Causal and Correlational Interdependencies Between Precursor and Secondary Pollutants: A Granger Causality and Cross Correlation Analysis of Urban Air Quality Dynamics (2024 2025), Abuja, Nigeria
Author
Otu Simon Achu, Ebam Martin, Bello Muazu Maccido, Mba Ojong Ndoma-Egba, Victor Ikechukwu Ekpunobi
Abstract
The formation of secondary air pollutants, particularly ground-level ozone (O3) and fine particulate matter (PM2.5), is governed by complex, non-linear interactions between precursor species including nitrogen dioxide (NO2) and carbon monoxide (CO). While the photochemical and physical mechanisms linking these pollutants are well-established in atmospheric chemistry, the directional, temporal, and causal nature of these relationships at the urban scale remains insufficiently quantified in observational data. This study investigates the interdependencies between precursor gases (NO2, CO) and secondary pollutants (O3, PM2.5) using a comprehensive two-year hourly dataset (January 2024 to December 2025) from an urban monitoring network in Abuja, Nigeria. We employ a multi-methodological framework integrating Pearson and Spearman correlation analysis, lagged cross-correlation, Granger causality testing within a Vector Autoregression (VAR) framework, and Convergent Cross Mapping (CCM) as a non-linear robustness check, to determine whether historical variations in precursor concentrations provide statistically significant predictive information for future concentrations of secondary pollutants. Across 17,544 hourly observations, both NO2 and CO Granger-cause O3 and PM2.5 at high significance (p < 0.001) in bivariate and multivariate VAR settings, with peak predictive lags of 7 to 24 hours. However, reverse-direction tests (secondary pollutants Granger-causing precursors) are equally or more significant, indicating that the observed predictive relationships partly reflect shared diurnal and meteorological drivers rather than one-way mechanistic causation. Lag-0 correlations between NO2/CO and O3 are negative (Pearson r = -0.28 and -0.40 respectively), consistent with titration dominating the instantaneous relationship in this traffic-influenced urban airshed, while positive cross-correlation peaks at 0-to-7-hour lags point to delayed photochemical production. We discuss these findings against the reviewed literature and outline the implications and limitations of Granger-based causal inference in a tropical, single-city setting.
Keywords
Granger causality; pollutant interdependencies; ozone formation; NO2-O3 coupling; PM2.5; urban air quality; time-series analysis; causal inference; Abuja
Full Text:
References
Das, S., Shukla, S., Yadav, A., & Chakraborti, A. (2026). Causal links between anthropogenic emissions and air pollution dynamics in Delhi. arXiv preprint. https://arxiv.org/abs/2503.18912
Deng, W., Wang, L., Huang, J., Guo, Z., Yang, J., Xiong, J., Liu, H., & Wen, M. (2026). Photochemical ozone formation oriented VOC source apportionment and health economic burdens in Pearl River Delta. npj Clean Air, 2, 16. https://doi.org/10.1038/s44407-026-00055-8
El Mghouchi, Y., & Udristioiu, M. T. (2026). Causal analysis of ground-level ozone formation using advanced AI-based methods in Craiova, Romania. European Physical Journal Plus, 141, 277. https://doi.org/10.1140/epjp/s13360-026-07495-x
Huang, C., Wang, J., Jin, Y., Min, M., Fan, Q., & Kim, J. (2026). O3-NOx-VOCs sensitivity in major Chinese regions: Detailed insights from GEMS satellite hourly observations. Atmospheric Chemistry and Physics, 26, 10221-10240. https://doi.org/10.5194/acp-26-10221-2026
Huang, H., Chen, T. K., Kamalanathan, S., Ehsan, R. M., Subasre, R., Liang, Z. F., & Luo, Y. S. (2025). Spatiotemporal analysis of urban air pollution dynamics: Linking emission sources, meteorological influences, and public health risks through long-term monitoring. Journal of Environmental Sciences, 10559-10587.
Kurniawan, T. A., Priya, A. K., Lei, T. M. T., Dissanayake, K. K., Onn, C. W., Jumaniyozov, K., Eshmetov, R., & Mohyuddin, A. (2026). Urban air pollution as a driver of climate forcing in Central and South Asia: Causal evidence from Tashkent (Uzbekistan) and Lahore (Pakistan). Research Square. https://doi.org/10.21203/rs.3.rs-10432281/v1
Liu, Y., Gong, Y., Wang, N., Shi, G., Yang, F., Zhang, Y., & Lu, K. (2026). Photochemical mechanism-dependent ozone formation and precursor sensitivity under varying NOx conditions. EGUsphere. https://doi.org/10.5194/egusphere-2026-1786
Mitra, B., Hridoy, A.-E. E., Mahmud, K., Uddin, M. S., Talha, A., Das, N., Nath, S. K., Shafiullah, M., Rahman, S. M., & Rahman, M. M. (2024). Exploring spatial and temporal dynamics of Red Sea air quality through multivariate analysis, trajectories, and satellite observations. Remote Sensing, 16(2), 381. https://doi.org/10.3390/rs16020381
Robertson, M. L., Arellano, A. F., & Sorooshian, A. (2026). Multi-platform analysis of ozone-precursor relationships in a semi-arid urban environment: Insights from TEMPO, Pandora, and surface observations in Tucson, Arizona. EGUsphere. https://doi.org/10.5194/egusphere-2026-3397
Rybarczyk, Y., Zalakeviciute, R., & Ortiz-Prado, E. (2024). Causal effect of air pollution and meteorology on the COVID-19 pandemic: A convergent cross mapping approach. Heliyon, 10(1), e25134. https://doi.org/10.1016/j.heliyon.2024.e25134
Tam, B. C. M., Tang, S.-K., & Cardoso, A. (2026). A multi-aspect feature dependency screening for augmentation of derivative feature space boosting deep learning training: A case of air quality in subtropical areas. IEEE Access, 14, 15637-15658.
Waudby, C. M. (2025). Beyond thunderstorm asthma - extending our understanding of the meteorology, pollen and asthma relationship [Doctoral dissertation, University of New South Wales]. https://doi.org/10.26190/unsworks/31339
Yang, X., Wang, Z.-H., & Wang, C. (2026). Directed causal coupling between urban heat and air pollution in U.S. cities. Environmental Research Letters, 21(6), 064029. https://doi.org/10.1088/1748-9326/ae53fd
Yuan, L., Han, W., Meng, J., Wang, Y., Yu, H., & Li, W. (2025). Uncovering the impact of urban functional zones on air quality in China. Atmospheric Chemistry and Physics, 25, 10421-10442. https://doi.org/10.5194/acp-25-10421-2025