A new hazard assessment workflow to assess soil contamination from large and artisanal scale gold mining

Gold mining activities are undertaken both at large and artisanal scale, often resulting in serious 'collateral' environmental issues, including environmental pollution and hazard to human and ecosystem health. Furthermore, some of these activities are poorly regulated, which can produce long-lasting damage to the environment and local livelihoods. The aim of this study was to identify a new workflow model to discriminate anthropogenic versus geogenic enrichment in soils of gold mining regions. The Kedougou region (Senegal, West Africa) was used as a case study. Ninety-four soil samples (76 topsoils and 18 bottom soils) were collected over an area of 6,742 km2 and analysed for 53 chemical elements. Robust spatial mapping, compositional and geostatistical models were employed to evaluate sources and elemental footprint associated with geology and mining activities. Multivariate approaches highlighted anomalies in arsenic (As) and mercury (Hg) distribution in several areas. However, further interpretation with enrichment factor (EFs) and index of geoaccumulation (IGeo) emphasised high contamination levels in areas approximately coinciding with the ones where artisanal and small scale mining (ASGM) activities occur, and robust compositional contamination index (RCCI) isolated potentially harmful elements (PHE) contamination levels in very specific areas of the Kedougou mining region. The study underlined the importance of complementary approaches to identify anomalies and, more significantly, contamination by hazardous material. In particular, the analyses helped to identify discrete areas that would require to be surveyed in more detail to allow a comprehensive and thorough risk assessment, to investigate potential impacts to both human and ecosystem health.

Rare earth elements (REEs): geochemical patterns and contamination aspects in Brazilian benchmark soils

Rare earth elements have been increasingly used in modern societies and soils are likely to be the final destination of several REE-containing (by)products. This study reports REE contents for topsoils (0-20 cm) of 175 locations in reference (n = 68) and cultivated (n = 107) areas in Brazil. Benchmark soil samples were selected accomplishing a variety of environmental conditions, aiming to: i) establishing natural background and anthropogenic concentrations for REE in soils; ii) assessing potential contamination of soils - via application of phosphate fertilizers - with REE; and, iii) predicting soil-REE contents using biomes, soil type, parent material, land use, sand content, and biomes-land use interaction as forecaster variables through generalized least squares multiple regression. Our hypotheses were that the variability of soil-REE contents is influenced by parent material, pedogenic processes, land use, and biomes, as well as that cultivated soils may have been potentially contaminated with REE via input of phosphate fertilizers. The semi-total concentrations of REE were assessed by inductively coupled plasma mass spectrometry (ICP-MS) succeeding a microwave-assisted aqua regia digestion. Analytical procedures followed a rigorous QA/QC protocol. Soil physicochemical composition and total oxides were also determined. Natural background and anthropogenic concentrations for REE were established statistically from the dataset by the median plus two median absolute deviations method. Contamination aspects were assessed by REE-normalized patterns, REE fractionation indices, and Ce and Eu anomalies ratios, as well as enrichment factors. The results indicate that differences in the amounts of REE in cultivated soils can be attributed to land use and agricultural sources (e.g., phosphate-fertilizer inputs), while those in reference soils can be attributed to parent materials, biomes, and pedogenic processes. The biomes, land use, and sand content helped to predict concentrations of light REE in Brazilian soils, with parent material being also of special relevance to predict heavy REE contents in particular.

Tracing the sources of sediment and associated particulate nitrogen from different land uses in the Johnstone River catchment, Wet Tropics, north-eastern Australia

While the ecosystem of the Great Barrier Reef (GBR), north-eastern Australia, is being threatened by the elevated levels of sediments and nutrients discharged from adjacent coastal river systems, the source of these detrimental pollutants are not well understood. Here we used a combined isotopic (δ¹³C, δ¹⁵N) and geochemical (Zn, Pt and S) signatures and stable isotope analysis in R (SIAR) mixing model to estimate the contribution of different land uses to the sediment and associated particulate nitrogen delivered to the Johnstone River. Results showed that rainforest was the largest contributor of suspended and bed sediments in the river estuary (both 33.1%), followed by banana (26.7%, 20.4%), sugarcane (21.5%, 21.4%) and grazing (18.7%, 25.1%). However, bananas and sugarcane land uses had the highest contribution to sediments delivered to the coast per unit of area. This will help land managers to prioritise on-ground activities to improve water quality in the GBR lagoon.

Nonnegative tensor factorization for contaminant source identification

Unsupervised Machine Learning (ML) is becoming increasingly popular for solving various types of data analytics problems including feature extraction, blind source separation, exploratory analyses, model diagnostics, etc. Here, we have developed a new unsupervised ML method based on Nonnegative Tensor Factorization (NTF) for identification of the original groundwater types (including contaminant sources) present in geochemical mixtures observed in an aquifer. Frequently, groundwater types with different geochemical signatures are related to different background and/or contamination sources. The characterization of groundwater mixing processes is a challenging but very important task critical for any environmental management project aiming to characterize the fate and transport of contaminants in the subsurface and perform contaminant remediation. This task typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. Additionally, the application of inverse methods may introduce biases in the analyses through the assumptions made in the model development process. Here, we substitute the model inversion with unsupervised ML analysis. The ML analysis does not make any assumptions about underlying physical and geochemical processes occurring in the aquifer. Our ML methodology, called NTFk, is capable of identifying (1) the unknown number of groundwater types (contaminant sources) present in the aquifer, (2) the original geochemical concentrations (signatures) of these groundwater types and (3) spatial and temporal dynamics in the mixing of these groundwater types. These results are obtained only from the measured geochemical data without any additional site information. In general, the NTFk methodology allows for interpretation of large high-dimensional datasets representing diverse spatial and temporal components such as state variables and velocities. NTFk has been tested on synthetic and real-world site three-dimensional datasets. The NTFk algorithm is designed to work with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

Contaminant source identification using semi-supervised machine learning

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. The NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).