Pre-Conference Trainings

These pre-conference trainings are designed to provide some basic background and knowledge to attendees about air quality sensors and applications. These trainings will add to knowledge of applications and ideas discussed at the conference.

Trainings are approximately 5 hours each on April 30, 2024, from 1:00 pm to 6:00 pm at the Riverside Convention Center and have limited space. These trainings will only be offered in-person. You will only be able to register for one training, but are welcome to attend one, two, or all three sessions within the workshop, or bounce between the two. Room numbers will be announced to registrants before the conference. 

Registration for these trainings is available through the conference registration.  If you registered for the conference before these trainings were available, instructions for changing your registration were emailed.

Web-Based Data Visualization Tools:  No coding required!

Max 50 participants.

Training Leads: Andrea Clements, US EPA & Carl Malings, NASA GSFC GMAO

Data visualization tools that are quick and easy to use, require no coding skills, and can be used with any type of sensor are in high demand. In addition to visualizing their own data, users often wish to compare the data they collect with other data sources such as nearby instruments that measure the same pollutants, wind measurements, satellite remote sensing data, or atmospheric model outputs. This training will cover a few free web-based tools to do just that! Participants to this training do not need any prior coding experience, but should have proficiency using internet web browsers and understand basic concepts in data analysis and statistics (correlation, time series analysis, averaging over specific subsets of data, etc.). Participants should also plan to bring their own WiFi-enabled laptop, or plan to share with a friend. 

This training is divided into three parts:

  • Part 1: REal TIme Geospatial Data Viewer (RETIGO)
  • The REal TIme Geospatial Data Viewer (RETIGO) is a free, web-based visualization tool developed by U.S.EPA to offer the features mentioned above. Stationary or mobile user-supplied datasets containing time stamps, latitude and longitude locations, and pollutant/environmental variables can be imported from the user’s computer where it is retained and secure. The tool then uses a web-interface where data can be displayed on an interactive map and in graphs for analysis by clicking available options. In recent years, the tool has been enhanced with interface updates, more user control over display options, new plots, new public data sources for comparison, new satellite overlays for visualization, and export functions that support 3-D visualization. This training session will introduce users to RETIGO as a quick data visualization tool and will review the new features included in the Version 4 update.  A hands-on tutorial outline will be reviewed for use in Part 2.
  • Part 2: Complimentary Tool to RETIGO
  • This training session will briefly introduce a web-based tool for developing correction factors, developed by Professor Daniel Westervelt’s research group.  It’s a great complement to RETIGO. Use RETIGO to find the nearest regulatory monitor for comparison and then use the web-based tool to develop your correction factor.  Bring your laptop or share with a friend to gain hands-on experience with RETIGO using a tutorial outline and some example datasets, then explore the correction factor tool.
  • Part 3: Intro to Satellite Remote Sensing for Air Quality Applications
  • This part will give a basic introduction to satellite remote sensing for air quality applications. It will begin with an overview of how satellite remote sensing can be used to detect parameters relevant for air quality. We will discuss the advantages and limitations of these satellite remote sensing estimates. There will be an overview of current and upcoming satellite missions and data products which are most relevant to particulate matter and trace gas sensing. Finally, the training will introduce and have participants explore two free online NASA resources for satellite data visualization and analysis: Worldview and Giovanni. Participants should bring their laptops and create a free NASA Earthdata account beforehand.

    This training is intended for those who already have a fairly good understanding of low-cost air sensors and how they work, and are looking for ways to analyze their data and use it alongside complementary datasets, but are not very comfortable with coding and prefer to work with graphic user interfaces, web-based tools, and/or spreadsheets for data analysis. This could include those from academia, research organizations, non-profit organizations engaged in policy and public advocacy work, or community scientists.

Prerequisites:

  • Proficiency using internet web browsers; internet browser installed on your personal computer
  • Basic knowledge of spreadsheet software (e.g., Microsoft Excel, Google Sheets); you should have access to one of these softwares and should be capable of loading a .csv file into it.
  • Understand basic concepts in data analysis and statistics (correlation, understanding a time series plot, averaging over different sub-sets of a dataset, etc.).

Hands-on with data (coding workshop)  

Max 50 participants.

Training Leads: David Hagan, QuantAQ, Dan Westervelt, Columbia University, and Jonathan Callahan, Desert Research Institute

Bring your laptop! This workshop will provide hands-on training for R and python users who wish to up their data analysis and data visualization skills. Participants will learn how to use open source R packages/python modules that can access data, perform analyses and create attractive data visualizations. To make the best use of limited time, attendees are highly encouraged to install all required packages/modules in advance of the workshop.

This training is divided into three parts:

  • Part 1: R Packages [Facilitator: Jonathan Callahan, Desert Research Institute]
  • This portion of the workshop will introduce participants to two open source R packages designed specifically for working with air quality data from regulatory monitors and low-cost sensors. The AirMonitor and AirSensor2 packages have been developed with funding from, and have been used by the US EPA, the US Forest Service and the South Coast Air Quality Management District. These R packages are part of a suite of R packages that provide core functionality for environmental monitoring at fixed locations. The full suite of packages represent a decade of continuous development with a focus on compact data formats, robust data analysis, compelling data visualization and a simple, easy-to-learn coding style.

    Goals: Attendees will become familiar with the AirMonitor and AirSensor2 R packages and will be able to quickly download, process and visualize large amounts of monitor and sensor data. Various analysis functions will be introduced and users will be able to choose their own sensors and monitors to create QC reports and end-user graphics.

    Audience: The R packages presented are designed for individuals who sometimes need to work independently, without the support of IT staff, for data ingest and manipulation. The target audience includes anyone who works with Air Quality data from regulatory monitors and low cost sensors and who is a regular user of R/RStudio. Attendees should have a basic understanding of R data types and common functions. Familiarity with the dplyr package will be especially helpful.

    The website for this R workshop, complete with setup instructions and all materials is available at: https://github.com/MazamaScience/ASIC-2024
  • Part 2: Python Packages [Facilitator: David Hagan, QuantAQ] 
  • Preparing visually appealing and scientifically valid figures is crucial for ensuring positive outcomes for communities and scientists alike when using air sensors. In attending this tutorial, you will learn how to make publication-quality figures to communicate the air sensor data you’ve collected for use in presentations, reports, and more. This session focuses on using the open-source programming language, python, to visualize data effectively. We will cover how to prepare your data and munge it into the correct format, create common figures, and use the “atmospy” python library to make your data visualization life easier.

    Goal: Learn how to make publication-quality, presentation-ready figures commonly used to communicate air quality sensor data such as diurnal profiles, time series, pollution roses, and calendar plots.

    Audience & Prereqs: Anyone interested in using Python to visualize their air sensor data (i.e., community members, students, professors, researchers, industry professionals, etc). Previous experience with Python is highly recommended as this session will not cover Python installation and basic usage.
  • Part 3: Correction Factors [Facilitator: Daniel Westervelt, Columbia University] 
  • We will cover hands on development of correction factors for PM2.5 sensors (such as PurpleAir). We will use both a pre-programmed web app (easy) and a step by step hands-on R/Python script (more difficult). Real world data from actual published studies will be used.

This training is intended for students, communities, and anyone who uses sensors and needs some help correcting the data.