Air Quality Atmosphere and Health最新文献

The objective of this study is to examine the association between air pollution and health burden in Thailand, Malaysia, and Singapore from 2000 to 2021. PM_2.5 concentrations and life expectancy are measures of air pollution. Health outcomes include death, incidence, and prevalence of respiratory infections and tuberculosis. The data are obtained from the Global Burden of Disease and the Air Quality Life Index. A Generalized Additive Mixed Model is used to analyze spatial and temporal patterns in a panel dataset with repeated annual observations across countries. This method allows for the detection of nonlinear relationships and considers cross-country variation and time series. The results show that Thailand has a significantly lower death rate from respiratory infections and tuberculosis compared to Malaysia, while the difference for Singapore is not statistically significant. Incidence, prevalence, and time are significantly associated with the death rate. PM_2.5 concentrations and life expectancy do not show significant effects in the model. The study contributes to the understanding of how disease-specific indicators and time trends influence death. It provides cross-country evidence from Southeast Asia and supports the use of burden-specific indicators in public health planning and surveillance.

Urban green space (UGS) plays a critical role in mitigating air pollution and enhancing urban sustainability through ecosystem services. However, the complex interactions between UGS and air pollution present both benefits and challenges. By adopting an ecosystem services perspective, the study pursues two main objectives: on the ond hand, to conduct a comprehensive review of the interactions between UGS and air pollution; and on the other hand, to identify current limitations in the field and highlight future research trends. Our study examines this relationship through a comprehensive bibliometric analysis spanning from 2003 to 2025. Using data from the Web of Science, we analyze 422 relevant publications through co-occurrence analysis, keyword clustering analysis, and keyword burst detection, employing CiteSpace and VOSviewer for visualization. The results show that: (1) Our study identifies research hotspots, including the regulatory role of UGS in air pollution control, the impact of air pollution on green space quality, and the interactions between UGS, air quality, and public health. (2) Findings reveal three distinct research phases: initial focus on pollutant removal mechanisms, expansion into urban sustainability frameworks, and recent emphasis on climate adaptation and ecosystem resilience. (3) Future research should pay greater attention to the reciprocal interactions between UGS and air pollution. Our study provides valuable insights to guide future studies and supports the development of more effective strategies for integrating urban greenery into air quality management and sustainable urban planning.

In response to concerns about urban air quality, this study examines particulate matter (PM2.5) exposure and data completeness across diverse demographics in Chicago using low-cost PurpleAir sensors. Chicago’s air quality, periodically worsened by both local emissions and external sources like wildfire smoke, presents health risks that are unevenly distributed across the city. PurpleAir public sensors started to be placed in 2018 with just 7 sensors, the network in Chicago has since expanded to 75 sensors by 2023, with new placements from community members helping to spread spatial and demographic gaps and enabling more detailed air quality data collection across diverse areas. Sensor coverage, data completeness, and correlations with community demographics were assessed over time, revealing that missing data was initially more prevalent in lower-income, majority-Hispanic neighborhoods. Results indicate that increased sensor placement has reduced demographic disparities in data completeness, yet certain communities remain underrepresented. Missingness remains a significant concern, especially during initial deployment and setup, highlighting the need for support to ensure data reliability. While increased coverage has improved spatial representation, targeted placement in high-risk areas and denser neighborhood-level sensor distribution are essential for equitable air quality monitoring. These findings underscore the need for further sensor deployment and maintenance strategies to ensure that air quality data effectively informs public health initiatives in all Chicago neighborhoods.

This study examines the spatio-temporal dynamics of surface ozone (O₃) pollution and the critical role of meteorological conditions in shaping its distribution across a major coastal province in eastern China. Based on high-resolution observational data from 2019 to 2023, we identify distinct spatial gradients and seasonal patterns in O₃ concentrations, with pronounced differences between urban and coastal zones. O₃ pollution frequency peaks at approximately 60% in June, with concentrations reaching ~ 200 µg m⁻³. Although pollution levels generally decline after July, elevated O₃ persists into September, particularly in urban areas south of the Yangtze River. Meteorological drivers exhibit strong regional heterogeneity: near-surface temperature dominates O₃ variability in inland urban areas, whereas solar radiation and wind fields become increasingly influential in coastal and industrialized regions. Crucially, we demonstrate that meteorological variations not only modulate daily pollution intensity but also extend the duration of high-O₃ episodes. This dual modulation effect contributes directly to the prolongation of the ozone season and the amplification of spatial disparities across the region. Our findings highlight the pivotal role of meteorology in exacerbating O₃ pollution in rapidly urbanizing coastal zones. These results are representative of many industrialized coastal cities in East Asia, where the convergence of urbanization, emissions, and complex coastal meteorology shapes evolving air quality challenges. The insights provided are essential for developing region-specific strategies to manage seasonal ozone risks under a changing climate.

O₃ and HCHO (Formaldehyde) exceedances occur periodically in Australian cities such as Sydney, Melbourne, and Brisbane. This study constructed spatio-temporal distribution patterns of pollutants in Australia based on daily O₃-HCHO data from 2012 to 2021. Influencing factors were examined using big data, slope-hurst analysis, and GTWR (geotemporally weighted regression) methods, while the Ben-Map model assessed health risk benefits of ozone reduction in Australia. The findings indicate: (1) Spatially, high-concentration area for both ozone and formaldehyde are distributed across southeastern Australia, with HCHO predominantly classified as Grade 4, Grade 3, and Grade 5.(2) Analysis of time series and trends indicates: ozone concentrations follow the pattern: winter > spring > autumn > summer, with a monthly average of 249.57 DU and an overall increasing trend; The maximum formaldehyde concentration occurs during summer, with a monthly average of 11.31 × 10¹⁵ molec/cm², exhibiting a predominantly decreasing trend. (3) Among natural sources, NDVI (normalised vegetation index), MC(moisture content) and T(temperature) exert the greatest influence on O₃-HCHO; amongst human activities, coal, oil and gas represent the primary contributing sources. (4) There was an overall decreasing trend in premature deaths due to ozone pollution in Australia, with the average number of deaths: all-cause (1105) > cardiovascular disease (703) > respiratory disease (430). The above research provides crucial theoretical support for the Australian government to enhance regional atmospheric environmental quality and public health benefits.

Air pollution is a major contributor to respiratory and cardiovascular diseases, prompting recent studies to adopt AI models for forecasting pollutant levels. In this work, we introduce a hybrid framework—SSA-LSTM-XGBoost—that applies a residual-correction strategy in four stages: (i) data preprocessing, (ii) training an LSTM network optimised with the Sparrow Search Algorithm (SSA), (iii) modelling the residuals with an SSA-optimised XGBoost learner, and (iv) fusing both outputs to obtain the final prediction. The framework is assessed on test and out-of-sample datasets and benchmarked against SVR, BiGRU, random forest, BiLSTM, and GRU. On the test set, SSA-LSTM-XGBoost attains the highest accuracy, recording an R(^{varvec{2}}) of 0.9554 and the lowest errors (RMSE = 2.5194, MAE = 1.4885, MAPE = 0.0635), amounting to an average error reduction of roughly 8.2% relative to the runner-up SSA-SVR. When validated on unseen data, it remains superior (R(^{varvec{2}}) = 0.8830, RMSE = 2.4838), achieving an average error decrease of about 3.5% compared with SSA-SVR despite the harsher evaluation conditions. These findings attest to the robustness and strong generalisability of the proposed framework for reliable PM(_{varvec{2.5}}) forecasting. In practice, such forecasts enable near-real-time hotspot alerts, short-term exposure advisories for vulnerable groups, and preventive traffic or industrial controls, thereby supporting municipal air-quality management and policy decisions in Duitama.

Ambient air pollution increases the risk of respiratory morbidity, but evidence concerning the effects of traffic-related air pollutants (TRAPs) on bronchitis admissions is scarce, and multi-city scale studies are needed. This study aimed to evaluate the associations of a set of TRAPs with bronchitis admission and to explore potential modifiers of the associations. Data on hospital admissions for bronchitis, air pollution, and meteorological factors from 1 January 2015 to 31 December 2021 were collected in 8 cities across Gansu Province, China. A generalized additive model (GAM) based on the Quasi-Poisson distribution, in combination with a distributed lag nonlinear model (DLNM), was used to evaluate the association between air pollutants and bronchitis admissions, controlling for calendar time (to control seasonality and long-term trend), meteorological factors (i.e., air temperature, relative humidity), and other possible confounders. We also performed a stratified analysis by gender, age, and season to assess potential effect modification within the study. Totally, 155,133 bronchitis patients were identified during the study period, including 2557 days. The daily number of hospital admissions for bronchitis ranged from 0 to 52. The results showed that exposure to PM_2.5, PM₁₀, NO₂, and CO was positively correlated with an increased risk of hospital admission for bronchitis. Each 10 µg/m³ increase in PM_2.5, PM₁₀, NO₂ was associated with a 3.2% (relative risk (RR) = 1.032, 95% confidence interval (CI): 1.020,1.045, lag 0–14 day), 13.5% (RR = 1.135, 95%CI: 1.103, 1.167, lag 0–11 day), and 9.5% (RR = 1.095, 95%CI: 1.081, 1.109, lag 0–14 day) increase in hospital admission for bronchitis, respectively. With a 1 mg/m³ increase in CO concentration, the risk of hospital admission for bronchitis increased by 7.7% (RR = 1.077, 95%CI: 1.047,1.108, lag 0–14 day). Subgroup analysis revealed that males and patients with bronchitis aged 0–14 years old were more sensitive to a change in PM_2.5, PM₁₀, NO₂, and CO. Additionally, in the cold season, exposure to PM_2.5, PM₁₀, NO₂, and CO was related to a significantly higher admission risk of bronchitis than in the warm season. In conclusion, exposure to PM_2.5, PM₁₀, NO₂, and CO is associated with an increase in hospital admissions for bronchitis in 8 cities of Gansu, China. Our analyses present the first empirical evidence that prevailing air pollution levels in Gansu, China, are associated with increased incidence of bronchitis, underscoring the need to reinforce policies aimed at further reducing air pollution in the region. This study also highlights the critical importance of mitigating the detrimental effects of air pollution on respiratory health, particularly to safeguard at-risk populations.

Rapid industrial growth has led to rise in energy demands, particularly fuelled by coal due to its cost-effectiveness and availability. However, coal mining is a significant source of air pollution, releasing harmful particulate matter that adversely affects human health. This study quantifies the prevalence, severity, and types of pulmonary impairment among residents living near high-sulfur coal mines in Northeast India and assesses the impact of coal mining, composition of particulate matter, and coal combustion in coke oven plants on lung function. Spirometry tests were conducted on the volunteer residents (N = 229, age 7–65 years, residing > 3 years) and 104 controls with similar characteristics. Using a questionnaire and spirometry, pulmonary impairment was estimated following the guidelines of the Indian Chest Society, 2019. Pulmonary impairment was found to be 34.93% in exposed individuals, compared to 18.26% in controls. Obstructive impairment was the most common lung disorder (17.90% vs. 10.57%), followed by mixed (9.17% vs. 3.84%) and restrictive (7.86% vs. 3.84%) in exposed and unexposed, respectively. The multiple regression analysis showed significant negative associations between exposure and FEV₁% (-9.519, 95% CI: -12.759, -6.279) and between exposure and FVC % (-9.952, 95% CI: -12.318, -7.586). Similarly, the logistic regression analysis revealed that the exposed individuals have 2.83 times more pulmonary impairment (OR: 2.83, 95% CI: 1.540–5.207). Exposed populations showed lower mean FEV₁ and FVC values and higher lung impairment compared to controls, with obstructive impairment being the most prevalent. Lung capacities were negatively associated with exposure and smoking.

While microplastic contamination in aquatic and terrestrial systems has been widely documented, atmospheric microplastics (AMPs) remain less understood, particularly regarding their sources, vertical behavior, and associated risks. This study examined the abundance, morphology, polymer types, and surface-adsorbed metals of AMPs in Iligan City, Philippines, comparing roadside and non-roadside environments at ground and elevated heights. Suspended particles were collected using a respirable dust sampler (1.4 m³/min) and filtered through Whatman GF/C paper. Analytical techniques included microscopy, attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX). AMP concentrations were higher in roadside (0.09 ± 0.04 MP/m³) than in non-roadside areas (0.08 ± 0.03 MP/m³), with the highest levels in elevated roadside samples (0.11 ± 0.04 MP/m³). Although the roadside–non-roadside difference was not statistically significant (p = 0.05772), this borderline value suggests a trend of increased MPs in traffic-influenced areas. Fibers dominated in shape, with black and transparent colors most common. Identified polymers included high-density polyethylene (HDPE), low-density polyethylene (LDPE), polypropylene (PP), polyvinyl chloride (PVC), and polyethylene terephthalate (PET). SEM-EDX detected toxic metals such as lead (Pb), mercury (Hg), and nickel (Ni), particularly in elevated roadside samples, indicating contributions from vehicular emissions and resuspended urban dust. This study highlights the vertical and spatial variability of AMPs and their role as carriers of heavy metals, offering important insights into their environmental behavior and potential health implications in urban Southeast Asian settings.

Air pollution exposure during pregnancy has been associated with reduced fetal size and birth weight, but the sensitive exposure windows for these associations are not well established. We examined the association between gestational air pollution exposure and altered fetal size (femur length, biparietal diameter, head circumference, abdominal circumference, estimated fetal weight), measured in mid-pregnancy (week 20) and late-pregnancy (week 34), and attained size at birth (Z-scores of birth weight, length, and head circumference). Within the UPSIDE cohort study (N = 312 pregnant women) in Rochester, New York, we estimated residential, daily, ambient PM_2.5 and NO₂ concentrations using a spatial-temporal model. Distributed lag models estimated increases/decreases in each marker associated with interquartile range (IQR) increases in gestational week mean PM_2.5 (3 µg/m³) and NO₂ (9 ppb) concentrations, adjusting for child sex, maternal pre-pregnancy body mass index, race/ethnicity, and smoking during pregnancy. For birth measures, gestational age at birth was also included. Each IQR increase in NO₂ in gestational weeks 18–33 was associated with decreased femur length at week 34 (-1.18 mm; 95% CI = -2.09, -0.27). However, each IQR increase during gestational weeks 8–9 was associated with increased femur length at week 20 (0.05 mm; 95% CI = 0.00, 0.09). No associations were observed between weekly NO₂/PM_2.5 concentrations and other fetal size or birth measures in models using all participants’ data. NO₂/femur length associations differed by child sex. Thus, there was limited support for an association between gestational PM_2.5/NO₂ exposure and ultrasound measures of fetal size in mid- and late- pregnancy.