Environmental Water Management Research Articles

Uncertainty analysis and economic value prediction of water environmental capacity based on Copula and Bayesian model: A case study of Yitong River, China

Water environmental capacity (WEC) is an indicator of environment management. The uncertainty analysis of WEC is more closely aligned with the actual conditions of the water body. It is crucial for accurately formulating pollution total emissions control schemes. However, the current WEC uncertainty analysis method ignored the connection between water quality and discharge, and required a large amount of monitoring data. This study analyzed the uncertainty of the WEC and predicted its economic value based on Copula and Bayesian model for the Yitong River in China. The Copula model was employed to calculate joint probabilities of water quality and discharge. And the posterior distribution of WEC with limited data was obtained by the Bayesian formula. The results showed that the WEC-COD in the Yitong River was 9009.67 t/a, while NH3-N had no residual WEC. Wanjinta Highway Bridge-Kaoshan Town reach had the most serious pollution. In order to make it have WEC, the reduction of COD and NH3-N was 5330.47 t and 3017.87 t. The economic value of WEC-COD was 5.97 107 CNY, and the treatment cost was 2.04 108 CNY to make NH3-N have residual WEC. The economic value distribution of WEC was extremely uneven, which could be utilized by adjusting the sewage outlet. In addition, since the treated water was discharged into the Sihua Bridge-Wanjinta Highway Bridge reach, the WEC-COD and the economic value were 19488.51 t/a and 8.24 107 CNY. Increasing the flow of rivers could effectively improve WEC and economic value. This study provided an evaluation tool for guiding river water environment management.

Phytoplankton community structure and its driving factors in autumn in Jinan, China

Human activities often cause multiple impacts on aquatic ecosystems, including changes in water quality, biodiversity, community composition, etc. However, the effects of human activities on phytoplankton in highly urbanized aquatic ecosystems are poorly understood. In this study, we conducted an in-depth analysis of phytoplankton communities and water environment factors in the Jinan City watershed in September 2020, focusing on 18 water environment factors. We found that nitrate nitrogen, total nitrogen, total phosphorus, and phosphate were the key drivers affecting the quality of the water environment in the area through principal component analysis (PCA). The results of the phytoplankton investigation showed that 143 species of phytoplankton from 8 phyla were found in the Jinan watershed, dominated by Chlorophyta, Bacillariophyta, and Cyanobacteria, of which cyanobacteria were dominant in density, while cryptophytes were the first in biomass. The assessment of phytoplankton diversity showed that the Jinan City watershed had high biodiversity, with an average Shannon Wiener Diversity Index of 3.12. RDA analysis revealed that permanganate index, total phosphorus, bathymetry, chlorophyll a, pH, and phosphate were significantly correlated with the structure of the phytoplankton community. Comprehensive trophic index (TLI) analysis showed that most of the watersheds in Jinan were moderately eutrophic. These results provide essential information for understanding the water quality status and aquatic biodiversity in Jinan City watersheds and provide a scientific basis for water environment management and protection.

An innovative method based on Gaussian cloud distribution and sample information richness for eutrophication assessment of Yangtze's lakes and reservoirs under uncertainty.

The precise assessment of a water body's eutrophication status is essential for making informed decisions in water environment management. However, conventional approaches frequently fail to consider the randomness, fuzziness, and inherent hidden information of water quality indicators. These would result in an unreliable assessment. An enhanced method was proposed for the eutrophication assessment under uncertainty in this study. The multi-dimension gaussian cloud distribution was introduced to capture the randomness and fuzziness. The Shannon entropy based on various sample size and trophic levels was proposed to maximize valuable information hidden in the datasets. Twenty-seven significant lakes and reservoirs located in the Yangtze River Basin were selected to demonstrate the proposed method. The sensitivity and consistency were used to evaluate the accuracy of the proposed method. Results indicate that the proposed method has the capability to effectively assess the eutrophication status of lakes and reservoirs under uncertainty and that it has a better sensitivity since it can identify more than 33-50% trophic levels compared to the traditional methods. Further scenario experiments analysis revealed that the sample information richness, i.e., sample size and the number of trophic levels is of great significance to the accuracy/robustness of the method. Moreover, a sample size of 60 can offer the most favorable balance between accuracy/robustness and the monitoring expenses. These findings are crucial to optimizing the eutrophication assessment.

Advancing Environmental Flows Science: Hindcasting and Forecasting Flow–Ecology Relationships

To advance current environmental flow standards in Texas, we developed field, laboratory, and analytical methods to model recruitment dynamics of indicator species selected to cover a range of population responses to flow variation. Preliminary investigations were conducted in three central Texas river basins to evaluate the feasibility of these methods for formulating and evaluating the state's environmental flow standards. Age analysis allowed back‐calculation of dates of fish hatching, mussel recruitment, and tree germination for estimation of temporal variation in recruitment. Recruitment data were then analyzed in relation to flow regime components. Statistical models were derived for flow–recruitment relationships of Shoal Chub Macrhybopsis hyostoma, Smallmouth Buffalo Ictiobus bubalus, threeridge mussel Amblema plicata, and boxelder Acer negundo. The Shoal Chub model was used to hindcast recruitment dynamics in the Brazos River, revealing a decline in recruitment after dam construction. The Smallmouth Buffalo model revealed that strong year‐classes were positively associated with springtime pulses of long duration but negatively associated with frequent short‐duration pulses. Results for the threeridge mussel were not as easily interpreted, but for all three river basins generally showed weaker recruitment during periods with extreme low and high flows. The boxelder model revealed positive associations between recruitment and high flow components and was used to forecast the probability of germination under five simulated flow regime scenarios, thereby providing an additional means for evaluating the suitability of environmental flow standards. Assessment of how hydrology influences recruitment provides an evidence‐based method to hindcast responses to past flow regimes and forecast responses to future flow scenarios, thereby providing a basis for evaluating environmental flow standards and water management options.

Analysis of dissolved oxygen influencing factors and concentration prediction using input variable selection technique: A hybrid machine learning approach

Accurate quantification of dissolved oxygen (DO) is critically important for the protection and management of aquatic ecosystems. Successful applications have utilized mechanistic and data-driven models to simulate DO content in aquatic ecosystems. However, mechanistic models present challenges due to their complex and difficult-to-solve conditions, making them less portable. Additionally, data-driven model predictions are hindered by the challenge of numerous input variables, impacting both the running speed and prediction performance of the model. To address these challenges, water quality data and meteorological data of the Tanjiang River were obtained. The maximum information coefficient (MIC) input variable selection technique was employed to identify primary environmental factors influencing DO changes. Furthermore, coupled with support vector regression (SVR), two models (SVR and MIC-SVR) were employed to estimate the DO concentration of the Tanjiang River, and the optimal model was established. The results indicated a shift in the primary pollution factor from ammonia nitrogen to total phosphorus after recent treatment in the Tanjiang River. In comparison with the SVR model, the root mean square error (RMSE) of the MIC-SVR model was reduced by 4.46%, and the Nash-efficiency coefficient (NSE) was improved by 45.85%. In addition, study of kernel function selection revealed that considering as many kernel functions as possible is necessary for improving the performance of the SVR model. Conclusively, the proposed MIC-SVR model serves as an effective tool to analyze the relationship between DO and environmental factors, identifying the primary causes of low DO, and accurately predict the DO concentration in the Tanjiang River (especially in its middle and lower reaches), thus providing a reference for governmental decision-making on water environmental protection and water resource management.

Chronic increasing nitrogen and endogenous phosphorus release from sediment threaten to the water quality in a semi-humid region reservoir

The water quality in the drinking water reservoir directly affects people's quality of life and health. When external pollution input is effectively controlled, endogenous release is considered the main cause of water quality deterioration. As the major nitrogen (N) and phosphorus (P) sources in reservoirs, sediment plays a vital role in affecting the water quality. To understand the spatial and temporal variation of N and P in the sediment, this study analyzed the current characteristics and cumulative effects of a semi-humid reservoir, Yuqiao Reservoir, in North China. The N and P concentrations in the reservoir sediment were decreased along the flow direction, while the minimum values were recorded at the central sediment profile. External input and algal deposition were the main factors leading to higher sediment concentrations in the east (Re-E) and west (Re-W) areas of reservoir sediment profiles. According to the long-term datasets, the peaks of both sediment total nitrogen content and deposition rate were observed in the 2010s, which has increased about three times and six times than in the1990s, respectively. Therefore, the increase in phosphorus concentration may be the main reason for eutrophication in water in recent years. The mineralization of organic matter has a significant promoting effect on releasing N and P from sediments, which will intensify eutrophication in water dominated by P and bring huge challenges to water environment management. This study highlights that the current imbalance in N and P inputs into reservoirs and the endogenous P release from sediment will have a significant impact on water quality.

Enhancing long-term river water quality prediction: Construction and validation of an improved hybrid model

To prevent eutrophication of river caused by excessive inputs of nutrients, accurately predicting these nutrient concentrations is of utmost importance. However, long-term accurate predictions of nutrients concentrations remain a challenge. In this study, an improved hybrid algorithm combining adaptive variational mode decomposition and multi-functional recurrent fuzzy neural network (AVMD-MFRFNN) was proposed. Firstly, whale optimization algorithm (WOA) was developed to identify the optimal number of modes for variational mode decomposition (VMD). Then VMD was employed to decompose input data into intrinsic mode fractions (IMFs), benefiting for reducing the noise effect and non-stationarity of data. Then, MFRFNN with the capability of characterizing complex data was developed to train and test the IMFs. The proposed AVMD-MFRFNN achieved impressive results with the root mean square errors (RMSEs) of 0.036 mg/L, 0.758 mg/L and 1.435 mg/L for predicting total phosphorus (TP), chemical oxygen demand (CODMn) and total nitrogen (TN) after 168 hours. These results signify a decrease of 5.3%, 14.1% and 30.1% compared to the second-best compared method. Moreover, this study demonstrates the potential that integrating the proposed AVMD with other methods can enhance the modeling performance of time series. Furthermore, sensitivity analysis results further validate the effectiveness of WOA in optimizing VMD parameter. Finally, the applicability of AVMD-MFRFNN was confirmed by the data from another river section. These results provide new insights and technical support into the management and preservation of river water environment.

Investigating First Flush Occurrence in Agro-Urban Environments in Northern Italy

The first flush (FF) phenomenon is commonly associated with a relevant load of pollutants, raising concerns about water quality and environmental management in agro-urban areas. An FF event can potentially transport contaminated water into a receiving water body by activating combined sewer overflow (CSO) systems present in the drainage urban network. Therefore, accurately characterizing FF events is crucial for the effective management of sewer systems and for limiting environmental degradation. Given the ongoing controversy in the literature regarding the delineation of FF event occurrences, there is an unavoidable necessity for further investigations, especially experimental-based ones. This study presents the outcomes of an almost two-year field campaign focused on assessing the water quantity and quality of two combined sewer systems in Northern Italy. For this purpose, various hydro-meteorological variables, including precipitation, flow rate, temperature, and solar radiation, in addition to water quality analytics, were measured continuously to capture stormwater events. Throughout the monitoring period, sixteen stormwater events were identified and analyzed using five indices usually adopted in the literature to identify FF occurrences. The results indicate that there is a strong positive correlation between the mass first flush ratios calculated for nutrients and three factors, including maximum rainfall intensity, maximum flow rate, and antecedent dry weather period. Furthermore, rainfall duration was found to possess a strong negative correlation with the mass first flush ratios calculated for nutrients. However, for the same rainfall event, the occurrence of FF has never been unanimously confirmed by the indices examined in this study. Moreover, different macro-groups of pollutants can behave differently. Thus, it becomes apparent that relying solely on a priori analyses, without the support of data from experimental monitoring campaigns, poses a risk when designing actions for the mitigation of FF occurrences.

Impact on urban river water quality and pollution control of water environmental management projects based on SMS-Mike21 coupled simulation

To explore the impact of expanding Nanyang Sewage Purification Center (NSPC) on the main sewage discharge area of Bai River, we constructed a 2D hydrodynamic-water quality model based on surface water modeling system (SMS) and Mike21. Simulating three sewage discharge conditions in wet, normal, and dry season, we evaluated three indicators (COD, NH3-N, and BOD5) by the single-factor pollution index and provided recommendations for water environment management. The results showed that, maximum absolute error of water level was 0.08 m, percentage bias coefficient of COD, NH3-N and BOD5 were 19.3%, 16.2% and 23.1%, indicating the SMS and Mike21 coupling model was applicable; water quality of the assessment section were upgraded from the original class IV, V, V (Condition 1) to class IV, III, II (Condition 2) and class IV, III, III (Condition 3) in the wet, normal and dry season, indicating that NSPC's expansion had improved the water quality of the assessment section; as the primary pollutant, BOD5 concentration in the downstream was lower than the upstream, which was due to the dilution effect of river. Therefore, on the basis of expanding NSPC, we recommend to remediation of BOD5 by physical, chemical, and biological methods. This study broadens new ideas for the application of Mike21, and provide a reference for the prevention and improvement of river water pollution in urban areas.

A hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit.

Dissolved oxygen is one of the important comprehensive indicators of river water quality, which reflects the degree of pollution in the water body. Monitoring and predicting dissolved oxygen are an important tool for water quality management, which helps to effectively maintain water ecological balance and prevent environmental problems. A single model cannot describe the dynamic characteristics of dissolved oxygen sequence, which affects the prediction accuracy. In order to obtain more accurate dissolved oxygen prediction results, decomposition techniques are commonly used to extract the main fluctuations and trends of water quality sequences. However, the high-frequency modes obtained from decomposition are still unstable. To solve this problem, this paper proposed a hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit. Firstly, dissolved oxygen sequence is preliminarily decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and obtain several intrinsic mode functions (IMF). The fuzzy entropy (FE) is calculated to quantify the complexity of the IMF. Then, variational mode decomposition improved by northern goshawk optimization is used to decompose the IMF with higher entropy. The nonlinearity and instability of the sequence are further weakened. Finally, the bidirectional gate recurrent unit (BiGRU) neural network is used to predict each IMF component, and the final prediction result is obtained by reconstructing the prediction results of each component. In order to verify the effectiveness of the proposed model, this paper selects the dissolved oxygen data of Xin'anjiang Reservoir as the research object. The experimental results show that the RMSE, MAE, MAPE, and R2 of the proposed model are 0.1164, 0.0894, 1.0403%, and 0.9939, respectively, which is best among other comparative prediction models (BP, LSTM, GRU, BiGRU, EMD-BiGRU, CEEMDAN-BiGRU, VMD-BiGRU, and GNO-VMD-BiGRU). Therefore, this model effectively deals with high volatility and nonlinear dissolved oxygen data and provides reference for water environment management and ecological protection.

Improving Analytic Hierarchy Process inside the Analytic Group Decision-Making Approach Method with Two-Dimensional Cloud Model for Water Resource Pollution Risk Warning Evaluation: A Case Study in Shandong Province, China

This study proposes a water resource pollution risk warning evaluation method. Firstly, an evaluation system is constructed, consisting of 15 secondary indicators in four aspects: water quality, ecology, utilization protection, and water disasters. Then, an improved AGA-AHP method and coefficient of variation method are used to determine the weights of each indicator. Cloud models are employed to describe the characteristics of standard clouds and evaluation clouds, establishing a two-dimensional cloud model with risk probability and hazard level as variables. Taking a certain region in Shandong Province, China, as an example, the quantitative analysis results indicate that the water pollution risk level in the area is classified as Level IV, with particular attention needed for water quality and management indicators. Simultaneously, a series of measures such as source control, monitoring and early warning, emergency response, and public participation are proposed to further reduce the risk. The research findings demonstrate the following: (1) The establishment of a comprehensive indicator system for multidimensional assessment; (2) The combination of the AGA-AHP method and cloud model for quantitative analysis; (3) The practicality of the method validated through the case study; (4) Providing a basis for subsequent decision-making. This study provides new insights for water environmental risk management, but a further optimization of the model to enhance predictive capability is required when applied in practical scenarios. Nevertheless, the preliminary validation of this method’s application prospects in water resource risk monitoring has been achieved.

Bibliometric analysis of global performance and trends of research on combined sewer overflows (CSOs) from 1990 to 2022.

Combined sewer overflows (CSOs) are one of the main sources of pollution in urban water systems and significantly impede the restoration of water body functionalities within urban rivers and lakes. To understand the research and frontier trends of CSOs comprehensively and systematically, a visual statistical analysis of the literature related to CSOs in the Web of Science core database from 1990 to 2022 was conducted using the bibliometric method using HistCite Pro and VOSviewer. The results reveal a total of 1,209 pertinent publications related to CSOs from 1990 to 2022, and the quantity of CSOs-related publications indicated an increasing trend. Investigations of the distribution and fate of typical pollutants in CSOs and their ecological effects on receiving waters and studies on pollution control technologies (source reduction, process control, and end-of-pipe treatment) are the current focus of CSOs research. CSOs pollution control technologies based on source reduction and the monitoring and control of emerging contaminants are at the forefront of scientific investigations on CSOs. This study systematically and comprehensively summarized current research topics and future research directions of CSOs, thus providing a reference for CSOs control and water environment management research.

Effectiveness of Financing Measures to Reduce Low Emissions in Poland - a Case Study

Local authorities are increasingly facing the challenges of climate change, which requires adaptation of various elements of local government systems, including infrastructure, communities and local economies. Operating in an ever-changing reality, local government units should plan for the necessary sustainable local government investments, with particular attention to structural gaps and financial mismatches. Local government is responsible for ensuring local energy security and thus for 'environmental sustainability'. The overall objective of this study is to assess the effectiveness of financing low-emission lowering measures from the Environmental Protection and Water Management Fund in municipalities in Lubusz Province in 2020. The article uses cause-effect relationship analysis (regression models). Investments implemented by local governments generally have a positive impact on the reduction of low emissions, although the results of the study indicate a negative impact of financing investments in alternative energy sources (RES). This may be related to large investment outlays, as well as difficulties in spatial planning, environmental impact and administrative inefficiency.

A Brief Review on Foodborne Zoonosis in Pakistan with the Perspective of One Health

Background: Zoonotic diseases pose a significant public health challenge globally, with developing countries like Pakistan facing heightened risks due to various factors such as climate change, environmental degradation, and socio-economic disparities. These diseases, transmitted between animals and humans, have profound implications for healthcare systems, food safety, and public health policies. Objective: This study aims to identify the key risk factors associated with the prevalence of zoonotic diseases in Pakistan and evaluate the effectiveness of the 'One Health' approach in addressing these multifaceted challenges. Methods: A comprehensive review of existing literature was conducted, focusing on zoonotic disease outbreaks in Pakistan, their associated risk factors, and the impact of integrated health strategies. Data from government reports, peer-reviewed articles, and international health organization databases were analyzed to assess the current state of zoonotic diseases in Pakistan. Results: The findings indicate that climate change, environmental issues, lack of public awareness, and socio-economic inequities are the primary drivers of zoonotic disease transmission in Pakistan. Despite some progress in disease surveillance and public health education, gaps in food and water safety practices, vector control, and environmental management persist. The 'One Health' approach has shown potential in improving cross-sectoral collaboration and enhancing disease prevention measures, yet its implementation remains inconsistent. Conclusion: Effective management of zoonotic diseases in Pakistan requires a concerted effort that encompasses improved surveillance, enhanced public awareness, and robust environmental and food safety policies. The 'One Health' approach emerges as a critical framework for fostering collaboration across human, animal, and environmental health sectors to mitigate the risks and impacts of zoonotic diseases. Keywords: Zoonotic Diseases, One Health Approach, Public Health, Pakistan, Climate Change, Environmental Health, Disease Surveillance, Food Safety, Vector Control, Socio-economic Factors.

Chemophysical and Metallic Characterization of Surface Water and Precipitation for Environmental Quality Assessment in Oyigbo L.G.A., Rivers State, Nigeria

This study investigates the repercussions of oil industry operations on environmental quality in the Niger Delta, with a specific focus on atmospheric soot contamination. By conducting chemophysical and metallic characterization of surface water (river) and precipitation (rain) in Oyigbo, Rivers State, Nigeria, the research evaluates various chemical and physical parameters, like pH, electrical conductivity (EC), total suspended solids (TSS), turbidity, as well as concentrations of heavy metals like lead (Pb), copper (Cu), cadmium (Cd), and zinc (Zn). The findings uncover a slightly acidic to neutral pH in water samples. The elevated EC in the Imo River, while meeting WHO standards, highlights the delicate balance between industrial development and environmental health. Turbidity values, meeting WHO standards but exceeding limits in some rainwater samples, prompt scrutiny of anthropogenic influences, with fishing and sand mining emerging as potential contributors to river turbidity, while TSS values, though higher along the Imo River, remain within WHO standards. Results reveal Cd, Cu, and Zn adhere to standards, while the elevated Pb levels necessitate further exploration into contamination sources. The heavy metals pollution index (HPI) categorizes the area as polluted and identifies lead as the primary contributor, underlining the urgency of interventions. The potential ecological risks index (PERI) classifies the pollution risk as slight, indicating a low ecological risk level, and reveals risk hierarchies, in the order of metals contributing to pollution as Pb > Cu > Cd > Zn for both river and rainwater. This study unravels the relationship between industrial activities and water quality in Oyigbo, contributing substantively to sustainable water resources and environmental management. The detailed findings stand as a cornerstone for informed decision-making, aiming to mitigate environmental impacts and safeguard ecosystems and communities reliant on vital water sources.

Distance makes a difference: The role of water pollution regulation on firms’ innovation in China

Throughout the imminent water crisis era and stringent water environment management, enterprises play a vital role in environmental governance. However, there is seldom analysis on how the water environment regulation influences an enterprise's innovation due to data deficiency. Using our database from 2000 to 2022, we combine geographic information from water quality monitoring stations with the data on listed companies in China to pinpoint what distance firms are exposed to the monitoring stations. Results from a spatial regression discontinuity design along the geographic distances demonstrate that water environmental regulation has a significant positive impact on the quantity and quality of enterprise innovation. This paper found that it is primarily driven by the decreased usage of intermediate goods, rising industry profits from the exit of low-productivity firms, and enhanced enterprise research and development (R&D) investment. Furthermore, the positive impact is more pronounced for privately-owned firms, firms closer to suppliers, polluting firms, and politically unaffiliated firms. Our study offers scientifically valuable insights for emerging market countries in formulating effective water environmental regulation policies to incentivize firms to innovate in environmental protection. Additionally, it provides novel ideas on synergizing water environmental regulation and firm innovation to foster a mutually beneficial relationship between environmental protection and sustainable development.

Sustainable Coastal-Based Tourism in Southern Sri Lanka: Assessing the Potentials of Blue Flag Certification’s Criteria

As an Island nation, Sri Lanka depends on coastal ecosystems to develop its economy by enhancing coastal-based tourism activities. Sri Lanka’s beaches are an essential part of coastal tourism as they attract many local and foreign tourists. With the ever-increasing demand for coastal tourism-based development, the beaches in Sri Lanka are susceptible to environmental degradation and pollution. The Blue Flag Certification (BFC), recognized globally for its stringent environmental standards, serves as a potential framework for promoting coastal tourism while ensuring the conservation of coastal ecosystems. The BFC requires any beach to achieve and sustain high standards in 33 criteria under the four categories: Environmental Education and Information, Water Quality, Environmental Management, and Safety and Services. Although many attractive beaches in the southern part of Sri Lanka usually receive a larger proportion of tourists annually, Sri Lanka has not obtained BFC for any of its beaches so far. Therefore, through a comprehensive assessment, including field surveys and analysis of the applicability of blue flag criteria, this study aims to gauge the feasibility and effectiveness of integrating BFC into the development of sustainable coastal-based tourism. Three beaches located in the Southern Province, namely Unawatuna, Mirissa, and Polhena were selected for this study. Observations and data gathered during the field investigations were analyzed for 30 criteria (excluding water quality criteria) using the guidelines and explanatory notes provided by the Foundation for Environmental Education (FEE). It was identified that Unawatuna has fully or partially achieved 13 criteria out of 30, while Mirissa and Polhena achieved only ninth and seventh criteria respectively. All three beaches have achieved most of the criteria under the environmental management category while lacking significant achievements in the safety and services category. However, all three beaches have achieved at least one criterion under the environmental education and information category. Enhanced cleanliness and aesthetic values of beaches were observed where the environmental management criteria were prioritized especially in Mirissa. This study ensures that accomplishing all BFC criteria helps to enhance the region’s coastal tourism potential while continuing to preserve its unique and pristine coastal ecosystems. Further, this research also provides valuable insights and a holistic approach for policymakers, local councils, and tourism development stakeholders to implement the necessary strategies to fulfil all the criteria. Therefore, this study recommends a steady and efficient incorporation of all the criteria by relevant authorities to promote sustainable tourism and therefore contribute to economic development.
  
 Keywords: Sustainable tourism, Environmental management, Blue flag, Water quality, Development
  

Study on financial cost evaluation of urban water environment management and pollution prevention and control

Abstract Nowadays, water pollution has become a major factor restricting social development. To address this, the government has issued a series of policy documents to control water environmental pollution and achieved certain results. However, on the whole, the prevention and control of the pollution of water environments requires a large amount of capital investment, but the corresponding results and benefits are not significant. Hence, this paper takes Shanghai, Nanjing, Hangzhou, and Hefei as examples to study the financial cost-effectiveness of the governance of urban water environments from the two dimensions of time and space. It is concluded that the cost of water environmental treatment has a negative effect on the comprehensive benefit of environmental treatment in the region in the short term and a positive effect in the long term, which indicates that water environmental pollution treatment is work that needs to be adhered to for a long time, and long-term planning is also needed for cost input. On this basis, strategies to improve the cost efficiency of water pollution treatment are presented.

A Study on the Efficiency of Green Technology Innovation in Listed Chinese Water Environment Treatment Companies

With the continuous advancement of the economy, the issues of resource scarcity and environmental damage are becoming increasingly severe. For example, in terms of water resources, the problems of environmental pollution and ecological imbalance in the water caused by industrial and agricultural wastewater are becoming more serious. In order to reduce water pollution, protect water resources, promote ecological balance, and reduce environmental risks, it is necessary to strengthen water environment management. This study uses the Malmquist DEA model to conduct a study on the green technology innovation efficiency (GTIE) of 24 water environment governance companies from 2019 to 2022. Corporate Research & Development investment and employee compensation are used as the input indicators, while the number of color patents obtained and operating income are employed as the output indicators. The evaluation criteria include pure technical efficiency, comprehensive technical efficiency, scale efficiency, and total factor productivity. The results show that there is significant room for improvement in the GTIE of the listed Chinese water environment governance enterprises, and there are considerable differences among different enterprises. The GTIE is significantly influenced by technological progress, the enterprise size, and the equity ratio. Therefore, water environment management enterprises should enhance their efforts in technological research and development and talent training, optimize resource allocation, improve the efficiency of green technology innovation, and effectively fulfill their social responsibilities. These measures will promote the efficient utilization of ecological water, the restoration of the water environment, and the establishment of a clean ecological environment.

Vertical water age and water renewal in a large riverine reservoir

Water quality and hypoxia in lakes and reservoirs are strongly associated with water renewal. Yet vertical water exchange is still not fully understood and challenging to evaluate in highly dynamic systems. Here, we applied a scaling approach using the vertical timescale, vertical water age (VWA), defined as time since a water parcel last touched the water surface. We established a 3D hydrodynamic-based age model to quantify the vertical water renewal in Xiangxi Bay, a tributary bay of the Three Gorges Dam. The integrated effects of hydrodynamic processes like stratification, intruding density currents from the mainstream, and upstream inflow on the vertical renewal were accounted for. Results indicated that the spatial–temporal distribution of VWA in Xiangxi Bay depended on stratification and forms of intruding density currents. Age was large in spring and summer, and small in autumn and winter, reaching a maximum of 25 days in April. The vertical water renewal was faster during bottom intrusions from the mainstream than during middle and surface intrusions. At times, the epilimnion contained old water due to circulations, and the hypolimnion contained young water due to upstream flushing. In contrast to natural lakes, the bottom water was often younger than overlying intermediate waters. This demonstrated that mixed layer depth was insufficient to fully capture the vertical exchange in riverine systems with significant surface/bottom intrusion. The findings suggested VWA as a quantitative measure of vertical water transport in highly dynamic systems and its usability for environmental water management.