📄 earth_science

Satellite-Based Assessment of Air Pollution Dynamics and Land Surface Characteristics in Riyadh, Saudi Arabia Using Sentinel-5P, MODIS, and Google Earth Engine

This study utilizes multi-source satellite data and Google Earth Engine to analyze the spatiotemporal dynamics of air pollutants and land surface characteristics in Riyadh from 2018 to 2025, revealing that while seasonal meteorological conditions and urban greening influence pollution patterns, emission intensity and atmospheric processes remain the dominant controlling factors.

Original authors: Fahad Alshehri, Hazem Abd El-Hamid

Published 2026-08-15

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CC BY 4.0

Original authors: Fahad Alshehri, Hazem Abd El-Hamid

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the sky above a city not as an empty blue canvas, but as a giant, invisible soup. In this soup, invisible ingredients like car exhaust and factory smoke mix with the sun's heat and the ground below. Scientists who study this "atmospheric soup" use a special toolkit called remote sensing. Instead of standing on a street corner with a mask, they use satellites—giant eyes in space—to take pictures of the air and the ground. They look for clues like Land Surface Temperature (how hot the ground feels, like a frying pan vs. a cool patch of grass) and NDVI (a score that tells us how green and healthy the plants are). Why does this matter? Because in cities growing super fast in hot, dry places, the way the ground looks and feels changes the air we breathe. If the ground gets too hot or the plants disappear, the "soup" can get toxic, making it hard for people to stay healthy. Understanding this dance between the ground, the plants, and the air is the key to building cities where people can thrive without choking on their own exhaust.

Now, let's zoom in on Riyadh, Saudi Arabia, a city that is expanding faster than a video game character leveling up. A team of researchers decided to play detective with this city's "atmospheric soup" from 2018 to 2025. They didn't just look at one thing; they combined data from three different satellite superpowers: Sentinel-5P (which sniffs out invisible gases), MODIS (which measures how hot the ground is and how green the plants are), and Sentinel-2 (which takes high-definition photos to see where buildings are popping up). They used a massive cloud computer called Google Earth Engine to crunch all this data, acting like a giant digital microscope to see patterns no human could spot with just their eyes.

Here is what they found: The air in Riyadh is a tale of two cities. The "bad guys" of the air pollution world—Nitrogen Dioxide (NO₂) and Carbon Monoxide (CO)—love to hang out in the busy city center and industrial zones, clustering right where the traffic jams and factories are. It's like a party where the exhaust fumes are the guests of honor. However, Ozone (O₃) is a sneaky trickster. It doesn't hang out in the city center; instead, it drifts downwind to the rural areas. Why? Because the sun's intense heat acts like a chemical kitchen, cooking up ozone from the city's exhaust only after it travels away from the source. Meanwhile, Sulfur Dioxide (SO₂) stays very local, sticking to specific industrial spots like a magnet.

The ground itself told a story too. The researchers watched the city get hotter and hotter, reaching a scorching maximum of 43.10°C in 2021. It was like the city was sitting on a heating element. But then, something interesting happened. After 2022, the heat started to drop, falling to 37.07°C by 2025. At the same time, the greenery (measured by NDVI) started to grow a little bit more, especially in planned parks and along the Wadi Hanifa river. It seems the city's new "greening" projects were like sprinkling a little water on that hot frying pan, cooling it down just a bit.

But here is the twist: while planting trees and cooling the ground helped lower the temperature, it didn't magically clean the air. When the scientists ran their math models, they found that the ground temperature and the amount of greenery could only explain a small slice of the pollution puzzle—about 12.4% to 23.1% of the changes. The rest? That's still controlled by how much pollution is being pumped out by cars and factories, and how the wind and weather move it around. The study suggests that while making Riyadh greener is a great idea for keeping the city cool, it's not a magic wand for air quality. To really fix the air, the city needs to tackle the exhaust at the source, not just the heat on the ground. It's a reminder that you can't just plant a garden to fix a smoggy sky; you have to stop the smoke coming out of the chimney first.

Technical Summary: Satellite-Based Assessment of Air Pollution Dynamics and Land Surface Characteristics in Riyadh, Saudi Arabia

Problem Statement
Rapid urbanization in arid regions, particularly in Riyadh, Saudi Arabia, has created a complex environmental system where anthropogenic emissions, land-use changes, and hyper-arid climatic conditions interact to degrade air quality. While ground-based monitoring stations offer high accuracy, their sparse spatial distribution limits the ability to capture regional atmospheric conditions across large metropolitan areas. Furthermore, previous research has often focused on individual pollutants or single environmental indicators, lacking an integrated assessment of the interplay between atmospheric composition, thermal environments, vegetation dynamics, and urban expansion in hyper-arid cities. This study addresses the need for a comprehensive, long-term evaluation of these interactions to support sustainable urban planning and air quality management.

Methodology
The study utilized a multi-source satellite data integration approach within the Google Earth Engine (GEE) cloud computing platform to analyze spatiotemporal dynamics from 2018 to 2025.

Data Sources:
- Atmospheric Pollutants: Sentinel-5P TROPOMI Level-3 products provided daily observations of tropospheric Nitrogen Dioxide (NO₂), Sulfur Dioxide (SO₂), Carbon Monoxide (CO), Ozone (O₃), and the Absorbing Aerosol Index (AAI).
- Land Surface Characteristics: MODIS products (MOD11A2 and MOD13A2) were used to derive Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI).
- Land Use/Land Cover (LULC): Sentinel-2 Level-2A imagery (10 m resolution) was processed using supervised classification to map five major classes: Built Area, Bare Ground, Rangeland, Vegetation, and Water Bodies.
Analytical Techniques:
- Spatiotemporal Analysis: Annual and seasonal composites were generated to visualize trends in pollutants, LST, NDVI, and LULC.
- Statistical Modeling: Pearson correlation analysis quantified linear relationships between variables. Principal Component Analysis (PCA) reduced dimensionality to identify dominant patterns of variability. Multiple Linear Regression (MLR) models were developed to evaluate the extent to which land surface characteristics (LST, NDVI, and Aridity Index [AI]) explain the variability of atmospheric pollutants.

Key Results

Pollutant Distribution: Primary pollutants (NO₂ and CO) were concentrated in urban and industrial zones, with NO₂ peaking in 2022 due to post-pandemic economic recovery. SO₂ exhibited localized hotspots near industrial complexes and power plants. In contrast, O₃ displayed an inverse urban-rural gradient, with higher concentrations in downwind rural areas driven by photochemical production, particularly during summer.
Land Surface Dynamics: The Built Area expanded continuously, replacing Bare Ground and Rangeland. Vegetation (NDVI) showed a modest increase after 2022, attributed to localized urban greening projects, though it remained fragmented. LST reached a maximum of 43.10°C in 2021 and subsequently declined to 37.07°C in 2025, suggesting a mitigation of surface thermal stress linked to greening initiatives.
Statistical Relationships:
- Correlation: A strong positive correlation was found between NO₂ and CO ( $r = 0.81$ ), indicating common emission sources (traffic and fossil fuel combustion).
- PCA: The first two principal components explained 55.8% of the total variance, highlighting seasonal meteorological conditions as the dominant driver of pollutant variability. Summer samples correlated with high LST, while winter samples reflected stable atmospheric conditions.
- Regression: MLR models yielded low-to-moderate coefficients of determination ( $R^2$ ranging from 0.124 for O₃ to 0.231 for SO₂). This indicates that land surface characteristics explain only a fraction of pollutant variability, with emission intensity, atmospheric transport, and photochemical processes remaining the primary controlling factors.

Significance and Claims
The paper claims that integrating multi-source satellite observations with cloud-based geospatial analysis provides a robust framework for long-term environmental monitoring in rapidly developing arid cities. The study demonstrates that while urban greening initiatives (evidenced by rising NDVI and declining LST) can mitigate surface thermal stress, they are insufficient on their own to significantly reduce gaseous pollutant concentrations. The authors conclude that effective air quality management in Riyadh requires a dual approach: expanding green infrastructure must be accompanied by stringent emission-control policies targeting transportation and industrial sectors. The findings offer scientific evidence to support sustainable urban planning under the Saudi Vision 2030, emphasizing that land surface variables are only one component of a complex system governed largely by anthropogenic emissions and atmospheric dynamics.

Technical Summary: Satellite-Based Assessment of Air Pollution Dynamics and Land Surface Characteristics in Riyadh, Saudi Arabia

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