城市环境空气中PM_(10)和PM_(2.5)质量浓度分布的均匀性研究

对长沙市环境空气中PM10、PM2.5质量浓度进行自动监测,并统计分析其分布的均匀性。结果表明,在1 d的4个典型时刻以及日内,PM2.5的质量浓度分布总体上较PM10均匀;从月内日均值及2013年1月—10月的月均值变化情况看,PM2.5质量浓度的相对标准偏差(RSD)总体高于PM10,表明PM2.5在长时间尺度上的分布较PM10更不均匀;就功能区分布而言,PM10、PM2.5质量浓度分布的均匀性没有明显的区域差异,两者的变化幅度与功能区类别没有必然联系。     

乌鲁木齐市冬季大气颗粒物PM_(10) 和PM_(2.5)污染水平及相关性研究

为研究乌鲁木齐市冬季采暖期间大气颗粒物污染特征,通过采样和在线监测二种手段分析了2015年1~2月大气颗粒物样品,采用重量法分析颗粒物质量浓度,并对其相关性进行分析。结果表明:依据《环境空气质量标准》(GB 3095-2012),采样期间乌鲁木齐市大气PM_(10) 和PM_(2.5)的日均质量浓度均超过了国家二级标准,颗粒物污染严重;PM_(10) 和PM_(2.5)存在显著相关性,PM_(2.5)和PM_(10) 浓度的比值均大于0.5,采暖期PM2.5对乌鲁木齐市大气颗粒物贡献显著。     

长沙市PM_(2.5)和PM_(10)质量浓度的变化特征

为检验PM_(2.5)和PM_(10)新监测标准实施近3年长沙大气颗粒物污染状况,利用近3年每日监测数据,对长沙10个国控自动监测点PM_(2.5)和PM_(10)达标情况、首要污染物及变化特征进行研究分析。结果表明,近3年长沙市PM_(2.5)和PM_(10)年均质量浓度均超过了新标准规定的年均值二级标准限值;2013年污染最严重。PM_(2.5)和PM_(10)月均值峰值出现在1月和11月,谷值在8月,各月PM_(2.5)超标天数和首要污染物为PM_(2.5)天数都大于PM_(10);PM_(2.5)和PM_(10)冬季日均值浓度明显高于其他季节,呈双峰型,峰值在上午10:00和20:00~21:00,夜晚浓度高于白天;PM_(2.5)春、夏、秋三季日变化呈单峰型,峰值在20:00~21:00;PM_(10)四季日变化呈双峰型。PM_(2.5)和PM_(10)浓度的比值(P)1月和2月最高,PM_(10)和PM_(2.5)日均值有着显著的线性相关性。     

环境空气PM_(2.5)和PM_(10)监测分析质量保证及其评价

为保证四城市ＰＭ２５和ＰＭ１０的监测数据准确,具有可比性,本研究规定了滤膜的选择、称量操作步骤的要求和滤膜称量的质控指标。研究结果表明,粗细颗粒物样品的采集和称量操作可行,监测数据准确、可靠,具有可比性。     

ICP-MS法测定南京市环境空气PM_(2.5)中多种元素

采用聚四氟乙烯膜采样,硝酸-过氧化氢-氢氟酸微波消解样品,ICP-MS法测定南京某国控点环境空气PM_(2.5)中30种元素,结果目标元素在0μg/L~500μg/L之间线性良好,方法检出限为0.02 ng/m~3~15 ng/m~3,实际样品6次测定结果的RSD为0.5%~19.6%,加标回收率为78.5%~126%;所测元素的年日均值为0.03 ng/m~3~1 462 ng/m~3,占PM_(2.5)总量的7.3%。主要来自化石燃烧、机动车排放和钢铁冶炼的Cd、Zn、Se、Pb、Sb、Cu、As富集程度较高,Al、Ba、Be、Fe主要来自土壤岩石等自然源,富集度低。元素测定值季节分布呈秋冬高、春夏低的态势,与PM_(2.5)的季节变化趋势一致。     

淮安市区大气中颗粒物PM_(10)、PM_(2.5)污染水平

通过对淮安市大气颗粒物中PM10、PM2．5的监测与污染水平分析,得出了淮安市区PM10与PM2．5浓度呈冬秋季高,夏春季低的特征。PM2.5和PM10的比值范围在0．62～0．65之间,即PM2．5在PM10以下颗粒物中所占比例大约为63％。     

新疆城市环境空气PM_(10)自动监测仪器质控巡检工作探析

随着城市环境空气质量自动监测业务现代化的飞速发展,新疆城市共安装38套PM10环境空气自动监测系统。本文通过对新疆19个城市空气自动监测系统PM10的巡检和质控考核,发现近几年来该系统运行维护中的诸多问题,从而提出仪器维护注意事项,以期提高新疆城市PM10监测数据质量,准确反映环境空气质量状况,为环境管理和政府决策提供更加科学、准确、可靠的基础监测数据。     

阿克苏市空气质量PM_(2.5)浓度变化研究与对策

近年来随着雾霾天气的频发和空气环境质量的不断下降,有关PM_(2.5)的研究逐渐成为研究的重点和热点。本研究利用阿克苏市2014年PM_(2.5)连续在线监测数据,对PM_(2.5)的污染现状和季节变化、月变化、日变化、昼夜变化规律进行探讨和分析。结果表明,阿克苏市PM_(2.5)质量浓度平均值春季最高,其次为冬季,夏季最低。春季沙尘天气和冬季采暖燃烧源是PM_(2.5)质量浓度增加的主要原因;阿克苏市PM_(2.5)质量浓度日均值为14.96~282.84μg/m3,年平均值为77.85μg/m3,是国家二级标准的1.04倍;阿克苏市PM_(2.5)质量浓度春季白天高于夜间,夏季和冬季白天低于夜间。     

苏州市区PM_(2.5)中元素污染特征

2014年使用EHM-X100型在线金属分析仪自动监测苏州市区大气PM2.5中Pb、Cu、K等24种元素质量浓度,并结合当地工业经济发展和降雨、土壤等环境状况对元素污染特征进行分析研究。结果表明:这24种元素的年均质量浓度在0.002μg/m3~0.834μg/m3之间,并总体呈现冬季质量浓度最高,春、秋季次之,夏季最低的变化趋势;Fe、Ca和Zn 3种元素在总质量浓度中占比较高,这可能与当地产业布局、建筑业及交通状况等有关,是人类活动所导致的污染。     

上海市城区典型居民住宅区PM2.5和PM10监测结果比较研究

在上海市环境空气质量连续自动监测网络中的一个城市居民住宅区监测点进行了为期一年的PM2.5和PM10的同步监测，监测结果表明：PM2.5和PM10日平均浓度之间的比值范围为0．194～0．889，月平均浓度之间的比值范围为0．420～0．667；冬季颗粒物中小粒径颗粒物PM2.5的比例较高，春季则较低；随着相对湿度的上升；颗粒物中小粒径颗粒物PM2.5的比例缓慢升高；比值变化的风向特征与监测点周围环境情况有关；PM2.5和PM10监测结果月均值之间和各月的日均值之间均线性相关，回归直线关系存在。     

Thermodynamic influences on size fractionated measurements (PM 2.5, PM 10) of ambient aerosols

Mass concentrations of PM 10 and PM 2.5 are planned as new standards for the monitoring of ambient air quality in the European Union. Standard procedure is the removal of particles > 10 microns and > 2.5 microns aerodynamic diameter, respectively, by impaction in a preseparator. Different samplers work according to different principles of flow control. The influence of ambient temperature, pressure and relative humidity on different devices is calculated to estimate the comparability of various aerosol samplers. Therefore, the effects of these ambient factors on the volume flow as well as on the cut-off dp50 are investigated. In a second step, the influence of relative humidity on the flow control device is calculated. The results show that the cut-off shifts (up to 6.4%) for varying ambient conditions. Therefore, the influence on the impaction process should not be neglected and an 'ideal sampler' would measure temperature, pressure and relative humidity and adapt the volume flow to avoid a systematic error in the cut-off.     

郑州市 PM2．5和 PM10质量浓度变化特征分析

根据郑州市2013年PM2．5和PM10颗粒物连续自动监测数据,对郑州市各国控站点的PM2．5和PM10的达标情况、变化趋势等进行探讨分析。结果表明：2013年郑州市PM10和PM2．5的年均质量浓度均超过了新标准规定的年均值二级标准限值。 PM10和PM2．5月均值峰值出现在1月和10月,谷值出现在8月,各月PM2．5的超标天数都大于PM10。PM10和PM2．5冬季的日均值浓度明显高于其他季节,呈双峰型,夜晚浓度整体高于白天;PM2．5春、夏、秋三季日变化呈单峰型,PM10夏季和秋季呈单峰型,春季呈双峰型。 PM2．5和PM10日均值有着非常显著的线性相关关系,PM2．5和PM10浓度的比值（p）10月最高。     

克拉玛依市大气PM2.5、PM10污染水平浅析

根据从2012年1月1日至2012年3月30日在同一个监测点取得的PM2.5和PM10监测数据,分析采暖期颗粒物污染水平特征。结果表明,PM2.5浓度和PM10浓度之间高度线性相关;克拉玛依市冬季空气环境中PM2.5是PM10中的主要组成成分;PM2.5浓度在一天内基本保持稳定,而PM10浓度在一天之中的变化幅度较大,峰值出现在中午上下班高峰期。     

Characteristics of ambient 1-min PM2.5 variation in Beijing

One-minute PM_2.5 concentration was obtained with LD-5C pocket microcomputer laser dust instrument from Dec. 15th, 2005 to Jan. 16th, 2006 and Mar. 17th to Apr. 28th, 2006 in Beijing. The concentration of SO₂, NO₂, O₃, CO, and PM₁₀ from Jan. 1st, 2001 to Dec. 31st, 2004 were obtained from the conversion of air pollution index. Results showed that all the pollutants showed cyclic characteristics. The longer yearly cycles was shown from SO₂, NO₂, O₃, CO, and PM₁₀, as the sampling time was 4-year long and daily collected. The shorter hourly and daily cycle was shown from 1-min PM_2.5, as the sampling time was about 1-month long and one collected at 1 min. The spectral density analysis confirmed this from the periodogram graphs. The longer yearly cycle (365, 180 days), the seasonal cycle (120, 60–90 days), and monthly cycle (21, 23, 27 days) of SO₂, NO₂, CO, O₃, and PM₁₀ were obviously shown. In addition, the shorter weekly cycle of 5–7 days is obviously shown, too. The shorter hourly cycle (8–12, 4–6, 3, 1–2 h, 20 min) of 1-min PM_2.5 was also indicated from spectral density analysis. Two major factors contribute the 1-min PM_2.5 cycles, i.e., the meteorological factors and source effects. Both the relative humidity and dew point showed consistent variation with PM_2.5, but the wind speed showed inverse variations with PM_2.5. Furthermore, the spectral density analysis of the meteorological factors (4–5, 2–2.5, 1–1.5 days, 12, 6–8, 3 h) may partially explain the cycles of PM_2.5. As for the sources effects, it can be shown from the strong dust storm of April 16–18th, 2006. PM_2.5 constantly increased tens and even hundreds of times high concentration within a few minutes due to the intensity of the dust sources.     

Distribution of PM2.5 and PM10-2.5 in PM10 Fraction in Ambient Air Due to Vehicular Pollution in Kolkata Megacity

This research paper aims at establishing baseline PM₁₀ and PM_2.5 concentration levels, which could be effectively used to develop and upgrade the standards in air pollution in developing countries. The relative contribution of fine fractions (PM_2.5) and coarser fractions (PM_10-2.5) to PM₁₀ fractions were investigates in a megacity which is overcrowded and congested due to lack of road network and deteriorated air quality because of vehicular pollution. The present study was carried out during the winter of 2002. The average 24h PM₁₀ concentration was 304 μg/m³, which is 3 times more than the Indian National Ambient Air Quality Standards (NAAQS) and higher PM₁₀ concentration was due to fine fraction (PM_2.5) released by vehicular exhaust. The 24h average PM_2.5 concentration was found 179 μg/m³, which is exceeded USEPA and EU standards of 65 and 50 μg/m³ respectively for the winter. India does not have any PM_2.5 standards. The 24 h average PM_10-2.5 concentrations were found 126 μg/m³. The PM_2.5 constituted more than 59% of PM₁₀ and whereas PM₁₀-PM_2.5 fractions constituted 41% of PM₁₀. The correlation between PM₁₀ and PM_2.5 was found higher as PM_2.5 comprised major proportion of PM₁₀ fractions contributed by vehicular emissions.     

PM10 in the ambient air of Chandrapur coal mine and its comparison with other environments

This study compares the ambient air particulate matter (PM10) data of 15 different coal mine environments. For most of these mine environments, the monitoring was carried out by different researchers using respirable dust sampler (RDS) that separates PM10 by centrifugal inertial separation. At two sites — Padmapur and Ghugus (Chandrapur, Maharashtra, India) — mass inertial impaction-based sampler was used for PM10 monitoring. It is observed that the spatiotemporal average value of ambient air PM10 monitored using mass inertial impactor reports relatively higher values (240–372 μg/m³) compared to those monitored using RDS (<227 μg/m³). In order to realize the severity of mine area pollution, it is compared with PM10 values found in an urban area (Delhi, India). It is found that PM10 values in Delhi (using mass inertial impactor) are much higher (300–400 μg/m³) than those reported for the mine environment. The data seems to indicate that the mine environment is relatively cleaner than urban air and therefore raises doubt about the appropriateness of using either mass impactor or RDS for PM10 sampling.     

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PM2.5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PM2.5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PM2.5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PM2.5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%。     

Indoor air quality modeling for PM10, PM2.5, and PM1.0 in naturally ventilated classrooms of an urban Indian school building

Assessment of indoor air quality (IAQ) in classrooms of school buildings is of prime concern due to its potential effects on student??s health and performance as they spend a substantial amount of their time (6?C7 h per day) in schools. A number of airborne contaminants may be present in urban school environment. However, respirable suspended particulate matter (RSPM) is of great significance as they may significantly affect occupants?? health. The objectives of the present study are twofold, one, to measure the concentrations of PM₁₀ (<10  $\upmu $ m), PM_2.5 (<2.5  $\upmu $ m), and PM_1.0 (<1.0  $\upmu $ m) in naturally ventilated classrooms of a school building located near a heavy-traffic roadway (9,755 and 4,296 vehicles/hour during weekdays and weekends, respectively); and second, to develop single compartment mass balance-based IAQ models for PM₁₀ (NVIAQM_pm10), PM_2.5 (NVIAQM_pm2.5), and PM_1.0 (NVIAQM_pm1.0) for predicting their indoor concentrations. Outdoor RSPM levels and classroom characteristics, such as size, occupancy level, temperature, relative humidity, and CO₂ concentrations have also been monitored during school hours. Predicted indoor PM₁₀ concentrations show poor correlations with observed indoor PM₁₀ concentrations (R ² = 0.028 for weekdays, and 0.47 for weekends). However, a fair degree of agreement (d) has been found between observed and predicted concentrations, i.e., 0.42 for weekdays and 0.59 for weekends. Furthermore, NVIAQM_pm2.5 and NVIAQM_pm1.0 results show good correlations with observed concentrations of PM_2.5 (R ² = 0.87 for weekdays and 0.9 for weekends) and PM_1.0 (R ² = 0.86 for weekdays and 0.87 for weekends). NVIAQM_pm10 shows the tendency to underpredict indoor PM₁₀ concentrations during weekdays as it does not take into account the occupant??s activities and its effects on the indoor concentrations during the class hours. Intense occupant??s activities cause resuspension or delayed deposition of PM₁₀. The model results further suggests conductance of experimental and physical simulation studies on dispersion of particulates indoors to investigate their resuspension and settling behavior due to occupant??s activities/movements. The models have been validated at three different classroom locations of the school site. Sensitivity analysis of the models has been performed by varying the values of mixing factor (k) and newly introduced parameter R _c. The results indicate that the change in values of k (0.33 to 1.00) does not significantly affect the model performance. However, change in value of R _c (0.001 to 0.500) significantly affects the model performance.     

PM2.5 and volatile organic compounds (VOCs) in ambient air: a focus on the effect of meteorology

PM(2.5) and VOCs (benzene, toluene, m-p-o-xylenes) concentrations were measured in an urban and a suburban site in Athens, Greece, during the period between April and November 2004. This period, which is considered to be the warmer period in Greece, is characterized by the development of sea-breeze over the Attica Basin. Additionally strong Northern, North-eastern winds called "The Etesians", predominate during the summer months (July-August), acting positively to the dispersion of pollutants. In this campaign, 24 days with sea-breeze development were observed, 15 days with northern winds, 6 days with southern winds while the rest of the days presented no specific wind profile. Maximum concentrations of PM(2.5), VOCs and nitrogen oxides, were detected during the days with sea-breeze, while minimum concentrations during the days with northern winds. Ozone was the only pollutant that appeared to have higher concentrations in the background site and not in the city centre, where benzene presented strong negative correlation with ozone, indicating the photochemical reaction of hydrocarbons that lead to the ozone formation. The BTX ratios were similar for both sites and wind profiles, indicating common sources for those pollutants. T/B ratio ranged in low levels, between 3-5 for site A and 2-5 for site B, suggesting vehicles emissions as the main sources of volatile compounds. Finally, the strong correlations of PM(2.5) and benzene concentrations, between the two sampling sites, indicate that both the city centre and the background site, are affected by the same sources, under common meteorological conditions (sea-breeze, northern winds).