宜都市PM2.5与O3季节性分布特征及潜在源区分析

为了解宜都市PM_2.5与O₃的污染特征及潜在来源,利用宜都市2020年3月至2022年2月在线监测数据及气象数据,对宜都市PM_2.5与O₃质量浓度变化特征、气象影响因素及潜在源区进行了分析,结果表明:宜都市PM_2.5质量浓度冬高夏低,日变化呈双峰特征,O₃质量浓度夏高冬低,日变化呈单峰特征。高湿、静稳的气象条件以及较强偏北风作用下的区域污染传输对PM_2.5污染有重要影响,高温以及中湿度对O₃污染过程有重要作用。春、夏、秋季偏南方向气流轨迹占主导,且携带较高的污染物浓度,冬季来自湖北东北及西南方向的气流占比较高且携带的PM_2.5浓度较高;宜都市PM_2.5、O₃的潜在源区具有季节性差异,总体来看,主要分布在河南南部、湖北东部及湖南的北部区域。     

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.     

某石化化工行业聚集区室内外PM2.5中多环芳烃分布特征及来源

于非采暖季和采暖季分别采集某石化化工行业聚集城市中心城区室内外PM_(2.5)样品,采用高效液相色谱法分析PM_(2.5)上载带的16种PAHs,对其分布特征、来源以及室外PAHs污染对室内污染的贡献进行了初步探讨。结果表明,研究区域非采暖季和采暖季室外PM_(2.5)中ΣPAHs浓度日均值分别为36.3、294 ng/m~3,室内PM_(2.5)中ΣPAHs浓度分别为14.8、84.6 ng/m~3,均以4、5环PAHs为主;室内PAHs主要来自室外渗透污染,但同时明显存在室内排放源贡献;PAHs来源分析进一步证实研究区域PAHs主要来自煤炭、石油等不完全燃烧,采暖季煤炭燃烧源贡献更突出。     

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.     

北方某市环境空气颗粒物中重金属污染状况研究

对我国北方某市大气颗粒物的污染状况进行了研究。在主导风向上设置采样点,于冬季、夏季分别采集TSP、PM₁₀、PM_2.5 3种不同动力学直径的大气颗粒物。采用ICP-MS对TSP、PM₁₀、PM_2.5中的元素浓度进行分析,并对3种不同粒径颗粒物中的10种重金属采用富集因子法进行评价。结果表明,北方某市环境空气颗粒物中富集程度最严重的为Cd、Pb;在采暖期,随着颗粒物粒径变小,Pb、Cd的富集指数呈现逐渐增大的趋势;非采暖期则无此趋势。     

2016—2017年武汉市城区大气PM2.5污染特征及来源解析

利用2016年1月至2017年9月湖北省环境监测中心站大气复合污染自动监测站的在线监测数据，对武汉市城区PM_2.5的污染特征及主要来源进行解析。结果表明，武汉市城区PM_2.5质量浓度呈现出明显的季节差异，季节变化规律为冬季>春季>秋季>夏季。水溶性离子的主要成分SO₄^2-、NO₃^-和NH₄⁺占总离子质量浓度的82.0%。PM_2.5中阴离子相对阳离子较为亏损，颗粒整体呈碱性。夏季气态污染物的氧化程度较高且SO₂较NO₂氧化程度高。后向轨迹分析结果表明，区域传输是武汉市PM_2.5的一个重要来源，在4个典型重污染阶段，武汉市分别受到局地、东北、西北及西南方向气团传输的影响。PMF模型解析出武汉市PM_2.5五大主要来源及平均贡献率：扬尘22.0%、机动车排放27.7%、二次气溶胶21.6%、重油燃烧14.9%和生物质燃烧13.8%。     

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。     

Assessment of indoor and outdoor particulate air pollution at an urban background site in Iran

The relationship between indoor and outdoor particulate air pollution was investigated at an urban background site on the Payambar Azam Campus of Mazandaran University of Medical Sciences in Sari, Northern Iran. The concentration of particulate matter sized with a diameter less than 1 μm (PM_1.0), 2.5 μm (PM_2.5), and 10 μm (PM₁₀) was evaluated at 5 outdoor and 12 indoor locations. Indoor sites included classrooms, corridors, and office sites in four university buildings. Outdoor PM concentrations were characterized at five locations around the university campus. Indoor and outdoor PM measurements (1-min resolution) were conducted in parallel during weekday mornings and afternoons. No difference found between indoor PM₁₀ (50.1 ± 32.1 μg/m³) and outdoor PM₁₀ concentrations (46.5 ± 26.0 μg/m³), indoor PM_2.5 (22.6 ± 17.4 μg/m³) and outdoor PM_2.5 concentration (22.2 ± 15.4 μg/m³), or indoor PM_1.0 (14.5 ± 13.4 μg/m³) and outdoor mean PM_1.0 concentrations (14.2 ± 12.3 μg/m³). Despite these similar concentrations, no correlations were found between outdoor and indoor PM levels. The present findings are not only of importance for the potential health effects of particulate air pollution on people who spend their daytime over a period of several hours in closed and confined spaces located at a university campus but also can inform regulatory about the improvement of indoor air quality, especially in developing countries.     

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。     

2021年青岛市一次PM_(2.5)和沙尘混合空气污染过程分析

以2021年3月青岛市空气自动站监测数据为依据,借助环境气象激光雷达、气溶胶激光雷达、在线离子色谱仪等技术手段,并利用后向轨迹模式(HYSPLIT)对青岛市一次PM_(2.5)和沙尘混合空气污染过程、气象条件、颗粒物组成以及传输路径等进行了综合分析。结果表明:静小风、湿度大、垂直方向逆温以及高空多次向近地面的污染物输送是第1阶段PM_(2.5)污染的主要原因,NO^(-)_(3)、SO^(2-)_(4)、NH^(+)_(4)浓度分别占水溶性离子浓度总和的51.7%,24.8%,22.4%,三者之和占ρ(PM_(2.5))的52.3%,机动车源、工业源和燃烧源贡献较大,其中尤以机动车源影响最显著;第2阶段各子站颗粒物浓度变化呈现明显的传输特征,PM_(2.5)中Ca^(2+)浓度升至第1阶段的6倍,沙尘源影响显著,污染气团主要来自蒙古国和我国内蒙古,前期由西北地区直接到达青岛,后期是经渤海湾、烟台到达青岛东南海域,最后回流至青岛;冷高压强度较弱导致近地面水平扩散条件不利,ρ(PM_(10))长时间维持在较高水平。     

Chemical composition of ambient particulate matter and redox activity

Exposure to ambient particulate matter (PM) has been associated with a number of adverse health effects. Increasing studies have suggested that such adverse health effects may derive from oxidative stress, initiated by the formation of reactive oxygen species (ROS) within affected cells. The study aimed to assess physical characteristics and chemical compositions of PM and to correlate the results to their redox activity. PM_2.5 (mass aerodynamic diameter ≤2.5 μm) and ultrafine particles (UFPs, mass media aerodynamic diameter <0.1 μm) were collected in an urban area, which had heavy traffic and represented ambient air pollution associated with vehicle exhaust. Background samples were collected in a rural area, with low traffic flow. Organic carbon (OC), elemental carbon (EC), polycyclic aromatic hydrocarbons (PAHs), and metals were analyzed. The dithiothreitol activity assay was used to measure the redox activity of PM. Results showed that UFPs have higher concentrations of OC, EC, and PAHs than those of PM_2.5. Several metals, including Fe, Cu, Zn, Ti, Pb, and Mn, were detected. Among them, Cu had the highest concentrations, followed by Fe and Zn. Organic carbon constituted 22.8% to 59.7% of the content on the surface of PM_2.5 and UFPs. Our results showed higher redox activity on a per PM mass basis for UFPs as compared to PM_2.5. Linear multivariable regression analyses showed that redox activity highly correlated with PAH concentrations and organic compounds, and insignificantly correlated with EC and metals, except soluble Fe, which increased redox activity in particle suspension due to the presence of ROS.     

中国城市PM2.5空气质量改善分阶段目标研究

中国城市细颗粒物(PM_(2.5))空气质量达标率低,且城市间的污染程度差异较大。为了整体改善PM_(2.5)空气质量,需要针对不同污染程度的城市,制定分阶段改善目标加以考核和管理,研究探讨了城市PM_(2.5)空气质量改善目标体系及不同污染程度城市各阶段目标值。首先运用文献综述法、国内外对比分析法梳理评述了WHO、欧美等发达国家PM_(2.5)的空气质量标准和达标要求,提出中国城市PM_(2.5)空气质量改善的考核目标体系,包括PM_(2.5)浓度目标值或下降率、严重污染天数上限、达标天数下限等指标。通过历史数据分析法研究了2000—2013年美国、日本一些城市和2013—2016年中国74个环保城市PM_(2.5)年均浓度的变化趋势,推论出中国城市PM_(2.5)年均浓度年均下降5%~8%是可能实现的;结合环境保护部及各省市PM_(2.5)污染防治规划,提出PM_(2.5)空气质量改善目标的设定原则和达标天数的回归计算方法;以2014年114个城市PM_(2.5)年均浓度为基数,计算得出不同污染程度城市2020、2025、2030年PM_(2.5)年均浓度年下降率和达标天数的目标值。     

中国中东部一次大范围重污染过程特征分析

采用嵌套网格空气质量预报模式(NAQPMS)模拟与气象、污染物观测资料相结合的方式,分析了2016年12月影响中国中东部地区的一次重污染过程中PM_(2.5)时空分布特征及来源成因。结果表明,重污染过程中PM_(2.5)具有较明显的时空变化规律,污染呈现一定程度的区域性分布特点,不同地理位置条件下,污染物浓度的累积和传输方式表现出不同的特征,细颗粒物快速二次生成及不利扩散条件下的持续积累可能是此次污染过程的主要原因,不利于污染物扩散的高低空天气形势的配合抑制了污染物的快速消散,为大气污染的形成及维持提供了稳定的大气环境背景,形成了此次污染过程污染浓度高、影响范围大的态势。     

山西省大气污染特征及对公众健康的空间影响

利用山西省11个地级市大气环境监测站的PM_2.5、PM₁₀和O₃浓度数据,分析了2015—2020年山西省PM_2.5、PM₁₀和O₃浓度时空变化特征,采用空间计量模型和岭回归方法,分析了空气污染对公众健康的空间影响。结果表明:PM_2.5和PM₁₀年均质量浓度总体下降,两者在2017年最高,2020年最低;O₃年均浓度总体增加。在季节尺度上,PM_2.5和PM₁₀质量浓度在冬季的12月和1月最高,夏季的8月最低;O₃浓度在6月最高。空间上,相较2015年,2020年山西省各地级市PM_2.5污染程度均有改善,其中长治改善效果最好;2020年山西各地级市PM₁₀污染兼有加重和减轻的情形,所有地级市PM_2.5和PM₁₀污染水平均超过国家二级污染浓度限值;2020年山西多数地级市O₃浓度升高。山西公众健康水平具有明显的空间离散特征,PM_2.5和PM₁₀浓度的局部空间自相关特征高度一致,呈现"南高北低"的格局,O₃浓度分布呈"南部高,中北部低"的格局。大气环境质量和经济发展水平均对医疗机构诊疗人数和健康体检人数的变化有正向影响,每万人卫生技术人员数量和公共财政支出比例对公众健康均有负向影响,其中经济发展水平和大气环境质量的影响最显著。山西省PM_2.5治理取得一定成效,但大部分城市PM_2.5和PM₁₀达标率较低,O₃浓度有持续升高的趋势,PM₁₀和O₃污染改善缓慢,深度减排仍面临挑战。PM_2.5和PM₁₀是危害山西公众健康的主要大气污染物,未来需要加强PM_2.5、PM₁₀和O₃的精细化管理及协同治理。     

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。     

襄阳市秋冬季PM2.5污染特征及潜在源区分析

为了解襄阳市秋冬季PM_2.5的污染特征及来源，基于2020年11月至2021年1月在线监测数据，对PM_2.5质量浓度、气象因素、化学组分、来源及潜在源区进行了分析。结果表明，襄阳市秋冬季污染天首要污染物均为PM_2.5,且随污染程度加重，PM_2.5与PM₁₀质量浓度比呈上升趋势，二次颗粒物的形成对PM_2.5的贡献更高。在PM_2.5化学组分中，水溶性离子占比最大，随着污染程度加重，二次离子(SNA)快速增长，二次离子的生成转化是污染的重要成因。轻度、中度污染时，湿度高、风速小、气温低，有利于污染的积累，重度污染时湿度大、风速回升，有利于上游污染的输送与二次转化。PMF模型解析出襄阳市PM_2.5主要来源及贡献率为二次源58.0%、工业企业源22.6%、机动车源10.7%、扬尘源8.7%。襄阳市潜在源区主要分布在河南省中北部、河北省南部、山东省西部、安徽省北部、江汉平原东部及南部区域，极少量分布在襄阳区域，长距离区域传输...     

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.     

蚌埠市夏季大气细颗粒物在线源解析

为了解蚌埠市环境空气中PM_(2.5)的来源,于2017年8月18日—9月18日,在百货大楼和高新区站点,利用单颗粒物气溶胶飞行时间质谱仪开展PM_(2.5)在线源解析。结果表明,百货大楼点位ρ(PM_(2.5))高于高新区点位,轻度污染比例(4.2%)明显高于高新区点位(0.8%),出现了中度污染(0.3%);SPAMS的PM_(2.5)质谱图显示百货大楼点位PM_(2.5)中K~+、Na~+特征明显,高新区点位HSO_4~-、NO_3~-、NO_2~-等无机信号较为明显;2个点位NO_3~-、NO_2~-、NH_4~+离子颗粒数占总颗粒数的百分比明显较高,且高新区点位NO_3~-、HSO_4~-离子数占比要明显高于百货大楼点位,燃料燃烧、工业工艺源、农田氮肥施用是其主要的人为污染源;2个点位PM_(2.5)成分主要为元素碳,分别占比42.4%,40.6%;污染时段,ρ(PM_(2.5))快速上升,除受本地机动车尾气源和燃煤源累积影响外,百货大楼点位扬尘源排放增加,高新区点位扬尘源和工业工艺排放源增加;2个点位机动车尾气源均为首要污染源,分别占比29.5%和30.9%,其次为燃煤源(24.3%和24.7%),扬尘源占比分别为22.9%和20.8%。     

Inorganic constituents of urban air pollution in the Lazio region (Central Italy)

A field study was carried out at six locations in the Lazio region (Central Italy) aimed at characterising atmospheric particulate matter (PM₁₀ and PM_2.5) from the point of view of the chemical composition and grain size distribution of the particles, the mixing properties of the atmosphere, the frequency and relevance of natural events. The combination of four different analytical techniques (ion chromatography, X-ray fluorescence and ICP for inorganic components, thermo-optical analysis for carbon compounds) yielded sound results in terms of characterisation of the air masses. During the first three months of the study (October-December 2004), many pollution events of natural (sea-salt or desert dust episodes) or anthropogenic nature were identified and characterised. More than 90% of the collected mass was identified by chemical analysis. The central role played by the mixing properties of the lower atmosphere when pollution events occurred was highlighted. The results show a major impact of primary anthropogenic pollutants on traffic stations and a homogeneous distribution of secondary pollutants over the regional area. An evaluation of the sources of PM and an identification of possible reliable tracers were obtained using a chemical fractionation procedure.     

Inorganic constituents of urban air pollution in the Lazio region (Central Italy)

A field study was carried out at six locations in the Lazio region (Central Italy) aimed at characterising atmospheric particulate matter (PM₁₀ and PM_2.5) from the point of view of the chemical composition and grain size distribution of the particles, the mixing properties of the atmosphere, the frequency and relevance of natural events. The combination of four different analytical techniques (ion chromatography, X-ray fluorescence and ICP for inorganic components, thermo-optical analysis for carbon compounds) yielded sound results in terms of characterisation of the air masses. During the first three months of the study (October–December 2004), many pollution events of natural (sea-salt or desert dust episodes) or anthropogenic nature were identified and characterised. More than 90% of the collected mass was identified by chemical analysis. The central role played by the mixing properties of the lower atmosphere when pollution events occurred was highlighted. The results show a major impact of primary anthropogenic pollutants on traffic stations and a homogeneous distribution of secondary pollutants over the regional area. An evaluation of the sources of PM and an identification of possible reliable tracers were obtained using a chemical fractionation procedure.