posted on 2007-04-01, 00:00authored byPaul J. Squillace, Michael J. Moran
Factors associated with sources, transport, and fate of
volatile organic compounds (VOCs) in groundwater from
aquifers throughout the United States were evaluated using
statistical methods. Samples were collected from 1631
wells throughout the conterminous United States between
1996 and 2002 as part of the National Water-Quality
Assessment (NAWQA) Program of the U.S. Geological
Survey. Water samples from wells completed in aquifers
used to supply drinking water were analyzed for more than
50 VOCs. Wells were primarily rural domestic water
supplies (1184), followed by public water supplies (216);
the remaining wells (231) supplied a variety of uses. The
median well depth was 50 meters. Age-date information shows
that about 60% of the samples had a fraction of water
recharged after 1953. Chloroform, toluene, 1,2,4-trimethylbenzene, and perchloroethene were some of the
frequently detected VOCs. Concentrations generally were
less than 1 μg/L. Source factors include, in order of
importance, general land-use activity, septic/sewer density,
and sites where large concentrations of VOCs are
potentially released, such as leaking underground storage
tanks. About 10% of all samples had VOC mixtures that
were associated with concentrated sources; 20% were
associated with dispersed sources. Important transport factors
included well/screen depth, precipitation/groundwater
recharge, air temperature, and various soil characteristics.
Dissolved oxygen was strongly associated with VOCs
and represents the fate of many VOCs in groundwater.
Well type (domestic or public water supply) was also an
important explanatory factor. Results of multiple analyses
show the importance of (1) accounting for both dispersed
and concentrated sources of VOCs, (2) measuring
dissolved oxygen when sampling wells to help explain the
fate of VOCs, and (3) limiting the type of wells sampled
in monitoring networks to avoid unnecessary variance in
the data, or controlling for this variance during data analysis.