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。     

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%。     

重庆城区不同粒径颗粒物元素组分研究及来源识别

为研究重庆市大气颗粒物的污染特征及其来源,于2010年3—10月在主城区分别采集PM_1.0、PM_2.5和PM₁₀3种粒径的颗粒物样品,利用XRF分析其中的26种元素浓度。结果表明,重庆市主城区S元素在各粒径中含量都较高,细粒子中K的含量较高,粗粒子中Si、Ca和Fe的浓度较大。富集因子分析表明,主城区Cd、S、Se等污染元素的富集系数较大,且粒径越小,富集现象越明显。利用因子分析得出土壤风沙、扬尘、燃煤的燃烧、机动车燃油产生的尾气排放、生物质燃烧排放是重庆市颗粒物污染的主要来源。     

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%。     

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。     

External Contribution to Urban Air Pollution

Elevated particulate matter concentrations in urbanlocations have normally been associated with local trafficemissions. Recently it has been suggested that suchepisodes are influenced to a high degree by PM₁₀sources external to urban areas. To further corroboratethis hypothesis, linear regression was sought betweenPM₁₀ concentrations measured at eight urban sites inthe U.K., with particulate sulphate concentration measuredat two rural sites, for the years 1993–1997. Analysis ofthe slopes, intercepts and correlation coefficientsindicate a possible relationship between urban PM₁₀and rural sulphate concentrations. The influences of winddirection and of the distance of the urban from the ruralsites on the values of the three statistical parametersare also explored. The value of linear regression as ananalysis tool in such cases is discussed and it is shownthat an analysis of the sign of the rate of change of theurban PM₁₀ and rural sulphate concentrations providesa more realistic method of correlation. The resultsindicate a major influence on urban PM₁₀ concentrations from the eastern side of the UnitedKingdom. Linear correlation was also sought using PM₁₀ data from nine urban sites in London and nearby ruralRochester. Analysis of the magnitude of the gradients andintercepts together with episode correlation analysisbetween the two sites showed the effect of transportedPM₁₀ on the local London concentrations. This articlealso presents methods to estimate the influence of ruraland urban PM₁₀ sources on urban PM₁₀ concentrations and to obtain a rough estimate of thetransboundary contribution to urban air pollution from thePM₁₀ concentration data of the urban site.     

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.     

Receptor model-based source apportionment of particulate pollution in Hyderabad, India

Air quality in Hyderabad, India, often exceeds the national ambient air quality standards, especially for particulate matter (PM), which, in 2010, averaged 82.2?±?24.6, 96.2?±?12.1, and 64.3?±?21.2 μg/m³ of PM₁₀, at commercial, industrial, and residential monitoring stations, respectively, exceeding the national ambient standard of 60 μg/m³. In 2005, following an ordinance passed by the Supreme Court of India, a source apportionment study was conducted to quantify source contributions to PM pollution in Hyderabad, using the chemical mass balance (version 8.2) receptor model for 180 ambient samples collected at three stations for PM₁₀ and PM_2.5 size fractions for three seasons. The receptor modeling results indicated that the PM₁₀ pollution is dominated by the direct vehicular exhaust and road dust (more than 60 %). PM_2.5 with higher propensity to enter the human respiratory tracks, has mixed sources of vehicle exhaust, industrial coal combustion, garbage burning, and secondary PM. In order to improve the air quality in the city, these findings demonstrate the need to control emissions from all known sources and particularly focus on the low-hanging fruits like road dust and waste burning, while the technological and institutional advancements in the transport and industrial sectors are bound to enhance efficiencies. Andhra Pradesh Pollution Control Board utilized these results to prepare an air pollution control action plan for the city.     

Sources of PM10 and PM2.5 in Cairo’s ambient air

A source attribution study was performed to assess the contributions of specific pollutant source types to the observed particulate matter (PM) levels in the greater Cairo Area using the chemical mass balance (CMB) receptor model. Three intensive ambient monitoring studies were carried out during the period of February 21–March 3, 1999, October 27–November 27, 1999, and June 8–June 26, 2002. PM₁₀, PM_2.5, and polycyclic aromatic hydrocarbons (PAHs) were measured on a 24-h basis at six sampling stations during each of the intensive periods. The six intensive measurement sites represented background levels, mobile source impacts, industrial impacts, and residential exposure. Major contributors to PM₁₀ included geological material, mobile source emissions, and open burning. PM_2.5 tended to be dominated by mobile source emissions, open burning, and secondary species. This paper presents the results of the PM₁₀ and PM_2.5, source contribution estimates.     

Atmospheric Aerosol over a Southeastern Region of Texas: Chemical Composition and Possible Sources

Speciated samples of PM_2.5 were collected at a site in Jefferson County, Texas by US EPA (Environmental Protection Agency) from July of 2003 to August of 2005. A total of 269 samples with 52 species were measured; however, 22 species were excluded in this study because of too many below-detection-limit data. The data set was analyzed by positive matrix factorization (PMF) to infer the sources of PM observed at the site. The analysis identified ten sources: sulfate-rich secondary aerosol I (35.9%), sulfate-rich secondary aerosol II (21.0%), cement/carbon-rich (11.7%), wood smoke (8.8%), metal processing (6.3%), motor vehicle/road dust (5.7%), nitrate-rich secondary aerosol (3.3%), soil (3.2%), sea salt (2.6%), and chloride depleted marine aerosol (1.6%). Sulfate and nitrate mainly exist as salts. The two sulfate-rich secondary aerosols account for almost 57% of the PM_2.5 mass concentration. The factor containing highest concentrations of Cl and Na was attributed to sea salt due to the proximity of the monitoring site to the Gulf of Mexico. The chloride depleted marine aerosol was related to the sea salt aerosol. Cement/carbon-rich, wood smoke, metal processing, and motor vehicle/road dust factor were likely to be the local sources.     

Atmospheric ambient trace element concentrations of PM10 at urban and sub-urban sites: source apportionment and health risk estimation

In this study, PM₁₀ concentrations and elemental (Al, Fe, Sc, V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Mo, Ag, Cd, Sn, Sb, Ba, Pb, and Bi) contents of particles were determined in Düzce, Turkey. The particulate matter samplings were carried out in the winter and summer seasons simultaneously in both urban and sub-urban sampling sites. The average PM₁₀ concentration measured in the winter season was 86.4 and 27.3 μg/m³, respectively, in the urban and sub-urban sampling sites, while it was measured as 53.2 and 34.7 μg/m³ in the summer season. According to the results, it was observed that the PM₁₀ levels and the element concentrations reached higher levels, especially at the urban sampling site, in the winter season. The positive matrix factorization model (PMF) was applied to the data set for source apportionment. Analysis with the PMF model revealed six factors for both the urban (coal combustion, traffic, oil combustion, industry, biomass combustion, and soil) and sub-urban (industry, oil combustion, traffic, road dust, soil resuspension, domestic heating) sampling sites. Loadings of grouped elements on these factors showed that the major sources of the elements in the atmosphere of Düzce were traffic, fossil fuel combustion, and metal industry-related emissions.     

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.     

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.     

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。     

利用SPAMS研究长沙市秋季PM2.5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。     

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。     

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。     

Analysis of a high PM10 episode observed at a coastal site nearby Shanghai, China

A high PM₁₀ episode observed at a coastal site nearby Shanghai during 18–19 January 2007 was analyzed in this study. The maximum hourly averaged PM₁₀ concentrations for the 2 days were 0.58 and 0.62 mg/m³, respectively. The meteorological condition during the episode was favorable for air pollution with large-scale stagnation. There was no dispersing effect by high wind, no scavenging function by precipitation, and no diluting process by clean marine air during the episode. The trajectories for 16–19 January all came from the northern region and kept in low levels, and during the episode peak time, from the morning of 18 to the morning of 19 January, trajectories all came from the northern inland areas and had passed over the coastal region of Jiangsu province before arriving at the site. The variation of the air pollution indexes (APIs) in the cities located in the upwind direction of the site during the episode days clearly shows a process of large-scale air pollution from north to south. The liner correlation coefficient for PM₁₀ and SO₂ concentrations is 0.774 during the episode, while for PM₁₀ and CO, it even reaches 0.995, which indicated that the high PM₁₀ was mainly emitted from the coal burning for domestic heating in winter. Therefore, the observed episode was caused by the transport of domestic heating pollutants accumulated in the boundary layer from northern continental areas.     

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。     

郑州市PM2.5浓度时空分布特征及预测模型研究

利用统计学原理和GIS技术,对郑州市2013年8月17—12月31日期间PM2.5浓度时空分布特征进行分析,同时结合气象资料与前一日污染数据,建立人工神经网络反向传播算法模型(BP-ANN)和多元线性回归模型用于该市细颗粒物污染的短期预测。结果表明,郑州市PM2.5浓度日变化呈单峰模式,随逆温现象的发生和交通的密集于上午11:00达到峰值,午后逐步下降。在工作日、周末与国庆节的对比中,国庆节期间颗粒物污染浓度高出平日32.8%,表明人为活动的加剧影响PM2.5的排放;周末与工作日期间无显著差异。在空间分布上,金水区、管城回族区污染最为严重,工业燃煤、地铁施工等源排放是造成污染的主要原因;位于远郊的岗里水库,受秸秆焚烧和市区污染输送等影响,PM2.5浓度亦维持较高水平。最后,研究将所构建的BP-ANN预测模型和多元线性回归模型对比,结果发现两模型在建模阶段预测值与真实值的拟合一致性指标分别为0.944、0.918,均方根误差分别为59.788、70.611;验证阶段拟合一致性指标分别为0.854、0.794,平均绝对误差分别为25.298、32.775,表明BP-ANN模型在预测郑州市PM2.5污染过程中更具优势。