Ozone Pollution: Which Regions Exceed Dangerous Thresholds and What Measures?

In 2026, half of the world's population was exposed to ozone levels considered dangerous to health, according to the report from the European Copernicus Observatory. This massive exposure far exceeds WHO targets and is part of an upward trend driven by climate change.

Global Ozone Exposure: Key Figures and Most Affected Zones

The Copernicus report, based on satellite data and ground station networks, reveals that average ozone concentrations regularly exceed the 100 μg·m⁻³ threshold in vast urban territories of South Asia, Latin America, and Southern Europe. Megacities like New Delhi, Mexico City, and Seville experience the most frequent peaks, especially in summer when a combination of heat and strong solar irradiation favors the formation of tropospheric ozone. In rural areas, exposure remains more moderate, but pollution episodes spread rapidly via surface winds.

Copernicus uses a predictive model powered by a neural network that integrates satellite data, ground measurements, and ECMWF forecasts. This approach enables daily exposure mapping with 10 km resolution, providing unprecedented visibility into at-risk populations. According to simulations, nearly 3 billion people live in areas where the danger threshold is exceeded at least once a week. While specific sub-regions remain unconfirmed at this stage, the general trend indicates continued worsening.

Mechanisms of Tropospheric Ozone Formation and the Role of Climate Change

Tropospheric ozone is not directly emitted by human activities; it forms from precursors like nitrogen oxides (NOx) and volatile organic compounds (VOCs). Under strong solar irradiation, these pollutants react to create ozone. Climate change amplifies this process by increasing surface temperatures, accelerating chemical reactions, and prolonging ozone lifespan. Additionally, changes in atmospheric circulation patterns, observed in ECMWF climate models, transport pollutants to regions previously less affected.

Machine learning models like GraphCast or Pangu-Weather are now integrated into Copernicus forecasting systems to anticipate ozone episodes. By combining observation data with climate model outputs, these neural networks can identify ozone-forming conditions up to 48 hours in advance, providing authorities with an early action lever. This predictive capability is crucial for issuing early warnings and taking protective measures.