In this study, we performed ab initio calculations and obtained the bimolecular rate coefficients for the CH2OO/CH3CHOO reactions with H2O/(H2O)2. The energies were calculated with QCISD(T)/CBS//B3LYP/6-311+G(2d,2p) and the partition functions were estimated with anharmonic vibrational corrections by using the second order perturbation theory. Furthermore, we directly measured the rate of the CH2OO reaction with water vapor at high temperatures (348 and 358 K) to reveal the contribution of the water monomer in the CH2OO decay kinetics. We found that the theoretical rate coefficients reproduce the experimental results of CH2OO for a wide range of temperatures. For anti- (syn-) CH3CHOO, we obtained theoretical rate coefficients of 1.60 × 10(-11) (2.56 × 10(-14)) and 3.40 × 10(-14) (1.98 × 10(-19)) cm(3) s(-1) for water dimer and monomer reactions at room temperature. From the detailed analysis of the quantum chemistry and approximations for the thermochemistry calculations, we conclude that our calculated values would be within a factor of 3 of the correct values. Furthermore, at [H2O] = 1 × 10(17) to 5 × 10(17) cm(-3), we estimate that the effective first-order rate coefficients for CH2OO, anti- and syn-CH3CHOO reactions with water vapor will be ∼10(3), ∼10(4), and ∼10(1) s(-1), respectively. Thereby, for Criegee intermediates with a hydrogen atom on the same side as the terminal oxygen atom, the reaction with water vapor will likely dominate the removal processes of these CIs in the atmosphere.

The Burning Index (BI) is commonly used as a predictor of wildfire activity. An examination of data on the BI and wildfires in Los Angeles County, California from January 1976 to December 2000 reveals that although the BI is positively associated with wildfire occurrence, its predictive value is quite limited. Wind speed alone has a higher correlation with burn area than BI, for instance, and a simple alternative point process model using wind speed, relative humidity, precipitation and temperature well outperforms the BI in terms of predictive power. The BI is generally far too high in Winter and too low in Fall, and may exaggerate the impact of individual variables such as wind speed or temperature during times when other variables, such as precipitation or relative humidity, render the environment ill-suited for wildfires.

This paper explores the use of, and problems that arise in, kernel smoothing and parametric estimation of the relationships between wildfire incidence and various meteorological variables. Such relationships may be treated as components in separable point process models for wildfire activity. The resulting models can be used for comparative purposes in order to assess the predictive performance of the Burning Index.

Conditional intensity, Covariates, Marked point processes, Separability, Testing, Wildfires,