How opportunistic mobile monitoring can enhance air quality assessment?

Abstract

The deteriorating air quality in urban areas, particularly in developing countries, has led to increased attention being paid to the issue. Daily reports of air pollution are essential to effectively manage public health risks. Pollution estimation has become crucial to expanding spatial and temporal coverage and estimating pollution levels at different locations. The emergence of low-cost sensors has enabled high-resolution data collection, either in fixed or mobile settings, and various approaches have been proposed to estimate air pollution using this technology. The objective of this study is to enhance the data from fixed stations by incorporating opportunistic mobile monitoring (OMM) data. The main research question we are dealing with is: How can we augment fixed station data through OMM? In order to address the challenge of limited OMM data availability, we leverage existing data collected during periods when the pollution maps align with those observed by the fixed stations. By combining the fixed and mobile data, we apply interpolation techniques to produce more accurate pollution maps. The efficacy of our approach is validated through experiments conducted on a real-life dataset.