保山市大气细颗粒物排放特征与理化特征分析

于2016年11月9日—14日,用单颗粒气溶胶在线源解析技术分析保山市体育馆监测点大气中PM_(2.5)的化学组成、粒径分布、来源及典型排放源质谱特征。结果表明:采集的颗粒可分为7类,主要以有机碳、元素碳和混合碳颗粒为主,占电离颗粒数的60%以上;不同类型颗粒粒径分布差异较为明显;机动车尾气为首要污染贡献源,且呈周期性变化,每日有两个上升时段,分别为凌晨1:00—10:00和12:00—20:00;其次为燃煤源,贡献率为10%～40%;工艺过程源与生物质燃烧源贡献率相一致,总体上夜间贡献率高于白天;扬尘源、二次无机源贡献率变化幅度不大。     

茂名市大气PM_(2.5)在线源解析

于2014年12月31日—2015年1月12日,利用单颗粒气溶胶质谱仪对茂名市大气中PM2.5进行在线监测和分析。结果表明,茂名市大气颗粒物污染来源分布(颗粒数占比)分别为扬尘6%、工业工艺源10.9%、生物质燃烧14.7%、机动车尾气27.5%、燃煤23.4%、二次无机源7.7%和其他9.9%。空气质量从重度污染转为优良天气过程中,机动车尾气的贡献率基本保持在20%以上,而燃煤占比从28.9%降至12.3%;空气质量从优良转为污染天气的过程中,工业工艺源、二次无机源、生物质燃烧、燃煤的占比增加,而机动车尾气占比不断下降。     

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。     

长三角城市群污染输送相互影响的敏感性试验

利用WRF-Chem模式,对2013年11月29日至12月11日长江三角洲地区的严重空气污染事件进行数值模拟,研究长三角核心区不同污染物本地源和外来输送所占比重。分析长三角核心区排放源对本地不同污染物浓度的污染贡献。结果表明,在2013年12月的这一次污染事件中,颗粒物平均本地贡献与外来输送基本比重相当;而SO_2、CO、NH_3、NO_x这4种气体污染物则以本地贡献为主,本地贡献的差异与气体的化学反应活性有关,活性越强本地贡献比重越大。污染过程中12月7日至12月9日00:00为污染最严重的时段,污染物的本地贡献有明显上升。区域间输送的方向和强度与地面风向、风速有紧密的联系。在边界层高度范围内,大部分污染物越往高空本地排放源的贡献越弱,外来输送主导作用增强,而硝酸盐在地面、1 km和1.5 km的本地贡献差异远小于其他污染物。     

江淮地区秋季一次典型污染过程分析

结合2018年10月15—20日国控站点监测数据、气象资料及激光雷达走航观测结果,对江淮地区一次重度污染过程进行了分析。利用拉格朗日粒子扩散模型和拉格朗日混合单粒子轨迹模型定性分析了区域污染来源,分别基于激光雷达和空气站实测数据提出了外来源占比的估算方法,结合嵌套网格空气质量预报模式(NAQPMS)的源解析结果,对比分析了外来源占比。以淮北市为例,结合NAQPMS和单颗粒气溶胶质谱的PM2.5在线源解析结果,对比分析此次污染过程的行业来源。结果表明,本地污染累积时段,主要以燃煤和机动车尾气混合源为主(占比>70%);受北方污染输送时段,机动尾气占比显著升高,从19.4%(16日00:00)升至66.7%(17日11:00),淮北市、蚌埠市、合肥市3个城市污染物外来输送占比分别为52.2%~70.6%、48.8%~58.8%、41.5%~59.0%。     

武汉城区秋季大气挥发性有机物污染特征及部分物种来源的初步分析

于2014年10月采用GC-MS挥发性有机物(VOCs)在线监测系统在武汉城区开展大气VOCs连续监测,并分析VOCs体积分数的时间变化特征、光化学活性差异及来源。结果表明,武汉城区总VOCs体积分数为45.16×10-9,从高到低依次为烷烃烯烃芳香烃;VOCs日变化呈双峰型特征,峰值分别出现在6:00—8:00和19:00—23:00;T/B和E/E的平均比值分别为0.94和0.61,表明气团受机动车影响显著,且存在老化现象;烯烃对OH消耗速率(LOH)和臭氧生成潜势(OFP)的贡献率最大,芳香烃次之,烷烃最低;以3-甲基戊烷为机动车排放示踪物,计算得出非机动车源对乙烯、甲苯和间/对-二甲苯的贡献率分别为85%、55%和70%。     

东莞市一次空气污染过程成因分析

基于东莞市大气复合污染超级监测站的监测数据,选取2017年12月一次典型空气污染过程,对污染期间气象要素、大气颗粒物组分特征和污染物来源进行综合研究。结果表明,在污染期间,首要污染物为PM_(2.5),日均值为86μg/m3,其主要化学组分依次是OC、NO_3~-和SO_4~(2-),分别占PM_(2.5)的19.7%,16.1%和14.9%;在不利的气象条件下,本地污染排放和外源输入的一次污染物快速生成二次有机物、硝酸盐和硫酸盐,是造成该次空气污染的主要原因; PM_(2.5)污染主要来源为机动车尾气(27.7%)及二次无机源(19.0%)。     

2015年10月南宁市区典型大气污染过程成因分析

2015年10月15日—24日南宁市出现了一次典型的大气细颗粒物（PM2.5）污染过程,利用单颗粒气溶胶质谱仪和大气颗粒物激光雷达仪器进行监测,结合气象、后向轨迹及卫星遥感影像等资料分析大气污染成因及远距离传输对该次污染过程的影响。研究表明：此次南宁市大气污染以PM2.5超标为主,PM2.5日均质量浓度最高为85.2 μg/m3,超过标准值13.6%,其中PM1占PM2.5的66.3%。此次污染过程是由本地污染源与外来源影响互相叠加,在静稳、高温、强光等天气情况下发生协同作用引起的,污染物主要来源为燃煤源、机动车尾气源和生物质燃烧源,占全部来源的75.0%～80.0%。     

济南市空气中颗粒物来源与防治对策

颗粒物(总悬浮颗粒物TSP及可吸入颗粒物PM10)已成为济南市空气污染的首要污染物,其主要来源为扬尘、煤烟尘和风沙尘.三类尘对TSP和PM10的贡献分别为扬尘:34%和30%、煤烟尘:25%和19%、风沙尘:18%和22%.文中在阐明颗粒物源解析的分析方法及结果基础上,提出了颗粒物污染的防治对策.     

2015—2017年江苏省秋、冬季空气质量状况

采用2015—2017年秋、冬季江苏省环境空气质量监测数据,从空气质量优良(达标)率、首要污染物、主要污染物浓度分析空气质量现状及特点。结果表明,江苏省秋、冬季空气质量优良(达标)率在60%左右,其中沿海地区空气质量达标率最高(71.1%),西北地区达标率最差(52.2%)。污染日的首要污染物主要为PM 2.5,占比高达91.5%。ρ(PM2.5)/ρ(PM 10)存在地区差异,江苏西北地区扬尘源贡献较大,江苏南部地区的二次颗粒物贡献更明显。ρ(NO2)/ρ(SO2)逐年持续升高,表明大气污染类型从燃煤性污染转变为复合型污染。     

Study of size and mass distribution of particulate matter due to crop residue burning with seasonal variation in rural area of Punjab, India

Emission from field burning of agricultural crop residue is a common environmental hazard observed in northern India. It has a significant potential health risk for the rural population due to respirable suspended particulate matter (RSPM). A study on eight stage size segregated mass distribution of RSPM was done for 2 wheat and 3 rice crop seasons. The study was undertaken at rural and agricultural sites of Patiala (India) where the RSPM levels remained close to the National Ambient Air quality standards (NAAQS). Fine particulate matter (PM(2.5)) contributed almost 55% to 64% of the RSPM, showing that, in general, the smaller particles dominated during the whole study period with more contribution during the rice crop as compared to that of wheat crop residue burning. Fine particulate matter content in the total RSPM increased with decrease in temperature. Concentration levels of PM(10) and PM(2.5) were higher during the winter months as compared to that in the summer months. Background concentration levels of PM(10), PM(2.5) and PM(10-2.5) were found to be around 97 ± 21, 57 ± 15 and 40 ± 6 μg m(-3), respectively. The levels increased up to 66, 78 and 71% during rice season and 51, 43 and 61% during wheat crop residue burning, respectively. Extensive statistical analysis of the data was done by using pair t-test. Overall results show that the concentration levels of different size particulate matter are greatly affected by agricultural crop residue burning but the total distribution of the particulate matter remains almost constant.     

Monitoring of cotton dust and health risk assessment in small-scale weaving industry

The present study describes the estimation of particulate matter (cotton dust) with different sizes, i.e., PM(1.0), PM(2.5), PM(4.0), and PM(10.0 μm) in small-scale weaving industry (power looms) situated in district Hafizabad, Punjab, Pakistan, and the assessment of health problems of workers associated with these pollutants. A significant difference was found in PM(1.0), PM(2.5), PM(4.0), and PM(10.0) with reference to nine different sampling stations with p values <0.05. Multiple comparisons of particulate matter with respect to size, i.e. PM(1.0), PM(2.5), PM(4.0), and PM(10.0), depict that PM(1.0) differs significantly from PM(2.5), PM(4.0), and PM(10.0), with p values <0.05 and that PM(2.5) differs significantly from PM(1.0) and PM(10.0), with p values <0.05, whereas PM(2.5) differs non-significantly from PM(4.0), with a p value >0.05 in defined sampling stations on an average basis. Majority of the workers were facing several diseases due to interaction with particulate matter (cotton dust) during working hours. Flue, cough, eye, and skin infections were the most common diseases among workers caused by particulate matter (cotton dust).     

Investigation into presence of atmospheric particulate matter in Marikana, mining area in Rustenburg Town, South Africa

An investigation to find out presence of particulate matter in Marikana, a mining area in Rustenburg town, South Africa, was carried out in the months of August and November of 2008. Samples were collected for measurements of particulate matter (PM) of particle diameters of PM10, PM2.5, and PM1. After gravimetric analysis of daily measurements, it was found that PM10 concentration values ranged between 3 and 9 ??g/m³, PM2.5 concentration values ranged between 16 and 26 ??g/m³, and PM1 concentration values ranged between 14 and 18 ??g/m³ for the month of August 2008. For the month of November, it was found that PM10 concentration values ranged between 2 and 8 ??g/m³, PM2.5 concentration values ranged between 0 and 5 ??g/m³, and PM1 concentration values ranged between 4 and 15 ??g/m³. This study was undertaken as preliminary work having in mind that mining activities could be emitting high levels of particulate matter in the atmosphere which might be degrading the quality of the air. It was observed, however, that the daily particulate matter especially of PM10 emitted were quite low when compared to laid down International Air Quality Standards. The standards did not give guidelines for particulate matter of diameter 2.5 ??m. It was concluded that particulate matter came from three major sources: platinum mining, domestic biomass burning, and traffic emissions due to fuel burning.     

Dynamics and origin of PM2.5 during a three-year sampling period in Beijing, China

Systematic sampling and analysis were performed to investigate the dynamics and the origin of suspended particulate matter smaller than 2.5 μm in diameter (PM(2.5)), in Beijing, China from 2005 to 2008. Identifying the source of PM(2.5) was the main goal of this project, which was funded by the German Research Foundation (DFG). The concentrations of 19 elements, black carbon (BC) and the total mass in 158 weekly PM(2.5) samples were measured. The statistical evaluation of the data from factor analysis (FA) identifies four main sources responsible for PM(2.5) in Beijing: (1) a combination of long-range transport geogenic soil particles, geogenic-like particles from construction sites and the anthropogenic emissions from steel factories; (2) road traffic, industry emissions and domestic heating; (3) local re-suspended soil particles; (4) re-suspended particles from refuse disposal/landfills and uncontrolled dumped waste. Special attention has been paid to seven high concentration "episodes", which were further analyzed by FA, enrichment factor analysis (EF), elemental signatures and backward-trajectory analysis. These results suggest that long-range transport soil particles contribute much to the high concentration of PM(2.5) during dust days. This is supported by mineral analysis which showed a clear imprint of component in PM(2.5). Furthermore, the ratios of Mg/Al have been proved to be a good signature to trace back different source areas. The Pb/Ti ratio allows the distinction between periods of predominant anthropogenic and geogenic sources during high concentration episodes. Backward-trajectory analysis clearly shows the origins of these episodes, which partly corroborate the FA and EF results. This study is only a small contribution to the understanding of the meteorological and source driven dynamics of PM(2.5) concentrations.     

2020年江苏省机动车排放污染分布特征及与大气质量耦合性研究

依据生态环境部2021年6月发布的《排放源统计调查产排污核算方法和系数手册》,结合本地实测数据,在对汽油车颗粒物(PM)排放系数进行测算的基础上,核算了2020年江苏省机动车PM、氮氧化物(NO_(X))、挥发性有机物(VOC_(S))的排放总量,分析了机动车排放污染分布特征及与大气质量的耦合关系。结果表明:2020年江苏省机动车PM、NO_(X)、VOC_(S)排放量分别为0.5×10^(4),3.71×10^(5),1.17×10^(5) t。从区域分布来看,苏州、南京、无锡3市的3项污染物排放总量及NO_(X)、VOC_(S)排放量均位列前3位,PM排放量位列前3位的是苏州、徐州、无锡。从车型、燃料类型和排放阶段来看,国Ⅳ及以下排放标准的汽油小型客车是机动车VOC_(S)排放控制的重点,国Ⅲ排放标准的重型柴油货车是机动车PM和NO_(X)排放控制的重点。分析区域机动车PM排放量与大气中PM_(2.5)来源解析结果的耦合关系,其间存在不同程度的正相关性,控制机动车污染对改善大气环境会产生积极成效,南京、徐州和盐城3市的成效会尤为明显。     

Chemical Composition and Sources of PM10 and PM2.5 Aerosols in Guangzhou, China

Aerosol samples of PM10 and PM2.5 are collected in summertime at four monitoring sites in Guangzhou, China. The concentrations of organic and elemental carbons (OC/EC), inorganic ions, and elements in PM10 and PM2.5 are also quantified. Our study aims to: (1) characterize the particulate concentrations and associated chemical species in urban atmosphere (2) identify the potential sources and estimate their apportionment. The results show that average concentration of PM2.5 (97.54 μg m⁻³) in Guangzhou significantly exceeds the National Ambient Air Quality Standard (NAAQS) 24-h average of 65 μg m⁻³. OC, EC, Sulfate, ammonium, K, V, Ni, Cu, Zn, Pb, As, Cd and Se are mainly in PM2.5 fraction of particles, while chloride, nitrate, Na, Mg, Al, Fe, Ca, Ti and Mn are mainly in PM2.5-10 fraction. The major components such as sulfate, OC and EC account for about 70–90% of the particulate mass. Enrichment factors (EF) for elements are calculated to indicate that elements of anthropogenic origins (Zn, Pb, As, Se, V, Ni, Cu and Cd) are highly enriched with respect to crustal composition (Al, Fe, Ca, Ti and Mn). Ambient and source data are used in the multi-variable linearly regression analysis for source identification and apportionment, indicating that major sources and their apportionments of ambient particulate aerosols in Guangzhou are vehicle exhaust by 38.4% and coal combustion by 26.0%, respetively.     

基于后向轨迹模式的区域大气污染及传输特征分析

选取南京市2017年PM2.5逐时观测数据,分析其颗粒物污染特征,并利用聚类分析、潜在源贡献因子法和GDAS气象数据,分析不同高度、季节下南京市主要气流输送路径及PM2.5污染的主要潜在源区。结果表明：南京市PM2.5污染冬季最严重,夏季最轻,逐时PM2.5浓度变化范围夏季小于冬季;夏季气流轨迹主要来自东南方向,秋冬春等季节以偏西和西北路径为主,且随着高度的增加,气流输送速度逐渐加快;冬季对南京市PM2.5污染的贡献最为显著,低层PM2.5污染贡献源区主要集中在近地区域,且贡献率较高,随着高度的增加,贡献源区由研究区域向四周辐散,贡献范围广,贡献率降低。     

Motor Vehicle Contributions to Ambient PM10 and PM2.5 at Selected Urban Areas in the USA

A source apportionment study was carried out to estimate the contribution of motor vehicles to ambient particulate matter (PM) in selected urban areas in the USA. Measurements were performed at seven locations during the period September 7, 2000 through March 9, 2001. Measurements included integrated PM_2.5 and PM₁₀ concentrations and polycyclic aromatic hydrocarbons (PAHs). Ambient PM_2.5 and PM₁₀ were apportioned to their local sources using the chemical mass balance (CMB) receptor model and compared with results obtained using scanning electron microscopy (SEM). Results indicate that PM_2.5 components were mainly from combustion sources, including motor vehicles, and secondary species (nitrates and sulfates). PM₁₀ consisted mainly of geological material, in addition to emissions from combustion sources. The fractional contributions of motor vehicles to ambient PM were estimated to be in the range from 20 to 76% and from 35 to 92% for PM_2.5 and PM₁₀, respectively.     

Phthalate levels in Norwegian indoor air related to particle size fraction

Phthalates are found in numerous consumer products, including interior materials like polyvinyl chloride (PVC). Several studies have identified phthalates in indoor air. A recent case-control study demonstrated associations between allergic symptoms in children and the concentration of phthalates in dust collected from their homes. Here we have analyzed the content of selected phthalates in particulate matter (PM): PM(10) and PM(2.5) filter samples collected in 14 different indoor environments. The results showed the presence of the phthalates di-n-butyl phthalate (DBP), butyl benzyl phthalate (BBP), dicyclohexyl phthalate (DCHP) and diethyl hexyl phthalate (DEHP) in the samples. The dominating phthalate in both PM(10) and PM(2.5) samples from all locations was DBP. More than a 10-fold variation in the mean concentration of total phthalates between sampling sites was observed. The highest levels of total phthalates were detected in one children's room, one kindergarten, in two primary schools, and in a computer room. The relative contribution of total phthalates in PM(10) and PM(2.5) was 1.1 +/- 0.3% for both size fractions. The contribution of total phthalates in PM(2.5) to total phthalates in PM(10) ranged from 23-81%, suggesting different sources. Of the phthalates that were analyzed in the PM material, DBP was found to be the major phthalate in rubber from car tyres. However, our analyses indicate that tyre wear was of minor importance for indoor levels of both DBP as well as total phthalates. Overall, these results support the notion that inhalation of indoor PM contributes to the total phthalate exposure.     

Relationship between different size classes of particulate matter and meteorology in three European cities

Evidence on the correlation between particle mass and (ultrafine) particle number concentrations is limited. Winter- and spring-time measurements of urban background air pollution were performed in Amsterdam (The Netherlands), Erfurt (Germany) and Helsinki (Finland), within the framework of the EU funded ULTRA study. Daily average concentrations of ambient particulate matter with a 50% cut off of 2.5 microm (PM2.5), total particle number concentrations and particle number concentrations in different size classes were collected at fixed monitoring sites. The aim of this paper is to assess differences in particle concentrations in several size classes across cities, the correlation between different particle fractions and to assess the differential impact of meteorological factors on their concentrations. The medians of ultrafine particle number concentrations were similar across the three cities (range 15.1 x 10(3)-18.3 x 10(3) counts cm(-3)). Within the ultrafine particle fraction, the sub fraction (10-30 nm) made a higher contribution to particle number concentrations in Erfurt than in Helsinki and Amsterdam. Larger differences across the cities were found for PM2.5(range 11-17 microg m(-3)). PM2.5 and ultrafine particle concentrations were weakly (Amsterdam, Helsinki) to moderately (Erfurt) correlated. The inconsistent correlation for PM2.5 and ultrafine particle concentrations between the three cities was partly explained by the larger impact of more local sources from the city on ultrafine particle concentrations than on PM2.5, suggesting that the upwind or downwind location of the measuring site in regard to potential particle sources has to be considered. Also, relationship with wind direction and meteorological data differed, suggesting that particle number and particle mass are two separate indicators of airborne particulate matter. Both decreased with increasing wind speed, but ultrafine particle number counts consistently decreased with increasing relative humidity, whereas PM2.5 increased with increasing barometric pressure. Within the ultrafine particle mode, nucleation mode (10-30 nm) and Aitken mode (30-100 nm) had distinctly different relationships with accumulation mode particles and weather conditions. Since the composition of these particle fractions also differs, it is of interest to test in future epidemiological studies whether they have different health effects.