Distance adaptive graph convolutional gated network-based smart air quality monitoring and health risk prediction in sensor-devoid urban areas

Abstract

Robust air quality early warning systems for sustainable development of cities have garnered significant attention. These systems enable health risk warnings and regulatory plans when harmful pollutant levels are forecasted. However, with current frameworks, forecasting is only possible at locations with sufficient sensor data and cannot be predicted at new areas. Furthermore, long-term sensor failure also limits their real-time monitoring and forecasting applications. We propose a distance adaptive graph convolutional gated network that provides simultaneous forecasts of primary air pollutants at multiple temporal horizons and locations of a mega-city via spatio-temporal sensor fusion. The framework also solves critical problems of early warning systems related to long-term sensor failure and prediction at a new location of city. The results suggest that for 12 h ahead PM2.5 forecast, the proposed model reduces prediction errors by 43.89% and 52.59% compared to the timeseries-Transformer and convolutional long-short term memory network, respectively. For long-term sensor failure imputation, the mean absolute error for CO, NO2, O3, PM10, and PM2.5 ranged between 0.081–0.160, 0.004–0.007, 0.005–0.0155, 9.41–13.5, and 5.75–8.12, respectively. Whereas the remotely forecasted concentrations at sensor-less locations showed a close similarity to the actual air quality distribution in the city area.