Spatial Trends and Distribution Patterns of Toxic Heavy Metal Contamination in an Urbanized Watershed
Publication Date: 05/04/2024
Author(s): Charles Kennedy, Ikebude Chiedozie Francis , Barango Daye Owuna.
Volume/Issue: Volume 7 , Issue 1 (2024)
Abstract:
Rapid urban growth in developing nations exacerbates pressures on water resources through increased pollution loading if management practices cannot adapt efficiently. This study evaluated industrial effluent impacts on river systems in Nigeria contaminated by discharge from beverage, oil and biscuit manufacturing plants. Physicochemical parameters and heavy metal concentrations were monitored at sites upstream and downstream from waste outfalls during wet and dry seasons. Results demonstrated exceedances of national water quality standards for indicators of organic pollution like biochemical oxygen demand and chemical oxygen demand. Notably, highly toxic heavy metals exceeded World Health Organization limits by over 100 times, posing serious public health concerns through various exposure pathways. Seasonal variations reflected changes in pollution inputs. Spatial trends showed metal levels decreasing with distance, though remaining well above safe levels 100m downstream. A predictive transport model was formulated based on field measurements incorporated into the advection-dispersion equation. Key coefficients for the dispersion rate and velocity/dispersion ratio were quantified, allowing simulation of concentration changes under differing scenarios. Model predictions closely aligned with observed metal distribution patterns. Findings highlight the need for upgraded wastewater treatment and emissions controls to mitigate pollution over-burdening natural assimilative capacity. Continuous monitoring programs should track remediation effectiveness. This study provides insights to help authorities balance rapid industrialization, environmental protection and sustainable development goals through evidence-based regulatory strategies ensuring public health.
Keywords:
Industrial effluent, heavy metals, water quality, predictive modeling, public health