Long-term monitoring and source apportionment of PM2.5/PM10 in Beijing, China

Comprehensive analysis of PM10 in Belgrade urban area on the basis of long-term measurements

In this study, we investigated the impact of potential emission sources and transport pathways on annual and seasonal PM10 loadings in an urban area of Belgrade (Serbia). The analyzed dataset comprised PM10 mass concentrations for the period 2003-2015, as well as their chemical composition (organic/elemental carbon, benzo[a]pyrene, As, Cd, Cr, Mn, Ni, Pb, Cl(-), Na(+), Mg(2+), Ca(2+), K(+), NO3 (-), SO4 (2-), and NH4 (+)), meteorological parameters, and concentrations of inorganic gaseous pollutants and soot for the period 2011-2015. The combination of different methods, such as source apportionment (Unmix), ensemble learning method (random forest), and multifractal and inverse multifractal analysis, was utilized in order to obtain a detailed description of the PM10 origin and spatio-temporal distribution and to determine their relationship with other pollutants and meteorological parameters. The contribution of long-range and regional transport was estimated by means of trajectory sector analysis, whereas the hybrid receptor models were applied to identify potential areas of concern.

Inter-annual variability of wintertime PM2.5 chemical composition in Xi'an, China: Evidences of changing source emissions

Chemical characteristics of PM2.5 in Xi'an in wintertime of 2006, 2008, and 2010 were investigated. Markers of OC2, EC1, and NO3(-)/SO4(2-) ratio were calculated to investigate the changes in PM2.5 emission sources over the 5-year period. Positive matrix factorization (PMF) model was used to identify and quantify the main sources of PM2.5 and their contributions. The results showed that coal combustion, motor vehicular emissions, fugitive dust, and secondary inorganic aerosol accounted for more than 80% of PM2.5 mass. The importance of these major sources to the PM2.5 mass varied yearly: coal combustion was the largest contributor (31.2% ± 5.2%), followed by secondary inorganic aerosol (20.9% ± 5.2%) and motor vehicular emissions (19.3% ± 4.8%) in 2006; the order was still coal combustion emissions (27.6% ± 3.4%), secondary inorganic aerosol (23.2% ± 6.9%), and motor vehicular emissions (20.9% ± 4.6%) in 2008; while coal combustion emission further decreased (24.1% ± 3.1%) with fugitive dust (19.4% ± 5.5%) increasing in 2010. The changes in PM2.5 chemical compositions and source contributions can be attributed to the social and economic developments in Xi'an, China, including energy structure adjustment, energy consumption, the expansion of civil vehicles, and the increase of urban construction activities.

Concentrations, correlations and chemical species of PM2.5/PM10 based on published data in China: Potential implications for the revised particulate standard

Particulate matter (PM) has been of great concern in China due to the increasing haze pollution in recent years. In 2012, the Chinese national ambient air quality standard (NAAQS) was amended with a "more strict" regulation on the PM concentrations, i.e., 35 and 70 µg/m(3) for annual PM2.5 and PM10 averages, respectively (Grade-Ⅱ, GB3095-2012). To evaluate the potential of China to attain such new NAAQS and provide a more generalized chemical profile of PM in China, a comprehensive statistical analysis was carried out based on the published data of parallel PM2.5 and PM10 mass concentrations and chemical compositions of PM2.5 and PM10. The results show that most of the measured concentrations far exceed the new NAAQS. PM2.5 and PM10 show a strong positive correlation (R(2) = 0.87, p < 0.01) with PM2.5 accounting for about 65% of PM10, suggesting that the abatement of PM2.5 is crucial for reducing PM pollution and hence improving air quality in China. Organic carbon (OC), sulfate and crustal species are the three major components of PM. The NO3(-)/SO4(2-) ratios are 0.43 ± 0.26 in PM2.5 and 0.56 ± 0.29 in PM10, and the OC/EC ratios are 3.63 ± 1.73 in PM2.5 and 4.17 ± 2.09 in PM10, signifying that the stationary emissions from coal combustion remain the main PM source. An evaluation of PM2.5 situation in current China was carried out and the results show that it would take about 27 years to meet the limit value of 35 µg/m(3) in the revised standard, implying a rigorous challenge in PM2.5 control in China in the future.

A Two-Stage Method to Estimate the Contribution of Road Traffic to PM₂.₅ Concentrations in Beijing, China

BACKGROUND

Fine particulate matters with aerodynamic diameters smaller than 2.5 micrometers (PM2.5) have been a critical environmental problem in China due to the rapid road vehicle growth in recent years. To date, most methods available to estimate traffic contributions to ambient PM2.5 concentration are often hampered by the need for collecting data on traffic volume, vehicle type and emission profile.

OBJECTIVE

To develop a simplified and indirect method to estimate the contribution of traffic to PM2.5 concentration in Beijing, China.

METHODS

Hourly PM2.5 concentration data, daily meteorological data and geographic information were collected at 35 air quality monitoring (AQM) stations in Beijing between 2013 and 2014. Based on the PM2.5 concentrations of different AQM station types, a two-stage method comprising a dispersion model and generalized additive mixed model (GAMM) was developed to estimate separately the traffic and non-traffic contributions to daily PM2.5 concentration. The geographical trend of PM2.5 concentrations was investigated using generalized linear mixed model. The temporal trend of PM2.5 and non-linear relationship between PM2.5 and meteorological conditions were assessed using GAMM.

RESULTS

The medians of daily PM2.5 concentrations during 2013-2014 at 35 AQM stations in Beijing ranged from 40 to 92 μg/m³. There was a significant increasing trend of PM2.5 concentration from north to south. The contributions of road traffic to daily PM2.5 concentrations ranged from 17.2% to 37.3% with an average 30%. The greatest contribution was found at AQM stations near busy roads. On average, the contribution of road traffic at urban stations was 14% higher than that at rural stations.

CONCLUSIONS

Traffic emissions account for a substantial share of daily total PM2.5 concentrations in Beijing. Our two-stage method is a useful and convenient tool in ecological and epidemiological studies to estimate the traffic contribution to PM2.5 concentrations when there is limited information on vehicle number and types and emission profile.

“Green” nano-filters: fine nanofibers of natural protein for high efficiency filtration of particulate pollutants and toxic gases

By combining the significant properties of nanofibers and the multi-functionality of pure proteins, “green” multifunctional air-filters with high removal efficiency of particulates and toxic gases is achieved.

Trace elements in particulate matter from metropolitan regions of Northern China: Sources, concentrations and size distributions

Public concerns over airborne trace elements (TEs) in metropolitan areas are increasing, but long-term and multi-site observations of size-resolved aerosol TEs in China are still lacking. Here, we identify highly elevated levels of atmospheric TEs in megacities and industrial sites in a Beijing-Tianjin-Hebei urban agglomeration relative to background areas, with the annual mean values of As, Pb, Ni, Cd and Mn exceeding the acceptable limits of the World Health Organization. Despite the spatial variability in concentrations, the size distribution pattern of each trace element was quite similar across the region. Crustal elements of Al and Fe were mainly found in coarse particles (2.1-9 μm), whereas the main fraction of toxic metals, such as Cu, Zn, As, Se, Cd and Pb, was found in submicron particles (<1.1 μm). These toxic metals were enriched by over 100-fold relative to the Earth's crust. The size distributions of Na, Mg, K, Ca, V, Cr, Mn, Ni, Mo and Ba were bimodal, with two peaks at 0.43-0.65 μm and 4.7-5.8 μm. The combination of the size distribution information, principal component analysis and air mass back trajectory model offered a robust technique for distinguishing the main sources for airborne TEs, e.g., soil dust, fossil fuel combustion and industrial emissions, at different sites. In addition, higher elemental concentrations coincided with westerly flow, indicating that polluted soil and fugitive dust were major sources of TEs on the regional scale. However, the contribution of coal burning, iron industry/oil combustion and non-ferrous smelters to atmospheric metal pollution in Northern China should be given more attention. Considering that the concentrations of heavy metals associated with fine particles in the target region were significantly higher than those in other Asian sites, the implementations of strict environmental standards in China are required to reduce the amounts of these hazardous pollutants released into the atmosphere.

Effects of Urban Landscape Pattern on PM2.5 Pollution--A Beijing Case Study

PM_2.5 refers to particulate matter (PM) in air that is less than 2.5μm in aerodynamic diameter, which has negative effects on air quality and human health. PM_2.5 is the main pollutant source in haze occurring in Beijing, and it also has caused many problems in other cities. Previous studies have focused mostly on the relationship between land use and air quality, but less research has specifically explored the effects of urban landscape patterns on PM_2.5. This study considered the rapidly growing and heavily polluted Beijing, China. To better understand the impact of urban landscape pattern on PM_2.5 pollution, five landscape metrics including PLAND, PD, ED, SHEI, and CONTAG were applied in the study. Further, other data, such as street networks, population density, and elevation considered as factors influencing PM_2.5, were obtained through RS and GIS. By means of correlation analysis and stepwise multiple regression, the effects of landscape pattern on PM_2.5 concentration was explored. The results showed that (1) at class-level, vegetation and water were significant landscape components in reducing PM_2.5 concentration, while cropland played a special role in PM_2.5 concentration; (2) landscape configuration (ED and PD) features at class-level had obvious effects on particulate matter; and (3) at the landscape-level, the evenness (SHEI) and fragmentation (CONTAG) of the whole landscape related closely with PM_2.5 concentration. Results of this study could expand our understanding of the role of urban landscape pattern on PM_2.5 and provide useful information for urban planning.

Forward research on transmission characteristics of near-surface particulate-matter-polluted atmosphere in mining area combined with CFD method

The optical radiation and radiation transfer characteristics of atmospheric particulate matter (PM) in mining area of northwest China were simulated and analyzed in this paper. Computational fluid dynamics (CFD) method was adopted to simulate the distribution of PM considering the local desertification and mining activities. The 1-D radiative transfer equation was solved using discrete ordinates method combined with Mie scattering model based on the CFD simulation results. The spectral aerosol optical depth and transmission characteristics of PM polluted atmosphere in the wavelength of 1-25μm under different intensity of dust releases, wind speeds and dust compositions were obtained and analyzed. The simulation results show that: the transmission characteristics are obviously enhanced with the increase of wind speed and sand particles' proportion but greatly decreased with the increase of the intensity of dust release.

Spatial and Temporal Distribution of PM2.5 Pollution in Xi'an City, China

The monitoring data of the 13 stations in Xi’an city for the whole years of 2013 and 2014 was counted and analyzed. Obtaining the spatial and temporal distribution characteristics of PM_2.5 was the goal. Cluster analysis and the wavelet transform were utilized to discuss the regional distribution characteristics of PM_2.5 concentration (ρ(PM_2.5)) and the main features of its yearly changes and sudden changes. Additionally, some relevant factors were taken into account to interpret the changes. The results show that ρ(PM_2.5) in Xi’an during 2013 was generally higher than in 2014, it is high in winter and low in summer, and the high PM_2.5 concentration centers are around the People’s Stadium and Caotan monitoring sites; For the regional PM_2.5 distribution, the 13 sites can be divided into three categories, in which Textile city is Cluster 1, and High-tech Western is Cluster 2, and Cluster 3 includes the remaining 11 monitoring sites; the coefficient of goodness of the cluster analysis is 0.6761, which indicates that the result is acceptable. As for the yearly change, apart from June and July, the average ρ(PM_2.5) concentration has been above the normal concentration criteria of Chinese National Standard (50 g/m³); cloudy weather and low winds are the major meteorological factors leading to the sudden changes of ρ(PM_2.5).

Aldehydes in Relation to Air Pollution Sources: A Case Study around the Beijing Olympics

This study was carried out to characterize three aldehydes of health concern (formaldehyde, acetaldehyde, and acrolein) at a central Beijing site in the summer and early fall of 2008 (from June to October). Aldehydes in polluted atmospheres come from both primary and secondary sources, which limits the control strategies for these reactive compounds. Measurements were made before, during, and after the Beijing Olympics to examine whether the dramatic air pollution control measures implemented during the Olympics had an impact on concentrations of the three aldehydes and their underlying primary and secondary sources. Average concentrations of formaldehyde, acetaldehyde and acrolein were 29.3±15.1 μg/m3, 27.1±15.7 μg/m3 and 2.3±1.0 μg/m3, respectively, for the entire period of measurements, all being at the high end of concentration ranges measured in cities around the world in photochemical smog seasons. Formaldehyde and acrolein increased during the pollution control period compared to the pre-Olympic Games, followed the changing pattern of temperature, and were significantly correlated with ozone and with a secondary formation factor identified by principal component analysis (PCA). In contrast, acetaldehyde had a reduction in mean concentration during the Olympic air pollution control period compared to the pre-Olympic period and was significantly correlated with several pollutants emitted from local emission sources (e.g., NO2, CO, and PM2.5). Acetaldehyde was also more strongly associated with primary emission sources including vegetative burning and oil combustion factors identified through the PCA. All three aldehydes were lower during the post-Olympic sampling period compared to the before and during Olympic periods, likely due to seasonal and regional effects. Our findings point to the complexity of source control strategies for secondary pollutants.

Outdoor PM₁₀ source apportionment in metropolitan cities--a case study

This study was carried out to specify contribution of different sources in PM10 emission in Tehran City using chemical mass balance method. This is the first time that this method is used in Iran. To this end, the metallic elements including V, Ni, As, Pb, Cd, Hg, Mn, Al, Ca, K, Na, Fe, Zn, Sc, and S were sampled on the filters of high-volume sampler installed at four stations in Tehran. Afterward, highly sensitive inductively coupled plasma (ICP-M90; model aurora-Elit) was used to determine concentration of the elements precipitated on the filters. The obtained results were then compared with standard values. According to the results, the concentration of Cd (16.8 ng/m(3)) was higher than the standard level of 5 ng/m(3) at District 16 on November 14th 2012 which is almost three times the permissible limit. None of the elements Pb, Mn, V, and Hg exceeded the permissible limits except for Ni at District 16. Subsequently, the enrichment factor of the elements was calculated to indicate that elements of anthropogenic origins (Zn, S, Ni, and Hg) are highly enriched with respect to crustal composition (Na, Fe, and Ca). Exceedance factor were calculated for elements of each site to show that all study sites were in low-pollution category. Afterward, the contribution of different pollution sources of road dust, vehicles, and industries in emission of outdoor PM10 was investigated through chemical mass balance (CMB) method. According to which, the highest contribution comes from road dust with a share of 95.4 % of the total outdoor PM10 emission in Tehran mainly originated from the wear and friction of car tires with asphalt pavement. High calcium concentration in all districts of the city confirms the claim. Furthermore, transportation, with a significant difference, has a contribution of 4.05 % of total outdoor PM10 released while industries share very little about 0.4 %. In overall, the quality of road pavement could be a determining factor in releasing considerable amount of outdoor PM10 in urban areas.

Analysis of the characteristics and evolution modes of PM2.5 pollution episodes in Beijing, China during 2013

Fine particulate matter (PM2.5) has been recognized as a serious hazard linked to deleterious health effects. In this study, all PM2.5 Pollution Episodes (PPEs) in Beijing during 2013 were investigated with hourly PM2.5 observations from the Olympic Sport Center site, and then their characteristics and evolution modes analysed. Results show that 80 PPEs, covering 209 days, occurred in Beijing during 2013. Average PM2.5 concentrations during PPEs were almost twice (1.86) the annual mean value, although the PPEs showed significant seasonal variations. The most hazardous PPEs tended to occur in winter, whereas PPEs with long duration occurred in autumn. The PPEs could be divided into six clusters based on their compositions of different pollution levels, which were strongly related to meteorological factors. We used series peaks of PM2.5 concentrations to analyse the evolution modes of PPEs and found that the more peaks there were within the evolution mode, the longer the duration, and the higher the average and maximum PM2.5 concentrations. Each peak within a PPE can be identified by "rise" and "fall" patterns. The "rise" patterns are widely related to relative humidity, whereas the "fall" patterns are affected principally by wind speed for one-peak PPEs and boundary layer height for multi-peak PPEs. The peak patterns cannot be explained fully by meteorological factors; however, they might also be closely related to complex and diversified human activities.

Chemical characteristics of water-soluble ions in particulate matter in three metropolitan areas in the North China Plain

PM_2.5 and PM₁₀ samples were collected simultaneously in each season in Beijing, Tianjin and Shijiazhuang to identify the characteristics of water-soluble ion compositions in the North China Plain. The water-soluble ions displayed significant seasonal variation. The dominant ions were NO₃⁻, SO₄²⁻, NH₄⁺ and Cl⁻, accounting for more than 90% and 86% to the mass of total water-soluble ions in PM_2.5 and PM₁₀, respectively. The anion/cation ratio indicated that the ion acidity of each city varied both between sites and seasonally. Over 50% of the ion species were enriched in small particles ≤1 µm in diameter. The [NO₃⁻]/[SO₄²⁻] ratio indicated that vehicles accounted for the majority of the particulate pollution in Beijing. Shijiazhuang, a city highly reliant on coal combustion, had a higher SO₄²⁻ concentration.

Health risk assessment of inhalable particulate matter in Beijing based on the thermal environment

Inhalable particulate matter (PM₁₀) is a primary air pollutant closely related to public health, and an especially serious problem in urban areas. The urban heat island (UHI) effect has made the urban PM₁₀ pollution situation more complex and severe. In this study, we established a health risk assessment system utilizing an epidemiological method taking the thermal environment effects into consideration. We utilized a remote sensing method to retrieve the PM₁₀ concentration, UHI, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). With the correlation between difference vegetation index (DVI) and PM₁₀ concentration, we utilized the established model between PM₁₀ and thermal environmental indicators to evaluate the PM₁₀ health risks based on the epidemiological study. Additionally, with the regulation of UHI, NDVI and NDWI, we aimed at regulating the PM₁₀ health risks and thermal environment simultaneously. This study attempted to accomplish concurrent thermal environment regulation and elimination of PM₁₀ health risks through control of UHI intensity. The results indicate that urban Beijing has a higher PM₁₀ health risk than rural areas; PM₁₀ health risk based on the thermal environment is 1.145, which is similar to the health risk calculated (1.144) from the PM₁₀ concentration inversion; according to the regulation results, regulation of UHI and NDVI is effective and helpful for mitigation of PM₁₀ health risk in functional zones.

Estimation of PM10 in the traffic-related atmosphere for three road types in Beijing and Guangzhou, China

The levels of roadside PM10 in Beijing, China, were investigated in 2011 and 2012 on a seasonal basis to estimate the population exposure to particulates for three road types. The measurements of PM10 were also conducted in the southern Chinese megacity of Guangzhou for comparison purposes. The results showed that roadside PM10 in Beijing correlated strongly with the PM10 background in the urban atmosphere. The levels of PM10 in street canyons were markedly higher than those along the open roads and in crossroad areas because of limited ventilation. An elevation of PM10 was observed in April, which was possibly due to the sand storms that frequently occur in the spring. Based on these observations, roadside PM10 in Beijing could have multiple origins and was to some extent dispersion-governed. In Guangzhou, the roadside PM10 did not closely relate to the background values. The PM10 pollution was greatly affected by local traffic conditions. The simulation of PM10 for different road types was completed during the study period using the Motor Vehicle Emissions Factor Model (MOBILE6.2) as an emission model and the California Line Source Dispersion Model (CALINE4) and Operational Street Pollution Model (OSPM) as dispersion models. The MOBILE6.2/CALINE4 software package was demonstrated to be sufficient for the simulation of PM10 in the open roads and crossroad areas in both Beijing and Guangzhou, and the simulation results of roadside PM10 in the street canyons by the MOBILE6.2/OSPM package were in close agreement with those of the measurements.

Spatial and temporal characteristics of particulate matter in Beijing, China using the Empirical Mode Decomposition method

Air pollution has become a serious problem in Beijing, China. Daily PM10 mass concentration measurements were collected at 27 stations in Beijing over a 5-year period from January 1, 2008 to October 31, 2012. We used a new clustering method (kernel K-means) and a new period and trend decomposition method (Empirical Mode Decomposition, EMD) to explore the spatial and temporal characteristics of the PM10 mass concentration in the City. The temporal period and trend of each cluster center were decomposed using the EMD method, which is an adaptive data analysis method that requires no prior information. The daily PM10 mass concentrations varied greatly from 5 μg/m(3) to more than 600 μg/m(3). All of the stations were partitioned into three clusters by the kernel K-means method, and which represent the low-, middle- and high-pollution stations, respectively. The first cluster contained nine stations, mainly located in the north suburban area. The second cluster, whose degree of pollution was much more serious than the first cluster, contained 13 stations distributed in urban and peri-urban areas. The pollution level in the southern part of Beijing was much more serious than in the northern part of the City. The third cluster contained five stations located outside the second-cluster stations. The total decreased amplitudes of the three clusters during the whole period were 19 μg/m(3), 10 μg/m(3) and 4 μg/m(3), respectively. Although the global trend of the PM10 mass concentration decreased in general, it was not the same for each season and station. The trends in summer and winter declined, while in spring, it has been increasing in recent years. Five types of trends can be found for stations, including monotonic decreasing, rise fall, fall rise fall, fall rise and rise. The rising trend of the regional background air pollution monitoring station, Miyun-reservoir, indicates an increase in the City's background PM10 mass concentration.

Evaluation of PM10 forecasting based on the artificial neural network model and intake fraction in an urban area: a case study in Taiyuan City, China

Primary fine particulate matters with a diameter of less than 10 microm (PM10) are important air emissions causing human health damage. PM10 concentration forecast is important and necessary to perform in order to assess the impact of air on the health of living beings. To better understand the PM10 pollution health risk in Taiyuan City, China, this paper forecasted the temporal and spatial distribution of PM10 yearly average concentration, using Back Propagation Artificial Neural Network (BPANN) model with various air quality parameters. The predicted results of the models were consistent with the observations with a correlation coefficient of 0.72. The PM10 yearly average concentrations combined with the population data from 2002 to 2008 were given into the Intake Fraction (IF) model to calculate the IFs, which are defined as the integrated incremental intake of a pollutant released from a source category or a region over all exposed individuals. The results in this study are only for main stationary sources of the research area, and the traffic sources have not been included. The computed IFs results are therefore under-estimations. The IFs of PM10 from Taiyuan with a mean of 8.5 per million were relatively high compared with other IFs of the United States, Northern Europe and other cities in China. The results of this study indicate that the artificial neural network is an effective method for PM10 pollution modeling, and the Intake Fraction model provides a rapid population risk estimate for pollutant emission reduction strategies and policies.

IMPLICATIONS

The PM10 (particulate matter with an aerodynamic diameter < or = 10 microm) yearly average concentration of Taiyuan, with a mean of 0.176 mg/m3, was higher than the 65 microg/m3 recommended by the U.S. Environmental Protection Agency (EPA). The spatial distribution of PM10 yearly average concentrations showed that wind direction and wind speed played an important role, whereas temperature and humidity had a lower effect than expected. Intake fraction estimates of Taiyuan were relatively high compared with those observed in other cities. Population density was the major factor influencing PM10 spatial distribution. The results indicated that the artificial neural network was an effective method for PM10 pollution modeling.

Estimation of citywide air pollution in Beijing

There has been discrepancies between the daily air quality reports of the Beijing municipal government, observations recorded at the U.S. Embassy in Beijing, and Beijing residents’ perceptions of air quality. This study estimates Beijing’s daily area PM_2.5 mass concentration by means of a novel technique SPA (Single Point Areal Estimation) that uses data from the single PM_2.5 observation station of the U.S Embassy and the 18 PM₁₀ observation stations of the Beijing Municipal Environmental Protection Bureau. The proposed technique accounts for empirical relationships between different types of observations, and generates best linear unbiased pollution estimates (in a statistical sense). The technique extends the daily PM_2.5 mass concentrations obtained at a single station (U.S. Embassy) to a citywide scale using physical relations between pollutant concentrations at the embassy PM_2.5 monitoring station and at the 18 official PM₁₀ stations that are evenly distributed across the city. Insight about the technique’s spatial estimation accuracy (uncertainty) is gained by means of theoretical considerations and numerical validations involving real data. The technique was used to study citywide PM_2.5 pollution during the 423-day period of interest (May 10, 2010 to December 6, 2011). Finally, a freely downloadable software library is provided that performs all relevant calculations of pollution estimation.

Composition, seasonal variation, and sources of PM₁₀ from world heritage site Taj Mahal, Agra

Air samples for PM(10) (dp < 10 μm in aerodynamic diameter) were collected from March 2007 to February 2008 near Taj Mahal-a historically sensitive site in Agra. Each sample collected on 20.3 × 25.4-cm Whatman quartz microfiber filter using respirable dust sampler was analyzed gravimetrically for mass concentrations and chemically for elements such as Na, Mg, Al, Si, S, Ca, Sc, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Br, Rb, Cd, Ba, and Pb by inductively coupled plasma atomic emission spectroscopy and inorganic ions such as NH (4) (+) , K(+), SO (4) (2-) , NO (3) (-) , F(-), and Cl(-) by ion chromatograph. Annual average of PM(10) 155.47 ± 77.97 μg/m(3) was three times high the annual average NAAQ standard of 50 μg/m(3) for sensitive area. PM(10) as well as K(+), Cl(-), As, and Pb were higher in winter while crust elements and secondary aerosols were higher in summer. The average equivalent ratio of NH (4) (+) to sum up of SO (4) (2-) and NO (3) (-) was greater than unity which indicates high source strength of ammonia and alkaline nature of aerosols in Agra. Source apportionment of PM(10) was carried out by factor analysis using principal component analysis (varimax rotated factor matrix method) of SPSS. Five sources contributing to PM(10) were identified as crust material, vehicular emissions, industrial emissions, coal and biomass burning, and secondary aerosols.

Hydroxyl radical generation mechanism during the redox cycling process of 1,4-naphthoquinone

Airborne quinones contribute to adverse health effects of ambient particles probably because of their ability to generate hydroxyl radicals (·OH) via redox cycling, but the mechanisms remain unclear. We examined the chemical mechanisms through which 1,4-naphthoquinone (1,4-NQ) induced ·OH, and the redox interactions between 1,4-NQ and ascorbate acid (AscH(2)). First, ·OH formation by 1,4-NQ was observed in cellular and acellular systems, and was enhanced by AscH(2). AscH(2) also exacerbated the cytotoxicity of 1,4-NQ in Ana-1 macrophages, at least partially due to enhanced ·OH generation. The detailed mechanism was studied in an AscH(2)/H(2)O(2) physiological system. The existence of a cyclic 1,4-NQ process was shown by detecting the corresponding semiquinone radical (NSQ·-) and hydroquinone (NQH(2)). 1,4-NQ was reduced primarily to NSQ·- by O2·- (which was from AscH(2) reacting with H(2)O(2)), not by AscH(2) as normally thought. At lower doses, 1,4-NQ consumed O2·- to suppress ·OH; however, at higher doses, 1,4-NQ presented a positive association with ·OH. The reaction of NSQ·- with H(2)O(2) to release ·OH was another important channel for OH radical formation except for Haber-Weiss reaction. As a reaction precursor for O2·-, the enhanced ·OH response to 1,4-NQ by AscH(2) was indirect. Reducing substrates were necessary to sustain the redox cycling of 1,4-NQ, leading to more ·OH and a deleterious end point.

Dispersion model on PM₂.₅ fugitive dust and trace metals levels in Kuwait Governorates

Frequent dust storms and recent environmental changes were found to affect the human health especially in residents of arid countries. Investigations on the PM(2.5) fugitive dust in six Kuwait Governorate areas using dispersion Gaussian plume modeling revealed significant relationship between low rate of pollutant emission, low wind velocity, and stable weather conditions' matrix causing high rate of dust deposition in summer than in winter. The rate of dust deposition and trace metals levels in PM(2.5) were in the sequence of G-VI > G-I > G-II > G-V > G-III > G-IV. Trace metals were observed in the sequence of Al > Fe > Zn > Ni > Pb > Cd irrespective of the Governorate areas and the two seasons. The high rate of dust deposition and trace metals in PM(2.5) was reflected by the vast open area, wind velocity, and rapid industrialization besides natural and anthropogenic sources. A combination of air dispersion modeling and nephalometric and gravimetric studies of this kind not only determines the seasonal qualitative and quantitative analyses on the PM(2.5) dust deposition besides trace metals apportionment in six Kuwait Governorate areas, but also characterizes air pollution factors that could be used by environmentalist to deduce preventive measures.

Influence of different weather events on concentrations of particulate matter with different sizes in Lanzhou, China

The formation and development of weather events has a great impact on the diffusion, accumulation and transport of air pollutants, and causes great changes in the particulate pollution level. It is very important to study their influence on particulate pollution. Lanzhou is one of the most particulate-polluted cities in China and even in the world. Particulate matter (PM) including TSP, PM >10, PM2.5-10, PM2.5 and PM1.0 concentrations were simultaneously measured during 2005-2007 in Lanzhou to evaluate the influence of three kinds of weather events--dust, precipitation and cold front--on the concentrations of PM with different sizes and detect the temporal evolution. The main results are as follows: (1) the PM pollution in Lanzhou during dust events was very heavy and the rate of increase in the concentration of PM2.5-10 was the highest of the five kinds of particles. During dust-storm events, the highest peaks of the concentrations of fine particles (PM2.5 and PM1.0) occurred 3 hr later than those of coarse particles (PM>10 and PM2.5-10). (2) The major effect of precipitation events on PM is wet scavenging. The scavenging rates of particles were closely associated with the kinds of precipitation events. The scavenging rates of TSP, PM>10 and PM2.5-10 by convective precipitation were several times as high as those caused by frontal precipitation for the same precipitation amount, the reason being the different formation mechanism and precipitation characteristics of the two kinds of precipitation. Moreover, there exists a limiting value for the scavenging rates of particles by precipitation. (3) The major effect of cold-front events on particles is clearance. However, during cold-front passages, the PM concentrations could sometimes rise first and decrease afterwards, which is the critical difference in the influence of cold fronts on the concentrations of particulate pollutants vs. gaseous pollutants.

Driver exposure to particulate matter in Bangkok

The aims of this study were to determine the particulate matter with aerodynamic diameters > or = 2.5 microm (PM2.5) and 2.5-10 microm (PM10-2.5) exposure levels of drivers and to analyze the proportion of elemental carbon (EC) and organic carbon (OC) in PM2.5 in Bangkok, Thailand. Four bus routes were selected. Measurements were conducted over 10 days in August (rainy season) 2008 and 8 days in January (dry season) 2009. The mean PM2.5 exposure level of the Tuk-tuk drivers was 86 microg/m3 in August and 198 microg/m3 in January. The mean for the non-air-conditioned bus drivers was 63 microg/m3 in August and 125 microg/m3 in January. The PM2.5 and PM10-2.5 exposure levels of the drivers in January were approximately twice as high as those in August. The proportion of total carbon (TC) in PM2.5 to the PM2.5 level in August (0.97 +/- 0.28 microg/m3) was higher than in January (0.65 +/- 0.13 microg/m3). The proportion of OC in the TC of the PM2.5 in August (0.51 +/- 0.08 microg/m3) was similar to that in January (0.65 +/- 0.07 microg/m3). The TC exposure by PM25 in January (81 +/- 30 microg/m3) remained higher than in August (56-21 microg/m3). The mean level of OC in the PM2.5 was 29 +/- 13 microg/m3 in August and 50 +/- 24 microg/m3 in January. In conclusion, the PM exposure level in Bangkok drivers was higher than that in the general environment, which was already high, and it varied with the seasons and vehicle type. This study also demonstrated that the major component of the PM was carbon, likely derived from vehicles.

Vertical characteristics and source identification of PM10 in Tianjin

Ambient PM10 (particulate matter with a diameter less than 10 microm) concentrations were measured on a 255 meter tower in Tianjin, China. The samples were collected at four vertical levels (10, 40, 120 and 220 m). Vertical characteristics for PM10 samples were studied. The results showed that the concentrations of PM10 and constituent species had a negative correlation with the sampling height. The highest concentrations of PM10 and species were obtained at the 10 m level, and the lowest concentrations were measured at the 220 m level. For the fractions of species to total mass, SO4(2-) and NO3- had higher values (fraction) at greater height; while Ca had a higher fraction at lower height. Possible source categories for the PM10 ambient dataset were identified by the principal component analysis method. The possible source categories included crustal dust, vehicles, cement dust, and incineration as well as secondary sulfate and nitrate sources. Analysis of meteorological factors on PM10 concentrations indicated that wind speed and inversion may be the main factors contributing to different concentrations of PM10 at different heights.

Variations of PM10 concentrations in Wuhan, China

Concentrations of PM(10) (particulate matter as a key urban air pollutant) were obtained from nine monitor stations within the city of Wuhan for analysis after an intensive observational data collection period that commenced in January 2006 and concluded in December 2008. According to our data, PM(10) pollution intensified and reached a high alert level of air pollution during the month of November each year. It remained at a high alert level until the following April where it again decreased to a low alert level during the summer months. During the winter and spring months, the occurrence rate (in percent)of PM(10) was five to eight times higher (high alert level) than measurements detected during the summer months. The effects of intrinsic factors (pollution sources) and remote preconditions (dust storm propagation and formation of secondary aerosol) on severe PM(10) concentrations in Wuhan are first analyzed. After which, suggestions to reduce PM(10) pollutants are provided.

Ambient air quality trends and driving factor analysis in Beijing, 1983-2007

The rapid development in Beijing, the capital of China, has resulted in serious air pollution problems. Meanwhile great efforts have been made to improve the air quality, especially since 1998. The variation in air quality under the interaction of pollution and control in this mega city has attracted much attention. We analyzed the changes in ambient air quality in Beijing since the 1980's using the Daniel trend test based on data from long-term monitoring stations. The results showed that different pollutants displayed three trends: a decreasing trend, an increasing trend and a flat trend. SO2, dustfall, B[a]P, NO2 and PM10 fit decreasing trend pattern, while NOx showed an increasing trend, and CO, ozone pollution, total suspended particulate (TSP), as well as Pb fit the flat trend. The cause of the general air pollution in Beijing has changed from being predominantly related to coal burning to mixed traffic exhaust and coal burning related pollution. Seasonally, the pollution level is typically higher during the heating season from November to the following March. The interaction between pollution sources change and implementation of air pollution control measures was the main driving factor that caused the variation in air quality. Changes of industrial structure and improved energy efficiency, the use of clean energy and preferred use of clean coal, reduction in pollution sources, and implementation of advanced environmental standards have all contributed to the reduction in air pollution, particularly since 1998.

Urban particulate matter in Beijing, China, enhances allergen-induced murine lung eosinophilia

It has been reported that ambient particulate matter (PM) in some large cities, such as Beijing, China, causes adverse respiratory health effects. However, there is currently no experimental report on the relationship between bronchial asthma and urban PM (UPM) in northeast Asia. In this study, the microbial and chemical substances adsorbed onto UPM collected in Beijing were excluded by heat-treatment at 360 degrees C for 30 min. The effects of UPM or heated UPM (H-UPM) toward allergic lung inflammation were compared in murine lungs to investigate the role of organic substances. ICR mice were administrated intratracheally with the two kinds of UPM and/or ovalbumin (OVA) 4 times at 2-week intervals. UPM and H-UPM enhanced eosinophil recruitment induced by OVA in the alveoli and in the submucosa of the airway, which has a goblet cell proliferation in the bronchial epithelium. UPM and H-UPM synergistically increased Th-2 cytokines--interleukin (IL)-4 and IL-13, eosinophil-relevant cytokines and chemokines, such as IL-5 and monocyte chemotactic protein-3 (MCP-3), induced by OVA in bronchoalveolar lavage fluid (BALF). The enhancing effects were much greater in UPM than in H-UPM. UPM induced adjuvant effects on specific immunoglobulin E (IgE) and IgG1 production by OVA. In an in vitro study using RAW264.7 cells, UPM increased the expression of Toll-like receptor 2 (TLR2) mRNA, but not TLR4 mRNA. H-UPM caused no expression of both TLR mRNAs. These results suggest that the aggravated lung eosinophilia in UPM was due to activation of a Th2-associated immune response via the activation of TLR2 by microbial materials. Chemical materials of air pollutant origin contained in UPM, and inorganic components (elemental carbon, mineral elements) in H-UPM, could also cause the aggravation.