Decoding Urban Air Quality Through Sensor-Driven Predictive Intelligence
Keywords:
Air pollution, Environmental monitoring, Predictive intelligence, Sensor networks, Urban air qualityAbstract
Monitoring of air quality in urban areas is increasingly a day-to-day, continuous, scalable and data-driven process to meet the demands of capturing dynamic air pollution patterns. This study investigates the feasibility of using responses from metal-oxide sensors, reference measurements of pollutants and meteorological data to estimate urban air pollution by sensor-driven predictive intelligence. The hourly observations were preprocessed for missing values and then analysed descriptively, in correlation, in time and predictive. Using chronological training and testing datasets, the linear regression model, Random Forest model and Gradient Boosting model were evaluated for the estimation of carbon monoxide (CO). Many good correlations were obtained between CO and benzene (r = 0.931), PT08.S2(NMHC) (r = 0.916), and PT08.S1(CO) (r = 0.879). Linear regression showed the best predictive ability with an R² of 0.862, RMSE of 0.484 mg/m³, and MAE of 0.344 mg/m³. PT08.The most prevalent predictive sensor signal was S2(NMHC) and meteorological variables gave the surrounding environmental context. Results show that it is possible to combine the responses of the different sensors with predictive analytics to help with traditional monitoring to provide scalable, intelligent and sustainable air-quality surveillance in urban environments.
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