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。     

上海市浦东新区大气细颗粒物中重金属污染特征及来源解析

于2017年对浦东城区和郊区大气PM2.5中的重金属特征和来源进行了分析。结果表明,K、Fe、Na、Ca、Mg、Al等矿物元素为浦东新区PM2.5中含量最高的金属元素,其中K的年均值为297.3 ng/m^3。浦东城区的不同元素在季节变化上呈现较为不同的变化规律,郊区的金属元素值大部分呈现春季先逐月下降,在夏、秋季有起伏波动,在10月之后逐渐上升;沙尘+道路源+建筑扬尘、煤燃烧、工业排放、金属冶炼、船舶排放、海盐+垃圾焚烧+生物质燃烧为浦东城区PM2.5中重金属元素的6大类主要来源。其中沙尘+道路源+建筑扬尘对Ca的贡献率为82.7%,煤燃烧对As的贡献率为86.6%,工业排放对SO4^2-的贡献率达到65.9%,金属冶炼对Cr的贡献率为75.7%,船舶排放对V的贡献率为97.5%、海盐+垃圾焚烧+生物质燃烧对Cl^-的贡献率为93.0%。煤燃烧和金属冶炼主要来自于西部方向。船舶排放分布在长江口及其延伸带。浦东新区PM2.5中重金属元素的质量浓度与本地源排放强度、外界传输和大气扩散条件均有密切关系。     

渝东南典型城区冬季一次罕见的PM2.5污染过程及溯源分析

2020年12月底,以生态旅游业为主的重庆市渝东南地区出现了一次较为罕见的PM_2.5污染过程,持续时间长且污染程度重。以渝东南地区武隆区为例,应用污染特征雷达图、后向轨迹模型及潜在源污染贡献估算等方法分析了本次PM_2.5污染的特征及来源,结果表明：(1)在污染前期主要受扬尘、燃煤和机动车等污染排放影响,污染源直接排放贡献较大;中、后期污染受二次颗粒物影响显著,扬尘影响也较为明显。(2)污染期间的气流轨迹均为短距离输送,轨迹主要来自东北方向(65%)。(3)除自身污染排放贡献外,渝东北地区和主城都市区是武隆区PM_2.5污染的主要潜在源区,对武隆区传输贡献占比超50%。     

大同市大气颗粒物浓度与水溶性离子季度分布特征

为研究大同市大气颗粒物质量浓度与水溶性离子组成特征,于2013年2、7、9、12月,分别对大同市及其对照点庞泉沟国家大气背景点进行了PM2.5及PM10的采样,通过超声萃取-IC法测定了样品中的9种水溶性离子,结果表明,大同市大气颗粒物污染1、4季度重于2、3季度,PM2.5季度均值全年均未超标,PM10仅第1季度超标1.4倍,污染状况总体良好,PM2.5与PM10相关系数R为0.75,说明大同市颗粒物污染有较为相近的来源,且不同季节均以粗颗粒物为主;大同市PM2.5中水溶性离子浓度分布为SO2-4、NO-3、NH+4Cl-、Ca2+K+、Na+F-、Mg2+,PM10中Ca2+浓度仅次于SO2-4、NO-3,控制扬尘将有效降低PM10的浓度;PM2.5及PM10中的9种水溶性离子在不同季度的浓度与颗粒物浓度分布规律类似,1、4季度较高,2、3季度较低;由阴阳离子平衡计算结果可知,相关性方程的斜率K为1.045,表明大同市大气颗粒物中阳离子相对亏损,大气细粒子组分偏酸性。NO-3与SO2-4浓度比值均小于1,大同市以硫酸型污染为主,大气中的SO2-4主要来源于人类活动排放。     

Analysis of acidic components, heavy metals and PAHS of particulate in the Changwon–Masan area of Korea

This study is an analysis of the concentrations and components of heavy metals in PM_2.5 and the total suspended particulate (TSP) collected at a mechanical industrial complex (IC) site in Changwon and at a residential site in Masan, Korea. Particulate was collected during two sampling periods, from the late summer to the early fall and from the middle to late fall, at the IC site and one sampling period, from the middle fall to the early winter, at the residential site. PM_2.5 and TSP samples were taken by an annular denuder system and a hi-volume air sampler, respectively. The authors also identified the concentrations and components of heavy metals extracted from the PM_2.5 and TSP filters, the acidic components extracted from the PM_2.5 filters, and the polycyclic aromatic hydrocarbons (PAHs) extracted from polyurethane foam (PUF) plug. The average concentrations of the PM_2.5 collected at the IC and residential sites were very similar. Major sources of PM_2.5 at the study sites, however, were air emissions from vehicles and industry as well as emissions from residential heating and soil origins, respectively. The higher concentrations of the TSP at the IC site, as compared to those at the residential site, were due to either increased suspended dust from vehicle emissions or re-suspended road dust because of increased vehicle speeds near the IC site. Heavy metal concentrations in the TSPs were higher than those in the PM_2.5. The heavy metal concentrations in the PM_2.5 and TSP at the IC site with heavy traffic were substantially greater than those at the residential site. The concentrations of TSP and heavy metals and PAHs in PM during the period of the middle to late fall was much higher than those during the period of the late summer to early fall at the IC site. This is because of the difference in meteorological characteristics and energy uses between two periods. The residential site also showed higher concentrations of acidic anions while the IC site showed higher concentrations of acidic cation. Secondary aerosols or particulates, such as ammonium nitrate or ammonium nitrite, might have been important constituents of the PM_2.5 at the residential site. The PAHs in the TSP collected at the IC site was greatly affected by traffic and industry emissions consisting mostly of high molecular weight PAHs with two to four rings. PAHs in the TSP at the site, however, were affected by residential heating and air emissions from small chemical plants having higher concentrations of low molecular weight PAHs with five to six rings.     

青藏高原典型城市拉萨市大气颗粒物污染源成分谱建立研究与特征分析

系统研究建立高原典型城市拉萨市开放源(土壤风沙尘、道路扬尘、施工扬尘、采矿扬尘),移动源(机动车尾气尘),固定源(工业烟粉尘、生物质燃烧尘及餐饮油烟)共3类8种大气颗粒物(PM_(2.5)、PM_(10))污染源化学成分谱。研究结果表明:开放源以地壳类元素为主,自然背景特征明显;移动源源成分谱中元素碳含量明显高于其他城市,在PM_(2.5)、PM_(10)源谱中分别占60.15%、51.86%,有机碳含量也相对较高,均超过20%;固定源中,牛粪和松柏枝两类生物质燃烧污染源的有机碳含量显著高于其他组分,工业烟粉尘中Ca远高于其他组分,在PM_(2.5)、PM_(10)源谱中分别占21.32%、21.21%。移动源、固定源源成分谱均显示出高原城市的独特特征。     

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。     

工业链条炉燃用型煤与原煤颗粒物排放特征

选取燃烧型煤和原煤的典型链条炉,应用自行设计的固定源烟气颗粒物稀释采样系统,现场测试细颗粒物PM_(2.5)、PM_(10)和金属元素的排放特征。结果表明,型煤燃烧细颗粒物的排放比例高于原煤,型煤燃烧除尘器进口、出口PM_(2.5)质量比原煤燃烧分别增加715%和708%。燃烧型煤时,As和Pb在各粒径段的质量比均比原煤大。同时,由于型煤燃烧可吸入颗粒物的排放比例增加,包含或附着在烟尘上的金属元素排放比例也相应增加。     

中国北方地区采暖期颗粒物污染现状

分析了2013—2016年冬季采暖期与非采暖期中国北方地区颗粒物污染现状及时空变化特征。结果表明:中国北方地区空气污染比较严重,采暖期尤为突出。2016年,中国北方地区重度及以上污染天数比例超过10%,采暖期优良天数比例较非采暖期下降22.8%,重度及以上污染天数比例升高10.1个百分点。颗粒物浓度呈现明显的冬季高、夏季低的特点,最高值一般出现在12月至次年1月,最低值一般出现在7—9月。2013—2016年,北方地区空气质量呈较为明显的改善趋势,PM_(10)和PM_(2.5)浓度总体呈下降趋势,但2014年以来采暖期同期比较显示,PM2.5浓度呈缓慢升高趋势,采暖期空气污染形势十分严峻。颗粒物浓度呈现明显的空间分布规律,采暖期石家庄、保定、衡水、邢台、邯郸、安阳等城市为京津冀区域污染最严重的城市。     

南京市大气颗粒物中多环芳烃变化特征

逐月采集南京市大气中不同粒径的颗粒物,采用HPLC分析了2010年每个月PM_(10)和PM_(2.5)颗粒物样品中的多环芳烃(PAHs)的种类和浓度水平。结果表明:PM_(10)中PAHs年均值为25.07 ng/m~3,范围为11.03~53.56 ng/m3;PM_(2.5)中PAHs年均值为19.04 ng/m~3,范围为10.82~36.43 ng/m~3。PM_(10)和PM_(2.5)中PAHs总体浓度有着相似的变化趋势,呈现凹形变化曲线;在南京市大气颗粒物中吸附的PAHs大部分以5~6环的高环数组分为主,大部分PAHs和∑PAHs的相关性较好,年度变化幅度不大,分析结果表明,颗粒物中PAHs的来源与稳定的排放源相关,机动车排放不容忽视,与北方城市燃煤污染有着较大的区别。     

西宁市非采暖季和采暖季PM2.5中14种金属元素特征

于2012年11月采暖季和2013年9月非采暖季,在青藏高原典型城市西宁市4个采样点采集细颗粒物(PM_(2.5))样品,共获得40个有效样品。用微波消解-ICP-MS法、原子荧光法分析了样品中14种重点防控金属。结果表明:14种重点防控金属中Ag、Tl平均质量浓度为0.10~0.50 ng/m~3,Co、Sb、Hg平均质量浓度为0.50~4.00 ng/m~3,V、Cd、Cr、Ni、Cu、As平均质量浓度为4.00~50.0 ng/m~3,Mn、Pb、Zn平均质量浓度为50.0~2 000 ng/m~3。采样期间,采暖季相比非采暖季,PM_(2.5)质量浓度有下降趋势,不同采样区金属元素浓度有增有减。富集因子分析结果表明,重点防控金属元素在非采暖季主要来源于土壤风沙扬尘、机动车尾气和工业排放,采暖季主要来源于土壤风沙扬尘、燃煤、燃油、机动车尾气和工业排放。非采暖季Zn、Ag、Cd、Hg、Tl和Pb富集因子较高,采暖季Zn、As、Ag、Cd、Sb、Hg、Tl、Pb富集因子较高,更容易受到人为源的影响。     

Air Pollution Concentrations of PM2.5, PM10 and NO2 at Ambient and Kerbsite and Their Correlation in Metro City – Mumbai

Ambient concentrations of PM_2.5 and PM₁₀ are of concern with respect to effects on human health and environment. Increased levels of mortality and morbidity have been associated with respirable particulate air pollution. In India, it is not yet mandatory to monitor PM_2.5 levels therefore very limited information is available on PM_2.5 levels. To understand the fine particle pollution and also correlate with PM₁₀ which are monitored regularly in compliance with ambient air quality standards. This study was carried out to monitor PM_2.5, PM₁₀, and NO₂ for about one year in a residential cum commercial area of Mumbai city with a view to understand their correlation. The average PM_2.5 concentration at ambient and Kerbsite was 43 and 69 μg/m³. The correlation coefficients between PM_2.5 and PM₁₀ at ambient and Kerbsite were 0.83 and 0.85 respectively thus indicating that most of the PM_2.5 and PM₁₀ are from similar sources. TSP, PM₁₀ levels exceeded Central Pollution Control Board(CPCB) standard during winter season. PM_2.5 levels also exceeded 24 hourly average USEPA standard during winter season indicating unhealthy air quality.     

Characterisation of PM10, PM2.5 and Benzene Soluble Organic Fraction of Particulate Matter in an Urban Area of Kolkata, India

In this study, the relationship between inhalable particulate (PM₁₀), fine particulate (PM_2.5), coarse particles (PM_{2.5 – 10}) and meteorological parameters such as temperature, relative humidity, solar radiation, wind speed were statistically analyzed and modelled for urban area of Kolkata during winter months of 2003–2004. Ambient air quality was monitored with a sampling frequency of twenty-four hours at three monitoring sites located near traffic intersections and in an industrial area. The monitoring sites were located 3–5 m above ground near highly trafficked and congested areas. The 24 h average PM₁₀ and PM_2.5 samples were collected using Thermo-Andersen high volume samplers and exposed filter papers were extracted and analysed for benzene soluble organic fraction. The ratios between PM_2.5 and PM₁₀ were found to be in the range of 0.6 to 0.92 and the highest ratio was found in the most polluted urban site. Statistical analysis has shown a strong positive correlation between PM₁₀ and PM_2.5 and inverse correlation was observed between particulate matter (PM₁₀ and PM_2.5) and wind speed. Statistical analysis of air quality data shows that PM₁₀ and PM_2.5 are showing poor correlation with temperature, relative humidity and solar radiation. Regression equations for PM₁₀ and PM_2.5 and meteorological parameters were developed. The organic fraction of particulate matter soluble in benzene is an indication of poly aromatic hydrocarbon (PAH) concentration present in particulate matter. The relationship between the benzene soluble organic fraction (BSOF) of inhalable particulate (PM₁₀) and fine particulate (PM_2.5) were analysed for urban area of Kolkata. Significant positive correlation was observed between benzene soluble organic fraction of PM₁₀ (BSM10) and benzene soluble organic fraction of PM_2.5 (BSM2.5). Regression equations for BSM10 and BSM2.5 were developed.     

广州市春季一次沙尘天气过程综合观测

2017年4月21—23日广州市经历了一次远距离传输的沙尘天气过程,为了解沙尘过程对广州市空气质量的影响,基于广州市大气超级站,利用单颗粒气溶胶质谱(SPAMS)、气溶胶激光雷达观测数据并结合HYSPLIT后向轨迹模型分析了沙尘过程细颗粒物组分及污染来源贡献变化和沙尘气溶胶的来源及路径。结果表明:受沙尘过境影响,PM_(10)浓度大幅升高,PM_(2.5)/PM_(10)最小值仅为12.1%;沙尘过境期间影响近地面颗粒物的沙尘高度主要分布在1 km以下区域,近地面颗粒物消光系数均值为100.11 Mm~(-1),探测到最大退偏振比为0.28。SPAMS研究发现沙尘过境期间含硅酸盐颗粒物(SI)的细颗粒物数浓度比例达25.9%,是沙尘过境前的1.4倍;PM_(2.5)中扬尘贡献率明显增大,达到了17.3%,是沙尘过境前的1.9倍。后向轨迹模型HYSPLIT显示此次沙尘为典型的北方沙尘传输,沙尘源自中国西北地区,传输方向为自西北输送至华东地区后,转为东南方向影响广州市。     

Influence of Meteorological Parameters on Particulates and Atmospheric Pollutants at Taichung Harbor Sampling Site

Atmospheric aerosol particles and metallic concentrations, ionic species were monitored at the Experimental harbor of Taichung sampling site in this study. This work attempted to characterize metallic elements and ionic species associated with meteorological conditions variation on atmospheric particulate matter in TSP, PM_2.5, PM_2.5–10. The concentration distribution trend between TSP, PM_2.5, PM_2.5–10 particle concentration at the TH (Taichung harbor) sampling site were also displayed in this study. Besides, the meteorological conditions variation of metallic elements (Fe, Mg, Cr, Cu, Zn, Mn and Pb) and ions species (Cl⁻, NO₃ ⁻, SO₄ ²⁻, NH₄ ⁺, Mg²⁺, Ca²⁺ and Na⁺) concentrations attached with those particulate were also analyzed in this study. On non-parametric (Spearman) correlation analysis, the results indicated that the meteorological conditions have high correlation at largest particulate concentrations for TSP at TH sampling site in this study. In addition, the temperature and relative humidity of meteorological conditions that played a key role to affect particulate matter (PM) and have higher correlations then other meteorological conditions such as wind speed and atmospheric pressure. The parameter temperature and relative humidity also have high correlations with atmospheric pollutants compared with those of the other meteorological variables (wind speed, atmospheric pressure and prevalent wind direction). In addition, relative statistical equations between pollutants and meteorological variables were also characterized in this study.     

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%。     

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。     

北京地区不同季节PM2.5和PM10浓度对地面气象因素的响应

利用2013年1月—2014年12月北京地区PM_(2.5)和PM_(10)监测数据和同期近地面气象观测数据,采用非参数分析法(Spearman秩相关系数)研究了北京地区PM_(2.5)和PM_(10)的浓度对不同季节地面气象因素的响应。结果表明:北京地区大气颗粒物浓度水平具有明显的季节特征,冬季大气颗粒物污染最严重,夏季最轻。不同季节影响颗粒物浓度水平的气象因素各不相同,其中风速和日照时数为主要影响因素。PM_(2.5)和PM_(10)质量浓度对气象因素变化的响应程度也有较大区别,PM_(2.5)/PM_(10)比值冬季最高,PM_(2.5)影响最大,春季最低,PM_(10)影响最大。这些结论可对制订科学有效的大气污染控制策略提供参考。     

Distribution of PM2.5 and PM10-2.5 in PM10 Fraction in Ambient Air Due to Vehicular Pollution in Kolkata Megacity

This research paper aims at establishing baseline PM₁₀ and PM_2.5 concentration levels, which could be effectively used to develop and upgrade the standards in air pollution in developing countries. The relative contribution of fine fractions (PM_2.5) and coarser fractions (PM_10-2.5) to PM₁₀ fractions were investigates in a megacity which is overcrowded and congested due to lack of road network and deteriorated air quality because of vehicular pollution. The present study was carried out during the winter of 2002. The average 24h PM₁₀ concentration was 304 μg/m³, which is 3 times more than the Indian National Ambient Air Quality Standards (NAAQS) and higher PM₁₀ concentration was due to fine fraction (PM_2.5) released by vehicular exhaust. The 24h average PM_2.5 concentration was found 179 μg/m³, which is exceeded USEPA and EU standards of 65 and 50 μg/m³ respectively for the winter. India does not have any PM_2.5 standards. The 24 h average PM_10-2.5 concentrations were found 126 μg/m³. The PM_2.5 constituted more than 59% of PM₁₀ and whereas PM₁₀-PM_2.5 fractions constituted 41% of PM₁₀. The correlation between PM₁₀ and PM_2.5 was found higher as PM_2.5 comprised major proportion of PM₁₀ fractions contributed by vehicular emissions.     

Inorganic constituents of urban air pollution in the Lazio region (Central Italy)

A field study was carried out at six locations in the Lazio region (Central Italy) aimed at characterising atmospheric particulate matter (PM₁₀ and PM_2.5) from the point of view of the chemical composition and grain size distribution of the particles, the mixing properties of the atmosphere, the frequency and relevance of natural events. The combination of four different analytical techniques (ion chromatography, X-ray fluorescence and ICP for inorganic components, thermo-optical analysis for carbon compounds) yielded sound results in terms of characterisation of the air masses. During the first three months of the study (October-December 2004), many pollution events of natural (sea-salt or desert dust episodes) or anthropogenic nature were identified and characterised. More than 90% of the collected mass was identified by chemical analysis. The central role played by the mixing properties of the lower atmosphere when pollution events occurred was highlighted. The results show a major impact of primary anthropogenic pollutants on traffic stations and a homogeneous distribution of secondary pollutants over the regional area. An evaluation of the sources of PM and an identification of possible reliable tracers were obtained using a chemical fractionation procedure.