Scenario analysis of livestock-related PM2.5 pollution based on heteroskedastic geostatistical modelling

The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emissions levels. There is a large scientific consensus that the agricultural sector has a major impact on air quality. In Lombardy, livestock activities are widely acknowledged to be responsible for approximately 97% of regional ammonia emissions due to the high density of livestock. The main objective of our study is to quantify the relationship between ammonia emissions and PM2.5 concentrations in the Lombardy region and evaluate PM2.5 changes due to the reduction of ammonia emissions through scenario analysis. In particular, the study refers to the years between 2016 and 2020 inclusive. The information contained in the data is exploited using a spatiotemporal model capable of handling spatial and temporal correlation, as well as missing data. In this study, we propose a heteroskedastic extension of the Hidden Dynamic Geostatistical Model (HDGM) which is a two-level hierarchical model suitable for complex environmental processes. Scenario analysis will be carried out on high-resolution maps of the Lombardy region showing the changes in PM2.5 across the area. As a result, it is shown that a 26% reduction in NH3 emissions in the wintertime could reduce the PM2.5 average by 2.09 mg/m3 while a 50% reduction could reduce the PM2.5 average by 4.02 mg/m3 which corresponds to a reduction close to 5% and 10% respectively. Finally, results are detailed by province and land type.