An evaluation of the use of moderate resolution imaging spectroradiometer (MODIS)-derived aerosol optical depth to estimate ground level PM2.5 in Alaska
dc.contributor.author | Mathers, Alyson Marie McPhetres | |
dc.date.accessioned | 2019-06-12T23:55:53Z | |
dc.date.available | 2019-06-12T23:55:53Z | |
dc.date.issued | 2018-12 | |
dc.identifier.uri | http://hdl.handle.net/11122/10415 | |
dc.description | Thesis (M.S.) University of Alaska Fairbanks, 2018 | en_US |
dc.description.abstract | 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. | en_US |
dc.description.sponsorship | Alaska National Aeronautics and Space Administration (NASA) Established Program to Stimulate Competitive Research (EPSCoR) Program (N NNX15AK31A) | en_US |
dc.description.tableofcontents | Introduction -- Chapter 1: Particulate matter impacts in air quality and exposure in Alaska: current status and future directions -- Abstract -- 1.1: Introduction -- 1.2: Sources of particulate matter -- 1.2.1: Wood smoke -- 1.2.1.1: Residential wood burning -- 1.2.1.2: Wildfires-PM2.5 -- 1.2.1.3: Slash burning -- 1.2.2: Vehicular emissions -- 1.2.2.1: Gasoline -- 1.2.2.2: Diesel -- 1.2.3: Residential heating: distillate fuel oil -- 1.2.4: Ship emissions -- 1.2.5: Dust -- 1.2.5.1: Fugitive road dust -- 1.2.5.2: Windblown dust -- 1.2.6: Arctic haze-PM2.5 -- 1.2.7: Salt -- 1.2.8: Volcanic eruptions -- 1.3: Current methods of air quality analysis -- 1.4: Future directions -- 1.5: Conclusion -- 1.6: References -- 1.7: Tables and figures. Chapter 2: Evaluation of MODIS-retrieved aerosol optical depth in Alaska: a comparison of MODIS-retrieved and aeronet-retrieved AOD -- Abstract -- 2.1: Introduction -- 2.2: Validation methods -- 2.2.1: Aeronet AOD (ta) -- 2.2.2: MODIS AOD (tm) -- 2.2.3: Collocation -- 2.2.4: Analysis -- 2.3: Results and discussion -- 2.3.1: Bonanza Creek -- 2.3.1.1: Criterion 1: linear regression -- 2.3.1.2: Criterion 2: correlation -- 2.3.1.3: Criterion 3: error envelope (ee) -- 2.3.1.4: Error and bias -- 2.3.2: Utqiagvik (Barrow) -- 2.3.2.1: Criterion 1: linear regression -- 2.3.2.2: Criterion 2: Correlation -- 2.3.2.3: Criterion 3: Error envelope (ee) -- 2.3.2.4: Error and bias -- 2.4: Conclusion -- 2.5: References -- 2.6: Tables and figures. Chapter 3: Assessing the relationship between Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) and fine particulate matter (PM2.5) in Alaska's urban area -- Abstract -- 3.1: Introduction -- 3.2: Data -- 3.2.1: PM2.5 mass concentration data -- 3.2.2: MODIS data -- 3.2.3: Meteorological data -- 3.3: methods -- 3.3.1: Bias analysis -- 3.3.2: Regression analysis -- 3.3.2.1: Data -- 3.3.2.2: Model development -- 3.3.2.3: Validation -- 3.4: Results & discussion -- 3.4.1: Retrieval rates -- 3.4.2: Root mean square error -- 3.4.3: Bias -- 3.4.4: Multilinear models -- 3.4.5: Discussion -- 3.5: Conclusion -- 3.6: References -- 3.7: Tables and figures -- Conclusion -- References -- Appendix A -- Appendix B. | en_US |
dc.language.iso | en | en_US |
dc.subject | air quality | en_US |
dc.subject | Alaska | en_US |
dc.subject | forecasting | en_US |
dc.subject | pollution | en_US |
dc.subject | air | en_US |
dc.subject | MODIS | en_US |
dc.subject | spectroradiometer | en_US |
dc.title | An evaluation of the use of moderate resolution imaging spectroradiometer (MODIS)-derived aerosol optical depth to estimate ground level PM2.5 in Alaska | en_US |
dc.type | Thesis | en_US |
dc.type.degree | ms | en_US |
dc.identifier.department | Department of Civil Engineering | en_US |
dc.contributor.chair | Aggarwal, Srijan | |
dc.contributor.committee | Belz, Nathan | |
dc.contributor.committee | Perkins, Robert | |
refterms.dateFOA | 2020-03-06T02:41:34Z |