Description

Nitrogen dioxide (NO2) is unhealthy to breathe and is a necessary ingredient for the formation of unhealthy levels of surface ozone [NASA Air Quality]. NASA Aura's OMI sensor has been monitoring NO2 data since 2004 and has been used in a variety of health and air quality applications. The TROPOMI instrument onboard Sentinel-5P, launched in 2017, represents a significant improvement in spatial resolution over OMI. It will be better-suited for many applications currently using OMI data, including monitoring air pollution. In this advanced webinar, attendees will learn how to access and analyze TROPOMI data and learn about its applications.

Agenda Cite This Training

Objective

By the end of this training, attendees will be able to: 

  • Understand the available data products
  • Access and download TROPOMI data
  • Analyze the data using Python tools
Audience

This webinar is intended for end-users who are already familiar with satellite observation capabilities and have used online image archives or analysis tools at basic to intermediate levels for air quality applications, such as emissions estimation using satellite observations.

Course Format
  • Parts 1 & 2 will be one hour long.
  • Part 3 will be 2 hours long and include an opportunity for attendees to practice using TROPOMI data.
Sessions
Part One: Remote Sensing of NO2 with OMI

This webinar will provide an introduction to remote sensing of air quality, a description of OMI, an overview of available data products for NO2, and available data portals and tools. 

Materials:

Part Two: Introducing TROPOMI - High Resolution NO2 Observations from Space

This webinar will cover an introduction to TROPOMI, available data products for NO2, information about products detecting AI, CO, SO2, and HCHO, an overview of accessing TROPOMI data, and an exercise for downloading the data.

Materials:

Part Three: Python Tools for Analyzing NO2

This webinar will primarily consist of going through an exercise on using updated python codes to work with TROPOMI data. This will include reading, mapping, extracting over a point location, gridding the data, and dumping the data to a CSV file. 

Materials:

Prerequisites

 

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