A Python notebook for analyzing pollution CSV data.

• Aims to analyze the change in air quality index (AQI) for 4 major pollutants (Nitrogen Dioxide, Sulphur Dioxide, Carbon Monoxide, Ozone) that cover all 50 states of the United States from 2000 to 2016
• Procured from the Environmental Protection Agency (EPA)
• Consists of daily pollution data for the 4 major pollutants over 16 years for all 50 states (382 MB, 1746661 lines)
• Key points:
  • Organized by Date, dates repeat per county where the source data was acquired
  • NO2 and SO2 are in parts per billion while O3 and CO are in parts per million
  • The max hour describes what hour of the day the AQI was highest

• Reading Data:
  • Pandas to read the CSV
  • Data relatively unorganized¹ but contained diverse information
    • Grouped data by date
    • Calculated the means for each state
• Visualization:
  • Matplotlib - Graphs
  • Cartopy - Maps
  • Types of Visualizations:
    • Multi-line charts
    • Bubble maps
    • Multivariate linear regressions
    • Heat maps

Line Charts	Map Visualizations

• CO and NO₂ have decreased while O₃ and SO₂ have largely remained stagnant
• O₃ and NO₂ are recently the more prevalent pollutants compared to CO and SO₂
• When looking at results, be aware of:
  • Results may be affected by holes in the data as shown in the multi line charts and the heat maps
  • Differentiating between interpreting results on actual AQI index versus the change on various factors
  • Analyzing on case-by-case basis per pollutant is necessary to understand trends and how factors such as laws may affect pollution
• Awareness of air pollution is one big factor in the slow decline in some pollutants