9 research outputs found
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
Spatiotemporal modelling of PM 2.5 concentrations in Lombardy (Italy): a comparative study
This study presents a comparative analysis of three predictive models with an increasing degree of flexibility: hidden dynamic geostatistical models (HDGM), generalised additive mixed models (GAMM), and the random forest spatiotemporal kriging models (RFSTK). These models are evaluated for their effectiveness in predicting PM 2.5 concentrations in Lombardy (North Italy) from 2016 to 2020. Despite differing methodologies, all models demonstrate proficient capture of spatiotemporal patterns within air pollution data with similar out-of-sample performance. Furthermore, the study delves into station-specific analyses, revealing variable model performance contingent on localised conditions. Model interpretation, facilitated by parametric coefficient analysis and partial dependence plots, unveils consistent associations between predictor variables and PM 2.5 concentrations. Despite nuanced variations in modelling spatiotemporal correlations, all models effectively accounted for the underlying dependence. In summary, this study underscores the efficacy of conventional techniques in modelling correlated spatiotemporal data, concurrently highlighting the complementary potential of Machine Learning and classical statistical approaches
Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy
The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emission levels. There is a large scientific consensus that the agricultural sector has a significant impact on air quality. To support studies quantifying the role of the agricultural and livestock sectors on the Lombardy air quality, this paper presents a harmonised dataset containing daily values of air quality, weather, emissions, livestock, and land and soil use in the years 2016–2021, for the Lombardy region. The daily scale is obtained by averaging hourly data and interpolating other variables. In fact, the pollutant data come from the European Environmental Agency and the Lombardy Regional Environment Protection Agency, weather and emissions data from the European Copernicus programme, livestock data from the Italian zootechnical registry, and land and soil use data from the CORINE Land Cover project. The resulting dataset is designed to be used as is by those using air quality data for research
Spatiotemporal modelling of PM concentrations in Lombardy (Italy) -- A comparative study
This study presents a comparative analysis of three predictive models with an
increasing degree of flexibility: hidden dynamic geostatistical models (HDGM),
generalised additive mixed models (GAMM), and the random forest spatiotemporal
kriging models (RFSTK). These models are evaluated for their effectiveness in
predicting PM concentrations in Lombardy (North Italy) from 2016 to
2020. Despite differing methodologies, all models demonstrate proficient
capture of spatiotemporal patterns within air pollution data with similar
out-of-sample performance. Furthermore, the study delves into station-specific
analyses, revealing variable model performance contingent on localised
conditions. Model interpretation, facilitated by parametric coefficient
analysis and partial dependence plots, unveils consistent associations between
predictor variables and PM concentrations. Despite nuanced variations
in modelling spatiotemporal correlations, all models effectively accounted for
the underlying dependence. In summary, this study underscores the efficacy of
conventional techniques in modelling correlated spatiotemporal data,
concurrently highlighting the complementary potential of Machine Learning and
classical statistical approaches
Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy
The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emission levels. There is a large scientific consensus that the agricultural sector has a significant impact on air quality. To support studies quantifying the role of the agricultural and livestock sectors on the Lombardy air quality, this paper presents a harmonised dataset containing daily values of air quality, weather, emissions, livestock, and land and soil use in the years 2016–2021, for the Lombardy region. The daily scale is obtained by averaging hourly data and interpolating other variables. In fact, the pollutant data come from the European Environmental Agency and the Lombardy Regional Environment Protection Agency, weather and emissions data from the European Copernicus programme, livestock data from the Italian zootechnical registry, and land and soil use data from the CORINE Land Cover project. The resulting dataset is designed to be used as is by those using air quality data for research