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1.
Sci Total Environ ; 770: 144791, 2021 May 20.
Artigo em Inglês | MEDLINE | ID: mdl-33736401

RESUMO

The coupling between constructed wetlands and reservoir (CWs-R) afforded a novel ecosystem to improve the water quality and increase the emergency storage capacity of micro-polluted river drinking water source. In this study, spectroscopic characteristics of DOM in YL CWs-R ecosystem were first systematic studied based on a three-year field monitoring to investigate the chemical composition, sources and track the involved biogeochemical processes in the ecosystem. Three humic-like components (C1, C2, and C4, em >380 nm) and one protein-like component (C3, em < 380 nm) were identified by PARAFAC model. Significant spatiotemporal variations in concentration and composition of FDOM were observed in YL CWs-R ecosystem. The improved water transparency (SD) and, the increased hydraulic retention time (HRT) along YL CWs-R ecosystem enhance photochemical processes, leading to significant decreases in the intensities of humic-like components in effluent (P < 0.05) with lower degrees of aromaticity, molecular weights, and humification (decrease in HIX and increases in SR and BIX). In contrast, no significant spatial difference was observed for protein-like component (P > 0.05), which implies that the biodegradation and production of protein-like component may balance each other in the CWs-R ecosystem. The ecological pond unit plays a major role in the removal and transformation of DOM, especially in summer, while wetland purification unit contributes little to DOM reduction. In addition, the decay of aquatic macrophytes in wetland purification unit and the risk of algal bloom in the ecological pond unit might become important autochthonous sources of DOM, especially in summer and autumn. These findings are critical for further understanding the transformation processes of DOM in large-scale CWs-R ecosystems, and could provide important implications to improve sustainable safety of drinking water sources.


Assuntos
Ecossistema , Áreas Alagadas , China , Espectrometria de Fluorescência , Água
2.
Huan Jing Ke Xue ; 41(3): 1265-1275, 2020 Mar 08.
Artigo em Chinês | MEDLINE | ID: mdl-32608628

RESUMO

To explore the water purification efficiency and phytoplankton control efficiency of the water source ecological purification system, and evaluate the effectiveness of the functional group (FG) and morphology-based functional group (MBFG) in response to the internal environment and water quality of the water source ecological purification system, in the summer of 2018, the water quality and phytoplankton functional groups of each unit of the Yanlong Lake ecological purification system were monitored and analyzed. The results showed that the Yanlong Lake water source ecological purification system can effectively purify the water. The average values of total phosphorus, total nitrogen, dissolved oxygen, and turbidity in the influent water were 0.20 mg·L-1, 1.91 mg·L-1, 2.88 mg·L-1, and 60.23 NTU, respectively; after system treatment, these were 0.09 mg·L-1, 0.95 mg·L-1, 6.26 mg·L-1, and 39.53 NTU, respectively. Simultaneously, the spatial distribution of water quality within the system was heterogeneous, with significant spatial differences in dissolved oxygen (DO), pH, and turbidity (P<0.001). The Yanlong Lake water source ecological purification system could effectively control the density of phytoplankton (4.42×105-4.32×106 cells·L-1) when the effluent was in a mild eutrophication state. This reduced the risk of algal blooms. There were five absolute advantage FG:B, P, TC, J, and W1. There were six absolute advantage MBFG:GroupⅠ, GroupⅢ, GroupⅣ, GroupⅤ, GroupⅥ, and GroupⅦ. Both absolute dominant functional groups were effective in indicating changes in habitat conditions. The results of RDA analysis found that the environmental interpretation of the MBFG was higher than that of the FG. The results suggested that it is more appropriate to study the dynamics of phytoplankton in the Yanlong Lake ecological purification system in summer by selecting the MBFG classification method.

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