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Project 4
Spatiotemporal AQ Analysis
2022
Air pollution in today’s globalized, industrialized society poses a major environmental health risk, and millions of premature deaths from respiratory disease or heart attacks can be attributed to ambient pollution. This problem is particularly acute in urbanized areas. As part of the effort to identify and combat air pollution, air quality monitors have been deployed by governments to measure pollutants with high levels of accuracy. Advances in technology suggest that low-cost mobile monitors can help augment existing fixed monitors in understanding air quality levels on a more granular level, such as in a neighborhood or street by street. Wind speed can also impact the spread of pollutants, with suspended particulates responsible for smog conditions observed in London and Los Angeles.
Using existing data from a Tufts University study (Jiang, et al. 2020) on urban traffic and air quality, this project analyzed how atmospheric conditions have impacted levels of ultrafine particles (UFP), black carbon (BC) and nitric oxide (NO) through the binning of wind speeds and wind directions and comparing the mean concentration of the three pollutant types under each condition. Statistical data was analyzed through the Seaborn and Plotly libraries for visualization, and k-means clustering was done to compare unsupervised learning with categorization from the Beaufort Scale for wind speeds. Spatial data was saved as a GeoDataframe and uploaded to kepler.gl.
Data sources: Tufts Air Pollution Lab (TAPL)
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