Chicago PM2.5 Levels

Project Description

This prototype tool illustrates and compares 3 different PM 2.5 data sources, by day and neighborhood, in Chicago:

• ELPC community monitoring data
• Environmental Protection Agency public air sample data
• Purple Air self-reported data

Contributors: Linh Dinh

Frameworks/tools/packages used: Django, Webscraping (Selenium), API Requests, Plotly, S3, Heroku

Objectives

• illustrate the trends of PM 2.5 measurements in the Chicago area for 4 summers: 2017, 2018, 2019, 2020
• identify days where the discrepancies (in terms of PM 2.5 levels) between AirQuality, EPA, and PurpleAir data are significant and locate the neighborhoods where these discrepancies might be coming from
• provide a more detailed view into specific neighborhoods, more specifically:
  • locate blocks with much higher average PM 2.5 levels
  • identify hours/time periods with much higher average PM 2.5 levels

Results

More detailed (but also preliminary) analysis results can be found here

Usage

Online Access

The program was packaged and uploaded online to be accessed here. Graphs and visualizations are stored in S3.

Local Access

Activating your environment

To install the required packages in a new environment:

$ python3 -m venv env
$ source env/bin/ac
$ pip install -r requirements.txt

requirement.txt contains the required packages to run this program.

Running a command in your environment

Run shell script to launch a command-line utility that lets you interact with this Django project.

$ ./run_software.sh

Adding -d will re-pull the data from web scraping and API. If this is run, newer data (up to 14 days ago from run date) will get pulled and appended to the master data files.

Adding -g will recreate graphs from the master data files.

Once the interface is started, you can use it by pointing a browser to http://127.0.0.1:8000/.

If use locally, replace search/templates/index.html with search/templates/index_static.html and search/views.py with search/views_static.py

Structure of the software

1. Helper functions scripts:

   • pipeline.py: General helper functions to process, clean, and aggregate non-specific data.
   • process_data.py: Helper functions to process, clean, and aggregate air-quality-specific data (EPA, PurpleAir, AirQuality).
   • plot_functions.py: Helper functions to create visualizations from processed data.

2. Data collecting scripts:

   • load_geo_files.py: Retrieve relevant geo/shape files for Chicago. Store this data as .csv files in './data'. Utilize helper functions above.
   • load_data_files.py: Crawl EPA, PurpleAir, AirQuality and retrieve all relevant data. Then, combine this data with appropriate geo/shape files to create master data files. Store these master data files as .csv files in './data'. Utilize helper functions above. If this script is run, newer data (up to 14 days ago from run date) will get pulled and appended to the master data files.

3. Data visualization/display scripts:

   • create_visualizations.py: Python script that creates all graphs used by the Django application. If run locally, all the graphs are created and saved in the “static/graphs” folder. Need to be rerun if the data is updated.

4. Django scripts: Following the standard structure of a Django application, there are 3 main folders:

   • search folder contains html template and codes to display the correct visualizations selected by the users.
   • static folder contains css style and graphs generated from our data visualization scripts (to be displayed as static components in css structure).
   • ui folder contains Django default setup and scripts.

If use locally, replace search/templates/index.html with search/templates/index_static.html and search/views.py with search/views_static.py