Assessing the ecological stress in a Garonne River stretch, southwest France

Villanueva, Maria Ching; Ibarra, Alonso Aguilar

doi:10.1016/j.ecolind.2016.02.049

Cited by 9 publications

(6 citation statements)

References 30 publications

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“…However, such studies require complex laboratory analysis which affect data accuracy and precision, are time intensive, and are inapplicable at large scales. The total concentration of heavy metals can be used to evaluate the status of heavy metal pollution, potential ecological risks and sources (Villanueva and Ibarra 2016). In addition to comparison with national water quality standards, evaluation methods for heavy metals in water are well-established and include the single factor water quality index method (Pi) and multi-factor Water Quality Index method (WQI) (Cheng and Dan 2011;Parparov et al 1992), and the Nemero index method (Pn) (Tianxiang et al 2018).…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Heavy Metals in Water and Surface Sediments of the Fenghe River Basin, China: Assessment and Source Analysis

Luo

Kang

et al. 2021

Preprint

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This paper combines environmental science, inorganic chemistry, water quality monitoring and other disciplines, and uses several representative evaluation methods (WQI, Pn, I-geo, RI) for heavy metals in water and sediments. A preliminary assessment and source analysis of heavy metals (Cd, Cr, Fe, Mn, Zn, Cr, Ti, Ni, Cu, As, Pb, Sr) in water and surface sediments of the Fenghe River Basin, Shannxi Province, China was carried out in this study. Results indicate that most of the heavy metals in water are below national water quality standards. Exceptions include Mn, which exceeds national tertiary standards and Cr, which exceeds national drinking water standards. Most heavy metals in the sediments exceed the environmental standard values except Ni. Water quality index (WQI) and Nemero index (Pn) showed the same trend in contamination levels of sampling sites. According to the Geological Accumulation Index method (I-geo) and the Potential Ecological Risk Index method (RI), high concentrations of Cd poses a high ecological risk in some sampling locations. Pearson Correlation Analysis (CA), Hierarchical Clustering Analysis (HCA), Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) models are used to explore the relationships and sources of heavy metals. In general, upstream sources are similar, and middle and lower reaches are easily clustered into a large category except for some specific sampling points. For example, metals in sampling site FHK mainly come from surrounding residents and farms and heavy metals attributes in sampling site SLQ relate to the fact that municipal sewage is collected and treated. The factors or sources of heavy metals in water and sediment are revealed in detail through PMF models. In the water, the average contribution rate of these four source factors for heavy metals is 36.8%, 11.7%, 9.4% and 42.0%, while the average proportion of these four factors for heavy metals in sediment is 8.0%, 29.2%, 23.9% and 38.9% respectively. Results show that the main sources of pollution in the region are urban construction and transportation, electronics industry, machinery manufacturing, tourism and agriculture. These sectors should therefore be given sufficient attention in the prevention and management of heavy metal pollution.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Heavy Metals in Water and Surface Sediments of the Fenghe River Basin, China: Assessment and Source Analysis

Luo

Kang

et al. 2021

Preprint

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“…degradation but not necessarily strong indication of a system with many long-lived, large, individuals (i.e., an undegraded system). For context, a study comparing polluted vs. reference freshwater sites found the reference site to have a W statistic of only 0.30 vs. À0.09 and À0.12 at polluted sites (Villanueva and Ibarra, 2016), and data in this study maintained a positive mean value (0.196) whilst varying in yearly trends. When only the largely habitat invariable Chicago Sanitary and Ship Canal and Lockport Lock and Dam areas were sampled, and large abundances of small Lepomis spp.…”

Section: Index Specificitiesmentioning

confidence: 52%

“…ABC method or Warwick's W Statistic) provides a measure of community level changes thought to be interpretable on a level from disturbed (negative W values; higher abundances than biomass) to undisturbed (positive W values; higher biomass than abundance) (Clarke and Warwick, 2001b;Warwick and Clarke, 1994). Although initially developed with macroinvertebrates, comparisons between the W statistic of freshwater fish assemblages and water quality metrics suggests it is correlated with indicators of poor water quality (i.e., phosphorous, nitrogenous compounds, low dissolved oxygen), as long as other habitat variables are accounted for (Coeck et al, 1993;Pinto et al, 2006;Piperac et al, 2015;Villanueva and Ibarra, 2016), but apparently not metal pollutants (i.e., cadmium and zinc, Bervoets et al, 2005). The majority of W values for the fish assemblage of the CAWS were only slightly positive values (generally 0.25), suggesting a low level of Fig.…”

Section: Index Specificitiesmentioning

confidence: 99%

Increasing fish diversity of Chicago's waterways

Happel

2022

Knowl. Manag. Aquat. Ecosyst.

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Descriptions of shifts in biodiversity across time are desired for urban freshwater systems to better study ecosystem change and causal mechanisms. I document changes in fish diversity within the Chicago Area Waterways across 35 years using a battery of diversity metrics calculated on both abundance and biomass data. Has diversity of Chicago's waterways changed over 35 years and if so, do different diversity metrics exhibit different trends or breakpoints in trends? Diversity of the fish assemblage increased across the three decades of study. Breakpoint analysis suggested that trends across the timeseries were nonlinear. Changes in dominant species influenced behavior of less traditional metrics, whereas Margalef's Richness, Shannon-Weiner, and Gini-Simpson appeared more robust. Increases in richness and taxonomic diversity combined with decreases in species dominance suggest an ecosystem of increasing quality. The fish community of the Chicago Area Waterway System is more diverse both in abundance as well as biomass (i.e., energy flows) in the late 2010's than it was in the late 1980's. Although external factors related to wastewater treatment increased water quality and likely led to these assemblage-level changes breakpoint analysis did not allow verification of causal mechanisms.

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“…However, such research requires complex laboratory analysis, which affects the accuracy and precision of the data, requires a lot of time, and cannot be applied on a large scale. The total concentration of heavy metals can be used to evaluate the status of heavy metal pollution, potential ecological risks and sources (Villanueva & Ibarra 2016). In addition to comparison with national water quality standards, evaluation methods for heavy metals in water are well-established and include the single factor water quality index method (Pi) and multi-factor Water Quality Index method (WQI) (Cheng & Dan 2011;Adimalla 2019), and the Nemero index method (Pn) (Tianxiang et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Heavy metals in water and surface sediments of the Fenghe River Basin, China: assessment and source analysis

Luo

Kang

et al. 2021

Water Science and Technology

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This paper combines environmental science, inorganic chemistry, water quality monitoring and other disciplines to analyze and assess the heavy metals in the water bodies and sediments of the Fenghe River Basin (FRB) in Shaanxi Province, and reveal their sources. Water Quality Index (WQI), Nemero Index (Pn), Geological Accumulation Index (I-geo) and Potential Ecological Risk Index (RI) are used to assess heavy metals in water and sediments. Pearson correlation analysis (CA), hierarchical cluster analysis (HCA), principal component analysis (PCA) and positive matrix factorization (PMF) models are used to study the relationship and source of heavy metals. The results show that most of the residual heavy metals in the water are below the corresponding environmental quality standards for surface water. Most of the heavy metals in the sediment exceed the background value of the soil.The factors or sources of heavy metals in water and sediment are revealed in detail through PMF models. The main sources of pollution in the region are urban construction and transportation, electronics industry, machinery manufacturing and tourism. In water, the average contribution rates of these four sources to heavy metals were 36.8%, 11.7%, 9.4% and 42.0%, and in sediments were 8.0%, 29.2%, 23.9% and 38.9%. Therefore, these sectors should be given sufficient attention.

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Assessing the ecological stress in a Garonne River stretch, southwest France

Cited by 9 publications

References 30 publications

WITHDRAWN: Heavy Metals in Water and Surface Sediments of the Fenghe River Basin, China: Assessment and Source Analysis

WITHDRAWN: Heavy Metals in Water and Surface Sediments of the Fenghe River Basin, China: Assessment and Source Analysis

Increasing fish diversity of Chicago's waterways

Heavy metals in water and surface sediments of the Fenghe River Basin, China: assessment and source analysis

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