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
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
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
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
鉴于烟台市本地化源成分谱研究缺乏的现状,以及颗粒物精细化来源解析及环境管理的需求,采用NK-ZXF颗粒物再悬浮采样器,对6家烟台市典型工业下载灰源样品进行再悬浮采样,构建6类〔燃煤电厂、供热锅炉、生物质锅炉、钢铁(烧结)行业、玻璃...鉴于烟台市本地化源成分谱研究缺乏的现状,以及颗粒物精细化来源解析及环境管理的需求,采用NK-ZXF颗粒物再悬浮采样器,对6家烟台市典型工业下载灰源样品进行再悬浮采样,构建6类〔燃煤电厂、供热锅炉、生物质锅炉、钢铁(烧结)行业、玻璃行业和垃圾处理行业〕PM2.5源成分谱,并对PM2.5源成分谱特征及其排放颗粒物携带重金属特征进行评估.结果表明:①燃煤电厂PM2.5源成分谱的标识组分包括Si、Cl^-和SO4^2-,其质量分数分别为15.2%、9.3%和7.8%;与燃煤电厂相比,供热锅炉排放的PM2.5中w(OC)偏高、w(SO4^2-)偏低;生物质锅炉排放的主要组分有K、Cl^-和OC等,其质量分数分别为7.4%、13.3%和8.6%;钢铁(烧结)行业PM2.5源成分谱中w(Ca)、w(Fe)和w(Cl^-)较高;SO4^2-和Ca为玻璃行业PM2.5源成分谱的主要组分,其质量分数分别为20.6%、8.2%;垃圾处理行业重金属质量分数最高,其主要组分为Cl^-和SO4^2-.②CD (coefficient of divergence,分歧系数)计算结果表明,各源成分谱有一定相异性(CD范围为0.53~0.70),其中生物质锅炉与垃圾处理行业PM2.5源成分谱差异(CD为0.70)最大.③各典型工业排放PM2.5所携带重金属特征显示,垃圾处理行业排放PM2.5中的重金属质量分数(2.3%)最高,燃煤电厂、供热锅炉、生物质锅炉和玻璃行业排放的重金属中Cr、Ni和Cu相对质量分数较高,钢铁行业和垃圾处理行业排放的重金属中Pb相对质量分数较高.研究显示,所构建的烟台市各典型工业排放PM2.5源成分谱特征鲜明,能够反映各行业PM2.5排放特征.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
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
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.
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
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.
文摘鉴于烟台市本地化源成分谱研究缺乏的现状,以及颗粒物精细化来源解析及环境管理的需求,采用NK-ZXF颗粒物再悬浮采样器,对6家烟台市典型工业下载灰源样品进行再悬浮采样,构建6类〔燃煤电厂、供热锅炉、生物质锅炉、钢铁(烧结)行业、玻璃行业和垃圾处理行业〕PM2.5源成分谱,并对PM2.5源成分谱特征及其排放颗粒物携带重金属特征进行评估.结果表明:①燃煤电厂PM2.5源成分谱的标识组分包括Si、Cl^-和SO4^2-,其质量分数分别为15.2%、9.3%和7.8%;与燃煤电厂相比,供热锅炉排放的PM2.5中w(OC)偏高、w(SO4^2-)偏低;生物质锅炉排放的主要组分有K、Cl^-和OC等,其质量分数分别为7.4%、13.3%和8.6%;钢铁(烧结)行业PM2.5源成分谱中w(Ca)、w(Fe)和w(Cl^-)较高;SO4^2-和Ca为玻璃行业PM2.5源成分谱的主要组分,其质量分数分别为20.6%、8.2%;垃圾处理行业重金属质量分数最高,其主要组分为Cl^-和SO4^2-.②CD (coefficient of divergence,分歧系数)计算结果表明,各源成分谱有一定相异性(CD范围为0.53~0.70),其中生物质锅炉与垃圾处理行业PM2.5源成分谱差异(CD为0.70)最大.③各典型工业排放PM2.5所携带重金属特征显示,垃圾处理行业排放PM2.5中的重金属质量分数(2.3%)最高,燃煤电厂、供热锅炉、生物质锅炉和玻璃行业排放的重金属中Cr、Ni和Cu相对质量分数较高,钢铁行业和垃圾处理行业排放的重金属中Pb相对质量分数较高.研究显示,所构建的烟台市各典型工业排放PM2.5源成分谱特征鲜明,能够反映各行业PM2.5排放特征.
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.
