Abstract

It is an accepted fact that our atmosphere bears an increasing load of pollutants: carbon dioxide, ozone, oxides of nitrogen and sulfur, volatile organic compounds (VOCs), particulates, and heavy metals. The adverse health and environment effects of air pollution have been a major concern in shaping our environmental quality. The World Health Organization (WHO) estimates that 1.5 billion people living in the urban areas throughout the world are exposed to dangerous levels of air pollution and 2 million premature deaths occur annually. A year shortening of life expectancy by an average is also the result of air pollution. Air pollution risk assessment, especially in urban areas, is currently one of the most important environmental issues for human health.
Air quality model is a useful tool to simulate the complex dispersion of pollutants in the atmosphere and to predict the long-term effects on ground and spatial levels, and it plays an important role in air pollution risk assessment. Since there are inherent complexities and uncertainties associated with land use information, meteorological conditions, emission spatial allocation, as well as physical and chemical reactions in air quality modeling, it still needs to be further explored. The emergences of new spatial information technologies, such as satellite remote sensing technology and Geographic Information Systems (GIS) open a new era for air quality modeling and air pollution risk assessment, making it possible to predict the spatial concentration distributions of air pollutants on larger scales with finer details.
The objectives of the work in this thesis include the development of GIS-based air quality modeling system to predict the spatial concentration distributions of ambient air pollutants (PM2.5, NO2, SO2, and CO), the development of satellite remote sensing approach to retrieve aerosol optical depth (AOD) and to derive ground-level pollutant concentrations (PM2.5 and NO2), and the development of fuzzy aggregation risk assessment approach to evaluate the health risks of multiple air pollutants.
A GIS-based multi-source and multi-box (GMSMB) air quality modeling approach is developed to predict the spatial concentration distribution of four air pollutants (PM2.5, NO2, SO2, and CO) for the state of California. A satellite remote sensing approach is investigated to derive the ground-level NO2 concentrations from the satellite Ozone Monitoring Instrument (OMI) tropospheric NO2 column data for the same location and same period. The GMSMB modeling and satellite-derived results are cross-verified through comparing with each other and with the in-situ surface measurements. Furthermore, a fuzzy aggregation-ordered weighted averaging (OWA) risk assessment approach is developed to evaluate the integrated health risks of the four air pollutants.
An improved aerosol optical depth (AOD) retrieval algorithm is proposed for the MODIS satellite instrument at 1-km resolution. In order to estimate surface reflectances over variable cover types, including bright and dark surfaces, a modified minimum reflectance technique (MRT) is used. A new lookup table (LUT) is created using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) Radiative Transfer Code for the presumed aerosol types. The MODIS-retrieved AODs are used to derive the ground-level PM2.5 concentrations using the aerosol vertical profiles obtained from the GEOS-Chem simulation. The developed method has been examined to retrieve the AODs and evaluate the concentration distribution of PM2.5 over the city of Montreal, Canada in 2009. The satellite-derived PM2.5 concentrations are ranging from 1 to 14 µg/m3 in Montreal, which are in good agreement with the in-situ surface measurements at all monitoring stations. This suggests that the method in this study can retrieve AODs at a higher spatial resolution than previously and can operate on an urban scale for PM2.5 assessment.
Furthermore, the ground-level PM2.5 concentrations and corresponding health risks are investigated using the retrieved AOD from the satellite instruments of MODIS and MISR for the extended East Asia, including China, India, Japan, and South Korea. The results are validated with the monitoring values and literatures. Depending on the regression analysis, the GDP growth rates, population growth rates, and coal consumptions are the main reasons of the higher PM2.5 concentrations in Beijing. Some mitigating measurements are then proposed and the future trend is predicted. The developed method can be used to other regions for making cost-effective strategy to control and improve air pollution.