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.

翻译：意大利伦巴第大区因空气流通受限且排放水平较高，其空气质量位列欧洲污染最严重地区之一。科学界普遍认为农业部门对空气质量具有显著影响。在伦巴第，畜牧业活动被广泛认为贡献了该地区约97%的氨排放，这主要归因于该区域高密度的牲畜养殖。本研究旨在量化伦巴第大区氨排放与PM2.5浓度之间的关系，并通过情景分析评估因减少氨排放导致的PM2.5浓度变化。研究时段涵盖2016年至2020年。我们采用能够处理时空相关性及数据缺失问题的时空模型来挖掘数据信息。本研究提出隐动态地质统计模型（HDGM）的异方差扩展版本，该模型作为适用于复杂环境过程的双层分层模型，将基于伦巴第大区高分辨率地图进行情景分析，展示PM2.5浓度的区域变化。结果表明：冬季氨排放减少26%可使PM2.5均值下降2.09微克/立方米，而减少50%则可使PM2.5均值下降4.02微克/立方米，分别对应约5%和10%的降幅。最后，我们按省份和土地类型对结果进行了详细分解。