PM2.5 Spatiotemporal Evolution and Drivers in the Yangtze River Delta between 2005 and 2015

Spatiotemporal heterogeneity of the relationships between PM2.5 concentrations and their drivers in China's coastal ports

PM2.5 is one of the primary air pollutants that affect air quality and threat human health in the port areas. To prevent and control air pollution, it is essential to understand the spatiotemporal distributions of PM2.5 concentrations and their key drivers in ports. 19 coastal ports of China are selected to examine the spatiotemporal distributions of PM2.5 concentrations during 2013-2020. The annual average PM2.5 concentration decreases from 61.03 μg/m3 to 30.17 μg/m3, with an average decrease rate of 51.57%. Significant spatial autocorrelation exists among PM2.5 concentrations of ports. The result of the geographically and temporally weighted regression (GTWR) model shows significant spatiotemporal heterogeneity in the effects of meteorological and socioeconomic factors on PM2.5 concentrations. The effects of boundary layer height on PM2.5 concentrations are found to be negative in most ports, with a stronger effect found in the Pearl River Delta, Yangtze River Delta and some ports of the Bohai Rim Area. The total precipitation shows negative effects on PM2.5 concentrations, with the strongest effect found in ports of the Southeast Coast. The effects of surface pressure on PM2.5 concentrations are positive, with stronger effects found in Beibu Gulf Port and Zhanjiang Port. The effects of wind speed on PM2.5 concentrations generally increase from south to north. Cargo throughput shows strong and positive effects on PM2.5 concentrations in ports of Bohai Rim Area; the positive effects found in Beibu Gulf Port increased from 2013 to 2018 and decreased since 2019. The positive effects of GDP and nighttime light on PM2.5 concentrations gradually decrease and turn negative from south to north. Understandings obtained from this study can potentially support the prevention and control of air pollution in China's coastal ports.

Understanding Anthropogenic PM2.5 Concentrations and Their Drivers in China during 1998-2016

Air pollution poses serious challenges for human health and wellbeing. It also affects atmospheric visibility and contributes to climate change. As social and economic processes have increased, anthropogenic PM_2.5 pollution caused by intensive human activities has led to extremely severe air pollution. Spatiotemporal patterns and drivers of anthropogenic PM_2.5 concentrations have received increasing attention from the scientific community. Nonetheless, spatiotemporal patterns and drivers of anthropogenic PM_2.5 concentrations are still inadequately understood. Based on a time series of remotely sensed anthropogenic PM_2.5 concentrations, this study analyzed the spatiotemporal patterns of this crucial pollutant in China from 1998 to 2016 using Sen's slope estimator and the Mann-Kendall trend model. This, in combination with grey correlation analysis (GCA), was used to reveal the socioeconomic factors influencing anthropogenic PM_2.5 concentrations in eastern, central, and western China from 1998 to 2016. The results were as follows: (1) the average annual anthropogenic concentration of PM_2.5 in China increased quickly and reached its peak value in 2007, then remained stable in the following years; (2) only 63.30 to 55.09% of the land area reached the threshold value of 15 μg/m³ from 1998 to 2016; (3) regarding the polarization phenomenon of anthropogenic PM_2.5 concentrations existing in eastern and central China, the proportion of gradient 1 (≤15 μg/m³) gradually decreased and gradient 3 (≥35 μg/m³) gradually increased; and (4) the urbanization level (UR), population density (PD), and proportion of secondary industry to gross domestic product (SI) were the dominant socioeconomic factors affecting the formation of anthropogenic PM_2.5 concentrations in eastern, central, and western China, independently. The improvements in energy consumption per gross domestic product (EI) have a greater potential for mitigating anthropogenic PM_2.5 emissions in central and western China. These findings allow an interpretation of the spatial distribution of anthropogenic PM_2.5 concentrations and the mechanisms influencing anthropogenic PM_2.5 concentrations, which can help the Chinese government develop effective abatement strategies.

Effects of anthropogenic precursor emissions and meteorological conditions on PM2.5 concentrations over the "2+26" cities of northern China

Elucidating the characteristics and influencing mechanisms of PM_2.5 concentrations is the premise and key to the precise prevention and control of air pollution. However, the temporal and spatial heterogeneity of PM_2.5 concentrations and its driving mechanism are complex and need to be further analyzed. We analyzed the temporal and spatial variations of PM_2.5 concentrations in the "2 + 26" cities from 2015 to 2021, and quantified the influence of meteorological factors and anthropogenic emissions and their interactions on PM_2.5 concentrations based on geographic detector model. We find the inter-annual and inter-season PM_2.5 concentrations show downward trend from 2015 to 2021, and the inter-month PM_2.5 concentrations present a U-shaped distribution. The PM_2.5 concentrations in the "2 + 26" cities manifest a spatial distribution pattern of high in the south and low in the north, and high in the middle and low in the surroundings. Meteorological conditions have stronger effects on PM_2.5 concentrations than anthropogenic emissions, and planetary boundary layer height and temperature are the two main driving factors at the annual scale. On the seasonal scale, sunshine duration is the dominant factor of PM_2.5 concentrations in summer and autumn, and planetary boundary layer height is the dominant factor of PM_2.5 concentrations in winter. The effect of anthropogenic emissions on PM_2.5 concentration is higher in winter and spring than in summer and autumn, and ammonia and ozone have stronger effects on PM_2.5 concentrations than other anthropogenic emissions. Interactions between the factors significantly enhance the PM_2.5 concentrations. The interactions between planetary boundary layer height and other impacting factors play dominant roles on PM_2.5 concentrations at annual scale and in winter. Our results not only provide crucial information for further developing air quality policies of the "2 + 26" cities, but also bear out several important implications for clean air policies in China and other regions of the world.

Assessment of spatiotemporal changes of ecological environment quality of the Yangtze River Delta urban agglomeration in China based on MRSEI

The Ecological Environment Quality (EEQ) is an important foundation for the sustainable development of society and economy. To assess the spatiotemporal changes of the EEQ in the Yangtze River Delta Urban Agglomeration (YRDUA), we selected MODIS images of 2001, 2006, 2011, 2016 and 2021 to construct the Modified Remote Sensing Ecological Index (MRSEI) based on Google Earth Engine (GEE) platform and Principal Component Analysis (PCA). Then, we evaluated the spatiotemporal changes and spatial autocorrelation of the EEQ in the YRDUA. The results showed that: the EEQ of the YRDUA was improved from 2001 to 2011, deteriorated from 2011 to 2016, and improved from 2016 to 2021. The overall EEQ of the YRDUA was at moderate or excellent level, and the EEQ in the south was better than that in the north. The EEQ of the southern cities in the study area was better and more stable, while that of the northern cities was relatively poor and changes relatively drastic. The EEQ of the YRDUA was mainly unchanged and improved from 2001 to 2021. The regions with improved EEQ were mainly distributed in the north and west, while those with deteriorated EEQ were mainly distributed in the east and south. The EEQ of the YRDUA was improved gradually from 2001 to 2006, and relatively stable from 2006 to 2011. From 2011 to 2016, the changes were drastic and the EEQ deteriorated greatly; while from 2016 to 2021, the EEQ of the YRDUA was improved, and the area of ecological deterioration was significantly reduced. From 2001 to 2021, the Globalmoran’s I value ranged from 0.838 ~ 0.918. In the past 20 years, NS area in the YRDUA accounted for the highest proportion, while the HH aggregation was mainly distributed in the southern part of the YRDUA, while LL aggregation was mainly distributed in the northern part, indicated that the EEQ in the southern part was better than that in the northern part. This study provides a promising approach to assess the spatiotemporal changes of EEQ in urban areas, which is crucial to formulate the ecosystem protection policies and sustainable development strategies of YRDUA.

Exploring and attributing change to fractional vegetation coverage in the middle and lower reaches of Hanjiang River Basin, China

The middle and lower reaches of Hanjiang River Basin (MLHB), areas that have an important ecological function in China, have experienced great changes in the vegetation ecosystem driven by natural environmental change and human activity. Here, we explored the spatio-temporal dynamics of fractional vegetation coverage (FVC) and quantitatively analyzed its driving factors to advance current understanding of how the ecological environment has changed. Specifically, we used the dimidiate pixel model to calculate the FVC of the MLHB from 2001 to 2018 based on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data. We then used Theil-Sen median slope (Sen's slope) and coefficient of variation (CV) to explore spatial and temporal variations, as well as characteristics in fluctuations. Finally, we utilized a geographical detector model (with spatial scale effects and spatial data discretization tests) to quantify the influence of the detected natural and human factors. Results showed that average annual FVC was 0.30-0.75 for ~90% of the study area over the 19-year study period with a heterogeneous spatial distribution. FVC variation trend displayed stability and improvement. Areas with higher FVC displayed greater stability. All 10 detected natural and anthropogenic factors were responsible for changes in FVC. The primary factors causing FVC to change were precipitation (in 2001) and slope (in 2018), followed by landform type, distance to water, and nighttime light (NTL) (in 2018). Precipitation and slope consistently displayed the largest interaction across all years. The interaction between human and topographical factors had gradually increasing significance on changes in FVC over the research period. The range and type of factors suitable for promoting vegetation growth were detected in the study area. Results of this study can provide a scientific basis for developing effective strategies for local vegetation protection, restoration, and land resource management.

Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions

PM_2.5, which refers to fine particles with an equivalent aerodynamic diameter of less than or equal to 2.5 µm, can not only affect air quality but also endanger public health. Nevertheless, the spatial distribution of PM_2.5 is not well understood in data-poor regions where monitoring stations are scarce. Therefore, we constructed a random forest (RF) model and a bagging algorithm model based on ground-monitored PM_2.5 data, aerosol optical depth (AOD) and meteorological data, and auxiliary geographical variables to accurately estimate the spatial distribution of PM_2.5 concentrations in Xinjiang during 2015-2020 at a resolution of 1 km. Through 10-fold cross-validation (CV), the RF model and bagging algorithm model were verified and compared. The results showed the following: (1) The RF model achieved better model performance and thus can be used to estimate the PM_2.5 concentration at a relatively high resolution. (2) The PM_2.5 concentrations were high in southern Xinjiang and low in northern Xinjiang. The high values were concentrated mainly in the Tarim Basin, while most areas of northern Xinjiang maintained low PM_2.5 levels year-round. (3) The PM_2.5 values in Xinjiang showed significant seasonality, with the seasonally averaged concentrations decreasing as follows: winter (71.95 µg m^-3) > spring (64.76 µg m^-3) > autumn (46.01 µg m^-3) > summer (43.40 µg m^-3). Our model provides a way to monitor air quality in data-scarce places, thereby advancing efforts to achieve sustainable development in the future.

Understanding the distribution and drivers of PM2.5 concentrations in the Yangtze River Delta from 2015 to 2020 using Random Forest Regression

Understanding the drivers of PM_2.5 is critical for the establishment of PM_2.5 prediction models and the prevention and control of regional air pollution. In this study, the Yangtze River Delta is taken as the research object. Spatial cluster and outlier method was used to analyze the temporal and spatial distribution and variation of surface PM_2.5 in the Yangtze River Delta from 2015 to 2020, and Random Forest was utilized to analyze the drivers of PM_2.5 in this area. The results indicated that (1) based on the spatial cluster distribution of PM_2.5, the northwest and north of Yangtze River Delta region were mostly highly concentrated and surrounded by high concentrations of PM_2.5, while lowly concentrated and surrounded by low concentrations areas were distributed in the southern; (2) the relationship between PM_2.5 concentrations and drivers in the Yangtze River Delta was modeled well and the explanatory rate of drivers to PM_2.5 were more than 0.9; (3) temperature, precipitation, and wind speed were the main driving forces of PM_2.5 emission in the Yangtze River Delta. It should be noted that the repercussion of wildfire on PM_2.5 was gradually prominent. When formulating air pollution control measures, the local government normally considers the impact of weather and traffic conditions. In order to reduce PM_2.5 pollution caused by biomass combustion, the influence of wildfire should also be taken into account, especially in the fire season. Meanwhile, high leaf area was conducive to improving air quality, and the increasing green area will help reduce air pollutants.

The impact of technological progress on China's haze pollution-based on decomposition and rebound research

In order to effectively analyze and explore the socio-economic impact of haze pollution, the article constructs a comprehensive two-stage decomposition model to verify that technological progress plays a key role in controlling haze pollution. And for the first time, a macro-level research framework for the rebound effect of haze pollution has been constructed to compare and analyze the heterogeneity of the rebound effect of technological progress in different industries in different regions. The study found that (1) during the period 2000-2017, haze pollution situation deteriorated. Economic effects were the main reasons for haze pollution. Among these effects, technological progress was the main driving force for haze control, followed by the emission intensity during 2000-2011 and the reduction of industrial structure since 2014. (2) The significant drive of emission reduction is in the secondary industry, showing a trend of first increasing and then decreasing. Besides, there was a difference in spatial distribution, which shows an increased trend from east to west. (3) The rebound effect of haze pollution at the macro level in China presented high-level fluctuations, and there were certain spatial distribution differences. However, due to the convergence of technological development stages, regional differences have a gradual convergence trend. In the future, in the process of haze control, it is necessary to increase support for technological innovation, implement energy total control and price reform, promote technological progress, and implement differentiated haze reduction policies to solve problems according to local conditions.

Quantifying influences of administrative division adjustment on PM2.5 pollution in China's mega-urban agglomerations

China's mega-urban agglomerations have experienced severe particulate matter pollution that is accompanied by rapid economic growth and extensive administrative division adjustment (ADA). However, the precise roles of ADA on the environmental quality are unknown. Using the geographical detector and evolution tree model, this study quantifies the effects and mechanisms of ADA on the changes in PM_2.5 concentration in three mega-urban agglomerations: Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) during 2000-2017. Our results showed that: (1) ADA had strong positive effects on PM_2.5 concentrations in the 0-6 years lag and negative effects in the 7-10 years lag; (2) During 2000-2009, ADA elevated PM_2.5 concentration by 5.93% via stimulating the development and transfer of heavy industry and urban sprawl in the BTH; (3) YRD and PRD respectively reduced the ADA's exacerbating effect to 5.26% and 4.98% via reasonable industrial structures and comprehensive cooperation mechanisms; (4) During 2009-2017, BTH and YRD integrated industrial transformation and environmental protection services through ADA, which alleviated 9.51% and 8.49% of PM_2.5 pollution. PRD, meanwhile, accomplished orderly population dispersal and urban expansion by combining ADA with urban planning, thus reducing the PM_2.5 concentration by 8.01%. We located three agglomerations in the evolution tree, which provide a basis for formulating relevant policies and region-oriented air pollution joint prevention control strategies.

Exploring the convergence patterns of PM2.5 in Chinese cities

Economic development and ongoing urbanization are usually accompanied by severe haze pollution. Revealing the spatial and temporal evolution of haze pollution can provide a powerful tool for formulating sustainable development policies. Previous studies mostly discuss the differences in the level of PM2.5 among regions, but have paid little attention to the change rules of such differences and their clustering patterns over long periods. Therefore, from the perspective of club convergence, this study employs the log t regression test and club clustering algorithm proposed by Phillips and Sul (Econometrica 75(6):1771-1855, 2007. 10.1111/j.1468-0262.2007.00811.x) to empirically examine the convergence characteristics of PM2.5 concentrations in Chinese cities from 1998 to 2016. This study found that there was no evidence of full panel convergence, but supported one divergent group and eleven convergence clubs with large differences in mean PM2.5 concentrations and growth rates. The geographical distribution of these clubs showed significant spatial dependence. In addition, certain meteorological and socio-economic factors predominantly determined the convergence club for each city.

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Fine particulate matter in the lower atmosphere (PM2.5) continues to be a major public health problem globally. Identifying the key contributors to PM2.5 pollution is important in monitoring and managing atmospheric quality, for example, in controlling haze. Previous research has been aimed at quantifying the relationship between PM2.5 values and their underlying factors, but the spatial and temporal dynamics of these factors are not well understood. Based on random forest and Shapley additive explanation (SHAP) algorithms, this study analyses the spatiotemporal variations in selected key factors influencing PM2.5 in Zhejiang Province, China, for the period 2000–2019. The results indicate that, while factors influencing PM2.5 varied significantly during the period studied, SHAP values suggest that there is consistency in their relative importance as follows: meteorological factors (e.g., atmospheric pressure) > socioeconomic factors (e.g., gross domestic product, GDP) > topography and land cover factors (e.g., elevation). The contribution of GDP and transportation factors initially increased but has declined in the recent past, indicating that economic and infrastructural development does not necessarily result in increased PM2.5 concentrations. Vegetation productivity, as indicated by changes in NDVI, is demonstrated to have become more important in improving air quality, and the area of the province over which it constrains PM2.5 concentrations has increased between 2000 and 2019. Mapping of SHAP values suggests that, although the relative importance of industrial emissions has declined during the period studied, the actual area positively impacted by such emissions has actually increased. Despite developments in government policy, greater efforts to conserve energy and reduce emissions are still needed. The study further demonstrates that the combination of random forest and SHAP methods provides a valuable means to identify regional differences in key factors affecting atmospheric PM2.5 values and offers a reliable reference for pollution control strategies.

Exposure interval to ambient fine particulate matter (PM2.5) collected in Southwest China induced pulmonary damage through the Janus tyrosine protein kinase-2/signal transducer and activator of transcription-3 signaling pathway both in vivo and in vitro

PM2.5 is a well-known air pollutant threatening public health. Studies confirmed that exposure to the particles could impair pulmonary function, cause chronic obstructive pulmonary disease, and increase the incidence of lung cancer. The characteristic of PM2.5 varies across regions. The toxic function of PM2.5 in southwest China remains to be elucidated. This study aimed to investigate lung injury and its mechanisms induced by PM2.5 collected in Chengdu. Rats were administered with PM2.5 by intratracheal instillation for 4 weeks. Biochemical, cell count, and inflammation-related parameters were measured. Lung tissues were obtained for hematoxylin and eosin and Masson's trichrome staining. The expression levels of vascular endothelial growth factor (VEGF), Janus tyrosine protein kinase-2 (JAK-2), and signal transducer and activator of transcription-3 (STAT-3) were detected by immunohistochemistry assays. Meanwhile, A549 cells were treated with the PM2.5. The cell cycle, and apoptosis were measured by flow cytometry. mRNA and protein expressions of JAK-2, STAT-3, p-STAT-3, and VEGFA were detected using qPCR and Western blot analysis respectively. Results of in vivo study showed that PM2.5 induced lung pathological injury, aggravated the accumulation of inflammatory cells, and increased the serum levels of inflammatory factors. In vitro experiments showed that PM2.5 disrupted the cell growth cycle and increased cell apoptosis through the activation of the JAK-2/STAT-3 signaling pathway. Taken together, this study provided convincing experimental evidence that PM2.5 collected in southwest China could induce pulmonary injury as manifested by inflammatory response and lung fibrosis, possibly through the modulation of the JAK-2/STAT-3 signaling pathway.

A grey spatiotemporal incidence model with application to factors causing air pollution

The factors causing air pollution in China has caused extensive concern, but there are still many problems in the grey incidence model of identifying air pollution factors. The results produced by the existing grey incidence models are not stable when the order of objects in a given panel data is changed. In order to improve the reliability and uniformity of the grey incidence model, a new grey incidence model, called the grey spatiotemporal incidence model, abbreviated as the GSTI model, is designed in this paper. In the proposed model, the spatiotemporal data which can represent the spatial relationship among different objects rather than the three-dimensional panel data are defined. In addition, the new model includes two procedures. Firstly, the trend coefficient is used to measure the positive and negative connections between two data sequences. Secondly, the measurement coefficient is utilized to calculate the size of grey incidence degree. Subsequently, five properties of the GSTI model are discussed. To demonstrate its practicability and compatibility, the novel model is utilized to identify south Jiangsu province's main factors causing air pollution according to monthly data for 2018. The abundant comparison shows the applicability and superiority of the model in the identification of air pollution factors and the construction of grey incidence model.

Analysis of the Effectiveness of Air Pollution Control Policies Based on Historical Evaluation and Deep Learning Forecast: A Case Study of Chengdu-Chongqing Region in China

Air pollution is a common problem for many countries around the world in the process of industrialization as well as a challenge to sustainable development. This paper has selected Chengdu-Chongqing region of China as the research object, which suffers from severe air pollution and has been actively involved in air pollution control in recent years to achieve sustainable development. Based on the historical data of 16 cities in this region from January 2015 to November 2019 on six major air pollutants, this paper has first conducted evaluation on the monthly air quality of these cities within the research period by using Principal Component Analysis and the Technique for Order Preference by Similarity to an Ideal Solution. Based on that, this paper has adopted the Long Short-Term Memory neural network model in deep learning to forecast the monthly air quality of various cities from December 2019 to November 2020. The aims of this paper are to enrich existing literature on air pollution control, and provide a novel scientific tool for design and formulation of air pollution control policies by innovatively integrating commonly used evaluation models and deep learning forecast methods. According to the research results, in terms of historical evaluation, the air quality of cities in the Chengdu-Chongqing region was generally moving in the same trend in the research period, with distinct characteristics of cyclicity and convergence. Year- on-year speaking, the effectiveness of air pollution control in various cities has shown a visible improvement trend. For example, Ya’an’s lowest air quality evaluation score has improved from 0.3494 in 2015 to 0.4504 in 2019; Zigong’s lowest air quality score has also risen from 0.4160 in 2015 to 0.6429 in 2019. Based on the above historical evaluation and deep learning forecast results, this paper has proposed relevant policy recommendations for air pollution control in the Chengdu-Chongqing region.

Investigating the Impacts of Urbanization on PM2.5 Pollution in the Yangtze River Delta of China: A Spatial Panel Data Approach

Urbanization is a key determinant of fine particulate matter (PM2.5) pollution variability. However, there is a limited understanding of different urbanization factors’ roles in PM2.5 pollution. Using satellite-derived PM2.5 data from 2002 to 2017, we investigated the spatiotemporal evolution and the spatial autocorrelation of PM2.5 pollution in the Yangtze River Delta (YRD) region. Afterwards, the impacts of three urbanization factors (population urbanization, land urbanization and economic urbanization) on PM2.5 pollution were estimated by a spatial Durbin panel data model (SDM). Obtained results showed that: (i) PM2.5 pollution was larger in the north than in the south of YRD; (ii) Lianyungang and Yancheng cities had significant increasing trends in PM2.5 pollution from 2002 to 2017; (iii) the regional median center of PM2.5 pollution was observed in the Nanjing city, with gradual shifting to the northwest during the 16-year period; (iv) PM2.5 pollution showed significant and positive spatial autocorrelation and spillover effect; (v) population urbanization contributed more to the increase in PM2.5 pollution than land urbanization, while economic urbanization had no significant impact. The present study highlights the impacts of three urbanization factors on PM2.5 pollution which represent valuable and relevant information for air pollution control and urban planning.

Spatial Econometric Analysis of the Impact of Socioeconomic Factors on PM2.5 Concentration in China's Inland Cities: A Case Study from Chengdu Plain Economic Zone

Particulate matter with a diameter less than 2.5 µm (PM_2.5), one of the main sources of air pollution, has increasingly become a concern of the people and governments in China. Examining the socioeconomic factors influencing on PM_2.5 concentration is important for regional prevention and control. Previous studies mainly concentrated on the economically developed eastern coastal cities, but few studies focused on inland cities. This study selected Chengdu Plain Economic Zone (CPEZ), an inland region with heavy smog, and used spatial econometrics methods to identify the spatiotemporal distribution characteristics of PM_2.5 concentration and the socioeconomic factors underlying it from 2006 to 2016. Moran’s index indicates that PM_2.5 concentration in CPEZ does have spatial aggregation characteristics. In general, the spatial clustering from the fluctuation state to the stable low state decreased by 1% annually on average, from 0.190 (p < 0.05) in 2006 to 0.083 (p < 0.1) in 2016. According to the results of the spatial Durbin model (SDM), socioeconomic factors including population density, energy consumption per unit of output, gross domestic product (GDP), and per capita GDP have a positive effect on PM_2.5 concentration, while greening rate and per capita park space have a negative effect. Additionally, those factors have identified spatial spillover effects on PM_2.5 concentration. This study could be a reference and support for the formulation of more efficient air pollution control policies in inland cities.

Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta during the 2014-2017 Period

Large amounts of aerosol particles suspended in the atmosphere pose a serious challenge to the climate and human health. In this study, we produced a dataset through merging the Moderate Resolution Imaging Spectrometers (MODIS) Collection 6.1 3-km resolution Dark Target aerosol optical depth (DT AOD) with the 10-km resolution Deep Blue aerosol optical depth (DB AOD) data by linear regression and made use of it to unravel the spatiotemporal characteristics of aerosols over the Pan Yangtze River Delta (PYRD) region from 2014 to 2017. Then, the geographical detector method and multiple linear regression analysis were employed to investigate the contributions of influencing factors. Results indicate that: (1) compared to the original Terra DT and Aqua DT AOD data, the average daily spatial coverage of the merged AOD data increased by 94% and 132%, respectively; (2) the values of four-year average AOD were high in the north-east and low in the south-west of the PYRD; (3) the annual average AOD showed a decreasing trend from 2014 to 2017 while the seasonal average AOD reached its maximum in spring; and that (4) Digital Elevation Model (DEM) and slope contributed most to the spatial distribution of AOD, followed by precipitation and population density. Our study highlights the spatiotemporal variability of aerosol optical depth and the contributions of different factors over this large geographical area in the four-year period, and can, therefore, provide useful insights into the air pollution control for decision makers.

Health Risk and Resilience Assessment with Respect to the Main Air Pollutants in Sichuan

Rapid urbanization and industrialization in developing countries have caused an increase in air pollutant concentrations, and this has attracted public concern due to the resulting harmful effects to health. Here we present, through the spatial-temporal characteristics of six criteria air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃) in Sichuan, a human health risk assessment framework conducted to evaluate the health risk of different age groups caused by ambient air pollutants. Public health resilience was evaluated with respect to the risk resulting from ambient air pollutants, and a spatial inequality analysis between the risk caused by ambient air pollutants and hospital density in Sichuan was performed based on the Lorenz curve and Gini coefficient. The results indicated that high concentrations of PM_2.5 (47.7 μg m⁻³) and PM₁₀ (75.9 μg m⁻³) were observed in the Sichuan Basin; these two air pollutants posed a high risk to infants. The high risk caused by PM_2.5 was mainly distributed in Sichuan Basin (1.14) and that caused by PM₁₀ was principally distributed in Zigong (1.01). Additionally, the infants in Aba and Ganzi had high health resilience to the risk caused by PM_2.5 (3.89 and 4.79, respectively) and PM₁₀ (3.28 and 2.77, respectively), which was explained by the low risk in these two regions. These regions and Sichuan had severe spatial inequality between the infant hazard quotient caused by PM_2.5 (G = 0.518, G = 0.493, and G = 0.456, respectively) and hospital density. This spatial inequality was also caused by PM₁₀ (G = 0.525, G = 0.526, and G = 0.466, respectively), which is mainly attributed to the imbalance between hospital distribution and risk caused by PM_2.5 (PM₁₀) in these two areas. Such research could provide a basis for the formulation of medical construction and future air pollution control measures in Sichuan.