Air Pollution Analysis with GeoRetina AI (GRAI)

Air pollution analysis identifies, measures, and visualizes atmospheric pollutants that affect human health and the environment. This guide explains how to analyze air pollution data from the Sentinel-5P satellite using GeoRetina AI's conversational interface.

What is Sentinel-5P?

Sentinel-5 Precursor (Sentinel-5P) is a satellite launched by the European Space Agency (ESA) in 2017 as part of the Copernicus program. It carries a specialized instrument called TROPOMI (TROPOspheric Monitoring Instrument) designed to monitor air quality and atmospheric composition with:

High temporal resolution: Daily global coverage
Key pollutants: Nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), methane (CH₄), carbon monoxide (CO), and formaldehyde (HCHO)
Spatial resolution: 7km × 3.5km at nadir, improved to 5.5km × 3.5km since August 2019

This makes Sentinel-5P ideal for monitoring urban air quality, industrial emissions, and regional pollution events.

Performing Air Pollution Analysis with GRAI

GRAI makes atmospheric pollution analysis accessible through natural language interaction:

Start a Conversation: Navigate to the chat interface within GRAI.
Make Your Request: Specify an air pollution analysis by indicating:
- The pollutant of interest (NO₂, O₃, SO₂, CH₄, CO, HCHO)
- The location of interest
- The time period (specific date or date range)
Example requests:

Air Pollution Analysis Examples

Urban Air Quality

Analyze NO₂ concentrations over Beijing for the past month

Volcanic Emissions

Show me SO₂ pollution from the Etna volcano eruption in February 2023

Industrial Monitoring

Compare methane emissions in the Permian Basin between 2019 and 2022

Review the Results: GRAI will process the request and display the analysis directly in the chat.

Data Processing

GRAI applies corrections and filters to the Sentinel-5P data to ensure reliability. This includes cloud filtering (removing measurements affected by clouds), quality assurance filtering, and data averaging for time-series analysis.

Understanding the Results

The air pollution analysis by GRAI typically includes:

Pollutant Concentration Map: Spatial visualization of pollutant concentrations over the study area.
Concentration Statistics: Minimum, maximum, and average values for the selected pollutant.
Hotspot Identification: Areas with exceptionally high pollution levels.
Temporal Comparison: If a time range was specified, changes in pollution levels over time.
Context Information: Relevant information about potential pollution sources and health impacts.

Example Interaction

Here's a typical exchange with GRAI for air pollution analysis:

Madrid NO₂ Analysis

Analyze nitrogen dioxide levels in Madrid, Spain for the past three months

GRAI Response: "I'll analyze NO₂ concentrations over Madrid for the past three months using Sentinel-5P data. Processing your request...

Analysis complete for Madrid's NO₂ levels over the past three months. The data shows an average tropospheric NO₂ concentration of 32 μmol/m², with peak values of 78 μmol/m² occurring primarily on weekdays during morning rush hours. A clear pattern emerges with the city center and major highways showing 2-3× higher concentrations than surrounding areas. I've identified a 22% decrease in average concentration during weekends and a slight overall decreasing trend of 4% over the three-month period. Would you like to explore specific areas or time periods in greater detail?"