• An evaluation of the use of moderate resolution imaging spectroradiometer (MODIS)-derived aerosol optical depth to estimate ground level PM2.5 in Alaska

      Mathers, Alyson Marie McPhetres; Aggarwal, Srijan; Belz, Nathan; Perkins, Robert (2018-12)
      The air quality monitoring (AQM) network in Alaska is limited to major urban areas and national parks thus leaving a large proportion of the state unmonitored. To evaluate the use of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived aerosol optical depth (AOD) to predict ground-level PM2.5 concentrations and thereby increase the spatial coverage of the AQM network in Alaska, MODIS AOD was first validated against ground-based measurements of AOD in Utqiagvik and Bonanza Creek Alaska. MODIS AOD from 2000 to 2014 was obtained from MODIS collection 6 using the dark target land and ocean algorithms between the months of April and October. Based on validation results, individual Aqua and Terra products are valid for both locations at 10-kilometer and 3-kilometer resolution. In addition, combined Aqua and Terra MODIS AOD products are valid for both locations at 3-kilometer resolution and 10-kilometer resolution for Utqiagvik. The available PM2.5 data was then compared for satellite retrieval and all retrieval days to determine if there was sufficient data and the amount of bias introduced by possible low retrieval rates. Overall, Juneau had the lowest retrieval rates while Fairbanks and North Pole had the highest retrieval rates. In addition, Juneau appeared to have relatively high bias while stations located in Anchorage, Palmer, Fairbanks and North Pole had relatively low bias. Based on these findings, no models were developed for Juneau (southeast Alaska). Multilinear regression models were then developed for southcentral (Anchorage and Palmer) and interior (Fairbanks and North Pole) Alaska where the log-transform of PM2.5 was the response and meteorological data and the log-transform of MODIS AOD were the predictors. MODIS AOD appeared to be most highly correlated with PM2.5 in interior Alaska, while there was little to no correlation between MODIS AOD and PM2.5 in southcentral Alaska. All models underestimate surface PM2.5 concentrations which may be due to the high percentage of low PM2.5 values used to develop the models and the limited retrieval rates. Alternative modeling methods such as mixed-effects modeling may be necessary to develop adequate models for predicting surface PM2.5 concentrations. The MLR models did not perform well and should not be used to predict ground-based PM2.5 concentrations. Further research using alternative modeling methods should be performed. Model performance may also be improved by only using higher concentrations of PM2.5 to develop models. Overall, the limited spatial coverage of Alaska's air quality monitoring network and the low temporal resolution of MODIS-derived AOD make modeling the relationship between MODIS AOD and PM2.5 difficult in Alaska.
    • Near-Roadway Air Pollution: Evaluation of Fine Particulate Matter (PM2.5) and Ultrafine Particulate Matter (PM0.1) in Interior Alaska

      Aggarwal, Srijan; Kadir, Abdul; Belz, Nathan (2019-01-28)
      This report presents a study of fine (PM2.5) and ultrafine (PM0.1) particles in the Fairbanks North Star Borough (FNSB) in Interior Alaska, with specific emphasis on the relationship of ultrafine particles (UFPs) to vehicular traffic. Chapter 1 provides a summary of published literature on particulates in air from vehicular emissions. Chapter 2 provides a novel and robust GIS-based data analysis approach to PM2.5 data collected by the FNSB. This analysis approach is convenient for identifying hotspots, as well as locations where PM2.5 changes either abruptly or continuously or does not change at all. The results reveal that average on-roadway PM2.5 concentrations are higher in North Pole than in Fairbanks, and mean levels are higher in stationary background monitoring data than in mobile monitoring on-roadway data. Not surprisingly, significant negative correlations were found between temperature and PM2.5. Chapter 3 presents the results from the data collection campaign to measure UFPs at roadside locations in Fairbanks and North Pole and investigate the relationship of UFPs with traffic and meteorological parameters. Multilinear predictive models were developed for estimation of UFPs and PM2.5 based on weather and traffic parameters. Overall, this study improves our understanding of on- and near-roadway particulates in a cold-climate region.
    • User's guide for atmospheric carbon monoxide transport model

      Norton, William R.; Carlson, Robert F. (University of Alaska, Institute of Water Resources, 1976-06)
      In the winter months of Fairbanks, Alaska, a highly stable air temperature inversion creates high levels of carbon monoxide (CO) concentrations. As an aid to understanding this problem, a CO transport computer model has been created which provides a useful tool when used in conjunction with other measurement and analytic studies of traffic, meteorology, emissions control, zoning, and parking management. The model is completely documented and illustrated with several examples. Named ACOSP (Atmospheric CO Simulation Program), it predicts expected CO concentrations within a specific geographic area for a defined set of CO sources. At the present time, the model is programmed to consider automobile emissions as the major CO source and may include estimates of stationary sources. The model is coded for computer solution in the FORTRAN programming language and uses the finite-element method of numerical solution of the basic convective-diffusion equations. Although it has a potential for real-time analysis and control, at the present time the model will be most valuable for investigating and understanding the physical processes which are responsible for high CO levels and for testing remedial control measures at high speed and low cost.