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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 2019.Particularly,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 needed.Deteriorating meteorological condition during the COVID-19 lockdown obscured the actual PM2.5 reduction.Fireworks transport in 2020 Spring Festival(SF)triggered regional haze pollution.PM2.5 during the COVID-19 lockdown only reduced by 5.6%in Tianjin.Here 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 lockdown.In terms of PM2.5 chemical composition,significant NO_(3)−increase was observed during the COVID-19 lockdown.However,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 enhancement.Nitrogen 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 observed.Anomalously 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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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 China.This 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 stages.The 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 PM10.The 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 loading.OC,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 components.These fivemain components exhibited a bell-shaped size distribution in the 0.006–9.890μm range,whereas the other components exhibited a U-shaped distribution.Among 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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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 health.In 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,China.The collected samples were analyzed for the carbonaceous fractions,inorganic ions,elemental species,and specific marker sugar species.The 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 average.The 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 incineration.The biomass burning and primary biogenic contributions were determined by specific marker sugar species.The 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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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 attention.For many cities,it is still under debate whether the trend of surface O_(3)variation is driven by meteorology or the change in precursors emissions.In 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 China.After “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 emissions.The meteorology was unfavorable for O_(3)pollution in period of 2015-2016,and turned out to be favorable during 2017-2021.Specifically,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 2015.Since then,the favorable effects of meteorology on O_(3)pollution tended to be weaker.Temperature was the most important factor affecting O_(3)level,followed by air humidity in O_(3)pollution season.In 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 meteorology.To accurately evaluate the effect of control measures,the meteorological covariate must be corrected from the observations.This 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 technique.Overall,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 reductions.However,the concentration of O_(3) was found with no significant decrease with the reduction of precursors.Emission abatement notably improved air quality between 2015 and 2018.Such a decline in emissions tended to progressively slow down since 2018.Overall,the meteorological conditions in 2016–2017 and 2018–2019 were unfavorable for a better air quality,while it became favorable in 2020–2021.Specifically,emission abatement in 2021 further lowered the concentrations of SO_(2),NO_(2),CO,and PM_(2.5),while the emission of PM_(10) increased.And changes in precursors emissions worsened O_(3) air quality.To 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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Machine learning and theoretical analysis release the non-linear relationship among ozone,secondary organic aerosol and volatile organic compounds

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Journal of Environmental Sciences 2022年第4期34卷 75-84页

作者： feng Wang Zhongcheng Zhang Gen Wang Zhenyu Wang Mei Li Weiqing Liang Jie Gao Wei Wang Da Chen 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 State Key Laboratory on Odor Pollution Control Tianjin Academy of Environmental SciencesTianjin 300191China Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution Jinan University Institute of Mass Spectrometry and Atmospheric EnvironmentGuangzhou 510632China Guangdong-Hongkong-Macao Joint Laboratory of Collaborative Innovation for Environmental Quality Guangzhou 510632China Trusted AI System Laboratory College of Computer ScienceNankai UniversityTianjin 300350China Key Laboratory of Civil Aviation Thermal Hazards Prevention and Emergency Response Civil Aviation University of ChinaTianjin 300300China

Fine particulate matter(PM_(2.5))and ozone(O_(3))pollutions are prevalent air quality issues in China.Volatile organic compounds(VOCs)have significant impact on the formation of O_(3)and secondary organic aerosols(SOA)contributing PM_(2.5).Herein,we investigated 54 VOCs,O_(3)and SOA in Tianjin from June 2017 to May 2019 to explore the non-linear relationship among O_(3),SOA and VOCs.The monthly patterns of VOCs and SOA concentrations were characterized by peak values during October to March and reached a minimum from April to September,but the observed O_(3)was exactly the opposite.Machine learning methods resolved the importance of individual VOCs on O_(3)and SOA that alkenes(mainly ethylene,propylene,and isoprene)have the highest importance to O_(3)formation;alkanes(C_(n),n≥6)and aromatics were the main source of SOA formation.Machine learning methods revealed and emphasized the importance of photochemical consumptions of VOCs to O_(3)and SOA formation.Ozone formation potential(OFP)and secondary organic aerosol formation potential(SOAFP)calculated by consumed VOCs quantitatively indicated that more than 80%of the consumed VOCs were alkenes which dominated the O_(3)formation,and the importance of consumed aromatics and alkenes to SOAFP were 40.84%and 56.65%,respectively.Therein,isoprene contributed the most to OFP at 41.45%regardless of the season,while aromatics(58.27%)contributed the most to SOAFP in winter.Collectively,our findings can provide scientific evidence on policymaking for VOCs controls on seasonal scales to achieve effective reduction in both SOA and O_(3).

关键词： VOCs Machine learning Photochemical consumption Ozone formation potential Secondary organic aerosol formation potential

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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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Insight into the critical factors determining the particle number concentrations during summer at a megacity in China

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Journal of Environmental Sciences 2019年第1期31卷 169-180页

作者： Baoshuang Liu Xiaohui Bi Jiaying Zhang Jie Yuan Zhimei Xiao Qili Dai Yinchang Feng Yufen Zhang State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research College of Environmental Science and EngineeringNankai University Tianjin Environmental Monitoring Center

To identify the critical factors impacting the number concentration of particles with the aerodynamic diameters less than 2.5 μm(PNC_(2.5)), the continuous measurement of PNC_(2.5),chemical components in PM_(2.5), gaseous pollutants and meteorological conditions were conducted at an urban site in Tianjin in June 2015. Results indicated that the average PNC_(2.5) was 2839 ± 2430 d N/dlog Dp 1/cm^3 during the campaign. Compared to other meteorological parameters, the relative humidity(RH) had the strongest relationship with PNC_(2.5), with a Pearson's correlation coefficient of 0.53, and RH larger than 30% influenced strongly PNC_(2.5).The important influence of secondary reactions on PNC_(2.5) was inferred due to higher correlation coefficients between PNC_(2.5) and SO_4^(2-), NO_3^-, NH_4^+(r = 0.78–0.89; p < 0.01) and between PNC_(2.5) and ratios that represent the conversion of nitrogen and sulfur oxides to particulate matter(r = 0.42–0.49; p < 0.01). Under specific RH conditions, there were even stronger correlations between PNC_(2.5) and NO_3^-, SO_4^(2-), NH_4^+, while those between PNC_(2.5) and EC, OC were relatively weak, especially when RH exceeded 50%. Principal component analysis(PCA) and Pearson's correlation analysis indicated that secondary sources, vehicle emission and coal combustion might be major contributors to PNC_(2.5). Backward trajectory and potential source contribution function(PSCF) analysis suggested that the transport of air masses originated from these regions around Tianjin(Liaoning, Hebei, Shandong and Jiangsu) influenced critically PNC_(2.5). The north of Jiangsu, the west of Shandong, and the east of Hebei were distinguished as major potential source-areas of PNC_(2.5) by PSCF model.

关键词： Particle number concentration Correlation analysis Emission sources PSCF

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

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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 world.As 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 periods.Results show that aerosol liquid water content has a more significant effect on ammonium nitrate formation regardless of the severity of pollution.The dust source is dominant emission source in clean periods;while a combination of coal combustion and vehicle exhaust sources is more important in haze periods.And the control of ammonia emission is more effective in reducing the formation of ammonium nitrate.The 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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Vertical characteristics and source identification of PM_(10) in Tianjin

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Journal of Environmental Sciences 2012年第1期24卷 112-115页

作者： Zhimei Xiao Jianhui Wu Suqin Han Yufen Zhang Hong Xu Xiaoyong Zhang Guoliang Shi Yinchang Feng 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 Tianjin Meteorological Administration Tianjin 300071 China

Ambient PM 10 （particulate matter with a diameter less than 10μm） concentrations were measured on a 255 meter tower in Tianjin,China.The samples were collected at four vertical levels （10,40,120 and 220 m）.Vertical characteristics for PM 10 samples were studied.The results showed that the concentrations of PM 10 and constituent species had a negative correlation with the sampling height.The highest concentrations of PM 10 and species were obtained at the 10 m level,and the lowest concentrations were measured at the 220 m level.For the fractions of species to total mass,SO 4 2- and NO 3- had higher values （fraction） at greater height;while Ca had a higher fraction at lower height.Possible source categories for the PM 10 ambient dataset were identified by the principal component analysis method.The possible source categories included crustal dust,vehicles,cement dust,and incineration as well as secondary sulfate and nitrate sources.Analysis of meteorological factors on PM 10 concentrations indicated that wind speed and inversion may be the main factors contributing to different concentrations of PM 10 at different heights.

关键词： vertical characteristic PM 10 sources principal component analysis

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