Estimation of the PM2.5 health effects in China during 2000-2011

Effects of fine particulate matter on bone marrow-conserved hematopoietic and mesenchymal stem cells: a systematic review

The harmful effects of fine particulate matter ≤2.5 µm in size (PM2.5) on human health have received considerable attention. However, while the impact of PM2.5 on the respiratory and cardiovascular systems has been well studied, less is known about the effects on stem cells in the bone marrow (BM). With an emphasis on the invasive characteristics of PM2.5, this review examines the current knowledge of the health effects of PM2.5 exposure on BM-residing stem cells. Recent studies have shown that PM2.5 enters the circulation and then travels to distant organs, including the BM, to induce oxidative stress, systemic inflammation and epigenetic changes, resulting in the reduction of BM-residing stem cell survival and function. Understanding the broader health effects of air pollution thus requires an understanding of the invasive characteristics of PM2.5 and its direct influence on stem cells in the BM. As noted in this review, further studies are needed to elucidate the underlying processes by which PM2.5 disturbs the BM microenvironment and inhibits stem cell functionality. Strategies to prevent or ameliorate the negative effects of PM2.5 exposure on BM-residing stem cells and to maintain the regenerative capacity of those cells must also be investigated. By focusing on the complex relationship between PM2.5 and BM-resident stem cells, this review highlights the importance of specific measures directed at safeguarding human health in the face of rising air pollution.

Polycentric agglomeration and haze pollution: evidence from China

Polycentric agglomeration has gradually become a salient feature of rapid growth in urbanization in China. Using province-level balanced panel data over the period 2000-18, we examine the impact of polycentric agglomeration on haze pollution and its mechanism of action. The results show that the impact of polycentric agglomeration on haze pollution exhibits a significant inverted U-shaped feature. Nevertheless, except for a few provinces where polycentric agglomeration exceeds the turning point, the degree of polycentric concentration in most provinces lies to the left of the turning point. Further, a mediating effect model illustrates that industrial structure rationalization and technological progress are effective paths through which polycentric agglomeration affects haze pollution. Finally, we demonstrate that the effect of polycentric agglomeration on haze pollution is influenced by transportation and communication infrastructure; improved transportation and communication infrastructure contributes to the haze control effect of polycentric agglomeration.

Spatiotemporal distribution, trend, forecast, and influencing factors of transboundary and local air pollutants in Nagasaki Prefecture, Japan

The study of PM_2.5 and NO₂ has been emphasized in recent years due to their adverse effects on public health. To better understand these pollutants, many studies have researched the spatiotemporal distribution, trend, forecast, or influencing factors of these pollutants. However, rarely studies have combined these to generate a more holistic understanding that can be used to assess air pollution and implement more effective strategies. In this study, we analyze the spatiotemporal distribution, trend, forecast, and factors influencing PM_2.5 and NO₂ in Nagasaki Prefecture by using ordinary kriging, pearson's correlation, random forest, mann-kendall, auto-regressive integrated moving average and error trend and seasonal models. The results indicated that PM_2.5, due to its long-range transport properties, has a more substantial spatiotemporal variation and affects larger areas in comparison to NO₂, which is a local pollutant. Despite tri-national efforts, local regulations and legislation have been effective in reducing NO₂ concentration but less effective in reducing PM_2.5. This multi-method approach provides a holistic understanding of PM_2.5 and NO₂ pollution in Nagasaki prefecture, which can aid in implementing more effective pollution management strategies. It can also be implemented in other regions where studies have only focused on one of the aspects of air pollution and where a holistic understanding of air pollution is lacking.

The Relative Contributions of Different Wheat Leaves to the Grain Cadmium Accumulation

In the context of increasing atmospheric particles pollution, wheat cadmium (Cd) pollution caused by atmospheric deposition in agro-ecosystems has attracted increasing attention. However, the relative contribution of different wheat leaves-to-grain Cd accumulation is still unclear. We assessed the roles of different wheat leaves on grain Cd accumulation with field-comparative experiments during the filling stage. Results show that wheat leaves can direct uptake atmospheric Cd through stomata, and the flag leaf exhibited a higher Cd concentration compared to other leaves. The relative contribution of the leaves-to-grain Cd accumulation decreased gradually during the grain-filling period, from 34.44% reaching 14.48%, indicating that the early grain-filling period is the critical period for leaf Cd contributions. Moreover, the relative contribution of flag leaves (7.27%) to grain Cd accumulation was larger than that of the sum of other leaves (7.21%) at maturity. Therefore, the flag leaf is the key leaf involved in grain Cd accumulation, and controlling the transport of Cd from leaves to grains at the early filling period, particularly flag leaf, could help to ensure wheat grain safety, thus ensuring the safety of food production.

Health Impact Attributable to Improvement of PM2.5 Pollution from 2014–2018 and Its Potential Benefits by 2030 in China

With the advancement of urbanization and industrialization, air pollution has become one of the biggest challenges for sustainable development. In recent years, ambient PM2.5 concentrations in China have declined substantially due to the combined effect of PM2.5 control and meteorological conditions. To this end, it is critical to assess the health impact attributable to PM2.5 pollution improvement and to explore the potential benefits which may be obtained through the achievement of future PM2.5 control targets. Based on PM2.5 and population data with a 1 km resolution, premature mortality caused by exposure to PM2.5 in China from 2014 to 2018 was estimated using the Global Exposure Mortality Model (GEMM). Then, the potential benefits of achieving PM2.5 control targets were estimated for 2030. The results show that premature mortality caused by PM2.5 pollution decreased by 22.41%, from 2,361,880 in 2014 to 1,832,470 in 2018. Moreover, the reduction of premature mortality in six major regions of China accounted for 52.82% of the national total reduction. If the PM2.5 control target can be achieved by 2030, PM2.5-related premature deaths will further decrease by 403,050, accounting for 21.99% of those in 2018. Among them, 87.02% of cities exhibited decreases in premature deaths. According to the potential benefits in 2030, all cities were divided into three types, of which type III cities should set stricter PM2.5 control targets and further strengthen the associated monitoring and governance. The results of this study provide a reference for the formulation of air pollution control policies based on regional differences.

The associations of air pollution and socioeconomic factors with esophageal cancer in China based on a spatiotemporal analysis

Rapid urbanization and industrialization in China have incurred serious air pollution and consequent health concerns. In this study, we examined the modifying effects of urbanization and socioeconomic factors on the association between PM_2.5 and incidence of esophageal cancer (EC) in 2000-2015 using spatiotemporal techniques and a quasi-Poisson generalized linear model. The results showed a downward trend of EC and high-risk areas aggregated in North China and Huai River Basin. In addition, a stronger association between PM_2.5 and incidence was observed in low urbanization group, and the association was stronger for females than males. When exposure time-windows were adjusted as 0, 5, 10, 15 years, the incidence risk increased by 2.48% (95% CI: 2.23%, 2.73%), 2.20% (95% CI: 1.91%, 2.49%), 2.18% (95% CI%: 1.92%, 2.43%), 1.87% (95% CI%:1.64, 2.10%) for males, respectively and 4.03% (95% CI: 3.63%, 4.43%), 2.20% (95% CI: 1.91%, 2.49%), 3.97% (95% CI: 3.54%, 4.41%), 3.06% (95% CI: 2.71%, 3.41%) for females, respectively. The findings indicated people in low urbanization group faced with a stronger EC risk caused by PM_2.5, which contributes to a more comprehensive understanding of combating EC challenges related to PM_2.5 pollution.

Spatiotemporal variability in long-term population exposure to PM2.5 and lung cancer mortality attributable to PM2.5 across the Yangtze River Delta (YRD) region over 2010-2016: A multistage approach

The Yangtze River Delta region (YRD) is one of the most densely populated regions in the world, and is frequently influenced by fine particulate matter (PM_2.5). Specifically, lung cancer mortality has been recognized as a major health burden associated with PM_2.5. Therefore, this study developed a multistage approach 1) to first create dasymetric population data with moderate resolution (1 km) by using a random forest algorithm, brightness reflectance of nighttime light (NTL) images, a digital elevation model (DEM), and a MODIS-derived normalized difference vegetation index (NDVI), and 2) to apply the improved population dataset with a MODIS-derived PM_2.5 dataset to estimate the association between spatiotemporal variability of long-term population exposure to PM_2.5 and lung cancer mortality attributable to PM_2.5 across YRD during 2010-2016 for microscale planning. The created dasymetric population data derived from a coarse census unit (administrative unit) were fairly matched with census data at a fine spatial scale (street block), with R² and RMSE of 0.64 and 27,874.5 persons, respectively. Furthermore, a significant urban-rural difference of population exposure was found. Additionally, population exposure in Shanghai was 2.9-8 times higher than the other major cities (7-year average: 192,000 μg·people/m³·km²). More importantly, the relative risks of lung cancer mortality in high-risk areas were 28%-33% higher than in low-risk areas. There were 12,574-14,504 total lung cancer deaths attributable to PM_2.5, and lung cancer deaths in each square kilometer of urban areas were 7-13 times higher than for rural areas. These results indicate that moderate-resolution information can help us understand the spatiotemporal variability of population exposure and related health risk in a high-density environment.

Association between fine particle exposure and common test items in clinical laboratory: A time-series analysis in Changsha, China

Most studies on the health effects of PM_2.5 (fine particulate matter with diameter smaller than 2.5 μm) use indirect indicators, such as mortality and number of hospital visits. Recent research shows that biomarkers can also be used to evaluate the health effects of PM_2.5; however, these biomarkers are not very common. Clinical laboratories can provide a significant amount of test data that have been proven to have important diagnostic value. Therefore, we use big data analysis methods to find the associations between clinical laboratory common test items and PM_2.5 exposure. Data related to air pollution and meteorological information between 2014 and 2016 were obtained from the China National Environmental Monitoring Centre and the China National Meteorological Information Center. Additionally, data of 27 common test items from the same period were collected from Changsha Central Hospital. Primary analyses included a generalized additive model to analyze the associations between PM_2.5 concentration and common test items; the model was adjusted for time trends, weather conditions (temperature and humidity), and days of the week. Furthermore, we adjusted the effects of other air pollutants, such as PM₁₀, SO₂, NO₂, CO, and O₃. 17 items such as TP, ALB, ALT, AST, TBIL, DBIL, UREA, CREA, UA, GLU, LDL, WBC, K, Cl, Ca, TT, and FIB were significantly positively associated with PM_2.5 concentration (P< 0.05) and have concentration-response relationship. After adjusting the effect of PM₁₀+SO₂+NO₂+CO+O₃, TP, ALB, ALT, AST, TBIL, DBIL, UREA, CREA, UA, GLU, WBC, Cl, and Ca were still significantly associated with PM_2.5 concentration (P< 0.05). This current study suggested that clinical laboratory common test items may be used to assess and predict the health effects of PM_2.5 on the population.

Estimating Ground-Level Particulate Matter in Five Regions of China Using Aerosol Optical Depth

Aerosol optical depth (AOD) has been widely used to estimate near-surface particulate matter (PM). In this study, ground-measured data from the Campaign on Atmospheric Aerosol Research network of China (CARE-China) and the Aerosol Robotic Network (AERONET) were used to evaluate the accuracy of Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data for different aerosol types. These four aerosol types were from dust, smoke, urban, and uncertain and a fifth “type” was included for unclassified (i.e., total) aerosols. The correlation for dust aerosol was the worst (R2 = 0.15), whereas the correlations for smoke and urban types were better (R2 values of 0.69 and 0.55, respectively). The mixed-effects model was used to estimate the PM2.5 concentrations in Beijing–Tianjin–Hebei (BTH), Sichuan–Chongqing (SC), the Pearl River Delta (PRD), the Yangtze River Delta (YRD), and the Middle Yangtze River (MYR) using the classified aerosol type and unclassified aerosol type methods. The results suggest that the cross validation (CV) of different aerosol types has higher correlation coefficients than that of the unclassified aerosol type. For example, the R2 values for dust, smoke, urban, uncertain, and unclassified aerosol types BTH were 0.76, 0.85, 0.82, 0.82, and 0.78, respectively. Compared with the daily PM2.5 concentrations, the air quality levels estimated using the classified aerosol type method were consistent with ground-measured PM2.5, and the relative error was low (most RE was within ±20%). The classified aerosol type method improved the accuracy of the PM2.5 estimation compared to the unclassified method, although there was an overestimation or underestimation in some regions. The seasonal distribution of PM2.5 was analyzed and the PM2.5 concentrations were high during winter, low during summer, and moderate during spring and autumn. Spatially, the higher PM2.5 concentrations were predominantly distributed in areas of human activity and industrial areas.

Socioeconomic factors and regional differences of PM2.5 health risks in China

China is a country with one of the highest concentrations of airborne particulate matter smaller than 2.5 μm (PM_2.5) in the world, and it has obvious spatial-distribution characteristics. Areas of concentrated population tend to be regions with higher PM_2.5 concentrations, which further aggravate the impact of PM_2.5 pollution on population health. Using PM_2.5-concentration and socioeconomic data for 225 cities in China in 2015, we adopted a PM_2.5-health-risk-assessment method (with simplified calculation) and applied the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to analyze the effects of socioeconomic factors on PM_2.5 health risks. The results showed that: (1) At the national level, the order of contribution degree of each socioeconomic factor in the PM_2.5-health-risk and PM_2.5-concentration model is consistent. (2) From a regional perspective, in all three regions, the industrial structure is the decisive factor affecting PM_2.5 health risks, and reduction of energy intensity increases PM_2.5 health risks, but the impact of the total amount of urban central heating on PM_2.5 health risks is very low. In the eastern region, the increased urbanization rate and length of highways significantly increase PM2.5 health risks, but the increasing effect of the extent of built-up area is the lowest. In the central region, the increasing effects of the extent of built-up area on PM_2.5 health risks are significantly greater than the decreasing effects of the urbanization rate. In the western region, economic development has the least effect on reducing PM_2.5 health risks. Our research enriches PM_2.5-health-risk theory and provides some theoretical support for PM_2.5-health-risk diversity management in China.

Impact assessment of meteorological and environmental parameters on PM2.5 concentrations using remote sensing data and GWR analysis (case study of Tehran)

The PM_2.5 as one of the main pollutants in Tehran city has a devastating effect on human health. Knowing the key parameters associated with PM_2.5 concentration is essential to take effective actions to reduce the concentration of these particles. This study assesses the relationship between meteorological (humidity, pressure, temperature, precipitation, and wind speed) and environmental parameters (normalize difference vegetation index and land surface temperature of MODIS satellite data) on PM_2.5 concentration in Tehran city. The Geographically Weighted Regression (GWR) was employed to assess the impact of key parameters on PM_2.5 concentrations in winter and summer. For this purpose, first the seasonal average of meteorological data were extracted and synchronized to satellite data. Then, using the ordinary least square model, the important parameters related to PM_2.5 concentration were determined and evaluated. Finally, using the GWR model, the relationships between parameters related to PM_2.5 concentration were analyzed. The results of this study indicate that meteorological and environmental parameters in winter season (71%) have a much higher ability to explain PM_2.5 concentration than summer season (40%). In winter, PM_2.5 concentration has a negative correlation with vegetation at most parts of the study area, a negative correlation with LST in the western and a positive correlation in the eastern part of the study area, a positive correlation with temperature, and a negative correlation with wind speed in the northeastern part of the study area. Precipitation has a positive correlation with PM_2.5 concentration in most parts of the study area in both seasons. But, it was investigated in case of higher precipitation (more than 2 mm), PM_2.5 concentration decreases. But, there is no negative relationship in any of the dependent parameters with PM_2.5 concentration in summer. In this season, the air temperature parameter showed a high correlation with PM_2.5 concentration. Also, spatial variations of the local coefficients for all parameters are higher in winter than in summer.

Estimating Daily PM2.5 Concentrations in Beijing Using 750-M VIIRS IP AOD Retrievals and a Nested Spatiotemporal Statistical Model

Satellite-retrieved aerosol optical depth (AOD) data have been widely used to predict PM2.5 concentrations. Most of their spatial resolutions (~1 km or greater), however, are too coarse to support PM2.5-related studies at fine scales (e.g., urban-scale PM2.5 exposure assessments). Space-time regression models have been widely developed and applied to predict PM2.5 concentrations from satellite-retrieved AOD. Their accuracies, however, are not satisfactory particularly on days that lack a model dataset. The present study aimed to evaluate the effectiveness of recent high-resolution (i.e., ~750 m at nadir) AOD obtained from the Visible Infrared Imaging Radiometer Suite instrument (VIIRS) Intermediate Product (IP) in estimating PM2.5 concentrations with a newly developed nested spatiotemporal statistical model. The nested spatiotemporal statistical model consisted of two parts: a nested time fixed effects regression (TFER) model and a series of geographically weighted regression (GWR) models. The TFER model, containing daily, weekly, or monthly intercepts, used the VIIRS IP AOD as the main predictor alongside several auxiliary variables to predict daily PM2.5 concentrations. Meanwhile, the series of GWR models used the VIIRS IP AOD as the independent variable to correct residuals from the first-stage nested TFER model. The average spatiotemporal coverage of the VIIRS IP AOD was approximately 16.12%. The sample-based ten-fold cross validation goodness of fit (R2) for the first-stage TFER models with daily, weekly, and monthly intercepts were 0.81, 0.66, and 0.45, respectively. The second-stage GWR models further captured the spatial heterogeneities of the PM2.5-AOD relationships. The nested spatiotemporal statistical model produced more daily PM2.5 estimates and improved the accuracies of summer, autumn, and annual PM2.5 estimates. This study contributes to the knowledge of how well VIIRS IP AOD can predict PM2.5 concentrations at urban scales and offers strategies for improving the coverage and accuracy of daily PM2.5 estimates on days that lack a model dataset.

Satellite-Based Estimation of Hourly PM2.5 Concentrations Using a Vertical-Humidity Correction Method from Himawari-AOD in Hebei

Abstract: Particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) is related to various adverse health effects. Ground measurements can yield highly accurate PM2.5 concentrations but have certain limitations in the discussion of spatial-temporal variations in PM2.5. Satellite remote sensing can obtain continuous and long-term coverage data, and many previous studies have demonstrated the relationship between PM2.5 and AOD (aerosol optical depth) from theoretical analysis and observation. In this study, a new aerosol product with a high spatial-temporal resolution retrieved from the AHI (the Advance Himawari Imager) was obtained using a vertical-humidity correction method to estimate hourly PM2.5 concentrations in Hebei. The hygroscopic growth factor (fRH) was fitted at each site (in a total of 137 matched sites). Meanwhile, assuming that there was little change in fRH at a certain scale, the nearest fRH of each pixel was determined to calculate PM2.5 concentrations. Compared to the correlation between AOD and PM2.5, the relationship between the "dry" mass extinction efficiency obtained by vertical-humidity correction and the ground-measured PM2.5 significantly improved, with r coefficient values increasing from 0.19⁻0.47 to 0.61⁻0.76. The satellite-estimated hourly PM2.5 concentrations were consistent with the ground-measured PM2.5, with a high r (0.8 ± 0.07) and a low RMSE (root mean square error, 30.4 ± 5.5 μg/m³) values, and the accuracy in the afternoon (13:00⁻16:00) was higher than that in the morning (09:00⁻12:00). Meanwhile, in a comparison of the daily average PM2.5 concentrations of 11 sites from different cities, the r values were approximately 0.91 ± 0.03, and the RMSEs were between 13.94 and 31.44 μg/m³. Lastly, pollution processes were analyzed, and the analysis indicated that the high spatial-temporal resolution of the PM2.5 data could continuously and intuitively reflect the characteristics of regional pollutants (such as diffusion and accumulation), which is of great significance for the assessment of regional air quality.

Spatiotemporal Assessment of PM2.5-Related Economic Losses from Health Impacts during 2014⁻2016 in China

Background: Particulate air pollution, especially PM_2.5, is highly correlated with various adverse health impacts and, ultimately, economic losses for society, however, few studies have undertaken a spatiotemporal assessment of PM_2.5-related economic losses from health impacts covering all of the main cities in China. Methods: PM_2.5 concentration data were retrieved for 190 Chinese cities for the period 2014–2016. We used a log-linear exposure–response model and monetary valuation methods, such as value of a statistical life (VSL), amended human capital (AHC), and cost of illness to evaluate PM_2.5-related economic losses from health impacts at the city level. In addition, Monte Carlo simulation was used to analyze uncertainty. Results: The average economic loss was 0.3% (AHC) to 1% (VSL) of the total gross domestic product (GDP) of 190 Chinese cities from 2014 to 2016. Overall, China experienced a downward trend in total economic losses over the three-year period, but the Beijing–Tianjin–Hebei, Shandong Peninsula, Yangtze River Delta, and Chengdu-Chongqing regions experienced greater annual economic losses. Conclusions: Exploration of spatiotemporal variations in PM_2.5-related economic losses from long-term health impacts could provide new information for policymakers regarding priority areas for PM_2.5 pollution prevention and control in China.

Changes in gene expression in chronic allergy mouse model exposed to natural environmental PM2.5-rich ambient air pollution

Particulate matter (PM) air pollution has been associated with an increase in the incidence of chronic allergic diseases; however, the mechanisms underlying the effect of exposure to natural ambient air pollution in chronic allergic diseases have not been fully elucidated. In the present study, we aimed to investigate the cellular responses induced by exposure to natural ambient air pollution, employing a mouse model of chronic allergy. The results indicated that exposure to ambient air pollution significantly increased the number of eosinophils in the nasal mucosa. The modulation of gene expression profile identified a set of regulated genes, and the Triggering Receptor Expressed on Myeloid cells1(TREM1) signaling canonical pathway was increased after exposure to ambient air pollution. In vitro, PM2.5 increased Nucleotide-binding oligomerization domain-containing protein 1 (Nod1) and nuclear factor (NF)-κB signaling pathway activation in A549 and HEK293 cell cultures. These results suggest a novel mechanism by which, PM2.5 in ambient air pollution may stimulate the innate immune system through the PM2.5-Nod1-NF-κB axis in chronic allergic disease.

A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China

Satellite-derived aerosol optical depth (AOD) has been proven effective for estimating ground-level particles with an aerodynamic diameter <2.5μm (PM_2.5) concentrations. Using a time fixed effects regression model, we compared the capacity of two AOD sources, Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), to estimate ground-level PM_2.5 concentrations over a heavily polluted region in China. Regarding high-quality AOD data, the results show that the VIIRS model performs better than the MODIS model with respect to all model accuracy evaluation indexes (e.g., the coefficient of determination, R², of the VIIRS and MODIS models are 0.76 and 0.71 during model fitting and 0.72 and 0.66 in cross validation, respectively), the potential for capturing high PM_2.5 concentrations, and the precision of annual and seasonal PM_2.5 estimates. However, the spatiotemporal coverage of the high-quality VIIRS AOD is inferior to that of the MODIS AOD. We attempted to include medium-quality VIIRS AOD data to eliminate this, while exploring its influence on the performance of the VIIRS model. The results show that it improves the spatiotemporal coverage of the VIIRS AOD dramatically especially in winter, although a decline in model accuracy occurred. Compared to the MODIS model, the VIIRS model with both high-quality and medium-quality AOD data performs comparably or even better with respect to some model accuracy evaluation indexes (e.g., the model overfitting degree of the VIIRS and MODIS models are 7.46% and 5.82%, respectively), the potential for capturing high PM_2.5 concentrations, and the precision of annual and seasonal PM_2.5 estimates. Nevertheless, the VIIRS models did not perform as well as the MODIS model in summer. This study reveals the advantages and disadvantages of the MODIS and VIIRS AOD in simulating ground-level PM_2.5 concentrations, promoting research on satellite-based PM_2.5 estimates.

A Review of Recent Advances in Research on PM2.5 in China

PM_2.5 pollution has become a severe problem in China due to rapid industrialization and high energy consumption. It can cause increases in the incidence of various respiratory diseases and resident mortality rates, as well as increase in the energy consumption in heating, ventilation, and air conditioning (HVAC) systems due to the need for air purification. This paper reviews and studies the sources of indoor and outdoor PM_2.5, the impact of PM_2.5 pollution on atmospheric visibility, occupational health, and occupants’ behaviors. This paper also presents current pollution status in China, the relationship between indoor and outdoor PM_2.5, and control of indoor PM_2.5, and finally presents analysis and suggestions for future research.

The Relationships between PM2.5 and Meteorological Factors in China: Seasonal and Regional Variations

The interactions between PM_2.5 and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM_2.5 concentration and meteorological conditions have been mainly confined to a certain city or district, and the correlation over the whole of China remains unclear. Whether spatial and seasonal variations exist deserves further research. In this study, the relationships between PM_2.5 concentration and meteorological factors were investigated in 68 major cities in China for a continuous period of 22 months from February 2013 to November 2014, at season, year, city, and regional scales, and the spatial and seasonal variations were analyzed. The meteorological factors were relative humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS). We found that spatial and seasonal variations of their relationships with PM_2.5 exist. Spatially, RH is positively correlated with PM_2.5 concentration in north China and Urumqi, but the relationship turns to negative in other areas of China. WS is negatively correlated with PM_2.5 everywhere except for Hainan Island. PS has a strong positive relationship with PM_2.5 concentration in northeast China and mid-south China, and in other areas the correlation is weak. Seasonally, the positive correlation between PM_2.5 concentration and RH is stronger in winter and spring. TEM has a negative relationship with PM_2.5 in autumn and the opposite in winter. PS is more positively correlated with PM_2.5 in autumn than in other seasons. Our study investigated the relationships between PM_2.5 and meteorological factors in terms of spatial and seasonal variations, and the conclusions about the relationships between PM_2.5 and meteorological factors are more comprehensive and precise than before. We suggest that the variations could be considered in PM_2.5 concentration prediction and haze control to improve the prediction accuracy and policy efficiency.