Emission rates of greenhouse gases (GHGs) entering
into the atmosphere can be inferred using mathematical
inverse approaches that combine observations from a network
of stations with forward atmospheric transport models.
Some locations for collecting observations are better than
others for constraining GHG emissions through the inversion,
but the best locations for the inversion may be inaccessible
or limited by economic and other non-scientific factors.
We present a method to design an optimal GHG observing
network in the presence of multiple objectives that may be
in conflict with each other. As a demonstration, we use our
method to design a prototype network of six stations to monitor
summertime emissions in California of the potent GHG
1,1,1,2-tetrafluoroethane (CH2FCF3, HFC-134a). We use a
multiobjective genetic algorithm to evolve network configurations
that seek to jointly maximize the scientific accuracy
of the inferred HFC-134a emissions and minimize the associated
costs of making the measurements. The genetic algorithm
effectively determines a set of “optimal” observing
networks for HFC-134a that satisfy both objectives (i.e., the
Pareto frontier). The Pareto frontier is convex, and clearly
shows the tradeoffs between performance and cost, and the
diminishing returns in trading one for the other. Without dif-
ficulty, our method can be extended to design optimal networks
to monitor two or more GHGs with different emissions
patterns, or to incorporate other objectives and constraints
that are important in the practical design of atmospheric
monitoring networks.