Wildfires Impact Assessment on PM Levels Using Generalized Additive Mixed Models
Gianluca Leone,
Giorgio Cattani,
Mariacarmela Cusano,
Alessandra Gaeta,
Guido Pellis,
Marina Vitullo,
Raffaele Morelli
Affiliations
Gianluca Leone
Department for Environmental Evaluation, Control and Sustainability, Italian National Institute for Environmental Protection and Research (ISPRA), 00144 Rome, Italy
Giorgio Cattani
Department for Environmental Evaluation, Control and Sustainability, Italian National Institute for Environmental Protection and Research (ISPRA), 00144 Rome, Italy
Mariacarmela Cusano
Department for Environmental Evaluation, Control and Sustainability, Italian National Institute for Environmental Protection and Research (ISPRA), 00144 Rome, Italy
Alessandra Gaeta
Department for Environmental Evaluation, Control and Sustainability, Italian National Institute for Environmental Protection and Research (ISPRA), 00144 Rome, Italy
Guido Pellis
Department for Environmental Evaluation, Control and Sustainability, Italian National Institute for Environmental Protection and Research (ISPRA), 00144 Rome, Italy
Marina Vitullo
Department for Environmental Evaluation, Control and Sustainability, Italian National Institute for Environmental Protection and Research (ISPRA), 00144 Rome, Italy
Raffaele Morelli
Department for Environmental Evaluation, Control and Sustainability, Italian National Institute for Environmental Protection and Research (ISPRA), 00144 Rome, Italy
Wildfires are relevant sources of PM emissions and can have an important impact on air pollution and human health. In this study, we examine the impact of wildfire PM emissions on the Piemonte (Italy) air quality regional monitoring network using a Generalized Additive Mixed Model. The model is implemented with daily PM10 and PM2.5 concentrations sampled for 8 consecutive years at each monitoring site as the response variable. Meteorological data retrieved from the ERA5 dataset and the observed burned area data stored in the Carabinieri Forest Service national database are used in the model as explanatory variables. Spline functions for predictive variables and smooths for multiple meteorological variables’ interactions improved the model performance and reduced uncertainty levels. The model estimates are in good agreement with the observed PM data: adjusted R2 range was 0.63–0.80. GAMMs showed rather satisfactory results in order to capture the wildfires contribution: some severe PM pollution episodes in the study area due to wildfire air emissions caused peak daily levels up to 87.3 µg/m3 at the Vercelli PM10 site (IT1533A) and up to 67.7 µg/m3 at the Settimo Torinese PM2.5 site (IT1130A).
