Performance evaluation of gross pollutant trapping devices versus life cycle cost for best pollutant management practices in Klang River basin

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Nur Farazuien Binti Md Said, Ms.
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Due to rapid and uncontrolled development in Sg Klang, it leads to the production of increased amounts of waste material and garbage. Sg Klang has been identified as major contributor of waste that contributes over 67,000 metric tonnes of floating waste from 2016 until 2020. Thus, government has installed 119 units of Gross Pollutant Trap (GPT) in Sg Klang as a initiatives to trap gross pollutants from getting into the river system under River of Life (ROL) Project. ROL project is a project under the Economic Transformation Program (ETP) by the Malaysia Government, to reach greater Klang valley by transforming Sg Klang into a vibrant and liveable waterfront by 2020. In achieving this target, three components of the transformation program have been set up for Sg. Klang and the main tributaries within ROL area namely river cleaning, river beautification, and property development. Installation of GPT is one of the components under river cleaning component. Fundamentally, GPT is an engineered sediment trap built to capture stormwater and rising flow energy through its self-cleaning capacity. However, it was found that the GPT design criteria, efficiencies as well as operation and maintenance including life cycle cost (LCC) and cost effectiveness ratio (CER) of available GPT based on local data and experience are limited in information. Therefore, this study aims to evaluate the performance of GPTs in terms of wet load by 5 types of proprietary GPTs from 3 different types of land use (Residential, Commercial and Mix Development; to characterize gross pollutants trapped by 5 types of proprietary GPTs from 3 different types of land use at selected locations and to quantify life cycle costs (LCC) and cost effectiveness ratio (CER) with 5 types of proprietary GPTs. The study area includes 5 sub-catchments under ROL project, namely Sg Klang, Sg Kemensah, Sg Gisir and Sg Sering. The data collection is sourced from Pejabat Lembangan Sg Klang (PLSK) during the GPT maintenance every month. The wet load then stored in plastic bag and immediately weighed and recorded by following the ASCE standard method. The gross pollutant characterization was done at 14 selected sampling locations based on the amount of gross pollutant trapped in the GPT and the site suitability, which is availability of open space to conduct the sorting process. The gross pollutant trapped is sorted into its respective categories, then measured in terms of weight based on different landuse. Furthermore, this study takes into consideration the associated cost with regard to LCC and CER of installed GPTs in the study area. The highest annual gross pollutant wet load captured by year is from Sg Klang, with the value of 20372.5 kg/ha/yr for year 2015 and 17112.3 kg/ha/yr for year 2016. Total number of GPTs installed in this study area is 59, and most of the GPTs were installed in the residential area. In terms of gross pollutant characterization ̧ it shows a significant trend for residential area, where 72% of gross pollutants trapped in GPTs is vegetation due to surrounding trees around the residential area. In contrast, for commercial area, it was found that sediments has the most highest proportion for all sampling location, with a percentage range from 53% to 96% from 3 sampling locations. From the analysis, LCC of GPTs in the study area is ranging from RM 113,130 to RM 297,086 and RM 138,092 to RM 403,622 for project duration of 10 years and 20 years respectively. For CER analysis, in average NTVS is having a lowest average CER with value of RM 36.65 per kg per year manual method. However, it is found that GPT/SK/CDS/94 is having the lowest CER out of 119 proprietary GPTs with value of RM105.97. Additionally, the GPT inventory database for all catchment has been developed for future reference and will be uploaded into web based ArcGIS Online System and assist the authority through the GPT knowledge database system (DeGPTs) as well added value to the technical reference for MSMA Guidelines Chapter 10 enable better management of gross pollutant traps, in terms of cost and maintenance. Ultimately, the data and result obtained in this study provides information that can help future decision for GPTs installation and maintenance program and assist the engineers and local authorities to implement appropriate strategies for trapping gross pollutants in urban area, in terms of management and preparing budget allocation for GPTs operation & maintenance.
Gross Pollutant Trapping Devices