Multi-Model Analysis of Dispersion of Particulate and Gaseous Pollutants from Various Sources Over Major Towns in Benue State, Nigeria
Shiada Msugh Stephen
Department of Physics, Moses Orshio Adasu University, Makurdi, Nigeria.
Tyovenda A. Alexander
Department of Physics, Joseph Sarwuan Tarka University, Makurdi (JOSTUM), Nigeria.
Sombo Terver
Department of Physics, Joseph Sarwuan Tarka University, Makurdi (JOSTUM), Nigeria.
Tikyaa Verzua Emmanuel
Department of Physics, Joseph Sarwuan Tarka University, Makurdi (JOSTUM), Nigeria.
Frederick Gbaorun
Department of Physics, Moses Orshio Adasu University, Makurdi, Nigeria.
Ayaakaa Dalton Terkimbi
Department of Physics, Moses Orshio Adasu University, Makurdi, Nigeria.
Ikyumbur Jonathan Terseer *
Department of Physics, Moses Orshio Adasu University, Makurdi, Nigeria.
Igba Denen Solomon
Department of Physics, Joseph Sarwuan Tarka University, Makurdi (JOSTUM), Nigeria.
*Author to whom correspondence should be addressed.
Abstract
This study evaluated the performance of two Gaussian dispersion models in predicting ground-level concentrations of CO, SO₂, NO₂, PM₂.₅ and PM₁₀ from six major industrial point sources in Benue State, Nigeria. Field measurements were conducted during wet and dry seasons at downwind distances of 100–800 m from rice mills, cement plants, a biotechnology facility and a traditional brick kiln under neutral atmospheric stability (Pasquill class D). Emission rates were derived from fuel analysis and stack parameters, while meteorological inputs were obtained from on-site stations and NiMet records. The study was conducted during the wet and dry seasons of 2022 to capture seasonal variations. A total of 15 sampling sites were included, covering various source types such as six industrial point sources cement plants, rice mills, brick kilns, and a biotechnology facility along with other local emission sources. This research represents the first systematic head-to-head validation of emission estimates in the West African savannah, offering new insights beyond previous studies. The steady-state Gaussian Plume Model (GPM) demonstrated excellent agreement with observations, achieving Spearman rank correlation coefficients of 0.964–0.988, RMSE < 2.8 µg m⁻³ for gases and < 1.1 µg m⁻³ for particulates, and fractional bias within ±0.08 across all sites and pollutants. In contrast, the Gaussian Puff Model systematically under-predicted concentrations by 70–95 % beyond 300 m, confirming its unsuitability for continuous industrial emissions. Highest concentrations were recorded at cement plants (SO₂ and NO₂ > 50 µg m⁻³ at 100 m) and the brick kiln (PM₂.₅ and PM₁₀ > 10 µg m⁻³), while rice mills contributed elevated CO and organic-laden particulates. Dry-season values exceeded wet-season concentrations by 18–42 %, and near-field PM₂.₅ routinely surpassed the WHO 24-hour guideline of 15 µg m⁻³ within 300 m of all sources. Results validate the Gaussian Plume Model as the most accurate and appropriate tool for regulatory dispersion modelling in the West African savannah environment. The study recommends its immediate adoption by NESREA, mandatory installation of particulate control devices at cement and brick facilities, enforcement of low-sulphur fuel, and establishment of continuous monitoring networks in Makurdi, Gboko and Otukpo.
Keywords: Gaussian plume model, gaussian puff model, air-quality dispersion, PM₂.₅, industrial emissions