Computational fluid dynamics (CFD) simulation of airborne toxic pollutants and associated human health risks in industrial zones of Delta state, Nigeria

Nkechi Blessing  Chinedu; Gospel Effiong  Isangadighi; Ubong Bernard  Essien; Salami Basirat  Adedamola; Austin Uzochukwu  Orabuego; Patience Oinu  Momoh

doi:10.18502/japh.v11i1.21271

Nkechi Blessing Chinedu Department of Industrial Chemistry, Southern Delta University, Delta State, Nigeria
Gospel Effiong Isangadighi Environmental Management and Toxicology, Federal University of Petroleum Resources, Effurun, Delta State, Nigeria
Ubong Bernard Essien African Centre of Excellence in Public Health and Toxicological Research, University of Port Harcourt, Rivers State, Nigeria
Salami Basirat Adedamola Department of Chemical Sciences, Lead City University, Oyo State, Nigeria
Austin Uzochukwu Orabuego Pharmacology and Toxicology, Pharmaceutical Sciences University of Nigeria, Nsukka, Nigeria
Patience Oinu Momoh Department of Public Health, Global Health and Infectious Disease Control Institute (GHIDI), Nasarawa State University, Keffi, Nasarawa State, Nigeria

DOI: https://doi.org/10.18502/japh.v11i1.21271

Keywords: Computational fluid dynamics (CFD); Airborne toxic pollutants; Human health risk assessment (HHRA); Industrial emissions; Delta state, Nigeria

Abstract

Introduction: The rapid rate of industrial growth in Delta State, Nigeria, has led to an increase in the emission of airborne pollutants, including Particulate Matter (PM2.5), Sulfur dioxide (SO2), Nitrogen Oxides (NOx), and Volatile Organic Compounds (VOCs), which pose a threat to the environment and the health of the population. This paper utilises Computational Fluid Dynamics (CFD) and Human Health Risk Assessment (HHRA) to simulate the dispersion of pollutants and assess the risks associated with exposure in four industrial areas: Warri/Ekpan, Aladja, Ughelli, and Kwale.

Materials and methods: Simulations in three-dimensional CFD of ANSYS Fluent 2024 R1 were conducted using the actual meteorological, topographic and emission parameters provided in NiMet and EIA data. The Navier- Stokes equations were solved with the Realisable k-epsilon turbulence model. The model results were georeferenced and interpreted in ArcGIS Pro 3.2, generating exposure maps by combining the pollutant fields with the population fields. The Hazard Index (HI) and Lifetime Cancer Risk (LCR) were used in quantifying health risks in accordance with USEPA guidelines.

Results: The concentrations of VOCs and PM2.5 in the air were 115.6 μg/ m³ and 56.2 μg/m³, respectively, which exceeded the WHO levels. HI values were 14.7-21.4 (adults) and 26.138.0 (children), and LCR values (1.710;- 3.210; -3) represented that there was carcinogenic risk.

Conclusion: CFDH-HRA was the most accurate in predicting risks of pollution and exposure, highlighting hotspots in critical zones near Warri and Aladja. The importance of adopting CFD-based control and monitoring to achieve SDGs 3, 9, 11, and 13 lies in creating a cleaner and healthier environment