Search Results (107)

Accurate prediction of Air Quality Index (AQI) concentrations remains a critical challenge in environmental monitoring and public health management due to the complex nonlinear relationships among multiple atmospheric factors. To address this challenge, we propose a novel prediction model that integrates an adaptive-weight particle swarm optimization (AWPSO) algorithm with a back propagation neural network (BPNN). First, the random forest (RF) algorithm is used to scree the influencing factors of AQI concentration. Second, the inertia weights and learning factors of the standard PSO are improved to ensure the global search ability exhibited by the algorithm in the early stage and the ability to rapidly obtain the optimal solution in the later stage; we also introduce an adaptive variation algorithm in the particle search process to prevent the particles from being caught in local optima. Finally, the BPNN is optimized using the AWPSO algorithm, and the final values of the optimized particle iterations serve as the connection weights and thresholds of the BPNN. The experimental results show that the RFAWPSO-BP model reduces the root mean square error and mean absolute error by 9.17 μg/m³, 5.7 μg/m³, 2.66 μg/m³; and 9.12 μg/m³, 5.7 μg/m³, 2.68 μg/m³ compared with the BP, PSO-BP, and AWPSO-BP models, respectively; furthermore, the goodness of fit of the proposed model was 14.8%, 6.1%, and 2.3% higher than that of the aforementioned models, respectively, demonstrating good prediction accuracy. Full article

Background: The aim of the study is to determine whether environmental and climatic factors (air quality, precipitation rates, and air temperatures) alongside specific public health measures (social distancing and vaccination) have influenced total number of SARS CoV-2 positive cases (TOTAL CASES) and deaths (TOTAL DEATHS) from COVID-19 infection in the Republic of Serbia (RS). Method: An observational, retrospective study was conducted, covering the following three-year period in the RS: I (1 March 2020–1 March 2021); II (1 March 2021–1 March 2022); and III (1 March 2022–1 March 2023). Air quality was expressed as the values of the air quality index (AQI) and the concentrations of particulate matter 2.5 µm (PM_2.5). Precipitation rates (PREC) were expressed as the average monthly amount of rainfall (mm), while average air temperatures (AIR TEMP) were expressed in °C. Data were collected from relevant official and publicly available national and international resources. Data regarding the COVID-19 pandemic were collected from the World Health Organization. Results: No differences between the periods were observed for the average values of AIR TEMP (11.2–12.2 °C), PREC (56.1–66.8 mm), and AQI (57.2–58.8), while the average values of PM_2.5 significantly decreased in the III period (21.2 compared to 25.2, p = 0.03). Both TOTAL CASES and TOTAL DEATHS from COVID-19 infection showed positive correlation with the AQI and PM_2.5 and a negative correlation with the AIR TEMP. The correlation coefficient was strongest between TOTAL DEATHS and the AIR TEMP in the II period (r = −0.7; p = 0.007). The extent of rainfall and vaccination rates did not affect any of the observed variables. No differences in TOTAL CASES and TOTAL DEATHS were observed between the periods of increased social measures and other months, while both statistically significantly increased during the vaccination period compared to months without the vaccination campaign (p < 0.02, for both). Conclusions: Air quality, more precisely AQI and PM_2.5 and average air temperatures, but no precipitation rates, influenced the number of TOTAL CASES and TOTAL DEATHS from COVID-19 infection. These were the highest during the vaccination period, but vaccination could be considered as a confounding factor since the intensive vaccination campaign was conducted during the most severe phase of the COVID-19 pandemic. Social distancing measures did not reduce the number of TOTAL CASES or TOTAL DEATHS during the COVID-19 pandemic. Full article

Air pollution in urban areas has increased significantly over the past few years due to industrialization and population increase. Therefore, accurate predictions are needed to minimize their impact. This paper presents a neural network-based examination for forecasting Air Quality Index (AQI) values, employing two different models: a variable-depth neural network (NN) called slideNN, and a Gated Recurrent Unit (GRU) model. Both models used past particulate matter measurements alongside local meteorological data as inputs. The slideNN variable-depth architecture consists of a set of independent neural network models, referred to as strands. Similarly, the GRU model comprises a set of independent GRU models with varying numbers of cells. Finally, both models were combined to provide a hybrid cloud-based model. This research examined the practical application of multi-strand neural networks and multi-cell recurrent neural networks in air quality forecasting, offering a hands-on case study and model evaluation for the city of Ioannina, Greece. Experimental results show that the GRU model consistently outperforms the slideNN model in terms of forecasting losses. In contrast, the hybrid GRU-NN model outperforms both GRU and slideNN, capturing additional localized information that can be exploited by combining particle concentration and microclimate monitoring services. Full article

Since 1989, California pollution control efforts have caused annual PM_2.5 averages to decrease. Despite the decline in ambient air concentrations of PM_2.5, the San Joaquin Valley (SJV) of California continues to violate the federal standard for PM_2.5. This study evaluated PM_2.5 trends, diurnal and seasonal patterns, pollution sources, and air quality improvements from 2000 to 2022 in the SJV. Hourly and daily PM_2.5 data from CARB and EPA-certified monitors were analyzed using regression models, polar plots, and Air Quality Index (AQI) classification methods. Monthly PM_2.5 concentrations peaked in winter (November–January) and during commute periods, with higher levels observed on Fridays and Saturdays. In this study, the highest daily PM_2.5 levels observed in Fresno and Bakersfield occurred during the autumn, most likely due to agricultural activities and higher wind speeds, with daily values greater than 25 µgm⁻³ and 50 µgm⁻³, respectively. In contrast, in Clovis, the highest daily PM_2.5 concentrations occurred in the winter during episodes characterized by low wind speeds, with values greater than 22 µgm⁻³. While PM_2.5 has declined since 1999, progress has slowed significantly since 2010. However, all sites exceeded the new EPA standard of 9 µgm⁻³. Without substantial changes to emission sources, meeting federal standards will be difficult. Full article

Air pollution is a major environmental risk factor that contributes significantly to the global burden of disease, particularly through its impact on respiratory and cardiovascular health. The aim of this study is to investigate the temporal variations of ambient air pollutants and the influence of MPs (MPs) on their concentrations in the metropolitan area of Tehran from 2017 to 2021. Hourly data for PM_2.5, PM₁₀, O₃, NO₂, SO₂, and CO from all air quality monitoring stations were obtained. Effects of MPs for the same period were assessed. The results revealed that Tehran’s residents are continuously exposed to harmful levels of PM_2.5 (5.7 to 6.3 times), PM₁₀ (4.5–5.6 times), and NO₂ (8.7–10.0 times) that are significantly higher than the updated World Health Organization (WHO) air quality guidelines. All other air pollutants (except for O₃) showed the lowest and highest concentrations during summer and winter, respectively. The highest concentration of O₃ was found on weekends (weekend effect), while other ambient air pollutants had higher levels on weekdays (holiday effect). Although other air pollutants exhibited two peaks, in the morning and late evening, the hourly concentration of O₃ reached its maximum level at 3:00 pm. Approximately 51% to 65% of the Air Quality Index (AQI) values were classified as unhealthy for sensitive groups. Throughout the study period, PM_2.5 was identified as the primary pollutant affecting air quality in Tehran. Among MPs, temperature was the most important factor in increasing the concentration of O₃, while the other ambient pollutants decreased under the influence of wind speed. Given the current situation, effective and evidence-based air quality management strategies, like those that have been successfully applied elsewhere, are now a necessity to avoid the public health impact and economic losses from air pollution. Although this research focuses on Tehran as a model case of rapidly developing cities facing severe air quality challenges, the findings and recommendations have broader applicability to similar urban environments worldwide. Full article

A multivariable clustering methodology was evaluated using the LAMDA algorithm as an alternative tool for analyzing air quality data. This analysis was based on the assessment of marginal and global adequacy degrees for classification using temporal records of PM_2.5 data. This study was conducted before and during the COVID-19 pandemic in the Aburrá Valley, Colombia. A total of 244 samples were collected between 1 December 2018, and 23 November 2020, over 24-h periods at a frequency of three days per week, including weekends. A robust classifier was developed for the PM_2.5 dataset, demonstrating that the selected descriptors significantly influenced classification outcomes. The average value for each class fell within the established ranges of the air quality index (AQI). According to AQI scales, the “good” and “acceptable” categories accounted for 95.1% of the monitored days. Class C2 (“acceptable”) was the most prevalent, representing 66% of the records, while the category harmful to sensitive groups (4.5%) was observed in eleven instances. Additionally, only one record (0.4%) fell into the category harmful to health (C4). The proportions of C1 and C2 classifications before and during the pandemic were 93.7% and 97.7%, respectively. The improvement in air quality due to COVID-19 restrictions is evident, as 57% of the observations during the pandemic were classified as “good” (C1), compared to only 13.9% before the pandemic. The visualization of classification results through easily interpretable graphs serves as a valuable decision-making tool, integrating not only real-time PM_2.5 measurements but also historical trends of the study area. Full article

To better inform the public about ambient air quality and associated health risks and prevent cardiovascular and chronic respiratory diseases in Macau, the local government authorities apply the Air Quality Index (AQI) for air quality management within its jurisdiction. The application of AQI requires first determining the sub-indices for several pollutants, including respirable suspended particulates (PM₁₀), fine suspended particulates (PM_2.5), nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), and carbon monoxide (CO). Accurate prediction of AQI is crucial in providing early warnings to the public before pollution episodes occur. To improve AQI prediction accuracy, deep learning methods such as artificial neural networks (ANNs) and long short-term memory (LSTM) models were applied to forecast the six pollutants commonly found in the AQI. The data for this study was accessed from the Macau High-Density Residential Air Quality Monitoring Station (AQMS), which is located in an area with high traffic and high population density near a 24 h land border-crossing facility connecting Zhuhai and Macau. The novelty of this work lies in its potential to enhance operational AQI forecasting for Macau. The ANN and LSTM models were run five times, with average pollutant forecasts obtained for each model. Results demonstrated that both models accurately predicted pollutant concentrations of the upcoming 24 h, with PM₁₀ and CO showing the highest predictive accuracy, reflected in high Pearson Correlation Coefficient (PCC) between 0.84 and 0.87 and Kendall’s Tau Coefficient (KTC) between 0.66 and 0.70 values and low Mean Bias (MB) between 0.06 and 0.10, Mean Fractional Bias (MFB) between 0.09 and 0.11, Root Mean Square Error (RMSE) between 0.14 and 0.21, and Mean Absolute Error (MAE) between 0.11 and 0.17. Overall, the LSTM model consistently delivered the highest PCC (0.87) and KTC (0.70) values and the lowest MB (0.06), MFB (0.09), RMSE (0.14), and MAE (0.11) across all six pollutants, with the lowest SD (0.01), indicating greater precision and reliability. As a result, the study concludes that the LSTM model outperforms the ANN model in forecasting air pollutants in Macau, offering a more accurate and consistent prediction tool for local air quality management. Full article

This pilot study aims to evaluate the state of the natural environment in the Silver Maples Alley (SMA) in Łódź, Poland, by using interdisciplinary research methods combining landscape architecture and environmental microbiology. The research focuses on the ecological condition of the trees in SMA, a historical monument consisting of about 100 century-old silver maples (Acer saccharinum L.). As part of the analysis, the study examines the area’s soil properties, microbiological composition, and air quality, providing a comprehensive approach to assessing environmental quality. Microbial analyses were conducted to determine soil pH, the presence of polycyclic aromatic hydrocarbons (PAHs), and the activity of Bacillus bacteria that produce biosurfactants for pollutant degradation. The results were compared with control sites with different Air Quality Index (AQI) values, including a park, a rural area, and a revitalized urban space. The findings support the hypothesis that environmental cleanliness correlates with the presence of pollutant-degrading microorganisms, particularly in areas with better air quality. This research contributes to understanding the role of green infrastructure, particularly old tree alleys, in urban ecosystems and public health. It also provides valuable insights into future management practices for historical green spaces. It highlights the need for interdisciplinary collaboration between landscape architecture, microbiology, and environmental sciences to address pressing sustainable development challenges. Full article

Egypt must present a more thorough and accurate picture of the state of the air, as this can contribute to better environmental and public health results. Hence, the goal of the current study is to map and track the spatiotemporal air quality over Egypt’s Qena Governorate using remote sensing data. The current investigation is considered a pioneering study and the first attempt to map the air quality index in the studied area. Multisource remote sensing data sets from the Google Earth Engine (GEE) were used to achieve this. The first is Sentinel-5P’s average annual satellite image data, which were gathered for four important pollutants: carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and ozone (O₃) over a six year period from 2019 to 2024. The second is the MODIS aerosol optical density (AOD) product satellite image data from the GEE platform, which calculate the average annual particulate matter (PM_2.5 and PM₁₀). All mentioned pollutant images were used to calculate the air quality index (AQI) and aggregated air quality index (AAQI). Lastly, we used Landsat’s average yearly land surface temperature (LST) retrieval (OLI/TIRS). The aggregated air quality index (AAQI) was computed, and the U.S. Environmental Protection Agency’s (USEPA) air quality index (AQI) was created for each pollutant. According to the data, the AQI for CO, PM_2.5, and PM₁₀ in the research region ranged from hazardous to unhealthy; at the same time, the AQI for NO₂ varied between harmful and unhealthy for sensitive groups, with values ranging from 135 to 165. The annual average of the AQI for SO₂ throughout the studied period ranged from 29 to 339, with the categories ranging from good to hazardous. The constant AQI for ozone in the study area indicates that the ozone doses in Qena are surprisingly stable. Lastly, with a minimum value of 265 and a maximum of 489, the AAQI ranged from very unhealthy to dangerous in the current study. According to the data, the area being studied has poor air quality, which impacts the environment and public health. The results of this study have significant implications for environmental sustainability and human health and could be used in other areas. Full article

PM_2.5 pollution is increasing, causing health problems. The objective of this study was to model the behavior of PM_2.5AQI (air quality index) using machine learning (ML) predictive models of linear regression, lasso, ridge, and elastic net. A total of 16,543 records from the Huachac, Junin area in Peru were used with regressors of humidity in % and temperature in °C. The focus of this study is PM_2.5AQI and environmental variables. Methods: Exploratory data analysis (EDA) and machine learning predictive models were applied. Results: PM_2.5AQI has high values in winter and spring, with averages of 52.6 and 36.9, respectively, and low values in summer, with a maximum value in September (spring) and a minimum in February (summer). The use of regression models produced precise metrics to choose the best model for the prediction of PM_2.5AQI. Comparison with other research highlights the robustness of the chosen ML models, underlining the potential of ML in PM_2.5AQI. Conclusions: The predictive model found was α = 0.1111111 and a Lambda value λ = 0.150025, represented by PM_2.5AQI = 83.0846522 − 10.302222000 (Humidity) − 0.1268124 (Temperature). The model has an adjusted R² of 0.1483206 and an RMSE of 25.36203, and it allows decision making in the care of the environment. Full article

The efficient separation of photo-generated electrons and holes is significantly importance for enhancing photocatalytic performance. However, there are few reports on precisely constructing interfaces within a single nanocrystal to investigate the mechanism of photoinduced carrier transfer. In this study, nanorod heterodimer-structured CuS/Zn_xCd_1−xS heteronanocrystals (CuS/ZnCdS HNCs) were successfully synthesized as a typical model to explore the photoinduced carrier dynamics in the photocatalytic hydrogen evolution reaction (HER). The CuS/ZnCdS HNCs exhibited a photocatalytic hydrogen evolution activity of 146 mmol h⁻¹ g⁻¹ under visible light irradiation, which is higher than most reported values. Moreover, after 15 h of hydrogen production cycling tests, we found that the material maintained high hydrogen production performance, indicating excellent stability. The CuS/ZnCdS HNCs achieved an apparent quantum yield (AQY) of 69.2% at 380 nm, which is the highest value reported so far for ZnCdS- or CuS-based photocatalysts. The remarkable activity and stability of the CuS/ZnCdS HNCs were attributed to the strong internal electric field (IEF) and Z-scheme mechanism, which facilitate efficient charge separation, as demonstrated by in situ X-ray photoelectron spectroscopy (XPS) and electron paramagnetic resonance (EPR) analyses. This discovery provides a new approach for constructing Z-scheme heterogeneous copper-based nanocomposites within nanocrystals and offers guidance for improving photocatalytic activity. Full article

Urumqi is located at the northern foot of the Tianshan Mountains. Its topographical features have a significant impact on the transport and dispersion of air pollutants. Moreover, its winter is extremely long, lasting up to six months. A combination of an irrational energy consumption structure, unique meteorological conditions, and complex geographical terrains has led to a substantial increase in NO₂ emissions, severely damaging the local ecological environment. In this study, we integrate Geographic Information System (GIS) and Computational Fluid Dynamics (CFD). By leveraging GIS’s powerful spatial analysis capabilities and CFD’s high-precision fluid simulation technology, we significantly enhance the simulation accuracy of complex phenomena like airflow and pollutant diffusion. Additionally, the inverse distance weighted interpolation method is comprehensively employed to analyze the Air Quality Indices (AQIs) of typical pollutants in different districts of Urumqi during winter. The results reveal that high altitude causes instability of the dominant near-surface winds within the atmospheric boundary layer. The increasing frequency of surface calm winds reduces the advective transport of atmospheric pollutants. Topography and winter meteorological conditions are identified as the primary factors contributing to pollutant accumulation. This research not only unveils the fundamental mechanisms of pollutant dispersion in mountainous terrains but also validates the practicality of coupling GIS and CFD, providing a theoretical basis for pollution dispersion studies in this region. This study reveals the general laws of pollutant dispersion in mountainous terrain, resolves the issue of establishing complex geographical models, and demonstrates the feasibility of coupling the GIS and CFD. Meanwhile, it provides a theoretical basis for pollution dispersion in this region. Full article

This study presents the first comprehensive assessment of air pollution by ${\mathrm{PM}}_{2.5}$ and ${\mathrm{PM}}_{10}$ in the city of Cusco, aiming to determine atmospheric pollution levels, characterize air quality, and develop predictive models. The research, conducted during 2017–2020, systematically evaluated particulate matter (PM) contamination using a high-volume sampler (HiVol ECOTEC 3000) installed at 18 monitoring sites distributed across five urban districts. Multiple linear regression (MLR) models were developed and evaluated, incorporating meteorological, seasonal, and temporal variables under two approaches: direct linear (Model 1) and logarithmic transformation (Model 2). The model evaluation employed R², RMSE, MAE, MAPE, IOA, and CV statistical indicators. The results revealed concentrations significantly exceeding WHO guideline values, with ${\mathrm{PM}}_{2.5}$ ranging between 41.10 ± 3.2 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$ (2020) and 82.01 ± 5.1 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$ (2018), while ${\mathrm{PM}}_{10}$ values ranged from 45.07 ± 2.8 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$ (2020) to 72.35 ± 4.3 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$ (2017). A notable reduction was observed during 2020, attributable to COVID-19 pandemic restrictions. The Air Quality Index (AQI) indicated predominantly “Unhealthy” and “Very Unhealthy” levels during 2017–2018, improving to “Unhealthy for Sensitive Groups” in 2020. MLR models achieved maximum efficiency using logarithmic transformation, obtaining R² = 0.98 (p < 0.001) for ${\mathrm{PM}}_{2.5}$ in the 2020 rainy season and R² = 0.44 (p < 0.001) for ${\mathrm{PM}}_{10}$ in the 2018 annual model. These findings demonstrate the existence of nonlinear relationships between pollutants and predictor variables in Cusco’s atmospheric basin. Full article

Increased air pollution is driven by anthropogenic pollution emissions and climate change, which pose great challenges to environmental governance. Strengthening the monitoring of regional air quality levels and analyzing the causes of regional pollution is conducive to the management and sustainable development of the regional atmosphere. Functional data obtained on a wavelet basis were used in the fitting of air quality data of Fujian Province, and wavelet decomposition was performed to obtain low-frequency and high-frequency information. While the Fourier basis cannot adaptively adjust the time–frequency window, resulting in the loss of location information of special frequencies, the wavelet basis solves this problem. Functional analysis of variance was utilized for analyzing spatial differences in air pollution characteristics. Furthermore, the study established a multivariate functional linear regression model to explore the impact of meteorological factors and ozone precursor factors. The results indicated that the overall air quality was gradually improving in Fujian Province, but the concentration of ozone was progressively increasing. Air pollution in coastal areas was higher than that in inland areas. The p-values of the functional analysis of variance for energy values and crest values were less than 0.05. Moreover, the energy entropy and kurtosis values were greater than 0.05. There were significant differences of AQI in the fluctuation amplitude and variation characteristics of different cities. The total squared multiple correlation of regression model was above 50% on average. Ozone is currently the most serious pollution factor, mainly affected by wind speed, temperature, NO₂, and CO. In summer, it was principally influenced by VOCs. The findings of this study could act as a reference in exploring the time–frequency characteristics of air quality data and support of air pollution control. Full article

Air pollution has exceeded the anticipated safety limit and addressing this issue is crucial for sustainability, particularly in countries with high pollution levels. So, monitoring and forecasting air quality is essential for sustainable urban development. Therefore, this paper presents multiclass classification using two feature selection techniques, namely Sequential Forward Selection (SFS) and filtering, both with different machine learning and ensemble techniques, to predict air quality and make sure that the most relevant features are included in datasets for air quality determination. The results of the considered framework reveal that the SFS technique provides superior performance compared to filter feature selection (FFS) with different ML methods, including the AdaBoost Classifier, the Extra Tree Classifier, Random Forest (RF), and the Bagging Classifier, for efficiently determining the Air Quality Index (AQI). These models’ performances are assessed using predetermined performance metrics. The AdaBoost Classifier model with FFS has the lowest accuracy, while the RF model with SFS achieves the highest accuracy, at 78.4% and 99.99%, respectively. Based on the raw dataset, it was noted that the F1-score, recall, and precision values of the RF model with SFS are 99.96%, 99.97%, and 99.98%, respectively. Therefore, the experimental results undoubtedly show the supremacy, reliability, and robustness of the proposed approach in determining the AQI effectively. Full article