Effect of PM2.5 chemical constituents on atmospheric visibility impairment

PM_2.5 sampling was conducted at a curbside location in Delhi city for summer and winter seasons, to evaluate the effect of PM_2.5 and its chemical components on the visibility impairment. The PM_2.5 concentrations were observed to be higher than the National Ambient Air Quality Standards (NAAQS), indicating poor air quality. The chemical constituents of PM_2.5 (the water-soluble ionic species SO₄^2-, NO₃^?, Cl^?, and NH₄⁺, and carbonaceous species: organic carbon, elemental carbon) were analyzed to study their impact on visibility impairment by reconstructing the light extinction coefficient, b_ext. The visibility was found to be negatively correlated with PM_2.5 and its components. The reconstructed b_ext showed that organic matter was the largest contributor to b_ext in both the seasons which may be attributed to combustion sources. In summer season, it was followed by elemental carbon and ammonium sulfate; however, in winter, major contributions were from ammonium nitrate and elemental carbon. Higher elemental carbon in both seasons may be attributed to traffic sources, while lower concentrations of nitrate during summer, may be attributed to volatility because of higher atmospheric temperatures.

Implications: The chemical constituents of PM_2.5 that majorly effect the visibility impairment are organic matter and elemental carbon, both of which are products of combustion processes. Secondary formations that lead to ammonium sulfate and ammonium nitrate production also impair the visibility.     

天津地区环境监测点PM2.5量化分析模型

为了研究天津地区PM2.5与5种日常监测的污染物——PM10、SO2、CO、O3和NO2的定量关系,通过天津环保物联网获取了11个环境监测点2013年8月—2014年5月的日监测数据,分别尝试了多元一次回归模型、二次回归模型和基于主成分分析回归模型进行建模与量化分析。相关性上,天津地区各站点PM2.5与5种污染物显著相关,其中与PM10、SO2和NO2相关系数分别达到0.89±0.03、0.63±0.09和0.69±0.06。模型评价结果表明,利用5种污染物监测指标预估PM2.5浓度基本可行,基于主成分分析模型效果最好,拟合度能够达到0.85以上,平均误差在20%左右。     

基于混合效应模型的京津冀地区PM2.5数值模拟

采用2017年京津冀地区中分辨率成像光谱仪提供的3km气溶胶光学厚度(AOD)和气象数据,分别在有无气象因子作为自变量的情况下对PM_(2.5)与AOD的关系进行解释。比较基于空间、时间及时空的78种混合效应模型的模拟效果,并利用十折交叉模型进行验证。经相关系数、拟合程度和模型误差对比,最终确定将气温、相对湿度、风速和气压4种气象因子作为影响因子的基于时空的混合效应模型效果最佳。利用该模型估算2017年京津冀地区的PM_(2.5)监测值,结果表明,PM_(2.5)监测值与模拟值的R~2为0.90(经十折交叉模型校正后为0.81),均方根误差、平均绝对误差分别为13.44、10.12μg/m~3,模型的拟合精度较高。同时研究表明,整体来说,京津冀地区呈现南高北低、平原地区东南高西北低、非平原地区高纬度区域相对低的空间格局。     

北京市典型排放源PM2.5中多环芳烃成分谱特征

采用稀释通道采样系统对北京市部分污染源排放的PM2.5进行了采集,用气相色谱-质谱-质谱法分析了PM2.5中24种多环芳烃(PAHs)的浓度,获得典型排放源PM2.5中PAHs成分谱。结果表明,不同种类污染源排放的PAHs的组分浓度差异比较大,形成的百分浓度轮廓图有各自的特征。生物质燃烧和化石燃烧排放的PM2.5中PAHs含量高于其他污染源;燃煤电厂和供暖/工业锅炉排放的PM2.5中低环数的PAHs比例较高,而生物质燃烧和餐饮源则是高环数的污染物比例较高。燃烧温度高,燃烧较充分,采用布袋除尘方式的污染源排放的PAHs含量要低于其他污染源。     

监测数据反推下PM_(2.5)扩散模型及突发情况模拟

建立了PM2.5扩散模型和应急污染扩散评估方法。模型考虑了风向、风速、温度等影响,根据监测点位置和PM2.5浓度信息反推虚拟污染源位置及浓度信息;采用高斯扩散模型,得到稳定风向、风速、温度下的任意时间、接收点的PM2.5扩散衰减模型;突发情况下,用时间变量替换速度项,得到该地区时态变化的PM2.5变化规律。根据西北某城市PM2.5监测数据,建立了其四季PM2.5扩散衰减模型,并对其空气质量进行分级表征,同时对某监测点突发情况下PM2.5时空变化进行了预测评估。结果表明,该模型合理可行。     

An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

An enhanced PM_2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM_2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM_2.5 air quality is more likely to be critical for human health. The enhanced PM_2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM_2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM_2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.     

The sensitivity of PM2.5 source-receptor relationships to atmospheric chemistry and transport in a three-dimensional air quality model

Air quality model simulations constitute an effective approach to developing source-receptor relationships (so-called transfer coefficients in the risk analysis framework) because a significant fraction of particulate matter (particularly PM2.5) is secondary (i.e., formed in the atmosphere) and, therefore, depends on the atmospheric chemistry of the airshed. In this study, we have used a comprehensive three-dimensional air quality model for PM2.5 (SAQM-AERO) to compare three approaches to generating episodic transfer coefficients for several source regions in the Los Angeles Basin. First, transfer coefficients were developed by conducting PM2.5 SAQM-AERO simulations with reduced emissions of one of four precursors (i.e., primary PM, sulfur dioxide (SO2), oxides of nitrogen (NOx), and volatile organic compounds) from each source region. Next, we calculated transfer coefficients using two other methods: (1) a simplified chemistry for PM2.5 formation, and (2) simplifying assumptions on transport using information limited to basin-wide emission reductions. Transfer coefficients obtained with the simplified chemistry were similar to those obtained with the comprehensive model for VOC emission changes but differed for NOx and SOz emission changes. The differences were due to the parameterization of the rates of secondary PM formation in the simplified chemistry. In 90% of the cases, transfer coefficients estimated using only basin-wide information were within a factor of two of those obtained with the explicit source-receptor simulations conducted with the comprehensive model. The best agreement was obtained for VOC emission changes; poor agreement was obtained for primary PM2.5.     

Source profile and health risk assessment of PM2.5 from coal-fired power plants in Fuxin,China

In order to improve and establish the localized source profile of PM_2.5 in Fuxin, the ashes under dust catcher were collected from four typical coal-fired power plants in Fuxin and twenty-eight components were measured. The source profile of PM_2.5 in the soot of the four coal-fired power plants was established. SO₄^2? was the most abundant component in the PM_2.5 of the soot of the four coal-fired power plants, followed by Ca²⁺ and organic carbon (OC). The content of element components in PM_2.5 smoke ranges from 5.06 to 10.97%, the content of ionic components ranges from 36.53 to 48.59%, and the total carbon content ranges from 9.43 to 11.36%. The divergence coefficient of PM_2.5 source profile in Fuxin coal burning smoke is mostly similar to that of Fushun, whereas the divergence coefficient of Colorado reaches 0.65, indicating that Fuxin coal burning power plant smoke has no similarity to Colorado. The order of the geological accumulation index of Ni, Cu, V, Mn, and Cr was Cr (4.58) > Mn (4.42) > V (4.38) > Cu (4.09) > Ni (4.06), showing a heavy pollution level. The health risk assessment model recommended by the USEPA was used to assess the health risk of heavy metals in soot of coal-fired power plants, and the non-carcinogenic risk values of As for children and adults were 45.7 and 4.90, respectively. The carcinogenic risk values of Cr for adults and children were the highest, with values of 3.66 × 10^?5 and 2.06 × 10^?5, respectively, followed by As.

     

Source apportionment of PM2.5 during different haze episodes by PMF and random forest method based on hourly measured atmospheric pollutant

Environmental Science and Pollution Research - Hourly measured PM2.5-bound species, gases, and meteorological data were analyzed by the PMF receptor model to quantify source contributions, and by...     

Climatology of PM2.5 organic carbon concentrations from a review of ground-based atmospheric measurements by evolved gas analysis

In this work we have compared ground-based measurements of organic carbon (OC) in the fine aerosol (PM2.5) fraction that are reported in peer-reviewed publications as part of both short campaigns and continuous monitoring networks. The comparison provides a quantitative review of global OC measurements for the purpose of establishing the extent to which organic aerosol concentrations are known with sufficient geographic and historical resolution to constrain global climate models. Only North America has sufficient measurements to provide meaningful spatial and temporal trends, although available measurements from China and Japan indicate that the Asian region is the most polluted with OC concentrations of approximately 10 μg m^?3. These measurements have a low spatial resolution, with most sites located in highly urban areas within a small geographic region. OC concentrations in North America are approximately 1 μg m^?3 and are better characterized spatially, temporally, and historically by continuous monitoring networks established decades ago. OC concentration shows a weakly increasing trend in some regions from 1997 to 2006, although in most regions it has remained effectively constant over the last ten years. Eastern U.S. sites show maximum OC in the winter and western U.S. sites show maximum OC in the summer. There is no correlation at U.S. sites between OC concentration and sulfate, nitrate, or ammonium ions, with R² < 0.1 in each case.     

Source apportionment of Phoenix PM2.5 aerosol with the Unmix receptor model

The multivariate receptor model Unmix has been used to analyze a 3-yr PM2.5 ambient aerosol data set collected in Phoenix, AZ, beginning in 1995. The analysis generated source profiles and overall average percentage source contribution estimates (SCEs) for five source categories:gasoline engines (33 +/- 4%), diesel engines (16 +/- 2%), secondary SO4(2-) (19 +/- 2%), crustal/soil (22 +/- 2%), and vegetative burning (10 +/- 2%). The Unmix analysis was supplemented with scanning electron microscopy (SEM) of a limited number of filter samples for information on possible additional low-strength sources. Except for the diesel engine source category, the Unmix SCEs were generally consistent with an earlier multivariate receptor analysis of essentially the same data using the Positive Matrix Factorization (PMF) model. This article provides the first demonstration for an urban area of the capability of the Unmix receptor model.     

Decomposition analysis of PM2.5 emissions based on LMDI and Tapio decoupling model: study of Hunan and Guangdong

Environmental Science and Pollution Research - This paper takes energy consumption PM2.5 emission as research object, and quantitatively analyzes the PM2.5 emission level in Hunan and Guangdong...     

武汉市冬夏季大气PM2.5浓度及其烃类化合物的变化特征

分析了武汉市2009年冬夏两个季节大气PM2.5的浓度,并用气相色谱/质谱(GC/MS)技术研究了烃类化合物组成及变化特征。结果表明,PM2.5的质量浓度为25.0～302.4μg/m3,冬季明显高于夏季。检测出nC11～nC34正构烷烃,高碳数部分奇偶优势明显,碳优势指数(CPI)在1.1～2.9,具有高等植物蜡和人为源输入特征,冬夏季分布差异较大;藿烷和甾烷的普遍检出证实了大气颗粒物已明显受到化石燃料残余物的污染,且在冬季浓度相对较高;高环数多环芳烃含量较高,特征性诊断参数表明机动车排放相对较大。     

Short-term prediction of urban PM2.5 based on a hybrid modified variational mode decomposition and support vector regression model

Environmental Science and Pollution Research - PM2.5 (particulate matter with a size/diameter ≤ 2.5&nbsp;μm) is an important air pollutant that affects human health, especially...     

上海市大气PM2.5时空分布特征

对2014年上海市大气监测国控点的PM2.5浓度数据进行统计分析和聚类分析。统计分析结果表明,上海市PM2.5浓度冬春季高,夏秋季低,按月呈U形分布,且上海市大气PM2.5浓度在空间上总体趋势呈西高东低。利用MATLAB的聚类分析结果表明上海市的10个监测站可分为4类：1）跨省传输影响显著的青浦淀山湖站;2）受海洋大气影响显著的浦东川沙站;3）不稳定的过渡类,其包括杨浦四漂和浦东张江监测站;4）受本地排放影响显著的中心城区类,其包括普陀、十五厂（卢湾师专附小）、徐汇上师大、虹口凉城、静安和浦东新区监测站。本文聚类分析结果揭示了上海不同地理位置的大气PM2.5浓度的相互关系。     

Effect of local and long-range transport emissions on the elemental composition of PM10–2.5 and PM2.5 in Beirut

The elemental composition of PM_10−2.5 and PM_2.5 were studied in winter, summer, stormy and non-stormy dates during a period extending from February 2004 till January 2005, in a populated area of Beirut. Results of PIXE analysis and enrichment factor (E.F.) calculation, using Si as a reference of crustal material, showed that crustal elements (E.F.<10) like Si, Ca, K, Ti, Mn and Fe were more abundant in PM_10−2.5 while enriched elements (E.F.>10) like S, Cu, Zn and Pb predominated in PM_2.5. In PM_10−2.5, concentrations of crustal elements increased during stormy episodes, all time high Ca concentrations were due to the abundance of calcite and limestone rocks in Lebanon, and increased Cl levels correlated with marine air masses. In PM_2.5, sulfur concentrations were more prominent in the summer due to the enhancement of photochemical reactions. Sources of sulfur were attributed to local, sea-water and long-range transport from Eastern Europe, with the latter being the most predominate. Anthropogenic elements like Cu and Zn were generated from worn brakes and tires in high traffic density area and spikes of Pb were directly linked to a southerly wind originated from Egypt and/or Israel as determined by the air trajectory HYSPLIT model. In brief, elemental variations depended on the regional variability of the transport pattern and the different removal rates of aerosols.     

Effect of SME biodiesel blends on PM2.5 emission from a heavy-duty engine

To explore the effect of biodiesel and sulfur content on PM_2.5 emissions, engine dynamometer tests were performed on a Euro II engine to compare the PM_2.5 emissions from four fuels: two petroleum diesel fuels with sulfur contents of 50 and 100 ppm respectively, and two B20 fuels in which soy methyl ester (SME) biodiesel was added to each of the above mentioned petroleum diesel fuels (v/v: 80%/20% for petroleum diesel and SME respectively). Gaseous pollutants and PM_2.5 emissions were sampled with an AVL AMA4000 and Model 130 High-Flow Impactor (MSP Corp). Measurements were made of the PM_2.5 mass, organic carbon (OC), elemental carbon (EC) and the water-soluble ion distribution. The results showed that PM_2.5 emissions decreased with lower sulfur content or blending with SME biodiesel, and the decrease would be more by applying both two methods together. Particles of approximately 0.13 μm contributed 48–83% of PM_2.5 emissions. The impact of sulfur content on this percentage was different for low and high engine speed. The majority of PM_2.5 was comprised of OC and EC, and the carbon emission rate had the same trend as PM_2.5. Since the EC abatement of B20 was larger than OC, the OC/EC ratio of B20 was always larger than that of petroleum diesel. For petroleum diesel, the OC/EC increased with sulfur content, which was not the case for B20. The SO₄^2? had highest emission rate in the water-soluble ions of PM.     

Interpretation of trends of PM2.5 and reconstructed visibility from the IMPROVE network

Under the IMPROVE visibility monitoring network, federal land managers have monitored visibility and fine particle concentrations at 29 Class I area sites (mostly national parks and wilderness areas) and Washington, DC since 1988. This paper evaluates trends in reconstructed visibility and fine particles for the 10th (best visibility days), 50th (average visibility days), and 90th (worst visibility days) percentiles over the nine-year period from 1988-96. Data from these sites provides an indication of regional trends in air quality and visibility resulting from implementation of various emission reduction strategies.