Long-term sustainability of the water-agriculture-energy nexus in Brazil's MATOPIBA region: A case study using system dynamics.

The global demand for agricultural commodities has driven extensive land conversion to agriculture in Brazil, especially in the MATOPIBA region. This area encompasses the Rio Grande Basin, a major tributary of the São Francisco Basin that is known for expanding intensive irrigated agriculture and hydropower generation. However, recent data reveal declining precipitation and aquifer recharge, potentially exacerbating ongoing water and land conflicts. This study investigates the long-term sustainability of agricultural expansion amid the worsening water scarcity using a system dynamics model. Findings suggest that rising costs and decreasing profits due to irrigation water shortages may hinder the expansion of irrigated land. By 2040, the irrigation demand may remain partly unmet, while downstream flow and baseflow could decrease. Additionally, agricultural expansion will significantly raise energy demand, posing a developmental challenge. We suggest that ensuring the sustainability of the Rio Grande Basin depends on improved water management and exploring alternative energy sources to address existing constraints.

Excluding livestock access to farm dams reduces methane emissions and boosts water quality.

Farm dams, also known as 'agricultural ponds', are ubiquitous features of agricultural landscapes globally. Those accessed by livestock have high methane (CH4) emissions per unit area relative to other freshwater systems. Fencing dams and installing water troughs to prevent livestock from entering the dams are promising strategies to improve water quality and substantially reduce their carbon footprints. However, previous studies only measured the effects of fencing on methane diffusive emissions without considering ebullitive fluxes (i.e., methane bubbles), which is often the dominant emission pathway in smaller water bodies. Also, data is lacking on how the benefits of fencing farm dams vary across seasons. Using Australia as a test case, this study investigates the benefit of fencing off farm dams by monitoring total CH4 (diffusion + ebullition) and carbon dioxide (CO2) in summer and winter. Fenced dams had 72 % lower CH4 emissions in summer and 92 % lower in winter than unfenced dams. Similarly, CO2-equivalent (CO2 + CH4) fluxes were lower in fenced dams by 59 % in summer and 73 % in winter. Fenced dams had higher water quality, with 51 % less total dissolved nitrogen, 57 % less phosphorous, and 23-49 % more dissolved oxygen. Average daily air temperature was a key predictor of CH4 emissions from farm dams, underscoring the importance of considering temporal dynamics for estimating yearly farm dam emissions. We confirmed that excluding livestock from entering farm dams using fences significantly mitigates CH4 emissions and enhances water quality, and these benefits are maintained seasonally.

Ways to mitigate greenhouse gas production from rice cultivation.

Rice cultivation boasts a rich historical legacy, serving as the primary sustenance for over 50% of the global population. However, the cultivation process gives rise to the emission of methane (CH4) and nitrous oxide (N2O), two potent greenhouse gases. Notably, the global warming potential (GWP) of CH4 and N2O surpasses CO2 by 27-30 times and 273 times over 100 years, respectively. Addressing this environmental challenge necessitates exploring technical approaches and management strategies to curb gas emissions while sustaining rice yields. Several critical factors have been identified and analyzed for their potential to mitigate greenhouse gas production during rice cultivation. These include water management, fertilizer management, biochar application, cultivar selection, straw management, modified planting methods, and integration of new energy machinery. A comprehensive understanding and implementation of these methods can contribute significantly to achieving a dual objective: reducing emissions and maintaining optimal rice yields. Looking ahead, a synergistic integration of these diverse methods and management approaches holds promise for more effective results. Furthermore, the intricate water networks associated with rice cultivation should be carefully considered in the overall strategy. By adopting a holistic approach that addresses both emission reduction and sustainable water usage, the future of rice cultivation can be shaped to align with environmental stewardship and food security.

Long-term responses of internal environment dynamics in a freshwater lake to variations in external nutrient inputs: A model simulation approach.

Lake restoration usually focuses on reducing external nutrient sources. However, when sediments contain nutrients accumulated over multiple years, internal nutrient release can delay restoration progress. In lake restoration and management, it is important to understand the dynamic relationship between nutrient concentrations in a lake and internal and external nutrient sources. In this study, we quantified external nutrient inputs through measurements and compared them with internal sediment release from simulation using the PCLake+ model. Additionally, we evaluated alterations in the internal nutrient release, lake nutrient concentrations, and algae biomass (chlorophyll-a) within the lake following varying degrees of reduction in external nutrient loads. The results demonstrate that the PCLake+ effectively simulated the lake's nutrient concentration and algae biomass. Based on the PCLake+ estimates, internal nutrient loads accounted for 51 % of the total nitrogen (N) and 80 % of the total phosphorus (P) loadings in Lake Erhai in 2019. In 2020, the total contributions were 43 % for TN and 72 % for TP. We simulated four scenarios where external nutrient inputs were reduced to 25 %, 50 %, 75 %, and 99.99 % of their original levels. The 40-year simulation showed that the lake's ecological system initially exhibited a fast internal response but reach equilibrium after eight years. P concentrations took longer to reach equilibrium compared to N concentrations, probably due to the stronger binding characteristics of P. To meet the water quality target in the future, it is necessary to reduce external N and P inputs into Lake Erhai by at least 23 % and 15 %, respectively, under current conditions. Although reducing external nutrient loads can indirectly lower internal nutrient loads, water management should address both external and internal loads simultaneously, as internal release cannot be effectively reduced by external reductions alone. Additionally, the lake's internal release may continue for several years, even with reductions in external inputs.

Green gold or carbon beast? Assessing the environmental implications of cryptocurrency trading on clean water management and carbon emission SDGs.

This study addresses the ongoing debate concerning the environmental implications of cryptocurrencies. Specifically, it investigates the impact of Bitcoin trading volume on water and sanitation (Sustainable Development Goal (SDG) 6) and climate action (SDG 13). The research employs Ordinary Least Squares (OLS) panel data analysis to examine these relationships using a sample of 32 countries with available Bitcoin trading volume data from 2013 to 2020. The findings indicate that Bitcoin trading significantly and positively impacts progress towards SDG 6, suggesting potential benefits for water and sanitation initiatives. However, the study reveals a significant negative impact of higher Bitcoin trading volume on increased carbon emissions, underscoring the environmental costs associated with cryptocurrency activities. Similar impacts are observed for gold reserves, as their mining necessitates substantial energy consumption. These results highlight the need to regulate cryptocurrency trading and promote voluntary sustainable practices, particularly given the disparities between developed and emerging markets based on their governance frameworks. Additionally, the study considers the disparities between countries based on technology exports and economic policy uncertainty as influential determinants. The study's results emphasize the importance of proactive measures to ensure the responsible and sustainable use of cryptocurrencies. While cryptocurrencies offer significant economic returns, their early adoption stage necessitates further investigation into environmentally friendly approaches. Potential strategies include directing financial returns from cryptocurrencies towards alternative energy projects and supporting other environmental SDGs, thereby fostering a positive impact on the overall ecosystem. The study's implications extend to policymakers, regulators, and stakeholders, advocating for comprehensive and collaborative efforts to integrate sustainability into the rapidly evolving cryptocurrency market. This integration is crucial to ensure that the economic benefits of cryptocurrencies do not come at the cost of our environment.

Estimation of irrigated crop artificial irrigation evapotranspiration in China.

Agriculture water use accounts for 70% of the total water withdrawal worldwide. The evapotranspiration during crop growth is one of the important hydrological processes in the agricultural water cycle. This study proposed the concept of artificial irrigation evapotranspiration of irrigated crops to describe that the evapotranspiration caused by irrigation water use. Irrigated crops rely on two kinds of water sources: precipitation and irrigation water. With the construction of irrigation schemes, the artificial irrigation evapotranspiration plays an increasingly important role in the dualistic water cycle system of irrigated cropland. To reveal the amount of artificial irrigation evapotranspiration of 17 categories of irrigated crops in China, this study proposed a new quantitative model system which was established based on traditional evapotranspiration models and soil water balance models. Based on the new model system, we calculated the annual artificial irrigation evapotranspiration of irrigated crops for the period 2013 to 2017 in China. The results showed that the proportion of artificial irrigation evapotranspiration to the total evapotranspiration of irrigated crops was 41.3%, whose value was 228.1 km3 a-1. The artificial irrigation evapotranspiration in different agricultural water management regions were 90.0 km3 a-1 in the northeast region, 86.0 km3 a-1 in the southeast region, and relatively low 52.2 km3a-1 in the west region. The results of this study can provide methods for water management and policy-making in agricultural irrigated areas, and it can also provide a preliminary understanding of the influence of human activities on the dualistic water cycle in cropland.

Uncovering urban water consumption patterns through time series clustering and entropy analysis.

Sustainable urban water management is crucial for meeting the growing demands of urban populations. This study presents a novel approach that combines time series clustering, seasonal analysis, and entropy analysis to uncover residential water consumption patterns and their drivers. Using a three-year dataset from the SmartH2o project, encompassing 374 households, we identify nine distinct water consumption patterns through time series clustering, leveraging Dynamic Time Warping (DTW) as the optimal similarity measure. Multiple linear regression reveals key household characteristics influencing water usage behaviors, such as the number of bathrooms and appliance efficiency ratings. Seasonal analysis uncovers temporal dynamics, highlighting shifts towards lower consumption during summer months and increased variability in transitional seasons. Entropy analysis quantifies the diversity and complexity of water consumption at both cluster and household levels, informing targeted interventions. This comprehensive, granular approach enables the development of personalized water conservation strategies and policies, empowering water utilities to optimize resource management and contribute to sustainable urban water practices.

A bibliometric analysis-based literature review of the relationship between sustainable water management and green innovations in the agricultural sector.

Global water demand has grown intensively over the last three decades, and the predictions suggest this trend will continue. Sustainable Water Management (SWM) defines water-based principles and action frameworks interconnecting societal, economic, and environmental aspects to establish and maintain good practices serving long-term objectives related to water resources. Water scarcity, deterioration of water quality, less effective water technologies, hydrological changes caused by climate change, and increased water demand require the thorough revision of conventional approaches, new methods, and new policy measures. The research methodology in this paper includes a comprehensive review and bibliometric analysis of relevant literature on water management and sustainable development, including empirical studies, theoretical frameworks, and policy documents. The study explores the conceptual context of SWM, reveals the barriers hindering its core progress, evaluates the impact of green innovations on the development of novel operations, and gets an insight into the current policy and regulatory framework for SWM. Besides giving a review of the current practices and perspectives in SWM, the results of this study contribute to a deeper understanding of the complex relationship between sustainable water management and green innovations in the agricultural sector and provide possible directions toward adopting effective strategies and policies to promote a more intense permeation of the SWM approach.

Controlling exposure to As and Cd from rice via irrigation management.

Irrigation management controls biogeochemical cycles in rice production. Under flooded paddy conditions, arsenic becomes plant-available as iron-reducing conditions ensue, while oxic conditions lead to increased plant availability of Cd in acidic soils. Because Cd enters rice through Mn transporters, we hypothesized that irrigation resulting in intermediate redox could simultaneously limit both As and Cd in rice grain due to As retention in soil and Mn competition for Cd uptake. In a 2 year field study, we used 6 irrigation managements that varied in extent and frequency of inundation, and we observed strong effects of irrigation management on porewater chemistry, soil redox potentials, plant As and Cd concentrations, plant nutrient concentrations, and methane emissions. Plant As decreased with drier irrigation management, but in the grain this effect was stronger for organic As than for inorganic As. Grain organic As, but not inorganic As, was strongly and positively correlated with cumulative methane emissions. Conversely, plant Cd increased under more aerobic irrigation management and grain Cd was negatively correlated with porewater Mn. A hazard index approach showed that in the tested soil with low levels of As and Cd (5.4 and 0.072 mg/kg, respectively), irrigation management could not simultaneously decrease grain As and Cd. Many soil properties, such as reducible As, available Cd, soil pH, available S, and soil organic matter should be considered when attempting to optimize irrigation management when the goal is decreasing the risk of As and Cd in rice grain.

A Scalable and Robust Water Management Strategy for PEMFCs: Operando Electrothermal Mapping and Neutron Imaging Study.

Effective water management is crucial for the optimal operation of low-temperature polymer electrolyte membrane fuel cells (PEMFCs). Excessive liquid water production can cause flooding in the gas diffusion electrodes and flow channels, limiting mass transfer and reducing PEMFC performance. To tackle this issue, a nature-inspired chemical engineering (NICE) approach has been adopted that takes cues from the integument structure of desert-dwelling lizards for passive water transport. By incorporating engraved, capillary microchannels into conventional flow fields, PEMFC performance improves significantly, including a 15% increase in maximum power density for a 25 cm2 cell and 13% for a 100 cm2 cell. Electro-thermal maps of the lizard-inspired flow field demonstrate a more uniform spatial distribution of current density and temperature than the conventional design. Neutron radiography provides evidence that capillary microchannels in the lizard-inspired flow field facilitate the efficient transport and removal of generated liquid water, thereby preventing blockages in the reactant channels. These findings present a universally applicable and highly efficient water management strategy for PEMFCs, with the potential for widespread practical implementation for other electrochemical devices.

Quantitative Microbial Risk Assessment of Antibiotic-Resistant E. coli, Legionella pneumophila, and Mycobacteria in Nonpotable Wastewater Reuse Applications.

Antibiotic-resistant bacteria (ARB) have become a major threat to public health and modern medicine. A simple death kinetics-based dose-response model (SD-DRM) was incorporated into a quantitative microbial risk assessment (QMRA) to assess the risks of exposure to reclaimed wastewater harboring antibiotic-resistant E. coli, Legionella pneumophila, and Mycobacterium avium for multiple exposure scenarios. The fractions of ARB and trace antibiotics present in the body were incorporated to demonstrate their impact on infection risks. Both ARB and antibiotic susceptible bacteria, ASB, are assumed to have the same dose-response in the absence of antibiotics but behave differently in the presence of residual antibiotics in the body. Annual risk of L. pneumophila infection exceeded the EPA 10-4 pppy (per person per year) benchmark at concentrations in reclaimed water greater than 103-104 CFU/L, depending on parameter variation. Enteropathogenic E. coli infection risks meet the EPA annual benchmark at concentrations around 105-106 total E. coli. The results illustrated that an increase in residual antibiotics from 0 to 40% of the minimum inhibitory concentration (MIC) reduced the risk by about 1 order of magnitude for E. coli but was more likely to result in an untreatable infection.

Adequate water supply enhances seedling growth and metabolism in Festuca kryloviana: insights from physiological and transcriptomic analys.

BACKGROUND: Festuca kryloviana is a significant native grass species in the Qinghai Lake region, and its low emergence rate is a primary factor limiting the successful establishment of cultivated grasslands. The region's arid and low-rainfall climate characteristics result in reduced soil moisture content at the surface. Despite the recognized impact of water availability on plant growth, the specific role of moisture in seedling development remains not fully elucidated. This study aims to investigate the germination rate and seedling growth velocity of F. kryloviana seeds under varying moisture conditions, and to integrate physiological and transcriptomic analyses of seedlings under these conditions to reveal the mechanisms by which water influences seedling development. RESULTS: The emergence rate of F. kryloviana seedlings exhibited an initial increase followed by a decrease with increasing moisture content. The highest emergence rate, reaching 75%, was observed under 20% soil moisture conditions. By the eighth day of the experiment, the lengths of the plumules and radicles under the optimal emergence rate (full water, FW) were 21.82% and 10.87% longer, respectively, than those under closely matching the soil moisture content during the background survey (stress water, SW). The differential development of seedlings under varying moisture regimes is attributed to sugar metabolism within the seeds and the accumulation of abscisic acid (ABA). At FW conditions, enhanced sugar metabolism, which generates more energy for seedling development, is facilitated by higher activities of α-amylase, sucrose synthase, and trehalose-6-phosphate synthase compared to SW conditions. This is reflected at the transcriptomic level with upregulated expression of the α-amylase (AMY2) gene and trehalose-6-phosphate synthase (TPS6), while genes associated with ABA signaling and transduction are downregulated. Additionally, under FW conditions, the expression of genes related to the chloroplast thylakoid photosystems, such as photosystem II (PSII) and photosystem I (PSI), is upregulated, enhancing the seedlings' light-capturing ability and photosynthetic efficiency, thereby improving their autotrophic capacity. Furthermore, FW treatment enhances the expression of the non-enzymatic antioxidant system, promoting metabolism within the seeds. In contrast, SW treatment increases the activity of the enzymatic antioxidant system, including peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT), to cope with water stress. CONCLUSIONS: Our experiment systematically evaluated the impact of moisture conditions on the growth and development of F. kryloviana seedlings. Physiological and transcriptomic data collectively indicate that adequate water (20%) supply enhances seedling growth and development by reducing ABA levels and increasing α-amylase activity within seeds, thereby boosting sugar metabolism and promoting the growth of seedling, which in turn leads to an improved emergence rate. Considering water management in future cultivation practices may be a crucial strategy for enhancing the successful establishment of F. kryloviana in grassland ecosystems.

Factors Associated with Legionella Detection in the Water Systems of National Lodging Organization Facilities with Water Management Programs in the United States.

A better understanding of risk factors and the predictive capability of water management program (WMP) data in detecting Legionella are needed to inform the efforts aimed at reducing Legionella growth and preventing outbreaks of Legionnaires' disease. Using WMPs and Legionella testing data from a national lodging organization in the United States, we aimed to (1) identify factors associated with Legionella detection and (2) assess the ability of WMP disinfectant and temperature metrics to predict Legionella detection. We conducted a logistic regression analysis to identify WMP metrics associated with Legionella serogroup 1 (SG1) detection. We also estimated the predictive values for each of the WMP metrics and SG1 detection. Of 5435 testing observations from 2018 to 2020, 411 (7.6%) had SG1 detection, and 1606 (29.5%) had either SG1 or non-SG1 detection. We found failures in commonly collected WMP metrics, particularly at the primary test point for total disinfectant levels in hot water, to be associated with SG1 detection. These findings highlight that establishing and regularly monitoring water quality parameters for WMPs may be important for preventing Legionella growth and subsequent disease. However, while unsuitable water quality parameter results are associated with Legionella detection, this study found that they had poor predictive value, due in part to the low prevalence of SG1 detection in this dataset. These findings suggest that Legionella testing provides critical information to validate if a WMP is working, which cannot be obtained through water quality parameter measurements alone.

Spatial heterogeneity of soil moisture caused by drainage and its effects on cadmium variation in rice grain within individual fields.

Paddy drainage is the critical period for rice grain to accumulate cadmium (Cd), however, its roles on spatial heterogeneity of grain Cd within individual fields are still unknown. Herein, field plot experiments were conducted to study the spatial variations of rice Cd under continuous and intermittent (drainage at the tillering or grain-filling or both stages) flooding conditions. The spatial heterogeneity of soil moisture and key factors involved in Cd mobilization during drainages were further investigated to explain grain Cd variation. Rice grain Cd levels under continuous flooding ranged from 0.16 to 0.22 mg kg-1 among nine sampling sites within an individual field. Tillering drainage slightly increased grain Cd levels (0.19-0.31 mg kg-1) with little change in spatial variation. However, grain-filling drainage greatly increased grain Cd range to 0.33-0.95 mg kg-1, with a huge spatial variation observed among replicated sites. During two drainage periods, soil moisture decreased variously in different monitoring sites; greater variation (mean values ranged from 0.14 to 0.27 m3 m-3) was observed during grain-filling drainage. Accordingly, 2.9-3.3-fold variation in soil Eh and 0.55-0.67-unit variation in soil pH were observed among those sites. In the soil with low moisture, ferrous fractions such as ferrous sulfide (FeS) were prone to be oxidized to ferric fractions; meanwhile, the followed generation of hydroxyl radicals involved in Cd remobilization was enhanced. Consequently, soil dissolved Cd changed from 2.97 to 8.92 µg L-1 among different sampling sites during grain-filling drainage; thus, large variation was observed in grain Cd levels. The findings suggest that grain-filling drainage is the main process controlling spatial variation of grain Cd, which should be paid more attention in paddy Cd evaluation.

Nitrite-dependent microbial utilization for simultaneous removal of sulfide and methane in sewers.

Chemicals are commonly dosed in sewer systems to reduce the emission of hydrogen sulfide (H2S) and methane (CH4), incurring high costs and environmental concerns. Nitrite dosing is a promising approach as nitrite can be produced from urine wastewater, which is a feasible integrated water management strategy. However, nitrite dosing usually requires strict conditions, e.g., relatively high nitrite concentration (e.g., ∼200 mg N/L) and acidic environment, to inhibit microorganisms. In contrast to "microbial inhibition", this study proposes "microbial utilization" concept, i.e., utilizing nitrite as a substrate for H2S and CH4 consumption in sewer. In a laboratory-scale sewer reactor, nitrite at a relatively low concentrations of 25-48 mg N/L was continuously dosed. Two nitrite-dependent microbial utilization processes, i.e., nitrite-dependent anaerobic methane oxidation (n-DAMO) and microbial sulfide oxidation, successfully occurred in conjunction with nitrite reduction. The occurrence of both processes achieved a 58 % reduction in dissolved methane and over 90 % sulfide removal in the sewer reactor, with microbial activities measured as 15.6 mg CH4/(L·h) and 29.4 mg S/(L·h), respectively. High copy numbers of n-DAMO bacteria and sulfide-oxidizing bacteria (SOB) were detected in both sewer biofilms and sediments. Mechanism analysis confirmed that the dosed nitrite at a relatively low level did not cause the inhibition of sulfidogenic process due to the downward migration of activity zones in sewer sediments. Therefore, the proposed "microbial utilization" concept offers a new alternative for simultaneous removal of sulfide and methane in sewers.

Spatial distribution of physicochemical parameters and drinking and irrigation water quality indices in the Jhelum River, Pakistan.

Sustainable management of river systems is a serious concern, requiring vigilant monitoring of water contamination levels that could potentially threaten the ecological community. This study focused on the evaluation of water quality in the Jhelum River (JR), Azad Jammu and Kashmir, and northern Punjab, Pakistan. To achieve this, 60 water samples were collected from various points within the JR Basin (JRB) and subjected to a comprehensive analysis of their physicochemical parameters. The study findings indicated that the concentrations of physicochemical parameters in the JRB water remained within safety thresholds for both drinking and irrigation water, as established by the World Health Organization and Pakistan Environmental Protection Agency. These physicochemical parameters refer to various chemical and physical characteristics of the water that can have implications for both human health (drinking water) and agricultural practices (irrigation water). The spatial variations throughout the river course distinguished between the upstream, midstream, and downstream sections. Specifically, the downstream section exhibited significantly higher values for physicochemical parameters and a broader range, highlighting a substantial decline in its quality. Significant disparities in mean values and ranges were evident, particularly in the case of nitrates and total dissolved solids, when the downstream section was compared with its upstream and midstream counterparts. These variations indicated a deteriorating downstream water quality profile, which is likely attributable to a combination of geological and anthropogenic influences. Despite the observed deterioration in the downstream water quality, this study underscores that the JRB within the upper Indus Basin remains safe and suitable for domestic and agricultural purposes. The JRB was evaluated for various irrigation water quality indices. The principal component analysis conducted in this study revealed distinct covariance patterns among water quality variables, with the first five components explaining approximately 79% of the total variance. Recommending the continued utilization of the JRB for irrigation, we advocate for the preservation and enhancement of water quality in the downstream regions.

Spatiotemporal dynamics, bioaccumulation, and critical influencing factors of antibiotics in tilapia aquaculture: A study on source identification and environmental fate within typical farming systems.

This study investigated the spatiotemporal dynamics, bioaccumulation, and critical influencing factors of antibiotics in tilapia aquaculture, focusing on source identification and environmental fate within typical farming systems. The results revealed a progressive increase in antibiotic concentrations in pond water and sediments over the cultivation period, with suspended solids and chemical oxygen demand identified as significant environmental factors influencing the distribution and dissemination of antibiotics. The aquaculture water source was the primary contributor of antibiotics in the farming system. Furthermore, the bioaccumulation factor (BAF) calculations indicated varying degrees of antibiotic enrichment in tilapia tissues, with sulfadimethoxine exhibiting the highest BAFs. Correlation analyses, redundancy analysis, and multivariate linear regression analysis provided insights into the relationship between environmental factors and antibiotics, identifying key antibiotics and influencing factors. The study highlighted the importance of managing and treating water sources to reduce the inflow of antibiotics into aquaculture systems and emphasized the need for non-antibiotic aquaculture practices to minimize the impact on the environment and public health. In conclusion, this research contributes valuable information for the development of effective management strategies and policies aimed at curbing antibiotic pollution in aquaculture environments, ensuring the sustainability of the aquaculture industry, and protecting ecosystem and consumer health.

Different paradigms for dealing with urban water issues, using new technological and governance solutions, learning from Chinese, Indian and European experiences.

Three paradigms to deal with urban water issues are compared. The analysis focuses on their definition and objectives, the role of different stakeholders, the issues they deal with, and the possible solutions suggested. The paradigms differ in scope (from the narrow focus of the sponge city paradigm to the broad goals of eco-city paradigm) and in terms of the governance structures used to coordinate different stakeholders. The smart and sponge paradigms mainly use existing government structures. In the eco-cities approach, the citizens want to be involved through newly created governance structures. Smart and eco-city initiatives emphasize the involvement of stakeholders, while in the sponge cities approach, the initiative is often taken by the local government. Finally, in terms of expected solutions, the paradigms want to create eco- or healthy cities or improve water management to create a more healthy urban environment. After identifying the issue, alternative water-related technologies are available, like generating energy from wastewater or separating grey and brown water. Cities require different governance structures, and managing information flows in an integrated way to solve water and other issues. The experience in Europe, China, and India may help other cities choose the right paradigm.

Two-year evaluation of Legionella in an aging residential building: Assessment of multiple potable water remediation approaches.

Legionella is an opportunistic waterborne pathogen that is difficult to eradicate in colonized drinking water pipes. Legionella control is further challenged by aging water infrastructure and lack of evidence-based guidance for building treatment. This study assessed multiple premise water remediation approaches designed to reduce Legionella pneumophila within a residential building located in an aging, urban drinking water system over a two-year period. Samples (n = 745) were collected from hot and cold-water lines and quantified via most probable number culture. Building-level treatment approaches included three single heat shocks, three single chemical shocks, and continuous low-level chemical disinfection in the potable water system. The building was highly colonized with L. pneumophila with 71 % L. pneumophila positivity. Single heat shocks had a statistically significant L. pneumophila reduction one day post treatment but no significant L. pneumophila reduction at one week, two weeks, and four weeks post treatment. The first two chemical shocks resulted in statistically significant L. pneumophila reduction at two days and four weeks post treatment, but there was a significant L. pneumophila increase at four weeks following the third chemical shock. Continuous low-level chemical disinfection resulted in statistically significant L. pneumophila reduction at ten weeks post treatment implementation. This demonstrates that in a building highly colonized with L. pneumophila, sustained remediation is best achieved using continuous low-level chemical treatment.

Interpreting optimised data-driven solution with explainable artificial intelligence (XAI) for water quality assessment for better decision-making in pollution management.

In Saudi Arabia, water pollution and drinking water scarcity pose a major challenge and jeopardise the achievement of sustainable development goals. The urgent need for rapid and accurate monitoring and assessment of water quality requires sophisticated, data-driven solutions for better decision-making in water management. This study aims to develop optimised data-driven models for comprehensive water quality assessment to enable informed decisions that are critical for sustainable water resources management. We used an entropy-weighted arithmetic technique to calculate the Water Quality Index (WQI), which integrates the World Health Organization (WHO) standards for various water quality parameters. Our methodology incorporated advanced machine learning (ML) models, including decision trees, random forests (RF) and correlation analyses to select features essential for identifying critical water quality parameters. We developed and optimised data-driven models such as gradient boosting machines (GBM), deep neural networks (DNN) and RF within the H2O API framework to ensure efficient data processing and handling. Interpretation of these models was achieved through a three-pronged explainable artificial intelligence (XAI) approach: model diagnosis with residual analysis, model parts with permutation-based feature importance and model profiling with partial dependence plots (PDP), accumulated local effects (ALE) plots and individual conditional expectation (ICE) plots. The quantitative results revealed insightful findings: fluoride and residual chlorine had the highest and lowest entropy weights, respectively, indicating their differential effects on water quality. Over 35% of the water samples were categorised as 'unsuitable' for consumption, highlighting the urgency of taking action to improve water quality. Amongst the optimised models, the Random Forest (model 79) and the Deep Neural Network (model 81) proved to be the most effective and showed robust predictive abilities with R2 values of 0.96 and 0.97 respectively for testing dataset. Model profiling as XAI highlighted the significant influence of key parameters such as nitrate, total hardness and pH on WQI predictions. These findings enable targeted water quality improvement measures that are in line with sustainable water management goals. Therefore, our study demonstrates the potential of advanced, data-driven methods to revolutionise water quality assessment in Saudi Arabia. By providing a more nuanced understanding of water quality dynamics and enabling effective decision-making, these models contribute significantly to the sustainable management of valuable water resources.