Collected Water Quality Data Research Articles

Community-Based Monitoring Detects Sources and Risks of Mining-Related Water Pollution in Zimbabwe

Although mining and mineral processing are vital for many economies in the Global South, they are associated with enormous challenges of managing potentially devastating environmental impacts. In contexts where environmental oversight agencies often lack financial and personal capacities to fulfill their role, community-based monitoring might be a valid alternative to monitor potential environmental impacts. In this study, we present the setup and the implementation of a citizen science project to monitor water quality parameters in a river downstream of a coal mining area in Hwange, Western Zimbabwe. In a joint effort over 1.5 years, community monitors and scientists took close to 800 water samples in the Deka River and effluent channels. The data allowed identifying sources of pollution and relating these to past and present mining activities. The primary source of acid mine drainage came from abandoned underground mine sites. Illegal mine water dumping from active mine sites accentuated the problem and resulted in fish kills and food risks for the local population. Concentrations of manganese, nickel and arsenic were exceeding national fresh water guidelines and international drinking water standards. Manganese concentrations exceeded guidelines by a factor of 70 resulting in a public health risk. In this study, we showed that community-based monitoring offers a promising approach to establish a high-quality dataset for assessing mining-related risks if the implementation of sampling protocols is followed tightly. The monitoring scheme significantly improves the collection and interpretation of water quality data in challenging contexts where governmental institutions and industrial players are not enforcing environmental standards.

Mapping of Water Quality as Aquaculture Media, Comparative Study on Three Rivers in Riau Province

Kampar River, Siak River, and Rokan River have different watersheds, and the water from these rivers is not interconnected. The results of this study can be used as a basis for managing aquatic resources in the Kampar River, Siak River, and Rokan River, Riau Province, Indonesia. This research was conducted in April 2017 – March 2018. The collection and measurement of water quality for each river consisted of three stations which were carried out based on SNI through the survey method twice, namely in the dry season and rainy season. The observed water quality parameters include physical, chemical, and biological parameters. The results of the next analysis are calculated using the STORET method using a value system from the United State-Environmental Protection Agency (US-EPA) which is adjusted to the Class III Water Quality Standard based on PP. 22 of 2021. The results of the analysis of the rain index value in the highest dry season is a score of -44 on the Rokan River, classified as class D with a score of -31 heavily polluted. The STORET score in the Kampar River and Siak River obtained -24, each classified as Class C, is included in the moderately polluted category. Meanwhile, in the rainy season the pollution index with the same pattern is highest in the Rokan River -40, then Kampar River, and Siak River each -20. The water quality standard used in this study is class III because it still supports fish farming activities. Based on Government Regulation Number 22 of 2021 concerning water quality standards, class III is water whose designation can be used for freshwater fish aquaculture, animal husbandry, and gardens irrigation. The Storet method is carried out by collecting water quality data and measuring data from each water parameter compared to the value of class III water quality standards.

Analysis of Water Quality in the Mandaong River for Development of Freshwater Fish Cultivation

The Mandaong River is a river flow that originates from the springs of Mount Sibela which until now has not been known with certainty the upstream point of the Mandaong River. This river flows through 3 (three) villages, namely Mandaong Village which is a downstream area, Makean Village Village and Papaloang Village which is an area of the Mandaong River body. Taking into account the position of the Mandaong River which flows from the mountains through plantations and settlements, it is estimated that it will receive some input from both natural processes and from the activities of the surrounding community such as bathing, washing, excavating sand, agriculture and settlements. Disposal of domestic waste, organic and inorganic waste so that there will be a decrease in the quality of river water in the form of physical, chemical and biological aspects. This study aims to determine the water quality of the Mandaong river based on physical, chemical and biological parameters and to determine the status of the water quality of the Mandaong river for the development of freshwater fish farming. The method of collecting water quality data on physical and chemical parameters was measured directly in the field and water samples were taken for further testing in the laboratory. For macroinvertebrate sampling by using nets /nets hand, the laying of nets is carried out systematically on the left, middle and right bank of the river at each predetermined station. Based on the results of measurements of the water temperature in the Mandaong river, it ranges from 25°C–27°C, Flow Speed ranges from 1.02–2.06, River depth ranges from 45–97 cm. and water pH ranged from 6.76-7.31, DO content ranged from 6.31-6.78, BOD content ranged from 0.10-5.63, nitrate content ranged from 0.361-2.068, phosphate content ranged from 4.506 –6.753, ammonia content in the range of 0.002, detergent content in the range of 50. Family Biotic Index (FBI) values ranged from 3.36-4.16. The FBI index value shows that the water quality of the Mandaong River is in the very good category with the level of pollution not polluted by organic matter at stations 1 and 2, while at station 3 it is in the very good category with a slightly polluted level of organic matter. Based on the results of research and discussion, it can be concluded from this study that the testing of physical, chemical and biological parameters in research on the Mandaong river shows that the level of river water pollution is included in the very good category. Therefore, the water quality of the Mandaong River can be developed for freshwater fish farming activities.

Studi fisika kimia perairan untuk budidaya ikan nila (Oreochromis niloticus) yang dipelihara pada keramba jaring apung di Desa Pematang Limau

This study aimed to determine the water quality in the floating net cage area in the cultivation of tilapia (Oreochromis nilocitus) and to find out how the condition of the waters in Pematang Limau could support the cultivation activities. This research was conducted using 3 (three) stations. Water quality data collection was carried out 3 times. The data used included primary and secondary data. Parameters observed in situ included temperature, brightness, current velocity and pH. Ammonia, DO and BOD were analyzed at the Calibration and Health Laboratory of Central Kalimantan in Palangka Raya. Research resulted showed that the overall water quality in the floating net cage area can still be used to maintain tilapia culture. The water quality at each station showed a value that can still be used for cultivation activities except for BOD value that was high.

Adopting citizen science approach for water quality monitoring in Uzungöl, Turkey.

In the present study, adopting of citizen science approach for monitoring water quality of a lake (Uzungöl, Turkey) was investigated. The study consisted of selection of sampling points and water quality parameters, training of volunteers, sampling and analysis by volunteers, and development of a mobile application for data collection and storage. In the scope of the study, four measurement points around the lake were selected and elementary school students were trained both theoretically and practically to collect water quality data. During the project (June-December, 2018), volunteers collected water samples and measured temperature, pH, nitrate, and phosphate with the given test kits. The mobile application was developed using open source code and used to collect and store the volunteer data. According to the volunteer data, temperature values were between 8.5 and 15.5°C, pH values were between 2 and 8.5, nitrate values were between 0.5 and 2.5mg/L, and phosphate values were between 0.5 and 5mg/L in the lake. Most of the pH and temperature results were compatible with field measurements done by research group during field visits. The motivation of the volunteers and mobile application development were the achievements of the project. Low number of the data and its reliability were the main limitations. Therefore, this study showed that citizen science has both capabilities and constraints for collection of water quality data, however; outstanding potential of citizen science is obvious.

Integrative Survey of 68 Non-overlapping Upstate New York Watersheds Reveals Stream Features Associated With Aquatic Fecal Contamination

Aquatic fecal contamination poses human health risks by introducing pathogens in water that may be used for recreation, consumption, or agriculture. Identifying fecal contaminant sources, as well as the factors that affect their transport, storage, and decay, is essential for protecting human health. However, identifying these factors is often difficult when using fecal indicator bacteria (FIB) because FIB levels in surface water are often the product of multiple contaminant sources. In contrast, microbial source-tracking (MST) techniques allow not only the identification of predominant contaminant sources but also the quantification of factors affecting the transport, storage, and decay of fecal contaminants from specific hosts. We visited 68 streams in the Finger Lakes region of Upstate New York, United States, between April and October 2018 and collected water quality data (i.e., Escherichia coli, MST markers, and physical–chemical parameters) and weather and land-use data, as well as data on other stream features (e.g., stream bed composition), to identify factors that were associated with fecal contamination at a regional scale. We then applied both generalized linear mixed models and conditional inference trees to identify factors and combinations of factors that were significantly associated with human and ruminant fecal contamination. We found that human contaminants were more likely to be identified when the developed area within the 60 m stream buffer exceeded 3.4%, the total developed area in the watershed exceeded 41%, or if stormwater outfalls were present immediately upstream of the sampling site. When these features were not present, human MST markers were more likely to be found when rainfall during the preceding day exceeded 1.5 cm. The presence of upstream campgrounds was also significantly associated with human MST marker detection. In addition to rainfall and water quality parameters associated with rainfall (e.g., turbidity), the minimum distance to upstream cattle operations, the proportion of the 60 m buffer used for cropland, and the presence of submerged aquatic vegetation at the sampling site were all associated based on univariable regression with elevated levels of ruminant markers. The identification of specific features associated with host-specific fecal contaminants may support the development of broader recommendations or policies aimed at reducing levels of aquatic fecal contamination.

An integrated framework of sensing, machine learning, and augmented reality for aquaculture prawn farm management

The rapid growth of prawn farming on an international scale will play an important role in meeting the protein requirements of an expanding global population. Efficient management of the commercial ponds for healthy production of prawns is the key mantra of success in this industry. It is a necessity to maintain the water quality parameters in these ponds within specific ranges to create an ideal environment of optimal growth of healthy prawns. The current practice of water quality data collection and their usage for decision making on most farms is not efficient and does not take full advantage of the latest technologies. The research presented in this paper aimed at addressing this problem by systematic investigation and development of an integrated framework where (i) modern sensors were investigated for their suitability and deployed for continuous monitoring of the water quality variables in prawn ponds; (ii) novel machine learning models were investigated based on collected data and deployed to accurately forecast pond status over next 24 h. This provides farmers insight into upcoming situations and take necessary measures to avoid catastrophic situations; and (iii) augmented reality-based visualisation methods were investigated for improved data capture process and efficient decision making through real-time interactive interfaces. The paper presents the integrated framework as well as the details of sensing, machine learning, and augmented reality components. We found that (i) YSI EXO2 Multi-Sonde is the best sensor for continuous monitoring of prawn ponds; (ii) ForecastNet (our developed machine learning model) provides best forecasting results with symmetric mean absolute percentage error of 6.1 %, 9.6 %, and 8.5 % for dissolved oxygen, pH, and temperature; and (iii) augmented reality-based interactive interface achieves accuracy as high as 89.2 % for management decisions with at least 41 % less time. The experience of the project as presented in this paper can act as a guide for researchers as well as prawn farmers to take advantage of latest sensors, machine learning algorithms and augmented reality tools.

Introducing an Open-Source Regional Water Quality Data Viewer Tool to Support Research Data Access

Water quality data collection, storage, and access is a difficult task and significant work has gone into methods to store and disseminate these data. We present a tool to disseminate research in a simple method that does not replace but extends and leverages these tools. The tool is not geo-graphically limited and works with any spatially-referenced data. In most regions, government agencies maintain central repositories for water quality data. In the United States, the federal government maintains two systems to fill that role for hydrological data: the U.S. Geological Survey (USGS) National Water Information System (NWIS) and the U.S. Environmental Protection Agency (EPA) Storage and Retrieval System (STORET), since superseded by the Water Quality Portal (WQP). The Consortium of the Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) has developed the Hydrologic Information System (HIS) to standardize the search and discovery of these data as well as other observational time series datasets. Additionally, CUAHSI developed and maintains HydroShare.org (5 May 2021) as a web portal for researchers to store and share hydrology data in a variety of formats including spatial geographic information system data. We present the Tethys Platform based Water Quality Data Viewer (WQDV) web application that uses these systems to provide researchers and local monitoring organizations with a simple method to archive, view, analyze, and distribute water quality data. WQDV provides an archive for non-official or preliminary research data and access to those data that have been collected but need to be distributed prior to review or inclusion in the state database. WQDV can also accept subsets of data downloaded from other sources, such as the EPA WQP. WQDV helps users understand what local data are available and how they relate to the data in larger databases. WQDV presents data in spatial (maps) and temporal (time series graphs) forms to help the users analyze and potentially screen the data sources before export for additional analysis. WQDV provides a convenient method for interim data to be widely disseminated and easily accessible in the context of a subset of official data. We present WQDV using a case study of data from Utah Lake, Utah, United States of America.

Analyzing long-term water quality of lakes in Rhode Island and the northeastern United States with an anomaly approach.

Addressing anthropogenic impacts on aquatic ecosystems is a focus of lake management. Controlling phosphorus and nitrogen can mitigate these impacts, but determining management effectiveness requires long-term datasets. Recent analysis of the LAke multi-scaled GeOSpatial and temporal database for the Northeast (LAGOS-NE) United States found stable water quality in the northeastern and midwestern United States; however, sub-regional trends may be obscured. We used the University of Rhode Island’s Watershed Watch Volunteer Monitoring Program (URIWW) dataset to determine if there were sub-regional (i.e., 3000 km2) water quality trends. URIWW has collected water quality data on Rhode Island lakes and reservoirs for over 25 yr. The LAGOS-NE and URIWW datasets allowed for comparison of water quality trends at regional and sub-regional scales, respectively. We assessed regional (LAGOS-NE) and sub-regional (URIWW) trends with yearly median anomalies calculated on a per-station basis. Sub-regionally, temperature and chlorophyll a increased from 1993 to 2016. Total nitrogen, total phosphorus, and the nitrogen:phosphorus ratio (N:P) were stable. At the regional scale, the LAGOS-NE dataset showed similar trends to prior studies of the LAGOS-NE with chlorophyll a, total nitrogen, and N:P all stable over time. Total phosphorus did show a very slight increase. In short, algal biomass, as measured by chlorophyll a in Rhode Island lakes and reservoirs increased, despite stability in total nitrogen, total phosphorus, and the nitrogen to phosphorus ratio. Additionally, we demonstrated both the value of long-term monitoring programs, like URIWW, for identifying trends in environmental condition, and the utility of site-specific anomalies for analyzing for long-term water quality trends.

Planning Advanced Treatment of Tap Water Consumption in Universitas Pertamina

The wastewater treatment plant (WWTP) in Universitas Pertamina’s area is operating very well. The existence of a green campus program with a wastewater recycling indicator has become one of the challenges. Improving the WWTP effluent quality by adding an advanced treatment unit. This study aims to design advanced processing units and estimate the effluent yields and required costs. This study was conducted by conducting a field survey, collecting water quality data, and literature study. With planning discharge (Qpeak) of 8.45 m3 /hour, the land area required for the addition of advanced treatment is 105.85 m2 . Advanced treatment consists of 1 unit of Equalization Tank, 2 units of slow sand filter, 1 unit of sand washer, 2 units of microfiltration membrane, 1 disinfection body, and 2 reservoir units. These units it is expected to make effluent quality meets the drinking water quality standards with a TSS value of 0 mg/L, Ammonia (NH3-N) 0.35 mg/L, Organic Substances (KmnO4) 0.513 mg/L, Total Dissolved Solids (TDS) 23 mg/L, and Total Coliform 0 Total/100 ml of sample. The total cost needed to build an advance treatment for tap water consumption is Rp 374,727,334.

A GIS‐based approach for delineating suitable areas for cage fish culture in a lake

AbstractWe present a GIS‐based approach to the delineation of areas that have different levels of suitability for use as tilapia cage culture sites the Kenyan part of Lake Victoria, Africa. The study area was 4,100 km2. The method uses high‐resolution bathymetric data, newly collected water quality data from all major fishing grounds and cage culture sites, and existing spatial information from previous studies. The parameters considered are water depth, water temperature, levels of dissolved oxygen, chlorophyll‐aconcentrations, distances to the lake shoreline and proximity to other constraints on cage culture development. The results indicated that the area most suitable for fish cages comprised about 362 km2, or approximately 9% of the total area; the remaining 91% (i.e., 3,737 km2) was found to be unsuitable for tilapia cage culture. We conclude that the successful implementation of this approach would need stakeholder involvement in the validation and approval of potential sites, and in the incorporation of lake zoning into spatial planning policy and the regulations that support sustainable use while minimising resource use conflicts. The results of this study have broader applicability to the whole of Lake Victoria, other African Great Lakes, and any lakes in the world where tilapia cage culture already occurs or may occur in the future.

Shifts in precipitation and agricultural intensity increase phosphorus concentrations and loads in an agricultural watershed

Fertilizers and manure applied to cropland to increase yields are often lost via surface erosion, soil leaching, and runoff, increasing nutrient loads in surface and sub-surface waters, degrading water quality, and worsening the ‘dead zone’ in the Gulf of Mexico. We leverage spatial and temporal variation in agricultural practices and precipitation events to examine how these factors affect stream total phosphorus (TP) concentrations and loads in the Sugar River (Wisconsin), recently listed as impaired. To perform our analysis, we first collected water quality data from 1995 to 2017 from 40 sites along the Sugar River and its tributaries. Starting in 2004, three dairy farms expanded to become concentrated animal feeding operations (CAFOs) in this watershed. We then estimated how time of year, stream position, discharge volume, and proximity to the newly expanded CAFOs affected TP concentrations and loads. Total P concentrations, which ranged from 0.02 to 1.4 mg/L and often exceeded the EPA surface water standard of 0.1 mg/L, increased with increases in stream discharge and proximity to dairy operations, peaking in early spring to mid-summer coincident with extreme precipitation events. Our empirical analysis also shows that TP concentrations downstream from the newly permitted CAFOs increased by 19% relative to upstream concentrations. When examining total daily phosphorus loads (concentration × discharge) from this 780 km2 watershed, we found that loads ranged from 5.88 to 4801 kg. Compared to upstream TP loads, those downstream from the CAFOs increased by 91% after the expansions – over four times that of concentration increases – implying that the rate of downstream phosphorus transfer has increased due to CAFO expansion. Our results argue for standards that focus on loads rather than concentrations and monitoring that includes peak events. As agriculture intensifies and extreme rainfall events become more frequent, it becomes increasingly important to limit soil and TP runoff from manure and fertilizer. Siting CAFOs carefully, limiting their size, and improving farming practices in proximity to CAFOs in spring and early summer could considerably reduce nutrient loads.

A Beginner's Guide to Water Management – Common Aquatic Birds Using Florida Lakes Informational Circular 113

Florida LAKEWATCH is a citizen scientist program that monitors Florida's lakes. Since it was established in 1991, Florida LAKEWATCH has worked with thousands of volunteers to collect water quality data on more than 2,700 aquatic systems in Florida. It is the most comprehensive and longest-running water quality data source in the United States, if not the world. This 18-page circular written by Mark V. Hoyer and published by the UF/IFAS Program in Fisheries and Aquatic Sciences of the School of Forest Resources and Conservation describes Florida LAKEWATCH's volunteer aquatic bird-monitoring program. Citizen volunteers can provide a comprehensive and intimate understanding of birds and their interactions with Florida lake systems, helping to detect the changes in the types and numbers of birds using lakes that may indicate important natural or human-caused environmental trends. Whether you want to identify birds you’ve spotted or take an active role in the management and conservation of Florida's natural resources, this circular can help you learn the basics. https://edis.ifas.ufl.edu/fa229

Estimating nitrogen inputs, storage, and exports for a small watershed in the upper Mississippi river basin, USA

Nitrogen contributions to the Gulf of Mexico hypoxic zone from agriculture watersheds are well documented. Beargrass creek watershed a 5,985hectare agriculturally dominated watershed in northern Indiana was instrumented to collect water quality data. A nitrogen budget was developed to account for net input and export of nitrogen from the watershed. Inputs consisted of fertilizer application, soil mineralization, and atmospheric deposition. Exports consisted of nitrogen removal in grain and stream exportation. Water quality testing at two gage stations, fourteen access tubes, and nine drainage tiles determined where excess nitrogen was stored. Nitrogen inputs was estimated at 1,004,213kg/year or 168 kg/ha/year in 2015. Total nitrogen exports accounted for 860,250kg/ha or 144kg/ha/year: with 30% of the applied nitrogen removed by Beargrass Creek through baseflow, tile flows, and storm event runoff.

Smartphone-based Fluoride-specific Sensor for Rapid and Affordable Colorimetric Detection and Precise Quantification at Sub-ppm Levels for Field Applications

Higher levels of fluoride (F–) in groundwater constitute a severe problem that affects more than 200 million people spread over 25 countries. It is essential not only to detect but also to accurately quantify aqueous F– to ensure safety. The need of the hour is to develop smart water quality testing systems that would be effective in location-based real-time water quality data collection, devoid of professional expertise for handling. We report a cheap, handheld, portable mobile device for colorimetric detection and rapid estimation of F– in water by the application of the synthesized core–shell nanoparticles (near-cubic ceria@zirconia nanocages) and a chemoresponsive dye (xylenol orange). The nanomaterial has been characterized thoroughly, and the mechanism of sensing has been studied in detail. The sensor system is highly selective toward F– and shows unprecedented sensitivity in the range of 0.1–5 ppm of F–, in field water samples, which is the transition regime, where remedial measures may be needed. It addresses multiple issues expressed by indicator-based metal complexes used to determine F– previously. Consistency in the performance of the sensing material has been tested with synthetic F– standards, water samples from F– affected regions, and dental care products like toothpastes and mouthwash using a smartphone attachment and by the naked eye. The sensor performs better than what was reported by prior works on aqueous F– sensing.

Generation of geolocated and radiometrically corrected true reflectance surfaces in the visible portion of the electromagnetic spectrum over large bodies of water using images from a sUAS

The focus of this study was to develop true reflectance surfaces in the visible portion of the electromagnetic spectrum from small unmanned aerial system (sUAS) images obtained over large bodies of water when no ground control points were available. The goal of the research was to produce true reflectance surfaces from which reflectance values could be extracted and used to estimate optical water quality parameters utilizing limited in-situ water quality analyses. Multispectral imagery was collected using a sUAS equipped with a multispectral sensor, capable of obtaining information in the blue (0.475 μm), green (0.560 μm), red (0.668 μm), red edge (0.717 μm), and near infrared (0.840 μm) portions of the electromagnetic spectrum. To develop a reliable and repeatable protocol, a five-step methodology was implemented: (i) image and water quality data collection, (ii) image processing, (iii) reflectance extraction, (iv) statistical interpolation, and (v) data validation. Results indicate that the created protocol generates geolocated and radiometrically corrected true reflectance surfaces from sUAS missions flown over large bodies of water. Subsequently, relationships between true reflectance values and in-situ water quality parameters were developed.

Robustness analysis of storm water quality modelling with LID infrastructures from natural event-based field monitoring

Sponge city construction (SCC) in China, as a new concept and a practical application of low-impact development (LID), is gaining wide popularity. Modelling tools are widely used to evaluate the ecological benefits of SCC in stormwater pollution mitigation. However, the understanding of the robustness of water quality modelling with different LID design options is still limited due to the paucity of water quality data as well as the high cost of water quality data collection and model calibration. This study develops a new concept of ‘robustness’ measured by model calibration performances. It combines an automatic calibration technique with intensive field monitoring data to perform the robustness analysis of storm water quality modelling using the SWMM (Storm Water Management Model). One of the national pilot areas of SCC, Fenghuang Cheng, in Shenzhen, China, is selected as the study area. Five water quality variables (COD, NH3-N, TN, TP, and SS) and 13 types of LID/non-LID infrastructures are simulated using 37 rainfall events. The results show that the model performance is satisfactory for different water quality variables and LID types. Water quality modelling of greenbelts and rain gardens has the best performance, while the models of barrels and green roofs are not as robust as those of the other LID types. In urban runoff, three water quality parameters, namely, SS, TN and COD, are better captured by the SWMM models than NH3-N and TP. The modelling performance tends to be better under heavy rain and significant pollutant concentrations, denoting a potentially more stable and reliable design of infrastructures. This study helps to improve the current understanding of the feasibility and robustness of using the SWMM model in sponge city design.

From data to decisions: understanding information flows within regulatory water quality monitoring programs

Most countries maintain regulatory requirements for testing of drinking water supplies to guide treatment procedures and ensure safe water delivery to consumers. It is unclear, however, if water quality data are always used effectively, particularly in low-resource settings. Efforts to improve the use of water quality data will benefit from a comprehensive understanding of existing systems for managing and sharing information. This study evaluates the methods used to organize, analyze, and transmit drinking water quality data among 26 water supplier or surveillance institutions and two regulatory agencies in six countries of sub-Saharan Africa. Following extensive qualitative and quantitative data collection, we developed data flow diagrams to map formal and informal water quality networks. We found high levels of similarities between the information systems established by different institutions operating under different regulatory structures. We determined that the key barriers to information flows were the limited aggregation and analysis of data and the poor enforcement of data sharing requirements. Our results suggest that broad reforms are necessary to improve the use of these water quality data to manage water safety. These measures could include strengthening enforcement of testing and reporting, building staff capacity for managing and using data, and integrating collection of water quality data with other information systems.

Dataset on physicochemical and microbial properties of raw water in four drinking water treatment plants based in South Africa.

The present paper aims at determining the status of surface water quality by applying the treatability index for the raw water in four water treatment plants (WTPs), namely Vaalkop, Klipdrift, Wallmansthal, and Cullinan. These plants are based in South Africa. Sampling was conducted from July 2011 to June 2018 (7 years). The collected water samples were analysed on monthly basis over the specified period. Three Hundred and thirty six (336) water samples were collected and analysed. The Treatability Index (TI) was calculated for twenty-one physicochemical and microbial parameters, which include pH, conductivity, chloride, sodium, potassium, hardness, alkalinity, precipitation potential, turbidity, colour, E. coli, organic carbon, chlorophyll, nitrite, ammonia, nitrates, phosphate, iron, manganese, and sulphate. The computed TI values range from 0.1 to 1755.5 and the water quality was unsuitable for a number of defined uses. The data demonstrated high treatment demand for raw water. On that note, the surface water from the monitored places is not suitable for drinking purposes. The data and treatability index denoted the need for treatment prior consumption. The collected water quality data can be reused for future references, modelling, and trending of historic data to understand current and prospect future changes in the properties of our raw water qualities.

Accuracy of long-term volunteer water monitoring data: A multiscale analysis from a statewide citizen science program.

An increasing number of citizen science water monitoring programs is continuously collecting water quality data on streams throughout the United States. Operating under quality assurance protocols, this type of monitoring data can be extremely valuable for scientists and professional agencies, but in some cases has been of limited use due to concerns about the accuracy of data collected by volunteers. Although a growing body of studies attempts to address accuracy concerns by comparing volunteer data to professional data, rarely has this been conducted with large-scale datasets generated by citizen scientists. This study assesses the relative accuracy of volunteer water quality data collected by the Texas Stream Team (TST) citizen science program from 1992-2016 across the State of Texas by comparing it to professional data from corresponding stations during the same time period. Use of existing data meant that sampling times and protocols were not controlled for, thus professional and volunteer comparisons were refined to samples collected at stations within 60 meters of one another and during the same year. Results from the statewide TST dataset include 82 separate station/year ANOVAs and demonstrate that large-scale, existing volunteer and professional data with unpaired samples can show agreement of ~80% for all analyzed parameters (DO = 77%, pH = 79%, conductivity = 85%). In addition, to assess whether limiting variation within the source datasets increased the level of agreement between volunteers and professionals, data were analyzed at a local scale. Data from a single partner city, with increased controls on sampling times and locations and correction of a systematic bias in DO, confirmed this by showing an even greater agreement of 91% overall from 2009-2017 (DO = 91%, pH = 83%, conductivity = 100%). An experimental sampling dataset was analyzed and yielded similar results, indicating that existing datasets can be as accurate as experimental datasets designed with researcher supervision. Our findings underscore the reliability of large-scale citizen science monitoring datasets already in existence, and their potential value to scientific research and water management programs.