Utilizing PurpleAir Sensors for Real-Time Visualization and Analysis of Region-Specific PM2.5 Air Pollution Data

Abstract

Airborne particulate matter with a diameter of 2.5 micrometers or less (PM2.5) is a major health concern due to its adverse effects on human health and the environment. As air pollution worsens, understanding region-specific PM2.5 levels becomes crucial for targeted interventions. This study presents a novel approach using low-cost PurpleAir sensors that provide real-time PM2.5 data. Through the implementation of Python, a sophisticated visualization and analysis tool has been developed to map the real-time PM2.5 data on an interactive platform. Specific regions of interest can be effortlessly defined by selecting bounding boxes on the map, thereby focusing on areas that warrant immediate attention. Customizable filters, such as proximity of sensors to the selected region and the maximum allowable PM2.5 concentration, are incorporated into the tool to enhance data accuracy and streamline the data collection process. The strength of this approach lies in its ability to empower researchers and communities with timely information about region-specific PM2.5 levels, significantly advancing our understanding of air quality and facilitating a proactive approach towards a cleaner and healthier environment for all.