  For $\alpha\colon\mathbb{N}\rightarrow\mathbb{R}$, an $\alpha$-approximate
bi-kernel is a polynomial-time algorithm that takes as input an instance $(I,
k)$ of a problem $Q$ and outputs an instance $(I',k')$ of a problem $Q'$ of
size bounded by a function of $k$ such that, for every $c\geq 1$, a
$c$-approximate solution for the new instance can be turned into a
$c\cdot\alpha(k)$-approximate solution of the original instance in polynomial
time. This framework of \emph{lossy kernelization} was recently introduced by
Lokshtanov et al. We prove that for every nowhere dense class of graphs, every
$\alpha>1$ and $r\in\mathbb{N}$ there exists a polynomial $p$ (whose degree
depends only on $r$ while its coefficients depend on $\alpha$) such that the
connected distance-$r$ dominating set problem with parameter $k$ admits an
$\alpha$-approximate bi-kernel of size $p(k)$. Furthermore, we show that this
result cannot be extended to more general classes of graphs which are closed
under taking subgraphs by showing that if a class $C$ is somewhere dense and
closed under taking subgraphs, then for some value of $r\in\mathbb{N}$ there
cannot exist an $\alpha$-approximate bi-kernel for the (connected) distance-$r$
dominating set problem on $C$ for any function
$\alpha\colon\mathbb{N}\rightarrow\mathbb{R}$ (assuming the Gap Exponential
Time Hypothesis).
