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Temporal, Spatial Distribution and Source Simulation Analysis of NO3- in PM2.5 in Beijing City in 2013
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作者 Xln Liang Cheng Nianllang +1 位作者 Cheng Bingfen Meng Fan 《Meteorological and Environmental Research》 CAS 2015年第8期7-13,共7页
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
关键词 NO-3 Spatial and temporal distribution source pm2.5 Beijing CAMX
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Distribution and Source Apportionment of Polycyclic Aromatic Hydrocarbons from Atmospheric Particulate Matter PM2.5 in Beijing 被引量:9
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作者 刘大锰 高少鹏 安祥华 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2008年第2期297-305,共9页
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
关键词 distribution and occurrence source apportionment pm2.5 polycyclic aromatic hydrocarbons(PAHs) HPLC Beijing city
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Chemical characterization and sources of PM2.5 at 12-h resolution in Guiyang, China 被引量:4
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作者 Longchao Liang Na Liu +5 位作者 Matthew S. Landis Xiaohang Xu Xinbin Feng Zhuo Chen Lihai Shang Guangle Qiu 《Acta Geochimica》 EI CAS CSCD 2018年第2期334-345,共12页
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
关键词 Trace elements pm2.5 source apportionment
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Source Apportionment of Ambient PM_(10) in the Urban Area of Longyan City,China:a Comparative Study Based on Chemical Mass Balance Model and Factor Analysis Method 被引量:1
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作者 QIU Li-min LIU Miao +2 位作者 WANG Ju ZHANG Sheng-nan FANG Chun-sheng 《Chemical Research in Chinese Universities》 SCIE CAS CSCD 2012年第2期204-208,共5页
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
关键词 Factor analysis(FA) method Chemical mass balance(CMB) model source apportionment Atmospheric particle PM10
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A Research for Regional Contribution Rate of Internal Source and External Source of PM_(2.5) Based on Set Pair Analysis Method 被引量:1
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作者 Zhou Yejing Zhou Jingxuan Xiao Renbin 《Meteorological and Environmental Research》 CAS 2016年第2期36-40,44,共6页
A problem of the air pollution control in China is getting to know a regional contribution rate of internal and external source of PM2.5. In this paper,Set Pair Analysis( SPA) method is proposed to calculate the con... A problem of the air pollution control in China is getting to know a regional contribution rate of internal and external source of PM2.5. In this paper,Set Pair Analysis( SPA) method is proposed to calculate the contribution rate of PM2.5in Dongguan City. Due to geographic,meteorological factors and the low concentration of air pollutants in Qingxi area,the PM2.5in this place is mainly contributed by the regional transport of air pollutants from other inside areas of Dongguan,and less affected by the outside of Dongguan. So the concentration of PM2.5in Qingxi area can reflect the Dongguan's basic background concentration of PM2.5. On the basis of the basic background concentration,firstly the concentration of each pollutant components is divided into the internal part and the mixed part. Secondly using the source apportionment samples of five monitoring sites in Dongguan we can respectively construct a sample set A and an evaluation set B. Thirdly the SPA is operated onto the mixed part in terms of set B.At last the connection degree between the concentration of each pollutant components and external source and internal source will be calculated,that is the contribution rate. The research reveals that the contribution rate of internal source and external source of PM2.5in Dongguan City is 83%and 17% respectively,which roughly met expectations. This method is simple and effective and it can provide a reference for the government taking reduction measures to control PM2.5pollutants emission. 展开更多
关键词 Set Pair analysis Connection degree pm2.5 Internal source External source Contribution rate
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The Regression Analysis between the Meteorological Synthetic Index Sequence and PM2.5 Concentration
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作者 Weijuan Liang Zhaogan Zhang +4 位作者 Jing Gao Wanyu Li Xiaofan Liu Liyuan Bai Yufeng Gui 《Applied Mathematics》 2015年第11期1913-1917,共5页
Adapting daily meteorological data provided by China International Exchange Station, and using principal component analysis of meteorological index for dimension reduction comprehensive, the regression analysis model ... Adapting daily meteorological data provided by China International Exchange Station, and using principal component analysis of meteorological index for dimension reduction comprehensive, the regression analysis model between PM2.5 and comprehensive index is established, by making use of Eviews time series modeling of the comprehensive principal component, finally puts forward opinions and suggestions aim at the regression analysis results of using artificial rainfall to ease haze. 展开更多
关键词 METEOROLOGICAL INDEX Principal COMPONENT analysis Time Series Modeling pm2.5 HAZE
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Ambient Air Particulate PM2.5 Concentrations and Identification of Source Categories at Chihuahua, Mexico
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作者 Ana Canseco-Lajas Ramon Gomez Vargas Alfredo Campos-Trujillo 《Journal of Environmental Science and Engineering(A)》 2013年第3期147-154,共8页
Suspended particles in the air are pollutants formed by diverse materials and sizes. As determined in multiple studies, particulate matter equal to or less than 2.5 μrn also called breathable fraction is the one that... Suspended particles in the air are pollutants formed by diverse materials and sizes. As determined in multiple studies, particulate matter equal to or less than 2.5 μrn also called breathable fraction is the one that penetrates the inferior respiratory system. The present study shows the results of a sampling campaign of particulate matter PM25 through 12 months at 3 sites: C33 (Clinic 33), CIM (CIMAV) and A VA (Avalos) at Chihuahua, Mexico. The aim of this study was to determine the temporary concentrations of PM25, the concentration of metallic elements: Al, As, Co, Cu, Sb, Se, Fe, Mg, Mn, Ni, Pb and Zn, as well as their possible sources. The three sites had the same trend in the time, where in autumn and winter the higher concentrations were found. The yearly averages on sites C33, CIM, and AVA were 9.91, 12.74 and 21.67 μg·m^-3, respectively, finding that AVA site overpasses the standard of 15 μg·m^-3. The application of factor analysis to the data allowed to identify four source categories: crustal, related with Al-Co-Fe-Mg-Mn, vehicular, related with: As-Cu-Sb-Se; mixed, related with: As-Cu-Sb-Se-Pb-Zn and industrial related with Ni-Pb. 展开更多
关键词 PM25 trace metallic elements factor analysis source categories.
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Geographical Analysis of Lung Cancer Mortality Rate and PM2.5 Using Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth
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作者 Zhiyong Hu Ethan Baker 《Journal of Geoscience and Environment Protection》 2017年第6期183-197,共15页
Exposure to particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) may increase risk of lung cancer. The repetitive and broad-area coverage of satellites may allow atmospheric remote sensing to o... Exposure to particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) may increase risk of lung cancer. The repetitive and broad-area coverage of satellites may allow atmospheric remote sensing to offer a unique opportunity to monitor air quality and help fill air pollution data gaps that hinder efforts to study air pollution and protect public health. This geographical study explores if there is an association between PM2.5 and lung cancer mortality rate in the conterminous USA. Lung cancer (ICD-10 codes C34- C34) death count and population at risk by county were extracted for the period from 2001 to 2010 from the U.S. CDC WONDER online database. The 2001-2010 Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth dataset was used to calculate a 10 year average PM2.5 pollution. Exploratory spatial data analyses, spatial regression (a spatial lag and a spatial error model), and spatially extended Bayesian Monte Carlo Markov Chain simulation found that there is a significant positive association between lung cancer mortality rate and PM2.5. The association would justify the need of further toxicological investigation of the biological mechanism of the adverse effect of the PM2.5 pollution on lung cancer. The Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth dataset provides a continuous surface of concentrations of PM2.5 and is a useful data source for environmental health research. 展开更多
关键词 LUNG Cancer pm2.5 Remote Sensing GIS EXPLORATORY SPATIAL Data analysis SPATIAL Regression Bayesian MCMC Simulation
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Source Apportionment of PM2.5 in the Metropolitan Area of Costa Rica Using Receptor Models
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作者 Jorge Herrera Murillo Susana Rodríguez Roman +1 位作者 José Félix Rojas Marín Beatriz Cardenas 《Atmospheric and Climate Sciences》 2013年第4期562-575,共14页
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
关键词 pm2.5 Chemical COMPOSITION Costa Rica source APPORTIONMENT RECEPTOR Models
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2019至2021年郑州市采暖期和非采暖期大气PM_(2.5)中多环芳烃源解析与健康风险评估
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作者 阙菡雅 张思雨 +6 位作者 田梅 杨嵩泰 刘佳敏 王哲 宋帅 陈彦哲 周舫 《郑州大学学报(医学版)》 CAS 北大核心 2024年第5期650-655,共6页
目的:分析郑州市采暖期和非采暖期PM_(2.5)中多环芳烃(PAH)的污染特征,推测其可能的来源并评估对人群健康的影响。方法:检测郑州市2019年1月至2021年12月采暖期和非采暖期PM_(2.5)及各种PAH浓度,利用特征比值法、主成分分析法和PMF模型... 目的:分析郑州市采暖期和非采暖期PM_(2.5)中多环芳烃(PAH)的污染特征,推测其可能的来源并评估对人群健康的影响。方法:检测郑州市2019年1月至2021年12月采暖期和非采暖期PM_(2.5)及各种PAH浓度,利用特征比值法、主成分分析法和PMF模型推测PAH的来源,根据相关技术指南对PAH吸入途径的致癌风险进行评估。结果:采暖期PM_(2.5)和总PAH浓度中位数分别为72.00μg/m^(3)和7.28 ng/m^(3),非采暖期为50.00μg/m^(3)和7.16 ng/m^(3)。特征比值法分析结果显示,郑州市PAH污染主要来源于生物质和煤的燃烧以及机动车排放。主成分分析和PMF分析结果显示,采暖期PAH的主要来源为燃煤,非采暖期为机动车排放。郑州市大气PM_(2.5)中PAH对部分人群具有潜在的致癌风险。结论:郑州市采暖期PM_(2.5)中PAH主要来源于燃煤,非采暖期主要来源于机动车排放。PAH对人群具有潜在致癌风险,制定排放监测和控制方案势在必行。 展开更多
关键词 PM_(2.5) 多环芳烃 源解析 健康风险评估 郑州市
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西安PM_(2.5)碳组成及水溶性有机物分子特性和来源季节差异
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作者 杨毅 赵明佳 +1 位作者 张亚楠 刘伟 《安全与环境学报》 CAS CSCD 北大核心 2024年第7期2860-2868,共9页
利用平行因子分析和后向轨迹模型,采用紫外光谱法、三维荧光光谱法,分析西安市PM_(2.5)碳组成及水溶性有机物(Water-Soluble Organic Matters,WSOM)的荧光组分、分子特性和来源。结果显示,西安市各季节PM_(2.5)及其有机碳(Organic Carbo... 利用平行因子分析和后向轨迹模型,采用紫外光谱法、三维荧光光谱法,分析西安市PM_(2.5)碳组成及水溶性有机物(Water-Soluble Organic Matters,WSOM)的荧光组分、分子特性和来源。结果显示,西安市各季节PM_(2.5)及其有机碳(Organic Carbon,OC)和元素碳(Elemental Carbon,EC)的质量浓度由高到低依次为:冬、秋、春、夏,且南北郊差异不显著。PM_(2.5)中水溶性有机碳(Water-Soluble Organic Carbon,WSOC)质量浓度为3.50~17.29μg/m^(3),冬季WSOC质量浓度最高。四季的WSOM中均含有紫外光类腐殖质和可见光类腐殖质。秋、冬和夏季类富里酸的荧光强度占比最大。WSOM的E_(2)/E_(3)、E_(3)/E_(4)和AAE值由高到低依次是:冬、春、夏、秋。SUVA 254和MAE_(3)65值均在冬季最高,夏季最低。冬季WSOM的相对分子质量和腐殖化程度较小,分子苯环取代程度最大,光吸收能力对光吸收的波长依赖性较强;秋季WSOM的相对分子质量较大,腐殖化程度较强,光吸收的波长依赖性较弱;夏季WSOM的芳香化程度和光吸收能力及春季WSOM分子苯环取代程度最弱。碳组分质量浓度、UV 254、α350和荧光强度两两呈显著正相关(p<0.01)。WSOM的荧光指数(Fluorescence Index,FI)、生物源指数(Biogenic Index,BIX)和腐殖化指数(Humification Index,HIX)值分别为1.51~2.15、0.88~1.46、1.18~3.19。冬季WSOM的自生来源最高,夏季WSOM的陆源来源比例相对较大。西安市污染气团主要来自于陕西省区域气团传输。西安市四季PM_(2.5)碳组成及WSOM的荧光组分、分子特性和来源存在季节差异,但北郊和南郊的紫外荧光光谱特性和来源差异不显著。 展开更多
关键词 环境学 PM_(2.5) 水溶性有机物(WSOM) 荧光特性 平行因子分析 来源
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金昌市PM_(2.5)的化学组分特征及来源分析 被引量:1
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作者 喻恒 李忠勤 +4 位作者 周茜 张昕 蒋慧敏 王芳龙 罗雨甜 《环境化学》 CAS CSCD 北大核心 2024年第4期1188-1203,共16页
为研究金昌市PM_(2.5)及其化学组分(有机碳、元素碳和水溶性离子)的特征,于2020年5月—2021年3月对金昌市大气PM_(2.5)进行手工采样,并运用PMF模型和HYSPLIT模型解析污染来源.研究结果表明,观测期间金昌市PM_(2.5)年平均质量浓度为(62.2... 为研究金昌市PM_(2.5)及其化学组分(有机碳、元素碳和水溶性离子)的特征,于2020年5月—2021年3月对金昌市大气PM_(2.5)进行手工采样,并运用PMF模型和HYSPLIT模型解析污染来源.研究结果表明,观测期间金昌市PM_(2.5)年平均质量浓度为(62.2±10.4)μg·m^(−3),各季节平均质量浓度由高到低依次为春季、冬季、秋季、夏季.化学质量闭合研究表明:碳质组分(OM+EC)是金昌PM_(2.5)的主要组成部分.PM_(2.5)中的OC与EC的年平均质量浓度分别为(13.4±5.6)μg·m^(−3)、(2.9±1.5)μg·m^(−3),TC占PM_(2.5)质量浓度的16.3%,并且四季OC/EC的平均值均大于2,表明采样期间各个季节均存在二次污染.夏季OC与EC之间的相关系数最低,说明夏季污染物来源较其他季节更为复杂.金昌市PM_(2.5)中总水溶性离子的年平均质量浓度为(25.0±11.6)μg·m^(−3),占PM_(2.5)质量浓度的40.2%,其中,SO_(4)^(2−)、Ca^(2+)、NO_(3)^(−)和Cl^(−)是金昌市主要的4种离子,分别占总离子的22.5%、17.1%、16.8%、12.1%.对水溶性离子做离子平衡分析表明:夏季、秋季和冬季阴阳离子的相关性较好,没有重要的离子缺失,春季较差,有重要阴离子缺失.PMF模型表明金昌市PM_(2.5)的主要污染源为燃烧源(生物质+燃煤)(30.5%)、土壤尘(24.6%)、二次无机气溶胶(26.0%)和机动车尾气(18.9%),HYSPLIT模型表明金昌市春季PM_(2.5)浓度受外来污染源输入影响较大,夏季、秋季和冬季应主要考虑本地排放的贡献. 展开更多
关键词 PM_(2.5) 化学组分 来源分析 金昌市
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银川市冬季PM2.5重污染特征、来源与成因分析 被引量:12
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作者 李慧 张敬巧 +4 位作者 王涵 张萌 张玉龙 王平 王淑兰 《环境科学研究》 EI CAS CSCD 北大核心 2020年第2期289-295,共7页
近年来银川市冬季重污染过程频发,为明确银川市冬季PM2.5重污染的特征,分析其主要来源及成因,于2016年12月—2017年1月在银川市选取3个采样点开展PM2.5的样品采集与化学组分分析,利用CMB(化学质量平衡)模型对银川市冬季PM2.5进行来源解... 近年来银川市冬季重污染过程频发,为明确银川市冬季PM2.5重污染的特征,分析其主要来源及成因,于2016年12月—2017年1月在银川市选取3个采样点开展PM2.5的样品采集与化学组分分析,利用CMB(化学质量平衡)模型对银川市冬季PM2.5进行来源解析,对比分析了重污染日与非重污染日污染特征的差异.结果表明:①银川市冬季重污染日ρ(PM2.5)〔(181±33.6)μg/m^3〕是非重污染日的2.1倍;重污染日和非重污染日的ρ(NO^-3)/ρ(SO^2-4)均小于1,表明燃煤仍是银川市冬季PM2.5的重要来源.银川市冬季PM2.5中ρ(SOC)为(14.4±7.34)μg/m^3,约占ρ(OC)的65.2%.②与非重污染日相比,重污染日人为源无机元素As、Pb、Cd和Zn质量浓度在ρ(PM2.5)中的占比分别升高33.2%、18.4%、9.8%和2.9%,表明银川市冬季重污染主要受人为源贡献影响.③源解析结果表明,燃煤源、机动车尾气源、二次离子源和扬尘源是银川市PM2.5的主要污染源,与非重污染日相比,重污染日机动车尾气源的贡献率明显降低.研究显示,银川市冬季重污染受人为源污染物排放的影响较大,燃煤源是银川市冬季PM2.5的重要来源. 展开更多
关键词 西北地区 pm2.5 CMB 来源解析
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泰安市夏季大气PM_(2.5)污染特征及来源解析
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作者 田莎莎 宋梦迪 +4 位作者 祖可欣 宋锴 董华斌 曾立民 陆克定 《北京大学学报(自然科学版)》 EI CAS CSCD 北大核心 2024年第5期927-934,共8页
于2019年5月10日—6月10日对泰安市大气PM_(2.5)中的碳组分、水溶性离子进行在线监测,分析PM_(2.5)及其化学组分的变化特征,讨论泰安市细颗粒物的主要来源。结果表明,泰安市夏季PM_(2.5)的质量浓度为37.7μg/m^(3),是《环境空气质量标准... 于2019年5月10日—6月10日对泰安市大气PM_(2.5)中的碳组分、水溶性离子进行在线监测,分析PM_(2.5)及其化学组分的变化特征,讨论泰安市细颗粒物的主要来源。结果表明,泰安市夏季PM_(2.5)的质量浓度为37.7μg/m^(3),是《环境空气质量标准》(GB 3095—2012)二级标准阈值(35μg/m^(3))的1.1倍。水溶性离子在PM_(2.5)中占比最高,为47.3%。水溶性组分及其气态前体物存在明显的日变化规律,在早晨7:00出现峰值(单峰)。泰安市夏季OC/EC比值在1.1~17.5之间,说明泰安市主要受生物质燃烧、燃煤和尾气排放混合源的影响。正交矩阵因子分析(PMF)结果表明,PM_(2.5)结果中二次硝酸盐、生物质燃烧源、二次硫酸盐和燃煤源的贡献比分别为22.0%,46.7%,29.9%和1.4%。 展开更多
关键词 PM_(2.5) 化学组成 来源解析 泰安
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长春市冬春季环境空气中PM2.5污染特征与来源解析 被引量:9
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作者 董德明 杜山山 +5 位作者 黄亚司 满睿琪 姚梦颖 杜蕊含 梁大鹏 宁杨 《吉林大学学报(理学版)》 CAS 北大核心 2020年第5期1278-1286,共9页
为研究长春市冬季和春季大气PM2.5的主要来源及污染特征,于2018-01-06—2018-05-14连续采集PM2.5环境受体样品,分析其无机元素及水溶性阴离子组分.结果表明:采样期间长春市PM2.5的质量浓度为(46.4±24.4)μg/m^3,冬季和春季的平均... 为研究长春市冬季和春季大气PM2.5的主要来源及污染特征,于2018-01-06—2018-05-14连续采集PM2.5环境受体样品,分析其无机元素及水溶性阴离子组分.结果表明:采样期间长春市PM2.5的质量浓度为(46.4±24.4)μg/m^3,冬季和春季的平均质量浓度分别为(51.0±25.8)μg/m^3和(32.6±11.5)μg/m^3,超标率为11%,均在冬季超标,在春节假期中(2018-02-15—2018-02-21),PM2.5的质量浓度低且保持平稳;所测全部水溶性阴离子及部分无机元素(Al,As,Pb,Se,Ti)质量浓度呈冬季高于春季的趋势;长春市无机元素主要源于燃煤、交通和扬尘;长春市PM2.5中NO3^-和SO4^2-是燃煤和机动车尾气共同作用的结果,其中燃煤源的贡献率相对较高;长春市冬春季PM2.5主要来源为二次源(28.2%)、土壤尘源(12.6%)、交通排放源(10.7%)、燃煤源和建筑尘源(28.6%)、工业源和其他源(19.8%). 展开更多
关键词 pm2.5 无机元素 水溶性阴离子 源解析 污染
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台州市市区环境空气中PM2.5的多模型联用来源解析 被引量:3
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作者 何微娜 谢松青 +2 位作者 陶志华 王俏丽 李伟 《环境科学研究》 EI CAS CSCD 北大核心 2020年第6期1384-1392,共9页
为对台州市市区环境空气中PM 2.5的主要来源进行全面分析,运用CMAQ(空气质量模型)模型中的ISAM源追踪算法,计算了台州市本地各类污染源及外来源对PM 2.5的贡献,同时基于CMB模型的初步源解析结果,利用CMAQ模型解析二次前体物排放源的贡献... 为对台州市市区环境空气中PM 2.5的主要来源进行全面分析,运用CMAQ(空气质量模型)模型中的ISAM源追踪算法,计算了台州市本地各类污染源及外来源对PM 2.5的贡献,同时基于CMB模型的初步源解析结果,利用CMAQ模型解析二次前体物排放源的贡献,得到CMB-CMAQ联用模型的源解析结果,综合分析CMAQ模型和CMB-CMAQ联用模型解析结果最终获得台州市市区空气中PM2.5的贡献源数据.结果表明:①CMAQ模型和CMB-CMAQ联用模型解析结果均表明,台州市市区PM 2.5本地源中首要贡献源为工业源,两个模型中工业源贡献率分别为20.13%和26.94%,其次为扬尘源(贡献率分别为16.98%、19.37%)和道路移动源(贡献率分别为16.44%、18.14%).②CMB-CMAQ联用模型解析结果中工业源、扬尘源和道路移动源的贡献率均高于CMAQ模型解析结果,而外来源和电力源的贡献率均低于CMAQ模型解析结果.③CMAQ模型和CMB-CMAQ联用模型综合分析分配结果表明,外来源、工业源、扬尘源、道路移动源是对区域中PM 2.5贡献较大的4个污染源,贡献率分别为26.10%、22.38%、16.09%、15.07%.研究显示,台州市市区环境空气中PM 2.5污染呈以工业源、扬尘源为主,道路移动源污染突出的复合型污染特征,加强这三类源的排放管理对于台州市市区PM 2.5污染防治具有重要意义. 展开更多
关键词 pm2.5 源解析 CMB模型 CMAQ模型 CMB-CMAQ联用模型 台州市
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基于时空认知膨胀卷积网络与多源影响因素的PM_(2.5)细粒度预测模型
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作者 刘希亮 赵俊杰 +3 位作者 张羽民 林绍福 李建强 梅强 《北京工业大学学报》 CAS CSCD 北大核心 2024年第3期333-347,共15页
为实现精确化、细粒度的PM_(2.5)浓度预测,提出了基于时空认知膨胀卷积网络(spatial-temporal cognitive dilated convolution network,ST-C-DCN)的PM_(2.5)浓度预测模型ST-C-DCN。该模型将时空因素、气象因素运用于PM_(2.5)浓度预测,... 为实现精确化、细粒度的PM_(2.5)浓度预测,提出了基于时空认知膨胀卷积网络(spatial-temporal cognitive dilated convolution network,ST-C-DCN)的PM_(2.5)浓度预测模型ST-C-DCN。该模型将时空因素、气象因素运用于PM_(2.5)浓度预测,基于因果卷积网络提取时空特征,并采用时空注意力机制优化了时空特征的提取。基于海口市空气污染数据的实验测试表明:对于单个监测站,基线模型相比,ST-C-DCN的均方根误差(root mean square error,RMSE)平均下降24.7%,平均绝对误差(mean absolute error,MAE)平均下降9.93%,拟合优度(R-squared,R^(2))平均上升3.35%。对于全部监测站点的预测,ST-C-DCN在win-tie-loss(包括MSE、RMSE、MAE、R^(2))实验中,均获得了最多的获胜次数,分别为68,68、63和64。通过不同数据抽样条件下的Friedman检验,证明了ST-C-DCN对比基准有显著的性能提升。ST-C-DCN为细粒度PM_(2.5)预测提供了一个具有潜力的方向。 展开更多
关键词 PM_(2.5)预测 多源影响因素 膨胀卷积网络 贝叶斯优化 Shapley分析 Friedman检验
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PM2.5浓度影响因素的主成分回归分析 被引量:3
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作者 张红 董小刚 李群 《长春工业大学学报》 CAS 2017年第2期105-110,共6页
选取全国31个城市,对空气中细颗粒物(PM 2.5)浓度的影响因素进行分析。为处理自变量之间存在的共线性,选用主成分回归。确定主成分的个数,将原自变量的主成分代替原自变量进行回归分析。总结出造成空气中细颗粒物(PM 2.5)浓度上升的因... 选取全国31个城市,对空气中细颗粒物(PM 2.5)浓度的影响因素进行分析。为处理自变量之间存在的共线性,选用主成分回归。确定主成分的个数,将原自变量的主成分代替原自变量进行回归分析。总结出造成空气中细颗粒物(PM 2.5)浓度上升的因素分为两方面,直接因素中二氧化氮浓度和间接因素中汽车数量。 展开更多
关键词 pm2.5 主成分分析法 回归分析
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昆明市PM_(2.5)中水溶性无机离子的化学特征及来源解析
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作者 朱若珲 曹芳 范美益 《环境化学》 CAS CSCD 北大核心 2024年第6期2005-2016,共12页
为探究昆明市大气中水溶性无机离子的化学组分、季节差异及主要来源,本研究于2019年12月至2020年11月在云南大学进行PM_(2.5)样品采集,利用离子色谱仪分析样品中水溶性无机离子的质量浓度,并结合离子相关性分析、后向轨迹分析和主成分... 为探究昆明市大气中水溶性无机离子的化学组分、季节差异及主要来源,本研究于2019年12月至2020年11月在云南大学进行PM_(2.5)样品采集,利用离子色谱仪分析样品中水溶性无机离子的质量浓度,并结合离子相关性分析、后向轨迹分析和主成分分析等方法,阐明了昆明市大气中PM_(2.5)及其水溶性无机离子的季节污染特征及来源.结果表明,采样期间各季节总水溶性无机离子浓度均值排序为春季((5.6±2.2)μg·m^(−3))>冬季((5.5±2.6)μg·m^(−3))>秋季((4.3±2.8)μg·m^(−3))>夏季((3.6±2.2)μg·m^(−3)),水溶性无机离子年质量浓度的均值从大到小为SO_(4)^(2−)>Ca^(2+)>NO_(3)^(−)>NH_(4)^(+)>K^(+)>Cl^(−)>Na^(+)>Mg^(2+)>F^(−),其中SO_(4)^(2−)、Ca^(2+)、NO_(3)^(−)和NH_(4)^(+)是主要的水溶性无机离子.Ca^(2+)主要源于土壤粉尘,其他三者由前体物(SO_(2)、NO_(x)和NH_(3))二次转化生成,主要受化石燃料燃烧排放影响.SOR和NOR全年均值分别为0.20和0.02,表明在相同的环境里,SO_(2)二次转化为SO_(4)^(2−)的过程更易发生,且在秋季转化速率最大(SOR=0.23).SO_(4)^(2−)、NO_(3)^(−)和NH_(4)^(+)在秋季主要以NH_(4)NO_(3)和(NH_(4))_(2)SO_(4)的形式存在,其他三季则以NH_(4)HSO_(4)和NH_(4)NO_(3)的形式存在.昆明市大气PM_(2.5)中水溶性无机离子在冬、秋和春季一致,主要来自二次源和生物质燃烧源,其次是工业源和土壤尘,而夏季则主要来自机动车尾气、生物质燃烧源和土壤尘.除本地排放的影响外,冬季和夏季受到来自缅甸、老挝和贵州污染气团的影响,春季污染气团来自缅甸、云南本地和贵州,而秋季则受到云南东部和南部地区的气团输送影响. 展开更多
关键词 水溶性无机离子 PM_(2.5) 来源解析 昆明 后向轨迹分析
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ICP-MS法测定PM2.5中无机元素浓度及污染特征分析 被引量:2
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作者 王婉 王立 +4 位作者 张晓 张宝军 曹振红 张佳浩 朱媛媛 《环境工程技术学报》 CAS 2020年第4期631-638,共8页
采用微波消解、电感耦合等离子体质谱(ICP-MS)法同时直接测定了PM2.5中23种无机元素的浓度,并对消解方法、ICP-MS工作参数及条件进行了优化和选择。该方法的检出限为0.01~10.00 ng/mL,定量检出限为0.04~40.00 ng/mL。采用该方法测定了2... 采用微波消解、电感耦合等离子体质谱(ICP-MS)法同时直接测定了PM2.5中23种无机元素的浓度,并对消解方法、ICP-MS工作参数及条件进行了优化和选择。该方法的检出限为0.01~10.00 ng/mL,定量检出限为0.04~40.00 ng/mL。采用该方法测定了2017年10月-2018年1月秋冬季唐山市3个监测点位的PM2.5滤膜样品,结果表明:地壳元素中Si浓度最高,为2.30μg/m^3,大多元素浓度在采暖前高于采暖后;重金属元素中Zn浓度最高,为0.48μg/m^3,大多元素浓度在本次观测的11月和12月较高;所测元素浓度与其他文献数据具有可比性,说明该方法适用于环境大气PM2.5中的无机多元素分析测试。 展开更多
关键词 pm2.5 电感耦合等离子体质谱(ICP-MS)法 无机多元素分析
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