To what extent airborne particulate matters are influenced by ammonia and nitrogen oxides?

Intensive farming is known to significantly impact air quality, particularly fine particulate matter (PM$_{2.5}$). Understanding in detial their relation is important for scientific reasons and policy making. Ammonia emissions convey the impact of farming, but are not directly observed. They are computed through emission inventories based on administrative data and provided on a regular spatial grid at daily resolution. In this paper, we aim to validate \textit{lato sensu} the approach mentioned above by considering ammonia concentrations instead of emissions in the Lombardy Region, Italy. While the former are available only in few monitoring stations around the region, they are direct observations. Hence, we build a model explaining PM2.5 based on precursors, ammonia (NH3) and nitrogen oxides (NOX), and meteorological variables. To do this, we use a seasonal interaction regression model allowing for temporal autocorrelation, correlation between stations, and heteroskedasticity. It is found that the sensitivity of PM2.5 to NH3 and NOX depends on season, area, and NOX level. It is recommended that an emission reduction policy should focus on the entire manure cycle and not only on spread practices

Comparing air quality among Italy, Germany and Poland using BC indexes

In this paper we discuss air quality assessment in three Italian, German and Polish regions using the index methodology proposed in Bruno and Cocchi (2002, 2007). This analysis focuses first of all on the quality of the air in each of the countries being taken into consideration, and then adopts a more general approach in order to compare pollution severity and toxicity. This is interesting in a global European perspective where all countries are commonly involved in assessing air quality and taking proper measures for improving it. In this context, air quality indexes are a powerful data-driven tool which are easily calculated and summarize a complex phenomenon, such as air pollution, in indicators which are immediately understandable. In particular, the main objective of this work is to evaluate the index performances in distinguishing different air pollution patterns. This kind of analysis can be particularly useful, for example, in the perspective of constructing an indicator of air pollution. --

Adaptive LASSO estimation for functional hidden dynamic geostatistical model

We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). These models employ a classic mixed-effect regression structure with embedded spatiotemporal dynamics to model georeferenced data observed in a functional domain. Thus, the parameters of interest are functions across this domain. The algorithm simultaneously selects the relevant spline basis functions and regressors that are used to model the fixed-effects relationship between the response variable and the covariates. In this way, it automatically shrinks to zero irrelevant parts of the functional coefficients or the entire effect of irrelevant regressors. The algorithm is based on iterative optimisation and uses an adaptive least absolute shrinkage and selector operator (LASSO) penalty function, wherein the weights are obtained by the unpenalised f-HDGM maximum-likelihood estimators. The computational burden of maximisation is drastically reduced by a local quadratic approximation of the likelihood. Through a Monte Carlo simulation study, we analysed the performance of the algorithm under different scenarios, including strong correlations among the regressors. We showed that the penalised estimator outperformed the unpenalised estimator in all the cases we considered. We applied the algorithm to a real case study in which the recording of the hourly nitrogen dioxide concentrations in the Lombardy region in Italy was modelled as a functional process with several weather and land cover covariates

Adaptive LASSO estimation for functional hidden dynamic geostatistical models

We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). These models employ a classic mixed-effect regression structure with embedded spatiotemporal dynamics to model georeferenced data observed in a functional domain. Thus, the regression coefficients are functions. The algorithm simultaneously selects the relevant spline basis functions and regressors that are used to model the fixed effects. In this way, it automatically shrinks to zero irrelevant parts of the functional coefficients or the entire function for an irrelevant regressor. The algorithm is based on an adaptive LASSO penalty function, with weights obtained by the unpenalised f-HDGM maximum likelihood estimators. The computational burden of maximisation is drastically reduced by a local quadratic approximation of the log-likelihood. A Monte Carlo simulation study provides insight in prediction ability and parameter estimate precision, considering increasing spatiotemporal dependence and cross-correlations among predictors. Further, the algorithm behaviour is investigated when modelling air quality functional data with several weather and land cover covariates. Within this application, we also explore some scalability properties of our algorithm. Both simulations and empirical results show that the prediction ability of the penalised estimates are equivalent to those provided by the maximum likelihood estimates. However, adopting the so-called one-standard-error rule, we obtain estimates closer to the real ones, as well as simpler and more interpretable models

D-STEM v2: A Software for Modelling Functional Spatio-Temporal Data

Functional spatio-temporal data naturally arise in many environmental and climate applications where data are collected in a three-dimensional space over time. The MATLAB D-STEM v1 software package was first introduced for modelling multivariate space-time data and has been recently extended to D-STEM v2 to handle functional data indexed across space and over time. This paper introduces the new modelling capabilities of D-STEM v2 as well as the complexity reduction techniques required when dealing with large data sets. Model estimation, validation and dynamic kriging are demonstrated in two case studies, one related to ground-level air quality data in Beijing, China, and the other one related to atmospheric profile data collected globally through radio sounding.Comment: 29 pages, 11 figure

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

Space-Time Integration of Heterogeneous Networks in Air Quality Monitoring

Lo scopo di questo lavoro è di proporre un modello per l'integrazione di dati provenienti da reti eterogenee di monitoraggio al fine di valutare la qualità dell'aria. Per esempio, la rete di monitoraggio del PM10 nel Nord Italia `e composta per la gran parte da centraline che si basano su due sistemi di rilevamento, TEOM and LVG. Mentre i dati rilevati con il metodo TEOM sottostimano il “veroâ€Â? livello di PM10, le centraline LVG sono più precise e per questo sono state scelte dalla Comunità Europea come “strumenti di riferimentoâ€Â?. L’idea su cui si basa il lavoro è di utilizzare le concentrazioni giornaliere dei PM, misurate con gli strumenti più precisi, per correggere le misure meno esatte, rilevate da centraline “non equivalentiâ€Â? a quelle gravimetriche e che, non necessariamente si trovano nella stessa zona in cui sono situati i primi. A tal fine, introduciamo un modello di calibrazione multivariato spazio temporale che abbiamo denominato Geostatistical Dynamical Calibration model (GDC). La principale ipotesi su cui si basa il modello `e che entrambi gli strumenti siano contaminati da errori di misura e che le rilevazioni TEOM siano distorte, rispetto alle “vereâ€Â? concentrazioni, per un fattore addittivo ed uno moltiplicativo.Si assume, inoltre, che “veroâ€Â? livello di PM10 sia un processo spazio temporale latente, rappresentato dall’equazione di stato nella formulazione state space. Le stime dei valori calibrati si ottengono dall’applicazione del filtro di Kalman. Questo approccio può essere considerato un’estensione geostatistica del modello DDC (Dynamical Displaced Calibration) di Fass`o and Nicolis (2004)

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