Define two open sets by
The support of a distribution and its singular support are
Both are relatively closed because the removed sets are open. The locality of distributions gives . On an overlap of two open smoothness regions, the two smooth functions represent the same distribution, so their difference is zero almost everywhere and hence everywhere by continuity. They therefore join to one smooth function on . The same partition of unity argument shows that it represents there.
For , openness of implies
Indeed every neighborhood of a point in this relative closure meets in an open set containing a nonnegative test function with positive integral; outside the closure the indicator function is identically zero near the point.
The singular support of an indicator function needs a measure-sensitive boundary. For a Lebesgue measurable set , define its essential closure by
Then the general answer is
Outside this intersection, some ball has either no points of or a complement of Lebesgue measure zero. The regular distribution is respectively the constant zero or one on that ball and is smooth. Conversely, at a point of the intersection, every sufficiently small ball has positive Lebesgue measure in both sets. If a smooth function represented the indicator function there, its values would be in everywhere: any other value, by continuity, would give a ball of positive Lebesgue measure on which equality almost everywhere fails. On a connected ball such a continuous two-valued function is constant, contradicting the two positive measures.
In particular, the answer need not be the full topological boundary. For and , the indicator function is the constant one almost everywhere, so its singular support is empty although the boundary is . The full relative boundary is the answer when every neighborhood of each boundary point contains a positive Lebesgue measure portion of the complement, as for an open set with smooth boundary.
If , let be its extension by zero to . Its compact support is contained in , so this extension remains a test function. The restriction of a distribution is
Extension by zero preserves convergence in the space of test functions, so this defines a distribution on .
Choose a partition of unity subordinate to the neighborhoods from the hypothesis. For an arbitrary , local finiteness and compactness of give a finite decomposition . Each term is a test function supported in one . Thus
Since this holds for every test function, . This is the locality of distributions.