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.

翻译：意大利伦巴第平原的大气是欧洲污染最严重的地区之一，这归因于有限的大气环流和高排放水平。科学界普遍认为氨（NH₃）排放对空气质量具有首要影响，而伦巴第地区由于畜牧业高度密集，农业部门和畜牧业活动被广泛认为承担了该地区约97%的氨排放。本文量化了伦巴第平原氨排放与PM2.5浓度之间的关系，并通过“假设情景”分析评估了氨减排导致的PM2.5变化。利用一种能够处理空间与时间相关性及缺失数据的时空统计模型来挖掘数据中的信息。为此，我们提出了经典隐动态地统计模型的一种新型异方差扩展。通过期望最大化算法获得最大似然参数估计，并在新版本D-STEM软件中实现。基于2016年至2020年的数据，对伦巴第平原的高分辨率PM2.5地图进行了情景分析。结果表明，冬季氨排放减少26%可使PM2.5平均值降低1.44 mg/m³，而减少50%可使PM2.5平均值降低2.76 mg/m³，分别对应约3.6%和7%的降幅。最后，按省份和土地类型详细展示了结果。