Estimation of multitemporal dry deposition of air pollution by urban forests at city scale

Air pollution is a major problem for many cities worldwide. Urban forests provide a wide range of ecosystem services for human well-being, including recreation, urban temperature regulation, and air purification. The present study investigated the use of two Airborne Laser Scanning (ALS) datasets to estimate changes in dry deposition of particulate matter (PM10) by urban forests in the city of Florence. The spatial distribution of urban forests was mapped by photointerpretation of aerial images and classified into seven forest types. The leaf area index (LAI) was estimated using a regression model between LAI data and forest canopy cover from ALS data. The potential of PM10 removal by urban forests was estimated using pollution deposition equations and pollution concentrations measured at urban monitoring stations in 2013 and 2018. Our results show that PM10 removal by urban forests in the city of Florence decreased from 2013 to 2018. In the study area natural and human induced forest disturbances (e.g. wind storms and coppicing) occurred in the examined period, which reduced the forest canopy cover and the potential removal of air pollution by urban forest as well. Our study confirms that canopy cover is a good predictor for LAI. However, caution on the fact that our results were partially affected by the date of acquisition of remote sensing products.