Environmental Earth Sciences Progress Report 2020 and Outlook 2021.

The present editorial 2020 continues the series of status reports in Environmental Earth Sciences (EES) in previous years 2017 and 2019 (Kolditz et al. in Environ Earth Sci 77: 8, 2018, Kolditz et al. in Environ Earth Sci 79: 11, 2020). The year 2020 coming to an end was heavily influenced by the COVID-19 pandemic affecting all areas of life including research work and, therefore, scientific publishing as well ("Introduction"). One bright spot which shows longevity of journals that produce a quality product is that Environmental Earth Sciences (EES) is celebrating its 45th anniversary of publication. To this extent EES continues the tradition to honor the most cited papers contributing to the 2020 Impact Factor (IF) ("Highly and most cited topics") and provide information on the current status of EES as well as an outlook to 2021 ("Progress report").

Modelling spatial and temporal variability of water quality from different monitoring stations using mixed effects model theory.

Polder-type agricultural catchments within river deltas are specific land formations which management is highly demanding from several aspects. The close contact with the coastal sea may additionally affect the quality of adjacent marine environment. This study uses the case of the Lower Neretva Valley (LNV) to test the efficiency of applying Linear Mixed Effect (LME) theory in modelling spatial and temporal variations of surface and groundwater quality within a polder-type agricultural catchment. The methodology uses linear regressive techniques while taking into account spatial and temporal autocorrelation of residuals. The objective was to assess and model the spatial and temporal variability of the quality of surface- and ground-waters, in order to predict the impact of natural processes and human activities. A dataset of physicochemical properties of surface and groundwater quality of the LNV, recorded monthly in the period 2009-2017, was used to model the spatial and temporal variations of water salinity and nitrate concentrations. The network of water quality monitoring sites covers four polders on five thousand hectares of agricultural land, including the following types of water bodies: river streams, lateral canals, pumping stations, drainage canals and groundwater. The method of data analysis, based on LME theory with correlated spatial and temporal residuals, takes also into account the heteroscedasticity of the variance associated with each type of water quality monitoring station. The two Linear Mixed Effects models proposed for the prediction of electrical conductivity and nitrate concentration in the surface waters and groundwater, proved to be efficient at adequately reproducing the heterogeneity and complexity of the study area. However, the prediction of nitrate concentration in the water was not equally satisfactory of the one of electrical conductivity due to the large variation in nutrient concentrations. To improve spatial prediction, the density of monitoring network should be increased.

A Combined Approach of Sensor Data Fusion and Multivariate Geostatistics for Delineation of Homogeneous Zones in an Agricultural Field.

To assess spatial variability at the very fine scale required by Precision Agriculture, different proximal and remote sensors have been used. They provide large amounts and different types of data which need to be combined. An integrated approach, using multivariate geostatistical data-fusion techniques and multi-source geophysical sensor data to determine simple summary scale-dependent indices, is described here. These indices can be used to delineate management zones to be submitted to differential management. Such a data fusion approach with geophysical sensors was applied in a soil of an agronomic field cropped with tomato. The synthetic regionalized factors determined, contributed to split the 3D edaphic environment into two main horizontal structures with different hydraulic properties and to disclose two main horizons in the 0-1.0-m depth with a discontinuity probably occurring between 0.40 m and 0.70 m. Comparing this partition with the soil properties measured with a shallow sampling, it was possible to verify the coherence in the topsoil between the dielectric properties and other properties more directly related to agronomic management. These results confirm the advantages of using proximal sensing as a preliminary step in the application of site-specific management. Combining disparate spatial data (data fusion) is not at all a naive problem and novel and powerful methods need to be developed.

Effects of source rocks, soil features and climate on natural gamma radioactivity in the Crati valley (Calabria, Southern Italy).

The study, which represents an innovative scientific strategy to approach the study of natural radioactivity in terms of spatial and temporal variability, was aimed to characterize the background levels of natural radionuclides in soil and rock in the urban and peri-urban soil of a southern Italy area; to quantify their variations due to radionuclide bearing minerals and soil properties, taking into account nature and extent of seasonality influence. Its main novelty is taking into account the effect of climate in controlling natural gamma radioactivity as well as analysing soil radioactivity in terms of soil properties and pedogenetic processes. In different bedrocks and soils, activities of natural radionuclides ((238)U, (232)Th (4) K) and total radioactivity were measured at 181 locations by means of scintillation γ-ray spectrometry. In addition, selected rocks samples were collected and analysed, using a Scanning Electron Microscope (SEM) equipped with an Energy Dispersive Spectrometer (EDS) and an X-Ray Powder Diffraction (XRPD), to assess the main sources of radionuclides. The natural-gamma background is intimately related to differing petrologic features of crystalline source rocks and to peculiar pedogenetic features and processes. The radioactivity survey was conducted during two different seasons with marked changes in the main climatic characteristics, namely dry summer and moist winter, to evaluate possible effects of seasonal climatic variations and soil properties on radioactivity measurements. Seasonal variations of radionuclides activities show their peak values in summer. The activities of (238)U, (232)Th and (4) K exhibit a positive correlation with the air temperature and are negatively correlated with precipitations.

Assessment of lead pollution in topsoils of a southern Italy area: Analysis of urban and peri-urban environment.

Exposure to lead (Pb) may affect adversely human health. Mapping soil Pb contents is essential to obtain a quantitative estimate of potential risk of Pb contamination. The main aim of this paper was to determine the soil Pb concentrations in the urban and peri-urban area of Cosenza-Rende to map their spatial distribution and assess the probability that soil Pb concentration exceeds a critical threshold that might cause concern for human health. Samples were collected at 149 locations from residual and non-residual topsoil in gardens, parks, flower-beds, and agricultural fields. Fine earth fraction of soil samples was analyzed by X-ray Fluorescence spectrometry. Stochastic images generated by the sequential Gaussian simulation were jointly combined to calculate the probability of exceeding the critical threshold that could be used to delineate the potentially risky areas. Results showed areas in which Pb concentration values were higher to the Italian regulatory values. These polluted areas were quite large and likely, they could create a significant health risk for human beings and vegetation in the near future. The results demonstrated that the proposed approach can be used to study soil contamination to produce geochemical maps, and identify hot-spot areas for soil Pb concentration.

Mapping soil gas radon concentration: a comparative study of geostatistical methods.

Understanding soil gas radon spatial variations can allow the constructor of a new house to prevent radon gas flowing from the ground. Indoor radon concentration distribution depends on many parameters and it is difficult to use its spatial variation to assess radon potential. Many scientists use to measure outdoor soil gas radon concentrations to assess the radon potential. Geostatistical methods provide us a valuable tool to study spatial structure of radon concentration and mapping. To explore the structure of soil gas radon concentration within an area in south Italy and choice a kriging algorithm, we compared the prediction performances of four different kriging algorithms: ordinary kriging, lognormal kriging, ordinary multi-Gaussian kriging, and ordinary indicator cokriging. Their results were compared using an independent validation data set. The comparison of predictions was based on three measures of accuracy: (1) the mean absolute error, (2) the mean-squared error of prediction; (3) the mean relative error, and a measure of effectiveness: the goodness-of-prediction estimate. The results obtained in this case study showed that the multi-Gaussian kriging was the most accurate approach among those considered. Comparing radon anomalies with lithology and fault locations, no evidence of a strict correlation between type of outcropping terrain and radon anomalies was found, except in the western sector where there were granitic and gneissic terrain. Moreover, there was a clear correlation between radon anomalies and fault systems.

Stochastic analysis to assess the spatial distribution of groundwater nitrate concentrations in the Po catchment (Italy).

A large database including temporal trends of physical, ecological and socio-economic data was developed within the EUROCAT project. The aim was to estimate the nutrient fluxes for different socio-economic scenarios at catchment and coastal zone level of the Po catchment (Northern Italy) with reference to the Water Quality Objectives reported in the Water Framework Directive (WFD 2000/60/CE) and also in Italian legislation. Emission data derived from different sources at national, regional and local levels are referred to point and non-point sources. While non-point (diffuse) sources are simply integrated into the nutrient flux model, point sources are irregularly distributed. Intensive farming activity in the Po valley is one of the main Pressure factors Driving groundwater pollution in the catchment, therefore understanding the spatial variability of groundwater nitrate concentrations is a critical issue to be considered in developing a Water Quality Management Plan. In order to use the scattered point source data as input in our biogeochemical and transport models, it was necessary to predict their values and associated uncertainty at unsampled locations. This study reports the spatial distribution and uncertainty of groundwater nitrate concentration at a test site of the Po watershed using a probabilistic approach. Our approach was based on geostatistical sequential Gaussian simulation used to yield a series of stochastic images characterized by equally probable spatial distributions of the nitrate concentration across the area. Post-processing of many simulations allowed the mapping of contaminated and uncontaminated areas and provided a model for the uncertainty in the spatial distribution of nitrate concentrations.