Spatiotemporal Variations Of PM2 Research Articles

Spatio-temporal Variations in PM2.5and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration

To coordinate the contradiction between economic development and environmental pollution and achieve the sustainable development of the economy and society, the spatio-temporal variations in PM2.5 were analyzed based on PM2.5 concentration and meteorological data of the Yangtze River Delta (YRD) urban agglomeration. Wavelet transform coherence (WTC), partial wavelet coherence (PWC), and multiple wavelet coherence (MWC) were used to analyze the multi-scale coupling oscillation between PM2.5 and meteorological factors in the time-frequency domain. The results showed that:① the concentration of PM2.5 in the YRD decreased from northwest to southeast, and the spatial range with high PM2.5 concentration decreased annually. The spatial distribution characteristics of the seasonal average PM2.5 concentration were similar to those of the annual average PM2.5 concentration. PM2.5 concentration exhibited the seasonal variation characteristics of high in winter, low in summer, and transitioning between spring and autumn. ② PM2.5 concentration decreased from 2015 to 2021, and the compliance rate increased. The difference in annual average PM2.5 concentration was decreased with dynamic convergence characteristics. The convergence of PM2.5 concentration in summer was greater than that in winter. During the whole study period, the daily average PM2.5 concentration showed a "U" distribution, and the proportion of days with excellent and good PM2.5 levels were 49.72% and 41.45%, respectively. ③ The wavelet coherence between PM2.5 and meteorological factors was different in different time-frequency domains. The main factors affecting PM2.5 were different in different time-frequency scales. At all time-frequency scales, WTC and PWC showed that wind speed and temperature were the best explanatory variables of PM2.5 variation, respectively. ④ The larger the time-frequency scale, the stronger the interaction of multi-factor combinations to explain PM2.5 variations. The synergistic effect of temperature and wind speed could better explain the variation in PM2.5. These results can provide reference for air pollution control in the YRD.

Spatio-temporal variations of PM2.5 and O3 in China during 2013–2021: Impact factor analysis

Fine particulate matter (PM2.5) and ozone (O3) pollution are regarded as significant secondary air pollutants. The PM2.5 in most regions in China declined, and the decreasing rate in January was lower than the annual average. However, O3 concentration showed a steady increasing trend in most regions, and the increasing rate in July was slightly higher than the annual average. In particular, the annual average PM2.5 concentration and excess rate showed an increasing trend on the northern slope of the Tianshan Mountains. Conversely, O3 concentrations had shown a consistent increasing trend, exceeding the annual average limit of 100 μg/m3. Surface pressure exhibited positive correlations with PM2.5 in winter and O3 in summer across urban agglomerations. Moreover, soil temperature at different depths explained over 30% of the variations in PM2.5 and O3 in the Chengdu-Chongqing, Beijing-Tianjin-Hebei, and Lanzhou-Xining urban agglomerations. In winter, relative humidity demonstrated a positive correlation with urban agglomerations in northeast and northwest China, regions characterized by dry climates. During the COVID-19 period, the impacts of meteorological factors and soil temperature on PM2.5 and O3 differed significantly compared to preceding and subsequent periods. Notably, during the winter of 2020, the Harbin-Changchuan urban agglomeration exhibited a notable transition, as O3 and PM2.5 concentrations shifted from a strong negative correlation to a robust positive correlation. This remarkable shift, with deviations explained up to 60%, represents a unique phenomenon worth emphasizing in the study's findings.

Spatiotemporal characteristics and socioeconomic factors of PM2.5 heterogeneity in mainland China during the COVID-19 epidemic

Spatiotemporal variation of PM2.5 in 2018 and 2020 were compared to analyze the impacts of COVID-19, the spatial heterogeneity of PM2.5, and meteorological and socioeconomic impacts of PM2.5 concentrations heterogeneity in China in 2020 were investigated. The results showed that the annual average PM2.5 concentration in 2020 was 32.73 μg/m3 existing a U-shaped variation pattern, which has decreased by 6.38 μg/m3 compared to 2018. A consistent temporal pattern was found in 2018 and 2020 with significant high values in winter and low in summer. PM2.5 declined dramatically in eastern and central China, where are densely populated and economically developed areas during the COVID-19 epidemic compared with previous years, indicating that the significantly decline of social activities had an important effect on the reduction of PM2.5 concentrations. The lowest PM2.5 was found in August because that precipitation had a certain dilution effect on pollutants. January was the most polluted due to centralized coal burning for heating in North China. Overall, the PM2.5 concentrations in China were spatially agglomerated. The highly polluted contiguous zones were mainly located in northwest China and the central plains city group, while the coastal area and Inner Mongolia were areas with good air quality. Negative correlations were found between natural factors (temperature, precipitation, wind speed and relative humidity) and PM2.5 concentrations, with precipitation has the greatest impact on PM2.5, which are beneficial for reducing PM2.5 concentrations. Among the socio-economic factors, proportion of the secondary industry, number of taxis, per capita GDP, population, and industrial nitrogen oxide emissions have positive correlation effects on PM2.5, while the overall social electricity consumption, industrial sulfur dioxide emissions, green coverage in built-up areas, and total gas and liquefied gas supply have negative correlation effects on the PM2.5.

Spatio-temporal variations of PM2.5 concentrations and related premature deaths in Asia, Africa, and Europe from 2000 to 2018

Fine particulate matter (PM2.5) pollution is one of leading environmental problems worldwide, posing a serious threat to global public health. Based on long-time series of satellite-retrieved PM2.5 concentrations, this study estimated premature deaths from six diseases (acute lower respiratory infections (ALRI), chronic obstructive pulmonary disease (COPD), type 2 diabetes (DIA), ischemic heart disease (IHD), lung cancer (LNC), and stroke (STR)) attributable to PM2.5 in Asia, Africa, and Europe (AAE). The results showed that the 19-year average population-weighted PM2.5 concentration in AAE was 41.7 μg/m3, with 47.4, 33.9, and 15.5 μg/m3 for Asia, Africa, and Europe, respectively. In AAE, the total annual average premature deaths related to PM2.5 were 2819 thousand (95% confidence interval (CI): 2348–4021), with 2261 thousand (95% CI: 1880–3396) in Asia, 170 thousand (95% CI: 128–276) in Africa, and 388 thousand (95% CI: 305–599) in Europe. STR and IHD were the major contributors to premature deaths in AAE, accounting for 38.5% and 33.3%, respectively. DIA, as a newly considered disease, was responsible for 4.8% premature deaths of the total. Besides, high values of premature deaths were concentrated in China and India during 2000–2018, with 1228 thousand (95% CI: 912–1922) and 506 thousand (95% CI: 389–720) annual average premature deaths (43.6% and 17.9% of the total in AAE). The results of this study provide a reference for different countries in AAE to formulate PM2.5 pollution control strategies.

Spatio-temporal variation of PM2.5 pollution in Xinjiang and its causes: The growing importance in air pollution situation in China

Due to the differences in topographic features, water vapor distribution and emission structures between the north and south of Xinjiang Uygur Autonomous Region (hereinafter Xinjiang), the air pollution exhibits remarkable spatial heterogeneity in this region. In this study, Xinjiang is divided into four regions from north to south at the municipality scale based on the data from air quality monitoring sites recorded from 2013–2019, namely, the clean area in the north of northern Xinjiang (region I), the heavily polluted area of the Urumqi-Changji-Shihezi region (region Ⅲ), the moderately polluted area in the north of southern Xinjiang (region Ⅱ), and the severely polluted area in the south of southern Xinjiang (region Ⅳ). For highly polluted regions in northern and southern Xinjiang, regions Ⅲ and Ⅳ are compared with those typical polluted regions in central and eastern China, and the spatio-temporal variation characteristics and the causes of PM2.5 pollution in each sub-region are discussed. The results show that the region Ⅲ is a typical area with anthropogenic air pollution source, where the occurrence frequency of PM2.5 pollution with the intensity of moderate level or above (18%) is higher than the most heavily polluted area in central and eastern China (16%, regions from the south of North China to the west of Huang-Huai). The region IV is a typical area affected by dust weather, where the pollution frequency being about 52% is much higher than that in other typical polluted regions. In addition, under the combined effects of anthropogenic sources and dust aerosols, the pollution duration in region II is prolonged. Therefore, in contrast to the remarkable improvement of air quality in central and eastern China, the air pollution in typical regions of Xinjiang has become relatively more severe in recent years. Moreover, as Xinjiang is in the upstream of the central and eastern China in the mid-latitude westerlies, the heavy pollution due to sand and dust, anthropogenic sources and their mixing effects has a far-reaching impact on the downstream areas, further highlighting the growing importance of pollution prevention and control in Xinjiang.

Using spatiotemporal prediction models to quantify PM2.5 exposure due to daily movement

This study estimates exposure differences when considering spatiotemporal variations in PM2.5 concentration that a population may experience, using daily average land use regression estimates for 2017 in Pittsburgh, PA.

Assessing Spatio-Temporal Variations In PM2.5 Pollution And Associated Mortality In The South Asian Region

Assessing Spatio-Temporal Variations In PM2.5 Pollution And Associated Mortality In The South Asian Region

Spatiotemporal Variations of PM2.5 and O3 Relationship during 2014–2021 in Eastern China

Spatiotemporal Variations of PM2.5 and O3 Relationship during 2014–2021 in Eastern China

Fractional Vegetation Cover and Spatiotemporal Variations of PM2.5 Concentrations in the Beijing-Tianjin-Hebei Region of China

The Beijing-Tianjin-Hebei region (BTH) of China maintains high-pollution levels of particulate matter ≥2.5 μm (PM2.5). Accordingly, understanding the spatiotemporal distributions of PM2.5 and their relationship with fractional vegetation cover in this region is of great significance for effective air pollution treatment. In the present study, ground-based PM2.5 monitoring, MODIS-NDVI satellite data, spatial interpolation, dimidiate pixel model, and Spearman’s rank correlation analyses were used to explore this relationship in the years 2018 and 2019. The results indicated the following: (1) In the BTH, the average annual PM2.5 mass concentration was 50 μg·m−3 in 2019, a 9.2% decrease from 2018, but still in excess of China’s second-level environmental air quality standards (35 µg·m−3). (2) PM2.5 concentrations in the BTH were temporally distributed, exhibiting a roughly U-shaped pattern within a year, peaking in the winter, followed by the spring and autumn, and reaching its minimum in the summer. (3) Spatially, distributions of PM2.5 mass concentrations in the BTH were significantly lower in the north and higher in the south. PM2.5 in the central and southern areas displayed concentrated and continuous distribution trends. (4) PM2.5 concentrations were negatively correlated with fractional vegetation cover in the BTH, and the effect of fractional vegetation cover on PM2.5 mass concentration was more significant in the winter than in other seasons. According to the results of this study, improving vegetation cover and increasing vegetation area have a positive effect on PM2.5 deposition in the Beijing-Tianjin-Hebei region. Therefore, the author suggests that the ability of urban green spaces to mitigate PM2.5 pollution in the Beijing-Tianjin-Hebei region can be improved in the future by controlling the vegetation coverage of urban green spaces to a suitable extent, especially in winter. This study provides an important scientific basis for the quantitative analysis of the effect of vegetation cover on PM2.5 concentration distribution and air pollution control and environmental protection in the Beijing-Tianjin-Hebei region.

Demarcation of Coordinated Prevention and Control Regions in the Yangtze River Delta Based on Spatio-Temporal Variations in PM2.5 and O3 Concentrations

The PM2.5 and O3 pollution situation in the Yangtze River Delta (YRD) region is increasingly complex. Existing coordinated prevention and control programs are demarcated according to administrative regions, making fine-scale pollution control difficult. This study proposed a method for delineating regional control and pollution types based on the analysis of PM2.5 and O3 pollution characteristics. Four key indicators were used to comprehensively evaluate regional pollution trends: PM2.5 concentration ρ(PM2.5), O3 concentration ρ(O3), and the rates of change r(PM2.5) and r(O3). Our results demonstrate that from 2015 to 2020, cities in the YRD show a transition in the main pollution type from PM2.5 to O3. By 2020, the main types of pollution in the YRD region are co-pollution (ρ(PM2.5) > 35 µg/m3 and ρ(O3) > 100 µg/m3), O3 pollution (ρ(PM2.5) < 35 µg/m3 and ρ(O3) > 100 µg/m3), and low pollution (ρ(PM2.5) < 35 µg/m3 and ρ(O3) < 100 µg/m3), accounting for 41.9%, 26.3%, and 30% of the study area, respectively, which is roughly consistent with the remote sensing results (40.4%, 25.6%, and 33.7%, respectively). The proposed method can predict future trends in pollution and assist in decision-making for the coordinated prevention and control of PM2.5 and O3 pollution.

Predicting Daily PM2.5 Exposure with Spatially Invariant Accuracy Using Co-Existing Pollutant Concentrations as Predictors

The spatiotemporal variation of PM2.5 should be accurately estimated for epidemiological studies. However, the accuracy of prediction models may change over geographical space, which is not conducive for proper exposure assessment. In this study, we developed a prediction model to estimate daily PM2.5 concentrations from 2010 to 2017 in the Kansai region of Japan with co-existing pollutant concentrations as predictors. The overall objective was to obtain daily estimates over the study domain with spatially homogeneous accuracy. We used random forest algorithm to model the relationship between the daily PM2.5 concentrations and various predictors. The model performance was evaluated via spatial and temporal cross-validation and the daily PM2.5 surface was estimated from 2010 to 2017 at a 1 km × 1 km resolution. We achieved R2 values of 0.91 and 0.92 for spatial and temporal cross-validation, respectively. The prediction accuracy for each monitoring site was found to be consistently high, regardless of the distance to the nearest monitoring location, up to 10 km. Even for distances greater than 10 km, the mean R2 value was 0.88. Our approach yielded spatially homogeneous prediction accuracy, which is beneficial for epidemiological studies. The daily PM2.5 estimates will be used in a related birth cohort study to evaluate the potential impact on human health.

Predicting annual PM2.5 in mainland China from 2014 to 2020 using multi temporal satellite product: An improved deep learning approach with spatial generalization ability

The estimation of long-term fine particulate matter (PM2.5) concentrations and trend assessments play a critical role in preventing health risks to the human body. Deep learning methods based on time series have been highly accurate in predicting PM2.5. However, most time series models lack the ability for spatial generalization because they cannot combine the analysis at different spatial scales. In addition, temporal long-term trend analysis have not been reported in most of the studies. In this study, to reveal the spatiotemporal variability and trends of PM2.5, an improved deep learning framework named the SpatioTemporal Enhanced Neural Network (STENN) is developed for estimating PM2.5 concentrations with a spatial resolution of 1 km. Based on the bidirectional long short-term memory (LSTM) structure and attention mechanisms, the model provides a geographic-data-driven approach to incorporate the impact of the spatial heterogeneity and time dependence of PM2.5, which demonstrates that it has robust spatiotemporal transferable power with an R2 of 0.89 produced by cross validation (CV). High-resolution (1 km) and high-quality annual PM2.5 products for mainland China from 2014 to 2020 were constructed. In comparison with the current 1-km PM2.5 products, our framework demonstrates better stability in different regions, especially in terms of the high-value estimations and spatial continuity. The spatiotemporal PM2.5 distributions were also analyzed based on the time-series products. After the implementation of various control policies for atmospheric pollution, a declining trend of PM2.5 concentrations was observed in 88.79% of China between 2014 and 2020, with a mean decrease rate of 3.35 μg m−3 yr−1. This result indicates that the control policies of the Chinese government were effective in reducing PM2.5 concentrations. The PM2.5 concentrations in China reveal an exponential temporal trend, from a rapid decline to a gradual slowdown and a stable phase. To realize the vision of the Beautiful China Initiative, a regionally targeted policy for air pollution management is required. This study provides valuable implications for a more detailed analysis of the spatiotemporal variations in PM2.5 at small and medium spatial scales by developing an improved deep learning approach with a spatial generalization ability and integration with multi-temporal satellite products.

Spatiotemporal characteristics of PM2.5 and ozone concentrations in Chinese urban clusters

Despite China's public commitment to emphasise air pollution investigation and control, trends in PM2.5 and ozone concentrations in Chinese urban clusters remain unclear. This study quantifies the spatiotemporal variations in PM2.5 and surface ozone at the scale of Chinese urban clusters by using a long-term integrated dataset from 2015 to 2020. Nonlinear Granger causality testing was used to explore the spatial association patterns of PM2.5 and ozone pollution in five megacity cluster regions. The results show a significant downward trend in annual mean PM2.5 concentrations from 2015 to 2020, with a decline rate of 2.8 μg m-3 yr-1. By contrast, surface ozone concentrations increased at a rate of 2.1 μg m-3 yr-1 over the 6 years. The annual mean PM2.5 concentrations in urban clusters show significant spatial clustering characteristics, mainly in Beijing-Tianjin-Hebei (BTH), Fenwei Plain (FWP), Northern slope of Tianshan Mountains urban cluster (NSTM), Sichuan Basin urban cluster (SCB), and Yangtze River Delta (YRD). Surface ozone shows severe summertime pollution and distributional variability, with increased ozone pollution in major urban clusters. The highest increases were observed in BTH, Yangtze River midstream urban cluster (YRMR), YRD, and Pearl River Delta (PRD). Nonlinear Granger causality tests showed that PM2.5 was a nonlinear Granger cause of ozone, further supporting the literature's findings that PM2.5 reduction promoted photochemical reaction rates and stimulated ozone production. The nonlinear test statistic passed the significance test in magnitude and statistical significance. FWP was an exception, with no significant long-term nonlinear causal link between PM2.5 and ozone. This study highlights the challenges of compounded air pollution caused primarily by ozone and secondary PM2.5. These results have implications for the design of synergistic pollution abatement policies for coupled urban clusters.

Meteorological Influences on Spatiotemporal Variation of PM2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing-Tianjin-Hebei Region, China.

Understanding the spatiotemporal characteristics of PM2.5 concentrations and identifying their associated meteorological factors can provide useful insight for implementing air pollution interventions. In this study, we used daily air quality monitoring data for 28 air pollution transmission channel cities in the Beijing–Tianjin–Hebei region during 2014–2019 to quantify the relative contributions of meteorological factors on spatiotemporal variation in PM2.5 concentration by combining time series and spatial perspectives. The results show that annual mean PM2.5 concentration significantly decreased in 24 of the channel cities from 2014 to 2019, but they all still exceeded the Grade II Chinese Ambient Air Quality Standards (35 μg m−3) in 2019. PM2.5 concentrations exhibited clear spatial agglomeration in the most polluted season, and their spatial pattern changed slightly over time. Meteorological variables accounted for 31.96% of the temporal variation in PM2.5 concentration among the 28 cities during the study period, with minimum temperature and average relative humidity as the most critical factors. Spatially, atmospheric pressure and maximum temperature played a key role in the distribution of PM2.5 concentration in spring and summer, whereas the effect of sunshine hours increased greatly in autumn and winter. These findings highlight the importance of future clean air policy making, but also provide a theoretical support for precise forecasting and prevention of PM2.5 pollution.

Spatio-Temporal Variation of Pm2.5 And Their Associated Health Risk in Taipei City, Taiwan

Spatio-Temporal Variation of Pm2.5 And Their Associated Health Risk in Taipei City, Taiwan

Tracking PM2.5 and O3 Pollution and the Related Health Burden in China 2013-2020.

Based on the exposure data sets from the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/), we characterized the spatiotemporal variations in PM2.5 and O3 exposures and quantified the long- and short-term exposure related premature deaths during 2013-2020 with respect to the two-stage clean air actions (2013-2017 and 2018-2020). We find a 48% decrease in national PM2.5 exposure during 2013-2020, although the decrease rate has slowed after 2017. At the same time, O3 pollution worsened, with the average April-September O3 exposure increased by 17%. The improved air quality led to 308 thousand and 16 thousand avoided long- and short-term exposure related deaths, respectively, in 2020 compared to the 2013 level, which was majorly attributed to the reduction in ambient PM2.5 concentration. It is also noticed that with smaller PM2.5 reduction, the avoided long-term exposure associated deaths in 2017-2020 (13%) was greater than that in 2013-2017 (9%), because the exposure-response curve is nonlinear. As a result of the efforts in reducing PM2.5-polluted days with the daily average PM2.5 higher than 75 μg/m3 and the considerable increase in O3-polluted days with the daily maximum 8 h average O3 higher than 160 μg/m3, deaths attributable to the short-term O3 exposure were greater than those due to PM2.5 exposure since 2018. Future air quality improvement strategies for the coordinated control of PM2.5 and O3 are urgently needed.

Health risk and disease burden attributable to long-term global fine-mode particles

Particulate matter 2.5 (PM2.5) pollution has long been a global environmental problem and still poses a great threat to public health. This study investigates global spatiotemporal variations in PM2.5 using the newly developed satellite-derived PM2.5 dataset from 1998 to 2018. An integrated exposure–response (IER) model was employed to examine the characteristics of PM2.5-related deaths caused by chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD), lung cancer (LC), and stroke in adults (age≥25), as well as lower respiratory infection (LRI) in children (age≤5). The results showed that high annual PM2.5 concentrations were observed mainly in East Asia and South Asia. Over the 19-year period, PM2.5 concentrations constantly decreased in developed regions, but increased in most developing regions. Approximately 84% of the population lived in regions where PM2.5 concentrations exceeded 10 μg/m3. Meanwhile, the vast majority of the population (>60%) in East and South Asia was consistently exposed to PM2.5 levels above 35 μg/m3. PM2.5 exposure was linked to 3.38 (95% UI: 3.05–3.70) million premature deaths globally in 2000, a number that increased to 4.11 (95% UI: 3.55–4.69) million in 2018. Premature deaths related to PM2.5 accounted for 6.54%−7.79% of the total cause of deaths worldwide, with a peak in 2011. Furthermore, developing regions contributed to the majority (85.95%–95.06%) of PM2.5-related deaths worldwide, and the three highest-ranking regions were East Asia, South Asia, and Southeast Asia. Globally, IHD and stroke were the two main contributors to total PM2.5-related deaths, followed by COPD, LC, and LRI.

Using a distributed air sensor network to investigate the spatiotemporal patterns of PM2.5 concentrations

Spatiotemporal variations in PM2.5 are a key factor affecting personal pollution exposure levels in urban areas. However, fixed-site monitoring stations are so sparsely distributed that they hardly capture the dynamic and fine-scale variations in PM2.5 in urban areas with complex geographical features and urban forms. Recently, a distributed air sensor network (DASN) was deployed in Dezhou city, China, to monitor fine-scale air pollution information and obtain deep insight into variations in PM2.5. Based on the data collected by the DASN, this paper investigated the spatiotemporal patterns of PM2.5 using the time-series clustering method. The results demonstrated that there were four stages of PM2.5 daily variations, i.e., accumulation, continuous pollution, dispersion, and cleaning. Generally, the stage of dispersion occurred more rapidly than the stage of accumulation, and PM2.5 accumulated easily in warm and humid weather with low wind speeds. However, the stage of dispersion was affected mainly by high wind speeds and precipitation. Additionally, the results suggested that four variation stages did not strictly correspond to seasonal divisions. The spatial distributions of PM2.5 revealed that the main pollution source was located in a southeastern industrial park, which exhibited a significant impact throughout the four stages. Considering both the temporal and spatial characteristics of PM2.5, this study successfully identified pollution hotspots and confirmed the effect of industrial parks. The study demonstrates that the DASN has high prospective applicability for assessing the fine-scale spatial distribution of PM2.5, and the time-series clustering method can also assist environmental researchers in further exploring the spatiotemporal characteristics of urban air pollution.

Spatiotemporal characteristics of PM2.5 concentration in the Yangtze River Delta urban agglomeration, China on the application of big data and wavelet analysis

PM2.5 pollution has been one of the main environmental issues of concern for the Yangtze River Delta Urban Agglomeration (YRDUA) during the recent decade. In this paper, allied with big data and wavelet analysis, spatiotemporal variations of PM2.5 and its influencing factors (air pollutants and meteorological factors) are studied based on hourly concentrations of PM2.5 from 2015 to 2018 in the YRDUA. Results showed that PM2.5 presented a step-shaped decline from northwest to southeast in space and significant multi-scale temporal variations in time. On the macroscopic level, PM2.5 concentrations decreased from 2015 to 2018, showing a U-shaped pattern within a year. On the microscopic level, it had a four-stage annual variation (January to March, April to June, July to September, October to December) and the mutation events mainly occurred in winter. There were two dominant periods of PM2.5, an annual cycle on the time scale of 250–480 d and a semi-annual cycle on the time scale of 130–220 d. In addition, PM2.5 showed time scale-dependent correlations with air pollutants and meteorological factors. Among air pollutants, the correlation between PM2.5 and CO was the most consistent, and the correlation between PM2.5 and SO2/NO2 improved with the increase of time scale, while the correlation between PM2.5 and O3 was positive at shorter time scales but negative at broader time scales. Among meteorological factors, the correlations between PM2.5 and wind speed, precipitation, temperature, air pressure and relative humidity were mainly reflected at broader time scales. These findings would be helpful to improve the accuracy of prediction model and provide references for the ongoing joint prevention and control.

Spatio-Temporal Variations of Satellite-Based PM2.5 Concentrations and Its Determinants in Xinjiang, Northwest of China.

With the aggravation of air pollution in recent years, a great deal of research on haze episodes is mainly concentrated on the east-central China. However, fine particulate matter (PM2.5) pollution in northwest China has rarely been discussed. To fill this gap, based on the standard deviational ellipse analysis and spatial autocorrelation statistics method, we explored the spatio-temporal variation and aggregation characteristics of PM2.5 concentrations in Xinjiang from 2001 to 2016. The result showed that annual average PM2.5 concentration was high both in the north slope of Tianshan Mountain and the western Tarim Basin. Furthermore, PM2.5 concentrations on the northern slope of the Tianshan Mountain increased significantly, while showing an obviously decrease in the western Tarim Basin during the period of 2001–2016. Based on the result of the geographical detector method (GDM), population density was the most dominant factor of the spatial distribution of PM2.5 concentrations (q = 0.550), followed by road network density (q = 0.423) and GDP density (q = 0.413). During the study period (2001–2016), the driving force of population density on the distribution of PM2.5 concentrations showed a gradual downward trend. However, other determinants, like DEM (Digital elevation model), NSL (Nighttime stable light), LCT (Land cover type), and NDVI (Normalized Difference Vegetation Index), show significant increased trends. Therefore, further effort is required to reveal the role of landform and vegetation in the spatio-temporal variations of PM2.5 concentrations. Moreover, the local government should take effective measures to control urban sprawl while accelerating economic development.