Estimation Method of Regional Tank-Washing Wastewater Quantity Based on Multi-Source Data

Yong Xu (), Kaize Zhu and Huiling Zhong
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Yong Xu: Department of Electronic Commerce, South China University of Technology, Guangzhou 510006, China
Kaize Zhu: Department of Electronic Commerce, South China University of Technology, Guangzhou 510006, China
Huiling Zhong: Department of Electronic Commerce, South China University of Technology, Guangzhou 510006, China

Sustainability, 2023, vol. 16, issue 1, 1-15

Abstract: The growing demand for petrochemicals has led to an increase in the number of ships carrying hazardous goods, making the effective regulation of ship tank-washing wastewater collection and discharge more important. To attain this objective, it is crucial to conduct quantitative analyses of the quantity of tank-washing wastewater generated and its geographical spread in the region. However, current estimation methods are plagued by issues such as unreliability and inaccuracy. This study presents a methodology for estimating the quantity of regional tank-washing wastewater, which is based on multi-source data. Using this method to estimate the quantity of tank-washing wastewater generated in the Pearl River Delta region, it was found that in the first quarter of 2018, the demand for tank washing by dangerous goods ships accounted for approximately 7.4% of the total number of berthing events in the study area. If all of these demands were fulfilled, about 15,000 tons of tank-washing wastewater would be generated. A more precise estimation of tank-washing wastewater was achieved, and the geographical dispersal of quantity was identified. Estimating the quantity of tank-washing wastewater in the area forms the foundation for developing facilities for collecting and disposing such wastewater, as well as for the design and site selection of tank-washing stations.

Keywords: water transportation; data mining; automatic identification system; tank-washing wastewater; green shipping (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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