Number Of PM2 Research Articles

Optimal emission control plan aiming to basically eliminate PM2.5 heavy pollution in North and Northwest China in 2018

The fundamental eradication of heavy air pollution is the core objective during China's "14th Five-Year Plan" period. The purpose of this study was to basically eliminate fine particulate matter (PM2.5) heavy pollution in North and Northwest China during all PM2.5 heavy pollution process throughout 2018 through optimal emission control model based on linear programming technique. The results showed that the sensitivity coefficient of PM2.5 concentration to precursor emissions in North China increased with emissions disturbances, ranging from 0.23 to 0.93 with −80% disturbances. Compared to non-optimal control (NOC) scenario in North China, the optimal control (OC) scenario implemented in various precursors in various regions not only resulted in less precursors mitigation (with emission reduction of 4.1%, 5.9%, 16.6%, 3.5%, and 4.5% for primary PM2.5, SO2, NOx, NH3, and VOCs, respectively), but also led to greater air quality improvements (with the number of PM2.5 heavy pollution days (HPDs) reduced by 37 days, achieving the goal of basically eliminate PM2.5 heavy pollution in North China). If the polluting industries in North China were transferred to Northwest China and the synergistic control (SC scenario) measures were applied, both regions could achieve basically elimination of PM2.5 heavy pollution during the whole simulation periods. However, without implementing optimal control measures in Northwest China (FU scenario), the air quality in that region was poised to deteriorate. Specifically, the number of HPDs in Northwest China changed from 95 days under business as usual (BAU) scenario to 376 days under FU scenario and 29 days under SC scenario, and no new cities with PM2.5 heavy pollution emerged under SC scenario compared to BAU scenario. This study could provide scientific support for the battle against heavy pollution weather in China by redistributing the domestic industrial layout, rather than solely reducing local emissions.

Frequent haze events associated with transport and stagnation over the corridor between the North China Plain and Yangtze River Delta

Abstract. PM2.5 pollution is a major air quality issue that deteriorates human health, and numerous studies have focused on PM2.5 pollution in major regions such as the North China Plain (NCP) and Yangtze River Delta (YRD). However, the characteristics of PM2.5 concentrations and the associated formation mechanism in the transport corridor (referred to as SWLY) between the NCP and YRD are largely ignored. Based on observational data, we find that the number of PM2.5 pollution events in SWLY is comparable to that in the NCP, far exceeding that in the YRD, which is indicative of the severity of air pollution in this area. Utilizing a regional climate and air quality model, we isolate the effect of seesaw transport events, e.g., transport between the NCP and YRD, and atmospheric stagnation on the accumulation of PM2.5 over SWLY. Specifically, seesaw events and stagnation, comparable to each other, collectively account for an average of 67 % of pollution days, with PM2.5 exceeding 75 µg m−3, and this fraction (85 %) is even larger for severe haze events, with PM2.5 exceeding 150 µg m−3. Furthermore, the connection between seesaw transport and large-scale circulation is examined. The transregional transport of pollutants from the NCP to the YRD (YRD to NCP) is likely stimulated by positive (negative) to negative (positive) geopotential height anomalies at 500 hPa located in northern China. The health effect due to short-term PM2.5 exposure induced by the transregional transport and stagnation is investigated, yielding a total of 8634 (95 % CI: 6023–11 223) and 9496 (95 % CI: 6552–12 413) premature deaths, respectively, in SWLY during winter 2014–2019, which is as high as 9 % of the total premature deaths in China, even though SWLY takes up less than 1 % of China's area. While atmospheric stagnation is in general projected to occur more frequently under a warming climate, this study indicates the importance of regional emission control to alleviate PM2.5 pollution from seesaw transport and stagnation.

How Important Is Satellite-Retrieved Aerosol Optical Depth in Deriving Surface PM2.5 Using Machine Learning?

PM2.5 refers to the total mass concentration of tiny particulates in the atmosphere near the surface, obtained by means of in situ observations and satellite remote sensing. Given the highly limited number of ground observation stations of inhomogeneous distribution and an ill-posed remote sensing approach, increasing efforts have been devoted to the application of machine-learning (ML) models to both ground and satellite data. A key satellite-derived parameter, aerosol optical thickness (AOD), has been most commonly used as a proxy of PM2.5, although their correlation is fraught with large uncertainties. A critical question that has been overlooked concerns how much AOD helps to improve the retrieval of PM2.5 relative to its uncertainty incurred concurrently. The question is addressed here by taking advantage of high-density PM2.5 stations in eastern China to evaluate the contributions of AOD, determined as the difference in the accuracy of PM2.5 retrievals with and without AOD for varying densities of PM2.5 stations, using four popular ML models (i.e., Random Forest, Extra-trees, XGBoost, and LightGBM). Our results reveal that as the density of monitoring stations decreases, both the feature importance and permutation importance of satellite AOD demonstrate a consistent upward trend (p < 0.05). Furthermore, the ML models without AOD exhibit faster declines in overall accuracy and predictive ability compared with the models with AOD assessed using the sample-based and station-based (spatial) independent cross-validation approaches. Overall, a 10% reduction in the number of stations results in an increase of 0.7–1.2% and 0.6–1.2% in uncertainty in estimated and predicted accuracies, respectively. These findings attest to the indispensable role of satellite AOD in the PM2.5 retrieval process through ML because it can significantly mitigate the negative impact of the sparse distribution of monitoring sites. This role becomes more important as the number of PM2.5 stations decreases.

Availability of Third Molars as Donor Teeth for Autotransplantation to Replace Congenitally Absent Second Premolars in Children and Young Adults.

The aim of the study was to assess the presence and distribution of third molars (M3) regarding their autotransplantation in patients with congenital absence of second premolars (PM2). Additionally, M3 development in relation to patients' age and gender was investigated. Panoramic radiographs of non-syndromic patients with at least one congenitally absent PM2 were used to assess the localization and number of missing PM2 and the presence or absence of M3 (minimum age 10 years). The alternate logistic regression model was applied to analyze associations between the presence of PM2 and M3. A total of 131 patients with PM2 agenesis were identified (82 females, 49 males). At least one M3 was present in 75.6% and all M3 were present in 42.7% of patients. A statistically significant association between the number of PM2 and M3 agenesis was found; the effects of age and gender were not significant. More than half of M3 in patients between 14-17 years old had completed ¼ of their root development. The congenital absence of maxillary PM2 was associated with the absence of maxillary PM2, M3, and no correlation was found in the mandible. In patients with PM2 agenesis, at least one M3 is often present and can be considered as a donor tooth for autotransplantation.

Spatial association network of PM2.5 and its influencing factors in the Beijing-Tianjin-Hebei urban agglomeration.

In this paper, we empirically study the spatial association network of PM2.5 and the factors influencing those correlations using the gravity model, social network analysis (SNA), and the quadratic assignment procedure (QAP) based on data from the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) in China from 2005 to 2018. We draw the following conclusions. First, the spatial association network of PM2.5 exhibits relatively typical network structure characteristics: the network density and network correlations are highly sensitive to efforts to control air pollution, and there are obvious spatial correlations within the network. Second, cities in the center of the BTHUA have large network centrality values, while cities in the peripheral region have small centrality values. Tianjin is a core city in the network, and the spillover effect of PM2.5 pollution in Shijiazhuang and Hengshui is the most noticeable. Third, the 14 cities can be divided into four plates, with each plate having obvious geographical location characteristics and linkage effects. The cities in the association network are divided into three tiers. Beijing, Tianjin, and Shijiazhuang are located in the first tier, and a considerable number of PM2.5 connections are completed through these cities. Fourth, differences in geographical distance and urbanization are the main drivers of the spatial correlations of PM2.5. The greater the urbanization differences, the more likely the generation of PM2.5 links is, while the opposite is true for differences in geographical distance.

Decadal Application of WRF/Chem under Future Climate and Emission Scenarios: Impacts of Technology-Driven Climate and Emission Changes on Regional Meteorology and Air Quality

This work presents new climate and emissions scenarios to investigate changes on future meteorology and air quality in the U.S. Here, we employ a dynamically downscaled Weather Research and Forecasting model coupled with chemistry (WRF/Chem) simulations that use two Intergovernmental Panel on Climate Change scenarios (i.e., A1B and B2) integrated with explicitly projected emissions from a novel Technology Driver Model (TDM). The projected 2046–2055 emissions show widespread reductions in most gas and aerosol species under both TDM/A1B and TDM/B2 scenarios over the U.S. The WRF/Chem simulations show that under the combined effects of the TDM/A1B climate and emission changes, the maximum daily average 8-h ozone (MDA8 h O3) increases by ~3 ppb across the U.S. mainly due to widespread increases in near-surface temperature and background methane concentrations, with some contributions from localized TDM emission changes near urban centers. For the TDM/B2 climate and emission changes, however, the MDA8 h O3 is widely decreased, except near urban centers where the relative TDM emission changes and O3 formation regimes leads to increased O3. The number of O3 exceedance days (i.e., MDA8 h O3 > 70 ppb) for the entire domain is significantly reduced by a grid cell maximum of up to 43 days (domain average ~0.5 days) and 62 days (domain average ~2 days) for the TDM/A1B and TDM/B2 scenarios, respectively, while in the western U.S., larger O3 increases lead to increases in nonattainment areas, especially for the TDM/A1B scenario. The combined effects of climate and emissions (for both A1B and B2 scenarios) will lead to widespread decreases in the daily 24-h average (DA24 h) PM2.5 concentrations, especially in the eastern U.S. (max decrease up to 93 µg m−3). The PM2.5 changes are dominated by decreases in anthropogenic emissions for both the TDM/A1B and TDM/B2 scenarios, with secondary effects on decreasing PM2.5 from climate change. The number of PM2.5 exceedance days (i.e., DA24 h PM2.5 > 35 µg m−3) is significantly reduced over the eastern U.S. under both TDM/A1B and B2 scenarios, which suggests that both climate and emission changes may synergistically lead to decreases in PM2.5 nonattainment areas in the future.

Critical supply chains for mitigating PM2.5 emission-related mortalities in India

Air pollution and its health-related effects are a major concern globally, and many people die from air pollution-related diseases each year. This study employed a structural path analysis combined with a health impact inventory database analysis to estimate the number of consumption-based PM2.5 emission-related deaths attributed to India’s power supply sector. We identified critical supply chain paths for direct (production) electricity use and indirect (consumption) use. We also considered both domestic and foreign final demand and its effect on PM2.5 emission-related deaths. Several conclusions could be drawn from our results. First, the effect of indirect electricity usage on PM2.5 emission-related deaths is approximately four times larger than that for direct usage. Second, a large percentage of pollution-related deaths can be attributed to India’s domestic final demand usage; however, electricity usage in the intermediate and final demand sectors is inextricably linked. Third, foreign final demand sectors from the Middle East, the USA, and China contribute indirectly toward PM2.5 emission-related deaths, specifically in the rice export supply chain. The results show that the Indian government should implement urgent measures to curb electricity use in rice supply chains in order to reduce the number of PM2.5 emission-related deaths.

PM2.5 exposure induces alveolar epithelial cell apoptosis and causes emphysema through p53/Siva-1.

This study aims to investigate whether PM2.5 exposure is involved in the induction of alveolar epithelial cell apoptosis and the progression of emphysema in mice, and to further explore its specific molecular mechanism. A certain number of PM2.5 exposed mice and normal control mice were selected, and a lung resection operation was performed to collect the pulmonary tissue samples, which were then analyzed by hematoxylin and eosin (H&E) staining assay. Subsequently, the total protein in the pulmonary tissues of mice in PM2.5 exposure group and control group was extracted, and the p53 protein level was detected by Western blot. Meanwhile, in A549 cells, after treatment of different doses of PM2.5, the protein levels of p53, caspase3, and clv-caspase3 were examined by Western blot while the mRNA levels of p53, Siva-1, and clv-caspase3 were detected by quantitative Real Time-Polymerase Chain Reaction (qRT-PCR), respectively. In addition, flow cytometry was carried out to measure the incidence of cell apoptosis, while chromatin immunoprecipitation (ChIP) assay was performed to verify whether p53 binds to the Siva-1 promoter region and thus regulates its transcription process. H&E staining revealed that PM2.5 exposure caused pathological damage in the pulmonary tissues and the expansion of the spatial structure of alveoli, which led to emphysema in mice. Moreover, p53 protein expression in pulmonary tissue of mice in PM2.5 exposure group was remarkably higher than that in the control group. Subsequently, A549 cells were treated with 0, 25, 50, 100 μg/ml PM2.5 for 48 h, and it was found that, with the increase of PM2.5 exposure dose, the p53 protein level, Siva-1 mRNA level and cell apoptosis rate were all found increased in a dose-dependent manner, which could be partially reversed by transfection of si-p53 in A549 cells. In addition, CHIP experiments confirmed that p53 can bind to the Siva-1 promoter region and directly regulate Siva-1 transcription. In A549 cells, PM2.5 exposure increased the expression of the clv-caspase3 protein, which was reversed by the knockdown of p53; however, simultaneous overexpression of Siva-1 could further increase the clv-caspase3 protein level. Additionally, flow cytometry also revealed that PM2.5 exposure induced apoptosis of alveolar epithelial cells, while the knockdown of p53 reduced that, which could be promoted by the overexpression of Siva-1. PM2.5 exposure can promote the transcription of Siva-1 to induce apoptosis of alveolar epithelial cells and accelerate the progression of emphysema in mice by enhancing p53 protein expression.

Electrochemical dithiothreitol assay for large-scale particulate matter studies

Particulate matter (PM) air pollution is associated with human morbidity and mortality. Measuring PM oxidative potential has been shown to provide a predictive measurement between PM exposure and adverse health impacts. The dithiothreitol (DTT) assay is commonly used to measure the oxidative potential of PM2.5 (PM less than 2.5 μm aerodynamic diameter). In the common, kinetic form of this assay, the decay of DTT is quantified over time (indirectly) using 5,5'-dithiobis(2-nitrobenzoic acid) (DTNB, Ellman's reagent) via UV/vis absorbance spectroscopy. The loss of DTT can also be quantified directly using electrochemical detection. The objectives of this work were (1) to evaluate the electrochemical assay, using commercially available equipment, relative to the UV/vis absorbance assay, and (2) to apply the electrochemical method to a large (>100) number of PM2.5 aerosol filter samples. Also presented here is the comparison an end-point assay to the kinetic assay, in an attempt to reduce the time, labor, and materials neccssary to quantify PM oxidative potential. The end-point, electrochemical assay gave comparable results to the UV/vis absorbance assay for PM filter sample analysis. Finally, high filter mass loadings (higher than about 0.5 μg PM per mm2 filter) lead to sub-optimal DTT assay performance, which suggests future studies should limit particle mass loadings on filters.

Source apportionment of PM2.5 for 25 Chinese provincial capitals and municipalities using a source-oriented Community Multiscale Air Quality model

Source contributions to fine airborne particulate matter with aerodynamic diameters <2.5μm (PM2.5) during 2013 were determined for 25 Chinese provincial capitals and municipalities using a source-oriented version of the Community Multiscale Air Quality (CMAQ) model. Based on the hierarchical clustering analysis of the observed PM2.5 concentrations, the 25 cities were categorized into nine groups. Generally, annual PM2.5 concentrations were highest in the cities in the north (81–154μgm−3) and lowest in the cities close to seas in the south and east (27–57μgm−3). Seasonal PM2.5 observations in the cities were generally higher in winter than in the other seasons. Industrial or residential sources were predicted to be the largest contributor to PM2.5 for all the city groups, with annually fractional contributions of 25.0%–38.6% and 9.6%–27%, respectively. The annual contributions from power plants, agriculture NH3, windblown dust, and secondary organic aerosol (SOA) for the city groups were 8.7%–12.7%, 9.5%–12%, 6.1%–12.5%, and 5.4%–15.5%, respectively. Meanwhile, the annual contributions from transportation, sea salt, and open burning were relatively low (<8%, <2%, and <6%, respectively). Secondary PM2.5 accounted for 47%–63% of total annual PM2.5 concentrations in the cities and contributed to as much as 70% of daily PM2.5 concentrations on PM2.5 pollution days (daily concentrations>75μgm−3). Industrial or residential sources were generally the largest contributor on PM2.5 pollution days for all the city groups in each season, except that open burning, SOA, and windblown dust could be more important on some days, particularly in spring. The results of this study would be helpful to develop measures to reduce annual PM2.5 concentrations and the number of PM2.5 pollution days for different regions of China.

Outdoor infiltration and indoor contribution of UFP and BC, OC, secondary inorganic ions and metals in PM2.5 in schools

Infiltration of outdoor-sourced particles into indoor environments in 39 schools in Barcelona was assessed during school hours. Tracers of road traffic emissions (NO2, Equivalent Black Carbon (EBC), Ultrafine Particles (UFP), Sb), secondary inorganic aerosols (SO42−, NO3−, NH4+) and a number of PM2.5 trace elements showed median indoor/outdoor (I/O) ratios ≤ 1, indicating that outdoor sources importantly contributed to indoor concentrations. Conversely, OC and mineral components had I/O ratios>1. Different infiltration factors were found for traffic and secondary components (0.31–0.75 and 0.50–0.92, cold and warm season respectively), with maxima corresponding to EBC and Cd. Higher concentrations of indoor-generated particles were observed when closed windows hindered dispersion (cold season). Building age was not a major determinant of indoor levels. Neither were the window's material, except for NO2 (with an increase of 8 μg m−3 for wood framed windows) and the mineral components (also dependent on the presence of sand in a distance <20m) that reach the indoor environment via soil adhering to footwear with their dispersion being more barred by Aluminium/PVC framed windows than the wooden ones. Enlarged indoor concentrations of some trace elements suggest the presence of indoor sources that should be further investigated in order to achieve a healthier school indoor environment.

不同污染程度下毛白杨叶表面PM2.5颗粒的数量及性质和叶片气孔形态的比较研究

PDF HTML阅读 XML下载 导出引用 引用提醒 不同污染程度下毛白杨叶表面PM2.5颗粒的数量及性质和叶片气孔形态的比较研究 DOI: 10.5846/stxb201403190487 作者: 作者单位: 北京林业大学,北京林业大学,北京林业大学,北京林业大学,北京林业大学,北京林业大学,北京林业大学,北京林业大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家林业公益性行业科研专项(201304301) A comparative study of the stomata and PM2.5 particles on the leaf surface of Chinese white poplar (Populus tomentosa Carr.) in different polluted places Author: Affiliation: Beijing Forestry University,,,,,,Beijing Forestry University, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:选择了北京市环境PM2.5浓度不同的两个采样点的毛白杨(Populus tomentosa Carr.)作为研究对象,利用环境扫描电镜及X-射线能谱仪对杨树叶片表面滞留的PM2.5颗粒进行了观察、统计和成分分析,并研究了叶片气孔对环境颗粒物污染的适应性变化.结果表明:夏秋两季西直门叶片样品上下表面的PM2.5数量均多于森林公园样品,这说明环境PM2.5浓度是影响叶片表面滞留颗粒物数量的主要原因;其中叶片上表面是滞留PM2.5颗粒的主要区域.森林公园样品中PM2.5颗粒性质比较单一,硅铝酸盐颗粒和石英颗粒占很大比例,二者的主要来源均为天然源,如土壤扬尘、矿物颗粒等;而西直门采样点叶片样品滞留的PM2.5颗粒的元素组成更为复杂,其中50%以上的硅铝酸盐颗粒检测出了明显的铜、钾、氯、钠等元素的谱峰,其来源主要是工业排放;西直门样品PM2.5的含硫量高于森林公园样品,且夏季明显高于秋季.研究还发现有少数PM2.5颗粒进入了毛白杨叶片的气孔,而且不同污染程度下气孔的形态特征存在差异.与森林公园毛白杨叶片的气孔相比,西直门处的毛白杨叶片气孔的长度、宽度、面积和气孔密度均较小,说明较高的PM2.5污染程度对毛白杨叶片的形态发育有一定影响.研究结果可以为揭示植物叶片阻滞、吸收大气颗粒污染物的机制、合理选择和优化城市绿化树种从而改善空气质量提供一定的科学理论依据. Abstract:Observation and analysis of PM2.5 particles on a leaf surface can help us explore the correlation between PM2.5 particles and environmental factors and the response of stomata to particulate pollution in the air. This not only provides an important scientific reference for elucidating the mechanism of PM2.5 retention and absorption by leaves, but is also helpful for reasonably selecting and optimizing urban greening tree species to reduce particulate matter pollution and improve the atmospheric environment in the city. In Beijing, National Olympic Forest Park and Xizhimen traffic hub are two representative sites for monitoring the environment. In June and September 2013, we measured the atmospheric PM2.5 level of these monitoring sites using a real-time PM2.5 monitoring instrument, and found that the average atmospheric PM2.5 level in Xizhimen traffic hub was about 1.5 times higher than that in the National Olympic Forest Park. In this study, PM2.5 particles retained on the leaf surface of Chinese white poplar (Populus tomentosa Carr.) from these two sites were identified, counted, and analyzed by using an environmental scanning electron microscope (ESEM) and an X-ray energy dispersive spectrometer (EDS). At the same time, the adaptive response of leaf stomata to the PM2.5 pollution was studied. These results showed that the numbers of PM2.5 particles on both the adaxial and abaxial surfaces of leaf samples at the Xizhimen traffic hub were more than those in the National Olympic Forest Park in both summer and autumn, indicating that the number of PM2.5 particles adsorbed by leaves is largely affected by the atmospheric PM2.5 concentration. The adaxial surface of leaves is the main area of PM2.5 retention. The number of PM2.5 particles on the autumn samples was more than that on the summer samples. The PM2.5 particles on the leaves from the National Olympic Forest Park were mainly composed of aluminosilicate particles and quartz grains, and these particles are mainly from soil dust and mineral particles. However, the composition of PM2.5 particles from the Xizhimen traffic hub samples was more complex. The spectral peaks of Cu, Na, K, Cl, and other elements were found in the energy spectra in most aluminosilicate particles. These elements are mainly from industrial emissions. The sulfur content of PM2.5 from the Xizhimen traffic hub samples was higher than that from the National Olympic Forest Park samples in both summer and autumn. Moreover, the sulfur content was higher in summer than in autumn. Our results indicate that the amount and composition of particulate matter are affected by environmental factors to a great extent. It was also observed that a few PM2.5 particles are present in the stomata of P. tomentosa leaves. Also, the morphological characteristics of stomata varied at different atmospheric PM2.5 levels. The length, width, area, and density of stomata of the Xizhimen traffic hub samples were smaller than those of the National Olympic Forest park samples due to more serious PM2.5 pollution. Additionally, the secondary electron image and the back-scattering image were combined to better locate, observe, and analyze PM2.5 particles on the leaf surface of P. tomentosa. 参考文献 相似文献 引证文献

The Recent and Future Health Burden of Air Pollution Apportioned Across U.S. Sectors

Recent risk assessments have characterized the overall burden of recent PM2.5 and ozone levels on public health, but generally not the variability of these impacts over time or by sector. Using photochemical source apportionment modeling and a health impact function, we attribute PM2.5 and ozone air quality levels, population exposure and health burden to 23 industrial point, area, mobile and international emission sectors in the Continental U.S. in 2005 and 2016. Our modeled policy scenarios account for a suite of emission control requirements affecting many of these sectors. Between these two years, the number of PM2.5 and ozone-related deaths attributable to power plants and mobile sources falls from about 68,000 (90% confidence interval from 48,000 to 87,000) to about 36,000 (90% confidence intervals from 26,000 to 47,000). Area source mortality risk grows slightly between 2005 and 2016, due largely to population growth. Uncertainties relating to the timing and magnitude of the emission reductions may affect the size of these estimates. The detailed sector-level estimates of the size and distribution of mortality and morbidity risk suggest that the air pollution mortality burden has fallen over time but that many sectors continue to pose a substantial risk to human health.

Atmospheric ionic species in PM2.5 and PM1 aerosols in the ambient air of eastern central India

This study elucidates the characteristics of ambient PM2.5 (fine) and PM1 (submicron) samples collected between July 2009 and June 2010 in Raipur, India, in terms of water soluble ions, i.e. Na+, NH 4 + , K+, Mg2+, Ca2+, Cl−, NO 3 − and SO 4 2− . The total number of PM2.5 and PM1 samples collected with eight stage cascade impactor was 120. Annual mean concentrations of PM2.5 and PM1 were 150.9 ± 78.6 μg/m3 and 72.5 ± 39.0 μg/m3, respectively. The higher particulate matter (PM) mass concentrations during the winter season are essentially due to the increase of biomass burning and temperature inversion. Out of above 8 ions, the most abundant ions were SO 4 2− , NO 3 − and NH 4 + for both PM2.5 and PM1 aerosols; their average concentrations were 7.86 ± 5.86 μg/m3, 3.12 ± 2.63 μg/m3 and 1.94 ± 1.28 μg/m3 for PM2.5, and 5.61 ± 3.79 μg/m3, 1.81 ± 1.21 μg/m3 and 1.26 ± 0.88 μg/m3 for PM1, respectively. The major secondary species SO 4 2− , NO 3 − and NH 4 + accounted for 5.81%, 1.88% and 1.40% of the total mass of PM2.5 and 11.10%, 2.68%, and 2.48% of the total mass of PM1, respectively. The source identification was conducted for the ionic species in PM2.5 and PM1 aerosols. The results are discussed by the way of correlations and principal component analysis. Spearman correlation indicated that Cl− and K+ in PM2.5 and PM1 can be originated from similar type of sources. Principal component analysis reveals that there are two major sources (anthropogenic and natural such as soil derived particles) for PM2.5 and PM1 fractions.