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屋顶绿化系统处理稳定化黑水

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谢欣妤, 温兴泉, 郑向勇, 张业健, 赵敏, 胡湛波, 孔海南. 屋顶绿化系统处理稳定化黑水[J]. 环境工程学报, 2017, 11(3): 1355-1361. doi: 10.12030/j.cjee.201511186
引用本文: 谢欣妤, 温兴泉, 郑向勇, 张业健, 赵敏, 胡湛波, 孔海南. 屋顶绿化系统处理稳定化黑水[J]. 环境工程学报, 2017, 11(3): 1355-1361. doi: 10.12030/j.cjee.201511186
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XIE Xinyu, WEN Xingquan, ZHENG Xiangyong, ZHANG Yejian, ZHAO Min, HU Zhanbo, KONG Hainan. Treatment of pretreated toilet flushing water by green roof system[J]. Chinese Journal of Environmental Engineering, 2017, 11(3): 1355-1361. doi: 10.12030/j.cjee.201511186
Citation: XIE Xinyu, WEN Xingquan, ZHENG Xiangyong, ZHANG Yejian, ZHAO Min, HU Zhanbo, KONG Hainan. Treatment of pretreated toilet flushing water by green roof system[J]. Chinese Journal of Environmental Engineering, 2017, 11(3): 1355-1361. doi: 10.12030/j.cjee.201511186
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屋顶绿化系统处理稳定化黑水

  • 1.

    广西大学林学院, 南宁 530004

  • 2.

    温州大学生命与环境科学学院, 温州 325035

  • 3.

    上海交通大学环境科学与工程学院, 上海 200240

  • 基金项目:

    国家水体污染控制与治理科技重大专项（2013ZX07312-001-02）

    温州市科技项目（2015S0013）

  • 中图分类号: X505

Treatment of pretreated toilet flushing water by green roof system

  • 1.

    School of Forestry, Guangxi University, Nanning 530004, China

  • 2.

    School of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China

  • 3.

    School of Environmental Science and Engineering, Shanghai Jiaotong University, Shanghai 200240, China

  • Fund Project:

  • 摘要: 为了优化屋顶绿化系统处理稳定化黑水的工艺条件，以填料种类（A）、填料添加量（B）、植物种类（C）、土层厚度（D）为实验因素，设计了正交实验并研究了不同工艺条件下模拟装置对经过生物滤池稳定化处理后黑水的处理效果。结果表明，对NH4+-N去除率影响因素的主次顺序为A > D > C > B，对TN、TP去除率影响因素的主次顺序为A > D > B > C，对COD去除率影响因素的主次顺序为D > A > B > C。综合各污染物去除效果，得出较优的实验方案选用添加20%的柱状颗粒活性炭与泥炭土混合填料，土层厚度为20 cm，种植植物为黄秋葵。在实验进水氨氮、总氮、总磷及COD的平均浓度为76.85、88.71、14.83和167.15 m·L-1的条件下，不同正交实验装置的平均去除率可分别达到56.53%~87.30%、20.43%~46.91%、20.43%~83.11%和23.42%~52.36%。
    Abstract: To study the technological conditions for the treatment of the pretreated toilet flushing water by green roof systems,an orthogonal test was designed and different experimental installations were set up to study the treatment of trickling filter pretreated toilet flushing water. 4 factors were selected for the orthogonal test,which were lightweight filler (A),additional amount of filler (B),plant species (C)and thickness of soil (D). Results show that the optimal order of the 4 factors was A > D > C > B for NH4+-N removal efficiency,A > D > B > C for the removal efficiency of both TN and TP and D > A > B > C for COD removal efficiency. Considering the removal efficiency of every pollutant,suitable technology conditions were decided as follows:20% columnar activated carbon,80% peat soil,30 cm of soil thickness and planting of okra. While the average influent concentration of the test wastewater was 76.85,88.71,14.83 and 167.15 mg·L-1 for NH4+-N,TN,TP and COD, respectively,the average removal efficiency of each device was from 56.53% to 87.30%,20.43% to 46.91%,20.43% to 83.11% and 23.42% to 52.36%,respectively.
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  • [1] RASHIDI H, GHAFFARIANHOSEINI A, GhaffarianHoseini A, et al. Application of wastewater treatment in sustainable design of green built environments:A review[J]. Renewable and Sustainable Energy Reviews, 2015, 49:845-856
    [2] DEVKOTA J, SCHLACHTER H, ANAND C, et al. Development and application of EEAST:A life cycle based model for use of harvested rainwater and composting toilets in buildings[J]. Journal of Environmental Management, 2013, 130:397-404
    [3] 张荣祥. 分散建筑生活污水处理技术研究[J]. 山西建筑, 2009, 35(22):206-207
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出版历程
  • 收稿日期:  2016-03-01
  • 刊出日期:  2017-03-10

屋顶绿化系统处理稳定化黑水

  • 1. 广西大学林学院, 南宁 530004
  • 2. 温州大学生命与环境科学学院, 温州 325035
  • 3. 上海交通大学环境科学与工程学院, 上海 200240
  • 收稿日期:  2016-03-01
基金项目:

国家水体污染控制与治理科技重大专项（2013ZX07312-001-02）

温州市科技项目（2015S0013）

摘要: 为了优化屋顶绿化系统处理稳定化黑水的工艺条件，以填料种类（A）、填料添加量（B）、植物种类（C）、土层厚度（D）为实验因素，设计了正交实验并研究了不同工艺条件下模拟装置对经过生物滤池稳定化处理后黑水的处理效果。结果表明，对NH4+-N去除率影响因素的主次顺序为A > D > C > B，对TN、TP去除率影响因素的主次顺序为A > D > B > C，对COD去除率影响因素的主次顺序为D > A > B > C。综合各污染物去除效果，得出较优的实验方案选用添加20%的柱状颗粒活性炭与泥炭土混合填料，土层厚度为20 cm，种植植物为黄秋葵。在实验进水氨氮、总氮、总磷及COD的平均浓度为76.85、88.71、14.83和167.15 m·L-1的条件下，不同正交实验装置的平均去除率可分别达到56.53%~87.30%、20.43%~46.91%、20.43%~83.11%和23.42%~52.36%。

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