Search Results (744)

This study analyzes air pollution time-series big data to assess stationarity, seasonal patterns, and the performance of machine learning models in forecasting PM2.5 concentrations. Fifty-two low-cost sensors (LCS) were deployed across Krakow city and its surroundings (Poland), collecting hourly air quality data and generating nearly 20,000 observations per month. The network captured both spatial and temporal variability. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test confirmed trend-based non-stationarity, which was addressed through differencing, revealing distinct daily and 12 h cycles linked to traffic and temperature variations. Additive seasonal decomposition exhibited time-inconsistent residuals, leading to the adoption of multiplicative decomposition, which better captured pollution outliers associated with agricultural burning. Machine learning models—Ridge Regression, XGBoost, and LSTM (Long Short-Term Memory) neural networks—were evaluated under high spatial and temporal variability (winter) and low variability (summer) conditions. Ridge Regression showed the best performance, achieving the highest $R^2$ (0.97 in winter, 0.93 in summer) and the lowest mean squared errors. XGBoost showed strong predictive capabilities but tended to overestimate moderate pollution events, while LSTM systematically underestimated PM2.5 levels in December. The residual analysis confirmed that Ridge Regression provided the most stable predictions, capturing extreme pollution episodes effectively, whereas XGBoost exhibited larger outliers. The study proved the potential of low-cost sensor networks and machine learning in urban air quality forecasting focused on rare smog episodes (RSEs). Full article

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

As industrialization and economic growth accelerate, PM_2.5 pollution has become a critical environmental concern. Predicting PM_2.5 concentration is challenging due to its nonlinear and complex temporal dynamics, limiting the accuracy and robustness of traditional machine learning models. To enhance prediction accuracy, this study focuses on Ma’anshan City, China and proposes a novel hybrid model (QMEWOA-QCAM-BiTCN-BiLSTM) based on an “optimization first, prediction later” approach. Feature selection using Pearson correlation and RFECV reduces model complexity, while the Whale Optimization Algorithm (WOA) optimizes model parameters. To address the local optima and premature convergence issues of WOA, we introduce a quantum-enhanced multi-strategy improved WOA (QMEWOA) for global optimization. A Quantum Causal Attention Mechanism (QCAM) is incorporated, leveraging Quantum State Mapping (QSM) for higher-order feature extraction. The experimental results show that our model achieves a MedAE of 1.997, MAE of 3.173, MAPE of 10.56%, and RMSE of 5.218, outperforming comparison models. Furthermore, generalization experiments confirm its superior performance across diverse datasets, demonstrating its robustness and effectiveness in PM_2.5 concentration prediction. Full article

The air quality within urban public transport is a critical determinant of passenger health. In the crowded and poorly ventilated cabins of Almaty’s metro, buses, and trolleybuses, concentrations of CO₂ and PM_2.5 often accumulate, elevating the risk of respiratory and cardiovascular diseases. This study investigates the air quality along three of the city’s busiest transport corridors, analyzing how the concentrations of CO₂, PM_2.5, and PM₁₀, as well as the temperature and relative humidity, fluctuate with the passenger density and time of day. Continuous measurements were collected using the Tynys mobile IoT device, which was bench-calibrated against a commercial reference sensor. Several machine learning models (logistic regression, decision tree, XGBoost, and random forest) were trained on synchronized environmental and occupancy data, with the XGBoost model achieving the highest predictive accuracy at 91.25%. Our analysis confirms that passenger occupancy is the primary driver of in-cabin pollution and that these machine learning models effectively capture the nonlinear relationships among environmental variables. Since the surveyed routes serve Almaty’s most densely populated districts, improving the ventilation on these lines is of immediate importance to public health. Furthermore, the high-temporal-resolution data revealed short-term pollution spikes that correspond with peak ridership, advancing the current understanding of exposure risks in transit. These findings highlight the urgent need to combine real-time monitoring with ventilation upgrades. They also demonstrate the practical value of using low-cost IoT technologies and data-driven analytics to safeguard public health in urban mobility systems. Full article

Infrared thermography (IRT) is explored as a non-invasive method for indirectly measuring parameters related to animal performance and welfare. This study investigates a feature extraction method termed the “thermal signature” (TS), a descriptor vector derived from the temperature matrix of an animal’s body surface, representing the percentage distribution of temperatures within predefined ranges. The TS, combined with environmental data, serves as a predictor attribute for machine learning-based classifier models to assess heat stress levels. The methodology was applied to a dataset collected from two groups of five dairy calves housed in a climate-controlled chamber and exposed to two artificial heat waves over 13 days. Data, including IRT measurements, respiratory rate (RR), rectal temperature (RT), and environmental variables, were collected five times daily (from 6 a.m. to 10 p.m., every four hours). Classifier models were developed using random forest (RF), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN) algorithms. The RF models based on RR achieved the highest accuracies, 94.1% for two heat stress levels and 80.3% for three heat stress levels, using TS configurations with six temperature ranges. The integration of TS with machine learning-based models demonstrates promising results for developing or enhancing classifiers of heat stress levels in dairy calves. Full article

In this work, the development of Al-based metal matrix composites (MMCs) is achieved using hybrid SiC and ZrO₂ reinforcement particles for automotive applications. Powder metallurgy (PM) is employed with various combinations of important process parameters for the fabrication of MMCs. MMCs were characterized using scanning electron microscopy (SEM), X-ray diffractometry (XRD), and a microhardness study. All XRD graphs adequately exhibit Al, SiC, and ZrO₂ peaks, indicating that the hybrid MMC products were satisfactorily fabricated with appropriate mixing and sintering at all the considered fabrication conditions. Also, no impurity peaks were observed, confirming high composite purity. MMC products in all the XRD patterns, suitable for the desired applications. According to the SEM investigation, SiC and ZrO₂ reinforcement components are uniformly scattered throughout Al matrix in all produced MMC products. The occurrence of Al, Si, C, Zr, and O in EDS spectra demonstrates the effectiveness of composite ball milling and sintering under all manufacturing conditions. Moreover, an increase in interfacial bonding of fabricated composites at a higher sintering temperature indicated improved physical properties of the developed MMCs. The highest microhardness value is 86.6 HVN amid all the fabricated composites at 7% silica, 14% zirconium dioxide, 500° sintering temperature, 90 min sintering time, and 60 min milling time. An integrated Particle Swarm Optimization–Support Vector Machine (PSO-SVM) model was developed to predict microhardness based on the input parameters. The model demonstrated strong predictive performance, as evidenced by low values of various statistical metrics for both training and testing datasets, highlighting the PSO-SVM model’s robustness and generalization capability. Specifically, the model achieved a coefficient of determination of 0.995 and a root mean square error of 0.920 on the training set, while on the testing set, it attained a coefficient of determination of 0.982 and a root mean square error of 1.557. These results underscore the potential of the PSO-SVM framework, which can be effectively leveraged to optimize process parameters for achieving targeted microhardness levels for the developed Al-SiC-ZrO₂ Composites. Full article

Clean and safe air quality is essential for public health, yet particulate matter (PM) significantly degrades air quality and poses serious health risks. The Gulf Cooperation Council (GCC) countries are particularly vulnerable to frequent and intense dust storms due to their vast desert landscapes. This study presents the first health risk assessment of carcinogenic and non-carcinogenic risks associated with exposure to PM2.5 and PM10 bound heavy metals and polycyclic aromatic hydrocarbons (PAHs) based on air quality data collected during the years of 2016–2018 near Dubai International Airport and Abu Dhabi International Airport. The results reveal no significant carcinogenic risks for lead (Pb), cobalt (Co), nickel (Ni), and chromium (Cr). Additionally, AI-based regression analysis was applied to time-series dust monitoring data to enhance predictive capabilities in environmental monitoring systems. The estimated incremental lifetime cancer risk (ILCR) from PAH exposure exceeded the acceptable threshold (10⁻⁶) in several samples at both locations. The relationship between visibility and key environmental variables—PM1, PM2.5, PM10, total suspended particles (TSPs), wind speed, air pressure, and air temperature—was modeled using three machine learning algorithms: linear regression, support vector machine (SVM) with a radial basis function (RBF) kernel, and artificial neural networks (ANNs). Among these, SVM with an RBF kernel showed the highest accuracy in predicting visibility, effectively integrating meteorological data and particulate matter variables. These findings highlight the potential of machine learning models for environmental monitoring and the need for continued assessments of air quality and its health implications in the region. Full article

Accurately identifying the sources of fine particulate matter (PM_2.5) pollution is crucial for pollution control and public health protection. Taking the PM_2.5 pollution event that occurred in Suzhou in June 2023 as a typical case, this study analyzed the characteristics and components of PM_2.5, and quantified the contributions of meteorological conditions, regional transport, and local emissions to the summertime PM_2.5 surge in a typical Yangtze River Delta (YRD) city. Chemical composition analysis highlighted a sharp increase in nitrate ions (NO₃⁻, contributing up to 49% during peak pollution), with calcium ion (Ca²⁺) and sulfate ion (SO₄²⁻) concentrations rising to 2 times and 7.5 times those of clean periods, respectively. Results from the random forest model demonstrated that emission sources (74%) dominated this pollution episode, significantly surpassing the meteorological contribution (26%). The Weather Research and Forecasting model combined with the Community Multiscale Air Quality model (WRF–CMAQ) further revealed that local emissions contributed the most to PM_2.5 concentrations in Suzhou (46.3%), while external transport primarily originated from upwind cities such as Shanghai and Jiaxing. The findings indicate synergistic effects from dust sources, industrial emissions, and mobile sources. Validation using electricity consumption and key enterprise emission data confirmed that intensive local industrial activities exacerbated PM_2.5 accumulation. Recommendations include strengthening regulations on local industrial and mobile source emissions, and enhancing regional joint prevention and control mechanisms to mitigate cross-boundary transport impacts. Full article

Accurate estimation of potential evapotranspiration (PET) is of paramount importance for water resource management, especially in Mediterranean mountainous environments that are often data-scarce and highly sensitive to climate variability. This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and K-Nearest Neighbors (KNN)—in predicting daily PET using limited meteorological data from a high-altitude in Central Greece. The ML models were trained and tested using easily available meteorological inputs—temperature, relative humidity, and extraterrestrial solar radiation—on a dataset covering 11 years (2012–2023). Among the tested configurations, RFR showed the best performance (R² = 0.917, RMSE = 0.468 mm/d, MAPE = 0.119 mm/d) when all the above-mentioned input variables were included, closely approximating FAO56–PM outputs. Results bring to light the potential of machine learning models to reliably estimate PET in data-scarce conditions, with RFR outperforming others, whereas the inclusion of the easily estimated extraterrestrial radiation parameter in the ML models training enhances PET prediction accuracy. Full article

Quantum Dots (QDs) are small semiconductor nanoparticles (<10 nm) with strong, relatively stable, and tunable luminescent properties, which are increasingly applied in the sensing and detection of various analytes, including metal ions, biomarkers, explosives, proteins, RNA/DNA fragments, pesticides, drugs, and pollutants. In this review, we critically assess recent developments and advancements in luminescent QD-based sensors from an analytical perspective. We collected, tabulated, and analyzed relevant data reported in 124 peer-reviewed articles. The key analytical figures of merit, including the limit of detection (LOD), excitation and emission wavelengths, and size of the particles were extracted, tabulated, and analyzed with graphical representations. We calculated the geometric mean and median LODs from those tabulated publications. We found the following geometric mean LODs: 38 nM for QD-fluorescent-based sensors, 26 nM for QD-phosphorescent-based sensors, and an impressively low 0.109 pM for QD-chemiluminescent-based sensors, which demonstrate by far the best sensitivity in QD-based detection. Moreover, AI-based sensing methods, including the ATTBeadNet model, optimized principal component analysis(OPCA) model, and Support Vector Machine (SVM)-based system, were reviewed as they enhance the analytical performance of the detection. Despite these advances, there are still challenges that include improvements in recovery values, biocompatibility, stability, and overall performance. This review highlights trends to guide the future design of robust, high-performance, QD-based luminescent sensors. Full article

Particulate matter 2.5 (PM_2.5) pollution poses severe threats to public health, ecosystems, and urban sustainability. With increasing industrialization and urban sprawl, accurate pollutant monitoring and effective mitigation of PM_2.5 have become global priorities. Recent advancements in machine learning (ML) have revolutionized PM_2.5 sensing by enabling high-accuracy predictions, and scalable solutions through data-driven approaches. Meanwhile, sustainable green technologies—such as urban greening, phytoremediation, and smart air purification systems—offer eco-friendly, long-term strategies to reduce PM_2.5 levels. This review, covering research publications from 2021 to 2025, systematically explores the integration of ML models with conventional sensor networks to enhance pollution forecasting, pollutant source attribution, and intelligent pollutant monitoring. The paper also highlights the convergence of ML and green technologies, including nature-based solutions and AI-driven environmental planning, to support comprehensive air quality management. In addition, the study critically examines integrated policy frameworks and lifecycle-based assessments that enable equitable, sector-specific mitigation strategies across industrial, transportation, energy, and urban planning domains. By bridging the gap between cutting-edge technology and sustainable practices, this study provides a comprehensive roadmap for researchers to combat PM_2.5 pollution. Full article

Transcriptomic profiling has shown that exposure to PM_2.5, a common air pollutant, can modulate gene expression, which has been linked to negative health effects and diseases. However, there are few population-based cohort studies on the association between PM_2.5 exposure and specific gene set expression. In this study, we used an unbiased transcriptomic profiling approach to examine gene expression in a mouse model exposed to PM_2.5 and to identify PM_2.5-responsive genes. The gene expressions were further validated in both the human cell lines and a population-based cohort study. Two cohorts of healthy older adults (aged ≥ 65 years) were recruited from regions characterized by differing levels of PM_2.5. Logistic regression and decision tree algorithms were then utilized to construct predictive models for PM_2.5 exposure based on these gene expression profiles. Our results indicated that the expression of five genes (FAM102B, PPP2R1B, OXR1, ITGAM, and PRP38B) increased with PM_2.5 exposure in both cell-based assay and population-based cohort studies. Furthermore, the predictive models demonstrated high accuracy in classifying high-and-low PM_2.5 exposure, potentially supporting the integration of gene biomarkers into public health practices. Full article

Air pollution poses a significant global challenge, impacting human health and environmental sustainability worldwide. Accurate air quality forecasting is essential for effective mitigation strategies, particularly in rapidly urbanizing regions. This study focuses on Shenyang, China, as a representative case to analyze air quality dynamics and develop a high-precision forecasting tool. Using a comprehensive six-year dataset (2020–2025) of daily air quality and meteorological measurements, a rigorous preprocessing pipeline was applied to ensure data integrity. Five gradient-boosted decision-tree models were trained and combined through a ridge-regularized stacking ensemble to enhance the predictive accuracy. The ensemble achieved an R² of 94.17% and a mean absolute percentage error of 7.79%, outperforming individual models. The feature importance analysis revealed that ozone, PM₁₀, and PM_2.5 concentrations are the dominant drivers of daily air quality fluctuations. The resulting forecasting system delivers robust, interpretable predictions across seasonal variations, offering a valuable decision support tool for urban air quality management. This framework demonstrates how advanced machine learning techniques can be applied in a Chinese urban context to inform global air pollution mitigation efforts. Full article

Machine learning (ML) algorithms hold the potential to outperform the selection of patients for immunotherapy (ICIs) compared to previous biomarker studies. We analyzed the predictive performance of ML models and compared them to traditional clinical biomarkers (TCBs) in the field of gastrointestinal (GI) cancers. The study has been registered in PROSPERO (number: CRD42023465917). A systematic search of PubMed was conducted to identify studies applying different ML algorithms to GI cancer patients treated with ICIs using tumor RNA gene expression profiles. The outcomes included were response to immunotherapy (ITR) or survival. Additionally, we compared the ML methodology details and predictive power inherent in the published gene sets using 5-fold cross-validation and logistic regression (LR), on an available well-defined ICI-treated metastatic gastric cancer (GC) cohort (n = 45). A set of standard clinical ICI biomarkers (MLH, MSH, and CD8 genes, plus PMS2 and PD-L1)) and de-novo calculated principal components (PCs) of the original datasets were also included as additional points of comparison. Nine articles were identified as eligible to meet the inclusion criteria. Three were pan-cancer studies, five assessed GC, and one studied colorectal cancer (CRC). Classification and regression models were used to predict ICI efficacy. Next, using LR, we validated the predictive power of applied ML algorithms on RNA signatures, using their reported receiver operating characteristics (ROC) analysis area under the curve (AUC) values on a well-defined ICI-treated gastric cancer (GC) dataset (n = 45). In two cases our method has outperformed the published results (reported/LR comparison: 0.74/0.831, 0.67/0.735). Besides the published studies, we have included two benchmarks: a set of TCBs and using principal components based on the whole dataset (PCA, 99% explained variance, 40 components). Interestingly, a study using a selected gene set (immuno-oncology panel) with AUC = 0.83 was the only one that outperformed the TCB (AUC = 0.8) and the PCA (AUC =0.81) results. Cross-validation of the predictive performance of these genes on the same GC dataset and an investigation of their prognostic role on a collated multi-cohort GC dataset of n = 375 resected, or chemotherapy-treated patients revealed that genes mannose-6-phosphate receptor (M6PR), Indoleamine 2,3-Dioxygenase 1 (IDO1), Neuropilin-1 (NRP1), and MAGEA3 performed similarly, or better than established biomarkers like PD-L1 and MSI. We found an immuno-oncology panel with an AUC = 0.83 that outperformed the clinical benchmark or the PC results. We recommend further investigation and experimental validation in the case of M6PR, IDO1, NRP1, and MAGEA3 expressions based on their strong predictive power in GC ITR. Well-designed studies with larger sample sizes and nonlinear ML models might help improve biomarker selections. Full article

This study introduces a digital twin paradigm that uses both stationary and mobile sensors and cutting-edge machine learning for urban air quality monitoring. By boosting $R^2$ values from 0.29 to 0.87–0.95, our two-step calibration method increased the accuracy of low-cost PM sensors, showing the possibility of growing monitoring networks without sacrificing measurement accuracy. Significant temporal and spatial variability in PM concentrations was found by mobile sensor deployments, with variations of up to 300% over short distances, predominantly during heavy traffic. During rush hours, peak concentrations were found on multi-lane boulevards and intersections, indicating important exposure concerns usually overlooked by stationary monitoring networks. According to our Graph Neural Network model, which successfully described pollutant dispersion patterns, road dust resuspension predominates in residential areas, while vehicle emissions account for 65% of PM_2.5 along high-traffic corridors. Urban green areas lower PM levels by 30%, yet when the current low-emission zones were first implemented, they had no discernible effect on air quality. Municipal authorities can use this digital twin strategy to acquire practical insights for focused air quality improvements. The method helps make evidence-based traffic management and urban planning judgments by identifying unidentified pollution hotspots and source contributions. The technique offers a scalable option for establishing healthier urban development and marks a substantial leap in environmental monitoring. Full article