Study Summary
Urban air pollution is a major public health concern in megacities worldwide, yet monitoring networks typically rely on chemical sensors that measure individual pollutant species independently. This study explores a complementary approach: using the atmospheric electric field, measured as the potential gradient (PG), as an electrical indicator of anthropogenic air pollution in São Paulo, Brazil, one of the largest and most polluted megacities in the Southern Hemisphere.
The research is based on a long-term dataset spanning from February 2018 to December 2024, combining continuous rooftop PG measurements at Mackenzie Presbyterian University with hourly concentrations of six major pollutants (CO, NO, NO₂, NOx, SO₂, and PM₁₀) from a nearby air-quality station operated by CETESB. Fair-weather periods were carefully identified using a two-step procedure that combines surface meteorological screening with satellite-based cloud filtering from GOES-16 and GOES-19 imagery, ensuring that the PG signal reflects aerosol and pollution effects rather than meteorological disturbances.
The analysis reveals a reproducible sensitivity hierarchy in the coupling between the electric field and air pollutants. Primary combustion-related gases (nitrogen oxides NOx, NO, and carbon monoxide CO) show the strongest associations with PG, with median daily Pearson correlations on the order of r ≈ 0.6. Particulate matter (PM₁₀) shows weaker and more variable coupling. Crucially, this relationship is strongly regime-dependent: the PG-pollutant coupling strengthens under stable atmospheric conditions and weak ventilation (typically at night), and weakens markedly during daytime convective mixing when the boundary layer is well-ventilated.
The study also examines the impact of large-scale emission perturbations. During the 2020 COVID-19 lockdown, when vehicular traffic and industrial activity dropped sharply, the fair-weather PG exhibited a persistent reduction relative to the multi-year baseline, providing independent electrical evidence of reduced anthropogenic emissions. A machine learning framework based on Random Forest regression was additionally used to test the predictive capability of PG for urban air pollution levels, confirming the regime-dependent nature of the coupling. These results position the atmospheric electric field not as a universal pollution proxy, but as a physically grounded, regime-dependent indicator of near-surface air quality in urban environments.
How to cite this work:
Rubén M. Romero, J.C. Tacza, Angel Vara-Vela, S. Szpigel, J.-P. Raulin, Regime-dependent sensitivity of the atmospheric potential gradient to anthropogenic air pollution in São Paulo, Brazil, Atmospheric Research, 2026, 109002, ISSN 0169-8095, https://doi.org/10.1016/j.atmosres.2026.109002