It's easy to take air for granted: of course we have to; otherwise we could only think of each breath.
But what we breathe shapes our futures.
Air quality has far-reaching impacts on people's health, economic prospects, and quality of life as a whole. In our project, we propose to look to tell a cohesive story of how air quality is linked with seemingly-disaperate metrics.
Learn more ↓This (below) graph visualizes the ratio of rated-'good' AQI days to the total number of measured days that each county had in 2022. It's important that this is a ratio and not a nonweighted number because each county has a different number of days on which AQI was measured.
Los Angeles and the surrounding Inland Empire stand out as having had particularly poor air quality in 2022, dropping well below the bounds of the color scale. San Bernadino County had a jaw-droppingly low 0.13 days with a 'good' AQI per measured day. Its life expectancy, as will be seen in the next graph, appears to suffer as well, falling one or two years short of its neighbors. Other pockets of poor air quality emerge around Colorado's Front Range, which suffers particularly from ozone pollution, areas in and around the manufacturing-heavy Rust Belt, and pockets of the southern part of the country (particularly around Texas).
This choropleth shows, as you might expect, average life expectancies for each county, which were averaged from their contained census tracts.
Most notably, the South has significantly lower life expectancies than the rest of the country. But the Rust Belt area, which as mentioned experiences lower air quality as well, similarly sees lower life expectancies. Detroit, again, stands out, especially in relation to its neighbors.
Here's another way of looking at the link between life expectancy and air quality: plotting them directly in relation to one another.
The correlation here was not nearly as strong as we were expecting, but nonetheless life expectancy does increase with better air quality. (One thing that might be skewing the results here is that we only are looking at data from 2022.) This graph also further emphasizes how awful of an air quality season the Los Angeles area had: three of the four bubbles off to the left are LA, Riverside, and San Bernadino Counties.
And finally, here is one more way of visualizing the same relationship. The "Good days percent" below again refers to the percentage of measured days which were rated to have good air quality.
Again, it's perhaps even more obvious in this chart that higher air quality often links to higher life expectancy. And Los Angeles stands out again: its are some of the darkest (most populous) counties that dramatically slope down from relatively good life expectancy to poor percentage of good AQI days. It will be interesting to see whether poor air quality from wildfires continues to recur in that area, and, if so, whether there will be a corresponding drop in life expectancy in the years to come.
This scatter plot compares the populations of each US county to natural disaster risk and median air quality index. The size of each point corresponds to the population for that county. The x axis corresponds to the natural disaster risk rating. A higher rating means that the county is at a greater risk for natural disasters. The y axis is the median air quality for that county in 2022. Hover over the county to see the name.
In general, we noticed that counties with a higher population tended to be at a greater risk for natural disasters, but they did not necessarily have a lower air quality index. Los Angeles County, as well as some neighboring California counties, stand out as having a high natural disaster rating, high air quality index, and high population.
This bar graph can be compared to the median air quality index bar graph to see the correlation and relationship. States such as New Jersey, Connecticut, Massachusetts, and Maryland stand out as having higher median incomes. States such as Arkansas, West Virginia, Alabama, and New Mexico have lower median incomes.
The states with the highest median income tend to be fairly close together and in the northeast region of the United States. The states with lower median incomes tend to be southern, landlocked states.
This bar graph shows the median air quality index for each state in 2022. A higher air quality index correlates to worse air quality. The states with the best air quality index were Hawaii, Idaho, Washington, and Oregon.
We were surprised to see that many states that were on the higher end for median income also tended to be on the higher end for air quality index. For example, New Jersey, New York, and California had some of the highest median incomes and the best air quality. We suspect that this could be because these states are more urban.
As shown throughout these visualizations, air quality is deeply linked with a number of important metrics that add up to impact the quality of life in geographic areas. Air quality is far too important to dismiss, though it's easy to do so as its effects are nearly always cumulative and not immediately noticable, and hopefully addressing it we can begin to improve the futures of those who live in regions with poor air quality.
a project by Shaelin Murphy and Jack MapelLentz