TY - JOUR
TI - Low-processing data enrichment and calibration for PM2.5 low-cost sensors
AU - Stojanović Danka B
AU - Kleut Duška N
AU - Davidović Miloš D
AU - De Vito Saverio
AU - Jovašević-Stojanović Milena V
AU - Bartonova Alena
AU - Lepioufle Jean-Marie
JN - Thermal Science
PY - 2023
VL - 27
IS - 3
SP - 2229
EP - 2240
PT - Article
AB - Particulate matter (PM) in air has been proven to be hazardous to human health. Here we focused on analysis of PM data we obtained from the same campaign which was presented in our previous study. Multivariate linear and random forest models were used for the calibration and analysis. In our linear regression model the inputs were PM, temperature and humidity measured with low-cost sensors, and the target was the reference PM measurements obtained from SEPA in the same timeframe.