Lake Water Quality Monitoring Research Articles

Review on the control and mitigation strategies of lake cyanobacterial blooms

由于人类活动和全球气候变化的叠加影响，湖泊富营养化和蓝藻水华仍是未来相当长一段时间内的水生态环境问题.蓝藻水华暴发会引发湖泊生态系统的灾害和饮用水安全风险，因此湖内蓝藻水华防控必不可少.现有蓝藻水华防控长效方法主要基于营养盐控制理论、浅水湖泊稳态转换理论和生物操纵理论，技术措施包括内源营养盐控制、生态修复、生物操纵.应急处置方法可以削减局部水域蓝藻水华强度，主要包括物理、化学方法.本文基于国内外研究治理案例，梳理了单项蓝藻防控技术运用的边界条件、蓝藻削减效果及防控成本.湖泊富营养化控制和蓝藻水华防控是一个长期而艰巨的系统工程，必须采取流域污染削减和湖内防控相结合的治理策略.湖泊水质与水生态的持续跟踪监测以及水质和水生态预测模型的构建，是动态调整治理方案、保障治理效果长效稳定的基础.;Lake eutrophication and cyanobacterial blooms are water ecological and environmental problems for a long time to come, due to the synergistic effects of human activities and global climate change. The outbreak of lake cyanobacterial blooms would entail water ecological risk and drinking water crisis, thus curative methods to suppress the proliferation of cyanobacteria bloom are imperative. Nutrient control, shallow lake regime shifts and biological manipulation are theoretical basis for the long-term strategies for cyanobacteria blooms prevention and control. Accordingly, external and internal nutrient loading reduction, ecological restoration and biomanipulation are sustainable control and management approaches. In addition, methods used for emergency situations can mitigate the intensity of cyanobacterial bloom, mainly including physical and chemical approaches. Based on the review of the domestic and international cases on cyanobacterial bloom control, we sort out the applicable conditions, cyanobacterial mitigation efficiency and performance cost of each approach. In fact, lake eutrophication management and cyanobacterial bloom mitigation are a long-term and systematic process. These in-lake strategies should invariably be accompanied by nutrient input reductions. The continuous monitoring of lake water quality and water ecology as well as the building of prediction model support the further adjustment of strategies and measures to ensure long-term success and sustainability.

Analysis of recurring patchiness in satellite-derived chlorophyll a to aid the selection of representative sites for lake water quality monitoring

Horizontal patchiness of water quality attributes in lakes substantially influences the ability to accurately determine an average condition of a lake from traditional in situ sampling. Therefore, spatial variability has to be accounted for in monitoring programmes which aim at determining the states and trends of ecosystem attributes. We used five years of Sentinel-2 Multispectral Instrument (MSI) data and conducted spatial analyses of surface chlorophyll a (Chl) concentration to map its variability and provide concrete recommendations for resource managers to design in situ sampling programmes. First, we developed a regional calibration of Chl predictions by C2RCC, an openly available processor for atmospheric corrections and water constituent retrieval, using in situ data from eleven temperate lakes in the central North Island of Aotearoa New Zealand. Using 93 match-up samples, we re-fitted C2RCC’s partitioning of constituent absorption coefficients to achieve an improved prediction accuracy for Chl (r2 = 0.79, root mean square error = 5.4 mg m−3). The new relationship was applied to all cloud-free images for thirteen regional lakes for further spatial analysis. We found that the medians calculated within areas of different sizes around in situ sampling locations may increase or decrease, illustrating an unpredictable uncertainty of the representativeness of any in situ sample. We went on to summarise five years of spatial variability by assessing each pixel for its tendency to be near the lake median Chl, higher (near the upper quartile) or lower (near the lower quartile). This spatiotemporal analysis revealed recurring patchiness that we converted to an indication of the representativeness of any location in the lake useful for the selection of more representative sites for future monitoring programmes.

Assessment of Spatial and Vertical Variability of Water Quality: Case Study of a Polymictic Polish Lake

UE regulations focus on methods of water quality monitoring and their use in rational management practices. This study investigated horizontal and vertical variations of electrical conductivity (EC), pH, dissolved oxygen (DO), and chlorophyll a (Chl-a) in a shallow polymictic lake. Monitoring of short-term variability of physical and chemical lake water parameters is a critical component in lake management, as it influences aquatic life. Based on the field research, maps of spatial distribution of the parameters were drawn. Using two methods: (1) a classical approach to water column measurements, from the top to the bottom (TB), in which the reference point is always a surface layer (SL), and (2) a newly introduced method of lake water quality monitoring based on a nearest neighbor (NN) approach; a comparison of higher and lower layers of the water column. By subtracting partial maps of spatial variability for different depths, final raster images were obtained. The NN method is rather absent in the limnology literature worldwide. Vertical and horizontal variability of the tested parameters in the polymictic, shallow Lake Bikcze (Poland) was presented in the results. In the presented paper, the commonly used TB method emphasized the role of the surface layer in shaping the variability of physicochemical parameters of lake waters. It shows a general trend of parameters’ changes from the top, to the bottom. The newly presented NN method, which has a major advantage in its simplicity and objectivity, emphasized structural differentiation within the range of variability. The nearest neighbor method was more accurate in showing the actual structure of fluctuation of parameters with higher fluctuation in the water column. Its advantage is a detailed recognition of the vertical variability of selected parameters in the water column. The method may be used regardless of the lake depth, its location in climatic zone, and/or region.

Chemical speciation of selected toxic metals and multivariate statistical techniques used to assess water quality of tropical Mexican Lake Chapala.

Mexican Lake Chapala is used as water supply for human consumption. Consequently, water quality of this lake is of paramount importance for the lake's wellbeing. The contribution presented in this paper investigates monitoring and assessment of lake water quality using water quality index (WQI), metal chemical speciation, and multivariate statistical techniques. Descriptive statistics shows total metal concentrations undetected conferring the lake a healthy status. Dissolved Cd and Pb exceed criterion continuous concentration limit, whereas Zn is below this limit indicating that water quality is satisfactory for aquatic life. However, WQI indicates poor water quality attributed to failure of conductivity, total solids, nitrogen, and phosphates, due to industrial and agro-industrial effluents. Metal speciations indicate that the presence of low concentrations of dissolved metals reflect interactions with gills of fish through metal-biotic ligand complexes affecting water quality. Positive correlations are obtained between conductivity and nitrates, indicating that agricultural activities and fertilizer runoffs increase the conductivity and that the environmental state of lake is being altered by human activities. Factors F1 (31%), F2 (19%), and F3 (11%) represent 61% of variability; F1 and F2 corroborate the pressure exerted by pollutants related with fertilizers and agrochemicals; F3 contains Zn and Pb with positive loads attributed to influx of tourist visitors. Sites S4, S5, S6, and S9 are identified as the most environmentally affected by COD, Alk*, pH, Cl-, nitrites, phosphates, and TS. Multivariate techniques permit to conclude that environmental stress of Lake Chapala is caused by variables pertaining to agrochemical, fertilizers and municipal wastes.

Citizen Scientists Contribute to Real-Time Monitoring of Lake Water Quality Using 3D Printed Mini Secchi Disks

Citizen science aims to mobilise the general public, motivated by curiosity, to collect scientific data and contribute to the advancement of scientific knowledge. In this article, we describe a citizen science network that has been developed to assess the water quality in a 100 km long tropical lake-estuarine system (Vembanad Lake), which directly or indirectly influences the livelihood of around 1.6 million people. Deterioration of water quality in the lake has resulted in frequent outbreaks of water-associated diseases, leading to morbidity and occasionally, to mortality. Water colour and clarity are easily measurable and can be used to study water quality. Continuous observations on relevant spatial and temporal scales can be used to generate maps of water colour and clarity for identifying areas that are turbid or eutrophic. A network of citizen scientists was established with the support of students from 16 colleges affiliated with three universities of Kerala (India) and research institutions, and stakeholders such as houseboat owners, non-government organisations (NGOs), regular commuters, inland fishermen, and others residing in the vicinity of Vembanad Lake and keen to contribute. Mini Secchi disks, with Forel-Ule colour scale stickers, were used to measure the colour and clarity of the water. A mobile application, named “TurbAqua,” was developed for easy transmission of data in near-real time. In-situ data from scientists were used to check the quality of a subset of the citizen observations. We highlight the major economic benefits from the citizen network, with stakeholders voluntarily monitoring water quality in the lake at low cost, and the increased potential for sustainable monitoring in the long term. The data can be used to validate satellite products of water quality and can provide scientific information on natural or anthropogenic events impacting the lake. Citizens provided with scientific tools can make their own judgement on the quality of water that they use, helping toward Sustainable Development Goal 6 of clean water. The study highlights potential for world-wide application of similar citizen-science initiatives, using simple tools for generating long-term time series data sets, which may also help monitor climate change.

Quantifying Turbidity Variation for Lakes in Daqing of Northeast China Using Landsat Images From 1984 to 2018

Water turbidity is an important proxy to measure water quality and environmental conditions. Based on extensive field data and Landsat data (2011–2018), this article developed a retrieval model suitable for turbidity. The determination coefficient ( R 2) of the model was 0.946 and the root-mean-square error was 23.82 NTU. The model was implemented to obtain the turbidity information of hundreds of lakes in Northeast China (1984–2018). The results revealed the distinctive spatial pattern of water turbidity values of the lakes (i.e., high turbidity in the south; low turbidity in the northwest; and moderate turbidity in the east). In terms of temporal pattern, the water turbidity values of most lakes trended downward at an average rate of 1.39 NTU/a ( P R = 0.56, P R P < 0.05). Water turbidity varies because of the interaction of multiple factors (e.g., area, temperature, water depth, precipitation, and land use) instead of one factor. This article highlights the potential of remote sensing in large-scale and long-term monitoring of lake water quality, and provides important information and support for water quality management in China.

基于深度神经网络算法的水体透明度反演方法

水体透明度能够直观反映湖泊水质状态，掌握长时间大尺度湖泊水体透明度是控制和改善湖泊水生态环境的关键。由于滇池的水质原位监测工作起步较晚，导致长时间序列的历史湖泊水体透明度数据的缺失。为此，以滇池为研究区，以深度神经网络算法为理论基础，以原位监测和MODIS遥感影像为数据，对2001年1月1日-2018年12月31日滇池水体透明度进行反演，并利用地理空间分析方法探讨了滇池湖泊水体透明度时空变化特征。研究结果表明：（1）提出的反演模型具有较好的性能（RMSE=0.1359，MAE=0.1134），能够客观反映湖泊水体透明度状况；（2）时间变化特征分析结果表明，滇池水体透明度总体呈现下降趋势，综合变化率为-0.08 m/10 a；（3）空间变化特征分析结果表明，水体透明度较高的区域下降率较大，水体透明度较低的区域变化趋势相对稳定，距离城区及居民区较近的水体透明度相对较低；人类活动将成为影响滇池水体透明度变化的重要因素，同时也是造成滇池水体污染的主要因素。;Lake is an important ecological resource, which not only determines the quality of regionally ecological environment, but also guarantees the sustainable development of the city. Lake ecological environment can reflect the environmental situation of the region. Secchi depth (SD) can directly reflect the state of lake water quality, and mastering long-term and large-scale lake SD is the key to control and improve the lake water environment. Dianchi Lake, located in the middle of Yunnan Guizhou Plateau, is the sixth largest freshwater lake in China. It is the main water area for irrigation in Kunming area, and it is also the basic condition for urban development. Therefore, Dianchi Lake is chosen as the research area. Because the in-situ monitoring of lake water quality for this study area was started late, there are few historical SD data. So, it is necessary to simulate and estimate SD by remote sensing images. In this paper, deep neural network algorithm was used to invert the SD of Dianchi Lake from January 1, 2001 to December 31, 2018 based on in-situ data and MODIS remote sensing images, and explored its spatial-temporal variation characteristics by geospatial analysis. The in-situ data was the daily monitoring values of 10 stations (Baiyukou, Caohai Center, Dianchi South, Duanqiao, Guanyinshan East, Guanyinshan West, Guanyinshan Center, Haikou West, Huiwan Center, and Luojiaying) from 2001 to 2010 and the monthly monitoring values of 2 regions (Caohai and Waihai) from 2018, which was provided by Yunnan Academy of Environmental Sciences; MODIS data was provided by NASA LAADS Web, with daily time resolution and 500 m spatial resolution. The methods used in this paper included Grey Relational Analysis (GRA), Long-Short Term Memory (LSTM), and Theil-Sen slope estimation. In the construction of SD inversion estimation model, the main methods include Correlation Analysis (C), Outlier Processing (O), Denoising Processing (D), and estimation model (COD-LSTM). The results showed that (1) the inversion model proposed in this paper had better performance (RMSE=0.1359, MAE=0.1134), which can objectively reflect SD status. (2) The analysis of temporal variation characteristics indicated that the SD of Dianchi Lake presented a downward trend with an average comprehensive change rate of -0.08 m/10 a. (3) The analysis of spatial variation characteristics indicated that the rate of decline in the region with higher SD was larger, and the trend in the region with lower SD was relatively stable. The SD near the urban and residential areas was relatively low. Human activities would become an important factor affecting the SD changes in Dianchi Lake, and it was also the main factor causing lake pollution.

An Open-source Wireless Platform for Real-time Water Quality Monitoring with Precise Global Positioning

Sustainable development associated with the agri-cultural field of Arequipa, a region in economic growth, is vul-nerable to contamination of water resources, putting production systems and food security at risk. Therefore, it is necessary to implement an automated system to control, management, and monitor this vital resource. The proposed work proposed a system to measure water quality monitoring in reservoirs and lakes with high accurate related to global positioning. It includes an embedded computer, multiparameter sonde, and an additional dual GNSS/INS in hardware architecture. The software architecture is fully open-source with compatibility, modularity, and interoperability features between Python and MySQL, al-lowing data management for real-time data in visual interface on a platform that stores unlimited data logging, monitors and analyzes. The proposed system is validated in an experimental test that measures the water quality of a huge agricultural reservoir, where certified instrumentation is mandatory, as compared to other methods used locally for this action.

Study on the temporal and spatial distribution of chlorophyll a in Erhai Lake based on multispectral data from environmental satellites

Satellite remote sensing technology presents advantages of macroscopicity, timeliness and cost effectiveness and has been increasingly used in lake water quality monitoring. In this paper, an empirical model for the remote sensing inversion of the chlorophyll a (Chl-a) concentration in Erhai Lake was established using ground monitoring Chl-a concentration data and multispectral remote sensing data from environmental satellites from 2010 to 2017. The average absolute error and relative error were 1.92 mg/m3 and 22%, respectively. A 10-year remote sensing inversion of Erhai blooms identified the temporal and spatial distribution characteristics of blooms and showed that the occurrence frequency of blooms was 37%, and they were mainly in the form of light algal blooms at a local scale. Moderate and severe blooms occurred at a frequency of 42%, mainly from Oct. to Jan. Moderate algal blooms were distributed along the southern and northern coasts and in coastal areas, while severe algal blooms were distributed in the northern section and across the entire lake. During the bloom period, the growth rate of the bloom area reached 102 km2/d, which was faster than the reduction rate (90 km2/d). Bloom events generally lasted for 6–37 days. The inflow of pollution sources led to a higher frequency of blooms in the coastal zone than in the lake center, and the frequency in the northern section was nearly twice as high as that in the southern section. Most blooms in Erhai Lake occurred from late summer to winter (i.e., Jul. to Jan. of the following year) because of the higher average air temperature (AT) and lower wind speed (WS) in winter and spring and the amount of precipitation in summer and autumn. The remote sensing method captured the high-risk areas and the spatial-temporal evolution trend of algal blooms, and the model provided support for the prevention and control of lake algal blooms; however, this work should be complemented by ground monitoring data for cloudy days.

CROWDSOURCING WATER QUALITY WITH THE SIMILE APP

Abstract. This paper aims at presenting the application for lake water quality monitoring which has been developed in the framework of SIMILE (Informative System for the Integrated Monitoring of Insubric Lakes and their Ecosystems) Interreg Italy-Switzerland project. The objective of SIMILE project is to facilitate the monitoring of the Maggiore, Como, and Lugano lakes through the integration of different techniques: in situ monitoring with buoys, remote sensing and citizen science. A mobile application has been designed in agreement with the project partners, who are also actors working for lake quality monitoring, such as CNR (Italian National Research Council) and ARPA (Agency for prevention and environmental protection). The developed application allows to collect data over the area of interest, such as pictures and parameters which can be acquired by visual inspection as well as with appropriate tools, depending on the user typology. The application has then been implemented with open source software to foster its use also for other projects with similar goals. In the paper, the design choices, the architecture and the implementation details are described.

Estimating Chlorophyll-a of Inland Water Bodies in Greece Based on Landsat Data

Assessing chlorophyll-a (Chl-a) pigments in complex inland water systems is of key importance as this parameter constitutes a major ecosystem integrity indicator. In this study, a methodological framework is proposed for quantifying Chl-a pigments using Earth observation (EO) data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and 8 Operational Land Imager (OLI) sensors. The first step of the methodology involves the implementation of stepwise multiple regression (MLR) analysis of the available Chl-a dataset. Then, principal component analysis (PCA) is performed to explore Greek lakes’ potential interrelationships based on their Chl-a values in conjunction with certain criteria: their characteristics (artificial/natural), typology, and climatic type. Additionally, parameters such as seasonal water sampling and the date difference between sampling and satellite overpass are taken into consideration. Next, is implemented a stepwise multiple regression analysis among different groups of cases, formed by the criteria indicated from the PCA itself. This effort aimed at exploring different remote sensing-derived Chl-a algorithms for various types of lakes. The practical use of the proposed approach was evaluated in a total of 50 lake water bodies (natural and artificial) from 2013–2018, constituting the National Lake Network Monitoring of Greece in the context of the Water Framework Directive (WFD). All in all, the results evidenced the suitability of Landsat data when used with the proposed technique to estimate log-transformed Chl-a. The proposed scheme resulted in the development of models separately for natural (R = 0.78; RMSE = 1.3 μg/L) and artificial lakes (R = 0.76; RMSE = 1.29 μg/L), while the model developed without criteria proved weaker (R = 0.65; RMSE = 1.85 μg/L) in comparison to the other ones examined. The methodological framework proposed herein can be used as a useful resource toward a continuous monitoring and assessment of lake water quality, supporting sustainable water resources management.

Simulation System of Lake Eutrophication Evolution based on RS & GIS Technology—a Case Study in Wuhan East Lake

Water is the source of life and monitoring water quality is very important. Traditional water quality monitoring often uses artificial methods, taking water samples on the spot, and then analysing them in the laboratory. This method is time-consuming and labour-intensive, and is affected by time, weather and human activities. It is not only inaccurate, but also difficult to multi-period. It is difficult to monitor large areas of water. This study proposes an intelligent water quality monitoring system— Simulation System of Lake Eutrophication Evolution based on RS & GIS Technology (SSLEE). Based on the .NET Framework, the system interface design is completed through Windows Form platform. C#, IDL and ArcGIS Engine are used to develop the whole system. The system can manage the measured data, remote sensing image data, preprocess the remote sensing image and retrieve the chlorophyll a concentration. Taking Wuhan East Lake as an example, the applicability of SSLEE system is demonstrated. Compared with the measured results, the error is small, indicating that the system is reliable in water quality monitoring of inland lakes.

Comprehensive environmental monitoring and assessment of protected wetland and lake water quality in Croatia and Serbia.

The irresponsible and uncontrolled use of water bodies for long period of time leads to pollution and water quality degradation, particularly in sensitive protected terrestrial and aquatic areas as lakes and wetlands situated in the vicinity of human habitations. Vulnerable ecosystems, the Lake Zobnatica, Serbia and the Wetlands of Tompojevci, Croatia, are characterized as protected natural areas and are surrounded by agricultural land and developed anthropogenic activities. In the samples of surface, ground water and agricultural runoff the key physicochemical parameters were determined. The results of analysed water characterized the Lake water quality class as IV-V. The measured parameters for the Lake show the high values of nutrients, particularly in ground water and agricultural runoff, pH value higher than 8.5 and lower values of dissolved oxygen (DO) than 5mg/L in the most of the examined water samples. According to the results and maximal allowable values Wetlands belong to the class V water quality, with high trophic state and poor eco-status according to Croatian and Serbian national Laws and By-Laws. The pH value higher than 8.5 of ground water in Wetlands of Tompojevci signifies alkaline groundwater. The maximum concentrations of orthophosphates in ground water and agricultural runoff (10.875 and 10.886mg/L, respectively) imply the high level of agricultural pollution. Research study of water samples has been performed from 2017 to 2019 within the Interreg IPA CBC Croatia-Serbia Project AF_HR-RS135_SeNs_Wetlands. This type of parallel and comparative research in Serbia and Croatia was conducted for the first time.

The Use of Multisource Optical Sensors to Study Phytoplankton Spatio-Temporal Variation in a Shallow Turbid Lake

Lake water quality monitoring has the potential to be improved through integrating detailed spatial information from new generation remote sensing satellites with high frequency observations from in situ optical sensors (WISPstation). We applied this approach for Lake Trasimeno with the aim of increasing knowledge of phytoplankton dynamics at different temporal and spatial scales. High frequency chlorophyll-a data from the WISPstation was modeled using non-parametric multiplicative regression. The ‘day of year’ was the most important factor, reflecting the seasonal progression of a phytoplankton bloom from July to September. In addition, weather factors such as the east–west wind component were also significant in predicting phytoplankton seasonal and diurnal patterns. Sentinel 3-OLCI and Sentinel 2-MSI satellites delivered 42 images in 2018 that successfully mapped the spatial and seasonal change in chlorophyll-a. The potential influence of localized inflows in contributing to increased chlorophyll-a in mid-summer was visualized. The satellite data also allowed an estimation of quality status at a much finer scale than traditional manual methods. Good correspondence was found with manually collected field data but more significantly, the greatly increased spatial and temporal resolution provided by satellite and WISPstation sensors clearly offers an unprecedented resource in the research and management of aquatic resources.

Trophic status assessment of small turbid lakes comparing remote sensing and in situ data: case study at lower Danube floodplain

The aim of the study was to compare in situ and remote sensing data, in order to assess the applicability of satellite images in water quality monitoring of floodplain lakes. Two indicators of trophic status were compared: chlorophyll a and total suspended matter. Two lakes on Lower Danube floodplain were selected: Srebarna and Malak Preslavets. Data were obtained in July and August 2018. Sentinel 2 MSI L1c images were analyzed in SeNtinel Application Platform (SNAP), (v. 6.0). According to in situ data, Srebarna Lake indicated status of eutrophication, while Malak Preslavets experienced hypertrophic conditions. Satellite data indicated eutrophic conditions for both lakes. Comparing the results from in situ and satellite data, chlorophyll a showed higher correlation (r = 0.66) and comparable results. On the other hand, significantly overestimation of suspended matter according to satellite data were found, as well weaker correlation (r = 0.57) between both methods. Remote sensing i.e. Sentinel products are emerging as a powerful tool in environmental observation. Although weather conditions could have significant impact on environmental dynamic especially in floodplain lakes, combining and comparing of different methods could improve the preciseness of the methodology as well as assessment reliability.

Application of High-resolution Satellite Imagery in Water Quality Monitoring of Rivers and Lakes

The traditional water quality monitoring needs a lot of manpower and financial resources, but with the advantage of high time efficiency and other advantages of high-score data for information extraction, analysis and monitoring can be timely, large-scale monitoring of specific water, make up for the shortcoming of traditional water quality environmental monitoring. This paper introduces the theoretical method and technical process of monitoring surface water body with high score data. First, the original remote sensing data are preprocessed by radiometric calibration, geometric correction, atmospheric correction and water extraction, and then an inversion model is established to invert the chlorophyll a concentration, and then the main monitoring indicators are established, such as suspended matter, transparency and eutrophication index.

Development of a Robotic Airboat for Online Water Quality Monitoring in Lakes

Maintenance of water resources through collection of water followed by laboratory analysis, is a key factor in the measurement of water quality. The main difficulty for water collection and analysis is the logistics of the process, since the collections are often made by mall boats in very distant aquifers, applying manual processes, and are sometimes based on few samples. In this paper, the development, construction, and implementation of a robotic airboat to measure water quality in lakes has been described. The airboat was developed in the form of a mini-boat, in a fiberglass structure, whose interior housed a battery, a Raspberry Pi mini-computer, a Wi-Fi router for connection to a notebook, tablet or cell phone, and sensors connected to the Arduino platform. The airboat was validated in two lagoons in Fortaleza, and in a reservoir in the city of Pacatuba. The results were collected with the purpose of analyzing the parameters of each lagoon. The main contribution of this work was the development of an autonomous system of acquiring water parameters from several points of the lagoon.

Spatial Distribution Characteristics and Influencing Factors of Weishan Lake

Weishan Lake is one of the four lakes in the Nansi Lake and an important water transport hub for the east route of the South-to-North Water Transfer Project. The water quality study of Weishan Lake is conducive to a comprehensive understanding of the water quality and ensure the smooth progress of the South-to-North Water Transfer Project. This paper uses the water quality comprehensive pollution index evaluation method to analyse the water quality of Weishan Lake, and uses ArcGIS drawing software to spatially distribute the concentration of CODMn, NH3-N, TP, TN and SS in Weishan Lake water quality monitoring. Visualize to analyse the maximum impact factor for each space. At the same time, this paper combines the water quality evaluation results of Weishan Lake and the cause of water pollution, and puts forward the control countermeasures suitable for the pollution control of the basin, which provides a basis for the Weishan Lake watershed management and the realization of the water quality target of the South-to-North Water Diversion Project.

Smartphone-based System for water quality analysis

Water quality in rural areas is difficult to monitor due to lack of connectivity from different water laboratories. In other areas, location-based real-time water quality data collection is a tedious job and highly dependent on human intervention. The presented paper introduces a low-cost battery operated smartphone-based embedded system design to measure different water quality parameters in various remote locations. Developed system measures pH, total dissolved salt (TDS) and temperature of the water samples using off the shelf available sensors. Measured pH and TDS dataset have been used to derive other water quality parameters using standard mathematical relationships such as salinity, oxygen reduction potential and conductivity. Front-end readout interface circuit has been designed and interfaced with 8-bit microcontroller along with classical Bluetooth module for measurement, data acquisition, and logging purpose. A dedicated smartphone-based application offers analysis and cloud data storage possibilities. It also provides facility to analyze water quality data with location information on Google map for quick judgment and easy understanding. The developed smartphone-based application provides the facility of auto-calibration feature for rapid and on-site usage. Developed smartphone-based application also opens up the possibility to share the data and warnings using different options such as SMS, WhatsApp and E-mail. Overall device has dimensions of 11.0 × 8.0 × 4.0 (in cm), weighs 350 g and runs with 9-V rechargeable battery. Obtained results have been validated with standard water quality measurement system from Eutech Instruments, and it has been observed that measured and calculated parameters are acceptable according to Indian water quality standards. Various statistical and artificial neural network-based modeling techniques have been used to convert measured water quality parameters to a single water quality index for easy and rapid judgment. The developed water quality measurement system has been used for multiple applications to explore the utility of the system such as instant water quality judgment and real-time water quality analysis of different water sources. One of the other explored applications is the real-time water quality monitoring of small ponds and lakes.

An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators

In-situ monitoring of lake water quality in synergy with satellite remote sensing represents the latest scientific trend in many water quality monitoring programs worldwide. This study investigated the suitability of the Operational Land Imager (OLI) instrument onboard the Landsat 8 satellite platform in accurately estimating key water quality parameters such as chlorophyll-a and nutrient concentrations. As a case study the largest freshwater body of Greece (Trichonis Lake) was used. Two Landsat 8 images covering the study site were acquired on 30 October 2013 and 30 August 2014 respectively. Near concurrent in-situ observations from two water sampling campaigns were also acquired from 22 stations across the lake under study. In-situ measurements (nutrients and chlorophyll-a concentrations) were statistically correlated with various spectral band combinations derived from the Landsat imagery of year 2014. Subsequently, the most statistically promising predictive models were applied to the satellite image of 2013 and validation was conducted using in-situ data of 2013 as reference. Results showed a relatively variable statistical relationship between the in-situ and reflectances (R logchl-a: 0.58, R NH4+: 0.26, R chl-a: 0.44). Correlation coefficient (R) values reported of up to 0.7 for ammonium concentrations and also up to 0.5 and up to 0.4 for chl-a concentration and chl-a concentrations respectively. These results represent a higher accuracy of Landsat 8 in comparison to its predecessors in the Landsat satellites series, as evidenced in the literature. Our findings suggest that Landsat 8 has a promising capability in estimating water quality components in an oligotrophic freshwater body characterized by a complete absence of any quantitative, temporal and spatial variance, as is the case of Trichonis lake. Yet, even with the presence of a lot of ground information as was the case in our study, a quantitatively accurate estimation of water quality constituents in coastal/inland waters remains a great challenge. The launch of sophisticated spaceborne sensing systems, such as that of Landsat 8, can assist in improving our ability to estimate freshwater lake properties from space.