唐山农村地区冬季室内外PM_(2.5)浓度污染特征

为了解中国北方农村地区冬季室内外PM_(2.5)污染特征,选择河北唐山某农村燃煤与非燃煤室内外PM_(2.5)进行实验研究。结果表明:(1)燃煤采样点室内外PM_(2.5)分别为47.9~370.0、14.8~145.0μg/m~3,非燃煤采样点室内外PM_(2.5)分别为13.6~217.0、10.9~131.0μg/m~3。(2)室内外PM_(2.5)浓度具有一定的相关性。(3)采样期间的20d内,根据《环境空气质量标准》(GB 3095—2012)二级标准(PM_(2.5)24h均值限值为75μg/m~3),燃煤采样点室外PM_(2.5)超标率为10%,而非燃煤采样点为5%;根据GB 3095—2012一级标准(PM_(2.5)24h均值限值为35μg/m~3),燃煤采样点室外PM_(2.5)超标率为35%,而非燃煤采样点为20%;根据《建筑通风效果测试与评价标准》(JGJ/T 309—2013)规定室内PM_(2.5)的日均值应小于75μg/m~3,燃煤采样点室内PM_(2.5)超标率为65%,而非燃煤采样点为35%。     

西安城郊PM_(10)和PM_(2.5)化学组分特征研究——以户县草堂寺为例

为研究西安城郊农村大气PM_(10)和PM_(2.5)中主要化学组分特征,于2014年12月至2015年10月在西安户县草堂寺采集颗粒物样品,分析了每组样品中的16种无机元素、8种水溶性离子、有机碳(OC)和元素碳(EC),对颗粒物和化学组分的浓度水平、时间变化特征进行了讨论。结果表明:(1)PM_(2.5)、PM_(10)年平均值分别为(79.78±59.12)、(118.09±79.27)μg/m~3。(2)PM_(2.5)及PM10中地壳元素浓度总体表现为春季高、秋季低;微量元素浓度表现为冬季高、夏季低。(3)PM_(2.5)和PM_(10)中SO_4~(2-)、NH_4~+、NO_3~-浓度总体表现为冬季秋季春季夏季。(4)冬、春季OC、EC明显高于夏、秋季;由OC/EC的最小值估算得到PM_(2.5)、PM_(10)中二次有机碳(SOC)年平均值分别为(7.90±8.89)、(8.55±8.50)μg/m~3,冬、春季SOC明显高于夏、秋季;秋、冬季OC、EC相关性较强,而春、夏季较弱。     

乌鲁木齐市PM_(2.5)浓度与气象条件耦合分析

为探究气象条件对污染物浓度的影响,于2013年10月至2014年10月在乌鲁木齐市主城区采集PM_(2.5)样品,并选取同期气象站监测的气象数据进行分析。结果表明:(1)乌鲁木齐市采暖期PM_(2.5)日均值平均达到84.70μg/m~3,超出了《环境空气质量标准》(GB 3095—2012)中24h平均二级限值(75μg/m~3),是非采暖期(20.66μg/m~3)的4倍多。(2)采暖期风速、相对湿度、气温、水汽压与PM_(2.5)日均值极显著相关,非采暖期相对湿度与PM_(2.5)日均值极显著相关。     

西安市全年中PM_(2.5)水溶性无机离子变化特征

采用ICS-1100型离子色谱仪在2014年6月到2015年6月期间对西安市大气中PM_(2.5)水溶性离子(NO_3~-、NH_4~+、SO_4~(2-)、NO_2~-、Cl~-、Na~+、Ca~(2+)、Mg~(2+)、K+)进行的实时监测,分析了全年PM_(2.5)中水溶性无机离子变化特征。结果显示:采样期间,西安市PM_(2.5)中NO_3~-、NH_4~+、SO_4~(2-)和Cl~-年均值占总离子的89.49%,且有明显月变化趋势,峰值出现在11和12月份,月浓度均值较往年同期降低,最高达到30.26、15.19、11.43和16.60μg·m~(-3)。Na~+、Ca~(2+)、Mg~(2+)和K~+浓度变化趋势与主离子不完全一致。NO_3~-均值大于SO_4~(2-)均值,表明PM_(2.5)中水溶性离子的主要贡献者为移动源。NO_3~-小时均值高于SO_4~(2-)小时均值,且在10:00和20:00处形成2个峰值。PM_(2.5)中NO_3~-与NO_2~-在0.05水平上显著相关,SO_4~(2-)与Cl~-的在0.01水平上极显著相关。     

道路区域PM_(2.5)浓度的影响因素研究

为探究人为因素和气象因素对道路区域PM_(2.5)浓度的影响,选择南京仙林大学城某条典型道路开展大气PM_(2.5)监测实验。结果表明,道路清扫抬升PM_(2.5)浓度,白天的抬升作用较傍晚和夜间更加显著。各类交通流对道路区域PM_(2.5)浓度的影响程度排序为:柴油车汽油车燃气车道路行人。PM_(2.5)浓度阴天高于晴天和多云天,霾日(209.3、80.5μg/m~3)高于非霾日(47.0、62.0μg/m~3);在霾日变化特征各异,在非霾日均呈"三峰"分布特征。非霾日,道路区域PM_(2.5)浓度的高值区与相对湿度的高值区,温度、风速的低值区重合;PM_(2.5)浓度的低值区与相对湿度的低值区,温度、风速的高值区重合。温度与PM_(2.5)浓度呈负相关(r=-0.501,P0.05),是影响PM_(2.5)污染程度的关键气象因子。由此可见,道路清扫、交通流和各类气象因素对道路区域PM_(2.5)浓度影响显著。     

北京市无风天人体高度的PM_(2.5)、PM_(10)变化特征及扩散机制研究

为了解无风天情况下PM_(2.5)、PM_(10)的人体暴露水平及扩散机制,对人体呼吸高度的PM_(2.5)、PM_(10)浓度及近地面不同高度处的温度、相对湿度进行连续监测,分析了垂直温度梯度、相对湿度的相对变化速率对PM_(2.5)、PM_(10)浓度的影响,并利用回归分析法建立PM_(2.5)、PM_(10)浓度与不同高度处温度、相对湿度的单、多变量回归模型,从中选取最优回归模型。结果表明:(1)晴天的PM_(2.5)、PM_(10)浓度在研究时段(9:00—21:00)内总体呈先降低再升高的趋势,而阴天、小雨天PM_(2.5)、PM_(10)浓度呈多峰变化,起伏较大;晴天不同高度的温度差异大,阴天、小雨天温度差异相对较小;晴天不同高度的相对湿度曲线总体均呈U型分布,相较而言,阴天及小雨天各层的相对湿度曲线波动较大;(2)垂直温度梯度是影响晴天PM_(2.5)、PM_(10)扩散的主要原因,相对湿度变化是影响颗粒物扩散的另一重要因素。(3)PM_(2.5)、PM_(10)浓度的单、多变量最优回归模型表明,低污染晴天,温度是影响颗粒物扩散的主要因素,高污染晴天则主要受相对湿度的影响,介于上述两种污染状况之间时,PM_(2.5)、PM_(10)浓度不仅受各层相对湿度的控制,还受到温度的影响。阴天PM_(2.5)、PM_(10)浓度的最优回归模型相对复杂,模型精度不及晴天。     

杭州市区空气污染特征及灰霾成因分析

利用2013年3月1日至2014年2月28日杭州市区4种常见污染物(NO_2、CO、PM_(2.5)和PM_(10))的小时浓度监测数据对杭州市区全年空气污染特征进行分析,并针对2014年1月17至19日的一次灰霾过程进行了污染特征与成因分析。结果表明,杭州市区NO_2质量浓度年均值为51.55μg/m~3,CO为0.87mg/m~3,PM_(2.5)为67.02μg/m~3,PM_(10)为102.06μg/m~3,均表现为冬季浓度高夏季浓度低的特征。4种污染物基本都在每天的9:00—10:00和19:00—20:00出现两个峰值。杭州市区PM_(2.5)主要来自于二次污染物转化,灰霾过程中PM_(2.5)质量浓度最高值接近300μg/m~3。这次灰霾过程的主要潜在源区包括京津地区、山东中部和江苏南部等地区,杭州市区本身气象条件加剧了这次污染的严重程度。     

京津冀地区PM_(2.5)的时空效应研究

PM_(2.5)污染已成为当前经济发展中亟待解决的难题。从年、季、日变化及周末效应4个时间尺度和空间自相关分析研究了京津冀地区PM_(2.5)的时空效应,并构建空间回归模型量化分析相关社会经济因素对PM_(2.5)的影响。结果显示:(1)2013—2016年京津冀地区PM_(2.5)污染整体呈下降趋势,但污染程度依然很高,基本都没有达到《环境空气质量标准》(GB 3095—2012)二级标准(35μg/m~3)。四季的达标天数夏季春季秋季冬季。中南部的石家庄、保定、衡水、邢台、邯郸为PM_(2.5)浓度高值区,日变化曲线为单峰型,受工业企业生产排放的影响较大;北部的张家口、承德、秦皇岛为PM_(2.5)浓度低值区,中东部的天津、北京、沧州、唐山、廊坊为PM_(2.5)浓度中值区,日变化曲线均为双峰型,受机动车尾气排放的影响较大。石家庄、北京的周末效应表现为白天PM_(2.5)浓度工作日高于周末,晚上周末高于工作日。(2)京津冀地区PM_(2.5)存在显著的空间正相关性,2013—2016年石家庄、衡水、邢台、邯郸始终表现出高-高集聚特征,张家口、承德、秦皇岛始终保持低-低集聚特征。汽车尾气排放是京津冀地区PM_(2.5)污染的重要影响因素,而能源消耗的影响不显著。     

夏季海淀公园内PM_(2.5)浓度水平梯度分布规律

于2014年夏季,通过观测海淀公园不同区域沿道路不同宽度处PM_(2.5)浓度,研究PM_(2.5)浓度日变化规律、水平梯度分布规律、净化效益及其影响因素。结果表明,海淀公园内PM_(2.5)浓度日变化规律呈白天低晚上高的趋势,09:00—15:00时PM_(2.5)浓度达到国家标准Ⅱ类功能区浓度质量要求,05:00时PM_(2.5)浓度最高。不同观测区域一定宽度范围内出现PM_(2.5)浓度积聚,之后开始下降。总体上,海淀公园在13:00时对PM_(2.5)浓度净化效益最显著,09:00时净化效益最差。环城高速路区域与城市主干道区域165 m以上宽度处、城市次干道区域60 m以上宽度处为正净化效益,并维持正净化效益。海淀公园内PM_(2.5)浓度与气象因子之间相关关系表明,PM_(2.5)浓度与平均温度、相对湿度呈显著相关,与其他气象因素没有显著相关性。     

海口市PM_(2.5)演变特征及其与气象因素的关系

利用海口市PM_(2.5)逐时数据、常规气象观测资料、FNL全球分析资料和HYSPLIT模式,对比分析海口市PM_(2.5)变化特征及其与气象因素的关系。结果表明:(1)2014年1月1日至2016年6月30日,海口市PM_(2.5)日均值以达到《环境空气质量标准》(GB 3095—2012)一级标准为主;年均值为23μg/m~3,达到GB 3095—2012二级标准;月均值整体呈周期性波动,秋冬季高、春夏季低;季节均值排序为冬季秋季春季夏季。(2)降水对PM_(2.5)有清除作用;风速加大会使PM_(2.5)浓度减小。(3)污染个例分析表明,海口市PM_(2.5)浓度增大,是因为东北风将外地污染物传输经过本地,并配合有利的天气形势,最终造成大气污染事件的发生。     

Elemental characterization of PM10, PM2.5 and PM1 in the town of Genoa (Italy)

The particulate matter (PM) concentration and composition, the PM10, PM2.5, PM1 fractions, were studied in the urban area of Genoa, a coastal town in the northwest of Italy. Two instruments, the continuous monitor TEOM and the sequential sampler PARTISOL, were operated almost continuously on the same site from July 2001 to September 2004. Samples collected by PARTISOL were weighted to obtain PM concentration and then analysed by PIXE (particle induced X-ray emission) and by ED-XRF (energy dispersion X-ray fluorescence), obtaining concentrations for elements from Na to Pb. Some of the filters used in the TEOM microbalance were analysed by ED-XRF to calculate Pb concentration values averaged over 7-30 d periods.     

关于PM2.5的综述

综述了大气PM25的来源,样品采集分析,化学组成,病毒机理,对人类健康和大气能见度的影响,以及国内外的研究进展.     

Monitoring of PM10 and PM2.5 around primary particulate anthropogenic emission sources

Investigations on the monitoring of ambient air levels of atmospheric particulates were developed around a large source of primary anthropogenic particulate emissions: the industrial ceramic area in the province of Castelló (Eastern Spain). Although these primary particulate emissions have a coarse grain-size distribution, the atmospheric transport dominated by the breeze circulation accounts for a grain-size segregation, which results in ambient air particles occurring mainly in the 2.5–10 μm range. The chemical composition of the ceramic particulate emissions is very similar to the crustal end-member but the use of high Al, Ti and Fe as tracer elements as well as a peculiar grain-size distribution in the insoluble major phases allow us to identify the ceramic input in the bulk particulate matter. PM2.5 instead of PM10 monitoring may avoid the interference of crustal particles without a major reduction in the secondary anthropogenic load, with the exception of nitrate. However, a methodology based in PM2.5 measurement alone is not adequate for monitoring the impact of primary particulate emissions (such as ceramic emissions) on air quality, since the major ambient air particles derived from these emissions are mainly in the range of 2.5–10 μm. Consequently, in areas characterised by major secondary particulate emissions, PM2.5 monitoring should detect anthropogenic particulate pollutants without crustal particulate interference, whereas PM10 measurements should be used in areas with major primary anthropogenic particulate emissions.     

A four-year size-segregated characterization study of particles PM10, PM2.5 and PM1 depending on air mass origin at Melpitz

PM₁₀ measurements were started in November 1992 at Melpitz site. The mean PM₁₀ concentration in 1993 was 38 μg m^?3 in the summer season (May until October) and about 44 μg m^?3 in the winter season (November until April). The mean PM₁₀ level decreased until 1999 and varies now in ranges from 20–34 μg m^?3 to 17–24 μg m^?3 (minimum and maximum mean values for 1999–2008) in winter and summer seasons, respectively. High volume filter samples of particles PM₁₀, PM_2.5 and PM₁ were characterized for mass, water-soluble ions, organic and elemental carbon from 2004 until 2008. The percentage of PM_2.5 in PM₁₀ varies between summer (71.6%) and winter seasons (81.9%). Mean concentrations of PM₁₀, PM_2.5 and PM₁ in Melpitz were 20, 15, and 13 μg m^?3 in 2004, 22, 18, and 13 μg m^?3 in 2005, 24, 19, and 12 μg m^?3 in 2006 and 22, 17, and 12 μg m^?3 in 2007, respectively. In the four winters the rural background concentration PM₁₀ at Melpitz exceeded the daily 50 μg m^?3 limit for Europe on 8, 8, 7 and 6 days, respectively.Findings for a simple two-sector-classification of the samples (May 2004 until April 2008) using 96-h backward trajectories for the identification of source regions are: Air masses were transported most of time (60%) from the western sector and secondly (17%) from the eastern sector. The lowest daily mean mass concentration PM₁₀ were found during western inflow in summer (17 μg m^?3) containing low amounts of sulphate (2.4 μg m^?3), nitrate (1.7 μg m^?3), ammonium (1.1 μg m^?3) and TC (3.7 μg m^?3). In opposite the highest mean mass concentration PM₁₀ was found during eastern inflow in winter (35 μg m^?3) with high amounts of sulphate (6.1 μg m^?3), nitrate (5.4 μg m^?3), ammonium (3.8 μg m^?3) and TC (9.4 μg m^?3). An estimation of secondary formed OC (SOA) shows 0.8–0.9 μg m^?3 for air masses from West and 2.1–2.2 μg m^?3 from East. The seasonal difference can be neglected.The half-hourly measurements of the particle mass concentration PM₁₀ evaluated as mean daily courses using a TEOM^® show low values (14–21 μg m^?3) in summer and winter for air masses transported from West and the highest concentrations (31–38 μg m^?3) in winter for air masses from East.The results demonstrate the influence of meteorological parameters on long-range transport, secondary particle mass formation and re-emission which modify mass concentration and composition of PM₁₀, PM_2.5 and PM₁. Melpitz site is located in the East of Germany faraway from strong local anthropogenic emissions (rural background). Therefore, this site is suitable for investigation of the influence of long-range transport of air pollution in continental air masses from the East with source regions inside and outside of the European Union.     

Elemental composition of PM10 and PM2.5 in urban environment in South Brazil

The purpose of the present study is to analyze the elemental composition and the concentrations of PM₁₀ and PM_2.5 in the Guaíba Hydrographic Basin with HV PM₁₀ and dichotomous samplers. Three sampling sites were selected: 8° Distrito, CEASA and Charqueadas. The sampling was conducted from October 2001 to December 2002. The mass concentrations of the samplers were evaluated, while the elemental concentrations of Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu and Zn were determined using the Particle-Induced X-ray Emission (PIXE) technique. Factor Analysis and Canonical Correlation Analysis were applied to the chemical and meteorological variables in order to identify the sources of particulate matter. Industrial activities such as steel plants, coal-fired power plants, hospital waste burning, vehicular emissions and soil were identified as the sources of the particulate matter. Concentration levels higher than the daily and the annual average air quality standards (150 and 50 μg m⁻³, respectively) set by the Brazilian legislation were not observed.     

Wintertime PM2.5 and PM10 Source Apportionment at Sacramento,California

ABSTRACT

The chemical mass balance (CMB) model was applied to winter (November through January) 1991–1996 PM2.5 and PM10 data from the Sacramento 13th and T Streets site in order to identify the contributions from major source categories to peak 24-hr ambient PM_2.5 and PM₁₀ levels. The average monthly PM₁₀ monitoring data for the nine-year period in Sacramento County indicate that elevated concentrations are typical in the winter months. Concentrations on days of highest PM₁₀ are dominated by the PM2.5 fraction. One factor contributing to increased PM_2.5 concentrations in the winter is meteorology (cool temperatures, low wind speeds, low inversion layers, and more humid conditions) that favors the formation of secondary nitrate and sulfate aerosols. Residential wood burning also elevates fine particulate concentrations in the Sacramento area.

The results of the CMB analysis highlight three key points. First, the source apportionment results indicate that primary motor vehicle exhaust and wood smoke are significant sources of both PM_2.5 and PM₁₀ in winter. Second, nitrates, secondarily formed as a result of motor-vehicle and other sources of nitrogen oxide (NO_x), are another principal cause of the high PM_2.5 and PM₁₀ levels during the winter months. Third, fugitive dust, whether it is resuspended soil and dust or agricultural tillage, is not the major contributor to peak winter PM_2.5 and PM10 levels in the Sacramento area.     

PM10, PM2.5 and PM1.0—Emissions from industrial plants—Results from measurement programmes in Germany

Emission measurement programmes were carried out at industrial plants in several regions of Germany to determine the fine dust in the waste gases; the PM₁₀, PM_2.5 and PM_1.0 fractions were sampled using a cascade impactor technique. The installations tested included plants used for: combustion (brown coal, heavy fuel oil, wood), cement production, glass production, asphalt mixing, and processing plants for natural stones and sand, ceramics, metallurgy, chemical production, spray painting, wood processing/chip drying, poultry farming and waste treatment. In addition waste gas samples were taken from small-scale combustion units, like domestic stoves, firing lignite briquettes or wood.In total 303 individual measurement results were obtained during 106 different measurement campaigns. In the study it was found that in more than 70% of the individual emission measurement results from industrial plants and domestic stoves the PM₁₀ portion amounted to more than 90% and the PM_2.5 portion between 50% and 90% of the total PM (particulate matter) emission. For thermal industrial processes the PM_1.0 portion constituted between 20% and 60% of the total PM emission.Typical particle size distributions for different processes were presented as cumulative frequency distributions and as frequency distributions. The particle size distributions determined for the different plant types show interesting similarities and differences depending on whether the processes are thermal, mechanical, chemical or mixed. Consequently, for the groups of plant investigated, a major finding of this study has been that the particle size distribution is a characteristic of the industrial process. Attempts to correlate particle size distributions of different plants to different gas cleaning technologies did not lead to usable results.     

区域大气环境中PM2．5／PM10空间分布研究

提出了一种利用移动监测技术研究区域大气环境中PM2.5／PM10空间分布的方法,并在2004年12月进行了宁波市全市域PM2.5／PM10空间分布的研究。数据显示：相同路径所代表的地区PM2.5和PM10具有很好的相关性,多数路径上PM2.5与PM10数据的相关系数平方在0．95以上,而不同路径上PM2.5与PM10的比值不同。文中给出了宁波市PM2.5／PM10污染的空间分布图,直观地显示出PM2.5／PM10污染的空间分布情况,突出了污染的重点点位和地区。     

Particulate matter in California: part 1--Intercomparison of several PM2.5, PM10-2.5, and PM10 monitoring networks

It will be many years before the recently deployed network of fine particulate matter with an aerodynamic diameter less than 2.5 microm (PM2.5) Federal Reference Method (FRM) samplers produces information on nonattainment areas, trends, and source impacts. However, data on PM2.5 and its major constituents have been routinely collected in California for the past 20 years. The California Air Resources Board operated as many as 20 dichotomous (dichot) samplers for PM2.5 and coarse PM (PM10-2.5). The California Acid Deposition Monitoring Program (CADMP) collected 12-h-average PM2.5 and PM10 from 1988 to 1995 at ten urban and rural sites and 24-h-average PM2.5 at five urban sites since 1995. Beginning in 1994, the Children's Health Study collected 2-week averages of PM2.5 in 12 communities in southern California using the Two-Week Sampler (TWS). Comparisons of collocated samples establish relationships between the dichot, CADMP, and TWS samplers and the 82-site network of PM2.5 FRM samplers deployed since 1999 in California. PM mass data from the different monitoring programs have modest to high correlation to FRM mass data, fairly small systematic biases and negative proportional biases ranging from 7 to 22%. If the biases are taken into account, all of the programs should be considered comparable with the FRM program. Thus, historical data can be used to develop long-term PM trends in California.     

Analysis of PM10, PM2.5, and PM2 5-10 concentrations in Santiago, Chile, from 1989 to 2001

Daily particle samples were collected in Santiago, Chile, at four urban locations from January 1, 1989, through December 31, 2001. Both fine PM with da < 2.5 microm (PM2.5) and coarse PM with 2.5 < da < 10 microm (PM2.5-10) were collected using dichotomous samplers. The inhalable particle fraction, PM10, was determined as the sum of fine and coarse concentrations. Wind speed, temperature and relative humidity (RH) were also measured continuously. Average concentrations of PM2.5 for the 1989-2001 period ranged from 38.5 microg/m3 to 53 microg/m3. For PM2.5-10 levels ranged from 35.8-48.2 microg/m3 and for PM10 results were 74.4-101.2 microg/m3 across the four sites. Both annual and daily PM2.5 and PM10 concentration levels exceeded the U.S. National Ambient Air Quality Standards and the European Union concentration limits. Mean PM2.5 levels during the cold season (April through September) were more than twice as high as those observed in the warm season (October through March); whereas coarse particle levels were similar in both seasons. PM concentration trends were investigated using regression models, controlling for site, weekday, month, wind speed, temperature, and RH. Results showed that PM2.5 concentrations decreased substantially, 52% over the 12-year period (1989-2000), whereas PM2.5-10 concentrations increased by approximately 50% in the first 5 years and then decreased by a similar percentage over the following 7 years. These decreases were evident even after controlling for significant climatic effects. These results suggest that the pollution reduction programs developed and implemented by the Comisión Nacional del Medio Ambiente (CONAMA) have been effective in reducing particle levels in the Santiago Metropolitan region. However, particle levels remain high and it is thus imperative that efforts to improve air quality continue.