Search Results (190)

Air pollution, particularly fine (PM_2.5) and coarse (PM₁₀) particulate matter, poses significant risks to public health and environmental sustainability. This study aims to develop robust predictive and forecasting models for hourly PM concentrations in Craiova, Romania, using advanced hybrid Artificial Intelligence (AI) approaches. A five-year dataset (2020–2024), comprising 20 meteorological and pollution-related variables recorded by four air quality monitoring stations, was analyzed. The methodology consists of three main phases: (i) data preprocessing, including anomaly detection and missing value handling; (ii) exploratory analysis to identify trends and correlations between PM concentrations (PMs) and predictor variables; and (iii) model development using 23 machine learning and deep learning algorithms, enhanced by 50 feature selection techniques. A deep Nonlinear AutoRegressive Moving Average with eXogenous inputs (Deep-NARMAX) model was employed for multi-step-ahead forecasting. The best-performing models achieved R² values of 0.85 for PM_2.5 and 0.89 for PM₁₀, with low RMSE and MAPE scores, demonstrating high accuracy and generalizability. The GEO-based feature selection method effectively identified the most relevant predictors, while the Deep-NARMAX model captured temporal dynamics for accurate forecasting. These results highlight the potential of hybrid AI models for air quality management and provide a scalable framework for urban pollution monitoring, predicting, and forecasting. Full article

This study assessed the influence of meteorological conditions on particulate matter (PM) concentrations in Campo Grande, Brazil, from May to December 2021. Using statistical analyses, including Pearson’s correlation coefficient and multivariate regression, we analyzed secondary data on PM2.5 and PM10 concentrations and meteorological variables from the Federal University of Mato Grosso do Sul’s Physics Department. Daily PM concentrations complied with Brazil’s National Ambient Air Quality Standards (PQAr). The PM2.5/PM10 ratios averaged 0.436 (hourly) and 0.442 (daily), indicating a mix of fine and coarse particles. Significant positive correlations were found with temperature, while relative humidity showed a negative correlation, reducing PM levels through deposition. Wind speed had no significant impact. Meteorological influences suggest that air quality management should be tailored to regional conditions, particularly addressing local emission sources like vehicular traffic and biomass burning. Full article

Dust emissions from unpaved haul roads in open-pit coal mining pose a significant risk to air quality, health, and operational efficiency of mining operations. This study integrated real-time field monitoring with numerical simulations using ANSYS Fluent 2023 R1 to investigate the generation, dispersion, and migration of particulate matter (PM) at the Ha’erwusu open-pit coal mine under varying meteorological conditions. Real-time measurements of PM2.5, PM10, and TSP, along with meteorological variables (wind speed, wind direction, humidity, temperature, and air pressure), were collected and analyzed using Pearson’s correlation and multivariate linear regression analyses. Wind speed and air pressure emerged as dominant factors in winter, whereas wind and temperature were more influential in summer (R² = 0.391 for temperature vs. PM2.5). External airflow simulations revealed that truck-induced turbulence and high wind speeds generated wake vortices with turbulent kinetic energy (TKE) peaking at 5.02 m²/s², thereby accelerating particle dispersion. The dust migration rates reached 3.33 m/s within 6 s after emission and gradually decreased with distance. The particle settling velocities ranged from 0.218 m/s for coarse dust to 0.035 m/s for PM2.5, with dispersion extending up to 37 m downwind. The highest simulated dust concentration reached 4.34 × 10⁻² g/m³ near a single truck and increased to 2.51 × 10⁻¹ g/m³ under multiple-truck operations. Based on spatial attenuation trends, a minimum safety buffer of 55 m downwind and 45 m crosswind is recommended to minimize occupational exposure. These findings contribute to data-driven, weather-responsive dust suppression planning in open-pit mining operations and establish a validated modeling framework for future mitigation strategies in this field. Full article

Despite spending a substantial proportion of their time indoors, the mental health effects of indoor air pollution on children and adolescents remain inadequately explored. This study aimed to elucidate the spatiotemporal variations and sociodemographic inequalities in exposure to multiple indoor pollutants and to assess their potential associations with depressive symptoms among school-aged children in Beijing. Using real-time portable monitors, concentrations of fine particulate matter (PM_2.5), coarse particulate matter (PM₁₀), carbon dioxide (CO₂), formaldehyde (HCHO), total volatile organic compounds (TVOC), temperature, and humidity in classrooms and bedrooms were measured during both weekdays and weekends. Moreover, substantial spatiotemporal heterogeneity was observed. It was found that concentrations of PM_2.5, PM₁₀, and TVOC peaked in classrooms during weekday daytime, while CO₂ levels were highest in bedrooms on weekend nights. Exposure levels were notably higher among children whose mothers had lower educational attainment and those living in recently renovated homes, indicating marked socio-demographic disparities. In multivariable logistic regression models, indoor exposure to CO₂ and TVOC was significantly associated with increased odds of depressive symptoms. These findings highlight the critical need to improve indoor air quality through enhanced ventilation and the mitigation of emissions from indoor sources, particularly within school and residential settings. The results offer valuable empirical evidence to guide the development of targeted environmental interventions and public health policies designed to support and enhance the psychological well-being of children. Full article

This study was conducted in an urban park in a temperate-continental city of Europe (Timișoara, Romania) and aimed to investigate the contribution of urban vegetation in maintaining air quality and mitigating the heat in the analyzed city. The following air parameters were monitored: fine particulate matter PM_2.5, coarse particulate matter PM₁₀, AQI (Air Quality Index) (resulted from PM_2.5 and PM₁₀), particle number, air temperature, relative air humidity, TVOC (total volatile organic compounds), and HCHO (formaldehyde). The results of this study show that urban vegetation remains a reliable factor in reducing PM_2.5 and PM₁₀ in city air and in keeping the AQI within the limits corresponding to good air quality, but also that relative air humidity counteracts the contribution of vegetation in achieving this goal. Inside the park, the HCHO concentration increased by up to 4–5 times compared to the outside, and this increase was not caused by vehicle traffic but rather by the photochemical reactions generating HCHO. Regarding the cooling effect on air temperature, the studied green space did not exhibit this effect, as the air temperature inside it increased by up to 1–6 °C compared to the outside. Our results contrast with the general perception that urban parks and green spaces are cooler islands within the cities and draw attention to the fact that having a green space in a city does not necessarily mean achieving environmental goals, such as reducing the heat risk of cities. Based on the results, we consider that the main limitations in achieving these objectives were the park’s small size (88 hectares) and its morphology and architecture resulting from the integration of the species that compose it. It follows from these data that it is not enough for an urban green space to be established, but its design must be combined with urban morphology strategies if the heat mitigation effect is to be achieved and the cooling benefits are to be maximized in cities. Full article

Short-term control measures are often implemented during major events to improve air quality and protect public health. In preparation for the 11th National Traditional Games of Ethnic Minorities of China (denoted as “NMG”), held from 8 to 16 September 2019 in Zhengzhou, China, the authorities introduced several air pollution control measures, including traffic restrictions and dust control. In the study presented herein, we applied automated machine learning-based weather normalisation combined with an augmented synthetic control method (ASCM) to evaluate the effectiveness of these interventions. Our results show that the impacts of the NMG control measures were not uniform, varying significantly across pollutants and monitoring stations. On average, nitrogen dioxide (NO₂) concentrations decreased by 8.6% and those of coarse particles (PM₁₀) decreased by 3.0%. However, the interventions had little overall effect on fine particles (PM_2.5), despite clear reductions observed at the traffic site, where NO₂ and PM_2.5 levels decreased by 7.2 and 5.2 μg m⁻³, respectively. These reductions accounted for 56.3% of the NMG policy’s effect on NO₂ concentration and 73.2% of its effect on PM_2.5 concentration at the traffic site. Notably, the control measures led to an increase in ozone (O₃) concentrations. Our results demonstrate the moderate effect of the short-term NMG intervention, emphasising the necessity for holistic strategies that address pollutant interactions, such as nitrogen oxides (NO_X) and volatile organic compounds (VOCs), as well as location-specific variability to achieve sustained air quality improvements. Full article

Low-cost particulate matter sensors have seen increased use for monitoring at personal and local levels due to their affordability, ease of operation, and high time resolution. However, the quality of data reported by these sensors can be questionable, and a thorough evaluation of their performance is necessary. This study evaluated the particle sizing accuracy of several commonly used optical sensors, including the Alphasense optical particle counter (OPC), TSI DustTrak DRX aerosol monitor, Plantower PMS5003 sensor, and Sensirion SPS30 sensor, using laboratory-generated monodisperse particles. The OPC and DRX agreed partially with reference instruments and showed promise in detecting coarse-size particles. However, the PMS5003 and SPS30 did not correctly size fine and coarse particles. Furthermore, their reported mass distributions do not directly correspond to their number distribution. Despite these limitations, field measurements involving a dust storm period showed that the SPS30 correlated reasonably well with reference instruments for both PM_2.5 and PM₁₀, though the regression slopes differed significantly. These findings underscore the need for caution when interpreting data from low-cost optical sensors, particularly for coarse particles. Recommendations for improving the performance of these sensors are also provided. Full article

This study aimed to assess the long-term trends in PM₁₀, PM_2.5, and hazardous trace elements in Makkah from 2020 to 2024, evaluating seasonal variations, health risks, and potential mitigation strategies. The results indicated that the PM₁₀ concentrations ranged from a minimum of 127.7 ± 14.2 µg/m³ (2020) to a maximum of 138.3 ± 15.7 µg/m³ (2024), while PM_2.5 levels varied between 100.7 ± 18.7 µg/m³ and 109.8 ± 21.3 µg/m³. A seasonal analysis showed the highest PM₁₀ and PM_2.5 levels during winter (147.8 ± 16.4 µg/m³ and 119.5 ± 21.7 µg/m³ in 2024, respectively), coinciding with lower wind speeds and reduced dispersion. Among the nine trace elements analyzed, Cr VI exhibited the highest increase from 0.008 ± 0.001 µg/m³ (2020) to 0.012 ± 0.001 µg/m³ (2024), while Cd and Ni also rose significantly. The excess cancer risk (ECR) associated with these pollutants exceeded the recommended threshold, with a strong correlation between PM₁₀ and ECR (r = 0.85–0.93, p < 0.01). These findings highlight the need for enhanced air quality monitoring and sustainable urban planning. Future research should focus on identifying the dominant pollution sources and assessing the long-term health impacts to support evidence-based air quality management in Makkah. Full article

Dust is one of the most important problems of human societies in arid and semi-arid areas. This study analyzed the rising and propagation of the dust storm occurring from 21 to 23 May 2016 in Isfahan province (Central Iran) by using the WRF-Chem and HYSPLIT models. The dust storm was visualized using visible imagery and coarse-mode aerosol optical depth data from satellite sensor data, and dust emission and transport were simulated for Central Iran by using WRF-Chem with the AFWA and GOCART schemes. The results show that the dust concentration in Sistan and Baluchistan province and the Persian Gulf was as high as 2000 µg/m³, and both schemes estimate the highest amount of dust emissions from the central parts of Iran and the eastern part of Isfahan province. PM10 data of Yazd station was used to verify the model outputs, which showed that the AFWA dust scheme has a higher correlation coefficient with observations (0.62) than the GOCART dust scheme. This case study suggests that WRF-Chem dust schemes simulate dust rising and propagation in Central Iran with reasonably good reliability, though further determination and enhancement are still required for an accurate prediction of dust concentration and extents. Full article

Transport and vehicle traffic are closely connected with particulate matter (PM) pollution, inducing various fractions into the atmosphere, some of them forming significant deposits on the surface of the car. They are washed away during carwash-inducing slurries collecting the PM deposits, which are characteristic of a large area. Crystalline PM matter was investigated by XRD coupled with polarized optical microscopy (POM). Organic matters were investigated by Fourier-Transform Infrared spectrometry (FTIR) and gas chromatography, GC-MS. Their microstructure and elemental composition were investigated by SEM-EDX. The crystalline features contain mainly quartz, calcite, and clay (muscovite and kaolinite) particles having traces of goethite and lepidocrocite. Slurry particle size distribution was established by sieving on the following meshes: 63 µm, 125 µm, 250 µm, 500 µm, 1000 µm, 2000 µm, and 4000 µm. Coarse fractions of 250–4000 μm are dominated by quartz and calcite particles. The quartz and calcite amount decreases with particle size, while the muscovite and kaolinite amount increases in the finest fractions of 0–125 μm. Organic matter was evidenced, firstly, by FTIR spectroscopy, revealing mostly CH₂; C=O, and NH₄ bonds that are more intense for the fine particulate fractions. The organic deposits form mainly amorphous crusts associated with micro- and nano-plastic particles related to the phthalates and traces of the washing detergents. Atomic Force Microscopy revealed their size range between 60 and 90 nm and evidenced nanoparticles within samples. The nanofractions adhere to the bigger particles in humid environments, assuring their immobilization to reduce their hazardous potential. Carwash slurry blending with fertile soil ensures proper grass seed germination and growth at mixtures of up to 60% slurry, allowing its sustainable reconversion as soil for landfill and dump rehabilitation, preventing the PM emission hazard. Blended compositions containing more than 60% slurry have noxious effects on the grass seeds, inhibiting their germination. Full article

Inflammatory cell activation in asthma may lead to reactive oxygen species (ROS) overproduction with an imbalance between oxidant levels and antioxidant capacity, called oxidative stress (OS). Since particulate matter (PM) airborne exposure may also contribute to ROS generation, it is unclear whether PM contributes more to OS than inflammatory cell activation. In our ASTHMA-FENOP study, which included 44 asthma patients and 37 matched controls, we aimed to characterize OS using five serum markers: total ROS content, protein carbonyl content, oxidized low-density lipoprotein (OxLDL), 8-hydroxydeoxyguanosine, and glutathione. Volunteers wore personal samplers for 24 h, collecting fine and coarse PM fractions separately, and the oxidative potential (OP) was determined using two methods. We observed differences between asthmatic and non-asthmatic volunteers in some OS markers, such as OxLDL, with an adjusted mean difference of 50,059.8 ng/mL (p < 0.001). However, we did not find an association between higher PM-OP and increased systemic OS. This suggests that at our PM-OP exposure levels, OS generated by the inflammatory cells themselves is more relevant than that generated by airborne PM. This supports the idea that asthma is a heterogeneous disease at the molecular level, mediated by inflammatory cell activation, and that OS may have potential clinical implications. Full article

Background/Objectives: This study compared chest high-resolution computed tomography (HRCT) findings between patients with anti-melanoma differentiation-associated gene 5 (MDA5) antibody-positive and antibody-negative progressive pulmonary fibrosis (PPF) with polymyositis/dermatomyositis (PM/DM). Methods: Of the 85 patients with PM/DM-interstitial lung disease (ILD), 17 were anti-MDA5 antibody-positive, and 68 were antibody-negative. Among these, 5 anti-MDA5 antibody-positive and 9 antibody-negative cases met the criteria for PPF and were enrolled in the study. The chest HRCT findings and the duration from treatment initiation to the appearance of key fibrotic changes were analyzed. Results: In the anti-MDA5-positive group, all patients were diagnosed with PPF within 6 months of treatment initiation, compared to only 22.2% in the anti-MDA5-negative group. While there was no difference between the anti-MDA5 antibody-positive and antibody-negative groups in terms of chest HRCT findings associated with PPF, the duration to the appearance of increased traction bronchiectasis and bronchiolectasis, and new ground-glass opacity with traction bronchiectasis was significantly shorter in the anti-MDA5-positive group (p = 0.016 and p = 0.023, respectively). The appearance of new fine reticulations and increased coarseness of reticular abnormalities tended to be shorter in the anti-MDA5 antibody-positive group than in the antibody-negative group. Conclusions: Pulmonary fibrosis in patients with anti-MDA5 antibody-positive ILD can rapidly progress within 6 months, despite immunosuppressive therapy. Frequent HRCT monitoring and early combination therapy with antifibrotic agents are crucial for managing the progression of fibrosis. Full article

Air pollution, particularly from coarse particulate matter (PM₁₀), is a major public health concern, significantly contributing to respiratory and cardiovascular diseases, especially among vulnerable populations. In 2024, Brazil introduced a new air quality resolution (CONAMA Resolution No. 506/2024), aligning its ultimate goal with the World Health Organization’s 2021 guidelines while establishing specific timelines for the interim targets. However, these interim targets, set for 2025, 2033, and 2044, along with the absence of a deadline for the final target of 15 µg/m³, raise concerns about their adequacy in addressing the urgent health impacts of air pollution. This study evaluates the economic and public health benefits of accelerating these targets in the city of Rio Grande, a region characterized by an industrial and port-driven economy and an aging population particularly vulnerable to air pollution. Using health impact assessments, economic cost analyses, and mortality estimates, we modeled three scenarios with PM₁₀ concentration limits of 30 µg/m³, 20 µg/m³, and 15 µg/m³, corresponding to the resolution’s 2033 and 2044 goals and the undated final target. Our findings indicate that achieving the 15 µg/m³ target by 2025 could prevent 2568 respiratory hospitalizations, 1551 cardiac hospitalizations, and 1128 air pollution-related deaths in Rio Grande, resulting in approximately BRL 7.3 million in healthcare savings. When extrapolated to cities with similar pollution profiles across Brazil, these results suggest substantial potential for reducing the health burdens and economic costs nationwide. This study underscores the urgent need to establish more ambitious timelines in Brazil’s air quality policies to maximize public health benefits and mitigate the economic impacts of air pollution. Full article

Air pollution presents a serious hazard to human health and the environment for the global rise in industrialization and urbanization. While fine-grained monitoring is crucial for understanding the formation and control of air pollution and their effects on human health, existing macro-regional level or ground-level methods make air pollution inference in the same spatial scale and fail to address the spatiotemporal correlations between cross-grained air pollution distribution. In this paper, we propose a 3D spatiotemporal attention super-resolution model (AirSTFM) for fine-grained air pollution inference at a large-scale region level. Firstly, we design a 3D-patch-wise self-attention convolutional module to extract the spatiotemporal features of air pollution, which aggregates both spatial and temporal information of coarse-grained air pollution and employs a sliding window to add spatial local features. Then, we propose a bidirectional optical flow feed-forward layer to extract the short-term air pollution diffusion characteristics, which can learn the temporal correlation contaminant diffusion between closeness time intervals. Finally, we construct a spatiotemporal super-resolution upsampling pretext task to model the higher-level dispersion features mapping between the coarse-grained and fined-grained air pollution distribution. The proposed method is tested on the PM_2.5 pollution datatset of the Yangtze River Delta region. Our model outperforms the second best model in RMSE, MAE, and MAPE by 2.6%, 3.05%, and 6.36% in the 100% division, and our model also outperforms the second best model in RMSE, MAE, and MAPE by 3.86%, 3.76%, and 12.18% in the 40% division, which demonstrates the applicability of our model for different data sizes. Furthermore, the comprehensive experiment results show that our proposed AirSTFM outperforms the state-of-the-art models. Full article

Accurate and timely air quality forecasting is crucial for mitigating pollution-related hazards and protecting public health. Recently, there has been a growing interest in integrating visual data for air quality prediction. However, some limitations remain in existing literature, such as their focus on coarse-grained classification, single-moment estimation, or reliance on indirect and unintuitive information from visual images. Here we present a dual-channel deep learning model, integrating surveillance images and multi-source numerical data for air quality forecasting. Our model, which combines a single-channel hybrid network consisting of VGG16 and LSTM (named VGG16-LSTM) with a single-channel Long Short-Term Memory (LSTM) network, efficiently captures detailed spatiotemporal features from surveillance image sequences and temporal features from atmospheric, meteorological, and temporal data, enabling accurate time-series forecasting of PM_2.5 and PM₁₀ concentrations. Experiments conducted on the 2021 Shanghai dataset demonstrate that the proposed model significantly outperforms traditional machine learning methods in terms of accuracy and robustness for time-series forecasting, achieving R² values of 0.9459 and 0.9045 and RMSE values of 4.79 μg/m³ and 11.51 μg/m³ for PM_2.5 and PM₁₀, respectively. Furthermore, validation results on the datasets from two stations in Kaohsiung, Taiwan, with average R² values of 0.9728 and 0.9365 and average RMSE values of 1.89 μg/m³ and 5.69 μg/m³ for PM_2.5 and PM₁₀ using a pretrain–finetune training strategy, confirm the model’s adaptability across diverse geographical contexts. These findings highlight the potential of integrating surveillance images to enhance air quality prediction, offering an effective supplement to ground-level environmental monitoring. Future work will focus on expanding datasets and optimizing network architectures to further improve forecasting accuracy and computational efficiency, enhancing the model’s scalability for broader regional air quality management. Full article