Description

This training covers the theoretical approaches and key datasets for estimating the near-surface concentration of particulate matter smaller than 2.5 micrometers in diameter (PM_2.5) by using satellite aerosol optical depth (AOD) and other data sources (ground based monitors, model estimates). 

Participants will acquire knowledge about different methodologies and their strengths and limitations for inferring surface PM_2.5 from satellite AOD. They will be able to identify access methods for using existing datasets of surface PM_2.5 estimated from satellite sensors and other data sources. They will also learn how to compare satellite-based estimates of PM_2.5 to in situ measurement data through hands-on coding exercises employing a generic code template and suitable co-location methodology. This would be the first step in evaluating the suitability of these datasets for the users’ applications and regions.

Prerequisites

• Fundamentals of Remote Sensing
• Introduction to Satellite Remote Sensing for Air Quality Applications

Objectives

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

• Match the required inputs, strengths, and limitations with different basic methods of inferring surface PM2.5 from satellite AOD;
• Differentiate between available data products for surface PM_2.5 from NASA and Washington University in St. Louis based on their methodologies, strengths, and weaknesses; 
• Use NASA Earthdata and the SatPM website to access these surface PM_2.5 data products for a location and time period of interest;
• Compare these satellite-derived PM_2.5 estimates to in situ measurements using appropriate co-location strategies and evaluation metrics.

Audience

• Researchers looking to use existing surface air quality estimates derived from satellites and to better understand the methodology and limitations
• Researchers in air quality management and/or public health seeking to use satellite data to derive custom surface-level air quality estimates
• Air quality managers seeking to understand the potential for satellites to supplement ground-based monitors in remote areas

Course Format

• The complete course consists of three 2-hour parts, with Part 1 offered on July 8, Part 2 on July 15, and Part 3 on July 22.
• On each day, there are two opportunities to take the course (one in English, one in Spanish):
  • English Session: 10:00 a.m. to 12:00 p.m. EDT (UTC-4)
  • Spanish Session: 3:00 p.m. to 5:00 p.m. EDT (UTC-4)
• Each part will include a 30-minute live Q&A.
• Those who attend Parts 1, 2, and 3 and complete the homework by the due date will receive a certificate of attendance.

Part 1: AOD Versus PM_2.5 – When They Are and Are Not Related

• About ARSET
• Training Overview
• Part 1 Introduction
• The Pieces – Ground-based measurements, AOD, and chemical transport models (CTM)
• Why (or when) is AOD related to PM_2.5?
• Simple relations of AOD to PM_2.5
• Part 1 Summary
• Q&A Session

ARSET Instructor: Carl Malings

Part 2: Estimation of PM_2.5 from AOD – Methodologies and Available Datasets

• Part 1 Review
• Part 2 Introduction
• Geophysical and Hybrid Models
• SatPM_2.5 at the Atmospheric Composition Analysis Group at Washington University in St. Louis
• Machine Learning Methods
• Bias-corrected MERRA-2
• Part 2 Summary
• Q&A Session

ARSET Instructors: Carl Malings, Pawan Gupta

Guest Instructors: Aaron van Donkelaar, Junhyeon Seo

Part 3: Case Study – Comparing Available Satellite-Derived PM_2.5 Products to Ground-Based Measurements

• Part 2 Review
• Part 3 Introduction
• Overview of the Case Study Exercise
• Orientation to Google Colab
• Accessing the SatPM Dataset
• Accessing Bias-Corrected MERRA-2 CNN
• Accessing Ground-based Data
• Basic Evaluation
• Training Summary
• Q&A Session

ARSET Instructor: Carl Malings

Guest Instructor: Sebastian Diez