Air Quality Atmosphere and Health最新文献

Climate has significant impacts on humans in many aspects. Therefore, people have considered climate parameters in various areas of life, as climate influences numerous activities, including economic, social, and physical environmental shaping. Over time, the human population has increasingly concentrated in urban areas compared to rural ones, leading to several issues. Among the most critical issues seen in cities are those related to climate and human health. This situation poses numerous adverse effects on human health. It is known that individuals feel comfortable, safe, and healthy within specific ranges of temperature, humidity, or wind values. The concept of Bioclimatic Comfort, which emerges within this context, is an essential concept for identifying such areas within cities. In this study, the climatic comfort conditions in Turkey’s Eastern Anatolia Region were investigated using the Wind Chill Index (WCI). The high altitudes, rugged topography, and harsh climate conditions of the region cause perceived temperatures to drop significantly, particularly during winter, due to low temperatures and high wind speeds. The research was conducted using average minimum monthly temperature and monthly wind speed data obtained from the Turkish State Meteorological Service for the period from 1991 to 2023, and spatial analyses were performed using ArcGIS Pro software. It was found that perceived temperatures during winter months (December, January, February) ranged from − 10 °C to -27 °C, falling into the “Moderate Risk” category. In some areas, temperatures dropped further, entering the “High Risk” category. This poses serious health risks, such as frostbite and cold weather shock. During spring, risk levels decreased, and in summer, the risk of cold weather shock almost entirely disappeared. However, in autumn, cold weather risk was observed to rise again. The climatic challenges of the Eastern Anatolia Region have significant impacts on public health and quality of life. This study serves as an essential reference for developing measures against cold weather conditions in the region and ensuring better protection for the local population against these harsh conditions. Future research could contribute to a broader evaluation of climatic risks by comparing the effects of the WCI across different geographical regions.

The escalating practice of rice-residue burning in agricultural regions has become a critical environmental and public health concern, necessitating a comprehensive assessment of its impact. With increasing evidence linking crop residue burning to air pollution, health risks, and socio-economic burdens, there is an urgent need to quantify its effects on burned areas, pollutant emissions, and associated health outcomes. This study analyzes the spatial distribution of burned areas, PM2.5 emissions, and their associated health impacts, including asthma cases, premature deaths, Years of Life Lost (YLLs), and Disability-Adjusted Life Years (DALYs). The data reveals alarming increases in burned areas, with regions like Makloor, Papannapet, and Armoor showing substantial rises. Emerging hotspots such as Dubbak and Athmakur (M) demonstrate exponential growth in burned hectares and corresponding health impacts, underscoring the urgent need for targeted interventions. Urban centers like Ghatkesar and Shamirpet face disproportionate health burdens due to high population exposure and regional pollutant transport. These findings highlight the critical need for comprehensive strategies, including promoting alternative residue management practices, strengthening regulations, and enhancing public awareness, to effectively mitigate the adverse effects of crop residue burning.

Accurate residential air quality assessment is crucial for studying health risks, evaluating local mitigation measures, and empowering citizens. Low-cost-sensors (LCS) have gained popularity for enhancing monitoring coverage and providing individuals with air quality measurement tools. This study examines the added value of a low-cost sensor in estimating residential fine particulate matter (PM_2.5) concentrations in the Netherlands. We employed a real-time Sensirion SPS30 dust sensor to monitor residential PM_2.5 concentrations. 73 sensors were deployed outdoors at participants’ residences with an average measurement time of 131 days. Data from LCS were compared with that of regulatory stations, using hourly and daily averages for comparison. Spatial variability of sensor measurements was assessed, and determinants were explored that explain potential differences between PM_2.5 concentrations from regulatory stations and LCS measurements. After data cleaning, 95.7% of measurements were retained. Meteorological factors did not impact sensor performance. The mean Pearson temporal correlation between the LCS and regulatory network was 0.75 for hourly and 0.88 for daily PM_2.5 averages. The average difference ranged from − 0.17 to 0.63 µg/m³, and the average absolute difference ranged from 2.42 to 4.50 µg/m³. Spatial variability of LCS-based average concentrations at similar locations was larger than that of regulatory stations. LCS measuring direction, traffic intensity, humidity, LCS readings, and distance to nearest background station had a significant effect on the difference between sensor and regulatory station concentrations. This study demonstrates that PM_2.5 can be accurately measured over extended periods using LCS, offering a dynamic, high-quality perspective on air quality, recording variations that regulatory stations and predictive air quality models may overlook.

Exposure to air pollution adversely affects health and is highly associated with birth outcomes. This study analyzes the relationship between air pollution and low birth weight (LBW; less than 2,500 g at birth) at the county level in the United States. Using the latest publicly available data from the County Health Rankings and Roadmaps across 3,027 counties and equivalents, simple and quantile regression analyses were conducted to test the association between the average daily density of fine particulate matter (PM_2.5) and LBW. The LBW data were from 2016 to 2022 from the National Center for Health Statistics and PM_2.5 data were obtained from Environmental Public Health Tracking Network from 2019. We controlled for key covariates. Simple regression found that PM_2.5 (p < .001) and children in poverty (p < .001) were positively associated with LBW, while rural population (p < .001) was negatively associated. Quantile regression results showed that PM_2.5 was associated at 10th (p = .001) and 50th quantiles (p < .001), but not 90th quantile (p = .32), implying that air pollution may play a more pronounced role in counties with lower to moderate LBW rates, whereas counties with highest LBW may be driven by other socioeconomic factors such as children in poverty (p < .001) and uninsured population (p = .01). The findings highlight the strong link between air pollution and LBW which could have long-term health consequences. The differential associations of PM_2.5 and LBW across the LBW distribution have important implications for potential interventions that may require tailored approach to risk profiles of the geography.

This study represents the extensive investigation of particulate matter (PM) concentrations in indoor environments in India, analyzing PM₁₀, PM_2.5, and PM₁ levels across 109 locations in the Mumbai Metropolitan Region (MMR), India. The monitored locations included residences, offices, restaurants, shopping malls, and cinema theatres, providing comprehensive insights into spatial and temporal variations. Results indicate that indoor PM levels frequently exceed WHO and NAAQS guidelines, with the highest concentrations observed in restaurants and residences. Restaurants recorded the highest mean PM₁₀ (94.2 ± 45.1 µg m^-3) and PM_2.5 (38.3 ± 25.7 µg m^-3) primarily due to cooking emissions and inadequate ventilation. Residences followed, with mean PM₁₀ and PM_2.5 levels of 83.6 ± 39.3 µg m^-3 and 30.7 ± 17.8 µg m^-3, respectively. Homes near industrial zones had significantly elevated PM₁₀ levels (116.2 ± 59.0 µg m^-3) highlighting the impact of external pollution infiltration. In contrast, private offices and shopping malls with well-maintained HVAC systems exhibited lower PM10 concentrations (65.8 ± 40.0 µg m^-3 and 72.1 ± 31.2 µg m^-3, respectively). Temporal analysis revealed that peak-hour PM₁₀ concentrations in cinema halls (105.1 ± 52.9 µg m^-3) were nearly 1.7 times higher than during non-peak hours (63.8 ± 23.1 µg m^-3), largely due to foot traffic and dust resuspension. The findings underscore the urgent need for IAQ regulations, enhanced ventilation strategies, and stricter emission controls to mitigate health risks. This research provides crucial data for policymakers and urban planners, facilitating evidence-based interventions to improve indoor air quality and safeguard public health.

Ambient air pollution represents the leading contributor to the global burden of respiratory diseases. Thus, it’s necessary to evaluate the additional hospitalization burden for respiratory diseases resulting from air pollutants among different population subgroups. Both single-pollutant and multi-pollutant Generalized Additive Model (GAM) analyses were performed to investigate the correlation between air pollutants and hospitalizations for respiratory disease in total population, males, females, people < 65 years old and people ≥ 65 years old, in Hengyang, China from 2019 to 2023. We found that in single-pollutant GAMs, the cumulative concentration over 2 days (lag02) of SO₂ and the concentration with a one-day lag (lag1) of O₃ for respiratory diseases hospitalizations showed more effects on total population. And the excessive risk (ER) were 6.43 (1.40, 11.71) and 0.52 (0.02, 1.03). The lag02 ER of SO₂ were 7.85 (1.85, 14.20) in males. In females, the lag1 ER of NO₂ was 2.85 (0.27, 5.50). In people < 65 years old, the lag02 ER of SO₂ and the lag1 ER of O₃ were 5.81 (0.24, 11.68) and 0.76 (0.20, 1.32). And in people ≥ 65 years old, the lag02 ER of SO₂ was 7.90 (0.36, 16.01). After including PM_2.5 into GAMs, we obtained similar results. In conclusions, exposure to air pollutants SO₂, NO₂ and O₃ had lag effects on the increased hospitalizations for respiratory diseases. Specifically, females were more susceptible to the impact of NO₂, while males were more susceptible to the impact of SO₂. And people < 65 years old were more sensitive to O₃ compared to those ≥ 65 years old.

PM_2.5 is a highly hazardous air pollutant that threatens public health, environmental sustainability, and SDGs progress. Accurate spatiotemporal prediction of its concentrations enables effective air quality management and early warnings. This study presents a novel deep learning framework for PM_2.5 prediction, which integrates a spatiotemporal graph convolutional network with multi-level attention mechanisms. The proposed model incorporates both satellite remote sensing data and meteorological variables to comprehensively represent the factors affecting PM_2.5 dynamics. A weighted spatial graph is constructed to model inter-station dependencies, and a hybrid architecture combining Graph Attention Networks (GAT) and Gated Recurrent Units (GRU) is employed to jointly capture spatial and temporal patterns. Furthermore, a global attention module is introduced to enhance the learning of long-range spatiotemporal dependencies. The model is evaluated using PM_2.5 observations from monitoring stations in the Beijing–Tianjin–Hebei region. Experimental results demonstrate that the proposed approach significantly outperforms conventional baseline models, highlighting the advantages of incorporating attention-based spatiotemporal learning and multi-source data fusion for fine-grained air quality prediction.

Mercury (Hg) is a toxic pollutant with significant global health impacts, yet its urban-industrial sources in South Asia remain insufficiently characterized. This study presents the first long-term (2018–2024) observational dataset of gaseous elemental mercury (GEM) across three major Indian cities: Delhi, Ahmedabad, and Pune. Daily average GEM concentrations were highest in Delhi (6.9 ± 4.2 ng/m³), followed by Ahmedabad (2.1 ± 0.7 ng/m³) and Pune (1.5 ± 0.4 ng/m³). Elevated nighttime GEM levels in Delhi were consistent with conditions of atmospheric stability and shallow boundary layer height, although these associations are presented qualitatively due to modeling constraints. A significant correlation between GEM and carbon monoxide (CO) (p < 0.05), along with GEM/CO slopes of 0.0064, 0.0023, and 0.001 ng/m³/ppbv for Delhi, Ahmedabad, and Pune respectively, suggests that combustion related urban emissions, particularly from coal and industrial sources are key contributors. Receptor based source apportionment using Positive Matrix Factorization (PMF) revealed that anthropogenic sources accounted for 72–92% of GEM levels, primarily from fossil fuel combustion, industrial activities, and vehicular emissions. Natural contributions (8–28%) were attributed to re-emission from soil and photochemical processes. Backward air mass trajectory analysis using NOAA’s HYSPLIT model further supported the influence of both local emissions and regional transport, particularly during high-GEM episodes in Delhi and Ahmedabad. Annual GEM emissions were estimated at 1.6–90.5 kg/year, with Delhi showing a 32% reduction over the study period, indicating the potential effectiveness of emission control measures. Health risk assessment based on the hazard quotient (HQ) indicated a higher chronic exposure risk in Delhi, though values remained below World Health Organization thresholds. Overall, this study highlights the spatial variability, source pathways, and potential health implications of urban Hg pollution in Indian cities and underscores the need for integrated monitoring and policy interventions.