The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geographic information system (GIS)-based probabilistic and machine learning methods, including the frequency ratio (FR) model and a convolutional neural network (CNN). Ten influencing factors, namely elevation, slope, the topographic wetness index (TWI), valley depth, fault density, lithology, and the average soil copper (Cu), calcium oxide (Cao), ferric oxide (Fe₂O₃), and lead (Pb) concentrations, were analyzed. In total, 27 rock samples with high activity concentration index values were divided randomly into training and validation datasets (70:30 ratio) to train the models. Areas were categorized as very high, high, moderate, low, and very low radon areas. According to the models, approximately 40% of the study area was classified as very high or high risk. Finally, the radon potential maps were validated using the area under the receiver operating characteristic curve (AUC) analysis. This showed that the CNN algorithm was superior to the FR method; for the former, AUC values of 0.844 and 0.840 were obtained using the training and validation datasets, respectively. However, both algorithms had high predictive power. Slope, lithology, and TWI were the best predictors of radon-affected areas. These results provide new information regarding the spatial distribution of radon, and could inform the development of new residential areas. Radon screening is important to reduce public exposure to high levels of naturally occurring radiation.

Nitrogen pollution of groundwater has created problems worldwide. Riparian zones form a connection hub for terrestrial and aquatic ecosystems. As a potential source of ammonium in groundwater, aquitards have an important effect on the environment of riparian zones. The spatial distribution and factors influencing the ammonium content in the riparian zone aquitard of a small watershed were analyzed through three geological boreholes with increasing distances from the river: boreholes A > B > C. The results show that the distribution of ammonium was closely related to the lithology of sediments. Under the influence of the river and floods, the average content of ion exchange form of ammonium of sediments in borehole A (stable sedimentary environment) was 94.31 mg kg⁻¹, accounting for 21.2% of the transferable ammonium. The average proportions of ion exchange form of ammonium in the transferable ammonium of boreholes B and C (unstable sedimentary environment) were 19.1% and 17.4%, respectively. The carbonate and iron-manganese oxide forms of ammonium content of sediments in three boreholes were 0.96–15.28 mg kg⁻¹ and 2.3–54.4 mg kg⁻¹, respectively; this was mainly affected by the pH and Eh of the sedimentary environment. Organic sulfide, the form of transferable ammonium of sediments mainly exists in organic matter. The ammonium content in pore water generally increased with depth and was mainly derived from the mineralization of humic-like organic matter in borehole A. The ammonium in pore water in boreholes B and C mixed with ammonium from the mineralization of organic matter and the desorption of ion exchange form ammonium within sediments. The ammonium content in the pore water (up to 5.34 mg L⁻¹) was much higher than the limit for drinking water of 0.5 mg L⁻¹ in China. Therefore, the aquitard has a high risk of releasing ammonium and poses a certain threat to the quality of groundwater.

The present study attempts to decipher the seasonal variations in hydro-geochemistry of groundwater in the Terekhol River Basin, western coastal region, Maharashtra, India. A total of 65 groundwater samples of post-monsoon (POMS) and pre-monsoon (PRMS) seasons were collected and analyzed for major ion composition using standard analytical procedures of APHA. Piper and Gibbs plots is used to elucidate the controlling factors which altering the groundwater composition. Scatter plots of ions indicate that major ions from lithologies exposed in the study area and anthropogenic activities are altering the groundwater chemistry. Statistical analysis includes correlation, factor analysis and cluster analysis used to interpret the hydrochemical data. As compared to the WHO drinking standards, all the groundwater samples are fit for drinking. Irrigation water suitability was ascertained based on SAR, %Na and KR indices. Overall, the groundwater chemistry in study area is reflects changes in natural processes rather than anthropogenic inputs.

Beach sediments of Kanyakumari at the southernmost India were evaluated for metals and trace elements and to assess their possible impact on coastal ecosystems. Positive correlations (except for Cd and Sr) between them indicated metamorphic lithologies and heavy mineral deposits as possible sources. Significant-extremely high enrichment and very high contamination of Th, Zr, Mo, Ti and U reflected the presence of different heavy minerals. The geo-accumulation index, however, mirrored their variable abundances at different sites. Association of Cd with P suggested the influence of anthropogenic solid waste from fishing industry. It might have caused >41-fold enrichment of Cd and the Fe- Mn-oxides possibly acted as scavengers for 13-fold enrichment of As compared to UCC. Concentrations of Zn and Cr between ERL and ERM in 13% and 93% of the samples, and Ni > ERM in 87% of sediments suggest their bioavailability to seawater with a potential risk for coastal aquifers.

Lanthanoids in the southern Gulf of California (GC) seawater are reported for the first time. Lanthanoids showed differences between peninsular and continental coastline, coastal or marine ecosystems, and dry or rainy season. The chondrite-normalized values showed high variability but followed a same pattern. Light lanthanoids were more enriched than heavy ones. Values of ∑Ln and La/Lu were higher in continental than peninsular coastlines, coastal than adjacent marine ecosystems, and rainy than dry season. Differences were related to the lithology and perturbation degree of the ecosystem watersheds. The chondrite-normalized patterns are typical of geological origin. Slightly negative Ce anomaly was related to the low levels of oxygen in water for the oxidation of Ce (III) to Ce (IV) and its posterior scavenging. Negative δEu anomaly is explained by an influx of fluvial and eolian materials from the upper continental, while a positive Eu anomaly related to hydrothermal vent inputs was non-evidenced.

Thirty samples of sediments were taken from Bosumtwi Lake (also called Bosomtwe Lake) in Ghana and analyzed for the contents of Fe, As, Hg, Co, Cr, Ni, Cd, and Pb. Several pollution indices (enrichment factor (EF), contamination factor (CF), geoaccumulation (Igₑₒ), and pollution load index (PLI)) were used to determine sedimentary pollution levels, and the risk of environmental exposure was calculated using Hakanson’s potential ecological risk (PER) indices. The results from PER assessments have indicated that sediments from the Bosumtwi Lake present a moderate environmental risk. According to EF calculations, Hg in Bosumtwi lake sediments is the element of concern that is being severely enriched. Hg was the largest contributor to PER with a 97% risk contribution. Multivariate statistical analysis revealed that the main sources of Hg were agrochemicals and atmospheric deposition, whereas the sources of Fe, As, Co, Cr, and Ni to Bosumtwi Lake were natural processes and are derived from the local lithology. There was no strong significant correlation among the contents of the heavy metals, sediment grain sizes, and total organic carbon (TOC), suggesting their lack of control in the distribution of heavy metals, the source, and the transport pathway. Finally, it is strongly recommended to do a study on Hg bioavailability in Bosumtwi Lake sediments. These findings will be relevant to Bosumtwi Lake’s profiling and historical development of heavy metal loads.

Microplastics are contaminants of great concern all over the world. Microplastics constitute pollutants themselves; moreover, other contaminants such as metals are easily absorbed on their plastic surface, becoming bioavailable to marine biota such as zooplankton.We collected marine zooplankton from Mediterranean Sea to investigate trace elements associated with microplastics. Samples were subjected to visual sorting by a stereomicroscope, collected with sterile tweezers, pooled and subjected to sonication, filtration, and drying before being subjected to acid extraction. An ICP-MS was utilized for multi-elemental determination.Aluminum, iron, chromium, zinc, nickel, molybdenum, manganese, lead cobalt, and copper were found at concentrations of mg/kg while arsenic, vanadium, rubidium, and cadmium at level of μg kg⁻¹. Other elements such as silver, beryllium, bismuth, selenium, tin, and thallium were under the limit of quantitation. Lower levels of iron and manganese in samples from Italy were found in comparison to England and Brazil, while aluminum, copper, and zinc registered comparable values. The presence of metals in marine waters is strictly related to sediment lithology and anthropogenic inputs, but plastic plays a key role as vectors for metal ions in the marine system, being able to concentrate metals several order of magnitude higher than in surrounding waters and exerting potential toxicity for living beings after chronic exposure.

In addition to global population growth due to migration from rural areas to urban areas, population density is constantly increasing in certain regions, thereby necessitating the introduction of new settlements in these regions. However, in the selection of settlement areas, no sufficient preliminary examinations have been conducted; consequently, various natural disasters may cause significant life and property losses. Herein, the most suitable settlement areas were determined using GIS (geographic information systems) in Canik District, where the population is continuously increasing. Therefore, this study aimed to incorporate a new perspective into studies on this subject. Within the scope of the study, landslide and flood risks, which are among the most important natural disasters in the region, were primarily evaluated, and high-risk areas were determined. Elevation, slope, aspect, curvature, lithology, topographic humidity index (TWI), and proximity to river parameters were used to produce flood susceptibility maps. A digital elevation model (DEM) of the study area was produced using contours on the 1/25,000 scaled topographic map. The elevation, slope, aspect, curvature, and TWI parameters were produced from the DEM using the relevant analysis routines of ArcGIS software. The raster map of each parameter was divided into 5 subclasses using the natural breaks classification method. In the reclassified raster maps, the most flood-sensitive or flood-prone subclasses were assigned a value of 5, and the least sensitive subclasses were assigned a value of 1. Then, the reclassified maps of the 7 parameters were collected using the “map algebra” function of ArcGIS 10.5 software, and the flood susceptibility index (FSI) map of the study area was obtained. The flood susceptibility map of the study area was obtained by dividing the FSI into 5 subclasses (very low, low, moderate, high, and very high) according to the natural breaks classification method. Thereafter, suitable and unsuitable areas in terms of biocomfort, which affects people’s health, peace, comfort, and psychology and is significant in terms of energy efficiency, were determined. At the last stage of the study, the most suitable settlement areas that were suitable in terms of both biocomfort and low levels of landslide and flood risks were determined. The calculated proportion of such areas to the total study area was only 2.1%. Therefore, because these areas were insufficient for the establishment of new settlements, areas that had low landslide and flood risks but were unsuitable for biocomfort were secondarily determined; the ratio of these areas was calculated as 56.8%. The remaining areas were inconvenient for the establishment of settlements due to the risk of landslides and floods; the ratio of these areas was calculated as 41.1%. This study is exemplary in that the priority for the selection of settlement areas was specified, and this method can be applied for selecting new settlements for each region considering different criteria. Due to the risk of landslides or flooding in the study area, the areas unsuitable for establishing a settlement covered approximately 41.1% of the total study area. The areas that had low flood and landslide risks but were suitable for biocomfort constituted only 2.1% of the study area. In approximately 56.8% of the study area, the risk of landslides or floods was low, and these areas were unsuitable in terms of biocomfort. Therefore, these areas were secondarily preferred as settlement areas. The most suitable areas for settlements constituted only 0.19% of the total study area, and these areas will not be able to meet the increasing demand for settlement area. Therefore, it is recommended to select areas that do not have the risk of landslides and floods but are unsuitable for biocomfort. This study reveals that grading should be performed in the selection of settlement areas. When choosing a settlement area in any region, possible natural disasters in the region should be identified first, and these disasters should be ordered in terms of their threat potential. Moreover, biocomfort areas suitable for settlements should be considered. In the next stages of settlement area selection, the criteria that affect the peace and comfort of people, such as distance to pollution sources, distance to noise sources, and proximity to natural areas, should also be evaluated. Thus, a priority order should be created for the selection of settlement areas using various other criteria.

Remote sensing and GIS technology were very helpful to determine an appropriate location of freshwater storage in Amhara, Ethiopia. The techniques were used to investigate the impact of lithology, surface geomorphology, slope parameters, drainage flow, drainage density, lineament density, land cover parameters on relief, and aerial and linear features and to understand their interrelationships. Morphometric parameters such as mean stream length (Lsm), stream length ratio (RL), bifurcation ratio (Rb), mean bifurcation ratio (Rbm), relief ratio (Rh), drainage density (Dd), stream frequency (Fs), drainage texture (Rt), form factor (Rf), circularity ratio (Rc), and elongation ratio (Re) were calculated. Spatial maps of morphometric parameters were produced by using AHP (analytical hierarchy process) of ArcGIS 10.3. Final priority map was generated by the overlay of those parameters with five categories of poor (16.6%), low (41.63%), moderate (29.61%), high (8.88%), and very high (3.28%) storage locations. The map showed that this study area belonged to the low to moderate storage location. The results exhibit precision-based assessment of the suitability for the dam construction sites of 6, 7, and 9 sub-basin zones. The outcome of this study strengthens the knowledge of geospatial analysis for water resources vulnerability and also allows policymakers in this drought-prone area to sustainably manage water supplies.

Understanding the spatial distribution of soil salinity is required to conserve land against degradation and desertification. Against this background, this study is the first attempt to predict soil salinity in the Jaghin basin, in southern Iran, by applying and comparing the performance of four deep learning (DL) models (deep convolutional neural networks—DCNNs, dense connected deep neural networks—DenseDNNs, recurrent neural networks-long short-term memory—RNN-LSTM and recurrent neural networks-gated recurrent unit—RNN-GRU) and six shallow machine learning (ML) models (bagged classification and regression tree—BCART, cforest, cubist, quantile regression with LASSO penalty—QR-LASSO, ridge regression—RR and support vectore machine—SVM). To do this, 49 environmental landsat8-derived variables including digital elevation model (DEM)-extracted covariates, soil-salinity indices, and other variables (e.g., soil order, lithology, land use) were mapped spatially. For assessing the relationships between soil salinity (EC) and factors controlling EC, we collected 319 surficial (0–5 cm depth) soil samples for measuring soil salinity on the basis of electrical conductivity (EC). We then selected the most important features (covariates) controlling soil salinity by applying a MARS model. The performance of the DL and shallow ML models for generating soil salinity spatial maps (SSSMs) was assessed using a Taylor diagram and the Nash Sutcliff coefficient (NSE). Among all 10 predictive models, DL models with NSE ≥ 0.9 (DCNNs was the most accurate model with NSE = 0.96) were selected as the four best models, and performed better than the six shallow ML models with NSE ≤ 0.83 (QR-LASSO was the weakest predictive model with NSE = 0.50). Based on DCNNs-, the values of the EC ranged between 0.67 and 14.73 dS/m, whereas for QR-LASSO the corresponding EC values were 0.37 to 19.6 dS/m. Overall, DL models performed better than shallow ML models for production of the SSSMs and therefore we recommend applying DL models for prediction purposes in environmental sciences.