Health impacts under different ozone mitigation pathways in Beijing-Tianjin-Hebei and its surroundings.

To evaluate the effects of the various ozone (O3) control approaches on environmental health and health inequalities, 121 reduction scenarios for nitrogen oxides (NOx) and volatile organic compounds (VOCs) were developed, and their environmental health impacts were calculated. With the target of achieving the 90th percentile of the daily maximum 8 h mean O3 concentration (MDA8-90th) of 160 µg/m3 in Beijing-Tianjin-Hebei and its surroundings ("2 + 26" cities), three typical scenarios namely, High-NOx reduction ratio (HN, NOx/VOCs = 6:1), High-VOCs reduction ratio (HV, NOx/VOCs = 3:7), and Balanced reduction ratio (Balanced, NOx/VOCs = 1:1) were investigated. The results show that O3 formation is currently NOx-limited at the regional scale, while some developed cities are VOC-limited, indicating that NOx mitigation should be the core for achieving the targeted concentration (160 µg/m3) at the regional scale, whereas cities such as Beijing in the short term should focus on VOCs mitigation. The population-weighted O3 concentrations in the HN, Balanced, and HV scenarios were 159.19, 159.19, and 158.44 µg/m3, respectively. In addition, the O3-related premature mortality was 41,320 in "2 + 26" cities; control measures under HN, Balanced, and HV could potentially decrease O3-related premature deaths by 59.94 %, 60.25 %, and 71.48 %, respectively. The HV scenario has been found to be more advantageous in lowering the O3-related environmental health impacts than the HN and Balanced scenarios. It was further found that premature deaths avoided by the HN scenario were mainly concentrated in economically unadvanced regions, whereas those prevented by the HV scenario were mainly concentrated in developed cities. This may lead to geographical inequities in environmental health. As ozone pollution in large cities with high population density is primarily VOC-limited, decrease in VOCs should be focused on in the short term to avoid more O3-related premature deaths, whereas NOx control may be more important in decreasing ozone concentrations and ozone-related mortality in the future.

Synergistic assessment of air pollution and carbon emissions from the economic perspective in China.

The synergistic evaluation integrating air quality, human health, climate impact, and socioeconomic development is significant for green and low-carbon transition. Here, we quantified the contribution of pollutant emissions in 30 provinces (source) to PM2.5 concentration and related premature mortality in each 20 km grid (receptor) of China in 2020 by an integrated model for the first time. Further, we established a cross-province contribution matrix of health impact intensity (HII, PM2.5-related deaths per GDP). According to HII and CEI (carbon emission intensity, defined as CO2 emission per GDP) levels, 30 provinces were divided into 4 regions including LL, HL, LH and HH. In order to assess the synergy in air pollution and carbon emission, we established an index system consisting of ISEC-AC (index for synergistic assessment) and its two sub index: IHI (index for HII assessment), and ICE (index for CEI assessment). Results showed that the ISEC-AC was more easily influenced by IHI as the variance of IHI was much higher than that of ICE. Influenced by various factors, e.g., economic structure, population density, pollution transport, ISEC-AC exhibited substantial spatial heterogeneity. In general, the ISEC-AC of southeast provinces was higher than that of central and western, indicating the environmental and climate impact per GDP was relatively lower in southeast China. For provinces, ISEC-AC of SH and GD were ~ 16 times higher than NX. For regions, due to low carbon emission intensity and health impact intensity, ISEC-AC of LL was the highest with 176; followed by HL (128), LH (126) and HH (77). Further, we figured out the main control problems and then put forward targeted synergetic control suggestions for air pollution and carbon emission from the perspective of energy structure, industry structure and industry layout, which can provide insights into future green and low-carbon policy making in China and other countries.

Mitigating China's Ozone Pollution with More Balanced Health Benefits.

China is confronting the challenge of opposite health benefits (OHBs) during ambient ozone (O3) mitigation because the same reduction scheme might yield opposite impacts on O3 levels and associated public health across different regions. Here, we used a combination of chemical transport modeling, health benefit assessments, and machine learning to capture such OHBs and optimize O3 mitigation pathways based on 121 control scenarios. We revealed that, for the China mainland, Beijing-Tianjin-Hebei and its surroundings ("2 + 26" cities), Yangtze River Delta, and Pearl River Delta, there could be at most 2897, 920, 1247, and 896 additional O3-related deaths in urban areas, respectively, accompanying 21,512, 3442, 5614, and 642 avoided O3-related deaths in rural areas, respectively, at the same control stage. Additionally, potential disbenefits during O3 mitigation were "pro-wealthy", that is, residents in developed regions are more likely to afford additional health risks. In order to avoid OHBs during O3 abatement, we proposed a two-phase control strategy, whereby the reduction ratio of NOX (nitrogen oxide) to VOCs (volatile organic compounds) was adjusted according to health benefit distribution patterns. Our study provided novel insights into China's O3 attainment and references for other countries facing the dual challenges of environmental pollution and associated inequality issues.

Analysis of vehicular CO2 emission in the Central Plains of China and its driving forces.

The Central Plains of China, represented by Henan province, faces a dramatic rise in vehicular stock and CO2 emissions. The refined-resolution(1 km × 1 km) vehicular CO2 emission inventory for Henan province was developed to identify emission patterns. Results show that CO2 emissions in Henan province reached 77.04 Mt in 2019, and LDGV and HDDT were the major sources that emitted 42.34% and 35.96% of CO2 emissions, respectively. Based on gridded emission, Moran's Index was used to identify spatial distribution patterns of vehicular CO2. The higher CO2 emission intensity areas were concentrated in the central and northern of the province and urban areas in each city, especially in Zhengzhou and its surrounding cities. Moreover, the analysis of the driving forces behind the differences in emissions among cities using the multi-regional (M-R) spatial decomposition model revealed that income and population-scale are significant impacts. In cities such as Zhengzhou, emissions may be dramatically increase owing to high economic growth expectations. 'Polarization phenomenon' of CO2 emission distribution should be vigilant. Findings provided insights for refined policy-making in Henan province to limit CO2 emission: (1) Take cities as transportation hubs, e.g., Zhengzhou and Shangqiu, and that in the traffic radiation circle, e.g., Jiaozuo and Zhoukou, as the critical areas for CO2 emission reduction; (2) Promote electric vehicles as replacement for traditional fuel vehicles; especially for cities with large passenger car emissions, such as Zhengzhou, and cities with large truck emissions, such as Shangqiu and Zhoukou; actively guide new consumer groups to choose EVs, especially in cities with high growth expectations such as Zhengzhou; (3) Rely on the advantages of transportation network to promote the 'road to railway' of bulk cargo transportation and mainly focus on highways with higher CO2 density, such as Beijing-Hong Kong&Macao Expressway, Shanghai-Xi'an Expressway, Da Guang Expressway, and Lian Huo Expressway.

[Impacts of Meteorology and Emission Variations on PM2.5 Concentration Throughout the Country During the 2020 Epidemic Period].

This study analyzed the impacts of meteorological conditions and changes in air pollutant emissions on PM2.5 across the country during the first quarter of 2020 based on the WRF-CMAQ model. Results showed that the variations in meteorological conditions led to a national PM2.5 concentration decreased of 1.7% from 2020-01 to 2020-03, whereas it increased by 1.6% in January and decreased by 1.3% and 7.9% in February and March, respectively. The reduction of pollutants emissions led to a decrease of 14.1% in national PM2.5 concentration during the first quarter of 2020 and a decrease of 4.0%, 25.7%, and 15.0% in January, February, and March, respectively. Compared to the same period last year, the PM2.5 concentration measured in Wuhan City decreased more than in the entire country. This was caused by improved meteorological conditions and a higher reduction of pollutant emissions in Wuhan City. PM2.5 in Beijing increased annually before the epidemic outbreak and during the strict control period, mainly due to unfavorable meteorological conditions. However, the decrease in PM2.5 in Beijing compared to March 2019 was closely related to the substantial reduction of emissions. The measured PM2.5 in the "2+26" cities, the Fenwei Plain and the Yangtze River Delta (YRD) decreased during the first quarter of 2020, with the largest drop occurring in the Yangtze River Delta due to higher YRD emissions reductions. The meteorological conditions of "2+26" cities and Fenwei Plain were unfavorable before the epidemic outbreak and greatly improved during the strict control period, whereas the Yangtze River Delta had the most favorable meteorological conditions in March. The decrease in PM2.5 concentration caused by the reduction of pollutant emissions in the three key areas was highest during the strict control period.

Air quality benefits of achieving carbon neutrality in China.

Achieving carbon neutrality before 2060 newly announced in China are expected to substantially affect air quality. Here we project the pollutants emissions in China based on a carbon neutrality roadmap and clean air policies evolution; national and regional PM2.5 and O3 concentrations in 2030 (the target year of carbon peak), 2035 (the target year of "Beautiful China 2035" launched by the Chinese government to fundamentally improve air quality) and 2060 (the target year of carbon neutrality) are then simulated using an air quality model. Results showed that compared with 2019, emissions of SO2, NOx, primary PM2.5, and VOCs are projected to reduce by 42%, 42%, 44%, and 28% in 2030, by 57%, 58%, 60%, and 42% in 2035, by 93%, 93%, 90% and 61% in 2060 respectively. Consequently, in 2030, 2035, and 2060, the national annual mean PM2.5 will be 27, 23, and 11 µg m-3; and the 90th percentile of daily 8-h maxima of O3 (O3-8h 90th) will be 129, 123, and 93 µg m-3; 82%, 94%, and 100% of 337 municipal cities will reach the current national air quality standard, respectively. It's expected that the "Beautiful China 2035" target is very likely to be achieved, and about half of the 337 cities will meet the current WHO air quality guideline in 2060. In the near future, strict environmental policies driven by "Beautiful China 2035" are needed due to their substantial contribution to emission reductions. By 2060, the low-carbon policies driven by the carbon neutrality target are expected to contribute to larger than 80% of reductions in PM2.5 and O3-8h 90th concentrations relative to the 2020 levels, implying that more attention could be paid to low-carbon policies after 2035. Our research would provide implications for future co-governance of air pollution and climate change mitigation in China and other developing countries.

Impacts of meteorology and emission variations on the heavy air pollution episode in North China around the 2020 Spring Festival.

Based on the Weather Research and Forecasting model and the Models-3 community multi-scale air quality model (WRF-CMAQ), this study analyzes the impacts of meteorological conditions and changes in air pollutant emissions on the heavy air pollution episode occurred over North China around the 2020 Spring Festival (January to Februray 2020). Regional reductions in air pollutant emissions required to eliminate the PM2.5 heavy pollution episode are also quantified. Our results found that meteorological conditions for the Beijing-Tianjin-Hebei and surrounding "2+26" cities are the worst during the heavy pollution episode around the 2020 Spring Festival as compared with two other typical heavy pollution episodes that occurred after 2015. However, because of the substantial reductions in air pollutant emissions in the "2+26" cities in recent years, and the 32% extra reduction in emissions during January to February 2020 compared with the baseline emission levels of the autumn and winter of 2019 to 2020, the maximum PM2.5 level during this heavy pollution episode around the 2020 Spring Festival was much lower than that in the other two typical episodes. Yet, these emission reductions are still not enough to eliminate regional heavy pollution episodes. Compared with the actual emission levels during January to February 2020, a 20% extra reduction in air pollutant emissions in the "2+26" cities (or a 45% extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020) could help to generally eliminate regionwide severe pollution episodes, and avoid heavy pollution episodes that last three or more consecutive days in Beijing; a 40% extra reduction in emissions (or a 60% extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020) could help to generally eliminate regionwide and continuous heavy pollution episodes. Our analysis finds that during the clean period after the heavy pollution episode around the 2020 Spring Festival, the regionwide heavy pollution episode would only occur with at least a 10-fold increase in air pollutant emissions.

[Evaluation of Different ISORROPIA-â ¡ Modes and the Influencing Factors of Aerosol pH Based on Tianjin Online Data].

Aerosol acidity is closely related to particle properties and the explosive growth of secondary particles. Aerosol pH is difficult to measure directly but can be estimated indirectly by thermodynamic equilibrium modeling. ISORROPIA-Ⅱ is one of the most commonly used thermodynamic models and includes different modes (forward and reverse) and aerosol states (stable and metastable). Studies have shown that the calculated pH results vary with the selected mode and phase state. In addition to the selection of modes and phases, there are also other factors that influence the modeling results. In order to explore the appropriate mode and phase selection of ISORROPIA-Ⅱ as well as the factors influencing the model results under the air pollution characteristics of typical Chinese cities, the simulation results of different modes and aerosol states were analyzed by using online hourly data for Tianjin. The results showed that the pH calculations using the forward mode and metastable state were satisfactory at a higher RH. With increased temperature, the pH, aerosol water content, and concentration proportion in the aerosol phase of semi-volatile components all decreased. RH affected aerosol pH by influencing the aerosol water content and concentration of semi-volatile components. An increased cation concentration led to an increased pH and NH3 concentration but a decreased HNO3 concentration, whereas an increased anion concentration had the opposite effect. Ca2+, SO42-, NO3-, and NH4+ had a great influence on pH. Compared with SO42-, NO3- had less effect on pH. Sensitive areas exist in the influence of NH4+ on pH, and a high NH4+ concentration did not cause a continuous pH increase. This study can improve the understanding of aerosol pH simulation using ISORROPIA-Ⅱ, and provides reference for research on the pH-related secondary generation mechanism, semi-volatile component gas-particle distribution, and pollution control measures.

Detailed Analysis of Estimated pH, Activity Coefficients, and Ion Concentrations between the Three Aerosol Thermodynamic Models.

In this work, we utilize a rich set of simulated and ground-based observational data in Tianjin, China to examine and compare the differences in aerosol acidity and composition predicted by three popular thermodynamic equilibrium models: ISORROPIA II, the Extended Aerosol Inorganics Model vision IV (E-AIM IV), and the Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients model (AIOMFAC). The species used to estimate aerosol acidity for both simulated and ambient data were NH4+, Na+, SO42-, NO3-, and Cl-. For simulated data, there is good agreement between ISORROPIA II and E-AIM IV predicted acidity in the forward and metastable mode, resulting from the hydrogen ion activity coefficient (γ(H+)) and the molality (m(H+)) showing opposite trends. While almost all other inorganic species concentrations are found to be similar among the three models, such is not the case for the bisulfate ion (HSO4-), which is linked to m(H+). We find that differences in predicted bisulfate between the three models primarily result from differences in the treatment of the HSO4- ↔ H+ + SO42- reaction for highly acidic conditions. This difference in bisulfate is responsible for much of the difference in estimated pH for the ambient data (average pH of 3.5 for ISORROPIA II and 3.0 for E-AIM IV).

High-Resolution Data Sets Unravel the Effects of Sources and Meteorological Conditions on Nitrate and Its Gas-Particle Partitioning.

Nitrate is one of the most abundant inorganic water-soluble ions in fine particulate matter (PM2.5). However, the formation mechanism of nitrate in the ambient atmosphere, especially the impacts of its semivolatility and the various existing forms of nitrogen, remain under-investigated. In this study, hourly ambient observations of speciated PM2.5 components (NO3-, SO42-, etc.) were collected in Tianjin, China. Source contributions were analyzed by PMF/ME2 (Positive Matrix Factorization using the Multilinear Engine 2) program, and pH were estimated by ISORROPIA-II, to investigate the relationship between pH and nitrate. Five sources (factors) were resolved: secondary sulfate (SS), secondary nitrate (SN), dust, vehicle and coal combustion. SN and pH showed a triangle-shaped relationship. When SS was high, the fraction of nitrate partitioning into the aerosol phase exhibits a characteristic "S-curve" relationship with pH for different seasons. An index ( ITL) is developed and combined with pH to explore the sensitive regions of "S-curve". Controlling the emissions of anions (SO42-, Cl-), cations (Ca2+, Mg2+, etc.) and gases (NO x, NH3, SO2, etc.) will change pH, potentially reducing or increasing SN. The findings of this work provide an effective approach for exploring the formation mechanisms of nitrate under different influencing factors (sources, pH, and IRL).

[Source Apportionment and Heath Risk Quantification of Heavy Metals in PM2.5 in Yangzhou, China].

Recently, a new method combining positive matrix factorization (PMF) and heavy metal health risk (HMHR) assessment was proposed to apportion sources of heavy metals in ambient particulate matter and the associated heavy metal cancer health risk (HMCR), which has been applied to data collected in Yangzhou, China. The annual average concentrations of six measured heavy metals were Pb (64.4 ng·m-3), followed by Cr (25.24 ng·m-3), As (6.36 ng·m-3), Ni (5.36 ng·m-3), Cd (3.34 ng·m-3), and Co (1.21 ng·m-3). The results showed that the major sources of PM2.5 were secondary sources (37.7%), followed by coal combustion (19.4%), resuspended dust (17.5%), vehicle emissions (16.9%), construction dust (5.2%), and industrial emissions (3.4%). As was primarily emitted from coal combustion, vehicle emissions, and resuspended dust. Co originated from industry emissions. Pb was mainly emitted from coal combustion. Ni and Cd were from industrial emissions. The major sources that contributed to HMCR were resuspended dust, coal combustion, vehicle emissions, industry emissions, and construction dust. The high contributions of resuspended dust and coal to HMCR were likely due to the high heavy metals concentrations in coal and the resuspended dust profile as well as high emissions of these sources.

[Analysis of Chemical Composition of the Fine Particulate Matter in Summer in Tianjin City via a Single Particle Aerosol Mass Spectrometer (SPAMS)].

As an important megacity of the Beijing-Tianjin-Hebei air pollution transmission channel and the Bohai Sea Economic Zone, Tianjin is influenced by air pollution in recent years, thus research on the fine particulate matter in Tianjin is of vital value. In this study, single particle aerosol mass spectrometry (SPAMS) was used to measure data of Jinnan District in Tianjin during August 2017, to describe the chemical features of fine particles in summer ambient air and estimate the potential pollution sources of fine particles. By using the ART-2a clustering method, 12 classes of PM were acquired, such as elemental carbon particles, Fe-NO3 particles, Na-K particles, and metal particles. After monitoring the size distribution and diurnal variation of fine particles, it was concluded that the ratio of EC particles decreased as the size increased, whereas dust particles and Fe-NO3 particles showed the opposite trend; three types of EC particles varied differently in a day according to the photochemical reaction; and the ratio of Fe-NO3 particles was elevated in the daytime because of industrial production during that period. Backward trajectories of daily airflow at the measured spot were also calculated. When the monitoring site was affected by the air mass from the southwest, coal-burning particles may have contributed more; whereas, when the air mass from the southeast occurred more frequently, biomass burning and sea salt particles possibly contributed more.

Refined source apportionment of coal combustion sources by using single particle mass spectrometry.

In this study, samples of three typical coal combustion source types, including Domestic bulk coal combustion (DBCC), Heat supply station (HSS), and Power plant (PP) were sampled and large sets of their mass spectra were obtained and analyzed by SPAMS during winter in a megacity in China. A primary goal of this study involves determining representative size-resolved single particle mass spectral signatures of three source types that can be used in source apportionment activities. Chemical types describe the majority of the particles of each source type were extracted by ART-2a algorithm with distinct size characteristics, and the corresponding tracer signals were identified. Mass spectral signatures from three source types were different from each other, and the tracer signals were effective in distinguishing different source types. A high size-resolution source apportionment method were proposed in this study through matching sources' mass spectral signatures to particle spectra in a twelve days ambient sampling to source apportion the particles. Contributions of three source types got different size characteristics, as HSS source got higher contribution in smaller sizes, But PP source got higher contributions as size increased. Source contributions were also quantified during two typical haze episodes, and results indicated that HSS source (for central-heating) and DBCC source (for domestic heating and cooking) may contribute evidently to pollution formation.

Spatial, seasonal and diurnal patterns in physicochemical characteristics and sources of PM2.5 in both inland and coastal regions within a megacity in China.

Day and night PM2.5 samples were collected at coastal and inland stations in a megacity in China. Temporal, spatial, and directional characteristics of PM2.5 concentrations and compositions were investigated. Average PM2.5 concentration was higher at inland (153.28µg/m3) than at coastal (114.46µg/m3). PM2.5 were significantly influenced by season and site but insignificantly by diurnal pattern. Sources were quantified by a two-way and a newly developed three-way receptor models conducted using ME2. Secondary sulfate and SOC (SS&SOC, 25% and 23%), coal and biomass burning (CC&BB, 20% and 21%), crustal and cement dust (CRD&CED, 19% and 21%), secondary nitrate (SN, 13% and 18%), vehicular exhaust (VE, 14% and 17%), and sea salt (SEA, 6% and 2%) were major sources for coastal and inland. Different mechanisms of heavy pollution were observed: heavy PM2.5 caused by primary sources and secondary sources showed similar frequency at coast, while most of heavy pollutions at inland site might be associated with the elevation of secondary particles. For spatial characteristics, SS&SOC, CRD&CED contributions were higher at coastal; SN and VE presented higher fractions at inland. Higher SS&SOC, SN and CC&BB in winter might be attributed to intensive coal combustion for residential warming and to stable meteorological conditions.

Source apportionment for fine particulate matter in a Chinese city using an improved gas-constrained method and comparison with multiple receptor models.

PM2.5 is one of the most studied atmospheric pollutants due to its adverse impacts on human health and welfare and the environment. An improved model (the chemical mass balance gas constraint-Iteration: CMBGC-Iteration) is proposed and applied to identify source categories and estimate source contributions of PM2.5. The CMBGC-Iteration model uses the ratio of gases to PM as constraints and considers the uncertainties of source profiles and receptor datasets, which is crucial information for source apportionment. To apply this model, samples of PM2.5 were collected at Tianjin, a megacity in northern China. The ambient PM2.5 dataset, source information, and gas-to-particle ratios (such as SO2/PM2.5, CO/PM2.5, and NOx/PM2.5 ratios) were introduced into the CMBGC-Iteration to identify the potential sources and their contributions. Six source categories were identified by this model and the order based on their contributions to PM2.5 was as follows: secondary sources (30%), crustal dust (25%), vehicle exhaust (16%), coal combustion (13%), SOC (7.6%), and cement dust (0.40%). In addition, the same dataset was also calculated by other receptor models (CMB, CMB-Iteration, CMB-GC, PMF, WALSPMF, and NCAPCA), and the results obtained were compared. Ensemble-average source impacts were calculated based on the seven source apportionment results: contributions of secondary sources (28%), crustal dust (20%), coal combustion (18%), vehicle exhaust (17%), SOC (11%), and cement dust (1.3%). The similar results of CMBGC-Iteration and ensemble method indicated that CMBGC-Iteration can produce relatively appropriate results.

Mass spectra features of biomass burning boiler and coal burning boiler emitted particles by single particle aerosol mass spectrometer.

In this study, single particle mass spectra signatures of both coal burning boiler and biomass burning boiler emitted particles were studied. Particle samples were suspended in clean Resuspension Chamber, and analyzed by ELPI and SPAMS simultaneously. The size distribution of BBB (biomass burning boiler sample) and CBB (coal burning boiler sample) are different, as BBB peaks at smaller size, and CBB peaks at larger size. Mass spectra signatures of two samples were studied by analyzing the average mass spectrum of each particle cluster extracted by ART-2a in different size ranges. In conclusion, BBB sample mostly consists of OC and EC containing particles, and a small fraction of K-rich particles in the size range of 0.2-0.5µm. In 0.5-1.0µm, BBB sample consists of EC, OC, K-rich and Al_Silicate containing particles; CBB sample consists of EC, ECOC containing particles, while Al_Silicate (including Al_Ca_Ti_Silicate, Al_Ti_Silicate, Al_Silicate) containing particles got higher fractions as size increase. The similarity of single particle mass spectrum signatures between two samples were studied by analyzing the dot product, results indicated that part of the single particle mass spectra of two samples in the same size range are similar, which bring challenge to the future source apportionment activity by using single particle aerosol mass spectrometer. Results of this study will provide physicochemical information of important sources which contribute to particle pollution, and will support source apportionment activities.

Influence of quarry mining dust on PM2.5 in a city adjacent to a limestone quarry: Seasonal characteristics and source contributions.

To understand the influence of quarry mining dust on particulate matter, ambient PM2.5 and quarry mining dust source samples were collected in a city near quarry facilities during 2013-2014. Samples were subject to chemical analysis for dust-related species (Al, Si, Ca, Fe, Ti), tracer metals, carbon components and water-soluble ions. Seasonal variations of PM2.5 and its main chemical components were investigated. Distinctive seasonal variations of PM2.5 were observed, with the highest PM2.5 concentrations (112.42µgm(-3)) in fall and lowest concentrations in summer (45.64µgm(-3)). For dust-related species, mass fractions of Si and Al did not show obvious seasonal variations, whereas Ca presented higher fractions in spring and summer and lower fractions in fall and winter. A combined receptor model (PMF-CMB) was applied to quantify the quarry mining dust contribution to PM2.5. Seven sources were identified, including quarry mining dust, soil dust, cement dust, coal combustion vehicles, secondary sulfate and secondary nitrate. On a yearly average basis, the contribution of quarry mining dust to PM2.5 was 6%. The contribution of soil dust to PM2.5 was comparable with cement dust (13% and 13%, respectively). Other identified sources included vehicle, secondary sulfate, secondary nitrate and coal combustion, which contributed 23, 15, 9 and 18% of the total mass, respectively. Air mass residence time (AMRT) analysis showed that northeast and southeast regions might be the major PM2.5 source during the sampling campaign. The findings of this study can be used to understand the characteristics of quarry mining dust and control strategies for PM2.5.