Air Quality Atmosphere and Health最新文献

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.

Opencast coal mining releases a mixture of air pollutants that pose significant risks to surrounding communities. This cross-sectional epidemiological study assessed the respiratory health of residents living within a 10-kilometer radius of the Bashundhara West opencast coal mine in Odisha, India, with particular focus on chronic obstructive pulmonary disease (COPD). Pulmonary function tests revealed an overall COPD prevalence of 31%, with a disproportionately higher burden among women (39.8%) compared to men (20.7%). Geographical disparities were also evident: Zone 2 (3 to 10 km from the mine) exhibited a COPD prevalence of 36.1%, substantially higher than Zone 1 (within 3 km, 24%). Notably, relative risk (0.66) and odds ratio (0.56) analyses indicated a lower likelihood of COPD among residents living closer to the mine, a counterintuitive finding that may reflect modifying factors such as household biomass smoke exposure, forest cover acting as a natural buffer, or individual susceptibility patterns. In addition, high rates of tuberculosis and smoking were observed, compounding respiratory health risks in the study area. Using annual mean PM2.5 concentrations (31.9 µg m⁻³), an estimated five deaths (1.7% of all-cause mortality) were attributable to air pollution-related cardiovascular impacts. These findings demonstrate a high burden of COPD in coalfield communities, with clear gender and spatial disparities, and highlight the interaction between ambient emissions and indoor exposures. The results emphasize the urgent need for expanded rural air quality monitoring, stronger clean fuel adoption programs, community-based screening for COPD, and integration of ecological buffers into mine management to protect vulnerable populations.

Controlling PM_2.5 and O₃ synergistic pollution has emerged as a key factor in improving China’s air quality. Spatiotemporal characteristics of synergistic pollution across the Chengdu-Chongqing City Group (CCCG) were characterized using high-frequency monitoring data from 215 sites. Then, further implemented a dual-pollutant random forest modeling framework to quantify factor-specific contributions and assess precursor sensitivity (NO_X/VOCs/HCHO) for both pollutants, thereby identifying dominant precursors. From 2015 to 2020, the level of PM_2.5 and O₃ synergistic pollution in the CCCG decreased 53.48%. The consequence demonstrated that the relative contributions of precursors to PM_2.5 and O₃ are 42.16% and 29.51%, respectively, with NOx and VOCs identified as key driving factors. In addition, the relative contributions of meteorological factors to PM_2.5 and O₃ were 22.49% and 34.11%, respectively. Based on the seasonal patterns of heavy PM_2.5 and O₃ pollution, it was estimated that the seasonal total impacts of H₂O₂ and C₅H₈ on summer O₃ pollution were 19.91 µg/m³ and 17.1 µg/m³, respectively, therefore, during intensive O₃ pollution in summer (June to August), efforts should be focused on reducing emissions of C₅H₈ and H₂O₂. In winter, the seasonal total impacts of NO_X and C₃H₈ on PM_2.5 were 36.35 µg/m³ and 27.34 µg/m³, respectively. Thus, during intensive PM_2.5 pollution in winter (December, January to February), priority should be given to reducing emissions of NO_X and C₃H₈.

This study investigates the asymmetric long- and short-run determinants of fine particulate matter (PM_2.5) air pollution in Saudi Arabia over the period 1995–2023. Using a Nonlinear Autoregressive Distributed Lag (NARDL) model with Newey–West standard errors, the analysis incorporates gross domestic product (GDP) per capita, industrial activity, energy use, urban population growth, and agricultural development, with Wald tests employed to examine asymmetries and robustness checks performed through a conventional ARDL framework. The results reveal strong asymmetric effects: in the long run, a 1% increase in GDP per capita reduces PM_2.5 emissions by approximately 6.3%, consistent with the Environmental Kuznets Curve hypothesis, while negative GDP shocks deteriorate air quality in the short run. Industrial and energy expansions significantly elevate PM_2.5 emissions in the short run (coefficients ≈ + 1.44 and + 3.89, respectively), whereas contractions mitigate pollution. Urban growth exerts a positive short-run effect on emissions (+ 0.03) but contributes to reductions in the long run (− 0.08). Agricultural contractions consistently lower PM_2.5 concentrations both in the long run (− 0.13) and short run (− 0.12). By highlighting the asymmetric pollution–economy nexus in a resource-dependent economy, this study underscores the importance of policy strategies that promote clean economic growth, accelerate renewable energy adoption, integrate sustainable urban planning, and foster climate-smart agricultural practices, emphasizing that effective environmental management must account for both the scale and direction of economic and structural changes.