Air pollution is a serious issue that affects many parts of the world, Southeast Asia in particular. Nitrogen oxides, particulate matter, sulfur dioxide, and other emissions have negative impacts on human health as well as overall environmental quality. The major sources in Thailand are open burning and fossil fuel combustion, i.e. in vehicles, energy use in industries and power generation. Given increasing actual and projected GDP growth, subsequent increases in energy consumption are inevitable. The power generation system must grow and expand as well to meet changes in demand from industrial, commercial, and residential customers. The Ministry of Energy of Thailand has published the Power Development Plan 2015 (PDP 2015) to outline policies and goals of the growing power generation and transmission systems throughout the nation. Notably, the plan involves increasing the use of coal-fired generation. Using both the Greenhouse Gas and Air Pollution Interactions and Synergies Model (GAINS) and the Comprehensive Air Quality Model with Extensions (CAMx), we have compared two different emissions scenarios: one with standard emission control technology, and another with maximum feasible emission controls. The effectiveness of emission control technology varied by region and pollutant. The greatest increase in air quality was located around the Rayong province in central Thailand. For PM₁₀ in the northern Thailand, however, emission control technologies did little to improve the air quality because the main source of pollutant, biomass burning, was left unabaited. This forecast of air quality can show possible impacts from future emissions in Thailand and regions that may benefit from added emission control technology in the future.

In this work, we will identify the existence of 'rough dependence on initial conditions' in atmospheric phenomena, a concept which is a problem for weather analysis and forecasting. Typically, two initially similar atmospheric states will diverge slowly over time such that forecasting the weather using the Navier-Stokes equations is useless after some characteristic time-scale. With rough dependence, two initial states diverge quickly implying forecasting is impossible. Using previous research in atmospheric science, rough dependence is characterized by using quantities that can be calculated from atmospheric data. Rough dependence will be identified in atmospheric phenomena on different time scales. The nature of rough dependence will be studied using a research model. Data was provided for this project by archives outside MU, and using our MU RADAR at the South Farm experiment station.

Enstrophy in a fluid relates to the dissipation tendency in a fluid that has use in studying turbulent flows. It also corresponds to vorticity as kinetic energy does to velocity. Earlier work showed that the Integrated Regional Enstrophy (IRE) was related to the sum of the positive Lypunov Exponents. Lyapunov Exponents are the characteristic exponent(s) of a dynamic system or a measure of the divergence or convergence of system trajectories that are initially close. Relatively high values of IRE derived from an atmospheric flow field in the study of atmospheric blocking was identified with the onset or demise of blocking events, but also transitions of the large-scale flow in general. Kolmogorv Entropy (KolE) also known as metric entropy is related to the sum of the positive Lyapunov Exponents as well. This quantity can be thought of as a measure of predictability (higher values less predictability) and will be non-zero for a chaotic system. Thus, the measure of IRE is related to KolE as well. This study will show that relatively low (high) values of IRE derived from atmospheric flows correspond to more stable (transitioning) large-scale flow a greater (lesser) degree of predictability and KolE. The transition is least predictable and should be associated with higher IRE and KolE.