Search Results (300)

Traditional attribute control charts for defect counts are commonly developed under the assumption that count data follow a homogeneous Poisson distribution. However, this assumption is often violated in practical applications. To overcome this limitation, a two-parameter Poisson distribution, the Conway–Maxwell–Poisson (CMP or COM–Poisson) distribution, has been widely used to construct control charts capable of effectively monitoring count data exhibiting over- or under-dispersion. Furthermore, the multiple dependent state (MDS) sampling scheme evaluates the current process status not only based on the present sample but also by incorporating information from previous samples, thereby achieving higher detection efficiency than single sampling schemes. This study integrates the COM–Poisson distribution with the MDS sampling strategy to develop an attribute control chart based on the extended exponentially weighted moving average statistic. The average run length is obtained under various shift magnitudes using probability-based computations. The simulation results demonstrate that the proposed chart substantially outperforms existing approaches in the prompt detection of out-of-control conditions. A real-world air quality index (AQI) monitoring study showed that the proposed chart effectively detected increases in weekly AQI counts and provided earlier warnings of potential air quality deterioration. Full article

Rapid urban growth and industrial activity have significantly increased air pollution in Chattogram, Bangladesh. Among various pollutants, particulate matter (PM_2.5 and PM₁₀) has emerged as a major concern due to its severe effects on public health and the environment. This study investigates seasonal and temporal variations in air quality and identifies periods of high risk. Data were collected from the Department of Environment, including PM_2.5 and PM₁₀ measurements from 2013 to April 2025 and AQI data from 2022 to April 2025. The analysis focuses on seasonal trends, monthly fluctuations, extreme pollution events, and correlations among pollutants. The study examines which seasons are most polluted and how PM_2.5, PM₁₀, and AQI change over the year. Results indicate that winter experiences the highest pollution levels, while monsoon shows the lowest. Pre-monsoon and post-monsoon show moderate pollution, representing transitional periods with variable air quality. Monthly trends reveal that pollution peaks in January and reaches its lowest point in July, demonstrating clear temporal patterns. Extreme pollution events are most frequent in winter and the pre-monsoon period, emphasizing periods of highest health risk. Correlation analysis shows strong positive associations between PM_2.5, PM₁₀, and AQI (r > 0.80). Machine-learning models demonstrated moderate predictive skill for PM₁₀ forecasting (R² up to 0.65), while PM_2.5 models at Agrabad produced negative R² values, indicating performance worse than a mean-value benchmark; accordingly, long-term PM_2.5 projections at Agrabad are not reported. Exploratory PM₁₀ projections suggest that concentrations may remain above WHO guideline levels through 2030, although these projections should be interpreted cautiously because meteorological drivers were not included in the modelling framework. The findings highlight the need for seasonal interventions to control particulate matter, particularly in winter and pre-monsoon. Measures such as stricter emission regulations, industrial controls, and public awareness campaigns are essential for mitigating health risks. This study provides a comprehensive understanding of air-quality dynamics in Chattogram and offers a framework for targeted pollution-control strategies. Implementing these strategies can improve air quality, protect public health, and support sustainable urban development. Full article

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

The Beijing–Tianjin–Hebei (BTH) region faces complex air pollution driven by alternating particulate matter (PM) and ozone (O₃) dominance, regional transport, topography, and meteorology. This study develops a hybrid framework integrating air quality index (AQI) records, pollutants, meteorological variables, and MEIC emissions from the BTH region (2018–2025) to capture spatiotemporal evolution and short-term predictability. Results show a seasonal AQI cycle (winter/spring highs, summer/autumn lows) with a summer PM–O₃ seesaw. Spatially, three zones were identified: the northern and coastal ecological barrier zone, the central compound-pollution plain zone, and the southern heavy-industrial zone. Random Forest identifies PM as the dominant AQI compositional contributor, with visibility, dew point, humidity, and MEIC emissions (particulates, NH₃, organics) as key correlates. Forecast evaluation reveals progressive improvement: ARMA captures linear baselines (R² = 0.318, MAPE = 33.26%), XGBoost improves statistical prediction by incorporating nonlinear feature interactions and lagged meteorology (R² = 0.567, MAPE = 24.81%), and LSTM shows the strongest statistical predictive performance (R² = 0.613, MAPE = 22.32%). The improvement of LSTM over XGBoost is incremental and reflects enhanced data-driven representation of short-term AQI–meteorology temporal dependence, rather than identification of physical pollution mechanisms. Regional disparities persist, with higher predictability in the southern heavy-industrial zone and lower accuracy in the northern and coastal ecological barrier zone affected by intermittent dust intrusions and frontal passages. Overall, the results suggest that LSTM may support data-driven short-term AQI warning, but source-oriented mitigation still requires process-based tools, such as chemical-transport or source-apportionment models. Full article

This study applies a Long Short-Term Memory (LSTM) model to predict key air pollutants, i.e., sulphur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (PM_2.5), as well as the Air Quality Index (AQI) across South Africa using satellite-derived observations. The analysis focuses on comparing original pollutant fields with model-generated predictions for two consecutive days, highlighting both spatial patterns and predictive performance. Results reveal a persistent and intense pollution hotspot over the Mpumalanga Highveld, driven by coal-fired power generation and industrial activities. Elevated pollutant concentrations in this region translate into AQI levels ranging from Unhealthy to Very Unhealthy, while most other parts of the country remain within the Good category. Spatial comparison between original and predicted fields shows strong agreement, with only minor deviations in areas characterized by steep emission gradients and localized plumes. Quantitative evaluation using RMSE (0.020390) and MSE (0.000416) confirms the high accuracy of the predictive model, with error values remaining extremely low across all pollutants and AQI outputs. PM_2.5 exhibits the smallest errors (MSE = 4.230169 × 10⁻⁶), while slightly higher values for SO₂ (MSE = 2.628 × 10⁻⁴) and NO₂ (MSE = 1.39541 × 10⁻⁴) reflect the difficulty of capturing sharp spatial transitions associated with point-source emissions. Despite these localized discrepancies, the model demonstrates robust skill in replicating both pollutant magnitudes and AQI classifications. Overall, the findings indicate that machine-learning approaches offer a reliable, high-resolution tool for air-quality prediction in South Africa and have strong potential for supporting operational forecasting, exposure assessment, and environmental policy development. Full article

Air pollution episodes in Metropolitan Lima pose persistent challenges for urban health protection and timely environmental decision-making. However, many machine learning approaches for air-quality prediction remain difficult to operationalize due to high latency, extensive hyperparameter tuning, and limited interpretability. This study addresses this gap by adopting an engineering-driven predictive knowledge modeling approach grounded in the Knowledge Discovery in Databases (KDD) framework to evaluate an efficient probabilistic classifier—Gaussian Naïve Bayes (GNB)—for predicting regulatory air-quality categories in Metropolitan Lima. A total of 768,185 hourly observations from SENAMHI monitoring stations covering the 2020–2025 period were analyzed, considering PM₁₀, PM_2.5, NO₂ concentrations, and the Air Quality Index (AQI). Data were preprocessed through validity checks, explicit outlier handling, and categorical encoding based on regulatory thresholds, while a time-based train–test split preserved temporal structure and prevented data leakage. The proposed model achieved strong predictive performance (global accuracy ≥ 0.925) and excellent probabilistic calibration (overall Brier Score ≈ 0.023; AQI Brier Score ≈ 0.010). These results demonstrate that GNB provides a robust, interpretable, and computationally efficient solution for operational air-quality management and early warning support, contributing to evidence-based urban environmental decision-making aligned with Sustainable Development Goal 13 (Climate Action). Full article

This study uses data from Taichung Fengyuan Station in Taiwan’s Civil IoT to conduct short-term forecasting of the Air Quality Index (AQI). We compile multiple pollutant and meteorological features and develop three models—Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Autoregressive Integrated Moving Average with exogenous variables (ARIMAX)—together with a persistence baseline for comparison. The purpose of this study is to clarify whether deep sequence models and a classical statistical model can provide reliable one-hour-ahead AQI forecasts at the site level and to examine the practical value of such forecasts for early warning and air-quality management. Results show that GRU achieves the lowest overall prediction errors, followed by LSTM. The persistence baseline outperforms ARIMAX but remains clearly inferior to both recurrent models. In sum, the study shows that site-level AQI forecasting can benefit from recurrent deep-learning models not only in terms of numerical accuracy, but also in terms of capturing short-term temporal structure beyond a naive carry-forward baseline. These findings provide a benchmark-oriented and application-oriented reference for short-horizon AQI warning scenarios. Full article

(1) Background: Metabolic disorders, including hypertension, hyperlipidemia, and hyperglycemia (HHH), rank at the top of the disease burden in China. However, population-level assessment of pharmacological treatment remains limited by the lack of scalable metrics for monitoring medication use and outcomes. (2) Methods: We pioneered the use of standardized combined “HHH” medication usage—encompassing antihypertensive, antidiabetic, and lipid-lowering agents—as an integrated proxy for evaluating interventions for cardiovascular diseases and diabetes. Leveraging wastewater-based epidemiology (WBE), we quantified HHH medication loads (mg/d/1000 persons) across 30 prefectures covering all regions in China, and mapped the associated geographical disparities using independent t-tests. Associations with environmental, socioeconomic, demographic, social service, and health-related behavioral and lifestyle factors were further examined via correlation analysis. (3) Results: Our findings confirmed a pronounced north–south gradient in HHH medication uses (the mean standardized loads in the north were approximately twice as high as those in the south, p < 0.05). Furthermore, aging, sex ratio, nicotine consumption, obesity rate, the comprehensive Air Quality Index (AQI), precipitation and the Urban Wellness and Healthcare Index were identified as the top seven influencing factors (|r| values ranging from 0.37 to 0.71, all p < 0.05). (4) Conclusions: As a comprehensive national-scale analysis of multi-drug use for HHH via WBE, this study provides valuable insights into national multi-disease pharmacological treatment, offering evidence-based support for refining clinical prescribing guidelines and rationalizing the allocation of healthcare resources. Full article

Kuwait experiences persistently high levels of air pollution driven by industrial emissions, transportation, oil-related activities, and frequent desert dust storms. This study aims to synthesize and critically evaluate the available evidence on the relationship between air pollution, Air Quality Index (AQI), and health outcomes in Kuwait using a guided narrative review approach. A guided literature search identified 26 peer-reviewed studies published between 2014 and 2026 about Kuwait air pollution, which were assessed for methodological characteristics, pollutant types, health outcome categories, and vulnerable populations. The most frequently examined pollutants were particulate matter (PM_2.5: 69%; PM₁₀: 38%), followed by NO₂ (23%), multi-pollutant and AQI-based (19%), O₃ (12%), SO₂ (12%), VOCs and PAHs (8%). Health-related investigations most commonly addressed mortality and respiratory morbidity, while cardiovascular, metabolic, biomarker-based, and cancer-related outcomes were less frequently represented. Among studies reporting direct health outcomes, elevated PM_2.5 exposure was generally associated with increased risks of respiratory hospitalizations, cardiovascular events, and all-cause mortality. Susceptible populations identified across the literature include children, older adults, individuals with pre-existing chronic conditions, and outdoor workers, who may experience higher exposure levels and greater health vulnerability. However, a substantial proportion of the included studies focused primarily on exposure characterization or pollutant modeling without direct assessment of health outcomes. These studies nonetheless indicate consistently elevated pollutant levels and seasonal variability, which may plausibly contribute to population health risks. Overall, while the available Kuwait-specific evidence suggests potential adverse health effects linked to air pollution, the strength of direct epidemiological evidence remains limited. Important gaps persist, including the scarcity of long-term cohort studies, limited multi-pollutant analyses, and insufficient integration of AQI categories with health outcomes. These limitations highlight the need for more robust and longitudinal research to better quantify health risks and inform public health policy in Kuwait. Full article

In this study, global spatial autocorrelation, local spatial autocorrelation, Spearman correlation analysis, gray correlation analysis, entropy weight method, and the gravity model were used to analyze the spatiotemporal variation and environment-urban-economy-associated factors of air quality of 31 provinces in China during 2015~2022. From 2015 to 2022, the Air Quality Index (AQI) exhibited a downward trend in 30 out of 31 Chinese provinces, with the exception of Shaanxi Province. Concurrently, the annual average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO declined across the study period. High-high clusters and low-high outliers were observed in northern China, whereas low-low clusters and high-low outliers were distributed in southern China. Twelve provinces (38.7%) showed positive correlation (0.095~0.95), 18 provinces (58.1%) showed negative correlation (−0.76~0.095), and only Anhui showed no correlation between AQI and O₃. The comprehensive AQI quality presented a dual-core model in Sichuan (in the southwest) and Henan (in the central part) of China, while the comprehensive AQI improvement rate presented a single-core model in Jiangsu in the east of China. The gravity models incorporating AQI and GDP revealed that both air quality and economic performance improved over the study period. The spatial pattern of pollution evolved from a multi-core structure to a non-core structure, whereas the pattern of economic growth transitioned from a non-core structure to a dual-core structure, with the Beijing-Tianjin-Hebei region and the Yangtze River Delta emerging as the primary urban agglomerations. Full article

Air pollution forecasting is particularly challenging in basins with frequent winter seasons and temperature inversions. In this study, we developed and rigorously evaluated deep learning models to forecast PM₁₀ and the Air Quality Index (AQI) in Igdır, Türkiye, using a five-year, hourly dataset (2020–2024) from the Igdır/Central station (PM₁₀, NO₂, O₃, SO₂; meteorology: pressure, temperature, wind speed, relative humidity, precipitation, cloud cover). Using linear interpolation and Z-score normalization, sine/cosine features (hour, month) were used to encode temporal periodicity, and a 72-h lookback → 24-h look-ahead design was employed. LSTM, GRU, BiLSTM, and CNN-LSTM models were compared under a three-stage ablation (meteorology only; +cyclic encoders; +lagged targets), and their hyperparameters were tuned via Bayesian optimization. The deep learning results were further contextualized against a Multiple Linear Regression (MLR) baseline serving as a snapshot persistence model to evaluate the specific advantage of LSTM’s temporal memory in short-horizon forecasting. Multi-output forecasting is central to the proposed design, featuring a multi-task learning (MTL) framework based on a single shared temporal encoder with two task-specific regression heads that simultaneously predict PM₁₀ and AQI. Compared with separate single-task models, the multi-output setup exploits cross-target covariance (AQI’s dependence on pollutant loads under meteorology), improves data efficiency and generalization through shared representations, and promotes coherent, horizon-stable forecasts across targets, which is particularly valuable when winter stagnation regimes couple PM₁₀ and AQI dynamics. Moreover, this study introduces a structured ablation design to explicitly evaluate the added value of multi-output forecasting under inversion-dominated basin conditions. The results show stepwise gains from cyclic encoders and, most strongly, from lagged target histories. Under the optimized 24-h setting, LSTM performs best (R²_{PM₁₀} = 0.7989, RMSE = 48.74 µg/m³; R²_{AQI} = 0.6626, RMSE = 37.81), marginally surpassing GRU and clearly outperforming BiLSTM and CNN-LSTM. Horizon sensitivity confirms the benefit of nowcasting: when retrained for shorter horizons, LSTM attains R² = 0.9991 for PM₁₀ (MAE = 2.44; RMSE = 3.30 µg/m³) and 0.9535 for AQI (MAE = 4.87; RMSE = 14.03) at 1 h, and R² = 0.9792 (PM₁₀; MAE = 9.70; RMSE = 15.67) and 0.8849 (AQI; MAE = 11.19; RMSE = 22.08) at 6 h. Residual diagnostics reveal heteroskedastic, regime-dependent errors peaking near 0 °C and low winds, as well as a conservative bias that underpredicts extremes. Collectively, the findings show that multi-output, temporally aware deep models enable accurate operational forecasting in Igdır. The proposed framework provides real-time air quality alerts and daily planning, providing decision support for sustainable air quality management, public health protection, and evidence-based urban policy and is transferable to similar continental basin environments. Full article

This study presents an assessment of ambient air quality in Chichiri and Malawi University of Business and Applied Sciences (MUBAS) locations, Blantyre City, Southern Malawi. The study aimed at assessing temporal trends, identifying exceedance of thresholds, investigating relationships between pollutants and meteorological factors, and exploring the predictability of air quality index (AQI). Five pollutants: ${\mathrm{P}\mathrm{M}}_{2.5}$, ${\mathrm{P}\mathrm{M}}_{10}$, ${\mathrm{N}\mathrm{O}}_{\mathrm{x}}$, ${\mathrm{C}\mathrm{O}}_2$ and TVOC were assessed over a two-month period using fixed low-cost sensors. Daily and hourly temporal analysis showed that pollutants peak during morning and evening hours. A significant number of exceedances for ${\mathrm{P}\mathrm{M}}_{2.5}$ and ${\mathrm{P}\mathrm{M}}_{10}$ were observed when compared to indicative thresholds. Chichiri exhibited more frequent AQI classifications in the “unhealthy” range. A strong positive relationship between ${\mathrm{P}\mathrm{M}}_{2.5}$ and ${\mathrm{P}\mathrm{M}}_{10}$ (r $=$ 0.84) and positive correlations between ${\mathrm{N}\mathrm{O}}_{\mathrm{x}}$ and ${\mathrm{C}\mathrm{O}}_2$ were observed. A multiple linear regression model achieved a high coefficient of determination (${\mathrm{R}}^2$ $=$ 0.938), identifying ${\mathrm{P}\mathrm{M}}_{10}$ and ${\mathrm{N}\mathrm{O}}_{\mathrm{x}}$ as dominant predictors of AQI variability. Temperature and humidity showed modest inverse relationship with AQI, suggesting dispersion effects. A comparison with African cities showed that the study areas’ pollution levels were within regional norms, but that there is a need for targeted mitigation. These findings underscore the importance of continuous monitoring, data-driven policy making and regional collaboration to address urban air quality challenges. Full article

Accurate prediction of the air quality index (AQI) is crucial for understanding urban pollution dynamics and protecting public health. This study proposes a dual-branch fusion framework (CL-XGB-Season) to address seasonal heterogeneity in AQI prediction by integrating temporal dynamic features and static patterns. The CNN-LSTM branch captures short-term temporal fluctuations, while a seasonally split XGBoost branch fits long-term static patterns via independent submodels for spring, summer, autumn, and winter. SHAP-based interpretability analysis revealed the dominant drivers across different seasons: the “temperature × O₃” interaction feature plays a key role in summer, characterizing the ozone formation mechanism dominated by photochemical reactions under conditions of high temperature and strong solar radiation; whereas the PM_2.5/PM₁₀ ratio is crucial in winter (where pollution is primarily driven by pollutant accumulation). The dual-branch fusion framework was validated using hourly resolution data from Chongqing for the 2020–2025 period. Results indicate that the framework achieved a prediction accuracy of 0.197 root mean square error (nRMSE) and 0.9611 coefficient of determination (R²) on the test set, outperforming eight ablation variants and five baseline models (ARIMA, Transformer, etc.) in comparative experiments. Ablation studies confirm the necessity of dual branches and seasonal modeling, with the full model reducing nRMSE by 19–63% versus single-model variants. This framework maintains stable seasonal performance and provides actionable insights for targeted air quality management. Full article

The spatiotemporal dynamics of the Air Quality Index (AQI) and its response to vegetation regulation require further investigation. Using multi-source data from 2020 in Shandong Province, China, this study analyzed the effects of vegetation greenness (NDVI, LAI, EVI), ecological efficiency (Net Primary Productivity, NPP), and landscape structure on AQI within 3 km grids. Monthly correlation analyses revealed that AQI peaks in January (125.09), June (99.01), and December (105.81). PM_2.5, O₃, and PM₁₀ were the primary pollutants in winter, summer, and spring/autumn, respectively. Vegetation showed a significant purifying effect from June to September. NPP (r = −0.83) was more effective in mitigating air pollution than greenness-related indices (r = −0.48). Pollution mitigation was enhanced by vegetation patches with complex shapes and dispersed configurations. During the non-growing season, the vegetation alleviating effect weakened considerably, and a decoupling between greenness and ecological efficiency occurred. This decoupling was associated with a stronger positive correlation between population density and AQI. The findings highlight the importance of seasonal vegetation dynamics and landscape optimization for regional air quality management. Full article

The sugarcane industry in South Africa is ranked among the top 15 producers worldwide and plays a significant role in supporting the nation’s socioeconomic development, producing approximately 2.3 million tons annually. Harvesting is largely labour-intensive and commonly involves the pre-harvest burning of sugarcane. This widespread practice is associated with (a) local air quality deterioration driven by pollutants such as carbon monoxide (CO), black carbon (BC), and sulphur dioxide (SO₂) and (b) adverse public health outcomes, including respiratory and cardiovascular diseases. This study aims to assess the air quality across KwaZulu-Natal and compare inland and coastal sugarcane-growing regions during the May–August 2023 harvest season. The CO and SO₂ concentrations are obtained from Sentinel-5P, while the BC data are sourced from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The Air Quality Index (AQI) is calculated using the CO, SO₂, PM_2.5, and NO₂ data from the Copernicus Atmosphere Monitoring Service (CAMS). The findings consistently indicate higher pollutant concentrations in inland regions, suggesting more concentrated burning activities and lower atmospheric dispersion relative to coastal areas. Overall, the results highlight the greater prevalence of poor air quality in inland sugarcane regions compared with coastal zones. Full article