# Analysis of Pollutant Exposure and Toxicological Risk in Aquatic Species Using Knowledge Graphs with Louvain Community Detection
> Kamilah A.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_105035996302
Jurnal / Konferensi: Beyond Technology Summit on Informatics International Conference Bts I2c 2025
Tahun terbit: 2025
DOI: https://doi.org/10.1109/BTS-I2C67944.2025.11399323
Citations: 0

## Authors
- Kamilah A.

## Abstract
Heavy metal pollution poses significant risks to biodiversity and food safety. We developed a novel framework integrating Knowledge Graph (KG) technology with the Louvain community detection algorithm to identify multi-metal contamination patterns and assess cumulative toxicological risks in aquatic species. We constructed a comprehensive KG from 310 scientific papers containing 55,297 contamination records across 778 species and 559 pollutants in China. The KG models entities including heavy metals, aquatic species, and toxic effects, with relationships such as 'bioaccumulates_in' and 'poses_health_risk'. Risk quantification aggregates evidence paths within the KG, weighted by toxicity metrics including Target Hazard Quotient (THQ) and Incremental Lifetime Cancer Risk (ILCR). The Louvain algorithm identified 23 densely connected communities with exceptionally high modularity (Q = 0.847), indicating strong community structure where clusters represent distinct multi-metal contamination syndromes. Community 1, the largest cluster (187 nodes), revealed mercury, lead, cadmium, and chromium co-contamination affecting 45 species with mean Total Target Hazard Quotient (TTHQ) = 6.34, predominantly impacting carnivorous species in industrial coastal regions. The identified high-risk clusters aligned with known pollution hotspots in independent field studies. This methodology transforms traditional single-contaminant assessment into systemic, multi-pollutant risk evaluation, providing actionable intelligence for targeted environmental monitoring, evidence-based consumption guidelines, and policy intervention strategies.

## Keywords
- Hazard quotient
- Hazard
- Biodiversity
- Risk assessment
- Environmental science
- Pollutant
- Geography
- Pollution
- Hazard analysis
- Contamination
- Aquatic ecosystem
- Ecology
- Environmental resource management
- China
- Taxonomic rank
- Global biodiversity
- Environmental health
- Community structure
- Food chain
- Trophic level
- Water pollution
- Environmental monitoring
- Risk analysis (engineering)
- Environmental pollution
- Graph

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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