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New insights into the formation of ammonium nitrate from a physical and chemical level perspective

Frontiers of Environmental Science & Engineering 2023年第11期17卷 209-221页

作者： Yuting Wei Xiao Tian Junbo Huang Zaihua Wang Bo Huang Jinxing Liu Jie Gao Danni Liang Haofei Yu Yinchang Feng Guoliang Shi State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control Tianjin Key Laboratory of Urban Transport Emission ResearchCollege of Environmental Science and EngineeringNankai UniversityTianjin 300350China CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research(CLAER) College of Environmental Science and EngineeringNankai UniversityTianjin 300350China Institute of Resources Utilization and Rare Earth Development Guangdong Academy of SciencesGuangzhou 510650China Guangzhou Hexin Instrument Co.Ltd. Guangzhou 510530China State Key Laboratory of Precision Measuring Technology and Instruments Tianjin Key Laboratory of Air pollutants Monitoring TechnologySchool of Precision Instrument and Opto-electronics EngineeringTianjin UniversityTianjin 300072China Gigantic Technology(Tianjin)Co.Ltd. Tianjin 300384China Department of Civil Environmental and Construction EngineeringUniversity of Central FloridaOrlandoFL32816USA

High levels of fine particulate matter(PM_(2.5))is linked to poor air quality and premature deaths,so haze pollution deserves the attention of the *** abundant inorganic components in PM_(2.5),ammonium nitrate(NH_(4)NO_(3))formation includes two processes,the diffusion process(molecule of ammonia and nitric acid move from gas phase to liquid phase)and the ionization process(subsequent dissociation to form ions).In this study,we discuss the impact of meteorological factors,emission sources,and gaseous precursors on NH4NO3 formation based on thermodynamic theory,and identify the dominant factors during clean periods and haze *** show that aerosol liquid water content has a more significant effect on ammonium nitrate formation regardless of the severity of *** dust source is dominant emission source in clean periods;while a combination of coal combustion and vehicle exhaust sources is more important in haze *** the control of ammonia emission is more effective in reducing the formation of ammonium *** findings of this work inform the design of effective strategies to control particulate matter pollution.

关键词： Ammonium nitrate formation Thermodynamic theory Aerosol liquid water content Source apportionment

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Influence of Different Test Cycles on the CO2Emission Factor of Light Gasoline Vehicles

Influence of Different Test Cycles on the CO2Emission Factor...

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Power and Energy Engineering (ICPEE), International Conference on

作者： Rui Wu Daoyuan Yang Hao Zhang Hang Xu Laboratory of Transport Pollution Control and Monitoring Technology Transport Planning and Research Institute Ministry of Transport Beijing China CATARC Automotive Test Center (Tianjin) Co. Ltd. China Automotive Technology Research Center Co. LTD Tianjin China

Emission factor is an important fundamental parameter for measuring the carbon dioxide (CO 2 ) emissions of light gasoline vehicles. However, there are differences in the CO 2 emission factors calculated from emission data obtained under different test cycles. To investigate the impact of different test cycles on CO 2 emission factors for light gasoline vehicles, this study conducted an in-depth analysis based on the China light-duty vehicle test cycle (CLTC), the Worldwide harmonized light vehicles test cycle (WLTC), and the real driving emission test cycle (RDE). The vehicle specific power (VSP) and vehicle speed are utilized as the characterization parameters for the micro-scale operation mode of vehicles. Based on this, the Bin intervals are divided to construct Bin intervals that conform to the normal distribution. CO 2 emission data for 6 light gasoline vehicles under C TC, WLTC, and RDE are used to investigate the impact of different test cycles on CO 2 emission factors for light gasoline vehicles. The study found that the “CLTC-only” CO 2 mission factor and “WLTC-only” CO 2 emission factor are lower than the “RDE-only” CO 2 baseline emission factor.

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Particulate matter pollution in Kunshan High-Tech zone： Source apportionment with trace elements, plume evolution and its monitoring

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Journal of Environmental Sciences 2018年第9期30卷 119-126页

作者： Suwei Zhang Qijie Zhang Armand Albergel Didier Buty Liangmin Yu Haiting Wang Wuxia Bi Peng Cheng Fu Chen Jun Fang Ruirui Hou Xudong Luan Changgan Shu Jingjing Su Jiangsu Skyray Instrument Co. Ltd. Kunshan 215300 China ARIA Technologies SA Boulogne-Billancourt 92100 France Kunshan Environment Monitoring Station Kunshan 215300 China State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and Engineering Nankai University Tianjin 300000 China Center for Urban Transport Emission Research College of Environmental Science and Engineering Nankai University Tianjin 300000 China Ecole Nationale Superieure des Mines de Douai Douai France Kinton Environmental Technology Co. Ltd. Nanjing 210000 China Chengdu University of Information Science and Technology Chengdu 610000 China Hohai Uniuersity Nanjing 210000 China

Particulate matter （PM} in the Kunshan High-Tech zone is studied during a three-month campaign. PM and trace elements are measured by the online pollution monitoring, forecast-warning and source term retrieval system AS3. Hourly measured concentrations of PM10, PM2.5 and 16 trace elements in the PM2.5 section （Ca, Pb, Cu, C1, V, Cr, Fe, Ti, Mn, Ni, Zn, Ga, As, Se, Sr, Ba） are focused. Source apportionment of trace elements by Positive Matrix Factorization modeling indicates that there are five major sources, including dust, industrial processing, traffic, combustion, and sea salt with contribution rate of 23.68%, 21.66%, 14.30%, 22.03%, and 6.89%, respectively. Prediction ofptume dispersion from concrete plant and traffic emissions shows that PM20 pollution of concrete plant is three orders of magnitude more than that of the traffic. The influence range can extend to more than 3 km in 1 hr. Because the footprint of the industrial plumes is constantly moving according to the local meteorological conditions, the fixed monitoring sites scattered in a few hundred meters haven＇t captured the heaviest pollution plume at the local scale of a few km2. As a more intensive monitoring network is not operationally possible, the use of online modeling gives accurate and quantitative information of plume location, which increases the spatial pollution monitoring capacity and improves the understanding of measurement data. These results indicate that the development of the AS3 system, which combines monitoring equipment and air pollution modeling systems, is beneficial to the real-time pollution monitoring in the industrial zone.

关键词： Particulate matter pollution Trace elements Online pollution monitoring in a industrial zone Source apportionment Plume evolution

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Knowledge-guided machine learning reveals pivotal drivers for gasto-particle conversion of atmospheric nitrate

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Environmental Science and Ecotechnology 2024年第3期19卷 100-108页

作者： Bo Xu Haofei Yu Zongbo Shi Jinxing Liu Yuting Wei Zhongcheng Zhang Yanqi Huangfu Han Xu Yue Li Linlin Zhang Yinchang Feng Guoliang Shi State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control Tianjin Key Laboratory of Urban Transport Emission ResearchCollege of Environmental Science and EngineeringNankai UniversityTianjin300350China CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research(CLAER) College of Environmental Science and EngineeringNankai UniversityTianjin300350China Department of Civil Environmentaland Construction EngineeringUniversity of Central FloridaOrlandoFLUSA School of Geography Earth and Environment Sciences University of BirminghamBirminghamB152TTUK State Key Laboratory of Precision Measuring Technology and Instruments Tianjin Key Laboratory of air Pollutants Monitoring TechnologySchool of Precision Instrument and Opto-electronics EngineeringTianjin UniversityTianjin300072China Gigantic Technology(Tianjin)Co. LtdTianjin300072China College of Computer Science Nankai UniversityTianjin300350China China National Environmental Monitoring Centre Beijing100012China

Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion *** process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).The mechanism betweenε(NO_(3)^(-))and its drivers is highly complex and nonlinear,and can be characterized by machine learning ***,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 *** urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact ofε(NO_(3)^(-)).Here we introduce a supervised machine learning approachdthe multilevel nested random forest guided by theory *** approach robustly identifies NH4 t,SO_(4)^(2-),and temperature as pivotal drivers forε(NO_(3)^(-)).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual ***,our approach underscores the significance of NH4 t during both daytime(30%)and nighttime(40%)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest *** research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

关键词： Machine learning Data driven Theoretical approach Domain knowledge Guide

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Synergetic roadmap of carbon neutrality and clean air for China

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Environmental Science and Ecotechnology 2023年第4期16卷 1-25页

作者： Qiang Zhang Zhicong Yin Xi Lu Jicheng Gong Yu Lei Bofeng Cai Cilan Cai Qimin Chai Huopo Chen Hancheng Dai Zhanfeng Dong Guannan Geng Dabo Guan Jianlin Hu Cunrui Huang Jianing Kang Tiantian Li Wei Li Yongsheng Lin Jun Liu Xin Liu Zhu Liu Jinghui Ma Guofeng Shen Dan Tong Xuhui Wang Xuying Wang Zhili Wang Yang Xie Honglei Xu Tao Xue Bing Zhang Da Zhang Shaohui Zhang Shaojun Zhang Xian Zhang Bo Zheng Yixuan Zheng Tong Zhu Jinnan Wang Kebin He Ministry of Education Key Laboratory for Earth System Modelling Department of Earth System ScienceTsinghua UniversityBeijing100084China Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing210044China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Institute for Carbon Neutrality Tsinghua UniversityBeijing100084China State Key Joint Laboratory for Environment Simulation and Pollution Control College of Environmental Sciences and Engineering and Center for Environment and HealthPeking UniversityBeijing100871China Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental PlanningBeijing100012China Center for Carbon Neutrality Chinese Academy of Environmental PlanningBeijing100012China National Center for Climate Change Strategy and International CooperationBeijing100035China Nansen-Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing100029China College of Environmental Sciences and Engineering Peking UniversityBeijing100871China Institute of Environmental Policy Management Chinese Academy of Environmental PlanningBeijing100012China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control School of Environmental Science and EngineeringCollaborative Innovation Center of Atmospheric Environment and Equipment TechnologyNanjing University of Information Science and TechnologyNanjing210044China Vanke School of Public Health Tsinghua UniversityBeijing100084China Center for Energy and Environmental Policy Research Beijing Institute of TechnologyBeijing100081China China CDC Key Laboratory of Environment and Population Health National Institute of En

It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on greenhouse gas reduction,air quality improvement,and improved *** the context of carbon peak,carbon neutrality,and clean air policies,this perspective tracks and analyzes the process of the synergetic governance of air pollution and climate change in China by developing and monitoring 18 *** 18 indicators cover the following five aspects:air pollution and associated weather-climate conditions,progress in structural transition,sources,inks,and mitigation pathway of atmospheric composition,health impacts and benefits of coordinated control,and synergetic governance system and *** tracking the progress in each indicator,this perspective presents the major accomplishment of coordinated control,identifies the emerging challenges toward the synergetic governance,and provides policy recommendations for designing a synergetic roadmap of Carbon Neutrality and Clean Air for China.

关键词： Carbon neutrality Clean air Synergetic roadmap

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The Transition and Suggestions of Coastal Port Reclamation Policy in China

SSRN

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SSRN 2022年

作者： Wang, Nan Zhu, Gaoru Li, Xinyang Xu, Min Cheng, Jinxiang Yi, Wenbin Liu, Shengqiang Xie, Zhenglei College of Marine Science and Engineering Nanjing Normal University Jiangsu Nanjing China Laboratory of Transport Pollution Control and Monitoring Technology Transport Planning and Research Institute Ministry of Transport of the People’s Republic of China Beijing100028 China Key Laboratory of Coastal Salt Marsh Ecosystems and Resources Ministry of Natural Resources Nanjing210023 China Building 2 Jia 6 Shuangguangxili Chaoyang Beijing100028 China

China has the largest extent of reclamation in the world with an area of 2976 km2 from 2002 to 2018. The coastal reclamation was used for saltpan purposes in the 1950s and was applied for agriculture in the 1960s and 1970s. The aquatic products of China occupied large scale coastal wetland and provides huge amounts of fishes. Since 2000, the reclamation was used for port, industries and urban expansion. Ports are the key to land-sea coordination and regional development, and the study of port reclamation patterns and environmental strategies for port clusters is the focus of coastal zone sustainable development. Port is the only one type that depend on reclamation and port play an important role in driving economic development and world trades. Port reclamation in China has undergone several phases of acceleration, peak, deceleration, and stagnation since 2002. This research demonstrated the reclamation policy changes. New situation of port reclamation, such as the stronger demand for port reclamation and strict reclamation policies, ecological demands of port reclamation, the different reclamation stages, unbalanced spatial-temporal distribution of reclamation, coordination of reclamation-related policies, and imbalance between filling and dredging, were demonstrated. In the end, the research shows land use efficiency needs to be improved, the ecological restoration level should be enhanced, and the port reclamation model should be changed, port plan revision and land-sea integration should be improved. Therefore, the research has important implication for scientifically regulating the spatial pattern of port reclamation, and promoting the sustainable development of port-dense coastal zone. © 2022, The Authors. All rights reserved.

关键词： Reclamation

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Study on Ship-based Atmospheric Pollutant Emission Forecasting for Environmental Impact Assessment in Port Planning

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IOP Conference Series: Earth and Environmental Science 2021年第3期781卷

作者： Yonglin Zhang Yue Li Mingjun Li Xiaowen Yang Jinxiang Cheng Honglei Xu Transport Planning and Research Institute Ministry of Transport Laboratory of Transport Pollution Control and Monitoring Technology Beijing 100028 China

Vessels are major contributors to port-related atmospheric pollution. In recent years, as the environmental impact assessment s(hereinafter as EIA) in port planning become increasingly standardized, the lens on atmospheric pollution in EIA has gradually broadened from loading and unloading, piling and storage by cargo types to include the impact of ship emissions. However, the impact analysis ship-based atmospheric pollutants in port areas lack quantitative methodologies for accurate calculation, in particular where annual forecasting is commissioned in annual planning. By combining the characteristics of Chinese port planning and mainstream calculation methods of ship emissions in the world, the study arrives at several recommendations on selecting methodologies for the calculating of ship-based emissions in the EIA process that are tailored different planning scenarios and data availability, serving as reference for EIA practitioners in their endeavors to calculate the atmospheric pollutant emissions from ships in the EIA process mandated by port planning.

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Heavy-Duty Diesel Trucks on Highways in Beijing-Tianjin-Hebei: Constant Challenge in pollution control

SSRN

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SSRN 2024年

作者： Han, Yingying Wang, Chang Zhu, Yixue Wu, Rui Xue, Yifeng Zhang, Xiang Wang, Weiwei Cui, Yangyang Han, Jinxiu Shi, Zongbo Wang, Xiuyan Jin, Taosheng Tianjin Key Laboratory of Urban Transport Emission Research State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and Engineering Nankai University Tianjin300350 China Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China School of Environment Beijing Normal University Beijing100875 China Laboratory of Transport Pollution Control and Monitoring Technology Transport Planning and Research Institute Ministry of Transport Beijing100028 China National Engineering Research Center of Urban Environmental Pollution Control Beijing Municipal Research Institute of Eco-Environmental Protection Beijing100037 China Sustainable Development Research Institute for Economy and Society of Beijing School of Management and Economics Beijing Institute of Technology Beijing100081 China School of Geography Earth and Environmental Sciences The University of Birmingham BirminghamB15 2TT United Kingdom

Heavy-duty diesel trucks (HDDTs) contributed significantly to on-road air pollution. This study measured the traffic flow of HDDTs on seven national highways in Beijing-Tianjin-Hebei (BTH) region to estimate air pollutants of HDDTs from 2016 to 2020. The emission factors are corrected by the International Vehicle Emissions (IVE) model, traffic flow, and road length. Compared with 2016, traffic flow in 2019 increased by 41%, carbon monoxide (CO) and nitrogen oxide (NOx) emissions increased by 0.3% and 4%, while hydrocarbons (HC) and particulate matter (PM) emissions decreased by about 26% and 18%. The COVID-19 lockdowns did not cause significant annual changes in traffic flow and pollution emissions in 2020 compared with 2019, but there were substantial monthly variations in pollutant emissions. Our study showed that although control measures were implemented during the "Thirteenth Five-Year Plan" period, the substantial increase in regional HDDT transport posed a continuing challenge in reducing road traffic emissions. © 2024, The Authors. All rights reserved.

关键词： COVID-19

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Multi-scale analysis of the chemical and physical pollution evolution process from pre-co-pollution day to PM2.5 and O3 co-pollution day

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Science of the Total Environment 2024年 945卷 173729页

作者： Wang, Feng Zhang, Chun Ge, Yi Zhang, Zhang Shi, Guoliang Feng, Yinchang School of Environmental Science and Safety Engineering Tianjin University of Technology Tianjin300384 China Shaanxi Province Environmental Monitoring Center Xi'an710054 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 Tianjin300350 China Cooperative Laboratory for Atmospheric Environment-Health Research College of Environmental Science and Engineering Nankai University Tianjin300350 China

PM2.5 and O3 are two of the main air pollutants that have adverse impacts on climate and human health. The evolution process of PM2.5 and O3 co-pollution are of concern because of the increased frequency of PM2.5 and O3 co-pollution days. Here, we examined the chemical coupling and revealed the driving factors of the PM2.5 and O3 co-pollution evolution process from cleaning day, PM2.5 pollution day, or O3 pollution day, applied by theoretical analysis and model calculation methods. The results demonstrate that PM2.5 and O3 co-pollution day frequently occurred with high concentrations of gaseous precursors and higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), which we attribute to the enhancement of atmospheric oxidation capacity (AOC). The AOC is positively correlated with O3 and weakly correlated with PM2.5. In addition, we found that the correlation coefficients of PM2.5-NO2 (0.62) were higher than that of PM2.5-SO2 (0.32), highlighting the priority of NOx controlling to mitigate PM2.5 pollution. Overall, our discovery can provide scientific evidence to design feasible solutions for the controlling PM2.5 and O3 co-pollution process. © 2024

关键词： Oxidation

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Simulating the multi-media fate of perfluorooctane sulfonate (PFOS) and its precursors in the Bohai coastal region of China

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Science of the Total Environment 2024年 957卷 177772页

作者： Han, Guoxiang Song, Shuai Du, Di Cui, Haotian Lu, Yonglong Wang, Rui State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing100085 China State Key Laboratory of Marine Environmental Science and Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems College of the Environment and Ecology Xiamen University Fujian 361102 China University of Chinese Academy of Sciences Beijing100049 China Ministry of Emergency Management Big Data Center Beijing100010 China Key Laboratory of Cyberspace and Data Security Ministry of Emergency Management Beijing100010 China School of Ecology and Nature Conservation Beijing Forestry University Beijing100083 China Transport Planning and Research Institute Ministry of Transport Laboratory of Transport Pollution Control and Monitoring Technology Beijing100028 China

PFOS and its precursors are of great concern due to their persistence and widespread presence in the environment. However, few studies have been conducted on their transformation and fate at a regional scale. We aim to address this gap by investigating their fate in the Bohai coastal region of China. Emissions of PFOS and its precursors are estimated based on anticipated POSF production in China from 1976 to 2025. Comparison of emission sources revealed that PFOS primarily originated from direct emissions, while precursors were notably influenced by indirect sources. Dynamic concentration curves of these pollutants in various compartments were simulated and validated against measured values. Spatial analysis indicated higher concentrations of these pollutants in sections 25 (Beijing), 26 (Tianjin), and 48 (Shenyang), with lower concentrations observed in sections 8, 16, and 24. The mass flux results highlighted significantly different pathways: precursors primarily interacted between upper and lower air with notable degradation, while PFOS mainly moved from lower air to soil and freshwater. Mass fluxes into the Bohai Sea suggested that PFOS mainly entered via water flow, while precursors primarily came through air deposition. Uncertainty analysis revealed higher uncertainties in soil and sediments than the oceans, with additional uncertainties in estimated emissions, and seawater salinity. This study provides a theoretical basis for modelling and regulating PFOS. © 2024

关键词： Miocene

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