Volatile organic compounds(VOCs)are important precursors of secondary organic compounds and ozone,which raise major environmental concerns.To investigate the VOC emission characteristics,measurements of VOCs based on ...Volatile organic compounds(VOCs)are important precursors of secondary organic compounds and ozone,which raise major environmental concerns.To investigate the VOC emission characteristics,measurements of VOCs based on proton transfer reaction-mass spectrometry during 2017 were conducted in a coastal industrial area in Ningbo,Zhejiang Province,China.Based on seasonal variation in species concentration,the positive matrix factorization(PMF)receptor model was applied to apportion the sources of VOCs in each season.The PMF results revealed that unknown acetonitrile source,paint solvent,electronics industry,biomass burning,secondary formation and biogenic emission were mainly attributed to VOC pollution.Biomass burning and secondary formation were the major sources of VOCs and contributed more than 70%of VOC emissions in spring and autumn.Industry-related sources contributed 8.65%–31.2%of the VOCs throughout the year.The unknown acetonitrile source occurred in winter and spring,and contributed 7.6%–43.73%of the VOC emissions in the two seasons.Conditional probability function(CPF)analysis illustrated that the industry sources came from local emission,while biomass burning and biogenic emission mainly came from the northwest direction.The potential source contribution function(PSCF)model showed that secondary formation-related source was mainly from Jiangsu Province,northeastern China and the surrounding ocean.The potential source areas of unknown acetonitrile source were northern Zhejiang Province,southern Jiangsu Province and the northeastern coastal marine environments.展开更多
Receptor models have been proved as useful tools to identify source categories and quantitatively calculate the contributions of extracted sources.In this study,sixty surface sediment samples were collected from fourt...Receptor models have been proved as useful tools to identify source categories and quantitatively calculate the contributions of extracted sources.In this study,sixty surface sediment samples were collected from fourteen lakes in Jiangsu Province,China.The total concentrations of C_4–C_（14）-perfluoroalkyl carboxylic acids and perfluorooctane sulfonic acid（∑_（12）PFASs） in sediments ranged from 0.264 to 4.44 ng/g dw（dry weight）,with an average of 1.76 ng/g dw.Three commonly-applied receptor models,namely principal component analysis-multiple linear regression（PCA-MLR）,positive matrix factorization（PMF） and Unmix models,were employed to apportion PFAS sources in sediments.Overall,these three models all could well track the ∑_（12） PFASs concentrations as well as the concentrations explained in sediments.These three models identified consistently four PFAS sources：the textile treatment sources,the fluoropolymer processing aid/fluororesin coating sources,the textile treatment/metal plating sources and the precious metal sources,contributing 28.1%,37.0%,29.7% and 5.3% by PCA-MLR model,30.60%,39.3%,22.4% and 7.7% by PMF model,and 20.6%,52.4%,20.2% and 6.8% by Unmix model to the ∑_（12） PFASs,respectively.Comparative statistics of multiple analytical methods could minimize individual-method weaknesses and provide convergent results to enhance the persuasiveness of the conclusions.The findings could give us a better knowledge of PFAS sources in aquatic environments.展开更多
Long-term and synchronous monitoring of PMIo and PM2.s was conducted in Chengdu in China from 2007 to 2013. The levels, variations, compositions and size distributions were investigated. The sources were quantified by...Long-term and synchronous monitoring of PMIo and PM2.s was conducted in Chengdu in China from 2007 to 2013. The levels, variations, compositions and size distributions were investigated. The sources were quantified by two-way and three-way receptor models （PMF2, ME2-2way and ME2-3way）, Consistent results were found： the primary source categories contributed 63.4% （PMF2）, 64.8% （ME2-2way） and 66.8% （ME2-Bway） to PMIo, and contributed 60.9% （PMF2）, 65.5% （ME2-2way） and 61.0% （ME2-3way） to PM2.s. Secondary sources contributed 31.8% （PMF2）, 32.9% （ME2-2way） and 31.7% （ME2-3way） to PMIo, and 35.0% （PMF2）, 33.8% （ME2-2way） and 36.0% （ME2-3way） to PM2.s. The size distribution of source categories was estimated better by the ME2-3way method. The three-way model can simultaneously consider chemical species, temporal variability and PM sizes, while a two-way model independently computes datasets of different sizes. A method called source directional apportionment （SDA） was employed to quantify the contributions from various directions for each source category. Crustal dust from east-north-east （ENE） contributed the highest to both PM^o （12.7%） and PMzs （9.7%） in Chengdu, followed by the crustal dust from south-east （SE） for PMao （9.8%） and secondary nitrate ＆ secondary organic carbon from ENE for PMzs （9.6%）. Source contributions from different directions are associated with meteorological conditions, source locations and emission patterns during the sampling period. These findings and methods provide useful tools to better understand PM pollution status and tn dovolon offoctive nolhltion control gtrateMeg.展开更多
Source apportionment of particulate matter （PM10） measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal...Source apportionment of particulate matter （PM10） measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal component scores （PCA/APCS） and UNMIX. The results were compared with previous estimates generated using the positive matrix factorization （PMF） receptor model to investigate each model＇s source-apportioning capability. All models used the PM10 chemical composition （organic carbon （OC）, elemental carbon （EC）, water soluble inorganic ions （WSIC）, and trace elements） for source apportionment. The average PM10 concentration during the study period was 249.7±103.9 μg/m3 （range： 61.4-584.8 μg/m3）. The UNMIX model resolved five sources （soil dust （SD）, vehicular emissions （VE）, secondary aerosols （SA）, a mixed source of biomass burning （BB） and sea salt （SS）, and industrial emissions （IE））. The PCA/APCS model also resolved five sources, two of which also included mixed sources （SD, VE, SD＋SS, （SA＋BB＋SS） and 1E）. The PMF analysis differentiated seven individual sources （SD, VE, SA, BB, SS, IE, and fossil fuel combustion （FFC））. All models identified the main sources contributing to PM10 emissions and reconfirmed that VE, SA, BB, and SD were the dominant contributors in Delhi.展开更多
Zhengzhou is a developing city in China, that is heavily polluted by high levels of particulate matter. In this study, fine particulate matter （PM2.5） was collected and analyzed for their chemical composition （solu...Zhengzhou is a developing city in China, that is heavily polluted by high levels of particulate matter. In this study, fine particulate matter （PM2.5） was collected and analyzed for their chemical composition （soluble ions, elements, elemental carbon （EC） and organic carbon （OC）） in an industrial district of Zhengzhou in 2010. The average concentrations of PM2.5 were 181, 122, 186 and 211 μg/m3 for spring, summer, autumn and winter, respectively, with an annual average of 175 μg/m3, far exceeding the PM2.5 regulation of USA National Air Quality Standards （15 μg/m3）. The dominant components of PM2.5 in Zhengzhou were secondary ions （sulphate and nitrate） and carbon fractions. Soluble ions, total carbon and elements contributed 41%, 13% and 3% of PM2.5 mass, respectively. Soil dust, secondary aerosol and coal combustion, each contributing about 26%, 24% and 23% of total PM2.5 mass, were the major sources of PM2.5, according to the result of positive matrix factorization analysis. A mixed source of biomass burning, oil combustion and incineration contributed 13% of PM2.5. Fine particulate matter arising from vehicles and industry contributed about 10% and 4% of PM2.5, respectively.展开更多
基金supported by the National Nature Science Foundation for Young Scientists of China(Nos.41605094,41905115).
文摘Volatile organic compounds(VOCs)are important precursors of secondary organic compounds and ozone,which raise major environmental concerns.To investigate the VOC emission characteristics,measurements of VOCs based on proton transfer reaction-mass spectrometry during 2017 were conducted in a coastal industrial area in Ningbo,Zhejiang Province,China.Based on seasonal variation in species concentration,the positive matrix factorization(PMF)receptor model was applied to apportion the sources of VOCs in each season.The PMF results revealed that unknown acetonitrile source,paint solvent,electronics industry,biomass burning,secondary formation and biogenic emission were mainly attributed to VOC pollution.Biomass burning and secondary formation were the major sources of VOCs and contributed more than 70%of VOC emissions in spring and autumn.Industry-related sources contributed 8.65%–31.2%of the VOCs throughout the year.The unknown acetonitrile source occurred in winter and spring,and contributed 7.6%–43.73%of the VOC emissions in the two seasons.Conditional probability function(CPF)analysis illustrated that the industry sources came from local emission,while biomass burning and biogenic emission mainly came from the northwest direction.The potential source contribution function(PSCF)model showed that secondary formation-related source was mainly from Jiangsu Province,northeastern China and the surrounding ocean.The potential source areas of unknown acetonitrile source were northern Zhejiang Province,southern Jiangsu Province and the northeastern coastal marine environments.
基金supported by the Mega-projects of Science Research for Water Environmental Improvement(No.2012ZX07101-002)the National Natural Science Foundation of China(No.41521003)
文摘Receptor models have been proved as useful tools to identify source categories and quantitatively calculate the contributions of extracted sources.In this study,sixty surface sediment samples were collected from fourteen lakes in Jiangsu Province,China.The total concentrations of C_4–C_（14）-perfluoroalkyl carboxylic acids and perfluorooctane sulfonic acid（∑_（12）PFASs） in sediments ranged from 0.264 to 4.44 ng/g dw（dry weight）,with an average of 1.76 ng/g dw.Three commonly-applied receptor models,namely principal component analysis-multiple linear regression（PCA-MLR）,positive matrix factorization（PMF） and Unmix models,were employed to apportion PFAS sources in sediments.Overall,these three models all could well track the ∑_（12） PFASs concentrations as well as the concentrations explained in sediments.These three models identified consistently four PFAS sources：the textile treatment sources,the fluoropolymer processing aid/fluororesin coating sources,the textile treatment/metal plating sources and the precious metal sources,contributing 28.1%,37.0%,29.7% and 5.3% by PCA-MLR model,30.60%,39.3%,22.4% and 7.7% by PMF model,and 20.6%,52.4%,20.2% and 6.8% by Unmix model to the ∑_（12） PFASs,respectively.Comparative statistics of multiple analytical methods could minimize individual-method weaknesses and provide convergent results to enhance the persuasiveness of the conclusions.The findings could give us a better knowledge of PFAS sources in aquatic environments.
基金supported by the Tianjin Natural Science Foundation(No.16JCQNJC08700)the Fundamental Research Funds for the Central Universities+4 种基金National Key Research and Development Program of China(No.2016YFC0208500)the National Natural Science Foundation of China(No.21407174)the Tianjin Research Program of Application Foundation(No.14JCQNJC08100)the Tianjin Science and Technology Project(Nos.16YFZCSF00260,14ZCDGSF00027,14ZCDGSF00029)the Special Funds for Research on Public Welfares of the Ministry of Environmental Protection of China(201309072)
文摘Long-term and synchronous monitoring of PMIo and PM2.s was conducted in Chengdu in China from 2007 to 2013. The levels, variations, compositions and size distributions were investigated. The sources were quantified by two-way and three-way receptor models （PMF2, ME2-2way and ME2-3way）, Consistent results were found： the primary source categories contributed 63.4% （PMF2）, 64.8% （ME2-2way） and 66.8% （ME2-Bway） to PMIo, and contributed 60.9% （PMF2）, 65.5% （ME2-2way） and 61.0% （ME2-3way） to PM2.s. Secondary sources contributed 31.8% （PMF2）, 32.9% （ME2-2way） and 31.7% （ME2-3way） to PMIo, and 35.0% （PMF2）, 33.8% （ME2-2way） and 36.0% （ME2-3way） to PM2.s. The size distribution of source categories was estimated better by the ME2-3way method. The three-way model can simultaneously consider chemical species, temporal variability and PM sizes, while a two-way model independently computes datasets of different sizes. A method called source directional apportionment （SDA） was employed to quantify the contributions from various directions for each source category. Crustal dust from east-north-east （ENE） contributed the highest to both PM^o （12.7%） and PMzs （9.7%） in Chengdu, followed by the crustal dust from south-east （SE） for PMao （9.8%） and secondary nitrate ＆ secondary organic carbon from ENE for PMzs （9.6%）. Source contributions from different directions are associated with meteorological conditions, source locations and emission patterns during the sampling period. These findings and methods provide useful tools to better understand PM pollution status and tn dovolon offoctive nolhltion control gtrateMeg.
文摘Source apportionment of particulate matter （PM10） measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal component scores （PCA/APCS） and UNMIX. The results were compared with previous estimates generated using the positive matrix factorization （PMF） receptor model to investigate each model＇s source-apportioning capability. All models used the PM10 chemical composition （organic carbon （OC）, elemental carbon （EC）, water soluble inorganic ions （WSIC）, and trace elements） for source apportionment. The average PM10 concentration during the study period was 249.7±103.9 μg/m3 （range： 61.4-584.8 μg/m3）. The UNMIX model resolved five sources （soil dust （SD）, vehicular emissions （VE）, secondary aerosols （SA）, a mixed source of biomass burning （BB） and sea salt （SS）, and industrial emissions （IE））. The PCA/APCS model also resolved five sources, two of which also included mixed sources （SD, VE, SD＋SS, （SA＋BB＋SS） and 1E）. The PMF analysis differentiated seven individual sources （SD, VE, SA, BB, SS, IE, and fossil fuel combustion （FFC））. All models identified the main sources contributing to PM10 emissions and reconfirmed that VE, SA, BB, and SD were the dominant contributors in Delhi.
基金part of the Science and Technology Plan Project in Zhengzhou funded by Henan Administration of Foreign Experts Affairs and Science and Technology Bureau of Zhengzhou City (grant no.094SYJH36069)support from Peking University and Taiwan Yunlin University of Science and Technology
文摘Zhengzhou is a developing city in China, that is heavily polluted by high levels of particulate matter. In this study, fine particulate matter （PM2.5） was collected and analyzed for their chemical composition （soluble ions, elements, elemental carbon （EC） and organic carbon （OC）） in an industrial district of Zhengzhou in 2010. The average concentrations of PM2.5 were 181, 122, 186 and 211 μg/m3 for spring, summer, autumn and winter, respectively, with an annual average of 175 μg/m3, far exceeding the PM2.5 regulation of USA National Air Quality Standards （15 μg/m3）. The dominant components of PM2.5 in Zhengzhou were secondary ions （sulphate and nitrate） and carbon fractions. Soluble ions, total carbon and elements contributed 41%, 13% and 3% of PM2.5 mass, respectively. Soil dust, secondary aerosol and coal combustion, each contributing about 26%, 24% and 23% of total PM2.5 mass, were the major sources of PM2.5, according to the result of positive matrix factorization analysis. A mixed source of biomass burning, oil combustion and incineration contributed 13% of PM2.5. Fine particulate matter arising from vehicles and industry contributed about 10% and 4% of PM2.5, respectively.
