"90 percent of the variation" is misleading when comparing the levels of one time-series against another. It's very easy to find two time-series that regress almost perfectly on one another because both steadily increase. Looking at first-differences is more informative about possible causal relations. The image Cyan posted is effectively two data points from a first difference perspective: both went up and then both went down.
Another graph from Nevin's website is slightly more persuasive:

There you can see 4-6 corresponding changes in trend. Still not that impressive, but maybe enough to start looking more closely.
Interesting to see the murder rate stay almost flat through the 2000s even as lagged lead use plummets by 80% or so.
A friend has been asking my views on the likelihood that there's anything to a correlation between changing levels of lead in paint (and automotive exhaust) and the levels of crime. He quoted from a Reason Blog:
I responded with the following:
He's apparently continued to pursue the question, and just forwarded these remarks from Steven Pinker that I thought were very illuminating, and probably deserve a place in this community's toolkit for skeptics. Pinker's main point is that the association between Lead and crime is a long tenuous chain of suppositions, and several of the intermediate points should be far easier to measure. Finding correlations at this distance is not very informative.
http://stevenpinker.com/files/pinker/files/pinker_comments_on_lead_removal_and_declining_crime.pdf
Does the phrase "long-chain correlation" stick in your head and make it easier to dismiss this kind of argument?