A comprehensive monitoring approach for a naturally anoxic aquifer beneath a controlled landfill.

The processes leading to high levels of arsenic (As), iron (Fe), and manganese (Mn) in groundwater, in a naturally reducing aquifer at a controlled municipal landfill site, are investigated. The challenge is to distinguish the natural water-rock interaction processes, that allow these substances to dissolve in groundwater, from direct pollution or enhanced dissolution of hydroxides as undesired consequences of the anthropic activities above. Ordinary groundwater monitoring of physical-chemical parameters and inorganic compounds (major and trace elements) was complemented by environmental isotopes of groundwater (tritium, deuterium, oxygen-18 and carbon-13) and dissolved gases (carbon-13 of methane and carbon dioxide and carbon-14 of methane). Pearson/Spearman correlation indices, as well as Principal Component Analysis (PCA), were used to determine the main correlations among variables. The concurrent presence of As, Fe and CH4, as reported in similar anoxic environments, suggests that anaerobic oxidation of methane could drive the reductive dissolution of As-rich Fe(III)(hydro)oxides. Manganese is more sensitive to carbon dioxide, possibly due to a decrease in pH which accelerates the dissolution of Mn-oxides. Finally, we found that tritium and deuterium, which have been used for decades as leachate tracer in groundwater, may be subject to false positives due to the reuse of water recovered from leachate treatment (which has the same isotopic signature of leachate) within the plants, to comply with the requirements of the circular economy. The integration of the environmental isotope analysis into the traditional monitoring approach can effectively support the comprehension of processes. However, this strategy needs to be complemented by a good conceptual hydrogeological model and expert evaluation to avoid misinterpretations.

Antarctic permafrost degassing in Taylor Valley by extensive soil gas investigation.

Ongoing studies conducted in northern polar regions reveal that permafrost stability plays a key role in the modern carbon cycle as it potentially stores considerable quantities of greenhouse gases. Rapid and recent warming of the Arctic permafrost is resulting in significant greenhouse gas emissions, both from physical and microbial processes. The potential impact of greenhouse gas release from the Antarctic region has not, to date, been investigated. In Antarctica, the McMurdo Dry Valleys comprise 10 % of the ice-free soil surface areas in Antarctica and like the northern polar regions are also warming albeit at a slower rate. The work presented herein examines a comprehensive sample suite of soil gas (e.g., CO2, CH4 and He) concentrations and CO2 flux measurements conducted in Taylor Valley during austral summer 2019/2020. Analytical results reveal the presence of significant concentrations of CO2, CH4 and He (up to 3.44 vol%, 18,447 ppmv and 6.49 ppmv, respectively) at the base of the active layer. When compared with the few previously obtained measurements, we observe increased CO2 flux rates (estimated CO2 emissions in the study area of 21.6 km2 ≈ 15 tons day-1). We suggest that the gas source is connected with the deep brines migrating from inland (potentially from beneath the Antarctic Ice Sheet) towards the coast beneath the permafrost layer. These data provide a baseline for future investigations aimed at monitoring the changing rate of greenhouse gas emissions from Antarctic permafrost, and the potential origin of gases, as the southern polar region warms.

Global thermal spring distribution and relationship to endogenous and exogenous factors.

Here we present digitization and analysis of the thermal springs of the world dataset compiled by Gerald Ashley Waring in 1965 into a collection of analog maps. We obtain the geographic coordinates of ~6,000 geothermal spring areas, including complementary data (e.g., temperature, total dissolved solids, flow rate), making them available in electronic format. Using temperature and flow rate, we derive the heat discharged from 1483 thermal spring areas (between ~10-5 and ~103 MW, with a median value of ~0.5 MW and ~8300 MW in total). We integrate this data set with other global data sets to study the relationship between thermalism and endogenous and exogenous factors with a supervised machine learning algorithm. This analysis confirms a dominant role of the terrestrial heat flow, topography, volcanism and extensional tectonics. This data set offers new insights and will boost future studies in geothermal energy exploration.

Geographically weighted regression and geostatistical techniques to construct the geogenic radon potential map of the Lazio region: A methodological proposal for the European Atlas of Natural Radiation.

In many countries, assessment programmes are carried out to identify areas where people may be exposed to high radon levels. These programmes often involve detailed mapping, followed by spatial interpolation and extrapolation of the results based on the correlation of indoor radon values with other parameters (e.g., lithology, permeability and airborne total gamma radiation) to optimise the radon hazard maps at the municipal and/or regional scale. In the present work, Geographical Weighted Regression and geostatistics are used to estimate the Geogenic Radon Potential (GRP) of the Lazio Region, assuming that the radon risk only depends on the geological and environmental characteristics of the study area. A wide geodatabase has been organised including about 8000 samples of soil-gas radon, as well as other proxy variables, such as radium and uranium content of homogeneous geological units, rock permeability, and faults and topography often associated with radon production/migration in the shallow environment. All these data have been processed in a Geographic Information System (GIS) using geospatial analysis and geostatistics to produce base thematic maps in a 1000 m × 1000 m grid format. Global Ordinary Least Squared (OLS) regression and local Geographical Weighted Regression (GWR) have been applied and compared assuming that the relationships between radon activities and the environmental variables are not spatially stationary, but vary locally according to the GRP. The spatial regression model has been elaborated considering soil-gas radon concentrations as the response variable and developing proxy variables as predictors through the use of a training dataset. Then a validation procedure was used to predict soil-gas radon values using a test dataset. Finally, the predicted values were interpolated using the kriging algorithm to obtain the GRP map of the Lazio region. The map shows some high GRP areas corresponding to the volcanic terrains (central-northern sector of Lazio region) and to faulted and fractured carbonate rocks (central-southern and eastern sectors of the Lazio region). This typical local variability of autocorrelated phenomena can only be taken into account by using local methods for spatial data analysis. The constructed GRP map can be a useful tool to implement radon policies at both the national and local levels, providing critical data for land use and planning purposes.

An assessment of gas emanation hazard using a geographic information system and geostatistics.

This paper describes the use of geostatistical analysis and GIS techniques to assess gas emanation hazards. The Mt. Vulsini volcanic district was selected for this study because of the wide range of natural phenomena locally present that affect gas migration in the near surface. In addition, soil gas samples that were collected in this area should allow for a calibration between the generated risk/hazard models and the measured distribution of toxic gas species at surface. The approach used during this study consisted of three general stages. First data were digitally organized into thematic layers, then software functions in the GIS program "ArcView" were used to compare and correlate these various layers, and then finally the produced "potential-risk" map was compared with radon soil gas data in order to validate the model and/or to select zones for further, more-detailed soil gas investigations.