徐州市大气颗粒物PM_(10)与PM_(2.5)污染水平分析

在徐州市的7个典型城市功能区采集大气颗粒物样品,对PM_(10)和PM_(2.5)的污染水平进行了分析。结果表明,徐州市PM_(10)和PM_(2.5)的污染较严重,超标率分别为26.3%和31.2%;空间上,工业区和交通居住混合区污染严重;时间上,污染水平呈现为冬季春季秋季夏季;PM_(2.5)在PM_(10)中的比重大于粗颗粒物,约占58%,应重视对其监测与治理。     

某核电厂施工期大气TSP、PM_(10)污染水平及相关性研究

为研究核电项目建设工程施工期大气颗粒物污染特征,分别于2012年5月和11月采集TSP,PM10样品,用重量法分析其质量浓度,并对其相关性进行分析。结果表明,用环境空气质量标准(GB 3095-2012)来衡量,监测周期内大气颗粒物TSP,PM10的日均浓度均符合标准;TSP=1.0308 PM10+0.0361,相关性较好。     

PM2.5和PM10监测数据“倒挂”与环境温度、湿度的关系

采用赛默飞世尔FH62C14型和5030 SHARP型在线监测仪分别对环境空气中的PM10和PM2.5开展监测,统计监测数据同时记录环境温度和湿度信息,探讨PM2.5和PM10在线监测数据"倒挂"现象与环境温度和湿度之间的关系。结果表明,一般情况下(温度5~40℃,相对湿度30%~90%),PM2.5和PM10在线监测数据"倒挂"的概率呈现出温度升高则变小,湿度升高则变大的趋势。     

上海市PM2.5及散射系数的监测

对2015年上海监测站点PM2.5,散射系数和气象参数进行连续观测,得到PM2.5质量浓度和散射系数的年均值分别为52.9μg/m3和262.4 Mm-1,且随气象参数的季节变化而变化.当西北和西南方向风速在3～5 m/s左右时,气溶胶散射系数出现较大值,西北方向主要由于冬季污染物的远距离传输,而西南方向主要为局地排放...     

超声提取高效液相色谱法测定大气可吸入颗粒物PM10中的四种多环芳烃

建立了以甲醇-水为流动相,高效液相色谱法同时测定大气可吸入颗粒物PM10中四种多环芳烃化合物的方法。方法用中流量TH-16A四通道采样器,石英纤维滤膜采集大气颗粒物样品,以甲醇为溶剂,超声波提取样品;用甲醇-水作流动相进行高效液相色谱分离,荧光检测器检测。采用优化的分离和测定条件对四种多环芳烃化合物苯并(b)荧蒽、苯并(k)荧蒽、苯并(a)芘、苯并[ghi]芘进行加标回收率和精密度实验,回收率范围为86%~93%,相对标准偏差分别为2.0%~6.2%。     

广州冬季PM10和PM2.5中有机碳与元素碳浓度水平及分布特征

于2002年1月至2月在广州市3个点采集不同粒径(PM2.5和PM10)大气气溶胶样品,测定了PM2.5,PM10及其中的有机碳(OC)和元素碳(EC)浓度,探讨了广州市冬季碳气溶胶的污染特征.结果表明,PM2 5和PM10平均浓度分别为105.9 μg/m3及161.7 μg/m3,其中PM25中的OC,EC浓度分别为22.6μg/m3和8.3μg/m3,PM10中的OC,EC浓度分别为29.4和10.4 μg/m3.PM2.5和PM10的总碳气溶胶含量分别为40.5%和35.7%.PM2.5和PM10中OC/EC的比值平均为2.7,这与大多数城市大气的OC/EC比值接近.OC与EC的相关性很好(R＞0.9),表明OC与EC的排放源相同.估算的PM2.5中次生有机碳(SOC)平均浓度为12.1μg/m3,占PM2.5中总OC浓度的49.6%;PM10的SOC平均浓度为14.3 μg/m3,占PM10中总OC的44.8%.     

“乌昌石”区域PM2.5污染特征及其与气象要素的关系

基于2015～2020年“乌昌石”区域PM_2.5逐时监测数据及气象资料,分析了区域内PM_2.5污染特征及其与气象要素的关系。结果表明：2015～2020年“乌昌石”区域PM_2.5年均浓度介于53.61～65.31μg/m³之间,2017年后区域PM_2.5污染得以明显改善,但2020年仍高于国家标准限值,4城市PM_2.5年均浓度呈西低东高的分布特征。6年4城市PM_2.5日均浓度总污染天数比例为24.40%,以轻度污染和重度污染天数为主,不同城市PM_2.5污染等级天数比例差异较大,且主要出现在秋冬季,五家渠和石河子的污染较乌鲁木齐和昌吉更严重。“乌昌石”区域风速、气温、日照时数和降水量与PM_2.5浓度呈极显著负相关,湿度和气压与其呈极显著正相关。研究显示,“乌昌石”区域PM_2.5污染改善显著,但重度污染尚未消除,构建PM_2.5污染发生时的气象要素数据库...     

PM2.5 and PM10 Mass Measurements in California's San Joaquin Valley

PM _2.5 and PM ₁₀ mass measurements from different sampling systems and locations within California's San Joaquin Valley (SJV) are compared to determine how well mass concentrations from a unified data set can be used to address issues such as compliance with particulate matter (PM) standards, temporal and spatial variations, and model predictions. Pairwise comparisons were conducted among 20 samplers, including four Federal Reference Method (FRM) units, battery-powered MiniVols, sequential filter samplers, dichotomous samplers, Micro-Orifice Uniform Deposit Impactors (MOUDIs), beta attenuation monitors (BAMs), tapered element oscillating microbalances (TEOMs), and nephelometers. The differences between FRM samplers were less than 10 and 20% for 70 and 92% of the pairwise comparisons, respectively. The TEOM, operating at 50°C in this study, measured less than the other samplers, consistent with other comparisons in nitrate-rich atmospheres. PM _2.5 mass measured continuously with the BAM was highly correlated with filter-based PM _2.5 although the absolute bias was greater than 20% in 45% of the cases. Light scattering (B _sp ) was also highly correlated with filter-based PM _2.5 at most sites, with mass scattering efficiencies varying by 10 and 20% for B _sp measured with Radiance Research nephelometers with and without PM _2.5 size-selective inlets, respectively. Collocating continuous monitors with filter samplers was shown to be useful for evaluating short-term variability and identifying outliers in the filter-based measurements. Comparability among different PM samplers used in CRPAQS is sufficient to evaluate spatial gradients larger than about 15% when the data are pooled together for spatial and temporal analysis and comparison with models.     

Relationship Between Biologically Fluorescent Aerosol and Local Meteorological Conditions

Time-resolved characterization of biological aerosol is important both for understanding environmental processes that affect biological aerosols and for determining realistic test conditions for the evaluation of bioaerosol detection systems. Very little work has been done to develop an understanding of the temporal fluctuations in bioaerosol concentration. During an experiment from 1–10 November 2008 ambient biological aerosol and meteorological data were collected. A FLIR/ICx/S3I Instantaneous Bioaerosol Analysis and Collection sensor was used to count both the biological and nonbiological aerosol in two size bins. The data indicate that the ambient relative humidity affects the optically observable concentration of biological aerosol with higher relative humidity generally associated with higher biological aerosol concentrations. The short timescale over which these correlations exist implies an aerosol process, rather than a change in aerosol source.

© 2013 American Association for Aerosol Research     

北京市颗粒物污染及其与气象因素的关系

通过收集北京市2015年冬、春、夏、秋四个季节代表月1、4、7、10月大气细颗粒物PM2.s和PM10及相关的气象数据,分析研究了颗粒物质量浓度的季节变化及其与气象因素的相关性.结果表明,PM2.5和PM10浓度呈现出春冬季节浓度偏高、夏秋季节浓度偏低的特点.除了冬季之外,其余三季PM2.5浓度均未超出国家二级标准值(75 μg/m3).PM10在四个季节中均未超出国家二级标准值(150 μg/m3).北京地区细粒子污染严重,春夏秋冬四季中PM2.5/PM10比值分别为0.60、0.87、0.92和0.81,全年中比值为0.78.PM2.5和PM10随气温变化图都出现两个峰值,都出现在气温在0℃和16℃时.日均气温在14～16℃之间相关性最好,在此范围内随着温度升高,颗粒物浓度随之升高.颗粒物浓度有髓相对湿度的增加而增大的趋势,且PM2.5受湿度影响更为明显.风速与颗粒物浓度呈负相关关系,且风速对细粒子的去除作用更突出.     

夏秋季北京大气可吸入气溶胶(PM10)浓度及其中化学组分分析

观测了2004年10月和2005年6月北京大气PM10中水溶性组分、总碳、总有机碳及元素成分.分析发现,北京大气PM10的浓度2004年10月为(186.1±113.9)μg/m3,2005年6月为(113.4±82.1)μg/m3.其中总碳(TC)、总有机碳(TOC)、水溶性成分的浓度范围秋季(10月)分别为19.3～33.9,12.9～25.8,89.8～118.9μg/m3,夏季(6月)分别为16.8～59.4,11.8～56.0,49.7～69.8 μg/m3.PM10的浓度和水溶性浓度秋季(10月)高于夏季(6月),TOC的浓度夏季略高,TC和总元素浓度夏秋季节相差不大.     

PM10和PM2.5的危害及控制对策

PM10和PM2.5不止会降低环境质量,还会对人体产生巨大危害,直接威胁着人类健康。所以,如果不对此加以控制,人类的生存条件将迅速恶化,产生的后果是极为可怕的。为了减轻PM10和PM2.5对环境、人体的危害,相关部门必须尽快采取强有力的管理措施,遏制环境问题进一步恶化的趋势。文章从PM10和PM2.5造成的危害出发,提出了几项控制建议。     

气象条件对北京北郊冬季气溶胶污染的影响

对2004年1月1～12日北京大气物理所气象观测塔院内气溶胶浓度的观测资料和同期的气象资料进行了分析,结果表明北京冬季出现气溶胶污染的频率较高,采样期间超标率达58%,超标发生时地面风速较小,以偏东风为主,并伴有强的低空逆温;稳定性降雪过程对PM10有滞后的清除作用.气溶胶浓度与相对湿度和风速二元线性回归分析结果表明,平均风速对PM10日均浓度的影响较大,北京冬季PM10体现了细粒子的特征.天气形势对气溶胶浓度的影响显著,冬季当北京位于高压或两高压的均压场内,会出现地面高污染.     

西安大气中黑碳气溶胶的演化特征

2003年9月～2004年4月在西安站点利用黑碳测量仪(Aethalometer)获得了大气细粒子中每5 min黑碳气溶胶(BC)浓度的演化特征.每5 min浓度变化范围为0.5～101.3 μg/m3.BC浓度月变化以12月最高,为(27.1±11.2)μg/m3,4月最低,为(9.0±3.4)μg/m3,表明机动车尾气、居民燃煤等来源以及不利的气象条件等共同作用,造成了冬季的高浓度BC.冬、春季日变化和周变化模式类似,秋季则明显不同,这主要是由于冬春两季机动车尾气可能是BC的主要贡献源,而秋季可能主要是由于农村生物秸杆燃烧排放的BC而引起其变化模式与冬春季有所差异.选取的3个典型BC高浓度日,其每5 min浓度日变化模式也表明,秋季生物质燃烧对BC有显著贡献.与国内外一些城市、郊区、背景点大气中的BC浓度对比,西安大气中的BC处于高浓度水平,这指示了西安大气中存在碳污染,需要进一步采取措施控制.     

Simultaneous Measurements of PM10 and PM1 using a single TEOM#

A time sharing, sequential scanning inlet system has been developed for a tapered element oscillating microbalance (TEOM) particulate matter (PM) mass measurement instrument, allowing quasi simultaneous measurement of ambient PM₁₀ and PM₁. Compared to running two separate instruments this approach eliminates systematic biases between the PM₁₀ and PM₁ data due to slightly different response functions of separate instruments. Furthermore, this approach reduces the costs of the measurements considerably. The system has been successfully tested during the winter 2005/2006 in Zurich, Switzerland and 24-hour average data have been compared to ambient concentrations measured with reference high volume sampler systems of the Swiss national air pollution monitoring network (NABEL) showing an excellent correlation. We demonstrate the use of our instrument for characterizing PM by showing the significant differences between PM₁₀ and PM₁ in their respective diurnal variation patterns as well as the increase in their concentrations during events of air stagnation.