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1.
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   

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.
Air pollution monitoring networks are the primary tools for measuring, managing and assessing air quality. However, these networks need considerable financial resources due to expensive devices and analyses, as well as such issues as the likely redundancy in the number of samples. The primary objective of this study was to identify possible information and equipment redundancies in Turkish monitoring networks. Thus, it is expected that the results of this study may help reduce air pollution monitoring expenses and increase monitoring efficiency. For this purpose, the Fuzzy C-Means clustering algorithm and time series analyses were used. This study has two novelties. First, this is the first study to be conducted for this purpose in Turkey. Further, Dickey–Fuller test statistics and model parameters have not been used as clustering variables before. Thus, it is expected that both stochastic behavior and concentration levels of PM10 time series will be reflected simultaneously, and similarities among monitoring stations will be better identified.  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.
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   

6.
This paper presents the analysis and interpretation of ambient particulate matter concentrations measured as PM10 at a network of six air monitoring stations in Kathmandu valley during the years, 2003 through 2005. The purpose was to understand the pollution trends associated with different areas considering levels particulate matter concentrations representing the ambient air quality of Kathmandu valley. The study indicate that particulate concentrations (PM10) measured are persistently higher at air sampling sites representing roadside areas compared to the background sites. The inter-station network variability with respect the particulate pollution suggests optimizing resources. The comparison of annual average PM10 concentration observed at six air-monitoring sites in Kathmandu Valley with standard annual average concentration prescribed by World Health Organization as well as Europe Union indicates serious PM10 pollution in Kathmandu valley.  相似文献   

7.
As most air quality monitoring sites are in urban areas worldwide, machine learning models may produce substantial estimation bias in rural areas when deriving spatiotemporal distributions of air pollutants. The bias stems from the issue of dataset shift, as the density distributions of predictor variables differ greatly between urban and rural areas. We propose a data-augmentation approach based on the multiple imputation by chained equations (MICE-DA) to remedy the dataset shift problem. Compared with the benchmark models, MICE-DA exhibits superior predictive performance in deriving the spatiotemporal distributions of hourly PM2.5 in the megacity (Chengdu) at the foot of the Tibetan Plateau, especially for correcting the estimation bias, with the mean bias decreasing from –3.4 µg/m3 to –1.6 µg/m3. As a complement to the holdout validation, the semi-variance results show that MICE-DA decently preserves the spatial autocorrelation pattern of PM2.5 over the study area. The essence of MICE-DA is strengthening the correlation between PM2.5 and aerosol optical depth (AOD) during the data augmentation. Consequently, the importance of AOD is largely enhanced for predicting PM2.5, and the summed relative importance value of the two satellite-retrieved AOD variables increases from 5.5% to 18.4%. This study resolved the puzzle that AOD exhibited relatively lower importance in local or regional studies. The results of this study can advance the utilization of satellite remote sensing in modeling air quality while drawing more attention to the common dataset shift problem in data-driven environmental research.  相似文献   

8.
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   

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.
The respirable particle matter (PM10) concentration in urban areas has been a chronic cause concern and principal reason for increased morbidity rate among resident population. The present study aimed at estimating a discrete event like mortality rate associated and attributable to excess particulate matter pollution in the Kathmandu Valley area. The Government of Nepal conducts air monitoring of particulates at its air monitoring site network covering valley area. Adopting the data available with respect to PM10 and with several other considerations like cutoff value for PM10, mean annual concentration, demographic data of valley, exceedance to the reference cutoff value, attributable fraction evolution and computation relative risk attributable to PM10 was computed. Assumption was made about the relative risk of long-term average PM10 exposure on natural mortality estimated and reported from a previous study. The estimation or mortality rate in our case was 0.95% after all these considerations and computation. This implies that 95 deaths out of 10,000 deaths are due to particulate pollution existing in the Kathmandu Valley Area.  相似文献   

11.
Air particulate matter (PM) samples were collected from June 2006 to May 2007 for determination of chemical elements. PM samples were taken in two size fractions (PM2.5 and PM10) with MiniVolume air samplers on rooftops of various buildings (15–25 m above ground) in the city of Riyadh. The samples were subjected to X-ray fluorescence analysis to measure major (Na, Mg, Al, K, Ca, Si, P, S, and Fe) and trace elements (Mn, Ni, Cu, Zn, and Ba). The results showed that the PM concentrations were higher for PM10 compared to PM2.5, indicating that the major PM source was local dust. Also the spatial distribution with high PM concentrations was observed in the south and southeast of the city and the lowest levels were in the center and northeast of the city. This spatial distribution was attributed to different factors such as wind direction and velocity, emission from cement factories, and the presence of buildings, trees, and paved streets that reduce the amount of dust resuspended into the atmosphere. The air quality of the city was found to range from good to hazardous based on PM2.5, and from good to very hazardous based on PM10. The element-enrichment factors revealed two element groups according to their changing spatial behavior. The first group showed no significant spatial changes indicating they have the same common source. The second group (mainly S and Ni) exhibited significant changes as expected from anthropogenic inputs. The origin of S is possibly a combination of minerals (CaSO4) and fossil fuel combustion. The source of Ni is probably from fossil fuel combustion.  相似文献   

12.
Fine particulate matter (PM2.5) and volatile organic compounds (VOCs) coexist in ambient air and contribute to adverse health effects in human populations. Thus, it is helpful to identify the contributions of air pollutants from different sources in order to design effective control strategies. Nevertheless, different sampling time schedules for VOCs and PM2.5 result in difficulties for conventional receptor modeling. Additionally, a receptor model is unable to link the retrieved factors directly with actual source locations. To address these gaps, this study integrated back-trajectory data into an improved source apportionment model suitable for multiple time resolution data to estimate the locations of the regional transport-related factor. Within six potential source regions (PSRs) outlined by the above method, PSR 5 was suggested the primary one located near the industrial regions in the northeastern China. Constrained model results showed that the source contribution estimates with back trajectories passing over the PSRs were 3 and 9% of the selected VOCs and PM2.5 mass, respectively.  相似文献   

13.
苏志华  韩会庆  陈波 《中国岩溶》2020,39(3):442-452
选取贵阳市10个空气质量监测站发布的PM2.5、PM10、SO2、NO2、CO和O3实时浓度数据，通过时间序列分析法和插值法研究贵阳市大气污染物的时空变化和复合污染特征。结果表明:（1）贵阳市2014-2018年主要污染物PM2.5和PM10的年平均浓度逐渐下降，光化学污染物O3年平均浓度有所增加，空气质量逐渐转好，环境治理取得明显效果；（2）2018-2019自然年PM2.5、PM10、NO2和O3在春季污染最严重，SO2和CO在冬季污染最严重，反映出污染源、阶段性燃料燃烧和二次离子生成等因素对不同污染物的影响不同；（3）PM2.5和PM10日变化特征为“午峰晚峰”型，峰值发生的时间因季节而异，主要由不同季节人类作息的起止时间不同所致，O3日变化为单峰型，夜间O3浓度较低，从早晨8:00点开始随着太阳辐射的增大和温度的升高，在15:00-16:00点左右达到峰值；（4）PM2.5的空间分布呈现出部分郊区和工业区较高，市中心居民区较低的特征，指示城市建设向郊区推进。O3浓度呈现出市区低、郊区高的空间分布特征，反映郊区植被覆盖好，释放的天然源VOCs促进了O3生成；（5）主要污染物O3与颗粒物PM2.5和PM10在春季造成的复合污染最为严重，在夏季O3与PM10造成一定程度的复合污染，在秋冬季O3浓度最低，O3与颗粒物不产生复合污染；一天之内同一时刻O3与颗粒物不会产生叠加从而造成复合污染。   相似文献   

14.
Santiago, the capital of Chile, suffers from high air pollution levels, especially during winter. An extensive particulate matter (PM) monitoring and analysis program was conducted to quantify elemental concentrations of PM. Size-resolved PM samples (PM2.5 and PM10–2.5) from the La Paz and Las Condes stations in Santiago (2004–2005) were analyzed using ICP-MS. Most trace element concentrations (Cu, Pb, Zn, Mn, V, Sb, Pb and As) were higher during winter than during summer and were also higher at the La Paz station than at the Las Condes station. During the highest pollution events, As concentrations in PM2.5 (16 ng m?3) exceeded the annual average standard value (6 ng m?3). A 10-year time series showed decreasing Pb and As concentrations and slightly increasing Zn, Cu and Mn concentrations. Concentrations of Cr and Ni remained relatively constant. The implementation of new public policies in 1998 may explain the decreasing concentrations of Pb and As. Enrichment factor (EF) calculations identified two principal groups: elements with EF < 10 (Mg, Y, Zr, U Sr, Ca, Ti, and V) and EF > 10 (Rb, K, Cs, Fe, P, Ba, Mn, Ni, Cr, Co, Zn, Sn, Pb, Cu, Mo, Cd, As, Ag, and Sb), which were related to natural and anthropogenic PM sources, respectively. Three main PM sources were identified using factor analysis: a natural source (crustal matter and marine aerosol), combustion and copper smelting. Three other sources were identified using rare earth elements: fluid catalytic crackers, oil-fired power production and catalytic converters.  相似文献   

15.
Yadav  Ganesh  Singh  R. B.  Anand  Subhash  Pandey  B. W.  Mohanty  Ashutosh  Dash  Sushree Sangita 《GeoJournal》2021,87(4):469-483

Ambient air pollution, particularly in the urban environment of developing countries, has turned out to be a major health risk factor. We explore the compounded impact of age sensitivity, exposure, poverty, co-morbidity, etc., along with composite air pollution in determining morbidity and health burden of people in Lucknow, India. This cross-sectional study is confined to analyse respiratory health status across different socio-economic and geographic locations using n = 140 in-depth questionnaire method. We used mean daily ambient air pollution data of PM10, PM2.5, SO2, and NO2 for the 2008–2018 period. We used the ecological model framework to assess the risk at different hierarchical levels and compounded severity on a spatial scale. We also used Logistic regression model with log odds and odds ratio to analyze the association of risks outcomes with composite air pollution scores calculated using the principal component analysis method. There is a strong association of location-specific respiratory disease prevalence with an overall 32 percent prevalence. The prevalence of ecological model 1 (individual domain) is 4.3 percent, while ecological model 2 (community domain) has the highest prevalence at 32.4 percent. The logistic regression model shows that respiratory disease load is positively associated with age sensitivity (P < .001) and composite pollution level (P < .001). For another model with suffocation as the outcome variable, composite pollution level (P < .001) and exposure (P < .001) are positively associated. Optimum interventions are required at Ecological models 1, 2, and 3 levels for better respiratory health outcomes.

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16.
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   

17.
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   

18.
The rapid growth of Riyadh—capital of Saudi Arabia—is pushing the area to more pollution and incentive for reorganization. The aim of this research is to assess air pollutants in southeast of Riyadh and detect opinion of the population about their environment. The assessment was done by analyzing 405 questionnaires, evaluating thermal band of Landsat 8, and spatial analyzing of particular matter and chemicals in 19 air samples by geostatistical tool in the ArcMap. Most of the inhabitants stated that they are suffering from bad odor, sewage leakage, and dust mainly from a cement factory. The thermal band of Landsat clarified the location of the pollution sources mainly the 1st industrial city, Yammama Cement Factory, and power plant in Farouq area. The ordinary kriging maps showed that the highest concentration of PM10 (>403 μg/m3) lied to the northern and western side of the study area and caused a health issue to most inhabitants.  相似文献   

19.
Compliance with U.S. air quality regulatory standards for atmospheric fine particulate matter (PM2.5) is based on meeting average 24 hour (35 μ m?3) and yearly (15 μg m?3) mass‐per‐unit‐volume limits, regardless of PM2.5 composition. Whereas this presents a workable regulatory framework, information on particle composition is needed to assess the fate and transport of PM2.5 and determine potential environmental/human health impacts. To address these important non‐regulatory issues an integrated approach is generally used that includes (1) field sampling of atmospheric particulate matter on filter media, using a size‐limiting cyclone, or with no particle‐size limitation; and (2) chemical extraction of exposed filters and analysis of separate particulate‐bound fractions for total mercury, trace elements and organic constituents, utilising different USGS laboratories optimised for quantitative analysis of these substances. This combination of sampling and analysis allowed for a more detailed interpretation of PM2.5 sources and potential effects, compared to measurements of PM2.5 abundance alone. Results obtained using this combined approach are presented for a 2006 air sampling campaign in Shenandoah National Park (Virginia, USA) to assess sources of atmospheric contaminants and their potential impact on air quality in the Park. PM2.5 was collected at two sampling sites (Big Meadows and Pinnacles) separated by 13.6 km. At both sites, element concentrations in PM25 were low, consistent with remote or rural locations. However, element/Zr crustal abundance enrichment factors greater than 10, indicating anthropogenic input, were found for Hg, Se, S, Sb, Cd, Pb, Mo, Zn and Cu, listed in decreasing order of enrichment. Principal component analysis showed that four element associations accounted for 84% of the PM2.5 trace element variation; these associations are interpreted to represent: (1) crustal sources (Al, REE); (2) coal combustion (Se, Sb), (3) metal production and/or mobile sources (Mo, Cd, Pb, Cu, Zn) and (4) a transient marine source (Sr, Mg). Concentrations of Hg in PM2.5 at background levels in the single pg m?3 were shown by collection and analysis of PM2.5 on filters and by an automated speciation analyser set up at the Big Meadows air quality site. The speciation unit revealed periodic elevation of reactive gaseous mercury (RGM) that co‐occurred with peaks in SO2, indicating an anthropogenic source. GC/MS total ion current chromatograms for the two sites were quite similar indicating that organic signatures were regional in extent and/or that the same compounds were present locally at each site. Calculated carbon preference index values for n‐alkanes indicated that plant waxes rather than anthropogenic sources, were the dominant alkane source. Polycyclic aromatic hydrocarbons (PAHs) were detected, with a predominance of non‐alkylated, and higher molecular weight PAHs in this fraction, suggestive of a combustion source (fossil fuel or forest fires).  相似文献   

20.
China has currently entered a critical stage of coordinated control of fine particulate matter (PM2.5) and ozone (O3), it is thus of tremendous value to accurately acquire high-resolution PM2.5 and O3 data. In contrast to traditional studies that usually separately estimate PM2.5 and O3, this study proposes a knowledge-informed neural network model for their joint estimation, in which satellite observations, reanalysis data, and ground station measurements are used. The neural network architecture is designed with the shared and specific inputs, the PM2.5-O3 interaction module, and the weighted loss function, which introduce the prior knowledge of PM2.5 and O3 into neural network modeling. Cross-validation (CV) results indicate that the inclusion of prior knowledge can improve the estimation accuracy, with R2 increasing from 0.872 to 0.911 and from 0.906 to 0.937 for PM2.5 and O3 estimation under sample-based CV, respectively. In addition, the proposed joint estimation model achieves comparable performance with the separate estimation model, but with higher efficiency. Mapping results of PM2.5 and O3 derived by the proposed model have demonstrated interesting findings in the spatial and temporal trends and variations over China.  相似文献   

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