Global urban temporal trends in fine particulate matter (PM2·5) and attributable health burdens: estimates from global datasets

Exploring the nexus of urban form, transport, environment and health in large-scale urban studies: A state-of-the-art scoping review

BACKGROUND

As the world becomes increasingly urbanised, there is recognition that public and planetary health relies upon a ubiquitous transition to sustainable cities. Disentanglement of the complex pathways of urban design, environmental exposures, and health, and the magnitude of these associations, remains a challenge. A state-of-the-art account of large-scale urban health studies is required to shape future research priorities and equity- and evidence-informed policies.

OBJECTIVES

The purpose of this review was to synthesise evidence from large-scale urban studies focused on the interaction between urban form, transport, environmental exposures, and health. This review sought to determine common methodologies applied, limitations, and future opportunities for improved research practice.

METHODS

Based on a literature search, 2958 articles were reviewed that covered three themes of: urban form; urban environmental health; and urban indicators. Studies were prioritised for inclusion that analysed at least 90 cities to ensure broad geographic representation and generalisability. Of the initially identified studies, following expert consultation and exclusion criteria, 66 were included.

RESULTS

The complexity of the urban ecosystem on health was evidenced from the context dependent effects of urban form variables on environmental exposures and health. Compact city designs were generally advantageous for reducing harmful environmental exposure and promoting health, with some exceptions. Methodological heterogeneity was indicative of key urban research challenges; notable limitations included exposure and health data at varied spatial scales and resolutions, limited availability of local-level sociodemographic data, and the lack of consensus on robust methodologies that encompass best research practice.

CONCLUSION

Future urban environmental health research for evidence-informed urban planning and policies requires a multi-faceted approach. Advances in geospatial and AI-driven techniques and urban indicators offer promising developments; however, there remains a wider call for increased data availability at local-levels, transparent and robust methodologies of large-scale urban studies, and greater exploration of urban health vulnerabilities and inequities.

Clean air policy reduces the atherogenic lipid profile levels: Results from China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) Study

Evidence of the associations between long-term exposure to PM2.5 and O3 and human blood lipid concentrations is abundant yet inconclusive. Whether clean air policies could improve lipid profiles remains unclear. In total, 2979312 participants from a Chinese nationwide prospective study were included. For cross-sectional analyses, linear mixed-effects models were utilized to assess the associations of pollutants with lipid profiles (TC, LDL-C, TG, HDL-C). For longitudinal analyses, a quasi-experimental design and difference-in-differences models were employed to investigate the impact of China's Clean Air Act. In the cross-sectional analyses, each IQR increase in PM2.5 was associated with 2.49 % (95 % CI: 2.36 %, 2.62 %), 2.51 % (95 % CI: 2.26 %, 2.75 %), 3.94 % (95 % CI: 3.65 %, 4.23 %), and 1.54 % (95 % CI: 1.38 %, 1.70 %) increases in TC, LDL-C, TG, and HDL-C, respectively. For each IQR increase in O3, TC, LDL-C, TG, and HDL-C changed by 1.06 % (95 % CI: 0.95 %, 1.17 %), 1.21 % (95 % CI: 1.01 %, 1.42 %), 1.78 % (95 % CI: 1.54 %, 2.02 %), and -0.63 % (95 % CI: -0.76 %, -0.49 %), respectively. Longitudinal analyses showed that the intervention group experienced greater TC, LDL-C, and HDL-C reductions (1.77 %, 4.26 %, and 7.70 %, respectively). Our findings suggest that clean air policies could improve lipid metabolism and should be implemented in countries with heavy air pollution burdens.

Estimation of plant pollution removal capacity based on intensive air quality measurements

This study investigated the role of present vegetation in improving air quality in Bucharest (Romania) by analyzing six years of air quality data (PM10 and NO2) from multiple monitoring stations. The target value for human health protection is regularly exceeded for PM10 and not for NO2 over time. Road traffic has substantially contributed (over 70%) to ambient PM10 and NO2 levels. The results showed high seasonal variations in pollutant concentrations, with a pronounced effect of vegetation in reducing PM10 and NO2 levels. Indeed, air quality improvements of 7% for PM10 and 25% for NO2 during the growing season were reported. By using Principal Component Analysis and pollution data subtraction methodology, we have disentangled the impact of vegetation on air pollution and observed distinct annual patterns, particularly higher differences in PM10 and NO2 concentrations during the warm season. Despite limitations such as a lack of full tree inventory for Bucharest and a limited number of monitoring stations, the study highlighted the efficiency of urban vegetation to mitigate air pollution.

Selective capture of PM2.5 by urban trees: The role of leaf wax composition and physiological traits in air quality enhancement

Human health risks from particles with a diameter of less than 2.5 µm (PM2.5) highlight the role of urban trees as bio-filters in air pollution control. However, whether the size and composition of particles captured by various tree species differ or not remain unclear. This study investigates how leaf attributes affect the capture of PM2.5, which can penetrate deep into the lungs and pose significant health risks. Using a self-developed particulate matter (PM) resuspension chamber and single-particle aerosol mass spectrometer, we measured the size distribution and mass spectra of particles captured by ten tree species. Notably, Cinnamomum camphora (L.) J.Presl and Osmanthus fragrans Lour. are more effective at capturing particles under 1 µm, which are most harmful because they can reach the alveoli, whereas Ginkgo biloba L. and Platanus × acerifolia (Aiton) Willd. tend to capture larger particles, up to 1.6 µm, which are prone to being trapped in the upper respiratory tract. Leaf physiological traits such as stomatal conductance and water potential significantly enhance the capture of larger particles. The Adaptive Resonance Theory neural network (ART-2a) algorithm classified a large number of single particles to determine their composition. Results indicate distinct inter-species variations in chemical composition of particles captured by leaves. Moreover, we identified how specific leaf wax compositions-beyond the known sticky nature of hydrophobic waxes-contribute to particle adhesion, particularly highlighting the roles of fatty acids and alkanes in adhering particles rich in organic carbon and heavy metals, respectively. This research advances our understanding by linking leaf physiological and wax characteristics to the selective capture of PM2.5, providing actionable insights for urban forestry management. The detailed exploration of particle size and composition, tied to specific tree species, enriches the current literature by quantifying how and why different species contribute variably to air quality improvement. This adds a crucial layer of specificity to the general knowledge that trees serve as bio-filters, offering a refined strategy for planting urban trees based on their particulate capture profiles.

Associations of long-term exposure to low-level PM2.5 and brain disorders in 260,922 middle-aged and older adults

Long-term exposure to high-level ambient PM2.5 was associated with increased risks of brain disorders, while the associations remain uncertain when the exposure is lower than current air quality standards in numerous countries. This study aimed to assess the effects of PM2.5 exposure on the brain system in the population with annual mean concentrations ≤15 μg/m3. We analyzed data from 260,922 participants without preexisting brain diseases at baseline in the UK Biobank. The geographical distribution of PM2.5 in 2010 was estimated by a land use regression model and linked with individual residential address. We investigated associations of ambient PM2.5 with incident neurological (dementia, Parkinson's diseases [PD], epilepsy, and migraine) and psychiatric (major depressive disorder [MDD] and anxiety disorder) diseases through Cox proportional hazard models. We further estimated the links with brain imaging phenotypes by neuroimaging analysis. Results showed that in the population with PM2.5 concentrations ≤15 μg/m3, each interquartile range (IQR, 1.28 μg/m3) increment in PM2.5 was related to incidence risks of dementia, epilepsy, migraine, MDD, and anxiety disorder with hazard ratios of 1.08 (95% confidence interval [CI]: 1.03, 1.13), 1.12 (1.05, 1.20), 1.07 (1.00, 1.13), 1.06 (1.03, 1.09), and 1.05 (1.02, 1.08), respectively. We did not observe a significant association with PD. The association with dementia was stronger among the population with poor cardiovascular health (measured by Life's Essential 8) than the counterpart (P for interaction = 0.037). Likewise, per IQR increase was associated with specific brain imaging phenotypes, including volumes of total brain (β = -0.036; 95% CI: -0.050, -0.022), white matter (-0.030; -0.046, -0.014), grey matter (-0.030; -0.042, -0.017), respectively. The findings suggest long-term exposure to ambient PM2.5 at low-level still has an adverse impact on the neuro-psychiatric systems. The brain-relevant epidemiological assessment suggests that each country should update the standard for ambient PM2.5 following the World Health Organization Air Quality Guidelines 2021.

The oxidative potential of fine ambient particulate matter in Xinxiang, North China: Pollution characteristics, source identification and regional transport

Atmospheric fine particulate matter (PM2.5) can trigger the production of cytotoxic reactive oxygen species (ROS), which can trigger or exacerbate oxidative stress and pulmonary inflammation. We collected 111 daily (∼24 h) ambient PM2.5 samples within an urban region of North China during four seasons of 2019-2020. PM2.5 samples were examined for carbonaceous components, water-soluble ions, and elements, together with their oxidative potential (represent ROS-producing ability) by DTT assay. The seasonal peak DTTv was recorded in winter (2.86 ± 1.26 nmol min-1 m-3), whereas the DTTm was the highest in summer (40.6 ± 8.7 pmol min-1 μg-1). WSOC displayed the highest correlation with DTT activity (r = 0.84, p < 0.0001), but the influence of WSOC on the elevation of DTTv was extremely negligible. Combustion source exhibited the most significant and robust correlation with the elevation of DTTv according to the linear mixed-effects model result. Source identification investigation using positive matrix factorization displayed that combustion source (36.2%), traffic source (30.7%), secondary aerosol (15.7%), and dust (14.1%) were driving the DTTv, which were similar to the results from the multiple linear regression (MLR) analysis. Backward trajectory analysis revealed that the major air masses originate from local and regional transportation, but PM2.5 OP was more susceptible to the influence of short-distance transport clusters. Discerning the influence of chemicals on health-pertinent attributes of PM2.5, such as OP, could facilitate a deep understanding of the cause-and-effect relationship between PM2.5 and impacts.

The association between airborne particulate matter (PM2.5) exposure level and primary open-angle glaucoma

The eye is vulnerable to the adverse effects of air pollution. Previous experimental study found that fine particulate matter (PM2.5) had a direct toxic effect on intraocular tissues. However, clinical evidence for the impact of air pollutants exposure on functional and structural changes in glaucoma remains scarce. A total of 120 patients with primary open-angle glaucoma (POAG) who met the inclusion criteria were included in this retrospective study. The standardized ophthalmic examination, such as intraocular pressure (IOP), visual field, optical coherence tomography, and comprehensive physical examination, were performed. The air pollution data, including PM2.5 concentration and air quality index (AQI), were collected. PM2.5 and AQI for the day of the medical examination, as well as one month, and three months before the medical examination date, were investigated. In our results, higher average exposure levels for one-month and three-month, were associated with increased IOP (r=0.229, P=0.013; r=0.204, P=0.028, respectively) and decreased visual field mean sensitivity (MS) (r=-0.212, P=0.037; r=-0.305, P=0.002, respectively). PM2.5 concentrations for the day of the medical examination was not significantly associated with ocular parameters. In multiple linear regression analysis adjusted for demographic and clinical factors, higher PM2.5 exposure for one month was associated with elevated IOP (P=0.040, β=0.173, 95 %CI=0.008-0.337). We also found an association between PM2.5 and MS (one-month exposure: β=-0.160, P=0.029; three-month exposure: β=-0.238, P=0.002). The logistic regression analysis found that three-month average PM2.5 exposure level was significantly associated with the disease severity (β=0.043, P=0.025, 95 %CI=1.005-1.084). In conclusion, this study is the first to investigate the relationship between air pollution and detailed ocular parameters of POAG patients in Shanghai over a three-year period, and to explore the effects of different exposure times of PM2.5 on glaucoma. This study found that PM2.5 exposure was correlated with elevated IOP and decreased MS. The one-month PM2.5 exposure level had the most significant effects on IOP. The three-month PM2.5 exposure level was an independent risk factor for POAG severity. Current evidence suggests there may be an association between PM2.5 exposure and POAG.

Regulation-driven changes in PM2.5 sources in China from 2013 to 2019, a critical review and trend analysis

Identifying changes in source-specific fine particles (PM2.5) over time is essential for evaluating the effectiveness of regulatory measures and informing future policy decisions. After the extreme haze events in China during 2013-14, more comprehensive and stringent policies were implemented to combat PM2.5 pollution. To determine the effectiveness of these policies, it is necessary to assess the changes in the specific source types to which the regulations pertain. Multiple studies have been conducted over the past decade to apportion PM2.5. The purpose of this study was to explore the available literature and conduct a critical review of the reliable results. In total, 5008 articles were screened, but only 48 studies were included for further analysis given our inclusion criteria including covering a monitoring period of ≥1 year and having enough speciation data to provide mass closure. Using these studies, we analyzed temporal and spatial trends across China from 2013 to 2019. We observed the overall decrease in the concentration contributions from all main source categories. The reductions from industry, coal and heavy oil combustion, and the related secondary sulfate were more notable, especially from 2013 to 2016-17. The contributions from biomass burning initially decreased but then increased slightly after 2016 in some locations despite new constraints on agricultural and household burning practices. Although the contributions from vehicle emissions and related secondary nitrate decreased, they gradually became the primary contributors to PM2.5 by ∼2017. Despite the substantial improvements achieved by the air pollution regulation implementations, further improvements in air quality will require additional aggressive actions, especially those targeting vehicular emissions. Ultimately, source apportionment studies based on extended duration, fixed-site sampling are recommended to provide a more thorough understanding of the sources impacting areas and transformations in PM2.5 sources prompted by regulatory actions.

Urban Scaling Functions: Emission, Pollution and Health

Urban scaling is widely used to characterize the population dependence of city indicators including greenhouse gas emission. Here we analyze the population dependence of CO2 and PM2.5 emissions and concentrations across all European cities. Our analysis reveals considerable variations in emissions among cities of comparable population size which are not captured by conventional urban scaling. We thus characterize these fluctuations by multi-parameter scaling functions and multifractal spectral analysis. We find that the distribution of emissions and population is multifractal while that of air pollution is not, leading to non-trivial relations between emission and pollution in some large cities. We also analyze the impact of forests in curbing emission and the impact of air pollution on health. Our work provides a detailed picture of the fluctuations in the scaling of urban metabolism in Europe and suggests a general strategy that goes beyond conventional urban scaling laws.

Transcriptome analysis of avian livers reveals different molecular changes to three urban pollutants: Soot, artificial light at night and noise

Identifying key molecular pathways and genes involved in the response to urban pollutants is an important step in furthering our understanding of the impact of urbanisation on wildlife. The expansion of urban habitats and the associated human-introduced environmental changes are considered a global threat to the health and persistence of humans and wildlife. The present study experimentally investigates how short-term exposure to three urban-related pollutants -soot, artificial light at night (ALAN) and traffic noise-affects transcriptome-wide gene expression in livers from captive female zebra finches (Taeniopygia guttata). Compared to unexposed controls, 17, 52, and 28 genes were differentially expressed in soot, ALAN and noise-exposed birds, respectively. In soot-exposed birds, the enriched gene ontology (GO) terms were associated with a suppressed immune system such as interferon regulating genes (IRGs) and responses to external stimuli. For ALAN-exposed birds, enriched GO terms were instead based on downregulated genes associated with detoxification, redox, hormonal-, and metabolic processes. Noise exposure resulted in downregulation of genes associated with the GO terms: cellular responses to substances, catabolic and cytokine responses. Among the individually differentially expressed genes (DEGs), soot led to an increased expression of genes related to tumour progression. Likewise, ALAN revealed an upregulation of multiple genes linked to different cancer types. Both sensory pollutants (ALAN and noise) led to increased expression of genes linked to neuronal function. Interestingly, noise caused upregulation of genes associated with serotonin regulation and function (SLC6A4 and HTR7), which previous studies have shown to be under selection in urban birds. These outcomes indicate that short-term exposure to the three urban pollutants perturbate the liver transcriptome, but most often in different ways, which highlights future studies of multiple-stress exposure and their interactive effects, along with their long-term impacts for urban-dwelling wildlife.

Ambient PM2.5 exposure and rapid population aging: A double threat to public health in the Republic of Korea

Particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5) can infiltrate deep into the respiratory system, posing significant health risks. Notably, the health burden of PM2.5 is more pronounced among the older adult population. With an aging population, the public health burden attributable to PM2.5 could escalate even if the current PM2.5 level remains stable. This study evaluated the number of deaths attributable to long-term PM2.5 exposure in the Republic of Korea between 2020 and 2050 and identified the PM2.5 concentration required at least to maintain the current PM2.5 health burden. To calculate mortality for 2020-2050, we performed a health impact assessment using 3-year (2019-2021) average population-weighted PM2.5 concentrations, age-specific population and mortality rates. In 2020, 33,578 [95% confidence interval (CI) = 31,708-35,448] deaths were attributable to PM2.5 exposure. Projecting forward, if the 2019-2021 average PM2.5 level remains constant, mortality is projected to be 112,953 (95% CI = 109,963-115,943) in 2050, more than three times higher than in 2020. To maintain the same level of health burden in 2050 as in 2020, the PM2.5 concentration needs to be immediately reduced to 5.8 μg/m3. In an age-specific analysis, the proportion of older adults (ages 65+) to total mortality would increase from 83% (2020) to 96% (2050), indicating that the rising mortality is predominantly driven by the aging population. By region, the reduction of PM2.5 concentrations, which is required immediately in 2020 to have the health burden in 2050 equal to that in 2020, varied from 3.6 μg/m3 in Goheung-gun (25% reduction) to 20.8 μg/m3 in Heungdeok-gu (82% reduction). Our study emphasizes the critical need for air quality management to consider aging populations when establishing PM2.5 air quality standards, as well as their associated policies and regulations.

Impact of urban tree arrangement on pedestrian exposure to the size-fractional particulate matter in a city boulevard

Trees act as natural filters that mitigate roadside air pollution. However, the filtration impact of different tree arrangements on traffic pollutants with different particle diameters has rarely been analysed in real street canyon environments. To quantify how roadside tree arrangements impact pedestrian exposure to particle number concentrations (PNCs) of different diameters (0.25-32 μm), in situ field measurements were carried out in a boulevard-type street canyon in the city of Xi'an, China. This study analysed the experimental data of PNCs collected along segments of a pedestrian lane under four typical tree arrangements: open space without trees, a sparse-spaced tree arrangement, a medium-spaced tree arrangement, and a dense-spaced tree arrangement in a street canyon. Our results reveal that the effect of tree arrangement on PNCs depended on the particle diameter. In general, trees can significantly reduce coarse PNC (particles with diameters > 2.5 μm) but not the fine PNC. Quantitative analysis showed that a medium-spaced tree arrangement, in which tree crowns are adjacent to each other but do not overlap, is the most capable of reducing PNC, followed by a sparse-spaced tree arrangement, while a the dense-spaced tree arrangement has the least impact. The attenuation effect of trees on the PNCs increased with increasing particle diameter. Moreover, the presence of trees altered the local microclimate, which also affected how exposure to PNCs changed. Our empirical findings further highlight the complexity of how trees affect particulate pollutants in street canyons and provide timely insights for enhancing tree-planning management in cities from the perspective of air quality improvement.

Long-term exposure to ambient fine particulate matter (PM2.5) and attributable pulmonary tuberculosis notifications in Ningxia Hui Autonomous Region, China: a health impact assessment

INTRODUCTION

Long-term exposure to fine particulate matter (≤2.5 µm (PM2.5)) has been associated with pulmonary tuberculosis (TB) notifications or incidence in recent publications. Studies quantifying the relative contribution of long-term PM2.5 on TB notifications have not been documented. We sought to perform a health impact assessment to estimate the PM2.5- attributable TB notifications during 2007-2017 in Ningxia Hui Autonomous Region (NHAR), China.

METHODS

PM2.5 attributable TB notifications were estimated at township level (n=358), stratified by age group and summed across NHAR. PM2.5-associated TB-notifications were estimated for total and anthropogenic PM2.5 mass and expressed as population attributable fractions (PAFs). The main analysis used effect and uncertainty estimates from our previous study in NHAR, defining a counterfactual of the lowest annual PM2.5 (30 µg/m3) level, above which we assumed excess TB notifications. Sensitivity analyses included counterfactuals based on the 5th (31 µg/m3) and 25th percentiles (38 µg/m3), and substituting effect estimates from a recent meta-analysis. We estimated the influence of PM2.5 concentrations, population growth and baseline TB-notification rates on PM2.5 attributable TB notifications.

RESULTS

Over 2007-2017, annual PM2.5 had an estimated average PAF of 31.2% (95% CI 22.4% to 38.7%) of TB notifications while the anthropogenic PAF was 12.2% (95% CI 9.2% to 14.5%). With 31 and 38 µg/m3 as counterfactuals, the PAFs were 29.2% (95% CI 20.9% to 36.3%) and 15.4% (95% CI 10.9% to 19.6%), respectively. PAF estimates under other assumptions ranged between 6.5% (95% CI 2.9% to 9.6%) and 13.7% (95% CI 6.2% to 19.9%) for total PM2.5, and 2.6% (95% CI 1.2% to 3.8%) to 5.8% (95% CI 2.7% to 8.2%) for anthropogenic PM2.5. Relative to 2007, overall changes in PM2.5 attributable TB notifications were due to reduced TB-notification rates (-23.8%), followed by decreasing PM2.5 (-6.2%), and population growth (+4.9%).

CONCLUSION

We have demonstrated how the potential impact of historical or hypothetical air pollution reduction scenarios on TB notifications can be estimated, using public domain, PM2.5 and population data. The method may be transferrable to other settings where comparable TB-notification data are available.

Air Pollution in Cardio-Oncology and Unraveling the Environmental Nexus: JACC: CardioOncology State-of-the-Art Review

Although recent advancements in cancer therapies have extended the lifespan of patients with cancer, they have also introduced new challenges, including chronic health issues such as cardiovascular disease arising from pre-existing risk factors or cancer therapies. Consequently, cardiovascular disease has become a leading cause of non-cancer-related death among cancer patients, driving the rapid evolution of the cardio-oncology field. Environmental factors, particularly air pollution, significantly contribute to deaths associated with cardiovascular disease and specific cancers, such as lung cancer. Despite these statistics, the health impact of air pollution in the context of cardio-oncology has been largely overlooked in patient care and research. Notably, the impact of air pollution varies widely across geographic areas and among individuals, leading to diverse exposure consequences. This review aims to consolidate epidemiologic and preclinical evidence linking air pollution to cardio-oncology while also exploring associated health disparities and environmental justice issues.

Comparative in vitro toxicological effects of water-soluble and insoluble components of atmospheric PM2.5 on human lung cells

Fine particulates in city air significantly impact human health, but the hazardous compositional mechanisms are still unclear. Besides the toxicity of environmental PM2.5 to in vitro human lung epithelial cells (A549), the independent cytotoxicity of PM2.5-bound water-soluble (WS-PM2.5) and water-insoluble (WIS-PM2.5) fractions were also compared by cell viability, oxidative stress (reactive oxygen species, ROS), and inflammatory injury (IL-6 and TNF-α). The cytotoxicity of PM2.5 varied significantly by sampling season and place, with degrees greater in winter and spring than in summer and autumn, related to corresponding trend of air PM2.5 level, and also higher in industrial than urban site, although their PM2.5 pollution levels were comparable. The PM2.5 bound metals (Ni, Cr, Fe, and Mn) may contribute to cellular injury. Both WS-PM2.5 and WIS-PM2.5 posed significant cytotoxicity, that WS-PM2.5 was more harmful than WIS-PM2.5 in terms of decreasing cell viability and increasing inflammatory cytokines production. In particular, industrial samples were usually more toxic than urban samples, and those from summer were generally less toxic than other seasons. Hence, in order to mitigate the health risks of PM2.5 pollution, the crucial targets might be components of heavy metals and soluble fractions, and sources in industrial areas, especially during the cold seasons.

New insight into air pollution-related cardiovascular disease: an adverse outcome pathway framework of PM2.5-associated vascular calcification

Despite the air quality has been generally improved in recent years, ambient fine particulate matter (PM2.5), a major contributor to air pollution, remains one of the major threats to public health. Vascular calcification is a systematic pathology associated with an increased risk of cardiovascular disease. Although the epidemiological evidence has uncovered the association between PM2.5 exposure and vascular calcification, little is known about the underlying mechanisms. The adverse outcome pathway (AOP) concept offers a comprehensive interpretation of all of the findings obtained by toxicological and epidemiological studies. In this review, reactive oxygen species generation was identified as the molecular initiating event (MIE), which targeted subsequent key events (KEs) such as oxidative stress, inflammation, endoplasmic reticulum stress, and autophagy, from the cellular to the tissue/organ level. These KEs eventually led to the adverse outcome, namely increased incidence of vascular calcification and atherosclerosis morbidity. To the best of our knowledge, this is the first AOP framework devoted to PM2.5-associated vascular calcification, which benefits future investigations by identifying current limitations and latent biomarkers.

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

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

Characteristics and source apportionment of water-soluble organic nitrogen (WSON) in PM2.5 in Hong Kong: With focus on amines, urea, and nitroaromatic compounds

Water-soluble organic nitrogen (WSON) is ubiquitous in fine particulate matter (PM2.5) and poses health and environmental risks. However, there is limited knowledge regarding its comprehensive speciation and source-specific contributions. Here, we conducted chemical characterization and source apportionment of WSON in 65 PM2.5 samples collected in Hong Kong during a 1-yr period. Using various mass-spectrometry-based techniques, we quantified 22 nitrogen-containing organic compounds (NOCs), including 17 nitroaromatics (NACs), four amines, and urea. The most abundant amine and NACs were dimethylamine and 4-nitrocatechol, respectively. Two secondary (i.e., secondary formation and secondary nitrate) and five primary sources (i.e., sea salt, fugitive dust, marine vessels, vehicle exhaust, and biomass burning) of WSON and these three categories of NOCs were identified. Throughout the year, secondary sources dominated WSON formation (69.0%), while primary emissions had significant contributions to NACs (77.1%), amines (75.9%), and urea (83.7%). Fugitive dust was the leading source of amines and urea, while biomass burning was the main source of NACs. Our multi-linear regression analysis revealed the significant role of sulfate, NO3, nitrate, liquid water content, and particle pH on WSON formation, highlighting the importance of nighttime NO3 processing and heterogeneous and aqueous-phase formation of NOCs in the Hong Kong atmosphere.

A spatial approach to assessing PM2.5 exposure level of a brickmaking community in South Africa

Globally, particulate matter with an aerodynamic diameter of 2.5 µm or less poses a significant threat to human health. The first step in quantifying human health impacts caused by exposure to PM2.5 pollution is exposure assessment. Population-weighted exposure level (PWEL) estimation is one of the methods that provides a more precise exposure assessment since it incorporates the spatiotemporal distribution of population with the pollution concentration estimate. In this study, PM2.5 exposure levels in the local communities around brickmaking industries were investigated, using the population census data of the study area and 1-year data from nine PM2.5 monitoring stations installed in and around the brickmaking industries. The observed PM2.5 data was spatially interpolated using inverse distance weight (IDW). Data on PM2.5 levels across the study area were classified based on the World Health Organization interim target (IT) guidelines and the South African National ambient air quality standard (NAAQS). An annual PM2.5 population weighted exposure level of 27.6 µg/m3 was estimated for the study area. However, seasonal exposure levels of 28.9, 37.6, 26.5, and 20.7 µg/m3 were estimated for the autumn, winter, spring, and summer seasons, respectively. This implies that local communities around the brick kiln in the Vhembe District are exposed to high levels of PM2.5, especially in winter. The PM2.5 levels in the brickmaking industries as well as its other sources in the Vhembe District, therefore, need to be lowered. Findings from population exposure level to pollutants can provide valuable data for formulating policies and recommendations on exposure reduction and public health protection.Implications: PM2.5 concentration in any given environment has high spatial and temporal variability due to the presence of diffused sources in the environment. Using ambient air concentrations to directly estimate population exposure without taking into consideration the disproportionate spatial and temporal distribution of the pollutant and the population may not yield accurate results on human exposure levels. It is, therefore, important to assess the aggregated PM2.5 exposure of a populace within a given area. This study therefore examines the PM2.5 population-weighted-exposure level of the host communities of the brickmaking industry in Vhembe District, South Africa.

An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere

Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

Diesel exhaust particles exposure exacerbates pro-thrombogenic plasma features ex-vivo after cerebral ischemia and accelerates tPA-induced clot-lysis in hypertensive subjects

The combustion of fossil fuels, mainly by diesel engines, generates Diesel Exhaust Particles (DEP) which are the main source of Particulate Matter (PM), a major air pollutant in urban areas. These particles are a risk factor for stroke with 5.6% of cases attributed to PM exposure. Our aim was to evaluate the effect of DEP exposure on clot formation and lysis in the context of stroke. An ex-vivo clot formation and lysis turbidimetric assay has been conducted in human and mouse plasma samples from ischemic stroke or control subjects exposed to DEP or control conditions. Experimental DEP exposure was achieved by nasal instillation in mice, or by ex-vivo exposure in human plasma. Results show consistent pro-thrombogenic features in plasma after human ischemic stroke and mouse cerebral ischemia (distal MCAo), boosted by the presence of DEP. Otherwise, thrombolysis times were increased after ischemia in chronically exposed mice but not in the DEP exposed group. Finally, subjects living in areas with high PM levels presented accelerated thrombolysis compared to those living in low polluted areas. Overall, our results point at a disbalance of the thrombogenic/lytic system in presence of DEP which could impact on ischemic stroke onset, clot size and thrombolytic treatment.

Relationships between long-term exposure to major PM2.5 constituents and outpatient visits and hospitalizations in Guangdong, China

Ambient fine particulate matter (PM2.5) has attracted considerable attention due to its crucial role in the rising global disease burden. Evidence of health risks associated with exposure to PM2.5 and its major constituents is important for advancing hazard assessments and air pollution emission policies. We investigated the relationship between exposure to major constituents of PM2.5 and outpatient visits as well as hospitalizations in Guangdong Province, China, where 127 million residents live in a severe PM2.5 pollution environment. An approach that integrates the generalized weighted quantile sum (gWQS) regression with the difference-in-differences (DID) approach was used to assess the overall mixture effects and relative contributions of each constituent. We observed significant associations between long-term exposure to the mixture of PM2.5 constituents (WQS index) and outpatient visits (IR%, percentage increases in risk per unit WQS index increase:1.73, 95%CI: 1.72, 1.74) as well as hospitalizations (IR%:5.15, 95%CI: 5.11, 5.20). Black carbon (weight: 0.34) and nitrate (weight: 0.60) respectively exhibited the highest contributions to outpatient visits and hospitalizations. The overall mixture effects on outpatient visits and hospitalizations were higher with increased summer air temperatures (IR%: 7.54, 95%CI: 7.33, 7.74 and IR%: 9.55, 95%CI: 8.36, 10.75, respectively) or decreased winter air temperatures (IR%: 1.88, 95%CI: 1.68, 2.08 and IR%: 4.87, 95%CI: 3.73, 6.02, respectively). Furthermore, the overall mixture effects on outpatient visits and hospitalizations were significantly higher in populations with higher socioeconomic status (P < 0.01). It's crucial to address the primary sources of nitrate precursor substances and black carbon (mainly traffic-related and industrial-related air pollutants) and consider the complex interaction effects between air temperature and PM2.5 in the context of climate change. Of particular concern is the need to prioritize healthcare demands in economically disadvantaged regions and to address the health inequalities stemming from the uneven distribution of healthcare resources and PM2.5 pollution.

Ambient fine particulate matter and daily mortality: a comparative analysis of observed and estimated exposure in 347 cities

BACKGROUND

Model-estimated air pollution exposure products have been widely used in epidemiological studies to assess the health risks of particulate matter with diameters of ≤2.5 µm (PM2.5). However, few studies have assessed the disparities in health effects between model-estimated and station-observed PM2.5 exposures.

METHODS

We collected daily all-cause, respiratory and cardiovascular mortality data in 347 cities across 15 countries and regions worldwide based on the Multi-City Multi-Country collaborative research network. The station-observed PM2.5 data were obtained from official monitoring stations. The model-estimated global PM2.5 product was developed using a machine-learning approach. The associations between daily exposure to PM2.5 and mortality were evaluated using a two-stage analytical approach.

RESULTS

We included 15.8 million all-cause, 1.5 million respiratory and 4.5 million cardiovascular deaths from 2000 to 2018. Short-term exposure to PM2.5 was associated with a relative risk increase (RRI) of mortality from both station-observed and model-estimated exposures. Every 10-μg/m3 increase in the 2-day moving average PM2.5 was associated with overall RRIs of 0.67% (95% CI: 0.49 to 0.85), 0.68% (95% CI: -0.03 to 1.39) and 0.45% (95% CI: 0.08 to 0.82) for all-cause, respiratory, and cardiovascular mortality based on station-observed PM2.5 and RRIs of 0.87% (95% CI: 0.68 to 1.06), 0.81% (95% CI: 0.08 to 1.55) and 0.71% (95% CI: 0.32 to 1.09) based on model-estimated exposure, respectively.

CONCLUSIONS

Mortality risks associated with daily PM2.5 exposure were consistent for both station-observed and model-estimated exposures, suggesting the reliability and potential applicability of the global PM2.5 product in epidemiological studies.

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.

Effect of PM2.5 exposure on adhesion molecules and systemic nitric oxide in healthy adults: The role of metals, PAHs, and oxidative potential

Since there is limited evidence on the impact of PM2.5 content on cardiovascular biomarkers, we conducted a cross-sectional study on 89 healthy adults from October 12 to November 21, 2021. We measured daily PM2.5 in two distinct regions during different time windows: a high-traffic urban area and an industrial suburb. The concentrations of metals, PAHs, and oxidative potential (OP) were determined using ICP-MS, GC-MS, and dithiothreitol (DTT), respectively. Systemic biomarkers, including NO, sICAM-1, sVCAM-1, MDA, and CRP, were quantified in each subject simultaneously. A generalized linear model was used to examine the association between PM2.5 toxicity and each health endpoint. Our findings indicated that daily PM2.5 concentrations exceeded the WHO-recommended level by approximately sevenfold. We found that PM2.5 exposure was associated with adverse cardiovascular outcomes. Moreover, exposure to PM2.5 mass, total PAHs, and certain trace metals (Ni, Fe, V, As, and Pb) resulted in a decline in serum NO levels. At lag 3, exposure to PM2.5 mass resulted in a significant decrease in NO levels [1.32% (95% CI: -2.27, -0.12)] and total PAHs [2.05% (95% CI: -3.93, -0.12)]. In contrast, OP exhibited a mild correlation with NO level increases. Positive associations were observed between PM2.5 and its chemical constituents (PAHs, As, Cu, OP) and adhesion molecules at different lag times. An increase of 0.16 ppb in PAH concentrations at an interquartile range was associated with a 4.74% decline (95% CI, -7.80, -0.55) in the sVCAM-1 level. However, our study did not reveal any significant trend between pollutants and other biomarkers (sICAM-1, MDA, and CRP). Consequently, our findings suggest that different PM2.5 chemical compositions exhibit diverse behavior in biological responses.

Toward Better and Healthier Air Quality: Global PM2.5 and O3 Pollution Status and Risk Assessment Based on the New WHO Air Quality Guidelines for 2021

To reduce the high burden of disease caused by air pollution, the World Health Organization (WHO) released new Air Quality Guidelines (AQG) on September 22, 2021. In this study, the daily fine particulate matter (PM2.5) and surface ozone (O3) data of 618 cities around the world is collected from 2019 to 2022. Based on the new AQG, the number of attainment days for daily average concentrations of PM2.5 (≤ 15 µg m-3) and O3 (≤ 100 µg m-3) is approximately 10% and 90%, respectively. China and India exhibit a decreasing trend in the number of highly polluted days (> 75 µg m-3) for PM. Every year over 68% and 27% of cities in the world are exposed to harmful PM2.5 (> 35 µg m-3) and O3 (> 100 µg m-3) pollution, respectively. Combined with the United Nations Sustainable Development Goals (SDGs), it is found that more than 35% of the world's cities face PM2.5-O3 compound pollution. Furthermore, the exposure risks in these cities (China, India, etc.) are mainly categorized as "High Risk", "Risk", and "Stabilization". In contrast, economically developed cities are mainly categorized as "High Safety", "Safety", and "Deep Stabilization." These findings indicate that global implementation of the WHO's new AQG will minimize the inequitable exposure risk from air pollution.

Accumulation of Ambient Black Carbon Particles Within Key Memory-Related Brain Regions

Importance

Ambient air pollution is a worldwide problem, not only related to respiratory and cardiovascular diseases but also to neurodegenerative disorders. Different pathways on how air pollutants could affect the brain are already known, but direct evidence of the presence of ambient particles (or nanoparticles) in the human adult brain is limited.

Objective

To examine whether ambient black carbon particles can translocate to the brain and observe their biodistribution within the different brain regions.

Design, Setting, and Participants

In this case series a label-free and biocompatible detection technique of nonincandescence-related white light generation was used to screen different regions of biobanked brains of 4 individuals from Belgium with neuropathologically confirmed Alzheimer disease for the presence of black carbon particles. The selected biological specimens were acquired and subsequently stored in a biorepository between April 2013 and April 2017. Black carbon measurements and data analysis were conducted between June 2020 and December 2022.

Main Outcomes and Measures

The black carbon load was measured in various human brain regions. A Kruskal-Wallis test was used to compare black carbon loads across these regions, followed by Dunn multiple comparison tests.

Results

Black carbon particles were directly visualized in the human brain of 4 individuals (3 women [75%]; mean [SD] age, 86 [13] years). Screening of the postmortem brain regions showed a significantly higher median (IQR) number of black carbon particles present in the thalamus (433.6 [289.5-540.2] particles per mm3), the prefrontal cortex including the olfactory bulb (420.8 [306.6-486.8] particles per mm3), and the hippocampus (364.7 [342.0-448.7] particles per mm3) compared with the cingulate cortex (192.3 [164.2-277.5] particles per mm3), amygdala (217.5 [147.3-244.5] particles per mm3), and the superior temporal gyrus (204.9 [167.9-236.8] particles per mm3).

Conclusions and Relevance

This case series provides evidence that ambient air pollution particles are able to translocate to the human brain and accumulate in multiple brain regions involved in cognitive functioning. This phenomenon may contribute to the onset and development of neurodegenerative disorders.

Single Droplet Tweezer Revealing the Reaction Mechanism of Mn(II)-Catalyzed SO2 Oxidation

Sulfate aerosol is one of the major components of secondary fine particulate matter in urban haze that has crucial impacts on the social economy and public health. Among the atmospheric sulfate sources, Mn(II)-catalyzed SO2 oxidation on aerosol surfaces has been regarded as a dominating one. In this work, we measured the reaction kinetics of Mn(II)-catalyzed SO2 oxidation in single droplets using an aerosol optical tweezer. We show that the SO2 oxidation occurs at the Mn(II)-active sites on the aerosol surface, per a piecewise kinetic formulation, one that is characterized by a threshold surface Mn(II) concentration and gaseous SO2 concentration. When the surface Mn(II) concentration is lower than the threshold value, the reaction rate is first order with respect to both Mn(II) and SO2, agreeing with our traditional knowledge. But when surface Mn(II) concentration is above the threshold, the reaction rate becomes independent of Mn(II) concentration, and the reaction order with respect to SO2 becomes greater than unity. The measured reaction rate can serve as a tool to estimate sulfate formation based on field observation, and our established parametrization corrects these calculations. This framework for reaction kinetics and parametrization holds promising potential for generalization to various heterogeneous reaction pathways.

Population exposure to multiple air pollutants and its compound episodes in Europe

Air pollution remains as a substantial health problem, particularly regarding the combined health risks arising from simultaneous exposure to multiple air pollutants. However, understanding these combined exposure events over long periods has been hindered by sparse and temporally inconsistent monitoring data. Here we analyze daily ambient PM2.5, PM10, NO2 and O3 concentrations at a 0.1-degree resolution during 2003-2019 across 1426 contiguous regions in 35 European countries, representing 543 million people. We find that PM10 levels decline by 2.72% annually, followed by NO2 (2.45%) and PM2.5 (1.72%). In contrast, O3 increase by 0.58% in southern Europe, leading to a surge in unclean air days. Despite air quality advances, 86.3% of Europeans experience at least one compound event day per year, especially for PM2.5-NO2 and PM2.5-O3. We highlight the improvements in air quality control but emphasize the need for targeted measures addressing specific pollutants and their compound events, particularly amidst rising temperatures.

Evaluation of neural reflex activation as a potential mode of action for respiratory and cardiovascular effects of fine particulate matter

OBJECTIVES

Mortality from respiratory and cardiovascular health conditions contributes largely to the total mortality that has been associated with exposure to PM2.5 in epidemiology studies. A mode of action (MoA) for these underlying morbidities has not been established, but it has been proposed that some effects of PM2.5 occur through activation of neural reflexes.

MATERIALS AND METHODS

We critically reviewed the experimental studies of PM2.5 (including ambient PM2.5, diesel exhaust particles, concentrated ambient particles, diesel exhaust, and cigarette smoke) and neural reflex activation, and applied the principles of the International Programme on Chemical Safety (IPCS) MoA/human relevance framework to assess whether they support a biologically plausible and human-relevant MoA by which PM2.5 could contribute to cardiovascular and respiratory causes of death. We also considered whether the evidence from these studies supports a non-threshold MoA that operates at low, human-relevant PM2.5 exposure concentrations.

RESULTS AND DISCUSSION

We found that the proposed MoA of neural reflex activation is biologically plausible for PM2.5-induced respiratory effects at high exposure levels used in experimental studies, but further studies are needed to fill important data gaps regarding the relevance of this MoA to humans at lower PM2.5 exposure levels. A role for the proposed MoA in PM2.5-induced cardiovascular effects is plausible for some effects but not others.

CONCLUSIONS

Further studies are needed to determine whether neural reflex activation is the MoA by which PM2.5 could cause either respiratory or cardiovascular morbidities in humans, particularly at the ambient concentrations associated with total mortality in epidemiology studies.

Spatiotemporally continuous PM2.5 dataset in the Mekong River Basin from 2015 to 2022 using a stacking model

With the potential to cause millions of deaths, PM2.5 pollution has become a global concern. In Southeast Asia, the Mekong River Basin (MRB) is experiencing heavy PM2.5 pollution and the existing PM2.5 studies in the MRB are limited in terms of accuracy and spatiotemporal coverage. To achieve high-accuracy and long-term PM2.5 monitoring of the MRB, fused aerosol optical depth (AOD) data and multi-source auxiliary data are fed into a stacking model to estimate PM2.5 concentrations. The proposed stacking model takes advantage of convolutional neural network (CNN) and Light Gradient Boosting Machine (LightGBM) models and can well represent the spatiotemporal heterogeneity of the PM2.5-AOD relationship. In the cross-validation (CV), comparison with CNN and LightGBM models shows that the stacking model can better suppress overfitting, with a higher coefficient of determination (R2) of 0.92, a lower root mean square error (RMSE) of 5.58 μg/m3, and a lower mean absolute error (MAE) of 3.44 μg/m3. For the first time, the high-accuracy PM2.5 dataset reveals spatially and temporally continuous PM2.5 pollution and variations in the MRB from 2015 to 2022. Moreover, the spatiotemporal variations of annual and monthly PM2.5 pollution are also investigated at the regional and national scales. The dataset will contribute to the analysis of the causes of PM2.5 pollution and the development of mitigation policies in the MRB.

Estimates of global mortality burden associated with short-term exposure to fine particulate matter (PM2·5)

BACKGROUND

The acute health effects of short-term (hours to days) exposure to fine particulate matter (PM2·5) have been well documented; however, the global mortality burden attributable to this exposure has not been estimated. We aimed to estimate the global, regional, and urban mortality burden associated with short-term exposure to PM2·5 and the spatiotemporal variations in this burden from 2000 to 2019.

METHODS

We combined estimated global daily PM2·5 concentrations, annual population counts, country-level mortality rates, and epidemiologically derived exposure-response functions to estimate the mortality attributable to short-term PM2·5 exposure from 2000 to 2019, in the continental regions and in 13 189 urban centres worldwide at a spatial resolution of 0·1° × 0·1°. We tested the robustness of our mortality estimates with different theoretical minimum risk exposure levels, lag effects, and exposure-response functions.

FINDINGS

Approximately 1 million (95% CI 690 000-1·3 million) premature deaths per year from 2000 to 2019 were attributable to short-term PM2·5 exposure, representing 2·08% (1·41-2·75) of total global deaths or 17 (11-22) premature deaths per 100 000 population. Annually, 0·23 million (0·15 million-0·30 million) deaths attributable to short-term PM2·5 exposure were in urban areas, constituting 22·74% of the total global deaths attributable to this cause and accounting for 2·30% (1·56-3·05) of total global deaths in urban areas. The sensitivity analyses showed that our worldwide estimates of mortality attributed to short-term PM2·5 exposure were robust.

INTERPRETATION

Short-term exposure to PM2·5 contributes a substantial global mortality burden, particularly in Asia and Africa, as well as in global urban areas. Our results highlight the importance of mitigation strategies to reduce short-term exposure to air pollution and its adverse effects on human health.

FUNDING

Australian Research Council and the Australian National Health and Medical Research Council.

Life-Course Health Risk Assessment of PM2.5 Elements in China: Exposure Disparities by Species, Source, Age, Gender, and Location

Key stages in people's lives have particular relevance for their health; the life-course approach stresses the importance of these stages. Here, we applied a life-course approach to analyze the health risks associated with PM2.5-bound elements, which were measured at three sites with varying environmental conditions in eastern China. Road traffic was found to be the primary source of PM2.5-bound elements at all three locations, but coal combustion was identified as the most important factor to induce both cancer risk (CR) and noncancer risk (NCR) across all age groups due to the higher toxicity of elements such as As and Pb associated with coal. Nearly half of NCR and over 90% of CR occurred in childhood (1-6 years) and adulthood (>18 years), respectively, and females have slightly higher NCR and lower CR than males. Rural population is found to be subject to the highest health risks. Synthesizing previous relevant studies and nationwide PM2.5 concentration measurements, we reveal ubiquitous and large urban-rural environmental exposure disparities over China.

Maternal urban particulate matter (SRM 1648a) exposure disrupted the cellular immune homeostasis during early life: The potential attribution of altered placental transcriptome profile

Ambient fine particular matter (PM2.5) exposure has been associated with numerous adverse effects including triggering functional disorders of the placenta and inducing immune imbalance in offspring. However, how maternal PM2.5 exposure impacts immune development during early life is not fully understood. In the current study, we exposed mice with low-, middle-, and high-dose PM2.5 during pregnancy to investigate the potential link between the transcriptional changes in the placenta and immune imbalance in mice offspring induced by PM2.5 exposures. Using flow cytometry, we found that the proportions of B cells, CD3+CD4+ T cells, CD3+CD8+ T cells, and macrophage (Mφ) cells were altered in the blood of PM2.5-exposed mice pups but not dendritic cells (DCs) and natural killer cells (NKs). Using bulk RNA sequencing, we found that PM2.5 exposure altered the transcriptional profile which indicated an inhibition of the complement and coagulation cascades in the placenta. Weighted gene co-expression network analysis (WGCNA) revealed the potential crosstalk between the perturbation of placental gene expression and the changes of immune cell subsets in pups on postnatal day 10 (PND10). Specifically, WGCNA identified a cluster of genes including Defb15, Defb20, Defb25, Cst8, Cst12, and Adam7 that might regulate the core immune cell types in PND10 pups. Although the underlying mechanisms of how maternal PM2.5 exposure induces peripheral lymphocyte disturbance in offspring still remain much unknown, our findings here shed light on the potential role of placental dysfunction in these adverse effects.

Particulate matter-induced metabolic recoding of epigenetics in macrophages drives pathogenesis of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a group of illnesses associated with unresolved inflammation in response to toxic environmental stimuli. Persistent exposure to PM is a major risk factor for COPD, but the underlying mechanism remains unclear. Using our established mouse model of PM-induced COPD, we find that repeated PM exposure provokes macrophage-centered chronic inflammation and COPD development. Mechanistically, chronic PM exposure induces transcriptional downregulation of HAAO, KMO, KYNU, and QPRT in macrophages, which are the enzymes of de novo NAD+ synthesis pathway (kynurenine pathway; KP), via elevated chromatin binding of the CCCTC-binding factor (CTCF) near the transcriptional regulatory regions of the enzymes. Subsequent reduction of NAD+ and SIRT1 function increases histone acetylation, resulting in elevated expression of pro-inflammatory genes in PM-exposed macrophages. Activation of SIRT1 by nutraceutical resveratrol mitigated PM-induced chronic inflammation and COPD development. In agreement, increased levels of histone acetylation and decreased expression of KP enzymes were observed in pulmonary macrophages of COPD patients. We newly provide an evidence that dysregulated NAD+ metabolism and consecutive SIRT1 deficiency significantly contribute to the pathological activation of macrophages during PM-mediated COPD pathogenesis. Additionally, targeting PM-induced intertwined metabolic and epigenetic reprogramming in macrophages is an effective strategy for COPD treatment.

Functional differences of cultivable leaf-associated microorganisms in the native Andean tree Gevuina avellana Mol. (Proteaceae) exposed to atmospheric contamination

AIMS

This study aimed to evaluate and describe the functional differences of cultivable bacteria and fungi inhabiting the leaves of Gevuina avellana Mol. (Proteaceae) in an urban area with high levels of air pollution and in a native forest in the southern Andes.

METHODS AND RESULTS

Phyllosphere microorganisms were isolated from the leaves of G. avellana, their plant growth-promoting capabilities were estimated along with their biocontrol potential and tolerance to metal(loid)s. Notably, plants from the urban area showed contrasting culturable leaf-associated microorganisms compared to those from the native area. The tolerance to metal(loid)s in bacteria range from 15 to 450 mg l-1 of metal(loid)s, while fungal strains showed tolerance from 15 to 625 mg l-1, being especially higher in the isolates from the urban area. Notably, the bacterial strain Curtobacterium flaccumfaciens and the fungal strain Cladosporium sp. exhibited several plant-growth-promoting properties along with the ability to inhibit the growth of phytopathogenic fungi.

CONCLUSIONS

Overall, our study provides evidence that culturable taxa in G. avellana leaves is directly influenced by the sampling area. This change is likely due to the presence of atmospheric pollutants and diverse microbial symbionts that can be horizontally acquired from the environment.

Spatio-temporal correlation between admissions for ruptured abdominal aortic aneurysms and levels of atmospheric pollution in Italy

AIMS

The aim of our study was to determine a correlation between rates and number of patients admitted with ruptured abdominal aortic aneurysms (rAAA) in Italian regions with different levels of atmospheric pollution.

METHODS

We analyzed a possible correlation between the number and rate (ruptured versus not ruptured) of patients with rAAA admitted in eight Italian regions with different levels of atmospheric pollution.

RESULTS

Number and rates of patients with rAAA were statistically correlated with levels of air pollution and low air temperature (RR = 1.90, 95% CI: 1.42, 2.1.0) (p<0.01). Even if low temperatures amplified the correlation between admissions for rAAA and PMs exposure, also during Summer and Spring there were sudden increases of the number of admissions for rAAA patients in periods with higher air pollution. The regions with high levels of atmospheric pollution had higher rates of admissions of patients with rAAA in comparison with regions with low level of air pollution. However, there was no difference between regions with low and very low level of atmospheric pollution. Mean age, sex distribution, exposure to established risk factors were similar for the population of the eight analyzed Italian regions.

CONCLUSIONS

The findings of this study highlight the potential to reduce AAA related mortality and burden by addressing the negative effects of exposure to high levels of atmospheric pollution. The possibility of a dose-dependent effect of atmospheric pollution on the cardiovascular system opens research initiatives and discussions about when and how to modulate interventions to reduce atmospheric pollutants.

Internal exposure potential of water-soluble organic molecules in urban PM2.5 evaluated by non-covalent adductome of human serum albumin

Water-soluble organic molecules (WSOMs) in inhaled PM2.5 can readily translocate from the lungs into the blood circulation, facilitating their distribution to and health effects on distant organs and tissues in the human body. Human serum albumin (HSA), the most abundant protein carrier in the blood, readily binds exogenous substances to form non-covalent adducts and subsequently transports them throughout the circulatory system, thereby indicating their internal exposure. The direct internal exposure of WSOMs in PM2.5 needs to be understood. In this study, the non-covalent HSA-WSOM adductome was developed as a dosimeter to evaluate the internal exposure potential of WSOMs in urban PM2.5. The WSOM composition was acquired from non-target high-resolution mass spectrometry analysis coupled with multiple ionizations. The binding level of HSA-WSOM non-covalent adducts was obtained from surface plasma resonance. Machine learning combined WSOM composition and the binding level of HSA-WSOM non-covalent adducts to screen bindable (also internalizable) WSOMs. The concentration of WSOM ranged from 4 to 13 μg/m3 during our observation period. Of the 17,513 mass spectral features detected, 9,484 contributed to the non-covalent adductome and possessed the internal exposure potential. 102 major contributors accounted for 90.6 % of the HSA-WSOM binding level. The fraction of internalizable WSOMs in PM2.5 varied from 11.9 % to 61.3 %, averaging 26.2 %. WSOMs that have internal exposure potential were primarily lignin-like and lipid-like substances. The HSA-WSOMs non-covalent adductome represents direct internal exposure potential, which can provide crucial insights into the molecular diagnosis of PM2.5 exposure and precise assessments of PM2.5 health effects.

Dynamic Traffic Data in Machine-Learning Air Quality Mapping Improves Environmental Justice Assessment

Air pollution poses a critical public health threat around many megacities but in an uneven manner. Conventional models are limited to depict the highly spatial- and time-varying patterns of ambient pollutant exposures at the community scale for megacities. Here, we developed a machine-learning approach that leverages the dynamic traffic profiles to continuously estimate community-level year-long air pollutant concentrations in Los Angeles, U.S. We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (NO2), maximum daily 8-h average ozone (MDA8 O3), and fine particulate matter (PM2.5) simulations by 47%, 4%, and 15%, respectively. We successfully captured PM2.5 levels exceeding limits due to heavy traffic activities and providing an "out-of-limit map" tool to identify exposure disparities within highly polluted communities. In contrast, the model without real-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents' exposure to PM2.5. The underestimations are more severe for disadvantaged communities such as black and low-income groups, showing the significance of incorporating real-time traffic data in exposure disparity assessment.

Trends in global ambient fine particulate matter pollution and diabetes mortality rates attributable to it in the 1990-2019: 30 years systematic analysis of global burden of disease

AIM

To analyze the trends in ambient fine particulate matter pollution (PM2.5) and the age-standardized mortality rate (ASMR) of diabetes attributable to it from 1990 to 2019 by region, country, and socio-economic development status.

METHODS

The main data, including the summary exposure value (SEV) of ambient PM2.5 and the ASMR of diabetes due to ambient PM2.5, was collected from the Global Burden of Disease 2019 database. The socio-demographic index (SDI) was employed for assessing a particular region or country's degree of socio-economic development. Joinpoint regression analysis was used to assess the changes of ambient PM2.5 and ASMR of diabetes attributable to it.

RESULTS

Globally, the SEV of ambient PM2.5 increased from 15.65 μg/m3 in 1990 to 26.22 μg/m3 in 2019, with an annual average percent change (AAPC) of 1.788 (95 % CI 1.687-1.889) μg/m3. The ASMR of diabetes attributable to ambient PM2.5 increased from 1.57 per 100,000 population in 1990 to 2.47 per 100.000 population in 2019 (AAPC = 1.569 [95 % CI 1.42-1.718]). Most regions and countries had an increase of SEV of ambient PM2.5 and ASMR of diabetes attributable to ambient PM2.5. The largest increase of SEV of ambient PM2.5 was observed in South Asia (AAPC = 3.556 [95 % CI 3.329-3.875]), while the largest increase of ASMR of diabetes was in Central Asia (AAPC = 5.170 [95%CI 4.696-5.647]). Moreover, the increase of SEV of ambient PM2.5 and ASMR of diabetes attributable to it were positively associated with SDI in low SDI countries (SDI < 0.46), whereas the opposite result was observed when SDI ≥ 0.46.

CONCLUSION

From 1990 to 2019, the population's exposure to ambient PM2.5 and ASMR of diabetes attributable to it increased generally, especially in low-middle SDI regions. Ambient PM2.5 remains a threat to global health. Greater investment in ambient PM2.5 and the mortality attributable to it are needed.

Differential response to neoadjuvant endocrine therapy for Black/African American and White women in NCDB

PURPOSE

Compared to White women, there are higher mortality rates in Black/African American (BAA) women with hormone receptor-positive breast cancer (HR + BC) which may be partially due to differences in treatment resistance. We assessed factors associated with response to neoadjuvant endocrine therapy (NET).

METHODS

The National Cancer Database (NCDB) was queried for women with clinical stage I-III HR + BC diagnosed 2006-2017 and treated with NET. Univariate and multivariate analyses described associations between the sample, duration of NET, and subsequent treatment response, defined by changes between clinical and pathological staging.

RESULTS

The analytic sample included 9864 White and 1090 BAA women. Compared to White women, BAA women were younger, had more co-morbidities, were higher stage at presentation, and more likely to have > 24 weeks of NET. After excluding those with unknown pT/N/M, 3521 White and 365 BAA women were evaluated for NET response. On multivariate analyses, controlling for age, stage, histology, HR positivity, and duration of NET, BAA women were more likely to downstage to pT0/Tis (OR 3.0, CI 1.2-7.1) and upstage to Stage IV (OR 2.4, CI 1.002-5.6). None of the women downstaged to pT0/Tis presented with clinical stage III disease; only 2 of the women upstaged to Stage IV disease presented with clinical Stage I disease.

CONCLUSION

Independent of NET duration and clinical stage at presentation, BAA women were more likely to experience both complete tumor response and progression to metastatic disease. These results suggest significant heterogeneity in tumor biology and warrant a more nuanced therapeutic approach to HR + BC.

Environmental exposure disparities in ultrafine particles and PM2.5 by urbanicity and socio-demographics in New York state, 2013-2020

BACKGROUND

The spatiotemporal and demographic disparities in exposure to ultrafine particles (UFP; number concentrations of particulate matter (PM) with diameter ≤0.1 μm), a key subcomponent of fine aerosols (PM2.5; mass concentrations of PM ≤ 2.5 μm), have not been well studied.

OBJECTIVE

To quantify and compare the aerosol pollutant exposure disparities for UFP and PM2.5 by socio-demographic factors in New York State (NYS).

METHODS

Ambient atmospheric UFP and PM2.5 were quantified using a global three-dimensional model of chemical transport with state-of-the-science aerosol microphysical processes validated extensively with observations. We matched these to U.S. census demographic data for varied spatial scales (state, county, county subdivision) and derived population-weighted aerosol exposure estimates. Aerosol exposure disparities for each demographic and socioeconomic (SES) indicator, with a focus on race-ethnicity and income, were quantified for the period 2013-2020.

RESULTS

The average NYS resident was exposed to 4451 #·cm-3 UFP and 7.87 μg·m-3 PM2.5 in 2013-2020, but minority race-ethnicity groups were invariably exposed to greater daily aerosol pollution (UFP: +75.0% & PM2.5: +16.2%). UFP has increased since 2017 and is temporally and seasonally out-of-phase with PM2.5. Race-ethnicity exposure disparities for PM2.5 have declined over time; by -6% from 2013 to 2017 and plateaued thereafter despite its decreasing concentrations. In contrast, these disparities have increased (+12.5-13.5%) for UFP. The aerosol pollution exposure disparities were the highest for low-income minorities and were more amplified for UFP than PM2.5. DISCUSSION: We identified large disparities in aerosol pollution exposure by urbanization level and socio-demographics in NYS residents. Jurisdictions with higher proportions of race-ethnicity minorities, low-income residents, and greater urbanization were disproportionately exposed to higher concentrations of UFP and PM2.5 than other NYS residents. These race-ethnicity exposure disparities were much larger, more disproportionate, and unabating over time for UFP compared to PM2.5 across various income strata and levels of urbanicity.

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.

Data Gap: Air Quality Networks Miss Air Pollution from Concentrated Animal Feeding Operations

In the U.S., the agricultural sector is the largest controllable source of several air pollutants, including ammonia (NH3), which is a key precursor to PM2.5 formation. Livestock waste is the dominant contributor to ammonia emissions. In contrast to most controllable air pollutants, satellite records show ammonia mixing ratios are rising. The number of confined animal feeding operations (CAFOs) that generate considerable livestock waste is also increasing. Spatial and temporal trends in USDA-reported animal numbers normalized by county area at medium and large CAFOs provide plausible explanations for patterns in satellite-derived NH3 over the contiguous U.S. (CONUS). The correlation between summertime ammonia derived from the European Space Agency's (ESA) Infrared Atmospheric Sounding Interferometer (IASI) and CAFO animal unit density in 2017 is positive and significant (r = 0.642; p ≈ 0). The temporal changes from 2002 to 2017 in animal unit density and NH3 derived from NASA's Atmospheric Infrared Sounder (AIRS) are spatially similar. Trends and ambient concentrations of PM2.5 mass in agricultural regions are difficult to assess relative to those of urban population centers given the sparseness of rural monitors in regulatory surface networks. Results suggest that in agricultural areas where ammonia concentrations and animal density are highest, air quality improvement lags behind the national average.

Assessment of health impacts and costs attributable to air pollution in urban areas using two different approaches. A case study in the Western Balkans

In this study, two different air quality impact assessment methodologies were adopted and combined with a sensitivity analysis to estimate the unit costs. Air pollution health impact (mortality) assessment was carried out using one methodology based on log-linear concentration response functions (CRF) and another relying on the integrated exposure response curve (IER) from the Global Burden of Disease. Morbidity impacts were estimated with the CRF approach only. To assess the inequalities between low and high income countries, an area of low-medium income countries with a critical air pollution situation, was selected. The health impact and related external costs attributable to air pollution in 2019 were assessed in 30 urban areas of the Western Balkans region, one of Europe's air pollution hot spots. The evaluation was based on PM2.5, O3 and NO2 concentrations in background sites from official monitoring networks. In 2019, the cost of mortality attributable to PM2.5 in 26 urban areas was 7.8 and 9.0 billion Euro according to IER and CRF methodologies, respectively. The cost of O3 associated with all-cause mortality estimated with the CRF methodology in 17 urban areas was 1.0 billion Euro while the one attributable to NO2 pollution in 28 urban areas was 1.5 billion Euro. The study results suggest that the economic burden of air pollution in the Western Balkans is higher in terms of GDP than the one observed in EU27 in the same time window. The study concludes that CRF and IER methodologies are coherent, because the discrepancy in the results are explained by the differences in the assessed health outcomes. The two approaches are complementary because the combination of them makes it possible to obtain a wider range of outcomes. In addition, despite the different causes of death considered, the comparison between them is useful for cross-validation.

Redox and inflammatory mechanisms linking air pollution particulate matter with cardiometabolic derangements

Air pollution is the largest environmental risk factor for disease and premature death. Among the different components that are present in polluted air, fine particulate matter below 2.5 μm in diameter (PM2.5) has been identified as the main hazardous constituent. PM2.5 mainly arises from fossil fuel combustion during power generation, industrial processes, and transportation. Exposure to PM2.5 correlates with enhanced mortality risk from cardiovascular diseases (CVD), such as myocardial infarction and stroke. Over the last decade, it has been increasingly suggested that PM2.5 affects CVD already at the stage of risk factor development. Among the multiple biological mechanisms that have been described, the interplay between oxidative stress and inflammation has been consistently highlighted as one of the main drivers of pulmonary, systemic, and cardiovascular effects of PM2.5 exposure. In this context, PM2.5 uptake by tissue-resident immune cells in the lung promotes oxidative and inflammatory mediators release that alter tissue homeostasis at remote locations. This pathway is central for PM2.5 pathogenesis and might account for the accelerated development of risk factors for CVD, including obesity and diabetes. However, transmission and end-organ mechanisms that explain PM2.5-induced impaired function in metabolic active organs are not completely understood. In this review, the main features of PM2.5 physicochemical characteristics related to PM2.5 ability to induce oxidative stress and inflammation will be presented. Hallmark and recent epidemiological and interventional studies will be summarized and discussed in the context of current air quality guidelines and legislation, knowledge gaps, and inequities. Lastly, mechanistic studies at the intersection between redox metabolism, inflammation, and function will be discussed, with focus on heart and adipose tissue alterations. By offering an integrated analysis of PM2.5-induced effects on cardiometabolic derangements, this review aims to contribute to a better understanding of the pathogenesis and potential interventions of air pollution-related CVD.

Measuring the urban-rural and spatiotemporal heterogeneity of the drivers of PM2.5-attributed health burdens in China from 2008 to 2021 using high-resolution dataset

Urbanization has been considered a driver of PM2.5 pollution and the attributed health burden. This study systematically measured the spatiotemporal and urban-rural heterogeneity of PM2.5-attributed health burden drivers, including income, population, baseline mortality rate, and PM2.5 level. The results reveal the significantly positive contribution of disposable income and the periodical and urban-rural differentiation of population contribution to PM2.5-attributed health burden. The difference in driver performance due to socioeconomic development and urbanization stages might be an important determinant for different or even opposite results of previous studies. Policymaking for mitigating PM2.5-attributed health risk could incorporate the re-assessment and driver determination for PM2.5-attributed health burden into the construction and development plan from the overall urbanization perspective. The urbanization-perspective driver decomposition could be synergized with the flow analysis, equality evaluation, and policy benefit estimation to achieve further direction-determining and quantitative assessment of the urban-rural PM2.5 health risk management strategies.

Modeling spatial-temporal variability of PM2.5 concentrations in Belt and Road Initiative (BRI) region via functional adaptive density approach

The rapid development of the Belt and Road Initiative (BRI) has led to severe air pollution dominated by PM2.5 concentrations which can cause a profound negative impact on human health and economic activity. This problem poses a critical environmental challenge to efficiently handling large-scale spatial-temporal PM2.5 data in this extended region. Functional data analysis (FDA) technique offers powerful tools that have the potential to enhance the analysis of spatial distributions and temporal dynamic changes in high-dimensional pollution data. However, modeling the spatial-temporal variability of PM2.5 concentrations by FDA remains unrevealed in the BRI region. To address this research gap, our study aimed to achieve two main objectives: first, to model the spatial-temporal dynamic variability of PM2.5 in 125 BRI nations (1998-2021), and second, to identify the underlying clusters behind the variations. We employed the recently developed functional adaptive density peak (FADP) clustering approach to solve the current problem. The proposed method is based on the joint use of functional principal components (FPCs) and functional cluster analyses. The main results are as follows: (i) The first three FPCs almost captured 99% of the total variations involving all valuable information on PM2.5 concentrations. (ii) PM2.5 pollution was highly concentrated in the developing countries (Pakistan, Bangladesh, and Nigeria) and the developed countries (Arabian Gulf countries: Qatar, United Arab Emirates, Bahrain, Saudi Arabia, Oman), and the least developed countries (Yemen and Chad). (iii) Three optimal clusters were identified and thus classified the PM2.5 into three distinct degrees of pollution: severe, moderate, and light. (iv) Cluster 1 had a severe pollution effect degree with a high rate of change, and it covered the Arabian Peninsula countries, African countries (Cameroon, Egypt, Gambia, Mali, Mauritania, Nigeria, Sudan, Senegal, Chad), Bangladesh, and Pakistan. (v) About 62 BRI countries belonged to cluster 2 showing a light pollution degree with annul average of less than 20 [Formula: see text]; this pointed out that the PM2.5 concentration remains stable in the cluster 2-related countries. The findings of this research would benefit governments and policymakers in preventing and controlling PM2.5 pollution exposure in BRI. Furthermore, this research could pay attention to sustainable development goals and the vision of the Green BRI policy.

Forecasting PM 2.5 concentration based on integrating of CEEMDAN decomposition method with SVM and LSTM

With urbanization and increasing consumption, there is a growing need to prioritize sustainable development across various industries. Particularly, sustainable development is hindered by air pollution, which poses a threat to both living organisms and the environment. The emission of combustion gases containing particulate matter (PM 2.5) during human and social activities is a major cause of air pollution. To mitigate health risks, it is crucial to have accurate and reliable methods for forecasting PM 2.5 levels. In this study, we propose a novel approach that combines support vector machine (SVM) and long short-term memory (LSTM) with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to forecast PM 2.5 concentrations. The methodology involves extracting Intrinsic mode function (IMF) components through CEEMDAN and subsequently applying different regression models (SVM and LSTM) to forecast each component. The Naive Evolution algorithm is employed to determine the optimal parameters for combining CEEMDAN, SVM, and LSTM. Daily PM 2.5 concentrations in Kaohsiung, Taiwan from 2019 to 2021 were collected to train models and evaluate their performance. The performance of the proposed model is evaluated using metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R2) for each district. Overall, our proposed model demonstrates superior performance in terms of MAE (1.858), MSE (7.2449), RMSE (2.6682), and (0.9169) values compared to other methods for 1-day ahead PM 2.5 forecasting. Furthermore, our proposed model also achieves the best performance in forecasting PM 2.5 for 3- and 7-day ahead predictions.