Investigating the Sources of Urban Air Pollution Using Low-Cost Air Quality Sensors at an Urban Atlanta Site.
Laura Hyesung YangDavid H HaganJean C Rivera-RiosMakoto M KelpEben S CrossYuyang PengJennifer KaiserLeah R WilliamsPhilip L CroteauJohn T JayneNga Lee NgPublished in: Environmental science & technology (2022)
Advances in low-cost sensors (LCS) for monitoring air quality have opened new opportunities to characterize air quality in finer spatial and temporal resolutions. In this study, we deployed LCS that measure both gas (CO, NO, NO 2 , and O 3 ) and particle concentrations and co-located research-grade instruments in Atlanta, GA, to investigate the capability of LCS in resolving air pollutant sources using non-negative matrix factorization (NMF) in a moderately polluted urban area. We provide a comparison of applying the NMF technique to both normalized and non-normalized data sets. We identify four factors with different temporal trends and properties for both normalized and non-normalized data sets. Both normalized and non-normalized LCS data sets can resolve primary organic aerosol (POA) factors identified from research-grade instruments. However, applying normalization provides factors with more diverse compositions and can resolve secondary organic aerosol (SOA). Results from this study demonstrate that LCS not only can be used to provide basic mass concentration information but also can be used for in-depth source apportionment studies even in an urban setting with complex pollution mixtures and relatively low aerosol loadings.
Keyphrases
- low cost
- heavy metals
- water soluble
- electronic health record
- air pollution
- particulate matter
- big data
- drinking water
- pet ct
- healthcare
- optical coherence tomography
- cystic fibrosis
- data analysis
- machine learning
- lung function
- artificial intelligence
- climate change
- ionic liquid
- social media
- room temperature
- deep learning