Research on the purification enhancement of ecological ponds: Integrating water cycle optimization and plants layout
The hydrodynamic conditions of ponds are generally poor, which seriously affects the long–term water quality guarantee. In this research, the numerical simulation method was used to establish an integrated model of hydrodynamics and water quality for the simulation of the plant purification effect i...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Pan, Xiangdong [verfasserIn] Liu, Shengyun [verfasserIn] Li, Ran [verfasserIn] Sun, Hailong [verfasserIn] Feng, Jingjie [verfasserIn] Cheng, Xiaolong [verfasserIn] Yao, Jia [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of environmental management - Amsterdam [u.a.] : Elsevier, 1990, 344 |
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| Übergeordnetes Werk: |
volume:344 |
| DOI / URN: |
10.1016/j.jenvman.2023.118487 |
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| Katalog-ID: |
ELV064055086 |
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| 520 | |a The hydrodynamic conditions of ponds are generally poor, which seriously affects the long–term water quality guarantee. In this research, the numerical simulation method was used to establish an integrated model of hydrodynamics and water quality for the simulation of the plant purification effect in ponds. Based on the flushing time using the tracer method, the purification rate of plants was introduced to consider the purification effect of plants on water quality. In–situ monitoring was carried out at the Luxihe pond in Chengdu, and the model parameters such as the purification rate of typical plants were calibrated. The degradation coefficient of NH3–N in the non–vegetated area was 0.014 d−1 in August and 0.010 d−1 in November. In areas with vegetation, the purification rate of NH3–N was 0.10–0.20 g/(m2·d) in August and 0.06–0.12 g/(m2·d) in November. The comparison of the results in August and November showed that due to the higher temperature in August, the plant growth effect was better, and the degradation rate of pollutants and the purification rate of pollutants by plants were higher. | ||
| 650 | 4 | |a Mathematical model | |
| 650 | 4 | |a Ecological ponds | |
| 650 | 4 | |a Flushing time | |
| 650 | 4 | |a Purification rate | |
| 700 | 1 | |a Liu, Shengyun |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Ran |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Hailong |e verfasserin |0 (orcid)0000-0002-2510-6499 |4 aut | |
| 700 | 1 | |a Feng, Jingjie |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Xiaolong |e verfasserin |4 aut | |
| 700 | 1 | |a Yao, Jia |e verfasserin |4 aut | |
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10.1016/j.jenvman.2023.118487 doi (DE-627)ELV064055086 (ELSEVIER)S0301-4797(23)01275-6 DE-627 ger DE-627 rda eng 333.7 690 VZ 48.00 bkl Pan, Xiangdong verfasserin aut Research on the purification enhancement of ecological ponds: Integrating water cycle optimization and plants layout 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The hydrodynamic conditions of ponds are generally poor, which seriously affects the long–term water quality guarantee. In this research, the numerical simulation method was used to establish an integrated model of hydrodynamics and water quality for the simulation of the plant purification effect in ponds. Based on the flushing time using the tracer method, the purification rate of plants was introduced to consider the purification effect of plants on water quality. In–situ monitoring was carried out at the Luxihe pond in Chengdu, and the model parameters such as the purification rate of typical plants were calibrated. The degradation coefficient of NH3–N in the non–vegetated area was 0.014 d−1 in August and 0.010 d−1 in November. In areas with vegetation, the purification rate of NH3–N was 0.10–0.20 g/(m2·d) in August and 0.06–0.12 g/(m2·d) in November. The comparison of the results in August and November showed that due to the higher temperature in August, the plant growth effect was better, and the degradation rate of pollutants and the purification rate of pollutants by plants were higher. Mathematical model Ecological ponds Flushing time Purification rate Liu, Shengyun verfasserin aut Li, Ran verfasserin aut Sun, Hailong verfasserin (orcid)0000-0002-2510-6499 aut Feng, Jingjie verfasserin aut Cheng, Xiaolong verfasserin aut Yao, Jia verfasserin aut Enthalten in Journal of environmental management Amsterdam [u.a.] : Elsevier, 1990 344 Online-Ressource (DE-627)266892868 (DE-600)1469206-5 (DE-576)10434461X 1095-8630 nnns volume:344 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 344 |
| spelling |
10.1016/j.jenvman.2023.118487 doi (DE-627)ELV064055086 (ELSEVIER)S0301-4797(23)01275-6 DE-627 ger DE-627 rda eng 333.7 690 VZ 48.00 bkl Pan, Xiangdong verfasserin aut Research on the purification enhancement of ecological ponds: Integrating water cycle optimization and plants layout 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The hydrodynamic conditions of ponds are generally poor, which seriously affects the long–term water quality guarantee. In this research, the numerical simulation method was used to establish an integrated model of hydrodynamics and water quality for the simulation of the plant purification effect in ponds. Based on the flushing time using the tracer method, the purification rate of plants was introduced to consider the purification effect of plants on water quality. In–situ monitoring was carried out at the Luxihe pond in Chengdu, and the model parameters such as the purification rate of typical plants were calibrated. The degradation coefficient of NH3–N in the non–vegetated area was 0.014 d−1 in August and 0.010 d−1 in November. In areas with vegetation, the purification rate of NH3–N was 0.10–0.20 g/(m2·d) in August and 0.06–0.12 g/(m2·d) in November. The comparison of the results in August and November showed that due to the higher temperature in August, the plant growth effect was better, and the degradation rate of pollutants and the purification rate of pollutants by plants were higher. Mathematical model Ecological ponds Flushing time Purification rate Liu, Shengyun verfasserin aut Li, Ran verfasserin aut Sun, Hailong verfasserin (orcid)0000-0002-2510-6499 aut Feng, Jingjie verfasserin aut Cheng, Xiaolong verfasserin aut Yao, Jia verfasserin aut Enthalten in Journal of environmental management Amsterdam [u.a.] : Elsevier, 1990 344 Online-Ressource (DE-627)266892868 (DE-600)1469206-5 (DE-576)10434461X 1095-8630 nnns volume:344 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 344 |
| allfields_unstemmed |
10.1016/j.jenvman.2023.118487 doi (DE-627)ELV064055086 (ELSEVIER)S0301-4797(23)01275-6 DE-627 ger DE-627 rda eng 333.7 690 VZ 48.00 bkl Pan, Xiangdong verfasserin aut Research on the purification enhancement of ecological ponds: Integrating water cycle optimization and plants layout 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The hydrodynamic conditions of ponds are generally poor, which seriously affects the long–term water quality guarantee. In this research, the numerical simulation method was used to establish an integrated model of hydrodynamics and water quality for the simulation of the plant purification effect in ponds. Based on the flushing time using the tracer method, the purification rate of plants was introduced to consider the purification effect of plants on water quality. In–situ monitoring was carried out at the Luxihe pond in Chengdu, and the model parameters such as the purification rate of typical plants were calibrated. The degradation coefficient of NH3–N in the non–vegetated area was 0.014 d−1 in August and 0.010 d−1 in November. In areas with vegetation, the purification rate of NH3–N was 0.10–0.20 g/(m2·d) in August and 0.06–0.12 g/(m2·d) in November. The comparison of the results in August and November showed that due to the higher temperature in August, the plant growth effect was better, and the degradation rate of pollutants and the purification rate of pollutants by plants were higher. Mathematical model Ecological ponds Flushing time Purification rate Liu, Shengyun verfasserin aut Li, Ran verfasserin aut Sun, Hailong verfasserin (orcid)0000-0002-2510-6499 aut Feng, Jingjie verfasserin aut Cheng, Xiaolong verfasserin aut Yao, Jia verfasserin aut Enthalten in Journal of environmental management Amsterdam [u.a.] : Elsevier, 1990 344 Online-Ressource (DE-627)266892868 (DE-600)1469206-5 (DE-576)10434461X 1095-8630 nnns volume:344 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 344 |
| allfieldsGer |
10.1016/j.jenvman.2023.118487 doi (DE-627)ELV064055086 (ELSEVIER)S0301-4797(23)01275-6 DE-627 ger DE-627 rda eng 333.7 690 VZ 48.00 bkl Pan, Xiangdong verfasserin aut Research on the purification enhancement of ecological ponds: Integrating water cycle optimization and plants layout 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The hydrodynamic conditions of ponds are generally poor, which seriously affects the long–term water quality guarantee. In this research, the numerical simulation method was used to establish an integrated model of hydrodynamics and water quality for the simulation of the plant purification effect in ponds. Based on the flushing time using the tracer method, the purification rate of plants was introduced to consider the purification effect of plants on water quality. In–situ monitoring was carried out at the Luxihe pond in Chengdu, and the model parameters such as the purification rate of typical plants were calibrated. The degradation coefficient of NH3–N in the non–vegetated area was 0.014 d−1 in August and 0.010 d−1 in November. In areas with vegetation, the purification rate of NH3–N was 0.10–0.20 g/(m2·d) in August and 0.06–0.12 g/(m2·d) in November. The comparison of the results in August and November showed that due to the higher temperature in August, the plant growth effect was better, and the degradation rate of pollutants and the purification rate of pollutants by plants were higher. Mathematical model Ecological ponds Flushing time Purification rate Liu, Shengyun verfasserin aut Li, Ran verfasserin aut Sun, Hailong verfasserin (orcid)0000-0002-2510-6499 aut Feng, Jingjie verfasserin aut Cheng, Xiaolong verfasserin aut Yao, Jia verfasserin aut Enthalten in Journal of environmental management Amsterdam [u.a.] : Elsevier, 1990 344 Online-Ressource (DE-627)266892868 (DE-600)1469206-5 (DE-576)10434461X 1095-8630 nnns volume:344 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 344 |
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10.1016/j.jenvman.2023.118487 doi (DE-627)ELV064055086 (ELSEVIER)S0301-4797(23)01275-6 DE-627 ger DE-627 rda eng 333.7 690 VZ 48.00 bkl Pan, Xiangdong verfasserin aut Research on the purification enhancement of ecological ponds: Integrating water cycle optimization and plants layout 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The hydrodynamic conditions of ponds are generally poor, which seriously affects the long–term water quality guarantee. In this research, the numerical simulation method was used to establish an integrated model of hydrodynamics and water quality for the simulation of the plant purification effect in ponds. Based on the flushing time using the tracer method, the purification rate of plants was introduced to consider the purification effect of plants on water quality. In–situ monitoring was carried out at the Luxihe pond in Chengdu, and the model parameters such as the purification rate of typical plants were calibrated. The degradation coefficient of NH3–N in the non–vegetated area was 0.014 d−1 in August and 0.010 d−1 in November. In areas with vegetation, the purification rate of NH3–N was 0.10–0.20 g/(m2·d) in August and 0.06–0.12 g/(m2·d) in November. The comparison of the results in August and November showed that due to the higher temperature in August, the plant growth effect was better, and the degradation rate of pollutants and the purification rate of pollutants by plants were higher. Mathematical model Ecological ponds Flushing time Purification rate Liu, Shengyun verfasserin aut Li, Ran verfasserin aut Sun, Hailong verfasserin (orcid)0000-0002-2510-6499 aut Feng, Jingjie verfasserin aut Cheng, Xiaolong verfasserin aut Yao, Jia verfasserin aut Enthalten in Journal of environmental management Amsterdam [u.a.] : Elsevier, 1990 344 Online-Ressource (DE-627)266892868 (DE-600)1469206-5 (DE-576)10434461X 1095-8630 nnns volume:344 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 344 |
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333.7 690 VZ 48.00 bkl Research on the purification enhancement of ecological ponds: Integrating water cycle optimization and plants layout Mathematical model Ecological ponds Flushing time Purification rate |
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Research on the purification enhancement of ecological ponds: Integrating water cycle optimization and plants layout |
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Pan, Xiangdong |
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Pan, Xiangdong Liu, Shengyun Li, Ran Sun, Hailong Feng, Jingjie Cheng, Xiaolong Yao, Jia |
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research on the purification enhancement of ecological ponds: integrating water cycle optimization and plants layout |
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Research on the purification enhancement of ecological ponds: Integrating water cycle optimization and plants layout |
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The hydrodynamic conditions of ponds are generally poor, which seriously affects the long–term water quality guarantee. In this research, the numerical simulation method was used to establish an integrated model of hydrodynamics and water quality for the simulation of the plant purification effect in ponds. Based on the flushing time using the tracer method, the purification rate of plants was introduced to consider the purification effect of plants on water quality. In–situ monitoring was carried out at the Luxihe pond in Chengdu, and the model parameters such as the purification rate of typical plants were calibrated. The degradation coefficient of NH3–N in the non–vegetated area was 0.014 d−1 in August and 0.010 d−1 in November. In areas with vegetation, the purification rate of NH3–N was 0.10–0.20 g/(m2·d) in August and 0.06–0.12 g/(m2·d) in November. The comparison of the results in August and November showed that due to the higher temperature in August, the plant growth effect was better, and the degradation rate of pollutants and the purification rate of pollutants by plants were higher. |
| abstractGer |
The hydrodynamic conditions of ponds are generally poor, which seriously affects the long–term water quality guarantee. In this research, the numerical simulation method was used to establish an integrated model of hydrodynamics and water quality for the simulation of the plant purification effect in ponds. Based on the flushing time using the tracer method, the purification rate of plants was introduced to consider the purification effect of plants on water quality. In–situ monitoring was carried out at the Luxihe pond in Chengdu, and the model parameters such as the purification rate of typical plants were calibrated. The degradation coefficient of NH3–N in the non–vegetated area was 0.014 d−1 in August and 0.010 d−1 in November. In areas with vegetation, the purification rate of NH3–N was 0.10–0.20 g/(m2·d) in August and 0.06–0.12 g/(m2·d) in November. The comparison of the results in August and November showed that due to the higher temperature in August, the plant growth effect was better, and the degradation rate of pollutants and the purification rate of pollutants by plants were higher. |
| abstract_unstemmed |
The hydrodynamic conditions of ponds are generally poor, which seriously affects the long–term water quality guarantee. In this research, the numerical simulation method was used to establish an integrated model of hydrodynamics and water quality for the simulation of the plant purification effect in ponds. Based on the flushing time using the tracer method, the purification rate of plants was introduced to consider the purification effect of plants on water quality. In–situ monitoring was carried out at the Luxihe pond in Chengdu, and the model parameters such as the purification rate of typical plants were calibrated. The degradation coefficient of NH3–N in the non–vegetated area was 0.014 d−1 in August and 0.010 d−1 in November. In areas with vegetation, the purification rate of NH3–N was 0.10–0.20 g/(m2·d) in August and 0.06–0.12 g/(m2·d) in November. The comparison of the results in August and November showed that due to the higher temperature in August, the plant growth effect was better, and the degradation rate of pollutants and the purification rate of pollutants by plants were higher. |
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