Fine particulate matter (PM 2.5) in China at a city level

Responses of fine particulate matter (PM2.5) air quality to future climate, land use, and emission changes: Insights from modeling across shared socioeconomic pathways

Air pollution induced by fine particulate matter with diameter ≤ 2.5 μm (PM2.5) poses a significant challenge for global air quality management. Understanding how factors such as climate change, land use and land cover change (LULCC), and changing emissions interact to impact PM2.5 remains limited. To address this gap, we employed the Community Earth System Model and examined both the individual and combined effects of these factors on global surface PM2.5 in 2010 and projected scenarios for 2050 under different Shared Socioeconomic Pathways (SSPs). Our results reveal biomass-burning and anthropogenic emissions as the primary drivers of surface PM2.5 across all SSPs. Less polluted regions like the US and Europe are expected to experience substantial PM2.5 reduction in all future scenarios, reaching up to ~5 μg m-3 (70 %) in SSP1. However, heavily polluted regions like India and China may experience varied outcomes, with a potential decrease in SSP1 and increase under SSP3. Eastern China witness ~20 % rise in PM2.5 under SSP3, while northern India may experience ~70 % increase under same scenario. Depending on the region, climate change alone is expected to change PM2.5 up to ±5 μg m-3, while the influence of LULCC appears even weaker. The modest changes in PM2.5 attributable to LULCC and climate change are associated with aerosol chemistry and meteorological effects, including biogenic volatile organic compound emissions, SO2 oxidation, and NH4NO3 formation. Despite their comparatively minor role, LULCC and climate change can still significantly shape future air quality in specific regions, potentially counteracting the benefits of emission control initiatives. This study underscores the pivotal role of changes in anthropogenic emissions in shaping future PM2.5 across all SSP scenarios. Thus, addressing all contributing factors, with a primary focus on reducing anthropogenic emissions, is crucial for achieving sustainable reduction in surface PM2.5 levels and meeting sustainable pollution mitigation goals.

Dynamic patterns of particulate matter concentration and size distribution in urban street canyons: insights into diurnal and short-term seasonal variations

Time-varying characteristics of particulate matter (PM) pollution play a crucial role in shaping atmospheric dynamics, which impact the health and welfare of urban commuters. Previously published studies on the diurnal patterns of PMs are not consistent, especially in the context of field experiments in central China, and most field studies have only focused on particles with a single particle size. This study conducted regional-scale studies across 72 street canyon sets in Wuhan, China, investigated diurnal and seasonal PM concentration variations while also evaluating various PM size and the key driving factors. During summer (July, August, and September), evergreen tree-lined street canyons maintained a stable linear trend for smaller dp particulates (i.e., PM1, PM2.5, and PM4), while deciduous street canyons exhibited a bimodal distribution. In winter (January and February), fine particulates (i.e., PM1 and PM2.5) remained a linear trend in evergreen street canyons, while deciduous street canyons show a slightly wavy fluctuating pattern. Meanwhile, it exhibited quadrimodal-peak and triple-trough patterns in both PM7, PM10, and TSP concentrations. The lowest PM concentrations were observed between 14:00 and 16:00 for all particle sizes, with decreased summer pollution (7.81% lower in PM2.5, 53.47% lower in PM10, and 50.3% lower in TSP) noted in our seasonal analysis. Among the various meteorological factors, relative humidity (RH) was identified as the dominant influencing PM factor in both summer and winter. Results from this study will help us better understand field-based air pollutant dispersion processes within pedestrian spaces while laying the groundwork for future research into street PM experiments.

Research on characteristics and influencing factors of road dust emission in a southern city in China

One of the primary causes of urban atmospheric particulate matter, which is harmful to human health in addition to affecting air quality and atmospheric visibility, is road dust. This study used online monitoring equipment to examine the characteristics of road dust emissions, the effects of temperature, humidity, and wind speed on road dust, as well as the correlation between road and high-space particulate matter concentrations. A section of a real road in Jinhua City, South China, was chosen for the study. The findings demonstrate that the concentration of road dust particles has a very clear bimodal single-valley distribution throughout the day, peaking between 8:00 and 11:00 and 19:00 and 21:00 and troughing between 14:00 and 16:00. Throughout the year, there is a noticeable seasonal change in the concentration of road dust particles, with the highest concentration in the winter and the lowest in the summer. Simultaneously, it has been discovered that temperature and wind speed have the most effects on particle concentration. The concentration of road dust particles reduces with increasing temperature and wind speed. The particle concentrations of road particles and those from urban environmental monitoring stations have a strong correlation, although the trend in the former is not entirely consistent, and the changes in the former occur approximately 1 h after the changes in the latter.

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.

Cross-sectional analysis of socioeconomic drivers of PM2.5 pollution in emerging SAARC economies

Within the intricate interplay of socio-economic, natural and anthropogenic factors, haze pollution stands as a stark emblem of environmental degradation, particularly in the South Asian Association for Regional Cooperation (SAARC) region. Despite significant efforts to mitigate greenhouse gas emissions, several SAARC nations consistently rank among the world's most polluted. Addressing this critical research gap, this study employs robust econometric methodologies to elucidate the dynamics of haze pollution across SAARC countries from 1998 to 2020. These methodologies include the Pooled Mean Group (PMG) and Augmented Mean Group (AMG) estimator, Panel two-stage least squares (TSLS), Feasible Generalized Least Squares (FGLS) and Dumitrescu-Hurlin (D-H) causality test. The analysis reveals a statistically significant cointegrating relationship between PM2.5 and economic indicators, with economic development and consumption expenditure exhibiting positive associations and rainfall demonstrating a mitigating effect. Furthermore, a bidirectional causality is established between temperature and economic growth, both influencing PM2.5 concentrations. These findings emphasize the crucial role of evidence-based policy strategies in curbing air pollution. Based on these insights, recommendations focus on prioritizing green economic paradigms, intensifying forest conservation efforts, fostering the adoption of eco-friendly energy technologies in manufacturing and proactively implementing climate-sensitive policies. By embracing these recommendations, SAARC nations can formulate comprehensive and sustainable approaches to combat air pollution, paving the way for a healthier atmospheric environment for their citizens.

Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning

Global fine particulate matter (PM2.5) assessment is impeded by a paucity of monitors. We improve estimation of the global distribution of PM2.5 concentrations by developing, optimizing, and applying a convolutional neural network with information from satellite-, simulation-, and monitor-based sources to predict the local bias in monthly geophysical a priori PM2.5 concentrations over 1998-2019. We develop a loss function that incorporates geophysical a priori estimates and apply it in model training to address the unrealistic results produced by mean-square-error loss functions in regions with few monitors. We introduce novel spatial cross-validation for air quality to examine the importance of considering spatial properties. We address the sharp decline in deep learning model performance in regions distant from monitors by incorporating the geophysical a priori PM2.5. The resultant monthly PM2.5 estimates are highly consistent with spatial cross-validation PM2.5 concentrations from monitors globally and regionally. We withheld 10% to 99% of monitors for testing to evaluate the sensitivity and robustness of model performance to the density of ground-based monitors. The model incorporating the geophysical a priori PM2.5 concentrations remains highly consistent with observations globally even under extreme conditions (e.g., 1% for training, R2 = 0.73), while the model without exhibits weaker performance (1% for training, R2 = 0.51).

Amelioration of metabolic disorders in H9C2 cardiomyocytes induced by PM2.5 treated with vitamin C

OBJECTIVE

Particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) is a public health risk. We investigate PM2.5 on metabolites in cardiomyocytes and the influence of vitamin C on PM2.5 toxicity.

MATERIALS AND METHODS

For 24 hours, H9C2 were exposed to various concentrations of PM2.5 (0, 100, 200, 400, 800 μg/ml), after which the levels of reactive oxygen species (ROS) and cell viability were measured using the cell counting kit-8 (CCK-8) and 2',7'-dichlorofluoresceindiacetate (DCFH2-DA), respectively. H9C2 were treated with PM2.5 (200 μg/ml) in the presence or absence of vitamin C (40 μmol/L). mRNA levels of interleukin 6(IL-6), caspase-3, fatty acid-binding protein 3 (FABP3), and hemeoxygenase-1 (HO-1) were investigated by quantitative reverse-transcription polymerase chain reaction. Non-targeted metabolomics by LC-MS/MS was applied to evaluate the metabolic profile in the cell.

RESULTS

Results revealed a concentration-dependent reduction in cell viability, death, ROS, and increased expression of caspase-3, FABP3, and IL-6. In total, 15 metabolites exhibited significant differential expression (FC > 2, p < 0.05) between the control and PM2.5 group. In the PM2.5 group, lysophosphatidylcholines (LysoPC,3/3) were upregulated, whereas amino acids (5/5), amino acid analogues (3/3), and other acids and derivatives (4/4) were downregulated. PM2.5 toxicity was lessened by vitamin C. It reduced PM2.5-induced elevation of LysoPC (16:0), LysoPC (16:1), and LysoPC (18:1).

DISCUSSION AND CONCLUSIONS

PM2.5 induces metabolic disorders in H9C2 cardiomyocytes that can be ameliorated by treatment with vitamin C.

Phosphocholine-induced energy source shift alleviates mitochondrial dysfunction in lung cells caused by geospecific PM2.5 components

Fine particulate matter (PM2.5) is globally recognized for its adverse implications on human health. Yet, remain limited the individual contribution of particular PM2.5 components to its toxicity, especially considering regional disparities. Moreover, prevention solutions for PM2.5-associated health effects are scarce. In the present study, we comprehensively characterized and compared the primary PM2.5 constituents and their altered metabolites from two locations: Taiyuan and Guangzhou. Analysis of year-long PM2.5 samples revealed 84 major components, encompassing organic carbon, elemental carbon, ions, metals, and organic chemicals. PM2.5 from Taiyuan exhibited higher contamination, associated health risks, dithiothreitol activity, and cytotoxicities than Guangzhou's counterpart. Applying metabolomics, BEAS-2B lung cells exposed to PM2.5 from both cities were screened for significant alterations. A correlation analysis revealed the metabolites altered by PM2.5 and the critical toxic PM2.5 components in both regions. Among the PM2.5-down-regulated metabolites, phosphocholine emerged as a promising intervention for PM2.5 cytotoxicities. Its supplementation effectively attenuated PM2.5-induced energy metabolism disorder and cell death via activating fatty acid oxidation and inhibiting Phospho1 expression. The highlighted toxic chemicals displayed combined toxicities, potentially counteracted by phosphocholine. Our study offered a promising functional metabolite to alleviate PM2.5-induced cellular disorder and provided insights into the geo-based variability in toxic PM2.5 components.

Large contributions of emission reductions and meteorological conditions to the abatement of PM2.5 in Beijing during the 24th Winter Olympic Games in 2022

To guarantee the blue skies for the 2022 Winter Olympics held in Beijing and Zhangjiakou from February 4 to 20, Beijing and its surrounding areas adopted a series of emission control measures. This provides an opportunity to determine the impacts of large-scale temporary control measures on the air quality in Beijing during this special period. Here, we applied the WRF-CMAQ model to quantify the contributions of emission reduction measures and meteorological conditions. Results show that meteorological conditions in 2022 decreased PM2.5 in Beijing by 6.9 and 11.8 µg/m3 relative to 2021 under the scenarios with and without emission reductions, respectively. Strict emission reduction measures implemented in Beijing and seven neighboring provinces resulted in an average decrease of 13.0 µg/m3 (-41.2%) in PM2.5 in Beijing. Over the entire period, local emission reductions contributed more to good air quality in Beijing than nonlocal emission reductions. Under the emission reduction scenario, local, controlled regions, other regions, and boundary conditions contributed 47.7%, 42.0%, 5.3%, and 5.0% to the PM2.5 concentrations in Beijing, respectively. The results indicate that during the cleaning period with the air masses from the northwest, the abatements of PM2.5 were mainly caused by local emission reductions. However, during the potential pollution period with the air masses from the east-northeast and west-southwest, the abatements of PM2.5 were caused by both local and nonlocal emission reductions almost equally. This implies that regional coordinated prevention and control strategies need to be arranged scientifically and rationally when heavy pollution events are forecasted.

Evidence for ethnicity and location as regulators of the newborn blood metabolome: a monozygous twin study

Introduction

Monochorionic, diamniotic (MCDA) monozygotic twins share nearly all genetic variation and a common placenta in utero. Despite this, MCDA twins are often discordant for a range of common phenotypes, including early growth and birth weight. As such, MCDA twins represent a unique model to explore variation in early growth attributable primarily to in utero environmental factors.

Methods

MCDA twins with a range of within-pair birth weight discordance were sampled from the peri/postnatal epigenetic twin study (PETS, Melbourne; n = 26 pairs), Beijing twin study (BTS, Beijing; n = 25), and the Chongqing longitudinal twin study (LoTiS, Chongqing; n = 22). All PETS participants were of European-Australian ancestry, while all Chinese participants had Han ancestry. The average of the birth weight difference between the larger and smaller co-twins for all twin pairs was determined and metabolomic profiles of amino acids, TCA cycle intermediates, fatty acids, organic acids, and their derivatives generated from cord blood plasma by gas chromatograph mass spectrometry. Within and between co-twin pair analyses were performed to identify metabolites specifically associated with discordance in birth weight. Multivariable regression and pathway enrichment analyses between different regions were performed to evaluate the geographical effects on the metabolism of MCDA twin pairs.

Results

PETS twins showed a markedly different metabolic profile at birth compared to the two Chinese samples. Within-pair analysis revealed an association of glutathione, creatinine, and levulinic acid with birth weight discordance. Caffeine, phenylalanine, and several saturated fatty acid levels were uniquely elevated in PETS twins and were associated with maternal BMI and average within pair birth weight, in addition to birth weight discordance. LoTiS twins had higher levels of glutathione, tyrosine, and gamma-linolenic acid relative to PETS and BTS twins, potentially associated with eating habits.

Conclusion

This study highlights the potential role of underlying genetic variation (shared by MZ twins), in utero (non-shared by MZ twins) and location-specific (shared by MZ twins) environmental factors, in regulating the cord blood metabolome of uncomplicated MCDA twins. Future research is needed to unravel these complex relationships that may play a key role in phenotypic metabolic alterations of twins independent of genetic diversity.

The Cytotoxic Effects of Fine Particulate Matter (PM2.5) from Different Sources at the Air-Liquid Interface Exposure on A549 Cells

The health of humans has been negatively impacted by PM2.5 exposure, but the chemical composition and toxicity of PM2.5 might vary depending on its source. To investigate the toxic effects of particulate matter from different sources on lung epithelial cells (A549), PM2.5 samples were collected from residential, industrial, and transportation areas in Nanjing, China. The chemical composition of PM2.5 was analyzed, and toxicological experiments were conducted. The A549 cells were exposed using an air-liquid interface (ALI) exposure system, and the cytotoxic indicators of the cells were detected. The research results indicated that acute exposure to different sources of particulate matter at the air-liquid interface caused damage to the cells, induced the production of ROS, caused apoptosis, inflammatory damage, and DNA damage, with a dose-effect relationship. The content of heavy metals and PAHs in PM2.5 from the traffic source was relatively high, and the toxic effect of the traffic-source samples on the cells was higher than that of the industrial- and residential-source samples. The cytotoxicity of particulate matter was mostly associated with water-soluble ions, carbon components, heavy metals, PAHs, and endotoxin, based on the analysis of the Pearson correlation. Oxidative stress played an important role in PM2.5-induced biological toxicity.

The role of N6-methyladenosine methylation in PAHs-induced cancers

Polycyclic aromatic hydrocarbons (PAHs), which are a wide range of environmental toxicants, may act on humans through inhalation, ingestion, and skin contact, resulting in a range of toxic reactions. Epidemiological studies showed that long-term exposure to PAHs in the occupational and living environment results in a substantial rise in the incidence rate of many cancers in the population, so the prevention and treatment of these diseases have become a major worldwide public health problem. N6-methyladenosine (m6A) modification greatly affects the metabolism of RNA and is implicated in the etiopathogenesis of many kinds of diseases. In addition, m6A-binding proteins have an important role in disease development. The abnormal expression of these can cause the malignant proliferation, migration, invasion, and metastasis of cancers. Furthermore, a growing number of studies revealed that environmental toxicants are one of the cancer risk factors and are related to m6A modifications. Exposure to environmental toxicants can alter the methylation level of m6A and the expression of the m6A-binding protein, thus promoting the occurrence and development of cancers through diverse mechanisms. m6A may serve as a biomarker for early environmental exposure. Through the study of m6A, we can find the health injury early, thus providing a new sight for preventing and curing environmental health-related diseases.

Advancing application of satellite remote sensing technologies for linking atmospheric and built environment to health

Background

The intricate interplay between human well-being and the surrounding environment underscores contemporary discourse. Within this paradigm, comprehensive environmental monitoring holds the key to unraveling the intricate connections linking population health to environmental exposures. The advent of satellite remote sensing monitoring (SRSM) has revolutionized traditional monitoring constraints, particularly limited spatial coverage and resolution. This innovation finds profound utility in quantifying land covers and air pollution data, casting new light on epidemiological and geographical investigations. This dynamic application reveals the intricate web connecting public health, environmental pollution, and the built environment.

Objective

This comprehensive review navigates the evolving trajectory of SRSM technology, casting light on its role in addressing environmental and geographic health issues. The discussion hones in on how SRSM has recently magnified our understanding of the relationship between air pollutant exposure and population health. Additionally, this discourse delves into public health challenges stemming from shifts in urban morphology.

Methods

Utilizing the strategic keywords "SRSM," "air pollutant health risk," and "built environment," an exhaustive search unfolded across prestigious databases including the China National Knowledge Network (CNKI), PubMed and Web of Science. The Citespace tool further unveiled interconnections among resultant articles and research trends.

Results

Synthesizing insights from a myriad of articles spanning 1988 to 2023, our findings unveil how SRMS bridges gaps in ground-based monitoring through continuous spatial observations, empowering global air quality surveillance. High-resolution SRSM advances data precision, capturing multiple built environment impact factors. Its application to epidemiological health exposure holds promise as a pioneering tool for contemporary health research.

Conclusion

This review underscores SRSM's pivotal role in enriching geographic health studies, particularly in atmospheric pollution domains. The study illuminates how SRSM overcomes spatial resolution and data loss hurdles, enriching environmental monitoring tools and datasets. The path forward envisions the integration of cutting-edge remote sensing technologies, novel explorations of urban-public health associations, and an enriched assessment of built environment characteristics on public well-being.

Hindered visibility improvement despite marked reduction in anthropogenic emissions in a megacity of southwestern China: An interplay between enhanced secondary inorganics formation and hygroscopic growth at prevailing high RH conditions

The PM2.5-bound visibility improvement remains challenging in China despite vigorous control on anthropogenic emissions in recent years. One critical issue could exist in the distinct physicochemical properties especially of secondary aerosol components. Taken the COVID-19 lockdown as an extreme case, we focus on the relationship between visibility, emission cuts, and secondary formation of inorganics with changing optical and hygroscopic behaviors in Chongqing, a representative city characterized with humid weather and poor diffusion conditions in Sichuan Basin, southwest of China. It is found that the increased secondary aerosol abundance (e.g., PM2.5/CO and PM2.5/PM10 as a proxy) with enhanced atmospheric oxidative capacity (e.g., O3/Ox, Ox = O3 + NO2), combined with insignificant meteorological dilution effect, might partly offset the benefit on the improved visibility from substantial reduction in anthropogenic emissions during the COVID-19 lockdown. This is in line with the efficient oxidation rates of sulfur and nitrogen (i.e., SOR, NOR), increasing more significantly with PM2.5 and relative humidity (RH) in comparison to O3/Ox. The resulted larger fraction of nitrate and sulfate (i.e., fSNA) would promote the optical enhancement (i.e., f(RH)) and mass extinction efficiency (MEE) of PM2.5, especially under highly humid conditions (e.g., RH > 80 %, with approximately half of the occurrence frequency). This could further facilitate secondary aerosol formation via aqueous-phase reaction and heterogeneous oxidation, likely due to enhanced water uptake and enlarged size/surface area upon hydration. In combination of gradually increased atmospheric oxidative capacity, this positive feedback would in turn inhibit the visibility improvement particularly at high RH environment. Considering the current air pollution complex status over China, further work on the formation mechanisms of major secondary species (e.g., sulfate, nitrate, and secondary organics), size-resolved chemical and hygroscopic properties, together with their interactions are highly recommended. Our results are hoping to assist in the atmospheric pollution complex mitigation and prevention in China.

Statistical significance of PM2.5 and O3 trends in China under long-term memory effects

Over the past decade, the Chinese government has implemented the "Clean Air Action" measures to enhance the atmospheric environmental quality, primarily focusing on curbing PM2.5 and O3 concentrations. The efficacy of these strategies and the underlying causes (human factors or natural variability) of any observed increases or decreases in PM2.5 and O3 concentrations are of great importance. Examining the hourly PM2.5 and O3 concentration time series from six representative regions in China between 2015 and 2021 revealed an overall downward trend in PM2.5 concentrations. However, the O3 concentration time series indicated upward trends in some regions, except for the Northeast area (NE) and Sichuan Basin (SCB). In the context of conventional significance tests, the assumption is typically that the time series' samples are independent and therefore memoryless. However, in situations where the time series exhibits strong autocorrelation and limited sample size, this assumption can lead to an overestimation of the statistical significance of the linear trend. To account for this, we utilized a long-term memory model that can reproduce the long-term persistence of pollutant records to improve the accuracy of significance tests. By comparing the P-values of real and surrogate data generated by the long-term memory model, we found that only PM2.5 concentrations in the Pearl River Delta (PRD) were slightly insignificant. For the remaining five regions, the P-values of PM2.5 concentrations were smaller than the significant level of 0.05, suggesting that the observed downward trends in PM2.5 concentrations are not due to natural variability, thereby confirming the effectiveness of the government's policies aimed at curbing atmospheric particulate matter in recent years. Our results show that O3 pollution is significantly increasing only in the Beijing-Tianjin-Hebei (BTH) region, beyond natural variability. In contrast, the trends of O3 pollution in many regions of China are markedly impacted by natural and climate variability.

The air we breathe: An In-depth analysis of PM2.5 pollution in 1312 cities from 2000 to 2020

In recent decades, the phenomenon of rapid urbanization in various parts of the world has led to a significant increase in PM2.5 concentration, which has emerged as a growing social concern. In order to achieve the objective of sustainable development, the United Nations Global Sustainable Development Goals (SDGs) have established the goal of creating inclusive, safe, resilient, and sustainable cities and human habitats (SDG 11). Goal 11.6 aims to decrease the negative environmental impact per capita in cities, with an emphasis on urban air quality and waste management. However, the global distribution of PM2.5 pollution varies due to disparities in urbanization development in different regions. The purpose of this paper is to explore the global spatial distribution and temporal variation of PM2.5 in cities with populations greater than 300,000 from 2000 to 2020, to gain insight into the issue. The findings indicate that PM2.5 concentrations are expected to continue increasing as urbanization progresses, but the rate of evolution of PM2.5 concentration varies depending on the continent, country, and city. From 2000 to 2020, PM2.5 concentration increased significantly in Asia and Africa, with the majority of the increased concentrations located in Asian countries and some African countries. On the other hand, most European and American countries had lower PM2.5 concentrations. The results of this study have the potential to inform urbanization policy formulation by providing knowledge about the spatial distribution of PM2.5 pollution during global urbanization. Addressing the issue of PM2.5 pollution is critical in achieving SDG 11.6 and promoting sustainable and coordinated development in cities worldwide.

Changes of the acute myocardial infarction-related resident deaths in a transitioning region: a real-world study involving 3.17 million people

Background

The impact of acute myocardial infarction (AMI) on the life span of residents in a transitioning region has not been studied in depth. Therefore, we aimed to evaluate the changes in AMI-related resident deaths in a transitioning region in China.

Methods

A longitudinal, population-based study was performed to analyze the deaths with/of AMI in Pudong New Area (PNA), Shanghai from 2005 to 2021. The average annual percentage change (AAPC) of AMI in crude mortality rates (CMR), age-standardized mortality rates worldwide (ASMRW), and rates of years of life lost (YLLr) were calculated by the joinpoint regression. The impact of demographic and non-demographic factors on the mortality of residents who died with/of AMI was quantitatively analyzed by the decomposition method.

Results

In 7,353 residents who died with AMI, 91.74% (6,746) of them were died of AMI from 2005 to 2021. In this period, the CMR and ASMRW of residents died with/of AMI were 15.23/105 and 5.17/105 person-years, the AAPC of CMR was 0.01% (95% CI: -0.71,0.72, p = 0.989) and 0.06% (95% CI: -0.71,0.84, p = 0.868), and the ASMRW decreased by 2.83% (95% CI: -3.66,-2.00, p < 0.001) and 2.76% (95% CI: -3.56,-1.95, p < 0.001), respectively. The CMR of people died of AMI showed a downward trend (all p < 0.05) in people ≥60 years but an upward trend [AAPC = 2.47% (95% CI: 0.07,4.94, p = 0.045)] in people of 45-59 years. The change in CMR of people died with/of AMI caused by demographic factors was 28.70% (95% CI: 12.99,46.60, p = 0.001) and 28.07% (95% CI: 12.71,45.52, p = 0.001) per year, respectively.

Conclusion

Preventative strategies for AMI should be applied to enhance the health management of residents aged 45-59 years or with comorbidities in the transitioning region.

Application of satellite remote sensing data and random forest approach to estimate ground-level PM2.5 concentration in Northern region of Thailand

Numerous epidemiological studies have shown that particulate matter with aerodynamic diameter up to 2.5 μm (PM2.5) is associated with many health consequences, where PM2.5 concentration obtained from the monitoring station was normally applied as the exposure level, so that the concentration of PM2.5 in unmonitored areas has not been captured. The satellite-derived aerosol optical depth (AOD) product is then used to spatially predict ground truth of PM2.5 concentration that covers the locations with no air quality monitoring station, but this method has seldom been developed in Thailand. This study aimed at estimating ground-level PM2.5 concentration at 3 km × 3 km spatial resolution over Northern region of Thailand in 2021 using the random forest model integrating the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products from Terra and Aqua satellites, meteorological factors, and land use data. A random forest model contained 100 decision trees was utilized to train the model, and 10-fold cross-validation approach was implemented to validate the model performance. The good consistency between actual (observed) and predicted concentrations of PM2.5 in Northern region of Thailand was observed, where a coefficient of determination (R2) and root mean square error (RMSE) of the model fitting were 0.803 and 14.30 μg/m3, respectively, and those of 10-fold cross-validation approach were 0.796 and 14.64 μg/m3, respectively. The three most important predictors for estimating the ground-level concentrations of PM2.5 in this study were normalized difference vegetation index (NDVI), relative humidity, and number of fire hotspot, respectively. Findings from this study revealed that integrating the MODIS AOD, meteorological variables, and land use data into the random forest model precisely and accurately estimated ground-level PM2.5 concentration over Northern region of Thailand that can be further used to investigate the effects of PM2.5 exposure on health consequences, even in unmonitored locations, in epidemiological studies.

Haze Exposure Changes the Skin Fungal Community and Promotes the Growth of Talaromyces Strains

Haze pollution has been a public health issue. The skin microbiota, as a component of the first line of defense, is disturbed by environmental pollutants, which may have an impact on human health. A total of 74 skin samples from healthy students were collected during haze and nonhaze days in spring and winter. Significant differences of skin fungal community composition between haze and nonhaze days were observed in female and male samples in spring and male samples in winter based on unweighted UniFrac distance analysis. Phylogenetic diversity whole-tree indices and observed features were significantly increased during haze days in male samples in winter compared to nonhaze days, but no significant difference was observed in other groups. Dothideomycetes, Capnodiales, Mycosphaerellaceae, etc. were significantly enriched during nonhaze days, whereas Trichocomaceae, Talaromyces, and Pezizaceae were significantly enriched during haze days. Thus, five Talaromyces strains were isolated, and an in vitro culture experiment revealed that the growth of representative Talaromyces strains was increased at high concentrations of particulate matter, confirming the sequencing results. Furthermore, during haze days, the fungal community assembly was better fitted to a niche-based assembly model than during nonhaze days. Talaromyces enriched during haze days deviated from the neutral assembly process. Our findings provided a comprehensive characterization of the skin fungal community during haze and nonhaze days and elucidated novel insights into how haze exposure influences the skin fungal community. IMPORTANCE Skin fungi play an important role in human health. Particulate matter (PM), the main haze pollutant, has been a public environmental threat. However, few studies have assessed the effects of air pollutants on skin fungi. Here, haze exposure influenced the diversity and composition of the skin fungal community. In an in vitro experiment, a high concentration of PM promoted the growth of Talaromyces strains. The fungal community assembly is better fitted to a niche-based assembly model during haze days. We anticipate that this study may provide new insights on the role of haze exposure disturbing the skin fungal community. It lays the groundwork for further clarifying the association between the changes of the skin fungal community and adverse health outcomes. Our study is the first to report the changes in the skin fungal community during haze and nonhaze days, which expands the understanding of the relationship between haze and skin fungi.

Outdoor Health Risk of Atmospheric Particulate Matter at Night in Xi'an, Northwestern China

The deterioration of air quality via anthropogenic activities during the night period has been deemed a serious concern among the scientific community. Thereby, we explored the outdoor particulate matter (PM) concentration and the contributions from various sources during the day and night in winter and spring 2021 in a megacity, northwestern China. The results revealed that the changes in chemical compositions of PM and sources (motor vehicles, industrial emissions, coal combustion) at night lead to substantial PM toxicity, oxidative potential (OP), and OP/PM per unit mass, indicating high oxidative toxicity and exposure risk at nighttime. Furthermore, higher environmentally persistent free radical (EPFR) concentration and its significant correlation with OP were observed, suggesting that EPFRs cause reactive oxygen species (ROS) formation. Moreover, the noncarcinogenic and carcinogenic risks were systematically explained and spatialized to children and adults, highlighting intensified hotspots to epidemiological researchers. This better understanding of day-night-based PM formation pathways and their hazardous impact will assist to guide measures to diminish the toxicity of PM and reduce the disease led by air pollution.

The effect of natural and socioeconomic factors on haze pollution from global and local perspectives in China

Analyzing the factors that cause haze and the regional differences in the influence of factors on haze is the premise and critical to precise prevention and control of haze pollution. This paper explores the global effects of haze pollution drivers and the spatial heterogeneity of factors on haze pollution using global and local regression models. The results show that, from a global perspective, a 1 μg/m³ increase in the average PM_2.5 concentration of a city's neighbors will increase the city's PM_2.5 concentration by 0.965 μg/m³. Temperature, atmospheric pressure, population density, and green coverage of built-up areas are positively associated with haze, while GDP per capita is the opposite. From a local perspective, each factor has different influencing scales on haze pollution. Specifically, technical support is on a global scale, and for every 1 unit increase in technical support level, the PM_2.5 concentration will decrease by 0.106-0.102 μg/m³. The influencing scales of other drivers are local. In southern China, the concentration of PM_2.5 decreases by 0.001-0.075 μg/m³ for every 1 °C increase in temperature, while in northern China, the concentration of PM_2.5 increases by 0.001-0.889 μg/m³. In the region around the Bohai Sea in eastern China, the concentration of PM_2.5 will decrease by 0.001-0.889 μg/m³ for every 1 m/s increase in wind speed. Population density positively impacts haze pollution, and the impact intensity gradually increases from 0.097 to 1.140 from south to north. For every 1% increase in the proportion of the secondary industry in southwest China, the PM_2.5 concentration will increase by 0.001-0.284 μg/m³. For cities in northeast China, for every 1% increase in the urbanization rate, the PM_2.5 concentration will decrease by 0.001-0.203 μg/m³. These findings help policymakers develop targeted joint prevention and control policies for haze pollution, considering regional differences.

Do heat waves worsen air quality? A 21-year observational study in Seoul, South Korea

Heat waves are generally known to deteriorate air quality. However, the impacts of heat waves on air quality can substantially vary depending on characteristics of heat waves. In this study, we examine air quality changes in Seoul during heat waves and their associations with large-scale atmospheric patterns. For this, air quality data from 25 stations and meteorological data from 23 weather stations and reanalysis datasets during July and August of 2001-2021 are used. Under heat waves, the mean daily PM₁₀, NO₂, and CO concentrations decrease by 7.9 %, 6.1 %, and 4.6 %, respectively, whereas the mean daily PM_2.5, O₃, and SO₂ concentrations increase by 4.1 %, 17.2 %, and 2.9 %, respectively. The atmospheric circulation under heat waves is less favorable for long-range transport of air pollutants to Seoul. The PM_2.5/PM₁₀ ratio increases under heat waves, indicating that the secondary formation of aerosols becomes more important under heat waves. 37 % of the heat wave days are accompanied by severe O₃ pollution exceeding the O₃ concentration standard in South Korea. There is a significant variability of air quality in Seoul within heat waves. The heat wave days with higher concentrations of PM_2.5, PM₁₀, O₃, NO₂, and CO than their non-heat wave means exhibit a prominent difference in large-scale atmospheric pattern from the heat wave days with lower concentrations. This difference is characterized by a zonal wave-like pattern of geopotential height, which is similar to the circumglobal teleconnection pattern known as one of the major patterns for heat waves in South Korea. This zonal wave-like pattern produces more stagnant conditions over Seoul.

Air pollution in heavy industrial cities along the northern slope of the Tianshan Mountains, Xinjiang: characteristics, meteorological influence, and sources

The spatiotemporal characteristics, relationship with meteorological factors, and source distribution of air pollutants (January 2017-December 2021) were analyzed to better understand the air pollutants on the northern slope of the Tianshan Mountains (NSTM) in Xinjiang, a heavily polluted urban agglomeration of heavy industries. The results showed that the annual mean concentrations of SO₂, NO₂, CO, O₃, PM_2.5, and PM₁₀ were 8.61-13.76 μg m^-3, 26.53-36.06 μg m^-3, 0.79-1.31 mg m^-3, 82.24-87.62 μg m^-3, 37.98-51.10 μg m^-3, and 84.15-97.47 μg m^-3. The concentrations of air pollutants (except O₃) showed a decreasing trend. The highest concentrations were in winter, and in Wujiaqu, Shihezi, Changji, Urumqi, and Turpan, the concentrations of particulate matter exceeded the NAAQS Grade II during winter. The west wind and the spread of local pollutants both substantially impacted the high concentrations. According to the analysis of the backward trajectory in winter, the air masses were mainly from eastern Kazakhstan and local emission sources, and PM₁₀ in the airflow had a more significant impact on Turpan; the rest of the cities were more affected by PM_2.5. Potential sources included Urumqi-Changj-Shihezi, Turpan, the northern Bayingol Mongolian Autonomous Prefecture, and eastern Kazakhstan. Consequently, the emphasis on improving air quality should be on reducing local emissions, strengthening regional cooperation, and researching transboundary transport of air pollutants.

A Study of Urban Haze and Its Association with Cold Surge and Sea Breeze for Greater Bangkok

This study deals with haze characteristics under the influence of the cold surge and sea breeze for Greater Bangkok (GBK) in 2017-2022, including haze intensity and duration, meteorological classification for haze, and the potential effects of secondary aerosols and biomass burning. A total of 38 haze episodes and 159 haze days were identified. The episode duration varies from a single day to up to 14 days, suggesting different pathways of its formation and evolution. Short-duration episodes of 1-2 days are the most frequent with 18 episodes, and the frequency of haze episodes decreases as the haze duration increases. The increase in complexity in the formation of relatively longer episodes is suggested by a relatively higher coefficient of variation for PM2.5. Four meteorology-based types of haze episodes were classified. Type I is caused by the arrival of the cold surge in GBK, which leads to the development of stagnant conditions favorable for haze formation. Type II is induced by sea breeze, which leads to the accumulation of air pollutants due to its local recirculation and development of the thermal internal boundary layer. Type III consists of the haze episodes caused by the synergetic effect of the cold surge and sea breeze while Type IV consists of short haze episodes that are not affected by either the cold surge or sea breeze. Type II is the most frequent (15 episodes), while Type III is the most persistent and most polluted haze type. The spread of haze or region of relatively higher aerosol optical depth outside GBK in Type III is potentially due to advection and dispersion, while that in Type IV is due to short 1-day episodes potentially affected by biomass burning. Due to cold surge, the coolest and driest weather condition is found under Type I, while Type II has the most humid condition and highest recirculation factor due to the highest average sea breeze duration and penetration. The precursor ratio method suggests the potential effect of secondary aerosols on 34% of the total haze episodes. Additionally, biomass burning is found to potentially affect half of the total episodes as suggested by the examination of back trajectories and fire hotspots. Based on these results, some policy implications and future work are also suggested.

Spatiotemporal Evolution and Driving Forces of PM2.5 in Urban Agglomerations in China

With the rapid development of China's economy, the process of industrialization and urbanization is accelerating, and environmental pollution is becoming more and more serious. The urban agglomerations (UAs) are the fastest growing economy and are also areas with serious air pollution. Based on the monthly mean PM_2.5 concentration data of 20 UAs in China from 2015 to 2019, the spatiotemporal distribution characteristics of PM_2.5 were analyzed in UAs. The effects of natural and social factors on PM_2.5 concentrations in 20 UAs were quantified using the geographic detector. The results showed that (1) most UAs in China showed the most severe pollution in winter and the least in summer. Seasonal differences were most significant in the Central Henan and Central Shanxi UAs. However, the PM_2.5 was highest in March in the central Yunnan UA, and the Harbin-Changchun and mid-southern Liaoning UAs had the highest PM_2.5 in October. (2) The highest PM_2.5 concentrations were located in northern China, with an overall decreasing trend of pollution. Among them, the Beijing-Tianjin-Hebei, central Shanxi, central Henan, and Shandong Peninsula UAs had the highest concentrations of PM_2.5. Although most of the UAs had severe pollution in winter, the central Yunnan, Beibu Gulf, and the West Coast of the Strait UAs had lower PM_2.5 concentrations in winter. These areas are mountainous, have high temperatures, and are subject to land and sea breezes, which makes the pollutants more conducive to diffusion. (3) In most UAs, socioeconomic factors such as social electricity consumption, car ownership, and the use of foreign investment are the main factors affecting PM_2.5 concentration. However, PM_2.5 in Beijing-Tianjin-Hebei and the middle and lower reaches of the Yangtze River are chiefly influenced by natural factors such as temperature and precipitation.

Statistical Analysis for Understanding PM2.5 Air Quality and the Impacts of COVID-19 Social Distancing in Several Provinces and Cities in Vietnam

Air pollution, especially in urban regions, is receiving increasing attention in Vietnam. Consequently, this work aimed to study and analyze the air quality in several provinces and cities in the country focusing on PM_2.5. Moreover, the impacts of COVID-19 social distancing on the PM_2.5 level were investigated. For this purpose, descriptive statistic, Box and Whisker plot, correlation matrix, temporal variation, and trend analysis were conducted. R-based program and the R package "openair" were employed for the calculations. Hourly PM_2.5 data were obtained from 8 national air quality monitoring sites. The study results indicated that provinces and cities in the North experienced more PM_2.5 pollution compared to the Central and South. PM_2.5 concentrations at each monitoring site varied significantly. Among monitoring sites, the northern sites showed high PM_2.5 correlations with each other than the other sites. Seasonal variation was observed with high PM_2.5 concentration in the dry season and low PM_2.5 concentration in the wet season. PM_2.5 concentration variation during the week was not so different. Diurnal variation showed that PM_2.5 concentration rose at peak traffic hours and dropped in the afternoon. There was mainly a decreasing trend in PM_2.5 concentration over the studied period. The COVID-19 pandemic has contributed to PM_2.5 reduction. In the months implemented social distancing for preventing the epidemic, PM_2.5 concentration declined but it would mostly increase in the following months. This study provided updated and valuable assessments of recent PM_2.5 air quality in Vietnam.

Spatio-temporally differentiated impacts of temperature inversion on surface PM2.5 in eastern China

Temperature inversion (TI) is one of the meteorological conditions that significantly affect regional air quality. Knowledge gap regarding the impacts of TI on surface PM_2.5 in different topographies still existed. In the present study, the occurrence frequency, temperature lapse rate (TLR), depth, and the diurnal variations of TI, surface-based TI (SBTI), elevated TI (ElTI), and multiple layers of TIs (MultiTI) and their impacts on near-surface PM_2.5 concentrations over eastern China that covers a range of topographies and climates, are systematically investigated based on global reanalysis ERA5 and the nationwide monitoring PM_2.5 dataset from 2014 to 2020. TIs occurred mostly in the early morning. Different types of TIs present distinctive seasonal and spatial patterns. The majority of SBTIs and ElTIs occurred during nighttime in northern China and daytime in southern China, respectively, as the result of their formation mechanisms. SBTIs usually had larger TLR while ElTIs had deeper depth. SBTIs showed strong enhancement effects on PM_2.5 concentration over the study domain while ElTIs showed more obvious impacts on northern nocturnal PM_2.5. The peak time of PM_2.5 was found around 18:00-22:00 LST, and TLR and depth of TIs are thought to be more relevant to PM_2.5 peak concentration due to their coincident peak times. The strength of TIs is therefore more crucial in regulating PM_2.5 than its occurrence frequency. Based on statistical analysis, our study provided a large picture of the generic spatiotemporal patterns of TIs and illustrated the impacts of different TIs on surface PM_2.5 pollution on a diurnal basis. For a deeper understanding of the formation of PM_2.5 pollution, more attention needs to be paid to the nocturnal PM_2.5 not only at surface level but also at higher levels in the presence of TIs.

Comprehensive evaluation method of urban air quality statistics based on environmental monitoring data and its application

Air quality monitoring is effective for timely understanding of the current air quality status of a region or city. Currently, the huge volume of environmental monitoring data, which has reasonable real-time performance, provides strong support for in-depth analysis of air pollution characteristics and causes. However, in the era of big data, to meet current demands for fine management of the atmospheric environment, it is important to explore the characteristics and causes of air pollution from multiple aspects for comprehensive and scientific evaluation of air quality. This study reviewed and summarized air quality evaluation methods on the basis of environmental monitoring data statistics during the 13th Five-Year Plan period, and evaluated the level of air pollution in the Beijing-Tianjin-Hebei region and its surrounding areas (i.e., the "2+26" region) during the period of the three-year action plan to fight air pollution. We suggest that air quality should be comprehensively, deeply, and scientifically evaluated from the aspects of air pollution characteristics, causes, and influences of meteorological conditions and anthropogenic emissions. It is also suggested that a three-year moving average be introduced as one of the evaluation indexes of long-term change of pollutants. Additionally, both temporal and spatial differences should be considered when removing confounding meteorological factors.

Highly time-resolved measurements of elements in PM2.5 in Changzhou, China: Temporal variation, source identification and health risks

The temporal variation, sources, and health risks of elemental composition in fine particles (PM_2.5) were explored using online measurements of 19 elements with a time resolution of 1 h at an urban location in Changzhou, China, from December 10, 2020 to March 31, 2021. The mass concentration of PM_2.5 was 50.1 ± 32.6 μg m^-3, with a range of 3-218 μg m^-3. The total concentration of 19 elements (2568 ± 1839 ng m^-3) accounted for 5.1 % of PM_2.5 mass concentration. S, Cl, Si, and Fe were the dominant elementary species, accounting for 90 % of total element mass concentrations during the whole campaign. Positive matrix factorization (PMF) model was applied to identify the major emission sources of elements in PM_2.5. Seven factors, named secondary sulfate mixed with coal combustion, Cl-rich, traffic, iron and steel industry, soil dust, fireworks, and shipping, were identified. The major sources for elements were iron and steel industry, followed by soil dust and secondary sulfate mixed with coal combustion, explaining 32.0 %, 23.5 % and 16.7 % of the total source contribution, respectively. The total hazard index (HI) of elements was 3.01 for children and 1.18 for adults, much greater than the admissible level (HI = 1). The total carcinogenic risk (CR) in Changzhou was estimated to be 5.87 × 10^-5, which was above the acceptable CR level (1 × 10^-6). Among the calculated metal elements, Cr, Co and As have higher carcinogenic risk, and Co was found to trigger the highest noncarcinogenic risk to Children. Our results indicate that industrial emission is the dominant CR contributor, emphasizing the necessity for stringent regulation of industry sources. Overall, our study provides useful information for policymakers to reduce emissions and health risks from elements in the Yangtze River Delta region.

Do anthropogenic and natural factors elevate the haze pollution in the South Asian countries? Evidence from long-term cointegration and VECM causality estimation

Anthropogenic and natural factors lead to substantial environmental degradation. This shift is aligned with the country's overall development, resulting in high demand for energy resources and a dramatic shift in human activities that contribute to haze pollution. Some of the countries in the South Asian region are ranked between one and twenty on the list of countries with the highest levels of PM2.5 pollution. The member countries have taken many steps to tackle global warming, but concern about haze pollution was found limited. Moreover, very little research was conducted on haze pollution, which led us to conduct this research in this region. This study used the panel data from 1998 to 2018 and a set of econometric models like long-term cointegrating relationship, fully modified ordinary least squares, and vector error-correction model Granger causality tests to examine the major drivers like anthropogenic and natural factors that might elevate haze pollution. Furthermore, our empirical results depict that (1) there is a long-term cointegrating relation between haze and the factors studied. (2) Energy consumption, urbanisation, and economic growth are the primary drivers of environmental degradation. (3) Rainfall has the most substantial influence on reducing haze pollution. The study concluded that (a) if the countries continue to develop at the same pace, all factors studied will continue to drive haze pollution to rise. (b) A decrease in PM2.5 pollution requires improvements in regional rainfall through vegetation, reducing reliance on fossil fuel-based energy sources, and increasing environmental education. (c) Slowing down the drive for urbanisation would not be cost-effective in reducing haze pollution in the region in the short run. Thus, reducing haze by adjusting the factors studied would not be easy in the short run and require the careful adoption of long-term policies.

Air Pollutants in Metropolises of Eastern Coastal China

Recently released hourly particular matter (PM:PM_2.5 and PM₁₀) and gaseous pollutants (SO₂, NO₂, CO, and O₃) data observed in Qingdao, Hangzhou, and Xiamen from 2015 to 2019 were utilized to reveal the current situation of air pollution over eastern coastal China. The PM pollution situation over the three metropolises ameliorated during studied period with the concentrations decreasing about 20-30%. Gas pollutants, excepting SO₂, generally exhibit no evident reduction tendencies, and a more rigorous control standard on gaseous pollutants is neededEven for the year 2018 with low pollution levels among the study period, these levels (<10% of PM_2.5, <6% of PM₁₀, and <15% of O₃) surpass the Grade II of the Chinese Ambient Air Quality Standard (CAAQS) over these metropolises of eastern coast China. No matter in which year, both SO₂ and CO concentrations are always below the Grade-II standards. According to the comparative analysis of PM_2.5/PM₁₀ and PM_2.5/CO during episode days and non-episode days, the formation of secondary aerosols associated with stagnant weather systems play an important role in the pollutant accumulation as haze episodes occurred. The stronger seasonal variations and higher magnitude occur in Qingdao and Hangzhou, while weaker seasonal variations and lower magnitudes occur in Xiamen. In Qingdao and Hangzhou, PM, NO₂, SO₂, and CO show relatively high levels in the cold wintertime and low levels in summer, whereas O₃ shows a completely opposite pattern. Xiamen exhibits high levels of all air pollutants except O₃ in spring due to its subtropical marine monsoon climate with mild winters. According to the back trajectory hierarchical clustering and concentration weighted trajectory (CWT) analysis, the regional transmission from adjacent cities has a significant impact on the atmospheric pollutant concentrations under the control of the prejudiced winds. Thus, besides local emission reduction, strengthening regional environmental cooperation and implementing joint prevention are effective measures to mitigate air pollution in the eastern coastal areas of China.

Heavy Metal Pollution and Risk Assessment of Surface Dust in the Arid NW China

High concentrations of heavy metals (HMs) in urban surface dust (USD) can be extremely hazardous to urban ecology and human health. Oasis cities are located at the edge of deserts and are more exposed to salt/sandstorms, and they face a significantly higher accumulation of USD than wet or semi-humid areas. However, systematic studies on the pollution and risk assessment of HMs in USD in oasis cities have rarely been conducted. This study systematically analyzed the enrichment status, spatial distribution, pollution levels, health risks, and sources of HMs in USD in a typical oasis city (Changji city). The results showed that the average concentrations of Pb, Ni, As, Cd, Hg, and Cu in the USD of Changji city were 46.83, 26.35, 9.92, 0.21, 0.047, and 59.33 mg/kg, respectively, and the results of the pollution index evaluation showed moderate Pb, Hg, and Cu pollution, mild Cd pollution, and no Ni or As pollution. The spatial distribution of HM concentrations in the USD was substantially heterogeneous. High values of Pb, Hg, and Cu concentrations were mainly observed in areas with relatively intensive transportation and commercial activities, and high values of Cd and Ni were observed in industrial areas. The health risk assessment showed that HMs do not pose non-carcinogenic risks to humans at their current level, but they pose a carcinogenic risk to children, with As contributing the largest carcinogenic and non-carcinogenic risks. The source identification of HMs showed that the main pollution of HMs were traffic sources for Pb and Cu, industrial sources for Ni and Cd, natural sources for As, and coal-fired sources for Hg. According to the results of the quantitative analysis with the positive matrix factorization, the contribution of pollution sources followed this order: industrial sources (31.08%) > traffic sources (26.80%) > coal-fired sources (23.31%) > natural sources (18.81%).

Exposure and Inequality of PM2.5 Pollution to Chinese Population: A Case Study of 31 Provincial Capital Cities from 2000 to 2016

Fine particulate matter (PM_2.5) exposure has been linked to numerous adverse health effects, with some disadvantaged subgroups bearing a disproportionate exposure burden. Few studies have been conducted to estimate the exposure and inequality of different subgroups due to a lack of adequate characterization of disparities in exposure to air pollutants in urban areas, and a mechanistic understanding of the causes of these exposure inequalities. Based on a long-term series of PM_2.5 concentrations, this study analyzed the spatial and temporal characteristics of PM_2.5 in 31 provincial capital cities of China from 2000 to 2016 using the coefficient of variation and trend analyses. A health risk assessment of human exposure to PM_2.5 from 2000 to 2016 was then undertaken. A cumulative population-weighted average concentration method was applied to investigate exposures and inequality for education level, job category, age, gender and income population subgroups. The relationships between socioeconomic factors and PM_2.5 exposure concentrations were quantified using the geographically and temporally weighted regression model (GTWR). Results indicate that the PM_2.5 concentrations in most of the capital cities in the study experienced an increasing trend at a rate of 0.98 μg m^-3 per year from 2000 to 2016. The proportion of the population exposed to high PM_2.5 (above 35 μg m^-3) increased annually, mainly due to the increase of population migrating into north, east, south and central China. The higher educated, older, higher income and urban secondary industry share (SIS) subgroups suffered from the most significant environmental inequality, respectively. The per capita GDP, population size, and the share of the secondary industry played an essential role in unequal exposure to PM_2.5.

Tropical modulation of East Asia air pollution

Understanding air pollution in East Asia is of great importance given its high population density and serious air pollution problems during winter. Here, we show that the day-to-day variability of East Asia air pollution, during the recent 21-year winters, is remotely influenced by the Madden-Julian Oscillation (MJO), a dominant mode of subseasonal variability in the tropics. In particular, the concentration of particulate matter with aerodynamic diameter less than 10 micron (PM10) becomes significantly high when the tropical convections are suppressed over the Indian Ocean (MJO phase 5-6), and becomes significantly low when those convections are enhanced (MJO phase 1-2). The station-averaged PM10 difference between these two MJO phases reaches up to 15% of daily PM10 variability, indicating that MJO is partly responsible for wintertime PM10 variability in East Asia. This finding helps to better understanding the wintertime PM10 variability in East Asia and monitoring high PM10 days.

Spatiotemporal characterization of aerosols and trace gases over the Yangtze River Delta region, China: impact of trans-boundary pollution and meteorology

BACKGROUND

The spatiotemporal variation of observed trace gases (NO₂, SO₂, O₃) and particulate matter (PM_2.5, PM₁₀) were investigated over cities of Yangtze River Delta (YRD) region including Nanjing, Hefei, Shanghai and Hangzhou. Furthermore, the characteristics of different pollution episodes, i.e., haze events (visibility < 7 km, relative humidity < 80%, and PM_2.5 > 40 µg/m³) and complex pollution episodes (PM_2.5 > 35 µg/m³ and O₃ > 160 µg/m³) were studied over the cities of the YRD region. The impact of China clean air action plan on concentration of aerosols and trace gases is examined. The impacts of trans-boundary pollution and different meteorological conditions were also examined.

RESULTS

The highest annual mean concentrations of PM_2.5, PM₁₀, NO₂ and O₃ were found for 2019 over all the cities. The annual mean concentrations of PM_2.5, PM₁₀, and NO₂ showed continuous declines from 2019 to 2021 due to emission control measures and implementation of the Clean Air Action plan over all the cities of the YRD region. The annual mean O₃ levels showed a decline in 2020 over all the cities of YRD region, which is unprecedented since the beginning of the China's National environmental monitoring program since 2013. However, a slight increase in annual O₃ was observed in 2021. The highest overall means of PM_2.5, PM₁₀, SO₂, and NO₂ were observed over Hefei, whereas the highest O₃ levels were found in Nanjing. Despite the strict control measures, PM_2.5 and PM₁₀ concentrations exceeded the Grade-1 National Ambient Air Quality Standards (NAAQS) and WHO (World Health Organization) guidelines over all the cities of the YRD region. The number of haze days was higher in Hefei and Nanjing, whereas the complex pollution episodes or concurrent occurrence of O₃ and PM_2.5 pollution days were higher in Hangzhou and Shanghai.The in situ data for SO₂ and NO₂ showed strong correlation with Tropospheric Monitoring Instrument (TROPOMI) satellite data.

CONCLUSIONS

Despite the observed reductions in primary pollutants concentrations, the secondary pollutants formation is still a concern for major metropolises. The increase in temperature and lower relative humidity favors the accumulation of O₃, while low temperature, low wind speeds and lower relative humidity favor the accumulation of primary pollutants. This study depicts different air pollution problems for different cities inside a region. Therefore, there is a dire need to continuous monitoring and analysis of air quality parameters and design city-specific policies and action plans to effectively deal with the metropolitan pollution.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12302-022-00668-2.

Evaluating the performance of chemical transport models for PM2.5 source apportionment: An integrated application of spectral analysis and grey incidence analysis

Evaluating the performance of source apportionment (SA) models is difficult due to the non-observable nature of source contribution in reality. Here we propose a new approach to assess the performance of Chemical Transport Models (CTMs) for SA based on wavelet time-frequency spectral analysis and Grey Incidence Analysis (GIA). For each source category, certain species that better reflect the periodic characteristics of the emission sources were selected as the chemical tracers. The consistency of the time series between the simulated source contributions and the observed source-specific chemical tracers was then examined using a GIA model based on the perspective of similarity, and characterized by the GIA scores. By applying this method to six typical pollution episodes, we evaluated the performance of the Comprehensive Air Quality Model with Extensions-Particle Source Apportionment Technology (CAMx-PSAT) model for PM_2.5 SA from different temporal and spatial scales. The source- and episode-dependent optimal average time and main source regions were obtained. This approach is robust for facilitating a relatively meticulous evaluation of the performance of CTMs for PM_2.5 SA, and provides additional insight for decision-making for heavy pollution emergencies.

Sources and processes of organic aerosol in non-refractory PM1 and PM2.5 during foggy and haze episodes in an urban environment of the Yangtze River Delta, China

Organic aerosol (OA) generally accounts for a large fraction of fine particulate matter (PM_2.5) in the urban atmosphere. Despite significant advances in the understanding their emission sources, transformation processes and optical properties in the submicron aerosol fraction (PM₁), larger size fractions - e.g., PM_2.5 - still deserve complementary investigations. In this study, we conducted a comprehensive analysis on sources, formation process and optical properties of OA in PM₁ and PM_2.5 under haze and foggy environments in the Yangtze River Delta (eastern China), using two aerosol chemical speciation monitors, as well as a photoacoustic extinctiometer at 870 nm. Positive matrix factorization analysis - using multilinear engine (ME2) algorithm - was conducted on PM₁ and PM_2.5 organic mass spectra. Four OA factors were identified, including three primary OA (POA) factors, i.e., hydrocarbon-like OA (HOA), cooking OA (COA), and biomass burning OA (BBOA), and a secondary OA (SOA) factor, i.e., oxidized oxygenated OA (OOA). An enhanced PM_1-2.5 COA concentration was clearly observed during cooking peak hours, suggesting important contribution of fresh cooking emissions on large-sized particles (i.e., PM_1-2.5). The oxidation state and concentration of PM_2.5 HOA were higher than that in PM₁, suggesting that large-sized HOA particles might be linked to oxidized POA. High contribution (44%) of large-sized OOA to non-refractory PM_2.5 mass was observed during haze episodes. During foggy episodes, PM₁ and PM_2.5 OOA concentrations increased as a positive relationship over time, along with an exponential increase in the PM_2.5-OOA to PM₁-OOA ratio. Meanwhile, OOA loadings increased with the aerosol liquid water content (ALWC) during foggy episodes. Random forest cross-validation analysis also supported the important influence of ALWC on OOA variations, supporting substantial impact of aqueous process on SOA formation during haze and/or foggy episodes. Obtained results also indicated high OOA contributions (21%-36%) and low POA contributions (6%-14%) to the PM_2.5 scattering coefficient during haze and foggy episodes, respectively. Finally, we could illustrate that atmospheric vertical diffusion and horizontal transport have important but different effects on the concentrations of different primary and secondary OA factors in different particle size fractions.

Health impacts of PM2.5-bound metals and PAHs in a medium-sized Brazilian city

Rio Grande is a medium-sized industrial city located in the extreme south of Brazil, and previous studies in this city have shown contamination by metal(loids) and polycyclic aromatic hydrocarbons (PAHs) in water, soil, and sediment and in the atmosphere. In Brazil, the incorporation of PM_2.5 monitoring in environmental legislation is recent (2018) and, like other developing countries, the number of studies is still small. This study aimed to investigate the levels of PM_2.5 in the industrial and urban area of Rio Grande, to determine the concentration of metal(loid)s As, Cd, Cu, and Pb and of 16 PAHs in the samples of PM_2.5, to perform the health risk assessment for these contaminants and the health impact assessment for two possible scenarios of reduction of PM_2.5 levels. Our main findings regarding the PM_2.5 samples include the following: (1) The levels of this pollutant in the city of Rio Grande were higher than those allowed in current Brazilian legislation, in both the industrial and urban areas; (2) the existence of non-carcinogenic and carcinogenic risks for metals present in all samples; (3) the absence of carcinogenic risk for the assessed PAHs; and (4) the reduction scenarios proposed pointed to a reduction of up to 22 deaths annually in conjunction with reductions in health-related expenditures. Thus, these results may serve as a basis for the development of public health policies aimed at improving air quality, jointly assisting health surveillance and directing future studies towards a better intrinsic approach to the problem.

Spatiotemporal Regularity and Socioeconomic Drivers of the AQI in the Yangtze River Delta of China

Air pollution has caused adverse effects on the climate, the ecological environment and human health, and it has become a major challenge facing the world today. The Yangtze River Delta (YRD) is the region with the most developed economy and the most concentrated population in China. Identifying and quantifying the spatiotemporal characteristics and impact mechanism of air quality in this region would help in formulating effective mitigation policies. Using annual data on the air quality index (AQI) of 39 cities in the YRD from 2015 to 2018, the spatiotemporal regularity of the AQI is meticulously uncovered. Furthermore, a geographically weighted regression (GWR) model is used to qualify the geographical heterogeneity of the effect of different socioeconomic variables on the AQI level. The empirical results show that (1) the urban agglomeration in the YRD presents an air pollution pattern of being low in the northwest and high in the southeast. The spatial correlation of the distribution of the AQI level is verified. The spatiotemporal regularity of the “high clustering club” and the “low clustering club” is obvious. (2) Different socioeconomic factors show obvious geographically heterogeneous effects on the AQI level. Among them, the impact intensity of transportation infrastructure is the largest, and the impact intensity of the openness level is the smallest. (3) The upgrading of the industrial structure improves the air quality status in the northwest more than it does in the southeast. The impact of transportation infrastructure on the air pollution of cities in Zhejiang Province is significantly higher than the impact on the air pollution of other cities. The air quality improvement brought by technological innovation decreases from north to south. With the expansion of urban size, there is a law according to which air quality first deteriorates and then improves. Finally, the government should promote the upgrading of key industries, reasonably control the scale of new construction land, and increase the cultivation of local green innovative enterprises.

Relationships between Springtime PM2.5, PM10, and O3 Pollution and the Boundary Layer Structure in Beijing, China

Complex pollution with high aerosol and ozone concentrations has recently been occurring in several densely populated cities in China, raising concerns about the influence of meteorological factors, including synoptic circulation and local conditions. In this study, comprehensive analyses on the associations between PM2.5, PM10, and O3 and meteorological conditions were conducted based on observations from radar wind profiler, microwave radiometer, automatic weather station, and air quality monitoring sites in Beijing during the spring of 2019. The results showed that the boundary layer height and temperature inversion were negatively (positively) correlated with PM (O3) concentrations, modulating the degree of air pollution. Five identified synoptic patterns were derived using geopotential height data of the ERA5 reanalysis, among which Type 1, characterised by south-westerly prevailing winds with high pressure to the south, was considered to be associated with severe PM and O3 contamination. This indicates that air pollutants originating from southern regions exert a major influence on Beijing through the transportation effect. In addition, high temperature, relative humidity, and low wind velocity exacerbate pollution. Overall, this study provides significant information for understanding the vital roles played by meteorological elements at both the regional and local scales in regulating air contamination during spring in Beijing.

Random Forest Estimation and Trend Analysis of PM2.5 Concentration over the Huaihai Economic Zone, China (2000–2020)

Consisting of ten cities in four Chinese provinces, the Huaihai Economic Zone has suffered serious air pollution over the last two decades, particularly of fine particulate matter (PM2.5). In this study, we used multi-source data, namely MAIAC AOD (at a 1 km spatial resolution), meteorological, topographic, date, and location (latitude and longitude) data, to construct a regression model using random forest to estimate the daily PM2.5 concentration over the Huaihai Economic Zone from 2000 to 2020. It was found that the variable expressing time (date) had the greatest characteristic importance when estimating PM2.5. By averaging the modeled daily PM2.5 concentration, we produced a yearly PM2.5 concentration dataset, at a 1 km resolution, for the study area from 2000 to 2020. On comparing modeled daily PM2.5 with observational data, the coefficient of determination (R2) of the modeling was 0.85, the root means square error (RMSE) was 14.63 μg/m3, and the mean absolute error (MAE) was 10.03 μg/m3. The quality assessment of the synthesized yearly PM2.5 concentration dataset shows that R2 = 0.77, RMSE = 6.92 μg/m3, and MAE = 5.42 μg/m3. Despite different trends from 2000–2010 and from 2010–2020, the trend of PM2.5 concentration over the Huaihai Economic Zone during the 21 years was, overall, decreasing. The area of the significantly decreasing trend was small and mainly concentrated in the lake areas of the Zone. It is concluded that PM2.5 can be well-estimated from the MAIAC AOD dataset, when incorporating spatiotemporal variability using random forest, and that the resultant PM2.5 concentration data provide a basis for environmental monitoring over large geographic areas.

Temporal variability of PM2.5 and its possible sources at the tropical megacity, Bengaluru, India

The mass concentrations of PM_2.5 were measured at a tropical megacity, Bengaluru, India, for the year 2015. The mean mass concentrations showed large fluctuations on day to day basis with values less than the Indian National Ambient Air Quality Standard (INAAQS) of 60 µg m^-3. The observed annual mean mass concentration of 28 ± 11 µg m^-3 is also within the INAAQS value of 40 µg m^-3. The diurnal trend of PM_2.5 concentration showed bimodal distribution, with the primary peak in the morning and the secondary one during the late evening hours. The timing of the peaks matched with rush traffic hours. Strong seasonality is observed in the diurnal concentration of PM_2.5 with the highest value during winter (50 ± 22 µg m^-3) and the lowest of (11 ± 5 µg m^-3) in the monsoon. The weekend PM_2.5 mass concentrations were less than those on the weekdays up to a maximum of 100%. The decrease in PM_2.5 mass concentration was also observed on the day of the strike when many busses were off the road. Vehicular traffic is suggested as one of the primary contributors of PM_2.5 in this region. The health risk assessment in this study, points to ischemic heart disease as the primary cause of PM_2.5-induced death.

Short-term effect of COVID-19 lockdowns on atmospheric CO2, CH4 and PM2.5 concentrations in urban environment

The COVID-19 pandemic has changed all areas of human activity as it forced the authorities around the world to enact unprecedented restrictions such as "lockdowns". The low economic activity reduced the anthropogenic impact on the environment, in particular, greenhouse gases and aerosols emissions were decreased. However, the associated change in air quality is difficult to directly observe and quantify, since concentrations of these components in urban areas are affected by many other factors. In this work statistical analysis of atmospheric CO2, CH4 and PM2.5, measured in 2017-2020 in the city of Ekaterinburg, Russia, are presented. A detailed focus was made on the lockdown period from March 28 to April 30, 2020. A significant decrease in concentrations and inter-hourly variations of all studied components were observed only in the short "self-isolation" period from April 6 to April 8. The anthropogenic origin of this effect, primarily associated with the reduction in vehicular traffic, was concluded from mean diurnal cycles and air temperature correlations of all components. A decrease in the difference between measured and background CO2 and CH4 mole fractions was also found during this period. The difference was 1.3±0.2 ppm for CO2 and 8±4 ppb for CH4, which was many times lower than during any other observed periods, suggesting a short-term effect of lockdown restrictions. Overall, a negative impact on the atmosphere quickly resumed after the recovery of economic activity. The approaches in this study can be used to detect weak fluctuations of atmospheric components in other urban territories.

Short-term effects of ambient air pollution on daily emergency room visits for abdominal pain: a time-series study in Wuhan, China

Short-term exposure to ambient air pollution has been proven to result in respiratory, cardiovascular, and digestive diseases, leading to increased emergency room visits (ERVs). Abdominal pain complaints provide a large proportion of the ERVs, as yet few studies have focused on the correlations between ambient air pollution and abdominal pain, especially in emergency departments within China. Daily data for daily ERVs were collected in Wuhan, China (from January 1, 2016 to December 31, 2018), including air pollution concentration (SO₂, NO₂, PM_2.5, PM₁₀, CO, and O₃), and meteorological variables. We conducted a time-series study to investigate the potential correlation between six ambient air pollutants and ERVs for abdominal pain and their effects, in different genders, ages, and seasons. A total of 16,318 abdominal pain ERVs were identified during the study period. A 10-μg/m³ increase in concentration of SO₂, NO₂, PM_2.5, PM₁₀, CO, and O₃ corresponded respectively to incremental increases in abdominal pain of 4.89% (95% confidence interval [CI]: - 1.50-11.70), 1.85% (95% CI: - 0.29-4.03), 0.83% (95% CI: - 0.05-1.72), - 0.22% (95% CI: - 0.73-0.30), 0.24% (95% CI: 0.08-0.40), and 0.86% (95% CI: 0.04 - 1.69). We observed significant correlations between CO and O₃ and increases in daily abdominal pain ERVs and positive but insignificant correlations between the other pollutants and ERVs (except PM₁₀). The effects were stronger for females (especially SO₂ and O₃: 13.53% vs. - 2.46%; 1.20% vs. 0.47%, respectively) and younger people (especially CO and O_3: 0.25% vs. 0.01%; 1.36% vs. 0.15%, respectively). Males (1.38% vs. 0.87%) and elders (1.27% vs. 0.99%) were more likely to be affected by PM_2.5. The correlations with PM_2.5 were stronger in cool seasons (1.25% vs. - 0.07%) while the correlation with CO was stronger in warm seasons (0.47% vs. 0.14%). Our time-series study suggests that short-term exposure to air pollution (especially CO and O₃) was positively correlated with ERVs for abdominal pain in Wuhan, China, and that the effects varied by season, gender and age. These data can add evidence on how air pollutants affect the human body and may prompt hospitals to take specific precautions on polluted days and maintain order in emergency departments made busier due to the pollution.

Unexpected rise of atmospheric secondary aerosols from biomass burning during the COVID-19 lockdown period in Hangzhou, China

After the global outbreak of COVID-19, the Chinese government took many measures to control the spread of the virus. The measures led to a reduction in anthropogenic emissions nationwide. Data from a single particle aerosol mass spectrometer in an eastern Chinese megacity (Hangzhou) before, during, and after the COVID-19 lockdown (5 January to February 29, 2020) was used to understand the effect lockdown had on atmospheric particles. The collected single particle mass spectra were clustered into eight categories. Before the lockdown, the proportions of particles ranked in order of: EC (57.9%) < K-SN (13.6%) < Fe-rich (10.2%) < ECOC (6.7%) < K-Na (6.6%) < OC (3.4%) < K-Pb (1.0%) < K-Al (0.7%). During the lockdown period, the EC and Fe-rich particles decreased by 42.8% and 93.2% compared to before lockdown due to reduced vehicle exhaust and industrial activity. By contrast, the K-SN and K-Na particles containing biomass burning tracers increased by 155.2% and 45.2% during the same time, respectively. During the lockdown, the proportions of particles ranked in order of: K-SN (39.7%) < EC (38.1%) < K-Na (11.0%) < ECOC (7.7%) < OC (1.2%) < K-Pb (0.9%) < Fe-rich (0.8%) < K-Al (0.6%). Back trajectory analysis indicated that both inland (Anhui and Shandong provinces) and marine transported air masses may have contributed to the increase in K-SN and K-Na particles during the lockdown, and that increased number of fugitive combustion points (i.e., household fuel, biomass combustion) was a contributing factor. Therefore, the results imply that regional synergistic control measures on fugitive combustion emissions are needed to ensure good air quality.

Ambient Air Pollution and Socioeconomic Status in China

BACKGROUND

Air pollution disparities by socioeconomic status (SES) are well documented for the United States, with most literature indicating an inverse relationship (i.e., higher concentrations for lower-SES populations). Few studies exist for China, a country accounting for 26% of global premature deaths from ambient air pollution.

OBJECTIVE

Our objective was to test the relationship between ambient air pollution exposures and SES in China.

METHODS

We combined estimated year 2015 annual-average ambient levels of nitrogen dioxide (NO 2 ) and fine particulate matter [PM ≤ 2.5 μ m in aerodynamic diameter (PM 2.5 )] with national demographic information. Pollution estimates were derived from a national empirical model for China at 1 -km spatial resolution; demographic estimates were derived from national gridded gross national product (GDP) per capita at 1 -km resolution, and (separately) a national representative sample of 21,095 individuals from the China Health and Retirement Longitudinal Study (CHARLS) 2015 cohort. Our use of global data on population density and cohort data on where people live helped avoid the spatial imprecision found in publicly available census data for China. We quantified air pollution disparities among individual's rural-to-urban migration status; SES factors (education, occupation, and income); and minority status. We compared results using three approaches to SES measurement: individual SES score, community-averaged SES score, and gridded GDP per capita.

RESULTS

Ambient NO 2 and PM 2.5 levels were higher for higher-SES populations than for lower-SES population, higher for long-standing urban residents than for rural-to-urban migrant populations, and higher for the majority ethnic group (Han) than for the average across nine minority groups. For the three SES measurements (individual SES score, community-averaged SES score, gridded GDP per capita), a 1-interquartile range higher SES corresponded to higher concentrations of 6 - 9   μ g / m 3 NO 2 and 3 - 6   μ g / m 3 PM 2.5 ; average concentrations for the highest and lowest 20th percentile of SES differed by 41-89% for NO 2 and 12-25% for PM 2.5 . This pattern held in rural and urban locations, across geographic regions, across a wide range of spatial resolution, and for modeled vs. measured pollution concentrations.

CONCLUSIONS

Multiple analyses here reveal that in China, ambient NO 2 and PM 2.5 concentrations are higher for high-SES than for low-SES individuals; these results are robust to multiple sensitivity analyses. Our findings are consistent with the idea that in China's current industrialization and urbanization stage, economic development is correlated with both SES and air pollution. To our knowledge, our study provides the most comprehensive picture to date of ambient air pollution disparities in China; the results differ dramatically from results and from theories to explain conditions in the United States. https://doi.org/10.1289/EHP9872.

Changes in stoichiometric characteristics of ambient air pollutants pre-to post-COVID-19 in China

To prevent the Corona Virus Disease 2019 (COVID-19) spreading, Chinese government takes a series of corresponding measures to restrict human mobility, including transportation lock-down and industries suspension, which significantly influenced the ambient air quality and provided vary rare time windows to assess the impacts of anthropological activities on air pollution. In this work, we divided the studied timeframe (2019/12/24-2020/2/24) into four periods and selected 88 cities from 31 representative urban agglomerations. The indicators of PM_2.5/PM₁₀ and NO₂/SO₂ were applied, for the first time, to analyze the changes in stoichiometric characteristics of ambient air pollutants pre-to post-COVID-19 in China. The results indicated that the ratios of NO₂/SO₂ presented a responding decline, especially in YRD (-5.01), YH (-3.87), and MYR (-3.84), with the sharp reduction of traffic in post-COVID-19 periods (P3-P4: 2.34 ± 0.94 m/m) comparing with pre-COVID-19 periods (P1-P2: 4.49 ± 2.03 m/m). Whereas the ratios of PM_2.5/PM₁₀ increased in P1-P3, then decreased in P4 with relatively higher levels (>0.5) in almost all urban agglomerations. Furthermore, NO₂ presented a stronger association with PM_2.5/PM₁₀ variation than CO; and PM_2.5 with NO₂/SO₂ variation than PM₁₀. In summary, the economic structure, lockdown measures and meteorological conditions could explain the noteworthy variations in different urban agglomerations. These results would be in great help for improving air quality in the post-epidemic periods.

Exploring the relationships between ground-measured particulate matter and satellite-retrieved aerosol parameters in China

In this study, the PM_2.5 and PM₁₀ concentrations from 367 cities in China were integrated with MODIS-retrieved aerosol optical depth (AOD) and Angstrom exponent (AE) data to explore the relationship between ground-measured surface particle concentrations and remote-sensing aerosol parameters. The impact of meteorological and topographical factors and seasonality were also taken into consideration and the partial least squares (PLS) regression model was adopted to evaluate the effects of surface particulate matter (PM) concentration and meteorological factors on the variation of aerosol parameters. PM concentrations and aerosol parameters all presented strong spatial disparity and seasonal patterns in China. After implementation of stringent clean air actions and policies, both the ground-measured and satellite-retrieved aerosol parameters revealed that the concentrations of suspended particles in China's cities declined dramatically from 2015 to 2018. The PM/AOD ratio showed conspicuous south-north and west-east differences. The ratio was strongly correlated to meteorological and topographic factors, and it tended to be higher in arid and less polluted regions. Moreover, the dominant factors affecting seasonal PM/AOD ratios varied among China's five regions. The correlations of daily PM-AOD were always strong in southwest China and in basin terrain (e.g., Sichuan Basin and Tarim Basin). In contrast, the PM-AOD correlation was found to be negative in some cities on the Tibetan Plateau because local relative humidity makes a greater contribution to AOD variation. Since the climate is arid and the ratio of coarse particles (e.g., PM₁₀) is much higher, PM tended to have a significantly negative correlation with AE in northwestern cities. Whereas in many southern cities, PM was positively correlated with AE because of the area's high relative humidity and aerosol hygroscopic properties.

A three-dimensional LUR framework for PM2.5 exposure assessment based on mobile unmanned aerial vehicle monitoring

Land use regression (LUR) models have been widely used in epidemiological studies and risk assessments related to air pollution. Although efforts have been made to improve the performance of LUR models so that they capture the spatial heterogeneity of fine particulate matter (PM_2.5) in high-density cities, few studies have revealed the vertical differences in PM_2.5 exposure. This study proposes a three-dimensional LUR (3-D LUR) assessment framework for PM_2.5 exposure that combines a high-resolution LUR model with a vertical PM_2.5 variation model to investigate the results of horizontal and vertical mobile PM_2.5 monitoring campaigns. High-resolution LUR models that were developed independently for daytime and nighttime were found to explain 51% and 60% of the PM_2.5 variation, respectively. Vertical measurements of PM_2.5 from three regions were first parameterized to produce a coefficient of variation for the concentration (CVC) to define the rate at which PM_2.5 changes at a certain height relative to the ground. The vertical variation model for PM_2.5 was developed based on a spline smoothing function in a generalized additive model (GAM) framework with an adjusted R² of 0.91 and explained 92.8% of the variance. PM_2.5 exposure levels for the population in the study area were estimated based on both the LUR models and the 3-D LUR framework. The 3-D LUR framework was found to improve the accuracy of exposure estimation in the vertical direction by avoiding exposure estimation errors of up to 5%. Although the 3-D LUR-based assessment did not indicate significant variation in estimates of premature mortality that could be attributed to PM_2.5, exposure to this pollutant was found to differ in the vertical direction. The 3-D LUR framework has the potential to provide accurate exposure estimates for use in future epidemiological studies and health risk assessments.

Changes in Long-Term PM2.5 Pollution in the Urban and Suburban Areas of China’s Three Largest Urban Agglomerations from 2000 to 2020

Particulate matter (PM2.5) is a significant public health concern in China, and the Chinese government has implemented a series of laws, policies, regulations, and standards to improve air quality. This study documents the changes in PM2.5 and evaluates the effects of industrial transformation and clean air policies on PM2.5 levels in urban and suburban areas of China’s three largest urban agglomerations, Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) based on a new degree of urbanization classification method. We used high-resolution PM2.5 concentration and population datasets to quantify the differences in PM2.5 concentrations in urban and suburban areas of these three urban agglomerations. From 2000 to 2020, the urban areas have expanded while the suburban areas have shrunk. PM2.5 concentrations in urban areas were approximately 32, 10, and 7 μg/m3 higher than those in suburban areas from 2000 to 2020 in BTH, YRD, and PRD, respectively. Since 2013, the PM2.5 concentrations in the urban regions of BTH, YRD, and PRD have declined at average annual rates of 7.30, 5.50, and 5.03 μg/m3/year, respectively, while PM2.5 concentrations in suburban areas have declined at average annual rates of 3.11, 4.23 and 4.69 μg/m3/year, respectively. By 2018, all of the urban and suburban areas of BTH, YRD, and PRD satisfied their specific targets in the Air Pollution and Control Action Plan. By 2020, the PM2.5 declines of BTH, YRD, and PRD exceeded the targets by two, three, and four times, respectively. However, the PM2.5 exposure risks in urban areas are 10–20 times higher than those in suburban areas. China will need to implement more robust air pollution mitigation policies to achieve the World Health Organization’s Air Quality Guideline (WHO-AQG) and reduce long-term PM2.5 exposure health risks.