Air Quality Atmosphere and Health最新文献

Urban air quality, particularly the concentration of fine particulate matter less than 2.5 micrometers in diameter (PM_2.5), poses a significant environmental and public health challenge, necessitating accurate forecasting in major metropolitan areas. This study develops and evaluates daily PM_2.5 concentration forecasting models for Mashhad, one of Iran’s largest metropolitan centers. Long Short-Term Memory (LSTM) neural networks were utilized to model the complex PM_2.5 concentration time series at three air quality monitoring stations: Taghiabad, Nakhrisi, and Resalat. The input dataset comprised the previous day’s mean PM_2.5 concentration, meteorological parameters (temperature, relative humidity, wind speed and direction, precipitation, and solar radiation), and temporal features (day of the week and month of the year), spanning the period from 2018 to 2023. A feature importance analysis was conducted using Normalized Mutual Information (NMI) to identify the most influential predictors—specifically, the previous day’s PM_2.5 concentration and relative humidity—and to exclude variables with low correlation, such as wind direction and solar radiation. To optimize forecasting performance and ensure station-specific adaptation, the LSTM model’s hyperparameters were independently tuned for each station using a Genetic Algorithm (GA). The results showed that the proposed LSTM models delivered strong and reliable performance. The Taghiabad station achieved the highest accuracy, with R² = 0.86 and RMSE = 5.82 µg/m³, followed by the Resalat and Nakhrisi stations with R² values of 0.83 and 0.73 and RMSEs of 6.37 µg/m³ and 8.36 µg/m³, respectively. These predictive accuracies highlight the considerable potential of the proposed model as an effective tool for urban air quality management and timely public health advisories in megacities with similar environmental conditions.

The widespread use of plastics has greatly improved people's daily lives, but at the same time, it has also brought serious environmental challenges. Microplastics (MPs) pose a serious threat to ecosystems and human health due to their potential toxicity and ability to carry other pollutants. Especially MPs suspended in the atmosphere, which humans continuously inhale during daily breathing. The atmosphere, as an important link in the Earth's ecosystem, plays a crucial role in the global spread of MPs. Therefore, the research on atmospheric MPs has become a hot topic of global concern. Researchers used passive or active sampling methods to obtain atmospheric MPs samples, but there were significant differences in the sampling details among various studies. The inconsistency of sampling strategies and the non standardized sampling process limit the comparability and credibility of relevant research data. This review reviews the development history of atmospheric MPs and analyzes the sampling methods used to collect atmospheric MPs in 92 articles published between 2015 and 2023, including key factors such as sampling tools, time settings, sampling locations, sources, and intake flow rates. The focus was on comparing the advantages and disadvantages of different methods and evaluating the potential impact of the sampling process on the research results. To promote in-depth research on global atmospheric MPs pollution, it is urgent to establish standardized sampling methods to ensure the accuracy and comparability of data. Highlights Analyze and compare different sampling methods for atmospheric MPs. Establish a standardized sampling method for global atmospheric MPs. Non standardized sampling methods can affect the credibility and comparability of observation results. Summarize the current research status of global atmospheric MPs pollution. The atmosphere is the main transport pathway for MPs.

Graphical abstract

Airborne microplastics (MPs) are an emerging class of particulate pollutants with profound implications for environmental and human health. This study presents the first comprehensive assessment of airborne MPs across indoor and outdoor public spaces in Kerala, India—a tropical region underrepresented in global MPs research. Air dust samples were collected from locations including bus terminals, supermarkets, educational institutions, government offices, libraries, laboratories, and hospitals. Analyses using stereomicroscopy and Raman spectroscopy revealed variations in abundance, shape, and polymer composition. The highest MP concentrations were found in bus terminals (960 ± 53 particles/kg), followed by supermarkets, while the hospitals showed the lowest concentrations (111 ± 10 particles/kg). Fibres (47.2%) were the commonly occurring morphology of microplastics, while polyethene (37.8%) and polyvinyl chloride (PVC) were the dominant polymers. Health risk assessments, such as the Polymer Risk Index (PRI), Estimated Daily Intake (EDI), and Microplastic Cancer Risk (MPCR), highlighted the major health threats posed by Poly Vinyl Chloride (PRI: 1333.4; MPCR: 11.51 mg/kg/day) and polystyrene. Infants were the most vulnerable group (EDI: 6.06 mg/kg/day) to MPs pollution than adults. These findings underscore the growing risk of MP inhalation in densely occupied public areas and call for targeted interventions, including improved plastic waste management, source control of indoor particles, and reduced plastic usage in high-traffic spaces. The study provides critical baseline data for airborne MPs in tropical India and supports the need for regulatory and epidemiological actions.

Graphical Abstract

Urban Green Infrastructure (UGI) is increasingly recognized as vital for sustainable urban development due to the ecosystem services it provides. Using 2023 data, this study employed I-Tree Canopy 7.1 to evaluate traditional gardens in Qazvin Province (~2,500 ha) in terms of their ecosystem services and economic value. The results indicate substantial air pollution reduction, with ozone being the most removed pollutant (151.45 tons annually) and carbon monoxide the least (2.79 tons annually). The gardens also contribute significantly to carbon sequestration and storage, with 30.96 kilotons of CO₂ sequestered annually (valued at $1,440,054) and 777.51 kilotons stored in total (valued at $36,165,161). In water management, the vegetation helps control surface runoff, preventing 3.2 thousand gallons per square mile each year (valued at $29). While the economic value of runoff reduction is modest, it represents a key ecosystem service; broader benefits such as flood mitigation and water quality improvement are not fully captured by the model. These findings demonstrate the measurable benefits of UGI and offer guidance for optimizing urban green spaces to enhance environmental performance.

The extensive industrialization, rapid urbanization, and strong fossil fuel use in the Middle East (ME) significantly contribute to increasing air pollution levels. Gas and oil production are the main sources of nitrogen dioxide (NO₂), methane (CH₄), ozone (O₃), sulphur dioxide (SO₂), carbon monoxide (CO), and formaldehyde (HCHO) in countries like Saudi Arabia, Iran, Iraq, and Kuwait. Current progress in satellite remote sensing (RS) has enabled record valuations of the air pollution across country borders, helping data-driven policymaking and regional assistance in handling air quality (AQ). Therefore, this study examines monthly and annual air pollutants and aerosol optical depth (AOD) in the ME from 2019 to 2024 using Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-5P images. The study specifies that highest AOD decreased from 0.5360 in 2019 to 0.5277 in 2024, while further air pollutants varies annually, such as SO₂ (0.0014 mol/m² in 2019 to 0.0027 mol/m² in 2024), CO (0.117 mol/m² in 2019 to 0.171 mol/m² in 2024), O₃ (0.146 mol/m² in 2019 to 0.155 mol/m² in 2024), and CH₄ (2304.23 ppb in 2019 to 2457.17 ppb in 2024). In ME countries where CH₄ emissions from fossil fuel processes are dominant (like Saudi Arabia, Iran, and Iraq), measured stages often surpass 1900 ppb. Although CO and HCHO trends propose minor improvements, recurrent SO₂ spikes exemplify the tenacity of high-impact emission procedures. These outlines highlight the necessity for stronger regional air pollution monitoring and mitigation plans, reflecting AQ dynamics in key ME cities.

Rapid urbanization and land use change are intensifying the Urban Heat Island (UHI) effect and its associated health risks. It is the most common problem in the cities across the Global South. Chattogram the second-largest city in Bangladesh that has highly urbanized and experiencing transformation of croplands and vegetation into impervious surfaces. The changes contribute to the increase in the surface temperature and vulnerability of urban residents. This study aims to determine spatiotemporal patterns of UHI between 2011 and 2024 and the Heat Vulnerability Index (HVI) for 2024 and its associated health risk of Chattogram District. The HVI was developed using eighteen geospatial, demographic, and socioeconomic indicators grouped under exposure, sensitivity, and adaptive capacity. For weighting and normalizing variables, Principal Component Analysis (PCA) was used, and for spatial statistics Global Moran’s I, Local Moran’s I, and Getis-Ord Gi were used to detect clustering patterns of heat risk. Results indicate that the average Land Surface Temperature (LST) rose by 1.8° C during the study period, and hotspots were concentrated in middle Thanas of Sadarghat, Kotwali, and Double Mooring. The HVI map shows that the vulnerability is very low at 94.4% although very high vulnerability is only 0.8% and is concentrated in the urban core. These findings highlight differential exposure to heat stress in Chattogram and need to incorporate vulnerability assessment into urban planning to inform climate adaptation and reduce possible public health effects in fast-growing cities.

Global health is threatened by air pollution, as particulate matter (PM₁₀ and PM_2.5) exposure worsens cardiopulmonary diseases. Malaysia’s haze conditions mandate machine-learning forecasts beyond traditional linear models. This study examines Support Vector Regression (SVR) and Multiple Linear Regression (MLR) using five years of PM₁₀ and PM_2.5 monthly averages data (2018–2022) from four regulatory monitoring stations in Pulau Pinang to assess the efficacy of SVR and MLR models in forecasting particulate matter concentrations, predictive accuracy, error characteristics, and robustness in haze events. The analysis incorporates atmospheric and meteorological predictors; carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), Sulphur dioxide (SO₂), temperature, and aerosol optical depth (AOD). SVR consistently outperformed MLR across all locations, achieving higher coefficient of determination (R² = 0.88–0.99), lower root mean square error (RMSE = 2.1–0.71), and reduced mean absolute error (MAE = 0.96 − 0.28) relative to MLR. A scatter index (SI = 0.080 to 0.040) supports relative performance improvements by enhancing error homogeneity and stability. SVR’s performance index (PI) consistently exceeded MLR’s between 0.66 and 0.89, indicating improved generalization and dependability suggesting that SVR offers better generalization and dependability in its performance. SVR predicted the peak size and onset date of southwest monsoon haze with 50% to 65% accuracy. Seasonal investigations revealed that regional smoke transport and climatic effects increased PM_2.5 concentrations during haze events. These findings indicate that SVR is an effective framework for forecasting particulate matter in monsoon-affected urban areas, showing its potential for integration into Malaysia’s real-time air quality warning and haze management systems.

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.