Investigation of Leachate Characteristics in Field-Scale Landfill Test Cells

2019 ◽  
Vol 13 (5) ◽  
pp. 829-842 ◽  
Author(s):  
Selin Top ◽  
Gülizar Kurtoğlu Akkaya ◽  
Ahmet Demir ◽  
Şenol Yıldız ◽  
Vahit Balahorli ◽  
...  
2006 ◽  
Vol 134 (1-3) ◽  
pp. 19-26 ◽  
Author(s):  
Bestamin Ozkaya ◽  
Ahmet Demir ◽  
M. Sinan Bilgili

2019 ◽  
Vol 21 (2) ◽  
pp. 153-162 ◽  

<p>This paper presents the results of an experimental study carried out in field-scale test cells in order to determine the effect of aeration and leachate recirculation on waste decomposition rate, solid waste characteristics, landfill gas composition and settlement in the landfill body. Four landfill test cells with the dimensions of 20 m x 40 m x 5 m were constructed in Komurcuoda Sanitary Landfill, Istanbul. Solid wastes representing Istanbul Asian side waste characteristics were landfilled in the test cells and they were operated simulating anaerobic (AN1), leachate recirculated anaerobic (AN2), semi-aerobic (A1) and aerobic landfilling (A2) methods. Alternative landfilling methods for accelerating solid waste stabilization in landfills were investigated by means of solid waste characteristics (elemental analysis, pH, moisture content, TOC, TKN, C/N ratio, volatile solid content (VS), biochemical methane potential (BMP), and stability index (SI) analysis), landfill gas components (CH4, CO2, O2, and H2S), temperature variations in landfill body, and landfill settlement. The study indicated that aeration and leachate recirculation accelerate biodegradation rate. Higher rates of MSW biodegradation eventually provide reduction in the contaminant life span of the landfill by achieving a high waste volume reduction in a relatively short duration than anaerobic test cells, decrease the cost of long term monitoring incurred with post-closure of landfill sites. In case of impossibility of aerobic landfilling based on the results of the cost benefit analysis, it was stated out that semi-aerobic landfilling technology is also a viable method in shortening the stabilization time and accelerating the landfill gas production.</p>


2000 ◽  
Author(s):  
Michael Schuller ◽  
Brad Fiebig ◽  
Patricia Hudson ◽  
Alicia Williams
Keyword(s):  

1991 ◽  
Vol 24 (5) ◽  
pp. 85-96 ◽  
Author(s):  
Qingliang Zhao ◽  
Zijie Zhang

By means of simulated tests of a laboratory–scale oxidation pond model, the relationship between BOD5 and temperature fluctuation was researched. Mathematical modelling for the pond's performance and K1determination were systematically described. The calculation of T–K1–CeCe/Ci) was complex but the problem was solved by utilizing computer technique in the paper, and the mathematical model which could best simulate experiment data was developed. On the basis of experiment results,the concept of plug–ratio–coefficient is also presented. Finally the optimum model recommended here was verified with the field–scale pond data.


2016 ◽  
Vol 3 (2) ◽  
pp. 118-130
Author(s):  
Tarek Abichou ◽  
Haykel Melaouhia ◽  
Bentley Higgs ◽  
Jeff Chanton ◽  
Roger Green

2021 ◽  
Vol 13 (15) ◽  
pp. 3024
Author(s):  
Huiqin Ma ◽  
Wenjiang Huang ◽  
Yingying Dong ◽  
Linyi Liu ◽  
Anting Guo

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R2 of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively.


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