Annual haze in Northern Thailand has become increasingly severe,impacting health and the environment.How-ever,the sources of the haze remain poorly quantified due to limited observational data on aerosol molecular tra...Annual haze in Northern Thailand has become increasingly severe,impacting health and the environment.How-ever,the sources of the haze remain poorly quantified due to limited observational data on aerosol molecular tracers.This study comprehensively investigates chemical composition of PM_(2.5),including both inorganic and organic compounds throughout haze and post-haze periods in 2019 at a rural site of Northern Thailand.Average PM_(2.5) concentrations during haze and post-haze period were 87±36 and 21±11μg/m^(3),respectively.Organic matter was the dominant contributor in PM_(2.5) mass,followed by water soluble inorganic ions and mineral dust.Molecular markers,including levoglucosan,dehydroabietic acid,and 4-nitrocatechol,and ions(Cl^(-),and K^(+)),were used to characterize low haze(PM_(2.5)<100μg/m^(3))and episodic haze(PM_(2.5)>100μg/m^(3)).Low haze is associated with local aerosols from agricultural waste burning,while episodic haze is linked to aged aerosols from mixed agricultural waste,softwood,and hardwood burning.Source apportionment incorporating these molecular markers in receptor modelling(Positive matrix factorization),identified three distinct biomass burning sources:mixed,local,and aged biomass burnings,contributing 31,19 and 13%of PM_(2.5) during haze period.During post-haze period,contributions shifted,with local biomass burning(32%)comparable to secondary sulfate(34%)and mixed dust and traffic sources(26%).These findings demonstrate that both regional and local sources con-tribute to severe haze,highlighting the need for integrated policies for cross-border cooperation as well as stricter regulations to reduce biomass burning in Northern Thailand and Southeast Asia.展开更多
Oxidative potential(OP)can be used as an indicator of the health risks of particulate matter in the air.To study the variation and sources of OP,we conducted an observation of PM_(2.5) in a megacity in southern China ...Oxidative potential(OP)can be used as an indicator of the health risks of particulate matter in the air.To study the variation and sources of OP,we conducted an observation of PM_(2.5) in a megacity in southern China in winter and spring of 2021.The results show that the average concentration of PM_(2.5) decreased by 47%from winter to spring,while volume-normalized and mass-normalized OP(i.e.,OP_(v) and OP_(m))increased by 6%and 69%,respectively.It suggests that the decline of PM_(2.5) may not necessarily decrease the health risks and the intrinsic toxicity of PM_(2.5).Variations of OP_(v) and OP_(m) among different periods were related to the different source contributions and environmental conditions.The positive matrix factorization model was used to identify the major sources of OP_(v).OP_(v) was mainly contributed by biomass burning/industrial emissions(29%),soil/road dust(20%),secondary sulfate(14%),and coal combustion(13%)in winter.Different major sources were resolved to be secondary sulfate(36%),biological sources(21%),and marine vessels(20%)in spring,presenting the substantial contribution of biological sources.The analysis shows strong associations between OP_(v) and both live and dead bacteria,further confirming the important contribution of bioaerosols to the enhancement of OP.This study highlights the importance of understanding OP in ambient PM_(2.5) in terms of public health impact and provides a new insight into the biological contribution to OP.展开更多
The source apportionment of PM2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI)model to acquire ...The source apportionment of PM2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI)model to acquire more accurate results.First,the SEI of secondary component precursors(SO2,NOx,NH3,and VOCs)was compiled to acquire the emission ratios of these sources for the precursors.Then,a regular CMB simulation was executed to obtain the contributions of primary particle sources and secondary components(SO4^2-,NO3^-3,NH4^+,and SOC).Afterwards,the contributions of secondary components were apportioned into primary sources according to the source emission ratios.The final source apportionment results combined the contributions of primary sources by CMB and SEI.This integrated approach was carried out via a case study of three coastal cities(Zhoushan,Taizhou,and Wenzhou;abbreviated WZ,TZ,and ZS)in Zhejiang Province,China.The regular CMB simulation results showed that PM2.5 pollution was mainly affected by secondary components and mobile sources.The SEI results indicated that electricity,industrial production and mobile sources were the largest contributors to the emission of PM2.5 gaseous precursors.The simulation results of the CMB-SEI model showed that PM2.5 pollution in the coastal areas of Zhejiang Province presented complex pollution characteristics dominated by mobile sources,electricity production sources and industrial production sources.Compared to the results of the CMB and SEI models alone,the CMB-SEI model completely apportioned PM2.5 to primary sources and simultaneously made the results more accurate and reliable in accordance with local industrial characteristics.展开更多
A total of 11 PM2.5 samples were collected from October 2003 to October 2004 at 8 sampling sites in Beijing city. The PM2.5 concentrations are all above the PM2.5 pollution standard （65 μg m^-3） established by Envi...A total of 11 PM2.5 samples were collected from October 2003 to October 2004 at 8 sampling sites in Beijing city. The PM2.5 concentrations are all above the PM2.5 pollution standard （65 μg m^-3） established by Environmental Protection Agency, USA （USEPA） in 1997 except for the Ming Tombs site. PM2.5 concentrations in winter are much higher than in summer. The 16 Polycyclic aromatic hydrocarbons （PAHs） listed as priority pollutants by USEPA in PM2.5 were completely identified and quantified by high performance liquid chromatography （HPLC） with variable wavelength detector （VWD） and fluorescence detector （FLD） employed. The PM2.5 concentrations indicate that the pollution situation is still serious in Beijing. The sum of 16 PAHs concentrations ranged from 22.17 to 5366 ng m^-3. The concentrations of the heavier molecular weight PAHs have a different pollution trend from the lower PAHs. Seasonal variations were mainly attributed to the difference in coal combustion emission and meteorological conditions. The source apportionment analysis suggests that PAHs from PM2.5 in Beijing city mainly come from coal combustion and vehicle exhaust emission. New measures about restricting coal combustion and vehicle exhaust must be established as soon as possible to improve the air pollution situation in Beijing city.展开更多
Based on the chemical composition data of a regional long-lasting haze event that occurred in the Yangtze River Delta(YRD)region from 17 December 2023 to 8 January 2024,the evolutionary characteristics of the chemical...Based on the chemical composition data of a regional long-lasting haze event that occurred in the Yangtze River Delta(YRD)region from 17 December 2023 to 8 January 2024,the evolutionary characteristics of the chemical components and sources of fine particulate matter(PM2.5)under different pollution levels were comparatively analyzed using PMF(Positive Matrix Factorization)and backward trajectory analysis.SNA(NO_(3)^(-),NH_(4)^(+),SO_(4)^(2-))was found to be the primary chemical component of PM2.5,making up 63.6%(clean days)to 69.7%(heavy pollution)of it.The NO_(3)^(-)concentration was 3.14(clean days)to 6.01(heavy pollution)times higher than that of SO_(4)^(2-).NO_(3)^(-),POC,Fe,Mn,Al concentrations increased,while SOC,EC,crustal elements(Ca,Si)and other water-soluble ions(WSIs)concentrations decreased as the pollution level increased.The contribution of secondary inorganics and biomass-burning emissions and industrial and ship emissions increased significantly as the pollution level increased,which accounted for 40.3%and 36.7%,respectively,in the heavy pollution stage.The contribution of traffic sources decreases gradually with increasing pollution levels,accounting for only 59.1%of the light pollution stage in the heavy pollution stage.PM_(2.5) and its main chemical components showed similar potential source distribution,located in the northwest(Fuyang,Huainan,Nanjing),south(Taizhou,Lishui,Jiande)and north(Taizhou,Yancheng).However,distinct transport routes were observed under the different air quality levels.During the heavy pollution period,the polluted air masses primarily came from the harbor regions,whereas during the light pollution period they were transported from the southeast(Taizhou)and the North China Plain.展开更多
In this work, receptor models were used to identify the PM2.5 sources and its contribution to the air quality in residential, comercial and industrial sampling sites in the Metropolitan Area of Costa Rica. Principal c...In this work, receptor models were used to identify the PM2.5 sources and its contribution to the air quality in residential, comercial and industrial sampling sites in the Metropolitan Area of Costa Rica. Principal component analysis with absolute principal component scores (PCA-APCS), UNIMX and positive matrix factorization (PMF) was applied to analyze the data collected during 1 year of sampling campaign (2010-2011). The PM2.5 samples were characterized through its composition looking for trace elements, inorganic ions and organic and elemental carbon. These three models identified some common sources of PM2.5: marine aerosol, crustal material, traffic, secondary aerosols (secondary sulfate and secondary nitrate resolved by PMF), a mixed source of heavy fuels combustion and biomass burning, and industrial emissions. The three models predicted that the major sources of PM2.5 in the Metropolitan Area of Costa Rica were related to anthropogenic sources (73%, 65% and 69%, respectively, for PCA-APCS, Unmix and PMF) although natural sources also contributed to PM2.5 (21%, 24% and 26%). On average, PCA and PMF methods resolved 94% and 95% of the PM2.5 mass concentrations, respectively. The results were comparable to the estimate using UNMIX.展开更多
During 2001-2006,PM2.5 (particle matter with aerodynamic diameter less than 2.5 microns) and PM10 (particle matter with aerodynamic diameter less than 10 microns) were collected at the Beijng Normal University (BNU) s...During 2001-2006,PM2.5 (particle matter with aerodynamic diameter less than 2.5 microns) and PM10 (particle matter with aerodynamic diameter less than 10 microns) were collected at the Beijng Normal University (BNU) site,China,and in 2006,at a background site in Duolun (DL).The long-term monitoring data of elements,ions,and black carbon showed that the major constituents of PM2.5 were black carbon (BC) crustal elements,nitrates,ammonium salts,and sulfates.These five major components accounted for 20%-80% of...展开更多
Previous studies have reported associations of short-term exposure to different sources of ambient fine particulate matter(PM2.5)and increased mortality or hospitalizations for respiratory diseases.Few studies,however...Previous studies have reported associations of short-term exposure to different sources of ambient fine particulate matter(PM2.5)and increased mortality or hospitalizations for respiratory diseases.Few studies,however,have focused on the short-term effects of source-specific PM2.5 on emergency room visits(ERVs)of respiratory diseases.Source apportionment for PM2.5 was performed with Positive Matrix Factorization(PMF)and generalized additive model was applied to estimate associations between source-specific PM2.5 and respiratory disease ERVs.The association of PM2.5 and total respiratory ERVs was found on lag4(RR=1.011,95%CI:1.002,1.020)per interquartile range(76μg/m3)increase.We found PM2.5 to be significantly associated with asthma,bronchitis and chronic obstructive pulmonary disease(COPD)ERVs,with the strongest effects on lag5(RR=1.072,95%CI:1.024,1.119),lag4(RR=1.104,95%CI:1.032,1.176)and lag3(RR=1.091,95%CI:1.047,1.135),respectively.The estimated effects of PM2.5 changed little after adjusting for different air pollutants.Six primary PM2.5 sources were identified using PMF analysis,including dust/soil(6.7%),industry emission(4.5%),secondary aerosols(30.3%),metal processing(3.2%),coal combustion(37.5%)and traffic-related source(17.8%).Some of the sources were identified to have effects on ERVs of total respiratory diseases(dust/soil,secondary aerosols,metal processing,coal combustion and traffic-related source),bronchitis ERVs(dust/soil)and COPD ERVs(traffic-related source,industry emission and secondary aerosols).Different sources of PM2.5 contribute to increased risk of respiratory ERVs to different extents,which may provide potential implications for the decision making of air quality related policies,rational emission control and public health welfare.展开更多
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...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.展开更多
Atmospheric extinction is impacted by the chemical composition of particles.To better understand the chemical composition of PM_(2.5)(particles with diameters of less than 2.5μm)and its relationship with extinction,o...Atmospheric extinction is impacted by the chemical composition of particles.To better understand the chemical composition of PM_(2.5)(particles with diameters of less than 2.5μm)and its relationship with extinction,one-month sampling campaigns were carried out in four different seasons from 2013 to 2014 in Jinan,China.The seasonal average concentrations of PM_(2.5)were 120.9(autumn),156.6(winter),102.5(spring),and 111.8μg/m^(3)(summer).The reconstructed PM_(2.5)chemical composition showed that sulfate,nitrate,chlorine salt,organic matter(OM),mineral dust,elemental carbon(EC)and others accounted for 25%,14%,2%,24%,22%,3%and 10%,respectively.The relationship between the chemical composition of PM_(2.5)and visibility was reconstructed by the IMPROVE method,and ammonium sulfate,ammonium nitrate,OM and EC dominated the visibility.Seven main sources were resolved for PM_(2.5),including secondary particles,coal combustion,biomass burning,industry,motor vehicle exhaust,soil dust and cooking,which accounted for 37%,21%,13%,13%,12%,3%and 1%,respectively.The contributions of different sources to visibility were similar to those to PM_(2.5).With increasing severity of air pollution,the contributions of secondary particles and coal combustion increased,while the contribution of motor vehicle exhaust decreased.The results showed that coal combustion and biomass burning were still the main sources of air pollution in Jinan.展开更多
The increasing emission of primary and gaseous precursors of secondarily formed atmospheric particulate matter due to continuing industrial development and urbanization are leading to an increased public awareness of ...The increasing emission of primary and gaseous precursors of secondarily formed atmospheric particulate matter due to continuing industrial development and urbanization are leading to an increased public awareness of environmental issues and human health risks in China. As part of a pilot study, 12-h integrated fine fraction particulate matter （PM2.5） filter samples were collected to chemically characterize and investigate the sources of ambient particulate matter in Guiyang City, Guizhou Province, southwestern China. Results showed that the 12-h integrated PM2.5 concentrations exhibited a daytime average of 51 ± 22 μg m^-3 （mean -4- standard deviation） with a range of 17-128 μg m^-3 and a nighttime average of 55 ± 32 μg m^-3 with a range of 4-186 μg m^-3. The 24-h integrated PM2.5 concentrations varied from 15 to 157 μg m^-3, with amean value of 53 ± 25 μg m^-3, which exceeded the 24-h PM2.5 standard of 35μg m^-3 set by USEPA, but was below the standard of 75 μg m^-3, set by China Ministry of Environmental Protection. Energy-dispersive X-ray fluorescence spectrometry （XRF） was applied to determine PM2.5 chemical element concentrations. The order of concentrations of heavy metals in PM2.5 were iron （Fe） 〉 zinc （Zn） 〉 manganese （Mn） 〉 lead （Pb） 〉 arsenic （As）〉 chromium （Cr）. The total concentration of 18 chemical elements was 13 ± 2 μg m^-3, accounting for 25% in PM2.5, which is comparable to other major cities in China, but much higher than cities outside of China.展开更多
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...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.展开更多
Based on the online and membrane sampling data of Yuncheng from January 1st to February 12th,2020,the formation mechanism of haze under the dual influence of Spring Festival and COVID-19(Corona Virus Disease)was analy...Based on the online and membrane sampling data of Yuncheng from January 1st to February 12th,2020,the formation mechanism of haze under the dual influence of Spring Festival and COVID-19(Corona Virus Disease)was analyzed.Atmospheric capacity,chemical composition,secondary transformation,source apportionment,backward trajectory,pollution space and enterprise distribution were studied.Low wind speed,high humidity and small atmospheric capacity inhibited the diffusion of air pollutants.Four severe pollution processes occurred during the period,and the pollution degree was the highest around the Spring Festival.In light,medium and heavy pollution periods,the proportion of SNA(SO_(4)^(2-),NO_(3)-and NH_(4)^(+))was 59.6%,56.0%and 54.9%,respectively,which was the largest components of PM_(2.5);the[NO_(3)-]/[SO_(4)^(2-)]ratio was 2.1,1.5 and 1.7,respectively,indicating that coal source had a great influence;the changes of NOR(nitrogen oxidation ratio,0.44,0.45,0.61)and SOR(sulphur oxidation ratio,0.40,0.49,0.65)indicated the accumulation of secondary aerosols with increasing pollution.The coal combustion,motor vehicle,secondary inorganic sources and industrial sources contributed 36.8%,26.59%,11.84%and 8.02%to PM_(2.5)masses,respectively.Backward trajectory showed that the influence from the east was greater during the Spring Festival,and the pollutants from the eastern air mass were higher,which would aggravate the pollution.Meteorological and Spring Festival had a great impact on heavy pollution weather.Although some work could not operate due to the impact of the COVID-19 epidemic,the emission of pollutants did not reduce much.展开更多
The constrained weighted-non-negative matrix factorization(CW-NMF)hybrid receptor model was applied to study the influence of steelmaking activities on PM_(2.5)(particulate matter with equivalent aerodynamic diameter ...The constrained weighted-non-negative matrix factorization(CW-NMF)hybrid receptor model was applied to study the influence of steelmaking activities on PM_(2.5)(particulate matter with equivalent aerodynamic diameter less than 2.5μm)composition in Dunkerque,Northern France.Semi-diurnal PM_(2.5)samples were collected using a high volume sampler in winter 2010 and spring 2011 and were analyzed for trace metals,water-soluble ions,and total carbon using inductively coupled plasma–atomic emission spectrometry(ICP-AES),ICP-mass spectrometry(ICP-MS),ionic chromatography and micro elemental carbon analyzer.The elemental composition shows that NO_(3)^(-),SO_(4)^(2-),NH_4~+and total carbon are the main PM_(2.5)constituents.Trace metals data were interpreted using concentration roses and both influences of integrated steelworks and electric steel plant were evidenced.The distinction between the two sources is made possible by the use Zn/Fe and Zn/Mn diagnostic ratios.Moreover Rb/Cr,Pb/Cr and Cu/Cd combination ratio are proposed to distinguish the ISW-sintering stack from the ISW-fugitive emissions.The a priori knowledge on the influencing source was introduced in the CW-NMF to guide the calculation.Eleven source profiles with various contributions were identified:8 are characteristics of coastal urban background site profiles and 3 are related to the steelmaking activities.Between them,secondary nitrates,secondary sulfates and combustion profiles give the highest contributions and account for 93%of the PM_(2.5)concentration.The steelwork facilities contribute in about 2%of the total PM_(2.5)concentration and appear to be the main source of Cr,Cu,Fe,Mn,Zn.展开更多
Long-lasting expansion of haze pollution in China has already presented a stern challenge to regional joint prevention and control. There is an urgent need to enlarge and reconstruct the coverage of joint prevention a...Long-lasting expansion of haze pollution in China has already presented a stern challenge to regional joint prevention and control. There is an urgent need to enlarge and reconstruct the coverage of joint prevention and control of air pollution in key area. Air quality models can identify and quantify the regional contribution of haze pollution and its key components with the help of numerical simulation, but it is difficult to be applied to larger spatial scale due to the complexity of model parameters. The time series analysis can recognize the existence of spatial interaction of haze pollution between cities, but it has not yet been used to further identify the spatial sources of haze pollution in large scale. Using econometric framework of time series analysis, this paper developed a new approach to perform spatial source apportionment. We applied this approach to calculate the contribution from spatial sources of haze pollution in China, using the monitoring data of particulate matter(PM_(2.5)) across 161 Chinese cities. This approach overcame the limitation of numerical simulation that the model complexity increases at excess with the expansion of sample range, and could effectively deal with severe large-scale haze episodes.展开更多
Cross-boundary transport of air pollution is a difficult issue in pollution control for the North China Plain.In this study,an industrial district(Shahe City)with a large glass manufactur-ing sector was investigated t...Cross-boundary transport of air pollution is a difficult issue in pollution control for the North China Plain.In this study,an industrial district(Shahe City)with a large glass manufactur-ing sector was investigated to clarify the relative contribution of fine particulate matter(PM_(2.5))to the city's high levels of pollution.The Nest Air Quality Prediction Model System(NAQPMS),paired with Weather Research and Forecasting(WRF),was adopted and applied with a spatial resolution of 5 km.During the study period,the mean mass concentrations of PM_(2.5),SO_(2),and NO_(2)were observed to be 132.0,76.1,and 55.5μg/m^(3),respectively.The model reproduced the variations in pollutant concentrations in Shahe at an acceptable level.The simulation of online source-tagging revealed that pollutants emitted within a 50-km radius of downtown Shahe contributed 63.4%of the city's total PM_(2.5)concentration.This contribu-tion increased to 73.9±21.2%when unfavorable meteorological conditions(high relative hu-midity,weak wind,and low planetary boundary layer height)were present;such conditions are more frequently associated with severe pollution(PM_(2.5)≥250μg/m^(3)).The contribution from Shahe was 52.3±21.6%.The source apportionment results showed that industry(47%),transportation(10%),power(17%),and residential(26%)sectors were the most important sources of PM_(2.5)in Shahe.The glass factories(where chimney stack heights were normally<70 m)in Shahe contributed 32.1%of the total PM_(2.5)concentration in Shahe.With an in-crease in PM_(2.5)concentration,the emissions from glass factories accumulated vertically and narrowed horizontally.At times when pollution levels were severe,the horizontally influ-enced area mainly covered Shahe.Furthermore,sensitivity tests indicated that reducing emissions by 20%,40%,and 60% could lead to a decrease in themass concentration of PM_(2.5) of of 12.0%,23.8%,and 35.5%,respectively.展开更多
In this paper,using concentration data of PM2. 5in 2013 in China and referring to a lot of literature,we preliminary studied the pollution of fine particulate matter and summarized PM2. 5source apportionment in the ke...In this paper,using concentration data of PM2. 5in 2013 in China and referring to a lot of literature,we preliminary studied the pollution of fine particulate matter and summarized PM2. 5source apportionment in the key cities in China. Our results showed that PM2. 5showed significant spatial and temporal distribution; high surface concentrations of PM2. 5concentrated mainly in the North China Plain,the Sichuan Basin,Yangtze River Delta and other regions; the average annual concentration of PM2. 5was about 80μg / m3 in North China Plain; Seasonal changes in the concentration of PM2. 5was winter > spring > autumn > summer; fired sources,industrial sources,vehicle exhaust were the major sources of PM2. 5; motor vehicle exhaust mostly contributed 10%- 30% to PM2. 5. This review provides a fundamental understanding of PM2. 5source apportionment and serves as an important reference for future source apportionment studies to be widely conducted in China.展开更多
In order to identify the day and night pollution sources of PM10 in ambient air in Longyan City,the authors analyzed the elemental composition of respirable particulate matters in the day and night ambient air samples...In order to identify the day and night pollution sources of PM10 in ambient air in Longyan City,the authors analyzed the elemental composition of respirable particulate matters in the day and night ambient air samples and various pollution sources which were collected in January 2010 in Longyan with inductivity coupled plasma-mass spectrometry（ICP-MS）.Then chemical mass balance（CMB） model and factor analysis（FA） method were applied to comparatively study the inorganic components in the sources and receptor samples.The results of factor analysis show that the major sources were road dust,waste incineration and mixed sources which contained automobile exhaust,soil dust/secondary dust and coal dust during the daytime in Longyan City,China.There are two major sources of pollution which are soil dust and mixture sources of automobile exhaust and secondary dust during the night in Longyan.The results of CMB show that the major sources are secondary dust,automobile exhaust and road dust during the daytime in Longyan.The major sources are secondary dust,soil dust and automobile exhaust during the night in Longyan.The results of the two methods are similar to each other and the results will guide us to plan to control the PM10 pollution sources in Longyan.展开更多
In this paper, the spatial, temporal distribution, transformation and source simulation of NO3- were analyzed systematically based on the monitoring data, literature review and numerical simulation （ CMAQ4.7.1 ）. An...In this paper, the spatial, temporal distribution, transformation and source simulation of NO3- were analyzed systematically based on the monitoring data, literature review and numerical simulation （ CMAQ4.7.1 ）. Analysis results showed that annual average concentration of NO3- in Beijing was between 6.69 and 12.48 μg/m3 with an increasing trend in recent years; concentration of NO3- in Beijing in 2013 was higher in winter and autumn than that in spring and summer and diurnal variation of NO3- showed bimedal distribution and spatial distribution of NO3- showed significant north-south gradient distribution; annual average NOR in Beijing was between 0.12 and 0.17 while it was between 0.17 and 0.20 during heavy air pollution days in 2013; the average ratio of NO3-/SO42- was between 0.97 and 1.06 while it was between 1.00 and 1.07 during heavy air pollution days in 2013; the emission sources of Beijing was being changed from fixed source to both fixed and moving sources in feature development; simulated local, external transportation, background and boundary condition were 40%, 44% and 16% respectively to the annual average concentration of NO3- in Beijing in 2013 while they were 31%, 57% and 12% respectively in heavy air pollution days, which indicated that extemal source played an important role to the concentration of NO3- in Beijing. Key words NO3- ; Spatial and temporal distribution; Source; PM2.5; Beijing; CAMx展开更多
Water-soluble organic nitrogen(WSON)affects the formation,hygroscopicity,acidity of organic aerosols,and nitrogen biogeochemical cycles.However,qualitative and quantitative characterizations of WSON remain limited due...Water-soluble organic nitrogen(WSON)affects the formation,hygroscopicity,acidity of organic aerosols,and nitrogen biogeochemical cycles.However,qualitative and quantitative characterizations of WSON remain limited due to its chemical complexity.In the study,1-year field samples of particulate matter 2.5(PM_(2.5))were collected fromJune 2022 to May 2023 to analyze the WSON concentration in PM_(2.5),and correlation analysis,positive matrix factor(PMF),and potential source contribution function(PSCF)modelswere employed to elucidate WSON source apportionment and transport pathways.The results revealed that the mean WSON concentrations reached 1.98±2.64μg/m^(3) with a mean WSON to water-soluble total nitrogen(WSTN)ratio of 21%.Further,WSON concentration exhibited a seasonal variation trend,with higher values in winter and lower in summer.Five sources were identified as contributors to WSON in PM_(2.5) within the reservoir area through a comprehensive analysis including correlation analysis,PSCF and concentration weighted trajectory(CWT),and PMF analyses.These sources were agricultural,dust,combustion,traffic,and industrial sources,of which agricultural source emerged as the primary contributor(76.69%).The atmosphere in the reservoir area were primarily influenced by the transport of northeastern air masses,local agricultural activities,industrial cities along the trajectory,and coastal regions,exerting significant influences on the concentration of WSON in the reservoir area.The findings of this study addressed the research gap concerning organic nitrogen in PM_(2.5) within the reservoir area,thereby offering a theoretical foundation and data support in controlling nitrogen pollution in the Danjiangkou Reservoir area.展开更多
文摘Annual haze in Northern Thailand has become increasingly severe,impacting health and the environment.How-ever,the sources of the haze remain poorly quantified due to limited observational data on aerosol molecular tracers.This study comprehensively investigates chemical composition of PM_(2.5),including both inorganic and organic compounds throughout haze and post-haze periods in 2019 at a rural site of Northern Thailand.Average PM_(2.5) concentrations during haze and post-haze period were 87±36 and 21±11μg/m^(3),respectively.Organic matter was the dominant contributor in PM_(2.5) mass,followed by water soluble inorganic ions and mineral dust.Molecular markers,including levoglucosan,dehydroabietic acid,and 4-nitrocatechol,and ions(Cl^(-),and K^(+)),were used to characterize low haze(PM_(2.5)<100μg/m^(3))and episodic haze(PM_(2.5)>100μg/m^(3)).Low haze is associated with local aerosols from agricultural waste burning,while episodic haze is linked to aged aerosols from mixed agricultural waste,softwood,and hardwood burning.Source apportionment incorporating these molecular markers in receptor modelling(Positive matrix factorization),identified three distinct biomass burning sources:mixed,local,and aged biomass burnings,contributing 31,19 and 13%of PM_(2.5) during haze period.During post-haze period,contributions shifted,with local biomass burning(32%)comparable to secondary sulfate(34%)and mixed dust and traffic sources(26%).These findings demonstrate that both regional and local sources con-tribute to severe haze,highlighting the need for integrated policies for cross-border cooperation as well as stricter regulations to reduce biomass burning in Northern Thailand and Southeast Asia.
基金supported by the National Natural Science Foundation of China(No.41975156)and the Fundamental Research Funds for the Central Universities.
文摘Oxidative potential(OP)can be used as an indicator of the health risks of particulate matter in the air.To study the variation and sources of OP,we conducted an observation of PM_(2.5) in a megacity in southern China in winter and spring of 2021.The results show that the average concentration of PM_(2.5) decreased by 47%from winter to spring,while volume-normalized and mass-normalized OP(i.e.,OP_(v) and OP_(m))increased by 6%and 69%,respectively.It suggests that the decline of PM_(2.5) may not necessarily decrease the health risks and the intrinsic toxicity of PM_(2.5).Variations of OP_(v) and OP_(m) among different periods were related to the different source contributions and environmental conditions.The positive matrix factorization model was used to identify the major sources of OP_(v).OP_(v) was mainly contributed by biomass burning/industrial emissions(29%),soil/road dust(20%),secondary sulfate(14%),and coal combustion(13%)in winter.Different major sources were resolved to be secondary sulfate(36%),biological sources(21%),and marine vessels(20%)in spring,presenting the substantial contribution of biological sources.The analysis shows strong associations between OP_(v) and both live and dead bacteria,further confirming the important contribution of bioaerosols to the enhancement of OP.This study highlights the importance of understanding OP in ambient PM_(2.5) in terms of public health impact and provides a new insight into the biological contribution to OP.
基金supported by the National Key Research and Development Program of China(No.2018YFC0214102)。
文摘The source apportionment of PM2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI)model to acquire more accurate results.First,the SEI of secondary component precursors(SO2,NOx,NH3,and VOCs)was compiled to acquire the emission ratios of these sources for the precursors.Then,a regular CMB simulation was executed to obtain the contributions of primary particle sources and secondary components(SO4^2-,NO3^-3,NH4^+,and SOC).Afterwards,the contributions of secondary components were apportioned into primary sources according to the source emission ratios.The final source apportionment results combined the contributions of primary sources by CMB and SEI.This integrated approach was carried out via a case study of three coastal cities(Zhoushan,Taizhou,and Wenzhou;abbreviated WZ,TZ,and ZS)in Zhejiang Province,China.The regular CMB simulation results showed that PM2.5 pollution was mainly affected by secondary components and mobile sources.The SEI results indicated that electricity,industrial production and mobile sources were the largest contributors to the emission of PM2.5 gaseous precursors.The simulation results of the CMB-SEI model showed that PM2.5 pollution in the coastal areas of Zhejiang Province presented complex pollution characteristics dominated by mobile sources,electricity production sources and industrial production sources.Compared to the results of the CMB and SEI models alone,the CMB-SEI model completely apportioned PM2.5 to primary sources and simultaneously made the results more accurate and reliable in accordance with local industrial characteristics.
基金Financial support from the National Natural Science Foundation of China （Grant No. 40475049） the Natural Sciences Foundation of Beijing city （Grant No. 8032012） are acknowledged.
文摘A total of 11 PM2.5 samples were collected from October 2003 to October 2004 at 8 sampling sites in Beijing city. The PM2.5 concentrations are all above the PM2.5 pollution standard （65 μg m^-3） established by Environmental Protection Agency, USA （USEPA） in 1997 except for the Ming Tombs site. PM2.5 concentrations in winter are much higher than in summer. The 16 Polycyclic aromatic hydrocarbons （PAHs） listed as priority pollutants by USEPA in PM2.5 were completely identified and quantified by high performance liquid chromatography （HPLC） with variable wavelength detector （VWD） and fluorescence detector （FLD） employed. The PM2.5 concentrations indicate that the pollution situation is still serious in Beijing. The sum of 16 PAHs concentrations ranged from 22.17 to 5366 ng m^-3. The concentrations of the heavier molecular weight PAHs have a different pollution trend from the lower PAHs. Seasonal variations were mainly attributed to the difference in coal combustion emission and meteorological conditions. The source apportionment analysis suggests that PAHs from PM2.5 in Beijing city mainly come from coal combustion and vehicle exhaust emission. New measures about restricting coal combustion and vehicle exhaust must be established as soon as possible to improve the air pollution situation in Beijing city.
基金supported by the National Key Research and Development Program of China(No.2022YFC3701204)the Natural Science Foundation of Jiangsu Province(No.BK20231300).
文摘Based on the chemical composition data of a regional long-lasting haze event that occurred in the Yangtze River Delta(YRD)region from 17 December 2023 to 8 January 2024,the evolutionary characteristics of the chemical components and sources of fine particulate matter(PM2.5)under different pollution levels were comparatively analyzed using PMF(Positive Matrix Factorization)and backward trajectory analysis.SNA(NO_(3)^(-),NH_(4)^(+),SO_(4)^(2-))was found to be the primary chemical component of PM2.5,making up 63.6%(clean days)to 69.7%(heavy pollution)of it.The NO_(3)^(-)concentration was 3.14(clean days)to 6.01(heavy pollution)times higher than that of SO_(4)^(2-).NO_(3)^(-),POC,Fe,Mn,Al concentrations increased,while SOC,EC,crustal elements(Ca,Si)and other water-soluble ions(WSIs)concentrations decreased as the pollution level increased.The contribution of secondary inorganics and biomass-burning emissions and industrial and ship emissions increased significantly as the pollution level increased,which accounted for 40.3%and 36.7%,respectively,in the heavy pollution stage.The contribution of traffic sources decreases gradually with increasing pollution levels,accounting for only 59.1%of the light pollution stage in the heavy pollution stage.PM_(2.5) and its main chemical components showed similar potential source distribution,located in the northwest(Fuyang,Huainan,Nanjing),south(Taizhou,Lishui,Jiande)and north(Taizhou,Yancheng).However,distinct transport routes were observed under the different air quality levels.During the heavy pollution period,the polluted air masses primarily came from the harbor regions,whereas during the light pollution period they were transported from the southeast(Taizhou)and the North China Plain.
文摘In this work, receptor models were used to identify the PM2.5 sources and its contribution to the air quality in residential, comercial and industrial sampling sites in the Metropolitan Area of Costa Rica. Principal component analysis with absolute principal component scores (PCA-APCS), UNIMX and positive matrix factorization (PMF) was applied to analyze the data collected during 1 year of sampling campaign (2010-2011). The PM2.5 samples were characterized through its composition looking for trace elements, inorganic ions and organic and elemental carbon. These three models identified some common sources of PM2.5: marine aerosol, crustal material, traffic, secondary aerosols (secondary sulfate and secondary nitrate resolved by PMF), a mixed source of heavy fuels combustion and biomass burning, and industrial emissions. The three models predicted that the major sources of PM2.5 in the Metropolitan Area of Costa Rica were related to anthropogenic sources (73%, 65% and 69%, respectively, for PCA-APCS, Unmix and PMF) although natural sources also contributed to PM2.5 (21%, 24% and 26%). On average, PCA and PMF methods resolved 94% and 95% of the PM2.5 mass concentrations, respectively. The results were comparable to the estimate using UNMIX.
基金the National Science Fund for Distinguished Young Scholars (No.20725723)
文摘During 2001-2006,PM2.5 (particle matter with aerodynamic diameter less than 2.5 microns) and PM10 (particle matter with aerodynamic diameter less than 10 microns) were collected at the Beijng Normal University (BNU) site,China,and in 2006,at a background site in Duolun (DL).The long-term monitoring data of elements,ions,and black carbon showed that the major constituents of PM2.5 were black carbon (BC) crustal elements,nitrates,ammonium salts,and sulfates.These five major components accounted for 20%-80% of...
基金supported by the National Natural Science Foundation of China (Nos. 81571130090, 91543112)the National Key Research and Development Program of China (Nos. 2016YFC0206506, 2017YFC0702700)+2 种基金the Ministry of Ecology and Environment: the research of national-level ecological and environmental planning (No. 14430019)the Peking University Health Science Center (No. BMU20160549)the National Young Thousand Talents Program of China
文摘Previous studies have reported associations of short-term exposure to different sources of ambient fine particulate matter(PM2.5)and increased mortality or hospitalizations for respiratory diseases.Few studies,however,have focused on the short-term effects of source-specific PM2.5 on emergency room visits(ERVs)of respiratory diseases.Source apportionment for PM2.5 was performed with Positive Matrix Factorization(PMF)and generalized additive model was applied to estimate associations between source-specific PM2.5 and respiratory disease ERVs.The association of PM2.5 and total respiratory ERVs was found on lag4(RR=1.011,95%CI:1.002,1.020)per interquartile range(76μg/m3)increase.We found PM2.5 to be significantly associated with asthma,bronchitis and chronic obstructive pulmonary disease(COPD)ERVs,with the strongest effects on lag5(RR=1.072,95%CI:1.024,1.119),lag4(RR=1.104,95%CI:1.032,1.176)and lag3(RR=1.091,95%CI:1.047,1.135),respectively.The estimated effects of PM2.5 changed little after adjusting for different air pollutants.Six primary PM2.5 sources were identified using PMF analysis,including dust/soil(6.7%),industry emission(4.5%),secondary aerosols(30.3%),metal processing(3.2%),coal combustion(37.5%)and traffic-related source(17.8%).Some of the sources were identified to have effects on ERVs of total respiratory diseases(dust/soil,secondary aerosols,metal processing,coal combustion and traffic-related source),bronchitis ERVs(dust/soil)and COPD ERVs(traffic-related source,industry emission and secondary aerosols).Different sources of PM2.5 contribute to increased risk of respiratory ERVs to different extents,which may provide potential implications for the decision making of air quality related policies,rational emission control and public health welfare.
基金supported by the National Technology Supporting, Kaifeng Environmental Protec-tion Bureau, Henan Province, China
文摘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.
基金supported by the National Key R&D Program of China(No.2017YFC0210000)the National Natural Science Foundation of China(Nos.41705113 and 41877312)+1 种基金the Young Talent Project of the Center for Excellence in Regional Atmospheric Environment CAS(CERAE201802)the Beijing Major Science and Technology Project(No.Z181100005418014)。
文摘Atmospheric extinction is impacted by the chemical composition of particles.To better understand the chemical composition of PM_(2.5)(particles with diameters of less than 2.5μm)and its relationship with extinction,one-month sampling campaigns were carried out in four different seasons from 2013 to 2014 in Jinan,China.The seasonal average concentrations of PM_(2.5)were 120.9(autumn),156.6(winter),102.5(spring),and 111.8μg/m^(3)(summer).The reconstructed PM_(2.5)chemical composition showed that sulfate,nitrate,chlorine salt,organic matter(OM),mineral dust,elemental carbon(EC)and others accounted for 25%,14%,2%,24%,22%,3%and 10%,respectively.The relationship between the chemical composition of PM_(2.5)and visibility was reconstructed by the IMPROVE method,and ammonium sulfate,ammonium nitrate,OM and EC dominated the visibility.Seven main sources were resolved for PM_(2.5),including secondary particles,coal combustion,biomass burning,industry,motor vehicle exhaust,soil dust and cooking,which accounted for 37%,21%,13%,13%,12%,3%and 1%,respectively.The contributions of different sources to visibility were similar to those to PM_(2.5).With increasing severity of air pollution,the contributions of secondary particles and coal combustion increased,while the contribution of motor vehicle exhaust decreased.The results showed that coal combustion and biomass burning were still the main sources of air pollution in Jinan.
基金The U.S. Environmental Protection Agency (EPA), through its Office of Research and Development, partially funded and participated in the research described here through cooperative agreement CR-833232-01 through the U.S. National Science Foundation-National Research Council Research Associateship Awardfunded by the National Key Basic Research Program of China (2013CB430004)the National Natural Science Foundation of China (No. 40773067)
文摘The increasing emission of primary and gaseous precursors of secondarily formed atmospheric particulate matter due to continuing industrial development and urbanization are leading to an increased public awareness of environmental issues and human health risks in China. As part of a pilot study, 12-h integrated fine fraction particulate matter （PM2.5） filter samples were collected to chemically characterize and investigate the sources of ambient particulate matter in Guiyang City, Guizhou Province, southwestern China. Results showed that the 12-h integrated PM2.5 concentrations exhibited a daytime average of 51 ± 22 μg m^-3 （mean -4- standard deviation） with a range of 17-128 μg m^-3 and a nighttime average of 55 ± 32 μg m^-3 with a range of 4-186 μg m^-3. The 24-h integrated PM2.5 concentrations varied from 15 to 157 μg m^-3, with amean value of 53 ± 25 μg m^-3, which exceeded the 24-h PM2.5 standard of 35μg m^-3 set by USEPA, but was below the standard of 75 μg m^-3, set by China Ministry of Environmental Protection. Energy-dispersive X-ray fluorescence spectrometry （XRF） was applied to determine PM2.5 chemical element concentrations. The order of concentrations of heavy metals in PM2.5 were iron （Fe） 〉 zinc （Zn） 〉 manganese （Mn） 〉 lead （Pb） 〉 arsenic （As）〉 chromium （Cr）. The total concentration of 18 chemical elements was 13 ± 2 μg m^-3, accounting for 25% in PM2.5, which is comparable to other major cities in China, but much higher than cities outside of China.
基金the Tianjin Science and Technology Program(No.18ZXSZSF00160)the Fundamental Research Funds for the Central Universities of China(Nos.ZB19500210 and ZB19000804)。
文摘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.
基金supported by the Ministry of Science and Technology of China(Nos.2016YFC0202000,2019YFC0214203)。
文摘Based on the online and membrane sampling data of Yuncheng from January 1st to February 12th,2020,the formation mechanism of haze under the dual influence of Spring Festival and COVID-19(Corona Virus Disease)was analyzed.Atmospheric capacity,chemical composition,secondary transformation,source apportionment,backward trajectory,pollution space and enterprise distribution were studied.Low wind speed,high humidity and small atmospheric capacity inhibited the diffusion of air pollutants.Four severe pollution processes occurred during the period,and the pollution degree was the highest around the Spring Festival.In light,medium and heavy pollution periods,the proportion of SNA(SO_(4)^(2-),NO_(3)-and NH_(4)^(+))was 59.6%,56.0%and 54.9%,respectively,which was the largest components of PM_(2.5);the[NO_(3)-]/[SO_(4)^(2-)]ratio was 2.1,1.5 and 1.7,respectively,indicating that coal source had a great influence;the changes of NOR(nitrogen oxidation ratio,0.44,0.45,0.61)and SOR(sulphur oxidation ratio,0.40,0.49,0.65)indicated the accumulation of secondary aerosols with increasing pollution.The coal combustion,motor vehicle,secondary inorganic sources and industrial sources contributed 36.8%,26.59%,11.84%and 8.02%to PM_(2.5)masses,respectively.Backward trajectory showed that the influence from the east was greater during the Spring Festival,and the pollutants from the eastern air mass were higher,which would aggravate the pollution.Meteorological and Spring Festival had a great impact on heavy pollution weather.Although some work could not operate due to the impact of the COVID-19 epidemic,the emission of pollutants did not reduce much.
基金financially supported by the Nord-Pas-de-Calais Region Councilthe Ministry of Higher Education and Research+1 种基金the European Regional Development FundsAdib Kfoury acknowledges the“Pole Metropolitain Cote d'Opale”(PMCO)for its PhD financial support
文摘The constrained weighted-non-negative matrix factorization(CW-NMF)hybrid receptor model was applied to study the influence of steelmaking activities on PM_(2.5)(particulate matter with equivalent aerodynamic diameter less than 2.5μm)composition in Dunkerque,Northern France.Semi-diurnal PM_(2.5)samples were collected using a high volume sampler in winter 2010 and spring 2011 and were analyzed for trace metals,water-soluble ions,and total carbon using inductively coupled plasma–atomic emission spectrometry(ICP-AES),ICP-mass spectrometry(ICP-MS),ionic chromatography and micro elemental carbon analyzer.The elemental composition shows that NO_(3)^(-),SO_(4)^(2-),NH_4~+and total carbon are the main PM_(2.5)constituents.Trace metals data were interpreted using concentration roses and both influences of integrated steelworks and electric steel plant were evidenced.The distinction between the two sources is made possible by the use Zn/Fe and Zn/Mn diagnostic ratios.Moreover Rb/Cr,Pb/Cr and Cu/Cd combination ratio are proposed to distinguish the ISW-sintering stack from the ISW-fugitive emissions.The a priori knowledge on the influencing source was introduced in the CW-NMF to guide the calculation.Eleven source profiles with various contributions were identified:8 are characteristics of coastal urban background site profiles and 3 are related to the steelmaking activities.Between them,secondary nitrates,secondary sulfates and combustion profiles give the highest contributions and account for 93%of the PM_(2.5)concentration.The steelwork facilities contribute in about 2%of the total PM_(2.5)concentration and appear to be the main source of Cr,Cu,Fe,Mn,Zn.
基金supposed by Shandong Natural Science Foundation[Grant number:ZR2016GM03]Ministry of Education[Grant number:17YJA790054]
文摘Long-lasting expansion of haze pollution in China has already presented a stern challenge to regional joint prevention and control. There is an urgent need to enlarge and reconstruct the coverage of joint prevention and control of air pollution in key area. Air quality models can identify and quantify the regional contribution of haze pollution and its key components with the help of numerical simulation, but it is difficult to be applied to larger spatial scale due to the complexity of model parameters. The time series analysis can recognize the existence of spatial interaction of haze pollution between cities, but it has not yet been used to further identify the spatial sources of haze pollution in large scale. Using econometric framework of time series analysis, this paper developed a new approach to perform spatial source apportionment. We applied this approach to calculate the contribution from spatial sources of haze pollution in China, using the monitoring data of particulate matter(PM_(2.5)) across 161 Chinese cities. This approach overcame the limitation of numerical simulation that the model complexity increases at excess with the expansion of sample range, and could effectively deal with severe large-scale haze episodes.
基金This work was supported by the National Key R&D Program of China(Grant 2017YFC0209904)National Natural Science Foundation of China(Grant 41877314)。
文摘Cross-boundary transport of air pollution is a difficult issue in pollution control for the North China Plain.In this study,an industrial district(Shahe City)with a large glass manufactur-ing sector was investigated to clarify the relative contribution of fine particulate matter(PM_(2.5))to the city's high levels of pollution.The Nest Air Quality Prediction Model System(NAQPMS),paired with Weather Research and Forecasting(WRF),was adopted and applied with a spatial resolution of 5 km.During the study period,the mean mass concentrations of PM_(2.5),SO_(2),and NO_(2)were observed to be 132.0,76.1,and 55.5μg/m^(3),respectively.The model reproduced the variations in pollutant concentrations in Shahe at an acceptable level.The simulation of online source-tagging revealed that pollutants emitted within a 50-km radius of downtown Shahe contributed 63.4%of the city's total PM_(2.5)concentration.This contribu-tion increased to 73.9±21.2%when unfavorable meteorological conditions(high relative hu-midity,weak wind,and low planetary boundary layer height)were present;such conditions are more frequently associated with severe pollution(PM_(2.5)≥250μg/m^(3)).The contribution from Shahe was 52.3±21.6%.The source apportionment results showed that industry(47%),transportation(10%),power(17%),and residential(26%)sectors were the most important sources of PM_(2.5)in Shahe.The glass factories(where chimney stack heights were normally<70 m)in Shahe contributed 32.1%of the total PM_(2.5)concentration in Shahe.With an in-crease in PM_(2.5)concentration,the emissions from glass factories accumulated vertically and narrowed horizontally.At times when pollution levels were severe,the horizontally influ-enced area mainly covered Shahe.Furthermore,sensitivity tests indicated that reducing emissions by 20%,40%,and 60% could lead to a decrease in themass concentration of PM_(2.5) of of 12.0%,23.8%,and 35.5%,respectively.
文摘In this paper,using concentration data of PM2. 5in 2013 in China and referring to a lot of literature,we preliminary studied the pollution of fine particulate matter and summarized PM2. 5source apportionment in the key cities in China. Our results showed that PM2. 5showed significant spatial and temporal distribution; high surface concentrations of PM2. 5concentrated mainly in the North China Plain,the Sichuan Basin,Yangtze River Delta and other regions; the average annual concentration of PM2. 5was about 80μg / m3 in North China Plain; Seasonal changes in the concentration of PM2. 5was winter > spring > autumn > summer; fired sources,industrial sources,vehicle exhaust were the major sources of PM2. 5; motor vehicle exhaust mostly contributed 10%- 30% to PM2. 5. This review provides a fundamental understanding of PM2. 5source apportionment and serves as an important reference for future source apportionment studies to be widely conducted in China.
基金Supported by the Natural Basic Research Program of China(No.2005CB422207)the Fund of Eco-enviromental Impacts and Protection in Devoloping and Utilizing of Oil-shale Resources(No.OSR-01-06)
文摘In order to identify the day and night pollution sources of PM10 in ambient air in Longyan City,the authors analyzed the elemental composition of respirable particulate matters in the day and night ambient air samples and various pollution sources which were collected in January 2010 in Longyan with inductivity coupled plasma-mass spectrometry（ICP-MS）.Then chemical mass balance（CMB） model and factor analysis（FA） method were applied to comparatively study the inorganic components in the sources and receptor samples.The results of factor analysis show that the major sources were road dust,waste incineration and mixed sources which contained automobile exhaust,soil dust/secondary dust and coal dust during the daytime in Longyan City,China.There are two major sources of pollution which are soil dust and mixture sources of automobile exhaust and secondary dust during the night in Longyan.The results of CMB show that the major sources are secondary dust,automobile exhaust and road dust during the daytime in Longyan.The major sources are secondary dust,soil dust and automobile exhaust during the night in Longyan.The results of the two methods are similar to each other and the results will guide us to plan to control the PM10 pollution sources in Longyan.
文摘In this paper, the spatial, temporal distribution, transformation and source simulation of NO3- were analyzed systematically based on the monitoring data, literature review and numerical simulation （ CMAQ4.7.1 ）. Analysis results showed that annual average concentration of NO3- in Beijing was between 6.69 and 12.48 μg/m3 with an increasing trend in recent years; concentration of NO3- in Beijing in 2013 was higher in winter and autumn than that in spring and summer and diurnal variation of NO3- showed bimedal distribution and spatial distribution of NO3- showed significant north-south gradient distribution; annual average NOR in Beijing was between 0.12 and 0.17 while it was between 0.17 and 0.20 during heavy air pollution days in 2013; the average ratio of NO3-/SO42- was between 0.97 and 1.06 while it was between 1.00 and 1.07 during heavy air pollution days in 2013; the emission sources of Beijing was being changed from fixed source to both fixed and moving sources in feature development; simulated local, external transportation, background and boundary condition were 40%, 44% and 16% respectively to the annual average concentration of NO3- in Beijing in 2013 while they were 31%, 57% and 12% respectively in heavy air pollution days, which indicated that extemal source played an important role to the concentration of NO3- in Beijing. Key words NO3- ; Spatial and temporal distribution; Source; PM2.5; Beijing; CAMx
基金supported by the National Natural Science Foundation of China(Nos.U23A2016,U1704241,and 42007175).
文摘Water-soluble organic nitrogen(WSON)affects the formation,hygroscopicity,acidity of organic aerosols,and nitrogen biogeochemical cycles.However,qualitative and quantitative characterizations of WSON remain limited due to its chemical complexity.In the study,1-year field samples of particulate matter 2.5(PM_(2.5))were collected fromJune 2022 to May 2023 to analyze the WSON concentration in PM_(2.5),and correlation analysis,positive matrix factor(PMF),and potential source contribution function(PSCF)modelswere employed to elucidate WSON source apportionment and transport pathways.The results revealed that the mean WSON concentrations reached 1.98±2.64μg/m^(3) with a mean WSON to water-soluble total nitrogen(WSTN)ratio of 21%.Further,WSON concentration exhibited a seasonal variation trend,with higher values in winter and lower in summer.Five sources were identified as contributors to WSON in PM_(2.5) within the reservoir area through a comprehensive analysis including correlation analysis,PSCF and concentration weighted trajectory(CWT),and PMF analyses.These sources were agricultural,dust,combustion,traffic,and industrial sources,of which agricultural source emerged as the primary contributor(76.69%).The atmosphere in the reservoir area were primarily influenced by the transport of northeastern air masses,local agricultural activities,industrial cities along the trajectory,and coastal regions,exerting significant influences on the concentration of WSON in the reservoir area.The findings of this study addressed the research gap concerning organic nitrogen in PM_(2.5) within the reservoir area,thereby offering a theoretical foundation and data support in controlling nitrogen pollution in the Danjiangkou Reservoir area.
