Spatial and temporal variations have been found in the levels of arsenic (As) throughout the groundwater of the Ghaghara basin. Fifteen out of twenty-five districts in this basin are reported to be affected by As, where the levels of As in groundwater and soil exceed the permissible limits set by the WHO (10 μgl-1) and FAO (20 mgkg-1) respectively. These districts include a total of four municipalities in Nepal and eighty-six blocks in India, all of which have varying degrees of As contamination. Approximately 17 million people are at risk of As poisoning, with more than two orders of magnitude higher potential lifetime incremental cancer risk, constituting over 153 thousand potential additional cases of cancer due to As-contaminated drinking water. Out of the 90 As-contaminated blocks in the Ghaghara basin, 4 blocks have about 7-fold higher potential risk of developing cancer, 49 blocks have 8-37-fold higher risk, and 37 blocks have up to 375-fold higher risk compared to the upper limit of the USEPA acceptable range, which is 1 × 10-6-1 × 10-4. High accumulation of As has been reported in the nails, hair, and urine of local inhabitants, with higher levels observed in females than males. The toxicity of As is manifested in terms of a higher occurrence of various diseases. Reproductive endpoints, such as increased incidences of preterm birth, spontaneous abortion, stillbirth, low-birth weight, and neonatal death, have also been reported in the basin. The level of As in tube wells has been found to be negatively correlated with the depth (r = -0.906), and tube wells with high levels of As (>150 μgl-1) are generally located within close proximity (<10 km) to abandoned or present meander channels in the floodplain areas of the Ghaghara river. In addition to As contamination, the water quality index (WQI) in the Ghaghara basin is poor according to the BIS standards for drinking water. Groundwater in six out of fifteen districts is unsuitable for drinking purposes, with a WQI exceeding 100. The levels of As in agricultural soil in many villages of Ballia, Bahraich, and Lakhimpur Kheri districts have exceeded the FAO limit. Water from deep tube wells has been found to be relatively safe in terms of As content, and thus can be recommended for drinking purposes. However, the use of surface water needs to be encouraged for irrigation purposes in order to preserve soil health and reduce As contamination in the food chain, thereby minimizing the risk of cancer.

Freshwater resources play a pivotal role in sustaining life and meeting various domestic, agricultural, economic, and industrial demands. As such, there is a significant need to monitor the water quality of these resources. Water quality index (WQI) models have gradually gained popularity since their maiden introduction in the 1960s for evaluating and classifying the water quality of aquatic ecosystems. WQIs transform complex water quality data into a single dimensionless number to enable accessible communication of the water quality status of water resource ecosystems. To screen relevant articles, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed to include or exclude articles. A total of 17 peer-reviewed articles were used in the final paper synthesis. Among the reviewed WQIs, only the Canadian Council for Ministers of the Environment (CCME) index, Irish water quality index (IEWQI) and Hahn index were used to assess both lotic and lentic ecosystems. Furthermore, the CCME index is the only exception from rigidity because it does not specify parameters to select. Except for the West-Java WQI and the IEWQI, none of the reviewed WQI performed sensitivity and uncertainty analysis to improve the acceptability and reliability of the WQI. It has been proven that all stages of WQI development have a level of uncertainty which can be determined using statistical and machine learning tools. Extreme gradient boosting (XGB) has been reported as an effective machine learning tool to deal with uncertainties during parameter selection, the establishment of parameter weights, and determining accurate classification schemes. Considering the IEWQI model architecture and its effectiveness in coastal and transitional waters, this review recommends that future research in lotic or lentic ecosystems focus on addressing the underlying uncertainty issues associated with the WQI model in addition to the use of machine learning techniques to improve the predictive accuracy and robustness and increase the domain of application.

Water quality monitoring has become more critical in recent years to ensure the availability of clean and safe water from natural aquifers and to understand the evolution of water contaminants across time and space. The conventional water monitoring techniques comprise of sample collection, preservation, preparation, tailed by laboratory testing and analysis with cumbersome wet chemical routes and expensive instrumentation. Despite the high accuracy of these methods, the high testing costs, laborious procedures, and maintenance associated with them don\'t make them lucrative for end end-users and field testing. As the participation of ultimate stakeholders, that is, common man for water quality and quantity can play a pivotal role in ensuring the sustainability of our aquifers, thus it is essential to develop and deploy portable and user-friendly technical systems for monitoring water sources in real-time or on-site. The present review emphasizes here on possible approaches including optical (absorbance, fluorescence, colorimetric, X-ray fluorescence, chemiluminescence), electrochemical (ASV, CSV, CV, EIS, and chronoamperometry), electrical, biological, and surface-sensing (SPR and SERS), as candidates for developing such platforms. The existing developments, their success, and bottlenecks are discussed in terms of various attributes of water to escalate the essentiality of water quality devices development meeting ASSURED criterion for societal usage. These platforms are also analyzed in terms of their market potential, advancements required from material science aspects, and possible integration with IoT solutions in alignment with Industry 4.0 for environmental application.

Water quality improvement is one of the top priorities in the global agenda endorsed by United Nation. In this review manuscript, a holistic view of water quality degradation such as concerned pollutants, source of pollution, and its consequences in major river basins around the globe (at least 1 from each continent and a total of 16 basins) is presented. Additionally, nine contemporary techniques such as field scale evaluation, watershed scale evaluation, strategies to identify critical source areas, optimization strategies for placement of best management practices (BMPs), social component in watershed modeling, machine learning algorithms to address water quality problems in complex natural systems concomitant with spatial heterogeneity, establishing a total maximum daily loads (TMDLs), remote sensing in monitoring water quality, and developing water quality index are discussed. Next, the existing barriers to improve water quality are classified into primary and secondary impediments. A detail discussion of three primary impediments (climate change, urbanization and industrial activities, and agriculture) and ten secondary impediments (availability of water quality data, complexity of system, lack of skilled person, environmental legislation, fragmented mandate, limitation in resources, environmental awareness, resistance to change, alteration of nutrient ratio by river damming, and emerging pollutants) are illustrated. Finally, considering all the existing knowledge gaps pertaining to contemporary strategies, a future direction of water quality research is outlined to significantly improve the water quality around the globe.

Rising food, housing and energy demand of increasing population creates an immense pressure on water resources, especially on water quality. The water quality around the globe is degrading primarily due to intense agricultural activities associated with rapid urbanization. This study attributes to cause of water quality problem, indices to measure water quality, methods to identify proper explanatory variables to water quality and it\'s processing to capture the special effect, and finally modeling of water quality using identified explanatory variables to provide insights. This would help policymakers and watershed managers to take necessary steps to protect water quality for the future as well as current generation. Finally, some knowledge gaps are also discussed which need to be addressed in the future studies.