Gravimetric analysis for PM2.5 mass concentration based on year-round monitoring at an urban site in Beijing

Daily PM_(2.5)(particulate matter with an aerodynamic diameter of below 2.5 μm) mass concentrations were measured by gravimetric analysis in Chinese Research Academy of Environmental Sciences(CRAES), in the northern part of the Beijing urban area, from December 2013 to April 2015. Two pairs of Teflon(T1/T2) and Quartz(Q1/Q2) samples were obtained, for a total number of 1352 valid filters. Results showed elevated pollution in Beijing,with an annual mean PM_(2.5)mass concentration of 102 μg/m~3. According to the calculated PM_(2.5)mass concentration, 50% of our sampling days were acceptable(PM_(2.5) 75 μg/m~3), 30% had slight/medium pollution(75–150 μg/m~3), and 7% had severe pollution( 250 μg/m~3). Sampling interruption occurred frequently for the Teflon filter group(75%) in severe pollution periods,resulting in important data being missing. Further analysis showed that high PM_(2.5)combined with high relative humidity(RH) gave rise to the interruptions. The seasonal variation of PM_(2.5)was presented, with higher monthly average mass concentrations in winter(peak value in February, 422 μg/m~3), and lower in summer(7 μg/m~3 in June). From May to August, the typical summer period, least severe pollution events were observed, with high precipitation levels accelerating the process of wet deposition to remove PM_(2.5). The case of February presented the most serious pollution, with monthly averaged PM_(2.5)of 181 μg/m~3 and 32% of days with severe pollution. The abundance of PM_(2.5)in winter could be related to increased coal consumption for heating needs.     

Characterization of atmospheric aerosol (PM10 and PM2.5) from a medium sized city in São Paulo state, Brazil

Air pollution causes deleterious effects on human health with aerosols being among the most polluting agents.The objective of this work is the characterization of the PM_(2.5) and PM_(10) aerosol mass in the atmosphere.The methods of analysis include WD-XRF and EDS.Data were correlated with meteorological information and air mass trajectories(model HYSPLIT)by multivariate analysis.A morphological structural analysis was also carried out to identify the probable sources of atmospheric aerosols in the city of Sao Jose do Rio Preto,Brazil.The mean mass concentration values obtained were 24.54 μg/m~3 for PM_(10),above the WHO annual standard value of 20 μg/m~3 and 10.88 μg/m~3 for PM_(2.5) whose WHO recommended limit is10 μg/m~3.WD-XRF analysis of the samples revealed Si and Al as major components of the coarse fraction.In the fine fraction,the major elements were Al and S.The SEM-FEG characterization allowed identifying the morphology of the particles in agglomerates,ellipsoids and filaments in the PM_(10),besides spherical in the PM_(2.5).The analysis by EDS corroborated WD-XRF results,identifying the crustal elements,aluminosilicates and elements of anthropogenic origin in the coarse fraction.For the fine fraction crustal elements were also identified;aluminosilicates,black carbon and spherical particles(C and O) originating from combustion processes were predominant.The use of multivariate analysis to correlate air mass trajectories with the results of the morpho-structural characterization of the particulate matter allowed confirmation of the complex composition of the particles resulting from the combination of both local and long-distance sources.     

Aerosol optical properties under different pollution levels in the Pearl River Delta (PRD) region of China

To clarify the aerosol hygroscopic growth and optical properties of the Pearl River Delta(PRD)region,integrated observations were conducted in Heshan City of Guangdong Province from October 19 to November 17,2014.The concentrations and chemical compositions of PM_(2.5),aerosol optical properties and meteorological parameters were measured.The mean value of PM_(2.5) increased from less than 35(excellent) to 35-75 μg/m~3(good) and then to greater than 75 μg/m~3(pollution),corresponding to mean PM_(2.5) values of 24.9,51.2,and 93.3 μg/m~3,respectively.The aerosol scattering hygroscopic growth factor(f(RH = 80%)) values were 2.0,2.12,and 2.18 for the excellent,good,and pollution levels,respectively.The atmospheric extinction coefficient(σext)and the absorption coefficient of aerosols(σ_(ap)) increased,and the single scattering albedo(SSA)decreased from the excellent to the pollution levels.For different air mass sources,under excellent and good levels,the land air mass from northern Heshan had lower f(RH) and σ_(sp) values.In addition,the mixed aerosol from the sea and coastal cities had lower f(RH) and showed that the local sources of coastal cities have higher scattering characteristics in pollution periods.     

Heavy metals and their source identification in particulate matter (PM2.5) in Isfahan City, Iran

The presence of heavy metals(HMs) in particulate matters(PMs) particularly fine particles such as PM_(2.5) poses potential risk to the health of human being. The purpose of this study was to analyze the contents of HMs in PM_(2.5) in the atmospheric monitoring stations in Isfahan city,Iran, in different seasons between March 2014 and March 2015 and their source identification using principle component analysis(PCA). The samples of PM_(2.5) were taken using a high volume sampler in 7 monitoring stations located throughout the city and industrial zones since March 2014 to March 2015. The HMs content of the samples was measured using ICP-MS.The results showed that the concentrations of As, Cd and Ni were in a range of 23–36, 1–12,and 5–76 ng/m~3 at all the stations which exceeded the US-EPA standards. Furthermore,the concentrations of Cr and Cu reached to 153 and 167 ng/m~3 in some stations which were also higher than the standard levels. Depending on the potential sources of HMs, their concentration in PM_(2.5) through the various seasons was different. PCA illustrated that the different potential sources of HMs in the atmosphere, showing that the most important sources of HMs originated from fossil fuel combustion, abrasion of vehicle tires, industrial activities(e.g., iron and steel industries) and dust storms. Management and control of air pollution of industrial plants and vehicles are suggested for decreasing the risk of the HMs in the region.     

Insights into the phenomenon of an explosive growth and sharp decline in haze: A case study in Beijing

A severe haze episode occurred in winter in the North China Plain(NCP),and the phenomenon of an explosive growth and sharp decline in PM_(2.5)(particulate matter with an aerodynamic diameter equal to or less than 2.5μm)concentration was observed.To study the systematic causes for this phenomenon,comprehensive observations were conducted in Beijing from November 26 to December 2,2015;during this period,meteorological parameters,LIDAR data,and the chemical compositions of aerosols were determined.The haze episode was characterized by rapidly varying PM2.5 concentration,and the highest PM_(2.5) concentration reached 667μg/m~3.During the haze episode,the NCP was dominated by a weak high-pressure system and continuously low PBL(planetary boundary layer)heights,which are unfavorable conditions for the diffusion of pollutants.The large increases in the concentrations of SNA(SO_4~(2-),NO_3~-and NH_4~+)during the haze implied that the formation of SNA was the largest contribution.Water vapor also played a vital role in the formation of haze by promoting the chemical transformation of secondary pollutants,which led to higher PM_(2.5) concentrations.The spatial distributions of PM_(2.5) in Beijing at different times and the backward trajectories of the air masses also indicated that pollutants from surrounding provinces in particular,contributed to the higher PM_(2.5)concentration.     

Baosteel emission control significantly benefited air quality in Shanghai

As the largest iron and steel producer in China, a part of Baosteel moved out of Shanghai deserves close attention due to its environmental impact. To understand the effect of Baosteel emission control on air quality in Shanghai, daily PM_(10), PM_(2.5), SO_2, NO_2 and CO were measured from 2010 to 2016. Concentrations of pollutants in Baoshan District presented a decreased trend during 2010–2016, with a reduction rate of 28.6% for PM_(10), 67.3% for SO_2, 8.6% for NO_2 and 42.0% for CO. However, fine particle pollution in Baoshan District during 2012–2016 seems to become more prominent, with PM_(2.5) concentration of 47 ± 28,45 ± 33, 38 ± 24, 54 ± 41 and 51 ± 34 μg/m3, respectively, indicating a slight increase of 8.5%in PM_(2.5). Concentrations of PM_(10) and CO decreased by 12.5% and 33.8% in the second half year in 2016(compared with that in 2015) probably due to closure of blast furnace of Baosteel. Baosteel was identified as the largest pollution source in Baoshan District.Emission from Baosteel accounted for 58.0% of SO_2, 43.6% of NO_2 and 79.3% of dust in total emission from Baoshan District during 2010–2015. Meanwhile, pollutant emission and coal consumption from Baosteel decreased by 52.0% for SO_2, 40.1% for NO_2, 15.7% for dust and22% for coal consumption. Energy consumption in Baoshan District reduced by 31% from2011 to 2015. Air quality improvement in Shanghai was attributed to local emission reduction, together with regional air quality improvement.     

Comparative study on water-soluble inorganic ions in PM2.5 from two distinct climate regions and air quality

Recently, air quality has significantly improved in developed country, but that issue is of concern in emerging megacity in developing country.In this study, aerosols and their precursor gas were collected by NILU filter pack at two distinct urban sites during the winter and summer in Osaka, Japan and dry and rainy seasons in Ho Chi Minh City(HCMC),Vietnam.The aims are to investigate the contribution of water-soluble inorganic ions(WSIIs) to PM_(2.5), thermodynamic characterization and possible formation pathway of secondary inorganic aerosol(SIA).The PM_(2.5) concentration in Osaka(15.8 μg/m~3) is lower than that in HCMC(23.0 μg/m~3), but the concentration of WSIIs in Osaka(9.0 μg/m~3) is two times higher than that in HCMC(4.1 μg/m~3).Moreover, SIA including NH_4~+, NO_3~-and SO_4~(2-)are major components in WSIIs accounting for 90% and 76%(in molar) in Osaka and HCMC,respectively.Thermodynamic models were used to understand the thermodynamic characterization of urban aerosols.Overall, statistical analysis results indicate that very good agreement(R~2 0.8) was found for all species, except for nitrate aerosol in HCMC.We found that when the crustal species present at high amount, those compositions should be included in model calculation(i.e.in the HCMC situation).Finally, we analyzed the characteristics of NH_4~+– NO_3~-– SO_4~(2-)system.A possible pathway to produce fine nitrate aerosol in Osaka is via the homogeneous reaction between NH_3 and HNO_3, while nonvolatile nitrate aerosols can be formed by the heterogeneous reactions in HCMC.     

Association of emergency room visits for respiratory diseases with sources of ambient PM2.5

Previous studies have reported associations of short-term exposure to different sources of ambient fine particulate matter(PM_(2.5)) and increased mortality or hospitalizations for respiratory diseases. Few studies, however, have focused on the short-term effects of source-specific PM_(2.5) on emergency room visits(ERVs) of respiratory diseases. Source apportionment for PM_(2.5) was performed with Positive Matrix Factorization(PMF) and generalized additive model was applied to estimate associations between source-specific PM_(2.5) and respiratory disease ERVs. The association of PM_(2.5) and total respiratory ERVs was found on lag4(RR = 1.011, 95%CI: 1.002, 1.020) per interquartile range(76 μg/m~3) increase.We found PM_(2.5) to be significantly associated with asthma, bronchitis and chronic obstructive pulmonary disease(COPD) ERVs, with the strongest effects on lag5(RR = 1.072,95%CI: 1.024, 1.119), lag4(RR = 1.104, 95%CI: 1.032, 1.176) and lag3(RR = 1.091, 95%CI: 1.047,1.135), respectively. The estimated effects of PM_(2.5) changed little after adjusting for different air pollutants. Six primary PM_(2.5) sources were identified using PMF analysis, including dust/soil(6.7%), industry emission(4.5%), secondary aerosols(30.3%), metal processing(3.2%),coal combustion(37.5%) and traffic-related source(17.8%). Some of the sources were identified to have effects on ERVs of total respiratory diseases(dust/soil, secondary aerosols, metal processing, coal combustion and traffic-related source), bronchitis ERVs(dust/soil) and COPD ERVs(traffic-related source, industry emission and secondary aerosols). Different sources of PM_(2.5) contribute to increased risk of respiratory ERVs to different extents, which may provide potential implications for the decision making of air quality related policies, rational emission control and public health welfare.     

Variation of airborne quartz in air of Beijing during the Asia-Pacific Economic Cooperation Economic Leaders'' Meeting

Quartz particles are a toxic component of airborne paniculate matter(PM).Quartz concentrations were analyzed by X-ray diffraction in eighty-seven airborne PM samples collected from three locations in Beijing before,during,and after the Asia-Pacific Economic Cooperation(APEC) Leaders' Meeting in 2014.The results showed that the mean concentrations of quartz in PM samples from the two urban sites were considerably higher than those from the rural site.The quartz concentrations in samples collected after the APEC meeting,when the pollution restriction lever was lifted,were higher than those in the samples collected before or during the APEC meeting.The quartz concentrations ranged from 0.97 to 13.2 μg/m~3,which were among the highest values amid those reported from other countries.The highest quartz concentration exceeded the Californian Office of Environmental Health Hazard Assessment reference exposure level and was close to the occupational threshold limit values for occupational settings.Moreover,a correlation analysis showed that quartz concentrations were positively correlated with concentrations of pollution parameters PM_(10),PM_(2.5),SO_2 and NO_x,but were negatively correlated with O_3 concentration.The results suggest that the airborne quartz particles may potentially pose health risks to the general population of Beijing.     

Chemical characteristics of size-resolved aerosols in winter in Beijing

Size-resolved aerosols were continuously collected by a Nano Sampler for 13 days at an urban site in Beijing during winter 2012 to measure the chemical composition of ambient aerosol particles. Data collected by the Nano Sampler and an ACSM(Aerodyne Aerosol Chemical Speciation Monitor) were compared. Between the data sets,similar trends and strong correlations were observed,demonstrating the validity of the Nano Sampler. PM10 and PM2.5concentrations during the measurement were 150.5 ± 96.0 μg/m3(mean ± standard variation)and 106.9 ± 71.6 μg/m3,respectively. The PM2.5/PM10 ratio was 0.70 ± 0.10,indicating that PM2.5dominated PM10. The aerosol size distributions showed that three size bins of 0.5–1,1–2.5 and 2.5–10 μm contributed 21.8%,23.3% and 26.0% to the total mass concentration(TMC),respectively. OM(organic matter) and SIA(secondary ionic aerosol,mainly SO42-,NO3-and NH4+) were major components of PM2.5. Secondary compounds(SIA and secondary organic carbon) accounted for half of TMC(about 49.8%) in PM2.5,and suggested that secondary aerosols significantly contributed to the serious particulate matter pollution observed in winter. Coal burning,biomass combustion,vehicle emissions and SIA were found to be the main sources of PM2.5. Mass concentrations of water-soluble ions and undetected materials,as well as their fractions in TMC,strikingly increased with deteriorating particle pollution conditions,while OM and EC(elemental carbon) exhibited different variations,with mass concentrations slightly increasing but fractions in TMC decreasing.     

海口市PM_(2.5)和PM_(10)来源解析

以海口市为例,研究了我国典型热带沿海城市——海口市环境空气颗粒物的污染特征和主要来源.2012年春季和冬季在海口市区4个采样点同步采集了环境空气中PM10和PM2.5样品,同时采集了多种颗粒物源样品,并使用多种仪器分析方法分析了源与受体样品的化学组成,建立了源化学成分谱.使用CMB(化学质量平衡)模型对海口市大气颗粒物进行源解析.结果表明:污染源贡献具有明显的季节特点,并存在一定的空间变化.冬季城市扬尘、机动车尾气尘、二次硫酸盐和煤烟尘是海口市PM10和PM2.5中贡献较大的源,在PM10和PM2.5中贡献率分别为23.6%、16.7%,17.5%、29.8%,13.3%、15.7%和13.0%、15.3%;春季机动车尾气尘、城市扬尘、建筑水泥尘和二次硫酸盐是海口市PM10和PM2.5中贡献较大的源,在PM10和PM2.5中贡献率分别为27.5%、35.0%,20.2%、14.9%,12.8%、6.0%和9.5%、10.5%.冬季较重的颗粒物污染可能来自于华南内陆地区的区域输送,特别是,本地排放极少的煤烟尘和二次硫酸盐受区域输送的影响更为显著.     

PM1切割器的评价及其与PM2.5切割器的切换研究

开展了PM₁切割器的性能评价研究,通过搭建基于静态箱法的评价系统,选用8种粒径的单分散聚苯乙烯微球,结合空气动力学粒径谱仪获取PM₁切割器的捕集效率,最终得出拟合曲线.通过该方法测量得出某台BGI的PM₁切割器在16.67 L·min^-1工作流量下的50%切割粒径分布在(1.0±0.1) μm范围内,几何标准偏差分布在1.2±0.1范围内.同时研究了采样流量对PM₁切割器性能的影响,发现50%切割粒径随流量增大呈现减小趋势,而几何标准偏差一致性较好,偏差不超过1%,尤其当采样流量为6.77 L·min^-1时,PM₁切割器的性能可调整为符合国家标准对PM_2.5切割器的要求,本研究成果为PM₁切割器评价方法及性能指标的确定提供了参考.     

天津市PM10, PM2.5和PM1连续在线观测分析

利用2010年9月1日─11月30日在中国气象局天津大气边界层观测站采集的ρ(PM10),ρ(PM2.5)和ρ(PM1)数据,分析了观测期间可吸入颗粒物的统计特征,结合同期气象观测资料,分析了典型天气条件下ρ(PM10),ρ(PM2.5)和ρ(PM1)的日变化特征及与风速、风向的关系.结果表明:观测期间,ρ(PM10)日均值有超过12的天数超过《国家环境空气质量标准》(GB 3095─1996)二级标准限值;ρ(PM2.5)有63 d超过美国国家环境保护局(US EPA)1997标准限值,超标率高达76.8%;不同天气条件下,ρ(PM10),ρ(PM2.5)和ρ(PM1)日变化特征明显,三者一般在大雾或扬沙浮尘天气条件下出现高值,有降水过程时出现低值;可吸入颗粒物以粗粒子(PM2.5～10)和PM1为主,PM2.5～10,PM1～2.5和PM1主要分布在风速小于3 m/s,风向为225°～280°和70°～110°范围内;风速大于3 m/s时,ρ(PM2.5～10)和ρ(PM1～2.5)有所增加.ρ(PM10),ρ(PM2.5)和ρ(PM1)未出现周末效应,但存在明显的周内变化.     

北京市PM2.5时空分布特征及其与PM10关系的时空变异特征

PM_(2.5)时空分布特征及其与其它污染物的相关关系是PM_(2.5)时空统计分析的主要研究内容.然而,现有的方法直接从监测站点的角度对时空分布特征进行分析,难以有效地揭示PM_(2.5)浓度的聚集分布特征;同时,常用的地理加权回归在对PM_(2.5)与其它污染物间关系进行建模的过程中,缺乏同时考虑时间异质性与空间异质性,从而不能准确地描述依赖关系的时空变异特征.为此,首先借助于空间聚类分析技术,对北京市2014年PM_(2.5)浓度的聚集结构进行探测,在此基础上,通过聚集结构来分析PM_(2.5)季节性时空分布特征.然后,利用地理时空加权回归对北京市PM_(2.5)与PM_(10)季节平均浓度间关系进行建模,依据回归结果分析PM_(2.5)-PM_(10)间关系的时空变异特征.实验结果表明,春夏季节PM_(2.5)污染程度及空间变异程度均低于秋冬季节,各季节PM_(2.5)浓度均表现为北部浓度低、南部浓度高的空间分布特征;地理时空加权回归具有更好的拟合效果,由回归系数进一步可发现,春夏季PM_(2.5)-PM_(10)相关性低于秋冬季PM_(2.5)-PM_(10)相关性;各季节均表现为西北部PM_(2.5)-PM_(10)的相关性高于东南部PM_(2.5)-PM_(10)的相关性.     

降水和风对大气PM2.5、PM10的清除作用分析

对合肥2015—2017年的降水、风和PM_(2.5)、PM_(10)浓度观测数据统计研究发现,降水对PM_(2.5)、PM_(10)有一定的清除作用,尤其在秋冬季节.秋冬季节小雨、中雨分别导致PM_(2.5)和PM_(10)浓度降低23.1%、40.4%和32.0%、63.7%.雨日PM_(2.5)/PM_(10)比例上升8.4%,表明降水对PM_(10)清除作用更显著.降水前后PM_(2.5)浓度变化与降水前PM_(2.5)浓度、降水强度、降水时长密切相关.当降水强度大于4 mm·h~(-1)或PM_(2.5)初始浓度高于115μg·m~(-3)时,降水对PM_(2.5)产生明显清除作用;而降水强度小于1 mm·h~(-1)或PM_(2.5)初始浓度低于115μg·m~(-3)时由于吸湿增长作用极易造成PM_(2.5)浓度反弹升高;且持续3 h以上雨强介于1～4 mm·h~(-1)的降水也对PM_(2.5)产生清除作用.降水前后PM_(10)浓度变化与初始浓度密切相关,而与雨强相关性较弱.当PM_(10)初始浓度大于50μg·m~(-3),降水就对PM_(10)产生明显清除作用,且PM_(10)初始浓度越高,降水后PM_(10)浓度下降越多.风速大于2 m·s~(-1)可显著降低PM_(2.5)浓度,因此,当风速大于4 m·s~(-1)时合肥较少出现中度及以上污染,但易造成地面起尘,使PM_(10)浓度不降反升.合肥冬季严重污染主要出现在西北风向,夏季中度以上污染天气较少,主要出现在风速低于3 m·s~(-1)的东南风向.     

淄博市SO2与PM10垂直空间变化规律及其相关性分析

本文对淄博市环境空气中主要污染物SO2和PM10在不同高度的浓度值进行分析,找出污染物垂直空间分布规律;并利用SO2和PM10日均浓度值分析两者之间的相关性,为淄博市环境空气质量进一步控制治理提供一定依据。研究结论如下:淄博市垂直空间SO2、PM10浓度变化基本呈随高度增加而逐步降低趋势;同时SO2和PM10浓度呈现较为明显的相关性。为进一步改善淄博市环境空气质量,不仅要在城市规划中充分考虑给城区以自然通风通道,增加城市对污染物扩散稀释的能力,而其要在开展针对建陶、水泥等行业专项行动,降低工业粉尘排放,加强对城区内市政、房地产建筑工地的监督管理,减少道路和建筑扬尘的同时,加强对SO2排放企业尤其燃煤企业的监管力度,控制SO2排放量,也会相应的进一步降低PM10浓度。     

重庆主城区大气PM10及PM2.5来源解析

为探讨重庆主城区4个季节大气PM₁₀和PM_2.5的主要来源,于2012年2—12月在重庆主城区的工业区、文教区和居住区5个环境监测点同步采集PM₁₀及PM_2.5样品,分析了无机元素、水溶性离子、有机碳和元素碳含量及其分布特征. 采集了重庆主城区土壤尘、建筑水泥尘、扬尘、移动源(包括机动车、施工机械及船舶)、工业源(包括固定燃烧源及工业工艺过程源)、生物质燃烧源及餐饮源等7类污染源,建立了重庆市本地化的污染源成分谱库. 利用CMB(化学质量平衡)受体模型及二重源解析技术分析了PM₁₀及PM_2.5的来源. 结果表明:重庆主城区大气中ρ(PM₁₀)及ρ(PM_2.5)的年均值分别为153.2和113.1 μg/m³,超过GB 3095—2012《环境空气质量标准》二级标准限值2倍以上. 大气PM₁₀的主要来源为扬尘、二次粒子和移动源(贡献率分别为23.9%、23.5%和23.4%),大气PM_2.5主要来源于二次粒子和移动源(贡献率分别为30.1%和27.9%).PM₁₀和PM_2.5的主要源类贡献率差别不大,表明研究区域内大气颗粒物污染控制应采取多源控制原则. 大气PM₁₀来源的季节性变化特征表现为春季和秋季主要以扬尘为主、夏季和冬季主要以二次粒子为主.     

2014年APEC期间北京市PM10和PM2.5氧化性损伤能力研究

为评估APEC会议期间联防联控措施对北京市大气可吸入颗粒物毒性的影响,采集2014年APEC会议前后3个月北京市大气PM₁₀和PM_2.5样品,应用质粒DNA损伤评价法来研究其氧化性损伤能力. 结果表明,APEC会议期间PM₁₀对DNA的损伤率高于PM_2.5,颗粒物对 DNA损伤率随剂量的增加而增加. 本研究用TD30值来指示颗粒物氧化性损伤能力,TD30为引起30%的DNA损伤率所需要的颗粒物剂量（单位为 μg·mL^-1）,TD30值越低,颗粒物氧化性损伤能力越强,APEC会议前后样品的TD30值表现为 APEC期间（11月）>APEC前（10月）>APEC后（12月）,说明氧化能力APEC后 >APEC前 >APEC期间. 用PM₁₀质量浓度乘上其在250 μg·mL^-1 剂量下的DNA损伤率得到颗粒物暴露毒性指数TI（toxic index）,与往年具有代表性月份样品的数据对比,TI大小顺序为2004年 >2014年 >2008年,说明大气中颗粒物暴露毒性随着政策控制力度的加大而降低.     

北京市大气PM10和PM2.5中有机物的时空变化

2005年四季在北京市不同功能区9个采样点采集大气PM₁₀和PM_2.5样品,并对其中有机物污染水平、分布特征及不同功能区PM₁₀和PM_2.5中有机物的相关性进行了探讨.结果表明,市区PM₁₀和PM_2.5中有机物年均值分别为41.39 μg/m³和34.84 μg/m³,是对照区十三陵的1.44倍和1.26倍;冬季有机物污染最严重,分别为春季的1.15、 1.82倍,秋季的2.06、 2.26倍,夏季的4.53、 6.26倍.不同季节PM_2.5与PM₁₀中EOM的比值超过0.60, 并呈现一定季节差异.各功能区有机污染表现出工业区(商业区)＞居民区(交通区、对照区)的变化趋势,且不同功能区PM_2.5中EOM对PM₁₀中EOM的影响程度各异.有机组分的年均值有非烃＞沥青质＞芳烃＞饱和烃的变化规律,而污染源的季节性排放是造成有机物组分季节变化的主要原因.     

夏季广州城区细颗粒物PM_(2.5)和PM_(1.0)中水溶性无机离子特征

于2008年7月1～31日在广州城区每天采集PM2.5和PM1.0样品.利用离子色谱分析了样品中Na+、NH4+、K+、Mg2+、Ca2+、F-、Cl-、NO3-和SO24-等9种离子组分质量浓度,并同步收集气象因子、大气散射系数、大气能见度以及SO2、NO2、O3气体污染物质量浓度等数据.结果表明,PM2.5和PM1.0中水溶性无机离子总浓度分别为(25.5±10.9)μg·m-3和(22.7±10.5)μg·m-3,分别占PM2.5和PM1.0质量浓度的(47.9±4.3)%和(49.3±4.3)%.SO42-占PM2.5和PM1.0中质量浓度百分比最高,分别为(25.8±4.0)%和(27.5±4.5)%.较高的温度和O3浓度有利于SO24-的生成,较高相对湿度有利于NO3-的生成.PM2.5和PM1.0中亲水性较强的SO42-、NH4+和NO3-对散射系数和能见度影响较大.