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This research delves into the air quality dynamics within Abu Dhabi, UAE, focusing on the analysis and future projection of NO2 , PM10 , and PM2.5 levels from 2015 through 2023. Through the application of ARIMA models for predictive analysis and ordinary kriging for spatial evaluation, the study meticulously assesses pollutant concentrations across 19 monitoring stations. Advanced data processing methods were employed, utilizing RStudio for statistical forecasting, complemented by the Openair package for the visualization and analysis of spatial data. Findings from this study unveil pronounced spatial distributions and evolving trends in pollutant concentrations, identifying notable pollution hotspots such as Mussafah, Hamdan Street, and Baniyas School, which consistently report higher levels of pollutants. The predictive accuracy of ARIMA models, as demonstrated by MAPE values, affirms their capability to project air quality trends accurately, particularly in identifying zones with increased pollution due to industrial operations and traffic congestion. The analysis highlights the profound impact of urban expansion and vehicle emissions on air quality. Predictions from ARIMA models are shown to be invaluable for guiding environmental strategy and policy making. The detailed examination not only illuminates current air quality conditions but also predicts a rise in pollutant levels in specific locales, signaling a pressing need for targeted environmental policies. Contributing significantly to the field of environmental science, this study proposes a comprehensive approach for evaluating and forecasting air quality in urban areas. It underscores the importance of predictive modeling in crafting and informing environmental policies and measures aimed at pollution reduction. In line with UAE's sustainability ambitions, the research advocates for improved air quality management practices to address pollution effectively and protect public health in Abu Dhabi and cities with similar environmental challenges, highlighting the results' practical implications for environmental policy and urban planning.
Ramadan et al. (Mon,) studied this question.