Search Results (4,165)

Long-term and accurate prediction of air pollutant concentrations can serve as a foundation for air pollution warning and prevention, which is crucial for social development and human health. In this study, we provide a model for predicting the concentration of air pollutants based on big data spatiotemporal correlation analysis and deep learning methods. Based on an improved temporal convolutional network (TCN) and a bi-directional gated recurrent unit (BiGRU) as the fundamental architecture, the model adds two attention mechanisms to improve performance: Squeeze and Excitation Networks (SENet) and Convolutional Block Attention Module (CBAM). The improved TCN moves the residual connection layer to the network’s front end as a preprocessing procedure, improving the model’s performance and operating efficiency, particularly for big data jobs like air pollution concentration prediction. The use of SENet improves the model’s comprehension and extraction of long-term dependent features from pollutants and meteorological data. The incorporation of CBAM enhances the model’s perception ability towards key local regions through an attention mechanism in the spatial dimension of the feature map. The TCN-SENet-BiGRU-CBAM model successfully realizes the prediction of air pollutant concentrations by extracting the spatiotemporal features of the data. Compared with previous advanced deep learning models, the model has higher prediction accuracy and generalization ability. The model is suitable for prediction tasks from 1 to 12 h in the future, with root mean square error (RMSE) and mean absolute error (MAE) ranging from 5.309~14.043 and 3.507~9.200, respectively. Full article

Accurate prediction of water quality involves early identification of future pollutant concentrations and water quality indicators, which is an important prerequisite for optimizing water environment management. Although deep learning algorithms have demonstrated considerable potential in predicting water quality parameters, their broader adoption remains hindered by limited interpretability. This study proposes an interpretable deep learning framework integrating an artificial neural network (ANN) model with Shapley additive explanations (SHAP) analysis to predict spatiotemporal variations in water quality and identify key influencing factors. A case study was conducted in the Poyang Lake Basin, utilizing multi-dimensional datasets encompassing topographic, meteorological, socioeconomic, and land use variables. Results indicated that the ANN model exhibited strong predictive performance for dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP), permanganate index (CODMn), ammonia nitrogen (NH₃N), and turbidity (Turb), achieving R² values ranging from 0.47 to 0.77. Incorporating land use and socioeconomic factors enhanced prediction accuracy by 37.8–246.7% compared to models using only meteorological data. SHAP analysis revealed differences in the dominant factors influencing various water quality parameters. Specifically, cropland area, forest cover, air temperature, and slope in each sub-basin were identified as the most important variables affecting water quality parameters in the case area. These findings provide scientific support for the intelligent management of the regional water environment. Full article

In this study, two air pollution abatement strategies are examined, focusing on sources and sinks. These include the reduction in ozone precursors (source) and impact of nature-based solutions (sink). For the first abatement strategy (source), two waves of COVID-19 lockdown periods are leveraged as proxies for the potential abatement of air quality pollutants in Toronto, Ontario, Canada, that could occur through electric vehicle deployment. Ground level ozone (O₃) and its precursors (NO, NO₂), were examined from April to December 2020, during the first two pandemic lockdown periods in Toronto. An ozone weekend effect framework was used to evaluate changes. Results showed that ozone precursors were the lowest of any of the preceding 10 years for both weekdays and weekends; however, ozone concentrations did not have a corresponding decrease but rather had a marked increase for both weekdays and weekends. These findings reflect reduced vehicular traffic and the ozone chemistry in an NOx-saturated (VOC-limited) environment. For the second abatement strategy (sink), a comparison of surface NO₂ observations and NO₂ satellite data showed the benefits of nature-based solutions as a sink abatement strategy, with the 2020 reduction amplified at the surface. Given the lack of ozone abatement realized through source reduction, deployment of nature-based solutions as a pollutant sink may present a more effective strategy for ground-level ozone abatement. Full article

In the present investigation, carbon dioxide (CO₂), carbon monoxide (CO), hydrocarbons (HC), nitric oxides (NO_X), particulate matter (PM), and fuel consumption were measured in a compression ignition internal combustion engine on a road route cycle in Quito, Ecuador. We used premium diesel and a mixture of diesel and cerium oxide at a concentration of 250 ppm. This research aimed to investigate the impact of cerium oxide on fossil fuels in terms of CO₂, CO, HC, NOx, PM, and fuel consumption. Five repetitions were performed for each fuel, and the results obtained were statistically analyzed using control charts. The experimental results showed a 27.1% reduction in PM, a 24.9% increase in NOx, and a 24.2% increase in HC, along with a 1% decrease in fuel consumption compared to the premium diesel case. We observed that the reduction in PM was due to the catalytic action of CeO₂, which enhances carbon oxidation. On the other hand, the increase in NOx was related to the higher temperature in the combustion chamber resulting from the improved thermal efficiency of the engine. This study provides guidelines for controlling air pollutants originating from vehicle emissions in high-altitude (over 2000 masl) road operations using cerium oxide as an additive. Full article

Volatile organic compounds (VOCs) are common indoor air pollutants known to pose significant health risks, yet little is known about how internal exposure varies across populations and environments. This study investigated the associations between indoor air pollutants and urinary VOC biomarkers in a nationally representative sample. We analyzed data from 1880 adults in the eighth Korea National Health and Nutrition Examination Survey (2020–2021) who completed an indoor air quality (IAQ) survey and provided urine samples, assessing the influence of sociodemographic, behavioral, and environmental factors. Indoor concentrations of PM_2.5, CO₂, formaldehyde, total VOCs, benzene, ethylbenzene, toluene, xylene, and styrene were measured, alongside the urinary concentrations of nine VOC biomarkers. Associations between pollutants, sociodemographic variables, and biomarkers were evaluated using univariate and multivariable linear regression with Bonferroni correction. Older age, female, lower socioeconomic status (SES), and smoking were associated with higher urinary VOC biomarker concentrations, with smoking showing the strongest associations. Indoor ethylbenzene, styrene, benzene, and CO₂ were also associated with multiple metabolites. These findings indicated significant associations between household air pollutants and urinary VOC metabolites, with disparities by age, sex, SES, and smoking status, underscoring the importance of targeted IAQ interventions for vulnerable populations. Full article

In this study, typical haze pollution influenced by biomass burning (BB) activities in Changsha in the autumn of 2024 was investigated through the mixing state and evolution process of BB particles via the real-time measurement of single-particle aerosol mass spectrometry (SPAMS). From the clean period to the haze period, the PM_2.5 concentration increased from 25 μg·m⁻³ at 12:00 to 273 μg·m⁻³ at 21:00 on 12 October, and the proportion of total BB single particles in the total detected particles increased from 17.2% to 54%. This indicates that the rapid increase in PM_2.5 concentration was accompanied by a concurrent increase in the contribution of particles originating from BB sources. The detected BB particles were classified into two types based on their mixing states and temporal variations: BB1 and BB2, which accounted for 71.7% and 28.3% of the total BB particles, respectively. The analysis of backward trajectories and fire spots suggested that BB1 particles originated from straw burning emissions at northern Changsha, while BB2 particles were primarily related to local nighttime cooking emissions in Changsha. In addition, a special type of K-containing single particles without K cluster ions was found closely associated with BB1 type particles, which were designated as secondarily processed BB particles (BB-sec). The BB-sec particles contained abundant sulfate and ammonium signals and showed lagged appearance after the peak of BB1-type particles, which was possibly due to the aging and formation of ammonium sulfate on the freshly emitted particles. In all, this study provides insights into understanding the substantial impact of BB sources on regional air quality during the crop harvest season and the appropriate disposal of crop straw, including conversion into high-efficiency fuel through secondary processing or clean energy via biological fermentation, which is of great significance for the mitigation of local haze pollution. Full article

Air pollution has a significant impact on the housing market, both in terms of property prices and buyer preferences, as well as urban development. Below, we present the main aspects of this impact. These may include a decline in property values in polluted areas, a change in buyer preferences (more buyers are taking environmental factors into account when choosing a home, including air quality—both outdoor and indoor—which translates into increased demand in ‘green’ neighborhoods), the development of energy-efficient and environmentally friendly buildings, the impact on spatial planning and urban policy, health effects, and the rental market. The study showed that air pollution has a significant negative impact on housing prices in Warsaw, particularly in relation to two pollutants: nitrogen dioxide (NO₂) and particulate matter (PM_2.5). As their concentrations decreased, housing prices increased, with the highest price sensitivity observed for smaller flats on the secondary market. The analysis used GRM and OLS statistical models, which confirmed the significance of the relationship between the concentrations of these pollutants and housing prices (per m²). NO₂ had a significant impact on prices in the primary market and on the largest flats in the secondary market, while PM_2.5 affected prices of smaller flats in the secondary market. No significant impact of other pollutants, meteorological factors, or their interaction on housing prices was detected. The study also showed that the primary and secondary markets differ significantly, requiring separate analyses. Attempts to combine them do not allow for the precise identification of key price-determining factors. Full article

PM_2.5 is an air pollutant that has a direct link to increased cardiovascular and respiratory morbidity and mortality, which has been demonstrated in numerous studies. Existing research highlights species-specific variations in the capacity of trees to capture and retain particulate matter (PM). However, a critical gap remains regarding sensitivity analyses of i-Tree Eco model assumptions. Such analyses are crucial for validating the model’s PM deposition estimates against empirically derived efficiencies, a deficiency that the present study addresses. The study consisted of two steps: a tree inventory was carried out at three selected sites, based on which, an ecosystem service analysis was performed using i-Tree Eco, and samples were taken from the leaves of trees at the analysed sites, which were the basis for comparing the data from the i-Tree Eco method and laboratory methods. The study focused on comparing PM_2.5 and PM₁₀ removal estimates derived from both the model and laboratory measurements. The results revealed significant discrepancies between the modelled and laboratory values. A comparison of the average annual PM₁₀ accumulation measured using laboratory methods for individual tree species showed that Tilia sp. achieved 24%, Fraxinus sp. 47.6%, Aesculus sp. 50.77%, and Quercus robur 23.4% of the PM₁₀ uptake efficiency estimated by the i-Tree Eco model. For PM_2.5 uptake, the values obtained through both methods were more consistent. Furthermore, trees growing under more challenging environmental conditions exhibited smaller diameter at breast height (DBH) and lower PM₁₀ and PM_2.5 removal efficiency according to both methods. While I-Tree Eco incorporates tree biophysical characteristics and health status, its methodology currently lacks the resolution to reflect site-specific environmental conditions and local pollutant concentrations at the individual tree level. Therefore, laboratory methods are indispensable for calibrating, validating, and supplementing i-Tree Eco estimates, especially when applied to diverse urban environments. Only the combined application of empirical and model-based methods provides a comprehensive understanding of the potential of urban greenery to improve air quality. Full article

Usually, biomass combustion in grate-fired boilers presents significant challenges due to the heterogeneous nature of the fuel, chemical composition variability, and complex thermal and chemical conversion processes along the grate. Accurate modeling of the fuel bed conversion is critical for optimizing combustion performance and reducing emissions. However, detailed bed models are often computationally intensive and time-consuming. To address this issue, the present work details a simplified empirical bed model that is integrated into a 3D computational fluid dynamics (CFD) simulation of a 35 MW industrial grate-fired boiler. The model successfully reproduced the flue gas mass flow rate, temperature, and chemical composition across different grate sections, predicting an average furnace outlet temperature of 994 °C, CO mass fraction of 779 mg/m³, CO₂ concentration of 12 vol.%, and O₂ concentration of 9.5 vol.%. These results fall within the range reported in recent CFD studies of similar systems and are consistent with operational monitoring data from the same plant. Sensitivity analyses showed that modifying the primary-to-secondary-air split ratio from 79/21 to 40/60 reduced the average CO mass fraction at the furnace outlet by more than 50%. Additionally, the average furnace temperature increased up to 1050 °C, enhancing combustion efficiency. The CFD model also demonstrated that relocating char combustion to later grate sections led to temperature imbalances near the boiler walls, emphasizing the importance of grate-specific conversion profiles. These results underscore the model’s ability to guide air distribution optimization, improve combustion performance, and reduce pollutant emissions in biomass boilers. Full article

Atmospheric aerosols are recognized as a major air pollutant with significant impacts on human health, air quality, and climate. Yet, the chemical composition and seasonal variability of aerosols remain underexplored in several Western Mediterranean regions. This study presents a year-long investigation of PM_2.5 and PM₁₀ in Tetouan, Northern Morocco, where both local emissions and regional transport influence air quality. PM_2.5 and PM₁₀ samples were collected and analysed for total mass and comprehensive chemical characterization, including organic carbon (OC), elemental carbon (EC), water-soluble ions (WSIs), and sugar tracers (levoglucosan, arabitol, and glucose). Concentration-weighted trajectory (CWT) modelling and air mass back-trajectory analyses were used to assess potential source regions and transport pathways. PM_2.5 concentrations ranged from 4.2 to 41.8 µg m⁻³ (annual mean: 18.0 ± 6.4 µg m⁻³), while PM₁₀ ranged from 11.9 to 66.3 µg m⁻³ (annual mean: 30.8 ± 9.7 µg m⁻³), with peaks in winter and minima in spring. The PM_2.5-to-PM₁₀ ratio averaged 0.59, indicating a substantial accumulation of particle mass within the fine fraction, especially during the cold season. Carbonaceous aerosols dominated the fine fraction, with total carbonaceous aerosol (TCA) contributing ~52% to PM_2.5 and ~34% to PM₁₀. Secondary organic carbon (SOC) accounted for up to 90% of OC in PM_2.5, reaching 7.3 ± 3.4 µg m⁻³ in winter. WSIs comprised ~39% of PM_2.5 mass, with sulfate, nitrate, and ammonium as major components, peaking in summer. Sugar tracers exhibited coarse-mode dominance, reflecting biomass burning and biogenic activity. Concentration-weighted trajectory and back-trajectory analyses identified the Mediterranean Basin and Iberian Peninsula as dominant source regions, in addition to local urban emissions. Overall, this study attempts to fill a critical knowledge gap in Southwestern Mediterranean aerosol research by providing a comprehensive characterization of PM_2.5 and PM₁₀ chemical composition and their seasonal dynamics in Tetouan. It further offers new insights into how a combination of local emissions and regional transport shapes the aerosol composition in this North African urban environment. Full article

The incidence of allergic diseases has increased notably in recent years. The reasons for this increase include air pollution, diet, and infectious factors. This study aims to analyze the interactions between aeroallergens, environmental pollutants, and meteorological factors and their impact on allergenic sensitization in Toledo, Spain. An aerobiological study was conducted over the past 30 years (1994–2023) using a Burkard collector and the SEAIC (Spanish Society of Allergology and Clinical Immunology) methodology. Meteorological data were obtained from the State Meteorological Agency (AEMET) and pollutant data were acquired from the Castilla-La Mancha Air Quality Monitoring Network. Patients presenting with seasonal allergic symptoms at the University Hospital of Toledo were selected for skin testing with various types of airborne pollen. A total of twenty pollen taxa were identified in the Toledo atmosphere, as follows: Cupressaceae (26.53%); Olea europaea (21.62%); Quercus (21.12%); Poaceae (10.30%); Urticaceae (2.58%); Plantago (2.48%); Platanus (2.00%); Amaranthaceae (1.72%); Rumex (1.68%); and Morus, Pistacia, Populus, Artemisia, Fraxinus, Alnus, Carex, and Ericaceae (less than 1% each). The average temperature increased by 1.2 °C, while the level of precipitation remained stable. Among all pollutants, only a moderate increase in ozone levels was observed; however, the concentrations of particulate matter and nitrogen oxides decreased. The prevalence of pollen sensitization in allergic patients ranged from 8% for Pinus nigra to 84% for Phleum pratense. In conclusion, the rise in temperature due to climate change, coupled with high concentrations of pollutants such as ozone, can result in increased concentrations of the main types of wind-borne pollen. Thus, this can lead to a greater sensitivity to pollen and, consequently, more people becoming allergic to pollen. Full article

Atmospheric pollution constitutes one of the key environmental challenges hindering Atmospheric pollution is a key environmental challenge constraining the sustainable development of Gansu Province’s land-based Belt and Road corridor and its regional ecological barrier function. The spatiotemporal heterogeneity of aerosol optical depth (AOD) profoundly impacts regional environmental quality. Based on MODIS AOD, NCEP reanalysis, and emission data, this study employed trend analysis (Mann–Kendall test) and attribution analysis (multiple linear regression combined with LMG and Spearman correlation) to investigate the spatiotemporal evolution of AOD over Gansu Province during 2009–2019 and its meteorological and emission drivers. Key findings include the following: (1) AOD exhibited significant spatial heterogeneity, with high values concentrated in the Hexi Corridor and central regions; monthly variation showed a unimodal pattern (peak value of 0.293 in April); and AOD generally declined slowly province-wide during 2009–2019 (52.8% of the area showed significant decreases). (2) Following the implementation of the Air Pollution Prevention and Control Action Plan in 2013 (2014–2019), AOD trends stabilized or declined in 99.8% of the area, indicating significant improvement. (3) Meteorological influences displayed distinct regional-seasonal specificity—the Hexi Corridor (arid zone) was characterized by strong negative correlations with relative humidity (RH2) and wind speed (WS) year-round, and positive correlations with temperature (T2) in spring but negative in summer in the north; the Hedong region (industrial zone) featured strong positive correlations with planetary boundary layer height (PBLH) in summer (r > 0.6) and with T2 in spring/summer; and the Gannan Plateau (alpine zone) showed positive WS correlations in spring and weak positive RH2 correlations in spring/autumn, highlighting the decisive regulatory role of underlying surface properties. (4) Emission factors (PM_2.5, ${\mathrm{SO}}_4^{2-}$, ${\mathrm{NO}}_3^{-}$, ${\mathrm{NH}}_4^{+}$, OM, and BC) dominated (>50% relative contribution) in 80% of seasonal scenarios, prevailing in most regions (Hexi: 71–95% year-round; Hedong: 68–80% year-round; and Gannan: 69–72% in spring/summer). Key components included BC (contributing > 30% in 11 seasons, e.g., 52.5% in Hedong summer), ${\mathrm{NO}}_3^{-}$ + ${\mathrm{NH}}_4^{+}$ (>57% in Hexi summer/autumn), and OM (20.3% in Gannan summer, 19.0% province-wide spring). Meteorological factors were the primary driver exclusively in Gannan winter (82%, T2-dominated) and province-wide summer (67%, RH2 + WS-dominated). In conclusion, Gansu’s AOD evolution is co-driven by emission factors (dominant province-wide) and meteorological factors (regionally and seasonally specific). Post-2013 environmental policies effectively promoted regional air quality improvement, providing a scientific basis for differentiated aerosol pollution control in arid, industrial, and alpine zones. Full article

Plastic pollution in river systems is a growing concern, especially in the Mekong Delta, where complex tidal dynamics facilitate downstream transport of plastic waste into the marine environment. This study assessed the density, composition, and temporal variability of floating plastic waste in the Hau River, approximately 30 km upstream of the Tran De River estuary. Floating net traps were deployed during both ebb and flood tides to quantify plastic waste with simultaneous meteorological and hydrological monitoring. The findings highlight that key meteorological factors, such as air temperature, humidity, wind speed, and wind direction, were found to indirectly influence plastic transport by altering surface currents and promoting plastic degradation. Meanwhile, hydrological conditions, especially tidal variability, play a direct and dominant role in determining the spatial and temporal distribution of plastic waste. Plastic debris was diverse in terms of items during both tidal phases. Although the number of plastic pieces was higher at ebb tide (134.33 pieces/h), the volume and concentration of plastic were greater at flood tide (1.22 kg/h and 0.73 kg/m³) than at ebb tide (0.81 kg/h and 0.29 kg/m³). Macroplastic debris was almost dominant during both ebb tide (97.29%) and flood tide (93.96%) compared to megaplastic and mesoplastic size. These findings highlight the importance of integrating tidal and climate factors into plastic waste management and support targeted interventions to reduce plastic discharge into coastal ecosystems. Full article

This study presents real-time measurements of particulate matter (PM₁, PM_2.5, PM₁₀) and carbon dioxide (CO₂) concentrations across five university indoor environments with varying occupancy levels and natural ventilation conditions. CO₂ concentrations frequently exceeded the 1000 ppm guideline, with peak values reaching 3018 ppm and 2715 ppm in lecture spaces, whereas one workshop environment maintained levels well below limits (mean = 668 ppm). PM concentrations varied widely: PM₁₀ reached 541.5 µg/m³ in a carpeted amphitheater, significantly surpassing the 50 µg/m³ legal daily limit, while a well-ventilated classroom exhibited lower levels despite moderate occupancy (PM₁₀ max = 116.9 µg/m³). Elevated PM values were strongly associated with flooring type and occupant movement, not just activity type. Notably, window ventilation during breaks reduced CO₂ concentrations by up to 305 ppm (p < 1 × 10⁻⁴⁷) and PM₁₀ by over 20% in rooms with favorable layouts. These findings highlight the importance of ventilation strategy, spatial orientation, and surface materials in shaping indoor air quality. The study emphasizes the need for targeted, non-invasive interventions to reduce pollutant exposure in historic university buildings where mechanical ventilation upgrades are often restricted. Full article

Air pollution is steadily increasing due to industrialization, economic activities, and transportation. High levels pose a significant threat to human health and well-being worldwide. Saudi Arabia is a growing country with air quality indices ranging from moderate to unhealthy. Although there are many monitoring stations distributed throughout the country, mathematical modeling of air pollution is still crucial for health and environmental decision-making. From this perspective, in this study, a data-driven approach based on pollutant records and a Deep Learning (DL) Long Short-Term Memory (LSTM) algorithm is carried out to perform temporal modeling of selected pollutants (PM₁₀, PM_2.5, CO and O₃) based on time series combined with a spatial modeling focused on selected cities (Riyadh, Jeddah, Mecca, Rabigh, Abha, Dammam and Taif), covering ~48% of the total population of the country. The best forecasts were provided by LSTM in cases where the datasets used were of relatively large size. Numerically, the obtained performance metrics such as the coefficient of determination (R²) ranged from 0.2425 to 0.8073. The best LSTM results were compared to those provided by two ensemble methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), where the merits of LSTM were confirmed mainly in terms of its ability to capture hidden relationships. We also found that overall, meteorological factors showed a weak association with pollutant concentrations, with ambient temperature exerting a moderate influence. However, incorporating ambient temperature into LSTM models did not lead to a significant improvement in predictive accuracy. The developed approach can be used to support decision-making in environmental and health domains, as well as to monitor pollutant concentrations based on historical time series records. Full article