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1.
2013年北京市PM2.5重污染日时空分布特征研究   总被引:3,自引:2,他引:1  
根据2013年北京市环境保护监测中心监测的PM2.5数据,系统分析了北京市重污染日PM2.5污染的时空分布特征,并利用克里格插值初步统计了全年和重污染日PM2.5不同浓度区间的国土面积。2013年全市PM2.5年均浓度为89.5μg/m3,重污染日平均浓度为218μg/m3,重污染日主要集中在冬季;PM2.5年均浓度呈现明显的南高北低梯度分布特征,而重污染日空间分布较均匀,南部及城六区存在明显的高污染区,平均浓度在180μg/m3以上;2013年北京市重污染日PM2.5平均浓度为150~250μg/m3,其对应的国土面积约为12 656 km2,PM2.5平均浓度在250μg/m3以上的国土面积约为883 km2,而全年无PM2.5平均浓度在150μg/m3以上所对应的国土面积。  相似文献   

2.
一种评估烟花爆竹燃放对大气PM2.5影响的新方法   总被引:5,自引:1,他引:4       下载免费PDF全文
基于北京市空气质量自动监测系统2013年2月常规污染物监测数据,提出了定量估算烟花爆竹燃放对大气PM2.5影响的污染物相对比值(PM2.5/CO)法。利用该方法研究表明,2013年北京除夕烟花爆竹燃放使PM2.5单站1小时平均浓度最大增加709μg/m3(石景山古城监测点);全市24小时平均浓度增加88μg/m3,达到159μg/m3,空气质量由良好升级为重度污染。元宵节夜间烟花爆竹燃放使PM2.5单站1小时平均浓度最大增加469μg/m3(海淀万柳监测点),全市24小时平均浓度增加54μg/m3。除夕夜、元宵夜全市平均烟花爆竹PM2.5浓度超过75μg/m3的时间分别为5、7 h,达到峰值后半衰期分别为0.9、1.7 h。城区烟花爆竹PM2.5浓度高于郊区,并可导致下风向郊区的PM2.5浓度显著增加。除夕、元宵节北京市区烟花爆竹排放PM2.5总量分别约为1.91×105kg、1.17×105kg。  相似文献   

3.
冬季大气中PM_(10)和PM_(2.5)污染特征及形貌分析   总被引:6,自引:4,他引:2  
2008年冬季采集大气中PM10和PM2.5样品,利用SPSS软件进行分析。结果表明,PM10质量浓度在92.87~384.7μg/m3之间,平均值为201.09μg/m3,超标率71.43%。PM2.5浓度跨度为57.27~230.21μg/m3,平均值为133.82μg/m3,超标率89.47%。PM10和PM2.5空间分布略有差异。PM2.5/PM10在29.10%~94.76%之间,均值为66.55%。PM2.5与PM10质量浓度之间有显著相关性,相关方程:PM2.5=0.7993×PM10-55.984(R2=0.9524,置信度为95%)。通过颗粒物形貌分析,初步判定冬季大气主要污染源为燃煤和机动车尾气排放。  相似文献   

4.
西安市区大气中PM2.5和PM10质量浓度污染特征   总被引:2,自引:1,他引:1  
2013年3月—2014年2月期间,设置1个监测点位,采集了西安市区大气环境中PM10和PM2.5样品,采用重量法测定了PM2.5和PM10质量浓度。结果表明,西安市区PM2.5质量浓度为16~558μg/m3,平均值为128μg/m3,超标率69.1%;PM10质量浓度范围为32~887μg/m3,平均值为249μg/m3,超标率71.8%。虽然PM2.5和PM10质量浓度的逐日变化幅度比较大,但是整体变化趋势非常相似,存在显著的正相关关系(r=0.831 9)。PM2.5和PM10质量浓度存在明显的季节变化,均为冬季最高,春季次之,秋季较低,夏季最低。ρ(PM2.5)/ρ(PM10)为0.245~0.822,平均值为0.510,说明PM2.5在PM10中所占比例大于PM2.5~10;此外,该比值呈现一定的季节变化规律,冬季、夏季较高,秋季次之,春季最低。霾天气发生时,该比值和PM2.5质量浓度明显高于无霾天气。  相似文献   

5.
北京清华园采暖前与采暖期PM10中含碳组分的理化特征   总被引:3,自引:1,他引:3  
采用美国rp公司生产的Series5400大气颗粒物碳质组分监测仪对清华园PM10中的碳质组分进行了连续在线监测(2002年9月~11月)。结合PM2.5中碳质组分浓度、PM10的浓度和气象数据,分析了碳质组分的污染特征。结果表明,采样期间清华园大气PM10中有机碳(OC)、元素碳(EC)的日平均质量浓度分别在4.07~65.81μg/m3、0.96~26.14μg/m3之间变化,平均值分别为20.8±12.1和7.0±5.1μg/m3。OC在总碳(TC)中占有很大比例,OC/TC平均值为75.84%;TC在PM10中的含量平均为25.0%。本文对9~10月份(秋季)和11月份(初冬)OC、EC的相关性分别进行了分析,结果表明OC、EC之间具有良好的相关性,9月份和10月份相关性系数(R2)为0.83;11月份为0.90。二次生成的OC(OCsec)浓度估算结果表明,9、10月份OCsec在OC中的比例(60.7%)比11月份(38.5%)大。碳质组分主要集中在细颗粒物中,PM10中的OC有70.3%存在于细颗粒物PM2.5中,TC则有58.6%存在于PM2.5中。  相似文献   

6.
广州冬季霾天气大气PM2.5污染特征分析   总被引:8,自引:4,他引:8  
收集了2005年12月至2006年2月的PM2.5浓度观测数据及同步气象数据,分析了冬季PM2.5质量浓度日变化趋势以及霾日期间PM2.5质量浓度日变化和小时变化趋势.结果发现,观测期间PM2.5日均值浓度为69μg/m3,霾日期间PM2.5日均值浓度为72μg/m3.冬季霾天气的发生频率为45%,霾天气过程最短持续2天,最长持续9天.较高的PM2.5浓度和较高的相对湿度及较小的风速是导致霾天气形成的主要原因.霾日期间PM2.5小时浓度变化趋势与人类活动周期和气象条件密切相关.  相似文献   

7.
2014年4月,应用热/光碳分析仪测定合肥市春季大气PM10和PM2.5中的有机碳(OC)、元素碳(EC)。结果显示,PM10、PM2.5的平均质量浓度分别为(124.0±34.3)μg/m3和(96.3±29.2) μg/m3,PM10中OC、EC的平均质量浓度分别为(15.1±5.5)μg/m3和(6.0±2.1) μg/m3,PM2.5中OC、EC的平均质量浓度分别为(12.1±3.5)μg/m3和(5.5±2.1) μg/m3。OC、EC在PM2.5中所占的比例均高于在PM10中的比例,说明合肥市春季PM2.5中碳的含量更高。通过分析8个碳组分及OC/EC比值,发现燃煤、机动车尾气和生物质燃烧是主要贡献源; OC易形成二次污染,EC排放以焦炭为主。  相似文献   

8.
一次连续在线观测分析天津市细颗粒物污染特征   总被引:2,自引:1,他引:1  
根据2005年的5月17日—5月23日GR IMM(1.109#)谱分析仪在线观测结果考察天津市细颗粒物浓度和质量浓度特征。观测期间,天津市颗粒物数浓度平均值为1 124 cm-3,粒径分布为0.25μm~0.60μm,98.5%粒子的粒径0.65μm。同期PM10日均质量浓度值为204μg/m3,ρ(PM2.5)为104μg/m3,ρ(PM1.0)为82.9μg/m3。ρ(PM1.0)/ρ(PM2.5)超过80%,粒径1μm超细颗粒物为天津城市大气颗粒物的主要成分。  相似文献   

9.
春节烟花爆竹燃放期间苏州市区PM2.5组分特征分析   总被引:3,自引:1,他引:2  
为了解春节期间烟花爆竹燃放对苏州市空气质量的影响,在苏州市南门监测点利用在线监测仪器(包括颗粒物分析仪、在线离子色谱、OC/EC分析仪和重金属分析仪)对环境空气中的PM2.5浓度水平、颗粒物水溶性离子、有机碳(OC)、元素碳(EC)和重金属浓度进行连续观测。通过比较烟花爆竹燃放时段和正常时段的PM2.5浓度水平和化学组成,分析并探讨烟花爆竹燃放对PM2.5浓度水平及其组分特征的影响。研究结果表明,大量烟花爆竹的集中燃放造成了PM2.5短时严重污染,最高质量浓度达到了571μg/m3,但随烟花爆竹燃放的减少,PM2.5浓度迅速降低。在烟花爆竹燃放高峰时段,SO42-、Cl-、K+、Mg2+和OC出现了明显的浓度峰值,SO42-质量浓度达到了93.2μg/m3,Cl-质量浓度达到了42.3μg/m3,K+质量浓度达到了115.6μg/m3,OC质量浓度达到了53.8μg/m3。另外,重金属浓度也出现了明显的峰值,Fe质量浓度达到了2.426μg/m3,Cu质量浓度达到了0.727μg/m3,Zn质量浓度达到了1.159μg/m3,Ba质量浓度达到了5.168μg/m3,Pb质量浓度达到了1.245μg/m3。烟花爆竹的燃放造成苏州市区环境空气中有毒有害物质的短期急剧上升,有必要限制烟花爆竹的燃放。  相似文献   

10.
内蒙古半干旱草原区大气气溶胶浓度以及散射等特性对生态环境、气候变化与预测研究有重要意义,文利用2009年1~4月在锡林浩特观象台草原站的观测资料,分析了冬、春季背景大气气溶胶质量浓度、黑碳质量浓度、散射系数的分布特征。研究发现,背景天气下,PM10、PM2.5、PM1.0浓度值都较低,平均值分别为22.7、9.5、6.1μg/m3,3种PM浓度值间的相关性不同;黑碳浓度平均值为0.59μg/m3,小粒子中的含量较高,其日分布规律受人类活动影响较大,与各PM浓度分布有较大不同;散射系数平均值为31.2Mm-1,与PM10、PM2.5、PM1.0、黑碳质量浓度都显著相关。三种PM中,PM2.5对散射和吸收的影响最大。风速、相对湿度对不同粒径的PM以及黑碳浓度、散射系数的影响有所不同。  相似文献   

11.
In this study, the size distribution of airborne particles and related heavy metals Co, Cd, Sn, Cu, Ni, Cr, Pb and V in two urban areas in Istanbul: Yenibosna and Goztepe, were examined. The different inhalable particles were collected by using a cascade impactor in eight size fractions (<0.4 μm, 0.4-0.7 μm, 1.1-2.1 μm, 2.1-3.3 μm, 3.3-4.7 μm, 4.7-5.8 μm, 5.8-9 μm and >9 μm) for six months at each station. Samples were collected on glass fiber filters and filters were extracted and analyzed using ICP-MS. Log-normal distributions showed that the particles collected at the Yenibosna site have a smaller size compared to the Goztepe samples and the size distribution of PM was represented the best by the tri-modal. The average total particle concentrations and standard deviations were obtained as 67.7 ± 17.0 μg m(-3) and 82.1 ± 21.2 μg m(-3), at the Yenibosna and G?ztepe sites, respectively. The higher metal rate in fine and medium coarse PM showed that the anthropogenic sources were the most significant pollutant source. Principal component analysis identified five components for PM namely traffic, road dust, coal and fuel oil combustion, and industrial.  相似文献   

12.
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.  相似文献   

13.
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.  相似文献   

14.
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.  相似文献   

15.
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.  相似文献   

16.
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。  相似文献   

17.
As users of indoor climbing gyms are exposed to high concentrations (PM(10) up to 4000 μg m(-3); PM(2.5) up to 500 μg m(-3)) of hydrated magnesium carbonate hydroxide (magnesia alba), reduction strategies have to be developed. In the present paper, the influence of the use of different kinds of magnesia alba on dust concentrations is investigated. Mass concentrations, number concentrations and size distributions of particles in indoor climbing gyms were determined with an optical particle counter, a synchronized, hybrid ambient real-time particulate monitor and an electrical aerosol spectrometer. PM(10) obtained with these three different techniques generally agreed within 25%. Seven different situations of magnesia alba usage were studied under controlled climbing activities. The use of a suspension of magnesia alba in ethanol (liquid chalk) leads to similar low mass concentrations as the prohibition of magnesia alba. Thus, liquid chalk appears to be a low-budget option to reduce dust concentrations. Magnesia alba pressed into blocks, used as powder or sieved to 2-4 mm diameter, does not lead to significant reduction of the dust concentrations. The same is true for chalk balls (powder enclosed in a sack of porous mesh material). The promotion of this kind of magnesia alba as a means of exposure reduction (as seen in many climbing gyms) is not supported by our results. Particle number concentrations are not influenced by the different kinds of magnesia alba used. The particle size distributions show that the use of magnesia alba predominantly leads to emission of particles with diameters above 1 μm.  相似文献   

18.
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.  相似文献   

19.
A long-term series (2001-2008) of chemical analysis of atmospheric particulate matter (PM(10) and PM(2.5)) collected in the city of Huelva (SW Spain) is considered in this study. The impact of emission plumes from one of the largest Cu-smelters in the world on air quality in the city of Huelva is evidenced by the high daily and hourly levels of As, other potentially toxic elements (e.g. Cu, Zn, Cd, Se, Bi, and Pb) in particulate matter, as well as the high levels of some gaseous pollutants (NO(2) and SO(2)). Mean arsenic levels in the PM10 fraction were higher than the target value set by European Directive 2004/107/EC (6 ngAs m(-3)) for 1(st) January 2013. Hourly peak concentrations of As and other metals and elements (Zn, Cu, P and Se) analyzed by PIXE can reach maximum hourly levels as high as 326 ngAs m(-3), 506 ngZn m(-3), 345 ngCu m(-3), 778 ngP m(-3) and 12 ngSe m(-3). The contribution of Cu-smelter emissions to ambient PM is quantified on an annual basis in 2.0-6.7 μg m(-3) and 1.8-4.2 μg m(-3) for PM(10) and PM(2.5), respectively. High resolution outputs of the HYSPLIT dispersion model show the geographical distribution of the As ambient levels into the emission plume, suggesting that the working regime of the Cu-smelter factory and the sea breeze circulation are the main factors controlling the impact of the Cu-smelter on the air quality of the city. The results of this work improve our understanding of the behaviour of industrial emission plumes and their impact on air quality of a city, where the population might be exposed to very high ambient concentrations of toxic metals during a few hours.  相似文献   

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