Abstract

Effective air quality regulation and climate change mitigation depend on reducing greenhouse gas emissions and air pollutants. To achieve sustainable green development, this study constructs a spatio-temporal hierarchical model to assess the Air Quality Index (AQI) across Nigerian states and to derive actionable insights for environmental sustainability. Specifically, this study develops a three-level Bayesian hierarchical model based on latent Gaussian likelihoods to capture both random effects and systematic differences among states in Nigeria. The model structure allows for state-level variation, covariate effects, additional unexplained variation and variance decomposition. Air Quality Index data from five geopolitical zones (covering 12 states) were used to validate the developed model. The study revealed the priority level and the actions required for each state, as well as the important contributors to air pollution in Nigeria. All industries must endeavour to reduce emissions and prepare for changes in air quality associated with rising temperature. Stakeholders should mitigate the effects of industry pollution on land biodiversity and preserve natural habitats against the degradation caused by air pollution. The model accuracy is very high, indicating high correlation between the observed and the predicted values.
The Bayesian model developed was properly validated, the model’s strong fit supports the analytical approach and affirms the demonstrated ability of identified predictors to explain variations in air quality across Nigerian states. States in the South South region requires emergency response to achieve sustainability. This study gives assurance of applying the model in policy interventions and projections in the future.