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。     

杭州秋冬季PM2.5污染特征分析

为研究杭州PM_2.5污染来源特征，利用2013—2019年杭州市PM_2.5监测数据和气象观测数据，分析了杭州市2013—2019年PM_2.5浓度变化，选取本地积累型和输入型2种PM_2.5污染过程，结合单颗粒气溶胶飞行时间质谱仪（SPAMS）和在线离子色谱数据，探讨杭州市PM_2.5化学组分和污染来源。结果表明：每年秋冬季（11月至次年3月）杭州以东北风、西北风及偏南风为主，风速低于4 m/s时，大气扩散条件差，受本地污染物积累影响，PM_2.5浓度容易出现超标；风速较大且为东北风和西北风时，受上游污染输入影响，易出现PM_2.5重度污染。本地积累型和输入型案例中，PM_2.5化学组分中占比最大的为NO₃^-、SO₄^2-和NH₄⁺；PM_2.5浓度上升过程中，二次NO₃^-和SO₄^2-转换率明显上升，其中NO₃^-上升更为显著，二次气溶胶污染严重。2次案例中，PM_2.5来源贡献占比前3位均为机动车尾气源、燃煤源和工业工艺源，其中本地积累型PM_2.5浓度上升阶段，机动车尾气源占比会明显上升；输入型案例中，输入阶段机动车尾气源占比显著上升，燃煤源贡献也小幅上升。     

连云港市冬季霾污染成因分析及应急管控效果评估

针对2022年1月5—14日连云港发生的细颗粒物（PM_2.5）连续污染事件（PM_2.5超标共计5 d），基于常规空气质量参数、气象要素、颗粒物组分参数等数据资料，系统分析了污染期间PM_2.5时空变化特征及污染成因，结合大气化学与天气预测模式（WRF Chem）和敏感性试验方法，定量评估了应急减排措施对连云港各区县PM_2.5浓度的影响。结果表明，5 d超标日中有3 d为轻度污染，2 d为中度污染，全市PM_2.5浓度呈现先上升后下降的趋势。不利的气象条件（静稳、小风、高湿）、本地排放（机动车尾气、工业工艺源）和二次生成共同导致了PM_2.5污染的发生。实施黄色预警管控后，ρ（PM_2.5）平均值下降了4.6μg/m³，降幅为5.2%，其中东海县和灌云县ρ（PM_2.5）的降幅最大，分别为6.1%和8.3%，同时污染天ρ（PM_2.5）峰值平均下降了9.4μg/m³（6.0%）。通过PM_2.5过程分析方法发现，应急减排导致人为排放、化学过程和背景浓度对近地面ρ（PM_2.5）正贡献的减少量要显著大于垂直混合、区域输送和对流过程负贡献的增加量。     

北京2019年国庆假日及随后一个星期PM2.5中金属元素来源分析

2019年10月1—14日，采集了北京五环内14份城区大气PM_2.5样品。用微波消解-电感耦合等离子体质谱法（ICP-MS）测试了样品中15种金属元素的质量浓度。结果表明：2019年国庆假日（10月1—7日）及随后一个星期（10月8—14日），北京五环内城区PM_2.5中15种金属元素的质量浓度总和分别为7 206、8 025.4 ng/m³。2个时段Na、K、Ca、Al占比较高，4种元素的质量浓度之和分别占元素总质量浓度的88.9%、83.5%。富集因子分析表明：2个研究时段内，北京城区Se、Cd均呈高度富集状态，受人为污染影响最大。根据因子分析结果以及各类污染源的标识元素判断，2个时段内，PM_2.5中的金属元素均来自地壳（土壤尘和建筑尘）、生物质燃烧、机动车排放、燃煤和工业排放的混合源。潜在源因子分析结果表明：国庆假日北京城区Se、Cd、Pb、Cr主要来源于河北、山东两省中部地区的区域性输送，Zn和Cu主要来源于北京周边工业排放源和城区机动车排放源；国庆假日之后，北京城区Se、Zn、Cd、Pb、Cr、Cu除受廊坊-天津-唐山污染带的区域性传输影响外，还受北京周边燃煤源、工业排放源及本地机动车排放源的共同影响。     

春节期间烟花爆竹燃放对海口市空气质量的影响

基于2018年与2019年春节期间常规空气污染物数据、2019年细颗粒物PM_2.5中水溶性离子和OC/EC的化学组分数据，结合气象资料，分析了春节期间烟花爆竹燃放对海口市空气质量的影响。结果表明：春节期间烟花爆竹燃放对海口市初一的空气质量影响较大，主要影响污染物为PM₁₀、PM_2.5和SO₂，对NO₂、CO和O₃影响相对较小；实施烟花爆竹严格管控后空气质量明显改善，2019年除夕夜PM₁₀、PM_2.5和SO₂质量浓度峰值较2018年分别下降67.4%、68.5%和66.7%；烟花爆竹集中燃放主要造成K⁺、Cl^-、Mg²⁺和SO₄^2-快速上升，其中K⁺影响最为显著，K⁺短时上升了16.21 μg/m³，增加17.01倍；对于NO₂^-、Na⁺、F^-、NO₃^-、Ca²⁺、NH₄⁺等6种离子和OC/EC影响较小。     

西宁市金属冶炼和压延加工业大气颗粒物排放清单

大气污染物排放清单是了解大气污染特征和控制对策的前提。根据排放因子方法，建立了2018年西宁市金属（包括黑色和有色金属）冶炼和压延加工业PM_2.5、PM₁₀大气污染物的排放清单，并对其时空分布特征和清单不确定性进行了分析。结果表明：西宁市黑色金属冶炼和压延加工业PM_2.5、PM₁₀的总排放量分别是4.88×10³、8.37×10³ t；该行业对PM_2.5、PM₁₀排放量贡献率最大的是城北区，分别为58.36%、49.61%。有色金属冶炼和压延加工业PM_2.5、PM₁₀的总排放量分别是1.85×10³、2.78×10³ t，该行业对PM_2.5、PM₁₀贡献率最大的是大通县，分别为53.51%、56.99%。黑色金属冶炼和压延加工业对PM_2.5、PM₁₀贡献率最大的产业是粗钢产业，贡献率分别是38.41%、30.28%。有色金属冶炼和压延加工业对PM_2.5、PM₁₀贡献率最大的是铝行业，贡献率分别是97.33%和98.01%。2个行业PM_2.5和PM₁₀的排放受月份影响较小，一天中09：00—18：00是排放高峰期。蒙特卡罗法模拟结果表明：黑色金属冶炼和压延加工业95%置信区间的不确定性较高，PM_2.5和PM₁₀的不确定性分别为-59.33%~58.55%和-47.51%~47.28%。     

2017年佛山市大气复合污染特征及来源分析

利用2017年佛山市8个国控监测点位的6项常规大气污染物自动监测数据，研究细颗粒物（PM_2.5）、可吸入颗粒物（PM₁₀）、臭氧（O₃）的时空变化和复合污染特征，并采用单颗粒气溶胶质谱仪对佛山市大气PM_2.5进行来源解析，分析O₃与二次气溶胶的协同增长关系。结果表明，2017年佛山市空气质量综合指数（AQI）为4.75，主要的空气质量污染物为PM_2.5、二氧化氮（NO₂）和O₃，除O₃呈现第2，3季度较高外，其他5项污染物均呈现第1，4季度较高的趋势。ρ（PM_2.5）和ρ（PM_2.5）/ρ（CO）在1—4月和11，12月较高，二次生成强度较大。机动车尾气源、燃煤源和工业工艺源是大气PM_2.5的主要来源。佛山市中心城区等道路密集以及交通枢纽地区的ρ（NO₂）较高，机动车尾气排放是大气NO₂的主要来源。O₃污染主要发生在4，5，7—10月。ρ（O₃）和ρ（PM_2.5）/ρ（CO）的日变化均在12:00—17:00达到峰值。ρ（PM_2.5）随光化学活性水平增强而提高，高度和中度光化学活性水平下ρ（PM_2.5）/ρ（CO）明显大于轻度和低光化学活性水平。在统计时段，PM_2.5和O₃协同增长的时间占37.3%，O₃污染对二次气溶胶的氧化生成有明显的促进作用。     

南通市一次典型沙尘污染过程分析

根据南通市大气超级站的观测结果和气象因素，对南通市2019年10月29日—11月2日一次典型沙尘污染过程、颗粒物化学组分、颗粒物消光和退偏进行分析。结果表明，在沙尘影响期间，PM₁₀小时峰值达311 μg/m³， ρ（Ca²⁺）较污染前上升了7.4倍；在沙尘颗粒物碱性环境条件下，二次组分OM和NO^-₃的快速生成，浓度分别较污染前上升了96.6 %和34.0 %；ρ（NO^-₃）/ρ（SO^-2₄）污染中（2.5）高于污染前（1.7），ρ（EC）/ρ（PM_2.5）污染中（4.2%）高于污染前（3.6%），受到明显的沙尘传输影响，而移动源排放也有一定贡献，在本地地面气压场较弱情况下，导致沙尘污染过程长时间持续。     

新冠肺炎疫情期间冀南地区空气质量时空变化特征

为研究新冠肺炎疫情期间冀南地区空气质量变化规律,明确防疫管控措施对空气质量的具体影响及大气污染物排放特征,笔者综合分析了研究区域的常规监测数据和部分大气超级站的PM_2.5组分数据。结果表明:疫情防控重点区域石家庄市、邢台市空气质量整体好转,细颗粒物和一次排放气态污染物浓度下降明显,PM_2.5来源中燃煤、生物质燃烧源占比上升,机动车尾气源占比下降,体现出交通管制、企业限产和道路工地扬尘管理等环保措施的有效性。疫情防控高风险区域石家庄市藁城区出现了明显的NO₂浓度降低、PM_2.5污染好转现象,而O₃浓度显著升高成为新的特征污染物。结果显示,藁城区综合防疫管控举措对本地一次排放污染物起到了明显抑制作用。疫情防控核心区域藁城区增村镇因实行最严格的封村、限行、停产等措施,人为污染源排放"触底",6项监测指标中除O₃浓度同比、环比均大幅度升高外,其他污染物浓度全时段降低,SO₂和CO昼夜差距缩小,环境质量明显优于周边乡镇。分析认为大规模持续化学消杀可能对O₃浓度升高有影响,此问题需要进一步探讨。     

北京城区降水对PM2.5和PM10清除作用分析

基于北京市PM_2.5和PM₁₀质量浓度、组分浓度以及降水数据，利用数理统计、相关性分析等方法分别从降水总量、降水时长和降水前颗粒物浓度3个角度研究降水对PM_2.5、PM₁₀的清除作用，同时以一次典型降水过程为例，具体分析降水对颗粒物的影响。结果表明：降水总量的增加有助于促进PM_2.5、PM₁₀的清除，随着降水总量增加，PM_2.5、PM₁₀的平均清除率提高，有效清除的比例增加；连续降水可增强对大气颗粒物的湿清除作用，连续降水达3d可有效降低PM_2.5、PM₁₀浓度；降水对PM_2.5、PM₁₀浓度的清除率和大气颗粒物前一日的平均浓度有较好的正相关性。降水对大气颗粒物的清除可分为清除、回升和平稳3个阶段，各个阶段大气颗粒物的变化趋势不同。降水对于大气气溶胶化学组分和酸碱性的改变具有明显作用，对于大气颗粒物各种组分的清除效果不完全相同。对于大气中OC、NO₃^-、SO₄^2-和NH₄⁺去除率较高，且这4种组分主要以颗粒态形式被冲刷进入降水中，加剧了北京市降水酸化程度。     

利用SPAMS初探盘锦市冬季PM2.5污染特征及来源

利用SPAMS 0515于2015年1月在盘锦市兴隆台空气质量自动监测点位采集PM_(2.5)样品,并分析其污染特征和来源。研究结果表明,盘锦市冬季PM_(2.5)的颗粒类型主要以OC颗粒、富钾颗粒、EC颗粒组成。其中,OC颗粒占比最高,为52.5%;PM_(2.5)污染的主要贡献源为燃煤、生物质燃烧、机动车尾气排放,占比分别为33.2%、25.7%、17.5%,特别是在PM_(2.5)质量浓度较高时段,燃煤和机动车尾气排放对污染的贡献较大。     

利用SPAMS研究长沙市秋季PM_(2.5)化学组成及来源

2019年10月12日—11月25日,使用单颗粒气溶胶飞行时间质谱仪(SPAMS)在位于长沙市的湖南省生态环境厅点位进行了为期45 d的定点监测。结果表明,监测期间长沙市总体空气质量小时级别优、良天气占比为80.3%。长沙市首要污染物为PM_(2.5),其主要来源为机动车尾气源,二次无机源次之,工业工艺源排在第三位,占比分别为27.4%,21.5%和17.4%。整体来看,监测期间PM_(2.5)质量浓度的升高大多伴随着以上3种污染源颗粒物的同步升高。机动车尾气源具有明显的早高峰,工业工艺源、生物质燃烧源和餐饮源夜间占比增加。在偏东方向气团主导下,工业工艺源和燃煤源贡献最大;在东北方向气团主导下,PM_(2.5)质量浓度最高,且机动车尾气源占比最高。     

质谱直接测量法解析盐城市大气细颗粒物来源

为全面了解盐城市大气颗粒物的组成,摸清以PM2.5为首要污染物的来源,说清其化学组分和源贡献率,于2014年12月16日00:00—2014年12月21日09:00,利用在线单颗粒气溶胶质谱仪,对盐城市细颗粒物进行实时在线源解析。结果表明,盐城首要污染物为燃煤,占比为23.7%,其次是机动车尾气,占比为18.3%,第三位是扬尘,占总颗粒数的15.7%,生物质燃烧占比为14.8%位列第四,工业工艺源、二次无机源和其他源贡献率相对较小。     

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.     

Vehicular particulate matter emissions in road tunnels in Sao Paulo, Brazil

In the metropolitan area of S?o Paulo, Brazil, ozone and particulate matter (PM) are the air pollutants that pose the greatest threat to air quality, since the PM and the ozone precursors (nitrogen oxides and volatile organic compounds) are the main source of air pollution from vehicular emissions. Vehicular emissions can be measured inside road tunnels, and those measurements can provide information about emission factors of in-use vehicles. Emission factors are used to estimate vehicular emissions and are described as the amount of species emitted per vehicle distance driven or per volume of fuel consumed. This study presents emission factor data for fine particles, coarse particles, inhalable particulate matter and black carbon, as well as size distribution data for inhalable particulate matter, as measured in March and May of 2004, respectively, in the Janio Quadros and Maria Maluf road tunnels, both located in S?o Paulo. The Janio Quadros tunnel carries mainly light-duty vehicles, whereas the Maria Maluf tunnel carries light-duty and heavy-duty vehicles. In the Janio Quadros tunnel, the estimated light-duty vehicle emission factors for the trace elements copper and bromine were 261 and 220 microg km(-1), respectively, and 16, 197, 127 and 92 mg km(-1), respectively, for black carbon, inhalable particulate matter, coarse particles and fine particles. The mean contribution of heavy-duty vehicles to the emissions of black carbon, inhalable particulate matter, coarse particles and fine particles was, respectively 29, 4, 6 and 6 times higher than that of light-duty vehicles. The inhalable particulate matter emission factor for heavy-duty vehicles was 1.2 times higher than that found during dynamometer testing. In general, the particle emissions in S?o Paulo tunnels are higher than those found in other cities of the world.     

Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF,PCFA and UNMIX receptor models

To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM_2.5 and PM_2.5?10 particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m³ for PM_2.5 and 331.36, 190.01, and 184.60 μg/m³ for PM_2.5?10 for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM_2.5, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM_2.5?10. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM_2.5 while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM_2.5?10. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.     

灰霾期间气溶胶的污染特征

从颗粒物的时空分布和浓度水平方面综述了灰霾期间气溶胶的污染特征,介绍了灰霾期间气溶胶中金属元素、水溶性离子、有机碳和元素碳的浓度特征,以及颗粒物与能见度的相关性研究进展。指出:灰霾天气多发生在冬季,且气溶胶中PM2.5占的比重大;气溶胶污染与地理环境、气候条件、经济发展水平等有密切关系;水溶性离子多集中在PM2.5中;能见度的下降与气溶胶特别是细颗粒物有很大关系。提出目前灰霾研究中主要存在3大问题:一是对灰霾期间气溶胶中含有的有机物类别及其对不同季节发生灰霾的贡献率仍需进一步研究;二是灰霾期间气溶胶中有机物的形成机理尚不明确;三是不同源排放的气溶胶对灰霾形成的贡献率有待探讨。建议系统地开展大气细颗粒物有害成分的鉴定、源排放颗粒物的物理化学特性、扩散过程中各种物质间的反应和转化等方面的研究,为大气污染防治法规的制定提供依据。     

新冠疫情管控期间西安细颗粒物中碳组分浓度变化特征

于2019年1月27日—3月18日及2020年1月27日—3月18日对西安市细颗粒物(PM_2.5)的碳组分浓度进行了在线观测，对比分析了非疫情与疫情期间各常规污染因子、气象要素、PM_2.5中有机碳(OC)和元素碳(EC)的污染特征。结果表明：非疫情与疫情期间西安市的气象条件总体水平较为相近。疫情期间的二氧化硫(SO₂)、臭氧(O₃)浓度相对升高。重污染天气下，除PM_2.5外，其他污染物浓度均降低，说明疫情管制对重污染天气污染物浓度的削弱作用明显。疫情期间，PM_2.5中的OC组分浓度及占比有显著升高，与疫情期间的各类交通管制导致的机动车尾气排放量显著降低有关。另外，OC与EC的相关性较强，说明污染来源与人类日常生活有关。疫情期间西安市颗粒物中碳组分主要来自各类生物质燃烧，并且存在SOC污染，SOC在OC中的占比达到37.8%。疫情期间重污染天气下，SOC在OC中的占比达到87.5%,说明SOC对重污染天气OC的贡献较大。     

Verifying emission factors and national POPs emission inventories for the UK using measurements and modelling at two rural locations

Different approaches are used to verify the adequacy of emission factors (EFs) and their use in emission inventories of persistent organic pollutants (POPs). The applicability of EFs was tested using atmospheric dispersion modelling to predict atmospheric concentrations of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) and resulting toxic equivalents (SigmaTEQ) and particulate matter <10 microm (PM(10)) in two rural locations in northern England (UK). The modelling was based on general assumptions of fuel composition, consumption and heating needs to simulate emissions of POPs from the domestic burning of coal and wood where ambient measurements were made in the winter of 1998. The model was used to derive the local contribution to ambient air concentrations, which were estimated independently based on comparative air measurements. The results support the hypothesis that in both villages, the majority of PAHs and the lower chlorinated PCDFs were locally released. The situation for PCBs and polychlorinated naphthalenes (PCNs) was different. While the EFs show the release of both compound groups from the domestic burning of coal and wood, the ambient levels of these "legacy POPs" in the villages were still clearly dominated by other sources. Rural areas relying mainly on fossil fuels can exceed the proposed UK ambient air quality standard for benzo[a]pyrene during winter. The measured EFs were then used to estimate the importance of the domestic burning of coal and wood to national emission inventories for these compound classes. Extrapolations to the UK suggest that the domestic burning of pure wood and coal were minor emitters for chlorinated POPs but contributed strongly to PAH and PM(10) levels in 2000. Finally, the UK's national POPs emission inventories based on source inventories and EF, as used here, were compared to estimates derived using the increase in atmospheric concentration of selected POPs.     

Characteristic, composition, and sources of TSP investigated by HRTEM/EDS and ESEM/EDS

Total suspended particle (TSP) collected at the fifth floor of House Dust in Hunan University, China, was analyzed in terms of microscopic morphology and chemical composition. The fine particles (50?nm-2?μm) in the TSP were analyzed by a high-resolution transmission electron microscope equipped with an energy-dispersive X-ray analyzer (HRTEM/EDS). Results showed that the particles were in shapes of plate, irregular and agglomerate. Based on EDS results, these fine particulate matter was primarily composed of Fe-rich (35.82-61.29%), Ca-rich (30.18-36.77%) and Si-rich (18.95-32.28%) particles. Other elements mainly including Mg (0.47-4.97%), Al (0.45-14.57%), S (0.45-4.73%), K (1.13-2.13%) and Zn (0.67-3.85%) were also observed. The sources analysis indicated that the HRTEM particles mainly originated from coal combustion, traffic emission, vehicles exhaust emission and fugitive soil or cement particulate matter. The coarse particles (4-50?μm) were detected by environmental scanning electron microscopy coupled with energy-dispersive X-ray detector (ESEM/EDS). Based on a simple algorithm, ESEM particles were categorized into five groups: C-bearing (46.15%, 67% and 86.98%), Si + Ca-bearing (21.48?+?11.80%, 16.51?+?10.81% and 16.32?+?10.62%), Si + Al-bearing (20.06?+?12.40%, 20.16?+?11.22% and 15.31?+?11.25%), Si-bearing (34.40%, 26.92% and 27.15%) particles and aggregates, most of which exhibit obvious crystalline structure, and these ESEM particles mainly derived from vehicles exhaust emission, coal combustion, soil, and biomass burning, while the aggregates are indicative of atmospheric reaction progress. HRTEM/EDS and ESEM/EDS are mutual complementary in analyzing the characteristic and determining the sources of TSP.