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1.
COVID-19 pandemic has forced to lockdown entire India starting from 24th March 2020 to 14th April 2020 (first phase), extended up to 3rd May 2020 (second phase), and further extended up to 17th May 2020 (third phase) with limited relaxation in non-hotspot areas. This strict lockdown has severely curtailed human activity across India. Here, aerosol concentrations of particular matters (PM) i.e., PM10, PM2.5, carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), ammonia (NH3) and ozone (O3), and associated temperature fluctuation in four megacities (Delhi, Mumbai, Kolkata, and Chennai) from different regions of India were investigated. In this pandemic period, air temperature of Delhi, Kolkata, Mumbai and Chennai has decreased about 3 °C, 2.5 °C, 2 °C and 2 °C respectively. Compared to previous years and pre-lockdown period, air pollutants level and aerosol concentration (?41.91%, ?37.13%, ?54.94% and ?46.79% respectively for Delhi, Mumbai, Kolkata and Chennai) in these four megacities has improved drastically during this lockdown period. Emission of PM2.5 has experienced the highest decrease in these megacities, which directly shows the positive impact of restricted vehicular movement. Restricted emissions produce encouraging results in terms of urban air quality and temperature, which may encourage policymakers to consider it in terms of environmental sustainability.  相似文献   

2.
Due to rapid economic growth of the country in the last 25 years, particulate matter (PM) has become a topic of great interest in China. The rapid development of industry has led to an increase in the haze created by pollution, as well as by high levels of urbanization. In 2012, the Chinese National Ambient Air Quality Standard (NAAQS) imposed ‘more strict’ regulation on the PM concentrations, i.e., 35 and 70 μg/m3 for annual PM2.5 and PM10 in average, respectively (Grade-II, GB3095-2012). The Pearson’s correlation coefficient was used to determine the linear relationship of pollution between pollution levels and weather conditions as well as the temporal and spatial variability among neighbouring cities. The goal of this paper was to investigate hourly mass concentration of PM2.5 and PM10 from June 1 to August 31, 2015 collected in the 11 largest cities of Gansu Province. This study has shown that the overall average concentrations of PM2.5 and PM10 in the study area were 26 and 66 μg/m3. In PM2.5 episode days (when concentration was more than 75 μg/m3 for 24 hrs), the average concentrations of PM2.5 was 2–3 times higher as compared to non-episode days. There were no observed clear differences during the weekday/weekend PM and other air pollutants (SO2, NO2, CO and O3) in all the investigated cities.  相似文献   

3.
Based on data from ground-based air quality stations, space–time variations of six principal atmospheric pollutants, such as particulate matter (PM2.5 and PM10) and gas pollutants (SO2, NO2, СО, and O3), obtained from January 1, 2014 to December 31, 2017 in the city of Lanzhou, have been studied. Average total concentrations of PM2.5 and PM10 were 53.2?±?26.91 and 124.54?±?82.33 µg/m3, respectively; however, the results showed that in 75.53% and 84.85% days, concentrations of these pollutants exceeded Chinese National Ambient Air Quality Standard and in 100% days exceeded World Health Organization guidelines standards. Daily mean values of aerosol optical depth and Ångström exponent based on data, received by satellite Moderate Resolution Imaging Spectroradiometer, show a broad range of values for aerosol optical depth (from 0.018 to 1.954) and Ångström exponent (from 0.003 to 1.8). Results of principal components analysis revealed three factor loadings. Thus, Factor 1 has the relevant loadings for PM2.5, PM10, CO, SO2, and NO2 (36%) and closely associated with transport emissions and industrial sources, which contribute to air pollution in Lanzhou. Factor 2 was heavily loaded with temperature and visibility (16.94%). Factor 3 consisted of relative humidity (14.11%). Cluster analysis revealed four subgroups: cluster 1 (PM2.5, NO2, SO2), cluster 2 (CO), cluster 3 (PM10) and cluster 4 (relative humidity, visibility, temperature, O3, wind speed), which were compliant with results, obtained from principal components analysis. Positive correlation was found among all pollutants, other than O3. According to processed backward trajectories obtained by Hybrid Single-Particle Lagrangian Integrated Trajectory model, it was found that movement of air masses occur from north, northwest, and west directions—the location of principal natural sources of aerosols.  相似文献   

4.
The objective of this paper is to analyze temporal and seasonal trends of air pollution in Bahrain between 2006 and 2012 by utilizing datasets from five air quality monitoring stations. The non-parametric and robust Theil-Sen approach is employed to study quantitatively temporal variations of particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). The calculated annual concentrations for PM10 and PM2.5 in Bahrain were substantially higher than recommended World Health Organization (WHO) guideline standards. Results showed increasing trends for PM10, PM2.5, and SO2 whereas O3 and its precursor NO2 showed decreasing behavior. The general increase in air pollution trends is in agreement with prediction of air pollution models for Middle East region due to economic growth, industrialization, and urbanization. The significances of long-term trends were examined. Additional to actual (unadjusted) trends, meteorological adjusted (deseasonalized) trends and seasonal trends were quantified. The box-plot analysis visually illustrated monthly variations of key air pollutants. It showed that only PM10 and PM2.5 exhibited seasonal pattern, and their concentrations increased during summer and decreased during winter. The effects of ambient air temperature, relative humidity, wind speed, and rainfall on particulate matter (PM) concentrations were further investigated. The Spearman correlation coefficient results demonstrated significant negative correlation between relative humidity and PM concentrations (??0.595 for PM10 and ??0.526 for PM2.5) while significant positive correlation was observed between temperature and PM concentrations (0.420 for PM10 and 0.482 for PM2.5).  相似文献   

5.
周莉  石贵勇  付宇  关瑶  陈来国 《岩矿测试》2016,35(3):302-309
PM2.5是近年来影响我国城市大气环境的首要污染物,其成因机制复杂。本文采用扫描电镜和ICP-MS研究了广州市大气颗粒物PM2.5的显微形貌及其化学组成特征,并应用富集因子法进行源解析。结果表明,PM2.5的颗粒形态以无定形态为主;主要物质表现为含Fe、Mg、Al、K、Na的硅酸盐组合,具有道路扬尘、建筑施工排放等一次粒子特征;单个无定形颗粒物能谱表现出硫酸盐+硝酸盐的组合特征,为汽车尾气所排放的前体污染气体NOx和SO2进入大气环境中,在特定的物理化学条件下通过成核作用发生相态改变所形成的二次粒子。PM2.5中高度富集Cd、Se、Zn、Cu、Pb、As等重金属,异常富集的Br主要为当地普遍使用的阻燃剂十溴联苯醚和拆解电子垃圾所致,稀土元素的浓度在0.022~0.582 ng/m3之间,具有重稀土元素富集的特征。这些特征反映出广州市PM2.5颗粒物的组成既有一次粒子,也有二次粒子,物质来源具有多重性。  相似文献   

6.
分析了2014~2018年北方5个典型中小盆地城市(兰州、银川、临汾、太原、南阳)PM10与PM2.5的浓度变化特征和大致来源类型。除2018年银川PM2.5浓度外,各市PM10和PM2.5年均浓度均超标;兰州、银川和南阳PM10与PM2.5呈逐年下降趋势,南阳下降最明显;临汾PM10与PM2.5呈逐年上升趋势;太原PM10与PM2.5稳定维持在一个高浓度状态。5个城市颗粒物浓度的季节变化特征一致:冬春高、夏秋低。对PM2.5/PM10值而言,冬季和夏季该比值较高,分别受取暖和降水的影响;春季和秋季该比值较低,分别受沙尘和秸秆焚烧及高强度建筑施工的影响。5市PM2.5和PM10浓度具有良好的线性关系,细颗粒占比大小顺序为临汾>南阳>太原>银川>兰州。  相似文献   

7.
Size distribution of PM10 mass aerosols and its ionic characteristics were studied for 2 years from January 2006 to December 2007 at central Delhi by employing an 8-stage Andersen Cascade Impactor sampler. The mass of fine (PM2.5) and coarse (PM10?2.5) mode particles were integrated from particle mass determined in different stages. Average concentrations of mass PM10 and PM2.5 were observed to be 306 ± 182 and 136 ± 84 μg m?3, respectively, which are far in excess of annual averages stipulated by the Indian National Ambient Air Quality Standards (PM10: 60 μg m?3 and PM2.5: 40 μg m?3). The highest concentrations of PM10?2.5 (coarse) and PM2.5 (fine) were observed 505 ± 44 and 368 ± 61 μg m?3, respectively, during summer (June 2006) period, whereas the lower concentrations of PM10?2.5 (35 ± 9 μg m?3) and PM2.5 (29 ± 13 μg m?3) were observed during monsoon (September 2007). In summer, because of frequent dust storms, coarse particles are more dominant than fine particles during study period. However, during winter, the PM2.5 contribution became more pronounced as compared to summer probably due to enhanced emissions from anthropogenic activities, burning of biofuels/biomass and other human activities. A high ratio (0.58) of PM2.5/PM10 was observed during winter and low (0.24) during monsoon. A strong correlation between PM10 and PM2.5 (r 2 = 0.93) was observed, indicating that variation in PM10 mass is governed by the variation in PM2.5. Major cations (NH4 +, Na+, K+, Ca2+ and Mg2+) and anions (F?, Cl?, SO4 2? and NO3 ?) were analyzed along with pH. Average concentrations of SO4 2? and NO3 ? were observed to be 12.93 ± 0.98 and 10.33 ± 1.10 μg m?3, respectively. Significant correlation between SO4 2? and NO3 ? in PM1.0 was observed indicating the major sources of secondary aerosol which may be from thermal power plants located in the southeast and incomplete combustion by vehicular exhaust. A good correlation among secondary species (NH+, NO3 ? and SO4 2?) suggests that most of NH4 + is in the form of ammonium sulfate and ammonium nitrate in the atmosphere. During winter, the concentration of Ca2+ was also higher; it may be due to entrainment of roadside dust particles, traffic activities and low temperature. The molar ratio (1.39) between Cl? and Na+ was observed to be close to that of seawater (1.16). The presence of higher Cl? during winter is due to western disturbances and probably local emission of Cl? due to fabric bleaching activity in a number of export garment factories in the proximity of the sampling site.  相似文献   

8.
Based on long-term PM2.5 data observed at high temporal and spatial resolution, the relationships between PM2.5, primary emission, and weather factors in China during four seasons were examined using statistical analysis. The results reveal that primary emission plays a decisive role in the spatial distribution and seasonal variability of PM2.5, except in western China, where PM2.5 is controlled by dust weather. In addition to the accumulation of primary emissions, unfavorable meteorological conditions for the diffusion of air pollution lead to the occurrence of PM2.5 pollution. The significant dynamic factors affecting PM2.5 concentration are surface wind speed, planet boundary layer height, and ventilation coefficient, especially in winter. The ventilation coefficient is inversely correlated with PM2.5. Better ventilation is more favorable for the dilution and outflow of local PM2.5. However, in spring and autumn, ventilation coefficient and PM2.5 are positively correlated over the southern regions with low emission, indicating that ventilation also affects the inflow of PM2.5 from outside the region. Wind shear, 850 hPa divergence, and vertical velocity have insignificant effects on the long-term variations in PM2.5. The significant thermal factor is 850 hPa temperature in winter, except in the Pearl River Delta and Xinjiang regions. In spring, the influence of each thermal factor is weak. In summer, the influences of temperature and humidity are more significant than in spring. In autumn, the influence of humidity is relatively obvious, compared with other thermal factors. The correlation coefficients between multi-factors regressed and observed PM2.5 concentrations pass the 95% confidence test, and are higher than that of single-factor regression over most regions. The observed data from December 2016 to February 2017 were chosen to test the regression equation. The test result reveals that the regression equation is effective for predicting PM2.5 concentrations over regions with high primary emission.  相似文献   

9.
The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not suitable for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM2.5 as measured by the 27 EPA ground monitoring stations was investigated. These results were also compared to conventional MODIS 10 km AOD retrievals (MOD04) for the same days and locations. The coefficients of determination for MOD04 and for MAIAC are R2 =0.45 and 0.50 respectively, suggested that AOD is a reasonably good proxy for PM2.5 ground concentrations. Finally, we studied the relationship between PM2.5 and AOD at the intra-urban scale (?10 km) in Boston. The fine resolution results indicated spatial variability in particle concentration at a sub-10 kilometer scale. A local analysis for the Boston area showed that the AOD-PM2.5 relationship does not depend on relative humidity and air temperatures below ～7 °C. The correlation improves for temperatures above 7–16 °C. We found no dependence on the boundary layer height except when the former was in the range 250–500 m. Finally, we apply a mixed effects model approach to MAIAC aerosol optical depth (AOD) retrievals from MODIS to predict PM2.5 concentrations within the greater Boston area. With this approach we can control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance. Our results show that the model-predicted PM2.5 mass concentrations are highly correlated with the actual observations (out-of-sample R2 of 0.86). Therefore, adjustment for the daily variability in the AOD-PM2.5 relationship provides a means for obtaining spatially-resolved PM2.5 concentrations.  相似文献   

10.
People living in the urban area and the surrounding suburban area have disparities in exposure and health risks due to different levels of ambient air pollutants. The main objective of this study is to investigate the concentrations, seasonal variations, and related health risks of ambient air pollutants (PM10, NO2, and SO2) in urban and suburban areas of Ningbo, China. The results showed that the average PM10, NO2, and SO2 concentrations in the urban area were 85.2, 49.3, and 37.4 μg/m3, which were 1.13, 1.25, and 1.41 times the values of the suburban area during the period of March 2009 to February 2010. For the potential health risk analysis, the residents have been divided into four age categories namely, infants, children (1 year), children (8–10 years), and adults. The analysis took into account age-specific breathing rates, body weights for different age categories. The results showed that the potential health risks to respiratory disease for all age categories living in urban area were higher than those in suburban area.  相似文献   

11.
In this study, several multivariate methods were used for forecasting hourly PM10 concentrations at four locations based on SO2 and meteorological data from the previous period. According to the results, boosted decision trees and multi-layer perceptrons yielded the best predictions. The forecasting performances were similar for all examined locations, despite the additional PM10 spatio-temporal analysis showed that the sites were affected by different emission sources, topographic and microclimatic conditions. The best prediction of PM10 concentrations was obtained for industrial sites, probably due to the simplicity and regularity of dominant pollutant emissions on a daily basis. Conversely, somewhat weaker forecast accuracy was achieved at urban canyon avenue, which can be attributed to the specific urban morphology and most diverse emission sources. In conclusion to this, the integration of advanced multivariate methods in air quality forecasting systems could enhance accuracy and provide the basis for efficient decision-making in environmental regulatory management.  相似文献   

12.
The objective of the study is to investigate spatio-temporal variations of PM10, PM2.5, and PM1 concentrations at seven residential sites, located in the vicinity of opencast coal projects, Basundhara Garjanbahal Area (BGA), India. Meteorological parameters such as wind speed, wind direction, relative humidity, and temperature were collected simultaneously with PM concentrations. Mean concentrations of PM10 in the range 215 ± 169–526 ± 412 μg m?3, PM2.5 in the range of 91 ± 79–297 ± 107 μg m?3, PM1 in the range of 68 ± 60–247 ± 84 μg m?3 were obtained. Coarse fractions (PM2.5–10) varied from 27 to 58% whereas fine fractions (PM1–2.5 and PM1) varied in the range of 51–73%. PM2.5 concentration was 41–74% of PM10 concentration, PM1 concentration was 31–62% of PM10 concentration, and PM1 concentration was 73–83% of PM2.5 concentration. Role of meteorology on PM concentrations was assessed using correlation analysis. Linear relationships were established among PM concentrations using least square regression analysis. With the aid of principal component analysis, two components were drawn out of eight variables, which represent more than 75% of variance. The results indicated that major sources of air pollutants (PM10, PM2.5, PM1, CO, CO2) at the residential sites are road dust raised by vehicular movement, spillage of coal generated during transportation, spontaneous combustion of coal, and biomass burning in village area.  相似文献   

13.
The 19th Common Wealth Games was organized at Delhi, India, during October 3 to 14, 2010, where more than 8,000 athletes from 71 Commonwealth Nations have participated. In order to give them better environment information for proper preparedness, mass concentrations of particulate matters below 10 microns (PM10) and 2.5 microns (PM2.5), black carbon (BC) particles and gaseous pollutants such as carbon monoxide (CO) and nitrogen oxide (NO) were monitored and displayed online for ten different locations around Delhi, including inside and outside the stadiums. This extensive information system for air quality has been set up for the period from September 24 to October 21, 2010, and data have been archived at 5-min interval for further research. During the study period, average concentration of PM10 and PM2.5 was observed to be 229.7 ± 85.5 and 112.1 ± 56.0 μg m?3, respectively, which is far in excess of the corresponding annual averages, stipulated by the national ambient air quality standards. Significant large and positive correlation (r = 0.93) between PM10 and PM2.5 implies that variations in PM10 mass are governed by the variations in PM2.5 mass. The mass concentrations of PM2.5 inside the stadium were found to be ~18 % lower than those outside; however, no large variations were observed in PM10. Mean concentrations of BC, CO and NO for the observation period were 10.9 μg m?3 (Min, 02 μg m?3; Max, 31 μg m?3), 1.83 ± 0.89 ppm (Min, 0.48 ppm; Max, 4.55 ppm) and 37.82 ppb (Min, 2.4 ppb; Max, 206.05 ppb), respectively. BC showed positive correlation (r = 0.73) with CO suggests unified source for both of them, mainly from combustion emissions. All the measured parameters, however, show a significant diurnal variation with enhanced peaks in the morning and late night hours and lower values during daytime.  相似文献   

14.
The rapid urbanization, industrialization, modernization, and the frequent Middle Eastern dust storms have negatively impacted the ambient air quality in Bahrain. The objective of this study is to identify the most critical atmospheric air pollutants with emphasis on their potential risk to health based on calculated AQI (air quality index) values using EPA approach. The air quality datasets of particulate matters (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) were measured in January 2012 and August 2012 using five mobile air quality monitoring stations located at different governorates. The results of this study demonstrated that PM10 and PM2.5 are the most critical air pollutants in Bahrain with PM2.5 prevailing during January 2012 and PM10 prevailing during August 2012. The corresponding AQI categories were utilized to evaluate spatial variability of particulate matters in five governorates. The impact of meteorological factors such as ambient air temperature, wind speed, relative humidity, and total precipitation on ambient air quality were discussed. The analysis demonstrated that the highest PM10 concentrations were observed in the Northern Governorate while the highest PM2.5 concentrations were observed in the Capital, Central, and Northern Governorates during August 2012. It was observed that the levels of PM2.5 pollution were higher within proximity of the industrial zone. The results suggested that the average PM2.5/PM10 ratio in August 2012 was lower than in January 2012 due to the Aeolian processes. This study concludes that higher wind speed, total precipitation, relative humidity rates, and lower ambient air temperature in January 2012 assisted with the dissipation of particulate matter thus lowering the pollution levels of both PM10 and PM2.5 in comparison to August 2012.  相似文献   

15.
Due to its negative impact on the living environment of human beings, ambient air pollution has become a global challenge to human health. In this study, surface observations of six criteria air pollutants, including PM2.5, PM10, SO2, NO2, CO and O3, were collected to investigate the spatial and temporal variation in the Beijing–Tianjin–Hebei (BTH) region during 2013–2016 and to explore the relationships between atmospheric pollutants and meteorological variables using quantile regression model (QRM) and multiple linear regression model (MLRM). The results show that BTH region has experienced significant air pollution, and the southern part generally has more severe conditions. The annual average indicates clear decreasing trends of the particulate matters, SO2 and CO concentrations over the last 4 years and slight increasing trends of NO2 and O3 in several cities. The seasonal and monthly characteristics indicate that the concentrations of five species reach their maxima in the winter and their minima in the summer, whereas O3 has the opposite behaviour. Finally, the pseudo R2 values show that the QRMs have the best performance in the winter, followed by spring, fall, and summer. Specifically, all the meteorological factors have significant impacts on air pollution but change with pollutants and seasons. The MLRM results are generally consistent with the QRM results in all seasons, and the inconsistencies are more common in the fall and winter. The results of this research provide foundational knowledge for predicting the response of air quality to climate change in the BTH region.  相似文献   

16.
The increasing emission of primary and gaseous precursors of secondarily formed atmospheric particulate matter due to continuing industrial development and urbanization are leading to an increased public awareness of environmental issues and human health risks in China. As part of a pilot study, 12-h integrated fine fraction particulate matter (PM2.5) filter samples were collected to chemically characterize and investigate the sources of ambient particulate matter in Guiyang City, Guizhou Province, southwestern China. Results showed that the 12-h integrated PM2.5 concentrations exhibited a daytime average of 51 ± 22 µg m?3 (mean ± standard deviation) with a range of 17–128 µg m?3 and a nighttime average of 55 ± 32 µg m?3 with a range of 4–186 µg m?3. The 24-h integrated PM2.5 concentrations varied from 15 to 157 µg m?3, with a mean value of 53 ± 25 µg m?3, which exceeded the 24-h PM2.5 standard of 35 µg m?3 set by USEPA, but was below the standard of 75 µg m?3, set by China Ministry of Environmental Protection. Energy-dispersive X-ray fluorescence spectrometry (XRF) was applied to determine PM2.5 chemical element concentrations. The order of concentrations of heavy metals in PM2.5 were iron (Fe) > zinc (Zn) > manganese (Mn) > lead (Pb) > arsenic (As) > chromium (Cr). The total concentration of 18 chemical elements was 13 ± 2 µg m?3, accounting for 25% in PM2.5, which is comparable to other major cities in China, but much higher than cities outside of China.  相似文献   

17.
Mass concentrations of PM10, PM2.5, and black smoke (BS) were measured in April 2003 during a 3-week campaign in a small village and at a nearby background location in the central part of the Czech Republic. In a pilot analysis, concentrations of selected trace elements (Al, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Sr, Cd, Sb, Cs, Pb) in the collected aerosol were determined by means of ICP-MS. Average concentrations of both PM fractions and BS were higher in the village (37, 26 and 26 μg m−3) than at the background location (26, 19 and 11 μg m−3) for PM10, PM2.5 and BS, respectively. Both PM10 and PM2.5 were reasonably correlated in the village (r = 0.80) and also at the background location (r = 0.79). Correlation between same fractions from the village and from the background site were even higher (r = 0.97 and r = 0.95 for the PM10 and PM2.5, respectively) suggesting that most of the aerosol in both locations may be influenced by similar sources. The ratio between PM10 and PM2.5 showed that sources in the village contributed about 33% and 35% to local aerosol concentration for PM10 and PM2.5, respectively. When the data from the two rural locations were compared with corresponding 24-h averages of PM10 concentrations obtained for the period of the campaign from fixed site monitors situated near larger towns, the highest concentration was found in Prague the Czech capital (49 μg m−3) followed by a district town Beroun (41 μg m−3) and the village (37 μg m−3). The lowest PM10 concentration was found in the village background (26 μg m−3). Elemental analysis revealed higher concentrations for most of the elements characteristic of combustion aerosol (namely Zn, Pb, As, Mn and Ti) in the PM collected in the village. The results support the idea that traditional heating in villages may contribute a great extent to local air pollution and may represent an important problem.  相似文献   

18.
This paper presents results of an atmospheric particulate matter (PM) monitoring and source apportionment study conducted during summer and fall 2010 in Cairo. These results are compared to those of similar studies in 1999 and 2002. Concentrations of PM2.5 and PM10 mass and their chemical constituents were determined and chemical mass balance modeling was conducted to estimate the source contributions to ambient PM. Emphasis was placed on characterizing the long-term trends in atmospheric lead (Pb) concentrations and their sources in Cairo. PM2.5 and PM10 concentrations were highest during fall 1999 at four of the five study sites. This was also the case for open (vegetative/trash) burning contributions, which showed a smaller increase during fall 2010. Burning of agricultural waste after the fall harvest continues to be a major source of PM in Cairo. Both PM2.5 and PM10 mass decreased dramatically at Shobra, an industrial site, from 1999 to 2010. A reduction of lead smelting has resulted in a decrease of ambient Pb concentrations of up to two orders of magnitude from 1999 to 2010 at Shobra, El-Zamalek, and El-Qualaly. From 1999 to 2010, the mobile source contribution has been relatively stable at most of the study sites. Future efforts to reduce ambient PM should focus on controlling emissions from motor vehicles and open burning and implementing mitigation strategies for reducing resuspended road and construction dust.  相似文献   

19.
In this study, AERMOD dispersion model has been applied for predicting the values of ambient concentrations of NO2 emissions due to the stacks of fourth gas refinery located in South Pars Gas Complex in Asaluyeh, Iran. First, the values of NO2 emissions from the stacks and the amounts of ambient concentrations of NO2 in nine monitoring stations have been measured in four seasons in 2013. Then, dispersion of NO2 emissions has been predicted by using AERMOD model in the region with the domain area of 10 × 10 km2, in average times of 1 h. Finally, the simulated and observed values of ambient NO2 concentrations in the nine receptors have been compared. Comparison of 1-h concentrations of the observed and predicted results with the international ambient standard levels shows that NO2 concentrations are higher than the standard value. The results show that AERMOD model can be used effectively for predicting the amounts of pollutants’ concentrations in the study area.  相似文献   

20.
Ambient air and coarse, fine and particulate-bound mercury (Hg(p)) pollutants were collected and analyzed from March 17 to May 22 and September 3, 2009 to March 5, 2010 at a highway traffic site located in Sha-Lu, central Taiwan. This study has the following objectives: (1) to measure the coarse and fine particulates concentrations and the particulate-bound mercury Hg(p) which was attached to these particulate; (2) to determine the average Hg(p) compositions in coarse and fine particulates and (3) to compare the Hg(p) concentrations and compositions particulate in this study to the those obtained in other studies. The results obtained in this study indicated that the average ambient air PM2.5, PM2.5–10 and PM10 were 18.79 ± 6.71, 11.22 ± 4.93 and 30.01 ± 10.27 μg/m3, respectively. The ranges of concentrations for Hg(p) in PM2.5 were from 0.0016 to 0.0557 ng/m3, from 0.0006 to 0.0364 ng/m3 in PM2.5–10 and from 0.0022 to 0.0862 ng/m3 in PM10. In addition, the highest particle-bound mercury compositions in PM2.5 were 16.85 ng/g and the lowest particle-bound mercury concentrations were 0.55 ng/g. The highest particle-bound mercury compositions in PM2.5–10 were 13.88 ng/g and the lowest particle-bound mercury in PM2.5–10 were 0.22 ng/g.  相似文献   

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