  Let $\{u(t,x)\}_{t>0,x\in{{\mathbb R}^{d}}}$ be the solution to a
$d$-dimensional parabolic Anderson model with initial condition ${\delta}_{0}$
and driven by a Gaussian noise that is white in time and has a spatially
homogeneous covariance given by a nonnegative-definite measure $f$ which
satisfies Dalang's condition. Let
$S_{N,t}:=N^{-d}\int_{{[0,N]}^d}{[U(t,x)-1]}~{\rm d}x$ denote the spatial
average on ${{\mathbb R}^{d}}$.
  We obtain various functional central limit theorems (CLTs) for spatial
averages based on the quantitative analysis of $f$ and spatial dimension $d$.
In particular, when $f$ is given by Riesz kernel, i.e., $f({\rm x})={\Vert x
\Vert}^{-\beta}{\rm d}x$, $\beta\in(0,2\wedge d)$, the functional CLT is also
based on the index $\beta$.
