Vehicle chase measurements used for studying real-world emissions apply various methods for calculating emission factors. Currently available methods are typically based on the dilution of emitted carbon dioxide (CO2) and the assumption that other emitted pollutants dilute similarly. A problem with the current methods arises when the studied vehicle is not producing CO2, e.g. during engine-motoring events, such as on downhill sections. This problem is also encountered when studying non-exhaust particulate emissions, e.g. from electric vehicles. In this study, we compare multiple methods previously applied for determining the dilution ratios. Additionally, we present a method applying multivariate adaptive regression splines and a new method called near-wake dilution. We show that emission factors for particulate emissions calculated with both methods are in line with the current methods for vehicles producing CO2. In downhill sections, the new methods were more robust to low CO2 concentrations than some of the current methods. The methods introduced in this study can hence be applied in chase measurements with changing driving conditions and be possibly extended to estimate non-exhaust emissions in the future.