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

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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我国典型区域二氧化碳和细颗粒物精细化协同减排路径

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科学通报 2024年第10期69卷 1373-1384页

作者：赵焕许博徐晗王振宇高洁黄俊波戴启立冯银厂史国良南开大学环境科学与工程学院国家环境保护城市空气颗粒物污染防治重点实验室天津300350 南开大学环境科学与工程学院中国气象局-南开大学大气环境与健康研究联合实验室天津300350

随着碳达峰、碳中和目标的提出,我国环境保护进入减污降碳协同治理新阶段.目前研究主要探寻空气质量政策或气候政策对二氧化碳和细颗粒物的减排效益.而从二氧化碳和细颗粒物排放终端能源消费重点行业、主要能源以及主导因素等角度出发,... 详细信息

随着碳达峰、碳中和目标的提出,我国环境保护进入减污降碳协同治理新阶段.目前研究主要探寻空气质量政策或气候政策对二氧化碳和细颗粒物的减排效益.而从二氧化碳和细颗粒物排放终端能源消费重点行业、主要能源以及主导因素等角度出发,来探究我国典型区域协同减排路径精细化方法的研究则鲜见报道.本研究基于2000~2020年我国京津冀、长江三角洲、珠江三角洲典型区域能源消耗数据,首先探明了3个典型区域二氧化碳和细颗粒物终端能源消费的重点排放行业均为工业;并进一步甄别了终端能源消费重点行业中PM_(2.5)排放主要能源为煤类能源,而CO_(2)排放主要能源由煤类能源逐渐转向煤、气类能源,但煤类仍为主导地位;随后使用因素分解模型解析了能源强度、技术进步等主导因素对单位国内生产总值的二氧化碳以及细颗粒物的影响效应;最终利用能源-环境核算预测模型,基于上述研究识别的终端能源消费重点行业、主要能源以及主导因素进行情景分析,旨在判断典型区域不同情景下碳达峰情况,进而寻找协同减排最优路径.结果发现,“技术进步”因素在前期减排效果最好;“能效提升”因素的减排效果在长时期碳污协同减排将起到关键作用,表明未来针对工业排放等终端能源消费重点行业,关注煤、气类等主要能源,采取能效提升、技术进步等手段措施,能够对减污降碳协同增效目标的实现产生最佳收益.希望本研究能为我国典型区域二氧化碳和细颗粒物合理化、精细化协同减排和管控提供科学证据.

关键词：二氧化碳细颗粒物 LEAP模型情景分析协同控制精细化

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Assessment of heavy metal pollution characteristics and human health risk of exposure to ambient PM_(2.5) in Tianjin,China

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Particuology 2015年第3期13卷 104-109页

作者： Peifei Chen Xiaohui Bi Jiaqi Zhang Jianhui Wu yinchang feng College of Environmental Science and Safety Engineering Tianjin University of Technology College of Environmental Science and Engineering Nankai University

To examine the features of heavy metal pollution of PM2.5 （particulate matter less than 2.5 μm） in Tianjin, China, as well as the exposure risk of PM2.5 to human health, we analyzed ambient PM2.5samples collected from a campus of Nankai University in June, August, and October 2012. The concentrations of PM2.5 and heavy metals （Ni, Cu, Pb, Zn, Cr, Cd, Hg, As and Mn） in PM2.5 were analyzed by gravimetric analysis and inductively coupled plasma-mass spectrometry, respectively. The results show that the heavy metals contained in PM2.5 were, in descending order, Cu, Zn, Pb, Mn, Cr, Ni, Cd, As, and Hg. The proportion of Cd exceeded the secondary level of National Ambient Air Quality Standard of China （GB 3095-2012） by 1.3 times, while others were within the limit. Enrichment factor analysis indicated that Cu, Zn, Cd, Pb, and Hg are mainly from anthropogenic sources. Principal component analysis indicated that the main sources of the heavy metals are vehicle exhaust, chemical waste, and coal-burning activities. The nine heavy metals which may cause health issues by exposure through the human respiratory system and should be further examined are Cr, Cd, As, Ni, Cu, Pb, Mn, Zn, and Hg, in the order of decreasing risk levels. With reference to the U.S. EPA standard the risk levels of all nine metals were below the acceptable level （10 6/year）.

关键词： PM2.5 Heavy metalEnrichment factorPrincipal component analysisHealth risk assessment

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Chemical characterization and source apportionment of PM_(1) and PM_(2.5) in Tianjin, China: Impacts of biomass burning and primary biogenic sources

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Journal of Environmental Sciences 2021年第1期33卷 196-209页

作者： Jahan Zeb Khan Long Sun Yingze Tian Guoliang Shi yinchang feng Center for Ecological Research&Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education College of ForestryNortheast Forestry UniversityHarbin150040China State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Enuironmental Science and EngineeringNankai UniversityTianjin300350China

The submicron particulate matter(PM_(1))and fine particulate matter(PM_(2.5))are very important due to their greater adverse impacts on the natural environment and human *** this study,the daily PM_(1) and PM_(2.5) samples were collected during early summer 2018 at a sub-urban site in the urban-industrial port city of Tianjin,*** collected samples were analyzed for the carbonaceous fractions,inorganic ions,elemental species,and specific marker sugar *** chemical characterization of PM_(1) and PM_(2.5) was based on their concentrations,compositions,and characteristic ratios(PM_(1)/PM_(2.5),AE/CE,NO3^-/SO4^2-,OC/EC,SOC/OC,OM/TCA,K^+/EC,levoglucosan/K^+,V/Cu,and V/Ni).The average concentrations of PM_(1) and PM_(2.5) were 32.4μg/m and 53.3μg/m^3,and PM_(1) constituted 63%of PM_(2.5) on *** source apportionment of PM_(1) and PM_(2.5) by positive matrix factorization(PMF)model indicated the main sources of secondary aerosols(25%and 34%),biomass burning(17%and 20%),traffic emission(20%and 14%),and coal combustion(17%and 14%).The biomass burning factor involved agricultural fertilization and waste *** biomass burning and primary biogenic contributions were determined by specific marker sugar *** anthropogenic sources(combustion,secondary particle formation,etc)contributed significantly to PM_(1) and PM_(2.5),and the natural sources were more evident in PM_(2.5).This work significantly contributes to the chemical characterization and source apportionment of PM_(1) and PM_(2.5) in near-port cities influenced by the diverse sources.

关键词： PM_(1) and PM_(2.5) Near-port city Chemical characterization Source apportionment Biomass burning Primary biogenic

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Size distribution and chemical characteristics of particles from crop residue open burning in North China

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Journal of Environmental Sciences 2021年第11期33卷 66-76页

作者： Tingkun Li Qili Dai Xiaohui Bi Jianhui Wu Yufen Zhang yinchang feng State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and EngineeringNankai UniversityTianjin300350China.

Crop residue open burning is an important emission source of ambient particles in *** study analyzed the particle emission characteristics of crop residue open burning through combustion experiments with a novel open combustion simulation device using three typical crop straws in north China(corn,wheat,and rice).Particle samples size ranging from 0.006–9.890μm were collected by an Electrical Low Pressure Impactor plus,a high size-resolution instrument capable of dividing particles into 14 size *** size distributions of organic carbon(OC),elemental carbon(EC),water-soluble ions,and elements were analyzed,and source chemical profiles were constructed for PM0.1,PM1,PM2.5,and *** number concentration of particles was concentrated in the Aiken nuclei mode(0.006–0.054μm),accounting for 75%of the total number,whereas the mass concentration was concentrated in the accumulation mode(0.054–0.949μm),accounting for 85.43%of the mass ***,EC,Cl−,and K(include total K and water-soluble K)were the major chemical components of the particles,whose mass percentage distributions differed from those of other *** fivemain components exhibited a bell-shaped size distribution in the 0.006–9.890μm range,whereas the other components exhibited a U-shaped *** the chemical profiles for PM0.1–PM10,OC was the most important component at 10–30%,followed by EC at 2%–8%.The proportions of K^(+),Cl^(−),and K varied substantially in different experimental groups,ranging from 0–15%,and K+and Cl−were significantly correlated(r=0.878,α=0.000).

关键词： Crop residue Open burning Size distribution Source profile Electrical Low Pressure Impactor plus(ELPI+)

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Impact of meteorological condition changes on air quality and particulate chemical composition during the COVID-19 lockdown

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Journal of Environmental Sciences 2021年第11期33卷 45-56页

作者： Jing Ding Qili Dai Yafei Li Suqin Han Yufen Zhang yinchang feng Tianjin Environmental Meteorological Center Tianjin 300074China State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and EngineeringNankai UniversityTianjin 300350China CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research Tianjin 300074China

Stringent quarantine measures during the Coronavirus Disease 2019(COVID-19)lockdown period(January 23,2020 to March 15,2020)have resulted in a distinct decrease in anthropogenic source emissions in North China Plain compared to the paralleled period of ***,22.7%decrease in NO_(2)and 3.0%increase of O_(3)was observed in Tianjin,nonlinear relationship between O_(3)generation and NO_(2)implied that synergetic control of NOx and VOCs is *** meteorological condition during the COVID-19 lockdown obscured the actual PM2.5 *** transport in 2020 Spring Festival(SF)triggered regional haze ***2.5 during the COVID-19 lockdown only reduced by 5.6%in *** we used the dispersion coefficient to normalize the measured PM2.5(DN-PM2.5),aiming to eliminate the adverse meteorological impact and roughly estimate the actual PM2.5 reduction,which reduced by 17.7%during the COVID-19 *** terms of PM2.5 chemical composition,significant NO_(3)−increase was observed during the COVID-19 ***,as a tracer of atmospheric oxidation capacity,odd oxygen(Ox=NO_(2)+O_(3))was observed to reduce during the COVID-19 lockdown,whereas relative humidity(RH),specific humidity and aerosol liquid water content(ALWC)were observed with noticeable *** oxidation rate(NOR)was observed to increase at higher specific humidity and ALWC,especially in the haze episode occurred during 2020SF,high air humidity and obvious nitrate generation was *** enhanced air humidity may response for the nitrate increase during the COVID-19 lockdown period.

关键词： COVID-19 lockdown Air quality Meteorological condition Air humidity Aerosol chemistry

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Using geoaccumulation index to study source profiles of soil dust in China

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Journal of Environmental Sciences 2008年第5期20卷 571-578页

作者： JI Yaqin feng yinchang WU Jianhui ZHU Tan BAI Zhipeng DUAN Chiqing State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and Engineering Nankai University Tianjin 300071 China.

Source apportionment studies of TSP （atmospheric particulate matter with aerodynamic diameters ≤ 100 μm） and PM10 （atmospheric particulate matter with aerodynamic diameters ≤ 10 μm） have revealed that soil dust is an important source of these particulates in China. In this study, the contamination of soil dust was assessed through the use of a geoaccumulation index （Igeo）. The mass concentration profiles of 17 elements （Na, Mg, Al, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Br, Ba, and Pb） were established for urban soil dusts. Geochemical compositions of soils from 15 cities were used to represent background urban soil compositions. The results of this study indicated that a number of cities are severely polluted by particulates containing Ca, Cr, Ni, and Cu in both size fractions （TSP and PM10）. Contamination with Zn, Pb, Co, and Br was moderate to severe （Igeo 〉 2）. The Al and Fe concentrations were not high enough for them to be considered contaminants.

关键词： geoaccumulation index soil dust source apportionment TSP PM10

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Impacts of meteorology and precursor emission change on O_(3) variation in Tianjin, China from 2015 to 2021

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Journal of Environmental Sciences 2023年第4期126卷 506-516页

作者： Jing Ding Qili Dai Wenyan Fan Miaomiao Lu Yufen Zhang Suqin Han yinchang feng Tianjin Environmental Meteorological Center Qi xiangtai roadTianjin 300074China State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and EngineeringNankai UniversityTianjin 300350China CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research Tianjin 300074China

Deterioration of surface ozone (O_(3)) pollution in Northern China over the past few years received much *** many cities,it is still under debate whether the trend of surface O_(3)variation is driven by meteorology or the change in precursors *** this work,a time series decomposition method (Seasonal-Trend decomposition procedure based on Loess (STL)) and random forest (RF) algorithm were utilized to quantify the meteorological impacts on the recorded O_(3)trend and identify the key meteorological factors affecting O_(3)pollution in Tianjin,the biggest coastal port city in Northern *** “removing” the meteorological fluctuations from the observed O_(3)time series,we found that variation of O_(3)in Tianjin was largely driven by the changes in precursors *** meteorology was unfavorable for O_(3)pollution in period of 2015-2016,and turned out to be favorable during ***,meteorology contributed 9.3μg/m^(3)O_(3)(13%) in 2019,together with the increase in precursors emissions,making 2019 to be the worst year of O_(3)pollution since *** then,the favorable effects of meteorology on O_(3)pollution tended to be *** was the most important factor affecting O_(3)level,followed by air humidity in O_(3)pollution *** the midday of summer days,O_(3)pollution frequently exceeded the standard level (>160μg/m^(3)) at a combined condition with relative humidity in 40%-50%and temperature>31℃.Both the temperature and the dryness of the atmosphere need to be subtly considered for summer O_(3)forecasting.

关键词： Ozone Meteorological conditions Time series decomposition Random forest Meteorological adjustment

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Quantifying the impacts of emissions and meteorology on the interannual variations of air pollutants in major Chinese cities from 2015 to 2021

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Science China Earth Sciences 2023年第8期66卷 1725-1737页

作者： Qili DAI Tianjiao DAI Linlu HOU Linxuan LI Xiaohui BI Yufen ZHANG yinchang feng State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and EngineeringNankai UniversityTianjin 300350China China Meteorological Administration-Nankai University Cooperative Laboratory for Atmospheric Environment-Health Research Tianjin 300350China

Air pollutant concentration is a function of emission rates and *** accurately evaluate the effect of control measures,the meteorological covariate must be corrected from the *** study quantified the impacts of emission abatement and meteorological condition on the interannual variations of SO_(2),NO_(2),CO,O_(3),PM_(10) and PM_(2.5) concentrations in 31 major Chinese cities using an optimized machine learning-based meteorological normalization ***,the annual average concentrations of SO_(2),NO_(2),CO,PM_(10)and PM_(2.5)were reduced by 86%,51%,99%,86% and 88%from 2015 to 2020,respectively,in the studied cities,attributable to their emission ***,the concentration of O_(3) was found with no significant decrease with the reduction of *** abatement notably improved air quality between 2015 and *** a decline in emissions tended to progressively slow down since ***,the meteorological conditions in 2016–2017 and 2018–2019 were unfavorable for a better air quality,while it became favorable in 2020–***,emission abatement in 2021 further lowered the concentrations of SO_(2),NO_(2),CO,and PM_(2.5),while the emission of PM_(10) *** changes in precursors emissions worsened O_(3) air *** meet the demand of improving air quality,more aggressive abatement measures need to be formulated to synergistically reduce NOx,volatile organic compounds,and coarse particles.

关键词： Air pollution Air quality Machine learning Meteorology Emissions Policy evaluation

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Secondary organic carbon quantification and source apportionment of PM_(10) in Kaifeng, China

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Journal of Environmental Sciences 2009年第10期21卷 1353-1362页

作者： WU Lin feng yinchang WU Jianhui ZHU Tan BI Xiaohui HAN Bo YANG Weihong YANG Zhiqiang State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and Engineering Nankai University Tianjin 300071 China Kaifeng Environmental Monitoring Center Kaifeng 475000 China.

During 2005, the filter samples of ambient PM10 from five sites and the source samples of particulate matter were collected in Kaifeng, Henan Province of China. Nineteen elements, water-soluble ions, total carbon （TC） and organic carbon （OC） contained in samples were analyzed. Seven contributive source types were identified and their contributions to ambient PM10 were estimated by chemical mass balance （CMB） receptor model. Weak associations between the concentrations of organic carbon and element carbon （EC） were observed during the sampling periods, indicating that there was secondary organic aerosol pollution in the urban atmosphere. An indirect method of ＂OC/EC minimum ratio＂ was applied to estimate the concentration of secondary organic carbon （SOC）. The results showed that SOC contributed 26.2%, 32.4% and 18.0% of TC in spring, summer-fall and winter, respectively, and the annual average SOC concentration was 7.07 μg/m^3, accounting for 5.73% of the total mass in ambient PM10. The carbon species concentrations in ambient PM10 were recalculated by subtracting SOC concentrations from measured concentrations of TC and OC to increase the compatibility of source and receptor measurements for CMB model.

关键词： PM10 source apportionment secondary organic carbon chemical mass balance

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