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. This study aims to enhance the data from fixed stations by incorporating opportunistic mobile participatory monitoring (MPM) data. The research question is: "How can we enrich fixed station data using MPM?" To overcome the limited availability of MPM data, we reuse existing data for periods with similar pollution maps observed by the fixed stations. The combined fixed and mobile data is then subjected to interpolation methods to generate more accurate pollution maps. The effectiveness of our approach is demonstrated by experiments conducted on a real-life dataset.