Effects of meteorological conditions on PM10 concentrations - A study in Macau

Review on the annual PM₁₀ concentrations over a 10-year period shows that Macau is subjected to severe fine particulate pollution. Investigations of its variation in monthly and daily time scales with the local meteorological records reveal further details. It is found that a distinct feature of the Asian monsoon climates, the changes of wind direction, mainly controls the general trend of PM₁₀ concentration in a year. The monsoon driven winter north-easterly winds bring upon Macau dry and particle enriched air masses leading to a higher concentration in that period while the summer south-westerly winds transport humid and cleaner air to the region leading to a lower PM₁₀ value. This distinct seasonal feature is further enhanced by the lower rainfall volume and frequency as well as mixing height in winter and their higher counterparts in summer. It is also found that the development of tropical cyclones near Macau could also impose episode like PM₁₀ concentration spikes due to the pre-typhoon induced stagnant air motion followed by the swing of wind direction to the northerly.     

Comparison of PM10 concentrations in high- and medium-volume samplers in a desert environment

The USEPA replaced TSP with PM₁₀ as the National Ambient Air Quality Standard for particulate matter. The commercially available PM₁₀ sampler is a high-volume model using quartz fiber filters. In certain investigations, such as source apportionment studies, chemical analysis of the filter is necessary, however, many analyses cannot be run on quartz filters. An alternate filter such as Teflon is amenable to XRF and ion chemical analyses but is not amenable to analysis for carbon. To overcome these problems DRI constructed a medium-volume PM₁₀ sampler that is capable of collecting particulates on both Teflon and quartz fiber filters simultaneously. This paper describes the design of the DRI medium-volume PM₁₀ sampler, discusses a method for determining equivalence of two samplers, the results of applying the method to test the equivalence of the medium-volume sampler and a commerical high-volume sampler, and examines differences between PM₁₀ and TSP measurements in a southwestern desert.     

Spatial & temporal variations of PM10 and particle number concentrations in urban air

The size of particles in urban air varies over four orders of magnitude (from 0.001 μm to 10 μm in diameter). In many cities only particle mass concentrations (PM10, i.e. particles <10 μm diameter) is measured. In this paper we analyze how differences in emissions, background concentrations and meteorology affect the temporal and spatial distribution of PM10 and total particle number concentrations (PNC) based on measurements and dispersion modeling in Stockholm, Sweden. PNC at densely trafficked kerbside locations are dominated by ultrafine particles (<0.1 μm diameter) due to vehicle exhaust emissions as verified by high correlation with NOx. But PNC contribute only marginally to PM10, due to the small size of exhaust particles. Instead wear of the road surface is an important factor for the highest PM10 concentrations observed. In Stockholm, road wear increases drastically due to the use of studded tires and traction sand on streets during winter; up to 90% of the locally emitted PM10 may be due to road abrasion. PM10 emissions and concentrations, but not PNC, at kerbside are controlled by road moisture. Annual mean urban background PM10 levels are relatively uniformly distributed over the city, due to the importance of long range transport. For PNC local sources often dominate the concentrations resulting in large temporal and spatial gradients in the concentrations. Despite these differences in the origin of PM10 and PNC, the spatial gradients of annual mean concentrations due to local sources are of equal magnitude due to the common source, namely traffic. Thus, people in different areas experiencing a factor of 2 different annual PM10 exposure due to local sources will also experience a factor of 2 different exposure in terms of PNC. This implies that health impact studies based solely on spatial differences in annual exposure to PM10 may not separate differences in health effects due to ultrafine and coarse particles. On the other hand, health effect assessments based on time series exposure analysis of PM10 and PNC, should be able to observe differences in health effects of ultrafine particles versus coarse particles.     

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.     

A combined analysis to identify airborne PM10 sources

A two step procedure that combines an air dispersion model with a receptor model was used to identify the key sources that contribute to air levels of suspended particulate matter. The contribution to PM(10) concentrations measured at four monitoring sites in San Nicolas, Argentina, of the following sources, a thermal power plant, an integrated steel mill, motor vehicle exhaust fumes, and finally dust from paved and unpaved roads, have been analysed. Moreover, an air dispersion model was used to estimate the contribution of the thermal power plant, emissions of which have been described in depth by means of hourly fuel consumption and specific emission factors. The ratio "apportionment coefficient" was introduced to relate the contribution of this source to the measured 24 h PM(10) concentrations by analysing the frequency of occurrence of connecting winds between the power plant and each monitoring site. In San Nicolas 70% of the PM(10) sampled at three of the four monitoring sites could be attributed to the power plant in those scenarios where winds connected the facility's tall point sources with the sampling locations. The contribution to the measured PM(10) levels of the rest of the sources that are present in the analysed area was confirmed by way of receptor models. For this purpose, the multielemental composition of 41 samples was determined by Wavelength Dispersive X-ray Fluorescence analysis. In order to ascertain the underlying correlations between PM(10) samples and potential sources, Principal Component Analysis was performed on the standard matrix of composition profiles, which comprises the measured PM(10) samples being enlarged with the composition profiles of the potential contributing sources. The diagonalization of the covariance matrix was used as a screening procedure to differentiate the most likely contributing sources from those that were not significant.     

Motor Vehicle Contributions to Ambient PM10 and PM2.5 at Selected Urban Areas in the USA

A source apportionment study was carried out to estimate the contribution of motor vehicles to ambient particulate matter (PM) in selected urban areas in the USA. Measurements were performed at seven locations during the period September 7, 2000 through March 9, 2001. Measurements included integrated PM_2.5 and PM₁₀ concentrations and polycyclic aromatic hydrocarbons (PAHs). Ambient PM_2.5 and PM₁₀ were apportioned to their local sources using the chemical mass balance (CMB) receptor model and compared with results obtained using scanning electron microscopy (SEM). Results indicate that PM_2.5 components were mainly from combustion sources, including motor vehicles, and secondary species (nitrates and sulfates). PM₁₀ consisted mainly of geological material, in addition to emissions from combustion sources. The fractional contributions of motor vehicles to ambient PM were estimated to be in the range from 20 to 76% and from 35 to 92% for PM_2.5 and PM₁₀, respectively.     

Indoor and outdoor PM mass and number concentrations at schools in the Athens area

Simultaneous indoor and outdoor PM₁₀ and PM_2.5 concentration measurements were conducted in seven primary schools in the Athens area. Both gravimetric samplers and continuous monitors were used. Filters were subsequently analyzed for anion species. Moreover ultrafine particles number concentration was monitored continuously indoors and outdoors. Mean 8-hr PM₁₀ concentration was measured equal to 229 ± 182 μg/m³ indoors and 166 ± 133 μg/m³ outdoors. The respective PM_2.5 concentrations were 82 ± 56 μg/m³ indoors and 56 ± 26 μg/m³ outdoors. Ultrafine particles 8-h mean number concentration was measured equal to 24,000 ± 17,900 particles/cm³ indoors and 32,000 ± 14,200 particles/cm³ outdoors. PM₁₀ outdoor concentrations exhibited a greater spatial variability than the corresponding PM_2.5 ones. I/O ratios were close or above 1.00 for PM₁₀ and PM_2.5 and smaller than 1.00 for ultrafine particles. Very high I/O ratios were observed when intense activities took place. The initial results of the chemical analysis showed that accounts for the 6.6 ± 3.5% of the PM₁₀ and for the 3.1 ± 1.4%.The corresponding results for PM_2.5 are 12.0 ± 7.7% for and 3.1 ± 1.9% for . PM_2.5 indoor concentrations were highly correlated with outdoor ones and the regression line had the largest slope and a very low intercept, indicative of no indoor sources of fine particulate . The results of the statistical analysis of indoor and outdoor concentration data support the use of as a proper surrogate for indoor PM of outdoor origin.     

Spatiotemporal analysis of the PM10 concentration over the Taranto area

In this paper, an analysis of air quality data is provided for the municipal area of Taranto (southern Italy) characterized by high environmental risks as formally decreed by the Italian government in the 1990s with two administrative measures. This is due to the massive presence of industrial sites with elevated environmental impact activities along the NW boundary of the city conurbation. The aforementioned activities have effects on the environment and on public health, as a number of epidemiological researches concerning this area reconfirm. The present study is focused on particulate matter as measured by PM10 concentrations at 13 monitoring stations, equipped with analogous instruments based on the Beta absorption technology, either reporting hourly, two-hourly, or daily measurements. Daily estimates of the PM10 concentration surfaces are obtained in order to identify areas of higher concentration (hot spots), possibly related to specific anthropic activities. Preliminary analysis involved addressing several data problems: (1) due to the use of two different validation techniques, a calibration procedure was devised to allow for data comparability; (2) imputation techniques were considered to cope with the large number of missing data, due to both different working periods and occasional malfunctions of PM10 sensors; and (3) reliable weather covariates (wind speed and direction, pressure, temperature, etc.) were obtained and considered within the analysis. Spatiotemporal modelling was addressed by a Bayesian kriging-based model proposed by Le and Zidek (2006) characterized by the use of time varying covariates and a semiparametric covariance structure. Advantages and disadvantages of the model are highlighted and assessed in terms of fit and performance. Estimated daily PM10 concentration surfaces are suitable for the interpretation of time trends and for identifying concentration peaks within the urban area.     

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.     

大气可吸入颗粒物(PM10)中矿物组分的X射线衍射研究

利用X射线衍射技术对北京2002春季和夏季的可吸入颗粒物进行了研究.结果表明,北京春季和夏季可吸入颗粒物的矿物组成明显不同,春季可吸入颗粒物中的矿物以硅铝酸盐为主,同时存在碳酸盐、硫酸盐、硫化物、铁的氧化物、粘土矿物以及难以鉴定的矿物;在夏季的样品中,矿物的种类有所减少,却有新的物种出现,如氯化氨、硫酸氨等.XRD定量分析显示,在沙尘天气时,可吸入颗粒物中石英和粘土矿物以及非晶质分别占到24.1%、28.5%和2 0%,斜长石和方解石分别占到10.4%和8.1%,其他矿物总共不到10%.矿物组分的确定对可吸入颗粒物来源的识别有一定的指导作用.     

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%。     

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。     

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。     

The collection of PM10 for toxicological investigation: comparisons between different collecting devices

A positive correlation has been established between increased levels of airborne particulate pollution and adverse health effects, the toxicological mechanisms of which are poorly understood. For toxicologists to unambiguously determine thesemechanisms, truly representative samples of ambient PM₁₀ are required. This presents problems, as PM₁₀ collecting equipment commonly employed, such as the Tapered Element Oscillating Microbalance (TEOM®), heat the inflow toexclude moisture or use fibrous filters, resulting in a PM₁₀sample that may have undergone significant chemical change on thefilter surface or is contaminated by filter fibres. Other systems(i.e. Negretti and Partisol) can successfully collect PM₁₀ without chemical alteration or filter contamination. Comparativecollections from Port Talbot, S. Wales suggest that TEOMs and Negretti/Partisol systems collect different PM₁₀'s; the principle difference arising from the TEOM's heating chamber, which precipitates water-soluble ions and volatilises some organic components. This results in both the mass and compositionof the PM₁₀'s being altered. Particle size distributionsfor Negretti and Partisol collections highlighted differences mainly attributed to different flow rates. The results of thiswork demonstrate that simple correlations between PM₁₀ massand adverse health effects are problematic. Furthermore, elucidation of the complex fractionation and chemical changes indifferent collectors is necessary.     

Identification of source regions of PM10 with backward trajectory-based statistical models during PM10 episodes

This study applies backward trajectory-based statistical techniques, residence time, conditional probability and emission attraction to evaluate potential source regions of PM₁₀ over a coastal region. PM₁₀ episodes were selected by principal component analysis for 1998–2005 over the Kaoping air quality basin. Residence time was applied to identify potential regions in which air parcels would remain over their 6- and 12-h trajectories. Emission attraction and conditional probability were used to analyze contribution ratios of distinct emission sources to air quality stations. The PM₁₀ episodes screen 175 days (6 % of total days) and 35.9 % of total station numbers. Residence time and emission attraction clearly identified potential areas in which backward trajectories remained during PM₁₀ episodes and high PM₁₀ events. Emission attraction evaluated relative contributions of various sources (stationary, line, and area) from specific jurisdictions, and provided information on specific sources for high-priority PM₁₀ emissions reduction. The conditional probabilities of emission attraction during high PM₁₀ events show that high values concentrated near stationary and area sources in the city of Kaohsiung.     

PM10 Concentration Measurements in Dublin City

Mass Concentration of ambient particulate matter with an aerodynamic diameter less than 10µm (PM10) are reported for the first time for a range of sites in Dublin City over a 6 month period from January 1st 1996 to June 30th 1996. PM10 gravimetric mass concentration measurements are made with low flow Partisol 2000 air samplers using an impaction type PM10 inlet and 47mm diameter glass fibre filters. In addition, much finer time resolution measurements (minimum sampling frequency of 30 minutes) are made using a tapered element oscillating microbalance (TEOM) PM10 mass monitor. These PM10 mass concentrations methods are also compared with mass concentration inferred using the standard black smoke method. Analysis of the ambient mass concentration data with reference to traffic density and meteorological influences are presented. Results for the first six months of 1996 show that the average PM10 values range from a high of 49 µg m-3 at the Dublin city centre site to 14 µg m-3 at one of the suburban sites. Intercomparison between PM10 and black smoke mass concentrations show that the relationship is site specific. Statistical analysis between PM10 levels and car traffic number show a positive correlation while a weak negative correlation is found between PM10 levels and rainfall amount, wind speed and air temperature.     

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。     

北京市主要PM10排放源成分谱分析

对北京市土壤尘、道路扬尘、城市扬尘、建筑施工尘、钢铁尘、煤烟尘等主要PM10无组织排放源和固定源进行采样、分析,建立相应的成分谱数据库,通过对其化学组分分析,确定各类PM10排放源的化学组分特征和标识元素。土壤尘、建筑施工扬尘、钢铁尘、煤烟尘PM10的标识元素分别为Si、Ca、Fe、Al,道路扬尘显示出明显的土壤尘、建筑施工尘和机动车污染的特征,城市扬尘成分谱与道路尘有很强的共线性,具有明显的道路扬尘特征。     

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。     

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.