Air Quality Predictions through Mathematical Modeling for Iron Ore Mine Project

Abstract

Mathematical modeling was deployed to predict air quality during the construction and operation phases of an iron ore mine project in Maharashtra, India. A survey of different models revealed that the ISCST3 model was the most applicable one to predict the air quality parameters, particularly the suspended particulate matter (SPM) and coarse particulate matter (PM10). Baseline air quality data, emission rates, local meteorology, and terrain information were used to simulate the ground-level concentrations. The simulation predicted SPM and PM10 peaks of 172 µg/m3 and 44 µg/m3, respectively. The prediction was within the prescribed limits of the national standards of 200 µg/m3 and 100 µg/m3, respectively, near the source, with minor exceedances in total SPM in two nearby villages and an impact on air quality due to proposed mining. Accordingly, mitigation strategies towards such villages were recommended and implemented. Later, the monitoring in the operation phase revealed that particulate matter could be controlled effectively with mitigation strategies and ensured compliance with air quality standards. The analysis also revealed strong correlations between the particulate matter and the distance of the localities and SPM and PM10. Continuous monitoring and adaptive mitigation based on real-time data were thus emphasized for long-term sustainability and responsible mining practices.

Keywords

• air quality
• ISCST3 modeling
• iron ore mining
• particulate matter
• construction phase
• operational phase