Fernando Garcia-Menendez

U.S. Air Quality and Health Benefits from Avoided Climate Change under Greenhouse Gas Mitigation.

We evaluate the impact of climate change on U.S. air quality and health in 2050 and 2100 using a global modeling framework and integrated economic, climate, and air pollution projections. Three internally consistent socioeconomic scenarios are used to value health benefits of greenhouse gas mitigation policies specifically derived from slowing climate change. Our projections suggest that climate change, exclusive of changes in air pollutant emissions, can significantly impact ozone (O3) and fine particulate matter (PM2.5) pollution across the U.S. and increase associated health effects. Climate policy can substantially reduce these impacts, and climate-related air pollution health benefits alone can offset a significant fraction of mitigation costs. We find that in contrast to cobenefits from reductions to coemitted pollutants, the climate-induced air quality benefits of policy increase with time and are largest between 2050 and 2100. Our projections also suggest that increasing climate policy stringency beyond a certain degree may lead to diminishing returns relative to its cost. However, our results indicate that the air quality impacts of climate change are substantial and should be considered by cost-benefit climate policy analyses.

Simulating smoke transport from wildland fires with a regional-scale air quality model: Sensitivity to spatiotemporal allocation of fire emissions

Air quality forecasts generated with chemical transport models can provide valuable information about the potential impacts of fires on pollutant levels. However, significant uncertainties are associated with fire-related emission estimates as well as their distribution on gridded modeling domains. In this study, we explore the sensitivity of fine particulate matter concentrations predicted by a regional-scale air quality model to the spatial and temporal allocation of fire emissions. The assessment was completed by simulating a fire-related smoke episode in which air quality throughout the Atlanta metropolitan area was affected on February 28, 2007. Sensitivity analyses were carried out to evaluate the significance of emission distribution among the models vertical layers, along the horizontal plane, and into hourly inputs. Predicted PM2.5 concentrations were highly sensitive to emission injection altitude relative to planetary boundary layer height. Simulations were also responsive to the horizontal allocation of fire emissions and their distribution into single or multiple grid cells. Additionally, modeled concentrations were greatly sensitive to the temporal distribution of fire-related emissions. The analyses demonstrate that, in addition to adequate estimates of emitted mass, successfully modeling the impacts of fires on air quality depends on an accurate spatiotemporal allocation of emissions.

The role of natural variability in projections of climate change impacts on U.S. ozone pollution: NATURAL VARIABILITY IN OZONE PROJECTIONS

Author(s): Garcia-Menendez, F; Monier, E; Selin, NE | Abstract: ©2017. American Geophysical Union. All Rights Reserved. Climate change can impact air quality by altering the atmospheric conditions that determine pollutant concentrations. Over large regions of the U.S., projected changes in climate are expected to favor formation of ground-level ozone and aggravate associated health effects. However, modeling studies exploring air quality-climate interactions have often overlooked the role of natural variability, a major source of uncertainty in projections. Here we use the largest ensemble simulation of climate-induced changes in air quality generated to date to assess its influence on estimates of climate change impacts on U.S. ozone. We find that natural variability can significantly alter the robustness of projections of the future climates effect on ozone pollution. In this study, a 15 year simulation length minimum is required to identify a distinct anthropogenic-forced signal. Therefore, we suggest that studies assessing air quality impacts use multidecadal simulations or initial condition ensembles. With natural variability, impacts attributable to climate may be difficult to discern before midcentury or under stabilization scenarios.

Atmospheric Plume Modeling with a Three-Dimensional Refinement Adaptive Grid Method

We present a three-dimensional fully-adaptive grid algorithm for chemical transport models. The method is designed to refine vertical and horizontal resolution by dynamically concentrating grid nodes within a region of interest. Exceptionally high grid resolution can be achieved in Eulerian air quality models using the method. Here the algorithm’s main operations are described. In addition, advection tests are used to demonstrate the algorithm’s ability to better capture concentration gradients in atmospheric plumes.

Development and Evaluation of an Air Quality Model for Predicting the Impacts of Prescribed Burns

A modeling system has been developed to predict accurately the downwind air quality impacts of prescribed burns. The system has been evaluated in applications to monitored burns and a long-range smoke event detected by the regional PM2.5 monitoring network in Southeastern USA. Uncertainties in the estimation of emissions have been identified and sensitivities of predicted PM2.5 levels to smoke injection height versus PBL height, and wind speed and direction have been quantified. More accurate wind predictions, currently provided by WRF, would significantly improve the performance of the modeling system.