A Sensor-Based Data Driven Framework to Investigate PM2.5 in the Greater Detroit Area

Abstract

PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> are inhalable particles with aerodynamic diameters of 2.5 micrometers or smaller. PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentrations require close monitoring as they impose negative effects on both human health and air quality. Monitoring PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentrations in the metropolitan Detroit Area is increasingly important as its residents are being disproportionately exposed to harmful air pollution due to health inequities through economic divestment, limited educational and employment opportunities. The relations between PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> and meteorological factors can be critical in understanding how particulate matter affects humans and the environment. This study utilizes PurpleAir sensors to measure PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> along with some methodological factors such as humidity and applies temporal analysis of the impact of meteorological factors on PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentrations and spatiotemporal analysis of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> distributions at different locations over the Greater Detroit Area via Long Short Term Memory (LSTM) neural networks and Dynamic Time Warping (DTW) algorithms, respectively. Our findings show that although LSTMs with exogenous variables (i.e., the current values of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentration, meteorological features, and weather conditions) can accurately (i.e., average RMSE of 3.2 μg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) predict levels of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> , but there is no significant relation between the mentioned meteorological factors and PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentrations over the Greater Detroit Area. Furthermore, DTW analysis portraits the similarity of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> behavioral patterns over the Greater Detroit Area.