Smart City Gnosys
Smart city article details
| Title | Environmental Data Analytics For Smart Cities: A Machine Learning And Statistical Approach |
|---|---|
| ID_Doc | 24215 |
| Authors | AlSalehy A.S.; Bailey M. |
| Year | 2025 |
| Published | Smart Cities, 8, 3 |
| DOI | http://dx.doi.org/10.3390/smartcities8030090 |
| Abstract | Highlights: What are the main findings? CO pollution in Jubail shows strong diurnal and moderate weekly patterns, with local sources dominating spatial variation. Ensemble machine learning models, especially Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost), achieved highly accurate CO forecasts (R2 > 0.95). What is the implication of the main finding? Predictive analytics enable proactive air quality management in smart cities, improving public health outcomes. Identifying pollution hotspots and weather interactions supports targeted interventions and smarter urban planning. Effectively managing carbon monoxide (CO) pollution in complex industrial cities like Jubail remains challenging due to the diversity of emission sources and local environmental dynamics. This study analyzes spatiotemporal CO patterns and builds accurate predictive models using five years (2018–2022) of data from ten monitoring stations, combined with meteorological variables. Exploratory analysis revealed distinct diurnal and moderate weekly CO cycles, with prevailing northwesterly winds shaping dispersion. Spatial correlation of CO was low (average 0.14), suggesting strong local sources, unlike temperature (0.92) and wind (0.5–0.6), which showed higher spatial coherence. Seasonal Trend decomposition (STL) confirmed stronger seasonality in meteorological factors than in CO levels. Low wind speeds were associated with elevated CO concentrations. Key predictive features, such as 3-h rolling mean and median values of CO, dominated feature importance. Spatiotemporal analysis highlighted persistent hotspots in industrial areas and unexpectedly high levels in some residential zones. A range of models was tested, with ensemble methods (Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost)) achieving the best performance ((Formula presented.)) and XGBoost producing the lowest Root Mean Squared Error (RMSE) of 0.0371 ppm. This work enhances understanding of CO dynamics in complex urban–industrial areas, providing accurate predictive models ((Formula presented.)) and highlighting the importance of local sources and temporal patterns for improving air quality forecasts. © 2025 by the authors. |
| Author Keywords | air quality monitoring; forecasting models; gas and weather data; machine learning for environmental data; multi-location data; predictive analytics; smart cities; spatiotemporal analysis; time-series analysis |
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