1
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Saharan US, Kumar R, Singh S, Mandal TK, Sateesh M, Verma S, Srivastava A. Hotspot driven air pollution during crop residue burning season in the Indo-Gangetic Plain, India. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 350:124013. [PMID: 38670421 DOI: 10.1016/j.envpol.2024.124013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 03/06/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024]
Abstract
Intensive crop residue burning (CRB) in northern India triggers severe air pollution episodes over the Indo-Gangetic Plain (IGP) each year during October and November. We have quantified the contribution of hotspot districts (HSDs) and total CRB to poor air quality over the IGP. Initially, we investigated the spatiotemporal distribution of CRB fire within the domain and pinpointed five HSD in each Punjab and Haryana. Furthermore, we have simulated air quality and quantified the impact of CRB using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), incorporating recent anthropogenic emissions (EDGAR v5) and biomass burning emissions (FINN v2.4) inventories, along with MOZART-MOSAIC chemistry. The key finding is that HSDs contributed ∼80% and ∼50% of the total fire counts in Haryana and Punjab, respectively. The model effectively captured observed PM₂.₅ concentrations, with a normalized mean bias (NMB) below 0.2 and R-squared (R2) exceeding 0.65 at the majority of validation sites. However, some discrepancies were observed at a few sites in Delhi, Punjab, Haryana, and West Bengal. The National Capital Region experienced the highest PM₂.₅ concentrations, followed by Punjab, Haryana, Uttar Pradesh, Bihar, and West Bengal. Moreover, HSDs were responsible for about 70% of the total increase in CRB-induced PM₂.₅ in the western, central, and eastern cities, and around 50% in the northern cities. By eliminating CRB emissions across the domain, we could potentially save approximately 18,000 lives annually. Policymakers, scientists, and institutions can leverage the framework to address air pollution at national and global scales by targeting source-specific hotspots. This approach, coupled with appropriate technological and financial solutions, can contribute to achieving climate change and sustainable development goals.
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Affiliation(s)
- Ummed Singh Saharan
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110012, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201 002, Uttar Pradesh, India
| | - Rajesh Kumar
- National Center for Atmospheric Research, Boulder, CO, USA
| | | | - Tuhin Kumar Mandal
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110012, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201 002, Uttar Pradesh, India.
| | - M Sateesh
- Climate Change Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Shubha Verma
- Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India
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2
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Gao Z, Zhou X. A review of the CAMx, CMAQ, WRF-Chem and NAQPMS models: Application, evaluation and uncertainty factors. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 343:123183. [PMID: 38110047 DOI: 10.1016/j.envpol.2023.123183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/28/2023] [Accepted: 12/15/2023] [Indexed: 12/20/2023]
Abstract
With the gradual deepening of the research and governance of air pollution, chemical transport models (CTMs), especially the third-generation CTMs based on the "1 atm" theory, have been recognized as important tools for atmospheric environment research and air quality management. In this review article, we screened 2396 peer-reviewed manuscripts on the application of four pre-selected regional CTMs in the past five years. CAMx, CMAQ, WRF-Chem and NAQPMS models are well used in the simulation of atmospheric pollutants. In the simulation study of secondary pollutants such as O3, secondary organic aerosol (SOA), sulfates, nitrates, and ammonium (SNA), the CMAQ model has been widely applied. Secondly, model evaluation indicators are diverse, and the establishment of evaluation criteria has gone through the long-term efforts of predecessors. However, the model performance evaluation system still needs further specification. Furthermore, temporal-spatial resolution, emission inventory, meteorological field and atmospheric chemical mechanism are the main sources of uncertainty, and have certain interference with the simulation results. Among them, the inventory and mechanism are particularly important, and are also the top priorities in future simulation research.
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Affiliation(s)
- Zhaoqi Gao
- Environment Research Institute, Shandong University, Qingdao, 266237, Shandong Province, China
| | - Xuehua Zhou
- Environment Research Institute, Shandong University, Qingdao, 266237, Shandong Province, China.
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3
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Lu B, Meng X, Dong S, Zhang Z, Liu C, Jiang J, Herrmann H, Li X. High-resolution mapping of regional VOCs using the enhanced space-time extreme gradient boosting machine (XGBoost) in Shanghai. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 905:167054. [PMID: 37714357 DOI: 10.1016/j.scitotenv.2023.167054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/10/2023] [Accepted: 09/11/2023] [Indexed: 09/17/2023]
Abstract
The accurate estimation of highly spatiotemporal volatile organic compounds (VOCs) is of great significance to establish advanced early warning systems and regulate air pollution control. However, the estimation of high spatiotemporal VOCs remains incomplete. Here, the space-time extreme gradient boost model (STXGB) was enhanced by integrating spatiotemporal information to obtain the spatial resolution and overall accuracy of VOCs. To this end, meteorological, topographical and pollutant emissions, was input to the STXGB model, and regional hourly 300 m VOCs maps for 2020 in Shanghai were produced. Our results show that the STXGB model achieve good hourly VOCs estimations performance (R2 = 0.73). A further analysis of SHapley Additive exPlanation (SHAP) regression indicate that local interpretations of the STXGB models demonstrate the strong contribution of emissions on mapping VOCs estimations, while acknowledging the important contribution of space and time term. The proposed approach outperforms many traditional machine learning models with a lower computational burden in terms of speed and memory.
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Affiliation(s)
- Bingqing Lu
- Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, PR China
| | - Xue Meng
- Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, PR China
| | - Shanshan Dong
- Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, PR China
| | - Zekun Zhang
- Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, PR China
| | - Chao Liu
- Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, PR China
| | - Jiakui Jiang
- Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, PR China
| | - Hartmut Herrmann
- Leibniz-Institut für Troposphärenforschung (IfT), Permoserstr. 15, 04318 Leipzig, Germany
| | - Xiang Li
- Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, PR China; Institute of Eco-Chongming (IEC), Shanghai 200241, China.
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4
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Chen Y, Liu C, Su W, Hu Q, Zhang C, Liu H, Yin H. Identification of volatile organic compound emissions from anthropogenic and biogenic sources based on satellite observation of formaldehyde and glyoxal. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:159997. [PMID: 36368395 DOI: 10.1016/j.scitotenv.2022.159997] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/09/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
Anthropogenic volatile organic compounds (VOCs) are serious pollutants in the atmosphere because of their toxicity and as precursors of secondary organic aerosols and ozone pollution. Although in-situ measurements provide accurate information on VOCs, their spatial coverage is limited and insufficient. In this study, we provide a global perspective for identifying anthropogenic VOC emission sources through the ratio of glyoxal to formaldehyde (RGF) based on satellite observations. We assessed typical cities and polluted areas in the mid latitudes and found that some Asian cities had higher anthropogenic VOC emissions than cities in Europe and America. For heavily polluted areas, such as the Yangtze River Delta (YRD), the areas dominated by anthropogenic VOCs accounted for 23 % of the total study areas. During the COVID-19 pandemic, a significant decline in RGF values was observed in the YRD and western United States, corresponding to a reduction in anthropogenic VOC emissions. Furthermore, developing countries appeared to have higher anthropogenic VOC emissions than developed countries. These observations could contribute to optimising industrial structures and setting stricter pollution standards to reduce anthropogenic VOCs in developing countries.
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Affiliation(s)
- Yujia Chen
- Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
| | - Cheng Liu
- Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China; Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China; Centre for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei 230026, China; Anhui Province Key Laboratory of Polar Environment and Global Change, University of Science and Technology of China, Hefei 230026, China.
| | - Wenjing Su
- Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China; Key Laboratory of Atmospheric Chemistry, China Meteorological Administration, Beijing 100089, China.
| | - Qihou Hu
- Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
| | - Chengxin Zhang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China
| | - Haoran Liu
- Institute of Physical Science and Information Technology, Anhui University, Hefei 230601, China
| | - Hao Yin
- Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
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Mondal A, Sharma SK, Mandal TK, Girach I, Ojha N. Frequency distribution of pollutant concentrations over Indian megacities impacted by the COVID-19 lockdown. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:85676-85687. [PMID: 34674132 PMCID: PMC8529380 DOI: 10.1007/s11356-021-16874-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 09/30/2021] [Indexed: 05/25/2023]
Abstract
The megacities experience poor air quality frequently due to stronger anthropogenic emissions. India had one of the longest lockdowns in 2020 to curb the spread of COVID-19, leading to reductions in the emissions from anthropogenic activities. In this article, the frequency distributions of different pollutants have been analysed over two densely populated megacities: Delhi (28.70° N; 77.10° E) and Kolkata (22.57° N; 88.36° E). In Delhi, the percentage of days with PM2.5 levels exceeding the National Ambient Air Quality Standards (NAAQS) between 25 March and 17 June dropped from 98% in 2019 to 61% in 2020. The lockdown phase 1 brought down the PM10 (particulate matter having an aerodynamic diameter ≤ 10 μm) levels below the daily NAAQS limit over Delhi and Kolkata. However, PM10 exceeded the limit of 100 μgm-3 during phases 2-5 of lockdown over Delhi due to lower temperature, weaker winds, increased relative humidity and commencement of limited traffic movement. The PM2.5 levels exhibit a regressive trend in the highest range from the year 2019 to 2020 in Delhi. The daily mean value for PM2.5 concentrations dropped from 85-90 μgm-3 to 40-45 μgm-3 bin, whereas the PM10 levels witnessed a reduction from 160-180 μgm-3 to 100-120 μgm-3 bin due to the lockdown. Kolkata also experienced a shift in the peak of PM10 distribution from 80-100 μgm-3 in 2019 to 20-40 μgm-3 during the lockdown. The PM2.5 levels in peak frequency distribution were recorded in the 35-40 μgm-3 bin in 2019 which dropped to 15-20 μgm-3 in 2020. In line with particulate matter, other primary gaseous pollutants (NOx, CO, SO2, NH3) also showed decline. However, changes in O3 showed mixed trends with enhancements in some of the phases and reductions in other phases. In contrast to daily mean O3, 8-h maximum O3 showed a reduction over Delhi during lockdown phases except for phase 3. Interestingly, the time of daily maximum was observed to be delayed by ~ 2 h over Delhi (from 1300 to 1500 h) and ~ 1 h over Kolkata (from 1300 to 1400 h) almost coinciding with the time of maximum temperature, highlighting the role of meteorology versus precursors. Emission reductions weakened the chemical sink of O3 leading to enhancement (120%; 11 ppbv) in night-time O3 over Delhi during phases 1-3.
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Affiliation(s)
- Arnab Mondal
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Marg, New Delhi, 110 012, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201 002, India.
| | - Sudhir Kumar Sharma
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Marg, New Delhi, 110 012, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201 002, India
| | - Tuhin Kumar Mandal
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Marg, New Delhi, 110 012, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201 002, India
| | - Imran Girach
- Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram, 695 022, India
| | - Narendra Ojha
- Physical Research Laboratory, Navrangpura, Ahmedabad, 380 009, India
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6
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Sharma S, Chandra M, Harsha Kota S. Four year long simulation of carbonaceous aerosols in India: Seasonality, sources and associated health effects. ENVIRONMENTAL RESEARCH 2022; 213:113676. [PMID: 35728639 DOI: 10.1016/j.envres.2022.113676] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/26/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
India's air quality is in a dismal state, with many studies ascribing it to PM2.5. Most of these corroborate that carbonaceous aerosol (CA) constitute significant fraction of PM2.5. However, investigations on the effect of long-term meteorological or emission changes on PM2.5 and its components, and their associated health effects are rare. In this work, WRF-Chem simulations for three seasons over four years (2016-2019) were carried out to cogitate the spatial and temporal changes in PM2.5 and its components in India. Model predicted PM2.5 concentrations were in good agreement with the ground-based observations for 25 cities. PM2.5 was highest in winter and lowest in pre-monsoon. PM2.5 reduced by ∼8% in Indo-Gangetic Plain (IGP) but increased by ∼38% and ∼130% in south and northeast India, respectively, from 2016 to 2019. IGP witnessed three times higher average PM2.5 concentrations than south India. No significant interannual change in CA contributions was observed, however, it peaked in the winter season. Other inorganics (OIN) were the major component of PM2.5, contributing more than 40%. Primary organic aerosol (POA) fractions were higher in north India, while secondary inorganic aerosol (SIA) dominated south India. Transport and residential sectors were the chief contributors to CA across India. Biomass burning contributed up to ∼23% of PM2.5 in regions of IGP during post-monsoon, with CA fractions up to 50%. Associations between PM2.5 and its components with daily inpatient admissions from a tertiary care centre in Delhi showed that PM2.5 and OIN had lower associations with daily hospital admissions than CA. Every 10 μg/m3 increase in POA, black carbon (BC), and secondary organic aerosol (SOA) were associated with ∼1.09%, ∼3.07% and ∼4.93% increase in the risk of daily hospital admissions. This invigorates the need for more policies targeting CA rather than PM2.5 to mitigate associated health risks, in India.
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Affiliation(s)
- Shubham Sharma
- Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110 016, India
| | - Mina Chandra
- Department of Psychiatry, Centre of Excellence in Mental Health, Atal Bihari Vajpayee Institute of Medical Sciences and Dr Ram Manohar Lohia Hospital, New Delhi, 110001, India
| | - Sri Harsha Kota
- Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110 016, India; Arun Duggal Centre of Excellence for Research in Climate Change and Air Pollution (CERCA), IIT Delhi, New Delhi, 110016, India.
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7
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Ali MA, Bilal M, Wang Y, Qiu Z, Nichol JE, Mhawish A, de Leeuw G, Zhang Y, Shahid S, Almazroui M, Islam MN, Rahman MA, Mondol SK, Tiwari P, Khedher KM. Spatiotemporal changes in aerosols over Bangladesh using 18 years of MODIS and reanalysis data. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 315:115097. [PMID: 35504182 DOI: 10.1016/j.jenvman.2022.115097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 04/13/2022] [Accepted: 04/16/2022] [Indexed: 06/14/2023]
Abstract
In this study, combined Dark Target and Deep Blue (DTB) aerosol optical depth at 550 nm (AOD550 nm) data the Moderate Resolution Imaging Spectroradiometer (MODIS) flying on the Terra and Aqua satellites during the years 2003-2020 are used as a reference to assess the performance of the Copernicus Atmosphere Monitoring Services (CAMS) and the second version of Modern-Era Retrospective analysis for Research and Applications (MERRA-2) AOD over Bangladesh. The study also investigates long-term spatiotemporal variations and trends in AOD, and determines the relative contributions from different aerosol species (black carbon: BC, dust, organic carbon: OC, sea salt: SS, and sulfate) and anthropogenic emissions to the total AOD. As the evaluations suggest higher accuracy for CAMS than for MERRA-2, CAMS is used for further analysis of AOD over Bangladesh. The annual mean AOD from both CAMS and MODIS DTB is high (>0.60) over most parts of Bangladesh except for the eastern areas of Chattogram and Sylhet. Higher AOD is observed in spring and winter than in summer and autumn, which is mainly due to higher local anthropogenic emissions during the winter to spring season. Annual trends from 2003-2020 show a significant increase in AOD (by 0.006-0.014 year-1) over Bangladesh, and this increase in AOD was more evident in winter and spring than in summer and autumn. The increasing total AOD is caused by rising anthropogenic emissions and accompanied by changes in aerosol species (with increased OC, sulfate, and BC). Overall, this study improves understanding of aerosol pollution in Bangladesh and can be considered as a supportive document for Bangladesh to improve air quality by reducing anthropogenic emissions.
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Affiliation(s)
- Md Arfan Ali
- Lab of Environmental Remote Sensing (LERS), School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing, 210044, China
| | - Muhammad Bilal
- Lab of Environmental Remote Sensing (LERS), School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing, 210044, China
| | - Yu Wang
- Lab of Environmental Remote Sensing (LERS), School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing, 210044, China
| | - Zhongfeng Qiu
- Lab of Environmental Remote Sensing (LERS), School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing, 210044, China.
| | - Janet E Nichol
- Department of Geography, School of Global Studies, University of Sussex, Brighton, BN19RH, UK
| | - Alaa Mhawish
- Lab of Environmental Remote Sensing (LERS), School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing, 210044, China
| | - Gerrit de Leeuw
- Royal Netherlands Meteorological Institute (KNMI), R & D Satellite Observations, 3730AE De Bilt, the Netherlands; Aerospace Information Research Institute, Chinese Academy of Sciences (AirCAS), No.20 Datun Road, Chaoyang District, Beijing, 100101, China; School of Atmospheric Physics, Nanjing University of Information Science and Technology (NUIST), Nanjing, 210044, China; School of Environment Science and Spatial Informatics, University of Mining and Technology, Xuzhou, Jiangsu, 221116, China
| | - Yuanzhi Zhang
- Lab of Environmental Remote Sensing (LERS), School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing, 210044, China
| | - Shamsuddin Shahid
- Department of Hydraulics & Hydrology, University Technology Malaysia, Malaysia
| | - Mansour Almazroui
- Center of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, UK
| | - M Nazrul Islam
- Center of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Muhammad Ashfaqur Rahman
- Weather and Climate Model Earth Science Technology and Policy Services Ltd. (ESTEPS), Dhaka, 1000, Bangladesh
| | - Sanjit Kumar Mondol
- School of Geographical Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | | | - Khaled Mohamed Khedher
- Department of Civil Engineering, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia
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Yang T, Liu B, Yang Y, Dai Q, Zhang Y, Feng Y, Hopke PK. Improved positive matrix factorization for source apportionment of volatile organic compounds in vehicular emissions during the Spring Festival in Tianjin, China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 303:119122. [PMID: 35276248 DOI: 10.1016/j.envpol.2022.119122] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 02/26/2022] [Accepted: 03/07/2022] [Indexed: 06/14/2023]
Abstract
Photochemical losses of volatile organic compounds (VOCs) and uncertainties in calculated factor profiles can reduce the accuracy of source apportionment by positive matrix factorization (PMF). We developed an improved PMF method (termed ICLP-PMF) that estimated the reaction-corrected ("initial") concentrations of VOCs. Source profiles from literature provided constraints. ICLP-PMF evaluated the vehicular emission contributions to hourly speciated VOC data from December 2020 to March 2021 and estimated gasoline and diesel vehicles contributions to Tianjin's VOC concentrations around the Chinese Spring Festival (SF). The average observed and initial total VOCs (TVOCs) concentrations were 24.2 and 42.9 ppbv, respectively. Alkanes were the highest concentration VOCs while aromatics showed the largest photochemical losses during the study period. Literature gasoline and diesel profiles from representative Chinese cities were constructed and provided constraints. Source apportionment was performed using ICLP-PMF method and three other PMF approaches. Photochemical losses of alkenes and aromatic hydrocarbons induced differences between calculated factor profiles and literature profiles. Using observed concentrations and unconstrained profiles produced underestimated SF contributions (∼121% and 72% for gasoline and diesel vehicles, respectively). According to the ICLP-PMF results, the contributions of gasoline and diesel vehicles during the SF were 25.6% and 23.2%, respectively, lower than those before and after the SF. No diel diesel vehicle contribution variations were found during the SF likely due to the decline in truck activity north of the study site during the holiday period.
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Affiliation(s)
- Tao Yang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin, 300350, China
| | - Baoshuang Liu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin, 300350, China.
| | - Yang Yang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin, 300350, China
| | - Qili Dai
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin, 300350, China
| | - Yufen Zhang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin, 300350, China
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin, 300350, China
| | - Philip K Hopke
- Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA; Institute for a Sustainable Environment, Clarkson University, Potsdam, NY, 13699, USA
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9
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Soni M, Girach I, Sahu LK, Ojha N. Photochemical evolution of air in a tropical urban environment of India: A model-based study. CHEMOSPHERE 2022; 297:134070. [PMID: 35231476 DOI: 10.1016/j.chemosphere.2022.134070] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 02/18/2022] [Accepted: 02/19/2022] [Indexed: 05/25/2023]
Abstract
The photochemical processes over tropical Indian region impact the atmospheric composition and air quality over local to global scales; nevertheless, studies on detailed atmospheric chemistry remain sparse in this region. In this study, we investigate the photochemical evolution of air in the downwind of a tropical semi-arid urban environment (Ahmedabad) in India using the Master Mechanism model. The 5-days long chemical evolution has been simulated for the winter conditions - when this region experiences strong ozone build up. Model environment has been set up by including the meteorological conditions, overhead ozone, and aerosol loading, etc. Nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3), and several volatile organic compounds (VOCs) have been initialized in the model based on the wintertime observations. The model predicts large O3 production (∼115 ppbv) in the downwind regions, followed by a gradual decrease from the 3rd day onwards. Additionally, significant amounts of the secondary inorganics, e.g. nitric acid (∼17 ppbv), hydrogen peroxide (∼9 ppbv), and organics, e.g. ketones (∼11 ppbv), are also simulated. The noontime maximum levels of hydroxyl (OH) and hydroperoxyl (HO2) radicals are simulated to be 0.3 and 44 pptv, respectively. While the production of OH is dominated by the reaction of NO with HO2 on the first day, photolysis of O3 dominates subsequently with reduction in NOx levels. VOCs are the major OH sink during day 1, however contribution of CO is greater on further days. The air mass trajectory analysis suggests the outflow of ozone-rich air over the rural areas and the Arabian Sea, in agreement with measurements and a global model. Our study highlights the strong impact of the urban outflows on the regional atmospheric composition. The continuous measurements of VOCs and radicals are needed over tropical regions to complement the models and further improve the understanding of air chemistry.
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Affiliation(s)
- Meghna Soni
- Physical Research Laboratory, Ahmedabad, India; Indian Institute of Technology, Gandhinagar, Gujarat, India
| | - Imran Girach
- Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram, India; Presently at Space Application Centre, Indian Space Research Organisation, Ahmedabad, India
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Kant R, Trivedi A, Ghadai B, Kumar V, Mallik C. Interpreting the COVID effect on atmospheric constituents over the Indian region during the lockdown: chemistry, meteorology, and seasonality. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:274. [PMID: 35286487 PMCID: PMC8918593 DOI: 10.1007/s10661-022-09932-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 03/05/2022] [Indexed: 06/14/2023]
Abstract
Most of the published articles which document changes in atmospheric compositions during the various lockdown and unlock phases of COVID-19 pandemic have made a direct comparison to a reference point (which may be 1 year apart) for attribution of the COVID-mediated lockdown impact on atmospheric composition. In the present study, we offer a better attribution of the lockdown impacts by also considering the effect of meteorology and seasonality. We decrease the temporal distance between the impacted and reference points by considering the difference of adjacent periods first and then comparing the impacted point to the mean of several reference points in the previous years. Additionally, we conduct a multi-station analysis to get a holistic effect of the different climatic and emission regimes. In several places in eastern and coastal India, the seasonally induced changes already pointed to a decrease in PM concentrations based on the previous year data; hence, the actual decrease due to lockdown would be much less than that observed just on the basis of difference of concentrations between subsequent periods. In contrast, northern Indian stations would normally show an increase in PM concentration at the time of the year when lockdown was effected; hence, actual lockdown-induced change would be in surplus of the observed change. The impact of wind-borne transport of pollutants to the study sites dominates over the dilution effects. Box model simulations point to a VOC-sensitive composition.
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Affiliation(s)
- Rahul Kant
- Department of Atmospheric Science, Central University of Rajasthan, Ajmer, 305801, India
| | - Avani Trivedi
- Department of Atmospheric Science, Central University of Rajasthan, Ajmer, 305801, India
| | - Bibhutimaya Ghadai
- Department of Atmospheric Science, Central University of Rajasthan, Ajmer, 305801, India
| | - Vinod Kumar
- Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128, Mainz, Germany
| | - Chinmay Mallik
- Department of Atmospheric Science, Central University of Rajasthan, Ajmer, 305801, India.
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Ojha N, Girach I, Sharma K, Sharma A, Singh N, Gunthe SS. Exploring the potential of machine learning for simulations of urban ozone variability. Sci Rep 2021; 11:22513. [PMID: 34795336 PMCID: PMC8602617 DOI: 10.1038/s41598-021-01824-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 11/01/2021] [Indexed: 11/17/2022] Open
Abstract
Machine learning (ML) has emerged as a powerful technique in the Earth system science, nevertheless, its potential to model complex atmospheric chemistry remains largely unexplored. Here, we applied ML to simulate the variability in urban ozone (O3) over Doon valley of the Himalaya. The ML model, trained with past variations in O3 and meteorological conditions, successfully reproduced the independent O3 data (r2 ~ 0.7). Model performance is found to be similar when the variation in major precursors (CO and NOx) were included in the model, instead of the meteorology. Further the inclusion of both precursors and meteorology improved the performance significantly (r2 = 0.86) and the model could also capture the outliers, which are crucial for air quality assessments. We suggest that in absence of high-resolution measurements, ML modeling has profound implications for unraveling the feedback between pollution and meteorology in the fragile Himalayan ecosystem.
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Affiliation(s)
| | - Imran Girach
- Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram, India.
| | - Kiran Sharma
- Graphic Era (Deemed to be University), Dehradun, India
| | - Amit Sharma
- Department of Civil and Infrastructure Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India
| | - Narendra Singh
- Aryabhatta Research Institute of Observational Sciences, Nainital, India
| | - Sachin S Gunthe
- EWRE Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India.,Laboratory for Atmospheric and Climate Sciences, Indian Institute of Technology Madras, Chennai, India
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12
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Laskar AH, Maurya AS, Singh V, Gurjar BR, Liang MC. A new perspective of probing the level of pollution in the megacity Delhi affected by crop residue burning using the triple oxygen isotope technique in atmospheric CO 2. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 263:114542. [PMID: 32311636 DOI: 10.1016/j.envpol.2020.114542] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 04/03/2020] [Accepted: 04/04/2020] [Indexed: 06/11/2023]
Abstract
Air quality in the megacity Delhi is affected not only by local emissions but also by pollutants from crop residue burning in the surrounding areas of the city, particularly the rice straw burning in the post monsoon season. As a major burning product, gaseous CO2, which is rather inert in the polluted atmosphere, provides an alternative solution to characterize the impact of biomass burning from a new perspective that other common tracers such as particulate matters are limited because of their physical and chemical reactiveness. Here, we report conventional ([CO2], δ13C, and δ18O) and unconventional (Δ17O) isotope data for CO2 collected at Connaught Place (CP), a core area in the megacity Delhi, and two surrounding remote regions during a field campaign in October 18-20, 2017. We also measured the isotopic ratios near a rice straw burning site in Taiwan to constrain their end member isotopic compositions. Rice straw burning produces CO2 with δ13C, δ18O, and Δ17O values of -29.02 ± 0.65, 19.63 ± 1.16, and 0.05 ± 0.02‰, respectively. The first two isotopic tracers are less distinguishable from those emitted by fossil fuel combustion but the last one is significantly different. We then utilize these end member isotopic ratios, with emphasis on Δ17O for the reason given above, for partitioning sources that affect the CO2 level in Delhi. Anthropogenic fraction of CO2 at CP ranges from 4 to 40%. Further analysis done by employing a three-component (background, rice straw burning, and fuel combustion) mixing model with constraints from the Δ17O values yields that rice straw burning contributes as much as ∼70% of the total anthropogenic CO2, which is more than double of the fossil fuel contribution (∼30%), during the study days.
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Affiliation(s)
- Amzad H Laskar
- Geosciences Division, Physical Research Laboratory, Ahmedabad, 380009, Gujarat, India
| | - Abhayanand S Maurya
- Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India
| | - Vishvendra Singh
- Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India
| | - Bhola R Gurjar
- Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India
| | - Mao-Chang Liang
- Institute of Earth Sciences, Academia Sinica Taipei, Taiwan.
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13
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Ojha N, Sharma A, Kumar M, Girach I, Ansari TU, Sharma SK, Singh N, Pozzer A, Gunthe SS. On the widespread enhancement in fine particulate matter across the Indo-Gangetic Plain towards winter. Sci Rep 2020; 10:5862. [PMID: 32246046 PMCID: PMC7125076 DOI: 10.1038/s41598-020-62710-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 03/09/2020] [Indexed: 11/20/2022] Open
Abstract
Fine particulate matter (PM2.5, aerodynamic diameter ≤2.5 µm) impacts the climate, reduces visibility and severely influences human health. The Indo-Gangetic Plain (IGP), home to about one-seventh of the world's total population and a hotspot of aerosol loading, observes strong enhancements in the PM2.5 concentrations towards winter. We performed high-resolution (12 km × 12 km) atmospheric chemical transport modeling (WRF-Chem) for the post-monsoon to winter transition to unravel the underlying dynamics and influences of regional emissions over the region. Model, capturing the observed variations to an extent, reveals that the spatial distribution of PM2.5 having patches of enhanced concentrations (≥100 µgm-3) during post-monsoon, evolves dramatically into a widespread enhancement across the IGP region during winter. A sensitivity simulation, supported by satellite observations of fires, shows that biomass-burning emissions over the northwest IGP play a crucial role during post-monsoon. Whereas, in contrast, towards winter, a large-scale decline in the air temperature, significantly shallower atmospheric boundary layer, and weaker winds lead to stagnant conditions (ventilation coefficient lower by a factor of ~4) thereby confining the anthropogenic influences closer to the surface. Such changes in the controlling processes from post-monsoon to winter transition profoundly affect the composition of the fine aerosols over the IGP region. The study highlights the need to critically consider the distinct meteorological processes of west-to-east IGP and changes in dominant sources from post-monsoon to winter in the formulation of future pollution mitigation policies.
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Affiliation(s)
- Narendra Ojha
- Space and Atmospheric Sciences division, Physical Research Laboratory, Ahmedabad, India.
| | - Amit Sharma
- EWRE Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India
- Laboratory for Atmospheric Research, Washington State University, Pullman, USA
| | - Manish Kumar
- Atmospheric Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany
| | - Imran Girach
- Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram, India
| | - Tabish U Ansari
- Lancaster Environment Centre, Lancaster University, Lancaster, UK
| | - Som K Sharma
- Space and Atmospheric Sciences division, Physical Research Laboratory, Ahmedabad, India
| | - Narendra Singh
- Aryabhatta Research Institute of observational sciencES (ARIES), Nainital, India
| | - Andrea Pozzer
- Atmospheric Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany
| | - Sachin S Gunthe
- EWRE Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India.
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