Mixing, dissipation enhancement and convergence rates for scaling limit of SPDEs with transport noise.

We prove the mixing property for stochastic linear transport equations on the torus, and dissipation enhancement in the viscous case. Our approach works also for the scaling limit of stochastic 2D (inviscid) fluid dynamical equations with transport noise to deterministic viscous equations. Quantitative estimates on the convergence rates are provided by combining analytic and probabilistic arguments, especially heat kernel properties and maximal estimates for stochastic convolutions. Similar ideas are applied to the stochastic 2D Keller-Segel model, yielding explicit choice of noise to ensure that the blow-up probability is less than any given threshold.