High ambient ozone (O 3 ) concentrations are a widespread and persistent problem globally. Although studies have documented the role of forests in removing O 3 and one of its precursors, nitrogen dioxide (NO 2 ), the cost effectiveness of using peri-urban reforestation for O 3 abatement purposes has not been examined. We develop a methodology that uses available air quality and meteorological data and simplified forest structure growth-mortality and dry deposition models to assess the performance of reforestation for O 3 precursor abatement. We apply this methodology to identify the cost-effective design for a hypothetical 405-ha, peri-urban reforestation project in the Houston-GalvestonBrazoria O 3 nonattainment area in Texas. The project would remove an estimated 310 tons of (t) O 3 and 58 t NO 2 total over 30 y. Given its location in a nitrogen oxide (NO x )-limited area, and using the range of Houston area O 3 production efficiencies to convert forest O 3 removal to its NO x equivalent, this is equivalent to 127-209 t of the regulated NO x . The cost of reforestation per ton of NO x abated compares favorably to that of additional conventional controls if no land costs are incurred, especially if carbon offsets are generated. Purchasing agricultural lands for reforestation removes this cost advantage, but this problem could be overcome through cost-share opportunities that exist due to the public and conservation benefits of reforestation. Our findings suggest that peri-urban reforestation should be considered in O 3 control efforts in Houston, other US nonattainment areas, and areas with O 3 pollution problems in other countries, wherever O 3 formation is predominantly NO x limited.air pollution | ecosystem services | natural infrastructure | state implementation plan

In the domain of fluid dynamics, the problem of shape optimization is relevant because is essential to increase lift and reduce drag forces on a body immersed in a fluid. The current state of the art in this aspect consists of two variants: (1) evolution from an initial guess, using optimization to achieve a very specific effect, (2) creation and genetic breeding of random individuals. These approaches achieve optimal shapes and evidence of response under parameter variation. Their disadvantages are the need of an approximated solution and / or the trial-and-error generation of individuals. In response to this situation, this manuscript presents a method which uses Fluid Mechanics indicators (e.g. streamline curvature, pressure difference, zero velocity neighborhoods) to directly drive the evolution of the individual (in this case a wing profile). This pragmatic strategy mimics what an artisan (knowledgeable in a specific technical domain) effects to improve the shape. Our approach is not general, and it is not fully automated. However, it shows to efficiently reach wing profiles with the desired performance. Our approach shows the advantage of application domain-specific rules to drive the optimization, in contrast with generic administration of the evolution.

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