Access to clean, non-polluted air is fundamental for the wellbeing of all living organisms and the environment. It’s especially important having clean air in areas we often frequent like campus, work since long term exposure to airborne pollution can negatively impact our respiratory, circulatory, and immune systems. Long term exposure also increases our chances of having a heart attack, stroke and can even lead to death. The University of San Diego (USD) is on top of a mesa in Mission Valley, San Diego about 5-7 miles away from the Pacific Ocean. San Diego typically has westward winds (except during Santa Ana conditions). Pacific Ocean winds move inland passing through two major freeways, residential housing, and an airport before reaching USD. These winds can potentially introduce aerosol pollutants like sea spray, dust, smoke.
Hydroxyl (OH) free radicals typically help us remove these aerosol pollutants, but they also have the potential to produce other aerosol pollutants like particulate matter (PM2.5, PM10) ozone (O3) which can be harmful to humans and the environment. OH radicals react with volatile organic carbons (VOCS) in the atmosphere forming a peroxy bond. In heavily polluted areas, when nitrous oxide (NO2 or NOx) is introduced from car emissions it reacts with the peroxy-bonded molecule producing atmospheric pollutants like ozone (O3) and particulate matter (PM 2.5 and PM10) which can have detrimental effects on the air quality.
Often the abundance and type of vegetation we plant in our urban areas can influence how well our air pollutants are reduced. For example, areas having more abundant vegetation have been found to have lower particulate matter concentrations. Some other studies have found that trees and shrubs are better at reducing air pollution in urban areas compared to other vegetation types like green walls or garden plants. When using vegetation to improve air quality it’s important to consider the proximity of plants and the wind direction transporting pollutants.
Georgia State University found that planting more trees and bushes near highways significantly reduced the air pollution sourced from cars suggesting that higher abundance of the appropriate plant type within proximity of pollution sources can potentially help reduce NOx and particulate matter. However, they didn’t account for the wind direction which can be useful to understand the pathways of air pollution. While plants are useful for mitigating air pollution they emit volatile organic carbons (VOCs) which can contribute to the formation of ozone in highly polluted environments when NOx is introduced.
While vegetation can be useful for reducing air pollution and enhancing air quality it depends on the plant abundance, plant type and the sources of air pollution within that area. Our experiment considered the variation in concentrations of OH radicals and airborne pollutants (NO2, VOC, PM2.5, PM10) between six sites that were observed as having different vegetative abundances, anthropogenic inputs.
We expected the sites with richer vegetation to correspond to a cleaner atmosphere with lower concentrations of OH radicals, NO2, VOCs, PM2.5, and PM10. We expected the sites with little to no vegetation and/or exposed to higher anthropogenic inputs to correspond to a polluted atmosphere with higher concentrations of OH radicals, NO2, PM2.5, and PM10. We considered the SCST strata plaza (Site 1), KIPJ garden (Site 3), and the outdoor seating area of Burt’s Bistro (Site 5) as our areas with ideal vegetation for air pollution reduction. We chose the SCST loading dock (Site 2), SCST balcony (Site 3), and Copley Library (Site 6) as our areas that had little to no vegetation or potentially higher amounts of air pollution.
To test our hypothesis, we used low-cost air quality Flow sensors by Plume labs to measure NO2, VOC, PM 2.5, PM10 concentrations at each site over the course of our three week experiment. The flow sensor was connected to a smartphone via Bluetooth and then data was exported to Excel to find the average air pollutant data from each site from each week. To determine to OH radicals at each site we used a bubbler with a frit containing about 10-14.5 mL of our TPA (terephthalic acid) solution which was attached to a vacuum pump to collect gaseous OH in our liquid samples. These liquid samples were then analyzed by the spectrofluorometer to eventually help us find the concentrations of OH radicals per L of air in the air at each site.
Winds came from the west or southwest for all sites with our sampling (via bubbler and flow) downwind from less than 10 feet away from surrounding plants or trees. For our vegetation sites the strata plaza and KIPJ garden and (Site 1 and 3) were surrounded by large clusters of plants, appearing to have a wide variety of plant species including succulents, bushes, flowers, small trees, grass, etc. The outdoor seating area of Bert’s Bistro had a few small clusters of succulents lining a fountain just a couple feet away. It is worth noting that field sampling at Bert’s Bistro was taken only about ten feet away from the cooking grill.
The SCST loading dock (Site 2) had little to no surrounding vegetation within ten feet. The SCST balcony (Site 3) had no surrounding vegetation and had a view of the busy road down the hill, so we expected to see higher concentrations of OH radicals and airborne pollutants. The outside of the Copley Library (Site 6) was largely surrounded by cement except for a gigantic tree and some surrounding grass and flowers about 10 feet away.
Our OH radical concentrations were somewhat consistent with what we expected. We saw lower OH concentrations for all our vegetation sites while we saw higher OH concentrations for every non-vegetation site except for the Copley library which might be a result of the tree that was downwind of the flow and bubbler sampling. Week 3’s non veg loading dock site had higher average air pollutant concentrations for every pollutant (NO2, PM2.5, PM10) except VOCs while the veg strada plaza site had elevated VOCs which is consistent with other studies’ claims that plants reduce particulate matter (PM2.5 and PM10) while continuously emitting VOCS into the air. The other higher air pollutant concentrations at the non veg loading dock site on week 3 might be a result of the lack of vegetation.
Comparing the four sites on week 4 we had the highest concentrations of NO2, VOCs PM2.5, PM10 at our veg burts bistro site likely because of the grills nearby which emit NO2 and contribute to particulate matter. The non veg SCST back balcony and veg KIPJ garden sites displayed relatively higher NO2 and VOC concentrations with reductions in particulate matter concentrations in comparison to Burt’s Bistro. We expected little VOC emission at the SCST back balcony due to the lack of plants and heightened NO2 emission from the traffic seen downhill but it had increased VOCs, NO2, and even OH radicals concentrations but less particulate matter at this site. The non veg Copley library site had lower NO2, PM2.5, PM10 but higher VOCs which could be because of the sampling downwind of the tree. This suggests the tree might help reduce these air pollutants better than the other succulent plants, etc found at the other 3 sites that were both veg and non veg that week.
Also comparing the first and last week of data we saw consistent VOCs for the veg strada plaza site and non veg loading doc but lower NO2, PM 2.5, PM10 for both sites during the last week of our experiment. This could mean there was some some change in the dominant westward wind flow from the ocean carrying airborne pollutants and anthropogenic inputs
Based on our study, the amount of vegetation doesn’t always associate with the concentrations of OH radicals, NO2, VOCs, PM2.5, and PM10. There also needs to be the consideration of the surrounding plant types and abundances and nearby sources of urban inputs that can influence air quality. Further studies investigating the relationship between air quality and vegetation might want to consider potentially having the same vegetation types in different areas varying based on the proximity or type of of nearby urban inputs.