Global Endeavors to Address the Health Effects of Urban Air Pollution

Screening Organic Components and Toxicogenic Structures from Regional Fine Particulate Matters Responsible for Myocardial Fibrosis in Male Mice

Numerous studies indicate that fine particulate matters (PM2.5) and its organic components are urgent risk factors for cardiovascular diseases (CVDs). Combining toxicological experiments, effect-directed analyses, and nontarget identification, this study aims to explore whether PM2.5 exposure in coal-combustion areas induces myocardial fibrosis and how to identify the effective organic components and their toxic structures to support regional risk control. First, we constructed an animal model of real-world PM2.5 exposure during the heating season and found that the exposure impaired cardiac systolic function and caused myocardial fibrosis, with chemokine Ccl2-mediated inflammatory response being the key cause of collagen deposition. Then, using the molecular event as target coupled with two-stage chromatographic isolation and mass spectrometry analyses, we identified a total of 171 suspect organic compounds in the PM2.5 samples. Finally, using hierarchical characteristic fragment analysis, we predicted that 40 of them belonged to active compounds with 6 alert structures, including neopentane, butyldimethylamine, 4-ethylphenol, hexanal, decane, and dimethylaniline. These findings provide evidence for risk management and prevention of CVDs in polluted areas.

A novel framework for quantitative attribution of particulate matter pollution mitigation to natural and socioeconomic drivers

Quantifying drivers contributing to air quality improvements is crucial for pollution prevention and optimizing local policies. Despite advances in machine learning for air quality analysis, their limited interpretability hinders attribution on global and local scales, vital for informed city management. Our study introduces an innovative framework quantifying socioeconomic and natural impacts on mitigation of particulate matter pollution in 31 Chinese major cities from 2014 to 2021. Two indices, formulated based on the additivity of Shapley additive explanations, are proposed to measure driver contributions globally and locally. Our analysis explores the self-contained and interactive effects of these drivers on particulate levels, pinpointing critical threshold values where these drivers trigger shifts in particulate matter levels. It is revealed that SO2, NOx, and dust emission reductions collectively account for 51.58 % and 51.96 % of PM2.5 and PM10 decreases at the global level. Moreover, our findings unveil a significant heterogeneity in driver contributions to pollutant mitigation across distinct cities, which can be instrumental in crafting location-specific policy recommendations.

Insight Study of Trace Elements in PM2.5 During Nine Years in Delhi, India: Seasonal Variation, Source Apportionment, and Health Risks Assessment

This study investigated the concentrations, seasonal variations, sources, and human health risks associated with exposure to heavy elements (As, Al, Pb, Cr, Mn, Cu, Zn, and Ni) of PM2.5 at an urban location of Delhi (28° 38' N, 77° 10' E; 218 m amsl), India, from January 2013 to December 2021. The average mass concentration of PM2.5 throughout the study period was estimated as 127 ± 77 µg m-3, which is exceeding the National Ambient Air Quality Standards (NAAQS) limit (annual: 40 µg m-3; 24 h: 60 µg m-3). The seasonal mass concentrations of PM2.5 exhibited at the order of post-monsoon (192 ± 110 µgm-3) > winter (158 ± 70 µgm-3) > summer (92 ± 44 µgm-3) and > monsoon (67 ± 32 µgm-3). The heavy elements, Al (1.19 µg m-3), Zn (0.49 µg m-3), Pb (0.43 µg m-3), Cr (0.21 µg m-3), Cu (0.21 µg m-3), Mn (0.07 µg m-3), and Ni (0.14 µg m-3) exhibited varying concentrations in PM2.5, with the highest levels observed in the post-monsoon season, followed by winter, summer, and monsoon seasons. Six primary sources throughout the study period, contributing to PM2.5 were identified by positive matrix factorization (PMF), such as dust (paved/crustal/soil dust: 29.9%), vehicular emissions (17.2%), biomass burning (15.4%), combustion (14%), industrial emissions (14.2%), and Br-rich sources (9.2%). Health risk assessments, including hazard quotient (HQ), hazard index (HI), and carcinogenic risk (CR), were computed based on heavy elements concentrations in PM2.5. Elevated HQ values for Cr and Mn linked with adverse health impacts in both adults and children. High carcinogenic risk values were observed for Cr in both adults and children during the winter and post-monsoon seasons, as well as in adults during the summer and monsoon seasons. The combined HI value exceeding one suggests appreciable non-carcinogenic risks associated with the examined elements. The findings of this study provide valuable insights into the behaviour and risk mitigation of heavy elements in PM2.5, contributing to the understanding of air quality and public health in the urban environment of Delhi.

Predicting high-resolution air quality using machine learning: Integration of large eddy simulation and urban morphology data

Accurately predicting air pollutants, especially in urban areas with well-defined spatial structures, is crucial. Over the past decade, machine learning techniques have been widely used to forecast urban air quality. However, traditional machine learning approaches have limitations in accuracy and interpretability for predicting pollutants. In this study, we propose a convolutional neural network (CNN) model to predict the spatial distribution of CO concentration in Nanjing urban area at 10 m resolution. Our model incorporates various factors as input, such as building height, topography, emissions, and is trained against the outputs simulated by the parallelized large-eddy simulation model (PALM). The PALM model has 48 different scenarios that varied in emissions, wind speeds, and wind directions. The results display a strong consistency between the two models. Furthermore, we evaluate the performance of our model using a 10-fold cross-validation and out-of-sample cross-validation approach. This yields a robust correlation (with both R2 > 0.8) and a low RMSE between the CO predicted by the PALM and CNN models, which demonstrates the generalization capability of our CNN model. The CNN can extract crucial features from the resulted weight contribution map. This map indicates that the CO concentration at a location is more influenced by nearby buildings and emissions than distant ones. The interpretable patterns uncovered by our model are related to neighborhood effects, wind speeds, directions, and the impact of orientation on urban CO distribution. The model also shows high prediction accuracy (R > 0.8) when applied to another city. Overall, the integration of our CNN framework with the PALM model enhances the accuracy of air quality predictions, while enabling a fluid dynamic laws interpretation, providing effective tools for air quality management.

Causal effect of air pollution on the risk of cardiovascular and metabolic diseases and potential mediation by gut microbiota

BACKGROUND

Epidemiological studies have explored the relationship between air pollution and cardiovascular and metabolic diseases (CVMDs). Accumulating evidence has indicated that gut microbiota deeply affects the risk of CVMDs. However, the findings are controversial and the causality remains uncertain. To evaluate whether there is the causal association of four air pollutants with 19 CVMDs and the potential effect of gut microbiota on these relationships.

METHODS

Genetic instruments for particulate matter (PM) with aerodynamic diameter < 2.5 μm (PM2.5), <10 μm (PM10), PM2.5 absorbance, nitrogen oxides (NOx) and 211 gut microbiomes were screened. Univariable Mendelian randomization (UVMR) was used to estimate the causal effect of air pollutants on CVMDs in multiple MR methods. Additionally, to account for the phenotypic correlation among pollutant, the adjusted model was constructed using multivariable Mendelian randomization (MVMR) analysis to strength the reliability of the predicted associations. Finally, gut microbiome was assessed for the mediated effect on the associations of identified pollutants with CVMDs.

RESULTS

Causal relationships between NOx and angina, heart failure and hypercholesterolemia were observed in UVMR. After adjustment for air pollutants in MVMR models, the genetic correlations between PM2.5 and hypertension, type 2 diabetes mellitus (T2DM) and obesity remained significant and robust. In addition, genus-ruminococcaceae-UCG003 mediated 7.8 % of PM2.5-effect on T2DM.

CONCLUSIONS

This study firstly provided the genetic evidence linking air pollution to CVMDs and gut microbiota may mediate the association of PM2.5 with T2DM. Our findings highlight the significance of air quality in CVMDs risks and suggest the potential of modulating intestinal microbiota as novel therapeutic targets between air pollution and CVMDs.

Concentration prediction and spatial origin analysis of criteria air pollutants in Shanghai

Severe air pollution events still occur frequently in Shanghai. In order to predict when Shanghai air quality satisfies the National Ambient Air Quality Standards of China (NAAQSC) and identify potential source areas of criteria air pollutants for the regional joint prevention and control of air pollution, concentration data of PM_2.5, PM₁₀, SO₂, NO₂ and O₃ were collected in 2014-2022 at fourteen monitoring sites across Shanghai and surrounding areas. A first - order rate equation with harmonic regression analysis was employed for time series analysis and concentration prediction. Decreasing concentrations were observed widely over all sites except O₃ and NO₂. It is very likely that the secondary NAAQSC standards for PM_x, and SO₂ would be met by 2025 and O₃ and NO₂ would likely become the critical pollutants that determine air quality level after 2025. Regional transport was predominant for PM_x and SO₂ pollution. A 3D - CWT multisite joint location method was developed to identify their potential source areas at different spatial resolutions. Weighting function correction was assigned via information entropy of endpoint numbers in each cell. A probabilistic parameter WI_PSA was proposed to quantify and normalize the probability that grid cells are source areas in order to achieve fourteen - site joint location, and it was comparable and compatible at different spatial resolutions. Potential source areas of PM_2.5 and PM₁₀ were similar, including Henan, Shandong, Hebei and Anhui, while origin domains of SO₂ mainly covered Henan and Hebei. In all seasons, air pollution that was transported to Shanghai (i.e., PM_x and SO₂) originated mainly from the North China Plain; the contribution of marine sources was neglectable.

Overview of PM2.5 and health outcomes: Focusing on components, sources, and pollutant mixture co-exposure

PM_2.5 varies in source and composition over time and space as a complicated mixture. Consequently, the health effects caused by PM_2.5 varies significantly over time and generally exhibit significant regional variations. According to numerous studies, a notable relationship exists between PM_2.5 and the occurrence of many diseases, such as respiratory, cardiovascular, and nervous system diseases, as well as cancer. Therefore, a comprehensive understanding of the effect of PM_2.5 on human health is critical. The toxic effects of various PM_2.5 components, as well as the overall toxicity of PM_2.5 are discussed in this review to provide a foundation for precise PM_2.5 emission control. Furthermore, this review summarizes the synergistic effect of PM_2.5 and other pollutants, which can be used to draft effective policies.

Radiolabelling and in vivo radionuclide imaging tracking of emerging pollutants in environmental toxicology: A review

Emerging pollutants (EPs) have become a global concern, attracting tremendous attention because of serious threats to human and animal health. EP diversity emanates from their behaviour and ability to enter the body via multiple pathways and exhibit completely different distribution, transport, and excretion. To better understand the in vivo behaviour of EPs, we reviewed radiolabelling and in vivo radionuclide imaging tracking of various EPs, including micro- and nano-plastics, perfluoroalkyl substances, metal oxides, pharmaceutical and personal care products, and so on. Because this accurate and quantitative imaging approach requires the labelling of radionuclides onto EPs, the main strategies for radiolabelling were reviewed, such as synthesis with radioactive precursors, element exchange, proton beam activation, and modification. Spatial and temporal biodistribution of various EPs was summarised in a heat map, revealing that the absorption, transport, and excretion of EPs are markedly related to their type, size, and pathway into the body. These findings implicate the potential toxicity of diverse EPs in organs and tissues. Finally, we discussed the potential and challenges of radionuclide imaging tracking of EPs, which can be considered in future EPs studies.

Elemental Composition and Sources of Fine Particulate Matter (PM2.5) in Delhi, India

In this study we have analysed the elemental composition of fine particulate matter (PM_2.5) to examine the seasonal changes and sources of the elements in Delhi, India from January, 2017 to December, 2021. During the entire sampling period, 19 elements (Al, Fe, Ti, Cu, Zn, Cr, Ni, As, Mo, Cl, P, S, K, Pb, Na, Mg, Ca, Mn, and Br) of PM_2.5 were identified by Wavelength Dispersive X-ray Fluorescence Spectrometer. The higher annual mean concentrations of S (2.29 µg m^-3), Cl (2.26 µg m^-3), K (2.05 µg m^-3), Ca (0.96 µg m^-3) and Fe (0.93 µg m^-3) were recorded during post-monsoon season followed by Zn > Pb > Al > Na > Cu > Ti > As > Cr > Mo > Br > Mg > Ni > Mn > and P. The annual mean concentrations of elemental composition of PM_2.5 accounted for 10% of PM_2.5 (pooled estimate of 5 year). Principal Component Analysis (PCA) identified the five main sources [crustal/soil/road dust, combustion (BB + FFC), vehicular emissions (VE), industrial emissions (IE) and mixed source (Ti, Cr and Mo rich-source)] of PM_2.5 in Delhi, India.

Assessment of indoor air quality and their inter-association in hospitals of northern India-a cross-sectional study

This study was commenced to evaluate the indoor and outdoor air quality concentrations of PM_2.5, sub-micron particles (PM_>2.5, PM_1.0-2.5, PM_{0.50 -1.0}, PM_0.25-0.50, and PM_<0.25), heavy metals, and microbial contaminants along with their identification in three different hospitals of Lucknow City. The study was conducted from February 2022 to April 2022 in hospitals situated in the commercial, residential, and industrial belts of the city. The indoor concentration trend of particulate matter as observed during the study suggested that most of the highest concentrations belonged to the hospital situated in an industrial area. The highest obtained indoor and outdoor concentrations for PM_1.0-2.5, PM_0.50-1.0, PM_0.25-0.50, and PM_<0.25 are 40.44 µg/m³, 56.08 µg/m³, 67.20 µg/m³, 74.50 µg/m³, 61.9 µg/m³, 79.3 µg/m³, 82.0 µg/m³, and 93.9 µg/m³, respectively, which belonged to hospital C situated in the industrial belt. However, for PM_>2.5, the highest indoor concentration obtained belonged to hospital B, i.e., 30.7 µg/m³, which is situated in the residential belt of the city. Regarding PM_2.5, the highest indoor and outdoor concentrations obtained are 149.41 µg/m³ and 227.45 µg/m³, which were recorded at hospital A and hospital C, respectively. The present study also observed that a high bacterial load of 1389.21 CFU/m³ is recorded in hospital B, and the fungi load was highest in hospital C with 786.34 CFU/m³. Henceforth, the present study offers thorough information on the various air pollutants in a crucial indoor setting, which will further aid the researchers in the field to identify and mitigate the same more precisely.

Heterogeneous Variations on Historical and Future Trends of CO2 and Multiple Air Pollutants from the Cement Production Process in China: Emission Inventory, Spatial-Temporal Characteristics, and Scenario Projections

Cement production is a major contributor to carbon dioxide (CO₂) and multiple hazardous air pollutant (HAP) emissions, threatening climate mitigation and urban/regional air quality improvement. In this study, we established a comprehensive emission inventory by coupling the unit-based bottom-up and mass balance methods, revealing that emissions of most HAPs have been remarkably controlled. However, an increasing 6.0% of atmospheric mercury emissions, as well as 14.1 and 23.7% of fuel-related and process-related CO₂ emission growth were witnessed unexpectedly. Industrial adjustment policies have imposed a great impact on the spatiotemporal changes in emission characteristics. Monthly emissions of CO₂ and multiple HAPs decreased from December to February due to the "staggered peak production," especially in northern China after implementing the intensified action plan for air pollution control in winter. Upgrading environmental technologies and adjusting capacity structures are identified as dominant driving forces for reducing HAP emissions. Besides, energy intensity improvement can help offset some of the impact caused by the increase in clinker/cement production. Furthermore, scenario analysis results show that ultra-low emission and low-carbon technology transformation constitute the keys to achieve the synergic reduction of CO₂ and multiple HAP emissions in the future.