Groundwater Quality Monitoring Using In-Situ Measurements and Hybrid Machine Learning with Empirical Bayesian Kriging Interpolation Method

This article discusses the assessment of groundwater quality using a hybrid technique that would aid in the convenience of groundwater (GW) quality monitoring. Twenty eight (28) GW samples representing 62 barangays in Calapan City, Oriental Mindoro, Philippines were analyzed for their physicochemical characteristics and heavy metal (HM) concentrations. The 28 GW samples were collected at suburban sites identified by the coordinates produced by Global Positioning System Montana 680. The analysis of heavy metal concentrations was conducted onsite using portable handheld X-Ray Fluorescence (pXRF) Spectrometry. Hybrid machine learning—geostatistical interpolation (MLGI) method, specific to neural network particle swarm optimization with Empirical Bayesian Kriging (NN-PSO+EBK), was employed for data integration, GW quality spatial assessment and monitoring. Spatial map of metals concentration was produced using the NN-PSO-EBK. Another, spot map was created for observed metals concentration and was compared to the spatial maps. Results showed that the created maps recorded significant results based on its MSEs with values such as 1.404 × 10−4, 5.42 × 10−5, 6.26 × 10−4, 3.7 × 10−6, 4.141 × 10−4 for Ba, Cu, Fe, Mn, Zn, respectively. Also, cross-validation of the observed and predicted values resulted to R values range within 0.934–0.994 which means almost accurate. Based on these results, it can be stated that the technique is efficient for groundwater quality monitoring. Utilization of this technique could be useful in regular and efficient GW quality monitoring.

Scientific Programming Tools for Water Management

This special issue delivers a platform in which researchers expose intersections between algorithm design, software platforms, and hardware architectures to deal with emerging challenges in the scientific field of management of water and water-dependent resources. Since the call for papers was announced in June 2019, this special issue has received 10 manuscripts. After a rigorous review process, 6 papers have been finally accepted for publication. Published papers deal with groundwater quality monitoring, coastal groundwater-dependent irrigation agriculture, desertification risk, water recovery from tailings, future scenarios of water resources, and vulnerability of coastal aquifers.

Towards a European denitrification concept for improved groundwater quality management and chemical status assessment

<p>Denitrification potential is an important parameter to know for adequate and efficient management and assessment of groundwater vulnerability and chemical status. Denitrification removes nitrate in groundwater, but the denitrification capacity is highly variable in space and time, and it may be used up with time. When linking pressure and impact the effect of partial or complete denitrification and denitrification capacity should be taken into account. In some areas, denitrification is seen as an advantage, allowing higher N release below soil without leading to a decrease of the groundwater quality and eventually concentrations in groundwater higher than the WFD and DWD threshold values, which EU member states have to establish to protect drinking water and groundwater dependent terrestrial and associated aquatic ecosystems.    </p><p>Within the GEOERA HOVER project, the aim was to assess the spatial extent and importance of denitrification. The studied cases permitted at a first step to highlight the heterogeneities of the approaches due to the variability of information obtained i.e. the likelihood of denitrification, depth and thickness of redox transition zone, complete denitrification status. The parameters used to define the denitrification vary also from one country to another based on a large set of redox sensitive ions (Eh, O<sub>2</sub>, NO<sub>3</sub>, NO<sub>2</sub>, Fe, Mn, SO<sub>4</sub>, CH<sub>4</sub>, δ<sup>18</sup>O<sub>-NO3</sub> et δ<sup>15</sup>N-<sub>NO3</sub>, H<sub>2</sub>S or N<sub>2</sub>). Some of these parameters can be accessed by standard methods in most laboratories, used for groundwater quality monitoring, while others require specialized analysis and interpretations.</p><p>Considering groundwater and hydrogeological data available in most of the EU countries, a simple method is proposed in order to classify the monitoring points into three classes: oxic, anoxic and mixed. After being tested in different well-known areas the method will be applied in various lithologies and hydrogeological contexts The proposed method will enable the development of European maps supporting groundwater quality management across Europe.</p>