MARATTO

article · Open Journal of Physical Science (ISSN 2734-2123)

CHEMOMETRIC APPROACH TO THE PREDICTION OF POLLUTANTS DISTRIBUTION IN WATER SOURCES AROUND ASHAKA CEMENT INDUSTRY

Abstract

Cement production can negatively affect nearby water resources. This study used chemometrics to predict the source and distribution of pollution indicators around the Ashaka Cement Industry in Nigeria. Seventy-two water samples were collected from twelve stations between July and October 2023 and analyzed for pH, dissolved oxygen (DO), electrical conductivity (EC), total dissolved solids (TDS), turbidity, salinity, total suspended solids (TSS), chemical oxygen demand (COD), biochemical oxygen demand (BOD), and the BOD/COD ratio using standard methods. Results showed that while most parameters were within WHO limits, elevated TSS and turbidity were observed at all stations, with high salinity at one sampling station. Generally, there were increases in the concentrations of most physicochemical parameters during August and September compared to July and October. Thus, while many individual parameters fell within WHO guidelines, the widespread and persistent issues with TSS and turbidity, along with the localized high salinity, indicate that the water sources were not of consistently good quality. Also, seasonal variations need to be considered in water management. Multivariate data analysis (PCA, HCA, CA, and DA) using IBM SPSS effectively correlated and classified physicochemical indicators of pollution based on their sources and spatial distribution patterns. This study demonstrates the potential of chemometrics for assessing industrial pollution and provides insights into the relationships between water quality indices and their sources around the cement industry. Thus, this information can guide monitoring and mitigation to minimize the industry's environmental impact around its environs.

Research topics

  • Analytical chemistry methods development
  • Environmental Chemistry and Analysis
  • Water Quality Monitoring and Analysis

Read the original research

This page summarises published work. The authoritative version sits with the publisher.

DOI: 10.52417/ojps.v6i1.758

Is something wrong with this record? Report it or request removal.

Discussion

Discuss this research

Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.

No discussion yet. Open the first thread.