AI Ensemble Learns to Catch Tehran’s Dangerous Ozone Peaks Before They Strike

Ground-level ozone is one of the most deceptive pollutants in the urban atmosphere. Unlike the black smoke from a diesel bus or the brown haze hanging over a highway, ozone is often invisible, building silently on hot, sunny afternoons when sunlight cooks traffic exhaust and industrial emissions into a corrosive gas. For the roughly nine million residents of Tehran, one of the largest and most topographically constrained megacities in the Middle East, those invisible peaks can trigger asthma attacks, inflame airways, damage crops, and push concentrations past regulatory limits with only a few hours of warning. A new study published in Environmental Science and Pollution Research argues that the missing ingredient in urban ozone forecasting is not more physics, but smarter error correction, and it demonstrates the idea with a hybrid deep learning framework that achieved a city-wide coefficient of determination of 0.87 for hourly predictions across eight monitoring stations.

The research, led by Sina Dadras and Ehsan Hajighasemi of the University of Tehran’s Department of Environmental Engineering together with colleagues Khosro Ashrafi and Majid Shafiepour Motlagh, introduces a framework the authors call the Residual-Aware Meta Ensemble, or RAME. The central problem the team set out to solve is a familiar one in air quality modeling: conventional forecasting systems tend to perform reasonably well on average but systematically underestimate the extreme values that matter most. When ozone climbs toward a health-threatening peak during the photochemically active hours around midday, the difference between a forecast of 90 parts per billion and an actual reading of 120 parts per billion is not a statistical footnote. It is the difference between an ordinary afternoon and a day on which children, the elderly, and people with respiratory disease should stay indoors.

The architecture of RAME reflects a two-stage philosophy that has deep roots in machine learning but has rarely been tuned so explicitly for peak capture. In the first stage, an ensemble of base learners generates foundational forecasts. Each base learner is trained on the previous 24 hours of historical pollutant concentrations combined with real-time meteorological parameters such as temperature, solar radiation, wind speed, and humidity, all of which govern the photochemical reactions that produce and destroy ozone near the surface. The ensemble approach matters because individual models carry different inductive biases: a recurrent network may track the diurnal rhythm of ozone well, while a convolutional component may excel at detecting abrupt changes. By weighting and combining their outputs, the ensemble smooths out the idiosyncratic errors of any single learner, a strategy the authors trace back to the stacked generalization framework introduced by David Wolpert in 1992 and refined by Leo Breiman’s work on stacked regressions.

The second stage is where RAME earns its name. Rather than accepting the weighted ensemble forecast as the final answer, the framework trains a meta-learner to model the residual, meaning the discrepancy between what the ensemble predicted and what the monitoring stations actually measured. This residual-correction mechanism is built from bidirectional recurrent units paired with dilated convolutions. The bidirectional design allows the network to read the time series both forward and backward, capturing how an ozone peak is shaped by the hours that precede it and the conditions that follow it. Dilated convolutions, which skip inputs at increasing intervals, let the model perceive patterns across multiple time scales simultaneously, from the sharp hour-by-hour fluctuations driven by traffic cycles to the slower multi-day evolution of heat waves and stagnant air masses that set the stage for severe episodes.

Trained and evaluated on observations from eight air quality monitoring stations distributed across Tehran, RAME delivered a mean 24-hour averaged R-squared of 0.87, with a root mean square error of 8.12 parts per billion and a mean absolute error of 5.30 parts per billion. Those figures represent the model’s ability to explain nearly nine-tenths of the variance in hourly ozone concentrations across a city whose monitoring sites span dramatically different microenvironments, from dense traffic corridors to residential districts shaped by the mountains that wall in the Tehran basin. Crucially, the model consistently outperformed every individual baseline it was compared against, and it did so across heterogeneous urban sites rather than excelling in one favorable location while failing elsewhere. For a forecasting system intended to guide city-wide public health decisions, that spatial consistency is arguably as important as the headline accuracy numbers.

The most striking result concerns the events that regulators and health officials care about most. RAME correctly identified 96 percent of critical ozone days, the episodes on which concentrations approached or exceeded regulatory thresholds. The authors attribute this skill to the residual-correction stage, which learns precisely where and when the ensemble’s smoothed predictions fall short. Conventional systems, the study notes, tend to significantly underestimate concentrations during photochemical peak periods, because the statistical averaging that makes a model robust on typical days also flattens its response to extremes. By explicitly training a second network on the ensemble’s failures, RAME converts the systematic bias of the first stage into a learnable signal, effectively teaching the system to recognize the atmospheric fingerprints of an impending exceedance.

The choice of Tehran as the test bed is not incidental. The city sits at the southern edge of the Alborz mountains, in a semi-enclosed basin where temperature inversions, intense summer solar radiation, and a large vehicle fleet combine to create conditions that are almost laboratory-perfect for photochemical ozone production. Previous research by the same group and collaborators has documented the substantial health burden and economic cost of ambient air pollution in Tehran, and studies of summertime ozone and nitrogen oxide dynamics between 2017 and 2019 have mapped the regional contributions to the city’s ozone problem. Against that backdrop, an hourly forecasting tool that reliably flags peak events has immediate operational value: it can inform traffic restriction schemes, industrial curtailments, school activity decisions, and public health advisories issued hours before the worst air arrives rather than after.

The methodological lineage of the work is also worth noting. Over the past decade, researchers have applied long short-term memory networks, gated recurrent units, convolutional architectures, temporal convolutional networks, and transformer models to pollutant forecasting in cities from Beijing to Hangzhou to Bogota. Ensemble and hybrid approaches have repeatedly shown advantages over single models, and national-scale exposure mapping efforts in China and the United States have demonstrated the scalability of machine learning for ozone estimation. What distinguishes RAME is its explicit focus on the residual structure of ensemble error at hourly resolution, and its demonstration that a meta-learner can be trained to compensate for the specific failure mode, peak underestimation, that has limited earlier systems. The authors describe the framework as robust, scalable, and methodologically adaptable, suggesting that the same residual-aware design could be transferred to other pollutants and other cities without fundamental redesign.

Limitations and open questions remain, as they do in any modeling study. The framework depends on the quality and continuity of monitoring data, and missing observations in air quality networks remain a persistent challenge that researchers have addressed with imputation techniques ranging from interpolation to nearest-neighbor methods. The model’s reliance on 24 hours of historical data means its skill is anchored to the recent past, and rapid synoptic changes or unprecedented emission events could still surprise it. The authors also note that data will be made available on request, which opens the door to independent validation by other research groups. Nevertheless, the performance achieved across eight heterogeneous stations in one of the world’s most meteorologically challenging megacities suggests that the approach is more than a local curiosity.

The broader significance of the study lies in what it says about the future of urban air quality management. As climate change intensifies heat waves and stagnation episodes across the Middle East and beyond, the photochemical conditions that generate dangerous ozone are expected to become more frequent and more severe. Cities cannot remove ozone from the atmosphere the way they can remove a point source of particulate matter; they can only anticipate it and act. A forecasting system that captures 96 percent of critical ozone days, with errors measured in single-digit parts per billion, converts air quality management from a reactive exercise into a genuinely predictive one. For Tehran’s residents, and potentially for millions of others living in sun-baked basins and valleys around the world, that shift could mean the difference between breathing a warning and breathing the peak itself.

Subject of Research: Hybrid deep learning ensemble forecasting of hourly surface ozone concentrations in Tehran, Iran

Article Title: Hourly ozone prediction using ensemble forecasting to accurately capture peak concentration: a case study of Tehran, Iran

Article References: Dadras, S., Hajighasemi, E., Ashrafi, K., & Motlagh, M. S. (2026). Hourly ozone prediction using ensemble forecasting to accurately capture peak concentration: a case study of Tehran, Iran. Environmental Science and Pollution Research, 33(28), 14217-14240. https://doi.org/10.1007/s11356-026-38197-7

Image Credits: AI Generated

DOI: 10.1007/s11356-026-38197-7

Keywords: ozone prediction, ensemble forecasting, deep learning, Tehran, air quality, peak concentration, residual correction, meta-learner, urban pollution, public health, machine learning, Environmental Science and Pollution Research