Optical water quality and human perceptions: a synthesis

Negative health impacts of water insecurity are often felt most in poor and rural communities and communities of color, who are more likely to be underserved by water infrastructure and disproportionately subject to socioeconomic stressors. Despite mandated efforts to allocate significant federal resources to infrastructure funding in ‘disadvantaged communities,’ communities with the most need risk systematic exclusion from access to resources, decision-making structures, and even benefits of research intended to address inequitable infrastructure services and health outcomes in their own communities. This project aims to describe groundwork and preliminary findings from community-engaged environmental research conducted within an ongoing community-based participatory research partnership in Robeson County, NC, a majority–minority county with the lowest median household income of NC’s 100 counties. Semi-structured interviews conducted with community members were analyzed to identify concerns about drinking water security (including safety, affordability, and reliability), perceptions of water quality, averting behaviors taken due to water insecurity, and ideas for improving water security. Findings suggest that there is a high level of mistrust in community water supplies, with perceptions of poor water quality driving a reliance on bottled water. Those relying on private wells expressed greater trust in their water and lower reliance on bottled water. Concerns about affordability were less prominent than those about water quality. Insufficient water reliability (low flow) was mentioned by many respondents, including those with community water service and those relying on private wells. Most supported increasing taxes to improve water security and also recommended increasing communications between water service providers and the public to improve trust. Overall, this work suggests the need for a comprehensive assessment of the quality and reliability of community water services in Robeson County, interventions to address problems identified, and much more engagement with the community about identifying and allocating funding to solve water security problems.

How do people's perceptions of water quality influence the life cycle environmental impacts of drinking water in Uganda?

Abstract. Our current capacity to model stream water quality is limited – particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatiotemporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years and across 102 catchments (which span over 130 000 km2). The modeling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate–nitrite (NOx) and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which were identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explained (19.9 %), the model explains 38.2 % (NOx) to 88.6 % (EC) of the total spatiotemporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; the temporal variability remains largely unexplained across all catchments, although long-term trends are well captured. The model is best used to predict proportional changes in water quality on a Box–Cox-transformed scale, but it can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot spots and hot moments for waterway pollution; (2) predicting the effects of catchment changes on water quality, e.g., urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on the following: (1) alternative statistical model structures to improve fitting for truncated data (for constituents where a large amount of data fall below the detection limit); and (2) better representation of nonconservative constituents (e.g., FRP) by accounting for important biogeochemical processes.

The present study aims to test a model of tap water risk and quality perception. A questionnaire was designed and applied to a convenience sample of 499 people in Portugal. The model includes aesthetic variables (colour, odour, and flavour), contextual indicators and risk perception. Other variables were also considered, including external information, trust in water companies, familiarity with tap water, and past water-related health problems. A behavioural outcome--drinking tap water--was also studied. Due to multivariate non-normality (Mardia's Coefficient (G2,P) = 460.3), the AGLS estimator was used for the structural equation model (SEM). A triangulation approach with multivariate regression analysis was used to explain the use of tap water to drink and to propose a more holistic model that could not be tested using SEM. Results show that perceived water quality is largely influenced by flavour. Other factors, such as perceived risk and contextual indicators, also have a role but their relevance is relatively weak. On the other hand, risk perception seems to be mainly a result of external information, past health problems, and water colour. Finally, the use of tap water to drink at home can be moderately explained and depends mostly on the ability to use alternatives (i.e. bottled water), tap water taste, and perceived risk.

In the context of water resource management and pollution control, the characterization of water quality impairments and identification of dominant pollutants are of critical importance. In this study, water quality impairment was assessed on the basis of 7 hydrochemical variables that were monitored bimonthly at 17 sites in 2010 along the rural-suburban-urban portion of the Wen-Rui Tang River in eastern China. Seven methods were used to assess water quality in the river system. These methods included single-factor assessment, water quality grading, comprehensive pollution index, the Nemerow pollution index, principle component analysis, fuzzy comprehensive evaluation, and comprehensive water quality identification index. Our analysis showed that the comprehensive water quality identification index was the best method for assessing water quality in the Wen-Rui Tang River due to its ability to effectively characterize highly polluted waters with multiple impairments. Furthermore, a guideline for the applications of these methods was presented based on their characteristics and efficacy. Results indicated that the dominant pollutant impairing water quality was total nitrogen comprised mainly of ammonium. The temporal variation of water quality was closely related to precipitation as a result of dilution. The spatial variation of water quality was associated with anthropogenic influences (urban, industrial, and agriculture activities) and water flow direction (downstream segments experiencing cumulative effects of upstream inputs). These findings provide valuable information and guidance for water pollution control and water resource management in highly polluted surface waters with multiple water quality impairments in areas with rapid industrial growth and urbanization.

Karst aquifers in subtropical regions are characterized by high variability of water availability and quality due to changes associated with rainy and dry seasons. An additional challenge for water management is the combination of surface-water and karst groundwater systems since high spatiotemporal dynamics cause high variability of water quality. In these cases, adapted protection strategies are required. In this study, a protection approach for the catchment of a river-water diversion point in a rural area in northern Vietnam is developed. The variability of water quality was evaluated by rainy and dry season synoptic surveys of suspended particles and microbial contamination at 49 sites and time series at three sets of paired sites under constant hydraulic conditions. The anthropogenic land-use activities in the catchment were mapped to identify potential contamination sources and to highlight the challenging combination of surface-water and karst groundwater management. The analyzed data indicate differences in water quality between the dry and rainy seasons and a higher influence on water quality from land use than from hydrologic conditions. Furthermore, the results suggest a high risk of contamination resulting from residential areas, agriculture, and livestock farming, and reveal the necessity of implementation of appropriate measures such as restricted farming and the hook-up of buildings to municipal sewage disposal. Finally, the data show that water quality can be improved by adjusting water withdrawals by the time of day. The applied methods can be transferred to other surface-water and karst groundwater systems in similar subtropical environments.

The appearance of the water is just one aspect of a waterscape that can be appreciated aesthetically. Water appearance is affected by water clarity and water colour. Here, an aesthetic assessment model of waterscape was suggested. In the model, water clarity and colour have direct effects, whereas water quality and phytoplankton biomass have indirect effects, on tourists' aesthetic assessment of water bodies. The preferred water colour is aquamarine to blue, regardless of depth of clarity. Water colour ranges from pastel yellow to yellow-green are not favoured by tourists. Four water-quality parameters were correlated with water clarity and phytoplankton biomass. The coefficient of indirect effect of river pollution index on tourists' aesthetic valuation of aquamarine to blue water colour was -0.457, and for pastel yellow to yellowgreen, it was -0.209. The research results showed observation of water colour could not only reflect waterscape aesthetic value, but also serve as a guide of judging water quality, and the status of phytoplankton benefited to simplify the process of water-quality management for waterscape.

Numerous studies have demonstrated that citizen science can provide valuable data on physical, chemical, and biological aspects of water quality. These studies mainly used quantitative methods. Qualitative approaches have been used to describe water quality conditions for much longer, but remain largely overlooked. The color, odor, and presence of aquatic vegetation or garbage influence human perceptions of river water quality, the use of the water, and thus the relation between humans and waterbodies. Yet, few studies have assessed how visual water quality indicators and local knowledge of water quality or sources of pollution can be used in citizen science projects, despite recent studies calling for greater attention to qualitative data sources. Qualitative data can enhance the interpretation of quantitative data and deepen the understanding of human-water relations. This paper evaluates qualitative water quality descriptors collected through the citizen science smartphone app CrowdWater and analyses how citizen scientists perceive and assess water quality in the app. Our analysis not only indicates that some citizen scientists already take quantitative physical–chemical measurements of water quality (even though this is not part of the app) but also that they frequently report their perception of water quality based on visual indicators and local knowledge. Our study makes a methodological contribution to traditional approaches in citizen science and water quality studies, highlighting the need to explore less frequently used methods and data sources and less frequently studied aspects of water quality.

Monitoring water quality is indispensable for the identification of threats to water environment and later management of water resources. Accurate monitoring and assessment of water quality have been long-term challenges. In this study, multivariate statistical techniques (MST) and water quality identification index (WQII) were applied to analyze spatiotemporal variation in water quality and determine the major pollution sources in the Qinhuai River, East China. A rotated principal component analysis (PCA) identified three potential pollution sources during the wet season (mixed pollution, physicochemical, and nonpoint sources of nutrients) and the dry season (nutrient, primary environmental, and organic sources) and they explained 81.14% of the total variances in the wet season and 78.42% of total variances in the dry season. The result of redundancy analysis (RDA) showed that population density, urbanization, and wastewater discharge are the main sources of organic pollution, while agricultural fertilizer consumption and industrial wastewater discharge are the main sources of nutrients such as nitrogen and phosphorus. The water quality of the Qinhuai River basin was determined to be mainly Class III (slightly polluted) and Class IV (moderately polluted) based on WQII. Temporally, the change trend of WQII showed that water quality gradually deteriorated between 1990 and 2005, improved between 2006 and 2010, and then deteriorated again. Spatially, the WQII distribution map showed that areas with more developed urbanization were relatively more polluted. Our results show that MST and WQII are useful tools to help the public and decision makers to evaluate the water quality of aquatic environment.

Spatio-temporal changes in surface water quality and sediment phosphorus content of a large reservoir in Turkey

Influence of recreational activity on water quality perceptions and concerns in Utah: A replicated analysis

Recreation demand models typically incorporate measures of the physical attributes of recreational sites; e.g., Secchi depth or phosphorous levels in case of water quality. Moreover, most studies show that individuals do respond to these physical characteristics in choosing where to recreate. However, the question remains as to whether the available physical measures accurately capture individual perceptions of water quality and if there is a additional role to be played by elicited perception measures in modeling recreation demand. In this paper, we use data from the 2004 Iowa Lakes Survey to model recreation demand as a function of both the physical water quality at 131 lakes in the state and household perceptions of lake water quality. In general, water quality perceptions are correlated with the available physical measures, but not perfectly so, and both actual and perceived water quality are found to significantly impact recreational site choice.

The deterioration of water quality has become a primary environmental concern worldwide. Understanding the status of water quality and identifying the influencing factors are important for water resources management. However, reported analyses have mostly been conducted in small and focused areas. It is still unclear if factors driving spatial variation in water quality would be different in extended spatial scales. In this paper, we analyzed spatial pattern of inland surface water quality in China using a dataset with four water quality parameters (i.e., pH, DO, NH4+-N and CODMn) and the water quality level. We tested the effects of anthropogenic (i.e., land use and socio-economic) and natural (i.e., climatic and topographic) factors on spatial variation in water quality. The study concluded that the overall inland surface water quality in China was at level III (fair). Water quality level was strongly correlated with CODMn and NH4+-N concentration. In contrast to reported studies that suggested land use patterns were the determinants of inland surface water quality, this study revealed that both anthropogenic and natural factors played important roles in explaining spatial variation of inland surface water quality in China. Among the tested explanatory variables, mean elevation within watershed appeared as the best predictor for pH, while annual precipitation and mean air temperature were the most important explanatory variables for CODMn and DO, respectively. NH4+-N concentration and water quality level were most strongly correlated with the percent of forest cover in watershed. Compared to studies at smaller spatial scales, this study found different influencing factors of surface water quality, suggesting that factors may play different roles at different spatial scales of consideration. Therefore management policies and measures in water quality control must be established and implemented accordingly. Since currently adopted parameters for monitoring of inland surface water quality in China are largely influenced by natural variables, additional physicochemical and biological indicators are needed for a robust assessment of human impacts on water quality.

Acquired underwater images often suffer from severe quality degradation, such as color shift and detail loss due to suspended particles' light absorption and scattering. In this paper, we propose a Dual-path Joint Correction Network (DJC-NET) to cope with the above degenerate issues, preserving different unique properties of underwater images in a dual-branch way. The design of the light absorption correction branch is to improve the selective absorption of light in water and remove color distortion, while the light scattering correction branch aims to improve the blur caused by scattering. Concretely, in the light absorption correction path, we design the triplet color feature extraction module, which balances the triplet color distribution of the degraded image through independent feature learning between R, G, and B channels. In the light scattering correction path, we develop a dual dimensional attention mechanism to extract the texture information from the features, aiming to recover sufficient details by more effective feature extraction. Furthermore, our method utilizes the multi-scale U-net to adaptively fusion features from different paths to generate enhanced images. Extensive visual and objective experimental results demonstrate that our method outperforms state-of-the-art methods in various underwater scenes.

Mining cities have undergone the process of extensive exploitation, which always results in a series of water issues. Integrated water resource management is necessary in improving water supply, allocation and quality without damaging economic development. This article constructs a linear optimization model including a ‘Top-Down’ socio-economic mode, and ‘Bottom-Up’ water quality control and water supply–demand modes with integrated water resource management focused on water allocation and water reclamation. Based on computer simulation, the model can propose a water resource management under the constraints of water supply–demand and water quality control, and the model can precisely predict the influences of water resource management on economic development, water utilization and water quality. Taking Ordos, a Chinese national resource city, as a case study, this model addresses a detailed water resource management, including a water allocation plan among industries and water reclamation plan with technologies, selection, arrangement and subsidies. The implementation of water resource management can fulfill multiple objectives on water quantity, water quality and sustainable economic development. This study indicates that water resource management with a comprehensive dynamic model can be a maneuverable approach to realize the sustainable development of economic growth and water resource utilization, as well as formulate the regional development plan.