标題: Performance investigation of electrochemical assisted HClO/Fe2+ process for the treatment of landfill leachate

作者: Ye, ZH (Ye, Zhihong); Miao, F (Miao, Fei); Zhang, H (Zhang, Hui)

來源出版物: ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH DOI: 10.1007/s11356-022-19174-2 提前訪問日期: FEB 2022

摘要: The feasibility of removal of chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N) from landfill leachate by an electrochemical assisted HClO/Fe2+ process is demonstrated for the first time. The performance of active chlorine generation at the anode was evaluated in Na2SO4/NaCl media, and a higher amount of active chlorine was produced at greater chloride concentration and higher current density. The probe experiments confirmed the coexistence of hydroxyl radical ((OH)-O-center dot) and Fe(IV)-oxo complex ((FeO2+)-O-IV) in the HClO/Fe2+ system. The influence of initial pH, Fe2+ concentration, and applied current density on COD and NH4+-N abatement was elaborately investigated. The optimum pH was found to be 3.0, and the proper increase in Fe2+ dosage and current density resulted in higher COD removal due to the accelerated accumulation of (OH)-O-center dot and (FeO2+)-O-IV in the bulk liquid phase, whereas, the NH4+-N oxidation was significantly affected by the applied current density because of the effective active chlorine generation at higher current but was nearly independent of Fe2+ concentration. The reaction mechanism of electrochemical assisted HClO/Fe2+ treatment of landfill leachate was finally proposed. The powerful (OH)-O-center dot and (FeO2+)-O-IV, in concomitance with active chlorine and M((OH)-O-center dot), were responsible for COD abatement, and active chlorine played a key role in NH4+-N oxidation. The proposed electrochemical assisted HClO/Fe2+ process is a promising alternative for the treatment of refractory landfill leachate.

入藏号: WOS:000755428200002

PubMed ID: 35169949

語言: English

文獻類型: Article; Early Access

作者關鍵詞: Advanced oxidation process; Active chlorine; Electrochemical Fenton-type process; Landfill leachate; COD; NH4+-N

地址: [Ye, Zhihong; Miao, Fei; Zhang, Hui] Wuhan Univ, Sch Resource & Environm Sci, Dept Environm Sci & Engn, Wuhan 430079, Peoples R China.

[Ye, Zhihong] Chongqing Univ, Coll Environm & Ecol, Key Lab Ecoenvironm Three Gorges Reservoir Reg, Chongqing 400045, Peoples R China.

通訊作者地址: Ye, ZH; Zhang, H (通訊作者)，Wuhan Univ, Sch Resource & Environm Sci, Dept Environm Sci & Engn, Wuhan 430079, Peoples R China.

Ye, ZH (通訊作者)，Chongqing Univ, Coll Environm & Ecol, Key Lab Ecoenvironm Three Gorges Reservoir Reg, Chongqing 400045, Peoples R China.

電子郵件地址: yezhihong@cqu.edu.cn; eeng@whu.edu.cn

影響因子：4.223

博士生沈航，李霖的論文在ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION刊出

标題: A Pricing Model for Urban Rental Housing Based on Convolutional Neural Networks and Spatial Density: A Case Study of Wuhan, China

作者: Shen, H (Shen, Hang); Li, L (Li, Lin); Zhu, HH (Zhu, Haihong); Li, F (Li, Feng)

來源出版物: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 卷: 11 期: 1 文獻号: 53 DOI: 10.3390/ijgi11010053 出版年: JAN 2022

摘要: With the development of urbanization and the expansion of floating populations, rental housing has become an increasingly common living choice for many people, and housing rental prices have attracted great attention from individuals, enterprises and the government. The housing rental prices are principally estimated based on structural, locational and neighborhood variables, among which the relationships are complicated and can hardly be captured entirely by simple one-dimensional models; in addition, the influence of the geographic objects on the price may vary with the increase in their quantities. However, existing pricing models usually take those structural, locational and neighborhood variables as one-dimensional inputs into neural networks, and often neglect the aggregated effects of geographical objects, which may lead to fluctuating rental price estimations. Therefore, this paper proposes a rental housing price model based on the convolutional neural network (CNN) and the synthetic spatial density of points of interest (POIs). The CNN can efficiently extract the complex characteristics among the relevant variables of housing, and the two-dimensional locational and neighborhood variables, based on the synthetic spatial density, effectively reflect the aggregated effects of the urban facilities on rental housing prices, thereby improving the accuracy of the model. Taking Wuhan, China, as the study area, the proposed method achieves satisfactory and accurate rental price estimations (coefficient of determination (R-2) = 0.9097, root mean square error (RMSE) = 3.5126) in comparison with other commonly used pricing models.

入藏号: WOS:000758407400001

語言: English

文獻類型: Article

作者關鍵詞: rental housing price; POI; geographic information systems; deep learning

地址: [Shen, Hang; Li, Lin; Zhu, Haihong; Li, Feng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

通訊作者地址: Li, L (通訊作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

電子郵件地址: shenhang@whu.edu.cn; lilin@whu.edu.cn; hhzhu@whu.edu.cn; liwind@whu.edu.cn

影響因子：2.899