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<p>Print out 2nd level header (shortened pandoc.header)</p>

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Enable users to evaluate long-term trends using a Generalized
Additive Modeling (GAM) approach. The model development includes selecting a
GAM structure to describe nonlinear seasonally-varying changes over time,
incorporation of hydrologic variability via either a river flow or salinity,
the use of an intervention to deal with method or laboratory changes
suspected to impact data values, and representation of left- and
interval-censored data. The approach has been applied to water quality data
in the Chesapeake Bay, a major estuary on the east coast of the United
States to provide insights to a range of management- and research-focused
questions.  Methodology described in Murphy (2019)
<doi:10.1016/j.envsoft.2019.03.027>.

Erik Leppo

.H2: Print out 2nd level header (shortened pandoc.header)

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