因子分析法解析北京市大气颗粒物PM10的来源

2004年10月份在北京市6个采样点采集了大气PM10样品,分析了大气颗粒物的质量浓度、元素组成、离子、有机碳(OC)和元素碳(EC)的浓度,并用因子分析模型对颗粒物的来源进行了研究。结果显示,北京市大气颗粒物的来源主要有6类:建筑水泥尘/机动车尾气尘/燃煤尘、土壤风沙尘、二次粒子尘、工业粉尘、生物质燃烧尘和燃油尘。用模型计算得到的各源对PM10的贡献率分别为建筑水泥尘/机动车尾气尘/燃煤尘占36.57%、土壤风沙尘占16.07%、二次粒子尘占12.33%、工业粉尘占10.29%、生物质燃烧尘占6.07%、燃油尘占3.84%、其它占14.84%。其中建筑水泥/机动车尾气尘/燃煤尘、土壤风沙尘、二次粒子尘、工业粉尘是大气颗粒物PM10的主要来源。实验表明,在缺少源成分谱时可以用因子分析模型来分析大气颗粒物的来源及其相对贡献。     

杭州市大气PM2.5和PM10污染特征及来源解析

2006年在杭州市两个环境受体点位采集不同季节大气中PM2.5和PM10样品,同时采集了多种颗粒物源类样品,分析了其质量浓度和多种化学成分,包括21种无机元素、5种无机水溶性离子以及有机碳和元素碳等,并据此构建了杭州市PM2.5和PM10的源与受体化学成分谱;用化学质量平衡(CMB)受体模型解析其来源。结果表明,杭州市PM2.5和PM10污染较严重,其年均浓度分别为77.5μg/m3和111.0μg/m3;各主要源类对PM2.5的贡献率依次为机动车尾气尘21.6%、硫酸盐18.8%、煤烟尘16.7%、燃油尘10.2%、硝酸盐9.9%、土壤尘8.2%、建筑水泥尘4.0%、海盐粒子1.5%。各主要源类对PM10贡献率依次为土壤尘17.0%、机动车尾气尘16.9%、硫酸盐14.3%、煤烟尘13.9%、硝酸盐粒8.2%、建筑水泥尘8.0%、燃油尘5.5%、海盐粒子3.4%、冶金尘3.2%。     

对应分析在大气颗粒物源解析中的应用研究

采用对应分析方法定性研究龙岩市大气颗粒物来源问题。结果表明:龙岩市4个监测点位中龙岩市环境监测站的主要污染源为高岭土矿和土壤风沙尘;龙岩学院与闽西职业技术学院的主要污染源为机动车尾气尘、燃煤尘和土壤风沙尘;龙岩师专的主要污染源为燃煤尘、小煤炉尘、水泥成品、水泥厂除尘器集尘和钢铁厂除尘器集尘。该方法能将R型因子分析和Q型因子分析结合起来,综合体现样品和元素之间的关系,为大气颗粒物源解析提供了一种新的途径。     

石家庄市春节期间大气颗粒物有机碳和元素碳的变化特征

为研究石家庄市大气颗粒物的污染特征及其来源,于2013年2月6—19日春节期间在石家庄市采集大气颗粒物TSP、PM10、PM2.5样品,对其有机碳、元素碳进行分析测定。结果表明,石家庄TSP、PM10、PM2.5日平均质量浓度分别为389、330、245μg/m3,颗粒物污染严重;碳组分在颗粒物中占有较大比重,且随着粒径的减少,碳组分比重逐渐增加;存在不严重的次生有机碳污染;OC与EC的相关系数较高,说明两者有较为相似的污染源,主要为燃煤、机动车排放源。各种气象条件对PM2.5、OC、EC浓度和OC/EC的变化都有不同程度的影响。     

石家庄市大气颗粒物元素组分特征分析

为研究石家庄市大气颗粒物的污染特征及其来源,于2013年4—5月在主城6区分别采集TSP、PM10和PM2.5颗粒物样品,利用ICP-MS分析其中的22种元素浓度。结果表明,石家庄市城区Ca、Fe元素在各粒径颗粒物中含量都较高,PM2.5中的S、K含量较高,PM10和TSP中Mg、Al的浓度相对较高。颗粒物的主要来源为燃煤尘、道路尘和建筑尘,TSP、PM10和PM2.5具有较好的统计相关性和同源性。     

沈阳市大气中PM2.5来源解析研究

通过2015年在沈阳市采集PM2.5样品及源类样品,分析样品的质量浓度和化学组成,用化学质量平衡(CMB)模型对该市PM2.5来源进行解析。结果表明:沈阳市大气中PM2.5浓度时空变化特征明显;各主要源类对沈阳市PM2.5的分担率依次为煤烟尘(28.03%)、二次无机离子(22.63%)、机动车尾气尘(17.27%)、城市扬尘(13.28%)、建筑尘(5.94%)、土壤风沙尘(5.82%)、道路尘(3.04%)、生物质燃烧尘(2.74%)和冶金尘(1.25%)。燃煤和机动车的有效控制既能降低本类源的贡献,也能降低二次无机离子,体现了多源类综合治理原则。     

应用化学质量平衡模型解析西宁大气PM2.5的来源

为研究影响西宁市大气环境PM_(2.5)污染水平的主要来源,于2014年采暖季、风沙季和非采暖季依托西宁市大气地面观测网络在11个监测点采集大气PM_(2.5)样品,对其化学组分(元素、离子和碳)进行分析。研究同步采集了4类固定源、14类移动源和4类开放源的PM_(2.5)样品,并构建源排放成分谱。应用化学质量平衡受体模型(CMB)开展源解析研究。源解析结果表明,观测期间西宁市PM_(2.5)主要来源包括城市扬尘(分担率为26.4%)、燃煤尘(14.5%)、机动车尾气(12.8%)、二次硫酸盐(9.0%)、生物质燃烧(6.6%)、二次硝酸盐(5.7%)、钢铁尘(4.7%)、锌冶炼尘(3.4%)、建筑尘(4.4%)、土壤尘(4.4%)、餐饮排放(2.9%)和其他未识别的来源(5.2%)。大力开展城市扬尘为主的开放源污染控制,严格控制本地燃煤、机动车等污染源的PM_(2.5)排放,是改善西宁市空气质量的重要途径。     

宁波PM10中有机碳和元素碳的季节变化及来源分析

为了探讨宁波市大气颗粒物中浓度水平与季节变化,2010年1、5、8、11月分季节采集了宁波市大气中PM10样品,在宁波连续观测了PM10以及有机碳(OC)、元素碳(EC)的浓度变化,并探讨宁波全年各季碳气溶胶污染变化特征;PM10中OC和EC相关性较好,说明OC与EC的来源相同,各采样点PM10中OC/EC的各季均值大部分超过2.0,表明宁波空气中存在一定的二次污染。宁波秋季SOC占OC含量高于其他季节。从PM10中8个碳组分丰度初步判断宁波市颗粒物中碳的主要来源是汽车尾气、道路扬尘及燃煤。     

潍坊市大气环境中PM2.5和PM10的污染特征及来源解析

于2017年1月—2018年1月在潍坊市城区8个监测点位按季节采集了环境空气颗粒物样品，对其组分进行分析；采用电子低压冲击仪（ELPI）稀释采样法和稀释四通道法2种源采样方法同步采集源样品，建立了潍坊市本地化的燃煤源、钢铁源等排放源的颗粒物源成分谱；结合排放源清单，利用化学质量平衡受体模型（CMB）开展不同行业的细颗粒物（PM_2.5）和可吸入颗粒物（PM₁₀）的精细化来源解析。结果表明，各监测点位ρ（PM_2.5）、ρ（PM₁₀）年均值均超过环境空气质量二级标准；潍坊市城市扬尘、土壤风沙尘、建筑水泥尘特征组分分别为硅（Si）、Si、钙（Ca），燃煤尘和造纸碱回收尘的特征组分均为硫酸根离子（SO₄^2-）；PM_2.5首要的贡献源类为煤烟尘，分担率为36%；其次为机动车尘，分担率为25.4%；扬尘的分担率为21.8%；煤烟尘中分担率最高的是工业燃煤（18%）；机动车尘中以载货汽车分担率最大（14%）。PM₁₀首要的贡献源类也是煤烟尘，分担率为30.9%，其次是扬尘（27.6%）、机动车尘（21.5%）；煤烟尘中分担率最高的是工业燃煤，为15.4%，机动车尘中以载货汽车分担率最大，为11.8%。工艺过程的分担率均较低。     

东营春季PM10中有机碳和元素碳的污染特征及来源

2010年4月采集了东营市大气PM10样品,测定了PM10的浓度,并采用IMPROVE-TOR方法准确测量了样品中的8个碳组分.结果表明,采样期间,东营市大气PM10的平均浓度为(147.02±56.22) μg/m3；PM10中有机碳(0C)、元素碳(EC)浓度平均值分别为11.82、3.68 μg/m 3；PM10中OC和EC显著相关,表明OC、EC的来源相同；所有采样点PM10中OC/EC均大于2.15,表明存在二次有机碳(SOC)的贡献；PM10中SOC平均质量浓度是3.91 μg/m3,占OC质量浓度的33.08％；通过计算PM10中8个碳组分丰度,初步判断东营市颗粒物中碳的主要来源是汽车尾气、道路扬尘和燃煤.     

北京市主要PM10排放源成分谱分析

对北京市土壤尘、道路扬尘、城市扬尘、建筑施工尘、钢铁尘、煤烟尘等主要PM10无组织排放源和固定源进行采样、分析,建立相应的成分谱数据库,通过对其化学组分分析,确定各类PM10排放源的化学组分特征和标识元素。土壤尘、建筑施工扬尘、钢铁尘、煤烟尘PM10的标识元素分别为Si、Ca、Fe、Al,道路扬尘显示出明显的土壤尘、建筑施工尘和机动车污染的特征,城市扬尘成分谱与道路尘有很强的共线性,具有明显的道路扬尘特征。     

杭州市大气PM2.5和PM10污染特征及来源解析

2006年在杭州市两个环境受体点位采集不同季节大气中PM_2.5和PM₁₀样品,同时采集了多种颗粒物源类样品,分析了其质量浓度和多种化学成分,包括21种无机元素、5种无机水溶性离子以及有机碳和元素碳等,并据此构建了杭州市PM_2.5和PM₁₀的源与受体化学成分谱;用化学质量平衡(CMB)受体模型解析其来源。结果表明,杭州市PM_2.5和PM₁₀污染较严重,其年均浓度分别为77.5μg/m³和111.0μg/m³;各主要源类对PM_2.5的贡献率依次为机动车尾气尘21.6%、硫酸盐18.8%、煤烟尘16.7%、燃油尘10.2%、硝酸盐9.9%、土壤尘8.2%、建筑水泥尘4.0%、海盐粒子1.5%。各主要源类对PM₁₀贡献率依次为土壤尘17.0%、机动车尾气尘16.9%、硫酸盐14.3%、煤烟尘13.9%、硝酸盐粒8.2%、建筑水泥尘8.0%、燃油尘5.5%、海盐粒子3.4%、冶金尘3.2%。     

Chemical compositions and sources of atmospheric PM10 in heating, non-heating and sand periods at a coal-based city in northeastern China

Mass concentrations and chemical components (18 elements, 9 ions, organic carbon [OC] and elemental carbon [EC]) in atmospheric PM(10) were measured at five sites in Fushun during heating, non-heating and sand periods in 2006-2007. PM(10) mass concentrations varied from 62.0 to 226.3 μg m(-3), with 21% of the total samples' mass concentrations exceeding the Chinese national secondary standard value of 150 μg m(-3), mainly concentrated in heating and sand periods. Crustal elements, trace elements, water-soluble ions, OC and EC represented 20-47%, 2-9%, 13-34%, 15-34% and 13-25% of the particulate matter mass concentrations, respectively. OC and crustal elements exhibited the highest mass percentages, at 27-34% and 30-47% during heating and sand period. Local agricultural residuals burning may contribute to EC and ion concentrations, as shown by ion temporal variation and OC and EC correlation analysis. Heavy metals (Cr, Ni, Zn, Cu and Mn) from coal combustion and industrial processes should be paid attention to in heating and sand periods. The anion/cation ratios exhibited their highest values for the background site with the influence of stationary sources on its upper wind direction during the sand period. Secondary organic carbon were 1.6-21.7, 1.5-23.0, 0.4-17.0, 0.2-33.0 and 0.2-21.1 μg m(-3), accounting for 20-77%, 44-88%, 4-77%, 8-69% and 4-73% of OC for the five sampling sites ZQ, DZ, XH, WH and SK, respectively. From the temporal and spatial variation analysis of major species, coal combustion, agricultural residual burning and industrial emission including dust re-suspended from raw material storage piles were important sources for atmospheric PM(10) in Fushun at heating, non-heating and sand periods, respectively. It was confirmed by principal component analysis that coal combustion, vehicle emission, industrial activities, soil dust, cement and construction dust and biomass burning were the main sources for PM(10) in this coal-based city.     

淮南市大气PM_(10)中多环芳烃污染特征的研究

对2008年05至11月淮南市5个采样点大气可吸入颗粒物(PM10)样品进行分析,总结了研究区内PM10及其中16种PAHs的浓度特征、季节变化规律和来源解析。研究区内16种PAHs浓度总和的范围在15.20~111.58ng.m-3之间,平均值为40.40ng.m-3,中位数为33.34ng.m-3。PAHs总量的季节变化与采样时环境温度显示出较好的负相关性,即秋季>春季>夏季;运用多环芳烃比值综合判断,淮南市大气PM10中PAHs主要以燃煤和机动车尾气混合来源为主,石油源和木材燃烧来源的贡献较小。     

天津市开放源可吸入颗粒物污染问题分析

通过高斯面源反演的计算方法对天津市扬尘污染源进行反演计算,建立开放源可吸入颗粒物污染源强数据库,系统分析了城市扬尘污染问题。数值试验模拟结果表明,扬尘控制措施与环境质量呈现很好的线性相关关系,通过模拟2004年天津市建筑施工扬尘对城市可吸入颗粒物污染贡献,提出扬尘污染问题解决方案。     

Regional and seasonal characteristics of emission sources of fine airborne particulate matter collected in the center and suburbs of Tokyo, Japan as determined by multielement analysis and source receptor models

Airborne particulate matter, suspected to induce adverse effects on human health, have been one of the most important concerns regarding recent air pollution issues in Japan. To characterize regional and seasonal variations in emission sources of fine airborne particulate matter (d < 2 microm), monthly samples (n = 36 for each site) were collected at urban (Tokyo), suburban (Maebashi), and mountainous (Akagi) sites in Japan from April 2003 to March 2006. Multielement analysis of chemical species (Na, Al, K, Ca, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Sb, and Pb) was performed by inductively coupled plasma-atomic emission spectrometry and inductively coupled plasma-mass spectrometry. The combined source receptor model, which consists of positive matrix factorization and chemical mass balance, determined the contributions of nine emission sources (local and continental soils, road dust, coal and oil combustion, waste incineration, steel industry, brake wear, and diesel exhaust) to the observed elemental concentrations. Large regional differences were identified in the source contributions among the observational sites. Diesel exhaust was identified as the most significant source (70% of identified contributions) at the urban site. Local and continental soils, coal combustion, and diesel exhaust were intricately assigned (20-30% each) to the suburban site. Continental soil was the predominant source (65%) at the mountainous site. Respective significant source contributions dominated the seasonal variations of total elemental concentrations at each site. These results suggest that a better understanding of the regional and seasonal characteristics of impacting emission sources will be important for improving regional environments.     

Atmospheric Aerosols over a Southwestern Region of Texas

Speciated samples of PM_2.5 were collected at the Big Bend site from July of 2003 to June 2006 and the McDonald Observatory site from July of 2003 to August of 2005 in southwestern Texas, respectively, by the US Environmental Protection Agency. A total of 175 samples for the Big Bend site and 105 samples for the McDonald Observatory site with 52 species were measured; however, 30 and 32 species from the Big Bend and McDonald Observatory sites, respectively, were excluded because of too much below-detection-limit data. Due to the laboratory change about November 1 of 2004 and possible analytical artifacts, phosphorous was excluded as well. Among the species excluded, 31 species are common to both sites. The two data sets were analyzed by positive matrix factorization to infer the sources of PM observed at the two sites. The analysis resolved five source-related factors for Big Bend and four for McDonald Observatory. Sulfate-rich secondary aerosol, coal burning, motor vehicle/road dust, and a mixed factor were identified as common sources to both sites. The other factor identified for Big Bend is related to soil. Sulfate mainly exists as ammonium salts. The sulfate-rich secondary aerosols account for about 62% and 66% of the PM_2.5 mass concentration at the two sites, respectively. The highest concentration of Si associated with Ca, Fe, \textSO₄^{2 -} {\text{SO}}_4^{2 - } , and organic carbon at the two sites was possibly attributed to the coal-fired power plants in the region. Basically, the factor of sulfate and coal burning at the two sites showed similar chemical composition profiles and seasonal variation that reflect the regional characteristics of these sources. The regional factors of sulfate, coal burning, and soil showed predominantly low-frequency variations; however, the area-related and/or local factors showed both high and low frequency variations. The motor vehicle/road dust and the mixed factors were likely to be area-related and/or local source.