Dust exposure in indoor climbing halls

The use of hydrated magnesium carbonate hydroxide (magnesia alba) for drying the hands is a strong source for particulate matter in indoor climbing halls. Particle mass concentrations (PM10, PM2.5 and PM1) were measured with an optical particle counter in 9 indoor climbing halls and in 5 sports halls. Mean values for PM10 in indoor climbing halls are generally on the order of 200-500 microg m(-3). For periods of high activity, which last for several hours, PM10 values between 1000 and 4000 microg m(-3) were observed. PM(2.5) is on the order of 30-100 microg m(-3) and reaches values up to 500 microg m(-3), if many users are present. In sports halls, the mass concentrations are usually much lower (PM10 < 100 microg m(-3), PM2.5 < or = 20 microg m(-3)). However, for apparatus gymnastics (a sport in which magnesia alba is also used) similar dust concentrations as for indoor climbing were observed. The size distribution and the total particle number concentration (3.7 nm-10 microm electrical mobility diameter) were determined in one climbing hall by an electrical aerosol spectrometer. The highest number concentrations were between 8000 and 12 000 cm(-3), indicating that the use of magnesia alba is no strong source for ultrafine particles. Scanning electron microscopy and energy-dispersive X-ray microanalysis revealed that virtually all particles are hydrated magnesium carbonate hydroxide. In-situ experiments in an environmental scanning electron microscope showed that the particles do not dissolve at relative humidities up to 100%. Thus, it is concluded that solid particles of magnesia alba are airborne and have the potential to deposit in the human respiratory tract. The particle mass concentrations in indoor climbing halls are much higher than those reported for schools and reach, in many cases, levels which are observed for industrial occupations. The observed dust concentrations are below the current occupational exposure limits in Germany of 3 and 10 mg m(-3) for respirable and inhalable dust. However, the dust concentrations exceed the German guide lines for work places without use of hazardous substances. In addition, minimizing dust concentrations to technologically feasible values is required by the current German legislation. Therefore, substantial reduction of the dust concentration is required.     

宁波和温州地区夏季大气中不同粒径颗粒物特征分析

对宁波地区北仑和奉化站、温州地区乐清站3个监测点夏季TSP、PM10、PM2.5和PM1.0进行监测,测试分析各种粒径颗粒物浓度水平和粒径分布特征,并通过化学质量平衡(CMB)受体模型对颗粒物进行源解析。监测结果显示,夏季宁波、温州地区TSP和PM10日均浓度为0.049～0.134mg/m3和0.025～0.084mg/m3,均未超过我国环境空气质量二级标准;PM2.5日均浓度为0.007～0.069mg/m3,按美国2006年EPA最新标准限值0.035mg/m3衡量,奉化、乐清、北仑站的超标天数占总监测天数的比例分别为75%、40%和37.5%。粒径分布统计结果显示,3个监测站点PM10占TSP的比例为48.78%～86.96%;PM2.5占TSP的比例为33.33%～72.46%;奉化和乐清监测点PM10中PM2.5和PM1.0的比例平均值在50%以上。源解析结果显示,夏季TSP主要来源于土壤尘,其次是建筑尘和煤烟尘,其贡献率分别为40.70%～55.49%、9.62%～13.64%和5.85%～17.28%。     

Source apportionment of PM10 and PM(2.5) at Tocopilla, Chile (22 degrees 05' S, 70 degrees 12' W)

Tocopilla is located on the coast of Northern Chile, within an arid region that extends from 30 degrees S to the border with Perú. The major industrial activities are related to the copper mining industry. A measurement campaign was conducted during March and April 2006 to determine ambient PM10 and PM(2.5) concentrations in the city. The results showed significantly higher PM10 concentrations in the southern part of the city (117 microg/m3) compared with 79 and 80 (microg/m3) in the central and northern sites. By contrast, ambient PM2.5 concentrations had a more uniform spatial distribution across the city, around 20 (microg/m3). In order to conduct a source apportionment, daily PM10 and PM(2.5) samples were analyzed for elements by XRF. EPA's Positive Matrix Factorization software was used to interpret the results of the chemical compositions. The major source contributing to PM(2.5) at sites 1, 2 and 3, respectively are: (a) sulfates, with approximately 50% of PM2.5 concentrations at the three sites; (b) fugitive emissions from fertilizer storage and handling, with 16%, 21% and 10%; (c) Coal and residual oil combustion, with 15%, 15% and 4%; (d) Sea salt, 5%, 6% and 16%; (e) Copper ore processing, 4%, 5% and 15%; and (f) a mixed dust source with 11%, 7% and 4%. Results for PM10--at sites 1, 2 and 3, respectively--show that the major contributors are: (a) sea salt source with 36%, 32% and 36% of the PM10 concentration; (b) copper processing emissions mixed with airborne soil dust with 6.6%, 11.5% and 41%; (c) sulfates with 31%, 31% and 12%; (d) a mixed dust source with 16%, 12% and 10%, and (e) the fertilizer stockpile emissions, with 11%, 14% and 2% of the PM10 concentration. The high natural background of PM10 implies that major reductions in anthropogenic emissions of PM10 and SO2 would be required to attain ambient air quality standards for PM10; those reductions would curb down ambient PM(2.5) concentrations as well.     

Speciation and sources of atmospheric aerosols in a highly industrialised emerging mega-city in central China

Monitoring air quality in large urban agglomerations is the key to the prevention of air pollution-related problems in emerging mega-cities. The city of Wuhan is a highly industrialised city with >9 million inhabitants in Central China. Simultaneous PM10 sampling was performed during 1 year at one urban and one industrial site. Mean PM10 daily levels (156 microg m(-3) at the urban site and 197 microg m(-3) at the industrial hotspot) exceed the US-EPA or EU annual limit values by 3-4 times. A detailed study of daily speciation showed that the mean chemical composition of PM10 presents minimal differences between peak and low PM episodes. This implies that PM10 aerosols in the study area result from local emissions, and air quality management and abatement strategies in Wuhan should thus focus on local anthropogenic sources. The levels of some elements of environmental concern are relatively high (409-615 ngPb m(-3), 66-70 ngAs m(-3), 116-227 ngMn m(-3), 10-12 ngCd m(-3)) due to industrial, but also urban emissions. Principal component analysis identified a mineral source (probably cement and steel manufacture) and smelting as the main contributors to PM10 levels at the industrial site (34%), followed by a coal fired power plant (20%) and the anthropogenic regional background (16%). At the urban site the major PM10 source is a mixed coal combustion source (31%), followed by the anthropogenic regional background (28%) and traffic (16%).     

Is PM10 mass measurement a reliable index for air quality assessment? An environmental study in a geographical area of north-eastern Italy

The aim of this study was to measure the concentration of some metals (Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Ti) in PM(10) samples collected in one urban and one industrial site and to assess that PM(10) total mass measurement may be not sufficient as air quality index due to its complex composition. Metals were determined by inductively coupled plasma-atomic emission spectroscopy (ICP-AES) and differential pulsed anodic stripping voltammetry (DPASV). The measured concentrations were used to calculate the content of metals in the PM(10) total mass, and to estimate the enrichment factors and the correlations between PM(10), metal concentrations and meteorological data for the two sites. The mean PM10 concentration during the sampling period in the urban site exceeded the annual European Union (EU) standard (40 microg/m(3)) and, for some sampling days, the daily EU standard (50 microg/m(3)) was also exceeded. In opposite, both EU standards were never exceeded in the industrial site. The overall metal content was nearly double in the industrial site compared to the urban one, and the mean Ni concentration exceeded the EU annual limit value (10 ng/m(3)). The metals with the highest enrichment factor were Cd, Cu, Ni and Pb for both sites, suggesting a dominant anthropogenic source for these metals. Metal concentrations were very low and typical of rural background during Christmas holidays, when factories were closed. PM(10) total mass measurement is not a sufficient air quality index since the metal content of PM(10) is not related to its total mass, especially in sites with industrial activities. This measurement should be associated with the analysis of toxic metals.     

A Statistical Analysis of Particulate Data Sets for Jawaharlal Nehru Port and Surrounding Harbour Region in India

The concentrations of total suspended particulate matter (TSP) and particulate matter less than 10 microns (PM10) were measured at various locations in a Jawaharlal Nehru port and surrounding harbour region. Meteorological data was also collected to establish the correlation with air pollutant concentration. The results are analysed from the standpoint of monthly and seasonal variations, annual trends as well as meteorological effects. The monthly mean concentration of TSP was in the range of 88.2 to 199.3 microg m(-3). The maximum and minimum-recorded value of PM10 was 135.8 and 20.3 microg m(-3), respectively. The annual average concentration of PM10 was 66.1 microg m(-3). There are clear associations between TSP and PM10 data set at all the measured three sites with a correlation coefficient of 0.89, 0.69 and 0.81, respectively. PM10 data appears to be a constant fraction of the TSP data throughout the year, indicating common influences of meteorology and sources. Particle size analysis showed PM10 to be 47% of the total TSP concentration, which is lower than reported for industrial area and traffic junctions in Mumbai. Anthropogenic sources contribute significantly to the PM10 fraction in an industrial region, while contributions from natural sources are more in a port and harbour area. Statistical analysis of air quality data shows that TSP is strongly correlated with wind speed but weakly correlated with temperature. There appears to be a simple inverse relationship between TSP and wind speed data, indicating the dilution and transport by winds.     

PM sources in a highly industrialised area in the process of implementing PM abatement technology. Quantification and evolution

Principal component analysis (PCA) coupled with a multilinear regression analysis (MLRA) was applied to PM(10) speciation data series (2002-2005) from four sampling sites in a highly industrialised area (ceramic production) in the process of implementing emission abatement technology. Five common factors with similar chemical profiles were identified at all the sites: mineral, regional background (influenced by the industrial estate located on the coast: an oil refinery and a power plant), sea spray, industrial 1 (manufacture and use of glaze components, including frit fusion) and road traffic. The contribution of the regional background differs slightly from site to site. The mineral factor, attributed to the sum of several sources (mainly the ceramic industry, but also with minor contributions from soil resuspension and African dust outbreaks) contributes between 9 and 11 microg m(-3) at all the sites. Source industrial 1 entails an increase in PM(10) levels between 4 and 5 microg m(-3) at the urban sites and 2 microg m(-3) at the suburban background site. However, after 2004, this source contributed less than 2 microg m(-3) at most sites, whereas the remaining sources did not show an upward or downward trend along the study period. This gradual decrease in the contribution of source industrial 1 coincides with the implementation of PM abatement technology in the frit fusion kilns of the area. This relationship enables us to assess the efficiency of the implementation of environmental technologies in terms of their impact on air quality.     

Seasonal variation, risk assessment and source estimation of PM 10 and PM10-bound PAHs in the ambient air of Chiang Mai and Lamphun, Thailand

Daily PM10 concentrations were measured at four sampling stations located in Chiang Mai and Lamphun provinces, Thailand. The sampling scheme was conducted during June 2005 to June 2006; every 3 days for 24 h in each sampling period. The result revealed that all stations shared the same pattern, in which the PM10 (particulate matters with diameter of less than 10 microm) concentration increased at the beginning of dry season (December) and reached its peak in March before decreasing by the end of April. The maximum PM10 concentration for each sampling station was in the range of 140-182 microg/m(3) which was 1.1-1.5 times higher than the Thai ambient air quality standard of 120 microg/m(3). This distinctly high concentration of PM10 in the dry season (Dec. 05-Mar. 06) was recognized as a unique seasonal pattern for the northern part of Thailand. PM10 concentration had a medium level of negative correlation (r = -0.696 to -0.635) with the visibility data. Comparing the maximum PM10 concentration detected at each sampling station to the permitted PM10 level of the national air quality standard, the warning visibility values for the PM10 pollution-watch system were determined as 10 km for Chiang Mai Province and 5 km for Lamphun Province. From the analysis of PM10 constituents, no component exceeded the national air quality standard. The total concentrations of PM10-bond polycyclic aromatic hydrocarbons (PAHs) are calculated in terms of total toxicity equivalent concentrations (TTECs) using the toxicity equivalent factors (TEFs) method. TTECs in Chiang Mai and Lamphun ambient air was found at a level comparable to those observed in Nagasaki, Bangkok and Rome and at a lower level than those reported at Copenhagen. The annual number of lung cancer cases for Chiang Mai and Lamphun Provinces was estimated at two cases/year which was lower than the number of cases in Bangkok (27 cases/year). The principal component analysis/absolute principal component scores (PCA/APCS) model and multiple regression analysis were applied to the PM10 and its constituents data. The results pointed to the vegetative burning as the largest PM10 contributor in Chiang Mai and Lamphun ambient air. Vegetative burning, natural gas burning & coke ovens, and secondary particle accounted for 46-82%, 12-49%, and 3-19% of the PM10 concentrations, respectively. However, natural gas burning & coke ovens as well as vehicle exhaust also deserved careful attention due to their large contributions to PAHs concentration. In the wet season and transition periods, 42-60% of the total PAHs concentrations originated from vehicle exhaust while 16-37% and 14-38% of them were apportioned to natural gas burning & coke ovens and vegetative burning, respectively. In the dry period, natural gas burning & coke ovens, vehicle exhaust, and vegetative burning accounted for 47-59%, 20-25%, and 19-28% of total PAHs concentrations. The close agreement between the measured and predicted concentrations data (R(2) > 0.8) assured enough capability of PCA/APCS receptor model to be used for the PM10 and PAHs source apportionment.     

Simulation of PM<Subscript>10</Subscript> concentration patterns for a 2010 traffic scenario in Bologna,Italy

Three state of the art traffic–emission–dispersion models dealing with particulate matter have been tested and validated over the Bologna metropolitan area with 2001 data and a future scenario has been developed in order to estimate expected PM concentrations in 2010. The modelling system is composed by a traffic model (VISUM) evaluating vehicle fluxes as a function of mobility demand and road network in the area, an emission model (Trefic) estimating pollutants emitted in atmosphere as a function of vehicle fluxes amount and composition and of environmental conditions and a dispersion model (ADMS) evaluating PM concentrations on the area, given the meteorological variables. The three models compose a cascade sequence and results of the previous one feed the next one. PM concentrations computed by the model suite for the town of Bologna, in northern Italy, for the reference period (January 2001) have been compared with air quality stations measurements suggesting the modelling system being especially suitable for evaluating traffic induced PM. Qualitative and quantitative changes in the circulating vehicle fleet have been supposed in order to obtain a realistic scenario for year 2010. Forecasted concentrations have been then compared with limits fixed by current EU legislation for particulate matter.     

Dynamics and origin of PM2.5 during a three-year sampling period in Beijing, China

Systematic sampling and analysis were performed to investigate the dynamics and the origin of suspended particulate matter smaller than 2.5 μm in diameter (PM(2.5)), in Beijing, China from 2005 to 2008. Identifying the source of PM(2.5) was the main goal of this project, which was funded by the German Research Foundation (DFG). The concentrations of 19 elements, black carbon (BC) and the total mass in 158 weekly PM(2.5) samples were measured. The statistical evaluation of the data from factor analysis (FA) identifies four main sources responsible for PM(2.5) in Beijing: (1) a combination of long-range transport geogenic soil particles, geogenic-like particles from construction sites and the anthropogenic emissions from steel factories; (2) road traffic, industry emissions and domestic heating; (3) local re-suspended soil particles; (4) re-suspended particles from refuse disposal/landfills and uncontrolled dumped waste. Special attention has been paid to seven high concentration "episodes", which were further analyzed by FA, enrichment factor analysis (EF), elemental signatures and backward-trajectory analysis. These results suggest that long-range transport soil particles contribute much to the high concentration of PM(2.5) during dust days. This is supported by mineral analysis which showed a clear imprint of component in PM(2.5). Furthermore, the ratios of Mg/Al have been proved to be a good signature to trace back different source areas. The Pb/Ti ratio allows the distinction between periods of predominant anthropogenic and geogenic sources during high concentration episodes. Backward-trajectory analysis clearly shows the origins of these episodes, which partly corroborate the FA and EF results. This study is only a small contribution to the understanding of the meteorological and source driven dynamics of PM(2.5) concentrations.     

乌鲁木齐市可吸入颗粒物水溶性离子特征及来源解析

采暖期时在乌鲁木齐市采集了环境空气中的可吸入颗粒物,对可吸入颗粒物质量浓度及8种水溶性离子的特征和来源进行了分析。结果表明,细粒子和粗粒子的月平均质量浓度分别是53.5～233.3μg/m3和38.9～60.9μg/m3;细粒子和粗粒子中水溶性离子主要由SO24-、NH4+和NO3-组成;粗粒子中NH4+与NO3-和SO24-的相关性分别是0.70和0.66,细粒子中NH4+与NO3-和SO24-的相关性分别是0.89和0.93,铵盐是乌鲁木齐可吸入颗粒物主要存在形式;煤烟尘是乌鲁木齐市采暖期可吸入颗粒物的主要来源。     

Relationship between different size classes of particulate matter and meteorology in three European cities

Evidence on the correlation between particle mass and (ultrafine) particle number concentrations is limited. Winter- and spring-time measurements of urban background air pollution were performed in Amsterdam (The Netherlands), Erfurt (Germany) and Helsinki (Finland), within the framework of the EU funded ULTRA study. Daily average concentrations of ambient particulate matter with a 50% cut off of 2.5 microm (PM2.5), total particle number concentrations and particle number concentrations in different size classes were collected at fixed monitoring sites. The aim of this paper is to assess differences in particle concentrations in several size classes across cities, the correlation between different particle fractions and to assess the differential impact of meteorological factors on their concentrations. The medians of ultrafine particle number concentrations were similar across the three cities (range 15.1 x 10(3)-18.3 x 10(3) counts cm(-3)). Within the ultrafine particle fraction, the sub fraction (10-30 nm) made a higher contribution to particle number concentrations in Erfurt than in Helsinki and Amsterdam. Larger differences across the cities were found for PM2.5(range 11-17 microg m(-3)). PM2.5 and ultrafine particle concentrations were weakly (Amsterdam, Helsinki) to moderately (Erfurt) correlated. The inconsistent correlation for PM2.5 and ultrafine particle concentrations between the three cities was partly explained by the larger impact of more local sources from the city on ultrafine particle concentrations than on PM2.5, suggesting that the upwind or downwind location of the measuring site in regard to potential particle sources has to be considered. Also, relationship with wind direction and meteorological data differed, suggesting that particle number and particle mass are two separate indicators of airborne particulate matter. Both decreased with increasing wind speed, but ultrafine particle number counts consistently decreased with increasing relative humidity, whereas PM2.5 increased with increasing barometric pressure. Within the ultrafine particle mode, nucleation mode (10-30 nm) and Aitken mode (30-100 nm) had distinctly different relationships with accumulation mode particles and weather conditions. Since the composition of these particle fractions also differs, it is of interest to test in future epidemiological studies whether they have different health effects.     

Comparison of the SidePak personal monitor with the Aerosol Particle Sizer (APS)

The aim of this study was to compare the performance of the TSI Aerodynamic Particle Sizer (APS) and the TSI portable photometer SidePak to measure airborne oil mist particulate matter (PM) with aerodynamic diameters below 10 μm, 2.5 μm and 1 μm (PM(10), PM(2.5) and PM(1)). Three SidePaks each fitted with either a PM(10), PM(2.5) or a PM(1) impactor and an APS were run side by side in a controlled chamber. Oil mist from two different mineral oils and two different drilling fluid systems commonly used in offshore drilling technologies were generated using a nebulizer. Compared to the APS, the SidePaks overestimated the concentration of PM(10) and PM(2.5) by one order of magnitude and PM(1) concentrations by two orders of magnitude after exposure to oil mist for 3.3-6.5 min at concentrations ranging from 0.003 to 18.1 mg m(-3) for PM(10), 0.002 to 3.96 mg m(-3) for PM(2.5) and 0.001 to 0.418 mg m(-3) for PM(1) (as measured by the APS). In a second experiment a SidePak monitor previously exposed to oil mist overestimated PM(10) concentrations by 27% compared to measurements from another SidePak never exposed to oil mist. This could be a result of condensation of oil mist droplets in the optical system of the SidePak. The SidePak is a very useful instrument for personal monitoring in occupational hygiene due to its light weight and quiet pump. However, it may not be suitable for the measurement of particle concentrations from oil mist.     

Traffic emission factors of ultrafine particles: effects from ambient air

Ultrafine particles have a significant detrimental effect on both human health and climate. In order to abate this problem, it is necessary to identify the sources of ultrafine particles. A parameterisation method is presented for estimating the levels of traffic-emitted ultrafine particles in terms of variables describing the ambient conditions. The method is versatile and could easily be applied to similar datasets in other environments. The data used were collected during a four-week period in February 2005, in Gothenburg, as part of the G?te-2005 campaign. The specific variables tested were temperature (T), relative humidity (RH), carbon monoxide concentration (CO), and the concentration of particles up to 10 μm diameter (PM(10)); all indicators are of importance for aerosol processes such as coagulation and gas-particle partitioning. These variables were selected because of their direct effect on aerosol processes (T and RH) or as proxies for aerosol surface area (CO and PM(10)) and because of their availability in local monitoring programmes, increasing the usability of the parameterization. Emission factors are presented for 10-100 nm particles (ultrafine particles; EF(ufp)), for 10-40 nm particles (EF(10-40)), and for 40-100 nm particles (EF(40-100)). For EF(40-100) no effect of ambient conditions was found. The emission factor equations are calculated based on an emission factor for NO(x) of 1 g km(-1), thus the particle emission factors are easily expressed in units of particles per gram of NO(x) emitted. For 10-100 nm particles the emission factor is EF(ufp) = 1.8×10(15)×(1 - 0.095×CO - 3.2×10(-3)×T) particles km(-1). Alternative equations for the EFs in terms of T and PM(10) concentration are also presented.     

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.     

重庆市内环货车错时限行对空气质量的影响

在分析货车实施错时限行后内环车流量时段分布变化基础上,通过对PM_(2.5)、NO_2等指标的ADMS模型模拟和实际监测数据对比分析,探讨了内环货车错时限行对环境空气质量的影响。结果表明,货车错时限行后主城区环境空气中PM_(2.5)、NO_2小时平均质量浓度分别降低了9.4%和6.0%,峰值浓度明显降低,晚上出现峰值时间往后推移了2~3 h。经ADMS模型模拟计算,内环高峰时段机动车排放对主城区NO_2、PM、VOCs的浓度贡献分别降低了54.1%、56.3%、17.5%,CO浓度贡献不大。内环货车错时限行措施对重庆市主城区空气质量的改善有一定的积极作用。     

桂林市细颗粒物部分典型排放源的粒径谱及成分分析

采用在线单颗粒气溶胶质谱技术源解析方法,对桂林市PM2.5典型排放源的粒径和化学成分进行质谱分析,采集燃煤/燃气源、工业工艺源、扬尘源、油烟源4类共计7个典型排放源。结果表明,桂林市4类排放源细颗粒物的粒径分布为0.25~1.25μm,80%以上的细颗粒分布在0.2~1.0μm的小粒径范围,峰值约0.68μm。细颗粒物离子成分含有Na~+、Mg~+、K~+、NH~+4、Fe~+、Pb~+、Cd~+、V~+、Mn~+、Li~+、Al~+、Ca~+、Cu~+、Zn~+、Cr~+、CN~-、PO_3~-、NO_2~-、NO_3~-、Cl~-、SO_4~(2-)、SiO_3~-等成分,桂林市细颗粒物为元素碳、有机碳元素碳、有机碳、富锰颗粒、富铁颗粒、富钾颗粒、矿物质、左旋葡聚糖以及其他金属等9类。     

Estimation of the contributions of long range transported aerosol in East Asia to carbonaceous aerosol and PM concentrations in Seoul, Korea using highly time resolved measurements: a PSCF model approach

The contributions of long range transported aerosol in East Asia to carbonaceous aerosol and particulate matter (PM) concentrations in Seoul, Korea were estimated with potential source contribution function (PSCF) calculations. Carbonaceous aerosol (organic carbon (OC) and elemental carbon (EC)), PM(2.5), and PM(10) concentrations were measured from April 2007 to March 2008 in Seoul, Korea. The PSCF and concentration weighted trajectory (CWT) receptor models were used to identify the spatial source distributions of OC, EC, PM(2.5), and coarse particles. Heavily industrialized areas in Northeast China such as Harbin and Changchun and East China including the Pearl River Delta region, the Yangtze River Delta region, and the Beijing-Tianjin region were identified as high OC, EC and PM(2.5) source areas. The conditional PSCF analysis was introduced so as to distinguish the influence of aerosol transported from heavily polluted source areas on a receptor site from that transported from relatively clean areas. The source contributions estimated using the conditional PSCF analysis account for not only the aerosol concentrations of long range transported aerosols but also the number of transport days effective on the measurement site. Based on the proposed algorithm, the condition of airmass pathways was classified into two types: one condition where airmass passed over the source region (PS) and another condition where airmass did not pass over the source region (NPS). For most of the seasons during the measurement period, 249.5-366.2% higher OC, EC, PM(2.5), and coarse particle concentrations were observed at the measurement site under PS conditions than under NPS conditions. Seasonal variations in the concentrations of OC, EC, PM(2.5), and coarse particles under PS, NPS, and background aerosol conditions were quantified. The contributions of long range transported aerosols on the OC, EC, PM(2.5), and coarse particle concentrations during several Asian dust events were also estimated. We also investigated the performance of the PSCF results obtained from combining highly time resolved measurement data and backward trajectory calculations via comparison with those from data in low resolutions. Reduced tailing effects and the larger coverage over the area of interest were observed in the PSCF results obtained from using the highly time resolved data and trajectories.     

Airborne particulate matter and BTEX in office environments

Total suspended particulate (TSP), PM(2.5) and BTEX were collected in nine offices in the province of Antwerp, Belgium. Both indoor and outdoor aerosol samples were analysed for their weight, elemental composition, and water-soluble fraction. Indoor TSP and PM(2.5) concentrations ranged from 7-31 microg m(-3) and 5-28 microg m(-3), with an average of 18 and 11 microg m(-3), respectively. Of all the elements analysed in indoor TSP, more than 95% was represented by Al, Si, K, Ca, Fe, Cl and S, accounting for 12% of the TSP by mass. The other elements showed significant enrichment relative to the earth's crust. The water-soluble ionic fraction accounted for almost 30% of the sampled indoor TSP by weight, and was enriched by anthropogenic activities. It was shown that the indoor PM levels varied among the offices, depending on the ventilation pattern, location, and occupation density of the office. Indoor BTEX levels ranged together from 5-47 microg m(-3) and were considerably higher than the corresponding outdoor levels. It was observed that some recently constructed and renovated buildings were clearly burdened with elevated levels for toluene, ethyl benzene, and xylenes, while outdoor air was found to be the main source for BTEX levels at the 'older' offices.     

Temporal variations of airborne particle concentration in an arid region

Airborne particle concentration (APC) measurements were carried out at the Sede Boker experimental station located in the northern Negev Desert, about 50 km south of Beer Sheva, during the years 1987--1997. The basic sampling period used in 1987--1993 was 12 h (day and night) and in 1994--1997 the sampling period was 24 h. For the entire study period, the average airborne particle concentration (APC) was 123.8 microg/m3, the highest value was 4204.2 microg/m3; and the lowest, near 5 microg/m3. For the 24 h average, about 90% of the cases were defined as normal situations (APC between 0-200 microg/m3) about, 8.5%, hazy periods (APC between 200-500 microg/m3), 1.4% dusty periods (APC between 500-1000 microg/m3) and about 0.7% were intense dusty periods (APC above 1000 microg/m3). Statistical analysis of the data showed significant seasonal and monthly fluctuations. The seasonal variation of the APC was further examined using different definitions of the seasons (astronomical, meteorological, and synoptic).