Air Quality Dashboard

Background & Methods

California experiences substantial variation in air quality across regions, with certain inland and agricultural areas facing persistently higher pollution levels than the rest of the state. The dataset used in this dashboard, top5_cities.rds, contains daily Air Quality Index (AQI) measurements for California cities from 1980 to 2023, along with AQI category classifications and population estimates. Each observation represents a single city-day, allowing us to observe both long-term patterns and short-term fluctuations.

Long-term exposure to fine particulate matter like (PM2.5) and ozone are key drivers of AQI has been consistently associated with increased risk of asthma exacerbation, cardiovascular disease, premature mortality, and disproportionate impacts in communities near traffic corridors or agricultural zones.

This dashboard focuses on the five California cities with the highest population-weighted long-run average AQI, identifying where pollution burdens are most concentrated. The visualizations address three guiding questions:

  1. How have monthly average AQI levels changed over time in these cities?
  2. How frequently do these cities experience days in more concerning AQI categories?
  3. Which cities face the greatest overall burden of unhealthy air?

This analysis is important for public health because long-term exposure to particulate matter and ozone is associated with increased respiratory symptoms, cardiovascular disease, and hospitalizations. Highlighting both seasonal patterns and cumulative burden provides policymakers, community stakeholders, and residents with accessible information that supports evidence-based decision-making about pollution mitigation strategies and environmental justice priorities.

Data & Processing

  • Dataset: top5_cities.rds, containing daily AQI measurements for California cities.
  • Unit of observation: city–day, including city name, date, AQI, category, and population.
  • The top 5 cities were selected using population-weighted mean AQI.
  • Monthly trends use aggregated mean AQI values for each month.
  • Daily scatterplot uses the most recent 365 days to keep the visualization readable.
  • All visuals apply lecture concepts on marks and channels, including position, color, and shape, as well as accessibility principles.

These visualization choices were intentional: the faceted time-series plot directly answers the question of how monthly AQI levels change over time, while the stacked bar chart summarizes how often each city experiences different AQI severity categories. Together, they provide complementary views of long-term trends and cumulative pollution burden.

Graphs

Graph 2 – Interactive AQI Category Mix by City

Interpretation

Graph 1 – Interpretation

Alt-Text: This line graph shows monthly mean AQI for five California cities from 1980–2023, faceted by city. Riverside and Bakersfield display consistently higher AQI values with strong seasonal peaks. Bishop shows the lowest overall AQI.

Graph 1 shows clear and persistent differences in monthly average AQI across the five most polluted cities. Although all cities show strong seasonal patterns—typically with peaks in summer and early fall—Riverside consistently experiences the highest monthly averages across the entire 40-year period. Bakersfield and Fresno also display elevated and highly variable AQI levels, while Bishop generally exhibits lower and more stable trends. Los Angeles shows improvement over time, with declining peaks in later years. These trends suggest that despite statewide progress in air quality regulation, certain inland regions continue to bear disproportionate pollution burdens.

Graph 2 – Interpretation

Alt-Text: This stacked bar chart shows the percentage of days in each AQI category for the five cities. Riverside and Fresno have the highest shares of “Unhealthy” and “Very Unhealthy” days, while Bishop has the highest share of “Good” and “Moderate” days.

Graph 2 summarizes the proportion of days that fall into each AQI category. Riverside and Fresno have the largest shares of Unhealthy and Very Unhealthy days, reinforcing their position as high-burden environments. In contrast, Bishop has the highest share of Good and Moderate days. The stacked bar chart highlights that cities with the highest long-term averages also experience more frequent extreme-category days, suggesting both chronic and episodic exposure risks. The interactive tooltips enhance interpretation by allowing viewers to compare exact percentages across cities.

Together, these visualizations show a cohesive pattern: California’s most polluted cities experience both persistently elevated average AQI and a disproportionate number of unhealthy air episodes, emphasizing environmental inequities and the need for targeted mitigation in high-burden regions.California’s inland and agricultural regions not only experience consistently higher average AQI but also face a disproportionate number of unhealthy-air days, underscoring the need for region-specific mitigation strategies.

Conclusion & Limitations

Conclusion

The patterns observed across these five cities reveal a consistent picture of unequal air-quality burdens in California. Riverside, Los Angeles, and Bakersfield show persistently elevated monthly average AQI, along with frequent spikes into unhealthy categories, indicating both chronic and episodic exposure risks. Several considerations should guide interpretation of these results. City-level AQI averages may mask substantial within-city differences, particularly in geographically large or topographically varied areas. Changes in monitoring station locations, technology, and measurement frequency over the 40-year period introduce some uncertainty in comparing early and recent data. AQI categories are designed for short-term health messaging and do not fully quantify cumulative exposure, which means that even cities with fewer extreme days may still experience meaningful long-term health impacts. Finally, population-weighted selection of cities emphasizes where the most people are exposed but may overlook smaller communities with very poor air quality. These limitations do not detract from the overall patterns but remind viewers that AQI trends represent an accessible approximation of much more complex environmental conditions.

Together, these visualizations demonstrate how long-term pollution burdens and daily category distributions reinforce one another, highlighting where exposure is most concentrated. These findings support a broader public-health understanding that certain inland regions continue to experience disproportionate air-quality challenges, underscoring the need for targeted mitigation strategies and policies that prioritize high-burden communities.