Stochastic trophic level index model: A new method for evaluating eutrophication state

Ding, Yang; Zhao, Jianing; Peng, Wenqi; Zhang, Jing; Qu-chang, Chen; Fu, Yicheng; Duan, Maoqing

doi:10.1016/j.jenvman.2020.111826

Cited by 32 publications

(12 citation statements)

References 27 publications

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“…TLI is widely used to evaluate the magnitude of eutrophication of rivers, reservoirs, and lakes in China ( Ding et al, 2021 ). We found that changes in the TLI in KZ were similar to changes in the Shannon index; exceptions to this general pattern were only observed at two time points (2020-11-03 and 2020-12-29; Figure 4 ).…”

Section: Resultsmentioning

confidence: 99%

“…Measurements of water quality parameters were taken following national standard specifications (Water quality—Determination of pH—Electrode method HJ1147-2020, Water quality—Determination of dissolved oxygen—Electrochemical probe method HJ 506-2009, Seville disc method, Water quality-Determination of permanganate index GB/T 11892-1989, Water quality—Determination of ammonia nitrogen—Salicylic acid spectrophotometry HJ 536-2009, Water quality-Determination of total nitrogen-Alkaline potassium persulfate digestion UV spectrophotometric method HJ 636-2012, Water quality-Determination of total phosphorus-Ammonium molybdate spectrophotometric method GB/T 11893-1989, and Water quality—Determination of chlorophyll a —Spectrophotometric method SL88-2012). The tropic level index (TLI) of water samples was calculated using TN, TP, permanganate index, transparency, and chlorophyll a indicators and equations from a previous study ( Ding et al, 2021 ). There were five trophic levels in the lake: hypereutrophic (TLI > 70), middle eutrophic (60 < TLI ≤ 70), mesotrophic (30 ≤ TLI ≤ 50), and oligotrophic (TLI < 30).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of environmental factors and microbial community structure in an important drinking-water reservoir across seasons

Feng¹,

Zhou²,

Zhao³

et al. 2023

Front. Microbiol.

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The composition of microbial communities varies in water and sediments, and changes in environmental factors have major effects on microbiomes. Here, we characterized variations in microbial communities and physicochemical factors at two sites in a large subtropical drinking water reservoir in southern China. The microbiomes of all sites, including the diversity and abundance of microbial species, were determined via metagenomics, and the relationships between microbiomes and physicochemical factors were determined via redundancy analysis. The dominant species in sediment and water samples differed; Dinobryon sp. LO226KS and Dinobryon divergens were dominant in sediment samples, whereas Candidatus Fonsibacter ubiquis and Microcystis elabens were dominant in water. The diversity was also significantly different in microbial alpha diversity between water and sediment habitats (p < 0.01). The trophic level index (TLI) was the major factor affecting the microbial community in water samples; Mycolicibacterium litorale and Mycolicibacterium phlei were significantly positively related to TLI. Furthermore, we also studied the distribution of algal toxin-encoding genes and antibiotic-resistant genes (ARGs) in the reservoir. It found that water samples contained more phycotoxin genes, with the cylindrospermopsin gene cluster most abundant. We found three genera highly related to cylindrospermopsin and explored a new cyanobacteria Aphanocapsa montana that may produce cylindrospermopsin based on the correlation through network analysis. The multidrug resistance gene was the most abundant ARG, while the relationship between ARGs and bacteria in sediment samples was more complicated than in water. The results of this study enhance our understanding of the effects of environmental factors on microbiomes. In conclusion, research on the properties, including profiles of algal toxin-encoding genes and ARGs, and microbial communities can aid water quality monitoring and conservation.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Evaluation of environmental factors and microbial community structure in an important drinking-water reservoir across seasons

Feng¹,

Zhou²,

Zhao³

et al. 2023

Front. Microbiol.

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“…GEE is an open cloud platform developed by Google for geoscience data processing and analysis [53]. It supports online programming and interactive display without downloading images [54]. A seasonal median reducer was applied to all remaining Sentinel-2 scenes from 2019 to 2020, calculating seasonal mean hue angle for FUI classification and implementing the FUI-based trophic state assessment algorithm.…”

Section: Processing Platform Geementioning

confidence: 99%

An Improved Eutrophication Assessment Algorithm of Estuaries and Coastal Waters in Liaodong Bay

Sun

et al. 2021

Remote Sensing

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Eutrophication is considered to be a significant threat to estuaries and coastal waters. Various localized studies on the world’s oceans have recognized and confirmed that the Forel-Ule Color Index (FUI) or optical measurements are proportional to several water quality variables based on the relatively clear Chl-a-based waters. However, the application potential of FUI in the turbid estuary with complex optics has not been explored. In this study, we selected the coastal waters in the northern Liaodong Bay as the study area, using the field hyperspectral reflectances (Rrs) collected in 2018 to correct the hue angle and verify the Sentinel-2 images algorithm of FUI by in situ FUI in 2019–2020. The results show that there is a good agreement (R2 = 0.81, RMSE = 1.32, MAPE = 1.25%). Trophic Level Index (TLI) was used to evaluate the eutrophication status. The relationship between the in situ FUI and TLI collected in 2018 was discussed based on the difference in the dominant components of waters, while a number of non-algae suspended solids in the estuaries and coastal waters led to the overestimation of eutrophication based on FUI. The R(560)–R(704) (when FUI is between 11 and 15) and R(665)/R(704) (when FUI is between 19 and 21) was employed to distinguish total suspended matter (TSM)-dominated systems in the FUI-based eutrophication assessment. Based on the analysis, a new approach to assessing the eutrophication of coastal waters in Liaodong Bay was developed, which proved to have good accuracy by the field data in 2019 and 2020 (accuracy is 79%). Finally, we used Sentinel-2 images from Google Earth from 2019 to 2020 and locally processed data from 2018 to analyze the FUI spatial distribution and spatial and temporal statistics of the trophic status in the northern Liaodong Bay. The results show that the northern Liaodong Bay always presented the distribution characteristics of high inshore and low outside, high in the southeast and low in the northwest. The nutrient status is the worst in spring and summer.

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“…However, their practical application is often hampered by the lack of knowledge of many complex and highly non-linear processes, and the difficulties in the calibration of numerous model parameters (typically over 20-30 parameters). As an alternative approach, DD modelling techniques, such as Regression and Indexes (Mchau et al 2019, Ding et al 2021, Artificial Neural Networks (Yang et al 2018, Park et al 2021 and Bayesian Networks (Mu et al 2021) prospered in algal bloom modelling, combined with remote sensing (Li et al 2022a). DD models are mainly based on data mining algorithms and statistical techniques that analyse and identify patterns amongst monitoring data to create anticipating rules linked to algal bloom dynamics.…”

Section: Introductionmentioning

confidence: 99%

Environmentally driven risk assessment for algal bloom occurrence in shallow lakes

Zhang

Mao²,

Cai³

et al. 2022

Environ. Res. Lett.

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An algal bloom is a complex hydro-biological phenomenon driven by multi-attribute environmental processes and thus is still difficult to predict. In this paper, a comprehensive modelling framework for forecasting algal bloom risks in shallow lakes is presented, which is based on long-term field observation and modelling of eutrophic shallow lakes. In the procedure, the major factors and their suitable ranges are investigated, and the individual influence of various driving factors is evaluated quantitatively, using an integrated approach of orthogonal design and regression analysis. By analyzing the possible combined effects of the major driving factors and the relationship between algal bloom risk and major bloom-driving factors, a cost-effective environmentally driven risk assessment model is developed to forecast the likelihood of algal bloom occurrence, through a parameter optimization and prediction comparison routine. The risk model has been calibrated and validated against long-term field observations of algal blooms in Taihu Lake, with the prediction accuracy higher than 70%, which only requires readily available meteorological and water quality data. It is noted that for the closed shallow lake, the influence of hydrodynamics can be indirectly reflected by the variation of wind speed; and, TP, water temperature (WT), photosynthetically active radiation (PAR), and average wind speed (AWS) could be used as major bloom-driving factors in Taihu Lake generally. This study provides a practical framework for the development of algal bloom early warning schemes for shallow lakes and helps to understand the combined function of complex bloom-driving factors.

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Stochastic trophic level index model: A new method for evaluating eutrophication state

Cited by 32 publications

References 27 publications

Evaluation of environmental factors and microbial community structure in an important drinking-water reservoir across seasons

Evaluation of environmental factors and microbial community structure in an important drinking-water reservoir across seasons

An Improved Eutrophication Assessment Algorithm of Estuaries and Coastal Waters in Liaodong Bay

Environmentally driven risk assessment for algal bloom occurrence in shallow lakes

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