Ecological status classification of the Taizi River Basin, China: a comparison of integrated risk assessment approaches

Fan, Jun; Semenzin, Elena; Meng, Wei; Giubilato, Elisa; Zhang, Yuan; Critto, Andrea; Zabeo, Alex; Zhou, Yixin; Ding, Sen; Wan, Jun; He, Mengchang; Lin, Chunye

doi:10.1007/s11356-015-4629-x

Cited by 17 publications

(7 citation statements)

References 22 publications

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“…It has recently become evident that the assessment of riverine environments requires introducing biological evaluation [4,5]. Moreover, the determination of the full image of the degradation status of aquatic ecosystems requires the analysis of the hydro-morphological status of river-beds and their riparian zones in order to assess the nature of the habitats and the degree of their transformation/naturalness [6][7][8][9][10]. This is also currently stipulated by the Water Framework Directive (WFD) [11].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, these are usually proposals of assessments [6,[12][13][14][15][16][17][18][19][20][21]. Considerably more seldom, they are works aimed at the determination of the sensitivity of methods and the degree of their compatibility and complementarity in the final assessment of a river's ecological state [9,10,20,22], although they can provide information supporting river management decisions [9,23,24].…”

Section: Introductionmentioning

confidence: 99%

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Quality of Rivers: Comparison of Hydro-Morphological, Physical-Chemical and Biological Methods

Gorzel

Kornijów

Buczyńska

2018

Ecological Chemistry and Engineering S

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The study was performed in five fourth-order tributaries of the Bystrzyca Lubelska River (Eastern Poland, Lublin Upland), differing in the degree of river-bed transformation and level of pollution. Hydro-morphological methods (descriptive method by Ilnicki and Lewandowski - IL, and index method by Oglecki and Pawlat - OP) and biological indices based on the composition of zoobenthos (Diversity - D, and index based on proportions between the density of Oligochaeta and Chironomidae - O/Ch) permitted distinguishing of four classes, from II to V. The distinguishing of only two quality classes (III and IV) was possible by means of physical-chemical methods and by benthic index BMWP_PL. Those two methods seem to show the lowest sensitivity to the spatial variability of the environment quality. The BMWP_PL index was the least sensitive to year-to-year environmental changes, and O/Ch was the most sensitive. Relatively high conformity was obtained between hydro-morphological assessments performed by means of the OP and IL methods. Results obtained by means of these tools weakly corresponded with the physical-chemical assessments. The latter assessments were the most similar to those obtained by means of the BMWP_PL (degree of similarity = 57%) and D (47%) indices, and considerably less in the case of O/Ch (36%). The BMWP_PL and D indices better corresponded with the results of the hydro-morphological assessment performed by means of the IL method than with those performed by means of the OP method while D index showed a reverse pattern. The O/CH index proved useful for the assessment of the degree of organic pollution of the river’s water, but not the sediments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quality of Rivers: Comparison of Hydro-Morphological, Physical-Chemical and Biological Methods

Gorzel

Kornijów

Buczyńska

2018

Ecological Chemistry and Engineering S

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“…This indicates that, in the Taizi River, the SVM model can be a reliable prediction tool for fish and algae communities using the selected environmental factors, while the ability of the model to predict the macroinvertebrate community was poor. The result of ecological status classification of the Taizi River reveals that the macroinvertebrate fauna was significantly impaired, while the fish community and algae community were less damaged [23]. This indicates that species with considerable or moderate tolerance occurred among the macroinvertebrate fauna, so their sensitivity to environmental stress was not very great.…”

Section: Discussionmentioning

confidence: 97%

“…The available dataset included data on biological communities (i.e., fish, algae, and macroinvertebrates), physico-chemical parameters (i.e., DO, EC, NH 3 -N, COD, BOD 5 , pH, TP, TN), and hydromorphological parameters (i.e., water quantity, channel change, morphology diversity). These indicators were selected for ecological status classification of the Taizi River Basin [23]. The results of previous studies showed that there was a negative trend in the ecological status from the highlands to the lowlands of the Taizi River Basin, and that the biological communities were significantly impaired, with varying degrees of damage to each species caused by environmental pressure.…”

Section: The Available Datasetmentioning

confidence: 99%

Predicting Bio-indicators of Aquatic Ecosystems Using the Support Vector Machine Model in the Taizi River, China

Fan

Kong

et al. 2017

Sustainability

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Numerous studies have sought to clarify the link between biological communities and environmental factors in freshwater, but an appropriate model is still needed to predict the effect of water quality and hydromorphology improvement on biological communities and to provide useful information for ecological restoration planning. In this study, a support vector machine (SVM) was used to predict the bio-indicators of an aquatic ecosystem (i.e., macroinvertebrates, fish, algae communities) in the Taizi River, northeast China. Environmental factors, including physico-chemical (i.e., dissolved oxygen (DO), electricity conductivity (EC), ammonia nitrogen (NH 3-N), chemical oxygen demand (COD), biological oxygen demand in five days (BOD 5), total phosphorus (TP), total nitrogen (TN)) and hydromorphology parameters (i.e., water quantity, channel change, morphology diversity) were used as the input variables to train and validate the SVM model. The sensitivity of the input variables for the prediction was examined by removing a variable from the SVM model. Results revealed that the SVM model reproduced the variation in bio-indicators of fish and algae communities well, based on the input variables. The sensitivity for the input variables applied in SVM showed that in the Taizi River the most sensitive variables for predicting macroinvertebrate and algae communities were channel change, DO, TN, and TP, while the most sensitive variables for predicting fish communities were DO and BOD 5. This study proposed an effective method for predicting biological communities, which will improve freshwater quality and hydromorphology management schemes. The outputs can guide the decision-making process in river basin management, support the prioritization of actions and resource allocation, and help to monitor and evaluate the effectiveness of interventions.

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“…Each country has a set of external and internal conditions to ensure integrated planning and management, starting with the objectives, such as legal, institutional, technical, economic, and social. In water management projects, water-related policies may include government decisions on environmental protection and social issues, such as enhancing the quality of water in reservoirs [4,18,21,51]. Four broad evaluation aspects were adopted in this study, namely: water quality, environmental, economic, and social aspects.…”

Section: Step 2: Identification and Selection Of Criteria And Sub-crimentioning

confidence: 99%

Strategic Evaluation Tool for Surface Water Quality Management Remedies in Drinking Water Catchments

Almaaofi

Etemad‐Shahidi

Stewart

2017

Water

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Drinking water catchments (DWC) are under pressure from point and nonpoint source pollution due to the growing human activities. This worldwide challenge is causing number of adverse effects, such as degradation in water quality, ecosystem health, and other economic and social pressures. Different evaluation tools have been developed to achieve sustainable and healthy drinking water catchments. However, a holistic and strategic framework is still required to adequately consider the uncertainty associated with feasible management remedies of surface water quality in drinking water catchments. A strategic framework was developed to adequately consider the uncertainty associated with management remedies for surface water quality in drinking water catchments. A Fuzzy Multiple Criteria Decision Analysis (FMCDA) approach was embedded into a strategic decision support framework to evaluate and rank water quality remediation options within a typical fixed budget constraint faced by bulk water providers. The evaluation framework consists of four core aspects; namely, water quality, environmental, economic and social, and number of associated quantitative and qualitative criteria and sub-criteria. Final remediation strategy ranking was achieved through the application of the Euclidean Distance by the In-center of Centroids (EDIC).

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Ecological status classification of the Taizi River Basin, China: a comparison of integrated risk assessment approaches

Cited by 17 publications

References 22 publications

Quality of Rivers: Comparison of Hydro-Morphological, Physical-Chemical and Biological Methods

Quality of Rivers: Comparison of Hydro-Morphological, Physical-Chemical and Biological Methods

Predicting Bio-indicators of Aquatic Ecosystems Using the Support Vector Machine Model in the Taizi River, China

Strategic Evaluation Tool for Surface Water Quality Management Remedies in Drinking Water Catchments

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