  The air in the Lombardy Plain, Italy, is one of the most polluted in Europe
due to limited atmosphere circulation and high emission levels. There is broad
scientific consensus that ammonia (NH$_3$) emissions have a primary impact on
air quality, and, in Lombardy, the agricultural sector and livestock activities
are widely recognised as being responsible for approximately 97% of regional
ammonia emissions due to the high density of livestock. In this paper, we
quantify the relationship between ammonia emissions and PM2.5 concentrations in
the Lombardy Plain and evaluate PM2.5 changes due to the reduction of ammonia
emissions through a "what-if" scenario analysis. The information in the data is
exploited using a spatiotemporal statistical model capable of handling spatial
and temporal correlation, as well as missing data. To do this, we propose a new
heteroskedastic extension of the well-established Hidden Dynamic Geostatistical
Model. Maximum likelihood parameter estimates are obtained by the
expectation-maximisation algorithm and implemented in a new version of the
D-STEM software. Considering the years between 2016 and 2020, the scenario
analysis is carried out on high-resolution PM2.5 maps of the Lombardy Plain. As
a result, it is shown that a 26% reduction in NH3 emissions in the wintertime
could reduce the PM2.5 average by 1.44 mg/m^3 while a 50% reduction could
reduce the PM2.5 average by 2.76 mg / m^3 which corresponds to a reduction
close to 3.6% and 7% respectively. Finally, results are detailed by province
and land type.
