A new method for the analysis of ambient air data

The emission rates of pollutant sources and the resulting pollutant ground level concentration (GLC) at surrounding locations are typically monitored on an hourly basis. These vast amount of data collected at substantial cost are normally used only to check for compliance with local governmental standards. The number of these measurements precludes their analysis by conventional methods. In this thesis, a new CDFT (Convolution and Deconvolution using Fourier Transforms) method is presented. This method utilizes the emission rate data of a single source and ground level concentration data at a single receptor efficiently in the form of probability distributions, to determine a characteristic Function E and its associated probability distribution f[sub E]. This function characterizes the local air shed between the source and the E receptor and can further be used to predict a new GLC pattern in response to a new emission rate pattern. The method is first developed in analytical form in the theory section. This analytical technique is applicable to those GLC and emission rate data which can be fitted by analytical (continuous) probability distributions, such as lognormal distributions. This method is further adapted for numerical computation, applicable to other GLC and emission rate data which cannot be well represented by analytical probability distributions. A computer program to carry out these numerical analyses is presented. It is first subjected to various tests to establish that it can be accurately applied to distributions of varying complexities. Then the sensitivity of the program to the effect of interval size, round off errors and instrument errors were ascertained. Practical applications of both the analytical and numerical techniques are demonstrated in examples. Possible extensions of the method to more complex situations are also discussed.Applied Science, Faculty ofChemical and Biological Engineering, Department ofGraduat