Assessment and Modelling of Particulate Matter (PM2.5 and PM10) Concentration around North Central Region of Nigeria

Abstract

Air Pollution has become one of the most conspicuous pollutants around the globe today, for which Particulate Matter (PM2.5 and PM10) is inclusive, having the highest air pollutant index (API) value contrasted with the other criteria contaminations. Long-term exposure to these pollutants may lead to a marked reduction in life expectancy due to increase in cardiopulmonary and lung disease mortality. This study provides baseline data on Particulate Matter (PM2.5 and PM10) concentration in the region and models it to indirect data using the average direct satellite captured data sourced 24 Hours from NASA through NASRDA software. A number of anthropogenic activities increase the concentration of PM2.5 and PM10 in the North Central Region of Nigeria, which are linked to health issues in the area. PM2.5 and PM10 are fine atmospheric particles and coarse particles, respectively that contribute to the low life expectancy of less than 60 years in Nigeria. The absence of Particulate Matter monitoring stations and inadequate equipment in the Country for timely prediction of its status for information that permits the regulatory authority and local community to take prudent steps and lessen the effect of particulate contamination, calls for the use of forecast models that would readily ensure data availability. This study applies the Multiple Linear Regression (MLR) model to predict Particulate Matter (PM2.5 and PM10) concentration and Air Quality Index prediction in Six States and the Federal Capital Territory which has two main seasons (Dry and Wet) in 2020, 2021 and 2022. The meteorological variables (of temperature) for the average of 12 Months was used to model the concentration. The Air Quality Index was calculated with the indirect data and it showed a percentile difference of about 5% between direct and indirect Particulate Matter concentration. The highest AQI for PM10 concentration for the direct data across the locations were 103µg/m3, 101 µg/m3 and 103 µg/m3 for 2020, 2021 and 2022 respectively while the lowest were 79 µg/m3, 82 µg/m3 and 83 µg/m3 for the same 2020, 2021 and 2022 respectively. For PM2.5 concentration, the AQI for the direct data across the locations were 51µg/m3, 53 µg/m3 and 53 µg/m3 in 2020, 2021 and 2022 respectively while the lowest were 32 µg/m3, 45 µg/m3 and 27 µg/m3 for 2020, 2021 and 2022 respectively. The highest AQI for PM10 concentration for the indirect data across the locations were 113µg/m3, 111 µg/m3 and 113 µg/m3 in 2020, 2021 and 2022 respectively while the lowest were 89 µg/m3, 92 µg/m3 and 93 µg/m3 in 2020, 2021 and 2022 respectively. The highest AQI for PM2.5 concentration indirect data across the locations were 53µg/m3, 53 µg/m3 and 52 µg/m3 in 2020, 2021 and 2022 respectively while the lowest were 45 µg/m3, 41 µg/m3 and 39 µg/m3 in 2020, 2021 and 2022 respectively. The AQIs with highest concentration for all the years exceeded the WHO World Annual Standard of 24 Hours for both PM2.5 and PM10 which is <50 µg/m3 and <100 µg/m3 respectively. This result is unhealthy for such locations and is a contributing factor to cases of cardiopulmonary and lung disease mortality around the region. The performance indicators used are Root Mean Square Error (RMSE) and the value of coefficient determination (R2). The error in the model was evaluated based on RMSE and the accuracy was assessed using R2. The increasing values of R2 and decreasing RMSE indicated that the Particulate Matter (PM2.5 and PM10) is very well explained by the input variable in the model being developed. It’s either RMSE was decreasing or increasing and R2 increasing or decreasing for each location. The indicator showed that the calculated indirect AQI and concentration can be relied on across the locations in the absence of direct data from Polar Satellites.