UC Berkeley

Atmospheric aerosols are solid particles and liquid droplets that are usually smaller than the diameter of a human hair. They can be found drifting in the air in every ecosystem on Earth, leaving significant impacts on human health and our climate. Understanding the spatial and temporal distribution of different atmospheric aerosols, therefore, is an important first step to decode the complex system of aerosols and further, their effects on public health and climate.

The development of remote-sensing radiometers provides a powerful tool to monitor the amount of atmospheric aerosols, as well as their compositions. Radiometers aboard satellites measure the amount of electromagnetic solar radiation. The amount of atmospheric aerosols is further quantified by aerosol optical depth (AOD), defined as the amount of solar radiation that aerosols scatter and absorb in the atmosphere and generally prevent from reaching the Earth surface. Despite efforts to improve remote-sensing instruments and a great demand for a detailed profile of aerosol spatial distribution, methods needed to provide AOD estimation at a reasonably fine resolution, are lacking. The quantitative uncertainties in the amount of aerosols, and especially aerosol compositions, limit the utility of traditional methods for aerosol retrieval at a fine resolution.

In Chapter 2 and 3 of this thesis, we exploit the use of statistical methods to estimate aerosol optical depth using remote-sensed radiation. A Bayesian hierarchy proves to be useful for modeling the complicated interactions among aerosols of different amount and compositions over a large spatial area. Based on the hierarchical model, Chapter 2 estimates and validates aerosol optical depth using Markov chain Monte Carlo methods, while chapter 3 resorts to an optimization-based approach for faster computation. We extend our study focus from the aerosol amount to the aerosol compositions in Chapter 4.

Chapter 1 briefly reviews the characteristics of atmospheric aerosols, including the different types of aerosols and their major impacts on human health. We also introduce a major remote-sensing instrument, NASA's Multi-angle Imaging SpectroRadiometer (MISR), which collects the observations our studies base on. Currently, the MISR operational aerosol retrieval algorithm provides estimates of aerosol optical depth at the spatial resolution of 17.6 km.

In Chapter 2, we embed MISR's operational weighted least squares criterion and its forward calculations for aerosol optical depth retrievals in a likelihood framework. We further expand it into a hierarchical Bayesian model to adapt to finer spatial resolution of 4.4 km. To take advantage of the spatial smoothness of aerosol optical depth, our method borrows strength from data at neighboring areas by postulating a Gaussian Markov Random Field prior for aerosol optical depth. Our model considers aerosol optical depth and mixing vectors of different types of aerosols as continuous variables. The inference is then carried out using Metropolis-within-Gibbs sampling methods. Retrieval uncertainties are quantified by posterior variabilities. We also develop a parallel Markov chain Monte Carlo algorithm to improve computational efficiency. We assess our retrieval performance using ground-based measurements from the AErosol RObotic NETwork (AERONET) and satellite images from Google Earth. Based on case studies in the greater Beijing area, China, we show that 4.4 km resolution can improve both the accuracy and coverage of remote-sensed aerosol retrievals, as well as our understanding of the spatial and seasonal behaviors of aerosols. This is particularly important during high-AOD events, which often indicate severe air pollution.

Chapter 3 of this thesis continues to improve our statistical aerosol retrievals for better accuracy and more efficient computation by switching to an optimization-based approach. We first establish objective functions for aerosol optical depth and aerosol compositions, based upon MISR operational weighted least squares criterion and its forward calculations. Our method also borrows strength from aerosol spatial smoothness by constructing penalty terms in the objective functions. The penalties correspond to a Gaussian Markov Random Field prior for aerosol optical depth and a Dirichlet prior for aerosol mixing vectors under our hierarchical Bayesian scheme; the optimization-based approach corresponds to Bayesian Maximum a Posteriori (MAP) estimation. Our MAP retrieval algorithm provides computational efficiency almost 60 times that of our Bayesian retrieval algorithm presented in Chapter 2. To represent the increasing heterogeneity of urban aerosol sources, our model continues to expand the pre-fixed aerosol mixtures used in the MISR operational algorithm by considering aerosol mixing vectors as continuous variables. Our retrievals are again validated using ground-based AERONET measurements. Case studies in the greater Beijing and Zhengzhou areas of China reassure that 4.4 km resolution can improve the accuracy and spatial coverage of remotely-sensed retrievals and enhance our understanding of the spatial behaviors of aerosols.

When comparing our aerosol retrievals to the extensive ground-based measurements collected in Baltimore, Maryland, we encountered greater uncertainties of aerosol compositions. It is a result from both the complex terrain structures of Baltimore and its various aerosol emission sources. Chapter 4, as result, extends the flexibility of our previous aerosol retrievals by incorporating a complete set of the eight commonly observed types of aerosols. The consequential rise in model complexity is met by a warm-start Markov chain Monte Carlo sampling scheme. We first design two Markov sub-chains, each representing an aerosol mixture containing only four types of the commonly observed aerosols. Combining the samples generated by these two sub-chains, we propose an initialization for the Markov chain that contains all eight types of commonly observed aerosols. Partial information on the interactions of different types of aerosols from the samples generated by the sub-chains proves to be useful in choosing a more efficient initial point for the complete Markov chain. Faster computation is achieved without compromising the retrieval accuracy nor the spatial resolution of the estimated aerosol optical depth. In the end, through case studies of aerosol retrievals for the Baltimore area, we explore the potentials of remote-sensed retrievals in improving our understanding of aerosol compositions.