Application of Co3O4 nanocrystal/rGO for simultaneous electrochemical detection of cadmium and lead in environmental waters

Sensing of cadmium (Cd) and lead (Pb) in environmental samples is crucial for identifying potential health risks associated with exposure to these heavy metals as well as understanding the extent of heavy metal contamination in different environments and its impact on the ecosystem. The present study elucidates the development of a novel electrochemical sensor that can detect Cd (II) and Pb (II) ions simultaneously. This sensor is fabricated using reduced graphene oxide (rGO) and cobalt oxide nanocrystals (Co₃O₄ nanocrystals/rGO). The characterization of Co₃O₄ nanocrystals/rGO was done by using various analytical techniques. The incorporation of cobalt oxide nanocrystals with intense absorption properties results in an amplification of the electrochemical current generated on the surface of the sensor by heavy metals. This, when coupled with the unique properties of the GO layer, enables the identification of trace levels of Cd (II) and Pb (II) in the surrounding environment. The electrochemical testing parameters were meticulously optimized to obtain high sensitivity and selectivity. The Co₃O₄ nanocrystals/rGO sensor exhibited exceptional performance in detecting Cd (II) and Pb (II) within a concentration range of 0.1-450 ppb. Notably, the limits of detection (LOD) for Pb (II) and Cd (II) were found to be highly impressive at 0.034 ppb and 0.062 ppb, respectively. The Co₃O₄ nanocrystals/rGO sensor integrated with the SWASV method displayed notable resistance to interference and exhibited consistent reproducibility and stability. Therefore, the suggested sensor has the potential to serve as a technique for detecting both ions in aqueous samples using SWASV analysis.

Characteristics of drought propagation and effects of water resources on vegetation in the karst area of Southwest China

The topography is complex in the southwest karst region of China, with severe surface water scarcity but abundant groundwater resources. Studying drought propagation and vegetation demand for water is important to effectively protect the ecological environment and improve the management of water resources. We employed CRU precipitation data, GLDAS, and GRACE data to calculate SPI (Standardized precipitation index), SSI (Standardized soil moisture index), SRI (Standardized runoff index), and GDI (Groundwater drought index), characterizing meteorological, agricultural, surface water and groundwater droughts, respectively. The Pearson correlation coefficient was adopted to study the propagation time of these four types of droughts. The random forest method was used to identify the importance of precipitation, 0-10 cm soil water, 10-200 cm soil water, surface runoff, and groundwater for NDVI (Normalized difference vegetation index), SIF (Solar-induced chlorophyll fluorescence), and NIRV (Near-infrared reflectance index of vegetation) at the pixel scale. The propagation time of meteorological drought to agricultural drought and agricultural drought to groundwater drought in the karst area of southwest China was significantly reduced by 1.25 months compared with the non-karst area. The response of SIF to meteorological drought was faster than that of NDVI and NIRV. The importance of water resources for vegetation in the whole study period (2003-2020) was ranked as precipitation, soil water, groundwater, and surface runoff. The contribution of soil water and groundwater in forest was 38.66 %, while 31.66 % and 21.67 % for grassland and cropland, respectively, which indicated that the demands of soil water and groundwater in forest were greater than that of grassland and cropland. When drought occurred (2009-2010), soil water, precipitation, runoff, and groundwater were ranked in order of importance. The importance of 0-200 cm soil water was 48.67 %, 57 %, and 41 % in forest, grassland, and cropland, respectively, higher than precipitation, runoff, and groundwater, indicating that soil water was the main water resource for vegetation to cope with drought. Since the cumulative effect of drought on SIF was more obvious, SIF showed a more serious negative anomaly than NDVI and NIRV from March to July 2010. The correlation coefficients between SIF, NDVI, NIRV, and precipitation were 0.94, 0.79, and 0.89 (P < 0.001), and the correlation coefficients with groundwater were -0.27 (P < 0.001), -0.02 (P > 0.05) and -0.15 (P < 0.05), respectively. Compared to NDVI and NIRV, SIF was more sensitive to meteorological drought and groundwater drought and had great potential in drought monitoring.

Comparison of machine learning algorithms to predict dissolved oxygen in an urban stream

Water quality monitoring for urban watersheds is critical to identify the negative urbanization impacts. This study sought to identify a successful predictive machine learning model with minimal parameters from easy-to-deploy, low-cost sensors to create a monitoring system for the urban stream network, Hunnicutt Creek, in Clemson, SC, USA. A multiple linear regression model was compared to machine learning algorithms k-nearest neighbor, decision tree, random forest, and gradient boosting. These algorithms were evaluated to understand which best predicted dissolved oxygen (DO) from water temperature, conductivity, turbidity, and water level change at four locations along the urban stream. The random forest algorithm had the highest performance in predicting DO for all four sites, with Nash-Sutcliffe model efficiency coefficient (NSE) scores > 0.9 at three sites and > 0.598 at the fourth site. The random forest model was further examined using explainable artificial intelligence (XAI) and found that temperature influenced the DO predictions for three of the four sites, but there were different water quality interactions depending on site location. Calculating the land cover type in each site's sub-watershed revealed that different amounts of impervious surface and vegetation influenced water quality and the resulting DO predictions. Overall, machine learning combined with land cover data helps decision-makers better understand the nuances of urban watersheds and the relationships between urban land cover and water quality.

Evaluation of Phthalic acid Tri-ethylene diamine (TED) and Folic acid-based carbon quantum dots for detection of heavy metals in water resources using fiber-optic instrumentation

Heavy metal pollution and toxicity from water resources has remained a great concern for entire population. This research demonstrates the capability of carbon quantum dots (CQD) for the fluorescence-based heavy metals detection in different water resource using a fiber optic spectrometer device. Two different types of CQDs (phthalic acid & triethylenediamine (PT CQDs) and Folic acid (FCQDs) were synthesized using microwave irradiation & hydrothermal method, respectively. CQD were characterized using several techniques like TEM, EDX, XPS and FTIR. PTCQD & FCQDs both were tested for sensing capability in water reservoirs like household and river water. The results indicate that both CQDs were able to detect all six heavy metal ions (Pb²⁺, Co²⁺, Mn³⁺, Hg²⁺, Ni²⁺, Cr³⁺) tested in the study in the range of 0-100 µM. The accuracy of heavy metal detection from both CQDs FCQDs and PTCQDs respectively showed that (Mercury (98.96%±1.04&106.6%±6.6), Nickle (99.29%±2.9&99.18%±102.8%±2.8), Lead (103.3%±3.3 &104.8%±4.8), Manganese (101.3 % ± 1.3 & 101.4 % ± 1.4), Chromium (101.9 % ± 1.9 & 101.9 % ± 1.9), Cobalt (100.8 %± 0.8 & 101.9%±1.9). It was found that FCQDs shows a three-fold higher sensitivity and greater resolution than PTCQDs for all the heavy metals samples. The CQDs sensing capability shows that CQDs are able to achieve a limit of detection in the range of 0.15-3µM along with 100% accuracy in terms of recovery with minimal error, these results clearly indicate that both CQDs have a tremendous potential to be used as a sensor for the detection of heavy metals even in complex water matrices. FCQDs shows more sensitivity for all metals as compared to PTCQDs and used in future as a sensing tool for heavy metal detection with better sensitivity and accuracy with less response time.

Occurrence and environmental risk assessment of 22 pesticides in Brazilian freshwaters

Pesticide contamination in water resources is a global threat. Although usually found at low concentrations, pesticides raise considerable toxicological concerns, mainly when mixtures are considered. The occurrence of 22 pesticides (2,4 D, alachlor, aldicarb, aldrin, atrazine, carbendazim, carbofuran, chlordane, chlorpyrifos, DDT, diuron, glyphosate, lindane, mancozeb, methamidophos, metolachlor, molinate, profenofos, simazine, tebuconazole, terbufos, and trifluralin) was investigated, through consolidated database information, in surface freshwaters of Brazil. Moreover, scenarios of environmental risk assessment considering isolated compounds and mixtures were performed, as well as a meta-analytic approach for toxicity purposes. Pesticides in freshwater have been reported from 719 cities (12.9% of Brazilian cities), where 179 (3.2%) showed pesticide occurrence above the limit of detection or quantification. Considering cities with more than five quantified, 16 cities were prone to environmental risks considering individual risks. However, the number increased to 117 cities when the pesticide mixture was considered. The mixture risk was driven by atrazine, chlorpyrifos, and DDT. The national maximum acceptable concentrations (MAC) for nearly all pesticides are higher than the predicted no-effect concentration (PNEC) for the species evaluated, except aldrin. Our results show the need to consider mixtures in the environmental risk assessment to avoid underestimation and review MAC to protect aquatic ecosystems. The results presented here may guide the revision of the national environmental legislation to ensure the protection of Brazilian aquatic ecosystems.

Modelling seasonal household variation in harvested rainwater availability: a case study in Siaya County, Kenya

Rainwater harvesting reliability, the proportion of days annually when rainwater demand is fully met, is challenging to estimate from cross-sectional household surveys that underpin international monitoring. This study investigated the use of a modelling approach that integrates household surveys with gridded precipitation data to evaluate rainwater harvesting reliability, using two local-scale household surveys in rural Siaya County, Kenya as an illustrative case study. We interviewed 234 households, administering a standard questionnaire that also identified the source of household stored drinking water. Logistic mixed effects models estimated stored rainwater availability from household and climatological variables, with random effects accounting for unobserved heterogeneity. Household rainwater availability was significantly associated with seasonality, storage capacity, and access to alternative improved water sources. Most households (95.1%) that consumed rainwater faced insufficient supply of rainwater available for potable needs throughout the year, with intermittencies during the short rains for most households with alternative improved sources. Although not significant, stored rainwater lasts longer for households whose only improved water source was rainwater (301.8 ± 40.2 days) compared to those having multiple improved sources (144.4 ± 63.7 days). Such modelling analysis could enable rainwater harvesting reliability estimation, and thereby national/international monitoring and targeted follow-up fieldwork to support rainwater harvesting.

Monitoring the impact of climate extremes and COVID-19 on statewise sentiment alterations in water pollution complaints

The COVID-19 pandemic and associated prevention policies can directly or indirectly alter the sentiment of individuals while registering water pollution complaints, but observational evidence remains limited. Here, we conducted a sentiment analysis on over 10,000 water pollution complaints from residents in Alabama, USA (2012-2021) to better understand how and to what extent COVID-19 has altered emotion (polarity score-based) and attitude (subjectivity) of water pollution complaints. We found that the 2017 state-wise drought significantly increased the percentage of negative water pollution complaints by +35%, with no significant alternation in attitude before the COVID-19 pandemic. Since COVID-19, the percentage of negative and subjective water pollution complaints significantly decreased and increased by -30 and +20%, respectively, and these sentiment alternations were maintained by 2021. This study provides a new direction for environmental governance and management, requiring a timely response to changes in the public's emotions and attitudes during the next climate extremes and pandemics.

Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms

The consequences of droughts are far-reaching, impacting the natural environment, water quality, public health, and accelerating economic losses. Applications of remote sensing techniques using satellite imageries can play an influential role in identifying drought severity (DS) and impacts for a broader range of areas. The Barind Tract (BT) is a region of Bangladesh located northwest of the country and considered one of the hottest, semi-arid, and drought-prone regions. This study aims to assess and predict the drought vulnerability over BT using Landsat satellite images from 1996 to 2031. Several indices, including Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Soil Moisture Content (SMC), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). VHI has been used to identify and predict DS based on VCI and TCI characteristics for 2026 and 2031 using Cellular Automata (CA)-Artificial Neural Network (ANN) algorithms. Results suggest an increasing patterns of DS accelerated by the reduction of healthy vegetation (19 %) and surface water bodies (26 %) and increased higher temperature (>5 °C) from 1996 to 2021. In addition, the VHI result signifies a massive increase in extreme drought conditions from 1996 (2 %) to 2021 (7 %). The DS prediction witnessed a possible expansion in extreme and severe drought conditions in 2026 (15 % and 13 %) and 2031 (18 % and 24 %). Understanding the possible impacts of drought will allow planners and decision-makers to initiate mitigating measures for enhancing the communities preparedness to cope with drought vulnerability.

A 350,000-year history of groundwater recharge in the southern Great Basin, USA

Estimating groundwater recharge under various climate conditions is important for predicting future freshwater availability. This is especially true for the water-limited region of the southern Great Basin, USA. To investigate the response of groundwater recharge to different climate states, we calculate the paleo recharge to a groundwater basin in southern Nevada over the last 350,000 years. Our approach combines a groundwater model with paleo-water-table data from Devils Hole cave. The minimum water-table during peak interglacial conditions was more than 1.6 m below modern levels, representing a recharge decline of less than 17% from present-day conditions. During peak glacial conditions, the water-table elevation was at least 9.5 m above modern levels, representing a recharge increase of more than 233-244% compared to present-day conditions. The elevation of the Devils Hole water-table is 3-4 times more sensitive to groundwater recharge during dry interglacial periods, compared to wet glacial periods. This study can serve as a benchmark for understanding long-term effects of past and future climate change on groundwater resources.

An empirical assessment of the impact of digital economy and environmental regulation on regional water resources efficiency in the context of COP26

The 26th Conference of the Parties to the United Nations Framework Convention on Climate Change (COP26) highlighted the importance of nuclear techniques in mitigating the impact of climate change on water resources, and improvements in water efficiency were considered an important step towards the achievement of the COP26 goals. We selected the super-efficiency SBM method to measure water resource utilization efficiency between the period of 2007 and 2020, and we focused on the relationship between the digital economy and water resource use efficiency (WRE). Conclusions as follows: (1) China's water resource use efficiency value is 0.441. The water resource use efficiency in the eastern region is the highest, and that in the western region is the lowest. (2) The digital economy can significantly promote the improvement of water resource use efficiency in the whole country and the eastern region. The impact of the digital economy on water resource use efficiency in the central region is not significant, and that in the western region is inhibitory. (3) In addition to the central region, environmental regulations in the east, west, and the whole country have made positive contributions to the efficiency of green water resource use. (4) Economic development promotes WRE in the whole country, the east, and the middle, but not in the west. The impact of industry on WRE is always negative. The impact of technological progress on WRE in the central and western regions and the whole country is not significant, but the impact in the eastern region is positive. The level of opening to the outside world has no significant impact on the WRE of the eastern region, but negative impact on the WRE of other regions. Abundance of water resources has a negative impact on WRE in all regions.

Potential benefits of public-private partnerships to improve the efficiency of urban wastewater treatment

For emerging economies lacking public budgets, continuous improvement of urban wastewater treatment efficiency (UWTE) requires effective government supervision of wastewater treatment infrastructures (WTIs) and participation of private capital seeking to profit-maximising. However, to what extent this public-private partnership (PPP) model, aimed at a reasonable sharing of benefit and risk, in delivering WTIs can improve the UWTE is unknown. We evaluated the impact of the PPP model on the UWTE by collecting data from 1303 urban wastewater treatment PPP projects in 283 prefecture-level cities in China from 2014 to 2019 and used data envelopment analysis and Tobit regression model. The UWTE was significantly higher in prefecture-level cities that introduced the PPP model in the construction and operation of WTIs, particularly those with a feasibility gap subsidy, competitive procurement, privatised operation, and non-demonstration. Moreover, the effects of PPPs on UWTE were limited by the economic development level, marketisation, and climatic conditions.

The accuracy and usability of point-of-use fluoride biosensors in rural Kenya

Geogenic fluoride contaminates the water of tens of millions of people. However, many are unaware of the fluoride content due in part to shortcomings of detection methods. Biosensor tests are a relatively new approach to water quality testing that address many of these shortcomings but have never been tested by non-experts in a "real-world" setting. We therefore sought to assess the accuracy and usability of a point-of-use fluoride biosensor using surveys and field tests in Nakuru County, Kenya. Biosensor tests accurately classified elevated fluoride (≥1.5 ppm) in 89.5% of the 57 samples tested. Usability was also high; all participants were able to use the test and correctly interpreted all but one sample. These data suggest that biosensor tests can provide accurate, meaningful water quality data that help non-experts make decisions about the water they consume. Further scaling of these technologies could provide new approaches to track global progress towards Sustainable Development Goal 6.

Spatiotemporal evolution of water ecological footprint based on the emergy-spatial autocorrelation method

To quantify and analyze the human demand for water resources and the available supply of water resource systems, this study combined emergy analysis and spatial autocorrelation analysis to establish a quantification and analysis system for water ecological footprint (WEF). First, the emergy theory of ecological economics and WEF were combined to propose an emergy quantification method for WEF and water ecological carrying capacity (WEC). Based on the spatial autocorrelation method, three-dimensional ecological footprint indicators (footprint size and depth) were introduced to analyze the spatial correlation and spatial aggregation of capital flow occupation and capital stock consumption in the water resource system. Using the Yellow River Basin (YRB) as the study area to verify the applicability of the WEF quantification and analysis system based on the emergy-spatial autocorrelation method, the following results were obtained. (1) From 2003 to 2018, the per capita WEF of the YRB generally showed a slow growth trend. (2) Compared to the upper and lower reaches of the YRB, the middle reaches had a higher WEF, and the WEC of the YRB was generally high in the west and low in the east. (3) Utilization of the water resources capital in the basin was generally unsustainable. It is necessary to take measures to promote rational allocation and efficient utilization of water resources for the coordinated development of society, the economy, and the environment in the YRB. (4) The emergy-spatial autocorrelation method is applied to basin/region water sustainability studies for decision makers.

Spatiotemporal impacts of climate change and human activities on water resources and ecological sensitivity in the Mekong subregion in Cambodia

Water resources in the Mekong subregion in Cambodia (MSC) have experienced dramatic changes in past decades, threatening regional ecosystem quality and sustainable development. Thus, it is important to explore the spatiotemporal impacts of climate change and human activities on water resources and ecological sensitivity. This study proposed an effective framework including spatiotemporal analysis of land use/cover change (LUCC) and ecological sensitivity assessment by combining remote sensing (RS) and geographic information system/science (GIS). An optimized feature space and a machine learning classification algorithm were constructed to extract four typical land cover types in the MSC from 1990 to 2020. An ecological sensitivity evaluation system, including four sub-sensitivities calculated by twelve indicators, was then constructed. The results suggest that severe shrinkage of water resources occurred before 2006, decreasing by 21.68%. The correlation between water resources and climate conditions displays a high to low level as human activity becomes involved. A significant spatiotemporal evolutionary pattern of ecological sensitivity was observed under the impact of external interference. Generally, the largest proportion of MSC belongs to the lightly sensitive level, which is mainly concentrated in the lower reaches, with an average of 33.93%. The highly sensitive area with a significant value in ecological protection has a slightly downward trend from 23.72 in 1990 to 22.55% in 2020.

Comparative Economic and Life Cycle Analysis of Future Water Supply Mix Scenarios for Hong Kong - A Water Scarce City

Increasing urbanization and changes in climate have placed increasing stress on urban water supply systems. Policy makers have increasingly adopted alternative water supply sources, such as desalination and water reclamation to meet this challenge, however these technologies may increase the negative environmental impacts of the water supply system. These alternative sources are energy intensive, and more expensive to produce, which raises questions about their sustainability. In this study, a Life Cycle Assessment (LCA) and a economic portfolio choice model were used to determine the impacts of Hong Kong's long term water policy. The results of our study show that the current water policy will increase the carbon emissions of producing 1 m³ of freshwater by 11% to 1.65 kg CO2-Eq due to the addition of desalination. However, a fit-for-purpose water policy approach only increases emission by 4%, to 1.54 kg CO2-Eq, by instead relying on water reclamation to offset freshwater consumption. Impacts from increased energy consumption were mitigated by improved wastewater treatment, which reduced CH₄ emissions. Although, ozone layer impacts increased due to higher NOx and N₂O emissions, highlighting the need to consider emissions from wastewater treatment processes when evaluating water reclamation processes. Impacts to water prices were also minimized when reclaimed water was chosen over desalination, due to its lower unit production cost. By considering both cost and environmental impacts of such system level changes, decision makers can more accurately evaluate different water supply approaches for data-driven policymaking.

Investigation of SARS-CoV-2 RNA contamination in water supply resources of Tabriz metropolitan during a peak of COVID-19 pandemic

It is crucial to have access to clean water resources during the COVID-19 pandemic for hygiene, since virus infection through wastewater leaks in metropolitan areas can be a threat. Accurate monitoring of urban water resources during the pandemic seems to be the only way to confirm safe and infected resources. Here, in this study, the amount of Severe Acute Respiratory Syndrome Coronavirus 2's Ribonucleic Acid (SARS-CoV-2 RNA) in the Tabriz urban water network located in the northwest of Iran was investigated by an extensive sampling of the city's water sources at a severe peak of the COVID-19 pandemic. The sampling process comprised a range of water sources, including wells, qanats, water treatment facilities, dams, and reservoirs. For each sample, a combination of polyethylene glycol (PEG) and sodium chloride (NaCl) was used for concentration and a laboratory RNA-based method was conducted for quantification. Before applying the extraction and quantification procedure to real samples, the proposed concentration method was verified with synthetic serum samples for the first time. After the concentration, RNA extraction was done by the BehPrep extraction column method, and Reverse Transcription Polymerase Chain Reaction (RT-PCR) detection of the virus was done by Covitech COVID-19 RT-PCR kit. In none of the water supply resources, SARS-COV-2 RNA has been detected except in a sample grabbed from a well adjacent to an urban wastewater discharge point downstream. The results of molecular analysis for the positive sample showed that the CT value and concentration of the virus genome were equal to 32.57 and 5720 copies/L, respectively. Quantitative analysis of real samples shows that the city's water network was safe at the time of the study. However, given that the positive sample was exposed to wastewater leakage, periodic sampling from wells and qanats is suggested during the pandemic until it can be proven that the leakage to these water sources is impossible.

Use of Wild Rice (Zizania palustris L.) in Paddy-Scale Bioassays for Assessing Potential Use of Mining-Influenced Water for Irrigation

As surface water resources become more intensely used, and occasionally non-useable, consideration of non-conventional water resources for anthropogenic use has become more prevalent. Potentially critical non-conventional water sources include flooded mine-pit lakes. However, water in these lakes can contain potentially problematic concentrations of contaminants of concern. We evaluated the potential use of elevated sulphate (SO₄) mining-influenced waters with low to non-detect metals concentrations for irrigation of wild rice (Zizania palustris L.; WR), a culturally and economically important species. Two flow-through in-situ paddies were developed adjacent to two mine-pit lakes with differing chemical water characteristics; specifically, Pit A contained ≈350 mg SO4 L^-1 and Pit C contained ≈1350 mg SO4 L^-1. Throughout the course of multiple consecutive growing seasons, no adverse WR responses to these mining-influenced water exposures were observed. Based on data and observations from this study, potential use of mining-influenced waters containing elevated SO₄ as the primary contaminant for appropriate irrigation purposes is supported. However, site-specific conditions and potential environmental risks must be considered prior to use of mining-influenced waters for anthropogenic applications.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10230-022-00908-0.

Evaluating the BFAST method to detect and characterise changing trends in water time series: A case study on the impact of droughts on the Mediterranean climate

Mediterranean climate regions are facing increased aridity conditions and water scarcity, thus needing integrated management of water resources. Detecting and characterising changes in water resources over time is the natural first step towards identifying the drivers of these changes and understanding the mechanism of change. The aim of this study is to evaluate the potential of Breaks For Additive Seasonal and Trend (BFAST) method to identify gradual (trend) and abrupt (step- change) changes in the freshwater resources time series over a long-term period. This research shows an alternative to the Pettitt's test, LOESS (locally estimated scatterplot smoothing) filter, Mann-Kendall trend test among other common methods for change detection in hydrological data, and paves the way for further scientific investigation related to climate variability and its influence on water resources. We used the monthly accumulated stored water in three reservoirs, the monthly groundwater levels of three hydrological settings and a standardized precipitation index to show BFAST performance. BFAST was successfully applied, enabling: (1) assessment of the suitability of past management decisions when tackling drought events; (2) detection of recovery and drawdown periods (duration and magnitude values) of accumulated stored water in reservoirs and groundwater bodies after wet and dry periods; 3) measurement of resilience to drought conditions; (4) establishment of similarities/differences in trends between different reservoirs and groundwater bodies with regard to drought events.

Water reuse in industries: analysis of opportunities in the Paraíba do Sul river basin, a case study in Presidente Vargas Plant, Brazil

In recent years, the demand for clean water has been growing all over the world despite the different threats posed, including increasing pollution, increasing deforestation and climate change. Industrial activity is the second largest consumer of water, so highly industrialized regions are more susceptible to water stress. In this sense, reuse strategies have been progressively discussed and used around the world; however, in Brazil there is still place for many advances, whether due to lack of incentives, cultural issues in society, or poor regulation of the subject. The objective of this work was to carry out a diagnosis of raw water uptake by industries in one Hydrographic Region of the state of Rio de Janeiro and to propose a discussion on the adoption of water reuse practices for non-potable purposes from the use of treated effluents. A survey of the theoretical framework on the subject was carried out, as well as an analysis of sustainability indicators and reports of the companies, including the current licensing processes of large undertakings consuming water resources. With this study, it was possible to obtain the average cost of implementing a water reuse unit for an industry in the state of Rio de Janeiro-Brazil, which, despite still being expensive, has a strong tendency to use due to world water shortages. Finally, it was concluded that the state of Rio de Janeiro has a threat of water scarcity that could be aggravated in the coming years, if measures and investments in supply alternatives are not adopted (water reuse), and improvement in all stages of water management water resources.

Temporal changes in the water quality of urban tropical streams: An approach to daily variation in seasonality

Using water quality indices (WQIs) is one way to monitor watercourses for water quality. Currently, there is a lack of information about their behavior in the diurnal cycle because sampling is often carried out in the morning. Also, few articles focus on assessing the urban impact on the spatial variability of WQIs in tropical first-order streams. Such streams receive many pollutants varying in intensity according to population habits, justifying the possible diurnal variation in water quality in addition to climatic attributes. This paper aims to evaluate the fluctuations in Brazilian WQIs and their parameters (temperature, turbidity, total solids [TS], hydrogen potential, dissolved oxygen [DO], biochemical oxygen demand [BOD], total phosphorus [TP], total nitrogen [TN], and Escherichia coli) between the morning (8 AM), afternoon (2 PM), and night (7 PM) periods for an urban first-order tropical stream. Overall, the lowest DO concentrations and highest values of TS, turbidity, BOD, TP, TN, and E. coli were obtained in the morning, possibly representing population habits: the greatest generation of pollutants occurs overnight and in the early morning because there are clandestine domestic wastewater inputs into the stream, whose hourly periodicity generates a similar periodicity in the WQI of the evaluated stream. Although there was a significant variation in WQI average values between morning (15.50 ± 1.97) and afternoon (20.83 ± 5.42) only during the dry season (p < 0.05), different results were common throughout the day in all months, and the water quality was often classified in distinct categories: very bad, bad, and regular. Our findings present another dimension to be considered when assessing urban water quality, leading to direct benefits to the management and use of urban waters. Integr Environ Assess Manag 2022;18:1260-1271. © 2021 SETAC.

Development, hotspots and trend directions of groundwater salinization research in both coastal and inland areas: a bibliometric and visualization analysis from 1970 to 2021

As a global concern, the issue of groundwater salinization refers to the phenomenon of an increase in the overall chemical content over background levels in the groundwater. It involves a long-term process that could degrade groundwater quality and restrict its availability for drinking, irrigation and industry. For the effective protection and further research of groundwater resources, policy strongly depends on understanding the development, hotspots and trend directions of groundwater salinization research, which involves the degree, sources and processes of global groundwater salinization. However, such a comprehensive and systematic analysis has not been performed, and it is difficult to have a deeper understanding of groundwater salinization. The purpose of this paper is to analyze the knowledge structure, hot topics and trends in the field of groundwater salinization based on 6651 Web of Science (WoS) publications combined with CiteSpace for in-depth bibliometric and visual analysis. The results showed that 292 institutions in 125 countries have published articles in this field from 1970 to 2021. The USA was one of the most prolific contributors, with the largest number of publications and active institutions. Cooperation among authors has become frequent in recent years, and they tend to cooperate in groups. According to the analysis of co-occurrence keywords and co-cited articles, "water resources", "sea level rise" and "variable density flow" were identified as three hot topics. A keyword burst analysis revealed the emerging trends of concerns about global climate change and the sustainable utilization of water resources. In addition, the possible opportunities and challenges were explored that may be faced in groundwater salinization research. The outcomes of this study are significant for future research on groundwater management and pollution control.

"Green" economic development in China: quantile regression evidence from the Yangtze River Economic Belt

As China's economy began transitioning from one focused on high-speed growth to one focusing on high-quality development, sustainable green development has become the main goal pursued by the government. This study empirically measures the marginal impact of per capita GDP, technological innovation level, industrial structure, openness, fiscal decentralization, and urbanization level on per capita wastewater discharge in 11 provinces (cities) along the Yangtze River Economic Belt (YREB) from 2008 to 2018 using a quantile model. The key findings were as follows: (1) factors such as the per capita GDP, industrial structure, foreign direct investment, and urbanization in the YREB significantly increased water resource pollution; (2) the quantile model regression results showed that the relationship between economic growth and ecological pollution followed the so-called environmental Kuznets inverted U-curve. Wastewater discharge per capita was low in areas with low per capita GDP, meaning that the ecological environment in these areas was more fragile and that the environmental pollution costs due to economic growth were therefore relatively much higher in these areas; (3) fiscal decentralization significantly reduced water resource pollution in relatively developed areas although the effects in the relatively developing areas were not significant; and (4) the effects of technological innovation on reducing water resource pollution in the YREB were positive but not very significant. The results also confirmed that traditional patterns of economic growth increased water pollution in the YREB. For this reason, the government needs to urgently improve policies-for example, upgrading economic structures, preventing over-urbanization, speeding up technological innovation, introducing environmentally friendly foreign investment, and providing more rewards to best practitioners of environmental governance-that is conducive to the achievement of green ecological development.

Regional precipitation trends since 1500 CE reconstructed from calcite sublayers of a varved Mediterranean lake record (Central Pyrenees)

The Mediterranean region is expected to be highly impacted by global warming, although the uncertainty of future scenarios, particularly about precipitation patterns remains quite large. To better predict shifts in its current climate system and to test models, more regional climate records are needed spanning longer than the instrumental period. Here we provide a high-resolution reconstruction of autumn precipitation for the Central Pyrenees since 1500 CE based on annual calcite sublayer widths from Montcortès Lake (Central southern Pyrenees) varved sediments. The 500-yr calcite data series was detrended and calibrated with instrumental climate records by applying correlations and cross-correlations to regional precipitation anomalies. Highest relationships were obtained between a composite calcite series and autumn precipitation anomalies for the complete calibration period (1900-2002) and for the two halves of the full period. Applied statistical tests were significant, evidencing that the climatic signal could be reconstructed. The reconstructed precipitation anomalies show interdecadal shifts, and rainfall decrease within the coldest period of the LIA and during the second half of the 20th century, probably associated to current Global Warming. Neither increasing nor decreasing linear trends or periods of extreme precipitation events were identified. Our results are coherent with other palaeohydrological reconstructions for northern Iberian Peninsula. Correlations between the predicted autumn precipitation and the main teleconnections -NAO, ENSO and WEMO- were weak, although a potential relationship with the Atlantic Multidecadal Oscillation (AMO) pattern is suggested. The obtained reconstruction provides the first estimations of regional autumn precipitation shifts in the Central Pyrenees and is one of the few reconstructions that cover annual-to-century scale climate variability of precipitation in the Mediterranean region from the end of the Litte Ice Age (LIA) to the current period of Global Warming.

Source, fate, transport and modelling of selected emerging contaminants in the aquatic environment: Current status and future perspectives

The occurrence of emerging contaminants (ECs), such as pharmaceuticals and personal care products (PPCPs), perfluoroalkyl and polyfluoroalkyl substances (PFASs) and endocrine-disrupting chemicals (EDCs) in aquatic environments represent a major threat to water resources due to their potential risks to the ecosystem and humans even at trace levels. Mathematical modelling can be a useful tool as a comprehensive approach to study their fate and transport in natural waters. However, modelling studies of the occurrence, fate and transport of ECs in aquatic environments have generally received far less attention than the more widespread field and laboratory studies. In this study, we reviewed the current status of modelling ECs based on selected representative ECs, including their sources, fate and various mechanisms as well as their interactions with the surrounding environments in aquatic ecosystems, and explore future development and perspectives in this area. Most importantly, the principles, mathematical derivations, ongoing development and applications of various ECs models in different geographical regions are critically reviewed and discussed. The recommendations for improving data quality, monitoring planning, model development and applications were also suggested. The outcomes of this review can lay down a future framework in developing a comprehensive ECs modelling approach to help researchers and policymakers effectively manage water resources impacted by rising levels of ECs.

Ecotoxicological aspects related to the occurrence of emerging contaminants in the Dinaric karst aquifer of Jadro and Žrnovnica springs

Karst aquifers are globally important source of drinking water and harbor specific ecosystems that are vulnerable to anthropogenic contamination. This paper provides insights into the occurrence and ecotoxicological characterization of 21 emerging contaminants (ECs) detected in the karst catchment of Jadro and Žrnovnica springs (Dinarides, Croatia). Karst springs used for water supply, surface water, and groundwater were sampled during seven campaigns. The ECs concentration levels ranged from 0.3 ng/L (tramadol in Jadro spring) to 372 ng/L (1H-benzotriazole in Cetina River). DEET was the most frequently detected ECs with an average concentration of around 50 ng/L in both surface water and groundwater. To prioritise detected ECs, their persistence (P), bioaccumulation (B), mobility (M) and toxicity (T) were assessed based on in silico strategy for PBT assessment and recently developed REACH PMT guidelines. PBT scores ranging below the threshold of 0.5, indicated non-PBT compounds of expected low concern. However, only 4 out of 21 detected ECs were not assessed as PMT/vPvM. Concerningly, 20 ECs were categorised as very mobile. Karst springs exhibited larger proportions of ECs meeting PMT/vPvM criteria than surface water. To characterise the contamination extent and estimate the incidence of adverse effects of detected ECs, a preliminary environmental risk assessment (ERA) was conducted. Most ECs posed no environmental risk with RQ values predominantly below 0.01. The total risk quotient RQ_site accentuated Cetina River as having the highest risk compared to other sampling sites. This is the first study on ECs in Croatian karst, contributing to a growing need to understand the impacts of emerging contaminants in karst aquifers, which are still largely unexplored.

A review on magnetic sensors for monitoring of hazardous pollutants in water resources

Water resources have long been of interest to humans and have become a serious issue in all aspects of human life. The disposal of hazardous pollutants in water resources is one of the biggest global concerns and poses many risks to human health and aquatic life. Therefore, the control of hazardous pollutants in water resources plays an important role, when it comes to evaluating water quality. Due to low toxicity, good electrical conductivity, facile functionalization, and easy preparation, magnetic materials have become a good alternative in recent years to control hazardous pollutants in water resources. In the present study, the idea of using magnetic sensors in controlling and monitoring of pharmaceuticals, pesticides, heavy metals, and organic pollutants have been reviewed. The water pollutants in drinking water, groundwater, surface water, and seawater have been discussed. The toxicology of water hazardous pollutants has also been reviewed. Then, the magnetic materials were discussed as sensors for controlling and monitoring pollutants. Finally, future remarks and perspectives on magnetic nanosensors for controlling hazardous pollutants in water resources and environmental applications were explained.