Modeling arsenic in European topsoils with a coupled semiparametric (GAMLSS-RF) model for censored data.

Arsenic (As) is a versatile heavy metalloid trace element extensively used in industrial applications. As is carcinogen, poses health risks through both inhalation and ingestion, and is associated with an increased risk of liver, kidney, lung, and bladder tumors. In the agricultural context, the repeated application of arsenical products leads to elevated soil concentrations, which are also affected by environmental and management variables. Since exposure to As poses risks, effective assessment tools to support environmental and health policies are needed. However, the most comprehensive soil As data available, the Land Use/Cover Area frame statistical Survey (LUCAS) database, contains severe limitations due to high detection limits. Although within International Organization for Standardization standards, the detection limits preclude the adoption of standard methodologies for data analysis. The present work focused on developing a new method to model As contamination in European soils using LUCAS soil samples. We introduce the GAMLSS-RF model, a novel approach that couples Random Forests with Generalized Additive Models for Location, Scale, and Shape. The semiparametric model can capture non-linear interactions among input variables while accommodating censored and non-censored observations and can be calibrated to include information from other campaign databases. After fitting and validating a spatial model, we produced European-scale As concentration maps at a 250 m spatial resolution and evaluated the patterns against reference values (i.e., two action levels and a background concentration). We found a significant variability of As concentration across the continent, with lower concentrations in Northern countries and higher concentrations in Portugal, Spain, Austria, France and Belgium. By overcoming limitations in existing databases and methodologies, the present approach provides an alternative way to handle highly censored data. The model also consists of a valuable probabilistic tool for assessing As contamination risks in soils, contributing to informed policy-making for environmental and health protection.

Quantitative analysis of the compliance of EU Sewage Sludge Directive by using the heavy metal concentrations from LUCAS topsoil database.

In the European Union (EU), a common understanding of the potential harmful effect of sewage sludge (SS) on the environment is regulated by the Sewage Sludge Directive 86/278/EEC (SSD). Limit values (LVs) for concentrations of heavy metals in soil are listed in Impact Assessment of this directive, and they were transposed by EU member states using different criteria. Member states adopted either single limit values or based on soil factors such as pH and texture to define the maximum limit values for concentrations of heavy metals in soils. Our work presents the first quantitative analysis of the SSD at the European level by using the Land Use and Coverage Area Frame Survey (LUCAS) 2009 topsoil database. The reference values at the European level were arranged taking into account the upper value (EU_UL) and the lower value (EU_LL) for each heavy metal (arsenic, cadmium, copper, chromium, mercury, nickel, lead, and zinc) as well as taking into account the pH of the soil (cadmium, copper, mercury, nickel, lead, and zinc) as introduced in the SSD Annex IA. Single and integrated contamination rate indices were developed to identify those agricultural soils that exceeded the reference values for each heavy metal. In total, 10%, 36%, and 19% of the LUCAS 2009 topsoil samples exceeded the limit values. Additionally, 12% and 16% of agricultural soils exceeded the concentration of at least one single heavy metal when European LVs were fixed following the soil pH in Strategy II compared to those national ones in Strategy I. Generally, all member states apply similar or stricter limit values than those laid down in the SSD. Our work indicates that choosing LVs quantitatively affects further actions such as monitoring and remediation of contaminated soils. The actual soil parameters, such as heavy metal concentrations and soil pH values from the LUCAS 2009 topsoil database, could be used by SSD-involved policy stakeholders not only to lay down the LVs for concentrations of heavy metal in soils but also for monitoring the SSD compliance grade by using the LUCAS surveys over time (past and upcoming LUCAS datasets).

Spatial assessment of topsoil zinc concentrations in Europe.

Zinc (Zn) is essential to sustain crop production and human health, while it can be toxic when present in excess. In this manuscript, we applied a machine learning model on 21,682 soil samples from the Land Use and Coverage Area frame Survey (LUCAS) topsoil database of 2009/2012 to assess the spatial distribution in Europe of topsoil Zn concentrations measured by aqua regia extraction, and to identify the influence of natural drivers and anthropogenic sources on topsoil Zn concentrations. As a result, a map was produced showing topsoil Zn concentrations in Europe at a resolution of 250 m. The mean predicted Zn concentration in Europe was 41 mg kg-1, with a root mean squared error of around 40 mg kg-1 calculated for independent soil samples. We identified clay content as the most important factor explaining the overall distribution of soil Zn in Europe, with lower Zn concentrations in coarser soils. Next to texture, low Zn concentrations were found in soils with low pH (e.g. Podzols), as well as in soils with pH above 8 (i.e., Calcisols). The presence of deposits and mining activities mainly explained the occurrence of relatively high Zn concentrations above 167 mg kg-1 (the one percentile highest concentrations) within 10 km from these sites. In addition, the relatively higher Zn levels found in grasslands in regions with high livestock density may point to manure as a significant source of Zn in these soils. The map developed in this study can be used as a reference to assess the eco-toxicological risks associated with soil Zn concentrations in Europe and areas with Zn deficiency. In addition, it can provide a baseline for future policies in the context of pollution, soil health, human health, and crop nutrition.

From regional to parcel scale: A high-resolution map of cover crops across Europe combining satellite data with statistical surveys.

The reformed Common Agricultural Policy of 2023-2027 aims to promote a more sustainable and fair agricultural system in the European Union. Among the proposed measures, the incentivized adoption of cover crops to cover the soil during winter provides numerous benefits such as improved soil structure and reduced nutrient leaching and erosion. Despite this recognized importance, the availability of spatial data on cover crops is scarce. The increasing availability of field parcel declarations in the European Union has not yet filled this data gap due to its insufficient information content, limited public availability and a lack of standardization at continental scale. At present, the best information available is regionally aggregated survey data, which although indicative, hinders the development of spatially accurate studies. In this work, we propose a statistical model relating Sentinel-1 data to the existence of cover crops at the 100-m spatial resolution over the entirety of the European Union and United Kingdom and estimate its parameters using the spatially aggregated survey data. To validate the method in a spatially-explicit way, predictions were compared against farmers' registered declarations in France, where the adoption of cover crops is widespread. The results indicate a good agreement between predictions and parcel-level data. When interpreted as a binary classifier, the model yielded an Area Under the Curve (AUC) of 0.74 for the whole country. When the country was divided into five regions for the evaluation of regional biases, the AUC values were 0.77, 0.75, 0.74, 0.70, and 0.65 for the North, Center, West, East, and South regions respectively. Despite limitations such as the lack of data for validation outside France, and the non-standardized nomenclature for cover crops among Member States, this work constitutes the first effort to obtain a relevant cover crop map at a European scale for researchers and practitioners.