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Li JM, Zhao SM, Miao QY, Wu SP, Zhang J, Schwab JJ. Changes in source contributions to the oxidative potential of PM 2.5 in urban Xiamen, China. J Environ Sci (China) 2025; 149:342-357. [PMID: 39181647 DOI: 10.1016/j.jes.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 08/27/2024]
Abstract
The toxicity of PM2.5 does not necessarily change synchronously with its mass concentration. In this study, the chemical composition (carbonaceous species, water-soluble ions, and metals) and oxidative potential (dithiothreitol assay, DTT) of PM2.5 were investigated in 2017/2018 and 2022 in Xiamen, China. The decrease rate of volume-normalized DTT (DTTv) (38%) was lower than that of PM2.5 (55%) between the two sampling periods. However, the mass-normalized DTT (DTTm) increased by 44%. Clear seasonal patterns with higher levels in winter were found for PM2.5, most chemical constituents and DTTv but not for DTTm. The large decrease in DTT activity (84%-92%) after the addition of EDTA suggested that water-soluble metals were the main contributors to DTT in Xiamen. The increased gap between the reconstructed and measured DTTv and the stronger correlations between the reconstructed/measured DTT ratio and carbonaceous species in 2022 were observed. The decrease rates of the hazard index (32.5%) and lifetime cancer risk (9.1%) differed from those of PM2.5 and DTTv due to their different main contributors. The PMF-MLR model showed that the contributions (nmol/(min·m3)) of vehicle emission, coal + biomass burning, ship emission and secondary aerosol to DTTv in 2022 decreased by 63.0%, 65.2%, 66.5%, and 22.2%, respectively, compared to those in 2017/2018, which was consistent with the emission reduction of vehicle exhaust and coal consumption, the adoption of low-sulfur fuel oil used on board ships and the reduced production of WSOC. However, the contributions of dust + sea salt and industrial emission increased.
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Affiliation(s)
- Jia-Min Li
- Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, Xiamen 361102, China; Center for Marine Environmental Chemistry and Toxicology, College of Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Si-Min Zhao
- Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, Xiamen 361102, China; Center for Marine Environmental Chemistry and Toxicology, College of Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Qi-Yu Miao
- Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, Xiamen 361102, China; Center for Marine Environmental Chemistry and Toxicology, College of Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Shui-Ping Wu
- Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, Xiamen 361102, China; Center for Marine Environmental Chemistry and Toxicology, College of Environment and Ecology, Xiamen University, Xiamen 361102, China.
| | - Jie Zhang
- Atmospheric Sciences Research Center, University at Albany, SUNY, Albany 12226, USA
| | - James J Schwab
- Atmospheric Sciences Research Center, University at Albany, SUNY, Albany 12226, USA
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Zou X, Wang S, Liu J, Zhu J, Zhang S, Xue R, Gu C, Zhou B. Role of gas-particle conversion of ammonia in haze pollution under ammonia-rich environment in Northern China and prospects of effective emission reduction. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 934:173277. [PMID: 38754510 DOI: 10.1016/j.scitotenv.2024.173277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/09/2024] [Accepted: 05/13/2024] [Indexed: 05/18/2024]
Abstract
As an important precursor of secondary inorganic aerosols (SIAs), ammonia (NH3) plays a key role in fine particulate matter (PM2.5) formation. In order to investigate its impacts on haze formation in the North China Plain (NCP) during winter, NH3 concentrations were observed at a high-temporal resolution of 1 min by using the SP-DOAS in Tai'an from December 2021 to February 2022. During the observation period, the average NH3 concentration was 11.84 ± 5.9 ppbv, and it was determined as an ammonia-rich environment during different air quality conditions. Furthermore, the average concentrations of sulfate (SO42-), nitrate (NO3-) and ammonium (NH4+) were 9.54 ± 5.97 μg/m3, 19.09 ± 14.18 μg/m3 and 10.72 ± 6.53 μg/m3, respectively. Under the nitrate-dominated atmospheric environment, aerosol liquid water content (ALWC) was crucial for NH3 particle transformation during haze aggravation, and the gas-particle partitioning of ammonia played an important role in the SIAs formation. The reconstruction of the molecular composition further indicated that ammonium nitrate (NH4NO3) plays a dominant role in the increase of PM2.5 during haze events. Consequently, future efforts to mitigate fine particulate pollution in this region should focus on controlling NH4NO3 levels. In ammonia-rich environments, NO3- formation is more dependent on the concentration of nitric acid (HNO3). The sensitive analysis of TNO3 (HNO3 + NO3-) and NHX (NH3 + NH4+) reduction using the thermodynamic model suggested that the NO3- concentration decreases linearly with the reduction of TNO3. And the concentration of NO3- decreases rapidly only when NHX is reduced by 50-60 %. Reducing NOX emissions is the most effective way to alleviate nitrate pollution in this region.
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Affiliation(s)
- Xueting Zou
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Shanshan Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Institute of Eco-Chongming (IEC), No. 20 Cuiniao Road, Shanghai 202162, China.
| | - Jiaqi Liu
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Jian Zhu
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Sanbao Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Ruibin Xue
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Chuanqi Gu
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Bin Zhou
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Institute of Eco-Chongming (IEC), No. 20 Cuiniao Road, Shanghai 202162, China; Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China.
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3
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Zhu W, Shi J, Wang H, Yu Y, Tan R, Shen R, Chen J, Lou S, Hu M, Guo S. Understanding secondary particles in a regional site of Yangtze River Delta: Insights from mass spectrometric measurement. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 933:172994. [PMID: 38719033 DOI: 10.1016/j.scitotenv.2024.172994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/29/2024] [Accepted: 05/02/2024] [Indexed: 05/19/2024]
Abstract
Submicron particulate matter (PM1) poses significant risks to health risks and global climate. In this study, secondary organic aerosols (SOA) and inorganic compositions were examined for their physicochemical characteristics and evolution using high-resolution aerosol instruments in Changzhou over one-month period. The results showed that transport accompanied by regional static conditions leaded to the occurrence of heavy pollution. In addition, regional generation and local emissions also leaded to the occurrence of light and moderate pollution during the observation period in Changzhou. Organic aerosols (OA) and nitrate (NO3-) accounted for 45 % and 23 % of PM1, respectively. The increase in PM1 was dominated by the contribution of NO3- and OA. SOA was dominance in OA (63 % with 40 % MO-OOA), which was higher than primary organic aerosols (POA). Besides, photochemical reactions and the high oxidizing nature of the urban atmosphere promoted the production of OA, especially MO-OOA in Changzhou. Our results highlight that secondary particles contribute significantly to PM pollution in Changzhou, underlining the importance of controlling emissions of gaseous precursors, especially under high oxidation conditions.
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Affiliation(s)
- Wenfei Zhu
- School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200233, PR China
| | - Jialin Shi
- School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200233, PR China
| | - Hui Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China
| | - Ying Yu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China
| | - Rui Tan
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China
| | - Ruizhe Shen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China
| | - Jun Chen
- School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200233, PR China
| | - Shengrong Lou
- School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200233, PR China
| | - Min Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China
| | - Song Guo
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, PR China.
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4
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Chen X, Ma W, Zheng F, Wang Z, Hua C, Li Y, Wu J, Li B, Jiang J, Yan C, Petäjä T, Bianchi F, Kerminen VM, Worsnop DR, Liu Y, Xia M, Kulmala M. Identifying Driving Factors of Atmospheric N 2O 5 with Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:11568-11577. [PMID: 38889013 DOI: 10.1021/acs.est.4c00651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Dinitrogen pentoxide (N2O5) plays an essential role in tropospheric chemistry, serving as a nocturnal reservoir of reactive nitrogen and significantly promoting nitrate formations. However, identifying key environmental drivers of N2O5 formation remains challenging using traditional statistical methods, impeding effective emission control measures to mitigate NOx-induced air pollution. Here, we adopted machine learning assisted by steady-state analysis to elucidate the driving factors of N2O5 before and during the 2022 Winter Olympics (WO) in Beijing. Higher N2O5 concentrations were observed during the WO period compared to the Pre-Winter-Olympics (Pre-WO) period. The machine learning model accurately reproduced ambient N2O5 concentrations and showed that ozone (O3), nitrogen dioxide (NO2), and relative humidity (RH) were the most important driving factors of N2O5. Compared to the Pre-WO period, the variation in trace gases (i.e., NO2 and O3) along with the reduced N2O5 uptake coefficient was the main reason for higher N2O5 levels during the WO period. By predicting N2O5 under various control scenarios of NOx and calculating the nitrate formation potential from N2O5 uptake, we found that the progressive reduction of nitrogen oxides initially increases the nitrate formation potential before further decreasing it. The threshold of NOx was approximately 13 ppbv, below which NOx reduction effectively reduced the level of night-time nitrate formations. These results demonstrate the capacity of machine learning to provide insights into understanding atmospheric nitrogen chemistry and highlight the necessity of more stringent emission control of NOx to mitigate haze pollution.
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Affiliation(s)
- Xin Chen
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Wei Ma
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Feixue Zheng
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Zongcheng Wang
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Chenjie Hua
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yiran Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jin Wu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Boda Li
- Meta Platforms, Inc., Menlo Park, California 94025, United States
| | - Jingkun Jiang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Chao Yan
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210008, China
| | - Tuukka Petäjä
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Federico Bianchi
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Veli-Matti Kerminen
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Douglas R Worsnop
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Yongchun Liu
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Men Xia
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Markku Kulmala
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
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5
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Zheng X, Liu J, Zhong B, Wang Y, Wu Z, Chuduo N, Ba B, Yuan X, Fan M, Cao F, Zhang Y, Chen W, Zhou L, Ma N, Yu P, Li J, Zhang G. Insights into anthropogenic impact on atmospheric inorganic aerosols in the largest city of the Tibetan Plateau through multidimensional isotope analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 929:172643. [PMID: 38649049 DOI: 10.1016/j.scitotenv.2024.172643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/18/2024] [Accepted: 04/18/2024] [Indexed: 04/25/2024]
Abstract
Particulate inorganic nitrogen aerosols (PIN) significantly influence air pollution and pose health risks worldwide. Despite extensive observations on ammonium (pNH4+) and nitrate (pNO3-) aerosols in various regions, their key sources and mechanisms in the Tibetan Plateau remain poorly understood. To bridge this gap, this study conducted a sampling campaign in Lhasa, the Tibetan Plateau's largest city, with a focus on analyzing the multiple isotopic signatures (δ15N, ∆17O). These isotopes were integrated into a Bayesian mixing model to quantify the source contributions and oxidation pathways for pNH4+ and pNO3-. Our results showed that traffic was the largest contributor to pNH4+ (31.8 %), followed by livestock (25.4 %), waste (21.8 %), and fertilizer (21.0 %), underscoring the impact of vehicular emissions on urban NH3 levels in Lhasa. For pNO3-, coal combustion emerged as the largest contributor (27.3 %), succeeded by biomass burning (26.3 %), traffic emission (25.3 %), and soil emission (21.1 %). In addition, the ∆17O-based model indicated a dominant role of NO2 + OH (52.9 %) in pNO3- production in Lhasa, which was similar to previous observations. However, it should be noted that the NO3 + volatile organic component (VOC) contributed up to 18.5 % to pNO3- production, which was four times higher than the Tibetan Plateau's background regions. Taken together, the multidimensional isotope analysis performed in this study elucidates the pronounced influence of anthropogenic activities on PIN in the atmospheric environment of Lhasa.
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Affiliation(s)
- Xueqin Zheng
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Junwen Liu
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China.
| | - Bingqian Zhong
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Yujing Wang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; Department of Environmental Science and Engineering, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Zeyan Wu
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Nima Chuduo
- Lhasa Meteorological Administration, Lhasa 850010, China
| | - Bian Ba
- Lhasa Meteorological Administration, Lhasa 850010, China
| | - Xin Yuan
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Meiyi Fan
- School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Fang Cao
- School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Yanlin Zhang
- School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Weihua Chen
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Luxi Zhou
- Guangzhou Institute of Tropical and Marine Meteorology, Meteorological Administration, Guangzhou 510640, China
| | - Nan Ma
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Pengfei Yu
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Jun Li
- State Key Laboratory of Organic Geochemistry and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
| | - Gan Zhang
- State Key Laboratory of Organic Geochemistry and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
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6
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Xu M, Hu B, Zhao S, Yan G, Wen T, Zhao X. Size-resolved water-soluble organic carbon and its significant contribution to aerosol liquid water. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172396. [PMID: 38608903 DOI: 10.1016/j.scitotenv.2024.172396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/20/2024] [Accepted: 04/09/2024] [Indexed: 04/14/2024]
Abstract
Size-segregated aerosols collected in Beijing from 2021 to 2022 were used to investigate the contribution of organic aerosols to the aerosol liquid water content (ALWC), the influencing factors of ALWC, and the concentrations and size distribution characteristics of water-soluble organic carbon (WSOC) after clean air actions. The results showed that the concentration of WSOC in particulate matter (PM)1.8 was 3.52 ± 2.43 μg/m3 during the sampling period. Obvious changes were observed in the size distribution of WSOC after clean air actions, which may be attributed to the enhancement of atmospheric oxidation capacity and the decrease in PM concentration. The contribution of organic aerosols to the ALWC in fine PM was 18.1 % during the sampling period, which was more significant at lower particles concentration and smaller particle size ranges. The ambient relative humidity (RH) and the ratio of NO3-/SO42- had an apparent influence on ALWC. The continuous increase in the nitrate proportion significantly reduced the deliquescence point of the aerosols, making them prone to hygroscopic growth at lower RH. Analysis of the relation among nitrogen oxidation ratio (sulfur oxidation ratio), ALWC and PM1.8 mass concentrations suggests that organic matter has a significant effect on the formation of secondary inorganic aerosols in the initial phase of pollution formation and plays a crucial role in aerosol pollution formation in Beijing. These results are conducive to understanding the formation mechanism of aerosols and provide scientific data and theoretical support for the formulation of more effective emission-reduction measures.
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Affiliation(s)
- Min Xu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Bo Hu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
| | - Shuman Zhao
- College of Chemistry and Chemical Engineering, Dezhou University, Dezhou 253023, China
| | - Guangxuan Yan
- Key Laboratory for Yellow River and Huai River Water Environment and Pollution Control, Henan Key Laboratory for Environmental Pollution Control, School of Environment, Henan Normal University, Xinxiang 453007, China
| | - Tianxue Wen
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Xiaoxi Zhao
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
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7
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Gen M, Zheng H, Sun Y, Xu W, Ma N, Su H, Cheng Y, Wang S, Xing J, Zhang S, Xue L, Xue C, Mu Y, Tian X, Matsuki A, Song S. Rapid hydrolysis of NO 2 at High Ionic Strengths of Deliquesced Aerosol Particles. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:7904-7915. [PMID: 38661303 DOI: 10.1021/acs.est.3c08810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Nitrogen dioxide (NO2) hydrolysis in deliquesced aerosol particles forms nitrous acid and nitrate and thus impacts air quality, climate, and the nitrogen cycle. Traditionally, it is considered to proceed far too slowly in the atmosphere. However, the significance of this process is highly uncertain because kinetic studies have only been made in dilute aqueous solutions but not under high ionic strength conditions of the aerosol particles. Here, we use laboratory experiments, air quality models, and field measurements to examine the effect of the ionic strength on the reaction kinetics of NO2 hydrolysis. We find that high ionic strengths (I) enhance the reaction rate constants (kI) by more than an order of magnitude compared to that at infinite dilution (kI=0), yielding log10(kI/kI=0) = 0.04I or rate enhancement factor = 100.04I. A state-of-the-art air quality model shows that the enhanced NO2 hydrolysis reduces the negative bias in the simulated concentrations of nitrous acid by 28% on average when compared to field observations over the North China Plain. Rapid NO2 hydrolysis also enhances the levels of nitrous acid in other polluted regions such as North India and further promotes atmospheric oxidation capacity. This study highlights the need to evaluate various reaction kinetics of atmospheric aerosols with high ionic strengths.
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Affiliation(s)
- Masao Gen
- Faculty of Frontier Engineering, Institute of Science and Engineering, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
| | - Haotian Zheng
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment Health Research, Tianjin 300350, China
| | - Yele Sun
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Wanyun Xu
- State Key Laboratory of Severe Weather, Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition and Environmental Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Nan Ma
- Institute for Environmental and Climate Research (ECI), Jinan University, Guangzhou 511443, China
| | - Hang Su
- Max Planck Institute for Chemistry, Mainz 55128, Germany
| | - Yafang Cheng
- Max Planck Institute for Chemistry, Mainz 55128, Germany
| | - Shuxiao Wang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuping Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Likun Xue
- Environment Research Institute, Shandong University, Qingdao 266237, China
| | - Chaoyang Xue
- Laboratoire de Physique et Chimie de l'Environnement et de l'Espace (LPC2E), CNRS - Université Orléans - CNES, Orléans Cedex 2 45071, France
| | - Yujing Mu
- Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Xiao Tian
- 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
| | - Atsushi Matsuki
- Institute of Nature and Environmental Technology, Kanazawa University, Kanazawa 920-1192, Japan
| | - Shaojie Song
- CMA-NKU Cooperative Laboratory for Atmospheric Environment Health Research, Tianjin 300350, China
- 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
- Harvard-China on Energy, Economy, and Environment, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
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8
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Xu B, Yu H, Shi Z, Liu J, Wei Y, Zhang Z, Huangfu Y, Xu H, Li Y, Zhang L, Feng Y, Shi G. Knowledge-guided machine learning reveals pivotal drivers for gas-to-particle conversion of atmospheric nitrate. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 19:100333. [PMID: 38021366 PMCID: PMC10661687 DOI: 10.1016/j.ese.2023.100333] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 10/09/2023] [Accepted: 10/12/2023] [Indexed: 12/01/2023]
Abstract
Particulate nitrate, a key component of fine particles, forms through the intricate gas-to-particle conversion process. This process is regulated by the gas-to-particle conversion coefficient of nitrate (ε(NO3-)). The mechanism between ε(NO3-) and its drivers is highly complex and nonlinear, and can be characterized by machine learning methods. However, conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors. It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact of ε(NO3-). Here we introduce a supervised machine learning approach-the multilevel nested random forest guided by theory approaches. Our approach robustly identifies NH4+, SO42-, and temperature as pivotal drivers for ε(NO3-). Notably, substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results. Furthermore, our approach underscores the significance of NH4+ during both daytime (30%) and nighttime (40%) periods, while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis. This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.
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Affiliation(s)
- Bo Xu
- 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 (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Haofei Yu
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Zongbo Shi
- School of Geography Earth and Environment Sciences, University of Birmingham, Birmingham, B15 2TT, UK
| | - Jinxing Liu
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin Key Laboratory of air Pollutants Monitoring Technology, School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin, 300072, China
- Gigantic Technology (Tianjin) Co., Ltd, Tianjin, 300072, China
| | - Yuting Wei
- 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 (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Zhongcheng 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 (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Yanqi Huangfu
- 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 (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Han Xu
- 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 (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Yue Li
- College of Computer Science, Nankai University, Tianjin, 300350, China
| | - Linlin Zhang
- China National Environmental Monitoring Centre, Beijing, 100012, 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 (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Guoliang Shi
- 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 (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
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9
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Deng M, Wang C, Yang C, Li X, Cheng H. Nitrogen and oxygen isotope characteristics, formation mechanism, and source apportionment of nitrate aerosols in Wuhan, Central China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 921:170715. [PMID: 38331296 DOI: 10.1016/j.scitotenv.2024.170715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 01/20/2024] [Accepted: 02/03/2024] [Indexed: 02/10/2024]
Abstract
Understanding the sources and formation mechanisms of nitrate in PM2.5 is important for effective and precise prevention and control of particulate matter pollution. In this study, we detected stable nitrogen and oxygen isotope signatures of NO- 3 (expressed as δ15N-NO- 3 and δ18O-NO3-) in PM2.5 samples in Wuhan, the largest city in central China. The sources and formation pathways of NO3- were quantitatively analyzed using the modified version of the Bayesian isotope mixing (MixSIR) model, and the regional transport characteristics of NO3- were analyzed using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model and concentration-weighted trajectory (CWT) method. The results showed that NO3- significantly contributed to the ambient PM2.5 pollution and its driving effect increased with the gradient of pollution level. The average δ15N-NO3- and δ18O-NO3- values were 4.7 ± 0.9 ‰ and 79.7 ± 2.9 ‰, respectively. δ15N-NO3- and δ18O-NO3- were more enriched in winter and increased dramatically in heavily polluted days. The reaction pathway of NO2 + OH dominated nitrate formation in summer, while the reaction pathway of N2O5+ H2O dominated in other seasons and contributed more in polluted days than clean days. The contributions of vehicle emission, coal combustion, biomass burning, biogenic soil emission, and ship emission sources to NO3- were 26.4 %, 23.4 %, 22.8 %, 15.3 %, and 12.1 %, respectively. In addition to local emissions, air mass transport from the northern China had a significant impact on particulate NO3- in Wuhan. Overall, we should pay special attention to vehicle and ship emissions and winter coal combustion emissions in future policymaking.
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Affiliation(s)
- Mengjie Deng
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
| | - Cimou Wang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
| | - Chunmian Yang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
| | - Xiaoxiao Li
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China.
| | - Hairong Cheng
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China.
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10
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Yao W, Pan X, Tian Y, Liu H, Zhang Y, Lei S, Zhang J, Zhang Y, Wu L, Sun Y, Wang Z. Development and evaluation of an online monitoring single-particle optical particle counter with polarization detection. J Environ Sci (China) 2024; 138:585-596. [PMID: 38135422 DOI: 10.1016/j.jes.2023.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/10/2023] [Accepted: 04/10/2023] [Indexed: 12/24/2023]
Abstract
We developed a single-particle optical particle counter with polarization detection (SOPC) for the real-time measurement of the optical size and depolarization ratio (defined as the ratio of the vertical component to the parallel component of backward scattering) of atmospheric particles, the polarization ratio (DR) value can reflect the irregularity of the particles. The SOPC can detect aerosol particles with size larger than 500 nm and the maximum particle count rate reaches ∼1.8 × 105 particles per liter. The SOPC uses a modulated polarization laser to measure the optical size of particles according to forward scattering signal and the DR value of the particles by backward S and P signal components. The sampling rate of the SOPC was 106 #/(sec·channel), and all the raw data were processed online. The calibration curve was obtained by polystyrene latex spheres with sizes of 0.5-10 µm, and the average relative deviation of measurement was 3.96% for sub 3 µm particles. T-matrix method calculations showed that the DR value of backscatter light at 120° could describe the variations in the aspect ratio of particles in the above size range. We performed insitu observations for the evaluation of the SOPC, the mass concentration constructed by the SOPC showed good agreement with the PM2.5 measurements in a nearby state-controlled monitoring site. This instrument could provide useful data for source appointment and regulations against air pollution.
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Affiliation(s)
- Weijie Yao
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaole Pan
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
| | - Yu Tian
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Hang Liu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yuting Zhang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shandong Lei
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Junbo Zhang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yinzhou Zhang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lin Wu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yele Sun
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zifa Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
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11
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Zhang J, Cheng H, Zhu Y, Xie S, Shao X, Wang C, Chung SK, Zhang Z, Hao K. Exposure to Airborne PM 2.5 Water-Soluble Inorganic Ions Induces a Wide Array of Reproductive Toxicity. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:4092-4103. [PMID: 38373958 DOI: 10.1021/acs.est.3c07532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Water-soluble inorganic ions (WSIIs, primarily NH4+, SO42-, and NO3-) are major components in ambient PM2.5, but their reproductive toxicity remains largely unknown. An animal study was conducted where parental mice were exposed to PM2.5 WSIIs or clean air during preconception and the gestational period. After delivery, all maternal and offspring mice lived in a clean air environment. We assessed reproductive organs, gestation outcome, birth weight, and growth trajectory of the offspring mice. In parallel, we collected birth weight and placenta transcriptome data from 150 mother-infant pairs from the Rhode Island Child Health Study. We found that PM2.5 WSIIs induced a broad range of adverse reproductive outcomes in mice. PM2.5 NH4+, SO42-, and NO3- exposure reduced ovary weight by 24.22% (p = 0.005), 14.45% (p = 0.048), and 16.64% (p = 0.022) relative to the clean air controls. PM2.5 SO42- exposure reduced the weight of testicle by 5.24% (p = 0.025); further, mice in the PM2.5 SO42- exposure group had 1.81 (p = 0.027) fewer offspring than the control group. PM2.5 NH4+, SO42-, and NO3- exposure all led to lower birth than controls. In mice, 557 placenta genes were perturbed by exposure. Integrative analysis of mouse and human data suggested hypoxia response in placenta as an etiological mechanism underlying PM2.5 WSII exposure's reproductive toxicity.
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Affiliation(s)
- Jushan Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai, China 200092
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China 200072
- College of Environmental Science and Engineering, Tongji University, Shanghai, China 200092
| | - Haoxiang Cheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Yujie Zhu
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China 200072
| | - Shuanshuan Xie
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China 200072
| | - Xiaowen Shao
- Department of Obstetrics and Gynecology, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China 200072
| | - Changhui Wang
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China 200072
| | - Sookja Kim Chung
- Faculty of Medicine, Macau University of Science and Technology, Taipa, Macau SAR 999078, China
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Zhongyang Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Ke Hao
- State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai, China 200092
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China 200072
- College of Environmental Science and Engineering, Tongji University, Shanghai, China 200092
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12
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Cheng Y, Chen L, Wu H, Liu J, Ren J, Zhang F. Wintertime fine aerosol particles composition and its evolution in two megacities of southern and northern China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 914:169778. [PMID: 38176561 DOI: 10.1016/j.scitotenv.2023.169778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 12/28/2023] [Accepted: 12/28/2023] [Indexed: 01/06/2024]
Abstract
Study on fine aerosols composition can help understand the particles formation and is crucial for improving the accuracy of model simulations. Based on field data measured by a Q-ACSM (Quadrupole-Aerosol Chemical Speciation Monitor), we have comprehensively compared the characteristics, evolution, and potential formation mechanisms of the components in NR-PM2.5 during wintertime at two megacities (Beijing and Guangzhou) of southern and northern China. We show that as PM pollution intensifies, the mass fraction of the primary aerosols (e.g., COA, HOA) in PM2.5 in Guangzhou increased, along with a slight decline in proportion of both the secondary organic (SOA) and inorganic (SIA) aerosols; In contrast, in Beijing, the proportion of the SIA ramped up from 28 % to 53 % with the pollution evolution; and the fraction of SOA in total OA also increased due to a substantial increment in the proportion of MO-OOA (from 29 % to 48 %), suggesting a significance of the secondary processes in worsening aerosols pollution in Beijing. Our further analysis demonstrates a leading role of aqueous pathway in the secondary formation of aerosols at the Beijing site, presenting an exponential rising of SIA and SOA with the relative humidity (RH) increase. Compared to Beijing, however, we find that the photochemical oxidation other than aqueous process in Guangzhou plays a more critical role in those secondary aerosols formation. Combined with the Hysplit trajectory model, we identify the high humid conditions in Guangzhou are typically affected by clean marine air masses, explaining the slower response of secondary components to the RH changes. Moreover, the particles in Guangzhou were observed less hygroscopic that is adverse to the aerosol aqueous chemistry. The results provide basis for the precise control of PM pollution in different regions across China and would be helpful in improving model simulations.
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Affiliation(s)
- Yiling Cheng
- School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
| | - Lu Chen
- School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China; Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Hao Wu
- School of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China
| | - Jieyao Liu
- School of Geographical Sciences, Hebei Normal University, Shijiazhuang 050024, China
| | - Jingye Ren
- Xi'an Institute for Innovative Earth Environment Research, Xi'an 710061, China
| | - Fang Zhang
- School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.
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13
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Qu Q, Wang S, Zhao B, Hu R, Liang C, Zhang H, Li S, Feng B, Hou X, Yin D, Du J, Chu Y, Zhang Y, Wu Q, Wen Y, Wu X, Hu J, Zhang S, Hao J. Response of organic aerosol in Beijing to emission reductions during the XXIV Olympic Winter Games. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 914:170033. [PMID: 38220000 DOI: 10.1016/j.scitotenv.2024.170033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/05/2024] [Accepted: 01/07/2024] [Indexed: 01/16/2024]
Abstract
Organic aerosol (OA) serves as a crucial component of fine particulate matter. However, the response of OA to changes in anthropogenic emissions remains unclear due to its complexity. The XXIV Olympic Winter Games (OWG) provided real atmospheric experimental conditions on studying the response of OA to substantial emission reductions in winter. Here, we explored the sources and variations of OA based on the observation of aerosol mass spectrometer (AMS) combined with positive matrix factorization (PMF) analysis in urban Beijing during the 2022 Olympic Winter Games. The influences of meteorological conditions on OA concentrations were corrected by CO and verified by deweathered model. The CO-normalized primary OA (POA) concentrations from traffic, cooking, coal and biomass burning during the OWG decreased by 39.8 %, 23.2 % and 65.0 %, respectively. Measures controlling coal and biomass burning were most effective in reducing POA during the OWG. For the CO-normalized concentration of secondary OA (SOA), aqueous-phase related oxygenated OA decreased by 51.8 % due to the lower relative humidity and emission reduction in precursors, while the less oxidized‑oxygenated OA even slightly increased as the enhanced atmospheric oxidation processes may partially offset the efficacy of emission control. Therefore, more targeted reduction of organic precursors shall be enhanced to lower atmospheric oxidation capacity and mitigate SOA pollution.
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Affiliation(s)
- Qipeng Qu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Ruolan Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Chengrui Liang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Haowen Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Boyang Feng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xuan Hou
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jinhong Du
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yangxi Chu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yanning Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Qingru Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yifan Wen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xiaomeng Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jingnan Hu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Shaojun Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
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14
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Chen L, Zhang F, Ren J, Li Z, Xu W, Sun Y, Liu L, Wang X. Changes in wintertime visibility across China over 2013-2019 and the drivers: A comprehensive assessment using machine learning method. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169516. [PMID: 38135088 DOI: 10.1016/j.scitotenv.2023.169516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/05/2023] [Accepted: 12/17/2023] [Indexed: 12/24/2023]
Abstract
Effective emission reduction measures have largely lowered the particulate concentration in China, but low-visibility events still occur frequently, greatly affecting people's daily life, travel, and health. In the context of carbon neutrality strategy and climate change, the mechanisms governing visibility changes may be undergoing a transformation. To address this critical issue, we have undertaken a comprehensive assessment by employing a novel approach that combines site observations, model-derived datasets, and machine learning techniques. Our analysis of the dataset shows varying degrees of improvement in wintertime visibility in regions such as North China, South China, and the Fenwei Plain over 2013-2019, but an unexpected deterioration (approximately 1 km yr-1) in central and southern China (CSC). We further elucidate key roles of PM2.5 reduction in these regions with visibility improvement; whereas the unsatisfactory visibility trend in CSC was caused by combined effect of relative humidity (RH) increase (47 %), aerosol hygroscopicity (κ) enhancement (9 %), and boundary layer (BLH) reduction (8 %), which greatly overwhelms the effect of PM2.5 reduction recently. Moreover, the study reveals a growing influence of RH on the wintertime visibility, reaching 40 % ± 24 % to the total contribution in 2019, while that of PM2.5 declined to 18 % ± 19 % and is expected to further diminish with emission reduction. Note those often-neglected factors-temperature, wind speed, BLH, and κ, account for over 40 % of the total contribution. Though the importance of aerosol hygroscopic growth to visibility was found decreasing recently, it retains non-negligible impacts on driving inter-annual visibility trends. This study yields innovative insights for air pollution control, underscoring the imperative of region-specific strategies to mitigate low-visibility events.
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Affiliation(s)
- Lu Chen
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), 518055 Shenzhen, China
| | - Fang Zhang
- School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), 518055 Shenzhen, China.
| | - Jingye Ren
- Xi'an Institute for Innovative Earth Environment Research, Xi'an 710061, China
| | - Zhigang Li
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Weiqi Xu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Yele Sun
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Lingling Liu
- College of Environment and Science, Beijing Normal University, Beijing 100875, China
| | - Xinming Wang
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China.
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15
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Zheng H, Li S, Jiang Y, Dong Z, Yin D, Zhao B, Wu Q, Liu K, Zhang S, Wu Y, Wen Y, Xing J, Henneman LRF, Kinney PL, Wang S, Hao J. Unpacking the factors contributing to changes in PM 2.5-associated mortality in China from 2013 to 2019. ENVIRONMENT INTERNATIONAL 2024; 184:108470. [PMID: 38324930 DOI: 10.1016/j.envint.2024.108470] [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: 06/27/2023] [Revised: 01/30/2024] [Accepted: 01/30/2024] [Indexed: 02/09/2024]
Abstract
From 2013 to 2019, a series of air pollution control actions significantly reduced PM2.5 pollution in China. Control actions included changes in activity levels, structural adjustment (SA) policy, energy and material saving (EMS) policy, and end-of-pipe (EOP) control in several sources, which have not been systematically studied in previous studies. Here, we integrate an emission inventory, a chemical transport model, a health impact assessment model, and a scenario analysis to quantify the contribution of each control action across a range of major emission sources to the changes in PM2.5 concentrations and associated mortality in China from 2013 to 2019. Assuming equal toxicity of PM2.5 from all the sources, we estimate that PM2.5-related mortality decreased from 2.52 (95 % confidence interval, 2.13-2.88) to 1.94 (1.62-2.24) million deaths. Anthropogenic emission reductions and declining baseline incidence rates significantly contributed to health benefits, but population aging partially offset their impact. Among the major sources, controls on power plants and industrial boilers were responsible for the highest reduction in PM2.5-related mortality (∼80 %), followed by industrial processes (∼40 %), residential combustion (∼40 %), and transportation (∼30 %). However, considering the potentially higher relative risks of power plant PM2.5, the adverse effects avoided by their control could be ∼2.4 times the current estimation. Our power plant sensitivity analyses indicate that future estimates of source-specific PM2.5 health effects should incorporate variations in individual source PM2.5 effect coefficients when available. As for the control actions, while activity levels increased for most sources, SA policy significantly reduced the emissions in residential combustion and industrial boilers, and EOP control dominated the contribution in health benefits in most sources except residential combustion. Considering the emission reduction potential by source and control actions in 2019, our results suggest that promoting clean energy in residential combustion and enforcing more stringent EOP control in the iron and steel industry should be prioritized in the future.
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Affiliation(s)
- Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Qingru Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Kaiyun Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shaojun Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Ye Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yifan Wen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Lucas R F Henneman
- Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USA
| | - Patrick L Kinney
- Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
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16
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Cheng C, Yang S, Yuan B, Pei C, Zhou Z, Mao L, Liu S, Chen D, Cheng X, Li M, Shao M, Zhou Z. The significant contribution of nitrate to a severe haze event in the winter of Guangzhou, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 909:168582. [PMID: 37967633 DOI: 10.1016/j.scitotenv.2023.168582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/02/2023] [Accepted: 11/12/2023] [Indexed: 11/17/2023]
Abstract
A severe haze pollution occurred in Guangzhou from January 14 to 16, 2021, during which the mass concentration of PM2.5 ranged from 76 to 243 μg m-3. This level of pollution was rarely observed in recent years considering the improved air quality in Guangzhou. Therefore, it is crucial to comprehensively study the formation mechanisms of this severe haze pollution to prevent its reoccurrence. During the haze period, the concentrations of NO and NO2 sharply increased by 7.4 and 3.8 times, respectively, and total volatile organic compounds (TVOCs) increased 7 times, suggesting enhanced primary emissions from vehicles due to stagnant meteorological conditions. Nitrate concentration (43 ± 20 μg m-3) increased 6.7 times and became the dominant species in PM2.5 during the haze period. Notably, gaseous NH3, HONO and HNO3 also exhibited a sharp increase, suggesting the important role of nitrate chemistry in the evolution of haze pollution. The simulation results from chemical box model revealed that the OH + NO2 reaction was the dominant formation pathway for nitrate production (82 %) during the haze period. The net production rate of ROx radicals (including OH, HO2 and RO2) was 4.4 times higher during the haze period (5.8 ppb h-1) compared to the pre-haze period (1.3 ppb h-1). This was mainly attributed to the enhanced HONO and OVOCs photolysis, which increased from 0.6 ppb h-1 to 3.1 ppb h-1 and 0.4 ppb h-1 to 2.1 ppb h-1, respectively. Furthermore, the sensitivity tests demonstrated the reductions in VOCs and NOx were both beneficial for controlling nitrate production by influencing OH production and N2O5 uptake rate. These findings provide insights into the formation mechanisms of nitrate production during severe haze pollution and suggest that joint mitigation of PM2.5 and O3 can be achieved through the control of VOCs emission.
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Affiliation(s)
- Chunlei Cheng
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China; State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China; State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy Science, Xi'an 710061, China
| | - Suxia Yang
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China; Institute for Environment and Climate Research, Jinan University, Guangzhou 510632, China; Guangzhou Research Institute of Environment Protection Co., Ltd, Guangzhou 510620, China
| | - Bin Yuan
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China; Institute for Environment and Climate Research, Jinan University, Guangzhou 510632, China
| | - Chenglei Pei
- Guangzhou Environmental Monitoring Center, Guangzhou 510030, China.
| | - Zhihua Zhou
- Shenzhen Ecological and Environmental Monitoring Center of Guangdong Province, Shenzhen 518049, China
| | - Liyuan Mao
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Sulin Liu
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Duanying Chen
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Xiaoya Cheng
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Mei Li
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China.
| | - Min Shao
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China; Institute for Environment and Climate Research, Jinan University, Guangzhou 510632, China
| | - Zhen Zhou
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
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Arfin T, Pillai AM, Mathew N, Tirpude A, Bang R, Mondal P. An overview of atmospheric aerosol and their effects on human health. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:125347-125369. [PMID: 37674064 DOI: 10.1007/s11356-023-29652-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/29/2023] [Indexed: 09/08/2023]
Abstract
Epidemiologic investigations have previously been published in more than 200 papers, and several studies have examined the impacts of particle air pollution on health. The main conclusions now being made about the epidemiological evidence of particle pollution-induced health impacts are discussed in this article. Although there is no universal agreement, most reviewers conclude that particulate air pollution, particularly excellent combustion-cause contamination prevalent in many municipal and manufacturing environments, is a significant risk for cardiopulmonary sickness and mortality. Most epidemiological research has concentrated on the impacts of acute exposure, although the total public health implications of chronic acquaintance's outcome may be more extraordinarily significant. According to some reviewers, prolonged, repeated exposure raises the risk of cardiorespiratory death and chronic respiratory illness. A more general (but still universal) agreement is that short-term particle pollution exposure has been shown to aggravate pre-existing pulmonary and cardiovascular diseases and increase the number of community members who become sick, require medical treatment, or die. Several in-depth studies conducted in the global and Indian regions are addressed.
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Affiliation(s)
- Tanvir Arfin
- Air Pollution Control Division, CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur, 440020, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
| | - Anupama M Pillai
- Air Pollution Control Division, CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur, 440020, India
| | - Nikhila Mathew
- Air Pollution Control Division, CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur, 440020, India
| | - Abha Tirpude
- Air Pollution Control Division, CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur, 440020, India
| | - Roshani Bang
- Air Pollution Control Division, CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur, 440020, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Pabitra Mondal
- Air Pollution Control Division, CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur, 440020, India
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18
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Bhattarai N, Wang S, Xu Q, Dong Z, Chang X, Jiang Y, Zheng H. Nitrogen isotopes suggest agricultural and non-agricultural sources contribute equally to NH 3 and NH 4+ in urban Beijing during December 2018. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 326:121455. [PMID: 36934964 DOI: 10.1016/j.envpol.2023.121455] [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: 12/26/2022] [Revised: 03/07/2023] [Accepted: 03/16/2023] [Indexed: 06/18/2023]
Abstract
Agricultural and non-agricultural sources emission contribute to atmospheric ammonia (NH3) and particulate ammonium (NH4+). However, our understanding on the sources of NH3 and NH4+ in PM2.5 (particles smaller than 2.5 μm) during the winter period in the urban atmosphere is limited. Here, we measured the concentrations and stable nitrogen isotopic composition (δ15N) of NH3 and NH4+ in parallel during December 2018 in urban Beijing to assess the non-agricultural and agricultural sources contributions to NH3 and NH4+ in ambient air based on the Chemical Transport Model (CTM), a Bayesian isotope mixing model (SIMMR), and the δ15N signatures that we developed. Our study found weekly NH4+ and NH3 concentrations were on average 2.5 ± 1.4 μg m-3 and 3.8 ± 1.7 μg m-3, respectively during December 2018. Weekly concentration weighted δ15N(NH4+) values ranged from 4.5‰ to 13.7‰ with an average value of 8.2 ± 3.9‰ during December 2018. After accounting for nitrogen isotopic fractionation from NH3 gas to NH4+ conversion, initial δ15N(NH3) values ranged from -22.5‰ to -12.8‰ with an average value of -17.4 ± 3.5‰. Further, weekly measured δ15N(NH3) values ranged from -22.2‰ to -10.2‰ (after correction) with an average value of -15.6 ± 5.3‰ during December 2018. Results from two different isotope-based method showed non-agricultural sources contributed 31.2%-63.1%, with an average value of 47.5 ± 14.6%, to NH4+ and 32.3%-71.2%, with an average of 53.4 ± 16.1%, to ambient NH3 during December 2018 in Beijing. Results from CMAQ-ISAM suggest non-agricultural sources contributed on average 66.2 ± 1.9% to ambient NH4+ and 66.4 ± 1.9% to ambient NH3 during December 2018. Results from this study suggest that agricultural and non-agricultural sources nearly equally contributed to NH3 and NH4+ in urban Beijing during December 2018 with an uncertainty range of 13%-19% between isotope-based methods and CTM method.
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Affiliation(s)
- Noshan Bhattarai
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China.
| | - Qingcheng Xu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Xing Chang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
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19
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Wei Y, Nenes A, Gao J, Liang W, Liang D, Shi G, Feng Y, Russell AG. Abundant nitrogen oxide and weakly acidic environment synergistically promote daytime particulate nitrate pollution. JOURNAL OF HAZARDOUS MATERIALS 2023; 456:131655. [PMID: 37216807 DOI: 10.1016/j.jhazmat.2023.131655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 05/09/2023] [Accepted: 05/15/2023] [Indexed: 05/24/2023]
Abstract
Nitrate is formed through the chemical production of gas-phase nitric acid and subsequent partitioning to the aerosol phase during the daytime. Many studies in the past separated these two aspects, even though they occur simultaneously in the atmosphere. To better understand the nitrate formation mechanism and effectively mitigate its production, it is necessary to consider the synergy between these two mechanisms. For this, we analyze hourly-speciated ambient observations data, with EK&TMA (Empirical Kinetic & Thermodynamic Modeling Approach) map to comprehensively explore the factors controlling nitrate production. Results show that precursor NO2 concentration and aerosol pH, which are related to anthropogenic activities, are the two major factors for chemical kinetics production and gas/particle thermodynamic partitioning processes respectively. Abundant NO2 and weakly acidic environments are favorable conditions for daytime particulate nitrate pollution, thus collaborative control of coal source, vehicle source, and dust source is needed to alleviate nitrate pollution.
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Affiliation(s)
- Yuting Wei
- 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 (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Athanasios Nenes
- Laboratory of Atmospheric Processes and their Impacts, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland; Center for the Study of Air Quality and Climate Change, Institute of Chemical Engineering Sciences, Foundation for Research and Technology Hellas, Patras GR-26504, Greece
| | - Jie Gao
- 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 (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Weiqing Liang
- 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 (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Danni Liang
- 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 (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Guoliang Shi
- 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 (CLAER), College of Environmental Science and Engineering, Nankai University, 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 (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Armistead G Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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20
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Zhang J, Cheng H, Di Narzo A, Zhu Y, Xie S, Shao X, Zhang Z, Chung SK, Hao K. Profiling Microbiota from Multiple Sites in the Respiratory Tract to Identify a Biomarker for PM 2.5 Nitrate Exposure-Induced Pulmonary Damages. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:7346-7357. [PMID: 37133311 DOI: 10.1021/acs.est.2c08807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The microbiota present in the respiratory tract (RT) responds to environmental stimuli and engages in a continuous interaction with the host immune system to maintain homeostasis. A total of 40 C57BL/6 mice were divided into four groups and exposed to varying concentrations of PM2.5 nitrate aerosol and clean air. After 10 weeks of exposure, assessments were conducted on the lung and airway microbiome, lung functions, and pulmonary inflammation. Additionally, we analyzed data from both mouse and human respiratory tract (RT) microbiomes to identify possible biomarkers for PM2.5 exposure-induced pulmonary damages. On average, 1.5 and 13.5% inter-individual microbiome variations in the lung and airway were explained by exposure, respectively. In the airway, among the 60 bacterial OTUs (operational taxonomic units) > 0.05% proportion, 40 OTUs were significantly affected by PM2.5 exposure (FDR ≤ 10%). Further, the airway microbiome was associated with peak expiratory flow (PEF) (p = 0.003), pulmonary neutrophil counts (p = 0.01), and alveolar 8-OHdG oxidative lesions (p = 0.0078). The Clostridiales order bacteria showed the strongest signals. For example, the o_Clostridiales;f_;g_ OTU was elevated by PM2.5 nitrate exposure (p = 4.98 × 10-5) and negatively correlated with PEF (r = -0.585 and p = 2.4 × 10-4). It was also associated with the higher pulmonary neutrophil count (p = 8.47 × 10-5) and oxidative lesion (p = 7.17 × 10-3). In human data, we confirmed the association of airway Clostridiales order bacteria with PM2.5 exposure and lung function. For the first time, this study characterizes the impact of PM2.5 exposure on the microbiome of multiple sites in the respiratory tract (RT) and its relevance to airflow obstructive diseases. By analyzing data from both humans and mice, we have identified bacteria belonging to the Clostridiales order as a promising biomarker for PM2.5 exposure-induced decline in pulmonary function and inflammation.
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Affiliation(s)
- Jushan Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai 200072, China
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China
- College of Environmental Science and Engineering, Tongji University, Shanghai 200072, China
| | - Haoxiang Cheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029-6574, United States
| | - Antonio Di Narzo
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029-6574, United States
| | - Yujie Zhu
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China
| | - Shuanshuan Xie
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China
| | - Xiaowen Shao
- Department of Obstetrics and Gynecology, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China
| | - Zhongyang Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029-6574, United States
| | - Sookja Kim Chung
- Medical Faculty, Macau University of Science and Technology, Taipa, Macau SAR 999078, China
| | - Ke Hao
- College of Environmental Science and Engineering, Tongji University, Shanghai 200072, China
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029-6574, United States
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21
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Pei WX, Ma SS, Chen Z, Zhu Y, Pang SF, Zhang YH. Heterogeneous uptake of NO 2 by sodium acetate droplets and secondary nitrite aerosol formation. J Environ Sci (China) 2023; 127:320-327. [PMID: 36522064 DOI: 10.1016/j.jes.2022.05.048] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/25/2022] [Accepted: 05/25/2022] [Indexed: 06/17/2023]
Abstract
The high NO3- concentration in fine particulate matters (PM2.5) during heavy haze events has attracted much attention, but the formation mechanism of nitrates remains largely uncertain, especially concerning heterogeneous uptake of NOX by aqueous phase. In this work, the heterogeneous uptake of NO2 by sodium acetate (NaAc) droplets with different NO2 concentrations and relative humidity (RH) conditions is investigated by microscopic Fourier transform infrared spectrometer (micro-FTIR). The IR feature changes of aqueous droplets indicate the acetate depletion and nitrite formation in humid environment. This implies that acetate droplets can provide the alkaline aqueous circumstances caused by acetate hydrolysis and acetic acid (HAc) volatilization for nitrite formation during the NO2 heterogeneous uptake. Meanwhile, the nitrite formation will exhibit a pH neutralizing effect on acetate hydrolysis, further facilitating HAc volatilization and acetate depletion. The heterogeneous uptake coefficient increases from 5.2 × 10-6 to 1.27 × 10-5 as RH decreases from 90% to 60% due to the enhanced HAc volatilization. Furthermore, no obvious change in uptake coefficient with different NO2 concentrations is observed. This work may provide a new pathway for atmospheric nitrogen cycling and secondary nitrite aerosol formation.
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Affiliation(s)
- Wen-Xiu Pei
- School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Shuai-Shuai Ma
- College of Chemistry and Material Engineering, Quzhou University, Quzhou 324000, China
| | - Zhe Chen
- School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Yue Zhu
- School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Shu-Feng Pang
- School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China.
| | - Yun-Hong Zhang
- School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China.
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22
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Luo L, Wu S, Zhang R, Wu Y, Li J, Kao SJ. What controls aerosol δ 15N-NO 3-? NO x emission sources vs. nitrogen isotope fractionation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 871:162185. [PMID: 36775154 DOI: 10.1016/j.scitotenv.2023.162185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 01/04/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
Atmospheric δ15N-NO3- has been used to reveal NOx (NO + NO2) sources as NO3- is the ultimate sink of NOx. However, it remains questionable whether the nitrogen isotope fractionation among NOy (NO, NO2, NO3, N2O5, HNO3 and NO3-) engender the misjudgment of NOx emission sources by affecting δ15N-NOy. To explore this issue, we integrated the dataset of aerosol δ15N-NO3- values and ratios of fNO2 (fNO2 = NO2/(NO2 + NO)), calculated the nitrogen isotope fractionation factors (Δs) among NOy, compared the total energy consumption in Beijing-Tianjin-Hebei region (BTH) from 2013 to 2018. Results showed that, although the total energy consumption structure changed from 2013 to 2018 in BTH, there were fewer interannual variances of aerosol δ15N-NO3- values. Nitrogen isotope fractionation factors between NO and NO2 (Δ0), NO2 and NO3 (Δ2), NO2 and N2O5 (Δ3), NO2 and ClONO2 (Δ4) also displayed less interannual variations from 2013 to 2018 in BTH. But both aerosol δ15N-NO3- and Δs displayed significant seasonal patterns, and there was significant relationship between monthly aerosol δ15N-NO3- and Δs, which suggested that Δs have important influence on shaping aerosol δ15N-NO3- and further discriminating NOx emission sources. This study implies that we should refine the Δs when employing atmospheric δ15N-NO3- to quantify NOx source allocation.
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Affiliation(s)
- Li Luo
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China; Collaborative Innovation Center of Marine Science and Technology, Hainan University, Haikou 570228, China.
| | - Siqi Wu
- Max Planck Institute for Marine Microbiology, 28359, Bremen, Germany; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China
| | - Renjian Zhang
- Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yunfei Wu
- Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Jiawei Li
- Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Shuh-Ji Kao
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China; Collaborative Innovation Center of Marine Science and Technology, Hainan University, Haikou 570228, China
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23
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Liang C, Wang S, Hu R, Huang G, Xie J, Zhao B, Li Y, Zhu W, Guo S, Jiang J, Hao J. Molecular tracers, mass spectral tracers and oxidation of organic aerosols emitted from cooking and fossil fuel burning sources. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 868:161635. [PMID: 36657674 DOI: 10.1016/j.scitotenv.2023.161635] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
Secondary organic aerosol (SOA) composes a substantial fraction of atmospheric particles, yet the formation and aging mechanism of SOA remains unclear. Here we investigate the initial oxidation of primary organic aerosol (POA) and further aging of SOA in winter Beijing by using aerosol mass spectrometer (AMS) measurements along with offline molecular tracer analysis. Multilinear engine (ME-2) source apportionment was conducted to capture the characteristic of source-related SOA, and connect them with specific POA. Our results show that urban cooking and fossil fuel burning sources contribute significantly (17 % and 20 %) to total organic aerosol (OA) in winter Beijing. Molecular tracer analysis by two-dimensional gas chromatography-time-of-flight mass spectrometer (GC × GC-ToF-MS) reveals that cooking SOA (CSOA) is produced through both photooxidation and aqueous-phase processing, while less oxidized SOA (LO-SOA) is the photooxidation product of fossil fuel burning OA (FFOA) and may experience aqueous-phase aging to form more-oxidized oxygenated OA (MO-OOA). Furthermore, CHOm/z 69 and CHOm/z 85 are mass spectral tracers indicating the initial photooxidation, while CHO2+ and C2H2O2+ imply further aqueous-phase aging of OA. Tracer analysis indicates that the formation of diketones is involved in the initial photooxidation of POA, while the formation of glyoxal and diacids is involved in the further aqueous-phase aging of SOA.
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Affiliation(s)
- Chengrui Liang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Ruolan Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Guanghan Huang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jinzi Xie
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yuyang Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Wenfei Zhu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Song Guo
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Jingkun Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
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24
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The Effect of CaO on the CO and NOx Emission Characteristics of Fast-Growing Grass Combustion. Processes (Basel) 2023. [DOI: 10.3390/pr11030760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
Abstract
Fast-growing grass is a biomass material with characteristics such as high temperature and drought resistance; rapid growth and development; and repeated germination and cutting. Therefore, it is a popular biomass fuel. It is required that the pollutants produced during the biomass combustion process are appropriately controlled. For this purpose, our study analyses the influence of combustion temperature and calcium oxide (CaO) on the nitrogen oxides (NOx) and carbon monoxide (CO) emission characteristics of fast-growing grass combustion using the biomass combustion flue gas analysis and testing platform. The results of our analysis revealed that CaO additive can simultaneously reduce the peak and total NOx emissions at 750 °C. Particularly, 5% CaO demonstrated a significant control effect on the NOx emission from the fast-growing grass combustion process, with a peak and total emissions reduction of 47.05% and 56.81%, respectively. In addition, with an increase in temperature, the CO emission curve attains a second peak higher than the first peak, and the peak and total emissions show a decreasing trend.
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25
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Li T, Zhang Q, Peng Y, Guan X, Li L, Mu J, Wang X, Yin X, Wang Q. Contributions of various driving factors to air pollution events: Interpretability analysis from Machine learning perspective. ENVIRONMENT INTERNATIONAL 2023; 173:107861. [PMID: 36898175 DOI: 10.1016/j.envint.2023.107861] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/09/2023] [Accepted: 03/01/2023] [Indexed: 06/18/2023]
Abstract
The air quality in China has been improved substantially, however fine particulate matter (PM2.5) still remain at a high level in many areas. PM2.5 pollution is a complex process that is attributed to gaseous precursors, chemical, and meteorological factors. Quantifying the contribution of each variable to air pollution can facilitate the formulation of effective policies to precisely eliminate air pollution. In this study, we first used decision plot to map out the decision process of the Random Forest (RF) model for a single hourly data set and constructed a framework for analyzing the causes of air pollution using multiple interpretable methods. Permutation importance was used to qualitatively analyze the effect of each variable on PM2.5 concentrations. The sensitivity of secondary inorganic aerosols (SIA): SO42-, NO3- and NH4+ to PM2.5 was verified by Partial dependence plot (PDP). Shapley Additive Explanation (Shapley) was used to quantify the contribution of drivers behind the ten air pollution events. The RF model can accurately predict PM2.5 concentrations, with determination coefficient (R2) of 0.94, root mean square error (RMSE) and mean absolute error (MAE) of 9.4 μg/m3 and 5.7 μg/m3, respectively. This study revealed that the order of sensitivity of SIA to PM2.5 was NH4+>NO3->SO42-. Fossil fuel and biomass combustion may be contributing factors to air pollution events in Zibo in 2021 autumn-winter. NH4+ contributed 19.9-65.4 μg/m3 among ten air pollution events (APs). K, NO3-, EC and OC were the other main drivers, contributing 8.7 ± 2.7 μg/m3, 6.8 ± 7.5 μg/m3, 3.6 ± 5.8 μg/m3 and 2.5 ± 2.0 μg/m3, respectively. Lower temperature and higher humidity were vital factors that promoted the formation of NO3-. Our study may provide a methodological framework for precise air pollution management.
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Affiliation(s)
- Tianshuai Li
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Qingzhu Zhang
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Yanbo Peng
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China; Shandong Academy for Environmental Planning, Jinan 250101, PR China.
| | - Xu Guan
- Shandong Academy for Environmental Planning, Jinan 250101, PR China
| | - Lei Li
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Jiangshan Mu
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Xinfeng Wang
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Xianwei Yin
- Zibo Ecological Environment Monitoring Center of Shandong Province, Zibo 255040, PR China
| | - Qiao Wang
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
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Zhao X, Zhao X, Liu P, Chen D, Zhang C, Xue C, Liu J, Xu J, Mu Y. Transport Pathways of Nitrate Formed from Nocturnal N 2O 5 Hydrolysis Aloft to the Ground Level in Winter North China Plain. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:2715-2725. [PMID: 36722840 DOI: 10.1021/acs.est.3c00086] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Particulate nitrate (NO3-) has currently become the major component of fine particles in the North China Plain (NCP) during winter haze episodes. However, the contributions of formation pathways to ground NO3- in the NCP are not fully understood. Herein, the NO3- formation pathways were comprehensively investigated based on model simulations combined with two-month field measurements at a rural site in the winter NCP. The results indicated that the nocturnal chemistry of N2O5 hydrolysis aloft could contribute evidently to ground NO3- at the rural site during the pollution episodes with high aerosol water contents, achieving the contribution percentages of 25.2-30.4% of the total. In addition to the commonly proposed vertical mixing of breaking nocturnal boundary layer in the early morning, two additional transport pathways (frontal downdrafts and downslope mountain breezes) in the nighttime were found to make higher contributions to ground NO3-. Considering the dominant role (69.6-74.8%) of diurnal chemistry in NO3- formation, reduction of NOx emissions in the daytime may be an effective control measure for reducing regional NO3- in the NCP.
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Affiliation(s)
- Xiaoxi Zhao
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing100085, China
- Institute of Urban Meteorology, Chinese Meteorological Administration, Beijing100089, China
- University of Chinese Academy of Sciences, Beijing100049, China
| | - Xiujuan Zhao
- Institute of Urban Meteorology, Chinese Meteorological Administration, Beijing100089, China
| | - Pengfei Liu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing100085, China
- University of Chinese Academy of Sciences, Beijing100049, China
| | - Dan Chen
- Institute of Urban Meteorology, Chinese Meteorological Administration, Beijing100089, China
| | - Chenglong Zhang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing100085, China
- University of Chinese Academy of Sciences, Beijing100049, China
| | - Chaoyang Xue
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing100085, China
- Laboratoire de Physique et Chimie de l'Environnement et de l'Espace (LPC2E), CNRS-Université Orléans-CNES, CEDEX 2, Orléans45071, France
| | - Junfeng Liu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing100085, China
- University of Chinese Academy of Sciences, Beijing100049, China
| | - Jing Xu
- Institute of Urban Meteorology, Chinese Meteorological Administration, Beijing100089, China
| | - Yujing Mu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing100085, China
- University of Chinese Academy of Sciences, Beijing100049, China
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Chen X, Li X, Liang J, Li X, Li S, Chen G, Chen Z, Dai S, Bin J, Tang Y. Causes of the unexpected slowness in reducing winter PM 2.5 for 2014-2018 in Henan Province. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 319:120928. [PMID: 36565915 DOI: 10.1016/j.envpol.2022.120928] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Toughest-ever clean air actions in China have been implemented nationwide to improve air quality. However, it was unexpected that from 2014 to 2018, the observed wintertime PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) concentrations showed an insignificant decrease in Henan Province (HNP), a region in the west of the North China Plain. Emission controls seem to have failed to improve winter air quality in HNP, which has caused great confusion in formulating the next air improvement strategy. We employed a deweathering technique to decouple the impact of meteorological conditions. The results showed that the deweathered PM2.5 trend was -3.3%/yr in winter from 2014 to 2018, which had a larger decrease than the observed concentrations (-0.9%/yr), demonstrating that emission reduction was effective at improving air quality. However, compared with the other two megacity clusters, Beijing-Tianjin-Hebei (BTH) (-8.4%/yr) and Yangtze River Delta (YRD) (-7.4%/yr), the deweathered decreasing trend of PM2.5 for HNP remained slow. The underlying mechanism driving the changes in PM2.5 and its chemical components was further explored, using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Model simulations indicated that nitrate dominated the increase of PM2.5 components in HNP and the proportions of nitrate to total PM2.5 increased from 22.4% in January 2015 to 39.7% in January 2019. There are two primary reasons for this phenomenon. One is the limited control of nitrogen oxide emissions, which facilitates the conversion of nitric acid to particulate nitrate by ammonia. The other is unfavourable meteorological conditions, particularly increasing humidity, further enhancing nitrate formation through multiphase reactions. This study highly emphasizes the importance of reducing nitrogen oxide emissions owing to their impact on the formation of particulate nitrate in China, especially in the HNP region.
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Affiliation(s)
- Xuwu Chen
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China; School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha, 410205, PR China
| | - Xiaodong Li
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China.
| | - Jie Liang
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Xin Li
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Shuai Li
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Gaojie Chen
- College of Mathematics and Econometrics, Hunan University, Changsha, 410082, PR China
| | - Zuo Chen
- College of Information Science and Technology, Hunan University, Changsha, 410082, PR China
| | - Simin Dai
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Juan Bin
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Yifan Tang
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
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28
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Huyen TT, Sekiguchi K, Ly BT, Nghiem TD. Assessment of traffic-related chemical components in ultrafine and fine particles in urban areas in Vietnam. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:159869. [PMID: 36328252 DOI: 10.1016/j.scitotenv.2022.159869] [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: 04/30/2022] [Revised: 09/30/2022] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
Ultrafine particles (UFPs or PM0.1; aerodynamic diameter ≤ 0.1 μm) were monitored at a roadside site (RS) in a populated area of Hanoi, Vietnam. Meanwhile, UFPs and fine particles (FPs or PM2.5, aerodynamic diameter ≤ 2.5 μm) were monitored at an ambient site (AS), located at an on-campus a university, approximately 200 m away from the RS. Sampling was conducted in different seasons-summer, winter, and the transitional periods of summer-to-winter and winter-to-summer (STP and WTP, respectively). Carbonaceous and ionic species in UFPs and FPs-rarely investigated in the study area-were analyzed to observe the seasonal variations, characteristics of UFPs near the roadway, and spatial differences between the sites. The UFPs concentration at the AS was in the order of winter > STP > WTP > summer, whereas that of the FPs was winter > WTP > STP > summer. This seasonal variation of particle concentration was possibly affected by the meteorological conditions, which contribute to the highest concentration in winter. The higher FPs concentration in WTP than in STP resulted from the substantial increase in ionic concentrations, particularly sulfate, nitrate, and ammonium. This result indicates the effect of secondary formation processes under drizzle-like weather, which is typical during WTP in northern Vietnam. Compared with UFPs at the AS, traffic-related emissions were more noticeable in UFPs at the RS, including a higher EC concentration and lower OC/EC ratio. The possibility of particle growth under favorable conditions, including the presence of gas-phase pollutants and the availability of surface areas owing to high UFPs concentration in Hanoi, may explain the low correlation of the chemical components between UFPs and FPs in the sites. This study serves as a reference for further investigation of the relationship between UFPs and FPs under highly polluted conditions in big cities in Vietnam in future studies.
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Affiliation(s)
- Truong-Thi Huyen
- Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura, Saitama 338-8570, Japan
| | - Kazuhiko Sekiguchi
- Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura, Saitama 338-8570, Japan.
| | - Bich-Thuy Ly
- School of Environmental Science and Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi 100000, Viet Nam
| | - Trung-Dung Nghiem
- School of Environmental Science and Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi 100000, Viet Nam
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29
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Tao J, Huang J, Bian G, Zhang L, Zhou Z, Zhang Z, Li J, Miao Y, Yuan Z, Sha Q, Xiao L, Wang B. Fine particulate pollution driven by nitrate in the moisture urban atmospheric environment in the Pearl River Delta region of south China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 326:116704. [PMID: 36356536 DOI: 10.1016/j.jenvman.2022.116704] [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: 07/28/2022] [Revised: 10/17/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
To identify potential sources of fine particles (PM2.5, with aerodynamic diameter (Da) ≤ 2.5 μm) in urban Dongguan of south China, a comprehensive campaign was carried out in the whole 2019. Hourly PM2.5 and its dominant chemical components including organic carbon (OC), elemental carbon (EC), water-soluble inorganic ions (WSIIs) and thirteen elements were measured using online instruments. Gaseous pollutants including NH3, HNO3, NO2, NO and O3 and meteorological parameters were also synchronously measured. PM2.5 was dominated by carbonaceous aerosols in summer and by WSIIs in the other seasons. PM2.5 and its dominant chemical components mostly peaked around noon (10:00-14:00 LST). Furthermore, high PM2.5 levels during the daytime were closely related with the increased NO3- levels. The high mass concentrations of NO3- in urban Dongguan during the daytime were likely related with regional transport of NO3- from suburban Dongguan, which was originated from the reaction between NO2 and O3 under the moisture condition during the nighttime. Seven major source factors for PM2.5 including secondary sulfate, ship emission, traffic emission, secondary nitrate, industrial processes, soil dust and coal combustion were identified by positive matrix factorization (PMF) analysis, which contributed 26 ± 14%, 16 ± 16%, 16 ± 10%, 14 ± 11%, 12 ± 11%, 8 ± 6% and 8 ± 6%, respectively, to annual PM2.5 mass concentration. Although secondary sulfate contributed much more than secondary nitrate to PM2.5 on annual basis, the latter exceeded the former source factor when daily PM2.5 mass concentration was higher than 60 μg m-3, indicating the critical role nitrate played in PM2.5 episode events.
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Affiliation(s)
- Jun Tao
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China; South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, China.
| | - Junjun Huang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
| | - Guojian Bian
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, China
| | - Leiming Zhang
- Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, Toronto, Canada
| | - Zhen Zhou
- Dongguan Sub-branch of Guangdong Ecological and Environmental Monitoring Center, Dongguan, China
| | - Zhisheng Zhang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, China
| | - Jiawei Li
- RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Yucong Miao
- Chinese Academy of Meteorological Sciences, Beijing, China
| | - Ziyang Yuan
- Sailbri Cooper Inc., Tigard, Oregon, United States
| | - Qinge Sha
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
| | - Linhai Xiao
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
| | - Boguang Wang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
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30
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Li F, Zhou S, Du L, Zhao J, Hang J, Wang X. Aqueous-phase chemistry of atmospheric phenolic compounds: A critical review of laboratory studies. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 856:158895. [PMID: 36130630 DOI: 10.1016/j.scitotenv.2022.158895] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/12/2022] [Accepted: 09/16/2022] [Indexed: 06/15/2023]
Abstract
Phenolic compounds (PhCs) are crucial atmospheric pollutants typically emitted by biomass burning and receive particular concerns considering their toxicity, light-absorbing properties, and involvement in secondary organic aerosol (SOA) formation. A comprehensive understanding of the transformation mechanisms on chemical reactions in atmospheric waters (i.e., cloud/fog droplets and aerosol liquid water) is essential to predict more precisely the atmospheric fate and environmental impacts of PhCs. Laboratory studies play a core role in providing the fundamental knowledge of aqueous-phase chemical transformations in the atmosphere. This article critically reviews recent laboratory advances in SOA formation from the aqueous-phase reactions of PhCs. It focuses primarily on the aqueous oxidation of PhCs driven by two atmospheric reactive species: OH radicals and triplet excited state organics, including the important chemical kinetics and mechanisms. The effects of inorganic components (i.e., nitrate and nitrite) and transition metal ions (i.e., soluble iron) are highlighted on the aqueous-phase transformation of PhCs and on the properties and formation mechanisms of SOA. The review is concluded with the current knowledge gaps and future perspectives for a better understanding of the atmospheric transformation and SOA formation potential of PhCs.
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Affiliation(s)
- Fenghua Li
- School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
| | - Shengzhen Zhou
- School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Guangdong Provincial Field Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Sun Yat-sen University, Guangzhou 510275, China; Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai 519082, China.
| | - Lin Du
- Environment Research Institute, Shandong University, Qingdao 266237, China
| | - Jun Zhao
- School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Guangdong Provincial Field Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Sun Yat-sen University, Guangzhou 510275, China; Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai 519082, China
| | - Jian Hang
- School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Guangdong Provincial Field Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Sun Yat-sen University, Guangzhou 510275, China; Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai 519082, China
| | - Xuemei Wang
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510000, China
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Qi L, Zheng H, Ding D, Wang S. Responses of sulfate and nitrate to anthropogenic emission changes in eastern China - in perspective of long-term variations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 855:158875. [PMID: 36126708 DOI: 10.1016/j.scitotenv.2022.158875] [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: 06/23/2022] [Revised: 09/07/2022] [Accepted: 09/15/2022] [Indexed: 06/15/2023]
Abstract
We investigate responses of sulfate (SO42-) and nitrate (NO3-) to anthropogenic emission changes in 2006-2017 by fixing meteorology at the 2009 level using nested 3D chemical transport model GEOS-Chem. We find that sulfate concentration decreases following SO2 emissions, but with a relatively smaller reduction rate (by 16 % in North China Plain (NCP) and 28 % in Yangtze River Delta (YRD)) due to larger sulfur oxidation ratio (SOR) at lower SO2 level. SOR follows a power law with SO2 emissions in general except in winter in NCP, when and where both SO2 emission reduction and atmospheric oxidation capacity are critical to the inter-annual variations of SOR. Nitrate concentration ([pNO3-]) decreases along with NOx emission reduction in summer, but increases slightly in winter in 2011-2017. Equilibrium with gas phase HNO3, NO3- in particle phase (pNO3-) is determined by total HNO3 (TN = [pNO3-] + [gHNO3]) oxidized from NO2 and gas-particle partitioning (ε(NO3-) = [pNO3-]/TN). TN is decreasing faster in summer (~33 %) than in winter (~25 %) in 2011-2017. In contrast, ε(NO3-) changes marginally in summer (within 5 %) but increases by 36 % in NCP and by 51 % in YRD in winter in 2006-2017. The increasing of ε(NO3-) in winter is attributed to the strong reduction of [pSO42-], which increases the relative abundance of NH3 and thus favors partitioning of NO3- to the particle phase. The effect of increasing ε(NO3-) overcomes that of decreasing TN in winter. We suggest reduce SO2 emissions to further reduce [pSO42-] in eastern China. In addition, we recommend reduce NOx emissions in summer, and reduce atmospheric oxidation capacity and relative abundance of NH3 in winter to reduce [pNO3-].
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Affiliation(s)
- Ling Qi
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Dian Ding
- Institute for Atmospheric and Earth System Research (INAR)/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
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Kuang B, Zhang F, Shen J, Shen Y, Qu F, Jin L, Tang Q, Tian X, Wang Z. Chemical characterization, formation mechanisms and source apportionment of PM 2.5 in north Zhejiang Province: The importance of secondary formation and vehicle emission. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 851:158206. [PMID: 36028033 DOI: 10.1016/j.scitotenv.2022.158206] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/11/2022] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
Abstract
PM2.5 affects air quality, therefore, chemical evolution, formation mechanism and source identification of PM2.5 are essential to help figure out mitigation measures. PM2.5 and its constituents were comprehensively characterized with highly time-resolved measurements from 2019 to 2020 in north Zhejiang Province (Shanxi, SX) for the first time, with an emphasis on the contribution of secondary formation and vehicle emission to PM2.5. Secondary inorganic ions (sulfate: 3.86 μg/m3, nitrate: 7.82 μg/m3 and ammonium: 4.59 μg/m3, SNA) were found to be the major components (54%) in PM2.5 (29.70 μg/m3). The highly consistence of nitrate, sulfate and secondary organic compounds (SOC) with Ox (NO2 + O3) or RH indicated the importance of photochemical oxidation and heterogeneous reaction in different scenarios. Higher atmospheric oxidative potential facilitated the SOC formation in spring. The PM2.5 mass was apportioned to eight sources resolved by positive matrix factorization (PMF): secondary nitrate (9.63 μg/m3), secondary sulfate (5.14 μg/m3), vehicle emission (7.26 μg/m3), coal combustion (2.39 μg/m3), biomass burning (1.38 μg/m3), soil dust (0.86 μg/m3), industry emission (0.50 μg/m3), and ship emission (0.32 μg/m3). Secondary nitrate (35%) and sulfate (19%) formation and vehicle emission (26%) were the main factors contributing to the PM2.5. Furthermore, the contribution of secondary nitrate formation increased with elevating PM2.5 concentration. Regional transport was synthetically studied by chemical and backward trajectory analysis, reflecting that secondary nitrate contributed severely to the air quality at SX, while vehicle emission contribution enhanced when atmosphere was stagnant. This study first provides long-term comprehensive chemical characterization and source apportionments of PM2.5 pollution in north Zhejiang, which may provide some guidance for the air pollution control.
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Affiliation(s)
- Binyu Kuang
- College of Environmental and Resource Sciences, Zhejiang University, Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou 310058, China
| | - Fei Zhang
- College of Environmental and Resource Sciences, Zhejiang University, Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou 310058, China
| | - Jiasi Shen
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
| | - Yemin Shen
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
| | - Fangqi Qu
- College of Environmental and Resource Sciences, Zhejiang University, Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou 310058, China
| | - Lingling Jin
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
| | - Qian Tang
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
| | - Xudong Tian
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China.
| | - Zhibin Wang
- College of Environmental and Resource Sciences, Zhejiang University, Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou 310058, China; ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China.
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33
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Cheng C, Yu R, Chen Y, Yan Y, Hu G, Wang S. Quantifying the source and formation of nitrate in PM 2.5 using dual isotopes combined with Bayesian mixing model: A case study in an inland city of southeast China. CHEMOSPHERE 2022; 308:136097. [PMID: 35998735 DOI: 10.1016/j.chemosphere.2022.136097] [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: 05/31/2022] [Revised: 08/10/2022] [Accepted: 08/15/2022] [Indexed: 06/15/2023]
Abstract
Atmospheric nitrate has been attracting increasing attention because it is one of the important components of PM2.5. Understanding the sources and formation mechanism of nitrate in PM2.5 is essential to take effective measures to prevent and control the emission of nitrogen oxides in the atmosphere and reduce the formation of haze. In this study, PM2.5 samples were collected from Ganzhou, an inland city in southeast China, during summer and winter. The concentrations of PM2.5 and NO3- were determined, and the isotopic compositions of NO3- (δ15N-NO3- and δ18O-NO3-) were analyzed in order to quantify the relative contributions of different emission sources and formation pathways of nitrate in PM2.5. The results showed that PM2.5 and NO3- concentrations were lower in summer (39.80 ± 11.10 μg·m-3 and 1.79 ± 0.70 μg·m-3) while higher in winter (69.85 ± 29.58 μg·m-3 and 10.83 ± 9.89 μg·m-3). The values of δ18O-NO3- and δ15N-NO3- ranged from 42.84‰ to 56.80‰ and from -11.17‰ to -2.08‰ in summer, while from 55.86‰ to 78.66‰ and from -10.63‰ to -1.88‰ in winter, respectively. The results of δ15N-NO3- combined with Bayesian isotope mixing model showed that the relative contributions of vehicle exhaust, soil microbial activity, biomass combustion and coal fired power plants were 59.3%, 28.5%, 8.7% and 3.4% in summer, while 65.1%, 20.1%, 10.6% and 4.1% in winter, respectively. The results of δ18O-NO3- combined with Bayesian isotope mixing model showed that the possible relative contributions of pathway 1 (P1) (NO2 + ·OH), pathway 2 (P2) (NO3 + HC) and pathway 3 (P3) (N2O5 + H2O) were 73.5%, 12.4% and 14.1% in summer, while 41.6%, 28.9% and 29.5% in winter, respectively. Moreover, P2 and P3 contributed more when NO3- concentrations were higher, suggesting that P2 and P3 were of significance to the formation of nitrate in PM2.5, especially during winter.
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Affiliation(s)
- Chen Cheng
- College of Chemical Engineering, Huaqiao University, Xiamen, 361021, China; Key Laboratory of Global Change and Marine-Atmospheric Chemistry, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361021, China
| | - Ruilian Yu
- College of Chemical Engineering, Huaqiao University, Xiamen, 361021, China.
| | - Yanting Chen
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
| | - Yu Yan
- College of Chemical Engineering, Huaqiao University, Xiamen, 361021, China; Key Laboratory of Global Change and Marine-Atmospheric Chemistry, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361021, China
| | - Gongren Hu
- College of Chemical Engineering, Huaqiao University, Xiamen, 361021, China; Institute of Environmental and Ecological Engineering, Huaqiao University, Xiamen, 361021, China.
| | - Shanshan Wang
- Key Laboratory of Global Change and Marine-Atmospheric Chemistry, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361021, China
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Zhang Y, Tang A, Wang C, Ma X, Li Y, Xu W, Xia X, Zheng A, Li W, Fang Z, Zhao X, Peng X, Zhang Y, Han J, Zhang L, Collett JL, Liu X. PM 2.5 and water-soluble inorganic ion concentrations decreased faster in urban than rural areas in China. J Environ Sci (China) 2022; 122:83-91. [PMID: 35717093 DOI: 10.1016/j.jes.2021.09.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 08/26/2021] [Accepted: 09/25/2021] [Indexed: 06/15/2023]
Abstract
We investigated variations of PM2.5 and water-soluble inorganic ions chemical characteristics at nine urban and rural sites in China using ground-based observations. From 2015 to 2019, mean PM2.5 concentration across all sites decreased by 41.9 µg/m3 with a decline of 46% at urban sites and 28% at rural sites, where secondary inorganic aerosol (SIAs) contributed to 21% (urban sites) and 17% (rural sites) of the decreased PM2.5. SIAs concentrations underwent a decline at urban locations, while sulfate (SO42-), nitrate (NO3-), and ammonium (NH4+) decreased by 49.5%, 31.3% and 31.6%, respectively. However, only SO42- decreased at rural sites, NO3- increased by 21% and NH4+ decreased slightly. Those changes contributed to an overall SIAs increase in 2019. Higher molar ratios of NO3- to SO42- and NH4+ to SO42- were observed at urban sites than rural sites, being highest in the heavily polluted days. Mean molar ratios of NH3/NHx were higher in 2019 than 2015 at both urban and rural sites, implying increasing NHx remained as free NH3. Our observations indicated a slower transition from sulfate-driven to nitrate-driven aerosol pollution and less efficient control of NOx than SO2 related aerosol formation in rural regions than urban regions. Moreover, the common factor at urban and rural sites appears to be a combination of lower SO42- levels and an increasing fraction of NO3- to PM2.5 under NH4+-rich conditions. Our findings imply that synchronous reduction in NOx and NH3 emissions especially rural areas would be effective to mitigate NO3--driven aerosol pollution.
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Affiliation(s)
- Yangyang Zhang
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing 100193, China
| | - Aohan Tang
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing 100193, China.
| | - Chen Wang
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing 100193, China
| | - Xin Ma
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing 100193, China
| | - Yunzhe Li
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing 100193, China
| | - Wen Xu
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing 100193, China
| | - Xiaoping Xia
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing 100193, China
| | - Aihua Zheng
- Analysis and Testing Center, Beijing Normal University, Beijing 100875, China
| | - Wenqing Li
- Fujian Institute of Tobacco Agricultural Sciences, Fuzhou 350003, China
| | - Zengguo Fang
- College of Resources and Environment, Qingdao Agricultural University, Qingdao 266109, China
| | - Xiufen Zhao
- College of Resources and Environment, Qingdao Agricultural University, Qingdao 266109, China
| | - Xianlong Peng
- College of Resources and Environment, Northeast Agricultural University, Haerbin 150030, China
| | - Yuping Zhang
- College of Resources and Environment, Hunan Provincial Key Laboratory of Farmland Pollution Control and Agricultural Resources Use, Hunan Agricultural University, Changsha 410128, China
| | - Jian Han
- College of Resources and Environment, Hebei Agricultural University, Baoding 071001, China
| | - Lijuan Zhang
- College of Resources and Environment, Hebei Agricultural University, Baoding 071001, China
| | - Jeffrey L Collett
- Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA
| | - Xuejun Liu
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing 100193, China.
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35
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Zhang J, Cheng H, Di Narzo A, Zhu Y, Shan M, Zhang Z, Shao X, Chen J, Wang C, Hao K. Within- and cross-tissue gene regulations were disrupted by PM 2.5 nitrate exposure and associated with respiratory functions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 850:157977. [PMID: 35964746 DOI: 10.1016/j.scitotenv.2022.157977] [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: 05/01/2022] [Revised: 07/25/2022] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Pathogenesis of complex diseases often involves multiple organs/tissue-types. To date, the PM2.5 exposure's toxic effects and induced disease risks were not studied at multi-tissue level. METHODS C57BL/6 mice (n = 40) were exposed to PM2.5 NO3- and clean air, respectively, and afterwards assessed respiratory functions and transcriptome in relevant tissues: blood and lung. We constructed within- and cross-tissue gene regulation networks and identified network modules associated with exposure and respiratory functions. RESULTS PM2.5 NO3- exposure elevated naïve B cells proportion in blood (p = 0.0028). Among the 6000 highest expressed genes in blood, 18.8 % (1133 genes) were altered by exposure at p ≤ 0.05 level, among which 763 genes were also associated with respiratory function (enrichment folds = 7.63, p = 2.7E-189). The exposure disrupted blood genes were primarily in the immunoregulation pathways. Both within- and cross-tissue gene network modules were perturbed by exposure and associated with respiratory function. An immunodeficiency related cross-tissue module of 555 genes was affected by exposure (p = 0.0023) and strongly correlated with FEV0.05/FVC (r = 0.61 and p = 3E-5). CONCLUSIONS This study aims to fill in a major knowledge gap and investigated the effect of PM2.5 exposure simultaneously in multiple tissues. We provided novel evidence that PM2.5 NO3- exposure profoundly perturbed within- and cross-tissue gene regulations, and highlighted their roles in the etiology of respiratory decline.
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Affiliation(s)
- Jushan Zhang
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China; College of Environmental Science and Engineering, Tongji University, Shanghai, China
| | - Haoxiang Cheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Antonio Di Narzo
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yujie Zhu
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China
| | | | - Zhongyang Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xiaowen Shao
- Department of Obstetrics and Gynecology, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China
| | - Jia Chen
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Changhui Wang
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Sema4, Stamford, CT, USA.
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36
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Dust emission reduction enhanced gas-to-particle conversion of ammonia in the North China Plain. Nat Commun 2022; 13:6887. [PMID: 36371439 PMCID: PMC9653376 DOI: 10.1038/s41467-022-34733-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 11/04/2022] [Indexed: 11/13/2022] Open
Abstract
Ammonium salt is an important component of particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5) and has significant impacts on air quality, climate, and natural ecosystems. However, a fundamental understanding of the conversion kinetics from ammonia to ammonium in unique environments of high aerosol loading is lacking. Here, we report the uptake coefficient of ammonia (γNH3) on ambient PM2.5 varying from 2.2 × 10-4 to 6.0 × 10-4 in the North China Plain. It is significantly lower than those on the model particles under simple conditions reported in the literature. The probability-weighted γNH3 increases obviously, which is well explained by the annual decrease in aerosol pH due to the significant decline in alkali and alkali earth metal contents from the emission source of dust. Our results elaborate on the complex interactions between primary emissions and the secondary formation of aerosols and the important role of dust in atmospheric chemistry.
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Luo L, Bai X, Liu S, Wu B, Liu W, Lv Y, Guo Z, Lin S, Zhao S, Hao Y, Hao J, Zhang K, Zheng A, Tian H. Fine particulate matter (PM 2.5/PM 1.0) in Beijing, China: Variations and chemical compositions as well as sources. J Environ Sci (China) 2022; 121:187-198. [PMID: 35654509 DOI: 10.1016/j.jes.2021.12.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 12/02/2021] [Accepted: 12/11/2021] [Indexed: 05/16/2023]
Abstract
Particulate matter (i.e., PM1.0 and PM2.5), considered as the key atmospheric pollutants, exerts negative effects on visibility, global climate, and human health by associated chemical compositions. However, our understanding of PM and its chemical compositions in Beijing under the current atmospheric environment is still not complete after witnessing marked alleviation during 2013-2017. Continuous measurements can be crucial for further air quality improvement by better characterizing PM pollution and chemical compositions in Beijing. Here, we conducted simultaneous measurements on PM in Beijing during 2018-2019. Results indicate that annual mean PM1.0 and PM2.5 concentrations were 35.49 ± 18.61 µg/m3 and 66.58 ± 60.17 µg/m3, showing a positive response to emission controls. The contribution of sulfate, nitrate, and ammonium (SNA) played an enhanced role with elevated PM loading and acted as the main contributors to pollution episodes. Discrepancies observed among chemical species between PM1.0 and PM2.5 in spring suggest that sand particles trend to accumulate in the range of 1-2.5 µm. Pollution episodes occurred accompanied with southerly clusters and high formation of SNA by heterogeneous reactions in summer and winter, respectively. Results from positive matrix factorization (PMF) combined with potential source contribution function (PSCF) models showed that potential areas were seasonal dependent, secondary and vehicular sources became much more important compared with previous studies in Beijing. Our study presented a continuous investigation on PM and sources origins in Beijing, which provides a better understanding for further emission control as well as a reference for other cities in developing countries.
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Affiliation(s)
- Lining Luo
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Xiaoxuan Bai
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Shuhan Liu
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Bobo Wu
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Wei Liu
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Yunqian Lv
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Zhihui Guo
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Shumin Lin
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Shuang Zhao
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Yan Hao
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Jiming Hao
- School of Environment, Tsinghua University, Beijing 100084, China
| | - Kai Zhang
- Department of Environmental Health Sciences School of Public Health University at Albany, State University of New York One University Place Rensselaer, NY 12144, USA
| | - Aihua Zheng
- Analysis and Testing Center, Beijing Normal University, Beijing 100875, China
| | - Hezhong Tian
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
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Sharma B, Jia S, Polana AJ, Ahmed MS, Haque RR, Singh S, Mao J, Sarkar S. Seasonal variations in aerosol acidity and its driving factors in the eastern Indo-Gangetic Plain: A quantitative analysis. CHEMOSPHERE 2022; 305:135490. [PMID: 35760126 DOI: 10.1016/j.chemosphere.2022.135490] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 05/30/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
This study employs ISORROPIA-II for the evaluation of aerosol acidity and quantification of contributions from chemical species and meteorological parameters to acidity variation in the Indian context. PM2.5 samples collected during summer (April-July 2018), post-monsoon (September-November 2018), and winter (December 2018-January 2019) from a rural receptor location in the eastern Indo-Gangetic Plain (IGP) were analyzed for ionic species, water-soluble organic carbon (WSOC), and organic and elemental carbon (OC, EC) fractions. This was followed by estimation of the in situ aerosol pH and liquid water content (LWC) using the forward mode of ISORROPIA-II, which is less sensitive to measurement uncertainty compared to the reverse mode, for a K+-Ca2+-Mg2+-NH4+-Na+-SO42--NO3--Cl--H2O system. Aerosol pH was moderately acidic (summer: 2.93 ± 0.67; post-monsoon: 2.67 ± 0.23; winter: 3.15 ± 0.34) and was most sensitive to SO42- and total ammonium (TNH3) variation. The LWC of aerosol showed an increasing trend from summer (16.6 ± 13.6 μg m-3) through winter (32.9 ± 10.4 μg m-3). With summer as the baseline, the largest changes in aerosol pH during the other seasons was driven by SO42- (ΔpH: -0.70 to -0.82 units), followed by TNH3 (ΔpH: +0.25 to +0.38 units) with K+ and temperature being significant only during winter (ΔpH: +0.51 and + 0.46 units, respectively). The prevalent acidity regime provided three major insights: i) positive summertime Cl- depletion (49 ± 20%) as a consequence of SO42- substitution increased aerosol pH by 0.03 ± 0.20 units and decreased LWC by 2.4 ± 5.9 μg m-3; ii) the rate of strong acidity (H+str) neutralization and the [H+str]/[SO42-] molar ratio suggested the existence of bounded acidity in ammonium-rich (winter) conditions; and iii) significant correlations between LWC, WSOC, and secondary organics during post-monsoon and winter pointed towards a possible indirect role of WSOC in enhancing LWC of aerosol, thereby increasing pH. Given the inability of proxies such as H+str and charge ratios to accurately represent aerosol pH as demonstrated here, this study emphasizes the need for rigorous thermodynamic model-based evaluation of aerosol acidity in the Indian scenario.
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Affiliation(s)
- Bijay Sharma
- Department of Earth Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur, Nadia, West Bengal, 741246, India; School of Engineering, Indian Institute of Technology (IIT) Mandi, Kamand, Himachal Pradesh, 175075, India
| | - Shiguo Jia
- Guangdong Provincial Field Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Guangzhou, 510275, PR China; School of Atmospheric Sciences, Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, PR China
| | - Anurag J Polana
- Department of Earth Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur, Nadia, West Bengal, 741246, India
| | - Md Sahbaz Ahmed
- Department of Environmental Science, Tezpur University, Tezpur, Assam, 784028, India
| | - Raza Rafiqul Haque
- Department of Environmental Science, Tezpur University, Tezpur, Assam, 784028, India
| | - Shruti Singh
- Department of Earth Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur, Nadia, West Bengal, 741246, India
| | - Jingying Mao
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, 510632, PR China
| | - Sayantan Sarkar
- Department of Earth Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur, Nadia, West Bengal, 741246, India; School of Engineering, Indian Institute of Technology (IIT) Mandi, Kamand, Himachal Pradesh, 175075, India.
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39
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Hang Y, Meng X, Li T, Wang T, Cao J, Fu Q, Dey S, Li S, Huang K, Liang F, Kan H, Shi X, Liu Y. Assessment of long-term particulate nitrate air pollution and its health risk in China. iScience 2022; 25:104899. [PMID: 36039292 PMCID: PMC9418855 DOI: 10.1016/j.isci.2022.104899] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 06/26/2022] [Accepted: 08/04/2022] [Indexed: 11/06/2022] Open
Abstract
Air pollution is a major environmental and public health challenge in China and the Chinese government has implemented a series of strict air quality policies. However, particulate nitrate (NO3−) concentration remains high or even increases at monitoring sites despite the total PM2.5 concentration has decreased. Unfortunately, it has been difficult to estimate NO3− concentration across China due to the lack of a PM2.5 speciation monitoring network. Here, we use a machine learning model incorporating ground measurements and satellite data to characterize the spatiotemporal patterns of NO3−, thereby understanding the disease burden associated with long-term NO3− exposure in China. Our results show that existing air pollution control policies are effective, but increased NO3− of traffic emissions offset reduced NO3− of industrial emissions. In 2018, the national mean mortality burden attributable to NO3− was as high as 0.68 million, indicating that targeted regulations are needed to control NO3− pollution in China. We build a NO3− model using machine learning techniques incorporating satellite data We estimate spatiotemporal variations of NO3− concentration in China from 2005–2018 In 2018, the national mean mortality burden attributable to NO3− was about 0.68 million Targeted regulations on vehicle emissions are needed to control NO3− pollution in China
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Affiliation(s)
- Yun Hang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Xia Meng
- School of Public Health, Fudan University, Shanghai 200032, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Tijian Wang
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Junji Cao
- Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, Beijing 100101, China
| | - Qingyan Fu
- State Ecologic Environmental Scientific Observation and Research Station at Dianshan Lake, Shanghai Environmental Monitoring Center, Shanghai 200235, China
| | - Sagnik Dey
- Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Shenshen Li
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100101, China
| | - Kan Huang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Fengchao Liang
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen 518055, China
| | - Haidong Kan
- School of Public Health, Fudan University, Shanghai 200032, China
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
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Su J, Zhao P, Ge S, Ding J. Aerosol liquid water content of PM 2.5 and its influencing factors in Beijing, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 839:156342. [PMID: 35640746 DOI: 10.1016/j.scitotenv.2022.156342] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 05/20/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
Aerosol liquid water content (ALWC) has important influences on atmospheric radiation and aerosol chemical processes. In this work, the changes in ALWC of PM2.5 were investigated over four seasons based on hourly monitoring of inorganic water-soluble ions and their gaseous precursors using the thermodynamic model ISORROPIA II. The results showed that the ALWC concentrations exhibited pronounced seasonal (autumn > summer > spring > winter) and diurnal variation characteristics. The sensitivity tests indicated that ALWC depended strongly on TSO4 (total sulfate (gas and aerosols) expressed as equivalent H2SO4), followed by TNO3 (total nitrate (gas and aerosols) expressed as equivalent HNO3). The relatively low concentration of TCl (total chloride (gas and aerosols) expressed as equivalent HCl) limit its importance in the atmosphere. ALWC showed exponential growth features as a function RH in all four seasons. RH became the most influential factor on the variation of ALWC when RH exceeded 80% in all seasons. The seasonal average data showed that the ALWC increased from 2.92 μg·m-3 to 75.83 μg·m-3 when ambient RH increased from 30% to 90%, the associated sulfate, nitrate, and ammonium (abbreviated as SNA) mass fraction in PM2.5 rose from 0.39 to 0.58 in the atmosphere. The ALWC facilitated the formation of SNA through gas-particle conversion and partitioning. The self-amplifying processes between ALWC and SNA enhanced aerosol formation. By modeling ALWC under different seasonal atmospheric scenarios, it was found that reductions in chemical species could reduce ALWC concentrations in different degrees. Based on the current emission conditions, controlling excess NH3 emission could effectively reduce ALWC to a maximum of 45.71% in summer, indicating that NH3 control was crucial for reducing ALWC and PM2.5 concentrations under high levels of SO42- and NO3-.
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Affiliation(s)
- Jie Su
- Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
| | - Pusheng Zhao
- Joint Laboratory for Electron Microscopy Analysis of Atmospheric Particles, Beijing 100012, China; Beijing Met High-Tech Co., Ltd, Beijing 102299, China.
| | - Shuangshuang Ge
- Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China.
| | - Jing Ding
- Meteorological and Environmental Center of Tianjin, Tianjin 300074, China
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Zhang Z, Xu B, Xu W, Wang F, Gao J, Li Y, Li M, Feng Y, Shi G. Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM 2.5 pollution. ENVIRONMENTAL RESEARCH 2022; 212:113322. [PMID: 35460636 DOI: 10.1016/j.envres.2022.113322] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 04/10/2022] [Accepted: 04/13/2022] [Indexed: 06/14/2023]
Abstract
PM2.5 pollution is a complex process mainly affected by emission sources and meteorological conditions. However, it is hard to accurately assess the effects of emission sources and meteorological conditions on the variation of PM2.5 concentrations in the complex atmospheric environment. In this study, the Random Forest model with Shapley Additive exPlanations (RF-SHAP) and Partial Dependence Plot (RF-PDP) was combined with Positive Matrix Factorization (PMF) to evaluate the impacts of various factors on PM2.5 pollution. The results show that anthropogenic emissions and meteorological conditions contributed about 67% (40.5 μg/m3) and 33% (19.7 μg/m3) to variation in PM2.5 concentrations, respectively. Specifically, secondary nitrate (SN) had the greatest impact among all sources (about 45%). Hence, we further explore the impacts of the primary sources and meteorological conditions on SN formation. Coal combustion and vehicle emissions significantly contribute to the formation of SN by providing a large number of precursor NOX. Additionally, the RF-PDP method was further employed to estimate the synergistic effects of primary sources and meteorological conditions on SN formation. The results help reveal strategies to simultaneously reduce SN by controlling primary emissions under suitable meteorological conditions. This work also suggests that the machine learning model can utilize online datasets well and provide a reliable approach for analyzing the causes of PM2.5 pollution.
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Affiliation(s)
- Zhongcheng 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, PR China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, PR China
| | - Bo Xu
- 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, PR China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, PR China
| | - Weiman Xu
- Trusted AI System Laboratory, College of Computer Science, Nankai University, Tianjin, 300350, PR China
| | - Feng Wang
- 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, PR China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, PR China
| | - Jie Gao
- 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, PR China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, PR China
| | - Yue Li
- Trusted AI System Laboratory, College of Computer Science, Nankai University, Tianjin, 300350, PR China
| | - Mei Li
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for on-line Source Apportionment System of Air Pollution Jinan University, Guangzhou, 510632, PR China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou, 510632, PR 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, PR China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, PR China
| | - Guoliang Shi
- 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, PR China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, PR China.
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Dao X, Ji D, Zhang X, He J, Sun J, Hu J, Liu Y, Wang L, Xu X, Tang G, Wang Y. Significant reduction in atmospheric organic and elemental carbon in PM 2.5 in 2+26 cities in northern China. ENVIRONMENTAL RESEARCH 2022; 211:113055. [PMID: 35257685 DOI: 10.1016/j.envres.2022.113055] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/22/2022] [Accepted: 02/27/2022] [Indexed: 06/14/2023]
Abstract
To better understand the change characteristics and reduction in organic carbon (OC) and elemental carbon (EC) in particulate matter (PM) with a diameter of ≤2.5 μm (PM2.5) driven by the most stringent clean air policies and pandemic-related lockdown measures in China, a comprehensive field campaign was performed to measure the carbonaceous components in PM2.5 on an hourly basis via harmonized analytical methods in the Beijing-Tianjin-Hebei and its surrounding region (including 2 + 26 cities) from January 1 to December 31, 2020. The results indicated that the annual average concentrations of OC and EC reached as low as 6.6 ± 5.7 and 1.8 ± 1.9 μg/m3, respectively, lower than those obtained in previous studies, which could be attributed to the effectiveness of the Clean Air Action Plan and the impact of the COVID-19-related lockdown measures implemented in China. Marked seasonal and diurnal variations in OC and EC were observed in the 2 + 26 cities. Significant correlations (p < 0.001) between OC and EC were found. The annual average secondary OC levels level ranged from 1.8-5.4 μg/m3, accounting for 37.7-73.0% of the OC concentration in the 2 + 26 cities estimated with the minimum R squared method. Based on Interagency Monitoring of Protected Visual Environments (IMPROVE) algorithms, the light extinction contribution of carbonaceous PM to the total amount reached 21.1% and 26.0% on average, suggesting that carbonaceous PM played a less important role in visibility impairment than did the other chemical components in PM2.5. This study is expected to provide an important real-time dataset and in-depth analysis of the significant reduction in OC and EC in PM2.5 driven by both the Clean Air Action Plan and COVID-19-related lockdown policies over the past few years, which could represent an insightful comparative case study for other developing countries/regions facing similar carbonaceous PM pollution.
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Affiliation(s)
- Xu Dao
- China National Environmental Monitoring Station, Beijing, 100012, China
| | - Dongsheng Ji
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Atmosphere Sub-Center of Chinese Ecosystem Research Network, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100191, China.
| | - Xian Zhang
- China National Environmental Monitoring Station, Beijing, 100012, China
| | - Jun He
- Natural Resources and Environment Research Group, International Doctoral Innovation Centre, Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, 315100, China; Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Ningbo, 315101, China
| | - Jiaqi Sun
- China National Environmental Monitoring Station, Beijing, 100012, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Yu Liu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Atmosphere Sub-Center of Chinese Ecosystem Research Network, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100191, China
| | - Lili Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Atmosphere Sub-Center of Chinese Ecosystem Research Network, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100191, China
| | - Xiaojuan Xu
- Atmosphere Sub-Center of Chinese Ecosystem Research Network, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100191, China
| | - Guigang Tang
- China National Environmental Monitoring Station, Beijing, 100012, China
| | - Yuesi Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Atmosphere Sub-Center of Chinese Ecosystem Research Network, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100191, China
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Xie X, Hu J, Qin M, Guo S, Hu M, Wang H, Lou S, Li J, Sun J, Li X, Sheng L, Zhu J, Chen G, Yin J, Fu W, Huang C, Zhang Y. Modeling particulate nitrate in China: Current findings and future directions. ENVIRONMENT INTERNATIONAL 2022; 166:107369. [PMID: 35772313 DOI: 10.1016/j.envint.2022.107369] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/07/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
Particulate nitrate (pNO3) is now becoming the principal component of PM2.5 during severe winter haze episodes in many cities of China. To gain a comprehensive understanding of the key factors controlling pNO3 formation and driving its trends, we reviewed the recent pNO3 modeling studies which mainly focused on the formation mechanism and recent trends of pNO3 as well as its responses to emission controls in China. The results indicate that although recent chemical transport models (CTMs) can reasonably capture the spatial-temporal variations of pNO3, model-observation biases still exist due to large uncertainties in the parameterization of dinitrogen pentoxide (N2O5) uptake and ammonia (NH3) emissions, insufficient heterogeneous reaction mechanism, and the predicted low sulfate concentrations in current CTMs. The heterogeneous hydrolysis of N2O5 dominates nocturnal pNO3 formation, however, the contribution to total pNO3 varies among studies, ranging from 21.0% to 51.6%. Moreover, the continuously increasing PM2.5 pNO3 fraction in recent years is mainly due to the decreased sulfur dioxide emissions, the enhanced atmospheric oxidation capacity (AOC), and the weakened nitrate deposition. Reducing NH3 emissions is found to be the most effective control strategy for mitigating pNO3 pollution in China. This review suggests that more field measurements are needed to constrain the parameterization of heterogeneous N2O5 and nitrogen dioxide (NO2) uptake. Future studies are also needed to quantify the relationships of pNO3 to AOC, O3, NOx, and volatile organic compounds (VOCs) in different regions of China under different meteorological conditions. Research on multiple-pollutant control strategies involving NH3, NOX, and VOCs is required to mitigate pNO3 pollution, especially during severe winter haze events.
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Affiliation(s)
- Xiaodong Xie
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Momei Qin
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Song Guo
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Min Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Hongli Wang
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Shengrong Lou
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Jingyi Li
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jinjin Sun
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Xun Li
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Li Sheng
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jianlan Zhu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Ganyu Chen
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Junjie Yin
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Wenxing Fu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; CAS Center for Excellence in Regional Atmospheric Environment, Chinese Academy of Science, Xiamen 361021, China.
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44
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Sun M, Zhou Y, Wang Y, Qiao X, Wang J, Zhang J. Heterogeneous Reaction of Peroxyacetyl Nitrate on Real-World PM 2.5 Aerosols: Kinetics, Influencing Factors, and Atmospheric Implications. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:9325-9334. [PMID: 35704858 DOI: 10.1021/acs.est.2c03050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The formation and decomposition of peroxyacetyl nitrate (PAN), an important atmospheric nitrogen oxide reservoir, can impact the level and cycling of free radicals and nitrogen compounds in the atmosphere. PAN sinks are poorly understood, highlighting the importance of elucidating the heterogeneous reaction of PAN on aerosol surfaces. Here, we report for the first time the uptake behavior, kinetics, and potential mechanism of PAN uptake on real-world aerosol PM2.5 using a flow tube system. The uptake coefficients (γ) of PAN increased non-linearly from (1.5 ± 0.7) × 10-5 at 0% relative humidity (RH) to (9.3 ± 2.0) × 10-5 at 80% RH. The γ decrease with increasing initial PAN concentration is consistent with the Langmuir-Hinshelwood mechanism. Organic components of aerosols may promote heterogeneous loss of PAN through redox reactions. Higher γ occurs with higher water content, lower pH, and lower ionic strength in the aqueous phase of aerosols. The present study suggests that heterogeneous reaction of PAN on ambient aerosols plays a non-negligible role in the atmospheric PAN budget and provides new insights into the role of PAN in promoting atmospheric oxidation capacity during hazy periods with cold and wet weather conditions.
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Affiliation(s)
- Mei Sun
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
- Institute of Environmental Engineering, ETH Zürich, Zürich 8093, Switzerland
| | - Ying Zhou
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Yifei Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Xueqi Qiao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Jing Wang
- Institute of Environmental Engineering, ETH Zürich, Zürich 8093, Switzerland
- Laboratory for Advanced Analytical Technologies, Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf 8600, Switzerland
| | - Jianbo Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
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45
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Tang Y, Wang S, Li G, Han D, Liu K, Li Z, Wu Q. Elevated Gaseous Oxidized Mercury Revealed by a Newly Developed Speciated Atmospheric Mercury Monitoring System. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:7707-7715. [PMID: 35607915 DOI: 10.1021/acs.est.2c01011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Gaseous oxidized mercury (Hg2+) monitoring is one of the largest challenges in the mercury research field, where existing methods cannot simultaneously satisfy the measurement requirements of both accuracy and time precision, especially in high-particulate environments. Here, we verified that dual-stage cation exchange membrane (CEM) sampler is incapable of gaseous elemental mercury (Hg0) uptake even if particulate matter is trapped on CEM, whereas the Hg2+ capture efficiency of the sampler is more than 90%. We then developed a Cation Exchange Membrane-Coupled Speciated Atmospheric Mercury Monitoring System (CSAMS) by coupling the dual-stage CEM sampler with the commercial Tekran 2537/1130/1135 system and configuring a new sampling and analysis procedure, so as to improve the monitoring accuracy of Hg2+ and ensure the simultaneous measurement of Hg0, Hg2+, and Hgp in 2 h time resolution. We deployed the CSAMS in urban Beijing in September 2021 and observed an unprecedented elevated Hg2+ during the daytime with an average amplitude of 510 pg m-3. Using a zero-dimensional box model, the elevated Hg2+ production rate was attributed to high atmospheric oxidant concentrations, Hg0 heterogeneous and interfacial oxidation processes on the surface of atmospheric particles, or potential unknown oxidants.
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Affiliation(s)
- Yi Tang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Guoliang Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Deming Han
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Kaiyun Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Zhijian Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Qingru Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
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Hu R, Wang S, Zheng H, Zhao B, Liang C, Chang X, Jiang Y, Yin R, Jiang J, Hao J. Variations and Sources of Organic Aerosol in Winter Beijing under Markedly Reduced Anthropogenic Activities During COVID-2019. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:6956-6967. [PMID: 34786936 PMCID: PMC8610015 DOI: 10.1021/acs.est.1c05125] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/24/2021] [Accepted: 11/05/2021] [Indexed: 05/19/2023]
Abstract
The COVID-19 outbreak provides a "controlled experiment" to investigate the response of aerosol pollution to the reduction of anthropogenic activities. Here we explore the chemical characteristics, variations, and emission sources of organic aerosol (OA) based on the observation of air pollutants and combination of aerosol mass spectrometer (AMS) and positive matrix factorization (PMF) analysis in Beijing in early 2020. By eliminating the impacts of atmospheric boundary layer and the Spring Festival, we found that the lockdown effectively reduced cooking-related OA (COA) but influenced fossil fuel combustion OA (FFOA) very little. In contrast, both secondary OA (SOA) and O3 formation was enhanced significantly after lockdown: less-oxidized oxygenated OA (LO-OOA, 37% in OA) was probably an aged product from fossil fuel and biomass burning emission with aqueous chemistry being an important formation pathway, while more-oxidized oxygenated OA (MO-OOA, 41% in OA) was affected by regional transport of air pollutants and related with both aqueous and photochemical processes. Combining FFOA and LO-OOA, more than 50% of OA pollution was attributed to combustion activities during the whole observation period. Our findings highlight that fossil fuel/biomass combustion are still the largest sources of OA pollution, and only controlling traffic and cooking emissions cannot efficiently eliminate the heavy air pollution in winter Beijing.
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Affiliation(s)
- Ruolan Hu
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Chengrui Liang
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Xing Chang
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Rujing Yin
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Jingkun Jiang
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
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47
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Li Z, Xie G, Chen H, Zhan B, Wang L, Mu Y, Mellouki A, Chen J. Characterization of peroxyacetyl nitrate (PAN) under different PM 2.5 concentration in wintertime at a North China rural site. J Environ Sci (China) 2022; 114:221-232. [PMID: 35459488 DOI: 10.1016/j.jes.2021.08.040] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/17/2021] [Accepted: 08/17/2021] [Indexed: 06/14/2023]
Abstract
As a secondary pollutant of photochemical pollution, peroxyacetyl nitrate (PAN) has attracted a close attention. A four-month campaign was conducted at a rural site in North China Plain (NCP) including the measurement of PAN, O3, NOx, PM2.5, oxygenated volatile organic compounds (OVOCs), photolysis rate constants of NO2 and O3 and meteorological parameters to investigate the wintertime characterization of photochemistry from November 2018 to February 2019. The results showed that the maximum and mean values of PAN were 4.38 and 0.93 ± 0.67 ppbv during the campaign, respectively. The PAN under different PM2.5 concentrations from below 75 μg/m3 up to 250 μg/m3, showed different diurnal variation and formation rate. In the PM2.5 concentration range of above 250 μg/m3, PAN had the largest daily mean value of 0.64 ppbv and the fastest production rate of 0.33 ppbv/hr. From the perspective of PAN's production mechanism, the light intensity and precursors concentrations under different PM2.5 pollution levels indicated that there were sufficient light intensity and high volatile organic compounds (VOCs) and NOx precursors concentration even under severe pollution level to generate a large amount of PAN. Moreover, the bimodal staggering phenomenon of PAN and PM2.5 provided a basis that PAN might aggravate haze through secondary organic aerosols (SOA) formation.
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Affiliation(s)
- Zhuoyu Li
- Department of Environmental Science & Engineering, Fudan Tyndall Center, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
| | - Guangzhao Xie
- Department of Environmental Science & Engineering, Fudan Tyndall Center, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
| | - Hui Chen
- Department of Environmental Science & Engineering, Fudan Tyndall Center, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
| | - Bixin Zhan
- Department of Environmental Science & Engineering, Fudan Tyndall Center, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
| | - Lin Wang
- Department of Environmental Science & Engineering, Fudan Tyndall Center, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
| | - Yujing Mu
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Abdelwahid Mellouki
- Institut de Combustion, Aérothermique, Réactivité et Environnement, Centre National de la Recherche Scientifique, 45071 Orléans cedex 02, France
| | - Jianmin Chen
- Department of Environmental Science & Engineering, Fudan Tyndall Center, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
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48
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Yang X, Zheng M, Liu Y, Yan C, Liu J, Liu J, Cheng Y. Exploring sources and health risks of metals in Beijing PM 2.5: Insights from long-term online measurements. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 814:151954. [PMID: 34843775 DOI: 10.1016/j.scitotenv.2021.151954] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/17/2021] [Accepted: 11/21/2021] [Indexed: 06/13/2023]
Abstract
To gain a comprehensive understanding of sources, health risks, and regional transport of PM2.5-bound metals in Beijing, one-year continuous measurement (K, Fe, Ca, Zn, Pb, Mn, Ba, Cu, As, Se, Cr, and Ni) was conducted from December 2016 to November 2017 and Positive Matrix Factorization analysis (PMF) was applied for source apportionment. It was found that the seasonal variation of sources could vary significantly among metals. Sources of Ca, Ba, As, Se, and Cr did not show much seasonal variations, with the contribution of its predominant source higher than 35% in each season. However, the major sources of K, Fe, Zn, Pb, Mn, Cu, and Ni exhibited obvious seasonal variations. In addition, the characteristics of metals in haze episodes were comprehensively investigated. Haze episodes in Beijing were characterized by higher metal concentrations and health risks, which were about 2- 6 times higher than non-haze periods. Moreover, the types of haze episode were different in winter and spring. Haze episodes in winter were mostly influenced by coal combustion, the contribution of which increased greatly and accounted for about 30% of PM2.5. The metals such as K, Zn, Pb, As, and Se significantly increased, which were mainly transported from south of Beijing. During haze episodes in spring, dust was an important source, which contributed to higher concentrations of crustal metals that transported from northwest of Beijing. To quickly and effectively identify source regions of metals in Beijing during haze episodes, a new diagnostic ratio method using Ca as a reference was developed. The ratios of some anthropogenic metals to Ca significantly increased when air mass was mainly from south of Beijing during haze episodes while the ratios remained constantly low in non-haze periods, when local emissions dominated. This method could be useful for rapid identification and control of metal pollution in Beijing.
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Affiliation(s)
- Xi Yang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Mei Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
| | - Yue Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Caiqing Yan
- Environment Research Institute, Shandong University, Qingdao 266237, China
| | - Junyi Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Jiumeng Liu
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Yuan Cheng
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
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49
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Dao X, Di S, Zhang X, Gao P, Wang L, Yan L, Tang G, He L, Krafft T, Zhang F. Composition and sources of particulate matter in the Beijing-Tianjin-Hebei region and its surrounding areas during the heating season. CHEMOSPHERE 2022; 291:132779. [PMID: 34742769 DOI: 10.1016/j.chemosphere.2021.132779] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 10/25/2021] [Accepted: 11/02/2021] [Indexed: 06/13/2023]
Abstract
This paper aimed to analyze the composition and pollution sources of particulate matter (PM) in the Beijing-Tianjin-Hebei region and its surrounding areas (henceforth the BTH region) during the heating season to support the mitigation and control of regional air pollution. Manual monitoring data from the China National Environmental Monitoring Network for Atmospheric PM in the BTH region were collected and analyzed during the 2016 and 2018 heating seasons. The positive definite matrix factor analysis (PMF) model was used to analyze the PM sources in BTH cities during the heating season. The main PM components were organic matter (OM), nitrate (NO3-), sulfate (SO42-) and ammonium salt (NH4+). Direct emission sources have decreased since 2016, indicating the effectiveness of governmental controls on these sources; however, secondary pollution showed an increasing trend, suggesting control measures should be strengthened. Daily regional average concentrations of OM, SO42-, NH4+, elemental carbon (EC), chloride (Cl-) and trace elements all showed similar trends. When air quality worsened, the concentrations of the main PM components increased, but trends of change varied among components. In 2018, concentrations of OM and chloride were highest in the Taihang Mountains, and NO3 concentrations were highest in Anyang, Hebi, Jiaozuo and Xinxiang. The SO42- concentration was highest in the southern section of the Taihang Mountains. The NH4+ and EC concentrations were generally highest in the central and southern regions. The concentration of crustal substances was highest in some cities in the north and central parts of the BTH region. In the 2018 heating season, the pollution level of five transmission channels showed an increasing trend in the Northwest, Southeast, Yanshan, South and Taihang Mountain channels. These findings provide a scientific basis for the continued management of atmospheric PM pollution.
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Affiliation(s)
- Xu Dao
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Shiying Di
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Xian Zhang
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Panjun Gao
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Li Wang
- Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
| | - Luyu Yan
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Guigang Tang
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Lihuan He
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Thomas Krafft
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Fengying Zhang
- China National Environmental Monitoring Centre, Beijing, 100012, China; Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands.
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50
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Numerical Simulation of Topography Impact on Transport and Source Apportionment on PM2.5 in a Polluted City in Fenwei Plain. ATMOSPHERE 2022. [DOI: 10.3390/atmos13020233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The unique energy structure, high intensity of coal production, and complex terrain, make Fenwei Plain a highly polluted region in China. In this study, we characterized the transport characteristic and sources of PM2.5 (the fraction of particulate matter ≤ 2.5 μm) in Sanmenxia, a polluted city in canyon terrain. The results showed that special topography in Sanmenxia had an important role in the transport of particulates. Sanmenxia is located between two northeast-southwest facing mountains, showing a special local circulation. The local circulation was dominated by a downslope wind at nighttime, while the cross−mountain airflow and zonal wind were dominant during the daytime in the canyon terrain. PM2.5 accumulated near Sanmenxia with the influence of downslope, zonal wind, and topography. The main regional transport paths could be summarized into an eastern path, a northern path, and a western path during the severe haze episodes. The PM2.5 source apportionment revealed by an on-line tracer-tagged of the Nested Air Quality Prediction Model System (NAQPMS) showed that the main regional sources of Sanmenxia were Yuncheng, Sanmenxia, and Weinan. The contribution to PM2.5 concentration in Sanmenxia was 39%, 25%, and 11%, respectively. The northern path had the most important impact on Sanmenxia. The results can provide scientific basis for the establishment of severe haze control in Sanmenxia and regional joint control.
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