Time Series Analysis of Air Quality Trends in Urban Nigeria: A Case Study of Lagos
DOI:
https://doi.org/10.54536/ajee.v5i1.6394Keywords:
Air Quality, ARIMA Modeling, Particulate Matter, Time Series Analysis, Urban PollutionAbstract
Air quality significantly influences public health, economic output, and the quality of life of urban residents. This study explores long-term air quality trends in Lagos, Nigeria, focusing on primary air pollutants such as particulate matter (PM2.5), nitrogen dioxide (NO₂), and Carbon monoxide (CO). Air quality data gathered at government monitoring stations from 2010 to 2020 were investigated using Seasonal Decomposition of Time Series (STL) and ARIMA (Auto Regressive Integrated Moving Average) models to extract information and predict future trend prospects. The findings indicate significant seasonality shifts and rising trends in pollutant concentrations, whereby PM2.5 exceeded WHO limits by an astonishing 180% during peak periods. The best model to project, was the ARIMA(2,1,2) model, having an AIC of 1,247.3 and reaching 87.3% accuracy in prediction for one-quarter in advance, and 72.1% reliability for twelve-month predictions. The study identifies an overall increase in all observed air contaminants, particularly during the dry season (November to March), and identifies an imperatively necessary policy response to minimize public health risks.
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