Quantifying the degree of dissimilarity between two probability distributions on a finite metric space is a fundamental task in Computer Science and Computer Vision. A natural dissimilarity measure based on optimal transport is the Earth Mover's Distance (EMD). A key technique for analyzing this metric, pioneered by Charikar (2002) and Indyk and Thaper (2003), involves constructing low-distortion embeddings of EMD(X) into the Lebesgue space $L_1$. It became a key problem to investigate whether the upper bound of $O(\log n)$ can be improved for important classes of metric spaces known to admit low-distortion embeddings into $L_1$. In the context of Computer Vision, grid graphs, especially planar grids, are among the most fundamental. Indyk posed the related problem of estimating the $L_1$-distortion of the space of uniform distributions on $n$-point subsets of $R^2$. The Progress Report, last updated in August 2011, highlighted two key results: first, the work of Khot and Naor (2006) on Hamming cubes, which showed that the $L_1$-distortion for Hamming cubes meets the described above upper estimate, and second, the result of Naor and Schechtman (2007) for planar grids, which established that the $L_1$-distortion of for a planar $n$ by $n$ grid is $Ω(\sqrt{\log n})$. Our first result is the improvement of the lower bound on the $L_1$-distortion for grids to $Ω(\log n)$, matching the universal upper bound up to multiplicative constants. The key ingredient allowing us to obtain these sharp estimates is a new Sobolev-type inequality for scalar-valued functions on the grid graphs. Our method is also applicable to many recursive families of graphs, such as diamond and Laakso graphs. We obtain the sharp distortion estimates of $\log n$ in these cases as well.