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Keywords = Tangxun Lake

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15 pages, 3704 KiB  
Article
The Influence of Seasonal Variability of Eutrophication Indicators on Carbon Dioxide and Methane Diffusive Emissions in the Largest Shallow Urban Lake in China
by Bingjie Ma, Yang Wang, Ping Jiang and Siyue Li
Water 2024, 16(1), 136; https://doi.org/10.3390/w16010136 - 29 Dec 2023
Cited by 1 | Viewed by 1175
Abstract
Eutrophication is prevalent in urban lakes; however, a knowledge gap exists regarding eutrophication influences on carbon dynamics in these ecosystems. In the present study, we investigated the carbon dioxide (CO2) and methane (CH4) concentration and diffusion fluxes in Lake [...] Read more.
Eutrophication is prevalent in urban lakes; however, a knowledge gap exists regarding eutrophication influences on carbon dynamics in these ecosystems. In the present study, we investigated the carbon dioxide (CO2) and methane (CH4) concentration and diffusion fluxes in Lake Tangxun (the largest shallow Chinese urban lake) in the autumn and winter of 2022 and spring and summer of 2023. We found that Lake Tangxun served as a source of GHGs, with average emission rates of 5.52 ± 12.16 mmol CO2 m−2 d−1 and 0.83 ± 2.81 mmol CH4 m−2 d−1, respectively. The partial pressure of dissolved CO2 (pCO2) (averaging 1321.39 ± 1614.63 μatm) and dissolved CH4 (dCH4) (averaging 4.29 ± 13.71 μmol L−1) exceeded saturation levels. Seasonal variability was observed in the pCO2 and dCH4 as well as CH4 fluxes, while the CO2 flux remained constant. The mean pCO2 and dCH4, as well as carbon emissions, were generally higher in summer and spring. pCO2 and dCH4 levels were significantly related to total nitrogen (TN), total phosphorus (TP), and ammonium-nitrogen (N-NH4+), and N-NH4+ was a main influencing factor of pCO2 and dCH4 in urban eutrophic lakes. The positive relationships of pCO2, dCH4 and trophic state index highlighted that eutrophication could elevate CO2 and CH4 emissions from the lake. This study highlights the fact that eutrophication can significantly increase carbon emissions in shallow urban lakes and that urban lakes are substantial contributors to the global carbon budget. Full article
(This article belongs to the Special Issue Recent Progress in CO2 Emission from the World’s Rivers)
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Figure 1

Figure 1
<p>Locations and study sites of the Lake Tangxun (<b>a</b>–<b>c</b>). Numbers 1–20 are sampling sites.</p>
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<p>Seasonal variations of water quality in Lake Tangxun. (<b>a</b>) T<sub>W</sub>; (<b>b</b>) <span class="html-italic">U</span>; (<b>c</b>) SD; (<b>d</b>) pH; (<b>e</b>) DO; (<b>f</b>) EC; (<b>g</b>) Chl-a; (<b>h</b>) DOC; (<b>i</b>) N-NH<sub>4</sub><sup>+</sup>; (<b>j</b>) N-NO<sub>3</sub><sup>−</sup>; (<b>k</b>) TN; (<b>l</b>) TP. Letters a, b and c denote significant differences among seasons.</p>
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<p>TSI seasonal variations in Lake Tangxun (non-parametric Kruskal–Wallis method). The trophic status was distinguished using green dotted lines and the red line represents the TSI mean. Letters a, b denote significant differences among seasons.</p>
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<p>Seasonal variations in dissolved GHGs concentrations and diffusion fluxes in Lake Tangxun; (<b>a</b>,<b>b</b>) <span class="html-italic">p</span>CO<sub>2</sub> and FCO<sub>2</sub> among seasons; (<b>c</b>,<b>d</b>) <span class="html-italic">d</span>CH<sub>4</sub> and FCH<sub>4</sub> among seasons. Letters a, b and c denote significant differences among seasons.</p>
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<p>Relationship of <span class="html-italic">p</span>CO<sub>2</sub> with (<b>a</b>) pH; (<b>b</b>) DO; (<b>c</b>) Chl-a; (<b>d</b>) N-NH<sub>4</sub><sup>+</sup>; (<b>e</b>) TN; (<b>f</b>) TP. R<sup>2</sup> and <span class="html-italic">p</span> values are shown.</p>
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<p>Relationship of TSI with (<b>a</b>) <span class="html-italic">p</span>CO<sub>2</sub> (after removing three outliers) and (<b>b</b>) <span class="html-italic">d</span>CH<sub>4</sub>. R<sup>2</sup> and <span class="html-italic">p</span> values are shown. Solid circles in the red box represent outliers.</p>
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<p>Linear regression of <span class="html-italic">d</span>CH<sub>4</sub> with (<b>a</b>) pH; (<b>b</b>) DO; (<b>c</b>) N-NH<sub>4</sub><sup>+</sup>; (<b>d</b>) TN; (<b>e</b>)TP; (<b>f</b>) <span class="html-italic">p</span>CO<sub>2</sub>. R<sup>2</sup> and <span class="html-italic">p</span> values are shown.</p>
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21 pages, 6492 KiB  
Article
Characteristics of Hydrogen and Oxygen Isotope Composition in Precipitation, Rivers, and Lakes in Wuhan and the Ecological Environmental Effects of Lakes
by Ao Zhang, Xinwen Zhao, Jun He, Xuan Huang, Xingyuezi Zhao and Yongbo Zhao
Water 2023, 15(16), 2996; https://doi.org/10.3390/w15162996 - 19 Aug 2023
Viewed by 1360
Abstract
Wuhan has a dense network of rivers and lakes. Due to the city’s development, the water system has been fragmented, the degradation of lakes is becoming increasingly severe, and the eco-environment has been significantly damaged. By collecting samples of the central surface water [...] Read more.
Wuhan has a dense network of rivers and lakes. Due to the city’s development, the water system has been fragmented, the degradation of lakes is becoming increasingly severe, and the eco-environment has been significantly damaged. By collecting samples of the central surface water bodies in Wuhan, including Yangtze River water, Han River water, lake water, and precipitation, and by utilizing hydrogen and oxygen isotopes and multivariate statistical methods, the hydraulic connectivity and ecological environmental effects between the Yangtze River, the Han River, and the lakes were revealed. The results indicated the following: (1) The local meteoric water Line (LMWL) in the Wuhan area was δD = 7.47δ18O + 1.77. The river water line equation was approximately parallel to the atmospheric precipitation line in the Wuhan area. The intercept and slope of the lake waterline equation were significantly smaller. The enrichment degree of δ18O and δD was Yangtze River < Hanjiang River < lake water. (2) The cluster analysis showed that the lakes could be divided into two types, i.e., inner-flow degraded (IFD) lakes and outer-flow ecological (OFE) lakes. Urban expansion has resulted in fragmentation of the IFD lakes, changing the connectivity between rivers and lakes and weakening the exchange of water bodies between the Yangtze River and lakes. Simultaneously, evaporation has caused hydrogen and oxygen isotope fractionation, resulting in the relative enrichment of isotopes. The IFD lakes included the Taizi Lake, Yehu Lake, and the Shenshan Lake. The OFE lakes and the Yangtze River were active, evaporation was weak, and the hydrogen and oxygen isotopes were relatively depleted, mainly including the Huangjia Lake, the East Lake, the Tangxun Lake, etc. (3) The excessive deuterium (d-excess) parameter values in the Yangtze River and the Han River water were positive. In contrast, the d values in the lakes were mainly negative. In the case of a weakened water cycle, the effect of evaporation enrichment on lake water δ18O and δD had a significant impact. It is suggested that the water system connection project of “North Taizi Lake-South Taizi Lake-Yangtze River” and the small lakes connecting to large lakes project of “Wild Lake-Shenshan Lake-Tangxun Lake” should be implemented in time to restore the water eco-environment. Full article
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Figure 1
<p>(<b>a</b>) Location of the Wuhan; (<b>b</b>) Location of the study area; (<b>c</b>) Distribution of sampling points of surface water bodies in Wuhan.</p>
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<p>Variations of weighted mean values of δ<sup>18</sup>O and δD in Wuhan atmospheric precipitation by month.</p>
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<p>The relationship between δ<sup>18</sup>O/δD values and seasonal precipitation in the Wuhan area: 1—the value of δ<sup>18</sup>O/δD and precipitation in spring; 2—the value of δ<sup>18</sup>O/δD and precipitation in summer; 3—the value of δ<sup>18</sup>O/δD and precipitation in autumn; 4—the value of δ<sup>18</sup>O/δD and precipitation in winter; 5—the relationship curve between δ<sup>18</sup>O/δD and precipitation in spring; 6—the relationship curve between δ<sup>18</sup>O/δD and precipitation in summer; 7—the relationship curve between δ<sup>18</sup>O/δD and precipitation in autumn; 8—the relationship between δ<sup>18</sup>O/δD and precipitation in winter.</p>
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<p>The relationship between δ<sup>18</sup>O/δD values and seasonal temperature in the Wuhan area: 1—the value of δ<sup>18</sup>O/δD and temperature in spring; 2—the value of δ<sup>18</sup>O/δD and temperature in summer; 3—the value of δ<sup>18</sup>O/δD and temperature in autumn; 4—the value of δ<sup>18</sup>O/δD and temperature in winter; 5—the relationship between δ<sup>18</sup>O/δD and temperature in spring; 6—the relationship between δ<sup>18</sup>O/δD and temperature in summer; 7—the relationship between δ<sup>18</sup>O/δD and temperature in autumn; 8—the relationship between δ<sup>18</sup>O/δD and temperature in winter.</p>
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<p>Relationships between δ<sup>18</sup>O and δD of the Yangtze River, Han River, and lakes in Wuhan: 1—Yangtze River water; 2—Hanjiang River Water; 3—lake water; 4—LMWL; 5—lake water line; 6—river water line.</p>
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<p>The relationship between hydrogen and oxygen isotope composition and EC in the Wuhan area.</p>
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<p>Variations of δ<sup>18</sup>O and δD values of lake water in the Wuhan area.</p>
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<p>δ<sup>18</sup>O value distribution of lake water.</p>
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<p>δD value distribution of lake water.</p>
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<p>Cluster analysis of lakes in Wuhan based on δ<sup>18</sup>O and δD values. The abscissa “distance” represents the distance between the two sample categories, which is dimensionless. The color of a line represents the category the sample is divided into, and different colors represent different categories.</p>
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<p>Variations in deuterium excess parameter values of surface water in the Wuhan area.</p>
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<p>The <span class="html-italic">d</span> value distribution of surface water.</p>
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12 pages, 2263 KiB  
Article
Polycyclic Aromatic Hydrocarbons in the Water Bodies of Dong Lake and Tangxun Lake, China: Spatial Distribution, Potential Sources and Risk Assessment
by Kuo Yao, Zhanling Xie, Lihao Zhi, Zefan Wang and Chengkai Qu
Water 2023, 15(13), 2416; https://doi.org/10.3390/w15132416 - 29 Jun 2023
Cited by 4 | Viewed by 1965
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are a group of highly toxic organic pollutants. At present, there has only been limited research into PAH contamination in Tangxun Lake and Dong Lake, which are the first and second largest urban inland lakes in China, respectively. This [...] Read more.
Polycyclic aromatic hydrocarbons (PAHs) are a group of highly toxic organic pollutants. At present, there has only been limited research into PAH contamination in Tangxun Lake and Dong Lake, which are the first and second largest urban inland lakes in China, respectively. This study investigated the concentration, spatial distribution, sources, and ecological risks of PAHs in the water from Dong Lake and Tangxun Lake. The focus of this study is to use models to analyze the sources of PAHs, as well as their potential toxicity to humans, in the water bodies of Dong Lake and Tangxun Lake. This study performed liquid–liquid extraction to extract PAHs from lake water samples using dichloromethane and then used gas chromatography–mass spectrometry (GC-MS) to quantitatively analyze the PAHs in the samples. The total concentration of the ∑16PAHs showed high variability among different sampling points, ranging from 12.92 to 989.09 ng/L, with an arithmetic mean of 121.97 ng/L. The composition of the ∑16PAHs was mainly concentrated at a low molecular weight (>70%). The molecular distributions of PAH studies, combined with positive matrix factorization (PMF), indicate that oil and coal combustion are the main sources of PAHs in Dong Lake and Tangxun Lake. The model of PMF succeeded in identifying and quantifying five sources with similar contributions: the combustion of petroleum products, heavy oil burning, coal combustion, traffic emissions, and natural gas and oil combustion mixed. According to toxicity equivalency (TEQ) and lifelong cancer risk (ILCR) research, PAHs from traffic sources in the environment may be more toxic, and the potential carcinogenic risk of PAH pollution to humans in Tangxun Lake and Dong Lake water bodies is relatively inferior. Full article
(This article belongs to the Section Water Quality and Contamination)
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<p>Map of the study area and sampling sites.</p>
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<p>Composition patterns and concentrations of PAHs in Dong Lake and Tangxun Lake. (<b>a</b>) The total concentration of PAHs at each sampling point and the proportion of PAHs with different number of rings. (<b>b</b>) The proportion of 16 types of PAHs detected in each sampling point.</p>
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<p>Scatter diagram of molecular index used to recognize the source of PAHs.</p>
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<p>The individual contribution distribution map of each factor obtained through the PMF model.</p>
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<p>The TEQ level in relation to BaA, BbF, BaP, and BkF.</p>
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11 pages, 2824 KiB  
Case Report
Seasonal Succession of Bacterial Communities in Three Eutrophic Freshwater Lakes
by Bin Ji, Cheng Liu, Jiechao Liang and Jian Wang
Int. J. Environ. Res. Public Health 2021, 18(13), 6950; https://doi.org/10.3390/ijerph18136950 - 29 Jun 2021
Cited by 7 | Viewed by 2066
Abstract
Urban freshwater lakes play an indispensable role in maintaining the urban environment and are suffering great threats of eutrophication. Until now, little has been known about the seasonal bacterial communities of the surface water of adjacent freshwater urban lakes. This study reported the [...] Read more.
Urban freshwater lakes play an indispensable role in maintaining the urban environment and are suffering great threats of eutrophication. Until now, little has been known about the seasonal bacterial communities of the surface water of adjacent freshwater urban lakes. This study reported the bacterial communities of three adjacent freshwater lakes (i.e., Tangxun Lake, Yezhi Lake and Nan Lake) during the alternation of seasons. Nan Lake had the best water quality among the three lakes as reflected by the bacterial eutrophic index (BEI), bacterial indicator (Luteolibacter) and functional prediction analysis. It was found that Alphaproteobacteria had the lowest abundance in summer and the highest abundance in winter. Bacteroidetes had the lowest abundance in winter, while Planctomycetes had the highest abundance in summer. N/P ratio appeared to have some relationships with eutrophication. Tangxun Lake and Nan Lake with higher average N/P ratios (e.g., N/P = 20) tended to have a higher BEI in summer at a water temperature of 27 °C, while Yezhi Lake with a relatively lower average N/P ratio (e.g., N/P = 14) tended to have a higher BEI in spring and autumn at a water temperature of 9–20 °C. BEI and water temperature were identified as the key parameters in determining the bacterial communities of lake water. Phosphorus seemed to have slightly more impact on the bacterial communities than nitrogen. It is expected that this study will help to gain more knowledge on urban lake eutrophication. Full article
(This article belongs to the Section Water Science and Technology)
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Figure 1
<p>A map with the location of the three lakes and sampling sites.</p>
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<p>(<b>a</b>) Rarefaction curves of operational taxonomic units; (<b>b</b>) Venn diagram. The water samples in spring, summer, autumn, and winter, respectively, are denoted as JCTXL, JXTXL, JQTXL and JDTXL for Tangxun Lake, JCYZL, JXYZL, JQYZL and JDYZL for Yezhi Lake and JCNL, JXNL, JQNL and JDNL for Nan Lake.</p>
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<p>Bacterial community of each lake at phylum level (<b>a</b>) and class level (<b>b</b>); overall bacterial community of each season at phylum level (<b>c</b>) and class level (<b>d</b>). The water samples in spring, summer, autumn, and winter, respectively, are denoted as JCTXL, JXTXL, JQTXL and JDTXL for Tangxun Lake; JCYZL, JXYZL, JQYZL and JDYZL for Yezhi Lake and JCNL, JXNL, JQNL and JDNL for Nan Lake.</p>
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<p>Obvious different abundance in bacterial taxa at phylum level (<b>a</b>) and genus level (<b>b</b>). The water samples in spring, summer, autumn, and winter, respectively, are denoted as JCTXL, JXTXL, JQTXL and JDTXL for Tangxun Lake; JCYZL, JXYZL, JQYZL and JDYZL for Yezhi Lake and JCNL, JXNL, JQNL and JDNL for Nan Lake.</p>
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<p>Function prediction of bacterial taxa based on the second level KEGG module by PICRUSt.</p>
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<p>RDA analysis showing the relationships between bacterial communities and nutrient parameters. The water samples in spring, summer, autumn, and winter, respectively, are denoted as JCTXL, JXTXL, JQTXL and JDTXL for Tangxun Lake; JCYZL, JXYZL, JQYZL and JDYZL for Yezhi Lake and JCNL, JXNL, JQNL and JDNL for Nan Lake.</p>
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20 pages, 4606 KiB  
Article
Spatiotemporal Evolution of Lakes under Rapid Urbanization: A Case Study in Wuhan, China
by Chao Wen, Qingming Zhan, De Zhan, Huang Zhao and Chen Yang
Water 2021, 13(9), 1171; https://doi.org/10.3390/w13091171 - 23 Apr 2021
Cited by 22 | Viewed by 3438
Abstract
The impact of urbanization on lakes in the urban context has aroused continuous attention from the public. However, the long-term evolution of lakes in a certain megacity and the heterogeneity of the spatial relationship between related influencing factors and lake changes are rarely [...] Read more.
The impact of urbanization on lakes in the urban context has aroused continuous attention from the public. However, the long-term evolution of lakes in a certain megacity and the heterogeneity of the spatial relationship between related influencing factors and lake changes are rarely discussed. The evolution of 58 lakes in Wuhan, China from 1990 to 2019 was analyzed from three aspects of lake area, lake landscape, and lakefront ecology, respectively. The Multi-Scale Geographic Weighted Regression model (MGWR) was then used to analyze the impact of related influencing factors on lake area change. The investigation found that the total area of 58 lakes decreased by 15.3%. A worsening trend was found regarding lake landscape with the five landscape indexes of lakes dropping; in contrast, lakefront ecology saw a gradual recovery with variations in the remote sensing ecological index (RSEI) in the lakefront area. The MGWR regression results showed that, on the whole, the increase in Gross Domestic Product (GDP), RSEI in the lakefront area, precipitation, and humidity contributed to lake restoration. The growth of population and the proportion of impervious surface (IS) in the lakefront area had different effects on different lakes. Specifically, the increase in GDP and population in all downtown districts and two suburb districts promoted lake restoration (e.g., Wu Lake), while the increase in population in Jiangxia led to lake loss. The growth of RSEI in lakefront area promoted the restoration of most lakes. A higher proportion of IS in lakefront area normally resulted in more lake loss. However, in some cases, the growth of IS was caused by lake conservation, which contributed to lake restoration (e.g., Tangxun Lake). The study reveals the spatiotemporal evolution of multiple lakes in Wuhan and provides a useful reference for the government to formulate differentiated protection policies. Full article
(This article belongs to the Section Urban Water Management)
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Figure 1
<p>(<b>a</b>) Location overview of Wuhan City; (<b>b</b>) Distribution of Key Lakes in Wuhan.</p>
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<p>Statistics of Water Area in Downtown and Suburb Districts from 1990 to 2019.</p>
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<p>Statistics of Water Area of 58 lakes from 1990 to 2019.</p>
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<p>Variations of PAFRAC of Key Lakes from 1990 to 2019.</p>
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<p>Variations of Average RSEI in Lakefront of Key Lakes from 1990 to 2018.</p>
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<p>Spatial Coefficients of GDP, POP, IS, and RSEI. If there are multiple panels, they should be listed as: (<b>a</b>) Spatial Coefficients of GDP; (<b>b</b>) Spatial Coefficients of POP; (<b>c</b>) Spatial Coefficients of IS; (<b>d</b>) Spatial Coefficients of RSEI.</p>
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<p>Spatial Coefficients of TEM, HUM, and PRE. (<b>a</b>) Spatial Coefficients of TEM; (<b>b</b>) Spatial Coefficients of HUM; (<b>c</b>) Spatial Coefficients of PRE.</p>
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756 KiB  
Article
Evaluation of Hyperspectral Indices for Chlorophyll-a Concentration Estimation in Tangxun Lake (Wuhan, China)
by Yaohuan Huang, Dong Jiang, Dafang Zhuang and Jingying Fu
Int. J. Environ. Res. Public Health 2010, 7(6), 2437-2451; https://doi.org/10.3390/ijerph7062437 - 27 May 2010
Cited by 27 | Viewed by 10483
Abstract
Chlorophyll-a (Chl-a) concentration is a major indicator of water quality which is harmful to human health. A growing number of studies have focused on the derivation of Chl-a concentration information from hyperspectral sensor data and the identification of best [...] Read more.
Chlorophyll-a (Chl-a) concentration is a major indicator of water quality which is harmful to human health. A growing number of studies have focused on the derivation of Chl-a concentration information from hyperspectral sensor data and the identification of best indices for Chl-a monitoring. The objective of this study is to assess the potential of hyperspectral indices to detect Chl-a concentrations in Tangxun Lake, which is the second largest lake in Wuhan, Central China. Hyperspectral reflectance and Chl-a concentration were measured at ten sample sites in Tangxun Lake. Three types of hyperspectral methods, including single-band reflectance, first derivative of reflectance, and reflectance ratio, were extracted from the spectral profiles of all bands of the hyperspectral sensor. The most appropriate bands for algorithms mentioned above were selected based on the correlation analysis. Evaluation results indicated that two methods, the first derivative of reflectance and reflectance ratio, were highly correlated (R2 > 0.8) with the measured Chl-a concentrations. Thus, the spatial and temporal variations of Chl-a concentration could be conveniently monitored with these hyperspectral methods. Full article
(This article belongs to the Special Issue Recent Advances in Environmental Research)
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<p>Study area and sampling locations.</p>
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<p>Viewing geometry of spectra sampling.</p>
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<p>Reflectance spectra at 10 sampling sites in Tangxun Lake.</p>
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<p>The curve of correlation coefficients between reflectance and Chl-<span class="html-italic">a</span> concentration.</p>
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<p>The correlation coefficients between first-derivative of reflectance and Chl-<span class="html-italic">a</span>.</p>
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<p>Linear models with reflectance ratios of characteristic bands.</p>
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<p>The correlation coefficients between Chl-<span class="html-italic">a</span> and reflectance ratios.</p>
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<p>Scatter plots of Chl-<span class="html-italic">a versus</span> reflectance at 733.1 nm.</p>
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<p>Scatter plots of Chl-<span class="html-italic">a versus</span> first-derivative of reflectance at 446.9 nm.</p>
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