Application of Remotely-sensed Aerosol Optical Depth in Characterization and Forecasting of Urban Fine Particulate Matter
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| Language: | English |
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Ohio University / OhioLINK
2014
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1406732261 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-ohiou14067322612021-08-03T06:26:29Z Application of Remotely-sensed Aerosol Optical Depth in Characterization and Forecasting of Urban Fine Particulate Matter Grant, Shanique L. Atmosphere Engineering aerosol optical depth MODIS particulate matter Ohio River Valley Emissions from local industries, particularly coal-fired power plants, have been shown to enhance the ambient pollutant budget in the Ohio River Valley (ORV) region. One pollutant that is of interest is PM2.5 due to its established link to respiratory illnesses, cardiopulmonary diseases and mortality. State and local agencies monitor the impact of the local point sources on the ambient concentrations at specific sites; however, the monitors do not provide satisfactory spatial coverage. An important metric for describing ambient particulate pollution is aerosol optical depth (AOD). It is a dimensionless geo-physical product measured remotely using satellites or ground-based light detection ranging instruments. This study focused on assessing the effectiveness of using satellite aerosol optical depth (AOD) as an indicator for PM2.5 in the ORV and two cities in Ohio. Three models, multi-linear regression (MLR), principal component analysis (PCA) – MLR and neural network, were trained using 40% of the total dataset. The outcome was later tested to minimize error and further validated with another 40% of the dataset not included in the model development phase. Furthermore, to limit the effect of seasonality, four models representing each season were created for each city using meteorological variables known to influence PM2.5 and AOD concentration.GIS spatial analysis tool was employed to visualize and make spatial and temporal comparisons for the ORV region. Comparable spatial distributions were observed. Regression analysis showed that the highest and lowest correlations were in the summer and winter, respectively. Seasonal decomposition methods were used to evaluate trends at local Ohio monitoring stations to identify areas most suitable for improved air quality management. Over the six years of study, Cuyahoga County maintained PM2.5 concentrations above the national standard and in Hamilton County (Cincinnati) PM2.5 levels ranked above the national level for more than half the study period. Therefore, forecasting models were developed for these two locations. All models had AOD as a significant predictor variable. In Cincinnati, the neural network and MLR models were the most useful for the summer and fall seasons; while, in the neural network explained most of the observed variance in Cleveland. 2014-09-24 English text Ohio University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1406732261 http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1406732261 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
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NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Atmosphere Engineering aerosol optical depth MODIS particulate matter Ohio River Valley |
| spellingShingle |
Atmosphere Engineering aerosol optical depth MODIS particulate matter Ohio River Valley Grant, Shanique L. Application of Remotely-sensed Aerosol Optical Depth in Characterization and Forecasting of Urban Fine Particulate Matter |
| author |
Grant, Shanique L. |
| author_facet |
Grant, Shanique L. |
| author_sort |
Grant, Shanique L. |
| title |
Application of Remotely-sensed Aerosol Optical Depth in Characterization and Forecasting of Urban Fine Particulate Matter |
| title_short |
Application of Remotely-sensed Aerosol Optical Depth in Characterization and Forecasting of Urban Fine Particulate Matter |
| title_full |
Application of Remotely-sensed Aerosol Optical Depth in Characterization and Forecasting of Urban Fine Particulate Matter |
| title_fullStr |
Application of Remotely-sensed Aerosol Optical Depth in Characterization and Forecasting of Urban Fine Particulate Matter |
| title_full_unstemmed |
Application of Remotely-sensed Aerosol Optical Depth in Characterization and Forecasting of Urban Fine Particulate Matter |
| title_sort |
application of remotely-sensed aerosol optical depth in characterization and forecasting of urban fine particulate matter |
| publisher |
Ohio University / OhioLINK |
| publishDate |
2014 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1406732261 |
| work_keys_str_mv |
AT grantshaniquel applicationofremotelysensedaerosolopticaldepthincharacterizationandforecastingofurbanfineparticulatematter |
| _version_ |
1719436768155009024 |