PM2.5的化学物种采样与分析方法

对PM25的化学物种组成及其采样方法进行了概述,侧重介绍了主要的半挥发性成分硝酸铵与有机碳的采样问题.按照无机多元素、水溶性离子和含碳组分概述了PM25主要化学物种的分析方法.     

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.     

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.     

北京地区气象条件与区域传输对PM2.5浓度影响研究

通过采用KZ（Kolmogorov Zurbenko）滤波统计方法,结合WRF/CMAQ数值模型情景分析技术,定量分析气象条件和区域传输对北京市PM2.5浓度的影响。结果显示: 2018年7月—2019年6月,北京市PM2.5平均质量浓度为46.0 μg/m3,气象条件同比偏差6.9%,外来传输平均为43.7%。从日变化上看,外来传输在早晚高峰期间明显下降,体现出本地机动车排放贡献明显上升的特点,气象条件对PM2.5浓度的影响主要表现为白天整体有利于污染物扩散,夜间气象条件转为偏不利的特征。2018年10月—2019年3月秋冬季期间,气象条件同比偏差3.3%,在气象条件较为不利和区域同比反弹的情况下,北京市PM2.5浓度持续走低,主要为源减排的效益。     

Risk assessment of heavy metals in road and soil dusts within PM2.5, PM10 and PM100 fractions in Dongying city, Shandong Province, China

15 road and 14 soil dust samples were collected from an oilfield city, Dongying, from 11/2009-4/2010 and analyzed by inductively coupled plasma-mass spectroscopy (ICP-MS) for V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd and Pb within PM(2.5), PM(10) and PM(100) fractions synchronously. Metal concentrations, sources and human health risk were studied. Results showed that both soil and road dust exhibited higher values for Mn and Zn and lower values for Co and Cd for the three fractions. Mass concentration ratios of PM(2.5)/PM(10) and PM(10)/PM(100) for metals in road and soil dust indicate that most of the heavy metals tend to concentrate in fine particles. Geoaccumulation index and enrichment factors analysis showed that Cu, Zn and Cd exhibited moderate or heavy contamination and significant enrichment, indicating the influence of anthropogenic sources. Vanadium, Cr, Mn and Co were mostly not enriched and were mainly influenced by crustal sources. For Ni, As and Pb, they ranged from not enriched to moderately enriched and were influenced by both crustal materials and anthropogenic sources. The conclusions were confirmed by multivariate analysis methods. Principle component analysis revealed that the major sources were vehicle emission, industrial activities, coal combustion, agricultural activities and crustal materials. The risk assessment results indicated that metal ingestion appeared to be the main exposure route followed by dermal contact. The most likely cause for cancer and other health risks are both the fine particles of soil and road dusts.     

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%。     

宁波市PM10、PM2.5中水溶性无机阴离子浓度水平及分布特征

研究了宁波市PM10、PM2.5中无机阴离子浓度水平及分布特征。结果表明,PM10中Cl-为1.00μg/m3,具有较明显的海洋特征,SO24-、NO3-离子浓度为9.90、3.70μg/m3;Cl-主要存在于粒径为2.5-10μm的颗粒物中,而NO3-、SO42-主要存在于PM2.5中,成为PM2.5的重要组成部分。PM10中水溶性无机阴离子季节变化明显,呈冬天高,夏天低的趋势。     

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。     

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.     

我国居住建筑室内PM_(2.5)研究现状及进展

分析了我国居住建筑室内PM_(2.5)污染水平,总结了现阶段PM_(2.5)的研究方法,主要包括理论分析法、数值模拟法和实验测试法。指出室外源是室内PM_(2.5)的主要污染来源,而室内烟草烟雾、烹饪及人员活动也会严重影响室内PM_(2.5)浓度。针对目前研究中存在的问题,提出了标定典型建筑在不同影响因素下的I/O比范围、研究家具和家电的颗粒物释放状况、加强农村室内外空气颗粒物污染调查等建议。     

应用数据挖掘算法预测台风条件下PM_(2.5)质量浓度

利用6种数据挖掘算法对2012年台风和污染物浓度等数据建模,建立台风条件下的气象因素和环境空气中PM2.5的关系模型。通过对2013年数据的测试,表明6种数据挖掘方法模型的预测值与实测值具有很好的一致性。6种算法均能实现实时预测,其中Bagging算法预测结果的相关性整体最好,而对于测试样本,M5Rules算法最好,SMOreg算法次之。以2013年的超强台风"海燕"的预测为例,讨论台风条件下PM_(2.5)质量浓度的变化过程及对预测结果的解释。     

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。     

桂林市冬季大气PM2.5中重金属污染物健康风险评估

通过监测桂林市2014年冬季大气PM_(2.5)中11种元素的质量浓度,运用富集因子解析其来源,并开展重金属潜在生态风险、人体暴露和健康风险评价。结果表明:Cd富集因子10,表明Cd的来源主要是人为源;其他元素富集因子均10,主要来源为自然源。生态危害程度Cd和Hg为极强,Pb为强,As为较强,其他元素均为轻微。总的潜在生态风险指数(RI)为19 385.3,远高于600,生态风险为极强。各元素经呼吸摄入的非致癌风险由高到低为AsCdCrPbHg,致癌风险由高到低为CrAsCdPb;儿童和成人的总致癌风险分别为1.68×10~(-7) a~(-1)和3.84×10~(-7) a~(-1),风险甚微。     

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。     

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.     

Characterization of PM10 and PM2.5 and associated heavy metals at the crossroads and urban background site in Zabrze, Upper Silesia, Poland, during the smog episodes

The concentrations of seven heavy metals (Cd, Cr, Cu, Fe, Mn, Ni, and Pb) associated with PM10 and PM2.5 at the crossroads and the background sites have been studied in Zabrze, Poland, during smog episodes. Although the background level was unusually elevated due to both high particulate emission from the industrial and municipal sources and smog favorable meteorological conditions, significant increase of the concentration of PM2.5 and PM10 as well as associated heavy metals in the roadside air compared to the urban background has been documented. The average daily difference between the roadside and corresponding urban background aerosol concentration was equal to 39.5 μg m^???3 for PM10 and 41.2 μg m^???3 for PM2.5. The highest levels of the studied metals in Zabrze appeared for iron carried by PM10 particles: 1,706 (background) and 28,557 ng m^???3 (crossroads). The lowest concentration level (in PM10) has been found for cadmium: 7 and 77 ng m^???3 in the background and crossroads site, respectively. Also the concentrations of heavy metals carried by the fine particles (PM2.5) were very high in Zabrze during the smog episodes. Concentrations of all studied metals associated with PM10 increased at the roadside compared to the background about ten times (one order) while metals contained in PM2.5 showed two to three times elevated concentrations (except Fe—five times and Cr—no increase).     

Gravimetric and Chemical Features of Airborne PM10 AND PM2.5 in Mainland Portugal

This paper describes concentration amounts of arsenic (As), particulate mercury (Hg), nickel (Ni) and lead (Pb) in PM₁₀ and PM_2.5, collected since 1993 by the Technological and Nuclear Institute (ITN) at different locations in mainland Portugal, featuring urban, industrial and rural environments, and a control as well. Most results were obtained in the vicinity of coal- and oil-fired power plants. Airborne mass concentrations were determined by gravimetry. As and Hg concentrations were obtained through instrumental neutron activation analysis (INAA), and Ni and Pb concentrations through proton-induced X-ray emission (PIXE). Comparison with the EU (European Union) and the US EPA (United States Environmental Protection Agency) directives for Ambient Air has been carried out, even though the sampling protocols herein – set within the framework of ITN's R&D projects and/or monitoring contracts – were not consistent with the former regulations. Taking this into account, 1) the EU daily limit for PM₁₀ was exceeded a few times in all sites except the control, even if the number of times was still inferior to the allowed one; 2) the EU annual mean for PM₁₀ was exceeded at one site; 3) the EPA daily limit for PM_2.5 was exceeded one time at three sites; 4) the EPA annual mean for PM_2.5 was exceeded at most sites; 5) the inner-Lisboa site approached or exceeded the legislated PMs; 6) Pb levels stayed far below the EU limit value; and 7) concentrations of As, Ni and Hg were also far less than the reference values adopted by EU. In every location, Ni appeared more concentrated in PM_2.5 than in coarser particles, and its levels were not that different from site to site, excluding the control. The highest As and Hg concentrations were found in the neighbourhood of the coal-fired, utility power plants. The results may be viewed as a “worst-case scenario” of atmospheric pollution, since they have been obtained in busy urban-industrial areas and/or near major power-generation and waste-incineration facilities.     

新疆环境空气中PM_(2.5)/PM_(10)比值特征分析

随着环境空气质量新标准的全面实施,PM_(2.5)监测已经全面普及,并成为全国大部分城市关注的首要污染物,根据新疆环境空气质量监测网中不同区域、不同时段颗粒物(PM_(2.5)、PM_(10))质量浓度监测结果,对PM_(2.5)/PM_(10)质量浓度的比值关系进行深入分析,研究其在新疆典型区域特殊气象条件下的分布规律,为科学合理评价和考核新疆环境空气质量提供数据支持与参考。     

春节烟花爆竹燃放期间苏州市区PM2.5中水溶性离子特征分析

使用在线离子色谱分析了苏州市区春节期间PM2.5中水溶性离子.结果表明：春节期间存在2个空气污染高峰时段（初一和初五）,其中初一凌晨PM2.5达到最高峰,小时质量浓度为571μg/m3;烟花爆竹集中燃放时段,PM2.5中K＋、Cl-、Mg2＋质量浓度明显上升,初-凌晨与腊月三十凌晨相比,K＋、Cl-、Mg2＋质量浓度分别增加了117倍、80.7倍、18.0倍;相关性分析表明,PM2.5中K＋、Mg2＋、Na＋、Ca2＋在烟花爆竹燃放时段可能具有相同的来源.