Application of artificial neural networks for predictive model of municipal solid waste collection in tourist cities

Abstract

BACKGROUND AND OBJECTIVES: In recent decades, there has been significant advancement in scholarly research focused on detecting pollutants in marine environments and assessing the potential risks associated with seafood, particularly marine fish. The advancement has been predominantly influenced by the detection of microplastics, which have the ability to permeate food webs via both direct and indirect pathways. Microplastic pollution poses a substantial health risk to organisms at all levels of the food chain, including humans, who are top consumers. Despite the global concern, there is a lack of extensive research on microplastics in fish in Indonesia. The reliance of coastal communities in Indonesia on marine resources raises concerns regarding the potential impact of microplastic contamination. This study sought to assess the extent of microplastic pollutants in commercially caught marine fish from Jakarta Bay, a densely populated and industrialized coastal area accommodating more than 35 million inhabitants.METHODS: The study was conducted at five nearby fresh seafood markets in the northern part of Jakarta, where marine fish specimens were collected between December 2023 and January 2024. In total, 160 samples were gathered, with 20 individuals representing each of the eight diverse marine fish species. The approved protocol for extracting microplastics, which incorporates biological digestion, density separation, and microplastic identification, was strictly followed, although some adaptations were made as the process unfolded. Preventative actions were enacted in order to decrease the risk of microplastic cross-contamination.FINDINGS: It was determined through analysis that 93.75 percent (150 out of 160) of the fish studied contained microplastics, which were detected in samples obtained from both the gut and gill samples. On average, each fish had 3.65 ± 2.34 particles per individual, or approximately 0.12 ± 0.21 particles per gram. Microplastics were found in 81.25 percent of gut samples and 79.38 percent of gill samples. The abundance of microplastics in gut (1.79 ± 1.19 particles per individual) was slightly lower than in gills (1.86 ± 1.30 particles per individual). The variance in microplastic content between the two organs did not reach statistical significance. Fish with carnivorous feeding habits demonstrated a higher average microplastic content when contrasted with those utilizing omnivorous and planktivorous feeding strategies. Fish living in the benthopelagic region tended to have slightly more microplastic particles than those in benthic coastal water and pelagic coastal water. Most of the microplastics detected in commercial marine fish were in the size range of 2000-5000 micrometers, with the majority being in the form of fragments and fibers. The study also pinpointed seven specific polymer classifications, which consist of polyethylene, polypropylene, polystyrene, nylon, polyester, polybutadiene, and polyethylene terephthalate.CONCLUSION: The escalating levels of microplastics in the environment present a substantial threat to food security, marine ecosystems, and human health. It is imperative to develop a standardized risk assessment mechanism utilizing advanced tools and methodologies to quantify the levels of microplastics in the environment and living organisms as the study moves forward. It is imperative that both capture fisheries and aquaculture undergo thorough assessments of risks and hazards. This study underscores the significance of monitoring plastic waste in the Greater Jakarta area and its adjacent coastlines. Further study is essential to evaluate the magnitude of plastic pollution in fish tissues that are consumed by humans, and to assess the potential consequences for food safety.

Keywords

• artificial neural networks (ann)
• model prediction
• municipal solid waste (msw)
• tourism city
• urban metabolism