道路绿化带影响大气颗粒物分布实测研究

以福州市西三环快速路某路段为实验靶区，利用微型环境检测仪采集路边细颗粒物(PM_2.5)、亚微米颗粒物(PM_1.0)和黑碳(BC)的空间分布样本，解析其在道路绿化带前后(绿化带前是指干道和辅道外边缘线位置，其他位置均视为绿化带后)的变化特征及原因。结果表明：(1)颗粒物浓度随着采样点远离干道而整体趋于递减，呈现BC>PM_2.5>PM_1.0的衰减率变化特征，且植被稠密的路边环境对应更大的颗粒物浓度降幅。(2)夏季绿化带后的颗粒物浓度降幅高于冬季，冬季绿化带后部分采样点的PM_1.0和PM_2.5浓度甚至有所抬升。在植被茂密的路边环境下，风自干道吹向绿化带情景的路边空气质量介于风自绿化带吹向干道情景和风平行于干道情景之间。(3)BC对交通变化的敏感性高于PM_2.5和PM_1.0,植被茂密的绿化带后的颗粒物浓度降幅会因交通强度的上升而增大。风自干道吹向绿化带时，绿化带对颗粒物的调节作用会随交通源强和季节的变化而不同...     

呼和浩特市空气颗粒物影响因素研究

选择呼和浩特市城东腾飞路中段为研究地,在内蒙古环境监测中心站实验大楼楼顶设置空气质量自动监测点,于2009年11月至2012年12月连续3年对该区域空气质量进行研究性监测。采用数理统计方法,对实测数据进行分析和研究,利用AQI评价的结果显示,颗粒物已经成为影响当地空气质量的主要污染物;PM2.5/PM10年平均值为0.47,随季节变化明显,春夏季比值偏小(春季为0.361,夏季为0.398),秋冬季比值偏大(秋季为0.499,冬季为0.635);受风速和湿度影响明显,比值与风速在0.01水平上呈现显著负相关,与湿度呈现显著正相关;利用3年污染物数据建立回归方程,经检验所建立的方程有效,说明其他污染物对PM2.5浓度变化有较显著影响。     

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.     

Suspended Particulate Matter Distribution in Rural-Industrial Satna and in Urban-Industrial South Delhi

An air quality sampling program was designed and implemented to collect the baseline concentrations of respirable suspended particulates (RSP = PM₁₀), non-respirable suspended particulates (NRSP) and fine suspended particulates (FSP = PM_2.5). Over a three-week period, a 24-h average concentrations were calculated from the samples collected at an industrial site in Southern Delhi and compared to datasets collected in Satna by Envirotech Limited, Okhla, Delhi in order to establish the characteristic difference in emission patterns. PM_2.5, PM₁₀, and total suspended particulates (TSP) concentrations at Satna were 20.5 ± 6.0, 102.1 ± 41.1, and 387.6 ± 222.4 μg m⁻³ and at Delhi were 126.7 ± 28.6, 268.6 ± 39.1, and 687.7 ± 117.4 μg m⁻³. Values at Delhi were well above the standard limit for 24-h PM_2.5 United States National Ambient Air Quality Standards (USNAAQS; 65 μg m⁻³), while values at Satna were under the standard limit. Results were compared with various worldwide studies. These comparisons suggest an immediate need for the promulgation of new PM_2.5 standards. The position of PM₁₀ in Delhi is drastic and needs an immediate attention. PM₁₀ levels at Delhi were also well above the standard limit for 24-h PM₁₀ National Ambient Air Quality Standards (NAAQS; 150 μg m⁻³), while levels at Satna remained under the standard limit. PM_2.5/PM₁₀ values were also calculated to determine PM_2.5 contribution. At Satna, PM_2.5 contribution to PM₁₀ was only 20% compared to 47% in Delhi. TSP values at Delhi were well above, while TSP values at Satna were under, the standard limit for 24-h TSP NAAQS (500 μg m⁻³). At Satna, the PM₁₀ contribution to TSP was only 26% compared to 39% in Delhi. The correlation between PM₁₀, PM_2.5, and TSP were also calculated in order to gain an insight to their sources. Both in Satna and in Delhi, none of the sources was dominant a varied pattern of emissions was obtained, showing the presence of heterogeneous emission density and that nonrespirable suspended particulate (NRSP) formed the greatest part of the particulate load.     

石家庄市大气颗粒物元素组分特征分析

为研究石家庄市大气颗粒物的污染特征及其来源,于2013年4—5月在主城6区分别采集TSP、PM10和PM2.5颗粒物样品,利用ICP-MS分析其中的22种元素浓度。结果表明,石家庄市城区Ca、Fe元素在各粒径颗粒物中含量都较高,PM2.5中的S、K含量较高,PM10和TSP中Mg、Al的浓度相对较高。颗粒物的主要来源为燃煤尘、道路尘和建筑尘,TSP、PM10和PM2.5具有较好的统计相关性和同源性。     

南京市大气颗粒物中多环芳烃变化特征

逐月采集南京市大气中不同粒径的颗粒物,采用HPLC分析了2010年每个月PM_(10)和PM_(2.5)颗粒物样品中的多环芳烃(PAHs)的种类和浓度水平。结果表明:PM_(10)中PAHs年均值为25.07 ng/m~3,范围为11.03~53.56 ng/m3;PM_(2.5)中PAHs年均值为19.04 ng/m~3,范围为10.82~36.43 ng/m~3。PM_(10)和PM_(2.5)中PAHs总体浓度有着相似的变化趋势,呈现凹形变化曲线;在南京市大气颗粒物中吸附的PAHs大部分以5~6环的高环数组分为主,大部分PAHs和∑PAHs的相关性较好,年度变化幅度不大,分析结果表明,颗粒物中PAHs的来源与稳定的排放源相关,机动车排放不容忽视,与北方城市燃煤污染有着较大的区别。     

直接进样-热脱附-GC-MS快速测定大气细颗粒物中有机示踪物

建立了直接进样-热脱附-GC-MS快速测定细颗粒物中甾烷类和藿烷类有机示踪物的方法。经实验条件优化,13种目标化合物的线性回归方程的相关系数均在0.990以上,空白加标回收率为81.4%~102.3%,实际样品加标回收率为79.1%~112.9%,相对标准偏差小于13.2%。当采样体积为24 m~3时,各目标化合物的检出限为0.008~0.084 ng/m~3,方法灵敏度高。利用该方法测定了北京城区采暖季和非采暖季PM2.5实际样品,结果表明:各目标物均有检出,且采暖季的甾烷类和藿烷类化合物的总量均明显高于非采暖季。该方法无需复杂的前处理和有机溶剂,操作简便快捷,在颗粒物中非极性化合物的快速检测方面具有很大的应用价值。     

北京地区不同季节PM2.5和PM10浓度对地面气象因素的响应

利用2013年1月—2014年12月北京地区PM_(2.5)和PM_(10)监测数据和同期近地面气象观测数据,采用非参数分析法(Spearman秩相关系数)研究了北京地区PM_(2.5)和PM_(10)的浓度对不同季节地面气象因素的响应。结果表明:北京地区大气颗粒物浓度水平具有明显的季节特征,冬季大气颗粒物污染最严重,夏季最轻。不同季节影响颗粒物浓度水平的气象因素各不相同,其中风速和日照时数为主要影响因素。PM_(2.5)和PM_(10)质量浓度对气象因素变化的响应程度也有较大区别,PM_(2.5)/PM_(10)比值冬季最高,PM_(2.5)影响最大,春季最低,PM_(10)影响最大。这些结论可对制订科学有效的大气污染控制策略提供参考。     

Particulate matter levels in a South American megacity: the metropolitan area of Lima-Callao,Peru

The temporal and spatial trends in the variability of PM₁₀ and PM_2.5 from 2010 to 2015 in the metropolitan area of Lima-Callao, Peru, are studied and interpreted in this work. The mean annual concentrations of PM₁₀ and PM_2.5 have ranges (averages) of 133–45 μg m^?3 (84 μg m^?3) and 35–16 μg m^?3 (26 μg m^?3) for the monitoring sites under study. In general, the highest annual concentrations are observed in the eastern part of the city, which is a result of the pattern of persistent local winds entering from the coast in a south-southwest direction. Seasonal fluctuations in the particulate matter (PM) concentrations are observed; these can be explained by subsidence thermal inversion. There is also a daytime pattern that corresponds to the peak traffic of a total of 9 million trips a day. The PM_2.5 value is approximately 40% of the PM₁₀ value. This proportion can be explained by PM₁₀ re-suspension due to weather conditions. The long-term trends based on the Theil-Sen estimator reveal decreasing PM₁₀ concentrations on the order of ?4.3 and ?5.3% year^?1 at two stations. For the other stations, no significant trend is observed. The metropolitan area of Lima-Callao is ranked 12th and 16th in terms of PM₁₀ and PM_2.5, respectively, out of 39 megacities. The annual World Health Organization thresholds and national air quality standards are exceeded. A large fraction of the Lima population is exposed to PM concentrations that exceed protection thresholds. Hence, the development of pollution control and reduction measures is paramount.     

上海市浦东新区大气细颗粒物中重金属污染特征及来源解析

于2017年对浦东城区和郊区大气PM2.5中的重金属特征和来源进行了分析。结果表明,K、Fe、Na、Ca、Mg、Al等矿物元素为浦东新区PM2.5中含量最高的金属元素,其中K的年均值为297.3 ng/m^3。浦东城区的不同元素在季节变化上呈现较为不同的变化规律,郊区的金属元素值大部分呈现春季先逐月下降,在夏、秋季有起伏波动,在10月之后逐渐上升;沙尘+道路源+建筑扬尘、煤燃烧、工业排放、金属冶炼、船舶排放、海盐+垃圾焚烧+生物质燃烧为浦东城区PM2.5中重金属元素的6大类主要来源。其中沙尘+道路源+建筑扬尘对Ca的贡献率为82.7%,煤燃烧对As的贡献率为86.6%,工业排放对SO4^2-的贡献率达到65.9%,金属冶炼对Cr的贡献率为75.7%,船舶排放对V的贡献率为97.5%、海盐+垃圾焚烧+生物质燃烧对Cl^-的贡献率为93.0%。煤燃烧和金属冶炼主要来自于西部方向。船舶排放分布在长江口及其延伸带。浦东新区PM2.5中重金属元素的质量浓度与本地源排放强度、外界传输和大气扩散条件均有密切关系。     

Extension of the EUROS Integrated Air Quality Model to Fine Particulate Matter by Coupling to CACM/MADRID 2

The European Operational Smog (EUROS) integrated air quality modelling system has been extended to model fine particulate matter (PM). From an extended literature study, the Caltech Atmospheric Chemistry Mechanism and the Model of Aerosol Dynamics, Reaction, Ionisation and Dissolution were selected and recently coupled to EUROS. Currently, modelling of mass and chemical composition of aerosols in two size fractions (PM_2.5 and PM_10–2.5) is possible. The chemical composition is expressed in terms of seven components: ammonium, nitrate, sulphate, elementary carbon, primary inorganic compounds, primary organic compounds and secondary organic compounds. Calculated PM₁₀ concentrations and chemical composition are presented for two summer months of the year 2003 (1 July to 31 August).     

Using Archive Data to Investigate Trends in the Sources and Composition of Urban PM10 Particulate Matter: Application to Edinburgh (U.K.) Between 1992 and 1997

By extending the method of Stedman (1998), daily dataof atmospheric concentrations of gravimetricPM₁₀, black smoke (BS) and sulphate aerosol (SA)from national networks were analysed to determine thetrends in time of the contribution of different sources of particulate matter to total PM₁₀ measured in central Edinburgh. Since BS is an indicator of combustion-related primary sources of particulate matter, the quantity obtained by subtraction of daily BS from daily PM₁₀ is indicative of the contribution to total PM₁₀ from other primary sources and from secondary aerosol. This PM₁₀-BS statistic was regressed on SA, since SA is an indicator of variation in secondary aerosol source. For Edinburgh, SA is a considerably better indicator of PM₁₀-BS during summer than winter (reflecting the much greater photochemical generation of secondary aerosol in summer) and there is evidence that the contribution of other secondary aerosol (presumably nitrate aerosol) has increased relative to SA between 1992 and 1997. The concentration of non-combustion primary particulate material (marine aerosol, suspended dust) to PM₁₀ in Edinburgh has not changed over this period but is about twice that calculated as the U.K. national average. The increasing input to PM₁₀ from secondary aerosol sources at regional rather than urban scale has important implications for ensuring local air quality compliance. The method should have general applicability to other locations.     

Particulate matter concentration levels during intense haze event in an urban environment

This study assessed concentration levels of particulate matter (PM) in the ambient environment of Ilorin metropolis, Nigeria, during haze episodes. Meteorological data (wind speed and direction, rainfall data, sunshine data, relative humidity and temperature) were obtained. Aerocet 531S particle counter (MetOne Instruments, USA) was used to measure four mass concentration ranges of PM (PM_1.0, PM_2.5, PM₁₀ and the total suspended particles (TSP)) in 10 locations taking into consideration land use patterns. Surfer® version 8 (Golden Software LLC, USA) was used to model the spatial variation of particulate matter concentration levels using kriging interpolation griding method. Human exposure assessment was done using the total respiratory deposition dose (TRDD) estimates and statutory limit breach (SLB) approaches. The appearance of dominating weak southern atmospheric wind flow was observed as wind speed ranged from 0 to 6.811 m/s while solar radiation periods ranged from 0.3 to 3.5 h/day. The relative humidity of the metropolis ranged between 28 and 57%, while daily temperature was 15 to 36 °C. Highest concentration levels of PM measured were 73.4, 562.7, 7066.3 and 9907.8 μg/m³ for PM_1.0, PM_2.5, PM₁₀ and TSP, respectively. Very strong negative correlations existed between the PM concentration levels and microclimatic parameters. Spatial variation of the concentration level as modelled using Surfer® version 8 indicated that particulate concentration level increases from south to north. Concentration levels of PM for the 24-h averaging period were generally above the 24-h threshold limit value set by the regulatory agencies for all the locations.     

喀什市2022年环境空气质量特征分析

为摸清喀什市环境空气质量变化特征,为管理部门进行污染精准管控提供科学参考,基于喀什市2022年环境空气自动监测数据开展分析研究。结果表明:2022年喀什市二氧化硫、二氧化氮、臭氧、一氧化碳年均质量浓度均优于国家二级标准。超标污染物主要为可吸入颗粒物和细颗粒物。喀什市细颗粒物、可吸入颗粒物浓度呈春、秋、冬季高,夏季低的季节特征。     

Atmospheric ambient trace element concentrations of PM10 at urban and sub-urban sites: source apportionment and health risk estimation

In this study, PM₁₀ concentrations and elemental (Al, Fe, Sc, V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Mo, Ag, Cd, Sn, Sb, Ba, Pb, and Bi) contents of particles were determined in Düzce, Turkey. The particulate matter samplings were carried out in the winter and summer seasons simultaneously in both urban and sub-urban sampling sites. The average PM₁₀ concentration measured in the winter season was 86.4 and 27.3 μg/m³, respectively, in the urban and sub-urban sampling sites, while it was measured as 53.2 and 34.7 μg/m³ in the summer season. According to the results, it was observed that the PM₁₀ levels and the element concentrations reached higher levels, especially at the urban sampling site, in the winter season. The positive matrix factorization model (PMF) was applied to the data set for source apportionment. Analysis with the PMF model revealed six factors for both the urban (coal combustion, traffic, oil combustion, industry, biomass combustion, and soil) and sub-urban (industry, oil combustion, traffic, road dust, soil resuspension, domestic heating) sampling sites. Loadings of grouped elements on these factors showed that the major sources of the elements in the atmosphere of Düzce were traffic, fossil fuel combustion, and metal industry-related emissions.     

超声提取-气相色谱质谱法测定大气PM2.5中正构烷烃

建立了超声提取-气相色谱质谱法测定大气PM2.5中32种正构烷烃方法,经离子温度优化、前处理比较、空白滤膜考察等获得了最佳的实验条件。研究发现,高、中、低3种浓度标准曲线的相关系数均在0.995以上,3种浓度的空白样品加标回收率分别为72.2%~117.8%、73.5%~104.4%、73.8%~109.5%,精密度均小于10%,实际样品加标回收率为75.7%~108.9%。当采样体积为24 m3时,各目标化合物的方法检出限为0.046~2.6 ng/m~3;经正构烷烃浓度范围为0.17~64.3 ng/m~3的1月及浓度范围为0.53~7.67 ng/m~3的6月的实际样品验证,该方法的检出限和测量范围也可较好的满足实际样品的测定。     

川东北中心城市南充大气污染态势及其主要成因

基于南充市主城区6项大气污染物浓度数据,研究了2014-2020年南充市的空气质量指数、空气质量指数等级和首要污染物的时序分布。结果表明:随着大气污染防治的开展,南充市大气污染物浓度逐渐下降,出现首要污染物的天数逐年减少,空气质量逐步提高。受污染物节律性影响,空气质量呈现明显的季节差异,冬季空气质量最差,春季次之,夏季污染相对较轻,秋季最轻。首要污染物类型的季节分布特征表现为冬季出现首要污染物天数最多,春季和夏季次之,秋季最少。春、秋、冬季以PM_2.5污染为主,夏季以O₃污染为主。从全年来看,与O₃相比,PM_2.5对空气质量的影响更为突出。在持续控制大气污染物排放总量的同时,精细化协同管控细颗粒物、氮氧化物、挥发性有机物和二氧化硫排放将有助于现阶段的大气污染防治。     

Atmospheric aerosols at a regional background Himalayan site—Mukteshwar, India

Continuous aerosol measurements were made at a regional background station (Mukteshwar) located in a rural Himalayan mountain terrain from December 2005 to December 2008 for a period of 3 years. The average concentrations of particulate matter less than or equal to 10 μm (PM₁₀), particulate matter less than or equal to 2.5 μm (PM_2.5) and black carbon (BC) are 46.0, 26.6 and 0.85 μg/m³ during the study period. Majority of the PM₁₀ values lie below 100 μg/m³ while majority of the PM_2.5 values lie below 30 μg/m³. It is further seen that during the monsoon months, especially July and August, the average values are comparatively low. It is also noted that the PM_2.5/PM₁₀ ratios between 0.50 and 0.75 have the maximum frequency distribution in the data set. Furthermore, the monthly mean ratio of BC to PM_2.5 mass lies between 3.0 and 7.5 % during the study period. Though the average PM₁₀ and PM_2.5 concentrations during the study period are less than the respective Indian ambient air quality standards, however, they are still above the WHO guidelines and would have adverse health impacts. This shows that even in rural/background regions that are far away from major pollution sources or urban areas, the aerosol concentrations are significant and require long-term monitoring, source quantification and aerosol model simulations.     

济南市大气颗粒汞的浓度特征及污染来源解析

为了解冬季采暖对济南市大气PM_2.5中汞浓度的影响,在济南市城郊开展了为期超过两年的PM_2.5样品采集工作,共计采集有效样品481个,测定并分析其中的颗粒汞(PHg)浓度和汞含量变化特征。结果表明,济南市大气PHg在采暖期的浓度均值为583.1 pg/m³,约为非采暖期的1.4倍,在国内外城市中处于中等偏上水平。济南市大气PM_2.5对PHg具有极强的富集能力,且在采暖期更强,可能与燃煤等活动排放了更多的超细颗粒物有关。在采暖期,大气PHg浓度主要受煤炭燃烧源和交通排放源影响,两者分别贡献了总方差的39.2%和16.7%;在非采暖期,气象条件季节性变化、交通排放源、煤炭燃烧源的影响显著,三者分别贡献了总方差的32.4%、15.8%、12.0%。高浓度PHg主要来源于分布在采样站点东北偏东方向上的众多燃煤工业企业。此外,济南市大气PHg还主要受来源于鲁西南地区的区域污染气团的影响,途经污染较重的京津冀地区的污染气团对济南市PHg浓度也有较大贡献。在非采暖期,济南市PHg还受到来自东南和西南方向的清洁海洋气团的显著影响。     

Seasonal air quality profile of inorganic ionic composition of PM10 near Taj Mahal in Agra, India

Atmospheric aerosols and their impacts on the environment particularly on human health is an issue of significant public and governmental concern. Though studies on air quality related to total suspended particulate matter have done by various authors in India, yet respirable suspended particulate matter (PM₁₀) is not characterized so far particularly in a historical and world heritage city like Agra. This study presents seasonal variation in mass levels of PM₁₀ and its ionic composition. PM₁₀ samples were collected in the proximity of Taj Mahal and subjected to chemical analysis using ion chromatography technique. The preliminary findings reveal that the 24-h average of PM₁₀ mass level varies from 115 to 233, 155 to 321, and 33 to 178 μg/m³, respectively, in summer, winter, and rainy seasons indicating critical pollution situation. These values are very much higher than the National Ambient Air Quality Standards of 75 μg/m³ (prescribed by Central Pollution Control Board, India) in both of summer and winter seasons whereas quite near the permissible limits in rainy season. The equivalent ratios of NH₄ ⁺ to nonsea salt SO₄ ^2? and NO₃ ^? and ∑Cations to ∑Anios were found to be greater than unity indicating high source strength of ammonia and alkaline nature of aerosols. The study suggests the need for continuous and long-term systematical sampling and detailed physiochemical analysis of PM₁₀ and also to know the characteristics of PM in background areas for better understanding of the emission sources.