Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application

Global net climate effects of anthropogenic reactive nitrogen

Anthropogenic activities have substantially enhanced the loadings of reactive nitrogen (Nr) in the Earth system since pre-industrial times1,2, contributing to widespread eutrophication and air pollution3-6. Increased Nr can also influence global climate through a variety of effects on atmospheric and land processes but the cumulative net climate effect is yet to be unravelled. Here we show that anthropogenic Nr causes a net negative direct radiative forcing of -0.34 [-0.20, -0.50] W m-2 in the year 2019 relative to the year 1850. This net cooling effect is the result of increased aerosol loading, reduced methane lifetime and increased terrestrial carbon sequestration associated with increases in anthropogenic Nr, which are not offset by the warming effects of enhanced atmospheric nitrous oxide and ozone. Future predictions using three representative scenarios show that this cooling effect may be weakened primarily as a result of reduced aerosol loading and increased lifetime of methane, whereas in particular N2O-induced warming will probably continue to increase under all scenarios. Our results indicate that future reductions in anthropogenic Nr to achieve environmental protection goals need to be accompanied by enhanced efforts to reduce anthropogenic greenhouse gas emissions to achieve climate change mitigation in line with the Paris Agreement.

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.

Ct-defined emphysema prevalence in a Chinese and Dutch general population

PURPOSE

We determine and compare the prevalence, subtypes, severity, and risk factors for emphysema assessed by low-dose CT(LDCT) in Chinese and Dutch general populations.

METHODS

This cross-sectional study included LDCT scans of 1143 participants between May and October 2017 from a Chinese Cohort study and 1200 participants with same age range and different smoking status between May and October 2019 from a Dutch population-based study. An experienced radiologist visually assessed the scans for emphysema presence (≥trace), subtype, and severity. Logistic regression analyses, overall and stratified by smoking status, were performed and adjusted for fume exposure, demographic and smoking data.

RESULTS

The Chinese population had a comparable proportion of women to the Dutch population (54.9 % vs 58.9 %), was older (61.7 ± 6.3 vs 59.8 ± 8.1), included more never smokers (66.4 % vs 38.3 %), had a higher emphysema prevalence ([58.8 % vs 39.7 %], adjusted odds ratio, aOR = 2.06, 95 %CI = 1.68-2.53), and more often had centrilobular emphysema (54.8 % vs 32.8 %, p < 0.001), but no differences in emphysema severity. After stratification, only in never smokers an increased odds of emphysema was observed in the Chinese compared to the Dutch (aOR = 2.55, 95 %CI = 1.95-3.35). Never smokers in both populations shared older age (aOR = 1.59, 95 %CI = 1.25-2.02 vs 1.26, 95 %CI = 0.97-1.64) and male sex (aOR = 1.50, 95 %CI = 1.02-2.22 vs 1.93, 95 %CI = 1.26-2.96) as risk factors for emphysema.

CONCLUSIONS

Only never smokers had a higher prevalence of mainly centrilobular emphysema in the Chinese general population compared to the Dutch after adjusting for confounders, indicating that factors other than smoking, age and sex contribute to presence of CT-defined emphysema.

Machine learning based urban sprawl assessment using integrated multi-hazard and environmental-economic impact

The increasing demand for land development due to human activities has fueled urbanization. However, uncontrolled urban development in some regions has resulted in urban environmental problems arising from an imbalance between supply and demand. This study aims to develop an integrated model for evaluating and prioritizing the management of hazardous urban sprawl in the Bandung metropolitan region of Indonesia. The novelty of this study lies in its pioneering application of long-term remote sensing data-based and machine learning techniques to formulate an urban sprawl priority index. This index is unique in its consideration of the impacts stemming from human economic activity, environmental degradation, and multi-disaster levels as integral components. The analysis of hazardous urban sprawl across three distinct time periods (1985-1993, 1993-2008, and 2008-2018) revealed that the 1993-2008 period had the highest increase in human economic activity, reaching 172,776 ha. The 1985-1993 period experienced the highest level of environmental degradation in the study area. Meanwhile, the 1993-2008 period showed the highest concentration of multi-hazard locations. The combined model of hazardous urban sprawl, incorporating the three parameters, indicated that the highest priority for intervention was on the outskirts of urban areas, specifically in West Bandung Regency, Cimahi, Bandung Regency, and East Bandung Regency. Regions with high-priority indices require greater attention from the government to mitigate the negative impacts of hazardous urban sprawl. This model, driven by the urban sprawl priority index, is envisioned to regulate urban movement in a more sustainable manner. Through the efficient monitoring of urban environments, the study seeks to guarantee the preservation of valuable natural resources while promoting sustainable urban development practices.

Residential surrounding greenness and the incidence of childhood asthma: Findings from a population-based cohort in Ontario, Canada

Several epidemiological studies have investigated the possible role that living in areas with greater amounts of greenspace has on the incidence of childhood asthma. These findings have been inconsistent, and few studies explored the relevance of timing of exposure. We investigated the role of residential surrounding greenness on the risk of incident asthma using a population-based retrospective cohort study. We included 982,131 singleton births in Ontario, Canada between 2006 and 2013. Two measures of greenness, the Normalized Difference Vegetation Index (NDVI) and the Green View Index (GVI), were assigned to the residential histories of these infants from pregnancy through to 12 years of age. Longitudinally-based diagnoses of asthma were determined by using provincial administrative health data. The extended Cox hazards model was used to characterize associations between greenness measures and asthma (up to age 12 years) while adjusting for several risk factors. In a fully adjusted model, that included a term for traffic-related air pollution (NO2), we found no association between an interquartile range increase (0.08) of the NDVI during childhood and asthma incidence (HR = 0.99; 95 % CI = 0.99-1.01). In contrast, we found that an 0.08 increase in NDVI during childhood reduced the risk of asthma in children 7-12 years of age by 14 % (HR = 0.86, 95 % CI:0.79-0.95). Seasonal differences in the association between greenness and asthma were noted. Our findings suggest that residential proximity to greenness reduces the risk of asthma in children aged 7-12.

Is model-estimated PM2.5 exposure equivalent to station-observed in mortality risk assessment? A literature review and meta-analysis

Model-estimated air pollution exposure assessments have been extensively employed in the evaluation of health risks associated with air pollution. However, few studies synthetically evaluate the reliability of model-estimated PM2.5 products in health risk assessment by comparing them with ground-based monitoring station air quality data. In response to this gap, we undertook a meticulously structured systematic review and meta-analysis. Our objective was to aggregate existing comparative studies to ascertain the disparity in mortality effect estimates derived from model-estimated ambient PM2.5 exposure versus those based on monitoring station-observed PM2.5 exposure. We conducted searches across multiple databases, namely PubMed, Scopus, and Web of Science, using predefined keywords. Ultimately, ten studies were included in the review. Of these, seven investigated long-term annual exposure, while the remaining three studies focused on short-term daily PM2.5 exposure. Despite variances in the estimated Exposure-Response (E-R) associations, most studies revealed positive associations between ambient PM2.5 exposure and all-cause and cardiovascular mortality, irrespective of the exposure being estimated through models or observed at monitoring stations. Our meta-analysis revealed that all-cause mortality risk associated with model-estimated PM2.5 exposure was in line with that derived from station-observed sources. The pooled Relative Risk (RR) was 1.083 (95% CI: 1.047, 1.119) for model-estimated exposure, and 1.089 (95% CI: 1.054, 1.125) for station-observed sources (p = 0.795). In conclusion, most model-estimated air pollution products have demonstrated consistency in estimating mortality risk compared to data from monitoring stations. However, only a limited number of studies have undertaken such comparative analyses, underscoring the necessity for more comprehensive investigations to validate the reliability of these model-estimated exposure in mortality risk assessment.

Addressing air quality challenges: Comparative analysis of Barcelona, Venezuela, and Guayaquil, Ecuador

This study presents a willingness-to-pay (WtP) questionnaire that was designed, validated, and applied to assess perceptions of air quality and self-reported health in two middle-income South American cities: Barcelona and its neighboring cities (Venezuela) and Guayaquil (Ecuador). These cities lack air quality monitoring and control measures. The questionnaire is a reliable tool to assess air quality based on citizens' perceptions, and the results reveal that both populations perceive low air quality and accurately identify emission sources and air pollutants (industrial emissions and particulate matter in Barcelona and vehicular emissions and carbon monoxide in Guayaquil). The study also evaluated the efforts made by both cities to improve air quality using the United Nations Environment Programme to strengthen air quality in South America. Based on this evaluation, strengths were identified for enhancing air quality in both cities. The study finds that in Barcelona and its surroundings, investment is needed to improve urban transport, waste management, and update the environmental legislation regarding air quality at the national level. In contrast, Guayaquil has already taken some measures to improve air quality, but more investment in public transport and measures to lower vehicle emissions are needed.

Cancer burden and risk in the Chinese population aged 55 years and above: A systematic analysis and comparison with the USA and Western Europe

Background

We analysed the cancer burden among elderly Chinese people over the age of 55 years and compared them to USA and Western Europe to explore the cancer model in China.

Methods

We retrieved data on 29 cancers with 34 risk factors from the 2019 Global Burden of Disease database to evaluate the cancer burden in Chinese elderly individuals aged 55 years and older. We then used the age-standardised incidence rate (ASIR), age-standardised death rate (ASDR), age-standardised disability-adjusted life year (DALY) rate, and average annual percentage change (AAPC) to compare the characteristics and change trend of cancers among China, USA, and Western Europe.

Results

In 2019, the number of incident cases of 29 cancers among people aged 55 years and above in China increased more than 3-fold compared to 1990, while the number of deaths and DALYs approximately doubled. We also found that the cancer population in China was ageing; meanwhile, the cancer burden became significantly higher for men than for women, and the gap between men and women had widened. Cancers with the highest cancer DALYs were lung cancer (13 444 500; 95% uncertainty interval (UI) = 11 307 100, 15 853 700), stomach cancer (7 303 900; 95% UI = 6 094 600, 8 586 500), oesophageal cancer (4 633 500; 95% UI = 3 642 500, 5 601 200), colon and rectum cancer (4 386 500; 95% UI = 3 769 500, 5 067 200), liver cancer (2 915 100, 95% UI = 2 456 300, 3 463 900), and pancreatic cancer (2 028 400; 95% UI = 1 725 000, 2 354 900). Compared with 1990, the DALY rate and incidence rate of stomach cancer, oesophageal cancer, and liver cancer had markedly decreased. The DALY rate and incidence rate of lung, colon, rectum, and pancreatic cancer had increased significantly, as did the incidence rate of breast cancer in women. Smoking and diet were the top two cancer risk factors, and the impact of ambient particulate matter pollution on cancer increased each year. The overall 29 cancers age-standardised DALY rate and ASDR in China, USA, and Western Europe were similar, and all showed downward trend in the past 30 years. Compared with the USA and Western Europe, the age-standardised DALY rate of liver, nasopharyngeal, oesophageal, stomach, and cervical cancers in China was more prominent. The age-standardised DALY rate of lung cancer and colon and rectum cancer decreased annually in Western Europe and the USA, but increased in China.

Conclusions

Over the past 30 years, China had made progress in controlling stomach, oesophageal, and liver cancer. However, lung, colon, rectum, pancreatic, and breast cancers had become more prevalent, having risen alongside economic development. The risks of smoking and dietary were major issues that need to be addressed urgently. The cancer situation in China remains serious; future cancer prevention efforts need to balance economic development with people's physical health, identify key groups, improve the health environment of residents and guide them to live a healthy life, and expand the scope of cancer screening.

First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact

Here we retrieve global daily 1 km gapless PM2.5 concentrations via machine learning and big data, revealing its spatiotemporal variability at an exceptionally detailed level everywhere every day from 2017 to 2022, valuable for air quality monitoring, climate change, and public health studies. We find that 96%, 82%, and 53% of Earth's populated areas are exposed to unhealthy air for at least one day, one week, and one month in 2022, respectively. Strong disparities in exposure risks and duration are exhibited between developed and developing countries, urban and rural areas, and different parts of cities. Wave-like dramatic changes in air quality are clearly seen around the world before, during, and after the COVID-19 lockdowns, as is the mortality burden linked to fluctuating air pollution events. Encouragingly, only approximately one-third of all countries return to pre-pandemic pollution levels. Many nature-induced air pollution episodes are also revealed, such as biomass burning.

Addressing Biases in Ambient PM2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India

Ambient PM2.5 exposure statistics in countries with limited ground monitors are derived from satellite aerosol optical depth (AOD) products that have spatial gaps. Here, we quantified the biases in PM2.5 exposure and associated health burden in India due to the sampling gaps in AOD retrieved by a Moderate Resolution Imaging Spectroradiometer. We filled the sampling gaps and derived PM2.5 in recent years (2017-2022) over India, which showed fivefold cross-validation R2 of 0.92 and root mean square error (RMSE) of 11.8 μg m-3 on an annual scale against ground-based measurements. If the missing AOD values are not accounted for, the exposure would be overestimated by 19.1%, translating to an overestimation in the mortality burden by 93,986 (95% confidence interval: 78,638-110,597) during these years. With the gap-filled data, we found that the rising ambient PM2.5 trend in India has started showing a sign of stabilization in recent years. However, a reduction in population-weighted exposure balanced out the effect of the increasing population and maintained the mortality burden attributable to ambient PM2.5 for 2022 (991,058:798,220-1,183,896) comparable to the 2017 level (1,014,766:812,186-1,217,346). Therefore, a decline in exposure alone is not sufficient to significantly reduce the health burden attributable to ambient PM2.5 in India.

Living with particles: Disclosure of pollution information, individual responses, and health consequences

Drawing on a panel dataset-the China Health and Retirement Longitudinal Survey (CHARLS)-and other city- and individual-level datasets, this study examines the causal impact of pollution information disclosure on individual outdoor activities and the health status of the middle-aged and elderly. Using city-level variations in disclosure timing, we found that the adoption of pollution information disclosure (PID) significantly reduces the probability of outdoor exercise, especially for those living in more polluted cities. This occurs mainly through enhanced awareness of environmental pollution, particularly for those who are more educated. However, the adoption of PID does not lead to an improvement in health status.

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.

Source apportionment, health risk assessment, and trajectory analysis of black carbon and light absorption properties of black and brown carbon in Delhi, India

Black Carbon (BC) is an important atmospheric pollutant, well recognized for adverse health and climatic effects. The present work discusses the monthly and seasonal variations of BC sources, health risks, and light absorption properties. The measurement was done from January to December 2021 using a seven wavelength aethalometer. Annual average BC concentration during the study period was 12.2 ± 8.8 μg/m3 (ranged from 1.9 - 52.2 μg/m3). Results represent highest BC concentration during winter (W), followed by post-monsoon (P-M), summer (S), and monsoon (M) seasons where the fossil fuel (FF) combustion is the major source during W, S, and M seasons and biomass burning (BB) during the P-M season. The health risk assessment revealed that individuals in Delhi are exposed to BC levels equivalent to inhaling the smoke from 36 passively smoked cigarettes (PSC) everyday. The risk is highest during W reaching upto 71 PSC and minimum during M i.e., 9 PSC. The light absorption properties were calculated for BC (AbsBC) and Brown carbon (AbsBrC). AbsBC and varied from 229-89 Mm-1 between 370-950 nm and AbsBrC varied from 87-12 Mm-1 between 370-660 nm. AbsBC contributed substantially to total absorption at all wavelengths, while AbsBrC contribution is quite significant in the UV region only. Trajectory analysis confirmed significant influence of regional sources (e.g., biomass-burning aerosols from northwest and east direction) on air quality, health risks, and light absorption properties of BC over Delhi especially during the P-M season. The BB events of Punjab, Haryana, Uttar Pradesh, and eastern Pakistan seems to have significant influence on Delhi's air quality predominantly during P-M season.

Interaction mechanism and spatial effect of cross-regional haze pollution based on a multisectoral economy-energy-environment (3E) model and the evidence from China

The transboundary characteristics and multisectoral factor interaction mechanism of haze pollution have aroused widespread attention but remain understudied. This article proposes a comprehensive conceptual model that clarifies regional haze pollution, further establishes a theoretical framework on a cross-regional, multisectoral economy-energy-environment (3E) system, and attempts to empirically investigate the spatial effect and interaction mechanism employing a spatial-econometrics model based on China's province-level regions. The results demonstrate that (1) regional haze pollution is a transboundary atmospheric state formed by the accumulation and agglomeration of various emission pollutants; moreover, there is a "snowball" effect and a spatial spillover effect. (2) The formation and evolution of haze pollution are driven by the multisectoral factors of 3E system interaction, and the findings still hold after theoretical and empirical analysis and robustness tests. (3) Significant spatial autocorrelation exists for the 3E factors, presenting different clustering modes with a dynamic spatiotemporal evolution, particularly in the high-high (H-H) mode and low-low (L-L) mode. (4) Significant heterogeneous impacts of economic and energy factors on haze pollution are identified, namely, an inverted "U-shaped" relationship and a positive linear association, respectively. Further spatial analysis demonstrates a strong spatial spillover and obvious path dependence among local and neighboring regions. Policymakers are advised to consider multisectoral 3E system interaction and cross-regional collaboration. Integr Environ Assess Manag 2023;19:1525-1543. © 2023 SETAC.

The cardiovascular disease burden attributable to particulate matter pollution in South America: analysis of the 1990-2019 global burden of disease

OBJECTIVES

Fine particulate matter <2.5 microns (PM2.5) is the most studied air pollutant. Both short- and long-term exposure to PM2.5 have been linked to cardiovascular disease (CVD). Studies evaluating air pollution in South America are scarce. Therefore, the impact of exposure to PM2.5, household air pollution (HAP), and ambient air pollution (AAP) on CVD mortality and CVD disability-adjusted life years (DALYs) in South American countries from 1990 to 2019 was explored.

STUDY DESIGN AND METHODS

The Global Burden of Disease initiative exposure-response function was used to analyze the total PM2.5, ambient PM2.5, and household PM2.5-related CVD deaths and DALYs rates, per 100,000 individuals, in 12 South American countries between 1990 and 2019. The relative change in burden was also assessed by comparing the 1990-1994 to 2015-2019 periods.

RESULTS

In 2019, 70,668 deaths and 1,736,414 DALYs due to CVD were attributed to total PM2.5 exposure in South America. Substantial regional heterogeneity was observed concerning the absolute change in PM2.5 concentration levels comparing 1990 to 2019. All South American countries observed a relative decline in CVD deaths and DALYs comparing the 1990-1994 to 2015-2019 periods. No country was able to reach the current World Health Organization 5 μg/m3 recommended limit in 2019. Predominantly, AAP was the greatest contributor to the CVD burden.

CONCLUSION

Air pollution substantially impacted CVD in South America; however, this impact was heterogenous, and the relative reduction of HAP and AAP burden was also not uniform. Recognizing PM2.5 importance is key for developing target population and individual-level interventions, which could ultimately alleviate its burden.

Estimation of surface ozone concentration over Jiangsu province using a high-performance deep learning model

Recently, the global background concentration of ozone (O₃) has demonstrated a rising trend. Among various methods, groun-based monitoring of O₃ concentrations is highly reliable for research analysis. To obtain information on the spatial characteristics of O₃ concentrations, it is necessary that the ground monitoring sites be constructed in sufficient density. In recent years, many researchers have used machine learning models to estimate surface O₃ concentrations, which cannot fully provide the spatial and temporal information contained in a sample dataset. To solve this problem, the current study utilized a deep learning model called the Residual connection Convolutional Long Short-Term Memory network (R-ConvLSTM) to estimate daily maximum 8-hr average (MDA8) O₃ over Jiangsu province, China during 2020. In this research, the R-ConvLSTM model not only provides the spatiotemporal information of MDA8 O₃, but also involves residual connection to avoid the problem of gradient explosion and gradient disappearance with the deepening of network layers. We utilized the TROPOMI total O₃ column retrieved from Sentinel-5 Precursor, ERA5 reanalysis meteorological data, and other supplementary data to build a pre-trained dataset. The R-ConvLSTM model achieved an overall sample-base cross-validation (CV) R² of 0.955 with root mean square error (RMSE) of 9.372 µg/m³. Model estimation also showed a city-based CV R² of 0.896 with RMSE of 14.029 µg/m³, the highest MDA8 O₃ in spring being 122.60 ± 31.60 µg/m³ and the lowest in winter being 69.93 ± 18.48 µg/m³.

Reversal of trends in global fine particulate matter air pollution

Ambient fine particulate matter (PM2.5) is the world's leading environmental health risk factor. Quantification is needed of regional contributions to changes in global PM2.5 exposure. Here we interpret satellite-derived PM2.5 estimates over 1998-2019 and find a reversal of previous growth in global PM2.5 air pollution, which is quantitatively attributed to contributions from 13 regions. Global population-weighted (PW) PM2.5 exposure, related to both pollution levels and population size, increased from 1998 (28.3 μg/m3) to a peak in 2011 (38.9 μg/m3) and decreased steadily afterwards (34.7 μg/m3 in 2019). Post-2011 change was related to exposure reduction in China and slowed exposure growth in other regions (especially South Asia, the Middle East and Africa). The post-2011 exposure reduction contributes to stagnation of growth in global PM2.5-attributable mortality and increasing health benefits per µg/m3 marginal reduction in exposure, implying increasing urgency and benefits of PM2.5 mitigation with aging population and cleaner air.

Is there an EKC between economic growth and air pollutant emissions in SAARC countries? Evidence from disaggregated analysis

The manufacturing and construction (M&C) sector not only plays a vital role in promoting economic growth, but is also a significant contributor to global air pollution. Growing concerns regarding air pollutant emissions necessitate a more disaggregated (i.e., sectoral) investigation in order to identify the major contributors. This study employs aggregated and disaggregated data to determine the fundamental effects of economic growth (i.e., overall growth and sectoral growth) on air pollutant emissions (APE) (specifically, PM2.5 and PM10 released by the M&C sector) in SAARC economies between 1995 and 2018. It assesses the environmental Kuznets curve (i.e., inverted U-shaped and N-shaped) using the feasible generalized least squares (FGLS), panel-corrected standard errors (PCSE), and generalized method of moments (GMM) techniques. The sectoral analysis reveals the presence of an N-shaped EKC while the overall analysis indicates an inverted U-shaped EKC. Population, financial development (FD), and merchandise exports (MX) have no influence on the estimates. Population and FD increase APE in all models, whereas the effects of MX vary between models. As SAARC economies are capital-deficient, these economies can adopt unbalanced environmental protection policies. First, focus on major contributing sectors (e.g., M&C sector) to curb APE, then focus on less emitting sectors in turn. By implementing pollution reduction strategies on M&C sector activities, governments may reach their threshold (peak) points earlier than expected. A reduction in APE is impossible without rigorous monitoring and application. Being capital-deficient nations and given the collective nature of the problem, a Transboundary Haze/Pollution agreement is required to solve this issue.

Interaction patterns between economic growth and atmospheric environment in China under the "carbon neutrality" target

Clarifying the interaction patterns between economic growth and atmospheric environment (EG-AE) in China is important to achieve the "carbon neutrality" target. A conceptual framework of air pollutant emission in urban economic growth (APEUEG) was proposed to explore the interaction patterns in China from 2007 to 2017. The empirical analysis revealed that a N-shaped EKC exists between aerosol optical depth (AOD) and gross domestic product (GDP), with inflection points of $5000 and $27,000, respectively. Therefore, we speculated that when GDP per capita of a city exceeded $5000, the AOD gradually decreased. However, when GDP per capita of a city gained over $27,000, the economic growth and the atmospheric environment would be coordinated steadily. The interaction of EG-AE experienced three stages-pollution, improvement, and coordination-in China. Spatially, the interaction patterns of EG-AE presented five clusters, which were associated with the spatial distribution of city levels. China's prefecture-level cities have undergone the cluster of low AOD-low GDP (LL), the cluster of high AOD-high GDP (HH), and the cluster of low AOD-high GDP (LH), as urban level improves. By 2017, about 44% of Chinese cities had not completed the coordinated development yet. We found that policymakers should formulate differentiated urban greener economic development policies to reduce APEUEG.

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.

Prediction of PM2.5 concentration based on the CEEMDAN-RLMD-BiLSTM-LEC model

Air quality has emerged as a critical concern in recent years, with the concentration of PM2.5 recognized as a vital index for assessing it. The accuracy of predicting PM2.5 concentrations holds significant value for effective air quality monitoring and management. In response to this, a combined model comprising CEEMDAN-RLMD-BiLSTM-LEC has been introduced, analyzed, and compared against various other models. The combined decomposition method effectively underlines the fundamental characteristics of the data compared to individual decomposition techniques. Additionally, local error correction (LEC) efficiently addresses the issue of prediction errors induced by excessive disturbances. The empirical results of nine steps indicate that the combined CEEMDAN-RLMD-BiLSTM-LEC model outperforms single prediction models such as RLMD and CEEMDAN, reducing MAE, RMSE, and SAMPE by 36.16%, 28.63%, 45.27% and 16.31%, 6.15%, 37.76%, respectively. Moreover, the inclusion of LEC in the model further diminishes MAE, RMSE, and SMAPE by 20.69%, 7.15%, and 44.65%, respectively, exhibiting commendable performance in generalization experiments. These findings demonstrate that the combined CEEMDAN-RLMD-BiLSTM-LEC model offers high predictive accuracy and robustness, effectively handling noisy data predictions and severe local variations. With its wide applicability, this model emerges as a potent tool for addressing various related challenges in the field.

Environmental quality assessment and spatial spillover effects of three urban agglomerations in China: A Meta-EBM approach

The new development form of urban agglomeration has greatly promoted economic and social progress in recent years, but it is also facing severe environmental pollution problems. Understanding the status quo of environmental efficiency in urban agglomerations and its leading driving forces is an important prerequisite for formulating energy conservation and emission reduction policies. This research uses the Meta Epsilon Based Measure (Meta-EBM) model to measure the environmental emission efficiency of the Beijing-Tianjin-Hebei（BTH）, Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations in China from 2014 to 2018 so as to improve on the inability of traditional Data Envelopment Analysis (DEA) to combine linear and non-linear characteristics, and employs Moran's I index and spatial econometric methods to analyze their spatial dependence and main driving factors. The results demonstrate that the overall environmental efficiency of the three major urban agglomerations in the five years from 2014 to 2018 presents a wave-like development and then tends to be flat. The itemized efficiency of economic outputs has maintained a relatively high level with the environmental output index exhibiting the best efficiency for industrial wastewater, followed by industrial sulfur dioxide (SO2). The scores of the two indicators for inhalable fine particle emissions (PM2.5) and industrial smoke and dust in each urban agglomeration are not ideal, and there are obvious differences between regions. Among them, YRD and PRD are relatively inferior. From the perspective of spatial spillover effects, various indicators show diverse characteristics at different development stages of the regions. Population and Normalized Difference Vegetation Index (NDVI) have a positive effect on environmental efficiency, while both Gross Domestic Product (GDP) per capita and transportation tend to show greater negative effects on regional environmental optimization. This study proposes countermeasures as follows. Each urban agglomeration should set up measures suitable to local conditions and give full play to their location advantages. They can also use space radiation to promote sector economic development and optimize urban environmental benefits.

Health risk assessment of indoor air quality, sociodemographic and kitchen characteristics on respiratory health among women responsible for cooking in urban settings of Oromia region, Ethiopia: a community-based cross-sectional study

OBJECTIVES

In Ethiopia, where biomass fuel is used by the majority of the population, women who are primarily responsible for cooking are at a higher risk of having respiratory symptoms. However, there is limited information on the respiratory symptoms of exposed women. This study assessed the magnitude of respiratory disease symptoms and associated factors among women responsible for cooking in Mattu and Bedele towns, south-west Ethiopia.

METHODS

A community-based cross-sectional study was conducted among 420 randomly selected women in urban settings in south-west Ethiopia. Data were collected through face-to-face interviews using a modified version of the American Thoracic Society Respiratory Questionnaire. The data were cleaned, coded and entered into EpiData V.3.1 and exported into SPSS V.22 for analysis. Bivariable and multivariable logistic regression analyses were used to identify factors associated with respiratory symptoms at a value of p<0.05.

RESULTS

It is found that 34.9% of the study participants have respiratory symptoms (95% CI 30.6% to 39.4%). Unimproved floor (adjusted OR (AOR)=2.4 at 95% CI 1.42 to 4.15), presence of thick black soot in the ceiling (AOR=2.1 at 95% CI 1.2 to 3.6), using fuel wood (AOR=2.3 at 95% CI 1.1 to 4.7), using a traditional stove (AOR=3.37 at 95% CI 1.85 to 6.16), long duration of cooking (AOR=2.52 at 95% CI 1.4 to 4.5) and cooking room without a window (AOR=2.4 at 95% CI 1.5 to 3.9) were significantly associated with women's respiratory symptoms.

CONCLUSION

More than two in six women who cook had respiratory symptoms. Floor, fuel and stove type, soot deposits in the ceiling, duration of cooking and cooking in a room without a window were the identified factors. Appropriate ventilation, improved floor and stove design and the switch to high-efficiency, low-emission fuels could help to lessen the effects of wood smoke on women's respiratory health.

Mapping of salty aeolian dust-source potential areas: Ensemble model or benchmark models?

Considering the effects of dust on human health, environment, agriculture, and transportation, it is necessary to investigate dust emissions susceptibility. This study aimed to study the capability of different machine learning models in analyzing land susceptibility to dust emissions. At first, the dust-source areas were identified by examining the frequency of occurrence (FOO) of dusty days using the aerosol optical depth (AOD) of the MODIS sensor from 2000 to 2020 and field surveys. Then, the weighted subspace random forest (WSRF) model in comparison with three benchmark models-general linear model (GLM), boosted regression tree (BRT), and support vector machine (SVM)-was employed to predict land susceptibility to dust emissions and also to determine the importance of dust-drivers. The results revealed that the WSRF outperformed benchmark models. In a nutshell, the values of accuracy, Kappa, and probability of detection for all models were more than 97 %, and also the false alarm rate was less than 1 % for all models. Spatial analysis indicated a greater frequency of dust events in the outskirts of Urmia Lake (mainly in the eastern and southern parts). Furthermore, according to the map of land susceptibility to dust emissions produced by the WSRF model, about 4.5 %, 2.8 %, 1.8 %, 0.8 %, and 0.2 % of the salt land, rangeland, agricultural, dry-farming, and barren lands, respectively, associated with high and very high degrees of dust emissions susceptibility. Therefore, this study provided in-depth insights into the applicability of the ensemble model, WSRF, to precisely map dust emissions susceptibility.

Spatiotemporal variations and mechanism of PM2.5 pollution in urban area: The case of Guiyang, Guizhou, China

PM_2.5 has been a hot concern in the recent decade. Many studies have focused on metropolises or those areas with poor air quality, but the PM_2.5 of more widespread areas is less considered. Considering the challenges of rapid economic growth and environmental problems against a developing region, we took Guiyang as a study case to assess the spatiotemporal variations and mechanism of PM2.5 pollution in an urban area from 2000 to 2020 in an extended sense. Based on PM_2.5 concentration data from 14 monitoring points in Guiyang, spatiotemporal variations and formation mechanism were assessed using wavelet, moving maximal information coefficients, and spatial correlation analysis. The urban Nighttime light data was selected to evaluate the impacts of socioeconomic factors on PM2.5 concentration using spatial correlation analysis. Further, wavelet and statistical analysis were adopted to analyze multi-dimensional temporal variations of PM_2.5 hourly concentration and the relationship with pressure, temperature, vapor pressure, relative humidity, wind, and visibility. The PM_2.5 hourly concentration was obtained from the monitoring points in downtown Guiyang according to data continuity and availability. PM_2.5 had different temporal variations at daily, monthly, seasonal, and annual levels, and interannual variation was the most obvious. The temperature was the main factor leading to the interannual temporal variation of PM_2.5. Wind and pressure were more significant for the responses of a shorter period variation with -0.76 and -0.80 of the minimum of correlation coefficient, respectively. Meanwhile, human activities significantly influenced spatiotemporal variations of PM_2.5. A spatial correlation analysis between PM2.5 and the related influencing factors from 2000 to 2018 was implemented based on a geographic information system. Besides, the landcovers within a buffer zone with a radius of 1 km on 14 monitoring points were visually interpreted to analyze the relationship between PM_2.5 and landcovers. Moreover, multivariate wavelet coherence analysis revealed the PM2.5 interaction among monitoring points. The PM_2.5 concentration in Guiyang dropped from 49 μg/m³ in 2012 to about 27 μg/m³ in 2018, and the air quality greatly improved. As in most cities, Guiyang has a significant PM_2.5 pollution island effect, with traffic and building land density contributing to higher PM_2.5 concentrations. There were some typical nonlinear spatiotemporal variations between PM_2.5 and its influencing factors, and these variations varied with the selected scale.

A hybrid model for estimating the number concentration of ultrafine particles based on machine learning algorithms in central Taiwan

Modeling is a cost-effective measure to estimate ultrafine particle (UFP) levels. Previous UFP estimates generally relied on land-use regression with insufficient temporal resolution. We carried out in-situ measurements for UFP in central Taiwan and developed a model incorporating satellite-based measurements, meteorological variables, and land-use data to estimate daily UFP levels at a 1-km resolution. Two sampling campaigns were conducted for measuring hourly UFP concentrations at six sites between 2008-2010 and 2017-2021, respectively, using scanning mobility particle sizers. Three machine learning algorithms, namely random forest, eXtreme gradient boosting (XGBoost), and deep neural network, were used to develop UFP estimation models. The performances were evaluated with a 10-fold cross-validation, temporal, and spatial validation. A total of 1,022 effective sampling days were conducted. The XGBoost model had the best performance with a training coefficient of determination (R²) of 0.99 [normalized root mean square error (nRMSE): 6.52%] and a cross-validation R² of 0.78 (nRMSE: 31.0%). The ten most important variables were surface pressure, distance to the nearest road, temperature, calendar year, day of the year, NO₂, meridional wind, the total length of roads, PM_2.5, and zonal wind. The UFP levels were elevated along the main roads across different seasons, suggesting that traffic emission is an important contributor to UFP. This hybrid model outperformed prior land use regression models and thus can provide more accurate estimates of UFP for epidemiological studies.

Combustion, Chemistry, and Carbon Neutrality

Combustion is a reactive oxidation process that releases energy bound in chemical compounds used as fuels─energy that is needed for power generation, transportation, heating, and industrial purposes. Because of greenhouse gas and local pollutant emissions associated with fossil fuels, combustion science and applications are challenged to abandon conventional pathways and to adapt toward the demand of future carbon neutrality. For the design of efficient, low-emission processes, understanding the details of the relevant chemical transformations is essential. Comprehensive knowledge gained from decades of fossil-fuel combustion research includes general principles for establishing and validating reaction mechanisms and process models, relying on both theory and experiments with a suite of analytic monitoring and sensing techniques. Such knowledge can be advantageously applied and extended to configure, analyze, and control new systems using different, nonfossil, potentially zero-carbon fuels. Understanding the impact of combustion and its links with chemistry needs some background. The introduction therefore combines information on exemplary cultural and technological achievements using combustion and on nature and effects of combustion emissions. Subsequently, the methodology of combustion chemistry research is described. A major part is devoted to fuels, followed by a discussion of selected combustion applications, illustrating the chemical information needed for the future.

Using deep ensemble forest for high-resolution mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran

High-resolution mapping of PM2.5 concentration over Tehran city is challenging because of the complicated behavior of numerous sources of pollution and the insufficient number of ground air quality monitoring stations. Alternatively, high-resolution satellite Aerosol Optical Depth (AOD) data can be employed for high-resolution mapping of PM2.5. For this purpose, different data-driven methods have been used in the literature. Recently, deep learning methods have demonstrated their ability to estimate PM2.5 from AOD data. However, these methods have several weaknesses in solving the problem of estimating PM2.5 from satellite AOD data. In this paper, the potential of the deep ensemble forest method for estimating the PM2.5 concentration from AOD data was evaluated. The results showed that the deep ensemble forest method with [Formula: see text] gives a higher accuracy of PM2.5 estimation than deep learning methods ([Formula: see text]) as well as classic data-driven methods such as random forest ([Formula: see text]). Additionally, the estimated values of PM2.5 using the deep ensemble forest algorithm were used along with ground data to generate a high-resolution map of PM2.5. Evaluation of produced PM2.5 map revealed the good performance of the deep ensemble forest for modeling the variation of PM2.5 in the city of Tehran.

Haze and inbound tourism: Empirical evidence from China

The impact of climate change on tourism has always been an important topic for research in the field of international tourism, and haze has been widely recognized as the primary negative factor affecting the development of inbound tourism in China. In this study, we first conduct a theoretical analysis of the mechanism through which haze influences the tourism industry, and then we empirically analyze the impact on China's inbound tourism using surface particulate matter (PM2.5) concentrations as a proxy for haze, based on provincial panel data from 1998 to 2016. The empirical results show that haze not only has an inhibitory effect on inbound tourism, but also significantly reduces the average length of stay of international tourists. In addition, while there are significant regional differences in the crowding-out effect of haze pollution on inbound tourism, the effect varies depending on the origin of inbound tourists, exhibiting the greatest negative impact on inbound tourism from Taiwan and the smallest from foreign countries. Our research highlights that haze pollution can led to the change of human tourism behavior which enrich the literature on tourism and haze.

Mortality Attributable to Ambient Air Pollution: A Review of Global Estimates

Since the publication of the first epidemiological study to establish the connection between long-term exposure to atmospheric pollution and effects on human health, major efforts have been dedicated to estimate the attributable mortality burden, especially in the context of the Global Burden of Disease (GBD). In this work, we review the estimates of excess mortality attributable to outdoor air pollution at the global scale, by comparing studies available in the literature. We find large differences between the estimates, which are related to the exposure response functions as well as the number of health outcomes included in the calculations, aspects where further improvements are necessary. Furthermore, we show that despite the considerable advancements in our understanding of health impacts of air pollution and the consequent improvement in the accuracy of the global estimates, their precision has not increased in the last decades. We offer recommendations for future measurements and research directions, which will help to improve our understanding and quantification of air pollution-health relationships.

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

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

Ambient Aerosol Is Physically Larger on Cloudy Days in Bondville, Illinois

Particle chemical composition affects aerosol optical and physical properties in ways important for the fate, transport, and impact of atmospheric particulate matter. For example, hygroscopic constituents take up water to increase the physical size of a particle, which can alter the extinction properties and atmospheric lifetime. At the collocated AERosol RObotic NETwork (AERONET) and Interagency Monitoring of PROtected Visual Environments (IMPROVE) network monitoring stations in rural Bondville, Illinois, we employ a novel cloudiness determination method to compare measured aerosol physicochemical properties on predominantly cloudy and clear sky days from 2010 to 2019. On cloudy days, aerosol optical depth (AOD) is significantly higher than on clear sky days in all seasons. Measured Ångström exponents are significantly smaller on cloudy days, indicating physically larger average particle size for the sampled populations in all seasons except winter. Mass concentrations of fine particulate matter that include estimates of aerosol liquid water (ALW) are higher on cloudy days in all seasons but winter. More ALW on cloudy days is consistent with larger particle sizes inferred from Ångström exponent measurements. Aerosol chemical composition that affects hygroscopicity plays a determining impact on cloudy versus clear sky differences in AOD, Ångström exponents, and ALW. This work highlights the need for simultaneous collocated, high-time-resolution measurements of both aerosol chemical and physical properties, in particular at cloudy times when quantitative understanding of tropospheric composition is most uncertain.

The impact of air pollution on venture capital: evidence from China

Utilizing a dataset of VC investment from 2005 to 2018, we investigate the relationship between air pollution and venture capital (VC) investment. We find that startups suffering severe air pollution receive less investment from VCs and experience a lower probability of financing from VCs. Our findings are robust to considering endogeneity concerns and various robustness checks. Moreover, air pollution is detrimental to startup innovation ability and entrepreneurial activities through which air pollution negatively affects VC investment. In response, VCs prefer to co-invest with other VCs to mitigate the adverse effect of air pollution. Our paper sheds new light on the impact of air pollution on the private financial markets.

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.

Commentary: Facial Aesthetic Dermatological Procedures and Photoprotection in Chinese Populations

The medical literature on aesthetic dermatology has primarily focused on a light-skinned patient population, yet patients of darker skin types have different needs and priorities. In Chinese individuals, key concerns include altered pigmentation, which is perceived to age the individual, and also relates to the Chinese cultural standard of beauty of fair skin; many seek aesthetic treatment for this. Non-invasive cosmetic procedures such as lasers and injections are also gaining in popularity in the Chinese market, but this population is prone to hyperpigmentation as an adverse effect of such procedures. Considered and tailored approaches, both to primary concerns of photoaging and the side effects of cosmetic treatments, are warranted.

Estimates of PM2.5 concentrations spatiotemporal evolution across China considering aerosol components in the context of the Reform and Opening-up

With astonishing and rapid development in China since the Reform and Opening-up in 1978, serious air pollution has become a great challenge. A better understanding of the response of PM_2.5 pollution to socioeconomic development after the Reform and Opening-up policy is benefit for pollution control. However, heterogeneous influences of biophysical and socioeconomic activities on PM_2.5 pollution pose great challenges in statistical simulation of PM_2.5. Few statistical model regards aerosol species as the explanatory variables for heterogeneous formation mechanism to retrieve PM_2.5 concentration. In this research, monthly PM_2.5 concentration in China during 1980-2020 was reconstructed by a novel statistical strategy considering aerosol components (AC-RF). Three cross-validation (CV) methods, sample-based CV, spatial-based CV and temporal-based CV results indicated satisfactory performance of AC-RF model with correlation coefficient (R) of 0.92, 0.90, 0.86, respectively. A three-stage concluded on PM_2.5 concentration annual variation in China was drawn as followed: Before 2000, PM_2.5 level in China represented smooth evolution and mainly influenced by natural events with polluted region locating in Xinjiang province, North China and Central China. Since 2000, PM_2.5 concentration increased to high level in the context of rapid socioeconomic development. Severe air pollution covered Jing-Jin-Ji agglomeration, Central China and Sichuan Basin. During 2012-2020, PM_2.5 declined and polluted region shrank, which was benefited by the strictest-ever air pollution control measures. Based on aerosol components analysis, sulfate aerosol exhibited the most significant increase trend in recent 40 years and black aerosol variation is the most closely related to PM_2.5 pollution. In conclusion, unsustainable development is the culprit for air quality deterioration. Strict and continuous air pollution control strategies are effective for air quality improvement.

Effects of air pollution on cardiovascular health in patients with type 2 diabetes mellitus: Evidence from a large tertiary hospital in Shandong Province, China

Air pollution has posed serious threats to human health. Based on the microdata of a large tertiary hospital in Shandong Province from 2016 to 2021, combined with the macro data such as air quality monitoring data, meteorological data, and city-level regional socio-economic data, this paper empirically tests the impact of air pollution instrumented by thermal inversions on the cardiovascular health of patients with type 2 diabetes mellitus (T2DM) and its group differences. The results show that: (1) Air pollution has a negative impact on the cardiovascular health of patients with T2DM, that is, the cardiovascular health of patients with T2DM will decline in regions with high air pollution; (2) The impact of air pollution on cardiovascular health in T2DM patients is heterogeneous, with males and older patients bearing greater air pollution health losses; (3) From the perspective of the external environment, the negative effects of environmental pollution on patients' health were significantly reduced in areas with higher environmental regulation intensity and better public health conditions, indicating the necessity of strengthening environmental governance and increasing public health expenditure.

A review on the application of the exposome paradigm to unveil the environmental determinants of age-related diseases

Age-related diseases account for almost half of all diseases among adults worldwide, and their incidence is substantially affected by the exposome, which is the sum of all exogenous and endogenous environmental exposures and the human body’s response to these exposures throughout the entire lifespan. Herein, we perform a comprehensive review of the epidemiological literature to determine the key elements of the exposome that affect the development of age-related diseases and the roles of aging hallmarks in this process. We find that most exposure assessments in previous aging studies have used a reductionist approach, whereby the effect of only a single environmental factor or a specific class of environmental factors on the development of age-related diseases has been examined. As such, there is a lack of a holistic and unbiased understanding of the effect of multiple environmental factors on the development of age-related diseases. To address this, we propose several research strategies based on an exposomic framework that could advance our understanding—in particular, from a mechanistic perspective—of how environmental factors affect the development of age-related diseases. We discuss the statistical methods and other methods that have been used in exposome-wide association studies, with a particular focus on multiomics technologies. We also address future challenges and opportunities in the realm of multidisciplinary approaches and genome–exposome epidemiology. Furthermore, we provide perspectives on precise public health services for vulnerable populations, public communications, the integration of risk exposure information, and the bench-to-bedside translation of research on age-related diseases.

Industrial agglomeration and haze pollution: Evidence from China

The rapid industrialization has contributed to the miracle of economic growth in China, while also caused a series of environmental problems. As one of the most concerning urban disease in China, the aggravation of haze pollution is closely related to the accelerating urbanization and industrialization process in recent years, and the strong spatial diffusion makes the negative externality of haze pollution more harmful. This characteristic of haze pollution is closely related to the spatial structure of industrial sectors, especially the agglomeration of industrial sectors. This paper established a spatial economic framework to investigate the spillover effect of industrial agglomeration on haze pollution based on satellite raster map data of PM_2.5 and location entropy index, and further discuss the question that whether industrial agglomeration can achieve a balanced development for both economic growth and environmental quality. The results indicated that there is an inverted U-shaped relationship between industrial agglomeration and haze pollution, and the indirect effect from spatial spillover dominated this impact. Industrial agglomeration has a single threshold effect on the knowledge spillover and the utilization of pollution disposal infrastructure of industrial sectors. Only when the agglomeration level reaches the threshold value, the scale effect can be realized to promote the air quality. The resource allocation efficiency and knowledge spillovers of industrial agglomeration need to be promoted in order to reach the inflection point and realize the win-win situation.

How Does Digitalization Affect Haze Pollution? The Mediating Role of Energy Consumption

In the context of digital technology innovation, an in-depth investigation into the impact of digitalization on haze pollution is of great significance for scientifically understanding environmental effects of digitalization and building a livable civic environment. From the perspective of energy consumption intensity and structure, this paper theoretically analyzes the direct and indirect effects of digitalization on haze pollution. On this basis, the impact of digitalization on haze pollution for 81 countries over the period 2010-2019 is empirically investigated by using the system GMM and mediating effects model. Empirical results show that digitalization can effectively suppress haze pollution, and there is significant heterogeneity in this inhibiting effect. In addition, digitalization can indirectly restrain haze pollution by reducing energy consumption intensity and optimizing energy consumption structure. The findings of this paper can provide enlightenment for countries to promote digitalization, combat haze pollution, and thus enhance the health of community residents.

Fine particulate matter induces METTL3-mediated m6A modification of BIRC5 mRNA in bladder cancer

Long-term exposure to fine particulate matter (PM_2.5) is reportedly related to a variety of cancers including bladder cancer. However, little is known about the biological mechanism underlying this association. In the present study, PM_2.5 exposure was significantly associated with increased levels of m⁶A modification in bladder cancer patients and bladder cells. METTL3 expression was aberrantly upregulated after PM_2.5 exposure, and METTL3 was involved in PM_2.5-induced m⁶A methylation. Higher METTL3 expression was observed in bladder cancer tissues and METTL3 knockdown dramatically inhibited bladder cancer cell proliferation, colony formation, migration and invasion, inducing apoptosis and disrupting the cell cycle. Mechanistically, PM_2.5 enhanced the expression of METTL3 by inducing the promoter hypomethylation of its promoter and increasing the binding affinity of the transcription factor HIF1A. BIRC5 was identified as the target of METTL3 through m⁶A sequencing (m⁶A-Seq) and KEGG analysis. The methylated BIRC5 transcript was subsequently recognized by IGF2BP3, which increased its mRNA stability. In particular, PM_2.5 exposure promoted the m⁶A modification of BIRC5 and its recognition by IGF2BP3. In addition, BIRC5 was involved in bladder cancer proliferation and metastasis, as well as VEGFA-regulated angiogenesis_. This comprehensive study revealed that PM_2.5 exposure exerts epigenetic regulatory effects on bladder cancer via the HIF1A/METTL3/IGF2BP3/BIRC5/VEGFA network.

Comparing spatial patterns of 11 common cancers in Mainland China

Background

A stronger spatial clustering of cancer burden indicates stronger environmental and human behavioral effects. However, which common cancers in China have stronger spatial clustering and knowledge gaps regarding the environmental and human behavioral effects have yet to be investigated. This study aimed to compare the spatial clustering degree and hotspot patterns of 11 common cancers in mainland China and discuss the potential environmental and behavioral risks underlying the patterns.

Methods

Cancer incidence data recorded at 339 registries in 2014 was obtained from the “China Cancer Registry Annual Report 2017”. We calculated the spatial clustering degree of the common cancers using the global Moran’s Index and identified the hotspot patterns using the hotspot analysis.

Results

We found that esophagus, stomach and liver cancer have a significantly higher spatial clustering degree (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.05$$\end{document}p<0.05) than others. When by sex, female esophagus, male stomach, male esophagus, male liver and female lung cancer had significantly higher spatial clustering degree (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.001$$\end{document}p<0.001). The spatial clustering degree of male liver was significantly higher than that of female liver cancer (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.001$$\end{document}p<0.001), whereas the spatial clustering degree of female lung was significantly higher than that of male lung cancer (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.001$$\end{document}p<0.001). The high-risk areas of esophagus and stomach cancer were mainly in North China, Huai River Basin, Yangtze River Delta and Shaanxi Province. The hotspots for liver and male liver cancer were mainly in Southeast China and south Hunan. Hotspots of female lung cancer were mainly located in the Pearl River Delta, Shandong, North and Northeast China. The Yangtze River Delta and the Pearl River Delta were high-risk areas for multiple cancers.

Conclusions

The top highly clustered cancer types in mainland China included esophagus, stomach and liver cancer and, by sex, female esophagus, male stomach, male esophagus, male liver and female lung cancer. Among them, knowledge of their spatial patterns and environmental and behavioral risk factors is generally limited. Potential factors such as unhealthy diets, water pollution and climate factors have been suggested, and further investigation and validation are urgently needed, particularly for male liver cancer. This study identified the knowledge gap in understanding the spatial pattern of cancer burdens in China and offered insights into targeted cancer monitoring and control.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13926-y.

Mortality burden due to long-term exposure to ambient PM2.5 above the new WHO air quality guideline based on 296 cities in China

OBJECTIVE

Quantifying the spatial and socioeconomic variation of mortality burden attributable to particulate matters with aerodynamic diameter ≤ 2.5 µm (PM2.5) has important implications for pollution control policy. This study aims to examine the regional and socioeconomic disparities in the mortality burden attributable to long-term exposure to ambient PM2.5 in China.

METHODS

Using data of 296 cities across China from 2015 to 2019, we estimated all-cause mortality (people aged ≥ 16 years) attributable to the long-term exposure to ambient PM2.5 above the new WHO air quality guideline (5 µg/m3). Attributed fraction (AF), attributed deaths (AD), attributed mortality rate (AMR) and total value of statistical life lost (VSL) by regional and socioeconomic levels were reported.

RESULTS

Over the period of 2015-2019, 17.0% [95% confidence interval (CI): 7.4-25.2] of all-cause mortality were attributable to long-term exposure to ambient PM2.5, corresponding to 1,425.2 thousand deaths (95% CI: 622.4-2,099.6), 103.5/105 (95% CI: 44.9-153.3) AMR, and 1006.9 billion USD (95% CI: 439.8-1483.4) total VSL per year. The AMR decreased from 120.5/105 (95% CI: 52.9-176.6) to 92.7/105 (95% CI:39.9-138.5) from 2015 to 2019. The highest mortality burden was observed in the north region (annual average AF = 24.2%, 95% CI: 10.8-35.1; annual average AMR = 137.0/105, 95% CI: 60.9-198.5). The highest AD and economic loss were observed in the east region (annual average AD = 390.0 thousand persons, 95% CI: 170.3-574.6; annual total VSL = 275.6 billion USD, 95% CI: 120.3-406.0). Highest AMR was in the cities with middle level of GDP per capita (PGDP)/urbanization. The majority of the top ten cities of AF, AMR and VSL were in high and middle PGDP/urbanization regions.

CONCLUSION

There were significant regional and socioeconomic disparities in PM2.5 attributed mortality burden among Chinese cities, suggesting differential mitigation policies are required for different regions in China.

Full-Coverage PM2.5 Mapping and Variation Assessment during the Three-Year Blue-Sky Action Plan Based on a Daily Adaptive Modeling Approach

Owing to a series of air pollution prevention and control policies, China’s PM2.5 pollution has greatly improved; however, the long-term spatial contiguous products that facilitate the analysis of the distribution and variation of PM2.5 pollution are insufficient. Due to the limitations of missing values in aerosol optical depth (AOD) products, the reconstruction of full-coverage PM2.5 concentration remains challenging. In this study, we present a two-stage daily adaptive modeling framework, based on machine learning, to solve this problem. We built the annual models in the first stage, then daily models were constructed in the second stage based on the output of the annual models, which incorporated the parameter and feature adaptive tuning strategy. Within this study, PM2.5 concentrations were adaptively modeled and reconstructed daily based on the multi-angle implementation of atmospheric correction (MAIAC) AOD products and other ancillary data, such as meteorological factors, population, and elevation. Our model validation showed excellent performance with an overall R2 = 0.91 and RMSE = 9.91 μg/m3 for the daily models, along with the site-based cross-validation R2s and RMSEs of 0.86–0.87 and 12–12.33 μg/m3; these results indicated the reliability and feasibility of the proposed approach. The daily full-coverage PM2.5 concentrations at 1 km resolution across China during the Three-Year Blue-Sky Action Plan were reconstructed in this study. We analyzed the distribution and variations of reconstructed PM2.5 at three different time scales. Overall, national PM2.5 pollution has significantly improved with the annual average concentration dropping from 33.67–28.03 μg/m3, which demonstrated that air pollution control policies are effective and beneficial. However, some areas still have severe PM2.5 pollution problems that cannot be ignored. In conclusion, the approach proposed in this study can accurately present daily full-coverage PM2.5 concentrations and the research outcomes could provide a reference for subsequent air pollution prevention and control decision-making.

A Social Vulnerability Index for Air Pollution and Its Spatially Varying Relationship to PM2.5 in Uganda

Fine particulate matter (PM2.5) is a ubiquitous air pollutant that is harmful to human health. Social vulnerability indices (SVIs) are calculated to determine where vulnerable populations are located. We developed an SVI for Uganda to identify areas with high vulnerability and exposure to air pollution. The 2014 national census was used to create the SVI. Mean PM2.5 at the subcounty level was estimated using global PM2.5 estimates. The mean PM2.5 for Kampala at the parish level was estimated using low-cost PM2.5 sensors and spatial interpolation. A local indicator of spatial association (LISA) was performed to determine significant spatial clusters of social vulnerability, and a bivariate analysis was performed to identify where significant associations were between SVI and annual PM2.5 mean concentrations. The LISA results showed significant clustering of high SVI in the northern and western regions of the country. The spatial bivariate analysis showed positive linear associations between SVI and PM2.5 concentration in subcounties in the northern, western, and central regions of Uganda, as well as in certain northern parishes in Kampala. Our approach identified areas facing both high social vulnerability and air pollution levels. These areas can be prioritized for health interventions and policy to reduce the impact of ambient PM2.5.

Ambient air pollution and prostate cancer risk in a population-based Canadian case-control study

Ambient air pollution is a human carcinogen and a possible risk factor for prostate cancer.

Urban Particulate Matter Hazard Mapping and Monitoring Site Selection in Nablus, Palestine

Few air pollution studies have been applied in the State of Palestine and all showed an increase in particulate matter concentrations above WHO guidelines. However, there is no clear methodology for selecting monitoring locations. In this study, a methodology based on GIS and locally calibrated low-cost sensors was tested. A GIS-based weighted overlay summation process for the potential sources of air pollution (factories, quarries, and traffic), taking into account the influence of altitude and climate, was used to obtain an air pollution hazard map for Nablus, Palestine. To test the methodology, eight locally calibrated PM sensors (AirUs) were deployed to measure PM2.5 concentrations for 55 days from 7 January to 2 March 2022. The results of the hazard map showed that 82% of Nablus is exposed to a high and medium risk of PM pollution. Sensors’ readings showed a good match between the hazard intensity and PM concentrations. It also shows an elevated PM2.5 concentrations above WHO guidelines in all areas. In summary, the overall average for PM2.5 in the Nablus was 48 µg/m3. This may indicate the effectiveness of mapping methodology and the use of low-cost, locally calibrated sensors in characterizing air quality status to identify the potential remediation options.

Analysis of PM2.5 Variations Based on Observed, Satellite-Derived, and Population-Weighted Concentrations

Fine particulate matter (PM2.5), which can cause adverse human health effects, has been proven as the first air pollutant in China. In situ observations with ground-level monitoring and satellite-based concentrations have been used to analyze the variations in PM2.5. However, variation analyses based on these two kinds of measurement have mainly focused on the concentration itself and ignored the effects on the population. Therefore, this study not only investigated these two kinds of measurements, but also performed weighted population analyses to study the variations in PM2.5. Firstly, daily models of timely structure adaptive modeling (TSAM) were constructed to simulate satellite-derived PM2.5 levels from January 2013 to December 2016. Secondly, population-weighted concentrations were calculated based on TSAM-derived PM2.5 surfaces. Finally, observed, TSAM-derived, and population-weighted concentrations were used to analyze the variations in PM2.5. The results showed the different importance of various input parameters; AOD had the highest rank. Additionally, TSAM models demonstrated good performance, fitting R ranging from 0.86 to 0.91, and validating R from 0.82 to 0.89. According to the air quality standard in China, TSAM-derived PM2.5 showed that the increase in area lower than Level II was 29.03% and the increase in population was only 14.81%. This indicates that the air quality exhibited an overall improvement in spatial perspective, but some areas with high population density showed a relatively low improvement due to uneven distributions in China. The population-weighted PM2.5 concentration could better represent the health threats of air pollutants compared with in situ observations.

Machine Learning-Based Approach Using Open Data to Estimate PM2.5 over Europe

Air pollution is currently considered one of the most serious problems facing humans. Fine particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) is a very harmful air pollutant that is linked with many diseases. In this study, we created a machine learning-based scheme to estimate PM2.5 using various open data such as satellite remote sensing, meteorological data, and land variables to increase the limited spatial coverage provided by ground-monitors. A space-time extremely randomised trees model was used to estimate PM2.5 concentrations over Europe, this model achieved good results with an out-of-sample cross-validated R2 of 0.69, RMSE of 5 μg/m3, and MAE of 3.3 μg/m3. The outcome of this study is a daily full coverage PM2.5 dataset with 1 km spatial resolution for the three-year period of 2018–2020. We found that air quality improved throughout the study period over all countries in Europe. In addition, we compared PM2.5 levels during the COVID-19 lockdown during the months March–June with the average of the previous 4 months and the following 4 months. We found that this lockdown had a positive effect on air quality in most parts of the study area except for the United Kingdom, Ireland, north of France, and south of Italy. This is the first study that depends only on open data and covers the whole of Europe with high spatial and temporal resolutions. The reconstructed dataset will be published under free and open license and can be used in future air quality studies.