Differences in phytoaccumulation of organic pollutants in freshwater submerged and emergent plants

Fan, Senrong; Liu, Huan; Zheng, Guoxiang; Wang, Yilin; Wang, Shuran; Liu, Yong; Liu, Xueqin; Wan, Yi

doi:10.1016/j.envpol.2018.05.071

Cited by 20 publications

(6 citation statements)

References 44 publications

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“…Estimating water quality directly from water surface reflection spectra is by no means easy in complex water environments [10,11]. Emergent plants are one of the monitoring priorities in the aquatic environment [12,13]. The spectral characteristics of emergent vegetation reflect the stoichiometric characteristics of trace elements relevant to water quality; specifically, elements such as nitrogen will affect the structure of plant components, thereby affecting the spectral reflectance of leaves [14,15].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Liu

Ding

et al. 2021

Remote Sensing

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Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms of emergent plants, we coupled discrete wavelet transform (DWT) and fractional order discretization (FOD) techniques with three machine learning models (random forest (RF), bagging algorithm (bagging), and eXtreme Gradient Boosting (XGBoost)) to mine this potential spectral information. A total of 567 models were developed, and airborne hyperspectral data processed with various DWT scales and FOD techniques were compared. The effective information in the hyperspectral reflectance data were better emphasized after DWT processing. After DWT processing the original spectrum (OR), its sensitivity to TN in water was maximally improved by 0.22, and the correlation between FOD and TN in water was optimally increased by 0.57. The transformed spectral information enhanced the TN model accuracy, especially for FOD after DWT. For RF, 82% of the model R2 values improved by 0.02~0.72 compared to the model using FOD spectra; 78.8% of the bagging values improved by 0.01~0.53 and 65.0% of the XGBoost values improved by 0.01~0.64. The XGBoost model with DWT coupled with grey relation analysis (GRA) yielded the best estimation accuracy, with the highest precision of R2 = 0.91 for L6. In conclusion, appropriately scaled DWT analysis can substantially improve the accuracy of extracting TN from UAV hyperspectral images. These outcomes may facilitate the further development of accurate water quality monitoring in sophisticated global waters from drone or satellite hyperspectral data.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Liu

Ding

et al. 2021

Remote Sensing

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“…For LS, a macrophyte‐dominated Eastern Taihu Lake received and processed vast quantities of aquatic plant litter (Song et al ., 2013; Song et al ., 2019). Plant litter and phytoaccumulation of PAHs (Fan et al ., 2018b) were returned as recalcitrant organic carbon into the LS. The microbial community members for degradation of recalcitrant organic carbon could be enriched in LS (Supporting Information Table S3).…”

Section: Discussionmentioning

confidence: 99%

Functional potential and assembly of microbes from sediments in a lake bay and adjoining river ecosystem for polycyclic aromatic hydrocarbon biodegradation

Yan

Song

Wang

et al. 2020

Environmental Microbiology

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Summary Lake and adjoining river ecosystems are ecologically and economically valuable and are heavily threatened by anthropogenic activities. Determining the inherent capacity of ecosystems for polycyclic aromatic hydrocarbon (PAH) biodegradation can help quantify environmental impacts on the functioning of ecosystems, especially on that of the microbial community. Here, PAH biodegradation potential was compared between sediments collected from a lake bay (LS) and an adjoining river (RS) ecosystem. Microbial community composition, function, and their co‐occurrence patterns were also explored. In the RS, the biodegradation rates (KD) of pyrene or PAH were almost two orders of magnitude higher than those in the LS. Sediment functional community structure and network interactions were dramatically different between the LS and RS. Although PAH degradation genes (p450aro, quinoline, and qorl) were detected in the LS, the community activity of these genes needed to be biostimulated for accelerated bioremediation. In contrast, functional communities in the RS were capable of spontaneous natural attenuation of PAH. The degradation of PAH in the RS also required coordinated response of the complex functional community. Taken together, elucidating functions and network interactions in sediment microbial communities and their responses to environmental changes are very important for the bioremediation of anthropogenic toxic contaminants.

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“…The microorganisms present in these systems promoted organics and nitrogen removal, while the plants assisted in nitrogen and phosphorus absorption. Additionally, the roots of plants secreted oxygen [ [19] , [20] , [21] ], which was beneficial for nitrifying bacteria enrichment. So far, a large number of bio-ecological remediation technologies have been reported for the removal of organics [ 22 , 23 ], nitrogen [ [24] , [25] , [26] ], and phosphorus [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Ecological filter walls for efficient pollutant removal from urban surface water

Liao,

Qiu,

Tian

et al. 2024

Environmental Science and Ecotechnology

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Differences in phytoaccumulation of organic pollutants in freshwater submerged and emergent plants

Cited by 20 publications

References 44 publications

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Functional potential and assembly of microbes from sediments in a lake bay and adjoining river ecosystem for polycyclic aromatic hydrocarbon biodegradation

Ecological filter walls for efficient pollutant removal from urban surface water

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