A computation saving Jackknife approach to receptor model uncertainty statements for serially correlated data

The use of receptor modeling is now a widely accepted approach to model air pollution data. The resulting estimates of pollution source profiles have error and frequently the uncertainties are obtained under an assumption of independence. In addition traditional Bootstrap approaches are very computationally intensive. We present an intuitive Jackknife alternative that is much less computationally intensive and in simulation examples and actual data seems to demonstrate that it provides wider confidence intervals and larger standard errors for receptor model profile estimates than does the Bootstrap done under the assumption of independence.