Optimal WSN Deployment to Measure Air Quality in Dynamic Multi-Source Dispersion Scenarios: A Case Study in Arequipa

Air quality is a crucial determinant of public health, significantly impacting global populations. According to the World Health Organization, air pollution causes approximately 7 million premature deaths each year, with 99% of the global population exposed to air that fails to meet recommended quality standards. Poor air quality leads to severe health issues, including respiratory and cardiovascular diseases. Given these alarming statistic, it is imperative for governments, organizations, and communities to prioritize air quality management. Effective measurement strategies are the first step in reducing health risks associated with pollution. Thus, to address this public health challenge, this article introduces a novel dynamic multi-source dispersion model that accounts for variations in wind velocity and direction over time, recognizing that air pollution is influenced by rapidly changing environmental factors. Additionally, three optimization problems are presented to identify the optimal locations for sensor nodes. The first problem enhances pollution sensing capabilities, the second maximizes the population served, aiming to ensure that air quality information is accessible to as many residents as possible, and the third one prioritizes the needs of vulnerable groups. Finally, we present results from a case study conducted on the campus of the National University of San Agustín in Arequipa, Peru, demonstrating the practical application of our proposed methodology in real-world scenarios.