Abstract
Ambient air quality is the most important environmental factor affecting human health, estimated by the WHO to be responsible for 4.2 million deaths annually. Having timely estimates for air quality is critical for implementing public policies that can limit anthropogenic emissions, reduce human exposure and allow for preparation and interventions in the health sector. In Brazil, wildfires constitute an important source of particulate matter emission, particularly in the country’s northern and midwestern regions, areas that are under-served in terms of air quality monitoring infrastructure. In the absence of regulatory-grade monitoring networks, low-cost sensors offer a viable alternative for generating real-time, publicly available estimates of pollutant concentrations. Here, we examine data from two low-cost sensors deployed in Brasília, in the Federal District of Brazil, during the 2022 wildfire season and use NOAA’s HYSPLIT model to investigate the origin of a particulate matter peak detected by the sensors. There was high agreeability of the data from the two sensors, with the raw values showing that daily average PM2.5 concentrations reached peak values of 46 µg/m3 and 43 µg/m3 at the school and park sites, respectively. This study demonstrates the value of low-cost sensors and their possible application in real-time scenarios for environmental health surveillance purposes.