Geochemical modelling of arsenic release into the crystalline aquifers: preliminary study

<p>Arsenic (As) is a toxic element present in different natural systems. The aqueous As species and their concentrations in natural waters depend on a variety of parameters, including the presence of natural source and the local geochemical conditions. The primary source of As in natural waters is the oxidation of mineral sulphides like arsenopyrite (FeAsS) and As-rich pyrite (FeS<sub>2</sub>) [1]. The trivalent iron (Fe<sup>3+</sup>) can act as oxidant for pyrite oxidative dissolution together with dissolved oxygen.In this work the attention is focused in As- contaminated area of the Calabria Region (Southern Italy). The high arsenic concentration is a peculiar characteristic of the shallow groundwaters circulating in a limited area of the Calabria region, which represents an unexplored mineralized area. Indeed, although pyrite is widely present in the crystalline rocks, its spatial distribution is highly variable and not predicable [2]. Generally, the As content of the studied granite rocks is within the normal global range but the presence of not-surfacing, hydrothermally-altered granites, could be the cause of As contamination in limited areas.  In order to explain the As-rich groundwaters occurring into crystalline aquifer, a reaction path modelling of granite dissolution was performed by using EQ3/6 software package version 8a.  The dissolving granite was considered to be constituted by quartz, two types of plagioclase (representing the rim and the core of the mineral), K-feldspar, biotite, muscovite, chlorite, epidote, fluorapatite and pyrite.  The considered value of pyrite content and its As concentration fall within the global estimations [3]. Two simulations were performed allowing the precipitation of moganite, gibbsite, kaolinite, illite-py and the calcite-rich solid solution of trigonal carbonate. Moreover, two oxy-hydroxide solid solutions composed of amorphous Fe(OH)<sub>3 </sub>- amorphous ferric arsenate and 2 lines-ferrihydrite - scorodite were precipitated in two separate runs to evaluate their effects on dissolved As. Nine water samples were used to fix the boundary conditions as well as to validate the outcomes of geochemical modeling. The arsenic concentration detected ranging from 25 to 435 µg/L. The theoretical trend involving the precipitation of amorphous Fe(OH)<sub>3</sub> is in agreement with the groundwaters richest in As, because a higher amount of pyrite is dissolved due to a greater availability of trivalent Fe in the aqueous solution, which is caused by the higher solubility of amorphous Fe(OH)<sub>3</sub> compared to 2-line ferrihydrite. The analytical data of the As-rich groundwaters, as a whole, are well explained by the performed simulations, suggesting that these processes control the release and fate of arsenic during the water-rock interaction.</p><p> </p><p>[1]. Sracek, O., Bhattacharya, P., Jacks, G., Gustafsson, J. P., & Von Brömssen, M. ,2004. Behavior of arsenic and geochemical modeling of arsenic enrichment in aqueous environments. Applied Geochemistry, 19(2), 169-180.</p><p>[2]. Bonardi G., De Vivo B., Giunta G., Lima A., Perrone V., Zuppetta A., 1982. Mineralizzazioni dell’Arco Calabro Peloritano.Ipotesi genetiche e quadro evolutivo. Boll.Soc.Geol.It. 101</p><p>[3]. Smedley, P. L., & Kinniburgh, D. G.,2002. A review of the source, behaviour and distribution of arsenic in natural waters. Applied geochemistry, 17(5), 517-568.</p>

Distribution of Groundwater Arsenic in Uruguay Using Hybrid Machine Learning and Expert System Approaches

Groundwater arsenic in Uruguay is an important environmental hazard, hence, predicting its distribution is important to inform stakeholders. Furthermore, occurrences in Uruguay are known to variably show dependence on depth and geology, arguably reflecting different processes controlling groundwater arsenic concentrations. Here, we present the distribution of groundwater arsenic in Uruguay modelled by a variety of machine learning, basic expert systems, and hybrid approaches. A pure random forest approach, using 26 potential predictor variables, gave rise to a groundwater arsenic distribution model with a very high degree of accuracy (AUC = 0.92), which is consistent with known high groundwater arsenic hazard areas. These areas are mainly in southwest Uruguay, including the Paysandú, Río Negro, Soriano, Colonia, Flores, San José, Florida, Montevideo, and Canelones departments, where the Mercedes, Cuaternario Oeste, Raigón, and Cretácico main aquifers occur. A hybrid approach separating the country into sedimentary and crystalline aquifer domains resulted in slight material improvement in a high arsenic hazard distribution. However, a further hybrid approach separately modelling shallow (<50 m) and deep aquifers (>50 m) resulted in the identification of more high hazard areas in Flores, Durazno, and the northwest corner of Florida departments in shallow aquifers than the pure model. Both hybrid models considering depth (AUC = 0.95) and geology (AUC = 0.97) produced improved accuracy. Hybrid machine learning models with expert selection of important environmental parameters may sometimes be a better choice than pure machine learning models, particularly where there are incomplete datasets, but perhaps, counterintuitively, this is not always the case.

Hydrogeological controls on groundwater recharge in a weathered crystalline aquifer: A case study from the Makutapora groundwater basin, Tanzania

&lt;p&gt;Drylands (semi-arid/arid regions) represent &gt;35% of the Earth&amp;#8217;s surface, support a population of around 2 billion people, and are forecast to be increasingly water stressed in coming decades. Groundwater is the most reliable source of water in drylands, and it is likely that the structure and hydraulic properties of superficial geology play a crucial role in controlling groundwater recharge in these regions.&amp;#160; However, the spatio-temporal hydrogeological controls on the rates of groundwater recharge, and their sensitivity to environmental change are poorly resolved.&lt;/p&gt;&lt;p&gt;In the Makutapora groundwater basin (Tanzania), an analogue for semi-arid tropical areas underlain by weathered and fractured crystalline rock aquifers, we conducted a series of geophysical surveys using Electrical Resistivity Tomography (ERT) and frequency domain electromagnetic methods (FDEM). Using these data, in conjunction with borehole logs, we identify and delineate five major lithological units in the basin: 1) Superficial deposits of coarse sand (&gt;200 &amp;#937; m) 2) Highly conductive smectitic clays (1-10 &amp;#937; m) 3) Decomposed pedolitic soils (30-100 &amp;#937; m) 4) Weathered saprolite (100-700 &amp;#937; m) and 5) Fractured granitic basement (&gt;700 &amp;#937; m). We also identify 10-50m wide zones of normal faulting extending across the basin and cutting through these units, interpreted with the aid of analysis of a digital elevation model alongside the geophysics data.&lt;/p&gt;&lt;p&gt;These results are combined with existing long-term hydrological and hydrogeological records to build conceptual models of the processes governing recharge. We hypothesise that: 1) Zones of active faulting provide permeable pathways enabling greater recharge to occur; 2) Superficial sand deposits may act as collectors and stores that slowly feed recharge into these fault zones; 3) Windows within layers of smectitic clay underlying ephemeral streams may provide pathways for focused recharge via transmission losses; and 4) Overbank flooding during high-intensity precipitation events that inundate a greater area of the basin increases the probability of activating such permeable pathways.&lt;/p&gt;&lt;p&gt;Our results suggest that configurations of superficial geology may play a crucial role in controlling patterns, rates and timing of groundwater recharge in dryland settings. They also provide a physical basis to improve numerical models of groundwater recharge in drylands, and a conceptual framework to evaluate strategies (e.g. Managed Aquifer Recharge) to artificially enhance the availability of groundwater resources in these regions.&lt;/p&gt;

Petrology and Structural Features of Tsiga and its Environs Northwestern Nigeria

The major rock types mapped in the Tsiga area NW Nigeria, include gneisses and granites associated with minor rocks such as xenoliths and some structural features including foliation, joints, faults and veins (quartz and pegmatite).The mineralogy includes feldspar (orthoclase and plagioclase), quartz, biotite and some accessory minerals. These rock types and the associated structural features have their trends in N-S direction. The structural analysis of joints on the granites of the study area shows that the joints have a trend of NNE-SSW. The joints on the gneisses have a trend of NE-SW. In terms of age relationships of the rocks, gneisses are older than the granites based on deformed features. The gneisses probably originated from sediments, para-gneisses. The granites must have resulted from magma intrusion. The structures in the study area suggests the imprint of the Pan-African Orogeny, as indicated by the structural analysis of fractures. The major rivers in the study area include Yali and Moryaji with their tributaries which formed a dendritic drainage pattern, and are structurally controlled. Surface water occurs during the raining season and underground water are available in the area, occurring in shallow pit dug along the stream and river channels, and the fractured crystalline aquifer. The economic potentials in the area includes; rock aggregates, dimension stone, lateritic soil and alluvium deposits which are used for different purposes and include building houses and construction of roads and bridges and probably rare earth elements at the contact zones between the igneous and metamorphic rocks.