Showing papers on "Water quality published in 2022"

Managing nitrogen legacies to accelerate water quality improvement

Abstract: Increasing incidences of eutrophication and groundwater quality impairment from agricultural nitrogen pollution are threatening humans and ecosystem health. Minimal improvements in water quality have been achieved despite billions of dollars invested in conservation measures worldwide. Such apparent failures can be attributed in part to legacy nitrogen that has accumulated over decades of agricultural intensification and that can lead to time lags in water quality improvement. Here, we identify the key knowledge gaps related to landscape nitrogen legacies and propose approaches to manage and improve water quality, given the presence of these legacies. Agricultural nitrogen legacies are delaying improvements to water quality. Comprehensive management strategies that address legacy issues are needed to ensure better environmental outcomes.

Appraisal of shallow groundwater quality with human health risk assessment in different seasons in rural areas of the Guanzhong Plain (China)

Abstract: Groundwater is the major source of water for drinking and irrigation purposes in and around Hua County, Shaanxi Province, China. The main purposes of this research is to evaluate the groundwater quality in the rainy and dry seasons of Hua County and analyze the causes of seasonal differences and determine the areas with serious pollution. Groundwater quality was assessed in this study using entropy water quality index (EWQI) and some graphical approaches such as Gibbs and Piper diagrams. The contour maps of groundwater quality were drawn by Geographical Information System (GIS). According to the obtained results, the locations where groundwater quality was rated as excellent or good in both wet and dry seasons were mainly in the north and east of the research area. COD and NO3- are the parameters that have the most serious negative effect on water quality. The dominant factors influencing groundwater chemical evolution in the study area were rock weathering and dissolution, and the precipitation and evaporation during the wet and dry seasons do not cause significant changes in groundwater chemistry. Adults' health risks results revealed that 27.69% and 52.31% of the groundwater samples exceeded the acceptable limit for non-carcinogenic risk in the wet and dry season, respectively, while for children the ratios are 30.16% and 47.62%, respectively. The contributive percentages of nitrate, fluoride and nitrate to the total risk are 61.29%, 28.71% and 10.00% in the wet season and 68.84%, 20.85% and 10.31% in the dry season. The risk is higher in the south than in the north of the study area, and is especially high in the southwest of the study area.

Effects of Water Pollution on Human Health and Disease Heterogeneity: A Review

Abstract: Background: More than 80% of sewage generated by human activities is discharged into rivers and oceans without any treatment, which results in environmental pollution and more than 50 diseases. 80% of diseases and 50% of child deaths worldwide are related to poor water quality. Methods: This paper selected 85 relevant papers finally based on the keywords of water pollution, water quality, health, cancer, and so on. Results: The impact of water pollution on human health is significant, although there may be regional, age, gender, and other differences in degree. The most common disease caused by water pollution is diarrhea, which is mainly transmitted by enteroviruses in the aquatic environment. Discussion: Governments should strengthen water intervention management and carry out intervention measures to improve water quality and reduce water pollution’s impact on human health.

A review of the application of machine learning in water quality evaluation

Abstract: With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments. • Machine learning is widely used in water quality monitoring and prediction. • The performance of 45 machine learning algorithms is evaluated and discussed. • The challenges and opportunities of machine learning in water system are described.

Fuzzy comprehensive evaluation with AHP and entropy methods and health risk assessment of groundwater in Yinchuan Basin, northwest China

Abstract: Water quality evaluation and health risk assessment are not only the basis of environmental protection work, but also of great significance to water environment supervision and management. In this paper, the fuzzy comprehensive evaluation for water quality was improved by using the analytic hierarchy process (AHP) and Entropy, and a health risk assessment model based on triangular fuzzy theory was developed. The evaluation results show 5 water categories: Class-1 (n = 1, 2%), Class-2 (n = 14, 32%), Class-3 (n = 15, 34%), Class-4 (n = 8, 18%) and Class-5 (n = 6, 14%), manifesting about 67% of the phreatic water can be used for drinking purposes in the research area. The Chadha diagram provides hydrochemical facies of the phreatic water are mainly NaCl type (n = 16, 36%) and Ca–Mg–Na type (n = 15, 34%). Fluorine as non-carcinogenic factor in health risk assessment, showing moderate correlation with SO42– (r = 0.54) and low correlation with Na+ (r = 0.38) in Pearson correlation analysis. The order of non-carcinogenic risk per year is as follow: Class-2, Class-3, All, Class-4 and Class-5 with the mean of 0.29, 0.51, 0.67, 0.86 and 1.55 × 10–8 for adults, 0.54, 0.95, 1.27, 1.58 and 2.89 × 10–8 for children. Compare with adults, children undertake higher health risk, in research area. Particularly, the region accepting Class-5 water supplement encounter high non-carcinogenic risk, where risk level is 2.24 and 2.28 times to the average risk level for adults and children, respectively. This paper provides insights into solving uncertainties in groundwater management and environmental protection, as well as into fuzzy logic techniques addressing pollution.

Groundwater Suitability for Drinking and Irrigation Using Water Quality Indices and Multivariate Modeling in Makkah Al-Mukarramah Province, Saudi Arabia

Abstract: Water shortage and quality are major issues in many places, particularly arid and semi-arid regions such as Makkah Al-Mukarramah province, Saudi Arabia. The current work was conducted to examine the geochemical mechanisms influencing the chemistry of groundwater and assess groundwater resources through several water quality indices (WQIs), GIS methods, and the partial least squares regression model (PLSR). For that, 59 groundwater wells were tested for different physical and chemical parameters using conventional analytical procedures. The results showed that the average content of ions was as follows: Na+ > Ca2+ > Mg 2+ > K+ and Cl− > SO42− > HCO32− > NO3− > CO3−. Under the stress of evaporation and saltwater intrusion associated with the reverse ion exchange process, the predominant hydrochemical facies were Ca-HCO3, Na-Cl, mixed Ca-Mg-Cl-SO4, and Na-Ca-HCO3. The drinking water quality index (DWQI) has indicated that only 5% of the wells were categorized under good to excellent for drinking while the majority (95%) were poor to unsuitable for drinking, and required appropriate treatment. Furthermore, the irrigation water quality index (IWQI) has indicated that 45.5% of the wells were classified under high to severe restriction for agriculture, and can be utilized only for high salt tolerant plants. The majority (54.5%) were deemed moderate to no restriction for irrigation, with no toxicity concern for most plants. Agriculture indicators such as total dissolved solids (TDS), potential salinity (PS), sodium absorption ratio (SAR), and residual sodium carbonate (RSC) had mean values of 2572.30, 33.32, 4.84, and −21.14, respectively. However, the quality of the groundwater in the study area improves with increased rainfall and thus recharging the Quaternary aquifer. The PLSR models, which are based on physicochemical characteristics, have been shown to be the most efficient as alternative techniques for determining the six WQIs. For instance, the PLSR models of all IWQs had determination coefficients values of R2 ranging between 0.848 and 0.999 in the Cal., and between 0.848 and 0.999 in the Val. datasets, and had model accuracy varying from 0.824 to 0.999 in the Cal., and from 0.817 to 0.989 in the Val. datasets. In conclusion, the combination of physicochemical parameters, WQIs, and multivariate modeling with statistical analysis and GIS tools is a successful and adaptable methodology that provides a comprehensive picture of groundwater quality and governing mechanisms.

Current water quality guidelines across North America and Europe do not protect lakes from salinization

Abstract: Significance The salinity of freshwater ecosystems is increasing worldwide. Given that most freshwater organisms have no recent evolutionary history with high salinity, we expect them to have a low tolerance to elevated salinity caused by road deicing salts, agricultural practices, mining operations, and climate change. Leveraging the results from a network of experiments conducted across North America and Europe, we showed that salt pollution triggers a massive loss of important zooplankton taxa, which led to increased phytoplankton biomass at many study sites. We conclude that current water quality guidelines established by governments in North America and Europe do not adequately protect lake food webs, indicating an immediate need to establish guidelines where they do not exist and to reassess existing guidelines.

COVID-19 and urban rivers: Effects of lockdown period on surface water pollution and quality- A case study of the Zarjoub River, north of Iran

Abstract: Due to the spreading of the coronavirus (COVID-19) in Iran, restrictions and lockdown were announced to control the infection. In order to determine the effects of the lockdown period on the status of the water quality and pollution, the concentrations of Al, As, Ba, Cr, Cu, Mo, Ni, Pb, Se, and Zn, together with Na+, Mg2+, Ca2+ and electrical conductivity (EC), were measured in the Zarjoub River, north of Iran, in both pre-lockdown and post-lockdown periods. The results indicated that water pollution and associated human health risk reduced by an average of 30% and 39%, respectively, during the lockdown period. In addition, the multi-purpose water quality index also improved by an average of 34%. However, the water salinity and alkalinity increased during the lockdown period due to the increase of municipal wastewater and the use of disinfectants. The major sources of pollution were identified as weathering, municipal wastewater, industrial and agricultural effluents, solid waste, and vehicular pollution. PCA-MLR receptor model showed that the contribution of mixed sources of weathering and municipal wastewater in water pollution increased from 23 to 50% during the lockdown period. However, the contribution of mixed sources of industrial effluents and solid wastes reduced from 64 to 45%. Likewise, the contribution of traffic-related sources exhibited a reduction from 13% in the pre-lockdown period to 5% together with agricultural effluent in the post-lockdown period. Overall, although the lockdown period resulted in positive impacts on diminishing the level of water pollution caused by industrial and vehicular contaminants, the increase of municipal waste and wastewater is a negative consequence of the lockdown period.

Antimicrobial Resistance Monitoring of Water Environments: A Framework for Standardized Methods and Quality Control

Abstract: Antimicrobial resistance (AMR) is a grand societal challenge with important dimensions in the water environment that contribute to its evolution and spread. Environmental monitoring could provide vital information for mitigating the spread of AMR; this includes assessing antibiotic resistance genes (ARGs) circulating among human populations, identifying key hotspots for evolution and dissemination of resistance, informing epidemiological and human health risk assessment models, and quantifying removal efficiencies by domestic wastewater infrastructure. However, standardized methods for monitoring AMR in the water environment will be vital to producing the comparable data sets needed to address such questions. Here we sought to establish scientific consensus on a framework for such standardization, evaluating the state of the science and practice of AMR monitoring of wastewater, recycled water, and surface water, through a literature review, survey, and workshop leveraging the expertise of academic, governmental, consulting, and water utility professionals.

Prediction of estuarine water quality using interpretable machine learning approach

Abstract: Estuaries are principal sources of pollution in coastal areas. Estuarine water quality prediction models can provide early warnings to prevent major disasters in coastal ecosystems. In this study, several machine learning models—multiple linear regression, artificial neural networks, random forest, and extreme gradient boosting (XGBoost)—were developed to predict NH4+-N in the Xiaoqing River estuary, China. The results show that there is a strong nonlinear relationship between estuarine NH4+-N and NH4+-N of the upper reaches. The shapely additive explanations method was used to interpret the XGBoost model and discover the influence of the upper reaches of the river on the estuary. These explanations showed that two stations monitoring water quality in the upper reaches (Shicun and Sanchakou) had a critical impact on estuarine water quality. If NH4+-N concentration of the upper reaches is below 2 mg/L, estuarine NH4+-N would not be negatively influenced by the upper reaches. These results can support pollution warnings for improving estuarine water quality and the integrated environmental management of the river and costal area.

A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges

Abstract: Water pollution has become one of the most serious issues threatening water environments, water as a resource and human health. The most urgent and effective measures rely on dynamic and accurate water quality monitoring on a large scale. Due to their temporal and spatial advantages, remote sensing technologies have been widely used to retrieve water quality data. With the development of hyper-spectral sensors, unmanned aerial vehicles (UAV) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval owing to various data availabilities and retrieval methodologies. This article presents the application of remote sensing for water quality retrieval, and mainly discusses the research progress in terms of data sources and retrieval modes. In particular, we summarize some retrieval algorithms for several specific water quality variables, including total suspended matter (TSM), chlorophyll-a (Chl–a), colored dissolved organic matter (CDOM), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). We also discuss the significant challenges to atmospheric correction, remotely sensed data resolution, and retrieval model applicability in the domains of spatial, temporal and water complexity. Finally, we propose possible solutions to these challenges. The review can provide detailed references for future development and research in water quality retrieval.