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.     

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。     

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.     

广州不同环境空气PM2.5中金属元素污染特征与风险评价

根据2015年广州城区磨碟沙、内陆郊区天湖和近海郊区万顷沙不同环境空气PM_2.5中金属元素（Pb、Cd、Cr、Ni、As等23种）监测数据，分析其污染特征与富集程度，并评估潜在生态风险和健康风险，为大气污染风险防控提供支持。结果显示：广州大气PM_2.5中金属K、Na、Fe、Ca、Al和Zn含量相对较高。总元素浓度总体呈冬季最高、夏季最低的特征，且随PM_2.5污染加重而升高，但总元素在PM_2.5中的占比下降。Cd、Se、Zn、Cu、Mo、Pb和Na富集严重（富集因子>100），体现了人类活动的重要影响，磨碟沙城区站富集因子通常高于另2个站点。广州大气总金属元素潜在生态危害程度为"很强"，Cd贡献为主，Pb、Cu和As元素贡献分别在天湖、磨碟沙和万顷沙位列第二。As、Cr和Mn是大气金属元素健康风险的主要贡献者；磨碟沙的总致癌效应风险高于万顷沙和天湖，但万顷沙的总非致癌效应风险最高。     

PM2.5 and PM10 Concentrations from the Qalabotjha Low-Smoke Fuels Macro-Scale Experiment in South Africa

This article presents results from the particulate monitoringcampaign conducted at Qalabotjha in South Africa during the winter of 1997. Combustion of D-grade domestic coal and low-smoke fuels were compared in a residential neighborhood to evaluate the extent of air quality improvement by switchinghousehold cooking and heating fuels.Comparisons are drawn between the gravimetric results from the two types of filter substrates (Teflon-membrane and quartz-fiber) as well as between the integrated and continuous samplers. It is demonstrated that the quartz-fiber filters reported 5 to 10% greater particulate mass than the Teflon-membrane filters, mainly due to the adsorption of organic gases onto the quartz-fiber filters. Due to heating of sampling stream to 50 °C in the TEOM continuous sampler and the high volatile content of the samples, approximately 15% of the particulate mass was lost during sampling.The USEPA 24-hr PM_2.5 and PM₁₀ National Ambient Air Quality Standards (NAAQS) of 65 g m^-3 and 150 g m^-3, respectively, were exceeded on several occasions during the 30-day field campaign. Average PMconcentrations are highest when D-grade domestic coal was used, and lowest between day 11 and day 20 of the experiment when a majority of the low-smoke fuels were phased in. Source impacts from residential coal combustion are also found to be influenced by changes in meteorology, especially wind velocity.PM_2.5 and PM₁₀ mass, elements, water-soluble cations (sodium, potassium, and ammonium), anions (chloride, nitrate, and sulfate), as well as organic and elemental carbonwere measured on 15 selected days during the field campaign. PM_2.5 constituted more than 85% of PM₁₀ at three Qalabotjha residential sites, and more than 70% of PM₁₀ at the gradient site in the adjacent community of Villiers. Carbonaceous aerosol is by far the most abundant component, accounting for more than half of PM mass at the three Qalabotjha sites, and for more than a third of PM mass at the gradient site. Secondary aerosols such as sulfate, nitrate,and ammonium are also significant, constituting 8 to 12% of PM mass at the three Qalabotjha sites and 15 to 20% at the Villiers gradient site.     

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。     

抚顺市PM10中元素分布特征及来源分析

为了确定抚顺市PM10中元素的浓度特征及其来源,于2006—2007年的采暖季、风沙季和非采暖季在抚顺市的6个采样点采集PM10样品,并用等离子体原子发射光谱法(ICP-AES)测定样品中Ti、Al、Mn、Mg、Ca、Na、K、Cu、Zn、As、Pb、Cr、Ni、Co、Cd、Fe、V等17种元素的含量。结果表明,Al、Mg、Ca、Na、K、Mn、Fe等地壳元素在17种元素中占有较大比重,全年平均达到97.0%。富集因子分析结果表明,Cu、Zn、Pb、Cr、Co、Cd等元素在各季和各采样点明显受到人为活动影响,是典型的污染元素。主因子分析结果显示,土壤风沙尘、建筑尘、燃煤尘、道路扬尘、机动车尾气排放、金属冶炼、锰、铜、钛工业源是抚顺市PM10中元素的主要来源。     

Interpretation of roadside PM10 monitoring data from Sunderland,United Kingdom

Roadside PM₁₀ has been monitored by Partisol® at three sitesin Sunderland between August 1997 and February 1998. The sites chosen were an inner city kerbside site; a roadside site adjacentto a dual carriageway on the outskirts of Sunderland with an openaspect; and a rural site.The results indicate that there is a seasonal variation in the relationship between the sites in terms of monitored PM₁₀.In the winter there is a poor correlation between the sites whereas in the summer significant correlations are obtained. Of the sites monitored PM₁₀ is consistently highest at the inner city roadside site. During the summer, exceedances of theU.K. 50 g m^-3 standard (DETR, 2000) are associated with conditions suitable for the build-up of photochemical pollutionhowever during the winter period exceedances are recorded duringa variety of weather conditions.At the dual carriageway site PM_2.5 has also been recorded and contributions to measured PM₁₀ are 77% in summer and68% in winter. The results illustrate a number of inconsistencies between this study utilising the Partisol® andothers reporting results where PM₁₀ has been monitored by TEOM®.     

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.     

苏州大气颗粒物PM2.5和PM10质量浓度异常“倒挂”现象的成因及影响分析

为深入研究PM_2.5和PM₁₀质量浓度异常“倒挂”现象的成因及影响,在苏州市相城区国控点开展比对监测分析,回顾性分析了2016—2020年苏州全部国控点颗粒物浓度数据。苏州市相城区国控点PM_2.5浓度的比对分析结果表明：该国控点频繁出现PM_2.5浓度高于其他国控点PM_2.5浓度和高于该站点PM₁₀浓度(“倒挂”率高达34%)的“双高”现象,PM_2.5平均浓度比其他9个国控点高12.5%～37.2%,比位于同一站点的备用监测仪器(“倒挂”率为0)高38.1%。2016—2020年,苏州全部国控点“倒挂”时间的总体趋势都是逐年递增,且集中发生在相对湿度较高的20:00至次日07:00。这5年间各国控点PM_2.5浓度异常偏高导致的异常“倒挂”现象对全市年均浓度产生的正误差分别为1.6%、2.8%、6.0%、6.2%和4.1%,基本呈现出逐年递增的趋势。上述结果表明：苏州PM_2.5浓度偏高是由动态加...     

河南省典型城市PM2.5无机元素污染特征及来源分析

分析2012年采暖季和非采暖季郑州市、洛阳市和平顶山市大气细颗粒物(PM_(2.5))样品中22种无机元素含量和污染特征,采用富集因子法、因子分析法研究当地PM_(2.5)中无机元素来源。结果表明:3个城市PM_(2.5)中无机元素总量在采暖季均高于非采暖季,不同季节占PM_(2.5)质量浓度的比例为1.7%~3.6%。Al、Na、Ca等地壳元素在PM_(2.5)中占比与PM_(2.5)浓度呈负相关关系,而Zn、Pb、Cu等人为源元素的占比随PM_(2.5)浓度增加无明显下降趋势。3个城市PM_(2.5)中Se、Cd、Br的富集因子高于1 000,Pb、Zn、Cu的富集因子为100~1 000,Co、Sc、Cr、Ni、As、Mn、Ba的富集因子为10~100,说明这些元素主要来源于人为源。13种人为源元素质量浓度在22种元素中占比为18.9%~26.3%,K、Fe、Ca、Al等4种元素占比为67.9%~76.1%。因子分析结果表明:3个城市无机元素来源组成有很大相似性,主要来源于燃煤、机动车、扬尘和建筑尘等,但Ni、Co、Sr、Ba还有来自其他排放源的贡献。     

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。     

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.     

PM2.5中重金属形态分布及其在模拟酸雨中的释放

对西安市夏季PM_2.5中6种重金属元素的化学形态进行分析,并研究了模拟酸雨淋溶条件下PM_2.5中6种元素的释放过程。结果表明,近80%的Cr和Fe分布在有机质、氧化物、硫化物结合态和残渣态中,Cd在大气环境中的化学性质很活泼,具有很强的毒害性,Pb、As、和Hg 3种元素主要以碳酸盐态、可氧化态与可还原态存在于环境中;PM_2.5中6种元素均有不同程度的释放,Cd释放率高于其他元素,Fe释放率最低,pH降低有利于颗粒物中重金属元素的释放。     

Atmosphic pollutants study of particles ionic species during high wind speed (>7 m s-1) near Taiwan strait in central Taiwan from 2004 to 2005

This study monitored atmospheric pollutants during high wind speed (> 7 m s⁻¹) at two sampling sites: Taichung Harbor (TH) and Wuci traffic (WT) during March 2004 to January 2005 in central Taiwan. The correlation coefficient (R ²) between TSP, PM_2.5, PM_2.5−10 particle concentration vs. wind speed at the TH and WT sampling site during high wind speed (< 7 m s⁻¹) were also displayed in this study. In addition, the correlation coefficients between TSP, PM_2.5 and PM_2.5−10 of ionic species vs. high wind speed were also observed. The results indicated that the correlation coefficient order was TSP > PM_2.5−10 > PM_2.5 for particle at both sampling sites near Taiwan strait. In addition, the concentration of Cl⁻, NO₃ ⁻, SO₄ ²⁻, NH₄ ⁺, Mg²⁺, Ca²⁺ and Na⁺ were also analyzed in this study.     

西宁市城区冬季PM2.5和PM10中有机碳、元素碳污染特征

2014年11月—2015年1月对西宁市冬季开展PM_(2.5)和PM_(10)的连续监测。利用DRI 2001A型热光碳分析仪(美国)对有机碳和元素碳进行分析,结果表明:西宁市冬季PM_(2.5)和PM_(10)中碳气溶胶所占比例分别为33.13%±6.83%、24.21%±6.27%,说明碳气溶胶主要集中在PM_(2.5)中;OC/EC值均大于2,说明西宁市大气中存在二次污染;SOC占PM_(2.5)和PM_(10)的质量浓度比例分别为46.50%和57.40%,PM_(2.5)中SOC浓度占PM_(10)中SOC浓度的61.88%,说明SOC主要存在于PM_(2.5)中,且SOC形成的二次污染和直接排放的一次污染都是西宁市碳气溶胶的主要来源;与其他城市比较发现,西宁市冬季PM_(2.5)中的碳气溶胶含量普遍高于其他城市,PM_(10)中OC质量浓度相对其他城市较高,EC质量浓度偏低;OC和EC的相关性不显著,说明来源不统一;进一步对OC和EC各组分质量浓度进行分析知,西宁市冬季碳气溶胶主要来源于机动车汽油排放、燃煤和生物质燃烧。     

2018—2021年安徽省PM2.5和O3时空分布特征及其健康风险分析

利用2018—2021年安徽省空气质量监测数据分析了PM_2.5和O₃时空分布特征及其引发的健康风险。结果表明:从时间分布来看,2018—2021年安徽省PM_2.5年均值下降25.5%,而O₃-8 h年均值则保持持平;PM_2.5和O₃-8 h月均值具有明显的季节变化特征,PM_2.5月均质量浓度和超标天数均在冬季达到最大值,O₃-8 h月均值和超标天数则在夏季达到最大值。从空间分布来看,PM_2.5、O₃-8 h年均值和超标天数均为皖北最高,其次为皖中,最后为皖南。夏季O₃是主要的健康风险因子,冬季PM_2.5是主要的健康风险因子。当PM_2.5超标时,除2021年皖北地区外(PM₁₀是主要的健康风险因子),PM_2.5均是主要的健康风险因子;当O₃-8 h超标时,O₃是主要的健康风险因子。     

上海市秋季典型PM2.5污染过程数值预报分析

基于2012年10月上海出现的一次典型PM2.5污染案例,验证评估上海市空气质量数值预报系统Model-3/CMAQ预报性能,采用过程分析技术,定量评估不同大气物理化学过程对上海代表性点位PM2.5浓度变化的作用规律。结果表明:Model-3/CMAQ模式系统能较好地反映PM2.5的浓度变化趋势与特点。对于上海市区点位(徐汇上师大)和东南部点位(奉贤海湾和浦东惠南),PM2.5浓度上升主要受本地源排放影响,其贡献比例超过40%,其次是区域大气传输作用的影响。对于西北部点位(崇明监测站和青浦淀山湖),区域大气传输是PM2.5浓度上升的主要原因,贡献比例超过70%,其次是源排放。各点位PM2.5浓度的主要去除途径均为大气传输,贡献比例均超过70%,其次是干沉降。气溶胶过程对PM2.5主要起二次颗粒物生成的作用,特别是市区及东南部点位,贡献比例较西北部点位更高。     

济南市大气颗粒汞的浓度特征及污染来源解析

为了解冬季采暖对济南市大气PM_2.5中汞浓度的影响,在济南市城郊开展了为期超过两年的PM_2.5样品采集工作,共计采集有效样品481个,测定并分析其中的颗粒汞(PHg)浓度和汞含量变化特征。结果表明,济南市大气PHg在采暖期的浓度均值为583.1 pg/m³,约为非采暖期的1.4倍,在国内外城市中处于中等偏上水平。济南市大气PM_2.5对PHg具有极强的富集能力,且在采暖期更强,可能与燃煤等活动排放了更多的超细颗粒物有关。在采暖期,大气PHg浓度主要受煤炭燃烧源和交通排放源影响,两者分别贡献了总方差的39.2%和16.7%;在非采暖期,气象条件季节性变化、交通排放源、煤炭燃烧源的影响显著,三者分别贡献了总方差的32.4%、15.8%、12.0%。高浓度PHg主要来源于分布在采样站点东北偏东方向上的众多燃煤工业企业。此外,济南市大气PHg还主要受来源于鲁西南地区的区域污染气团的影响,途经污染较重的京津冀地区的污染气团对济南市PHg浓度也有较大贡献。在非采暖期,济南市PHg还受到来自东南和西南方向的清洁海洋气团的显著影响。     

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%。