This is a repository of (only) journal articles related to air pollution covering all the key subjects like emission inventories, emission factors, dispersion modeling, source apportionment, health impact studies, energy scenarios, etc. While the list is populated with India specific papers, a number of interesting and useful papers from other countries are also included. Follow the article links to the journal pages for full articles.
If you want to search the metadata of the papers,click here. Note that this is a repository of papers which we found interesting and we are sharing the title, abstract, and link to only those articles here.
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Lai, A M; Carter, E; Shan, M; Ni, K; Clark, S; Ezzati, M; Wiedinmyer, C; Yang, X; Baumgartner, J; Schauer, J J Science of the Total Environment, 646 , pp. 309-319, 2019, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Lai2019309b,
title = {Chemical composition and source apportionment of ambient, household, and personal exposures to PM2.5 in communities using biomass stoves in rural China}, author = {A M Lai and E Carter and M Shan and K Ni and S Clark and M Ezzati and C Wiedinmyer and X Yang and J Baumgartner and J J Schauer}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050466055&doi=10.1016%2fj.scitotenv.2018.07.322&partnerID=40&md5=c43036434a09c23de26005e2b68fa5bd}, doi = {10.1016/j.scitotenv.2018.07.322}, year = {2019}, date = {2019-01-01}, journal = {Science of the Total Environment}, volume = {646}, pages = {309-319}, publisher = {Elsevier B.V.}, abstract = {Fine particulate matter (PM2.5) has health effects that may depend on its sources and chemical composition. Few studies have quantified the composition of personal and area PM2.5 in rural settings over the same time period. Yet, this information would shed important light on the sources influencing personal PM2.5 exposures. This study investigated the sources and chemical composition of 40 personal exposure, 40 household, and 36 ambient PM2.5 samples collected in the non-heating and heating seasons in rural southwestern China. Chemical analysis included black carbon (BC), water-soluble components (ions, organic carbon), elements, and organic tracers. Source apportionment was conducted using organic tracer concentrations in a Chemical Mass Balance model. Biomass burning was the largest identified PM2.5 source contributor to household (average, SD: 48 ± 11%) and exposures (31 ± 6%) in both seasons, and ambient PM2.5 in winter (20 ± 4%). Food cooking also contributed to household and personal PM, reaching approximately half of the biomass contributions. Secondary inorganic aerosol was the major identified source in summertime ambient PM2.5 (32 ± 14%), but was present in all samples (summer: 10 ± 3% [household], 13 ± 6% [exposures]; winter: 18 ± 2% [ambient], 7 ± 2% [household], 8 ± 2% [exposures]). Dust concentrations and fractional contribution to total PM2.5 were higher in summer exposure samples (7 ± 4%) than in ambient or household samples (6 ± 1% and 2 ± 1%, respectively). Indoor sources comprised up to one-fifth of ambient PM2.5, and outdoor sources (vehicles, secondary aerosols) contributed up to 15% of household PM2.5. While household sources were the main contributors to PM2.5 exposures in terms of mass, inorganic components of personal exposures differed from household samples. Based on these findings, health-focused initiatives to reduce harmful PM2.5 exposures may consider a coordinated approach to address both indoor and outdoor PM2.5 source contributors. © 2018}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } Fine particulate matter (PM2.5) has health effects that may depend on its sources and chemical composition. Few studies have quantified the composition of personal and area PM2.5 in rural settings over the same time period. Yet, this information would shed important light on the sources influencing personal PM2.5 exposures. This study investigated the sources and chemical composition of 40 personal exposure, 40 household, and 36 ambient PM2.5 samples collected in the non-heating and heating seasons in rural southwestern China. Chemical analysis included black carbon (BC), water-soluble components (ions, organic carbon), elements, and organic tracers. Source apportionment was conducted using organic tracer concentrations in a Chemical Mass Balance model. Biomass burning was the largest identified PM2.5 source contributor to household (average, SD: 48 ± 11%) and exposures (31 ± 6%) in both seasons, and ambient PM2.5 in winter (20 ± 4%). Food cooking also contributed to household and personal PM, reaching approximately half of the biomass contributions. Secondary inorganic aerosol was the major identified source in summertime ambient PM2.5 (32 ± 14%), but was present in all samples (summer: 10 ± 3% [household], 13 ± 6% [exposures]; winter: 18 ± 2% [ambient], 7 ± 2% [household], 8 ± 2% [exposures]). Dust concentrations and fractional contribution to total PM2.5 were higher in summer exposure samples (7 ± 4%) than in ambient or household samples (6 ± 1% and 2 ± 1%, respectively). Indoor sources comprised up to one-fifth of ambient PM2.5, and outdoor sources (vehicles, secondary aerosols) contributed up to 15% of household PM2.5. While household sources were the main contributors to PM2.5 exposures in terms of mass, inorganic components of personal exposures differed from household samples. Based on these findings, health-focused initiatives to reduce harmful PM2.5 exposures may consider a coordinated approach to address both indoor and outdoor PM2.5 source contributors. © 2018
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Lai, A M; Carter, E; Shan, M; Ni, K; Clark, S; Ezzati, M; Wiedinmyer, C; Yang, X; Baumgartner, J; Schauer, J J Science of the Total Environment, 646 , pp. 309-319, 2019, (cited By 0). Abstract | Links | BibTeX | Tags: Aerosols; Air pollution; Biomass; Chemical analysis; Exposure controls; Fuels; Heating; Organic carbon; Particulate emissions; Rural areas; Thermal processing (foods), aluminum; arsenic; barium; black carbon; calcium; chloride; chromium; cobalt; copper; iron; lithium; magnesium; manganese; molybdenum; nickel; organic carbon; palladium; phosphorus; potassium; rubidium; selenium; silicon; sodium; strontium; sulfur; titanium; vanadium; yttrium; zinc, ambient air; biomass burning; black carbon; chemical composition; chemical mass balance; cooking appliance; particulate matter; pollution exposure; rural area; seasonal variation; source apportionment, Biomass-burning; Chemical mass balance; China; Particulate Matter; Solid fuels, China, Particles (particulate matter) @article{Lai2019309,
title = {Chemical composition and source apportionment of ambient, household, and personal exposures to PM2.5 in communities using biomass stoves in rural China}, author = {A M Lai and E Carter and M Shan and K Ni and S Clark and M Ezzati and C Wiedinmyer and X Yang and J Baumgartner and J J Schauer}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050466055&doi=10.1016%2fj.scitotenv.2018.07.322&partnerID=40&md5=c43036434a09c23de26005e2b68fa5bd}, doi = {10.1016/j.scitotenv.2018.07.322}, year = {2019}, date = {2019-01-01}, journal = {Science of the Total Environment}, volume = {646}, pages = {309-319}, publisher = {Elsevier B.V.}, abstract = {Fine particulate matter (PM2.5) has health effects that may depend on its sources and chemical composition. Few studies have quantified the composition of personal and area PM2.5 in rural settings over the same time period. Yet, this information would shed important light on the sources influencing personal PM2.5 exposures. This study investigated the sources and chemical composition of 40 personal exposure, 40 household, and 36 ambient PM2.5 samples collected in the non-heating and heating seasons in rural southwestern China. Chemical analysis included black carbon (BC), water-soluble components (ions, organic carbon), elements, and organic tracers. Source apportionment was conducted using organic tracer concentrations in a Chemical Mass Balance model. Biomass burning was the largest identified PM2.5 source contributor to household (average, SD: 48 ± 11%) and exposures (31 ± 6%) in both seasons, and ambient PM2.5 in winter (20 ± 4%). Food cooking also contributed to household and personal PM, reaching approximately half of the biomass contributions. Secondary inorganic aerosol was the major identified source in summertime ambient PM2.5 (32 ± 14%), but was present in all samples (summer: 10 ± 3% [household], 13 ± 6% [exposures]; winter: 18 ± 2% [ambient], 7 ± 2% [household], 8 ± 2% [exposures]). Dust concentrations and fractional contribution to total PM2.5 were higher in summer exposure samples (7 ± 4%) than in ambient or household samples (6 ± 1% and 2 ± 1%, respectively). Indoor sources comprised up to one-fifth of ambient PM2.5, and outdoor sources (vehicles, secondary aerosols) contributed up to 15% of household PM2.5. While household sources were the main contributors to PM2.5 exposures in terms of mass, inorganic components of personal exposures differed from household samples. Based on these findings, health-focused initiatives to reduce harmful PM2.5 exposures may consider a coordinated approach to address both indoor and outdoor PM2.5 source contributors. © 2018}, note = {cited By 0}, keywords = {Aerosols; Air pollution; Biomass; Chemical analysis; Exposure controls; Fuels; Heating; Organic carbon; Particulate emissions; Rural areas; Thermal processing (foods), aluminum; arsenic; barium; black carbon; calcium; chloride; chromium; cobalt; copper; iron; lithium; magnesium; manganese; molybdenum; nickel; organic carbon; palladium; phosphorus; potassium; rubidium; selenium; silicon; sodium; strontium; sulfur; titanium; vanadium; yttrium; zinc, ambient air; biomass burning; black carbon; chemical composition; chemical mass balance; cooking appliance; particulate matter; pollution exposure; rural area; seasonal variation; source apportionment, Biomass-burning; Chemical mass balance; China; Particulate Matter; Solid fuels, China, Particles (particulate matter)}, pubstate = {published}, tppubtype = {article} } Fine particulate matter (PM2.5) has health effects that may depend on its sources and chemical composition. Few studies have quantified the composition of personal and area PM2.5 in rural settings over the same time period. Yet, this information would shed important light on the sources influencing personal PM2.5 exposures. This study investigated the sources and chemical composition of 40 personal exposure, 40 household, and 36 ambient PM2.5 samples collected in the non-heating and heating seasons in rural southwestern China. Chemical analysis included black carbon (BC), water-soluble components (ions, organic carbon), elements, and organic tracers. Source apportionment was conducted using organic tracer concentrations in a Chemical Mass Balance model. Biomass burning was the largest identified PM2.5 source contributor to household (average, SD: 48 ± 11%) and exposures (31 ± 6%) in both seasons, and ambient PM2.5 in winter (20 ± 4%). Food cooking also contributed to household and personal PM, reaching approximately half of the biomass contributions. Secondary inorganic aerosol was the major identified source in summertime ambient PM2.5 (32 ± 14%), but was present in all samples (summer: 10 ± 3% [household], 13 ± 6% [exposures]; winter: 18 ± 2% [ambient], 7 ± 2% [household], 8 ± 2% [exposures]). Dust concentrations and fractional contribution to total PM2.5 were higher in summer exposure samples (7 ± 4%) than in ambient or household samples (6 ± 1% and 2 ± 1%, respectively). Indoor sources comprised up to one-fifth of ambient PM2.5, and outdoor sources (vehicles, secondary aerosols) contributed up to 15% of household PM2.5. While household sources were the main contributors to PM2.5 exposures in terms of mass, inorganic components of personal exposures differed from household samples. Based on these findings, health-focused initiatives to reduce harmful PM2.5 exposures may consider a coordinated approach to address both indoor and outdoor PM2.5 source contributors. © 2018
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2018 |
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Bhargava, N; Gurjar, B R; Mor, S; Ravindra, K Assessment of GHG mitigation and CDM technology in urban transport sector of Chandigarh, India Journal Article Environmental Science and Pollution Research, 25 (1), pp. 363-374, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Bhargava2018363b,
title = {Assessment of GHG mitigation and CDM technology in urban transport sector of Chandigarh, India}, author = {N Bhargava and B R Gurjar and S Mor and K Ravindra}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031895905&doi=10.1007%2fs11356-017-0357-8&partnerID=40&md5=a1971fc86b2319031d5cbd3d7905aefc}, doi = {10.1007/s11356-017-0357-8}, year = {2018}, date = {2018-01-01}, journal = {Environmental Science and Pollution Research}, volume = {25}, number = {1}, pages = {363-374}, publisher = {Springer Verlag}, abstract = {The increase in number of vehicles in metropolitan cities has resulted in increase of greenhouse gas (GHG) emissions in urban environment. In this study, emission load of GHGs (CO, N2O, CO2) from Chandigarh road transport sector has been estimated using Vehicular Air Pollution Inventory (VAPI) model, which uses emission factors prevalent in Indian cities. Contribution of 2-wheelers (2-w), 3-wheelers (3-w), cars, buses, and heavy commercial vehicles (HCVs) to CO, N2O, CO2, and total GHG emissions was calculated. Potential for GHG mitigation through clean development mechanism (CDM) in transport sector of Chandigarh under two scenarios, i.e., business as usual (BAU) and best estimate scenario (BES) using VAPI model, has been explored. A major contribution of GHG load (~ 50%) in Chandigarh was from four-wheelers until 2011; however, it shows a declining trend after 2011 until 2020. The estimated GHG emission from motor vehicles in Chandigarh has increased more than two times from 1065 Gg in 2005 to 2486 Gg by 2011 and is expected to increase to 4014 Gg by 2020 under BAU scenario. Under BES scenario, 30% of private transport has been transformed to public transport; GHG load was possibly reduced by 520 Gg. An increase of 173 Gg in GHGs load is projected from additional scenario (ADS) in Chandigarh city if all the diesel buses are transformed to CNG buses by 2020. Current study also offers potential for other cities to plan better GHG reduction strategies in transport sector to reduce their climate change impacts. © 2017, Springer-Verlag GmbH Germany.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } The increase in number of vehicles in metropolitan cities has resulted in increase of greenhouse gas (GHG) emissions in urban environment. In this study, emission load of GHGs (CO, N2O, CO2) from Chandigarh road transport sector has been estimated using Vehicular Air Pollution Inventory (VAPI) model, which uses emission factors prevalent in Indian cities. Contribution of 2-wheelers (2-w), 3-wheelers (3-w), cars, buses, and heavy commercial vehicles (HCVs) to CO, N2O, CO2, and total GHG emissions was calculated. Potential for GHG mitigation through clean development mechanism (CDM) in transport sector of Chandigarh under two scenarios, i.e., business as usual (BAU) and best estimate scenario (BES) using VAPI model, has been explored. A major contribution of GHG load (~ 50%) in Chandigarh was from four-wheelers until 2011; however, it shows a declining trend after 2011 until 2020. The estimated GHG emission from motor vehicles in Chandigarh has increased more than two times from 1065 Gg in 2005 to 2486 Gg by 2011 and is expected to increase to 4014 Gg by 2020 under BAU scenario. Under BES scenario, 30% of private transport has been transformed to public transport; GHG load was possibly reduced by 520 Gg. An increase of 173 Gg in GHGs load is projected from additional scenario (ADS) in Chandigarh city if all the diesel buses are transformed to CNG buses by 2020. Current study also offers potential for other cities to plan better GHG reduction strategies in transport sector to reduce their climate change impacts. © 2017, Springer-Verlag GmbH Germany.
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Dalaba, M; Alirigia, R; Mesenbring, E; Coffey, E; Brown, Z; Hannigan, M; Wiedinmyer, C; Oduro, A; Dickinson, K L Liquified Petroleum Gas (LPG) Supply and Demand for Cooking in Northern Ghana Journal Article EcoHealth, 2018, (cited By 0; Article in Press). Abstract | Links | BibTeX | Tags: @article{Dalaba2018b,
title = {Liquified Petroleum Gas (LPG) Supply and Demand for Cooking in Northern Ghana}, author = {M Dalaba and R Alirigia and E Mesenbring and E Coffey and Z Brown and M Hannigan and C Wiedinmyer and A Oduro and K L Dickinson}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051696933&doi=10.1007%2fs10393-018-1351-4&partnerID=40&md5=374058e60afe718f9bb41f36e0c62d22}, doi = {10.1007/s10393-018-1351-4}, year = {2018}, date = {2018-01-01}, journal = {EcoHealth}, publisher = {Springer New York LLC}, abstract = {Like many other countries, Ghana relies on biomass (mainly wood and charcoal) for most of its cooking needs. A national action plan aims to expand liquefied petroleum gas (LPG) access to 50% of the country’s population by 2020. While the country’s southern urban areas have made progress toward this goal, LPG use for cooking remains low in the north. The aim of this cross-sectional study was to characterize the current state of the LPG market in this area and examine opportunities and barriers to scale up LPG adoption. We interviewed 16 LPG suppliers (stove, cylinder, and fuel vendors) as well as 592 households in the Kassena-Nankana Districts (KND) of Ghana. We find large rural–urban differences in LPG uptake: less than 10% of rural households own LPG stoves compared with over half of urban households. Awareness of LPG is high across the region, but accessibility of fuel supply is highly limited, with just one refilling station located in the KND. Affordability is perceived as the main barrier to LPG adoption, and acceptability is also limited by widespread concerns about the safety of cooking with LPG. Transitioning to a cylinder recirculation model, and providing more targeted subsidies and credit options, should be explored to expand access to cleaner cooking in this region. © 2018, The Author(s).}, note = {cited By 0; Article in Press}, keywords = {}, pubstate = {published}, tppubtype = {article} } Like many other countries, Ghana relies on biomass (mainly wood and charcoal) for most of its cooking needs. A national action plan aims to expand liquefied petroleum gas (LPG) access to 50% of the country’s population by 2020. While the country’s southern urban areas have made progress toward this goal, LPG use for cooking remains low in the north. The aim of this cross-sectional study was to characterize the current state of the LPG market in this area and examine opportunities and barriers to scale up LPG adoption. We interviewed 16 LPG suppliers (stove, cylinder, and fuel vendors) as well as 592 households in the Kassena-Nankana Districts (KND) of Ghana. We find large rural–urban differences in LPG uptake: less than 10% of rural households own LPG stoves compared with over half of urban households. Awareness of LPG is high across the region, but accessibility of fuel supply is highly limited, with just one refilling station located in the KND. Affordability is perceived as the main barrier to LPG adoption, and acceptability is also limited by widespread concerns about the safety of cooking with LPG. Transitioning to a cylinder recirculation model, and providing more targeted subsidies and credit options, should be explored to expand access to cleaner cooking in this region. © 2018, The Author(s).
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Rafaj, P; Amann, M Decomposing air pollutant emissions in Asia: Determinants and projections Journal Article Energies, 11 (5), 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Rafaj2018b,
title = {Decomposing air pollutant emissions in Asia: Determinants and projections}, author = {P Rafaj and M Amann}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051663234&doi=10.3390%2fen11051299&partnerID=40&md5=fcb1e64edb53fefb5910842815f7f151}, doi = {10.3390/en11051299}, year = {2018}, date = {2018-01-01}, journal = {Energies}, volume = {11}, number = {5}, publisher = {MDPI AG}, abstract = {High levels of air pollution pose an urgent social and public health challenge in many Asian regions. This study evaluates the role of key factors that determined the changes in emission levels in China, India and Japan over the past 25 years. While emissions of air pollutants have been declining in Japan since the 1990s, China and India have experienced a rapid growth in pollution levels in recent years. Around 2005, control measures for sulfur emissions started to deliver expected reductions in China, followed by cuts in nitrogen oxides ten years later. Despite recent policy interventions, growing emission trends in India persist. A decomposition analysis of emission-driving factors indicates that emission levels would have been at least two-times higher without the improvements in energy intensity and efficiency, combined with end-of-pipe measures. Due to the continuous reliance on fossil fuels, the abatement effect of a cleaner fuel mix was in most cases significantly smaller than other factors. A reassessment of emission projections developed in the past suggests a decisive impact of energy and environmental policies. It is expected that targeted legislative instruments will play a dominant role in achieving future air-quality goals in Asia. © 2018 by the authors.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } High levels of air pollution pose an urgent social and public health challenge in many Asian regions. This study evaluates the role of key factors that determined the changes in emission levels in China, India and Japan over the past 25 years. While emissions of air pollutants have been declining in Japan since the 1990s, China and India have experienced a rapid growth in pollution levels in recent years. Around 2005, control measures for sulfur emissions started to deliver expected reductions in China, followed by cuts in nitrogen oxides ten years later. Despite recent policy interventions, growing emission trends in India persist. A decomposition analysis of emission-driving factors indicates that emission levels would have been at least two-times higher without the improvements in energy intensity and efficiency, combined with end-of-pipe measures. Due to the continuous reliance on fossil fuels, the abatement effect of a cleaner fuel mix was in most cases significantly smaller than other factors. A reassessment of emission projections developed in the past suggests a decisive impact of energy and environmental policies. It is expected that targeted legislative instruments will play a dominant role in achieving future air-quality goals in Asia. © 2018 by the authors.
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Pant, P; Huynh, W; Peltier, R E Exposure to air pollutants in Vietnam: Assessing potential risk for tourists Journal Article Journal of Environmental Sciences (China), 2018, (cited By 0; Article in Press). Abstract | Links | BibTeX | Tags: @article{Pant2018b,
title = {Exposure to air pollutants in Vietnam: Assessing potential risk for tourists}, author = {P Pant and W Huynh and R E Peltier}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044631676&doi=10.1016%2fj.jes.2018.01.023&partnerID=40&md5=db9950a58836c354e38450b3b2d3ff60}, doi = {10.1016/j.jes.2018.01.023}, year = {2018}, date = {2018-01-01}, journal = {Journal of Environmental Sciences (China)}, publisher = {Chinese Academy of Sciences}, abstract = {Tourism can form an important component of a nation’s GDP, and Vietnam is among the most visited countries in Southeast Asia. Most studies on personal exposure focus on the general population, or occupational cohorts with exposure to specific pollutants. However, short-term exposure to air pollutants while visiting regions with high levels of air pollution can lead to acute health effects. A personal exposure study was conducted across three cities in Vietnam to estimate exposure to particulate matter (PM2.5) and black carbon for tourists. Measurements were conducted during the wet season in 2014 in Ho Chi Minh City, Da Lat and Nha Trang using portable instrumentation. Average 24-hr PM2.5 and BC exposures were estimated as 18.9 ± 9.24 and 3.41 ± 1.33 μg/m3 and among the three cities, Ho Chi Minh was found to have the highest PM2.5 concentrations. Environmental tobacco smoke, commuting and street food stands were found to contribute to highest levels of exposure to PM2.5 and BC across all cities. © 2017}, note = {cited By 0; Article in Press}, keywords = {}, pubstate = {published}, tppubtype = {article} } Tourism can form an important component of a nation’s GDP, and Vietnam is among the most visited countries in Southeast Asia. Most studies on personal exposure focus on the general population, or occupational cohorts with exposure to specific pollutants. However, short-term exposure to air pollutants while visiting regions with high levels of air pollution can lead to acute health effects. A personal exposure study was conducted across three cities in Vietnam to estimate exposure to particulate matter (PM2.5) and black carbon for tourists. Measurements were conducted during the wet season in 2014 in Ho Chi Minh City, Da Lat and Nha Trang using portable instrumentation. Average 24-hr PM2.5 and BC exposures were estimated as 18.9 ± 9.24 and 3.41 ± 1.33 μg/m3 and among the three cities, Ho Chi Minh was found to have the highest PM2.5 concentrations. Environmental tobacco smoke, commuting and street food stands were found to contribute to highest levels of exposure to PM2.5 and BC across all cities. © 2017
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Kedia, S; Vellore, R K; Islam, S; Kaginalkar, A A study of Himalayan extreme rainfall events using WRF-Chem Journal Article Meteorology and Atmospheric Physics, 2018, (cited By 0; Article in Press). Abstract | Links | BibTeX | Tags: @article{Kedia2018c,
title = {A study of Himalayan extreme rainfall events using WRF-Chem}, author = {S Kedia and R K Vellore and S Islam and A Kaginalkar}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050693103&doi=10.1007%2fs00703-018-0626-1&partnerID=40&md5=9bb16b65d967094d8d913f83877989d4}, doi = {10.1007/s00703-018-0626-1}, year = {2018}, date = {2018-01-01}, journal = {Meteorology and Atmospheric Physics}, publisher = {Springer-Verlag Wien}, abstract = {The rising number of extreme rainfall events over the Himalayan foothill states of India during the recent decades has become a serious issue with the growing concern of aerosol influences. This study intends to provide some insight into aerosol and gas chemistry responses to changes in monsoon circulation and precipitation, and also assess the impact of aerosols on two recent infamous heavy rainfall events using coupled meteorology–chemistry–aerosol (WRF-Chem) model simulations. The sensitivity of aerosols and chemistry on rainfall distribution and the amount is evaluated using the simulations with and without chemistry. Results from this study show that the magnitude and spatial distribution of precipitation are significantly influenced by including aerosol and gas chemistry in the model simulations. Realistic meteorological conditions as well as rainfall amount and distribution are reproduced when aerosols and gasses are taken into account in the simulation. There is an overall enhancement of total cumulative rainfall as high as 20% due to aerosols and gas chemistry over the western Himalayan Indian states. This study shows that cloud-microphysical properties and the resulting precipitation distribution depend critically on the aerosol types and their concentrations under similar thermodynamic conditions. This study highlights the role of aerosol and gas chemistry and recognizes the importance of atmospheric chemistry in the model simulation for the analysis of Himalayan extreme precipitation events, and its further associations with the Himalayan hydrology. © 2018, Springer-Verlag GmbH Austria, part of Springer Nature.}, note = {cited By 0; Article in Press}, keywords = {}, pubstate = {published}, tppubtype = {article} } The rising number of extreme rainfall events over the Himalayan foothill states of India during the recent decades has become a serious issue with the growing concern of aerosol influences. This study intends to provide some insight into aerosol and gas chemistry responses to changes in monsoon circulation and precipitation, and also assess the impact of aerosols on two recent infamous heavy rainfall events using coupled meteorology–chemistry–aerosol (WRF-Chem) model simulations. The sensitivity of aerosols and chemistry on rainfall distribution and the amount is evaluated using the simulations with and without chemistry. Results from this study show that the magnitude and spatial distribution of precipitation are significantly influenced by including aerosol and gas chemistry in the model simulations. Realistic meteorological conditions as well as rainfall amount and distribution are reproduced when aerosols and gasses are taken into account in the simulation. There is an overall enhancement of total cumulative rainfall as high as 20% due to aerosols and gas chemistry over the western Himalayan Indian states. This study shows that cloud-microphysical properties and the resulting precipitation distribution depend critically on the aerosol types and their concentrations under similar thermodynamic conditions. This study highlights the role of aerosol and gas chemistry and recognizes the importance of atmospheric chemistry in the model simulation for the analysis of Himalayan extreme precipitation events, and its further associations with the Himalayan hydrology. © 2018, Springer-Verlag GmbH Austria, part of Springer Nature.
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Perugu, H Emission modelling of light-duty vehicles in India using the revamped VSP-based MOVES model: The case study of Hyderabad Journal Article Transportation Research Part D: Transport and Environment, 2018, (cited By 0; Article in Press). Abstract | Links | BibTeX | Tags: @article{Perugu2018b,
title = {Emission modelling of light-duty vehicles in India using the revamped VSP-based MOVES model: The case study of Hyderabad}, author = {H Perugu}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044656422&doi=10.1016%2fj.trd.2018.01.031&partnerID=40&md5=72dd86c54b7b4ae91fc4d8f84303ec7d}, doi = {10.1016/j.trd.2018.01.031}, year = {2018}, date = {2018-01-01}, journal = {Transportation Research Part D: Transport and Environment}, publisher = {Elsevier Ltd}, abstract = {US-EPA’s MOVES is the new generation mobile source emissions model, which is built on Vehicle Specific Power based assumptions and that makes it suitable to apply anywhere in the world. In this paper, we have successfully modified MOVES model for application in Hyderabad, India. As the model’s default underlying “Federal Test Procedure-based Driving Cycle” cannot represent India’s driving conditions, we have used “Modified Indian Driving Cycle” and local light-duty vehicle-specific driving cycles to revise the emission rates. On average, based on deterioration rate comparison, the emission rates in India are 9.54, 8.37 and 9.45 times higher than the default US emission rates, for CO, HC and NOx, respectively. Based on the results analysis and background information from other studies, the faster degradation of local vehicles are due to different local operating conditions like worse traffic congestion/slower vehicle speeds and local road conditions. The project-level dispersion modeling-based validation results showed high R2 values of 0.656 and 0.648 for CO and NOx, when our newer emission rates were used. Based on available literature, this is the first attempt that tried to revamp the VSP-based emission model, MOVES, for Indian context. In this study, the real-world traffic operational data was used to replace the fundamental parameters in the MOVES model and this research can be used as a reference for MOVES application in India as it provides all the necessary details to revise the emission rates. © 2018 Elsevier Ltd}, note = {cited By 0; Article in Press}, keywords = {}, pubstate = {published}, tppubtype = {article} } US-EPA’s MOVES is the new generation mobile source emissions model, which is built on Vehicle Specific Power based assumptions and that makes it suitable to apply anywhere in the world. In this paper, we have successfully modified MOVES model for application in Hyderabad, India. As the model’s default underlying “Federal Test Procedure-based Driving Cycle” cannot represent India’s driving conditions, we have used “Modified Indian Driving Cycle” and local light-duty vehicle-specific driving cycles to revise the emission rates. On average, based on deterioration rate comparison, the emission rates in India are 9.54, 8.37 and 9.45 times higher than the default US emission rates, for CO, HC and NOx, respectively. Based on the results analysis and background information from other studies, the faster degradation of local vehicles are due to different local operating conditions like worse traffic congestion/slower vehicle speeds and local road conditions. The project-level dispersion modeling-based validation results showed high R2 values of 0.656 and 0.648 for CO and NOx, when our newer emission rates were used. Based on available literature, this is the first attempt that tried to revamp the VSP-based emission model, MOVES, for Indian context. In this study, the real-world traffic operational data was used to replace the fundamental parameters in the MOVES model and this research can be used as a reference for MOVES application in India as it provides all the necessary details to revise the emission rates. © 2018 Elsevier Ltd
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Purohit, P Small and bad Journal Article Nature Sustainability, 1 (1), pp. 17-18, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Purohit201817b,
title = {Small and bad}, author = {P Purohit}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051988867&doi=10.1038%2fs41893-017-0012-x&partnerID=40&md5=82a7b7b7094a8b2e2cace04f699b4e40}, doi = {10.1038/s41893-017-0012-x}, year = {2018}, date = {2018-01-01}, journal = {Nature Sustainability}, volume = {1}, number = {1}, pages = {17-18}, publisher = {Nature Publishing Group}, abstract = {It is well known that electricity production from the combustion of fossil fuels is a major source of air pollutants and greenhouse gases. Now, research shows that large generation plants are not necessarily the worst emitters. © 2017 The Publisher.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } It is well known that electricity production from the combustion of fossil fuels is a major source of air pollutants and greenhouse gases. Now, research shows that large generation plants are not necessarily the worst emitters. © 2017 The Publisher.
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Verma, M; Pervez, S; Deb, M K; Majumdar, D Domestic use of cooking fuel in India: A review on emission characteristics and associated health concerns Journal Article Asian Journal of Chemistry, 30 (2), pp. 235-245, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Verma2018235b,
title = {Domestic use of cooking fuel in India: A review on emission characteristics and associated health concerns}, author = {M Verma and S Pervez and M K Deb and D Majumdar}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040113140&doi=10.14233%2fajchem.2018.21006&partnerID=40&md5=71ec5195e488a71ab14f70773a7d4d67}, doi = {10.14233/ajchem.2018.21006}, year = {2018}, date = {2018-01-01}, journal = {Asian Journal of Chemistry}, volume = {30}, number = {2}, pages = {235-245}, publisher = {Chemical Publishing Co.}, abstract = {One out of every three Indians use biomass fuels such as wood, animal dung and coal cake, crop residues as their primary domestic energy source. About 23 and 61 % of urban and rural Indian households, respectively, rely on traditional stoves (Chullah) for cooking practices. Household air pollution contains solid fuel burning emissions prominently, is reported to claim 4.3 million premature deaths yearly in developing countries. But most of the review studies to address air pollution scenario in India are focused on outdoor environments; major reason to review the current knowledge on emission estimates from household biomass burning and associated impacts on indoor air and human health. This review intends to critically discuss the variability associated with emission estimates and impacts of household air quality in different parts of India as presented in several research works, published during 2001-2015. About 27 and 11 % increase in PM2.5 and PM10, respectively has been observed in Indian house-indoors during the assessment period. Emission factors, emission budgets of aerosol fractions, carbonaceous matter and other chemical components for household biofuel burning emissions were also summarized for the period of 2001-2015. Health effects studies due to household air pollution in India were also summarized and discussed. Improvement in ventilation system and modification in the pattern of fuels may contribute to reduce the effect of the pollution on national health. As there are no specific regulations or acts for controlling of household air pollution in India, urgent need is felt for implementing the strategies to create public awareness.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } One out of every three Indians use biomass fuels such as wood, animal dung and coal cake, crop residues as their primary domestic energy source. About 23 and 61 % of urban and rural Indian households, respectively, rely on traditional stoves (Chullah) for cooking practices. Household air pollution contains solid fuel burning emissions prominently, is reported to claim 4.3 million premature deaths yearly in developing countries. But most of the review studies to address air pollution scenario in India are focused on outdoor environments; major reason to review the current knowledge on emission estimates from household biomass burning and associated impacts on indoor air and human health. This review intends to critically discuss the variability associated with emission estimates and impacts of household air quality in different parts of India as presented in several research works, published during 2001-2015. About 27 and 11 % increase in PM2.5 and PM10, respectively has been observed in Indian house-indoors during the assessment period. Emission factors, emission budgets of aerosol fractions, carbonaceous matter and other chemical components for household biofuel burning emissions were also summarized for the period of 2001-2015. Health effects studies due to household air pollution in India were also summarized and discussed. Improvement in ventilation system and modification in the pattern of fuels may contribute to reduce the effect of the pollution on national health. As there are no specific regulations or acts for controlling of household air pollution in India, urgent need is felt for implementing the strategies to create public awareness.
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Tong, D; Zhang, Q; Davis, S J; Liu, F; Zheng, B; Geng, G; Xue, T; Li, M; Hong, C; Lu, Z; Streets, D G; Guan, D; He, K Targeted emission reductions from global super-polluting power plant units Journal Article Nature Sustainability, 1 (1), pp. 59-68, 2018, (cited By 5). Abstract | Links | BibTeX | Tags: @article{Tong201859b,
title = {Targeted emission reductions from global super-polluting power plant units}, author = {D Tong and Q Zhang and S J Davis and F Liu and B Zheng and G Geng and T Xue and M Li and C Hong and Z Lu and D G Streets and D Guan and K He}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043579714&doi=10.1038%2fs41893-017-0003-y&partnerID=40&md5=de2a589d02d606b904e6bb28f2b20bd0}, doi = {10.1038/s41893-017-0003-y}, year = {2018}, date = {2018-01-01}, journal = {Nature Sustainability}, volume = {1}, number = {1}, pages = {59-68}, publisher = {Nature Publishing Group}, abstract = {There are more than 30,000 biomass- and fossil-fuel-burning power plants now operating worldwide, reflecting a tremendously diverse infrastructure, which ranges in capacity from less than a megawatt to more than a gigawatt. In 2010, 68.7% of electricity generated globally came from these power plants, compared with 64.2% in 1990. Although the electricity generated by this infrastructure is vital to economic activity worldwide, it also produces more CO2 and air pollutant emissions than infrastructure from any other industrial sector. Here, we assess fuel- and region-specific opportunities for reducing undesirable air pollutant emissions using a newly developed emission dataset at the level of individual generating units. For example, we find that retiring or installing emission control technologies on units representing 0.8% of the global coal-fired power plant capacity could reduce levels of PM2.5 emissions by 7.7-14.2%. In India and China, retiring coal-fired plants representing 1.8% and 0.8% of total capacity can reduce total PM2.5 emissions from coal-fired plants by 13.2% and 16.0%, respectively. Our results therefore suggest that policies targeting a relatively small number of ‘super-polluting’ units could substantially reduce pollutant emissions and thus the related impacts on both human health and global climate. © 2018 The Author.}, note = {cited By 5}, keywords = {}, pubstate = {published}, tppubtype = {article} } There are more than 30,000 biomass- and fossil-fuel-burning power plants now operating worldwide, reflecting a tremendously diverse infrastructure, which ranges in capacity from less than a megawatt to more than a gigawatt. In 2010, 68.7% of electricity generated globally came from these power plants, compared with 64.2% in 1990. Although the electricity generated by this infrastructure is vital to economic activity worldwide, it also produces more CO2 and air pollutant emissions than infrastructure from any other industrial sector. Here, we assess fuel- and region-specific opportunities for reducing undesirable air pollutant emissions using a newly developed emission dataset at the level of individual generating units. For example, we find that retiring or installing emission control technologies on units representing 0.8% of the global coal-fired power plant capacity could reduce levels of PM2.5 emissions by 7.7-14.2%. In India and China, retiring coal-fired plants representing 1.8% and 0.8% of total capacity can reduce total PM2.5 emissions from coal-fired plants by 13.2% and 16.0%, respectively. Our results therefore suggest that policies targeting a relatively small number of ‘super-polluting’ units could substantially reduce pollutant emissions and thus the related impacts on both human health and global climate. © 2018 The Author.
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Verma, M; Pervez, S; Majumdar, D; Chakrabarty, R; Pervez, Y F Emission estimation of aromatic and halogenated VOCs from household solid fuel burning practices Journal Article International Journal of Environmental Science and Technology, 2018, (cited By 0; Article in Press). Abstract | Links | BibTeX | Tags: @article{Verma2018c,
title = {Emission estimation of aromatic and halogenated VOCs from household solid fuel burning practices}, author = {M Verma and S Pervez and D Majumdar and R Chakrabarty and Y F Pervez}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051622746&doi=10.1007%2fs13762-018-1920-7&partnerID=40&md5=5a12b3cc2afd7ebf82b046da5c61bd3a}, doi = {10.1007/s13762-018-1920-7}, year = {2018}, date = {2018-01-01}, journal = {International Journal of Environmental Science and Technology}, publisher = {Center for Environmental and Energy Research and Studies}, abstract = {This study describes the emission factors (EFs) of 16 volatile organic compounds (VOCs) for the combustion of commonly used household solid fuels including coal balls (CB), fuelwood (FW), dung cakes (DC), crop residues (CR), and mixed fuels (MF: DC + FW), collected from ten states of India. Sum of 16 VOCs EF (g kg−1) have shown highest level (50.0 ± 22.7 g kg−1) for CB, followed by CR (23.71 ± 10.64 g kg−1), DC (19.08 ± 3.29 g kg−1), MF (15.77 ± 9.49 g kg−1), and FW (12.79 ± 5.69 g kg−1). These findings are multifold higher than those reported for biomass burning in test chamber studies. Benzene and dichloromethane EFs were found to be dominating among the aromatic and halogenated VOCs, respectively. Annual TVOCs emission estimates were evaluated to be 12.58 ± 5.92 Gg year−1 from household solid fuel burning practices. It was the 1/6th of TVOCs emission estimates (73 Gg year−1) from biomass burning in India during 2009. © 2018, Islamic Azad University (IAU).}, note = {cited By 0; Article in Press}, keywords = {}, pubstate = {published}, tppubtype = {article} } This study describes the emission factors (EFs) of 16 volatile organic compounds (VOCs) for the combustion of commonly used household solid fuels including coal balls (CB), fuelwood (FW), dung cakes (DC), crop residues (CR), and mixed fuels (MF: DC + FW), collected from ten states of India. Sum of 16 VOCs EF (g kg−1) have shown highest level (50.0 ± 22.7 g kg−1) for CB, followed by CR (23.71 ± 10.64 g kg−1), DC (19.08 ± 3.29 g kg−1), MF (15.77 ± 9.49 g kg−1), and FW (12.79 ± 5.69 g kg−1). These findings are multifold higher than those reported for biomass burning in test chamber studies. Benzene and dichloromethane EFs were found to be dominating among the aromatic and halogenated VOCs, respectively. Annual TVOCs emission estimates were evaluated to be 12.58 ± 5.92 Gg year−1 from household solid fuel burning practices. It was the 1/6th of TVOCs emission estimates (73 Gg year−1) from biomass burning in India during 2009. © 2018, Islamic Azad University (IAU).
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Mahapatra, P S; Sinha, P R; Boopathy, R; Das, T; Mohanty, S; Sahu, S C; Gurjar, B R Atmospheric Research, 199 , pp. 145-158, 2018, (cited By 1). Abstract | Links | BibTeX | Tags: @article{Mahapatra2018145b,
title = {Seasonal progression of atmospheric particulate matter over an urban coastal region in peninsular India: Role of local meteorology and long-range transport}, author = {P S Mahapatra and P R Sinha and R Boopathy and T Das and S Mohanty and S C Sahu and B R Gurjar}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032307133&doi=10.1016%2fj.atmosres.2017.09.001&partnerID=40&md5=6051ba5195a508a8f3dd84ffcf8b9757}, doi = {10.1016/j.atmosres.2017.09.001}, year = {2018}, date = {2018-01-01}, journal = {Atmospheric Research}, volume = {199}, pages = {145-158}, publisher = {Elsevier Ltd}, abstract = {Measurement of particulate matter (PM) over an urban site with relatively high concentration of aerosol particles is critically important owing to its adverse health, environmental and climate impact. Here we present a 3 years’ worth of measurements (January 2012 to December 2014) of PM2.5 (aerodynamic diameter of less than 2.5 μm) and PM10 (aerodynamic diameter of less than 10 μm) along with meteorological parameters and seasonal variations at Bhubaneswar an urban-coastal site, in eastern India. The concentrations of PM were determined gravimetrically from the filter samples of PM2.5 and PM10. It revealed remarkable seasonal variations with winter values (55.0 ± 23.4 μg/m3; 147.3 ± 42.4 μg/m3 for PM2.5 and PM10 respectively) about 3.5 times higher than that in pre-monsoon (15.7 ± 6.2 μg/m3; 41.8 ± 15.3 μg/m3). PM2.5 and PM10 were well correlated while PM2.5/PM10 ratios were found to be 0.38 and 0.32 during winter and pre-monsoon, indicating the predominance of coarse particles, mainly originating from long range transport of pollutants from northern and western parts of India and parts of west Asia as well. Concentration weighted trajectory (CWT) analysis revealed the IGP and North Western Odisha as the most potential sources of PM2.5 and PM10 during winter. The PM concentrations at Bhubaneswar were comparable with those at other coastal sites of India reported in the literature, but were lower than few polluted urban sites in India and Asia. Empirical model reproduced the observed seasonal variation of PM2.5 and PM10 very well over Bhubaneswar. © 2017 Elsevier B.V.}, note = {cited By 1}, keywords = {}, pubstate = {published}, tppubtype = {article} } Measurement of particulate matter (PM) over an urban site with relatively high concentration of aerosol particles is critically important owing to its adverse health, environmental and climate impact. Here we present a 3 years’ worth of measurements (January 2012 to December 2014) of PM2.5 (aerodynamic diameter of less than 2.5 μm) and PM10 (aerodynamic diameter of less than 10 μm) along with meteorological parameters and seasonal variations at Bhubaneswar an urban-coastal site, in eastern India. The concentrations of PM were determined gravimetrically from the filter samples of PM2.5 and PM10. It revealed remarkable seasonal variations with winter values (55.0 ± 23.4 μg/m3; 147.3 ± 42.4 μg/m3 for PM2.5 and PM10 respectively) about 3.5 times higher than that in pre-monsoon (15.7 ± 6.2 μg/m3; 41.8 ± 15.3 μg/m3). PM2.5 and PM10 were well correlated while PM2.5/PM10 ratios were found to be 0.38 and 0.32 during winter and pre-monsoon, indicating the predominance of coarse particles, mainly originating from long range transport of pollutants from northern and western parts of India and parts of west Asia as well. Concentration weighted trajectory (CWT) analysis revealed the IGP and North Western Odisha as the most potential sources of PM2.5 and PM10 during winter. The PM concentrations at Bhubaneswar were comparable with those at other coastal sites of India reported in the literature, but were lower than few polluted urban sites in India and Asia. Empirical model reproduced the observed seasonal variation of PM2.5 and PM10 very well over Bhubaneswar. © 2017 Elsevier B.V.
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Panicker, A S; Aditi, R; Beig, G; Ali, K; Solmon, F Radiative forcing of carbonaceous aerosols over two urban environments in northern India Journal Article Aerosol and Air Quality Research, 18 (4), pp. 884-894, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Panicker2018884b,
title = {Radiative forcing of carbonaceous aerosols over two urban environments in northern India}, author = {A S Panicker and R Aditi and G Beig and K Ali and F Solmon}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046400677&doi=10.4209%2faaqr.2017.01.0056&partnerID=40&md5=be00b4a3125e825ed6bf707cf7e7aaa5}, doi = {10.4209/aaqr.2017.01.0056}, year = {2018}, date = {2018-01-01}, journal = {Aerosol and Air Quality Research}, volume = {18}, number = {4}, pages = {884-894}, publisher = {AAGR Aerosol and Air Quality Research}, abstract = {The radiative forcing of elemental carbon (EC) and organic carbon (OC) has been estimated over two urban environments in Northern India (Jabalpur [JBL] and Udaipur [UDPR]) from November 2011 till November 2012 (till September 2012 over Jabalpur). The elemental carbon concentrations reached 7.36 ± 1.99 µg m–3 over JBL and were as high as 10.78 ± 4.85 µg m–3 over UDPR, whereas the corresponding OC concentrations were much higher in different months (as high as 19.37 ± 12.6 µg m–3 over JBL and 39.71 ± 13.05 µg m–3 over UDPR). The radiative forcing for OC and EC has been estimated using an optical model along with a radiative transfer model. The surface OC radiative forcing was found to range from –2.19 ± 1.93 W m–2 to –3.083 ± 2.29 W m–2 over JBL and –1.97 ± 1.37 to –5.89 ± 2.17 W m–2 over UDPR, whereas the estimated top of the atmosphere (TOA) forcing ranged from –0.87 ± 0.49 to –1.87 ± 0.90 W m–2 over JBL and from –1.23 ± 0.31 to –3.44 ± 1.51 W m–2 over UDPR. However, the effect of EC forcing (as high as –21.75 W m–2 at the surface of and +6.3 W m–2 at TOA over JBL and –38.21 W m–2 at the surface of and +5.05 W m–2 at TOA over UDPR) was found to be more than tenfold higher than OC forcing due to its strong atmospheric absorption, in spite of much lower concentrations compared to OC. © Taiwan Association for Aerosol Research.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } The radiative forcing of elemental carbon (EC) and organic carbon (OC) has been estimated over two urban environments in Northern India (Jabalpur [JBL] and Udaipur [UDPR]) from November 2011 till November 2012 (till September 2012 over Jabalpur). The elemental carbon concentrations reached 7.36 ± 1.99 µg m–3 over JBL and were as high as 10.78 ± 4.85 µg m–3 over UDPR, whereas the corresponding OC concentrations were much higher in different months (as high as 19.37 ± 12.6 µg m–3 over JBL and 39.71 ± 13.05 µg m–3 over UDPR). The radiative forcing for OC and EC has been estimated using an optical model along with a radiative transfer model. The surface OC radiative forcing was found to range from –2.19 ± 1.93 W m–2 to –3.083 ± 2.29 W m–2 over JBL and –1.97 ± 1.37 to –5.89 ± 2.17 W m–2 over UDPR, whereas the estimated top of the atmosphere (TOA) forcing ranged from –0.87 ± 0.49 to –1.87 ± 0.90 W m–2 over JBL and from –1.23 ± 0.31 to –3.44 ± 1.51 W m–2 over UDPR. However, the effect of EC forcing (as high as –21.75 W m–2 at the surface of and +6.3 W m–2 at TOA over JBL and –38.21 W m–2 at the surface of and +5.05 W m–2 at TOA over UDPR) was found to be more than tenfold higher than OC forcing due to its strong atmospheric absorption, in spite of much lower concentrations compared to OC. © Taiwan Association for Aerosol Research.
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Keerthi, R; Selvaraju, N; Varghese, Alen L; Anu, N Source apportionment studies for particulates (PM10) in Kozhikode, South Western India using a combined receptor model Journal Article Chemistry and Ecology, 2018, (cited By 0; Article in Press). Abstract | Links | BibTeX | Tags: @article{Keerthi2018b,
title = {Source apportionment studies for particulates (PM10) in Kozhikode, South Western India using a combined receptor model}, author = {R Keerthi and N Selvaraju and L Alen Varghese and N Anu}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052296080&doi=10.1080%2f02757540.2018.1508460&partnerID=40&md5=fd81555fa4b27eab1f109a48583c414b}, doi = {10.1080/02757540.2018.1508460}, year = {2018}, date = {2018-01-01}, journal = {Chemistry and Ecology}, publisher = {Taylor and Francis Ltd.}, abstract = {In the present work, source apportionment studies were carried out for particulate matter – one among the significant pollutants as addressed by The National Ambient Air Quality Standards. Advantages and disadvantages of each receptor model were addressed using a combined receptor model which integrates Factor Analysis (FA), Positive Matrix Factorisation (PMF) and Chemical Mass Balance (CMB). Verification of the approach was done using sets of synthetic data as well as field data from Kozhikode. Sampling was carried out in National Institute of Technology, Calicut for a period of over 26 days with 24-hour sampling. The sampling gave an average PM concentration value in the range of 29.174–129.176 µg m−3. Studies using field data revealed five dominant sources and their contributions obtained from CMB and PMF were compared. Soil dust (contribution from CMB: 18%; contribution from PMF: 16%), marine aerosol (17%; 25%), construction and aggregate processing (46%; 11%), garden waste combustion (18%; 45%), and vehicular exhaust (1%; 3%) were major contributors in the site under study. The outcomes of the study integrated with the support of local authorities and by the acceptance of residents can definitely curb the pollution levels in the site under the study. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.}, note = {cited By 0; Article in Press}, keywords = {}, pubstate = {published}, tppubtype = {article} } In the present work, source apportionment studies were carried out for particulate matter – one among the significant pollutants as addressed by The National Ambient Air Quality Standards. Advantages and disadvantages of each receptor model were addressed using a combined receptor model which integrates Factor Analysis (FA), Positive Matrix Factorisation (PMF) and Chemical Mass Balance (CMB). Verification of the approach was done using sets of synthetic data as well as field data from Kozhikode. Sampling was carried out in National Institute of Technology, Calicut for a period of over 26 days with 24-hour sampling. The sampling gave an average PM concentration value in the range of 29.174–129.176 µg m−3. Studies using field data revealed five dominant sources and their contributions obtained from CMB and PMF were compared. Soil dust (contribution from CMB: 18%; contribution from PMF: 16%), marine aerosol (17%; 25%), construction and aggregate processing (46%; 11%), garden waste combustion (18%; 45%), and vehicular exhaust (1%; 3%) were major contributors in the site under study. The outcomes of the study integrated with the support of local authorities and by the acceptance of residents can definitely curb the pollution levels in the site under the study. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
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Shaddick, G; Thomas, M L; Green, A; Brauer, M; van Donkelaar, A; Burnett, R; Chang, H H; Cohen, A; Dingenen, R V; Dora, C; Gumy, S; Liu, Y; Martin, R; Waller, L A; West, J; Zidek, J V; Prüss-Ustün, A Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution Journal Article Journal of the Royal Statistical Society. Series C: Applied Statistics, 67 (1), pp. 231-253, 2018, (cited By 4). Abstract | Links | BibTeX | Tags: @article{Shaddick2018231b,
title = {Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution}, author = {G Shaddick and M L Thomas and A Green and M Brauer and A van Donkelaar and R Burnett and H H Chang and A Cohen and R V Dingenen and C Dora and S Gumy and Y Liu and R Martin and L A Waller and J West and J V Zidek and A Prüss-Ustün}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020394880&doi=10.1111%2frssc.12227&partnerID=40&md5=dd51cd7c4f75e7da56573aeb65c18fe7}, doi = {10.1111/rssc.12227}, year = {2018}, date = {2018-01-01}, journal = {Journal of the Royal Statistical Society. Series C: Applied Statistics}, volume = {67}, number = {1}, pages = {231-253}, publisher = {Blackwell Publishing Ltd}, abstract = {Air pollution is a major risk factor for global health, with 3 million deaths annually being attributed to fine particulate matter ambient pollution (PM2.5). The primary source of information for estimating population exposures to air pollution has been measurements from ground monitoring networks but, although coverage is increasing, regions remain in which monitoring is limited. The data integration model for air quality supplements ground monitoring data with information from other sources, such as satellite retrievals of aerosol optical depth and chemical transport models. Set within a Bayesian hierarchical modelling framework, the model allows spatially varying relationships between ground measurements and other factors that estimate air quality. The model is used to estimate exposures, together with associated measures of uncertainty, on a high resolution grid covering the entire world from which it is estimated that 92% of the world’s population reside in areas exceeding the World Health Organization’s air quality guidelines. © 2017 World Health Organization, Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society.}, note = {cited By 4}, keywords = {}, pubstate = {published}, tppubtype = {article} } Air pollution is a major risk factor for global health, with 3 million deaths annually being attributed to fine particulate matter ambient pollution (PM2.5). The primary source of information for estimating population exposures to air pollution has been measurements from ground monitoring networks but, although coverage is increasing, regions remain in which monitoring is limited. The data integration model for air quality supplements ground monitoring data with information from other sources, such as satellite retrievals of aerosol optical depth and chemical transport models. Set within a Bayesian hierarchical modelling framework, the model allows spatially varying relationships between ground measurements and other factors that estimate air quality. The model is used to estimate exposures, together with associated measures of uncertainty, on a high resolution grid covering the entire world from which it is estimated that 92% of the world’s population reside in areas exceeding the World Health Organization’s air quality guidelines. © 2017 World Health Organization, Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society.
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van Lierop, D; Badami, M G; El-Geneidy, A M What influences satisfaction and loyalty in public transport? A review of the literature Journal Article Transport Reviews, 38 (1), pp. 52-72, 2018, (cited By 5). Abstract | Links | BibTeX | Tags: @article{vanLierop201852b,
title = {What influences satisfaction and loyalty in public transport? A review of the literature}, author = {D van Lierop and M G Badami and A M El-Geneidy}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014761338&doi=10.1080%2f01441647.2017.1298683&partnerID=40&md5=61635a1e5c8ed40e1062bc09dcc4ab05}, doi = {10.1080/01441647.2017.1298683}, year = {2018}, date = {2018-01-01}, journal = {Transport Reviews}, volume = {38}, number = {1}, pages = {52-72}, publisher = {Routledge}, abstract = {Public transport ridership retention is a challenge for many cities. To develop comprehensive strategies aimed at retaining riders, it is necessary to understand the aspects of public transport that influence users to become loyal to the system. This paper analyses relevant literature regarding the causes of satisfaction and loyalty in public transport. We find that the service factors most associated with satisfaction are on-board cleanliness and comfort, courteous and helpful behaviour from operators, safety, as well as punctuality and frequency of service. On the other hand, loyalty is associated with users’ perceptions of value-for-money, on-board safety and cleanliness, interactions with personnel and the image and commitment to public transport that users feels. Furthermore, the results elucidate that the concept of loyalty is best defined based on users’ intentions to continue using the service, their willingness to recommend it to others, their overall satisfaction, but also and most importantly, their image of and involvement with public transport. Public transport users who have a positive image of the agency and consider public transport an integral component of city life are more likely to demonstrate loyalty and act like ambassadors for public transport agencies. © 2017 Informa UK Limited, trading as Taylor & Francis Group.}, note = {cited By 5}, keywords = {}, pubstate = {published}, tppubtype = {article} } Public transport ridership retention is a challenge for many cities. To develop comprehensive strategies aimed at retaining riders, it is necessary to understand the aspects of public transport that influence users to become loyal to the system. This paper analyses relevant literature regarding the causes of satisfaction and loyalty in public transport. We find that the service factors most associated with satisfaction are on-board cleanliness and comfort, courteous and helpful behaviour from operators, safety, as well as punctuality and frequency of service. On the other hand, loyalty is associated with users’ perceptions of value-for-money, on-board safety and cleanliness, interactions with personnel and the image and commitment to public transport that users feels. Furthermore, the results elucidate that the concept of loyalty is best defined based on users’ intentions to continue using the service, their willingness to recommend it to others, their overall satisfaction, but also and most importantly, their image of and involvement with public transport. Public transport users who have a positive image of the agency and consider public transport an integral component of city life are more likely to demonstrate loyalty and act like ambassadors for public transport agencies. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
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Pommier, M; Fagerli, H; Gauss, M; Simpson, D; Sharma, S; Sinha, V; Ghude, S D; Landgren, O; Nyiri, A; Wind, P Impact of regional climate change and future emission scenarios on surface O3 and PM2.5 over India Journal Article Atmospheric Chemistry and Physics, 18 (1), pp. 103-127, 2018, (cited By 1). Abstract | Links | BibTeX | Tags: @article{Pommier2018103b,
title = {Impact of regional climate change and future emission scenarios on surface O3 and PM2.5 over India}, author = {M Pommier and H Fagerli and M Gauss and D Simpson and S Sharma and V Sinha and S D Ghude and O Landgren and A Nyiri and P Wind}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045846440&doi=10.5194%2facp-18-103-2018&partnerID=40&md5=a4936a447d58408519b82570cd64ab56}, doi = {10.5194/acp-18-103-2018}, year = {2018}, date = {2018-01-01}, journal = {Atmospheric Chemistry and Physics}, volume = {18}, number = {1}, pages = {103-127}, publisher = {Copernicus GmbH}, abstract = {Eleven of the world’s 20 most polluted cities are located in India and poor air quality is already a major public health issue. However, anthropogenic emissions are predicted to increase substantially in the short-term (2030) and medium-term (2050) futures in India, especially if no further policy efforts are made. In this study, the EMEP/MSC-W chemical transport model has been used to predict changes in surface ozone (O3) and fine particulate matter (PM2.5) for India in a world of changing emissions and climate. The reference scenario (for present-day) is evaluated against surface-based measurements, mainly at urban stations. The evaluation has also been extended to other data sets which are publicly available on the web but without quality assurance. The evaluation shows high temporal correlation for O3 (r = 0.9) and high spatial correlation for PM2.5 (r = 0.5 and r = 0.8 depending on the data set) between the model results and observations. While the overall bias in PM2.5 is small (lower than 6%), the model overestimates O3 by 35%. The underestimation in NOx titration is probably the main reason for the O3 overestimation in the model. However, the level of agreement can be considered satisfactory in this case of a regional model being evaluated against mainly urban measurements, and given the inevitable uncertainties in much of the input data. For the 2050s, the model predicts that climate change will have distinct effects in India in terms of O3 pollution, with a region in the north characterized by a statistically significant increase by up to 4% (2 ppb) and one in the south by a decrease up to -3% (-1.4 ppb). This variation in O3 is assumed to be partly related to changes in O3 deposition velocity caused by changes in soil moisture and, over a few areas, partly also by changes in biogenic non-methane volatile organic compounds. Our calculations suggest that PM2.5 will increase by up to 6.5% over the Indo-Gangetic Plain by the 2050s. The increase over India is driven by increases in dust, particulate organic matter (OM) and secondary inorganic aerosols (SIAs), which are mainly affected by the change in precipitation, biogenic emissions and wind speed. The large increase in anthropogenic emissions has a larger impact than climate change, causing O3 and PM2.5 levels to increase by 13 and 67% on average in the 2050s over the main part of India, respectively. By the 2030s, secondary inorganic aerosol is predicted to become the second largest contributor to PM2.5 in India, and the largest in the 2050s, exceeding OM and dust. © Author(s) 2018.}, note = {cited By 1}, keywords = {}, pubstate = {published}, tppubtype = {article} } Eleven of the world’s 20 most polluted cities are located in India and poor air quality is already a major public health issue. However, anthropogenic emissions are predicted to increase substantially in the short-term (2030) and medium-term (2050) futures in India, especially if no further policy efforts are made. In this study, the EMEP/MSC-W chemical transport model has been used to predict changes in surface ozone (O3) and fine particulate matter (PM2.5) for India in a world of changing emissions and climate. The reference scenario (for present-day) is evaluated against surface-based measurements, mainly at urban stations. The evaluation has also been extended to other data sets which are publicly available on the web but without quality assurance. The evaluation shows high temporal correlation for O3 (r = 0.9) and high spatial correlation for PM2.5 (r = 0.5 and r = 0.8 depending on the data set) between the model results and observations. While the overall bias in PM2.5 is small (lower than 6%), the model overestimates O3 by 35%. The underestimation in NOx titration is probably the main reason for the O3 overestimation in the model. However, the level of agreement can be considered satisfactory in this case of a regional model being evaluated against mainly urban measurements, and given the inevitable uncertainties in much of the input data. For the 2050s, the model predicts that climate change will have distinct effects in India in terms of O3 pollution, with a region in the north characterized by a statistically significant increase by up to 4% (2 ppb) and one in the south by a decrease up to -3% (-1.4 ppb). This variation in O3 is assumed to be partly related to changes in O3 deposition velocity caused by changes in soil moisture and, over a few areas, partly also by changes in biogenic non-methane volatile organic compounds. Our calculations suggest that PM2.5 will increase by up to 6.5% over the Indo-Gangetic Plain by the 2050s. The increase over India is driven by increases in dust, particulate organic matter (OM) and secondary inorganic aerosols (SIAs), which are mainly affected by the change in precipitation, biogenic emissions and wind speed. The large increase in anthropogenic emissions has a larger impact than climate change, causing O3 and PM2.5 levels to increase by 13 and 67% on average in the 2050s over the main part of India, respectively. By the 2030s, secondary inorganic aerosol is predicted to become the second largest contributor to PM2.5 in India, and the largest in the 2050s, exceeding OM and dust. © Author(s) 2018.
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Chatani, S; Yamaji, K; Sakurai, T; Itahashi, S; Shimadera, H; Kitayama, K; Hayami, H Overview of model inter-comparison in Japan’s study for reference air quality modeling (J-STREAM) Journal Article Atmosphere, 9 (1), 2018, (cited By 3). Abstract | Links | BibTeX | Tags: @article{Chatani2018f,
title = {Overview of model inter-comparison in Japan’s study for reference air quality modeling (J-STREAM)}, author = {S Chatani and K Yamaji and T Sakurai and S Itahashi and H Shimadera and K Kitayama and H Hayami}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040792001&doi=10.3390%2fatmos9010019&partnerID=40&md5=76fc5216b4ab606fe72baab1d0c27e64}, doi = {10.3390/atmos9010019}, year = {2018}, date = {2018-01-01}, journal = {Atmosphere}, volume = {9}, number = {1}, publisher = {MDPI AG}, abstract = {The inter-comparison of regional air quality models is an effective way to understand uncertainty in ambient pollutant concentrations simulated using various model configurations, as well as to find ways to improve model performance. Based on the outcomes and experiences of Japanese projects thus far, a new model inter-comparison project called Japan’s study for reference air quality modeling (J-STREAM) has begun. The objective of J-STREAM is to establish reference air quality modeling for source apportionment and effective strategy making to suppress secondary air pollutants including PM2.5 and photochemical ozone in Japan through model inter-comparison. The first phase focuses on understanding the ranges and limitations in ambient PM2.5 and ozone concentrations simulated by participants using common input datasets. The second phase focuses on issues revealed in previous studies in simulating secondary inorganic aerosols, as well as on the three-dimensional characteristics of photochemical ozone as a new target. The third phase focuses on comparing source apportionments and sensitivities under heavy air pollution episodes simulated by participating models. Detailed understanding of model performance, uncertainty, and possible improvements to urban-scale air pollution involving secondary pollutants, as well as detailed sector-wise source apportionments over megacities in Japan are expected. © 2018 by the authors.}, note = {cited By 3}, keywords = {}, pubstate = {published}, tppubtype = {article} } The inter-comparison of regional air quality models is an effective way to understand uncertainty in ambient pollutant concentrations simulated using various model configurations, as well as to find ways to improve model performance. Based on the outcomes and experiences of Japanese projects thus far, a new model inter-comparison project called Japan’s study for reference air quality modeling (J-STREAM) has begun. The objective of J-STREAM is to establish reference air quality modeling for source apportionment and effective strategy making to suppress secondary air pollutants including PM2.5 and photochemical ozone in Japan through model inter-comparison. The first phase focuses on understanding the ranges and limitations in ambient PM2.5 and ozone concentrations simulated by participants using common input datasets. The second phase focuses on issues revealed in previous studies in simulating secondary inorganic aerosols, as well as on the three-dimensional characteristics of photochemical ozone as a new target. The third phase focuses on comparing source apportionments and sensitivities under heavy air pollution episodes simulated by participating models. Detailed understanding of model performance, uncertainty, and possible improvements to urban-scale air pollution involving secondary pollutants, as well as detailed sector-wise source apportionments over megacities in Japan are expected. © 2018 by the authors.
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Shen, G; Hays, M D; Smith, K R; Williams, C; Faircloth, J W; Jetter, J J Evaluating the Performance of Household Liquefied Petroleum Gas Cookstoves Journal Article Environmental Science and Technology, 52 (2), pp. 904-915, 2018, (cited By 6). Abstract | Links | BibTeX | Tags: @article{Shen2018904b,
title = {Evaluating the Performance of Household Liquefied Petroleum Gas Cookstoves}, author = {G Shen and M D Hays and K R Smith and C Williams and J W Faircloth and J J Jetter}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041463615&doi=10.1021%2facs.est.7b05155&partnerID=40&md5=ea8acec257fefd9b0bd484ff9cbeb49b}, doi = {10.1021/acs.est.7b05155}, year = {2018}, date = {2018-01-01}, journal = {Environmental Science and Technology}, volume = {52}, number = {2}, pages = {904-915}, publisher = {American Chemical Society}, abstract = {Liquefied petroleum gas (LPG) cookstoves are considered to be an important solution for mitigating household air pollution; however, their performance has rarely been evaluated. To fill the data and knowledge gaps in this important area, 89 laboratory tests were conducted to quantify efficiencies and pollutant emissions from five commercially available household LPG stoves under different burning conditions. The mean thermal efficiency (±standard deviation) for the tested LPG cookstoves was 51 ± 6%, meeting guidelines for the highest tier level (Tier 4) under the International Organization for Standardization, International Workshop Agreement 11. Emission factors of CO2, CO, THC, CH4, and NOx on the basis of useful energy delivered (MJd) were 142 ± 17, 0.77 ± 0.55, 130 ± 196, 5.6 ± 8.2, and 46 ± 9 mg/MJd, respectively. Approximately 90% of the PM2.5 data were below the detection limit, corresponding to an emission rate below 0.11 mg/min. For those data above the detection limit, the average emission factor was 2.4 ± 1.6 mg/MJd, with a mean emission rate of 0.20 ± 0.16 mg/min. Under the specified gas pressure (2.8 kPa), but with the burner control set to minimum air flow rate, less complete combustion resulted in a visually yellow flame, and CO, PM2.5, EC, and BC emissions all increased. LPG cookstoves met guidelines for Tier 4 for both CO and PM2.5 emissions and mostly met the World Health Organization Emission Rate Targets set to protect human health. © 2017 American Chemical Society.}, note = {cited By 6}, keywords = {}, pubstate = {published}, tppubtype = {article} } Liquefied petroleum gas (LPG) cookstoves are considered to be an important solution for mitigating household air pollution; however, their performance has rarely been evaluated. To fill the data and knowledge gaps in this important area, 89 laboratory tests were conducted to quantify efficiencies and pollutant emissions from five commercially available household LPG stoves under different burning conditions. The mean thermal efficiency (±standard deviation) for the tested LPG cookstoves was 51 ± 6%, meeting guidelines for the highest tier level (Tier 4) under the International Organization for Standardization, International Workshop Agreement 11. Emission factors of CO2, CO, THC, CH4, and NOx on the basis of useful energy delivered (MJd) were 142 ± 17, 0.77 ± 0.55, 130 ± 196, 5.6 ± 8.2, and 46 ± 9 mg/MJd, respectively. Approximately 90% of the PM2.5 data were below the detection limit, corresponding to an emission rate below 0.11 mg/min. For those data above the detection limit, the average emission factor was 2.4 ± 1.6 mg/MJd, with a mean emission rate of 0.20 ± 0.16 mg/min. Under the specified gas pressure (2.8 kPa), but with the burner control set to minimum air flow rate, less complete combustion resulted in a visually yellow flame, and CO, PM2.5, EC, and BC emissions all increased. LPG cookstoves met guidelines for Tier 4 for both CO and PM2.5 emissions and mostly met the World Health Organization Emission Rate Targets set to protect human health. © 2017 American Chemical Society.
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Nagpure, A S; Reiner, M; Ramaswami, A Resource requirements of inclusive urban development in India: Insights from ten cities Journal Article Environmental Research Letters, 13 (2), 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Nagpure2018b,
title = {Resource requirements of inclusive urban development in India: Insights from ten cities}, author = {A S Nagpure and M Reiner and A Ramaswami}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048223764&doi=10.1088%2f1748-9326%2faaa4fc&partnerID=40&md5=882d02aa9b73642731600691f6baaa67}, doi = {10.1088/1748-9326/aaa4fc}, year = {2018}, date = {2018-01-01}, journal = {Environmental Research Letters}, volume = {13}, number = {2}, publisher = {Institute of Physics Publishing}, abstract = {This paper develops a methodology to assess the resource requirements of inclusive urban development in India and compares those requirements to current community-wide material and energy flows. Methods include: (a) identifying minimum service level benchmarks for the provision of infrastructure services including housing, electricity and clean cooking fuels; (b) assessing the percentage of homes that lack access to infrastructure or that consume infrastructure services below the identified benchmarks; (c) quantifying the material requirements to provide basic infrastructure services using India-specific design data; and (d) computing material and energy requirements for inclusive development and comparing it with current community-wide material and energy flows. Applying the method to ten Indian cities, we find that: 1%-6% of households do not have electricity, 14%-71% use electricity below the benchmark of 25 kWh capita-month-1; 4%-16% lack structurally sound housing; 50%-75% live in floor area less than the benchmark of 8.75 m2 floor area/capita; 10%-65% lack clean cooking fuel; and 6%-60% lack connection to a sewerage system. Across the ten cities examined, to provide basic electricity (25 kWh capita-month-1) to all will require an addition of only 1%-10% in current community-wide electricity use. To provide basic clean LPG fuel (1.2 kg capita-month-1) to all requires an increase of 5%-40% in current community-wide LPG use. Providing permanent shelter (implemented over a ten year period) to populations living in non-permanent housing in Delhi and Chandigarh would require a 6%-14% increase over current annual community-wide cement use. Conversely, to provide permanent housing to all people living in structurally unsound housing and those living in overcrowded housing (<5 m cap-2) would require 32%-115% of current community-wide cement flows. Except for the last scenario, these results suggest that social policies that seek to provide basic infrastructure provisioning for all residents would not dramatically increasing current community-wide resource flows. © 2018 The Author(s). Published by IOP Publishing Ltd.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } This paper develops a methodology to assess the resource requirements of inclusive urban development in India and compares those requirements to current community-wide material and energy flows. Methods include: (a) identifying minimum service level benchmarks for the provision of infrastructure services including housing, electricity and clean cooking fuels; (b) assessing the percentage of homes that lack access to infrastructure or that consume infrastructure services below the identified benchmarks; (c) quantifying the material requirements to provide basic infrastructure services using India-specific design data; and (d) computing material and energy requirements for inclusive development and comparing it with current community-wide material and energy flows. Applying the method to ten Indian cities, we find that: 1%-6% of households do not have electricity, 14%-71% use electricity below the benchmark of 25 kWh capita-month-1; 4%-16% lack structurally sound housing; 50%-75% live in floor area less than the benchmark of 8.75 m2 floor area/capita; 10%-65% lack clean cooking fuel; and 6%-60% lack connection to a sewerage system. Across the ten cities examined, to provide basic electricity (25 kWh capita-month-1) to all will require an addition of only 1%-10% in current community-wide electricity use. To provide basic clean LPG fuel (1.2 kg capita-month-1) to all requires an increase of 5%-40% in current community-wide LPG use. Providing permanent shelter (implemented over a ten year period) to populations living in non-permanent housing in Delhi and Chandigarh would require a 6%-14% increase over current annual community-wide cement use. Conversely, to provide permanent housing to all people living in structurally unsound housing and those living in overcrowded housing (<5 m cap-2) would require 32%-115% of current community-wide cement flows. Except for the last scenario, these results suggest that social policies that seek to provide basic infrastructure provisioning for all residents would not dramatically increasing current community-wide resource flows. © 2018 The Author(s). Published by IOP Publishing Ltd.
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Matawle, J L; Pervez, S; Deb, M K; Shrivastava, A; Tiwari, S PM2.5 pollution from household solid fuel burning practices in Central India: 2. Application of receptor models for source apportionment Journal Article Environmental Geochemistry and Health, 40 (1), pp. 145-161, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Matawle2018145b,
title = {PM2.5 pollution from household solid fuel burning practices in Central India: 2. Application of receptor models for source apportionment}, author = {J L Matawle and S Pervez and M K Deb and A Shrivastava and S Tiwari}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994316500&doi=10.1007%2fs10653-016-9889-y&partnerID=40&md5=b50bf8f6aaa63c903620776d0079857a}, doi = {10.1007/s10653-016-9889-y}, year = {2018}, date = {2018-01-01}, journal = {Environmental Geochemistry and Health}, volume = {40}, number = {1}, pages = {145-161}, publisher = {Springer Netherlands}, abstract = {USEPA’s UNMIX, positive matrix factorization (PMF) and effective variance-chemical mass balance (EV-CMB) receptor models were applied to chemically speciated profiles of 125 indoor PM2.5 measurements, sampled longitudinally during 2012–2013 in low-income group households of Central India which uses solid fuels for cooking practices. Three step source apportionment studies were carried out to generate more confident source characterization. Firstly, UNMIX6.0 extracted initial number of source factors, which were used to execute PMF5.0 to extract source-factor profiles in second step. Finally, factor analog locally derived source profiles were supplemented to EV-CMB8.2 with indoor receptor PM2.5 chemical profile to evaluate source contribution estimates (SCEs). The results of combined use of three receptor models clearly describe that UNMIX and PMF are useful tool to extract types of source categories within small receptor dataset and EV-CMB can pick those locally derived source profiles for source apportionment which are analog to PMF-extracted source categories. The source apportionment results have also shown three fold higher relative contribution of solid fuel burning emissions to indoor PM2.5 compared to those measurements reported for normal households with LPG stoves. The previously reported influential source marker species were found to be comparatively similar to those extracted from PMF fingerprint plots. The comparison between PMF and CMB SCEs results were also found to be qualitatively similar. The performance fit measures of all three receptor models were cross-verified and validated and support each other to gain confidence in source apportionment results. © 2016, Springer Science+Business Media Dordrecht.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } USEPA’s UNMIX, positive matrix factorization (PMF) and effective variance-chemical mass balance (EV-CMB) receptor models were applied to chemically speciated profiles of 125 indoor PM2.5 measurements, sampled longitudinally during 2012–2013 in low-income group households of Central India which uses solid fuels for cooking practices. Three step source apportionment studies were carried out to generate more confident source characterization. Firstly, UNMIX6.0 extracted initial number of source factors, which were used to execute PMF5.0 to extract source-factor profiles in second step. Finally, factor analog locally derived source profiles were supplemented to EV-CMB8.2 with indoor receptor PM2.5 chemical profile to evaluate source contribution estimates (SCEs). The results of combined use of three receptor models clearly describe that UNMIX and PMF are useful tool to extract types of source categories within small receptor dataset and EV-CMB can pick those locally derived source profiles for source apportionment which are analog to PMF-extracted source categories. The source apportionment results have also shown three fold higher relative contribution of solid fuel burning emissions to indoor PM2.5 compared to those measurements reported for normal households with LPG stoves. The previously reported influential source marker species were found to be comparatively similar to those extracted from PMF fingerprint plots. The comparison between PMF and CMB SCEs results were also found to be qualitatively similar. The performance fit measures of all three receptor models were cross-verified and validated and support each other to gain confidence in source apportionment results. © 2016, Springer Science+Business Media Dordrecht.
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Srivastava, P; Dey, S; Srivastava, A K; Singh, S; Tiwari, S Most probable mixing state of aerosols in Delhi NCR, northern India Journal Article Atmospheric Research, 200 , pp. 88-96, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Srivastava201888b,
title = {Most probable mixing state of aerosols in Delhi NCR, northern India}, author = {P Srivastava and S Dey and A K Srivastava and S Singh and S Tiwari}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033576152&doi=10.1016%2fj.atmosres.2017.09.018&partnerID=40&md5=757ceec2b9a84c44a11e8ae1093e429f}, doi = {10.1016/j.atmosres.2017.09.018}, year = {2018}, date = {2018-01-01}, journal = {Atmospheric Research}, volume = {200}, pages = {88-96}, publisher = {Elsevier Ltd}, abstract = {Unknown mixing state is one of the major sources of uncertainty in estimating aerosol direct radiative forcing (DRF). Aerosol DRF in India is usually reported for external mixing and any deviation from this would lead to high bias and error. Limited information on aerosol composition hinders in resolving this issue in India. Here we use two years of aerosol chemical composition data measured at megacity Delhi to examine the most probable aerosol mixing state by comparing the simulated clear-sky downward surface flux with the measured flux. We consider external, internal, and four combinations of core-shell (black carbon, BC over dust; water-soluble, WS over dust; WS over water-insoluble, WINS and BC over WINS) mixing. Our analysis reveals that choice of external mixing (usually considered in satellite retrievals and climate models) seems reasonable in Delhi only in the pre-monsoon (Mar-Jun) season. During the winter (Dec-Feb) and monsoon (Jul-Sep) seasons, ‘WS coating over dust’ externally mixed with BC and WINS appears to be the most probable mixing state; while ‘WS coating over WINS’ externally mixed with BC and dust seems to be the most probable mixing state in the post-monsoon (Oct–Nov) season. Mean seasonal TOA (surface) aerosol DRF for the most probable mixing states are 4.4 ± 3.9 (− 25.9 ± 3.9), − 16.3 ± 5.7 (− 42.4 ± 10.5), 13.6 ± 11.4 (− 76.6 ± 16.6) and − 5.4 ± 7.7 (− 80.0 ± 7.2) W m− 2 respectively in the pre-monsoon, monsoon, post-monsoon and winter seasons. Our results highlight the importance of realistic mixing state treatment in estimating aerosol DRF to aid in policy making to combat climate change. © 2017}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } Unknown mixing state is one of the major sources of uncertainty in estimating aerosol direct radiative forcing (DRF). Aerosol DRF in India is usually reported for external mixing and any deviation from this would lead to high bias and error. Limited information on aerosol composition hinders in resolving this issue in India. Here we use two years of aerosol chemical composition data measured at megacity Delhi to examine the most probable aerosol mixing state by comparing the simulated clear-sky downward surface flux with the measured flux. We consider external, internal, and four combinations of core-shell (black carbon, BC over dust; water-soluble, WS over dust; WS over water-insoluble, WINS and BC over WINS) mixing. Our analysis reveals that choice of external mixing (usually considered in satellite retrievals and climate models) seems reasonable in Delhi only in the pre-monsoon (Mar-Jun) season. During the winter (Dec-Feb) and monsoon (Jul-Sep) seasons, ‘WS coating over dust’ externally mixed with BC and WINS appears to be the most probable mixing state; while ‘WS coating over WINS’ externally mixed with BC and dust seems to be the most probable mixing state in the post-monsoon (Oct–Nov) season. Mean seasonal TOA (surface) aerosol DRF for the most probable mixing states are 4.4 ± 3.9 (− 25.9 ± 3.9), − 16.3 ± 5.7 (− 42.4 ± 10.5), 13.6 ± 11.4 (− 76.6 ± 16.6) and − 5.4 ± 7.7 (− 80.0 ± 7.2) W m− 2 respectively in the pre-monsoon, monsoon, post-monsoon and winter seasons. Our results highlight the importance of realistic mixing state treatment in estimating aerosol DRF to aid in policy making to combat climate change. © 2017
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Vaghmaria, N; Mevada, N; Maliakal, J Impact of Diwali festival on aerosol optical properties over an Urban city, Ahmedabad (India) Journal Article Aerosol and Air Quality Research, 18 (2), pp. 522-532, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Vaghmaria2018522b,
title = {Impact of Diwali festival on aerosol optical properties over an Urban city, Ahmedabad (India)}, author = {N Vaghmaria and N Mevada and J Maliakal}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044131456&doi=10.4209%2faaqr.2017.04.0124&partnerID=40&md5=933e8171d783d9982a08fe7f92155a59}, doi = {10.4209/aaqr.2017.04.0124}, year = {2018}, date = {2018-01-01}, journal = {Aerosol and Air Quality Research}, volume = {18}, number = {2}, pages = {522-532}, publisher = {AAGR Aerosol and Air Quality Research}, abstract = {An attempt has been made to investigate changes in the characteristics of aerosol optical properties induced during Diwali/ New Year celebrations of 2012 over an urban city, Ahmedabad (India). Ground based measurements of average Aerosol Optical Depth (AOD) were carried out using a Microtops-II at Physics Department, Gujarat University, Ahmedabad during 10thto 16thNov, 2012. AOD on the day just after Diwali is found to increase significantly; around 75% compared to pre-Diwali days for visible wavelength, remained at higher level for one more day (next day of New Year). Turbidity factor (β) also changed considerably, to a high value of 0.34 on the next day of Diwali, indicating a significant increase in aerosol loading associated with Diwali/ New Year festivities. Normal anti-correlation between Angstrom exponent (α) and turbidity parameter (β) is not seen on days following Diwali. The AOD and β values in the evening of the next day of Diwali increased to about 164% and 171% respectively compared to pre-Diwali days evenings; such an increase associated with the festivities has not been reported at any part of the country. Spectral variation of AOD also shows a significant change in the pattern during following two days of celebrations compared to pre-Diwali days. It is seen to follow power law more closely. Significant increase in AOD and change in spectral pattern suggest an increase of fine/accumulation mode particles due to Diwali/ New Year festivities. © Taiwan Association for Aerosol Research.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } An attempt has been made to investigate changes in the characteristics of aerosol optical properties induced during Diwali/ New Year celebrations of 2012 over an urban city, Ahmedabad (India). Ground based measurements of average Aerosol Optical Depth (AOD) were carried out using a Microtops-II at Physics Department, Gujarat University, Ahmedabad during 10thto 16thNov, 2012. AOD on the day just after Diwali is found to increase significantly; around 75% compared to pre-Diwali days for visible wavelength, remained at higher level for one more day (next day of New Year). Turbidity factor (β) also changed considerably, to a high value of 0.34 on the next day of Diwali, indicating a significant increase in aerosol loading associated with Diwali/ New Year festivities. Normal anti-correlation between Angstrom exponent (α) and turbidity parameter (β) is not seen on days following Diwali. The AOD and β values in the evening of the next day of Diwali increased to about 164% and 171% respectively compared to pre-Diwali days evenings; such an increase associated with the festivities has not been reported at any part of the country. Spectral variation of AOD also shows a significant change in the pattern during following two days of celebrations compared to pre-Diwali days. It is seen to follow power law more closely. Significant increase in AOD and change in spectral pattern suggest an increase of fine/accumulation mode particles due to Diwali/ New Year festivities. © Taiwan Association for Aerosol Research.
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Karambelas, A; Holloway, T; Kiesewetter, G; Heyes, C Constraining the uncertainty in emissions over India with a regional air quality model evaluation Journal Article Atmospheric Environment, 174 , pp. 194-203, 2018, (cited By 2). Abstract | Links | BibTeX | Tags: @article{Karambelas2018194b,
title = {Constraining the uncertainty in emissions over India with a regional air quality model evaluation}, author = {A Karambelas and T Holloway and G Kiesewetter and C Heyes}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036511314&doi=10.1016%2fj.atmosenv.2017.11.052&partnerID=40&md5=32501af16d56bd9c2bd373427946f4d4}, doi = {10.1016/j.atmosenv.2017.11.052}, year = {2018}, date = {2018-01-01}, journal = {Atmospheric Environment}, volume = {174}, pages = {194-203}, publisher = {Elsevier Ltd}, abstract = {To evaluate uncertainty in the spatial distribution of air emissions over India, we compare satellite and surface observations with simulations from the U.S. Environmental Protection Agency (EPA) Community Multi-Scale Air Quality (CMAQ) model. Seasonally representative simulations were completed for January, April, July, and October 2010 at 36 km × 36 km using anthropogenic emissions from the Greenhouse Gas-Air Pollution Interaction and Synergies (GAINS) model following version 5a of the Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants project (ECLIPSE v5a). We use both tropospheric columns from the Ozone Monitoring Instrument (OMI) and surface observations from the Central Pollution Control Board (CPCB) to closely examine modeled nitrogen dioxide (NO2) biases in urban and rural regions across India. Spatial average evaluation with satellite retrievals indicate a low bias in the modeled tropospheric column (−63.3%), which reflects broad low-biases in majority non-urban regions (−70.1% in rural areas) across the sub-continent to slightly lesser low biases reflected in semi-urban areas (−44.7%), with the threshold between semi-urban and rural defined as 400 people per km2. In contrast, modeled surface NO2 concentrations exhibit a slight high bias of +15.6% when compared to surface CPCB observations predominantly located in urban areas. Conversely, in examining extremely population dense urban regions with more than 5000 people per km2 (dense-urban), we find model overestimates in both the column (+57.8) and at the surface (+131.2%) compared to observations. Based on these results, we find that existing emission fields for India may overestimate urban emissions in densely populated regions and underestimate rural emissions. However, if we rely on model evaluation with predominantly urban surface observations from the CPCB, comparisons reflect model high biases, contradictory to the knowledge gained using satellite observations. Satellites thus serve as an important emissions and model evaluation metric where surface observations are lacking, such as rural India, and support improved emissions inventory development. © 2017 Elsevier Ltd}, note = {cited By 2}, keywords = {}, pubstate = {published}, tppubtype = {article} } To evaluate uncertainty in the spatial distribution of air emissions over India, we compare satellite and surface observations with simulations from the U.S. Environmental Protection Agency (EPA) Community Multi-Scale Air Quality (CMAQ) model. Seasonally representative simulations were completed for January, April, July, and October 2010 at 36 km × 36 km using anthropogenic emissions from the Greenhouse Gas-Air Pollution Interaction and Synergies (GAINS) model following version 5a of the Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants project (ECLIPSE v5a). We use both tropospheric columns from the Ozone Monitoring Instrument (OMI) and surface observations from the Central Pollution Control Board (CPCB) to closely examine modeled nitrogen dioxide (NO2) biases in urban and rural regions across India. Spatial average evaluation with satellite retrievals indicate a low bias in the modeled tropospheric column (−63.3%), which reflects broad low-biases in majority non-urban regions (−70.1% in rural areas) across the sub-continent to slightly lesser low biases reflected in semi-urban areas (−44.7%), with the threshold between semi-urban and rural defined as 400 people per km2. In contrast, modeled surface NO2 concentrations exhibit a slight high bias of +15.6% when compared to surface CPCB observations predominantly located in urban areas. Conversely, in examining extremely population dense urban regions with more than 5000 people per km2 (dense-urban), we find model overestimates in both the column (+57.8) and at the surface (+131.2%) compared to observations. Based on these results, we find that existing emission fields for India may overestimate urban emissions in densely populated regions and underestimate rural emissions. However, if we rely on model evaluation with predominantly urban surface observations from the CPCB, comparisons reflect model high biases, contradictory to the knowledge gained using satellite observations. Satellites thus serve as an important emissions and model evaluation metric where surface observations are lacking, such as rural India, and support improved emissions inventory development. © 2017 Elsevier Ltd
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Jayarathne, T; Stockwell, C E; Bhave, P V; Praveen, P S; Rathnayake, C M; Islam, Md R; Panday, A K; Adhikari, S; Maharjan, R; Goetz, Douglas J; Decarlo, P F; Saikawa, E; Yokelson, R J; Stone, E A Atmospheric Chemistry and Physics, 18 (3), pp. 2259-2286, 2018, (cited By 5). Abstract | Links | BibTeX | Tags: @article{Jayarathne20182259b,
title = {Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE): Emissions of particulate matter from wood-and dung-fueled cooking fires, garbage and crop residue burning, brick kilns, and other sources}, author = {T Jayarathne and C E Stockwell and P V Bhave and P S Praveen and C M Rathnayake and R Md Islam and A K Panday and S Adhikari and R Maharjan and J Douglas Goetz and P F Decarlo and E Saikawa and R J Yokelson and E A Stone}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042150260&doi=10.5194%2facp-18-2259-2018&partnerID=40&md5=a272d2dc2b562e3fc82875a3a1366853}, doi = {10.5194/acp-18-2259-2018}, year = {2018}, date = {2018-01-01}, journal = {Atmospheric Chemistry and Physics}, volume = {18}, number = {3}, pages = {2259-2286}, publisher = {Copernicus GmbH}, abstract = {The Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE) characterized widespread and under-sampled combustion sources common to South Asia, including brick kilns, garbage burning, diesel and gasoline generators, diesel groundwater pumps, idling motorcycles, traditional and modern cooking stoves and fires, crop residue burning, and heating fire. Fuel-based emission factors (EFs; with units of pollutant mass emitted per kilogram of fuel combusted) were determined for fine particulate matter (PM2.5), organic carbon (OC), elemental carbon (EC), inorganic ions, trace metals, and organic species. For the forced-draft zigzag brick kiln, EFPM2.5 ranged from 12 to 19gkg-1 with major contributions from OC (7%), sulfate expected to be in the form of sulfuric acid (31.9%), and other chemicals not measured (e.g., particle-bound water). For the clamp kiln, EFPM2.5 ranged from 8 to 13gkg-1, with major contributions from OC (63.2%), sulfate (23.4%), and ammonium (16%). Our brick kiln EFPM2.5 values may exceed those previously reported, partly because we sampled emissions at ambient temperature after emission from the stack or kiln allowing some particle-phase OC and sulfate to form from gaseous precursors. The combustion of mixed household garbage under dry conditions had an EFPM2.5 of 7.4±1.2gkg-1, whereas damp conditions generated the highest EFPM2.5 of all combustion sources in this study, reaching up to 125±23gkg-1. Garbage burning emissions contained triphenylbenzene and relatively high concentrations of heavy metals (Cu, Pb, Sb), making these useful markers of this source. A variety of cooking stoves and fires fueled with dung, hardwood, twigs, and/or other biofuels were studied. The use of dung for cooking and heating produced higher EFPM2.5 than other biofuel sources and consistently emitted more PM2.5 and OC than burning hardwood and/or twigs; this trend was consistent across traditional mud stoves, chimney stoves, and three-stone cooking fires. The comparisons of different cooking stoves and cooking fires revealed the highest PM emissions from three-stone cooking fires (7.6-73gkg-1), followed by traditional mud stoves (5.3-19.7gkg-1), mud stoves with a chimney for exhaust (3.0-6.8gkg-1), rocket stoves (1.5-7.2gkg-1), induced-draft stoves (1.2-5.7gkg-1), and the bhuse chulo stove (3.2gkg-1), while biogas had no detectable PM emissions. Idling motorcycle emissions were evaluated before and after routine servicing at a local shop, which decreased EFPM2.5 from 8.8±1.3 to 0.71±0.45gkg-1 when averaged across five motorcycles. Organic species analysis indicated that this reduction in PM2.5 was largely due to a decrease in emission of motor oil, probably from the crankcase. The EF and chemical emissions profiles developed in this study may be used for source apportionment and to update regional emission inventories. © 2018 Author(s).}, note = {cited By 5}, keywords = {}, pubstate = {published}, tppubtype = {article} } The Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE) characterized widespread and under-sampled combustion sources common to South Asia, including brick kilns, garbage burning, diesel and gasoline generators, diesel groundwater pumps, idling motorcycles, traditional and modern cooking stoves and fires, crop residue burning, and heating fire. Fuel-based emission factors (EFs; with units of pollutant mass emitted per kilogram of fuel combusted) were determined for fine particulate matter (PM2.5), organic carbon (OC), elemental carbon (EC), inorganic ions, trace metals, and organic species. For the forced-draft zigzag brick kiln, EFPM2.5 ranged from 12 to 19gkg-1 with major contributions from OC (7%), sulfate expected to be in the form of sulfuric acid (31.9%), and other chemicals not measured (e.g., particle-bound water). For the clamp kiln, EFPM2.5 ranged from 8 to 13gkg-1, with major contributions from OC (63.2%), sulfate (23.4%), and ammonium (16%). Our brick kiln EFPM2.5 values may exceed those previously reported, partly because we sampled emissions at ambient temperature after emission from the stack or kiln allowing some particle-phase OC and sulfate to form from gaseous precursors. The combustion of mixed household garbage under dry conditions had an EFPM2.5 of 7.4±1.2gkg-1, whereas damp conditions generated the highest EFPM2.5 of all combustion sources in this study, reaching up to 125±23gkg-1. Garbage burning emissions contained triphenylbenzene and relatively high concentrations of heavy metals (Cu, Pb, Sb), making these useful markers of this source. A variety of cooking stoves and fires fueled with dung, hardwood, twigs, and/or other biofuels were studied. The use of dung for cooking and heating produced higher EFPM2.5 than other biofuel sources and consistently emitted more PM2.5 and OC than burning hardwood and/or twigs; this trend was consistent across traditional mud stoves, chimney stoves, and three-stone cooking fires. The comparisons of different cooking stoves and cooking fires revealed the highest PM emissions from three-stone cooking fires (7.6-73gkg-1), followed by traditional mud stoves (5.3-19.7gkg-1), mud stoves with a chimney for exhaust (3.0-6.8gkg-1), rocket stoves (1.5-7.2gkg-1), induced-draft stoves (1.2-5.7gkg-1), and the bhuse chulo stove (3.2gkg-1), while biogas had no detectable PM emissions. Idling motorcycle emissions were evaluated before and after routine servicing at a local shop, which decreased EFPM2.5 from 8.8±1.3 to 0.71±0.45gkg-1 when averaged across five motorcycles. Organic species analysis indicated that this reduction in PM2.5 was largely due to a decrease in emission of motor oil, probably from the crankcase. The EF and chemical emissions profiles developed in this study may be used for source apportionment and to update regional emission inventories. © 2018 Author(s).
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Kumar, R; Barth, M C; Pfister, G G; Monache, Delle L; Lamarque, J F; Archer-Nicholls, S; Tilmes, S; Ghude, S D; Wiedinmyer, C; Naja, M; Walters, S How Will Air Quality Change in South Asia by 2050? Journal Article Journal of Geophysical Research: Atmospheres, 123 (3), pp. 1840-1864, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Kumar20181840b,
title = {How Will Air Quality Change in South Asia by 2050?}, author = {R Kumar and M C Barth and G G Pfister and L Delle Monache and J F Lamarque and S Archer-Nicholls and S Tilmes and S D Ghude and C Wiedinmyer and M Naja and S Walters}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042353464&doi=10.1002%2f2017JD027357&partnerID=40&md5=36a687e8c10a8014edc6631394d3c459}, doi = {10.1002/2017JD027357}, year = {2018}, date = {2018-01-01}, journal = {Journal of Geophysical Research: Atmospheres}, volume = {123}, number = {3}, pages = {1840-1864}, publisher = {Blackwell Publishing Ltd}, abstract = {Exposure to unhealthy air causes millions of premature deaths and damages crops sufficient to feed a large portion of the South Asian population every year. However, little is known about how future air quality in South Asia will respond to changing human activities. Here we examine the combined effect of changes in climate and air pollutant emissions projected by the Representative Concentration Pathways (RCP) 8.5 and RCP6.0 on air quality of South Asia in 2050 using a state-of-the-science Nested Regional Climate model with Chemistry (NRCM-Chem). RCP8.5 and RCP6.0 are selected to represent scenarios of highest and lowest air pollution in South Asia by 2050. NRCM-Chem shows the ability to capture observed key features of variability in meteorological parameters, ozone and related gases, and aerosols. NRCM-Chem results show that surface ozone and particulate matter of less than 2.5 μm in diameter will increase significantly by midcentury in South Asia under the RCP8.5 but remain similar to present day under RCP6.0. No RCP suggest an improvement in air pollution in South Asia by midcentury. Under RCP8.5, the frequency of air pollution events is predicted to increase by 20–120 days per year in 2050 compared to the present-day conditions, with particulate matter of less than 2.5 μm in diameter predicted to breach the World Health Organization ambient air quality guidelines on an almost daily basis in many parts of South Asia. These results indicate that while the RCP scenarios project a global improvement in air quality, they generally result in degrading air quality in South Asia. ©2018. American Geophysical Union. All Rights Reserved.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } Exposure to unhealthy air causes millions of premature deaths and damages crops sufficient to feed a large portion of the South Asian population every year. However, little is known about how future air quality in South Asia will respond to changing human activities. Here we examine the combined effect of changes in climate and air pollutant emissions projected by the Representative Concentration Pathways (RCP) 8.5 and RCP6.0 on air quality of South Asia in 2050 using a state-of-the-science Nested Regional Climate model with Chemistry (NRCM-Chem). RCP8.5 and RCP6.0 are selected to represent scenarios of highest and lowest air pollution in South Asia by 2050. NRCM-Chem shows the ability to capture observed key features of variability in meteorological parameters, ozone and related gases, and aerosols. NRCM-Chem results show that surface ozone and particulate matter of less than 2.5 μm in diameter will increase significantly by midcentury in South Asia under the RCP8.5 but remain similar to present day under RCP6.0. No RCP suggest an improvement in air pollution in South Asia by midcentury. Under RCP8.5, the frequency of air pollution events is predicted to increase by 20–120 days per year in 2050 compared to the present-day conditions, with particulate matter of less than 2.5 μm in diameter predicted to breach the World Health Organization ambient air quality guidelines on an almost daily basis in many parts of South Asia. These results indicate that while the RCP scenarios project a global improvement in air quality, they generally result in degrading air quality in South Asia. ©2018. American Geophysical Union. All Rights Reserved.
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Sabatino, Di S D; Buccolieri, R; Kumar, P Spatial distribution of air pollutants in Cities Book Springer International Publishing, 2018, (cited By 1). @book{DiSabatino201875b,
title = {Spatial distribution of air pollutants in Cities}, author = {S D Di Sabatino and R Buccolieri and P Kumar}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046145276&doi=10.1007%2f978-3-319-62731-1_5&partnerID=40&md5=7166019cc260dffbf89e487f3f60635d}, doi = {10.1007/978-3-319-62731-1_5}, year = {2018}, date = {2018-01-01}, journal = {Clinical Handbook of Air Pollution-Related Diseases}, pages = {75-95}, publisher = {Springer International Publishing}, note = {cited By 1}, keywords = {}, pubstate = {published}, tppubtype = {book} } |
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Bhuyan, P; Deka, P; Prakash, A; Balachandran, S; Hoque, R R Chemical characterization and source apportionment of aerosol over mid Brahmaputra Valley, India Journal Article Environmental Pollution, 234 , pp. 997-1010, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Bhuyan2018997b,
title = {Chemical characterization and source apportionment of aerosol over mid Brahmaputra Valley, India}, author = {P Bhuyan and P Deka and A Prakash and S Balachandran and R R Hoque}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038808847&doi=10.1016%2fj.envpol.2017.12.009&partnerID=40&md5=a5001e4dfa4e36a79efbaefb58711fbd}, doi = {10.1016/j.envpol.2017.12.009}, year = {2018}, date = {2018-01-01}, journal = {Environmental Pollution}, volume = {234}, pages = {997-1010}, publisher = {Elsevier Ltd}, abstract = {Aerosol samples (as PM10 n = 250) were collected from three rural/remote receptor locations in the mid Brahmaputra plain region and were chemically characterized for metals (Al, Fe, Co, Cu, Cr, Cd, Mn, Ni, Pb), ions (Ca2+, Mg2+, Na+, K+, NH4 +, F−, Cl−, NO3 −, SO4 2−), and carbon. Vital ratios like NO3 −/SO4 2−, EC/OC, K+/EC, K+/OC, enrichment factors and inter-species correlations were exploited to appreciate possible sources of aerosol. These empirical analyses pointed towards anthropogenic contributions of aerosol, particularly from biomass burning, vehicular emission, and road dust. The chemically characterized concentration data were subsequently fed into two receptor models viz. Principal Component Analysis-Multiple Linear Regression (PCA-MLR) and Chemical Mass Balance (CMB) for apportionment of sources of aerosol. The PCA-MLR estimates identified that the combustion sources together accounted for ∼42% of aerosol and the contribution of secondary formation to be 24%. Road and crustal dusts have been well apportioned by PCA-MLR, which together accounts for ∼26% of the aerosol. The CMB model estimates explained that the combustion sources taken together contributed ∼47% to the aerosol, which includes biomass burning (27%), vehicular emission (13%), coal (1%), kerosene (4%), and petroleum refining (2%). Other major sources that were apportioned were road dust (15%), crustal dust (26%), and construction dust (6%). There are inherent limitations in the source strength estimations because of uncertainty present in the source emission profiles that have been applied to the remote location of India. However, both the models (PCA-MLR and CMB) estimated the contribution of combustion sources to 42 and 47% respectively, which is comparable. PCA-MLR and CMB models resolved that combustion sources dominate the contribution to aerosol. Dusts originating from roads, crust and construction are other important sources. © 2017 Elsevier Ltd}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } Aerosol samples (as PM10 n = 250) were collected from three rural/remote receptor locations in the mid Brahmaputra plain region and were chemically characterized for metals (Al, Fe, Co, Cu, Cr, Cd, Mn, Ni, Pb), ions (Ca2+, Mg2+, Na+, K+, NH4 +, F−, Cl−, NO3 −, SO4 2−), and carbon. Vital ratios like NO3 −/SO4 2−, EC/OC, K+/EC, K+/OC, enrichment factors and inter-species correlations were exploited to appreciate possible sources of aerosol. These empirical analyses pointed towards anthropogenic contributions of aerosol, particularly from biomass burning, vehicular emission, and road dust. The chemically characterized concentration data were subsequently fed into two receptor models viz. Principal Component Analysis-Multiple Linear Regression (PCA-MLR) and Chemical Mass Balance (CMB) for apportionment of sources of aerosol. The PCA-MLR estimates identified that the combustion sources together accounted for ∼42% of aerosol and the contribution of secondary formation to be 24%. Road and crustal dusts have been well apportioned by PCA-MLR, which together accounts for ∼26% of the aerosol. The CMB model estimates explained that the combustion sources taken together contributed ∼47% to the aerosol, which includes biomass burning (27%), vehicular emission (13%), coal (1%), kerosene (4%), and petroleum refining (2%). Other major sources that were apportioned were road dust (15%), crustal dust (26%), and construction dust (6%). There are inherent limitations in the source strength estimations because of uncertainty present in the source emission profiles that have been applied to the remote location of India. However, both the models (PCA-MLR and CMB) estimated the contribution of combustion sources to 42 and 47% respectively, which is comparable. PCA-MLR and CMB models resolved that combustion sources dominate the contribution to aerosol. Dusts originating from roads, crust and construction are other important sources. © 2017 Elsevier Ltd
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Goel, R Modelling of road traffic fatalities in India Journal Article Accident Analysis and Prevention, 112 , pp. 105-115, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Goel2018105b,
title = {Modelling of road traffic fatalities in India}, author = {R Goel}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044648110&doi=10.1016%2fj.aap.2017.12.019&partnerID=40&md5=0b8ab7cd094c48bf9278d669420155db}, doi = {10.1016/j.aap.2017.12.019}, year = {2018}, date = {2018-01-01}, journal = {Accident Analysis and Prevention}, volume = {112}, pages = {105-115}, publisher = {Elsevier Ltd}, abstract = {Passenger modes in India include walking, cycling, buses, trains, intermediate public transport modes (IPT) such as three-wheeled auto rickshaws or tuk-tuks, motorised two-wheelers (2W) as well as cars. However, epidemiological studies of traffic crashes in India have been limited in their approach to account for the exposure of these road users. In 2011, for the first time, census in India reported travel distance and mode of travel for workers. A Poisson-lognormal mixture regression model is developed at the state level to explore the relationship of road deaths of all the road users with commute travel distance by different on-road modes. The model controlled for diesel consumption (proxy for freight traffic), length of national highways, proportion of population in urban areas, and built-up population density. The results show that walking, cycling and, interestingly, IPT are associated with lower risk of road deaths, while 2W, car and bus are associated with higher risk. Promotion of IPT has twofold benefits of increasing safety as well as providing a sustainable mode of transport. The mode shift scenarios show that, for similar mode shift across the states, the resulting trends in road deaths are highly dependent on the baseline mode shares. The most worrying trend is the steep growth of death burden resulting from mode shift of walking and cycling to 2W. While the paper illustrates a limited set of mode shift scenarios involving two modes at a time, the model can be applied to assess safety impacts resulting from a more complex set of scenarios. © 2018 The Author}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } Passenger modes in India include walking, cycling, buses, trains, intermediate public transport modes (IPT) such as three-wheeled auto rickshaws or tuk-tuks, motorised two-wheelers (2W) as well as cars. However, epidemiological studies of traffic crashes in India have been limited in their approach to account for the exposure of these road users. In 2011, for the first time, census in India reported travel distance and mode of travel for workers. A Poisson-lognormal mixture regression model is developed at the state level to explore the relationship of road deaths of all the road users with commute travel distance by different on-road modes. The model controlled for diesel consumption (proxy for freight traffic), length of national highways, proportion of population in urban areas, and built-up population density. The results show that walking, cycling and, interestingly, IPT are associated with lower risk of road deaths, while 2W, car and bus are associated with higher risk. Promotion of IPT has twofold benefits of increasing safety as well as providing a sustainable mode of transport. The mode shift scenarios show that, for similar mode shift across the states, the resulting trends in road deaths are highly dependent on the baseline mode shares. The most worrying trend is the steep growth of death burden resulting from mode shift of walking and cycling to 2W. While the paper illustrates a limited set of mode shift scenarios involving two modes at a time, the model can be applied to assess safety impacts resulting from a more complex set of scenarios. © 2018 The Author
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Goldemberg, J; Martinez-Gomez, J; Sagar, A; Smith, K R Household air pollution, health, and climate change: Cleaning the air Journal Article Environmental Research Letters, 13 (3), 2018, (cited By 2). Abstract | Links | BibTeX | Tags: @article{Goldemberg2018b,
title = {Household air pollution, health, and climate change: Cleaning the air}, author = {J Goldemberg and J Martinez-Gomez and A Sagar and K R Smith}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048262606&doi=10.1088%2f1748-9326%2faaa49d&partnerID=40&md5=140d630381283cc595fab43f9230e231}, doi = {10.1088/1748-9326/aaa49d}, year = {2018}, date = {2018-01-01}, journal = {Environmental Research Letters}, volume = {13}, number = {3}, publisher = {Institute of Physics Publishing}, abstract = {Air pollution from the use of solid household fuels is now recognized to be a major health risk in developing countries. Accordingly, there has been some shift in development thinking and investment from previous efforts, which has focused only on improving the efficiency of household fuel use, to those that focus on reducing exposure to the air pollution that leads to health impact. Unfortunately, however, this is occurring just as the climate agenda has come to dominate much of the discourse and action on international sustainable development. Thus, instead of optimizing approaches that centrally focus on the large health impact, the household energy agenda has been hampered by the constraints imposed by a narrow definition of sustainability – one primarily driven by the desire to mitigate greenhouse emissions by relying on renewable biomass fueling so-called improved cookstoves. In reality, however, solid biomass is extremely difficult to burn sufficiently cleanly in household stoves to reach health goals. In comparison to the international development community, however, some large countries, notably Brazil historically and more recently, India have substantially expanded the use of liquefied petroleum gas (LPG) in their household energy mix, using their own resources, having a major impact on their national energy picture. The net climate impact of such approaches compared to current biomass stoves is minimal or non-existent, and the social and health benefits are, in contrast, potentially great. LPG can be seen as a transition fuel for clean household energy, with induction stoves powered by renewables as the holy grail (an approach already being adopted by Ecuador as also discussed here). The enormous human and social benefits of clean energy, rather than climate concerns, should dominate the household energy access agenda today. © 2018 The Author(s). Published by IOP Publishing Ltd.}, note = {cited By 2}, keywords = {}, pubstate = {published}, tppubtype = {article} } Air pollution from the use of solid household fuels is now recognized to be a major health risk in developing countries. Accordingly, there has been some shift in development thinking and investment from previous efforts, which has focused only on improving the efficiency of household fuel use, to those that focus on reducing exposure to the air pollution that leads to health impact. Unfortunately, however, this is occurring just as the climate agenda has come to dominate much of the discourse and action on international sustainable development. Thus, instead of optimizing approaches that centrally focus on the large health impact, the household energy agenda has been hampered by the constraints imposed by a narrow definition of sustainability – one primarily driven by the desire to mitigate greenhouse emissions by relying on renewable biomass fueling so-called improved cookstoves. In reality, however, solid biomass is extremely difficult to burn sufficiently cleanly in household stoves to reach health goals. In comparison to the international development community, however, some large countries, notably Brazil historically and more recently, India have substantially expanded the use of liquefied petroleum gas (LPG) in their household energy mix, using their own resources, having a major impact on their national energy picture. The net climate impact of such approaches compared to current biomass stoves is minimal or non-existent, and the social and health benefits are, in contrast, potentially great. LPG can be seen as a transition fuel for clean household energy, with induction stoves powered by renewables as the holy grail (an approach already being adopted by Ecuador as also discussed here). The enormous human and social benefits of clean energy, rather than climate concerns, should dominate the household energy access agenda today. © 2018 The Author(s). Published by IOP Publishing Ltd.
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Prakash, J; Lohia, T; Mandariya, A K; Habib, G; Gupta, T; Gupta, S K Chemical characterization and quantitativ e assessment of source-specific health risk of trace metals in PM1.0 at a road site of Delhi, India Journal Article Environmental Science and Pollution Research, 25 (9), pp. 8747-8764, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Prakash20188747b,
title = {Chemical characterization and quantitativ e assessment of source-specific health risk of trace metals in PM1.0 at a road site of Delhi, India}, author = {J Prakash and T Lohia and A K Mandariya and G Habib and T Gupta and S K Gupta}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040333499&doi=10.1007%2fs11356-017-1174-9&partnerID=40&md5=09e75a7807bdbbb3021fac651a8e976e}, doi = {10.1007/s11356-017-1174-9}, year = {2018}, date = {2018-01-01}, journal = {Environmental Science and Pollution Research}, volume = {25}, number = {9}, pages = {8747-8764}, publisher = {Springer Verlag}, abstract = {This study presents the concentration of submicron aerosol (PM1.0) collected during November, 2009 to March, 2010 at two road sites near the Indian Institute of Technology Delhi campus. In winter, PM1.0 composed 83% of PM2.5 indicating the dominance of combustion activity-generated particles. Principal component analysis (PCA) proved secondary aerosol formation as a dominant process in enhancing aerosol concentration at a receptor site along with biomass burning, vehicle exhaust, road dust, engine and tire tear wear, and secondary ammonia. The non-carcinogenic and excess cancer risk for adults and children were estimated for trace element data set available for road site and at elevated site from another parallel work. The decrease in average hazard quotient (HQ) for children and adults was estimated in following order: Mn > Cr > Ni > Pb > Zn > Cu both at road and elevated site. For children, the mean HQs were observed in safe level for Cu, Ni, Zn, and Pb; however, values exceeded safe limit for Cr and Mn at road site. The average highest hazard index values for children and adults were estimated as 22 and 10, respectively, for road site and 7 and 3 for elevated site. The road site average excess cancer risk (ECR) risk of Cr and Ni was close to tolerable limit (10−4) for adults and it was 13–16 times higher than the safe limit (10−6) for children. The ECR of Ni for adults and children was 102 and 14 times higher at road site compared to elevated site. Overall, the observed ECR values far exceed the acceptable level. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } This study presents the concentration of submicron aerosol (PM1.0) collected during November, 2009 to March, 2010 at two road sites near the Indian Institute of Technology Delhi campus. In winter, PM1.0 composed 83% of PM2.5 indicating the dominance of combustion activity-generated particles. Principal component analysis (PCA) proved secondary aerosol formation as a dominant process in enhancing aerosol concentration at a receptor site along with biomass burning, vehicle exhaust, road dust, engine and tire tear wear, and secondary ammonia. The non-carcinogenic and excess cancer risk for adults and children were estimated for trace element data set available for road site and at elevated site from another parallel work. The decrease in average hazard quotient (HQ) for children and adults was estimated in following order: Mn > Cr > Ni > Pb > Zn > Cu both at road and elevated site. For children, the mean HQs were observed in safe level for Cu, Ni, Zn, and Pb; however, values exceeded safe limit for Cr and Mn at road site. The average highest hazard index values for children and adults were estimated as 22 and 10, respectively, for road site and 7 and 3 for elevated site. The road site average excess cancer risk (ECR) risk of Cr and Ni was close to tolerable limit (10−4) for adults and it was 13–16 times higher than the safe limit (10−6) for children. The ECR of Ni for adults and children was 102 and 14 times higher at road site compared to elevated site. Overall, the observed ECR values far exceed the acceptable level. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
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Gollapalli, M; Kota, S H Methane emissions from a landfill in north-east India: Performance of various landfill gas emission models Journal Article Environmental Pollution, 234 , pp. 174-180, 2018, (cited By 2). Abstract | Links | BibTeX | Tags: @article{Gollapalli2018174b,
title = {Methane emissions from a landfill in north-east India: Performance of various landfill gas emission models}, author = {M Gollapalli and S H Kota}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034748876&doi=10.1016%2fj.envpol.2017.11.064&partnerID=40&md5=9dfec6936b04f17312d418824f1af8d3}, doi = {10.1016/j.envpol.2017.11.064}, year = {2018}, date = {2018-01-01}, journal = {Environmental Pollution}, volume = {234}, pages = {174-180}, publisher = {Elsevier Ltd}, abstract = {Rapid urbanization and economic growth has led to significant increase in municipal solid waste generation in India during the last few decades and its management has become a major issue because of poor waste management practices. Solid waste generated is deposited into open dumping sites with hardly any segregation and processing. Carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) are the major greenhouse gases that are released from the landfill sites due to the biodegradation of organic matter. In this present study, CH4 and CO2 emissions from a landfill in north-east India are estimated using a flux chamber during September, 2015 to August, 2016. The average emission rates of CH4 and CO2 are 68 and 92 mg/min/m2, respectively. The emissions are highest in the summer whilst being lowest in winter. The diurnal variation of emissions indicated that the emissions follow a trend similar to temperature in all the seasons. Correlation coefficients of CH4 and temperature in summer, monsoon and winter are 0.99, 0.87 and 0.97, respectively. The measured CH4 in this study is in the range of other studies around the world. Modified Triangular Method (MTM), IPCC model and the USEPA Landfill gas emissions model (LandGEM) were used to predict the CH4 emissions during the study year. The consequent simulation results indicate that the MTM, LandGEM-Clean Air Act, LandGEM-Inventory and IPCC models predict 1.9, 3.3, 1.6 and 1.4 times of the measured CH4 emission flux in this study. Assuming that this higher prediction of CH4 levels observed in this study holds well for other landfills in this region, a new CH4 emission inventory (Units: Tonnes/year), with a resolution of 0.10 × 0.10 has been developed. This study stresses the importance of biodegradable composition of waste and meteorology, and also points out the drawbacks of the widely used landfill emission models. Predictions from commonly used landfill gas emission models were compared to in-situ observations. © 2017 Elsevier Ltd}, note = {cited By 2}, keywords = {}, pubstate = {published}, tppubtype = {article} } Rapid urbanization and economic growth has led to significant increase in municipal solid waste generation in India during the last few decades and its management has become a major issue because of poor waste management practices. Solid waste generated is deposited into open dumping sites with hardly any segregation and processing. Carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) are the major greenhouse gases that are released from the landfill sites due to the biodegradation of organic matter. In this present study, CH4 and CO2 emissions from a landfill in north-east India are estimated using a flux chamber during September, 2015 to August, 2016. The average emission rates of CH4 and CO2 are 68 and 92 mg/min/m2, respectively. The emissions are highest in the summer whilst being lowest in winter. The diurnal variation of emissions indicated that the emissions follow a trend similar to temperature in all the seasons. Correlation coefficients of CH4 and temperature in summer, monsoon and winter are 0.99, 0.87 and 0.97, respectively. The measured CH4 in this study is in the range of other studies around the world. Modified Triangular Method (MTM), IPCC model and the USEPA Landfill gas emissions model (LandGEM) were used to predict the CH4 emissions during the study year. The consequent simulation results indicate that the MTM, LandGEM-Clean Air Act, LandGEM-Inventory and IPCC models predict 1.9, 3.3, 1.6 and 1.4 times of the measured CH4 emission flux in this study. Assuming that this higher prediction of CH4 levels observed in this study holds well for other landfills in this region, a new CH4 emission inventory (Units: Tonnes/year), with a resolution of 0.10 × 0.10 has been developed. This study stresses the importance of biodegradable composition of waste and meteorology, and also points out the drawbacks of the widely used landfill emission models. Predictions from commonly used landfill gas emission models were compared to in-situ observations. © 2017 Elsevier Ltd
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Lu, X; Zhang, L; Liu, X; Gao, M; Zhao, Y; Shao, J Lower tropospheric ozone over India and its linkage to the South Asian monsoon Journal Article Atmospheric Chemistry and Physics, 18 (5), pp. 3101-3118, 2018, (cited By 2). Abstract | Links | BibTeX | Tags: @article{Lu20183101b,
title = {Lower tropospheric ozone over India and its linkage to the South Asian monsoon}, author = {X Lu and L Zhang and X Liu and M Gao and Y Zhao and J Shao}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042946218&doi=10.5194%2facp-18-3101-2018&partnerID=40&md5=ac1f8c5c06ba2309be1d09035a00e7a4}, doi = {10.5194/acp-18-3101-2018}, year = {2018}, date = {2018-01-01}, journal = {Atmospheric Chemistry and Physics}, volume = {18}, number = {5}, pages = {3101-3118}, publisher = {Copernicus GmbH}, abstract = {Lower tropospheric (surface to 600 hPa) ozone over India poses serious risks to both human health and crops, and potentially affects global ozone distribution through frequent deep convection in tropical regions. Our current understanding of the processes controlling seasonal and long-term variations in lower tropospheric ozone over this region is rather limited due to spatially and temporally sparse observations. Here we present an integrated process analysis of the seasonal cycle, interannual variability, and long-term trends of lower tropospheric ozone over India and its linkage to the South Asian monsoon using the Ozone Monitoring Instrument (OMI) satellite observations for years 2006-2014 interpreted with a global chemical transport model (GEOS-Chem) simulation for 1990- 2010. OMI observed lower tropospheric ozone over India averaged for 2006-2010, showing the highest concentrations (54.1 ppbv) in the pre-summer monsoon season (May) and the lowest concentrations (40.5 ppbv) in the summer monsoon season (August). Process analyses in GEOS-Chem show that hot and dry meteorological conditions and active biomass burning together contribute to 5.8 Tg more ozone being produced in the lower troposphere in India in May than January. The onset of the summer monsoon brings ozone-unfavorable meteorological conditions and strong upward transport, which all lead to large decreases in the lower tropospheric ozone burden. Interannually, we find that both OMI and GEOS-Chem indicate strong positive correlations (r = 0:55-0.58) between ozone and surface temperature in pre-summer monsoon seasons, with larger correlations found in high NOx emission regions reflecting NOx-limited production conditions. Summer monsoon seasonal mean ozone levels are strongly controlled by monsoon strengths. Lower ozone concentrations are found in stronger monsoon seasons mainly due to less ozone net chemical production. Furthermore, model simulations over 1990-2010 estimate a mean annual trend of 0.19±0.07 (p value<0.01) ppbv yr1 in Indian lower tropospheric ozone over this period, which are mainly driven by increases in anthropogenic emissions with a small contribution (about 7 %) from global methane concentration increases. © Author(s) 2018.}, note = {cited By 2}, keywords = {}, pubstate = {published}, tppubtype = {article} } Lower tropospheric (surface to 600 hPa) ozone over India poses serious risks to both human health and crops, and potentially affects global ozone distribution through frequent deep convection in tropical regions. Our current understanding of the processes controlling seasonal and long-term variations in lower tropospheric ozone over this region is rather limited due to spatially and temporally sparse observations. Here we present an integrated process analysis of the seasonal cycle, interannual variability, and long-term trends of lower tropospheric ozone over India and its linkage to the South Asian monsoon using the Ozone Monitoring Instrument (OMI) satellite observations for years 2006-2014 interpreted with a global chemical transport model (GEOS-Chem) simulation for 1990- 2010. OMI observed lower tropospheric ozone over India averaged for 2006-2010, showing the highest concentrations (54.1 ppbv) in the pre-summer monsoon season (May) and the lowest concentrations (40.5 ppbv) in the summer monsoon season (August). Process analyses in GEOS-Chem show that hot and dry meteorological conditions and active biomass burning together contribute to 5.8 Tg more ozone being produced in the lower troposphere in India in May than January. The onset of the summer monsoon brings ozone-unfavorable meteorological conditions and strong upward transport, which all lead to large decreases in the lower tropospheric ozone burden. Interannually, we find that both OMI and GEOS-Chem indicate strong positive correlations (r = 0:55-0.58) between ozone and surface temperature in pre-summer monsoon seasons, with larger correlations found in high NOx emission regions reflecting NOx-limited production conditions. Summer monsoon seasonal mean ozone levels are strongly controlled by monsoon strengths. Lower ozone concentrations are found in stronger monsoon seasons mainly due to less ozone net chemical production. Furthermore, model simulations over 1990-2010 estimate a mean annual trend of 0.19±0.07 (p value<0.01) ppbv yr1 in Indian lower tropospheric ozone over this period, which are mainly driven by increases in anthropogenic emissions with a small contribution (about 7 %) from global methane concentration increases. © Author(s) 2018.
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Gautam, S; Yadav, A; Pillarisetti, A; Smith, K; Arora, N 120 (1), Institute of Physics Publishing, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @conference{Gautam2018d,
title = {Short-Term Introduction of Air Pollutants from Fireworks during Diwali in Rural Palwal, Haryana, India: A Case Study}, author = {S Gautam and A Yadav and A Pillarisetti and K Smith and N Arora}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043982276&doi=10.1088%2f1755-1315%2f120%2f1%2f012009&partnerID=40&md5=51b3b5e8a5c0629dda9b6afc8e0967a1}, doi = {10.1088/1755-1315/120/1/012009}, year = {2018}, date = {2018-01-01}, journal = {IOP Conference Series: Earth and Environmental Science}, volume = {120}, number = {1}, publisher = {Institute of Physics Publishing}, abstract = {The contribution of firework-related air pollutants into the rural atmosphere was monitored by measuring ambient air concentrations of PM2.5, CO, and metals over Mitrol- Aurangabad, Haryana, India, before, during, and after the 2015 Diwali celebration. PM2.5 concentrations were observed to be approximately 5 times and 12 times higher than Indian and WHO 24-h standards, respectively. CO concentrations on the day of Diwali were found to be nearly 7.5 times and nearly 1.5 times higher than Indian standards and WHO 8-h standards, respectively. Increased concentrations of SO4, K, N3, Al, and Na were observed. SO4, K, N3, Al, and Na were found between approximately 2 and 5 times higher on festival days than on a normal, non-festival day in November. Use of firecrackers during Diwali and surrounding celebrations thus contribute to decreased air quality and elevated levels of air pollutants associated with adverse health impacts. Optimization or controlled use of firecrackers during Diwali is suggested in rural areas. © Published under licence by IOP Publishing Ltd.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {conference} } The contribution of firework-related air pollutants into the rural atmosphere was monitored by measuring ambient air concentrations of PM2.5, CO, and metals over Mitrol- Aurangabad, Haryana, India, before, during, and after the 2015 Diwali celebration. PM2.5 concentrations were observed to be approximately 5 times and 12 times higher than Indian and WHO 24-h standards, respectively. CO concentrations on the day of Diwali were found to be nearly 7.5 times and nearly 1.5 times higher than Indian standards and WHO 8-h standards, respectively. Increased concentrations of SO4, K, N3, Al, and Na were observed. SO4, K, N3, Al, and Na were found between approximately 2 and 5 times higher on festival days than on a normal, non-festival day in November. Use of firecrackers during Diwali and surrounding celebrations thus contribute to decreased air quality and elevated levels of air pollutants associated with adverse health impacts. Optimization or controlled use of firecrackers during Diwali is suggested in rural areas. © Published under licence by IOP Publishing Ltd.
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Gautam, S; Pillarisetti, A; Yadav, A; Singh, D; Arora, N; Smith, K Daily average exposures to carbon monoxide from combustion of biomass fuels in rural households of Haryana, India Journal Article Environment, Development and Sustainability, pp. 1-9, 2018, (cited By 3; Article in Press). Abstract | Links | BibTeX | Tags: @article{Gautam20181b,
title = {Daily average exposures to carbon monoxide from combustion of biomass fuels in rural households of Haryana, India}, author = {S Gautam and A Pillarisetti and A Yadav and D Singh and N Arora and K Smith}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043401357&doi=10.1007%2fs10668-018-0131-1&partnerID=40&md5=1d9e2cb210b87afa4dd1c9287a02b400}, doi = {10.1007/s10668-018-0131-1}, year = {2018}, date = {2018-01-01}, journal = {Environment, Development and Sustainability}, pages = {1-9}, publisher = {Springer Netherlands}, abstract = {Exposure to harmful by-products of combustion arising from the use of biomass fuels for cooking and heating in rural areas of developing countries results in poor air quality and is responsible for millions of deaths yearly. Little formal quantification and measurement of carbon monoxide (CO), one of these harmful air pollutants, have been performed in rural areas of North India. In the current study, we measured exposure to CO from cooking and heating in seven households using biomass and liquid petroleum gas (LPG) in open and closed kitchens. Exposures to CO ranged from 4.81 to 7.01, 0.20 to 1.81, and 0.02 to 0.75 mg m−3 for households cooking with biomass, cooking with LPG, and for households in which no cooking occurred, respectively. It was observed that the CO concentration in biomass-only households is much higher (78%) than in LPG-only households (14%). We found exposures in closed kitchens approximately two times higher than in open kitchens. Location of the kitchen (i.e., open vs. closed) was the most important determinant of exposure of primary cooks to CO in this geography. © 2018 Springer Science+Business Media B.V., part of Springer Nature}, note = {cited By 3; Article in Press}, keywords = {}, pubstate = {published}, tppubtype = {article} } Exposure to harmful by-products of combustion arising from the use of biomass fuels for cooking and heating in rural areas of developing countries results in poor air quality and is responsible for millions of deaths yearly. Little formal quantification and measurement of carbon monoxide (CO), one of these harmful air pollutants, have been performed in rural areas of North India. In the current study, we measured exposure to CO from cooking and heating in seven households using biomass and liquid petroleum gas (LPG) in open and closed kitchens. Exposures to CO ranged from 4.81 to 7.01, 0.20 to 1.81, and 0.02 to 0.75 mg m−3 for households cooking with biomass, cooking with LPG, and for households in which no cooking occurred, respectively. It was observed that the CO concentration in biomass-only households is much higher (78%) than in LPG-only households (14%). We found exposures in closed kitchens approximately two times higher than in open kitchens. Location of the kitchen (i.e., open vs. closed) was the most important determinant of exposure of primary cooks to CO in this geography. © 2018 Springer Science+Business Media B.V., part of Springer Nature
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Sahu, R K; Pervez, S; Chow, J C; Watson, J G; Tiwari, S; Panicker, A S; Chakrabarty, R K; Pervez, Y F Temporal and spatial variations of PM2.5 organic and elemental carbon in Central India Journal Article Environmental Geochemistry and Health, pp. 1-18, 2018, (cited By 0; Article in Press). Abstract | Links | BibTeX | Tags: @article{Sahu20181b,
title = {Temporal and spatial variations of PM2.5 organic and elemental carbon in Central India}, author = {R K Sahu and S Pervez and J C Chow and J G Watson and S Tiwari and A S Panicker and R K Chakrabarty and Y F Pervez}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044569162&doi=10.1007%2fs10653-018-0093-0&partnerID=40&md5=8bb15a87e6a2a970af1b3dfe3406e295}, doi = {10.1007/s10653-018-0093-0}, year = {2018}, date = {2018-01-01}, journal = {Environmental Geochemistry and Health}, pages = {1-18}, publisher = {Springer Netherlands}, abstract = {This study describes spatiotemporal patterns from October 2015 to September 2016 for PM2.5 mass and carbon measurements in rural (Kosmarra), urban (Raipur), and industrial (Bhilai) environments, in Chhattisgarh, Central India. Twenty-four-hour samples were acquired once every other week at the rural and industrial sites. Twelve-hour daytime and nighttime samples were acquired either a once a week or once every other week at the urban site. Each site was equipped with two portable, battery-powered, miniVol air samplers with PM2.5 inlets. Annual average PM2.5 mass concentrations were 71.8 ± 27 µg m−3 at the rural site, 133 ± 51 µg m−3 at the urban site, and 244.5 ± 63.3 µg m−3 at the industrial site, ~ 2–6 times higher than the Indian Annual National Ambient Air Quality Standard of 40 µg m−3. Average monthly nighttime PM2.5 and carbon concentrations at the urban site were consistently higher than those of daytime from November 2015 to April 2016, when temperatures were low. Annual average total carbon (TC = OC + EC) at the urban (46.8 ± 23.8 µg m−3) and industrial (98.0 ± 17.2 µg m−3) sites also exceeded the Indian PM2.5 NAAQS. TC accounted for 30–40% of PM2.5 mass. Annual average OC ranged from 17.8 ± 6.1 µg m−3 at the rural site to 64 ± 9.4 µg m−3 at the industrial site, with EC ranging from 4.51 ± 2.2 to 34.01 ± 7.8 µg m−3. The average OC/EC ratio at the industrial site (1.88) was 18% lower than that at the urban site and 52% lower than that at the rural site. OC was attributed to 43.0% of secondary organic carbon (SOC) at the rural site, twice that estimated for the urban and industrial sites. Mortality burden estimates for PM2.5 EC are 4416 and 6196 excess deaths at the urban and industrial sites, respectively, during 2015–2016. © 2018 Springer Science+Business Media B.V., part of Springer Nature}, note = {cited By 0; Article in Press}, keywords = {}, pubstate = {published}, tppubtype = {article} } This study describes spatiotemporal patterns from October 2015 to September 2016 for PM2.5 mass and carbon measurements in rural (Kosmarra), urban (Raipur), and industrial (Bhilai) environments, in Chhattisgarh, Central India. Twenty-four-hour samples were acquired once every other week at the rural and industrial sites. Twelve-hour daytime and nighttime samples were acquired either a once a week or once every other week at the urban site. Each site was equipped with two portable, battery-powered, miniVol air samplers with PM2.5 inlets. Annual average PM2.5 mass concentrations were 71.8 ± 27 µg m−3 at the rural site, 133 ± 51 µg m−3 at the urban site, and 244.5 ± 63.3 µg m−3 at the industrial site, ~ 2–6 times higher than the Indian Annual National Ambient Air Quality Standard of 40 µg m−3. Average monthly nighttime PM2.5 and carbon concentrations at the urban site were consistently higher than those of daytime from November 2015 to April 2016, when temperatures were low. Annual average total carbon (TC = OC + EC) at the urban (46.8 ± 23.8 µg m−3) and industrial (98.0 ± 17.2 µg m−3) sites also exceeded the Indian PM2.5 NAAQS. TC accounted for 30–40% of PM2.5 mass. Annual average OC ranged from 17.8 ± 6.1 µg m−3 at the rural site to 64 ± 9.4 µg m−3 at the industrial site, with EC ranging from 4.51 ± 2.2 to 34.01 ± 7.8 µg m−3. The average OC/EC ratio at the industrial site (1.88) was 18% lower than that at the urban site and 52% lower than that at the rural site. OC was attributed to 43.0% of secondary organic carbon (SOC) at the rural site, twice that estimated for the urban and industrial sites. Mortality burden estimates for PM2.5 EC are 4416 and 6196 excess deaths at the urban and industrial sites, respectively, during 2015–2016. © 2018 Springer Science+Business Media B.V., part of Springer Nature
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Tang, R; Tian, L; Thach, T -Q; Tsui, T H; Brauer, M; Lee, M; Allen, R; Yuchi, W; Lai, P -C; Wong, P; Barratt, B Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong Journal Article Environment International, 113 , pp. 100-108, 2018, (cited By 1). Abstract | Links | BibTeX | Tags: @article{Tang2018100b,
title = {Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong}, author = {R Tang and L Tian and T -Q Thach and T H Tsui and M Brauer and M Lee and R Allen and W Yuchi and P -C Lai and P Wong and B Barratt}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041655270&doi=10.1016%2fj.envint.2018.01.009&partnerID=40&md5=c6722cbdd30bc4e11ab310fe2786cf1d}, doi = {10.1016/j.envint.2018.01.009}, year = {2018}, date = {2018-01-01}, journal = {Environment International}, volume = {113}, pages = {100-108}, publisher = {Elsevier Ltd}, abstract = {Background: Epidemiological studies typically use subjects’ residential address to estimate individuals’ air pollution exposure. However, in reality this exposure is rarely static as people move from home to work/study locations and commute during the day. Integrating mobility and time-activity data may reduce errors and biases, thereby improving estimates of health risks. Objectives: To incorporate land use regression with movement and building infiltration data to estimate time-weighted air pollution exposures stratified by age, sex, and employment status for population subgroups in Hong Kong. Methods: A large population-representative survey (N = 89,385) was used to characterize travel behavior, and derive time-activity pattern for each subject. Infiltration factors calculated from indoor/outdoor monitoring campaigns were used to estimate micro-environmental concentrations. We evaluated dynamic and static (residential location-only) exposures in a staged modeling approach to quantify effects of each component. Results: Higher levels of exposures were found for working adults and students due to increased mobility. Compared to subjects aged 65 or older, exposures to PM2.5, BC, and NO2 were 13%, 39% and 14% higher, respectively for subjects aged below 18, and 3%, 18% and 11% higher, respectively for working adults. Exposures of females were approximately 4% lower than those of males. Dynamic exposures were around 20% lower than ambient exposures at residential addresses. Conclusions: The incorporation of infiltration and mobility increased heterogeneity in population exposure and allowed identification of highly exposed groups. The use of ambient concentrations may lead to exposure misclassification which introduces bias, resulting in lower effect estimates than ‘true’ exposures. © 2018 Elsevier Ltd}, note = {cited By 1}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background: Epidemiological studies typically use subjects’ residential address to estimate individuals’ air pollution exposure. However, in reality this exposure is rarely static as people move from home to work/study locations and commute during the day. Integrating mobility and time-activity data may reduce errors and biases, thereby improving estimates of health risks. Objectives: To incorporate land use regression with movement and building infiltration data to estimate time-weighted air pollution exposures stratified by age, sex, and employment status for population subgroups in Hong Kong. Methods: A large population-representative survey (N = 89,385) was used to characterize travel behavior, and derive time-activity pattern for each subject. Infiltration factors calculated from indoor/outdoor monitoring campaigns were used to estimate micro-environmental concentrations. We evaluated dynamic and static (residential location-only) exposures in a staged modeling approach to quantify effects of each component. Results: Higher levels of exposures were found for working adults and students due to increased mobility. Compared to subjects aged 65 or older, exposures to PM2.5, BC, and NO2 were 13%, 39% and 14% higher, respectively for subjects aged below 18, and 3%, 18% and 11% higher, respectively for working adults. Exposures of females were approximately 4% lower than those of males. Dynamic exposures were around 20% lower than ambient exposures at residential addresses. Conclusions: The incorporation of infiltration and mobility increased heterogeneity in population exposure and allowed identification of highly exposed groups. The use of ambient concentrations may lead to exposure misclassification which introduces bias, resulting in lower effect estimates than ‘true’ exposures. © 2018 Elsevier Ltd
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Dey, S On the theoretical aspects of improved fog detection and prediction in India Journal Article Atmospheric Research, 202 , pp. 77-80, 2018, (cited By 2). Abstract | Links | BibTeX | Tags: @article{Dey201877b,
title = {On the theoretical aspects of improved fog detection and prediction in India}, author = {S Dey}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034083902&doi=10.1016%2fj.atmosres.2017.11.018&partnerID=40&md5=930437ab369978196f5a626d7e9458b2}, doi = {10.1016/j.atmosres.2017.11.018}, year = {2018}, date = {2018-01-01}, journal = {Atmospheric Research}, volume = {202}, pages = {77-80}, publisher = {Elsevier Ltd}, abstract = {The polluted Indo-Gangetic Basin (IGB) in northern India experiences fog (a condition when visibility degrades below 1 km) every winter (Dec-Jan) causing a massive loss of economy and even loss of life due to accidents. This can be minimized by improved fog detection (especially at night) and forecasting so that activities can be reorganized accordingly. Satellites detect fog at night by a positive brightness temperature difference (BTD). However, fixing the right BTD threshold holds the key to accuracy. Here I demonstrate the sensitivity of BTD in response to changes in fog and surface emissivity and their temperatures and justify a new BTD threshold. Further I quantify the dependence of critical fog droplet number concentration, NF (i.e. minimum fog concentration required to degrade visibility below 1 km) on liquid water content (LWC). NF decreases exponentially with an increase in LWC from 0.01 to 1 g/m3, beyond which it stabilizes. A 10 times low bias in simulated LWC below 1 g/m3 would require 107 times higher aerosol concentration to form the required number of fog droplets. These results provide the theoretical aspects that will help improving the existing fog detection algorithm and fog forecasting by numerical models in India. © 2017}, note = {cited By 2}, keywords = {}, pubstate = {published}, tppubtype = {article} } The polluted Indo-Gangetic Basin (IGB) in northern India experiences fog (a condition when visibility degrades below 1 km) every winter (Dec-Jan) causing a massive loss of economy and even loss of life due to accidents. This can be minimized by improved fog detection (especially at night) and forecasting so that activities can be reorganized accordingly. Satellites detect fog at night by a positive brightness temperature difference (BTD). However, fixing the right BTD threshold holds the key to accuracy. Here I demonstrate the sensitivity of BTD in response to changes in fog and surface emissivity and their temperatures and justify a new BTD threshold. Further I quantify the dependence of critical fog droplet number concentration, NF (i.e. minimum fog concentration required to degrade visibility below 1 km) on liquid water content (LWC). NF decreases exponentially with an increase in LWC from 0.01 to 1 g/m3, beyond which it stabilizes. A 10 times low bias in simulated LWC below 1 g/m3 would require 107 times higher aerosol concentration to form the required number of fog droplets. These results provide the theoretical aspects that will help improving the existing fog detection algorithm and fog forecasting by numerical models in India. © 2017
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Police, S; Sahu, S K; Tiwari, M; Pandit, G G Chemical composition and source apportionment of PM2.5 and PM2.5–10 in Trombay (Mumbai, India), a coastal industrial area Journal Article Particuology, 37 , pp. 143-153, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Police2018143b,
title = {Chemical composition and source apportionment of PM2.5 and PM2.5–10 in Trombay (Mumbai, India), a coastal industrial area}, author = {S Police and S K Sahu and M Tiwari and G G Pandit}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039712998&doi=10.1016%2fj.partic.2017.09.006&partnerID=40&md5=b040f128b300b8bf48fd190d6165b0f6}, doi = {10.1016/j.partic.2017.09.006}, year = {2018}, date = {2018-01-01}, journal = {Particuology}, volume = {37}, pages = {143-153}, publisher = {Elsevier B.V.}, abstract = {PM2.5 and PM2.5–10 concentrations, elemental constituents, and sources in a densely populated coastal industrial area (Trombay, Mumbai) were investigated in 2010 and 2011. The PM2.5 and PM2.5–10 concentrations were 13.50–71.60 and 22.40–127.78 μg/m3, respectively. The daily PM2.5 concentrations exceeded the Indian Central Pollution Control Board limit (60 μg/m3) several days in winter. Of the elements analyzed, Si then Al had the highest concentrations in PM2.5–10, but black carbon then Si had the highest concentrations in PM2.5. The element concentrations varied widely by season. Al, Ca, Fe, Si, and Ti concentrations were highest in summer, Cl, Mg, and Na concentrations were highest in the monsoon season, and the other trace metal concentrations in both PM2.5 and PM2.5–10 were highest in winter. The PM2.5 and PM2.5–10 sources were apportioned by positive matrix factorization. PM2.5 and PM2.5–10 had six dominant sources, crustal material (8.7% and 25.3%, respectively), sea salt spray (6.1% and 15.0%, respectively), coal/biomass combustion (25.5% and 13.8%, respectively), fuel oil combustion (19.0% and 11.2%, respectively), road traffic (17.7% and 12.6%, respectively), and the metal industry (10.6% and 7.0%, respectively). Anthropogenic sources clearly contributed most to PM2.5 but natural sources contributed most to PM2.5–10. © 2017 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } PM2.5 and PM2.5–10 concentrations, elemental constituents, and sources in a densely populated coastal industrial area (Trombay, Mumbai) were investigated in 2010 and 2011. The PM2.5 and PM2.5–10 concentrations were 13.50–71.60 and 22.40–127.78 μg/m3, respectively. The daily PM2.5 concentrations exceeded the Indian Central Pollution Control Board limit (60 μg/m3) several days in winter. Of the elements analyzed, Si then Al had the highest concentrations in PM2.5–10, but black carbon then Si had the highest concentrations in PM2.5. The element concentrations varied widely by season. Al, Ca, Fe, Si, and Ti concentrations were highest in summer, Cl, Mg, and Na concentrations were highest in the monsoon season, and the other trace metal concentrations in both PM2.5 and PM2.5–10 were highest in winter. The PM2.5 and PM2.5–10 sources were apportioned by positive matrix factorization. PM2.5 and PM2.5–10 had six dominant sources, crustal material (8.7% and 25.3%, respectively), sea salt spray (6.1% and 15.0%, respectively), coal/biomass combustion (25.5% and 13.8%, respectively), fuel oil combustion (19.0% and 11.2%, respectively), road traffic (17.7% and 12.6%, respectively), and the metal industry (10.6% and 7.0%, respectively). Anthropogenic sources clearly contributed most to PM2.5 but natural sources contributed most to PM2.5–10. © 2017 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences
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Jain, S; Sharma, S K; Mandal, T K; Saxena, M Source apportionment of PM10 in Delhi, India using PCA/APCS, UNMIX and PMF Journal Article Particuology, 37 , pp. 107-118, 2018, (cited By 4). Abstract | Links | BibTeX | Tags: @article{Jain2018107b,
title = {Source apportionment of PM10 in Delhi, India using PCA/APCS, UNMIX and PMF}, author = {S Jain and S K Sharma and T K Mandal and M Saxena}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028594318&doi=10.1016%2fj.partic.2017.05.009&partnerID=40&md5=f3c6570cc2a054c99456e98bd9ec0141}, doi = {10.1016/j.partic.2017.05.009}, year = {2018}, date = {2018-01-01}, journal = {Particuology}, volume = {37}, pages = {107-118}, publisher = {Elsevier B.V.}, abstract = {Source apportionment of particulate matter (PM10) measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal component scores (PCA/APCS) and UNMIX. The results were compared with previous estimates generated using the positive matrix factorization (PMF) receptor model to investigate each model’s source-apportioning capability. All models used the PM10 chemical composition (organic carbon (OC), elemental carbon (EC), water soluble inorganic ions (WSIC), and trace elements) for source apportionment. The average PM10 concentration during the study period was 249.7 ± 103.9 μg/m3 (range: 61.4–584.8 μg/m3). The UNMIX model resolved five sources (soil dust (SD), vehicular emissions (VE), secondary aerosols (SA), a mixed source of biomass burning (BB) and sea salt (SS), and industrial emissions (IE)). The PCA/APCS model also resolved five sources, two of which also included mixed sources (SD, VE, SD+SS, (SA+BB+SS) and IE). The PMF analysis differentiated seven individual sources (SD, VE, SA, BB, SS, IE, and fossil fuel combustion (FFC)). All models identified the main sources contributing to PM10 emissions and reconfirmed that VE, SA, BB, and SD were the dominant contributors in Delhi. © 2017 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences}, note = {cited By 4}, keywords = {}, pubstate = {published}, tppubtype = {article} } Source apportionment of particulate matter (PM10) measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal component scores (PCA/APCS) and UNMIX. The results were compared with previous estimates generated using the positive matrix factorization (PMF) receptor model to investigate each model’s source-apportioning capability. All models used the PM10 chemical composition (organic carbon (OC), elemental carbon (EC), water soluble inorganic ions (WSIC), and trace elements) for source apportionment. The average PM10 concentration during the study period was 249.7 ± 103.9 μg/m3 (range: 61.4–584.8 μg/m3). The UNMIX model resolved five sources (soil dust (SD), vehicular emissions (VE), secondary aerosols (SA), a mixed source of biomass burning (BB) and sea salt (SS), and industrial emissions (IE)). The PCA/APCS model also resolved five sources, two of which also included mixed sources (SD, VE, SD+SS, (SA+BB+SS) and IE). The PMF analysis differentiated seven individual sources (SD, VE, SA, BB, SS, IE, and fossil fuel combustion (FFC)). All models identified the main sources contributing to PM10 emissions and reconfirmed that VE, SA, BB, and SD were the dominant contributors in Delhi. © 2017 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences
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Cusworth, D H; Mickley, L J; Sulprizio, M P; Liu, T; Marlier, M E; Defries, R S; Guttikunda, S K; Gupta, P Quantifying the influence of agricultural fires in northwest India on urban air pollution in Delhi, India Journal Article Environmental Research Letters, 13 (4), 2018, (cited By 2). Abstract | Links | BibTeX | Tags: @article{Cusworth2018b,
title = {Quantifying the influence of agricultural fires in northwest India on urban air pollution in Delhi, India}, author = {D H Cusworth and L J Mickley and M P Sulprizio and T Liu and M E Marlier and R S Defries and S K Guttikunda and P Gupta}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047762912&doi=10.1088%2f1748-9326%2faab303&partnerID=40&md5=47a8ef895ae6aea8d16e2c109dc296aa}, doi = {10.1088/1748-9326/aab303}, year = {2018}, date = {2018-01-01}, journal = {Environmental Research Letters}, volume = {13}, number = {4}, publisher = {Institute of Physics Publishing}, abstract = {Since at least the 1980s, many farmers in northwest India have switched to mechanized combine harvesting to boost efficiency. This harvesting technique leaves abundant crop residue on the fields, which farmers typically burn to prepare their fields for subsequent planting. A key question is to what extent the large quantity of smoke emitted by these fires contributes to the already severe pollution in Delhi and across other parts of the heavily populated Indo-Gangetic Plain located downwind of the fires. Using a combination of observed and modeled variables, including surface measurements of PM2.5, we quantify the magnitude of the influence of agricultural fire emissions on surface air pollution in Delhi. With surface measurements, we first derive the signal of regional PM2.5 enhancements (i.e. the pollution above an anthropogenic baseline) during each post-monsoon burning season for 2012-2016. We next use the Stochastic Time-Inverted Lagrangian Transport model (STILT) to simulate surface PM2.5 using five fire emission inventories. We reproduce up to 25% of the weekly variability in total observed PM2.5 using STILT. Depending on year and emission inventory, our method attributes 7.0%-78% of the maximum observed PM2.5 enhancements in Delhi to fires. The large range in these attribution estimates points to the uncertainties in fire emission parameterizations, especially in regions where thick smoke may interfere with hotspots of fire radiative power. Although our model can generally reproduce the largest PM2.5 enhancements in Delhi air quality for 1-3 consecutive days each fire season, it fails to capture many smaller daily enhancements, which we attribute to the challenge of detecting small fires in the satellite retrieval. By quantifying the influence of upwind agricultural fire emissions on Delhi air pollution, our work underscores the potential health benefits of changes in farming practices to reduce fires. © 2018 The Author(s). Published by IOP Publishing Ltd.}, note = {cited By 2}, keywords = {}, pubstate = {published}, tppubtype = {article} } Since at least the 1980s, many farmers in northwest India have switched to mechanized combine harvesting to boost efficiency. This harvesting technique leaves abundant crop residue on the fields, which farmers typically burn to prepare their fields for subsequent planting. A key question is to what extent the large quantity of smoke emitted by these fires contributes to the already severe pollution in Delhi and across other parts of the heavily populated Indo-Gangetic Plain located downwind of the fires. Using a combination of observed and modeled variables, including surface measurements of PM2.5, we quantify the magnitude of the influence of agricultural fire emissions on surface air pollution in Delhi. With surface measurements, we first derive the signal of regional PM2.5 enhancements (i.e. the pollution above an anthropogenic baseline) during each post-monsoon burning season for 2012-2016. We next use the Stochastic Time-Inverted Lagrangian Transport model (STILT) to simulate surface PM2.5 using five fire emission inventories. We reproduce up to 25% of the weekly variability in total observed PM2.5 using STILT. Depending on year and emission inventory, our method attributes 7.0%-78% of the maximum observed PM2.5 enhancements in Delhi to fires. The large range in these attribution estimates points to the uncertainties in fire emission parameterizations, especially in regions where thick smoke may interfere with hotspots of fire radiative power. Although our model can generally reproduce the largest PM2.5 enhancements in Delhi air quality for 1-3 consecutive days each fire season, it fails to capture many smaller daily enhancements, which we attribute to the challenge of detecting small fires in the satellite retrieval. By quantifying the influence of upwind agricultural fire emissions on Delhi air pollution, our work underscores the potential health benefits of changes in farming practices to reduce fires. © 2018 The Author(s). Published by IOP Publishing Ltd.
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Khedikar, S; Balasubramanian, R; Chattopadhyay, N; Beig, G; Kulkarni, N Monitoring and study the effect of weather parameters on concentration of surface ozone in the atmosphere for its forecasting Journal Article Mausam, 69 (2), pp. 243-252, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Khedikar2018243b,
title = {Monitoring and study the effect of weather parameters on concentration of surface ozone in the atmosphere for its forecasting}, author = {S Khedikar and R Balasubramanian and N Chattopadhyay and G Beig and N Kulkarni}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048314825&partnerID=40&md5=e66a05e7e09fed2b057bf8d7115ef6ab}, year = {2018}, date = {2018-01-01}, journal = {Mausam}, volume = {69}, number = {2}, pages = {243-252}, publisher = {India Meteorological Department}, abstract = {The surface ozone levels throughout the world have been on an increase for the last three decades. The present study has been conducted for five Indian cities viz., New Delhi, Pune, Nagpur, Kodaikanal and Thiruvananthapuram for a period of six to ten years between (2005 to 2015) to study the trends in surface ozone with respect to spatial, diurnal, monthly and annual variation. This surface ozone concentration which is measured in Dobson unit using electrochemical method (Brewer Bubbler ozone sensor) is analyzed for hourly, monthly and annual variations. This study reveals that New Delhi and Nagpur showing decreasing trend in annual maximum ozone concentration while Kodaikanal, Pune and Thiruvananthapuram showing increasing trend. Similarly, except Nagpur, all other four locations viz., New Delhi, Kodaikanal, Pune and Thiruvananthapuram showing increasing trend in annual mean surface ozone concentration. The influences of meteorological factors that affect ozone were investigated for these locations throughout the year with measurement analysis. It is found that there is close relation between meteorological factors and concentration of surface ozone. All five locations showed strong and positive correlation with maximum temperature. As temperature is rising (global warming), the levels of ozone will increase similarly. It is well known that weather parameters influencing the emission rate of ozone precursors. An attempt has been made to check the possibilities to predict the ozone concentration using available weather parameters in absence of data related to ozone precursor gases like CH4 and NOx. Ozone forecasting models were developed for all five locations using composite weather variables and the performance of the model in predicting ozone at various locations is quite satisfactory. © 2018, India Meteorological Department. All rights reserved.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } The surface ozone levels throughout the world have been on an increase for the last three decades. The present study has been conducted for five Indian cities viz., New Delhi, Pune, Nagpur, Kodaikanal and Thiruvananthapuram for a period of six to ten years between (2005 to 2015) to study the trends in surface ozone with respect to spatial, diurnal, monthly and annual variation. This surface ozone concentration which is measured in Dobson unit using electrochemical method (Brewer Bubbler ozone sensor) is analyzed for hourly, monthly and annual variations. This study reveals that New Delhi and Nagpur showing decreasing trend in annual maximum ozone concentration while Kodaikanal, Pune and Thiruvananthapuram showing increasing trend. Similarly, except Nagpur, all other four locations viz., New Delhi, Kodaikanal, Pune and Thiruvananthapuram showing increasing trend in annual mean surface ozone concentration. The influences of meteorological factors that affect ozone were investigated for these locations throughout the year with measurement analysis. It is found that there is close relation between meteorological factors and concentration of surface ozone. All five locations showed strong and positive correlation with maximum temperature. As temperature is rising (global warming), the levels of ozone will increase similarly. It is well known that weather parameters influencing the emission rate of ozone precursors. An attempt has been made to check the possibilities to predict the ozone concentration using available weather parameters in absence of data related to ozone precursor gases like CH4 and NOx. Ozone forecasting models were developed for all five locations using composite weather variables and the performance of the model in predicting ozone at various locations is quite satisfactory. © 2018, India Meteorological Department. All rights reserved.
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Gao, M; Han, Z; Liu, Z; Li, M; Xin, J; Tao, Z; Li, J; Kang, J -E; Huang, K; Dong, X; Zhuang, B; Li, S; Ge, B; Wu, Q; Cheng, Y; Wang, Y; Lee, H -J; Kim, C -H; Fu, J S; Wang, T; Chin, M; Woo, J -H; Zhang, Q; Wang, Z; Carmichael, G R Atmospheric Chemistry and Physics, 18 (7), pp. 4859-4884, 2018, (cited By 1). Abstract | Links | BibTeX | Tags: @article{Gao20184859b,
title = {Air quality and climate change, Topic 3 of the Model Inter-Comparison Study for Asia Phase III (MICS-Asia III) – Part 1: Overview and model evaluation}, author = {M Gao and Z Han and Z Liu and M Li and J Xin and Z Tao and J Li and J -E Kang and K Huang and X Dong and B Zhuang and S Li and B Ge and Q Wu and Y Cheng and Y Wang and H -J Lee and C -H Kim and J S Fu and T Wang and M Chin and J -H Woo and Q Zhang and Z Wang and G R Carmichael}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045270561&doi=10.5194%2facp-18-4859-2018&partnerID=40&md5=5f7cd4d8fd440d2421ce5818ad19e746}, doi = {10.5194/acp-18-4859-2018}, year = {2018}, date = {2018-01-01}, journal = {Atmospheric Chemistry and Physics}, volume = {18}, number = {7}, pages = {4859-4884}, publisher = {Copernicus GmbH}, abstract = {Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online coupled air quality models perform in simulating high aerosol pollution in the North China Plain region during wintertime haze events and evaluates the importance of aerosol radiative and microphysical feedbacks. A comprehensive overview of the MICS-Asia III Topic 3 study design, including descriptions of participating models and model inputs, the experimental designs, and results of model evaluation, are presented. Six modeling groups from China, Korea and the United States submitted results from seven applications of online coupled chemistry-meteorology models. Results are compared to meteorology and air quality measurements, including data from the Campaign on Atmospheric Aerosol Research Network of China (CARE-China) and the Acid Deposition Monitoring Network in East Asia (EANET). The correlation coefficients between the multi-model ensemble mean and the CARE-China observed near-surface air pollutants range from 0.51 to 0.94 (0.51 for ozone and 0.94 for PM2.5) for January 2010. However, large discrepancies exist between simulated aerosol chemical compositions from different models. The coefficient of variation (SD divided by the mean) can reach above 1.3 for sulfate in Beijing and above 1.6 for nitrate and organic aerosols in coastal regions, indicating that these compositions are less consistent from different models. During clean periods, simulated aerosol optical depths (AODs) from different models are similar, but peak values differ during severe haze events, which can be explained by the differences in simulated inorganic aerosol concentrations and the hygroscopic growth efficiency (affected by varied relative humidity). These differences in composition and AOD suggest that future models can be improved by including new heterogeneous or aqueous pathways for sulfate and nitrate formation under hazy conditions, a secondary organic aerosol (SOA) formation chemical mechanism with new volatile organic compound (VOCs) precursors, yield data and approaches, and a more detailed evaluation of the dependence of aerosol optical properties on size distribution and mixing state. It was also found that using the ensemble mean of the models produced the best prediction skill. While this has been shown for other conditions (for example, the prediction of high-ozone events in the US (McKeen et al., 2005)), this is to our knowledge the first time it has been shown for heavy haze events. © 2018 Author(s). This work is distributed under the Creative Commons Attribution 3.0 License.}, note = {cited By 1}, keywords = {}, pubstate = {published}, tppubtype = {article} } Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online coupled air quality models perform in simulating high aerosol pollution in the North China Plain region during wintertime haze events and evaluates the importance of aerosol radiative and microphysical feedbacks. A comprehensive overview of the MICS-Asia III Topic 3 study design, including descriptions of participating models and model inputs, the experimental designs, and results of model evaluation, are presented. Six modeling groups from China, Korea and the United States submitted results from seven applications of online coupled chemistry-meteorology models. Results are compared to meteorology and air quality measurements, including data from the Campaign on Atmospheric Aerosol Research Network of China (CARE-China) and the Acid Deposition Monitoring Network in East Asia (EANET). The correlation coefficients between the multi-model ensemble mean and the CARE-China observed near-surface air pollutants range from 0.51 to 0.94 (0.51 for ozone and 0.94 for PM2.5) for January 2010. However, large discrepancies exist between simulated aerosol chemical compositions from different models. The coefficient of variation (SD divided by the mean) can reach above 1.3 for sulfate in Beijing and above 1.6 for nitrate and organic aerosols in coastal regions, indicating that these compositions are less consistent from different models. During clean periods, simulated aerosol optical depths (AODs) from different models are similar, but peak values differ during severe haze events, which can be explained by the differences in simulated inorganic aerosol concentrations and the hygroscopic growth efficiency (affected by varied relative humidity). These differences in composition and AOD suggest that future models can be improved by including new heterogeneous or aqueous pathways for sulfate and nitrate formation under hazy conditions, a secondary organic aerosol (SOA) formation chemical mechanism with new volatile organic compound (VOCs) precursors, yield data and approaches, and a more detailed evaluation of the dependence of aerosol optical properties on size distribution and mixing state. It was also found that using the ensemble mean of the models produced the best prediction skill. While this has been shown for other conditions (for example, the prediction of high-ozone events in the US (McKeen et al., 2005)), this is to our knowledge the first time it has been shown for heavy haze events. © 2018 Author(s). This work is distributed under the Creative Commons Attribution 3.0 License.
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Lehtomäki, H; Korhonen, A; Asikainen, A; Karvosenoja, N; Kupiainen, K; Paunu, V -V; Savolahti, M; Sofiev, M; Palamarchuk, Y; Karppinen, A; Kukkonen, J; Hänninen, O Health impacts of ambient air pollution in Finland Journal Article International Journal of Environmental Research and Public Health, 15 (4), 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Lehtomäki2018b,
title = {Health impacts of ambient air pollution in Finland}, author = {H Lehtomäki and A Korhonen and A Asikainen and N Karvosenoja and K Kupiainen and V -V Paunu and M Savolahti and M Sofiev and Y Palamarchuk and A Karppinen and J Kukkonen and O Hänninen}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045402526&doi=10.3390%2fijerph15040736&partnerID=40&md5=d9e326e86ac7ecb2cd154df9ad5d33ce}, doi = {10.3390/ijerph15040736}, year = {2018}, date = {2018-01-01}, journal = {International Journal of Environmental Research and Public Health}, volume = {15}, number = {4}, publisher = {MDPI AG}, abstract = {Air pollution has been estimated to be one of the leading environmental health risks in Finland. National health impact estimates existing to date have focused on particles (PM) and ozone (O3). In this work, we quantify the impacts of particles, ozone, and nitrogen dioxide (NO2) in 2015, and analyze the related uncertainties. The exposures were estimated with a high spatial resolution chemical transport model, and adjusted to observed concentrations. We calculated the health impacts according to Word Health Organization (WHO) working group recommendations. According to our results, ambient air pollution caused a burden of 34,800 disability-adjusted life years (DALY). Fine particles were the main contributor (74%) to the disease burden, which is in line with the earlier studies. The attributable burden was dominated by mortality (32,900 years of life lost (YLL); 95%). Impacts differed between population age groups. The burden was clearly higher in the adult population over 30 years (98%), due to the dominant role of mortality impacts. Uncertainties due to the concentration–response functions were larger than those related to exposures. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } Air pollution has been estimated to be one of the leading environmental health risks in Finland. National health impact estimates existing to date have focused on particles (PM) and ozone (O3). In this work, we quantify the impacts of particles, ozone, and nitrogen dioxide (NO2) in 2015, and analyze the related uncertainties. The exposures were estimated with a high spatial resolution chemical transport model, and adjusted to observed concentrations. We calculated the health impacts according to Word Health Organization (WHO) working group recommendations. According to our results, ambient air pollution caused a burden of 34,800 disability-adjusted life years (DALY). Fine particles were the main contributor (74%) to the disease burden, which is in line with the earlier studies. The attributable burden was dominated by mortality (32,900 years of life lost (YLL); 95%). Impacts differed between population age groups. The burden was clearly higher in the adult population over 30 years (98%), due to the dominant role of mortality impacts. Uncertainties due to the concentration–response functions were larger than those related to exposures. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.
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David, L M; Ravishankara, A R; Kodros, J K; Venkataraman, C; Sadavarte, P; Pierce, J R; Chaliyakunnel, S; Millet, D B Aerosol Optical Depth Over India Journal Article Journal of Geophysical Research: Atmospheres, 123 (7), pp. 3688-3703, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{David20183688b,
title = {Aerosol Optical Depth Over India}, author = {L M David and A R Ravishankara and J K Kodros and C Venkataraman and P Sadavarte and J R Pierce and S Chaliyakunnel and D B Millet}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045281147&doi=10.1002%2f2017JD027719&partnerID=40&md5=a54d229ca14af50d6eb7d81e62ab6247}, doi = {10.1002/2017JD027719}, year = {2018}, date = {2018-01-01}, journal = {Journal of Geophysical Research: Atmospheres}, volume = {123}, number = {7}, pages = {3688-3703}, publisher = {Blackwell Publishing Ltd}, abstract = {Tropospheric aerosol optical depth (AOD) over India was simulated by Goddard Earth Observing System (GEOS)-Chem, a global 3-D chemical-transport model, using SMOG (Speciated Multi-pOllutant Generator from Indian Institute of Technology Bombay) and GEOS-Chem (GC) (current inventories used in the GEOS-Chem model) inventories for 2012. The simulated AODs were ~80% (SMOG) and 60% (GC) of those measured by the satellites (Moderate Resolution Imaging Spectroradiometer and Multi-angle Imaging SpectroRadiometer). There is no strong seasonal variation in AOD over India. The peak AOD values are observed/simulated during summer. The simulated AOD using SMOG inventory has particulate black and organic carbon AOD higher by a factor ~5 and 3, respectively, compared to GC inventory. The model underpredicted coarse-mode AOD but agreed for fine-mode AOD with Aerosol Robotic Network data. It captured dust only over Western India, which is a desert, and not elsewhere, probably due to inaccurate dust transport and/or noninclusion of other dust sources. The calculated AOD, after dust correction, showed the general features in its observed spatial variation. Highest AOD values were observed over the Indo-Gangetic Plain followed by Central and Southern India with lowest values in Northern India. Transport of aerosols from Indo-Gangetic Plain and Central India into Eastern India, where emissions are low, is significant. The major contributors to total AOD over India are inorganic aerosol (41–64%), organic carbon (14–26%), and dust (7–32%). AOD over most regions of India is a factor of 5 or higher than over the United States. ©2018. American Geophysical Union. All Rights Reserved.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } Tropospheric aerosol optical depth (AOD) over India was simulated by Goddard Earth Observing System (GEOS)-Chem, a global 3-D chemical-transport model, using SMOG (Speciated Multi-pOllutant Generator from Indian Institute of Technology Bombay) and GEOS-Chem (GC) (current inventories used in the GEOS-Chem model) inventories for 2012. The simulated AODs were ~80% (SMOG) and 60% (GC) of those measured by the satellites (Moderate Resolution Imaging Spectroradiometer and Multi-angle Imaging SpectroRadiometer). There is no strong seasonal variation in AOD over India. The peak AOD values are observed/simulated during summer. The simulated AOD using SMOG inventory has particulate black and organic carbon AOD higher by a factor ~5 and 3, respectively, compared to GC inventory. The model underpredicted coarse-mode AOD but agreed for fine-mode AOD with Aerosol Robotic Network data. It captured dust only over Western India, which is a desert, and not elsewhere, probably due to inaccurate dust transport and/or noninclusion of other dust sources. The calculated AOD, after dust correction, showed the general features in its observed spatial variation. Highest AOD values were observed over the Indo-Gangetic Plain followed by Central and Southern India with lowest values in Northern India. Transport of aerosols from Indo-Gangetic Plain and Central India into Eastern India, where emissions are low, is significant. The major contributors to total AOD over India are inorganic aerosol (41–64%), organic carbon (14–26%), and dust (7–32%). AOD over most regions of India is a factor of 5 or higher than over the United States. ©2018. American Geophysical Union. All Rights Reserved.
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Jat, R; Sahu, V; Gurjar, B R Pollution exposure to humans and its assessment Book IGI Global, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @book{Jat201893b,
title = {Pollution exposure to humans and its assessment}, author = {R Jat and V Sahu and B R Gurjar}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049643782&doi=10.4018%2f978-1-5225-3379-5.ch006&partnerID=40&md5=11bdeb81faf7fbf5d53be22f7bb7ddef}, doi = {10.4018/978-1-5225-3379-5.ch006}, year = {2018}, date = {2018-01-01}, journal = {Effective Solutions to Pollution Mitigation for Public Welfare}, pages = {93-121}, publisher = {IGI Global}, abstract = {Exposure analysis is the receptor-oriented approach of the pollution-level measurement. In this chapter, a detailed discussion is provided of the fundamentals of exposure analysis, methods of measurement, basics of models used for the prediction of pollution concentration indoors and outdoors, and a brief discussion about the health impact of selected pollutants. A detail of fundamental of indoor air quality (IAQ) models like mass balance and CFD models is discussed. Also, basic structures of community multiscale air quality model (CMAQ) and AIRMOD ambient air dispersion models are described. It is observed that measurement of pollution exposure by direct method requires more time and effort as compared with the integrated exposure and stationary measurement. AIRMODE is steady state model and based upon the Gaussian dispersion model. CMAQ is capable of simulating the pollution level for the range of geographic scale for multiple pollutants. © 2018, IGI Global.}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {book} } Exposure analysis is the receptor-oriented approach of the pollution-level measurement. In this chapter, a detailed discussion is provided of the fundamentals of exposure analysis, methods of measurement, basics of models used for the prediction of pollution concentration indoors and outdoors, and a brief discussion about the health impact of selected pollutants. A detail of fundamental of indoor air quality (IAQ) models like mass balance and CFD models is discussed. Also, basic structures of community multiscale air quality model (CMAQ) and AIRMOD ambient air dispersion models are described. It is observed that measurement of pollution exposure by direct method requires more time and effort as compared with the integrated exposure and stationary measurement. AIRMODE is steady state model and based upon the Gaussian dispersion model. CMAQ is capable of simulating the pollution level for the range of geographic scale for multiple pollutants. © 2018, IGI Global.
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Goel, R; Garcia, L M T; Goodman, A; Johnson, R; Aldred, R; Murugesan, M; Brage, S; Bhalla, K; Woodcock, J Estimating city-level travel patterns using street imagery: A case study of using Google Street View in Britain Journal Article PLoS ONE, 13 (5), 2018, (cited By 1). Abstract | Links | BibTeX | Tags: @article{Goel2018c,
title = {Estimating city-level travel patterns using street imagery: A case study of using Google Street View in Britain}, author = {R Goel and L M T Garcia and A Goodman and R Johnson and R Aldred and M Murugesan and S Brage and K Bhalla and J Woodcock}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046449331&doi=10.1371%2fjournal.pone.0196521&partnerID=40&md5=2766ecd65279b44597e8d6921cdcce4a}, doi = {10.1371/journal.pone.0196521}, year = {2018}, date = {2018-01-01}, journal = {PLoS ONE}, volume = {13}, number = {5}, publisher = {Public Library of Science}, abstract = {Background Street imagery is a promising and growing big data source providing current and historical images in more than 100 countries. Studies have reported using this data to audit road infrastructure and other built environment features. Here we explore a novel application, using Google Street View (GSV) to predict travel patterns at the city level. Methods We sampled 34 cities in Great Britain. In each city, we accessed 2000 GSV images from 1000 random locations. We selected archived images from time periods overlapping with the 2011 Census and the 2011–2013 Active People Survey (APS). We manually annotated the images into seven categories of road users. We developed regression models with the counts of images of road users as predictors. The outcomes included Census-reported commute shares of four modes (combined walking plus public transport, cycling, motorcycle, and car), as well as APS-reported past-month participation in walking and cycling. Results We found high correlations between GSV counts of cyclists (‘GSV-cyclists’) and cycle commute mode share (r = 0.92)/past-month cycling (r = 0.90). Likewise, GSV-pedestrians was moderately correlated with past-month walking for transport (r = 0.46), GSV-motorcycles was moderately correlated with commute share of motorcycles (r = 0.44), and GSV-buses was highly correlated with commute share of walking plus public transport (r = 0.81). GSV-car was not correlated with car commute mode share (r = –0.12). However, in multivariable regression models, all outcomes were predicted well, except past-month walking. The prediction performance was measured using cross-validation analyses. GSV-buses and GSV-cyclists are the strongest predictors for most outcomes. Conclusions GSV images are a promising new big data source to predict urban mobility patterns. Predictive power was the greatest for those modes that varied the most (cycle and bus). With its ability to identify mode of travel and capture street activity often excluded in routinely carried out surveys, GSV has the potential to be complementary to new and traditional data. With half the world’s population covered by street imagery, and with up to 10 years historical data available in GSV, further testing across multiple settings is warranted both for cross-sectional and longitudinal assessments. Copyright: © 2018 Goel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.}, note = {cited By 1}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background Street imagery is a promising and growing big data source providing current and historical images in more than 100 countries. Studies have reported using this data to audit road infrastructure and other built environment features. Here we explore a novel application, using Google Street View (GSV) to predict travel patterns at the city level. Methods We sampled 34 cities in Great Britain. In each city, we accessed 2000 GSV images from 1000 random locations. We selected archived images from time periods overlapping with the 2011 Census and the 2011–2013 Active People Survey (APS). We manually annotated the images into seven categories of road users. We developed regression models with the counts of images of road users as predictors. The outcomes included Census-reported commute shares of four modes (combined walking plus public transport, cycling, motorcycle, and car), as well as APS-reported past-month participation in walking and cycling. Results We found high correlations between GSV counts of cyclists (‘GSV-cyclists’) and cycle commute mode share (r = 0.92)/past-month cycling (r = 0.90). Likewise, GSV-pedestrians was moderately correlated with past-month walking for transport (r = 0.46), GSV-motorcycles was moderately correlated with commute share of motorcycles (r = 0.44), and GSV-buses was highly correlated with commute share of walking plus public transport (r = 0.81). GSV-car was not correlated with car commute mode share (r = –0.12). However, in multivariable regression models, all outcomes were predicted well, except past-month walking. The prediction performance was measured using cross-validation analyses. GSV-buses and GSV-cyclists are the strongest predictors for most outcomes. Conclusions GSV images are a promising new big data source to predict urban mobility patterns. Predictive power was the greatest for those modes that varied the most (cycle and bus). With its ability to identify mode of travel and capture street activity often excluded in routinely carried out surveys, GSV has the potential to be complementary to new and traditional data. With half the world’s population covered by street imagery, and with up to 10 years historical data available in GSV, further testing across multiple settings is warranted both for cross-sectional and longitudinal assessments. Copyright: © 2018 Goel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Kota, S H; Guo, H; Myllyvirta, L; Hu, J; Sahu, S K; Garaga, R; Ying, Q; Gao, A; Dahiya, S; Wang, Y; Zhang, H Year-long simulation of gaseous and particulate air pollutants in India Journal Article Atmospheric Environment, 180 , pp. 244-255, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Kota2018244b,
title = {Year-long simulation of gaseous and particulate air pollutants in India}, author = {S H Kota and H Guo and L Myllyvirta and J Hu and S K Sahu and R Garaga and Q Ying and A Gao and S Dahiya and Y Wang and H Zhang}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043394687&doi=10.1016%2fj.atmosenv.2018.03.003&partnerID=40&md5=862c418d8a553670799a150a1ca9cfc0}, doi = {10.1016/j.atmosenv.2018.03.003}, year = {2018}, date = {2018-01-01}, journal = {Atmospheric Environment}, volume = {180}, pages = {244-255}, publisher = {Elsevier Ltd}, abstract = {Severe pollution events occur frequently in India but few studies have investigated the characteristics, sources, and control strategies for the whole country. A year-long simulation was carried out in India to provide detailed information of spatial and temporal distribution of gas species and particulate matter (PM). The concentrations of O3, NO2, SO2, CO, as well as PM2.5 and its components in 2015 were predicted using Weather Research Forecasting (WRF) and the Community Multiscale Air Quality (CMAQ) models. Model performance was validated against available observations from ground based national ambient air quality monitoring stations in major cities. Model performance of O3 does not always meet the criteria suggested by the US Environmental Protection Agency (EPA) but that of PM2.5 meets suggested criteria by previous studies. The performance of model was better on days with high O3 and PM2.5 levels. Concentrations of PM2.5, NO2, CO and SO2 were highest in the Indo-Gangetic region, including northern and eastern India. PM2.5 concentrations were higher during winter and lower during monsoon season. Winter nitrate concentrations were 160–230% higher than yearly average. In contrast, the fraction of sulfate in total PM2.5 was maximum in monsoon and least in winter, due to decrease in temperature and solar radiation intensity in winter. Except in southern India, where sulfate was the major component of PM2.5, primary organic aerosol (POA) fraction in PM2.5 was highest in all regions of the country. Fractions of secondary components were higher on bad days than on good days in these cities, indicating the importance of control of precursors for secondary pollutants in India. Capsule abstract: Predicted gaseous and particulate air pollutants in India using WRF/CMAQ in 2015 were validated and the variations were analyzed for future source and health analysis. © 2018 Elsevier Ltd}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } Severe pollution events occur frequently in India but few studies have investigated the characteristics, sources, and control strategies for the whole country. A year-long simulation was carried out in India to provide detailed information of spatial and temporal distribution of gas species and particulate matter (PM). The concentrations of O3, NO2, SO2, CO, as well as PM2.5 and its components in 2015 were predicted using Weather Research Forecasting (WRF) and the Community Multiscale Air Quality (CMAQ) models. Model performance was validated against available observations from ground based national ambient air quality monitoring stations in major cities. Model performance of O3 does not always meet the criteria suggested by the US Environmental Protection Agency (EPA) but that of PM2.5 meets suggested criteria by previous studies. The performance of model was better on days with high O3 and PM2.5 levels. Concentrations of PM2.5, NO2, CO and SO2 were highest in the Indo-Gangetic region, including northern and eastern India. PM2.5 concentrations were higher during winter and lower during monsoon season. Winter nitrate concentrations were 160–230% higher than yearly average. In contrast, the fraction of sulfate in total PM2.5 was maximum in monsoon and least in winter, due to decrease in temperature and solar radiation intensity in winter. Except in southern India, where sulfate was the major component of PM2.5, primary organic aerosol (POA) fraction in PM2.5 was highest in all regions of the country. Fractions of secondary components were higher on bad days than on good days in these cities, indicating the importance of control of precursors for secondary pollutants in India. Capsule abstract: Predicted gaseous and particulate air pollutants in India using WRF/CMAQ in 2015 were validated and the variations were analyzed for future source and health analysis. © 2018 Elsevier Ltd
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Lee, H -H; Iraqui, O; Gu, Y; Yim, S H -L; Chulakadabba, A; Tonks, A Y -M; Yang, Z; Wang, C Impacts of air pollutants from fire and non-fire emissions on the regional air quality in Southeast Asia Journal Article Atmospheric Chemistry and Physics, 18 (9), pp. 6141-6156, 2018, (cited By 0). Abstract | Links | BibTeX | Tags: @article{Lee20186141b,
title = {Impacts of air pollutants from fire and non-fire emissions on the regional air quality in Southeast Asia}, author = {H -H Lee and O Iraqui and Y Gu and S H -L Yim and A Chulakadabba and A Y -M Tonks and Z Yang and C Wang}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046632874&doi=10.5194%2facp-18-6141-2018&partnerID=40&md5=d1dbcb7801adf079265abdbe03c19c36}, doi = {10.5194/acp-18-6141-2018}, year = {2018}, date = {2018-01-01}, journal = {Atmospheric Chemistry and Physics}, volume = {18}, number = {9}, pages = {6141-6156}, publisher = {Copernicus GmbH}, abstract = {Severe haze events in Southeast Asia caused by particulate pollution have become more intense and frequent in recent years. Widespread biomass burning occurrences and particulate pollutants from human activities other than biomass burning play important roles in degrading air quality in Southeast Asia. In this study, numerical simulations have been conducted using the Weather Research and Forecasting (WRF) model coupled with a chemistry component (WRF-Chem) to quantitatively examine the contributions of aerosols emitted from fire (i.e., biomass burning) versus non-fire (including fossil fuel combustion, and road dust, etc.) sources to the degradation of air quality and visibility over Southeast Asia. These simulations cover a time period from 2002 to 2008 and are driven by emissions from (a) fossil fuel burning only, (b) biomass burning only, and (c) both fossil fuel and biomass burning. The model results reveal that 39 % of observed low-visibility days (LVDs) can be explained by either fossil fuel burning or biomass burning emissions alone, a further 20 % by fossil fuel burning alone, a further 8 % by biomass burning alone, and a further 5 % by a combination of fossil fuel burning and biomass burning. Analysis of an 24 h PM2.5 air quality index (AQI) indicates that the case with coexisting fire and non-fire PM2.5 can substantially increase the chance of AQI being in the moderate or unhealthy pollution level from 23 to 34 %. The premature mortality in major Southeast Asian cities due to degradation of air quality by particulate pollutants is estimated to increase from ∼ 4110 per year in 2002 to ∼ 6540 per year in 2008. In addition, we demonstrate the importance of certain missing non-fire anthropogenic aerosol sources including anthropogenic fugitive and industrial dusts in causing urban air quality degradation. An experiment of using machine learning algorithms to forecast the occurrence of haze events in Singapore is also explored in this study. All of these results suggest that besides minimizing biomass burning activities, an effective air pollution mitigation policy for Southeast Asia needs to consider controlling emissions from non-fire anthropogenic sources. © 2018 Author(s).}, note = {cited By 0}, keywords = {}, pubstate = {published}, tppubtype = {article} } Severe haze events in Southeast Asia caused by particulate pollution have become more intense and frequent in recent years. Widespread biomass burning occurrences and particulate pollutants from human activities other than biomass burning play important roles in degrading air quality in Southeast Asia. In this study, numerical simulations have been conducted using the Weather Research and Forecasting (WRF) model coupled with a chemistry component (WRF-Chem) to quantitatively examine the contributions of aerosols emitted from fire (i.e., biomass burning) versus non-fire (including fossil fuel combustion, and road dust, etc.) sources to the degradation of air quality and visibility over Southeast Asia. These simulations cover a time period from 2002 to 2008 and are driven by emissions from (a) fossil fuel burning only, (b) biomass burning only, and (c) both fossil fuel and biomass burning. The model results reveal that 39 % of observed low-visibility days (LVDs) can be explained by either fossil fuel burning or biomass burning emissions alone, a further 20 % by fossil fuel burning alone, a further 8 % by biomass burning alone, and a further 5 % by a combination of fossil fuel burning and biomass burning. Analysis of an 24 h PM2.5 air quality index (AQI) indicates that the case with coexisting fire and non-fire PM2.5 can substantially increase the chance of AQI being in the moderate or unhealthy pollution level from 23 to 34 %. The premature mortality in major Southeast Asian cities due to degradation of air quality by particulate pollutants is estimated to increase from ∼ 4110 per year in 2002 to ∼ 6540 per year in 2008. In addition, we demonstrate the importance of certain missing non-fire anthropogenic aerosol sources including anthropogenic fugitive and industrial dusts in causing urban air quality degradation. An experiment of using machine learning algorithms to forecast the occurrence of haze events in Singapore is also explored in this study. All of these results suggest that besides minimizing biomass burning activities, an effective air pollution mitigation policy for Southeast Asia needs to consider controlling emissions from non-fire anthropogenic sources. © 2018 Author(s).
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