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Groundwater is the major resource for drinking and irrigation purposes in urban areas of Abaya-Chemo sub-basin of Great Rift Valley, Ethiopia. There is an incredible increase in demand in the sub-basin for good-quality groundwater resources. However, the exhaustive irrigation and rapid urbanization has posed a serious threat to groundwater quality in the urban districts of sub-basin like Arba Minch town. The aim of the study was to evaluate the groundwater quality status and to map their spatial distribution with respect to the suitability for drinking and irrigation purposes in Arba Minch town, Ethiopia. Fourteen bore well samples were examined for geochemical variations and groundwater qualities. The spatial distribution maps of quality parameters were prepared using the kriging method in ArcGIS 10.3. Drinking water quality index, sodium adsorption ratio (SAR), percentage sodium (Na%), residual sodium carbonate (RSC), and permeability index (PI) were considered for drinking and irrigation suitability assessment. Comparison of the hydrochemical results with the World Health Organization (WHO) and Ethiopian drinking water standards (ES) and various classifications revealed that the current status of the groundwaters is suitable for drinking and irrigation purposes except for a few sites at the northwestern part of the study area. The WQI results revealed that 7% and 64% of samples fall from excellent to good classes for drinking categories. Irrigation indices also demonstrated that 80% of samples fall in good classes for irrigation purposes. About 75 percent of samples belong to Ca–Mg-HCO3 facies, and the reaming samples belong to Ca–Mg–Cl facies. The results of the study concluded that the proposed approach is reliable and efficient for the groundwater pollution status evaluation and can also be applied in decision making for effective groundwater resources monitoring in the study area.

Evaluation of the hydrogeochemical processes governing the heavy metal distribution and the associated health risk is important in managing and protecting the health of freshwater resources. This study mainly focused on the health impacts due to the heavy metals pollution in a known Cretaceous-Tertiary (K/T) contact region (Tiruchinopoly, Tamilnadu) of peninsular India, using various pollution indices, statistical, and geochemical analyses. A total of 63 samples were collected from the hard rock aquifers and sedimentary formations during southwest monsoon and analysed for heavy metals, such as Li, Be, Al, Rb, Sr, Cs, Ba, pb, Mn, Fe, Cr, Zn, Ga, Cu, As, Ni, and Co. Ba was the dominant element that ranged from 441 to 42,638 μg/l in hard rock aquifers, whereas Zn was the major element in sedimentary formations, with concentrations that ranged from 44 to 118,281 μg/l. The concentrations of Fe, Ni, Cr, Al, Cr, and Ni fell above the permissible limit in both of the formations. However, the calculated heavy metal evaluation index (HEI), heavy metal pollution index (HPI), and the degree of contamination (Cd) parameters were higher in the sedimentary formation along the contact zone of the K/T boundary. Excessive health risks from consumption of contaminated groundwater were mostly confined to populations in the northern and southwestern regions of the study area. Carcinogenic risk assessment suggests that there are elevated risks of cancer due to prolonged consumption of untreated groundwater. Ba, Sr, and Zn were found to be geochemically highly mobile due to the partitioning between the rock matrix and groundwater, aided by the formation of soluble carbonato-complexes. Factor analysis indicates that the metals are mainly derived from the host rocks and anthropogenic inputs are relatively insignificant. Overall, this study indicated that groundwater in K/T contact zones is vulnerable to contamination because of the favorable geochemical factors. Long-term monitoring of such contact zones is required to avert the potential health hazards associated with consumption of the contaminated groundwater.

Heavy metal accumulated in soil pose substantial threats to soil environmental quality and human health. Accurate identification of pollution sources is critical for implementing targeted prevention and control strategies. In this study, 118 surface soil samples were collected from Jiangmen City to examine the concentration, spatial distribution, sources, and health risks of heavy metals in the soil. The results revealed that the average concentrations of Hg, Cd, Cu, Pb, and As exceeded their respective background values. Through the integration of Pearson correlation analysis and the Positive Matrix Factorization model, this study identified three primary pollution sources: industrial emissions (50%), land transport and shipping emissions (31%), and parent material and agricultural mixed sources (19%). Ecological risk assessment revealed that Jianghai and Pengjiang Districts posed the highest potential ecological risk, with an ecological risk index of 300. Health risk assessments further revealed that industrial emissions, as the primary influencing factor, pose potential carcinogenic and non-carcinogenic risks to both children and adults in Jiangmen City. This study provides a fundamental reference for understanding heavy metals contamination in the soil of Jiangmen City. The findings provide local authorities with targeted control measures to address long-term industrial pollution issues.

This research was carried out using the open-source database system along with the continuous air quality monitoring station results from global data sets during the COVID-19 pandemic lockdown in India and the global. Our purpose of this research is to study the improvement of air quality and human mortality rates in countries worldwide during the COVID-19 pandemic lockdown. Worldwide air quality data were collected from > 12,000 continuous air quality monitoring stations on six continents covering 1000 major cities from over 100 countries. Here, we discussed the implementation of the open-source data set of basic air pollutants such as PM 2.5, NO2, temperature, relative humidity, and Air Quality Index variation during the pre-lockdown and lockdown pandemic COVID-19 in India and described the global aspect. An average concentration of PM 2.5 (145.51 μg/m3), NO2 (21.64 μg/m3), and AQI index (55.58) continuously decreased. The variation of PM 2.5, NO2, normally shows more than 25 μg/m3 every year, but during the COVID-19 lockdown period (April 2020) continuously decreased below 20 μg/m3. Similarly, the AQI index and meteorological factors such as temperature, relative humidity, and wind speed variation decreased significantly in the many countries in the world. In Asian countries, air quality improved during the national lockdown especially in the most polluted cities globally such as Beijing, Delhi, and Nanjing and also in developed cities like Madrid, New York, Paris, Seoul, Sydney, Tokyo. Furthermore, the reduction of particulate matter was in about 46%, and other gaseous pollutants during the lockdown period were observed in a 54% reduction. We are witnessing pollution reductions which add significantly to improvements in air quality. This is due to the massive decrease in the use of fossil fuel, which in turn reduces production and traffic in general. People nowadays are now willing to see a comparatively healthier world with bleached skies and natural ecosystems. This research finding demonstrates potential safety benefits associated with improving air quality and mortality rates during the COVID-19 pandemic, resulting in decreases in mortality rates in India and around the world.

Estuaries are highly dynamic zones. They are confluence of sea, river, tributaries, effluents, and runoff water that modify constantly their physical conditions and affect growth, productivity, and survival of inhabiting biota. In the current study, we investigated the extent to which estuarine sediment fractions explain variation of grain size parameters (mean size: Mz, standard deviation: σI, skewness: Ski and kurtosis: KG), fish diversity, and phytoplankton richness in estuaries. We applied series of multivariate regression models and controlled for the effect of sediment origin, season, and measurement method. The models output showed that sand fraction explained consistently variation of all the four grain size parameters. However, silt fraction was significantly associated with Mz, Ski, and KG but not σI. We couldn't detect any significant relationship between sand or silt fraction with fish diversity. In contrast, we found that high richness in phytoplankton species was associated with low sand and low silt conditions. We therefore conclude that it is possible to reliably estimate Mz, σI, Ski, KG, and phytoplankton richness from sand and silt fractions. Moreover, managing sand and silt fractions ejected in estuaries may be an alternative of protecting and restoring estuarine biodiversity.

Mineral prospectivity mapping (MPM) is a pivotal technique in geoscientific mineral resource exploration. To address three critical challenges in current deep convolutional neural network applications for geoscientific mineral resource prediction—(1) model bias induced by imbalanced distribution of ore deposit samples, (2) deficiency in global feature extraction due to excessive reliance on local spatial correlations, and (3) diminished discriminative capability caused by feature smoothing in deep networks—this study innovatively proposes a T-GCN model integrating Transformer with graph convolutional neural networks (GCNs). The model achieves breakthrough performance through three key technological innovations: firstly, constructing a global perceptual field via Transformer’s self-attention mechanism to effectively capture long-range geological relationships; secondly, combining GCNs’ advantages in topological feature extraction to realize multi-scale feature fusion; and thirdly, designing a feature enhancement module to mitigate deep network degradation. In practical application to the PangXD ore district, the T-GCN model achieved a prediction accuracy of 97.27%, representing a 3.76 percentage point improvement over the best comparative model, and successfully identified five prospective mineralization zones, demonstrating its superior performance and application value under complex geological conditions.

This study aims to explore the state-wise assessment of SARS-CoV-2 (COVID-19) pandemic spread in Malaysia with focus on influence of meteorological parameters and air quality. In this study, state-wise COVID-19 data, meteorological parameters and air quality index (AQI) were collected from March 13 to April 30, 2020, which encompass three movement control order (MCO) periods in the country. Overall, total infected cases were observed to be higher in MCO phase 1 and 2 and significantly reduced in MCO phase 3. Due to the variation in the spatial interval of population density and individual immunity, the relationship of these parameters to pandemic spread could not be achieved. The study infers that temperature (T) between 23 and 25 °C and relative humidity (RH) (70–80%) triggered the pandemic spread by increase in the infected cases in northern and central Peninsular Malaysia. Selangor, WP Kuala Lumpur and WP Putrajaya show significantly high infected cases and a definite trend was not observed with respect to a particular meteorological factor. It is identified that high precipitation (PPT), RH and good air quality have reduced the spread in East Malaysia. A negative correlation of T and AQI and positive correlation of RH with total infected cases were found during MCO phase 3. Principal component analysis (PCA) indicated that T, RH, PPT, dew point (DP) and AQI are the main controlling factors for the spread across the country apart from social distancing. Vulnerability zones were identified based on the spatial analysis of T, RH, PPT and AQI with reference to total infected cases. Based on time series analysis, it was determined that higher RH and T in Peninsular Malaysia and high amount of PPT, RH and good air quality in East Malaysia have controlled the spreading during MCO phase 3. The predominance of D614 mutant was observed prior to March and decreases at the end of March, coinciding with the fluctuation of meteorological factors and air quality. The outcome of this study gives a general awareness to the public on COVID-19 and the influence of meteorological factors. It will also help the policymakers to enhance the management plans against the pandemic spreading apart from social distancing in the next wave of COVID-19.

Surface water pollution is a global problem and has been evident for a long period of time. Hence, the aim of the study was to evaluate the hydro-geochemical characteristics of Selamko farm reservoir water quality and its suitability for multipurpose uses using GIS-based water quality indices. The water sampling sites and parameters were selected systematically based on the land use, land cover, and anthropogenic activities around Selamko reservoir watershed in Debre Tabor, Ethiopia. Water samples were collected from 11 sampling stations from July 2019 to March 2020 using the composite sampling method and examined using standard procedures. The suitability of the reservoir's water quality for multipurpose use was investigated using drinking and irrigation water quality indices, and other tools. The spatial distribution maps of water quality parameters were prepared using the kriging method in ArcGIS 10.5. The results of the geospatial analysis indicated that the reservoir water quality parameters had spatial variation, which was caused by industrial and household wastewater inflow across the reservoir's watershed. Based on WHO and ES standards, the computed drinking water quality index results revealed that 81.81% and 18.18% of the reservoir's water quality fall into the poor to very poor classes, which indicates that the water in the reservoir is not fit for drinking. However, a Wilcox diagram, irrigation indices, and USEPA regulations revealed that the reservoir water quality is found to be safe and suitable for irrigation, fishing, and livestock purposes. The study concluded that Selamko reservoir's water quality is suitable for irrigation, fishing, and livestock watering with proper management accordingly.

Groundwater contamination from intensive agricultural and industrial activity can lead to deteriorated drinking water quality that poses serious health threats for humans. The intention of the study was to appraisal the level of groundwater pollution and the potential risks it could pose to the residents of Kombolcha Town, an area with significant industrial and agricultural activity. Samples were obtained from seventeen groundwater wells in August 2021 and examined with standard methods. The groundwater comprehensive and indices for irrigation were calculated to evaluate the appropriateness of water quality for various purposes. A USEPA model was applied to estimate human health risks from polluted groundwater. Results revealed that all hydrogeochemical characteristics and heavy metals, with the exception of chromium, were within Ethiopian and World Health Organization drinking water guideline. Groundwater quality pollution index results for drinking demonstrated that 5.88% of water samples were evaluated as very good, whereas 94.12% of water samples were rated excellent. Ca-HCO3 facies make up 53% of samples, and Ca–Mg–Cl facies account for the remaining samples. Total non-carcinogenic risks to health for males extended from 0.35 to 2.39, for women from 0.38 to 2.59, and for children from 0.49 to 3.38. Cancer risks ranged from 0 to 1.6 × 10−3 for men, 0 to 1.74 × 10−3 for females, and 0 to 2.24 × 10−3 for children. These results indicate that children in this region suffer more risks for health than adults. The study concluded that to safeguard water quality, reduce threats to health, and more efficiently use the groundwater resource, prompt and efficient actions on agricultural and industrial practices must be taken in the research area.