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

PM
_2.5
are inhalable particles with aerodynamic diameters of 2.5 micrometers or smaller. PM
_2.5
concentrations require close monitoring as they impose negative effects on both human health and air quality. Monitoring PM
_2.5
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
_2.5
and meteorological factors can be critical in understanding how particulate matter affects humans and the environment. This study utilizes PurpleAir sensors to measure PM
_2.5
along with some methodological factors such as humidity and applies temporal analysis of the impact of meteorological factors on PM
_2.5
concentrations and spatiotemporal analysis of PM
_2.5
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
_2.5
concentration, meteorological features, and weather conditions) can accurately (i.e., average RMSE of 3.2 μg/m
³
) predict levels of PM
_2.5
, but there is no significant relation between the mentioned meteorological factors and PM
_2.5
concentrations over the Greater Detroit Area. Furthermore, DTW analysis portraits the similarity of PM
_2.5
behavioral patterns over the Greater Detroit Area.