Leveraging Machine Learning for Ambient Air Pollutant Prediction: The Zambian Mining Environment Context

Abstract

Air quality monitoring in Zambian mining towns is an important issue due to the high levels of pollution caused by mining activities. Zambia is a country rich in minerals and mining is a significant contributor to its economy. However, mining activities have also led to increased levels of air pollution in mining towns, affecting the health of local communities. According to the Ministry of Mines, the major sources of air pollution in the Copperbelt are smelters, mining, and quarrying among others. Additionally, the Ministry of Mines reports that major pollutants include sulfur dioxide (SO2), oxides of nitrogen (NOx), particulate matter, carbon monoxide (CO), dust, Carbon dioxide, etc. There are several government agencies engaged in management that can help with these environmental issues, including the Zambia Environmental Management Agency (ZEMA). The research was investigated by using a thorough review of the literature, furthermore, a qualitative study was conducted at ZEMA the primary institution for environmental monitoring, and specifically, interviews were conducted. This was done in order to gain an in-depth overview of the current state of the art for environmental pollutant monitoring in affected mining towns. According to the findings presented here, the country has not made enough investments in environmental monitoring technologies and instead relies on funded projects that render the agency responsible for preventing and controlling ambient pollution inoperable after the projects are completed, despite the fact that there are plenty of mineral resources available and more are still to be discovered. The research suggested new techniques for comparing ambient air pollutant levels to national guideline limits based on the limitations of its results.