Studies of ambient and chamber aerosol composition using the aerosol mass spectrometer

This thesis presents composition measurements for atmospherically relevant inorganic and organic aerosol from laboratory and ambient measurements using the Aerodyne aerosol mass spectrometer. Studies include the oxidation of dodecane in the Caltech environmental chambers, and several aircraft- and ground-based field studies, which include the quantification of wildfire emissions off the coast of California, and Los Angeles urban emissions.

The oxidation of dodecane by OH under low NO conditions and the formation of secondary organic aerosol (SOA) was explored using a gas-phase chemical model, gas-phase CIMS measurements, and high molecular weight ion traces from particle- phase HR-TOF-AMS mass spectra. The combination of these measurements support the hypothesis that particle-phase chemistry leading to peroxyhemiacetal formation is important. Positive matrix factorization (PMF) was applied to the AMS mass spectra which revealed three factors representing a combination of gas-particle partitioning, chemical conversion in the aerosol, and wall deposition.

Airborne measurements of biomass burning emissions from a chaparral fire on the central Californian coast were carried out in November 2009. Physical and chemical changes were reported for smoke ages 0 – 4 h old. CO₂ normalized ammonium, nitrate, and sulfate increased, whereas the normalized OA decreased sharply in the first 1.5 - 2 h, and then slowly increased for the remaining 2 h (net decrease in normalized OA). Comparison to wildfire samples from the Yucatan revealed that factors such as relative humidity, incident UV radiation, age of smoke, and concentration of emissions are important for wildfire evolution.

Ground-based aerosol composition is reported for Pasadena, CA during the summer of 2009. The OA component, which dominated the submicron aerosol mass, was deconvolved into hydrocarbon-like organic aerosol (HOA), semi-volatile oxidized organic aerosol (SVOOA), and low-volatility oxidized organic aerosol (LVOOA). The HOA/OA was only 0.08–0.23, indicating that most of Pasadena OA in the summer months is dominated by oxidized OA resulting from transported emissions that have undergone photochemistry and/or moisture-influenced processing, as apposed to only primary organic aerosol emissions. Airborne measurements and model predictions of aerosol composition are reported for the 2010 CalNex field campaign.

Efficient methods for stochastic optimal control

The Hamilton Jacobi Bellman (HJB) equation is central to stochastic optimal control (SOC) theory, yielding the optimal solution to general problems specified by known dynamics and a specified cost functional. Given the assumption of quadratic cost on the control input, it is well known that the HJB reduces to a particular partial differential equation (PDE). While powerful, this reduction is not commonly used as the PDE is of second order, is nonlinear, and examples exist where the problem may not have a solution in a classical sense. Furthermore, each state of the system appears as another dimension of the PDE, giving rise to the curse of dimensionality. Since the number of degrees of freedom required to solve the optimal control problem grows exponentially with dimension, the problem becomes intractable for systems with all but modest dimension.

In the last decade researchers have found that under certain, fairly non-restrictive structural assumptions, the HJB may be transformed into a linear PDE, with an interesting analogue in the discretized domain of Markov Decision Processes (MDP). The work presented in this thesis uses the linearity of this particular form of the HJB PDE to push the computational boundaries of stochastic optimal control.

This is done by crafting together previously disjoint lines of research in computation. The first of these is the use of Sum of Squares (SOS) techniques for synthesis of control policies. A candidate polynomial with variable coefficients is proposed as the solution to the stochastic optimal control problem. An SOS relaxation is then taken to the partial differential constraints, leading to a hierarchy of semidefinite relaxations with improving sub-optimality gap. The resulting approximate solutions are shown to be guaranteed over- and under-approximations for the optimal value function. It is shown that these results extend to arbitrary parabolic and elliptic PDEs, yielding a novel method for Uncertainty Quantification (UQ) of systems governed by partial differential constraints. Domain decomposition techniques are also made available, allowing for such problems to be solved via parallelization and low-order polynomials.

The optimization-based SOS technique is then contrasted with the Separated Representation (SR) approach from the applied mathematics community. The technique allows for systems of equations to be solved through a low-rank decomposition that results in algorithms that scale linearly with dimensionality. Its application in stochastic optimal control allows for previously uncomputable problems to be solved quickly, scaling to such complex systems as the Quadcopter and VTOL aircraft. This technique may be combined with the SOS approach, yielding not only a numerical technique, but also an analytical one that allows for entirely new classes of systems to be studied and for stability properties to be guaranteed.

The analysis of the linear HJB is completed by the study of its implications in application. It is shown that the HJB and a popular technique in robotics, the use of navigation functions, sit on opposite ends of a spectrum of optimization problems, upon which tradeoffs may be made in problem complexity. Analytical solutions to the HJB in these settings are available in simplified domains, yielding guidance towards optimality for approximation schemes. Finally, the use of HJB equations in temporal multi-task planning problems is investigated. It is demonstrated that such problems are reducible to a sequence of SOC problems linked via boundary conditions. The linearity of the PDE allows us to pre-compute control policy primitives and then compose them, at essentially zero cost, to satisfy a complex temporal logic specification.