Fig nbsp xA Plot of hourly average

While there is evidence of limitations with how AERMOD treats aircraft exhaust dispersion, our findings could also be explained if regression models systematically underestimated concentrations further from the source. In the far-field, the aviation signal is smaller and statistically significant predictors are fewer. In our stepwise regression modeling, we omitted non-statistically significant terms. This implicitly assumes a zero contribution of that CB 65 predictor to ambient concentrations, when in reality the contribution may be positive if not statistically significant. This could create downward bias in aviation-attributable regression predictions at P4 and P5, potentially yielding a consistent difference between aviation-attributable regression predictions and dispersion model predictions at all 3 sites asteroid impacts measured BC. That said, the P4 and P5 models did include source terms for all runways and arrival/departure activity, so large systematic bias is unlikely.
4.3. Limitations
5. Conclusions