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.     

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.     

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。     

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.     

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。     

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.     

Temporal variations of airborne particles concentration in the Brussels environment

We report and analyze data on the PM10 fraction of airborne particles measured at five recording stations in the Brussels region from October 2002 till September 2003. These stations are representative of the various activity sectors of the Brussels urban area. The objective was the determination of the origin of the PM10 particles (particles up to 10 μm) that are recorded in that region in order to follow the EU directives concerning tolerance level of airborne particles concentration. In order to evaluate the impacts of local and external factors that inject solid particles in the atmosphere of Brussels we compared concentration data from working and not working (holidays) periods. Moreover, we also compared concentrations from periods of agricultural activity and rest in the Brabant provinces surrounding the Brussels region for various crop types. The results lead to the conclusion that the impact or urban traffic is rather limited while that of the agricultural activities is important. Moreover, there appears a clear-cut distinction between different types of crops.     

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.     

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.     

Characterization of ionic composition of TSP and PM10 during the Middle Eastern Dust (MED) storms in Ahvaz, Iran

Because of the recent frequent observations of major dust storms in southwestern cities in Iran such as Ahvaz, and the importance of the ionic composition of particulate matters regarding their health effects, source apportionment, etc., the present work was conducted aiming at characterizing the ionic composition of total suspended particles (TSP) and particles on the order of ～10?μm or less (PM(10)) during dust storms in Ahvaz in April-September 2010. TSP and PM(10) samples were collected and their ionic compositions were determined using an ion chromatography. Mean concentrations of TSP and PM(10) were 1,481.5 and 1,072.9?μg/m(3), respectively. Particle concentrations during the Middle Eastern Dust (MED) days were up to four times higher than those in normal days. Ionic components contributed to only 9.5% and 11.3% of the total mass of TSP and PM(10), respectively. Crustal ions were most abundant during dust days, while secondary ions were dominant during non-dust days. Ca(2+)/Na(+) and Cl(-)/Na(+) ratios can be considered as the indicators for identification of the MED occurrence. It was found that possible chemical forms of NaCl, (NH(4))(2)SO(4), KCl, K(2)SO(4), CaCl(2), Ca(NO(3))(2), and CaSO(4) may exist in TSP. Correlation between the anionic and cationic components suggests slight anion and cation deficiencies in TSP and PM(10) samples, though the deficiencies were negligible.     

Spatial and temporal variations in inhalable CuZnPb aerosols within the Mexico City pollution plume

We report on the CuPbZn content of PM10 and PM2.5 samples collected from three sites (urban T0, suburban T1 and rural T2) during the Mexico City MILAGRO campaign of March 2006. Daytime city centre concentrations of summation operator CuZnPb(PM10) were much higher (T0 > 450 ng m(-3)) than at the suburban site (T1 < 200 ng m(-3)). Rural site (T2) summation operator CuZnPb(PM10) concentrations exceeded 50 ng m(-3) when influenced by the megacity plume but dropped to 10 ng m(-3) during clean northerly winds. Nocturnal metal concentrations more than doubled at T0, as pollutants became trapped in the nightly inversion layer, but decreased at the rural site. Transient spikes in concentrations of different metals, e.g. a "copper event" at T0 (CuPM10 281 ng m(-3)) and "zinc event" at T1 (ZnPM10 1481 ng m(-3)) on the night of March 7-8, demonstrate how industrial pollution sources produce localised chemical inhomogeneities in the city atmosphere. Most metal aerosols are <2.5 microm and SEM study demonstrates the dominance of Fe, Ti, Ba, Cu, Pb and Zn (and lesser Sn, Mo, Sb, W, Ni, V, As, Bi) in metalliferous particles that have shapes including spherical condensates, efflorescent CuZnClS particles, cindery Zn, and Cu wire. Metal aerosol concentrations do not change in concert with PM10 mass, which is more influenced by wind resuspension than industrial emissions. Metalliferous particles can induce cell damage, and PM composition is probably more important than PM mass, with respect to negative health effects, so that better monitoring and control of industrial emissions would likely produce significant improvements in air quality.     

Air Quality and Monitoring Strategy in the Helsinki Metropolitan Area, Finland

The Helsinki Metropolitan Area Council (YTV) is responsible for air quality monitoring in the Helsinki area. Air quality has been monitored periodically since the late 1950s. An automatic SO2 monitoring network was constructed in 1975 and TSP measurements were added in 1978. Since then the network has been expanded and currently five automatic multicomponent stations form the basis of the network monitoring SO2, NO, NO2, CO, PM10 and O3 concentrations. Manual TSP and PM10 measurements are also conducted. Mobile monitoring units are also being used as well as special measurement campaigns. The effects of air pollution on nature are studied in bioindicator monitoring. An air quality index is used in order to inform the public of the current air quality situation. Changes in air quality are reflected in monitoring strategy. SO2 concentrations have decreased in the past two decades. Annual averages in 1995 were at or below 5 µg/m3. Traffic is the major source for pollutants even though catalytic converters have lowered traffic emissions somewhat. The highest annual average NO2 concentration at an urban site was 49 µg/m3 in 1995, and there has been no clear change in NO2 levels. There has been a decreasing trend in CO concentrations. Maximum annual TSP and PM10 averages in 1995 were 92 and 32 µg/m3, respectively. The highest average lead concentration was 0.01 µg/m3. Elevated concentrations are experienced from time to time. During the spring daily TSP and PM10 concentrations can go up to around 300 and 150 µg/m3, respectively. This is caused by resuspension mainly due to street sanding. Also a major winter NO2 episode occurred in December 1995. The highest hourly NO2 concentrations reached 400 µg/m3.     

Assessing PM10 source reduction in urban agglomerations for air quality compliance

The objective of this work was to study PM(10) and PM(2.5) concentration data available from monitoring stations in two large urban agglomerations in Greece and to estimate the emissions reduction required for compliance with the EU Air Quality Standards (AQS) for particulate matter. The cities studied are namely the Athens and Thessaloniki Metropolitan Areas (AMA and TMA, respectively). PM(10) concentrations during the period 2001-2010 have been evaluated for 15 air quality monitoring stations in the two urban areas. It was found that the concentrations of PM(10) during the period studied constantly exceeded the threshold values at the traffic and industrial stations in TMA and most of the traffic sites in AMA. Most of the occurrences of non-attainment to the daily AQSs were observed during the winter period at all stations (more pronounced for TMA stations). The reduction in current emission source strength to meet the air quality goal was calculated by the rollback equation using PM(10) day-averaged concentrations over the selected period at each station. Among the lognormal and Weibull distributions, the lognormal distribution was found to best fit the frequency distributions of PM(10) concentrations at the selected stations. The results showed that the minimum reduction required in order to meet the AQS in the AMA ranges from approximately 20 to 38% and up to 11% for traffic and background stations, respectively. Reductions in the range of 31% for traffic and 44% for industrial areas in TMA are also required. The same methodology was applied to PM(2.5) concentrations in the AMA and showed that emission reductions up to 31% are necessary in order to meet the 2020 EU AQS. Finally, continuous concentration data of organic (OC) and elementary carbon (EC) in PM(2.5) were used to study the possibility of achieving specific emission attenuation objectives in AMA.     

The composition of PM10 as collected by a conventional TEOM, a modified TEOM and a Partisol gravimetric monitor at a kerbside site in the North East of England

The composition of airborne particulate matter sampled by a conventional TEOM, an experimental modified TEOM, operated at a lower temperature but fitted with a drier to remove moisture and a Partisol, installed at a kerbside site in the North East of England, has been investigated. The results indicate that there is a seasonal variation in the composition of PM(10) as sampled by the three monitors, with chloride concentration being significantly higher in the winter. The Partisol was found to sample a higher mass of chloride and nitrate, however the differences between the monitors was only significant for chloride. Both TEOM's were found to sample a greater mass of sulphate, although the variability in the data collected meant that significance of the results was not proven statistically. The range of artifacts associated with PM(10) monitors is reviewed. Difficulties in the interpretation of results due to the variable nature of airborne particulate matter and the ability of filter based systems to accurately represent the composition of atmospheric particles are considered.     

Indoor and outdoor concentrations of fine particles, particle-bound PAHs and volatile organic compounds in Kaunas, Lithuania

This complex study presents indoor and outdoor levels of air-borne fine particles, particle-bound PAHs and VOCs at two urban locations in the city of Kaunas, Lithuania, and considers possible sources of pollution. Two sampling campaigns were performed in January-February and March-April 2009. The mean outdoor PM(2.5) concentration at Location 1 in winter was 34.5 ± 15.2 μg m(-3) while in spring it was 24.7 ± 12.2 μg m(-3); at Location 2 the corresponding values were 36.7 ± 21.7 and 22.4 ± 19.4 μg m(-3), respectively. In general there was little difference between the PM concentrations at Locations 1 and 2. PM(2.5) concentrations were lower during the spring sampling campaign. These PM concentrations were similar to those in many other European cities; however, the levels of most PAHs analysed were notably higher. The mean sum PAH concentrations at Locations 1 and 2 in the winter campaign were 75.1 ± 32.7 and 32.7 ± 11.8 ng m(-3), respectively. These differences are greater than expected from the difference in traffic intensity at the two sites, suggesting that there is another significant source of PAH emissions at Location 1 in addition to the traffic. The low observed indoor/outdoor (I/O) ratios indicate that PAH emissions at the locations studied arise primarily from outdoor sources. The buildings at both locations have old windows with wooden frames that are fairly permissive in terms of air circulation. VOC concentrations were mostly low and comparable to those reported from Sweden. The mean outdoor concentrations of VOC's were: 0.7 ± 0.2, 3.0 ± 0.8, 0.5 ± 0.2, 3.5 ± 0.3, and 0.2 ± 0.1 μg m(-3), for benzene, toluene, ethylbenzene, sum of m-, p-, o-xylenes, and naphthalene, respectively. Higher concentrations of VOCs were observed during the winter campaign, possibly due to slower dispersion, slower chemical transformations and/or the lengthy "cold start" period required by vehicles in the wintertime. A trajectory analysis showed that air masses coming from Eastern Europe carried significantly higher levels of PM(2.5) compared to masses from other regions, but the PAHs within the PM(2.5) are of local origin. It has been suggested that street dust, widely used for winter sanding activities in Eastern and Central European countries, may act not only as a source of PM, but also as source of particle-bound PAHs. Other potential sources include vehicle exhaust, domestic heating and long-range transport.     

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.     

Characterization of PM10 and PM2.5 and associated heavy metals at the crossroads and urban background site in Zabrze, Upper Silesia, Poland, during the smog episodes

The concentrations of seven heavy metals (Cd, Cr, Cu, Fe, Mn, Ni, and Pb) associated with PM10 and PM2.5 at the crossroads and the background sites have been studied in Zabrze, Poland, during smog episodes. Although the background level was unusually elevated due to both high particulate emission from the industrial and municipal sources and smog favorable meteorological conditions, significant increase of the concentration of PM2.5 and PM10 as well as associated heavy metals in the roadside air compared to the urban background has been documented. The average daily difference between the roadside and corresponding urban background aerosol concentration was equal to 39.5 μg m^???3 for PM10 and 41.2 μg m^???3 for PM2.5. The highest levels of the studied metals in Zabrze appeared for iron carried by PM10 particles: 1,706 (background) and 28,557 ng m^???3 (crossroads). The lowest concentration level (in PM10) has been found for cadmium: 7 and 77 ng m^???3 in the background and crossroads site, respectively. Also the concentrations of heavy metals carried by the fine particles (PM2.5) were very high in Zabrze during the smog episodes. Concentrations of all studied metals associated with PM10 increased at the roadside compared to the background about ten times (one order) while metals contained in PM2.5 showed two to three times elevated concentrations (except Fe—five times and Cr—no increase).     

基于GIS的石家庄市PM10和PM2.5时空分布研究

利用2018年261个乡镇环境空气自动监测站监测数据,结合GIS空间分析技术,对石家庄市PM10和PM2.5的时空污染特征进行了研究。结果表明,石家庄地区PM10和PM2.5污染的空间分布整体表现为西北部山区好于东南部的平原地区,主城区好于周边县(市、区)的特征。采暖期PM10和PM2.5的污染程度明显重于非采暖期。PM2.5稳定性差于PM10,PM10和PM2.5的稳定性与污染程度具有一定的负相关性,表现出污染越轻的区域稳定性越差。两者的日均值浓度变化在时间序列上呈极强正相关,且污染越重的区域时间相关性越强。与日均值相关性不同,污染程度越轻的区域PM10和PM2.5年均值的线性相关性越强。     

Aerosol size distribution and mass concentration measurements in various cities of Pakistan

During March and April 2010 aerosol inventories from four large cities in Pakistan were assessed in terms of particle size distributions (N), mass (M) concentrations, and particulate matter (PM) concentrations. These M and PM concentrations were obtained for Karachi, Lahore, Rawalpindi, and Peshawar from N concentrations using a native algorithm based on the Grimm model 1.109 dust monitor. The results have confirmed high N, M and PM concentrations in all four cities. They also revealed major contributions to the aerosol concentrations from the re-suspension of road dust, from sea salt aerosols, and from vehicular and industrial emissions. During the study period the 24 hour average PM(10) concentrations for three sites in Karachi were found to be 461 μg m(-3), 270 μg m(-3), and 88 μg m(-3), while the average values for Lahore, Rawalpindi and Peshawar were 198 μg m(-3), 448 μg m(-3), and 540 μg m(-3), respectively. The corresponding 24 hour average PM(2.5) concentrations were 185 μg m(-3), 151 μg m(-3), and 60 μg m(-3) for the three sites in Karachi, and 91 μg m(-3), 140 μg m(-3), and 160 μg m(-3) for Lahore, Rawalpindi and Peshawar, respectively. The low PM(2.5)/PM(10) ratios revealed a high proportion of coarser particles, which are likely to have originated from (a) traffic, (b) other combustion sources, and (c) the re-suspension of road dust. Our calculated 24 hour averaged PM(10) and PM(2.5) concentrations at all sampling points were between 2 and 10 times higher than the maximum PM concentrations recommended by the WHO guidelines. The aerosol samples collected were analyzed for crustal elements (Al, Fe, Si, Mg, Ca) and trace elements (B, Ba, Cr, Cu, K, Na, Mn, Ni, P, Pb, S, Sr, Cd, Ti, Zn and Zr). The averaged concentrations for crustal elements ranged from 1.02 ± 0.76 μg m(-3) for Si at the Sea View location in Karachi to 74.96 ± 7.39 μg m(-3) for Ca in Rawalpindi, and averaged concentrations for trace elements varied from 7.0 ± 0.75 ng m(-3) for B from the SUPARCO location in Karachi to 17.84 ± 0.30 μg m(-3) for Na at the M. A. Jinnah Road location, also in Karachi.