🌫️ Why do some parts of a city experience far worse air quality than others?
Air pollution is often discussed as a city-wide problem, yet pollution rarely affects all neighbourhoods equally. Some districts repeatedly experience severe pollution episodes, while others remain comparatively less affected. Understanding these hidden spatial and temporal patterns is essential for designing effective environmental policies.
Tehran provides an important case study. Like Delhi, Mexico City, and Los Angeles, it faces persistent air-quality challenges driven by transportation, urban growth, energy consumption, and atmospheric conditions. In 2022, one of the most polluted years experienced by the metropolitan area, understanding the causes of pollution became particularly important for urban sustainability and public health.
🛰️ Looking at pollution from space
To better understand these patterns, we used Sentinel-5P satellite observations and Google Earth Engine to analyse five major atmospheric pollutants across Tehran's 22 municipal districts during 2022:
• Carbon Monoxide (CO)
• Nitrogen Dioxide (NO₂)
• Sulphur Dioxide (SO₂)
• Ozone (O₃)
• Aerosol Index (AI)
Rather than focusing only on annual averages, we examined daily temporal variations and combined them with advanced spatial statistics to understand where pollution accumulates and why.
📊 What did we discover?
Several clear patterns emerged.
CO and NO₂ concentrations were highest during the first and last quarters of the year. These peaks coincided with school reopenings, increased traffic activity, and greater transport demand, highlighting the important role of transportation in shaping urban air quality.
Ozone displayed a different seasonal behaviour. As a secondary pollutant formed through photochemical reactions, its concentrations increased during warmer and sunnier periods.
Aerosols showed the greatest variability among all pollutants. Spring dust events, urban development activities, traffic emissions, and poor atmospheric mixing contributed to elevated particulate pollution levels across multiple districts.
🗺️ Pollution is not randomly distributed
One of the primary objectives of this study was to identify the drivers of air pollution during one of Tehran’s most polluted years.
The results revealed exceptionally strong spatial clustering. Global Moran’s I values exceeded 0.90 for all investigated pollutants, indicating that pollution across Tehran follows highly clustered spatial patterns rather than random distributions. Compared with many studies conducted in other highly polluted regions around the world, these values are remarkably high and suggest that pollution accumulation in Tehran is strongly concentrated in specific urban areas.
To identify where these clusters occur, we applied Local Indicators of Spatial Association (LISA).
The results revealed persistent pollution hotspots across the metropolitan area. Among all pollutants, aerosols exhibited the strongest clustering behaviour, with 282 High–High (HH) clusters and a total of 483 statistically significant clusters. These hotspots highlight districts where pollution repeatedly accumulates and where targeted interventions may have the greatest impact.
🔎 What drives these hotspots?
The findings suggest that different pollutants are associated with different urban processes.
🚗 Traffic congestion strongly influences CO and NO₂ concentrations.
🏗️ Urban expansion and construction activities contribute to particulate pollution.
🌪️ Dust transport and meteorological conditions amplify aerosol concentrations.
🏭 Fossil-fuel consumption by industries and power plants affects SO₂ patterns.
Rather than a single pollution source, Tehran experiences multiple overlapping pollution mechanisms operating simultaneously across space and time.
🌍 Lessons beyond Tehran
The findings are not only relevant to Tehran. Similar challenges have been reported in major metropolitan areas such as Delhi, Mexico City, and Los Angeles, where transportation systems, urban form, population growth, and environmental conditions interact to shape air quality.
The study demonstrates how satellite observations and spatial analytics can complement traditional monitoring systems and provide a more comprehensive understanding of urban pollution dynamics.
🎯 From pollution maps to policy action
Understanding where and when pollutants accumulate is critical for effective environmental management.
The identified hotspots can support:
• Targeted traffic-management strategies
• District-level air-quality interventions
• Urban greening initiatives
• Sustainable transportation planning
• Public-health risk assessment
• Evidence-based environmental policy
By moving beyond pollution mapping and identifying the underlying spatial drivers of contamination, cities can design more effective strategies to improve air quality, urban sustainability, and quality of life.
💬 Looking ahead
Can satellite-based monitoring help cities move from measuring pollution to understanding its causes?
I welcome discussion and collaboration with researchers and practitioners working in remote sensing, GIS, air-quality monitoring, environmental health, urban sustainability, and climate-resilient urban planning.
🔗 Full article: https://link.springer.com/article/10.1007/s41810-025-00303-6