Concentrations of PM2.5-associated OC, EC, and PCDD/Fs measured during the 2003 wildfire season in Missoula, Montana

Throughout August and September, 2003, wildfires burned in close proximity to Missoula, Montana, with smoke emanating from the fires impacting the valley for much of the summer. This presented the perfect opportunity to measure the levels of polychlorinated dibenzodioxins and dibenzofurans (PCDD/F) comprising ambient forest fire smoke particles impacting the Missoula Valley. An air sampler at the Montana Department of Environmental Quality's (DEQ) compliance site in Missoula measured hourly averages of PM₁₀ throughout the fire season. Three collocated PM_2.5 cyclones collected 24-h smoke samples using quartz filters and Polyurethane Foam (PUF) sorbent cartridges. From the quartz filters, concentrations of Organic and Elemental Carbon (OC/EC) were measured, while PCDD/F were measured from one set of a filter (particle phase) and PUF (vapor phase) aggregate of samples in an attempt to also investigate the different phases of PCDD/F in forest fire smoke impaired communities.Hourly PM₁₀ concentrations peaked at 302.9 μg m⁻³ on August 15. The highest OC concentration (115.6 μg m⁻³) was measured between August 21–22, and the highest EC concentration of 10.5 μg m⁻³ was measured August 20–21. Measurable concentrations of PM_2.5 associated PCDD/Fs were not detected from a representative aggregate sample, with the exception of small amounts of 1,2,3,4,6,7,8-heptachlorodibenzodioxin and octachlorodibenzodioxin. PM_2.5 samples collected during the smoke events were composed of approximately 65% OC. However, the OC fraction of the particles collected in the smoke impaired Missoula valley was not composed of significant amounts of PCDD/F.     

合肥市冬季细颗粒物中碳组分特征分析

利用2020年12月1日至2021年2月28日合肥市细颗粒物(PM_2.5)、有机碳(OC)和元素碳(EC)等环境空气质量监测数据和气象观测数据,分析了合肥市大气PM_2.5中OC和EC的污染特征,并探讨了其来源以及气象因素影响。结果表明：合肥市冬季碳质气溶胶是PM_2.5中主要组分,随着污染程度的加重,碳质气溶胶的质量浓度逐步增加,但其在PM_2.5中的占比先减小后增加。在以PM_2.5为首要污染物的不同污染级别天气条件下,OC和EC的相关性说明不同程度下碳质气溶胶来源复杂。OC/EC表明机动车尾气和燃煤源排放是碳质气溶胶的主要来源。二次有机碳(SOC)会随着污染程度的加重而呈现升高趋势。OC和EC在冬季受温度影响较小;较大的相对湿度对OC和EC具有一定的清除作用,明显降水或连续降水的清除作用更加显著;而风速对含碳气溶胶的影响主要出现在污染天气背景下。     

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.     

石家庄市春节期间大气颗粒物有机碳和元素碳的变化特征

为研究石家庄市大气颗粒物的污染特征及其来源,于2013年2月6—19日春节期间在石家庄市采集大气颗粒物TSP、PM10、PM2.5样品,对其有机碳、元素碳进行分析测定。结果表明,石家庄TSP、PM10、PM2.5日平均质量浓度分别为389、330、245μg/m3,颗粒物污染严重;碳组分在颗粒物中占有较大比重,且随着粒径的减少,碳组分比重逐渐增加;存在不严重的次生有机碳污染;OC与EC的相关系数较高,说明两者有较为相似的污染源,主要为燃煤、机动车排放源。各种气象条件对PM2.5、OC、EC浓度和OC/EC的变化都有不同程度的影响。     

APEC期间京津冀及周边地区PM2.5中碳组分变化特征及来源

在APEC会议期间和会期之后,分别采集北京、天津、石家庄、保定、济南5个采样点的PM2.5样品,通过分析碳组分的变化特征,研究京津冀地区污染物减排的影响以及减排后各指标的变化特征,分析大气颗粒物中碳气溶胶的可能来源。采用重量法测定组分中PM2.5的含量,利用热/光碳分析仪测定组分中OC、EC的含量,结果表明,由于采取了污染源减排措施,会议期间PM2.5、OC、EC的质量浓度均低于会期之后;会议期间和会期之后OC与EC均表现出了较好的相关性,r2为0.789~0.983,说明OC与EC的排放源基本相同;会议期间OC/EC为3.11~3.62,表明含碳气溶胶的来源主要是机动车排放,同时也存在一定的燃煤排放,会期之后为3.08~6.10,表明燃煤的排放在碳气溶胶中的比重明显增加,另外OC/EC也表明APEC会议期间和会期之后二次有机碳在各采样点均普遍存在。     

采暖期北京大气PM2.5中碳组分的分布特征及来源解析

在冬季采暖期采集北京大气中的PM_(2.5)样品,利用自动称重系统AWS-1和热/光碳分析仪测定样品中PM_(2.5)和OC/EC,研究碳组分的变化特征,并通过OC/EC的值和单颗粒气溶胶质谱仪(SPAMS 0515)分析大气颗粒物中碳气溶胶的可能来源。结果表明:PM_(2.5)污染天气的OC、EC在PM_(2.5)中的占比要比清洁天气时低,其中SOC在PM_(2.5)中的占比由清洁天气时的22.9%减少到了重污染天气的15.4%,这是因为大气中的PM_(2.5)有较强的消光作用,导致气溶胶的氧化能力降低,造成了SOC的生成量减少;通过分析OC/EC值表明,冬季采暖期北京大气碳气溶胶的主要来源为机动车尾气和燃煤,这与SPAMS 0515在线解析的结果一致。采用SPAMS 0515进行在线OC、EC分析,在PM_(2.5)质量浓度≤250μg/m3时同手工方法有较好的相关性。解析结果表明,燃煤和机动车尾气是北京冬季采暖期的首要污染物来源,占比分别为34.0%和26.4%。     

东营春季PM10中有机碳和元素碳的污染特征及来源

2010年4月采集了东营市大气PM10样品,测定了PM10的浓度,并采用IMPROVE-TOR方法准确测量了样品中的8个碳组分.结果表明,采样期间,东营市大气PM10的平均浓度为(147.02±56.22) μg/m3;PM10中有机碳(0C)、元素碳(EC)浓度平均值分别为11.82、3.68 μg/m 3;PM10中OC和EC显著相关,表明OC、EC的来源相同;所有采样点PM10中OC/EC均大于2.15,表明存在二次有机碳(SOC)的贡献;PM10中SOC平均质量浓度是3.91 μg/m3,占OC质量浓度的33.08％;通过计算PM10中8个碳组分丰度,初步判断东营市颗粒物中碳的主要来源是汽车尾气、道路扬尘和燃煤.     

西宁市城区冬季PM2.5和PM10中有机碳、元素碳污染特征

2014年11月—2015年1月对西宁市冬季开展PM_(2.5)和PM_(10)的连续监测。利用DRI 2001A型热光碳分析仪(美国)对有机碳和元素碳进行分析,结果表明:西宁市冬季PM_(2.5)和PM_(10)中碳气溶胶所占比例分别为33.13%±6.83%、24.21%±6.27%,说明碳气溶胶主要集中在PM_(2.5)中;OC/EC值均大于2,说明西宁市大气中存在二次污染;SOC占PM_(2.5)和PM_(10)的质量浓度比例分别为46.50%和57.40%,PM_(2.5)中SOC浓度占PM_(10)中SOC浓度的61.88%,说明SOC主要存在于PM_(2.5)中,且SOC形成的二次污染和直接排放的一次污染都是西宁市碳气溶胶的主要来源;与其他城市比较发现,西宁市冬季PM_(2.5)中的碳气溶胶含量普遍高于其他城市,PM_(10)中OC质量浓度相对其他城市较高,EC质量浓度偏低;OC和EC的相关性不显著,说明来源不统一;进一步对OC和EC各组分质量浓度进行分析知,西宁市冬季碳气溶胶主要来源于机动车汽油排放、燃煤和生物质燃烧。     

Seasonal Variation of Chemical Composition in Fine Particles at Gosan, Korea

PM_2.5 aerosol samples were collected at Gosan in Jeju Island during six intensive measurement periods between November 2001 and August 2003. In order to investigate the chemical composition of fine particles, major ion components, trace elements, and elemental and organic carbon were analyzed. Quite different seasonal characteristic in the chemical composition of fine particles was observed. The concentration of most secondary aerosol components showed a summer minimum and a winter maximum with higher correlation between them at Gosan. This fact clearly reveals the possibility of long-range transport of such pollutants in winter. On the other hand, OC and EC had the highest concentration and good correlation with ion components, such as K⁺, Ca²⁺ in fall. It means that biomass burning could significantly influence the ambient fine carbonaceous particulate in fall, which was primarily long-range transported.     

Interpretation of roadside PM10 monitoring data from Sunderland,United Kingdom

Roadside PM₁₀ has been monitored by Partisol® at three sitesin Sunderland between August 1997 and February 1998. The sites chosen were an inner city kerbside site; a roadside site adjacentto a dual carriageway on the outskirts of Sunderland with an openaspect; and a rural site.The results indicate that there is a seasonal variation in the relationship between the sites in terms of monitored PM₁₀.In the winter there is a poor correlation between the sites whereas in the summer significant correlations are obtained. Of the sites monitored PM₁₀ is consistently highest at the inner city roadside site. During the summer, exceedances of theU.K. 50 g m^-3 standard (DETR, 2000) are associated with conditions suitable for the build-up of photochemical pollutionhowever during the winter period exceedances are recorded duringa variety of weather conditions.At the dual carriageway site PM_2.5 has also been recorded and contributions to measured PM₁₀ are 77% in summer and68% in winter. The results illustrate a number of inconsistencies between this study utilising the Partisol® andothers reporting results where PM₁₀ has been monitored by TEOM®.     

Characterisation of PM10, PM2.5 and Benzene Soluble Organic Fraction of Particulate Matter in an Urban Area of Kolkata, India

In this study, the relationship between inhalable particulate (PM₁₀), fine particulate (PM_2.5), coarse particles (PM_{2.5 – 10}) and meteorological parameters such as temperature, relative humidity, solar radiation, wind speed were statistically analyzed and modelled for urban area of Kolkata during winter months of 2003–2004. Ambient air quality was monitored with a sampling frequency of twenty-four hours at three monitoring sites located near traffic intersections and in an industrial area. The monitoring sites were located 3–5 m above ground near highly trafficked and congested areas. The 24 h average PM₁₀ and PM_2.5 samples were collected using Thermo-Andersen high volume samplers and exposed filter papers were extracted and analysed for benzene soluble organic fraction. The ratios between PM_2.5 and PM₁₀ were found to be in the range of 0.6 to 0.92 and the highest ratio was found in the most polluted urban site. Statistical analysis has shown a strong positive correlation between PM₁₀ and PM_2.5 and inverse correlation was observed between particulate matter (PM₁₀ and PM_2.5) and wind speed. Statistical analysis of air quality data shows that PM₁₀ and PM_2.5 are showing poor correlation with temperature, relative humidity and solar radiation. Regression equations for PM₁₀ and PM_2.5 and meteorological parameters were developed. The organic fraction of particulate matter soluble in benzene is an indication of poly aromatic hydrocarbon (PAH) concentration present in particulate matter. The relationship between the benzene soluble organic fraction (BSOF) of inhalable particulate (PM₁₀) and fine particulate (PM_2.5) were analysed for urban area of Kolkata. Significant positive correlation was observed between benzene soluble organic fraction of PM₁₀ (BSM10) and benzene soluble organic fraction of PM_2.5 (BSM2.5). Regression equations for BSM10 and BSM2.5 were developed.     

贵州农村地区室内空气质量及细颗粒物污染特征

对贵州农村地区燃煤和燃柴典型村进行了室内外空气质量的监测,并对其中细颗粒物PM2.5的特征进行了研究。结果表明:燃煤家庭厨房和卧室PM2.5分别超过《环境空气质量标准》GB 3095—2012中标准限值(75μg/m3)的1.97、1.41倍,燃柴家庭分别超标0.74、0.06倍,而SO2、CO、NOx均低于标准限值。PAHs以燃煤村厨房最高,为53.92 ng/m3,燃柴村厨房为10.34 ng/m3,2种燃料所致PAHs均以中高环组分为主,对人体健康产生较大风险。PM2.5中氟和砷含量较低,低于参考浓度限值。燃煤村和燃柴村厨房内氟均值分别为0.14、0.11μg/m3,砷平均值分别为0.020、0.014μg/m3。需采取炉灶/燃料改良干预措施来降低农村室内空气污染物浓度,保障居民身体健康。     

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.     

重庆城区不同粒径颗粒物元素组分研究及来源识别

为研究重庆市大气颗粒物的污染特征及其来源,于2010年3—10月在主城区分别采集PM_1.0、PM_2.5和PM₁₀3种粒径的颗粒物样品,利用XRF分析其中的26种元素浓度。结果表明,重庆市主城区S元素在各粒径中含量都较高,细粒子中K的含量较高,粗粒子中Si、Ca和Fe的浓度较大。富集因子分析表明,主城区Cd、S、Se等污染元素的富集系数较大,且粒径越小,富集现象越明显。利用因子分析得出土壤风沙、扬尘、燃煤的燃烧、机动车燃油产生的尾气排放、生物质燃烧排放是重庆市颗粒物污染的主要来源。     

沈阳市冬季重污染过程PM2.5浓度变化及成因分析

利用多种污染物浓度数据、气象观测数据,结合HYSPLIT后向轨迹模式,对2015年11月6—10日发生在沈阳的一次较长时间重污染天气过程,从大气浓度变化、天气形势特征及成因机制等方面进行综合分析。结果表明,重污染期间日空气质量指数均超过重度污染限值200,首要污染物PM_(2.5)最高小时质量浓度达到1 326μg/m3,为沈阳市监测PM_(2.5)以来的历史峰值。此次空气污染是气象及人为因素共同作用的结果,重污染过程时段内高空场不利于气流上升运动的发展,地面倒槽、稳定的大气层结不利于污染物的扩散。此次重污染过程与大范围秸秆集中燃烧、大量污染物排放有一定关系。通过后向轨迹计算分析,发现颗粒物长距离输送对区域污染产生一定影响。     

江苏省级区域空气质量数值预报模式效果评估

采用中国科学院大气物理研究所开发的嵌套网格空气质量模式系统(NAQPMS),搭建江苏省级区域空气质量数值预报模式系统,并测试了该系统对2013年夏季江苏省PM2.5质量浓度未来24 h预报以及7 d潜势预测的效果。结果表明,该系统成功应用于江苏省的空气质量预报;所有地市的24 h预报效果均在合理范围内(平均分数偏差小于±60%且平均分数误差小于75%);7 d潜势预测效果比24 h预报效果略差,整体能准确把握PM2.5质量浓度的变化趋势。     

华北地区一次重污染天气的气象变化过程分析

利用大气环境监测数据、常规气象观测资料、探空数据以及HYSPLIT后向轨迹模式对2014年2月20—26日发生在华北区域的一次大面积重污染天气进行综合分析。利用风云卫星观测资料直观展示了污染的生成、消散状况。结果表明,在此次重污染天气过程中,华北地区主要城市均观测到高浓度的PM_(2.5),其中北京、石家庄PM_(2.5)小时浓度均值分别为286.1、371.2μg/m~3。该次污染与天气过程关系密切,平稳的高空环流形势、华北地面弱低压为污染天气的发生、发展提供了有利的气象条件。地面的静风或小风天气以及近地逆温的出现有利于污染的维持。后向轨迹分析表明,此次污染过程区域性明显,南部、西南部周边地区的污染物外源性输入对研究的主要城市有显著影响。     

内蒙古干草原冬、春季大气气溶胶的若干特征观测研究

内蒙古半干旱草原区大气气溶胶浓度以及散射等特性对生态环境、气候变化与预测研究有重要意义,文利用2009年1～4月在锡林浩特观象台草原站的观测资料,分析了冬、春季背景大气气溶胶质量浓度、黑碳质量浓度、散射系数的分布特征。研究发现,背景天气下,PM10、PM2.5、PM1.0浓度值都较低,平均值分别为22.7、9.5、6.1μg/m3,3种PM浓度值间的相关性不同;黑碳浓度平均值为0.59μg/m3,小粒子中的含量较高,其日分布规律受人类活动影响较大,与各PM浓度分布有较大不同;散射系数平均值为31.2Mm-1,与PM10、PM2.5、PM1.0、黑碳质量浓度都显著相关。三种PM中,PM2.5对散射和吸收的影响最大。风速、相对湿度对不同粒径的PM以及黑碳浓度、散射系数的影响有所不同。     

辛集市"十三五"期间空气质量特征分析

重点对河北省辛集市"十三五"期间整体空气质量变化情况以及影响辛集市优良天数的2个重要参数O₃和PM_2.5的污染规律进行了分析。结果表明,辛集市"十三五"期间空气质量改善明显,优良天数整体增加,污染天数整体减少。O₃浓度及其作为首要污染物出现的天数整体呈现上升趋势,对综合指数的贡献率逐年增加;O₃污染高发期主要集中在4—9月,高值区域分布差距较大,但市区污染持续突出。PM_2.5浓度逐年下降,以PM_2.5为首要污染物的天数逐年减少;PM_2.5浓度季节变化特征整体呈现"秋冬高、春夏低"的分布特点,空间分布呈"南北高、中间低"的污染特征。     

西安市区大气中PM2.5和PM10质量浓度污染特征

2013年3月—2014年2月期间,设置1个监测点位,采集了西安市区大气环境中PM10和PM2.5样品,采用重量法测定了PM2.5和PM10质量浓度。结果表明,西安市区PM2.5质量浓度为16~558μg/m3,平均值为128μg/m3,超标率69.1%;PM10质量浓度范围为32~887μg/m3,平均值为249μg/m3,超标率71.8%。虽然PM2.5和PM10质量浓度的逐日变化幅度比较大,但是整体变化趋势非常相似,存在显著的正相关关系(r=0.831 9)。PM2.5和PM10质量浓度存在明显的季节变化,均为冬季最高,春季次之,秋季较低,夏季最低。ρ(PM2.5)/ρ(PM10)为0.245~0.822,平均值为0.510,说明PM2.5在PM10中所占比例大于PM2.5~10;此外,该比值呈现一定的季节变化规律,冬季、夏季较高,秋季次之,春季最低。霾天气发生时,该比值和PM2.5质量浓度明显高于无霾天气。