Search Results (1,104)

Indoor formaldehyde pollution poses a serious threat to human health, and residential layout design plays a critical role in formaldehyde diffusion. However, current research on the correlation between typical residential layout shapes and formaldehyde diffusion mechanisms remains insufficient. This study summarizes and refines five typical residential layout prototypes in Wuhan through investigation and employs CFD simulation to analyze formaldehyde diffusion patterns under three ventilation conditions: still-air condition, southeasterlies (dominant in summer), and northwesterlies (dominant in winter), with varying airflow speeds. Key indices such as the Human Health Risk Index and the removal rates are calculated, and the spatial distribution characteristics are evaluated by integrating airflow organization properties. The findings reveal that: (1) Under unventilated conditions, formaldehyde diffusion differs markedly by layout: the T-shaped layout shows more concentrated high concentrations. (2) Ventilation leads to layout-specific accumulation (dead zones, vortices), requiring tailored removal measures. (3) Most layouts achieve optimal removal efficiency at 1 m/s airflow speed (O-shaped, I-shaped, and L-shaped layouts), whereas F-shaped layouts require >0.5 m/s in summer and >1 m/s in winter; T-shaped layouts require case-specific solutions. Based on simulations, optimized ventilation strategies (targeted window-opening sequences, high-risk zone removal measures) are proposed. Results provide insights for early-stage residential design optimization in subtropical monsoon climates, improving indoor air quality. Full article

The longitudinal impact of fine particulate matter (PM_2.5) and its specific chemical drivers on multi-system physiological dysregulation, quantified by the allostatic load index (ALI), remains unclear. Leveraging panel data from 7421 middle-aged and older adults in the China Health and Retirement Longitudinal Study (CHARLS), we employed individual fixed-effects models—strictly controlling for time-invariant confounding—to evaluate the associations of PM_2.5 and five components with ALI. Within-person increases in PM_2.5 and all evaluated components significantly elevated ALI scores. Notably, secondary inorganic aerosols (SIAs: nitrate, ammonium, and sulfate) exerted the most pronounced effects, exhibiting J-shaped non-linear dose-response curves that suggest high-concentration exposures may overwhelm homeostatic buffering thresholds. This study provides the first longitudinal evidence identifying SIAs as the primary pollutants driving cumulative multi-system physiological decline in aging populations, offering critical support for component-specific air quality management. Full article

In response to ecological degradation caused by uncontrolled dust emissions from open-pit mines, this study selected the Hequ open-pit coal mine as the study area and established a monitoring system to collect data on TSP, PM10, PM2.5, and environmental indicators across three zones: the mining pit, the main haul road, and the coal yard. The necessity of zoning was validated using the least significant difference (LSD) method. Pollutant correlations were examined using the individual air quality index (IAQI), Pearson correlation matrix analysis, and grey relational analysis. Univariate models, multiple linear regression (MLR), and principal component analysis–multiple linear regression (PCA–MLR) were applied to quantitatively analyze dust evolution patterns and the influence of environmental factors, with model accuracy verified by the mean relative error (MRE) method. The results showed significant differences in dust concentrations among the three zones. Dust concentrations of all particle sizes in the mining pit and coal yard exceeded the secondary standard limit, whereas those on the haul road only exceeded the primary limit, with pollution intensity ranked as mining pit > coal yard > haul road and PM2.5 identified as the core pollutant in all zones. Linear relationships were significant in univariate models, and multivariate fitting outperformed univariate fitting, with MLR prediction accuracy ranked as coal yard (3.02%) > haul road (9.46%) > mining pit (10.75%). In the mining pit, TSP and PM10 exhibited a strong positive correlation with atmospheric pressure, while PM2.5 showed a strong negative correlation with relative humidity. On the haul road, all particle size fractions displayed strong negative correlations with temperature and wind speed. In the coal yard, only a strong negative correlation with temperature was observed. The PCA–MLR model improved prediction accuracy by 56.63% and 13.41% compared to the direct MLR model. Comprehensive analysis indicates that the atmospheric environment of the Hequ open-pit mine urgently requires proactive restoration measures to optimize the sustainability of the ecological environment. Full article

Fine-scale ozone pollution estimation serves as a crucial prerequisite for promoting sustainable air quality management and facilitating the green industrial transition, especially in petrochemical parks where high concentrations of precursor pollutants are emitted. In this context, focusing on a typical petrochemical industrial park, this study adopts and compares both unsupervised and supervised machine learning methods to evaluate their O₃ concentration estimation performance. Six unsupervised algorithms were evaluated using the Silhouette Score (SS) and Calinski–Harabasz Index (CH). The proposed hybrid SK model (SOM + k-means) achieved the best clustering performance (SS = 0.6615, CH = 13,946.60), identifying nine meteorological patterns, two of which were strongly associated with elevated ozone. Among the supervised models, after hyperparameter tuning with 5-fold cross-validation, XGBoost outperformed support vector regression, random forest, and Convolutional Neural Networks on the held-out test set, achieving R² = 0.87, RMSE = 13.67 μg/m³, and MAE = 10.02 μg/m³. SHAP analysis indicated that northeasterly winds, moderate wind speed (≤4 m/s), low humidity, and temperatures between 10 and 20 °C were positively associated with high ozone levels, whereas southwesterly winds and high humidity showed negative associations. These findings highlight the complex interactions between meteorological factors and ozone dynamics. The proposed meteorology-driven estimation framework provides candidate indicators for ozone pollution risk screening within petrochemical industrial zones, offering preliminary meteorological references for subsequent pollution mechanism research and follow-up validated operational modeling, and contributing to regional environmental sustainability. Full article

Over the past two decades, air-quality prediction has moved from a mainly single-model paradigm toward ensemble systems that make explicit use of diversity across models, observations, and data streams. This review connects developments that are often treated separately: chemical transport model (CTM) ensembles, tree-based and hybrid machine learning ensembles, deep learning architectures, physics-informed neural networks, and distributed approaches such as federated learning. Evidence summarized from recent systematic reviews and coordinated modeling initiatives indicates that, within comparable validation settings, ensembles often outperform individual models for PM_2.5, PM₁₀, O₃, NO₂, CO, and SO₂ across a broad range of spatial scales and standard error metrics, including RMSE, MAE, and correlation. Operational CTM ensembles, such as the Copernicus Atmosphere Monitoring Service (CAMS) European system with eleven regional models, improve both forecast skill and uncertainty characterization for ozone and particulate matter. In data-driven applications, tree-based ensembles (Random Forest, gradient boosting, XGBoost, LightGBM) and hybrid deep architectures (CNN–LSTM models, attention-based multi-branch networks, graph neural networks) now form a core part of the state of the art for AQI (Air Quality Index) and particulate-matter estimation from structured and multi-source data. Reported performance can be very high on well-structured tabular datasets, with $R^2$ values above 0.99 in selected benchmarks and RMSE reductions of 23–45% relative to classical statistical baselines in multi-modal studies; however, these values are not directly interchangeable because pollutant type, prediction horizon, monitoring density, and validation design differ among studies. This review proposes a practical taxonomy of ensemble strategies and uses it to explain why diversity, rather than model count alone, is central to reliable air-quality prediction. Drawing on coordinated European and North American model-evaluation initiatives (AQMEII, HTAP) and on case studies in topographically and meteorologically complex Italian regions (the Po Valley, the Naples metropolitan area, and Campania), we show that effective ensemble design requires a balance among diversity, redundancy, computational feasibility, and interpretability. On the basis of a structured narrative synthesis, the main research gaps concern physics-informed and explainable ensemble frameworks, transferable and adaptive models, standardized benchmarks, severe-pollution-episode forecasting, and scalable distributed architectures. Open questions include how to design compact non-redundant CTM sub-ensembles and how to couple deep learning with chemical-transport physics in next-generation operational systems. Full article

Soil pollution is one of the main threats to soil ecosystem health, and the combination of microbial remediation with phytoremediation can promote contaminant degradation and contribute to the revitalization of ecosystem functions. Plant interactions with the surrounding physical and biological environments determine the formation of new ecosystems, which, similarly to natural ones, can provide several ecosystem services. Despite the increasing diffusion of phytoremediation and the large areas potentially suitable for the application of these technologies, the delivery of ecosystem services during phytoremediation interventions has rarely been investigated. In the framework of a bio-phytoremediation project, the present work explored the capacity of a new plant cover created at a site under bio-phytoremediation (area: 0.52 ha) to provide supporting, regulating, and cultural Ecosystem Services. The daily cumulative cooling effect due to plant transpiration during summer was 12.7 °C m⁻² day⁻¹, with species characterized by the highest Leaf Area Index and leaf transpiration rates (such as Chrysopogon zizanioides, evergreen shrub species, and Populus nigra) contributing the most to air cooling. Thanks to plant photosynthesis, 177 kg of C was sequestered each year, while the air pollution intercepted by the plant leaves amounted to 1.82 kg per year. After 30 months of phytoremediation, an increase in soil microbial activity indicated a progressive improvement in soil quality. Similarly, the cytometric analysis of hepatopancreatic cells from isopods indicated that the bioindicator organism actively and positively responded to the recovering environment, thus supporting the effectiveness of the ongoing remediation of soil quality. Finally, the implementation of the bio-phytoremediation intervention led to the creation of new habitats, increased ecological connectivity in the surrounding urban area, and generated an aesthetic value comparable to that of urban greenery. These analyses provided evidence that these techniques not only supported soil remediation and site securing but also contributed to improving citizen well-being in the proximity of the site and the environmental quality of the area. These significant positive externalities of phytoremediation should be considered when selecting technologies for environmental clean-up. Full article

Curbing industrial air pollution remains central to China’s sustainable transition under the dual-carbon commitment, and the digital economy has been proposed as an enabler of cleaner production. Yet most evidence centers on carbon emissions and single-dimensional digital measures, leaving the link between multi-dimensional digital development and sulfur dioxide (SO₂) emissions underexplored. We construct a multi-dimensional digital economy index (DEI) from three county-aggregated digital integration sub-indicators using principal component analysis and assemble a balanced panel of thirty provinces over 2000–2022 (N = 690). Using two-way fixed-effects estimation with cluster-robust inference, we find that DEI is negatively associated with provincial SO₂ emissions: a one-unit increase in DEI is associated with 19.2 percent lower emissions. The association survives wild cluster bootstrap inference, province-specific linear trends, alternative composite weights, two alternative SO₂ measures, and a Bartik-style exposure–shock instrumental-variable sensitivity analysis. Integration-type dimensions carry the stronger effect. A formal mediation analysis shows that digital development lowers coal reliance and raises regulation and innovation. Yet no observable channel statistically mediates the total effect. This pattern is consistent with abatement operating through emissions monitoring and compliance. The findings position digital development as a correlate of air-quality improvement and inform digital infrastructure planning under the dual-carbon framework. 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

Urban outdoor exercise spaces are important public infrastructures for physical activity, but their users may be exposed to concurrent air pollution and unfavorable thermal environmental conditions. This study developed an activity-weighted framework to assess the compound ozone–heat exposure risk in urban outdoor exercise spaces. Taking the central districts of Beijing as the study area, we integrated the mobile phone signaling-derived visitation frequency, 1 km ground-level O₃ estimates, the 30 m Landsat-derived land surface temperature (LST), the land cover composition, road network indicators, and three-dimensional building morphology variables. An activity-weighted compound ozone–heat exposure risk index (COHER) was constructed by combining the normalized daily visitation frequency, monthly mean O₃, and area of interest (AOI)-level mean LST. The results showed that the visitation frequency, O₃, and LST exhibited mismatched spatial patterns, highlighting the need for compound exposure assessment. COHER values ranged from 0.0000 to 0.1918 and were strongly right-skewed, with 49 outdoor exercise spaces identified as the top 10% high-risk sites. These high-risk spaces had a substantially higher visitation frequency and mean LST than the remaining spaces, whereas O₃ differences were small and not statistically significant. Exploratory XGBoost–SHAP analysis suggested that the built-up intensity, building height variability, and potential airflow obstruction were relatively important environmental correlates of COHER. The proposed framework provides a relative place-based screening tool for identifying priority outdoor exercise spaces for exposure-sensitive planning and risk mitigation. 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

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

In the context of SDG 7 and SDG 13 of the 2030 Sustainable Development Agenda, a new performance indicator has started to gain momentum in scientific research: renewable energy productivity. Understanding the drivers and the challenges of green energy productivity could help add on to the classical focus of renewable energy research on infrastructure, technical and economic feasibility, and environmental and social impacts, by considering the performance indicators in this field more. Only very few studies have explored the influencing factors of renewable energy productivity. Thus, this research aims to reveal the impact of social, economic, energy, and environmental variables on green energy productivity. The methodological approach involves bibliometric analyses of the literature on green energy productivity (GEP) and panel data regression models involving 16 independent variables. The main findings indicate positive effects of green taxes, female participation in the workforce, and highly educated people on GEP, pointing out the importance of green taxation, education, and gender equality in sustainable development. On the other hand, negative relationships of green energy productivity with economic growth, traditional energy variables, and air pollution were found for the European Union’s member states over 2007 and 2023. The results suggest that the analyzed European countries based their economic growth on traditional resources, with less importance given to renewable resources and green technologies, as the share of renewable resources of GDP was also negatively correlated. While private financial resources increase green energy productivity, questions about research and development investments, urbanization, and diversity index are still debatable. Full article

Urban air pollution, especially the problem of PM_2.5, is one of the major health challenges facing the planet today. To provide accurate PM_2.5 predictions despite data noise and missing data, the authors proposed a deep learning model. We constructed a Stacked Autoencoder–Convolutional Neural Network–Bidirectional Long Short-Term Memory–Long Short-Term Memory (SAE-CNN-BiLSTM-LSTM) model that (1) utilises convolutional layers to extract spatial features from the input data, (2) employs bidirectional LSTM layers to capture long-term temporal dependencies, and (3) utilises an autoencoder to learn latent representations of the data to mitigate the effects of missing data. The model was trained on a large dataset of hourly measurements of air quality and meteorological parameters collected between 2018 and 2020 in Klang, Malaysia. The performance of the model on data that were not used during training was evaluated using a range of metrics. The SAE-CNN-BiLSTM-LSTM model achieved a test RMSE of approximately 11.97 µg/m³ and an R² statistic of approximately 0.85 for PM_2.5 concentrations, outperforming the other models tested on the same datasets. The additional metrics of MAE, MAPE, Mean Bias Error, and Index of Agreement confirmed the model’s accuracy and low bias in the prediction of air pollution levels. Statistical tests, such as the Diebold–Mariano test, confirmed the significance of the model’s accuracy over the CNN-LSTM models. These findings indicate that the proposed model effectively captures the dynamics of the air pollution data. The proposed model structure efficiently achieved an accurate and lightweight model for urban air pollution forecasting. Full article

Urban Park vegetation plays a crucial role in mitigating air pollution by serving as a natural sink for gaseous and particulate pollutants, thereby enhancing the ecological sustainability of cities. Identifying tree species with high tolerance to air pollution is therefore essential for effective urban park planning and management in highly polluted urban environments. This study evaluated the air pollution tolerance of selected tree species commonly found in urban parks of Kandy City, Sri Lanka, using the Air Pollution Tolerance Index (APTI). Five tree species—Terminalia catappa (Indian almond), Cassia fistula (golden shower tree), Pongamia pinnata (Indian beech), Madhuca longifolia (butter tree), and Tabebuia rosea (pink poui)—were assessed at two urban park locations representing contrasting pollution levels, identified based on ambient SO₂, NO₂, and PM_2.5 concentrations. APTI was calculated using four leaf biochemical parameters: pH, ascorbic acid content, relative water content, and total chlorophyll content. Leaf samples were collected from ten replicates of each species at both sites. Madhuca longifolia exhibited the highest APTI values (17.06 at the HP site and 25.17 at the LP site), followed by Cassia fistula, Terminalia catappa, Tabebuia rosea, and Pongamia pinnata. These findings suggest that the identified species, particularly Madhuca longifolia and Cassia fistula, are well-suited for urban greening and can contribute to mitigating air pollution impacts. However, these findings are constrained by a single cross-sectional sampling term, limited species screening, sequential data collection variances, and fixed mathematical equations. Consequently, future research should implement continuous multi-station monitoring arrays, expand species diversity, establish localized biochemical weightings, and initiate long-term multi-seasonal tracking to resolve temporal dynamics in tropical urban ecosystems. Full article

This study focused on the spatial and temporal changes in photosynthetic pigment concentrations in the leaves of Ginkgo biloba and their integration into a new bioindicator index, the Pigment Integrity-to-Dust Ratio (PIDR), to assess urban air pollution stress on trees in Budapest, Hungary. High levels of chlorophyll and carotenoids in early summer indicated greater pigment integrity at the moderate-traffic site, whereas there were clear indications of reductions in the high-traffic area. The control site represented a low-traffic, pollution-free baseline. Chlorophyll concentrations dropped in the traffic-exposed leaves, and there were increased levels in the formation of pheophytin. It is thought that these reductions were caused by city stress. Responses of pigments were also variable at the moderate site, perhaps due to some form of recovery or adjustment in the study’s time frame. The observed negative relationships between selected pollutants and PIDR suggested that pollutant exposure was associated with pigment degradation and foliar dust deposition, although these associations should be interpreted as exploratory. The Air Pollution Tolerance Index (APTI) was significantly different between the pollution-exposed sites and the control, reflecting physiological tolerance in chronically exposed trees rather than directly measuring pigment damage. Therefore, the APTI and PIDR provide complementary information. Overall, the PIDR appears to be a promising exploratory bioindicator of physiological stress response, based on pigment concentration changes and dust deposition. Full article