Air Quality Atmosphere and Health最新文献

Air pollution causes various adverse health effects on millions of people every year. This study focused on provinces that house 65% of Türkiye’s population to analyze the trends and potential health impacts of PM_2.5, NO₂, and SO₂. Furthermore, a 2025 health impact forecast was developed based on observed pollutant trends. The health impact assessment was structured around two key variables: population size and population density. Concurrently, trend analysis was differentiated based on the provinces’ metropolitan status. The AirQ + assessment tool identified Hatay and Düzce provinces as having the most significant health impacts. Conversely, SO₂ trend analysis demonstrated higher average concentration values within non-metropolitan provinces. Over the five-year study period (2020–2024), specific mortality findings were documented. The highest PM_2.5-attributed mortality was due to stroke in the 25–29 age group. In contrast, the highest NO₂-attributed mortality was observed for Acute Lower Respiratory Tract Infections (ALRI) in adults aged 30 and over. Analysis revealed that the peak number of pollutant-attributed deaths occurred in 2022. The 2025 forecast offers valuable insights for future pollutant-based health impact assessments, with the outputs presented on a pollutant-oriented basis. The study concludes with various recommendations, ranging from individual protection measures to national-level investment priorities.

Effective air quality monitoring is essential for ensuring urban environmental comfort and protecting public health, especially within smart-city frameworks. In Lisbon, the performance of low-cost NO₂ and PM₁₀ sensors was assessed using a novel Correlation-Distance Index (CDI) coupled with Principal Component Analysis (PCA) weighting, alongside a demographic-weighted spatial association technique, benchmarked against reference monitoring stations. The analysis began by quantifying the inverse relationship between sensor–sensor correlation and Euclidean distance. This metric was then refined through the integration of PCA-derived weights and local population density, resulting in a composite CDI-PCA score assigned to each sensor. Spatial clustering of these scores identified zones of underperforming sensors, particularly in densely populated downtown and western neighborhoods. When these clusters were compared with census-block population data, there was a statistically significant association between CDI patterns and population density (χ²=406.5, p < 0.001). High-performing sensors were 22.5% more enriched in densely populated areas, indicating that demographic context provides meaningful spatial structure for network optimization, even though the modest effect size suggests the influence of multiple underlying factors. Robustness was verified through seasonal subsampling and leave-one-out cross-validation, confirming that distance-decay patterns and cluster boundaries remain consistent across environmental conditions and are not driven by individual outliers. The results demonstrate that coupling multivariate spatial statistics with demographic context supports targeted sensor calibration and redeployment strategies. Prioritizing high-population areas and dynamically adjusting sensor densities can improve real-time data accuracy and promote more equitable air quality monitoring.

With the accelerated pace of industrialization, energy demand continues to increase, exacerbating the contradictions between economic development and environmental protection. Rising carbon emissions have posed serious challenges to ecosystems. Achieving coordinated development among energy, economy, environment, and carbon emissions is therefore essential for regional sustainable development. Accordingly, this study investigates the coordinated development within the regional energy-economy-environment-carbon (EEEC) coupling system. Firstly, this research integrates energy consumption, economic industrial structure, ecological environmental quality, and carbon emission levels into a unified analytical framework, thereby developing an EEEC coupling system. Secondly, a coupling coordination model is employed to quantitatively assess the level of coordinated development within the regional EEEC system. Then, an optimization model aiming at maximizing the coupling coordination degree is established. This model comprehensively considers multiple constraints, such as economic and energy factors, facilitating the optimization of industrial structures and exploration of sustainable development pathways. Lastly, the developed model is empirically applied to Southwest China to verify its effectiveness. The main findings of this study are as follows: (1) Energy carbon emissions in the Southwest region show an overall upward trend. Electricity is the largest contributor to energy carbon emissions in the region, followed by raw coal, with carbon emission volumes of 76100.59 (({10^8}kgC{O_2}eq)) and 31942.16 (({10^8}kgC{O_2}eq)), respectively. (2) The coupling coordination degree of the Energy-Economy-Environment-Carbon system presents a gradually rising trend. In 2022, the coupling coordination degree across various regions was approximately 0.7; however, it remains in the intermediate coordination stage, indicating that the overall synergy level of the system still has room for further optimization. (3) Industry still holds a relatively large proportion in the industrial structure of various regions. The structures of agriculture, the construction industry, and other industries have improved, with average increases of 0.08%, 0.12%, and 9.88%, respectively.

Air pollution in megacities such as São Paulo is one of the major public health challenges, with fine particulate matter (PM2.5) and nitrogen dioxide (NO₂) among the most critical air pollutants due to their respiratory and cardiovascular effects. Intensified land use along the Urban Structuring Axes (EETU), a strategy within transit-oriented development, has promoted densification and verticalization, altering the urban microclimate and pollutant dispersion. This study analyzed how different urban morphological configurations influence the dispersion of PM2.5 and NO₂ in street canyons along the EETU in São Paulo city, considering the interaction between built form and street trees. The ENVI-met microclimatic model, based on Computational Fluid Dynamics (CFD), was used to simulate pollutant concentration and dispersion in five street canyons representative of distinct urban morphologies. Indicators such as urban density, verticality (average building height), H/W ratio, occupation, directionality (building orientation relative to prevailing winds), and Normalized Difference Vegetation Index (NDVI) were applied to characterize each area. Results show that compact and highly occupied urban forms exhibit substantially higher NO₂ concentrations at pedestrian level, with differences of up to ~ 80% between contrasting urban morphologies. In contrast, PM2.5 concentrations vary only modestly between canyons (< 5% variation), remaining close to urban background levels. PM2.5 deposition mass, however, shows pronounced sensitivity to urban form and vegetation, varying by over 60% between canyons, indicating that deposition responds differently from pollutant concentration. Urban form therefore plays a key role in shaping ventilation efficiency and air quality in dense areas of São Paulo. Strategies that combine building height variation, transversal block openings, orientation relative to prevailing winds, and context-sensitive street-tree management are essential to reduce population exposure to air pollution in compact urban environments.

This study examines the effect of Artificial Intelligence (AI) on infant mortality rates (IMR) across 102 countries from 2000 to 2023, considering the moderating roles of governance quality (GQI), digital infrastructure (DII), and medical infrastructure (MII). Grounded in the Health Production Function, Institutional, Technological Systems and Absorptive Capacity theories, the study explores how institutional, technological and healthcare readiness influence AI’s effectiveness in improving healthcare outcomes. Multiple estimation techniques, such as Pooled Ordinary Least Squares (POLS), Fully Modified Ordinary Least Squares (FMOLS), Driscoll–Kraay Standard Errors (DKSE), Two-Stage Least Squares (2SLS), and Method of Moments Quantile Regression (MMQR), were employed to address heterogeneity and endogeneity concerns. Results across all estimators are consistent and show that AI significantly reduces IMR by enhancing diagnostic precision, healthcare resource allocation, and data-driven decision-making. The moderating effects of GQI, DII, and MII are negative and significant, suggesting that strong governance, robust digital networks, and advanced medical systems amplify the effectiveness of AI. Quantile regression results indicate heterogeneous effects, with stronger impacts in technologically advanced and low-mortality contexts, and weaker impacts where structural constraints exist. Subsample analysis reveals that AI reduces IMR in both developing and developed economies, but the effect is greater in the latter due to institutional maturity. Overall, the study highlights that AI’s transformative potential in reducing infant mortality depends critically on governance capacity, digital readiness, and healthcare system strength.

In order to understand the influence of forest fires on one of the critical pollutants impacting air quality over a hilly station, this study was undertaken utilizing integrated observation from in-situ station, satellites and reanalysis data. Black Carbon (BC) surface mass concentration was measured over Mizoram, the southernmost state of North east India (NEI) for the first time during 4th March − 3rd April 2022 under a campaign mode. Mean BC during the study period was 6.18 ± 3.1 µg/m³ that varied between a daily mean of 2.43 ± 0.41 µg/m³ to 12.87 ± 2.12 µg/m³. Reanalysis data is found as not sufficiently representative for this region as MERRA-2 reanalysis significantly overestimated BC (~ 4 times than the observed mean) over Mizoram during the fire active period while underestimated during other times. The possibilities of long range transport increasing the BC load over Mizoram was found limited during the study period. An average absorption Ångström exponent (AAE) of 1.22 ± 0.1 suggests dominant influence of biomass burning, consistent with flaming combustion of local and regional wood sources. Higher forest fire activity within 250 km of the station coincided with increased BC levels and implicated forest fire as dominant BC emission source during this season. CALIPSO Lidar observations detected an elevated smoke layer at 4.5–5 km AMSL, indicating fire induced convective activities, which was gradually replaced by dust and polluted dust as the season advanced. This study highlights the importance of long-term in-situ measurements of aerosols over fragile ecosystems like the NEI.

Indoor CO₂ concentration is widely recognized as an effective proxy for infection risk and ventilation efficiency. In addition to improving indoor air quality, the necessity of performing indoor ventilation correctly, effectively, and efficiently to prevent or minimize the spread of SARS-CoV-2 has gained even more importance during the COVID-19 pandemic. In this study, carbon dioxide (CO₂), fine particulate matter (PM_2.5,) temperature, and humidity were monitored in sixteen different indoor environments belonging to four categories: markets, restaurants, public transportation (Metrobus), and shopping centers in Istanbul, Türkiye, during March and April 2021. Measurements were conducted using calibrated low-cost sensors (± 30 ppm accuracy for CO₂) that recorded data every 20 s for 15 min on four different days. The lowest variation in CO₂ measurements was observed in shopping centers (533–663 ppm), while the highest was in restaurants (532–974 ppm). Humidity, temperature, and PM_2.5 values were also recorded and discussed. Results indicate that restaurants exhibited the highest CO₂ based infection risk due to limited or natural ventilation, whereas shopping centers with central mechanical ventilation maintained CO₂ concentrations below 650 ppm, indicating low risk. Ventilation management through continuous CO₂ monitoring is emphasized as a crucial strategy for controlling epidemic risks indoors. Recommendations include improving ventilation efficiency, using portable air cleaners with HEPA/UV filters, and visually displaying CO₂ levels via a “traffic-light” system to inform occupants about indoor safety. This study provides the first quantitative comparison of CO₂-based infection risk across multiple indoor environments in Istanbul, providing baseline data for future research and public health policies.

Black carbon (BC) is an air pollutant of emerging concern, and further evidence is needed to understand its health effects. This study investigates the association between gestational BC exposure and birth outcomes, and whether these effects are independent of particulate matter with an aerodynamic diameter of ≤ 2.5 μm (PM_2.5) exposure. We used data from the population-based birth cohort, Maternal Air Pollution in Southern Sweden (MAPSS) for the years 2000–2009, including 43,676 mother-child pairs. Maternal exposure to air pollution at residential address was estimated using a high-resolution dispersion model. We used logistic and linear regressions to examine association of air pollution with birth outcomes adjusting for maternal age, Body Mass Index, smoking, education, maternal country of birth, sex of the child, parity, household income, and birth year. We found that BC exposure during pregnancy was associated with birth weight (decrease of 69 g per 1 µg/m³ (95% Confidence Interval (CI): -96, -42) and small for gestational age (SGA) (odds ratio (OR) of 1.29 (95% CI: 1.05, 1.59; p = 0.015) per 1 µg/m³ increase in BC). BC exposure during the second trimester (OR of 1.79 (95% CI: 1.03, 3.09) and PM_2.5 during pregnancy (OR of 1.06 (95% CI: 1.01,1.12) were associated with Low Birth Weight (LBW). Associations with BC remained after adjustment for PM_2.5. Our study contributes to further evidence of birth weight impact from BC exposure to an emerging pollutant of concern, even in a low-exposure environment. Further, we demonstrate effects of PM_2.5, but also differentiate independent effects of these two correlated pollutants.

This study investigates the spatiotemporal patterns and driving mechanisms of urban pollution islands (UPI) in Urumqi, a representative arid city in northwestern China, during heating and non-heating periods. Using PM₂.₅ monitoring data (2017–2023) from national control stations in urban and suburban areas combined with meteorological and pollution factors, we developed the Urban Pollution Island Intensity (UPII) and applied Pearson correlation analysis and principal component analysis (PCA) to systematically examine the spatiotemporal evolution and dominant drivers of UPI. Results reveal significant temporal differences in UPII intensity, with the heating period average (105 µg/m³) being 4.2 times higher than the non-heating period (25 µg/m³), highlighting the synergistic effect of coal-fired heating and unfavorable diffusion conditions. Spatially, a “northern rural > urban > southern rural” gradient was observed. During heating periods, UPII_N was more prominent due to industrial agglomeration and topographic blocking, while higher UPII_S during non-heating periods suggested regional transport. A “temporal shift” in pollution formation mechanisms was identified: non-heating periods were meteorologically dominated (e.g., temperature showed significant negative correlation with UPII_S, r = -0.480, p < 0.01), while heating periods exhibited strong emission-driven patterns, with PCA indicating the first principal component explained 41.2% of variance, mainly loaded by coal-combustion pollutants including SO₂, CO, and PM₁₀. The UPII index effectively distinguished between local emissions and regional transport contributions, further validated by the sharp UPII decline during COVID-19 lockdowns. This study proposes a “period-subarea-category” precision governance strategy to inform air pollution control in arid northwestern cities.

This study investigates the seasonal occurrence, sources and transformation mechanisms of piperazine and its nitrosated derivatives, mononitrosopiperazine and dinitrosopiperazine, alongside nitrate and nitrite in fine and coarse particulate matter in order to evaluate the influence of emissions and meteorological conditions. All target compounds exhibited significantly higher concentrations during the heating season, reflecting increased combustion activities as well as atmospheric conditions conducive to pollutant accumulation and secondary formation. The concentrations of fine and coarse particulates exhibited a highly significant correlation, suggesting the presence of shared sources, particularly residential and industrial heating, during the winter months. The secondary formation of mononitrosopiperazine and dinitrosopiperazine was a consequence of the atmospheric nitrosation of piperazine in the presence of nitrite. The formation of these compounds was found to be favoured by the following conditions: cold weather, stagnant conditions and low solar radiation. The dinitrosopiperazine exhibited a significant correlation with nitrite and nitrate, while demonstrating an inverse relationship with temperature and insolation, thereby supporting its secondary formation. However, a negative correlation was observed between relative humidity and mononitrosopiperazine, dinitrosopiperazine, nitrite, and nitrate, indicating enhanced scavenging or dilution processes. Principal component analysis (PCA) differentiated primary piperazine emissions from secondary nitrosamine formation, thereby reinforcing the role of NO_x-related chemistry. ANOVA analysis was also performed to back up the findings from seasonal concentration trends, correlation analysis and PCA, and to provide a consistent understanding of their behavior, sources and atmospheric dynamics. The findings under consideration demonstrate the combined effects of precursor availability, emission strength, and meteorology on reactive nitrogen compounds and nitrosamines.