Knowledge discovery for operational decision support in air quality management

Operational decision-making in air quality management systems requires intense efforts for assessing monitored data streams on time. In contrary with the previous works, which focus on air quality forecasting, this paper concentrates on near real time air quality assessment. Data uncertainty problems associated with environmental monitoring networks bring forth issues such as measurement validation and estimation of missing or erroneous values, which are critical for taking trustworthy decisions in a timely fashion. A remedy to these problems is proposed through knowledge discovery techniques. By employing classification techniques, an empirical approach is presented for supporting the decision making process involved in an environmental management system that monitors ambient air quality and triggers alerts when incidents occur. Specifically, exhaustive experiments with large, real world datasets have resulted to trustworthy predictive models, capable operational decision-making for measurement validation and estimation of missing or erroneous data. The outstanding performance of the induced predictive models signifies the added value of using data-driven approaches in operational air quality assessment.