Air Quality Atmosphere and Health最新文献

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.

Air quality monitoring with low-cost PM_2.5 (particulate matter with aerodynamic size lower than 2.5 microns) particle sensors is becoming increasingly common; nevertheless, the accuracy of these sensors varies among devices and operating conditions. Prior studies have investigated how environmental factors, such as temperature and relative humidity (RH), affect sensor accuracy and have identified significant impacts of these variables in numerous instances. The current study used three relative humidities, 20 ± 3%, 50 ± 3%, and 80 ± 3%, to test and calibrate two low-cost PM_2.5 sensors, the Plantower PMSA003(A) and the Sensirion SPS30, against the standard research-grade monitor Grimm 11-A. Hygroscopic particle NaCl and hydrophobic particle compressor oil are utilized for the purpose of calibration. To process the sensor response, five calibration models: linear, quadratic, piecewise linear, piecewise quadratic, and artificial neural network (ANN) are utilized. To determine the model performance, the coefficient of determination (R²), mean bias error (MBE), and root mean square error (RMSE) are calculated. The Sensirion SPS30 outperformed the other sensor (Plantower) in terms of its sensitivity to changes in relative humidity. The ANN model achieved R² values of over 0.9967 and RMSE values below 10% for both types of particles (using the Grimm 11-A as a reference). While sensors’ reactions to relative humidity can vary for hygroscopic particles (NaCl), they show very little fluctuation for hydrophobic particles (compressor oil) across all relative humidity levels. Using the calibration equations available for 20 ± 3%, 50 ± 3%, and 80 ± 3% relative humidity, a weighted calibration method is proposed to determine PM_2.5 levels at any relative humidity ranging from 20 to 80%.

This study examined the short-term association between the humidex—a composite index integrating ambient temperature and relative humidity—and the incidence of influenza A and B in Kawasaki City, Japan. Recognizing the limitations of analyzing temperature and humidity independently, we employed the humidex to better capture perceived environmental conditions. Daily influenza case data from March 2014 to December 2019 were sourced from the Kawasaki City Infectious Disease Surveillance System. A quasi-Poisson generalized linear model, combined with distributed lag non-linear models (DLNMs), was used to estimate exposure–response relationships, adjusting for multiple environmental confounders. A total of 181,895 influenza cases were reported during the study period, with influenza A accounting for 72.4% and influenza B for 27.6%. Compared to the median humidex (20.1), the cumulative relative risk (RR) for influenza A was significantly elevated at the 5th percentile (humidex: 3.0; RR: 4.91, 95% CI: 3.04–7.97) and moderately increased at the 95th percentile (humidex: 40.0; RR: 1.37, 95% CI: 0.59–3.22). Influenza B showed a similar but less pronounced pattern, with RRs of 3.79 (95% CI: 1.60–9.01) and 1.46 (95% CI: 0.93–2.28) at the respective percentiles. These findings indicate that low humidex values are associated with a heightened risk of influenza, particularly type A. Incorporating perceived humidity metrics into epidemiological models may enhance early warning systems and inform public health interventions during cold, dry conditions.

Air pollution interventions often focus on reducing emissions but rarely account for dynamic population exposure or public health outcomes. This study evaluates the effects of traffic-reduction scenarios in Lancaster (UK) using a novel Air Pollution Exposure Health Index (APEHI) framework, which builds on previous exposure risk metrics to support non-health experts in assessing exposure-related health risks. Baseline conditions were compared with three intervention scenarios using seasonal traffic and pedestrian counts, air quality measurements, spatiotemporal activity maps and regression models. Scenario 1 implemented full vehicle restrictions (excluding buses and emergency services) year-round, reducing by 80% and doubling pedestrian flow. Scenario 2 applied the same restrictions during daytime hours (07:00–18:00), cutting overall vehicle traffic by 63% and increasing pedestrian flow by 78%. Scenario 3 restricted traffic during peak periods (07:00–11:00 and 15:00–19:00), producing a 57% reduction in daily vehicle flow and increase in pedestrian flow. Across scenarios, NO₂ and PM₁₀ concentrations declined by up to 25% and 16%, respectively. However, a counterintuitive outcome emerged where, overall exposure risks and PM₁₀-related health impacts rose by 20–27% despite these reductions, largely due to rising pedestrian density and persistent background emissions. Sensitivity testing revealed that even small changes in intervention assumptions, such as variations in pedestrian density or traffic flow, can significantly influence exposure-related health outcomes. For instance, a 10% increase in pedestrian flow or a shortfall in traffic mitigation could raise exposure-related health risks by an additional 5–12%. These results show emission-reduction alone may not deliver proportional health benefits and should be complemented by exposure-aware strategies that consider human mobility patterns and residual pollution sources. Therefore, integrating tools such as APEHI into planning processes can help identify when and where people are most at risk, enabling targeted interventions that maximise health gains while maintaining urban functionality.

Graphical Abstract

Road dust serves as a major sink for toxic trace elements and fine particulates, which poses ecological and health risks in urbanizing cities. Therefore, this study evaluates road dust characteristics across four distinct land-use zones -residential, commercial, highway, and industrial in Guwahati and Nalbari, Assam, India, in which, silt load, particulate matter (PM) emission factors, elemental concentrations, potential sources, and associated ecological and human health risks analysis were focused. Results demonstrated that PM emission rates were higher at highway sites for both Guwahati (PM_2.5: 5.94 g/VKT; PM₁₀: 24.55 g/VKT) and Nalbari (PM_2.5: 2.68 g/VKT; PM₁₀: 11.10 g/VKT), with significantly higher values in Guwahati due to heavy commercial traffic in urban environment. High concentrations of Al, Ca, Mg, Fe, and Na were observed across all sites, while Cu, Ni, Zn, and Pb were notably elevated near highways and commercial areas. Cd exhibited the highest ecological risk (ER) and contamination factor (Cf) among all elements. Children exhibited greater vulnerability to both non-carcinogenic and carcinogenic risks than adults, with total carcinogenic risk higher in Guwahati (5.36 × 10^− 4) than in Nalbari (3.87 × 10^− 4) due to prevalence of Cd and Cr levels. Positive matrix factorization revealed that crustal material and vehicular abrasion were dominant sources in Guwahati, whereas natural weathering and tire-brake wear prevailed in Nalbari. These findings highlight strong influence of silt load and traffic density in different land-use areas. The combined application of source apportionment and risk assessment provides critical evidence for integrating road dust control into regional air quality management and urban planning.

The current study investigated the impact of Body Mass Index (BMI) on particle deposition and dose received in the human respiratory tract for three groups of exposed subjects (adult males, adult females and 10-year-old children). A dosimetry model, the Exposure Dose Model 2 (ExDoM2) was modified to take into account the subject’s BMI. BMI affects physiological parameters which in turn affect the deposition fraction and dose of particles in the respiratory tract. This study used literature data to incorporate the impact of BMI to physiological parameters. BMI influences particle deposition differently across different age groups and particle sizes. Higher BMI leads to higher particle deposition in children for all particle sizes while in adults BMI can either increase or decrease particle deposition depending on the particle size. Regarding particle dose, both mass dose of PM₁₀ and number dose of ultrafine particles (UFPs) increased with increasing BMI due to the higher inhalation rates. These results highlight that obese subjects receive higher dose in the respiratory tract than health weight subjects. After particle clearance, the highest dose was observed in oesophagus with the dose increasing by 86.2% for children for a BMI increase by 15 kg/m², whereas an increase of 47.9% for adult males and 46.7% for adult females was calculated for a BMI increase by 20 kg/m². This finding suggests a higher percentage increase of the dose received by children compared to adults.

Graphical Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality and a major contributor to morbidity worldwide. Emerging evidence indicates that climate change is a significant risk factor. Given Iran’s diverse climate and the high burden of CVDs, the country is particularly vulnerable to the effects of meteorological factors on cardiovascular health. The objective of this systematic review was to determine the effect of exposure to climate factors and cardiovascular outcomes in Iran. The study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. Three databases (Scopus, PubMed, and Web of Science) were searched for articles published from January 2000 to February 2025. Twenty-five articles were included in the systematic review after titles, abstracts, and full texts were screened. The results showed that exposure to cold temperature increases cardiovascular risk through physiological changes, including increased blood pressure and blood viscosity. High temperatures were significantly associated with an increased risk of cardiovascular mortality and hospital admissions. Air pollution modifies these relationships in some regions but not in others. These findings highlight the need for climate-responsive healthcare policies that are consistent with Iran's regional climate patterns.

To assess exposure levels and identify the toxicity sources of fine particulate matter (PM_2.5)-bound polycyclic aromatic hydrocarbons (PAHs) in Xi’an, this study conducted simultaneous ambient air and personal exposure monitoring. The campaign included seven sampling sites across major urban functional zones and recruited representative residents. PAH concentrations in PM_2.5 samples were determined via high-performance liquid chromatography. Correlation analysis and characteristic ratio methods were used to evaluate the relations between ambient and personal exposure levels and to identify PAH sources. Results revealed that the total concentration of PM_2.5-bound PAHs in winter was 702.43 ± 568.39 ng m⁻³, with a toxic equivalent quantity (TEQ) of 40.76 ± 25.50 ng m⁻³. Carcinogenic PAHs were ubiquitously detected (0.050–274.18 ng m⁻³). The daily average concentration of benzo[a]pyrene (BaP) reached 14.98 ng m⁻³, exceeding the Chinese National Ambient Air Quality Standard by up to 4.99 times, with an exceedance rate of 95.83%. The average TEQ (22.07 ng m⁻³) was 1.85 times that of BaP alone, underscoring the considerable contribution of other carcinogenic PAHs. Pentacyclic PAHs were the dominant toxic contributors, primarily originating from coal and petroleum combustion, and chemical products. This study demonstrates that personal PAH exposure in Xi’an is influenced by ambient air quality and individual lifestyles, providing critical insights for targeted exposure mitigation and public health protection.