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.
What are the required costs to sustain the electrification of the residential sector? What are the achievable primary energy savings? This paper tries to answer these questions, for the Italian ...residential sector, by providing coupled energetical and economic evaluations of the electrification pathways. To this end, this paper extends MOIRAE, a bottom-up modelling approach previously proposed by the authors. First, the input data have been upgraded by coupling, using ad-hoc statistical methods, different datasets provided by the Italian Institute of Statistics. Second, to estimate households’ time-variation, a socio-demographic model has been developed, validated, and implemented. Third, an economic model of fixed and variable costs for electrical and thermal appliances has been implemented. Subsequently, the modelling approach has been calibrated against detailed consumption data for the different Italian regions and validated against historical data. Finally, MOIRAE has been employed to unveil the electrification pathways with and without household budget constraints, aiming at replacing natural gas, LPG, diesel, and fuel oil energy carriers with electrical energy. For the different scenarios investigated, the changes in primary energy consumptions and the variation of variable and fixed costs have been included to consider both the energetic and the economic point of view.
•Time variable and economic evaluations are included within MOIRAE.•The model is calibrated with regionalized data.•The model is validated considering global perspectives in different years.•Electrification pathways of the Italian residential sector are discussed.•The economical burden of decarbonisation pathways is discussed.
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.
Abstract 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 $$\text {PM}_{2.5}$$ 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 $$\text {PM}_{2.5}$$ 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.
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.
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.
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 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.
