Release of reactive phosphorus from sediments in Dongting Lake linked with the Yangtze River

Environmental contextEutrophication caused by excessive inputs of phosphorus is a prevalent global environmental problem. Reactive phosphorus released from sediments was measured by two new in situ passive sampling techniques capable of high-resolution measurements of phosphorus concentration. The methods provide the scientific evidence for solving the problems associated with deteriorating surface water quality. AbstractInternal phosphorus (P) loading is regarded as a major eutrophication factor and may prevent improvements in lake water quality. Two new in situ passive sampling techniques, high-resolution pore-water equilibrators (HR-Peeper) and zirconium oxide-based diffusive gradients in thin films (Zr-oxide DGT), were combined to measure dissolved reactive phosphorus (DRP) (CPeeper) and labile phosphorus (CDGT) at five sites in South Dongting and West Dongting Lakes. The vertical distribution of CPeeper and CDGT displayed similarity, which demonstrated that the buffering capacity of the labile P in sediments was similar at different depths. The diffusion flux of P from the sediments at the sediment–water interface ranged from 1.9 to 88ng m–2 day–1, with an average value of 38ng m–2 day–1. The P flux at the entrances to the Yuan, Li and Zi Rivers was fairly large at all five sites. The sediments at the five sites released P into overlying water, indicating that the sediments are an important source of P for Dongting Lake.

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MARINE-EO project: Monitoring and forecast of eutrophication around fish farms, a Maliakos Gulf case (Eastern Mediterranean).

10.5194/egusphere-egu21-9961 ◽

2021 ◽

Author(s):

Amandine Declerck ◽

Matthias Delpey ◽

Thibaut Voirand ◽

Ioanna Varkitzi

Keyword(s):

Water Quality ◽

High Resolution ◽

Real Time ◽

Chlorophyll A ◽

Satellite Observation ◽

Risk Level ◽

Fish Farms ◽

Ocean Modelling ◽

Monitoring And Forecasting

Keywords: eutrophication; high resolution ocean modeling ; Chla satellite data ; biogeochemistryMaliakos Gulf corresponds to mesotrophic waters that can reach eutrophic conditions and are occasionally subject to Harmful Algal Blooms (HAB) (Varkitzi et al. 2018). At the same time, it is an important fish farming and aquaculture production area. A large issue is thus related to the monitoring and forecasting of the risk of occurrence of algae blooms in the Gulf. For this purpose, the present study couples predictions from a high-resolution numerical ocean model with satellite observation to improve the monitoring and anticipation of threats for the local fish farms induced by occasional eutrophication.This solution is developed in the frame of the MARINE-EO project (https://marine-eo.eu/). It combines satellite observation with high-resolution ocean modelling to provide detailed information as a support to fish farms management and operations. It is implemented in an operational platform, which provides continuous information in real time as well as short term predictions. The deployed solution uses CMEMS physical products as an input data and offers to refine this solution in order to provide a local information on site using a downscaling strategy. High resolution satellite products and ocean modelling allow to include the impact of local coastal processes on currents and water quality parameters to provide a proper monitoring and forecasting solution at the scale of a specific fish farm.To model specific eutrophication processes, a NPZD (Nutrients-Phytoplankton-Zooplankton-Detritus) biogeochemical model is used. Included in the MOHID Water modelling system, the water quality module (Mateus, 2006) considering 18 properties: nutrients and organic matter (nitrogen, phosphorus and silica biogeochemical cycles), oxygen and organisms (phytoplankton and zooplankton) was deployed in the western Aegean Sea. The simulated chlorophyll a concentrations are used to compute a risk level for the eutrophication occurrence. To complete this indicator, another risk level was based on the eutrophication variation following Primpas et al. (2010) formulation. In addition to model forecasts, ocean color observations from the Sentinel-2 MSI and Landsat-8 OLI sensors are used to provide high resolution chlorophyll a concentrations maps in case of bloom events. The processing chain uses the sixth version of the Quasi-Analytical Algorithm initially developed by Lee et al. (2002) and an empirical relation based on a database built using the HydroLight software to compute chlorophyll a concentration.Two past eutrophication events monitored in situ (Varkitzi et al. 2018) were studied to assess the accuracy of the developed tool. Although few in situ data were available on environmental input (as rivers flow and nutrient concentrations), it was possible using statistics to reproduce qualitatively these blooms. Finally, an operational demonstration was conducted during 2 months of the 2020 autumn season, to showcase real time monitoring and predictive perspectives.

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A Multi-sensor Process for In-Situ Monitoring of Water Pollution in Rivers or Lakes for High-Resolution Quantitative and Qualitative Water Quality Data

2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES) ◽

10.1109/cse-euc-dcabes.2016.171 ◽

2016 ◽

Cited By ~ 5

Author(s):

Sukanya Randhawa ◽

Sandeep S. Sandha ◽

Biplav Srivastava

Keyword(s):

Water Pollution ◽

Water Quality ◽

High Resolution ◽

In Situ Monitoring ◽

Quality Data ◽

Water Quality Data

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Shift of Sediments Bacterial Community in the Black-Odor Urban River during In Situ Remediation by Comprehensive Measures

Water ◽

10.3390/w11102129 ◽

2019 ◽

Vol 11 (10) ◽

pp. 2129 ◽

Cited By ~ 2

Author(s):

Jian Zhang ◽

Yun Tang ◽

Zhanguo Kou ◽

Xiao Teng ◽

Wei Cai ◽

...

Keyword(s):

Water Quality ◽

Bacterial Community ◽

Organic Pollutants ◽

In Situ Remediation ◽

Sediment Remediation ◽

Overlying Water ◽

Rapid Urbanization ◽

Associated Bacteria

The phenomenon of black-odor urban rivers with rapid urbanization has attracted extensive attention. In this study, we investigated the water quality and composition of sediment-associated bacteria communities in three remediation stages (before remediation, 30 days after remediation, and 90 days after remediation) based on the in situ remediation using comprehensive measures (physical, chemical, and biological measures). The results show that the overlying water quality was notably improved after in situ remediation, while the diversity and richness of sediment-associated bacterial communities decreased. A growing trend of some dominant genus was observed following the remediation of a black-odor river, such as Halomonas, Pseudomonas, Decarbonamis, Leptolina, Longilina, Caldiseericum, Smithella, Mesotoga, Truepera, and Ralstonia, which play an important role in the removal of nitrogen, organic pollutants and hydrogen sulfide (H2S) during the sediment remediation. Redundancy analysis (RDA) showed that the bacterial community succession may accelerate the transformation of organic pollutants into inorganic salts in the sediment after in situ remediation. In a word, the water quality of the black-odor river was obviously improved after in situ remediation, and the bacterial community in the sediment notably changed, which determines the nutrients environment in the sediment.

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Coupling machine learning with high resolution satellite imagery to estimate spatiotemporal changes of salinity in water bodies

10.5194/egusphere-egu2020-4156 ◽

2020 ◽

Author(s):

Majid Bayati ◽

Mohammad Danesh-Yazdi

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Water Quality ◽

Artificial Neural Networks ◽

High Resolution ◽

Satellite Imagery ◽

Water Bodies ◽

Wide Range ◽

High Resolution Satellite Imagery

The spatiotemporal dynamics of salinity in hypersaline lakes is strongly dependent on the rate of water flow feeding the lake, evaporation rate, and the phenomena of precipitation and dissolution. Although in-situ observations are most reliable in quantifying water quality variables, the spatiotemporal distribution of such data are typically limited or cannot be readily extrapolated for long-term projections. Alternatively, remotely-sensed imagery has facilitated less expensive and stronger ability to estimate water quality over a wide range of spatiotemporal resolutions. This study introduces a machine learning model that leverages in-situ measurements and high-resolution satellite imagery to estimate the salinity concentration in water bodies. To this end, 123 points were sampled in April and July of 2019 across the Lake Urmia surface covering the wide range of salinity fluctuations. Among the artificial neural networks, ANFIS, and linear regression tools examined to determine the relationship between salinity and surface reflectance, artificial neural networks yielded the best accuracy evidenced by R2 = 0.94 and RMSE = 6.8%. The results show that the seasonal change of salinity is linearly correlated with the volume of water feeding the lake, witnessing that dilution imposes a stronger control on the salinity than bed salt dissolution. The impact of disturbance in the lake circulation due to the causeway is also evident from the sharp changes of salinity around the bridge piers near spring when the mixing of fresh and hypersaline water from the southern and northern parts, respectively, takes place. The results of this study prove the promising potential of machine learning tools fed multi-spectral satellite information to map other water quality metrics than salinity as well.

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Assessment of sediment and porewater after one year of subaqueous capping of contaminated sediments in Hamilton Harbour, Canada

Water Science & Technology ◽

10.2166/wst.1998.0768 ◽

1998 ◽

Vol 37 (6-7) ◽

pp. 323-329 ◽

Cited By ~ 12

Author(s):

José M. Azcue ◽

Alex J. Zeman ◽

Alena Mudroch ◽

Fernando Rosa ◽

Tim Patterson

Keyword(s):

Soluble Reactive Phosphorus ◽

Coarse Sand ◽

Contaminated Sediments ◽

Overlying Water ◽

Hamilton Harbour ◽

Vertical Fluxes ◽

Reactive Phosphorus ◽

One Year ◽

In Situ Capping

In this manuscript, we present data from a demonstration in situ capping site (100 m × 100 m) in Hamilton Harbour, Lake Ontario, Canada. A layer of clean medium to coarse sand with the average thickness of 35 cm was placed at the site in the summer of 1995. Concentration of Zn, Cr, and Cd in the original sediments reached values over 6000, 300 and 15 μg/g, respectively. The predicted consolidation of the uppermost one meter of sediment was about 14 cm, which was in good agreement with values obtained from comparisons of moisture content values of pre-capping and post-capping cores. A thin layer of fresh moderately contaminated sediments has started to develop on the top of the cap. In general, the concentrations of elements were greater in porewater than in the overlying water, e.g., the concentration of Fe and soluble reactive phosphorus were 1000 times, and those of Mn 100 times greater. There was a significant reduction in the vertical fluxes of all the trace elements after the capping of the contaminated sediments.

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Adsorption-Desorption of Phosphate on Hohhot Dust in Yellow River Water

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.807-809.219 ◽

2013 ◽

Vol 807-809 ◽

pp. 219-222

Author(s):

Xiao Li Wang ◽

Hui Juan Wang

Keyword(s):

River Water ◽

Yellow River ◽

Soluble Reactive Phosphorus ◽

Phosphorus Concentration ◽

Overlying Water ◽

P Adsorption ◽

Isotherm Equation ◽

Langmuir Isotherm Equation ◽

Reactive Phosphorus ◽

Adsorption Desorption

The Equilibrium Phosphorus Concentration (EPC0) of Hohhot dust (HD) of Inner mongolia was measured to examine whether the HD acted as sources or sinks of soluble reactive phosphorus (SRP) to the Yellow River water column. The modified Langmuir isotherm equation was modified to describe phosphorus (P) adsorption on the HD in Yellow River water. The EPC0 was higher than P concentration in the overlying water, which indicates that the HD acted as sources of phosphate. In addition, solid concentration (Cs) effect existed obviously in P adsorption experiment and the hysteresis got bigger as Cs increased.

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Submerged macrophyte Ceratophyllum demersum affects phosphorus exchange at the sediment–water interface

Water Science & Technology ◽

10.2166/wst.2015.050 ◽

2015 ◽

Vol 71 (6) ◽

pp. 913-921 ◽

Cited By ~ 1

Author(s):

Yanran Dai ◽

Shuiping Cheng ◽

Wei Liang ◽

Zhenbin Wu

Keyword(s):

Submerged Macrophytes ◽

Soluble Reactive Phosphorus ◽

Mean Value ◽

Submerged Macrophyte ◽

Ceratophyllum Demersum ◽

Phosphorus Concentration ◽

Water Interface ◽

Adsorption Parameters ◽

Overlying Water ◽

Reactive Phosphorus

Substantial research efforts were made to assess the effects of submerged macrophytes on water quality improvement, but information on the mechanism of submerged macrophytes relative to the exchange of phosphorus (P) at the sediment–water interface is very limited. To help fill the void, a popular species, Ceratophyllum demersum L. was chosen to address the effects and mechanisms of submerged macrophyte growth on the processes of P exchange across the sediment–water interface. In treatment mesocosms (planted), equilibrium phosphorus concentration (EPC0) value falls from 68.4 to 36.0 µg/L, with a mean value of 52.5 µg/L. Conversely, the distribution coefficient (Kd) value has a predominantly increasing trend. But they are both significantly higher than an unplanted control (p < 0.05). Also, in the planted mesocosm, maximum phosphate sorption capacity (Qmax) was significantly reduced (4,721–3,845 mg/kg), and most of the linear correlations between different forms of phosphorus and sediment P adsorption parameters were affected (p < 0.05). The EPC0 Percentage Saturation percentages (EPCsat) in planted groups were 325% higher than that in control (p < 0.05). We conclude that C. demersum could promote the release of P from sediments, and soluble reactive phosphorus concentration in overlying water is probably the driving force for P exchange at the sediment–water interface.

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Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-1511-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 1511-1516 ◽

Cited By ~ 4

Author(s):

I. Theologou ◽

M. Patelaki ◽

K. Karantzalos

Keyword(s):

Water Quality ◽

High Resolution ◽

Satellite Data ◽

Landsat 8 ◽

Landsat 7 ◽

Multi Temporal ◽

Water Quality Status ◽

Quality Status ◽

Empirical Algorithms

Assessing and monitoring water quality status through timely, cost effective and accurate manner is of fundamental importance for numerous environmental management and policy making purposes. Therefore, there is a current need for validated methodologies which can effectively exploit, in an unsupervised way, the enormous amount of earth observation imaging datasets from various high-resolution satellite multispectral sensors. To this end, many research efforts are based on building concrete relationships and empirical algorithms from concurrent satellite and in-situ data collection campaigns. We have experimented with Landsat 7 and Landsat 8 multi-temporal satellite data, coupled with hyperspectral data from a field spectroradiometer and in-situ ground truth data with several physico-chemical and other key monitoring indicators. All available datasets, covering a 4 years period, in our case study Lake Karla in Greece, were processed and fused under a quantitative evaluation framework. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets towards the accurate prediction of key water quality indicators for shallow inland systems. Single linear regression models didn’t establish concrete relations across multi-temporal, multi-sensor observations. Moreover, the shallower parts of the inland system followed, in accordance with the literature, different regression patterns. Landsat 7 and 8 resulted in quite promising results indicating that from the recreation of the lake and onward consistent per-sensor, per-depth prediction models can be successfully established. The highest rates were for chl-a (r2=89.80%), dissolved oxygen (r2=88.53%), conductivity (r2=88.18%), ammonium (r2=87.2%) and pH (r2=86.35%), while the total phosphorus (r2=70.55%) and nitrates (r2=55.50%) resulted in lower correlation rates.

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Evaluation of RapidEye-3 Satellite Data for Assessing Water Turbidity of Lake Borabey

Proceedings ◽

10.3390/ecws-4-06424 ◽

2019 ◽

Vol 48 (1) ◽

pp. 14

Author(s):

Gordana Kaplan ◽

Zehra Yigit Avdan ◽

Serdar Goncu ◽

Ugur Avdan

Keyword(s):

Remote Sensing ◽

Water Quality ◽

High Resolution ◽

Remote Sensing Data ◽

Quality Parameters ◽

In Situ Measurements ◽

Water Quality Parameters ◽

Water Turbidity ◽

Sensing Data

In water resources management, remote sensing data and techniques are essential in watershed characterization and monitoring, especially when no data are available. Water quality is usually assessed through in-situ measurements that require high cost and time. Water quality parameters help in decision making regarding the further use of water-based on its quality. Turbidity is an important water quality parameter and an indicator of water pollution. In the past few decades, remote sensing has been widely used in water quality research. In this study, we compare turbidity parameters retrieved from a high-resolution image with in-situ measurements collected from Borabey Lake, Turkey. Here, the use of RapidEye-3 images (5 m-resolution) allows for detailed assessment of spatio-temporal evaluation of turbidity, through the normalized difference turbidity index (NDTI). The turbidity results were then compared with data from 21 in-situ measurements collected in the same period. The actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.84. The research findings support the use of remote sensing data of RadipEye-3 to estimate water quality parameters in small water areas. For future studies, we recommend investigating different water quality parameters using high-resolution remote sensing data.

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