Search Results (4,208)

Forecasting fine particulate matter with a diameter of 2.5 μm (PM_2.5) is critically important due to its adverse effects on human health and environmental sustainability. Although ground-based monitoring stations provide accurate measurements, their limited spatial coverage restricts large-scale PM_2.5 assessment, especially in complex urban regions. Consequently, aerosol optical depth (AOD) derived from satellite imagery, combined with advanced deep learning (DL) techniques, has emerged as an effective alternative by offering wide spatial coverage and rich spatio-temporal information. This paper proposed an optimization-driven fuzzy transformer–deep belief network (ODFT-DBN) for accurate PM_2.5 air pollution prediction. The proposed framework integrates a fuzzy inference module to model uncertainty and nonlinear environmental relationships, a transformer encoder to capture long-range spatio-temporal dependencies, and a DBN to extract hierarchical features and improve prediction robustness. In addition, a novel multi-objective gray wolf optimizer (NMOGWO) is employed to jointly optimize the model hyper-parameters and fuzzy membership functions. The proposed approach is implemented for the city of Tehran, Iran, using meteorological variables, topographical features, ground-based PM_2.5 measurements, and satellite-derived AOD data. The ODFT-DBN model is compared with several benchmark methods, including bidirectional encoder representations from transformers (BERT), transformer, long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), DBN, and extreme gradient boosting (XGBoost). Experimental results demonstrate that the proposed framework achieves superior predictive performance, attaining an R² value of 0.94 and root mean square error (RMSE) of 0.8 μg/m³. Scatter plot analyses indicate a strong agreement between predicted and observed PM_2.5 values, while the proposed model exhibits low variance, stable convergence behavior, and acceptable computational time. Overall, the results confirm the effectiveness, robustness, and practical applicability of the proposed ODFT-DBN framework for spatio-temporal PM_2.5 forecasting. Full article

South Africa faces significant challenges in monitoring air pollution from different provinces due to the sparse nature of the sensor network and heterogeneous pollutant sources. Notably, some provinces continue to record a limited amount of data on air pollution, thus making monitoring in those locations problematic. Fortunately, the capabilities of deep learning models to facilitate effective monitoring in data-scarce locations have been highlighted by researchers; however, these models within the context of transfer learning still lack transparency and uncertainty quantification. Using air pollutants and meteorological factors, this study proposes a transfer learning model for particulate matter (PM_2.5) prediction in a data-scarce region. This transfer learning (TL) model leverages an adaptive Bi-directional Gated Recurrent Unit (adaBiGRU) with explainable artificial intelligence (xAI) and uncertainty quantification (UQ) to provide a novel uncertainty-aware adaptation transfer learning (UATL_adaBiGRU) model for a data-scarce location. Variant models based on the adaBiGRU technique, such as the temporal convolution network adaBiGRU (TCN-adaBiGRU) and domain-adversarial neural network adaBiGRU (DANNadaBiGRU), are presented as comparative models. The performance evaluation metrics are root mean squared, R² score and mean squared error. The R² score of pre-trained models in source domain is adaBiGRU (0.888), DANN_adaBiGRU (0.7788) and TCN_adaBiGRU (0.876). Furthermore, other comparative TL models include GRU (0.898), MLP (0.802) and adaptive LSTM (0.886). Afterwards, the pre-trained baseline model (adaBiGRU) was fine-tuned in the target domain dataset and the unpromising result contributed to the proposition of the UATL_adaBiGRU model for a data-scarce location, with R² score of 0.9618. Uncertainty assessment metrics results were also presented for the proposed model. Ablation assessment demonstrates that each component of the UATL_adaBiGRU contributes to enhancing the predictive performance. Again, the Diebold–Mariano (DM) test statistic demonstrates a statistically significant difference between baseline model and UATL_adaBiGRU model. Finally, the local interpretable model-agnostic explanation highlights multi-scaled features as contributing towards the prediction of PM_2.5 in the target domain. In view of this result, model fine-tuning is strongly recommended to enhance the robustness of the proposed uncertainty-aware adaption model in data-limited regions in South Africa. Full article

Fine particulate matter with an aerodynamic diameter of 2.5 μm or smaller (PM2.5) is a major environmental health risk that threatens individuals’ health, quality of life, and sustainable well-being. In the digital era, protective behaviors are increasingly shaped by people’s ability to access, evaluate, and use health information from online sources. This cross-sectional descriptive correlational study examined the levels of health literacy, digital health literacy, and self-care behaviors for PM2.5 protection and examined their associations with self-care behaviors among adults in Mueang District, Samut Songkhram Province, Thailand. A proportionate stratified sample of 375 adults from 11 subdistricts completed structured questionnaires. Data were analyzed using descriptive statistics and multiple linear regression. Most participants had moderate health literacy (55.2%), digital health literacy (52.0%), and self-care behaviors for PM2.5 protection (56.3%). The health literacy and digital health literacy dimensions jointly explained 28.1% of the variance in self-care behaviors. Using digital information for health decision-making showed the largest unique association with self-care behaviors (β = 0.31), followed by decision-making for PM2.5 protection (β = 0.26) and evaluation of information credibility (β = 0.24). Understanding PM2.5 information did not contribute independently after the other literacy dimensions were considered. PM2.5 risk communication should therefore move beyond information provision and strengthen credibility assessment, information appraisal, and action-oriented decision-making while addressing socioeconomic and digital-access barriers. Full article

Rapid urbanization and climate change have intensified the interconnected challenges of surface heating, air pollution, and declining ecosystem functions, with important implications for regional sustainability. Taking Henan Province, China, as the study area, this study selected 2013, 2018, and 2023 as representative years and used land surface temperature (LST), fine particulate matter (PM2.5), ozone (O3), and net primary productivity (NPP) to characterize the thermal environment, air pollution, and carbon sequestration capacity. Pearson correlation analysis, multiple linear regression, and XGBoost-SHAP were integrated to examine bivariate associations, independent linear associations, factor importance, nonlinear responses, and potential threshold characteristics associated with natural, ecological, and anthropogenic factors. The results showed marked spatial differences in the four environmental variables. The multiple linear regression models explained 57.4–69.0% of the variation in LST, 23.8–72.0% in O3, 81.0–84.8% in PM2.5, and 57.4–62.5% in NPP. Natural factors generally showed relatively large and temporally stable standardized coefficients. Precipitation and potential evapotranspiration were positively associated with LST, whereas elevation and precipitation were negatively associated with PM2.5 and O3. NDVI showed an environmentally favorable pattern, being negatively associated with LST, PM2.5, and O3 but positively associated with NPP. Anthropogenic variables generally exhibited smaller and less temporally stable coefficients. The XGBoost models demonstrated good predictive performance, particularly for PM2.5, with R² values of 0.945, 0.920, and 0.905 in 2013, 2018, and 2023, respectively. SHAP analysis identified DEM, PRE, PET, and NDVI as the main contributors to model predictions and revealed nonlinear responses and potential threshold characteristics. These findings indicate that coordinated management of vegetation cover, hydrothermal conditions, and urban development can support heat mitigation, air pollution control, ecosystem productivity, and more sustainable, climate-resilient, and low-carbon development in rapidly urbanizing regions. Full article

Festive fireworks can substantially affect air quality by causing short-term increases in particulate matter (PM) concentrations. This study analysed spatial and temporal PM pollution patterns in three Slovenian municipalities—Novo mesto, Hrastnik, and Jesenice—during the 2025/2026 Christmas–New Year holiday period. Using descriptive statistics, threshold-based peak detection, temporal segmentation, Pearson correlation analysis, and normalized descriptive indicators, the study evaluated PM_2.5, PM₁₀, relative humidity, and CO₂ levels from late December to mid-January. Results revealed pronounced short-term pollution episodes, with PM_2.5 peaking at 109 µg/m³ in Novo mesto, 128 µg/m³ in Hrastnik, and 133 µg/m³ in Jesenice. Most peaks occurred during late-night and early-morning hours, although Jesenice showed a more dispersed peak pattern. Fine particles represented the dominant PM fraction, with mean PM_2.5/PM₁₀ ratios ranging from 0.91 to 0.93. Normalized indicators showed that Jesenice had the highest relative variability and peak-to-mean ratios despite the lowest average PM concentrations. These findings show that holiday-period air-quality assessment should consider not only average concentrations, but also short-term peak intensity, timing, and local pollution profiles. Full article

Air pollutants can cause respiratory diseases, highlighting the need for effective preventive and therapeutic strategies. We investigated the protective effects of heat-killed Lacticaseibacillus paracasei ATG-E1 against particulate matter plus diesel exhaust particle (PM₁₀D)-induced airway inflammation. BALB/c mice were intranasally injected with PM₁₀D and treated with heat-killed L. paracasei ATG-E1 via oral gavage for 5 days. In the bronchoalveolar lavage fluid (BALF) and lungs, inflammatory mediators, immune cell subtypes, and histological changes were analyzed, while gut microbiota composition was analyzed in the cecum. Heat-killed L. paracasei ATG-E1 suppressed the infiltration of immune cells, including neutrophils, T cells, and B cells. Furthermore, it decreased various inflammatory mediators, such as C-X-C Motif chemokine ligand (CXCL)-1, macrophage inflammatory protein (MIP)-2, interleukin (IL)-1α, and tumor necrosis factor (TNF)-α, in the BALF and lung tissue, as well as serum symmetric dimethylarginine (SDMA) levels in the PM₁₀D-induced airway inflammation model. Heat-killed L. paracasei ATG-E1 also exhibited a protective effect against lung damage induced by PM₁₀D. Furthermore, heat-killed L. paracasei ATG-E1 treatment shifted the gut microbiota composition, increasing several bacterial genera. The data demonstrate that heat-killed L. paracasei ATG-E1 acts as a protective agent against air pollutant-induced lung injury, suggesting its potential as a candidate adjunctive strategy for prevention. Full article

This study investigated indoor environmental air quality (IEAQ) across university workspaces at a higher education institution in Sharjah, United Arab Emirates (UAE), assessing environmental conditions that may influence occupant health, the surrounding environment, and sustainability. Physical parameters (temperature, relative humidity, noise, and illuminance), chemical parameters (indoor gases and particulate matter), and biological contaminants (airborne bacteria and fungi) were measured in semi-occupied indoor environments with a total of 68 random samples collected and analyzed. Perceived heat discomfort and environmental variability were assessed using the Thom Discomfort Index (TDI), Humidex Index, ANOVA, Kruskal–Wallis, Mann–Whitney U, and one-sample t-tests. Average measurements of relative humidity, temperature, noise, and illuminance were 60.7%, 21.6 °C, 57.5 dB, and 440 lux, respectively. Average concentrations of PM_2.5, PM₁₀, CO, and CO₂ were 1223 ppm, 104 ppm, 1 ppm, and 623 ppm, respectively. Microbial contamination was generally insignificant across most investigated workspaces. While most measured parameters remained within recommended threshold limit values (TLVs), elevated levels of noise, illuminance, and particulate matter were observed in selected workspaces. These findings demonstrate that university indoor environments generally maintain acceptable air quality conditions; however, targeted interventions, including improved HVAC maintenance and indoor pollutant management, are required to enhance sustainable university indoor environments and optimize occupant comfort. Full article

Cross-state air pollution can degrade air quality in regions surrounding urban areas. However, assessing health impacts and economic costs requires quantifying annual pollutant transport. This type of annual assessment poses significant technical challenges, including the need to differentiate between external and local pollution, which have different spatial distributions; the large number of required air quality simulations; and the extensive post-processing of results. The Mexico City Metropolitan Area is located within the most polluted airshed in the country and exports large quantities of atmospheric pollutants to neighboring airsheds; however, until now, the annual magnitude of the impact on the population and its associated costs had not been quantified. This paper presents the estimation of potentially avoidable premature mortality and the annual economic cost associated with importing PM_2.5 from the Mexico City Metropolitan Area into the metropolitan areas of Toluca and Cuernavaca. These estimates are based on 8640 simulations of air quality using the Hysplit 5.3.0 model, corresponding to each hour of 360 days in one year. Geoprocessing tools specifically designed in ArcGIS 10.4 were used to automate the calculations of PM_2.5 mass exchange, and the premature mortality and health costs were calculated using the BenMap-CE 1.5.0.4 program. Results indicate that the export of PM_2.5 from the Mexico City Metropolitan Area in 2018 may have resulted in 19,473 potentially avoidable premature deaths in the two recipient metropolitan areas. This impact could represent an annual economic cost of $12,197 million for the Toluca Valley Metropolitan Area and $4140 million for the Cuernavaca Metropolitan Area. Full article

Air pollution in Almaty, Kazakhstan, poses a critical public health challenge intensified by the city’s basin topography and seasonal thermal inversions that trap anthropogenic emissions. The sparse stationary network (~5 stations for ~2 million inhabitants) lacks the spatial and temporal resolution needed to capture intra-urban variability. We present the design, deployment, and field evaluation of a low-cost distributed Internet of Things (IoT) network of six custom nodes—Winsen ZPHS01B multi-parameter modules with Raspberry Pi Zero 2 W edge units, at an estimated principal-component cost of ~US$100 per node—operated during a winter-spring campaign (February–April 2025) and yielding over 70,000 measurements of PM_2.5, PM₁₀, CO₂, temperature, and relative humidity. The system’s novelty lies in three integrated engineering features: an Active Airflow Stabilization enclosure that decouples sampling from external wind, context-aware adaptive edge filtering that reduces transmitted data volume by ~40%, and a secure Edge-DMZ-Core telemetry pipeline. Node readings were cross-validated against a Qingping Air Monitor Pro with documented traceability to FEM-grade reference analyzers (R² = 0.89–0.95), and city-scale consistency was confirmed against the national monitoring dashboard; the network is therefore characterized as providing internally consistent low-cost observations rather than reference-equivalent concentrations. Daily mean PM_2.5 exceeded the WHO 24 h guideline (15 µg/m³) on 84% of monitored days, with February concentrations (54.4 µg/m³) significantly above March (21.9 µg/m³; p < 0.001). A high PM_2.5/PM₁₀ ratio (~0.96), measured at the physically consistent nodes, together with higher weekend concentrations, points to coal-based residential heating as the most likely dominant source. A coupled WRF-SILAM framework is configured for future model-observation integration. The system offers a reproducible, scalable, and cost-effective template for ambient particulate monitoring in resource-constrained cities with complex terrain. Full article

Air quality in China has markedly improved over the past decade, yet pollution levels remain high and continue to threaten public health. This study analyzed the spatiotemporal variations in six air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, O₃, CO) across seven regions in China (2015–2024) using Kriging interpolation. The performance of Recurrent Neural Network (RNN), Long Short-term Memory (LSTM), and Convolutional Neural Network-Long Short-term Memory (CNN-LSTM) models was assessed using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) metrics. Results showed that all pollutants exhibited overall declining trends, with SO₂ depicting the largest reduction (69.53%), while O₃ displayed intermittent increases from 2017 to 2024. North China recorded both the highest concentrations and the greatest reductions in PM_2.5, SO₂, and CO, whereas Southwest and South China maintained the lowest overall levels. Among the predictive models, LSTM achieved the highest overall accuracy (mean RMSE = 1.802, mean MAE = 0.915, R² > 0.99). These findings provide a comprehensive depiction of China’s air pollution evolution and highlight the potential of deep learning for region-specific air quality prediction and policy design. The results offer a quantitative foundation for optimizing differentiated control strategies and advancing precision air quality management. Full article

The Beijing–Tianjin–Hebei (BTH) region faces complex air pollution driven by alternating particulate matter (PM) and ozone (O₃) dominance, regional transport, topography, and meteorology. This study develops a hybrid framework integrating air quality index (AQI) records, pollutants, meteorological variables, and MEIC emissions from the BTH region (2018–2025) to capture spatiotemporal evolution and short-term predictability. Results show a seasonal AQI cycle (winter/spring highs, summer/autumn lows) with a summer PM–O₃ seesaw. Spatially, three zones were identified: the northern and coastal ecological barrier zone, the central compound-pollution plain zone, and the southern heavy-industrial zone. Random Forest identifies PM as the dominant AQI compositional contributor, with visibility, dew point, humidity, and MEIC emissions (particulates, NH₃, organics) as key correlates. Forecast evaluation reveals progressive improvement: ARMA captures linear baselines (R² = 0.318, MAPE = 33.26%), XGBoost improves statistical prediction by incorporating nonlinear feature interactions and lagged meteorology (R² = 0.567, MAPE = 24.81%), and LSTM shows the strongest statistical predictive performance (R² = 0.613, MAPE = 22.32%). The improvement of LSTM over XGBoost is incremental and reflects enhanced data-driven representation of short-term AQI–meteorology temporal dependence, rather than identification of physical pollution mechanisms. Regional disparities persist, with higher predictability in the southern heavy-industrial zone and lower accuracy in the northern and coastal ecological barrier zone affected by intermittent dust intrusions and frontal passages. Overall, the results suggest that LSTM may support data-driven short-term AQI warning, but source-oriented mitigation still requires process-based tools, such as chemical-transport or source-apportionment models. Full article

Sustainable air-quality governance requires not only local emission reduction but also a shift from single-pollutant control to coordinated PM_2.5–O₃ control, and from city-bounded management to regional governance under spatial spillovers. Based on balanced annual city-level panel data for the “2 + 26” urban agglomeration in the Beijing–Tianjin–Hebei region and surrounding areas from 2013 to 2020, this paper uses the dynamic Spatial Durbin Model (SDM) to analyze the spatial spillover effect of PM_2.5 and O₃ pollution and the effect of regional governance policies. The results show that both PM_2.5 and O₃ exhibit significant spatial autocorrelation and cross-city dependence, indicating that isolated local control measures are insufficient for sustainable air pollution prevention and that city-bounded governance cannot fully address regionally connected pollution risks. Economic output and secondary-industry employment remain important structural factors of pollution. The policy-text analysis shows that measures centered on coal-related control and industrial governance were more directly aligned with PM_2.5 reduction, whereas O₃-related governance lagged, suggesting that single-pollutant-oriented control may generate a sustainability trade-off when PM_2.5 reduction is not accompanied by coordinated O₃ control. These findings highlight two sustainability challenges in China’s regional air-quality governance: first, single-pollutant control can improve particulate pollution but may not ensure sustainable air-quality improvement when O₃ and its precursors are insufficiently addressed; second, isolated city-level governance may be insufficient when pollution outcomes exhibit significant spatial dependence across administrative boundaries. The study provides empirical evidence for sustainable air-quality governance by emphasizing differentiated PM_2.5 and O₃ responses, coordinated PM_2.5–O₃ control, regional governance beyond individual city boundaries, and the integration of spatial spillover assessment into regional environmental policy design. Full article

This study examines the seasonal and diurnal variations in ultrafine particles (PM_0.1) and their carbonaceous components (OC and EC), collected at the Institute of Technology of Cambodia in Phnom Penh. Sampling was conducted over 14 consecutive days in September 2024 (during the wet season) and February 2025 (during the dry season). The average mass concentration of PM_0.1 in February (8.5 μg/m³; range: 3.9–11.3 μg/m³) was approximately three times greater than that in September, driven by a corresponding increase in OC concentration. Conversely, average EC concentrations remained almost stable across both seasons, indicating consistent local emission sources. Total carbonaceous compounds (OC + EC) constitute approximately 50% of the PM_0.1 mass in both seasons. Primary organic carbon (POC) concentration increases almost four times in February compared to September. Secondary organic carbon (SOC) concentrations were significantly elevated during February daytime (1.4 ± 1.0 μg/m³), indicating active photochemical formation. Backward trajectory analysis and satellite hotspot data revealed that September air masses originated from maritime sources without significant local burning influences, while February pollution events were likely influenced by short-range transboundary transport from biomass-burning areas across the Cambodia–Vietnam border. Full article

Ambient fine particulate matter (${\mathrm{PM}}_{2.5}$) poses a significant health risk in Europe, where many cities are exposed to levels exceeding WHO and EU guidelines. Reducing population exposure, therefore, calls for targeted and effective mitigation strategies. To support the implementation of optimal ${\mathrm{PM}}_{2.5}$ reduction policies, high-resolution air quality modeling is necessary. In this context, source allocation studies aim to link the pollution at a specific location to different emitters, typically expressing the contribution of each in terms of concentration differences. An alternative approach is the use of relative potentials, defined as the share of ${\mathrm{PM}}_{2.5}$ concentration reduced at a given receptor resulting from the reduction in the emissions from a given source. To calculate relative potentials, Source-Receptor Relationships (SRRs) can be used to mimic Chemical Transport Models, saving significant computation time when simulating emission reduction scenarios. However, while the relative potential indicator is increasingly used to guide source allocation analyses, its robustness with respect to meteorological variability has not been systematically evaluated. Given that meteorology can be a major driver of ${\mathrm{PM}}_{2.5}$ inter-annual variability, assessing this robustness is a prerequisite for the optimal use of SRRs in air quality planning. To address this gap, we use the SRR model SHERPA, based on the Chemical Transport Model EMEP, to evaluate the robustness of relative potentials of 150 European cities across four contrasting meteorological years (2015, 2017, 2019, and 2021). The contributions of four spatial reduction scales, six emission sectors and five emission precursors are analyzed. Our results show that relative potentials vary little with meteorology for most cities, with low inter-annual ranges for most spatial scales, precursors and sectors. These trends are consistent with EMEP simulations. They establish the robustness of the relative potential indicator and of SRR-based source allocations with respect to meteorological variability, supporting their use in guiding targeted air quality policies in Europe. Full article

Fuel poverty is shaped by interacting social, environmental and housing conditions, yet these links remain underexplored at city scale. The analysis is framed as an ecological, cross-sectional assessment of spatial associations rather than as a causal proof of a closed feedback mechanism. This study examines the relationship between fuel poverty, deprivation, particulate air pollution and housing typology across 54 wards in Liverpool, UK. Ward-level fuel poverty and Index of Multiple Deprivation (IMD) data were integrated with 2023–2024 annual mean particulate matter (PM_2.5 and PM₁₀) from 58 low-cost air-quality sensors and classified housing types. Regression models were used to compare individual, additive and interaction effects. Fuel poverty ranged from 12.4% to 25.29%, while PM_2.5 and PM₁₀ frequently exceeded World Health Organization guideline values. IMD was the strongest individual predictor of fuel poverty (R² = 0.281, $p<0.001$). The preferred additive model including IMD, PM_2.5, PM₁₀ and housing type explained 43.5% of the variance, with Victorian Terraces emerging as a significant risk factor. Although interaction models suggested pollution-deprivation coupling, model selection and uncertainty diagnostics favoured the simpler additive specification. The findings support targeted retrofit, fuel-poverty and emissions-control policies in deprived urban neighbourhoods where inefficient housing and environmental stressors compound energy insecurity and where local action can contribute to more equitable urban sustainability. Full article