Long-term trends and spatial patterns of satellite-retrieved PM2.5 concentrations in South and Southeast Asia from 1999 to 2014

Optimizing health risk assessment for soil trace metals under low-precision sampling conditions: A case study of agricultural soil

Cost limitations often lead to the adoption of lower precision grids for soil sampling in large-scale areas, potentially causing deviations in the observed trace metal (TM) concentrations from their true values. Therefore, in this study, an enhanced Health Risk Assessment (HRA) model was developed by combining Monte Carlo simulation (MCS) and Empirical Bayesian kriging (EBK), aiming to improve the accuracy of health risk assessment under low-precision sampling conditions. The results showed that the increased sampling scale led to an overestimation of the non-carcinogenic risk for children, resulting in potential risks (the maximum Hazard index value was 1.08 and 1.64 at the 500 and 1000 m sampling scales, respectively). EBK model was suitable for predicting soil TM concentrations at large sampling scale, and the predicted concentrations were closer to the actual value. Furthermore, we found that the improved HRA model by combining EBK and MCS effectively reduced the possibility of over- or under-estimation of risk levels due to the increasing sampling size, and enhanced the accuracy and robustness of risk assessment. This study provides an important methodology support for health risk assessment of soil TMs under data limitation.

Crop residue burning in China (2019-2021): Spatiotemporal patterns, environmental impact, and emission dynamics

Crop residue burning (CRB) is a major contributor to air pollution in China. Current fire detection methods, however, are limited by either temporal resolution or accuracy, hindering the analysis of CRB's diurnal characteristics. Here we explore the diurnal spatiotemporal patterns and environmental impacts of CRB in China from 2019 to 2021 using the recently released NSMC-Himawari-8 hourly fire product. Our analysis identifies a decreasing directionality in CRB distribution in the Northeast and a notable southward shift of the CRB center, especially in winter, averaging an annual southward movement of 7.5°. Additionally, we observe a pronounced skewed distribution in daily CRB, predominantly between 17:00 and 20:00. Notably, nighttime CRB in China for the years 2019, 2020, and 2021 accounted for 51.9%, 48.5%, and 38.0% respectively, underscoring its significant environmental impact. The study further quantifies the hourly emissions from CRB in China over this period, with total emissions of CO, PM10, and PM2.5 amounting to 12,236, 2,530, and 2,258 Gg, respectively. Our findings also reveal variable lag effects of CRB on regional air quality and pollutants across different seasons, with the strongest impacts in spring and more immediate effects in late autumn. This research provides valuable insights for the regulation and control of diurnal CRB before and after large-scale agricultural activities in China, as well as the associated haze and other pollution weather conditions it causes.

Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval

Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for climate research. Here, we propose a novel pretrained deep learning framework that can extract information underlying each satellite pixel and use it to create new latent features that can be employed for improving retrieval accuracy in regions without in situ data. With the proposed model, we developed a new global fAOD (at 0.5 μm) data from 2001 to 2020, resulting in a 10% improvement in the overall correlation coefficient (R) during site-based independent validation and a 15% enhancement in non-AERONET site areas validation. Over the past two decades, there has been a noticeable downward trend in global fAOD (-1.39 × 10-3/year). Compared to the general deep-learning model, our method reduces the global trend's previously overestimated magnitude by 7% per year. China has experienced the most significant decline (-5.07 × 10-3/year), which is 3 times greater than the global trend. Conversely, India has shown a significant increase (7.86 × 10-4/year). This study bridges the gap between sparse in situ observations and abundant satellite measurements, thereby improving predictive models for global patterns of fAOD and other climate factors.

Spatial and temporal characteristic of PM2.5 and influence factors in the Yellow River Basin

The Yellow River Basin has been instrumental in advancing ecological preservation and fostering national high-quality development. However, since the advent of China's reform and opening-up policies, the basin has faced severe environmental pollution issues. This study leverages remote sensing data from 1998 to 2019. As per the "Basin Scope and Its Historical Changes" published by the Yellow River Conservancy Commission of the Ministry of Water Resources, the Yellow River Basin is categorized into upstream, midstream, and downstream regions for analysis of their spatial and temporal distribution traits using spatial autocorrelation methods. Additionally, we employed probes to study the effects of 10 factors, including mean surface temperature and air pressure, on PM2.5. The study findings reveal that (1) the annual average concentration of PM2.5 in the Yellow River Basin exhibited a fluctuating trend from 1998 to 2019, initially increasing, then decreasing, followed by another increase before ultimately declining. (2) The air quality in the Yellow River Basin is relatively poor, making it challenging for large-scale areas with low PM2.5 levels to occur. (3) The PM2.5 concentration in the Yellow River Basin exhibits distinct high and low-value concentration areas indicative of air pollution. Low-value areas are predominantly found in the sparsely populated central and southwestern plateau regions of Inner Mongolia, characterized by a better ecological environment. In contrast, high-value areas are prevalent in the inland areas of Northwest China, with poorer natural conditions, as well as densely populated zones with high energy demand and a relatively developed economy. (4) The overall population density in the Yellow River Basin, as well as in the upstream, midstream, and downstream regions, serves as a primary driving factor. (5) The primary drivers in the middle reaches and the entire Yellow River Basin remain consistent, whereas those in the upper and lower reaches have shifted. In the upstream, air pressure emerges as a primary driver of PM2.5, while in the downstream, NDVI and precipitation become the main influencing factors.

Assessment of Transboundary PM2.5 from Biomass Burning in Northern Thailand Using the WRF-Chem Model

Air pollution, particularly PM2.5, poses a significant environmental and public health concern, particularly in northern Thailand, where elevated PM2.5 levels are prevalent during the dry season (January-May). This study examines the influx and patterns of transboundary biomass burning PM2.5 (TB PM2.5) in this region during the 2019 dry season using the WRF-Chem model. The model's reliability was confirmed through substantial correlations between model outputs and observations from the Pollution Control Department (PCD) of Thailand at 10 monitoring stations. The findings indicate that TB PM2.5 significantly influences local PM2.5 levels, often surpassing contributions from local sources. The influx of TB PM2.5 began in January from southern directions, intensifying and shifting northward, peaking in March with the highest TB PM2.5 proportions. Elevated levels persisted through April and declined in May. Border provinces consistently exhibited higher TB PM2.5 concentrations, with Chiang Rai province showing the highest average proportion, reaching up to 45%. On days when PM2.5 levels were classified as 'Unhealthy for Sensitive Groups' or 'Unhealthy', TB PM2.5 contributed at least 50% to the total PM2.5 at all stations. Notably, stations in Chiang Rai and Nan showed detectable TB PM2.5 even at 'Very Unhealthy' levels, underscoring the significant impact of TB PM2.5 in the northern border areas. Effective mitigation of PM2.5-related health risks requires addressing PM2.5 sources both within and beyond Thailand's borders.

Intricate synergistic effects between air pollution and carbon emission: An emerging evidence from China

Due to global climate change and intensifying anthropogenic pollution, China confronts the dual challenge of controlling particulate matter 2.5 μm (PM2.5) pollution and reducing carbon emissions. Quantifying the characteristics of PM2.5 concentrations and CO2 emissions, as well as identifying the driving factors and synergistic effects of PM2.5 reduction and CO2 mitigation, are crucial steps in promoting sustainable urban development and achieving the Sustainable Development Goals (SDGs) in China. In this study, we selected 168 cities as our case-study, and quantified spatial characteristics of PM2.5 concentrations and CO2 emissions from 2015 to 2020 in China. Then we analyzed driving factors affecting the spatial heterogeneity of PM2.5 reduction and CO2 mitigation applying Multi-scale Geographically Weighted Regression (MGWR) model. By employing coupling coordination degree (CCD) model, we further detected the spatiotemporal evolution patterns of the synergistic effects between PM2.5 reduction and CO2 mitigation in key Chinese cities. The result showed that: (a) From 2015 to 2020, PM2.5 concentrations experienced a significant reduction from 59.78 μg/m3 to 49.83 μg/m3, while CO2 emissions increased from 44.88 × 106 t in 2015 to 45.77 × 106 t in 2020; (b) Green economy efficiency (gee), government attention (gover), and environmental regulation (envir) demonstrate the most pronounced synergistic effect on pollution reduction and carbon mitigation, with the drivers exhibiting obvious spatial heterogeneity; (c) The overall coupling coordination level of PM2.5 pollution and CO2 emissions in China dropped from 0.49 in 2015 to 0.46 in 2020, and the coupling coordination grade in northern cities was notably higher than that in southern cities. The result enhances our understanding of spatiotemporal patterns of synergistic effects between PM2.5 reduction and CO2 mitigation, and provides the theoretical basis for policy decision-making to realize pollution decrease and carbon neutral and regional environment governance.

Characterization and health risk assessment of PM2.5-bound polycyclic aromatic hydrocarbons in Yangon and Mandalay of Myanmar

To better understand the potential adverse health effects of atmospheric fine particles in the Southeast Asian developing countries, PM2.5 samples were collected at two urban sites in Yangon and Mandalay, representing coastal and inland cities in Myanmar, in winter and summer during 2016 and 2017. The concentrations of 21 polycyclic aromatic hydrocarbons (PAHs) in PM2.5 were determined using a gas chromatography-mass spectrometry (GC-MS). The concentrations of PAHs in PM2.5 in Yangon and Mandalay ranged from 7.6 to 180 ng m-3, with an average of 72 ng m-3. The PAHs were significantly higher in winter than in summer, and significantly higher in Mandalay than in Yangon. The health risk analysis of PAHs, based on the toxic equivalent quantity (TEQ) calculation, and the incremental lifetime cancer risk (ILCR) assessment indicated that PM2.5 in Myanmar has significant health risks with higher health risks in Mandalay compared to Yangon. Diagnostic ratios of PAHs, correlation of PAHs with other species in PM2.5 and the positive matrix factorization (PMF) analysis showed that TEQ is strongly affected by biomass burning and vehicular emissions in Myanmar. Additionally, it was found that the aging degree of aerosols and air mass trajectories had great influences on the concentration and composition of PAHs in PM2.5 in Myanmar, thereby affecting the toxicity of PM2.5.

Spatio-temporal analysis and risk modeling of foot-and-mouth disease outbreaks in China

FMD is an acute contagious disease that poses a significant threat to the health and safety of cloven-hoofed animals in Asia, Europe, and Africa. The impact of FMD exhibits geographical disparities within different regions of China. The present investigation undertook an exhaustive analysis of documented occurrences of bovine FMD in China, spanning the temporal range from 2011 to 2020. The overarching objective was to elucidate the temporal and spatial dynamics underpinning these outbreaks. Acknowledging the pivotal role of global factors in FMD outbreaks, advanced machine learning techniques were harnessed to formulate an optimal prediction model by integrating comprehensive meteorological data pertinent to global FMD. Random Forest algorithm was employed with top three contributing factors including Isothermality(bio3), Annual average temperature(bio1) and Minimum temperature in the coldest month(bio6), all relevant to temperature. By encompassing both local and global factors, our study provides a comprehensive framework for understanding and predicting FMD outbreaks. Furthermore, we conducted a phylogenetic analysis to trace the origin of Foot-and-mouth disease virus (FMDV), pinpointing India as the country posing the greatest potential hazard by leveraging the spatio-temporal attributes of the collected data. Based on this finding, a quantitative risk model was developed for the legal importation of live cattle from India to China. The model estimated an average probability of 0.002254% for FMDV-infected cattle imported from India to China. TA sensitivity analysis identified two critical nodes within the model: he possibility of false negative clinical examination in infected cattle at destination (P5) and he possibility of false negative clinical examination in infected cattle at source(P3). This comprehensive approach offers a thorough evaluation of FMD landscape within China, considering both domestic and global perspectives, thereby augmenting the efficacy of early warning mechanisms.

Spatiotemporal characteristic analysis of PM2.5 in central China and modeling of driving factors based on MGWR: a case study of Henan Province

Since the start of the twenty-first century, China's economy has grown at a high or moderate rate, and air pollution has become increasingly severe. The study was conducted using data from remote sensing observations between 1998 and 2019, employing the standard deviation ellipse model and spatial autocorrelation analysis, to explore the spatiotemporal distribution characteristics of PM2.5 in Henan Province. Additionally, a multiscale geographically weighted regression model (MGWR) was applied to explore the impact of 12 driving factors (e.g., mean surface temperature and CO2 emissions) on PM2.5 concentration. The research revealed that (1) Over a period of 22 years, the yearly mean PM2.5 concentrations in Henan Province demonstrated a trend resembling the shape of the letter "M", and the general trend observed in Henan Province demonstrated that the spatial center of gravity of PM2.5 concentrations shifted toward the north. (2) Distinct spatial clustering patterns of PM2.5 were observed in Henan Province, with the northern region showing a primary concentration of spatial hot spots, while the western and southern areas were predominantly characterized as cold spots. (3) MGWR is more effective than GWR in unveiling the spatial heterogeneity of influencing factors at various scales, thereby making it a more appropriate approach for investigating the driving mechanisms behind PM2.5 concentration. (4) The results acquired from the MGWR model indicate that there are varying degrees of spatial heterogeneity in the effects of various factors on PM2.5 concentration. To summarize the above conclusions, the management of the atmospheric environment in Henan Province still has a long way to go, and the formulation of relevant policies should be adapted to local conditions, taking into account the spatial scale effect of the impact of different influencing factors on PM2.5.

Assessment of variability in PM2.5 and its impact on human health in a West African country

PM2.5 has become a global challenge threatening human health, climate, and the environment. PM2.5 is ranked as the most common cause of premature mortality and morbidity. Therefore, the current study endeavors to probe the spatiodynamic characteristics of PM2.5 in the Republic of Niger and its impacts on human health from 1998 to 2019. Based on remotely sensed satellite datasets, the study found that the concentration of PM2.5 continued to rise in Niger from 68.85 μg/m3 in 1998 to 70.47 μg/m3 in 2019. During the study period, the annual average PM2.5 concentration is far above the WHO guidelines and the interim target-1 (35 μg/m3). The overall annual growth rate of PM2.5 concentration in Niger is 0.02 μg/m3/year. The health risk (HR) due to PM2.5 exposure is also escalated in Niger, particularly, in Southern Niger. The extent of the extremely high-risk areas corresponding to 1 × 104-9.4 × 105 μg.persons/m3 is increased from 0.9% (2000) to 2.8% (2019). Niamey, southern Dakoro, Mayahi, Tessaoua, Mirriah, Magaria, Matameye, Aguié, Madarounfa, Groumdji, Madaoua, Bouza, Keita, eastern Tahoua, eastern Illéla, Bkomnni, southern Dogon-Doutchi, Gaya, eastern Boboye, central Kollo, and western Tillabéry are experienced high HR due to long-term exposure to PM2.5. These findings indicate that PM2.5 causes a serious health risk across Niger. There is an immediate need to carry out its regional control. Therefore, policymakers and the Nigerien government should make conscious efforts to identify the priority target areas with radically innovative appropriate mitigation interventions.

Analysis of diurnal to seasonal variations and trends in air pollution potential in an urban area

Air pollution is the world's largest environmental health threat to humans and has wide-ranging adverse effects on the environment. The term ventilation coefficient (VC), which is a function of the average wind speed through the planetary boundary layer (PBL) and the PBL height (PBLH), can be used to estimate air pollution potential. We analyzed PBLH, wind speed through PBL, and VC over Tehran using ERA5, and PM2.5 surface concentration using MERRA-2 during 1991-2020. Both PBLH and VC undergo substantial diurnal variations, with higher values during the day and much lower values at night. As a result, PM2.5 concentration in Tehran is the maximum in the early morning, while it is relatively lower in the afternoon. The average wind speed through PBL shows the same diurnal variation in all seasons, except in winter when winds in PBL are stronger at night than during the day. Both PBLH and VC over Tehran show substantial seasonal variations, with much higher values in summer followed in decreasing order by spring, autumn, and winter, highlighting an extremely high air pollution potential in winter. Hence, due to high pollutant emissions, the occurrence of severe air pollution is expected to be a common feature in Tehran in winter. PBLH has significantly increased over Tehran both during the day and at night for the period 1991-2020, primarily in response to the surface warming in recent decades, while wind speed through PBL has significantly declined only at night. The overall impact of such changes is an increase in VC over Tehran both during the day and at night, although the increasing trend of VC is statistically significant only at night. Our results highlight the urgent need for the implementation of effective sustainable policies to reduce air pollution and its adverse effects in winter when air pollution potential is high in Tehran.

A multiscale analysis of the spatially heterogeneous relationships between non-point source pollution-related processes and their main drivers in Chaohu Lake watershed, China

A better understanding of the relationships between non-point source (NPS) pollution-related processes and their drivers will help to develop scientific watershed management measures. Although various studies have explored the drivers' impact on NPS pollution-related processes, quantitative knowledge of the properties within these relationships is still needed. This study uses the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to produce three related processes of NPS pollution, quick flow (QF), nitrogen export (NE), and sediment export (SE), in the upstream watershed of Chaohu Lake, China. The spatial distributions of QF, NE, and SE and their responses to multiple natural-socioeconomic drivers at nine spatial scales (1 km2, 10 km2, 20 km2, 30 km2, 50 km2, 75 km2, 100 km2, 200 km2, and town) were compared. The results showed that the spatial scale has little impact on the spatial distributions of NPS pollution-related processes. Across the nine scales, the socioeconomic drivers related to agricultural activities, area proportions of cultivated land (cultivated) and paddy field (paddy), have dominant impacts on NE, while the topographical drivers, the connectivity index (IC) and slope, have dominant impacts on both SE and QF. The magnitudes of single and paired natural-socioeconomic drivers' impacts on NPS pollution-related processes increase logarithmically or linearly with increasing spatial scale, but they tend to reach a stable threshold at a certain coarse scale. Our results emphasized the necessity and importance of embracing spatial scale effects in watershed water environmental management.

Black carbon pollution in China from 2001 to 2019: Patterns, trends, and drivers

Based on a near real-time 10 km × 10 km resolution black carbon (BC) concentration dataset, this study investigated the spatial patterns, trend variations, and drivers of BC concentrations in China from 2001 to 2019 with spatial analysis, trend analysis, hotspot clustering, and multiscale geographically weighted regression (MGWR). The results indicate that Beijing-Tianjin-Hebei, the Chengdu-Chongqing agglomeration, Pearl River Delta, and East China Plain were the hotspot centers of BC concentration in China. From 2001 to 2019, the average rate of decline in BC concentrations across China was 0.36 μg/m³/year (p < 0.001), with BC concentrations peaking around 2006 and sustaining a decline for the next decade or so. The rate of BC decline was higher in Central, North, and East China than in other regions. The MGWR model revealed the spatial heterogeneity of the influences of different drivers. A number of enterprises had significant effects on BC in East, North, and Southwest China; coal production had strong effects on BC in Southwest and East China; electricity consumption had better effects on BC in Northeast, Northwest, and East China than in other regions; the ratio of secondary industries had the greatest effects on BC in North and Southwest China; and CO₂ emissions had the strongest effects on BC in East and North China. Meanwhile, the reduction of BC emissions from the industrial sector was the dominant factor in the decrease of BC concentration in China. These findings provide references and policy prescriptions for how cities in different regions can reduce BC emissions.

Long term spatiotemporal trends and health risk assessment of remotely sensed PM2.5 concentrations in Nigeria

PM_2.5 is an important indicator reflecting air quality variations. Currently, environmental pollution related issues have become more severe that significantly threaten human health. The current study is an attempt to analyze the spatio-dynamic characteristics of PM_2.5 in Nigeria based on the directional distribution and trend clustering analysis from 2001 to 2019. The results indicated that PM_2.5 concentration increased in most of the Nigerian states, particularly in mid-northern and southern states. The lowest PM_2.5 concentration in Nigeria is even beyond the interim target-1 (35 μg/m³) of the WHO. During the study period, the average PM_2.5 concentration increased at a growth rate of 0.2 μg/m³/yr from 69 μg/m³ to 81 μg/m³. The growth rate varied from region to region. Kano, Jigawa, Katsina, Bauchi, Yobe, and Zamfara experienced the fastest growth rate of 0.9 μg/m³/yr with 77.9 μg/m³ mean concentration. The median center of the national average PM_2.5 moved toward the north indicating the highest PM_2.5 concentration in northern states. The Saharan desert dust is the dominant source of PM_2.5 in northern areas. Moreover, agricultural practices and deforestation activities along with low rainfall increase desertification and air pollution in these regions. Health risks increased in most of the mid-northern and southern states. The extent of ultra-high health risk (UHR) areas corresponding to the 8×10⁴-7.3×10⁶ μg⋅person/m³ increased from 1.5% to 2.8%. Mainly Kano, Lagos, Oyo, Edo, Osun, Ekiti, southeastern Kwara, Kogi, Enugu, Anambra, Northeastern Imo, Abia, River, Delta, northeastern Bayelsa, Akwa Ibom, Ebonyi, Abuja, Northern Kaduna, Katsina, Jigawa, central Sokoto, northeastern Zamfara, central Borno, central Adamawa, and northwestern Plateau are under UHR areas.

Ozone exposure and health risks of different age structures in major urban agglomerations in People's Republic of China from 2013 to 2018

High concentration of surface ozone (O₃) will cause health risks to people. In order to analyze the spatiotemporal characteristics of O₃ and assess O₃ exposure and health risks for different age groups in China, we applied multiple methods including standard deviation ellipse, spatial autocorrelation, and exposure-response functions. Results show that O₃ concentrations increased in 64.5% of areas in China from 2013 to 2018. The central plain urban agglomeration (CPU), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) witnessed the greatest incremental rates of O₃ by 16.7%, 14.3%, and 13.1%. Spatially, the trend of O₃ shows a significant positive autocorrelation, and high trend values primarily in central and east China. The proportion of the total population exposed to high O₃ (above 160 μg/m³) increased annually. Compared to 2013, the proportion of the young, adult, and old populations exposed to high O₃ increased to different extents in 2018 by 26.8%, 29.6%, and 27.2%, respectively. The extent of population exposure risk areas in China expanded in size, particularly in north and east China. The total premature respiratory mortalities attributable to long-term O₃ exposure in six urban agglomerations were about 177,000 in 2018 which has increased by 16.4% compared to that in 2013. Among different age groups, old people are more vulnerable to O₃ pollution, so we need to strengthen their relevant health protection of them.

The spatiotemporal evolution and influencing factors of urban green innovation in China

Green innovation (GI) is an important way to build an ecological civilization and an innovative country. The study on urban green innovation (UGI) is of great significance for enriching the research on GI and rationally formulating high-quality urban development policies. The green patent data obtained using a web crawler was used to represent the level of UGI. The spatiotemporal evolution and influencing factors of UGI in China were analyzed by standard deviation ellipses, spatial autocorrelation, and Geodetector. The research shows that: From 2005 to 2020, the level of UGI in China tended to rise rapidly. The center of gravity of UGI in China was located in the southeast of China's geometric center and tended to move to the south and west. The standard deviation ellipse was distributed in a "northeast-southwest" pattern, the area was gradually shrinking, and the length of the two semi-axes was shortening. UGI in China showed obvious global and local spatial autocorrelations. The degree of global spatial autocorrelation was gradually increasing. Among the types of local spatial autocorrelation, the largest number of low-low agglomeration cities was mainly located in the northwest and southwest part of China, while high-high agglomeration cities were distributed in Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta. The government intervention expressed by the proportion of scientific and technological expenditure in fiscal expenditure and environmental regulation is the dominant factor affecting UGI.

Surface ozone pollution in China: Trends, exposure risks, and drivers

Introduction

Within the context of the yearly improvement of particulate matter (PM) pollution in Chinese cities, Surface ozone (O₃) concentrations are increasing instead of decreasing and are becoming the second most important air pollutant after PM. Long-term exposure to high concentrations of O₃ can have adverse effects on human health. In-depth investigation of the spatiotemporal patterns, exposure risks, and drivers of O₃ is relevant for assessing the future health burden of O₃ pollution and implementing air pollution control policies in China.

Methods

Based on high-resolution O₃ concentration reanalysis data, we investigated the spatial and temporal patterns, population exposure risks, and dominant drivers of O₃ pollution in China from 2013 to 2018 utilizing trend analysis methods, spatial clustering models, exposure-response functions, and multi-scale geographically weighted regression models (MGWR).

Results

The results show that the annual average O₃ concentration in China increased significantly at a rate of 1.84 μg/m³/year from 2013 to 2018 (160 μg/m³) in China increased from 1.2% in 2013 to 28.9% in 2018, and over 20,000 people suffered premature death from respiratory diseases attributed to O₃ exposure each year. Thus, the sustained increase in O₃ concentrations in China is an important factor contributing to the increasing threat to human health. Furthermore, the results of spatial regression models indicate that population, the share of secondary industry in GDP, NOx emissions, temperature, average wind speed, and relative humidity are important determinants of O₃ concentration variation and significant spatial differences are observed.

Discussion

The spatial differences of drivers result in the spatial heterogeneity of O₃ concentration and exposure risks in China. Therefore, the O₃ control policies adapted to various regions should be formulated in the future O₃ regulation process in China.

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.

Spatial and temporal variations in PM2.5 and associated health risk assessment in Saudi Arabia using remote sensing

Air pollutants, especially ambient particulate matter (PM_2.5), detrimentally impact human health and cause premature deaths. The dynamic characteristics and associated health risks of PM_2.5 are analyzed based on the standard deviational ellipse (SDE) and trend analysis in Saudi Arabia (SAU) from 1998 to 2018 by utilizing recently updated satellite-derived PM_2.5 concentrations (V4.GL.03). The outcomes show that the national average PM_2.5 concentration increased from 28 μg/m³ to 45 μg/m³ with a growth rate of 2.3 μg/m³/year. The center of median PM_2.5 concentrations moved to the southeast over the years studied due to the presence of vast sandy deserts, sand dunes, a busy port, and coastal and industrial areas in this region. The areas of SAU that experienced PM_2.5 concentrations above 35 μg/m³ increased from 20% to 70%. The rapid-fast growth (RFG) class acquired from the unsupervised classification has the fastest growth rate of 2.5 μg/m³/yr, occurring in southeastern SAU, namely Ash-Sharqiyah, Ar-Riyad, and Najran. It covered ∼27% of the total area of SAU over the study period. Whereas, the slow growth (SG) class with a less than 0.2 μg/m³/yr growth rate covered 12% of the total area of SAU, distributed in northwestern regions. The extent of extremely-high risk areas corresponding to greater than 1 × 10³ μg·person/m³ increased from 4% to 11%, particularly in Makkah, Central Al-Madinah, and western Asir, Jizan, mid-eastern Najran, Al-Quassim, and mid-eastern Ar-Riyad and Ash Sharqiyah.

Spatiotemporal Distribution and Evolution of Digestive Tract Cancer Cases in Lujiang County, China since 2012

This study aims to analyze the spatiotemporal distribution and evolution of digestive tract cancer (DTC) in Lujiang County, China by using the geographic information system technology. Results of this study are expected to provide a scientific basis for effective prevention and control of DTC. The data on DTC cases in Lujiang County, China, were downloaded from the Data Center of the Center for Disease Control and Prevention in Hefei, Anhui Province, China, while the demographic data were sourced from the demographic department in China. Systematic statistical analyses, including the spatial empirical Bayes smoothing, spatial autocorrelation, hotspot statistics, and Kulldorff's retrospective space-time scan, were used to identify the spatial and spatiotemporal clusters of DTC. GM(1,1) and standard deviation ellipses were then applied to predict the future evolution of the spatial pattern of the DTC cases in Lujiang County. The results showed that DTC in Lujiang County had obvious spatiotemporal clustering. The spatial distribution of DTC cases increases gradually from east to west in the county in a stepwise pattern. The peak of DTC cases occurred in 2012-2013, and the high-case spatial clusters were located mainly in the northwest of Lujiang County. At the 99% confidence interval, two spatiotemporal clusters were identified. From 2012 to 2017, the cases of DTC in Lujiang County gradually shifted to the high-incidence area in the northwest, and the spatial distribution range experienced a process of "dispersion-clustering". The cases of DTC in Lujiang County will continue to move to the northwest from 2018 to 2025, and the predicted spatial clustering tends to be more obvious.

Does the Government’s Environmental Attention Affect Ambient Pollution? Empirical Research on Chinese Cities

Environmental pollution has attracted growing government attention. We employ a series of panel data regression models to measure and analyze the impact of environmental attention of 284 prefecture-level municipal governments on ambient pollution in China. The results show that: (1) The improvement of government environmental attention can curb ambient pollution. (2) The impact of government environmental attention on ambient pollution is heterogeneous in the difference of regional and local environmental pollution severity. (3) Government environmental attention inhibits ambient pollution through green development and industrial upgrading. The conclusions of this paper provide evidence and implications for environmental regulation in developing countries and cities.

Human Activity Intensity in China under Multi-Factor Interactions: Spatiotemporal Characteristics and Influencing Factors

Human activities involving nature have various environmental impacts. The assessment of the spatial and temporal evolution of human activity intensity (HAI) and its driving forces is significant for determining the effects of human activities on regional ecological environments and regulating such activities. This research quantified the HAI of China, assessed its spatiotemporal characteristics, and analyzed its influencing factors based on the land use data and panel data of 31 provinces in mainland China. The results indicate that the HAI in China is increasing, with the average value increasing from 15.83% in 1980 to 20.04% in 2018, and the HAI was relatively serious in the Beijing–Tianjin–Hebei region, Yangtze River Delta and Pearl River Delta in this period. The spatial differences in the HAI in China show a pattern of being strong in the east and weak in the west, and the spatial center of gravity of China’s HAI has gradually moved west, changing from a central enhancement mode to a point-like “core” enhancement mode. The dominant factors affecting spatial differences in HAI are economic and industrial levels. Labor, population, and capital factors also strongly impact HAI, and energy consumption and pollution emissions have little impact. These results deepen the understanding of the underlying mechanism of the environmental impact of human activities and provide a scientific basis for land-use-related decision making and eco-environment construction.

Long term influence of groundwater preservation policy on stubble burning and air pollution over North-West India

Stubble burning (SB) has been a major source of seasonal aerosol loading and pollution over northern India. The aftereffects of groundwater preservation act i.e., post 2010 era (2011-2020) has seen delay in crop harvesting thereby shifting the peak SB to May (Wheat SB) and to November (Paddy SB) by 8-10 and 10-12 days compared to pre-2010. Groundwater storage depletion rate of 29.2 mm yr^-1 was observed over the region. Post 2010 era shows an increase of 1.4% in wheat SB and 21% in Paddy SB fires over Punjab and Haryana with 70% of PM_2.5 air mass clusters (high probability > 0.8) advecting to the downwind regions leading to 23-26% increase in PM_2.5 and 4-6% in aerosol loading over National Capital Region (NCR). Although the objective of water conservation policy was supposed to preserve the groundwater by delaying the paddy transplantation and sowing, on the contrary the implementation of this policy has seen groundwater storage after 2013 depleting at a rate of 29.2 mmyr^-1 over these regions. Post policy implementation has led to shift and shrinking of harvest window with increased occurrences in SB fires which also increase associated particulate matter pollution over North India.

Spatial Differentiation and Influencing Factors of Water Pollution-Intensive Industries in the Yellow River Basin, China

The structure adjustment and layout optimization of water pollution-intensive industries (WPIIs) are crucial to the health and sustainable development of the watershed life community. Based on micro-detailed data of Chinese industrial enterprises from 2003 to 2013, we analyzed and revealed the spatial differentiation characteristics and influencing factors of WPIIs in the Yellow River Basin (YRB) from 2003 to 2013 by constructing a water pollution-intensive index and integrating kernel density estimation and geographically weighted regression models from a watershed perspective. The results show that: (1) the scale of WPIIs in the YRB showed a growth trend from 2003 to 2013, and the output value increased from 442.5 billion yuan in 2003 to 6192.4 billion yuan in 2013, an increase of 13 times. (2) WPIIs are generally distributed in an east-west direction, and their spatial distribution is river-side, with intensive distribution in the downstream areas and important tributaries such as Fen River and Wei River. (3) WPIIs are generally clustered in high density downstream, but the spatial clustering characteristics of different industries varied significantly. The chemical industries, paper industries, etc. were mainly concentrated in downstream areas. Processing of food from agricultural products was distributed in the upper, middle and downstream areas. Resource-intensive industries such as coal and oil were concentrated in energy-rich midstream areas. (4) Natural resource endowment was the main factor affecting the distribution of WPIIs in the midstream and upstream areas of the basin, and technological innovation played a significant role in the distribution of downstream industries. The level of economic development and industrial historical foundation promoted the geographical concentration of industries. The scale of wastewater discharge and the proximity of rivers influenced the concentration of industries in the midstream and downstream.

Will open waste burning become India's largest air pollution source?

India struggles with frequent exceedances of the ambient air quality standard for particulate matter and benzene. In the past two decades, India has made considerable progress in tackling indoor air pollution, by phasing out kerosene lamps, and pushing biofuel using households towards Liquefied Petroleum Gas (LPG) usage. In this study, we use updated emission inventories and trends in residential fuel consumption, to explore changes in the contribution of different sectors towards India's largest air pollution problem. We find that residential fuel usage is still the largest air pollution source, and that the <10% households using cow dung as cooking fuel contribute ∼50% of the residential PM_2.5 emissions. However, if current trends persist, residential biofuel usage in India is likely to be phased out by 2035. India's renewable energy policies are likely to reduce emissions in the heat and electricity sector, and manufacturing industries, in the mid-term. PM_2.5 emissions from open waste burning, on the other hand, hardly changed in the decade from 2010 to 2020. We conclude that without strong policies to promote recycling and upcycling of non-biodegradable waste, and the conversion of biodegradable waste to biogas, open waste burning is likely to become India's largest source of air pollution by 2035. While our study is limited to India, our findings are of relevance for other countries in the global South suffering from similar waste management challenges.

Spatiotemporal characteristics analysis and potential distribution prediction of peste des petits ruminants (PPR) in China from 2007-2018

Peste des petits ruminants (PPR) is a highly infectious disease that mainly infects small ruminants. To date, PPR has been confirmed in more than 70 countries. In China, PPR has occurred in more than 20 provinces and cities. In this study, based on geographic information system (GIS), spatial analysis was used to examine the occurrence of PPR in China from 2007 to 2018. The results showed that PPR first occurred in Tibet and gradually spread to other provinces. The outbreaks of PPR were concentrated in 2014, 2015 and 2018. Combining climate factors with the maximum entropy (MaxEnt), the results also suggested that the potential risk areas of PPR outbreaks in China were mainly Jiangsu, Yunnan and Anhui in Southeast China. Finally, a phylogenetic tree was used to analyse the evolutionary relationship between the peste des petits ruminants virus (PPRV) in China and the global ones, and it was found that the one in China had a close genetic relationship with the one in Mongolia, India and Bangladesh. Understanding and forecasting the distribution of PPR in China will help policymakers develop targeted monitoring plans. Likewise, analysing the global PPRV epidemic trends will play an important role in the elimination and prevention of PPR.

Spatio-temporal variations in fine particulate matter and evaluation of associated health risk over Pakistan

Human health and the environment are adversely affected by fine particulate matter. By utilizing standard deviation ellipse and trend analyses, we studied the spatial patterns and temporal trends of PM_2.5 over Pakistan from 1998 to 2016. The outcomes of these analyses indicated that PM_2.5 concentrations were considerably amplified in Pakistan, particularly in the provinces of Punjab and Sindh. The areal extent of PM_2.5 concentrations below 15 μg/m³ declined constantly, and the area with PM_2.5 concentrations above 35 μg/m³ increased significantly. The highly affected cities were Lahore, Faisalabad, Multan, Southern Gujranwala, Dera Ghazi Khan, Bahawalpur, Sukkur, and Larkana. Overall, the northwest-southeast axis experienced more rapid variations in the spatial pattern of PM_2.5 than the northeast-southwest axis; similarly, the east-north axis also experienced faster changes in the spatial distribution of this crucial pollutant than the west-south axis. To support nationwide air pollution control, a two-tier level was recommended for allocated regions in Pakistan depending on their PM_2.5 concentrations. From 1998 to 2016, health risks expanded and increased in Pakistan, particularly in Lahore, Karachi, Multan, Gujranwala, Faisalabad, and Hyderabad; these are Pakistan's most populated cities. The outcomes of this study suggest that human health is continuously affected by PM_2.5 in Pakistan, and that a plan of action to combat air pollution is immediately needed. Integr Environ Assess Manag 2021;17:1243-1254. © 2021 SETAC.

Atmospheric NO2 Distribution Characteristics and Influencing Factors in Yangtze River Economic Belt: Analysis of the NO2 Product of TROPOMI/Sentinel-5P

Nitrogen dioxide (NO2) has a great influence on atmospheric chemistry. Scientifically identifying the temporal-spatial characteristics of NO2 distribution and their driving factors will be of realistic significance to atmospheric governance in the Yangtze River Economic Belt (YREB). Based on the NO2 data derived from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 satellite (2017~present), spatial autocorrelation analysis, standard deviation ellipse (SDE), and geodetectors were used to systematically analyze the spatial-temporal evolution and driving factors of tropospheric NO2 vertical column density (NO2 VCD) in the YREB from 2019 to 2020. The results showed that the NO2 VCD in the YREB was high in winter and autumn and low in spring and summer (temporal distribution), and high in the northeast and low in the southwest (spatial distribution), with significant spatial agglomeration. High-value agglomeration zones were collectively and stably distributed in the east region, while low-value zones were relatively dispersed. The explanatory power of each potential factor for the NO2 VCD showed regional and seasonal variations. Surface pressure was found to be a core influencing factor. Synergistic effects of factors presented bivariate enhancement or nonlinear enhancement, and interaction between any two factors strengthened the explanatory power of a single factor for the NO2 VCD.

Particulate Matter and Premature Mortality: A Bayesian Meta-Analysis

BACKGROUND

We present a systematic review of studies assessing the association between ambient particulate matter (PM) and premature mortality and the results of a Bayesian hierarchical meta-analysis while accounting for population differences of the included studies.

METHODS

The review protocol was registered in the PROSPERO systematic review registry. Medline, CINAHL and Global Health databases were systematically searched. Bayesian hierarchical meta-analysis was conducted using a non-informative prior to assess whether the regression coefficients differed across observations due to the heterogeneity among studies.

RESULTS

We identified 3248 records for title and abstract review, of which 309 underwent full text screening. Thirty-six studies were included, based on the inclusion criteria. Most of the studies were from China (n = 14), India (n = 6) and the USA (n = 3). PM_2.5 was the most frequently reported pollutant. PM was estimated using modelling techniques (22 studies), satellite-based measures (four studies) and direct measurements (ten studies). Mortality data were sourced from country-specific mortality statistics for 17 studies, Global Burden of Disease data for 16 studies, WHO data for two studies and life tables for one study. Sixteen studies were included in the Bayesian hierarchical meta-analysis. The meta-analysis revealed that the annual estimate of premature mortality attributed to PM_2.5 was 253 per 1,000,000 population (95% CI: 90, 643) and 587 per 1,000,000 population (95% CI: 1, 39,746) for PM₁₀.

CONCLUSION

253 premature deaths per million population are associated with exposure to ambient PM_2.5. We observed an unstable estimate for PM₁₀, most likely due to heterogeneity among the studies. Future research efforts should focus on the effects of ambient PM₁₀ and premature mortality, as well as include populations outside Asia. Key messages: Ambient PM_2.5 is associated with premature mortality. Given that rapid urbanization may increase this burden in the coming decades, our study highlights the urgency of implementing air pollution mitigation strategies to reduce the risk to population and planetary health.

Impact of COVID-19 on PM2.5 Pollution in Fastest-Growing Megacity Dhaka, Bangladesh

OBJECTIVE

The purpose of the research was to investigate and identify the impact of COVID-19 lockdown on fine particulate matter (PM2.5) pollution in Dhaka, Bangladesh by using ground-based observation data.

METHODS

The research assessed air quality during the COVID-19 pandemic for PM2.5 from January 1, 2017 to August 1, 2020. The research considered pollution in pre-COVID-19 (January 1 to March 23), during COVID-19 (March 24 to May 30), and post-COVID-19 (May 31 to August 1) lockdown periods with current (2020) and historical (2017-2019) data.

RESULTS

PM2.5 pollution followed a similar yearly trend in year 2017-2020. The average concentration for PM2.5 was found 87.47 μg/m3 in the study period. Significant PM2.5 declines were observed in the current COVID-19 lockdown period compared with historical data: 11.31% reduction with an absolute decrease of 7.15 μg/m3.

CONCLUSIONS

The findings of the research provide an overview of how the COVID-19 pandemic affects air pollution. The results will provide initial evidence regarding human behavioral changes and emission controls. This research will also suggest avenues for further study to link the findings with health outcomes.

Health effects of particulate matter in major Indian cities

Background: Particulate matter (PM) is one among the crucial air pollutants and has the potential to cause a wide range of health effects. Indian cities ranked top places in the World Health Organization list of most polluted cities by PM. Objectives: Present study aims to assess the trends, short- and long-term health effects of PM in major Indian cities. Methods: PM-induced hospital admissions and mortality are quantified using AirQ+ software. Results: Annual PM concentration in most of the cities is higher than the National Ambient Air Quality Standards of India. Trend analysis showed peak PM concentration during post-monsoon and winter seasons. The respiratory and cardiovascular hospital admissions in the male (female) population are estimated to be 31,307 (28,009) and 5460 (4882) cases, respectively. PM_2.5 has accounted for a total of 1,27,014 deaths in 2017. Conclusion: Cities with high PM concentration and exposed population are more susceptible to mortality and hospital admissions.

Recessive Transition Mechanism of Arable Land Use Based on the Perspective of Coupling Coordination of Input–Output: A Case Study of 31 Provinces in China

In the rapid process of urbanization in China, arable land resources are faced with dual challenges in terms of quantity and quality. Starting with the change in the coupling coordination relationship between the input and output on arable land, this study applies an evaluation model of the degree of coupling coordination between the input and output (D_CCIO) on arable land and deeply analyzes the recessive transition mechanism and internal differences in arable land use modes in 31 provinces on mainland China. The results show that the total amount and the amount per unit area of the input and output on arable land in China have presented different spatio-temporal trends, along with the mismatched movement of the spatial barycenter. Although the D_CCIO on arable land increases slowly as a whole, 31 provinces show different recessive transition mechanisms of arable land use, which is hidden in the internal changes in the input–output structure. The results of this study highlight the different recessive transition patterns of arable land use in different provinces of China, which points to the outlook for higher technical input, optimized planting structure, and the coordination of human-land relationships.

Spatiotemporal variation and distribution characteristics of crop residue burning in China from 2001 to 2018

In this study, we integrated a remote-sensing fire product (MOD14A1) and land-use product (MCD12Q1) to extract the number of crop-residue burning (CRB) spots and the fire radiative power (FRP) in China from 2001 to 2018. Moreover, we conducted three trend analyses and two geographic distribution analyses to quantify the interannual variations and summarize the spatial characteristics of CRB on grid (0.25° × 0.25°) and regional scales. The results indicated that CRB presents distinctive seasonal patterns with each sub-region. All trend analyses suggested that the annual number of CRB spots in China increased significantly from 2001 to 2018; the linear trend reached 2615 spots/year, the Theil-Sen slope was slightly lower at 2557 spots/year, and the Mann-Kendal τ was 0.75. By dividing the study period into two sub-periods, we found that the five sub-regions presented different trends in the first and second sub-periods; e.g., the Theil-Sen slope of eastern China in the first sub-period (2001-2009) was 1021 spots/year but was -1599 spots/year in the second period (2010-2018). This suggests that summer CRB has been effectively mitigated in eastern China since 2010. Further, the average FRP of CRB spots presented a decreasing trend from 27.5 MW/spot in 2001 to only 15.8 MW/spot in 2018; this may be attributable to more scattered CRB rather than aggregated CRB. Collectively, the fire spots, FRP, and average FRP indicated that spring, summer, and autumn CRB had dropped dramatically over previous levels by 2018 due to strict mitigation measures by local governments.

Effects of the leaf functional traits of coniferous and broadleaved trees in subtropical monsoon regions on PM2.5 dry deposition velocities

Plants can intercept airborne particulate matter through deposition. Different types of plants exhibit different functional leaf traits, which can affect the dry deposition velocity (V_d). However, the most crucial leaf traits of coniferous and broadleaved trees remain unidentified. In this study, we selected 18 typical plants from the subtropical monsoon regions, where PM_2.5 (fine particulate matter with a diameter of ≤2.5 μm) concentrations are relatively high, and classified them into coniferous and broadleaved categories. Subsequently, we analyzed the relationships between V_d and leaf surface free energy (SFE), single leaf area (LA_s), surface roughness (SR), specific leaf area (SLA), epicuticular wax content (EWC), and width-to-length ratio (W/L). The results indicated that most coniferous trees exhibited a high V_d. The correlation analysis revealed that SFE, SR, LA_s, and W/L were the key factors that affected the V_d of all the tested species. SFE and SLA had the strongest influence on the V_d of broadleaved trees, whereas LA_s and SLA had the strongest effect on that of coniferous trees. Most coniferous trees had a high SLA, which can reduce water loss and hinder particle deposition. However, the stiff leaves of coniferous trees fluttered less, resulting in a larger leaf area that enhanced the capture efficiency. The leaf structure of broadleaved trees is more flexible, resulting in erratic flutter, which may impede deposition and lead to high resuspension. Coniferous and broadleaved trees may have different dominant leaf traits that affect particle deposition.

Spatio-temporal characteristics of the relationship between carbon emissions and economic growth in China's transportation industry

The economic and social development of a country rely heavily on transportation. In China, it has become the third largest energy consumption sector and generates substantial amounts of carbon emissions. In the present study, direct and indirect carbon emissions from the transportation industry throughout China's 30 provinces during 1997-2017 were calculated. Further, to reveal the spatio-temporal characteristics of the relationship between carbon emissions and economic growth, the standard deviational ellipse and Tapio decoupling method were employed. The main results are as follows. (1) The total carbon emissions from the transportation industry increased from 132.79 million tons (Mt) in 1997 to 849.64 Mt in 2017, with an average annual growth rate of 9.72%; direct carbon emissions accounted for approximately 86% of the total. (2) Carbon emissions as well as the added value of the transportation industry had the same spatial distribution characteristics, presenting a northeast-southwest pattern during 1997-2017. Although their spatial distribution patterns were mainly in the north-south direction, the development in the east-west direction became increasingly obvious. (3) The decoupling index in the transportation industry was greater than 0.8 for most years, with an expansive negative decoupling state or an expansive coupling state. The differences in carbon emissions and economic growth between various provinces showed a spatio-temporal disparity of the decoupling states in the transportation industry. The obtained results are of considerable interest for China's policymakers to set more reasonable carbon emission reduction goals and implement targeted policies according to the carbon emission situation at a local scale.

The changing PM2.5 dynamics of global megacities based on long-term remotely sensed observations

Satellite observations show that the rapid urbanization and emergence of megacities with 10 million or more residents have raised PM2.5 concentrations across the globe during the past few decades. This study examines PM2.5 dynamics for the 33 cities included on the UN list of megacities published in 2018. These megacities were classified into densely (>1500 residents per km²), moderately (300-1500 residents per km²) and sparsely (<300 residents per km²) populated areas to examine the effect of human population density on PM2.5 concentrations in these areas during the period 1998-2016. We found that: (1) the higher population density areas experienced higher PM2.5 concentrations; and (2) the megacities with high PM2.5 concentrations in these areas had higher concentrations than those in the moderately and sparsely populated areas of other megacities as well. The numbers of residents experiencing poor air quality is substantial: approximately 452 and 163 million experienced average annual PM2.5 levels exceeding 10 and 35 μg/m³, respectively in 2016. We also examined PM2.5 trends during the past 18 years and predict that high PM2.5 levels will likely continue in many of these megacities in the future without substantial changes in their economies and/or pollution abatement practices. There will be more megacities in the highest PM2.5 pollution class and the number of megacities in the lowest PM2.5 pollution class will likely not change. Finally, we analyzed how the PM2.5 pollution burden varies geographically and ranked the 33 megacities in terms of PM2.5 pollution in 2016. The most polluted regions are China, India, and South Asia and the least polluted regions are Europe and Japan. None of the 33 megacities currently fall in the WHO's PM2.5 attainment class (<10 μg/m³) while 9 megacities fall into the PM2.5 non-attainment class (>35 μg/m³). In 2016, the least polluted megacity was New York and most polluted megacity was Delhi whose average annual PM2.5 concentration of 110 μg/m³ is nearly three times the WHO's non-attainment threshold.

Source Apportionment of Coarse Particulate Matter (PM10) in Yangon, Myanmar

The Republic of the Union of Myanmar is one of many developing countries facing concerns about particulate matter (PM). Previously, a preliminary study of PM_2.5 in 2018 suggested that the main source of PM in Yangon, the former capital, was vehicle emissions. However, this suggestion was not supported by any chemical composition data. In this study, to fill that gap, we quantitatively determined source contributions to coarse particulate matter (PM₁₀) in Yangon, Myanmar. PM₁₀ samples were collected in Yangon from May 2017 to April 2018 and chemically analyzed to determine composition. Chemical composition data for these samples were then used in the Chemical Mass Balance (CMB) model to identify the major sources of particulate matter in this area. The results indicate that PM₁₀ composition varies seasonally according to both meteorological factors (e.g., precipitation and temperature) and human activities (e.g., firewood and yard waste burning). The major sources of PM in Yangon annually were dust, secondary inorganic aerosols (SIA), and secondary organic aerosols (SOA), while contributions from biomass burning were more important during the winter months.

Spatiotemporal Characteristics and Driving Force Analysis of Flash Floods in Fujian Province

Flash floods are one of the most destructive natural disasters. The comprehensive identification of the spatiotemporal characteristics and driving factors of a flash flood is the basis for the scientific understanding of the formation mechanism and the distribution characteristics of flash floods. In this study, we explored the spatiotemporal patterns of flash floods in Fujian Province from 1951 to 2015. Then, we analyzed the driving forces of flash floods in geomorphic regions with three different grades based on three methods, namely, geographical detector, principal component analysis, and multiple linear regression. Finally, the sensitivity of flash floods to the gross domestic product, village point density, annual maximum one-day precipitation (Rx1day), and annual total precipitation from days > 95th percentile (R95p) was analyzed. The analytical results indicated that (1) The counts of flash floods rose sharply from 1988, and the spatial distribution of flash floods mainly extended from the coastal low mountains, hills, and plain regions of Fujian (IIA2) to the low-middle mountains, hills, and valley regions in the Wuyi mountains (IIA4) from 1951 to 2015. (2) From IIA2 to IIA4, the impact of human activities on flash floods was gradually weakened, while the contribution of precipitation indicators gradually strengthened. (3) The sensitivity analysis results revealed that the hazard factors of flash floods in different periods and regions had significant differences in Fujian Province. Based on the above results, it is necessary to accurately forecast extreme precipitation and improve the economic development model of the IIA2 region.

Pareto law-based regional inequality analysis of PM2.5 air pollution and economic development in China

Regional inequality has caused large social and economic problems in China. Numerous researchers have sought to understand the status of economic inequality in the past decades. However, studies are lacking on other aspects of regional inequality, particularly when multiple facets must be considered. In this study, we have innovatively proposed a Pareto law-based method that can help assess multiple dimensions of regional inequality simultaneously. With this approach, we can rank multiple aspects of inequality and provide robust, reasonable goals for different groups of administrative districts. The proposed approach was successfully implemented by using Chinese data for 2015 and 2016, a period during which China was experiencing both severe PM_2.5 pollution and economic regional inequality. The results indicate that (1) Shanghai and Shenzhen represent the optimal condition of economic development; (2) different from the spatial distribution of economic inequality alone, inequality was higher in central China for both economic development and PM_2.5 air quality; (3) in the context of severe economic inequality in China, the tradeoff between economic development and air quality will result in a relatively equitable condition. In addition, the proposed method is open-ended and can be extended to incorporate more aspects of regional inequality. This approach appears to possess substantial potential for integration into decision-making regarding regional inequality.

Spatiotemporal analysis of ground and satellite-based aerosol for air quality assessment in the Southeast Asia region

Satellite observations for regional air quality assessment rely on comprehensive spatial coverage, and daily monitoring with reliable, cloud-free data quality. We investigated spatiotemporal variation and data quality of two global satellite Aerosol Optical Depth (AOD) products derived from MODIS and VIIRS imagery. AOD is considered an essential atmospheric parameter strongly related to ground Particulate Matter (PM) in Southeast Asia (SEA). We analyze seasonal variation, urban/rural area influence, and biomass burning effects on atmospheric pollution. Validation indicated a strong relationship between AERONET ground AOD and both MODIS AOD (R² = 0.81) and VIIRS AOD (R² = 0.68). The monthly variation of satellite AOD and AERONET AOD reflects two seasonal trends of air quality separately for mainland countries including Myanmar, Laos, Cambodia, Thailand, Vietnam, and Taiwan, Hong Kong, and for maritime countries consisting of Indonesia, Philippines, Malaysia, Brunei, Singapore, and Timor Leste. The mainland SEA has a pattern of monthly AOD variation in which AODs peak in March/April, decreasing during wet season from May-September, and increasing to the second peak in October. However, in maritime SEA, AOD concentration peaks in October. The three countries with the highest annual satellite AODs are Singapore, Hong Kong, and Vietnam. High urban population proportions in Singapore (40.7%) and Hong Kong (21.6%) were associated with high AOD concentrations as expected. AOD values in SEA urban areas were a factor of 1.4 higher than in rural areas, with respective averages of 0.477 and 0.336. The AOD values varied proportionately to the frequency of biomass burning in which both active fires and AOD peak in March/April and September/October. Peak AOD in September/October in some countries could be related to pollutant transport of Indonesia forest fires. This study analyzed satellite aerosol product quality in relation to AERONET in SEA countries and highlighted framework of air quality assessment over a large, complicated region.

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.

Assessment of urban environmental change using multi-source remote sensing time series (2000-2016): A comparative analysis in selected megacities in Eurasia

Excessive urban growth has led to an urban environmental degradation in megacities in less developed countries. Using fine particulate matter (PM_2.5) concentration, land surface temperature (LST), and normalized difference vegetation index (NDVI) data obtained by satellite remote sensing, we analysed the inter-annual variations and trends in the urban environment of 17 megacities in Eurasia from 2000 to 2016. Taking the average environmental condition for all the megacities in 2000 as the baseline, the urban environmental conditions were evaluated by a Comprehensive Environmental Index (CEI) from 2001 to 2016. The variation and trends analysis of CEI revealed that the overall environmental conditions in Chennai, Dhaka, Kolkata and Tianjin showed significant deterioration trends. Environmental qualities in newly developed urban areas experienced degradation in Bangalore, Beijing, and Mumbai. The area of environmentally deteriorated urban land has been expanding in Bangalore, Chennai, Delhi, Kolkata, and Mumbai in India and Dhaka in Bangladesh since 2001. By contrast, the area of environmentally degraded urban land in Chinese megacities expanded to the largest extent in the period of 2007-2009 and decreased afterwards. The result suggests that greening and strong emission control strategies significantly contributed to urban environmental quality enhancement in rapidly developing megacities.

AMPK activation attenuates inflammatory response to reduce ambient PM2.5-induced metabolic disorders in healthy and diabetic mice

Epidemiological and experimental studies have indicated that ambient fine particulate matter (PM_2.5) exposure is associated with the occurrence and development of metabolic disorders such as obesity and type 2 diabetes mellitus (T2DM). However, the mechanism is not clear yet, and there are few studies to explore the possible prevention measure. In this study, C57BL/6 and db/db mice were exposed to concentrated PM_2.5 or filtered air using Shanghai Meteorological and Environmental Animal Exposure System (Shanghai-METAS) for 12 weeks. From week 11, some of the mice were assigned to receive a subcutaneous injection of AMPK activator (AICAR). Lipid metabolism, glucose tolerance, insulin sensitivity and energy homeostasis were measured. Meanwhile, the respiratory, systemic and visceral fat inflammatory response was detected. The results showed that PM_2.5 exposure induced the impairments of glucose tolerance, insulin resistance, lipid metabolism disorders and disturbances of energy metabolism in both C57BL/6 and db/db mice. These impairments might be consistent with the increased respiratory, circulating and visceral adipose tissue (VAT) inflammatory response, which was characterized by the release of IL-6 and TNF-α in lung, serum and VAT. More importantly, AICAR administration led to the significant enhancement of energy metabolism, elevation of AMPK as well as the decreased IL-6 and TNF-α in VAT of PM_2.5-exposed mice, which suggesting that AMPK activation might attenuate the inflammatory responses in VAT via the inhibition of MAPKs and NFκB. The study indicated that exposure to ambient PM_2.5 under the concentration which is often seen in some developing countries could induce the occurrence of metabolic disorders in normal healthy mice and exacerbate metabolic disorders in diabetic mice. The adverse impacts of PM_2.5 on insulin sensitivity, energy homeostasis, lipid metabolism and inflammatory response were associated with AMPK inhibition. AMPK activation might inhibit PM_2.5-induced metabolic disorders via inhibition of inflammatory cytokines release. These findings suggested that AMPK activation is a potential therapy to prevent some of the metabolic disorders attributable to air pollution exposure.

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called “check-in behavior,” can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users’ daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term “check-in behavior,” which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as “Weibo”), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend.

Satellite-derived PM2.5 concentration trends over Eastern China from 1998 to 2016: Relationships to emissions and meteorological parameters

Fine particulate matter (PM_2.5) pollution in Eastern China (EC) has raised concerns due to its adverse effects on air quality, climate, and human health. This study investigated the long-term variation trend in satellite-derived PM_2.5 concentrations and how it was related to pollutant emissions and meteorological parameters over EC and seven regions of interest (ROIs) during 1998-2016. Over EC, the annual mean PM_2.5 increased before 2006 due to the enhanced emissions of primary PM_2.5, NO_x and SO₂, but decreased with the reduced SO₂ emissions after 2006 evidently in response to China's clean air policies. In addition, results from statistical analyses indicated that in the North China Plain (NCP), Northeast China (NEC), Sichuan Basin (SCB) and Central China (CC) planetary boundary layer height (PBLH) was the dominant meteorological driver for the PM_2.5 decadal changes, and in the Pearl River Delta (PRD) wind speed is the leading factor. Overall, the variation in meteorological parameters accounted for 48% of the variances in PM_2.5 concentrations over EC. The population-weighted PM_2.5 over EC increased from 36.4 μg/m³ in 1998-2004 (P1) to 49.4 μg/m³ in 2005-2010 (P2) then decreased to 46.5 μg/m³ in 2011-2016 (P3). In the NCP and NEC, the percentages of the population living above the World Health Organization (WHO) Interim Target-1 (IT-1, 35 μg/m³) have risen steadily over the past 20 yr, reaching maxima of 97.3% and 78.8% in P3, respectively, but decreases of ∼30% from P2 to P3 were found for the SCB and PRD.

Molecular markers for biomass burning associated with the characterization of PM2.5 and component sources during dry season haze episodes in Upper South East Asia

Severe air pollution in the form of smoke haze in the northern part of Southeast Asia (SEA) occurs annually in the dry season due to huge open area burning. Molecular markers of biomass burning were investigated by characterization of fine particles (PM_2.5) collected in the dry season (23 February-28 April 2016). The average PM_2.5, organic carbon (OC) and elemental carbon (EC) concentrations were 64.3 ± 17.6 μg m^-3, 23.6 ± 8.1 μg m^-3 and 2.85 ± 0.98 μg m^-3, respectively. SO₄^2- was the dominant species (8.73 ± 2.88 μg m^-3) of water-soluble ion, followed by NH₄⁺ (3.32 ± 1.01 μg m^-3) and NO₃^- (2.70 ± 0.51 μg m^-3). High concentrations of the biomass burning tracers K⁺ (1.27 ± 0.38 μg m^-3) and levoglucosan (1.22 ± 0.75 μg m^-3) were observed. The ratios of levoglucosan/K⁺ (0.92 ± 0.35) and levoglucosan/mannosan (20.4 ± 4.1) identified forest and agricultural waste burning as major contributors to the aerosol. Strong correlations (r > 0.800) between levoglucosan and OC, K⁺, anhydrosugar isomer (mannosan and galactosan) and other saccharides (mannose, arabitol and mannitol) verified that combustion of biomass was the major source of organic compounds associated with PM_2.5 aerosols. Oxalate was the most abundant (0.75 ± 0.17 μg m^-3; 53%) of the carboxylates. The concentration of oxalate was strongly correlated to that of PM_2.5 (r = 0.799) and levoglucosan (r = 0.615), indicating that oxalate originates mainly from primary emissions from biomass burning rather than secondary formation from photochemical processes. Backward trajectories indicated that long-range transport air masses influencing air quality in Northern Thailand originated to the west and southwest.

Value Assessment of Health Losses Caused by PM2.5 Pollution in Cities of Atmospheric Pollution Transmission Channel in the Beijing⁻Tianjin⁻Hebei Region, China

A set of exposure–response coefficients between fine particulate matter (PM_2.5) pollution and different health endpoints were determined through the meta-analysis method based on 2254 studies collected from the Web of Science database. With data including remotely-sensed PM_2.5 concentration, demographic data, health data, and survey data, a Poisson regression model was used to assess the health losses and their economic value caused by PM_2.5 pollution in cities of atmospheric pollution transmission channel in the Beijing–Tianjin–Hebei region, China. The results showed the following: (1) Significant exposure–response relationships existed between PM_2.5 pollution and a set of health endpoints, including all-cause death, death from circulatory disease, death from respiratory disease, death from lung cancer, hospitalization for circulatory disease, hospitalization for respiratory disease, and outpatient emergency treatment. Each increase of 10 μg/m³ in PM_2.5 concentration led to an increase of 5.69% (95% CI (confidence interval): 4.12%, 7.85%), 6.88% (95% CI: 4.94%, 9.58%), 4.71% (95% CI: 2.93%, 7.57%), 9.53% (95% CI: 6.84%, 13.28%), 5.33% (95% CI: 3.90%, 7.27%), 5.50% (95% CI: 4.09%, 7.38%), and 6.35% (95% CI: 4.71%, 8.56%) for above-mentioned health endpoints, respectively. (2) PM_2.5 pollution posed a serious threat to residents’ health. In 2016, the number of deaths, hospitalizations, and outpatient emergency visits induced by PM_2.5 pollution in cities of atmospheric pollution transmission channel in the Beijing–Tianjin–Hebei region reached 309,643, 1,867,240, and 47,655,405, respectively, accounting for 28.36%, 27.02% and 30.13% of the total number of deaths, hospitalizations, and outpatient emergency visits, respectively. (3) The economic value of health losses due to PM_2.5 pollution in the study area was approximately $28.1 billion, accounting for 1.52% of the gross domestic product. The economic value of health losses was higher in Beijing, Tianjin, Shijiazhuang, Zhengzhou, Handan, Baoding, and Cangzhou, but lower in Taiyuan, Yangquan, Changzhi, Jincheng, and Hebi.

Fine particulate matter (PM2.5) enhances FcεRI-mediated signaling and mast cell function

Persistent exposure to ambient fine particulate matter (PM_2.5) can exacerbate allergic diseases in humans. Mast cells play an important role in allergic inflammation in peripheral tissues, such as skin, mucosa, and lung. Engagement of the high-affinity Fc receptor leads to mast cell degranulation, releasing a variety of highly active mediators including histamine, leukotrienes, and inflammatory cytokines. How PM_2.5 exposure affects mast cell activation and function remains largely unknown. To characterize the effect of PM_2.5 on mast cells, we used bone marrow-derived mast cells (BMMCs) to examine whether PM_2.5 affected FcεRI-mediated signaling, cytokine production, and degranulation. Exposure to high doses of PM_2.5 caused pronounced apoptosis and death of BMMCs. In contrast, exposure to low doses of PM_2.5 enhanced mast cell degranulation and FcεRI-mediated cytokine production. Further analysis showed that PM_2.5 treatment increased Syk activation and subsequently phosphorylation of its substrates including LAT, PLC-γ1, and SLP-76. Moreover, PM_2.5 treatment led to activation of the PI3K and MAPK pathways. Intriguingly, water-soluble fraction of PM_2.5 were found responsible for the enhancement of FcεRI-mediated signaling, mast cell degranulation, and cytokine production. Our data suggest that PM_2.5, mainly water-soluble fraction of PM_2.5, could affect mast cell activation through enhancing FcεRI-mediated signaling.

Assessing Effect of Targeting Reduction of PM2.5 Concentration on Human Exposure and Health Burden in Hong Kong Using Satellite Observation

Targeting reduction of PM2.5 concentration lessens population exposure level and health burden more effectively than uniform reduction does. Quantitative assessment of effect of the targeting reduction is limited because of the lack of spatially explicit PM2.5 data. This study aimed to investigate extent of exposure and health benefits resulting from the targeting reduction of PM2.5 concentration. We took advantage of satellite observations to characterize spatial distribution of PM2.5 concentration at a resolution of 1 km. Using Hong Kong of China as the study region (804 satellite’s pixels covering its residential areas), human exposure level (cρ) and premature mortality attributable to PM2.5 (Mort) for 2015 were estimated to be 25.9 μg/m3 and 4112 people per year, respectively. We then performed 804 diagnostic tests that reduced PM2.5 concentrations by −1 μg/m3 in different areas and a reference test that uniformly spread the −1 μg/m3. We used a benefit rate from targeting reduction (BRT), which represented a ratio of declines in cρ (or Mort) with and without the targeting reduction, to quantify the extent of benefits. The diagnostic tests estimated the BRT levels for both human exposure and premature mortality to be 4.3 over Hong Kong. It indicates that the declines in human exposure and premature mortality quadrupled with a targeting reduction of PM2.5 concentration over Hong Kong. The BRT values for districts of Hong Kong could be as high as 5.6 and they were positively correlated to their spatial variabilities in population density. Our results underscore the substantial exposure and health benefits from the targeting reduction of PM2.5 concentration. To better protect public health in Hong Kong, super-regional and regional cooperation are essential. Meanwhile, local environmental policy is suggested to aim at reducing anthropogenic emissions from mobile and area (e.g., residential) sources in central and northwestern areas.

Spatial Patterns of Satellite-Retrieved PM2.5 and Long-Term Exposure Assessment of China from 1998 to 2016

Previous studies have shown that particulate matter with an aerodynamic diameter of less than 2.5 micrometers (PM_2.5) is tightly associated with adverse effects on human health, i.e., morbidity and mortality. Based on long-term satellite-derived PM_2.5 datasets, this study analyzed the spatial patterns and temporal trends of PM_2.5 concentrations in China from 1998 to 2016 using standard deviational ellipse and statistical analyses. A long-term assessment of exposure and health impacts due to PM_2.5 was undertaken by the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. The results show that concentrations of PM_2.5 increased nonlinearly in most areas of China from 1998 to 2016. Higher concentrations were found in eastern China and western Tarim Basin, and most areas exceeded the World Health Organization’s (WHO) annual PM_2.5 standards. The median center of average PM_2.5 concentration of the country shifted to the southeast and then returned during the examined time period. The proportion of the population exposed to equal PM_2.5 concentrations increased at first, then trended downward. The proportion of the population exposed to PM_2.5 over WHO Interim Target-1 (35 µg/m³) increased 20.6%, which was the largest growth compared with other WHO standard levels. The extent of health risk in China increased and expanded from 1998 to 2016, especially in the Beijing-Tianjin-Hebei region, the Yangtze River Delta, and the Pearl River Delta, which are China’s top three urban areas. The implementation of the Air Pollution Prevention and Control Action Plan has gradually paid off. If the government can achieve long-term adherence to its plan, great economic and health benefits will be gotten through the BenMAP-CE model analysis.