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.     

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.     

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.     

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.     

宁波市区冬季大气颗粒物及其主要组分的污染特征分析

为了更好地研究影响宁波市区环境空气质量的污染物变化特征,于2010年1月20—30日进行了加强监测。研究结果表明,宁波市区大气中PM₁₀和PM_2.5质量浓度较高,其中PM_2.5/PM₁₀为0.5~0.85。对PM₁₀和PM_2.5采样膜分析,水溶性粒子和含碳组分分别占PM₁₀和PM_2.5质量浓度的56.7%和66.9%,其中二次污染的水溶性离子SO₄^2-、NO₃^-和NH₄⁺是PM₁₀和PM_2.5中浓度较高的离子组分;PM_2.5样品中OC与EC的相关性较好,表明OC与EC的来源相对一致,可能主要来自机动车尾气的贡献;但PM₁₀样品中OC与EC的相关性较差,表明其来源相对复杂;其中SOC的浓度占OC的13%~35%,说明宁波市区冬季导致二次污染的光化学反应不活跃。     

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。     

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。     

Polycyclic aromatic hydrocarbons in bulk PM2.5 and size-segregated aerosol particle samples measured in an urban environment

To analyze polycyclic aromatic hydrocarbons (PAHs) at an urban site in Seoul, South Korea, 24-hr ambient air PM_2.5 samples were collected during five intensive sampling periods between November 1998 and December 1999. To determine the PAH size distribution, 3-day size-segregated aerosol samples were also collected in December 1999. Concentrations of the 16 PAHs in the PM_2.5 particles ranged from 3.9 to 119.9 ng m⁻³ with a mean of 24.3 ng m⁻³.An exceptionally high concentration of PAHs(∼120 ng m⁻³) observed during a haze event in December 1999 was likely influenced more by diesel vehicle exhaust than by gasoline exhaust, as well as air stagnation, as evidenced by the low carbon monoxide/elemental carbon (CO/EC) ratio of 205 found in this study and results reported by previous studies. The total PAHs associated with the size-segregated particles showed unimodal distributions. Compared to the unimodal size distributions of PAHs with modal peaks at < 0.12 μm measured in highway tunnels in Los Angeles (Venkataraman and Friedlander, 1994), four- to six-ring PAHs in our study had unimodal size distributions, peaking at the larger size range of 0.28–0.53 μm, suggesting the coagulation of freshly emitted ultrafine particles during transport to the sampling site. Further, the fraction of PAHs associated with coarse particles(> 1.8 μm) increased as the molecular weight of the PAHs decreased due to volatilization of fine particles followed by condensation onto coarse particles.     

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。     

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。     

北京市典型重污染过程中PM2.5载带水溶性无机离子污染特征分析

为研究北京地区冬季PM_(2.5)载带的水溶性无机离子组分污染特征,2013年1月在中国环境科学研究院内采用在线离子色谱(URG-9000B,AIM-IC)对PM_(2.5)中水溶性无机离子(SO_4~(2-)、NO_3~-、Cl~-、NH_4~+、Na~+、K~+、Mg~(2+)、Ca~(2+))进行监测与分析。结果表明,采样期间总水溶性无机离子(TWSI)浓度为61.0μg/m~3,其中二次无机离子SO_4~(2-)、NO_3~-、NH_4~+(SNA)占比达72.3%,在PM_(2.5)中占比为40.29%,表明北京市PM_(2.5)二次污染严重。重污染天[NO_3~-]/[SO_4~(2-)]表明,固定源污染较移动源更为显著。三元相图表明,在空气质量为优的情况下,NH_4~+(在SNA中占比为30.3%~65.5%,下同)主要以NH_4NO_3的形式存在,较少比例以(NH_4)_2SO_4存在;严重污染时,NH_4~+(47.3%~77.9%)主要以(NH_4)_2SO_4形式存在,其次以NH_4NO_3的形式存在,其余的NH_4~+以NH_4Cl的形式存在。[NO_3~-]/[SO_4~(2-)]日变化表明,早、晚机动车高峰影响北京重污染发生。     

2020年泰州市细颗粒物质量浓度高值区污染来源解析

2020年在位于泰州市主城区大气细颗粒物(PM_(2.5))质量浓度高值区的莲花国控空气站点进行手工采样,分析了大气PM_(2.5)的质量浓度和元素组成,以及离子、有机碳和元素碳的质量浓度。根据监测结果,采用正定矩阵因子分解(PMF)受体模型对其来源进行解析。结果显示,莲花站点大气PM_(2.5)中主要组分包括有机物、硝酸盐、硫酸盐、铵盐、地壳物质、氯盐、钾盐、黑炭、微量元素和钠盐,占比分别为35.7%,25.6%,13.9%,11.9%,6.1%,2.3%,1.5%,1.5%,0.8%和0.7%,有机物、硝酸盐、硫酸盐、铵盐为首要污染组分,这4类物质对PM_(2.5)的累计贡献为87.1%。根据解析结果与实际污染特征,提出应优化城市路网结构,强化工地扬尘管控,全面取缔燃煤炉和严抓秸秆禁烧工作等控制对策。     

Evaluation of A Sampling Strategy for Estimation of Long-term Pm2.5 Exposure for Epidemiological Studies

As part of the European Community Respiratory Health Survey (ECRHS) PM_2.5 (particles collected with an upper 50% cut point of 2.5 μm aerodynamic diameter) was measured using an EPA-WINS (Environmetal Protection Agency Well Impactor Ninety-six) sampler. The monitoring schedule was restricted to 7 days per month for one year. Simultaneously, during this one year study period a collocated Harvard Impactor (HI) was run on a daily basis in Erfurt, Germany. Here we validated the reliability of annual, seasonal and monthly means estimated using the ECRHS scheme (measurements taken less than 25% of the whole study period) with the ‘true’ long-term averages, which were estimated using all available daily means.The daily PM_2.5 means, obtained by both instruments operated in parallel, were only slightly different (the mean difference between EPA-WINS and HI was 1.8 μg m⁻³ and 2.8 μg m⁻³ for the winter means). The values obtained by the two instruments were highly correlated (r = 0.95).In view of that negligible difference, no additional bias was seen with respect to the annual and the winter means estimated by the two different sampling strategies (the difference was 1.7 μg m⁻³ and 2.7 μg m⁻³, respectively). Monthly means, however, can only be considered to be a crude estimate that may substantially under- or overestimate the true monthly mean value.     

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。     

Concentrations of PM2.5-associated OC, EC, and PCDD/Fs measured during the 2003 wildfire season in Missoula, Montana

Throughout August and September, 2003, wildfires burned in close proximity to Missoula, Montana, with smoke emanating from the fires impacting the valley for much of the summer. This presented the perfect opportunity to measure the levels of polychlorinated dibenzodioxins and dibenzofurans (PCDD/F) comprising ambient forest fire smoke particles impacting the Missoula Valley. An air sampler at the Montana Department of Environmental Quality's (DEQ) compliance site in Missoula measured hourly averages of PM₁₀ throughout the fire season. Three collocated PM_2.5 cyclones collected 24-h smoke samples using quartz filters and Polyurethane Foam (PUF) sorbent cartridges. From the quartz filters, concentrations of Organic and Elemental Carbon (OC/EC) were measured, while PCDD/F were measured from one set of a filter (particle phase) and PUF (vapor phase) aggregate of samples in an attempt to also investigate the different phases of PCDD/F in forest fire smoke impaired communities.Hourly PM₁₀ concentrations peaked at 302.9 μg m⁻³ on August 15. The highest OC concentration (115.6 μg m⁻³) was measured between August 21–22, and the highest EC concentration of 10.5 μg m⁻³ was measured August 20–21. Measurable concentrations of PM_2.5 associated PCDD/Fs were not detected from a representative aggregate sample, with the exception of small amounts of 1,2,3,4,6,7,8-heptachlorodibenzodioxin and octachlorodibenzodioxin. PM_2.5 samples collected during the smoke events were composed of approximately 65% OC. However, the OC fraction of the particles collected in the smoke impaired Missoula valley was not composed of significant amounts of PCDD/F.     

飞行时间二次离子质谱法在一次污染过程中PM2.5表面组分分析中的应用

团簇离子源的发明和使用，使飞行时间二次离子质谱（TOF-SIMS）法成为材料表面化学分析越来越重要的手段，TOF-SIMS法的主要测试功能包括表层质谱、化学成像及深度剖析3种。采用TOF-SIMS法对一次污染过程中的大气细颗粒物（PM_2.5）的表层进行检测，得到了PM_2.5表面成分的质谱及成像信息。结果表明：PM_2.5表层存在多种金属离子，通过扫描电子显微镜能谱仪的形貌与飞行时间二次离子质谱仪的成像亮度分析可知，K⁺、Na⁺、Mg²⁺响应强度相对较强，含量较高；通过原子吸收光谱仪进行金属离子定量可知，2018年11月4日和12月24日的K⁺、Na⁺、Mg²⁺的平均质量分别为1.809 5、0.443 8、1.526 2 mg；从形态分布上看，PM_2.5表面烟尘集合体含量较多，其次为燃煤飞灰、矿物颗粒和超细未知颗粒；PM_2.5表层的有机物离子C_xH_y片段也较多，经过m/z的测试数据进一步确定，表明颗粒物表层含有大量的脂肪烃和芳香烃；除此之外，还存在含N、O、S等的有机物和无机物。     

灰霾期间武汉城市区域大气污染物的理化特征

利用湖北省大气复合污染自动监测站2013年的全年监测数据,分析了灰霾期间武汉城市区域大气污染物的理化特征。霾日主要出现在春季、秋季和冬季。霾日与非霾日大气污染物质量浓度和气象参数的对比分析结果显示:高湿度、静风是武汉城市区域霾日的重要气象特征;PM1、PM_(2.5)、PM_(10)、NO_2、CO、NH3的质量浓度,SOR、NOR值以及PM_(2.5)中的二次无机离子(SO2-4、NO-3、NH+4)和部分元素(Pb、Se、Cd、Zn、K)的质量浓度均在霾日明显高于非霾日,而霾日SO2质量浓度仅在冬季略高于非霾日。选取2013年1月的连续灰霾日进行相关性分析,结果表明:污染组分主要来自当地排放(包括直接排放和二次形成),并受当地气象条件影响。此次灰霾过程中PM_(2.5)中的硫酸盐和硝酸盐主要来自气相反应,气态NO_2主要生成了气态HNO_3,而不是HNO_2。     

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。     

Numerical Simulations on the Effect of Sea–Land Breezes on Atmospheric Haze over the Pearl River Delta Region

The atmospheric haze over the Pearl River Delta (PRD) was investigated by using the Models-3 Community Multi-scale Air Quality modeling system with meteorological fields simulated by the Fifth-generation National Center for Atmospheric Research/Penn State University Mesoscale Model (MM5) from September 26th to September 30th, 2004. The model-simulated meteorological elements and particulate matter with aerodynamic diameter less than 10 μm (PM₁₀) were compared with observations at four air quality-monitoring stations. The results showed that MM5 successfully reproduced the diurnal variations of temperature, wind speed, and wind directions at these stations. The temporal variations of the simulated values were consistent with those of the observed (such as temperature, wind speed, and wind direction). The correlation coefficient was 0.91 for temperature and 0.56 for wind speed. The modeling results show that the spatial distributions of simulated PM₁₀ were closely related to the source emissions indicating three maxima of PM₁₀ over the PRD. The sea–land breezes diurnal cycle played a significant role in the redistribution and transport of PM₁₀. Nighttime land breeze could transport PM₁₀ to the coast and the sea, while daytime sea breeze (SB) could carry the accumulated PM₁₀ offshore back to the inland cities. PM₁₀ could also be transported vertically to a height of up to about 1000 m because of strong turbulence in the SB front. Process analyses indicated that the emission sources and the vertical diffusion were the major processes to influence the concentrations of particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5).     

Suspended Particulate Matter Distribution in Rural-Industrial Satna and in Urban-Industrial South Delhi

An air quality sampling program was designed and implemented to collect the baseline concentrations of respirable suspended particulates (RSP = PM₁₀), non-respirable suspended particulates (NRSP) and fine suspended particulates (FSP = PM_2.5). Over a three-week period, a 24-h average concentrations were calculated from the samples collected at an industrial site in Southern Delhi and compared to datasets collected in Satna by Envirotech Limited, Okhla, Delhi in order to establish the characteristic difference in emission patterns. PM_2.5, PM₁₀, and total suspended particulates (TSP) concentrations at Satna were 20.5 ± 6.0, 102.1 ± 41.1, and 387.6 ± 222.4 μg m⁻³ and at Delhi were 126.7 ± 28.6, 268.6 ± 39.1, and 687.7 ± 117.4 μg m⁻³. Values at Delhi were well above the standard limit for 24-h PM_2.5 United States National Ambient Air Quality Standards (USNAAQS; 65 μg m⁻³), while values at Satna were under the standard limit. Results were compared with various worldwide studies. These comparisons suggest an immediate need for the promulgation of new PM_2.5 standards. The position of PM₁₀ in Delhi is drastic and needs an immediate attention. PM₁₀ levels at Delhi were also well above the standard limit for 24-h PM₁₀ National Ambient Air Quality Standards (NAAQS; 150 μg m⁻³), while levels at Satna remained under the standard limit. PM_2.5/PM₁₀ values were also calculated to determine PM_2.5 contribution. At Satna, PM_2.5 contribution to PM₁₀ was only 20% compared to 47% in Delhi. TSP values at Delhi were well above, while TSP values at Satna were under, the standard limit for 24-h TSP NAAQS (500 μg m⁻³). At Satna, the PM₁₀ contribution to TSP was only 26% compared to 39% in Delhi. The correlation between PM₁₀, PM_2.5, and TSP were also calculated in order to gain an insight to their sources. Both in Satna and in Delhi, none of the sources was dominant a varied pattern of emissions was obtained, showing the presence of heterogeneous emission density and that nonrespirable suspended particulate (NRSP) formed the greatest part of the particulate load.