Collected Water Quality Data Research Articles

Screening the Performance of a Reverse Osmosis Pilot-Scale Process That Treats Blended Feedwater Containing a Nanofiltration Concentrate and Brackish Groundwater.

A two-stage pilot plant study has been completed that evaluated the performance of a reverse osmosis (RO) membrane process for the treatment of feedwater that consisted of a blend of a nanofiltration (NF) concentrate and brackish groundwater. Membrane performance was assessed by monitoring the process operation, collecting water quality data, and documenting the blended feedwater's impact on fouling due to microbiological or organic means, plugging, and scaling, or their combination. Fluorescence and biological activity reaction tests were used to identify the types of organics and microorganisms present in the blended feedwater. Additionally, scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) were used to analyze suspended matter that collected on the surfaces of cartridge filters used in the pilot's pretreatment system. SEM and EDS were also used to evaluate solids collected on the surfaces of 0.45 µm silver filter pads after filtering known volumes of NF concentrate and RO feedwater blends. Water quality analyses confirmed that the blended feedwater contained little to no dissolved oxygen, and a significant amount of particulate matter was absent from the blended feedwater as defined by silt density index and turbidity measurements. However, water quality results suggested that the presence of sulfate, sulfide, iron, anaerobic bacteria, and humic acid organics likely contributed to the formation of pyrite observed on some of the membrane surfaces autopsied at the conclusion of pilot operations. It was determined that first-stage membrane productivity was impacted by the location of cartridge filter pretreatment; however, second-stage productivity was maintained with no observed flux decline during the entire pilot operation's timeline. Study results indicated that the operation of an RO process treating a blend of an NF concentrate and brackish groundwater could maintain a sustainable and productive operation that provided a practical minimum liquid discharge process operation for the NF concentrate, while the dilution of RO feedwater salinity would lower overall production costs.

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy

In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area. Since traditional procedures to collect water quality data are time-consuming, labor-intensive, and costly, water resources management has shifted from gathering field measurement data to utilizing remote sensing (RS) data. WDD has been threatened by various driving forces in recent years, such as contamination from different sources, sedimentation, nutrient runoff, salinity intrusion, temperature fluctuations, and microbial contamination. Therefore, this study aimed to retrieve and map WQIs, namely dissolved oxygen (DO) and chlorophyll-a (Chl-a) of the Wadi Dayqah Dam (WDD) reservoir from Sentinel-2 (S2) satellite data using a new procedure of weighted averaging, namely Bayesian Maximum Entropy-based Fusion (BMEF). To do so, the outputs of four Machine Learning (ML) algorithms, namely Multilayer Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVRs), and XGBoost, were combined using this approach together, considering uncertainty. Water samples from 254 systematic plots were obtained for temperature (T), electrical conductivity (EC), chlorophyll-a (Chl-a), pH, oxidation–reduction potential (ORP), and dissolved oxygen (DO) in WDD. The findings indicated that, throughout both the training and testing phases, the BMEF model outperformed individual machine learning models. Considering Chl-a, as WQI, and R-squared, as evaluation indices, BMEF outperformed MLR, SVR, RFR, and XGBoost by 6%, 9%, 2%, and 7%, respectively. Furthermore, the results were significantly enhanced when the best combination of various spectral bands was considered to estimate specific WQIs instead of using all S2 bands as input variables of the ML algorithms.

“The Gems of Water”: a co-created scientist-citizen approach for water quality monitoring

To better understand local water quality pollution by organic contaminants and how it affects communities around the world, “The Gems of Water” project aims to build a co-creative global scientist-citizen approach, actively engaging citizens and connecting them to scientists and to advanced water monitoring tools. The project applies the Stir Bar Sorptive Extraction technique with an advanced wide-screening method, used to assess the occurrence of agrochemicals, industrial compounds and pharmaceuticals in surface and groundwaters. In collaboration between Coral Conservation and the Joint Research Centre (JRC) of the European Commission, a study was conducted to identify contamination patterns which could be affecting the coral reef by investigating water quality in local rivers in the south Caribbean region of Costa Rica, an area characterized by agricultural activities linked to bananas production. Coral Conservation is a youth-led non-governmental organization focused on activities for the conservation and protection of the coral reef. Sampling locations for the study were jointly determined with members of the Coral Conservation group and included 13 sampling points from 3 different rivers and 2 wells. The pilot study allowed evaluation of the workflow for scientist-citizen collection of water quality data, whereby participants from the local community conduct sampling and extraction activities, while the wide-screening analysis of about 230 contaminants is performed at the JRC Water Laboratory. Considering the lessons learned through the Costa Rican pilot case, in this paper we discuss the elements required for successful scientist-citizen projects. Challenges are described and outlooks provided to improve citizens’ engagement projects and others participatory water quality monitoring activities. “The Gems of Water” project contributes to data collection of rarely monitored compounds in rural and remote areas through a scientist-citizen approach, addressing knowledge gaps on water quality and building bridges between science and society. The outlook is for such an innovative approach to support bottom-up management actions which can lead to alternative solutions in water quality management.

Keanekaragaman Ikan Hasil Tangkapan Nelayan di Pelabuhan Pulau Baai Kota Bengkulu

Pulau Baai Port is a crucial resource used as the main sea transportation route for some commodities and goods needed by principals when traveling to or from Bengkulu. Data collection was carried out at three stations The method used to determine stations is purposive sampling. Water quality data collection was also carried out at the three stations. The fish identification method is carried out by looking at morphological characteristics to facilitate classification using the Marine Fishes of the south identification book. A total of 18 species of fish found in the waters of the City of Bengkulu landed at the port Pulau Baai namely white pomfret fish (Pampus argenteus), black pomfret fish (Parastromateus niger), mackerel fish (Scomberomorini), mujair fish (Oreochromis mossambicus), beledang fish (Trichiurus lepturus), next fish (Pleuronectiformes), manyung fish (Arius thalassinus ), kerong kerong fish (Terapon jarbua), sea cork fish (Channa striata), big eye tuba fish (Thunnus obesus), mullet fish (Moolgarda seheli), anchovies (Engraulidae), mackerel fish (Scomberomorini), beleberan fish (Opisthopterus tardoore), Cladi fish (Osphronemus goramy), Kape-kape fish (Pentaprion longimanus), Gulamo fish (Johnius trachycephalus), and Sengin fish (Eleutheronema tetradactylumThen, from the results of calculating the diversity index at these three stations, the values range from 2.46-2.07, (medium). It can be concluded that the results of the ecological analysis at the three stations have moderate diversity with a uniform composition and no species dominates.

Water Quality in the Habitat of Sembilang Fish (Plotosus canius) in the Estuary of Langsa City

Sembilang Fish (Plotosus canius) is one of the fish found in estuarine waters, and it's quite popular with the community. Excessive fishing by the community causes the habitat of sembilang fish to be threatened to decrease. The purpose of this study was to determine the water quality and examine the environmental conditions of sembilang fish in the dry season and rainy season in the estuary waters of Langsa City as the basis for domestication. This study used a descriptive method by collecting water quality data at 3 research stations and collecting fish data that was taken from fishermen's catches in Langsa estuary waters. The results showed that the water quality parameters observed in the dry season and rainy season in the estuary were still in decent condition for the life and existence of sembilang fish. Season greatly affects the body weight of both male and female fish. In the dry season, the female weight ranges from 120–220 g and the male 120–240 g, while in the rainy season, the female weight ranges from 134-572 g and the male 136–260 g.

MOLLUSKS DIVERSITY IN THE PROTECTED COASTLINE OF BERBAK-SEMBILANG NATIONAL PARK INDONESIA

Mollusk has a role as a food source in benthic ecosystems. Presumably, there has been a decrease in its diversity on the protected coastline of Berbak-Sembilang National Park (BSNP) due to fluctuations in water quality parameters that threaten the sustainability of another biota in the web food system. The purpose of this study was to describe the diversity of mollusks and their correlation with water quality in the protected coastline area. The research methodology was carried out, namely: water quality data collection, mollusk sampling, diversity analysis, and correlation analysis. Principal component analysis and similarity analysis were used. The results showed that there were 28 species of mollusks, which were classified into two classes: Bivalvia (79%), and Gatropoda (49%), with 21 species. The mollusk abundance means 263.25 ind.m-2, and the diversity is categorized as low H'1 except stations 2 and 4. It is supported by the dominant species, namely Anadara granosa (36.61%), and Nutricola sp. (35.47%. There are two clusters of mollusks: open coastal areas are characterized by higher temperatures, nutrients, salinity, pH, currents, and brightness, and estuary clusters are characterized by higher dissolved oxygen and mollusk abundance.

One-Week-Ahead Prediction of Cyanobacterial Harmful Algal Blooms in Iowa Lakes.

Cyanobacterial harmful algal blooms (CyanoHABs) pose serious risks to inland water resources. Despite advancements in our understanding of associated environmental factors and modeling efforts, predicting CyanoHABs remains challenging. Leveraging an integrated water quality data collection effort in Iowa lakes, this study aimed to identify factors associated with hazardous microcystin levels and develop one-week-ahead predictive classification models. Using water samples from 38 Iowa lakes collected between 2018 and 2021, feature selection was conducted considering both linear and nonlinear properties. Subsequently, we developed three model types (Neural Network, XGBoost, and Logistic Regression) with different sampling strategies using the nine selected variables (mcyA_M, TKN, % hay/pasture, pH, mcyA_M:16S, % developed, DOC, dewpoint temperature, and ortho-P). Evaluation metrics demonstrated the strong performance of the Neural Network with oversampling (ROC-AUC 0.940, accuracy 0.861, sensitivity 0.857, specificity 0.857, LR+ 5.993, and 1/LR- 5.993), as well as the XGBoost with downsampling (ROC-AUC 0.944, accuracy 0.831, sensitivity 0.928, specificity 0.833, LR+ 5.557, and 1/LR- 11.569). This study exhibited the intricacies of modeling with limited data and class imbalances, underscoring the importance of continuous monitoring and data collection to improve predictive accuracy. Also, the methodologies employed can serve as meaningful references for researchers tackling similar challenges in diverse environments.

Geostatistical based optimization of groundwater monitoring well network design

Monitoring groundwater quality in economically important and other aquifers is carried out regularly as part of regulatory processes for water and other resource development. Many water quality parameters are measured as part of baseline monitoring around mining and onshore gas resource development regions to develop improved understanding of the hydrogeological system as well as to inform managerial decisions to assess and manage contamination risks and health hazards. Water quality distribution in an aquifer is most often inferred from point measurements from limited number of bores drilled at arbitrary locations. Estimating the distribution of water quality parameters in the aquifer based on these point measurements is often a challenging task and results in high uncertainty in the estimates due to limited data availability. Minimizing uncertainty can be achieved by drilling more bores to collect water quality data and several approaches are available to identify optimal bore hole locations to minimize estimation uncertainty. However, optimization of borehole locations is difficult when multiple water quality parameters are of interest and have different spatial distributions in the aquifer. In this study we use geostatistical kriging to interpolate a large number of groundwater quality parameters. Then we integrate these predicted values and use the Differential Evolution algorithm to determine optimal locations for bores that would simultaneously reduce spatial prediction uncertainty of all parameters. The method is applied for designing a groundwater monitoring network in the Namoi region of Australia for monitoring groundwater quality in an economically important aquifer of the Great Artesian Basin. Optimal locations for 10 new monitoring bores are identified using this approach.

Mapping river turbidity at a large basin-scale with a spatially transferable ensemble model using Landsat 8 multispectral imagery

ABSTRACT Mapping river water quality at a basin scale with satellite image data represents a tremendous technical challenge, since traditional empirical remote sensing models calibrated for specific reaches of a river cannot be applied to all the rivers and streams in the basin with an adequate prediction accuracy. In this study, we developed a multi-predictor ensemble model to map and assess water turbidity for all rivers and streams in the Tombigbee River Basin with Landsat 8 multispectral images. Coincident with Landsat 8 satellite overpass on 2 May 2019, we conducted boat-based water quality data collection in the confluence area of Tombigbee River and Black Warrior River near the city of Demopolis, Alabama. Based on the coincident satellite image and field water samples, we calibrated and evaluated the multi-predictor ensemble model, which consists four component empirical models. Our evaluations suggest that the multi-predictor ensemble model can improve turbidity estimation accuracy by 79% in comparison with the best traditional empirical model. More importantly, our multi-predictor ensemble model can be applied to other river and stream reaches across three major sub-basins with a high prediction accuracy. With the multi-predictor ensemble model, we derived a turbidity map for all rivers and streams with a width greater than 90 m in the basin, by processing three Landsat 8 image scenes. This basin-wide turbidity map indicates that the turbidity of rivers and streams exhibits longitudinal gradients and distinct spatial trends within the basin, which were related to the basin land cover/land use, hydrometeorological condition, and the spatial distribution of dams. Owing to its strong spatial transferability and high prediction accuracy, the ensemble model, in conjunction with synoptic multispectral satellite image data, is recommended as an effective and efficient tool for mapping and monitoring river and stream water quality at a large basin or regional scale.

Community‐driven science and science education: Living in and navigating the edges of equity, justice, and science learning

Community‐driven science and science education: Living in and navigating the edges of equity, justice, and science learning

Citizen Science for Water Quality Monitoring in the Meki River, Ethiopia: Quality Assurance and Comparison with Conventional Methods

A lack of water quality information for many water bodies around the world makes it difficult to identify global change and discover early signs of myriad threats to freshwater resources. This problem is widely seen in Ethiopia due to absence of regular monitoring. Citizen science has a great potential to fill these gaps in water quality data, but there is concern about the accuracy of data collected by citizen scientists. Moreover, there is a gap to engage citizen scientists in water quality monitoring, and there is still insufficient awareness of how citizen scientists can become part of a collaborative scheme. This study aimed to evaluate the accuracy of water quality collected by citizen scientists and characterize the water quality of the Meki River with the involvement of citizen scientists. The suitability of the river water for irrigation was evaluated using a combination of citizen science and conventional water quality data collection methods. Water temperature, turbidity, ammonia, phosphate, nitrate, nitrite, total alkalinity, total hardness, and pH were analyzed by both citizen scientists and in a conventional laboratory. The citizen scientists’ data, expressed as percent of synthetic standard solution concentrations, indicated good agreement for selected water quality parameters: 123.8 ± 24.7% for PO43−, 115.6 ± 6.3% for NO3−, 105.8 ± 7.4% for pH, and 133.3 ± 23.6% for NH4+. Thus, citizen scientists can monitor and collect water quality data accurately. From the results, the Meki River water can be used for irrigation, but pollution sources should be controlled to reduce further quality deterioration as the population increases.

Evaluation of Low-Cost UV-Vis Spectroscopy for Measuring Nitrate Using Synthetic Water Samples

Highlights A novel low-cost, modular spectroscopy system is compared to a standard system using synthetic samples, principal component analysis, and partial least squares regression. The information contained in the data produced by the two systems is similar according to principal component analysis. The low-cost system was able to accurately predict nitrate concentrations in concentrated and diluted samples using partial least squares regression. The methodology could be applied to water quality analysis in agriculture and water resources management. Abstract. Water quality data collection is an essential component of water systems management. For instance, the effective management of nutrients in hydroponic systems is necessary for maximizing yields efficiently and sustainably. Additionally, nutrients in natural and engineered waterbodies must be monitored to ensure they are meeting the required chemical characteristics for their ecological and social functions. However, conventional water quality data collection methods place limitations on water systems management due to their high resource requirements. Nitrate (NO3) is a major nutrient in ecological and agricultural systems, which can be reliably measured with ultraviolet-visible (UV-Vis) spectroscopy, a highly established technique for water quality analysis. The goal of this research was to evaluate a novel, low-cost, modular UV-Vis spectroscopy setup (GatorSpec) for the measurement of NO3 concentration in chemically complex solutions. UV-Vis absorbance of synthetic samples was measured using the GatorSpec and a commonly used bench-top laboratory spectroscopy system, the NanoDrop2000C. These data were analyzed using principal component analysis (PCA) to compare the spectral data produced by each system and partial least squares (PLS) regression to compare their ability to predict NO3 concentration. Results showed that data from both measurement systems were similar, indicating that the low-cost GatorSpec provided similar measurement accuracy to that of the laboratory reference system, the NanoDrop2000C. The PLS results revealed that for the diluted samples, the models derived from both systems were very good at predicting NO3 concentration. With these outcomes, it can be concluded that the GatorSpec is effective at measuring NO3 concentration in complex solutions and is comparable in performance to that of the NanoDrop2000C. In the future, this low-cost setup could be used to manage NO3 concentrations more efficiently in various applications such as hydroponic plant production, environmental monitoring, and stormwater treatment, which, in turn, could reduce the economic and environmental costs of these systems. Keywords: Low-cost, Synthetic samples, Ultraviolet-visible absorption spectroscopy, Water quality.

Application of QUAL2kw and Forecasting models for assessment of pollutants in upper Athi River catchment in Kenya

Water pollution in the Athi river drainage basin, the fourth largest drainage basin in Kenya, has the potential of adversely impacting the aquatic biota and communities living downstream of the river. The upper part of the basin has experienced rapid industrialization in the recent past and there has also been increased encroachment of wetlands and forests for agriculture and settlement to cater for the increased population. The objective of this study was geared towards the generation of scenarios to predict future water quality trends in the upper parts of the Athi river basin. The prediction of the future water quality trends was undertaken in two phases: (i) the development of longitudinal water quality profile using QUAL2Kw model and (ii) prediction of the future water quality trends using the SPSS Forecasting tool. The collection of water quality data in established sampling stations located in the upper parts of the basin was carried out between January and December 2018. The archival data for the period 2015-2018 was obtained from Water Resources Authority (WRA) database for the determination of future trends in selected water quality parameters. The correlation analysis between the observed and predicted water quality parameters yielded strong R2 values for temperature-0.82, electrical conductivity-0.99, total dissolved solids-0.94, biochemical oxygen demand-0.66, chlorophyll a-0.89, total nitrogen-0.75 and total phosphorous-0.94. The standard error ranged between 0.01-0.42, indicating that the model is reliable for predicting water quality parameters in the river. The available archival monthly data from 2015 to 2018 were averaged for time series forecasting to generate future water quality scenarios of the river for the period 2019-2030. The results showed that dissolved oxygen exhibit a decreasing trend in future indicating depletion of oxygen in water. There will also be significant increasing trends for Electrical Conductivity, Total Suspended Solids, Biochemical Oxygen Demand and Iron in future. There is a risk that the pollutants will increase to unsafe levels in the Athi River if appropriate management actions are not taken by the concerned agencies

Analysis of the Interaction between Lake and Groundwater Based on Water-Salt Balance Method and Stable Isotopic Characteristics.

To better protect lacustrine ecologies and understand the evolutionary process of lake environments, it is critical to study the interacting mechanisms between lakes and the surrounding groundwater. The Wuliangsu Lake watershed is the largest wetland in the Yellow River basin and is the discharge area of the Hetao Irrigation District (HID), which is one of the three largest agricultural production areas in China. Due to the influence of human activities, the discharge water from the HID has led to the deterioration of the Wuliangsu Lake ecology and the degradation of the lake environment. Based on long-term observation data and water sampling data collected in 2021, a water–salt equilibrium model was used to analyze the recharge rate of groundwater to the lake. The contribution rate of groundwater to lake recharge in the study area was calculated with a Bayesian mixing model by combining D and 18O stable isotope data. Furthermore, the environmental evolutionary process of the lake was also analyzed using the collected water quality data. The results show that channel drainage was the main source of recharge to Wuliangsu Lake, accounting for more than 75%, while groundwater contributed less than 5% of lake recharge. After implementing the ecological water supplement plan, the concentration of various ions in the lake decreased, the concentration of the total dissolved solids (TDS) in the lake decreased from 1.7 g/L in 2016 to 1.28 g/L in 2021, and the ecological environment was improved. The contribution of groundwater to lake recharge was quantitatively analyzed. The results of this study can facilitate the development of vital strategies for preventing the further deterioration of lake water quality and for protecting wetland ecologies.

Impacts of residential fertilizer ordinances on Florida lacustrine water quality

AbstractDespite the assumption that residential fertilizer ordinances improve regional water quality, their impacts across space and time largely remain unknown. Here, we analyze changes in water quality of lakes throughout the State of Florida from 1987 to 2018, comparing trends in water quality parameters before and after implementation of county‐wide fertilizer ordinances. We used a large dataset of publicly collected water quality data and linear mixed models to analyze ordinance impacts on total nitrogen, total phosphorus, chlorophyll a, and Secchi depth across 160 lakes throughout Florida. We further analyze water quality impacts relative to the type of ordinance (winter fertilizer ban, summer ban, nonseasonal ban, no ban). We found fertilizer ordinances favorably impacted lacustrine water quality, and winter (dry season) fertilizer bans had the greatest effect across all water quality metrics. Results of this study can be used to support the effectiveness of fertilizer ordinances across humid tropical and subtropical climate regions.

Spatially referenced Bayesian state-space model of total phosphorus in western Lake Erie

Abstract. Collecting water quality data across large lakes is often done under regulatory mandate; however, it is difficult to connect nutrient concentration observations to sources of those nutrients and to quantify this relationship. This difficulty arises from the spatial and temporal separation between observations, the impact of hydrodynamic forces, and the cost involved in discrete samples collected aboard vessels. These challenges are typified in Lake Erie, where binational agreements regulate riverine loads of total phosphorus (TP) to address the impacts from annual harmful algal blooms (HABs). While it is known that the Maumee River supplies 50 % of the nutrient load to Lake Erie, the details of how the Maumee River TP load changes Lake Erie TP concentration have not been demonstrated. We developed a hierarchical spatially referenced Bayesian state-space model with an adjacency matrix defined by surface currents. This was applied to a 2 km-by-2 km grid of nodes, to which observed lake and river TP concentrations were joined. The model generated posterior samples describing the unobserved nodes and observed nodes on unobserved days. We quantified the impact plume of the Maumee River by experimentally changing concentration data and tracking the change in in-lake predictions. Our impact plume represents the spatial and temporal variation of how river concentrations correlate with lake concentrations. We used the impact plume to scale the Maumee River spring TP load to an effective Maumee River TP spring load for each node in the lake. By assigning an effective load to each node, the relationship between load and concentration is consistent throughout our sampling locations. A linear model of annual lake node mean TP concentration and effective Maumee River load estimated that, in the absence of the Maumee River load, lake concentrations at the sampled nodes would be 23.1 µg L−1 (±1.75, 95 % CI, credible interval) and that for each 100 t of spring TP effective load delivered to Lake Erie, mean TP concentrations increase by 11 µg L−1 (±1, 95 % CI). Our proposed modeling technique allowed us to establish these quantitative connections between Maumee TP load and Lake Erie TP concentrations which otherwise would be masked by the movement of water through space and time.

Identification and Apportionment of Potential Pollution Sources Using Multivariate Statistical Techniques and APCS-MLR Model to Assess Surface Water Quality in Imjin River Watershed, South Korea

Reliable water quality monitoring data, identifying potential pollution sources, and quantifying the corresponding potential pollution source apportionment are essential for future water resource management and pollution control. Here, we collected water quality data from seven monitoring sites to identify spatiotemporal changes in surface water in the Imjin River Watershed (IRW), South Korea, distinguish potential pollution sources, and quantify the source apportionment from 2018–2020. An analysis was performed based on multivariate statistical techniques (MST) and the absolute principal component score-multiple linear regression (APCS-MLR) model. Statistically significant groups were created based on spatiotemporally similar physicochemical water quality characteristics and anthropogenic activities: low-pollution (LP) and high-pollution (HP) regions, and dry season (DS) and wet season (WS). There were statistically significant mean differences in water quality parameters between spatial clusters, rather than between temporal clusters. We identified four and three potential factors that could explain 80.75% and 71.99% in the LP and HP regions, respectively. Identification and quantitative evaluation of potential pollution sources using MST and the APCS-MLR model for the IRW may be useful for policymakers to improve the water quality of target watersheds and establish future management policies.

Development of a sensor and measurement platform for water quality observations: design, sensor integration, 3D printing, and open-source hardware.

A measurement and development platform for collecting water quality data (the WaterWatcher) was developed. The platform includes sensors to measure turbidity, total dissolved solids (TDS), and water temperature as variables that are often collected to assess water quality. The design is extensible for research and monitoring purposes, and all the design files are provided under open-source permissive licenses for further development. System design and operation are discussed for illustrative purposes. A block diagram indicates elements of mechanical, electrical, and software design for this system. The mechanical assembly used to house circuit boards and sensors is designed using 3D printing for rapid prototyping. The electronic circuit board acts as a carrier for an Arduino 32-bit microcontroller board and an associated cellular module along with a GPS for geolocation of water quality measurements. The cellular module permits data transfer for Internet of Things (IoT) functionality. System operation is set up using a command line interface (CLI) and C + + code that allows for calibration coefficients and human-readable transfer functions to be defined so that sensor voltages are related to physical quantities. Data are cached on a secure digital (SD) card for backup. The circuit was calibrated, and system operation assessed by deployment on an urban reservoir. Biogeochemical cycles were identified in the collected data using spectrogram and semivariogram analyses to validate system operation. As a system with hardware and software released under an open source license, the WaterWatcher platform reduces the time and effort required to build and deploy low-cost water quality measurement sensors and provides an example of the basic hardware design that can be used for measurements of water quality.

Equilibrium analysis of dissolved oxygen in Lake Wuliangsuhai during ice-covered period

为探明中国北方浅水湖泊乌梁素海冰封期水体溶解氧平衡的内在机理，于2021年1—2月在湖心处布设了一台水质在线监测浮标，收集到包括溶解氧等在内的水质数据. 通过对溶解氧数据的小波降噪处理，结合气象资料，模拟分析了冰生长及稳定期内水体溶解氧的变化趋势，定性分析了水体溶解氧的平衡机理. 结果表明：湖泊的日均最高产氧速率为7.19 mg/(L ·d)，最低产氧速率为2.01 mg/(L ·d); 日均最高耗氧速率为7.13 mg/(L ·d)，最低耗氧速率为2.37 mg/(L ·d). 24 h的单位时间平均最高产氧速率为0.55 mg/(L ·h)，最低产氧速率为0 mg/(L ·h); 单位时间平均最高耗氧速率为0.36 mg/(L ·h)，最低耗氧速率为0.08 mg/(L ·h). 由此说明小时间尺度下溶解氧的补充消耗不均衡导致了大时间尺度下的溶解氧不平衡，进而产生了冬季湖泊的亏氧现象. 通过进一步溶解氧驱动因素与水环境因子响应关系的分析发现，浊度、水温与产氧速率呈显著负相关，叶绿素a与产氧速率和耗氧速率均呈显著正相关，表明了这些限制性水环境因子在一定程度上影响了冰下水体的溶解氧平衡.;In order to probe into the internal mechanism of dissolved oxygen (DO) equilibrium in the shallow Lake Wuliangsuhai in Northern China during the ice-covered period, a set of online water monitoring buoys were arranged in the center of lake for collection of water quality data from January to February, 2021. Upon the wavelet denoising processing of DO and based on the meteorological data, the DO changes during the growth and stability period of ice was simulated and analyzed, followed by a qualitative analysis of the mechanism of the DO equilibrium. Results show as follow: The lake's maximum daily average oxygen production rate was 7.19 mg/(L ·d); the minimum was 2.01 mg/(L ·d). The maximum daily oxygen consumption rate was 7.13 mg/(L ·d) and the minimum was 2.37 mg/(L ·d). The maximum 24-hour per-unit-time average oxygen production rate was 0.55 mg/(L ·h) and the minimum was 0 mg/(L ·d). The maximum per-unit-time average oxygen consumption rate was 0.36 mg/(L ·h) and the minimum was 0.08 mg/(L ·h). This study indicates that the disequilibrium between the DO production and consumption on small time scales results in DO disequilibrium on large time scales, causing oxygen deficiency in the lake in winter. Further analysis on the relationship between DO driving factors and water environment factors suggests a negative correlation between turbidity, water temperature and oxygen production rate and a positive correlation between chlorophyll-a and oxygen production and consumption rate, which indicates that these restrictive water environmental factors influence the DO equilibrium in water body under ice.

Evaluación de la calidad del agua subterránea en un sector de la Zona de Reglamentación Especial de Los Pantanos de Villa

Los Pantanos de Villa is a Protected Natural Area (ANP), a Ramsar site, and the only designated Wildlife Refuge in Lima. Urban growth, industrialization and low environmental awareness on the part of local inhabitants pose threats to the Pantanos de Villa Wildlife Refuge ecosystem. Located close to Pantanos de Villa is the Villa Baja human settlement, which does not have drinking water and sewage services, and therefore uses silos, as well as discharging its sewage (black and gray waters) into unlined canals. In order to evaluate the influence of anthropogenic activities on the quality of the groundwater in Pantanos de Villa, surveys of the local population were conducted and water samples were taken. For the taking of samples, the methodology contained in the US Geological Survey’s National Field Manual for the Collection of Water-Quality Data (NFM) was followed. The results obtained were compared with environmental quality standards (ECA), and a significant difference was observed between the samples collected outside and within the Protected Natural Area. The samples collected outside the ANP showed that certain parameters do not meet the aforementioned standards (nitrates, fecal coliforms, Escherichia coli), thereby constituting a risk to human and animal health. However, at the same time, the lower levels for certain parameters within the ANP demonstrate the regulatory and phytoremediation functions of the wetland.