Modeling zero-inflated response data is pretty straight-forward, especially when using families from brms. But I wonder, is there a means of incorporating information about a zero-inflated explanatory variable into such a model (or any kind of regression)? Or is it more common to split up the response data and model the zero and non-zero information separately?

For example, I have some explanatory ratio data (m/m^2) that is concentrated at zero, but there is huge variation in the response variables at that point (response is log-normally distributed, but also has zeros, so Iâ€™m using a hurdle model), creating a kind of â€śspikeâ€ť in uncertainty in the response when the explanatory variable is zero. Any way to handle this kind of thing?