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|Title:||Development of an infrared pollution index to identify ground-level compositional, particle size, and humidity changes using Himawari-8|
Air pollution monitoring
|Citation:||Sowden, M., Blake, D., Cohen, D., Atanacio, A., & Mueller, U. (2020). Development of an infrared pollution index to identify ground-level compositional, particle size, and humidity changes using Himawari-8. Atmospheric Environment, 229, 117435. doi:10.1016/j.atmosenv.2020.117435|
|Abstract:||Speciated air quality data informs health studies and quantitates impacts. However, monitoring is concentrated around populated regions whilst, large remote and rural regions remain unmonitored despite risks of dust-storms or wild-fires. Sub-hourly, infrared, geostationary data, such as the 10-min data from Himawari 8, could potentially be used to quantify regional air quality continually. Monitoring of Aerosol Optical Depth (AOD) is restricted to visible spectra (i.e. daytime only), while newer quantification methods using geostationary infrared (IR) data have focused on detecting the presence, or absence, of an event. Limited attention has been given to the determination of particle size and aerosol composition (such as sulfates, black carbon, sea-salt, and mineral dust), using IR exclusively, and more appropriate methods are required to improve the understanding of source impacts. Hourly data were collected for a three-year study period (July 2015 to July 2018) across the greater Sydney region in Eastern Australia from seventeen ground-based sites that measured meteorological data and quantified ambient concentrations of NO, NO2, SO2, PM2.5, PM10, and O3. This data was combined with source-apportioned categories (soil, sea-spray, smoke, secondary sulfates, and vehicles) from positive matrix factorization (PMF) of elemental aerosol collected on daily filters at five monitoring sites across the region. Regression analysis of five brightness temperature difference (BTD) infrared indices were used to determine a pollution index. The pollution index was shown to be related to humidity, particle size, and compositional changes. Unlike fixed thresholds, the continual index function can be aggregated spatially and temporarily. Good resolution is obtained between PM2.5 and O3. BTD appears insensitive to concentration, and the pollution index was used to detect and identify composition prior to determining concentration. © 2020 Elsevier Ltd.|
|Appears in Collections:||Journal Articles|
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