2005 Progress Report: Air Quality, Emissions, Growth, and Change: A Method to Prescribe a Desirable FutureEPA Grant Number: R831838
Title: Air Quality, Emissions, Growth, and Change: A Method to Prescribe a Desirable Future
Investigators: Chang, Michael E. , Akhtar, Farhan , Carpenter, Ann , Chapman, James , Noonan, Douglas , Russell, Armistead G. , Weber, Ed
Current Investigators: Chang, Michael E. , Akhtar, Farhan , Carpenter, Ann , Chapman, James , Chirico, Jennifer M , Noonan, Douglas , Russell, Armistead G.
Institution: Georgia Institute of Technology
Current Institution: Georgia Institute of Technology , Lawrence Frank and Company, Inc.
EPA Project Officer: Chung, Serena
Project Period: December 1, 2004 through November 30, 2007 (Extended to November 30, 2009)
Project Period Covered by this Report: December 1, 2004 through November 30, 2005
Project Amount: $649,999
RFA: Regional Development, Population Trend, and Technology Change Impacts on Future Air Pollution Emissions (2004) RFA Text | Recipients Lists
Research Category: Global Climate Change , Climate Change , Air
At the scale of 50 years, we postulate that emissions and the activities, processes, and infrastructure associated with them, are pliant that is, they are adaptable. Rather than trying to predict how emissions will change in the future and what impact they will have on future air quality (and hoping that the impact is beneficial), we propose a method in which we define a desirable air quality state and then derive the emissions and activity profiles required to achieve it. The objectives of this research project are to: (1) develop and apply at the regional community scale a reasonable method for defining one or more desirable future air quality states; (2) develop and demonstrate an “inverse” approach that utilizes the future desired air quality to derive the emissions, activities, processes, and infrastructure that are needed to achieve the desired future; and (3) identify the types and amounts of land use modifications, technology advancements, and other changes that will be required to transform or morph the present emissions scenario into the future desired emissions scenario.
During this initial project year, we focused efforts on the first objective: to develop and apply at the regional community scale a reasonable method for defining one or more desirable future air quality states. From a purely economic perspective, we investigated different approaches to examine the question of “what is the ideal air quality in Atlanta in 2050?” We drafted a white paper that described the different approaches available and the ones that we later employed, which included a locational equilibrium model, benefit transfer methodology, and a compensating differentials approach. Valuations were estimated for projected scenarios for Atlanta in 2050. This multiple-method approach yielded multiple estimates of the optimal air quality, enabling the sensitivity of the results to various modeling assumptions to be assessed. The optimal air quality levels were quite robust to several different modeling assumptions. This furthers our project goal of inverse-modeling the ideal air quality for Atlanta in 2050 by providing the “target” or final outcome air quality profile. Even without the inverse modeling that is to follow in Year 2 of the project, such a result can inform decision makers about future needs. For example, because the optimal air quality identified is substantially cleaner than the status quo air quality in Atlanta, a practical application of the findings would be to use them to argue for cost-effective improvements in air quality: up to the identified point, we can be confident that improvements in air quality will provide marginal net benefits for society. Additional and necessary preliminary work was completed in anticipation of both phase 2, in which an inverse model is used with the desired air quality in 2050 to derive the required emissions in 2050, and phase 3, in which the emissions are related back to land use.
Year 2 of the project will be dedicated to the second objective: to develop and demonstrate an “inverse” approach that utilizes the future desired air quality to derive the emissions, activities, processes, and infrastructure that are needed to achieve the desired future. In this regard, we have commenced developing the computer codes and programs that will be used to run an existing and validated air quality model application (CMAQ) for the Atlanta, Georgia metropolitan area in an inverse mode. In inverse modeling, the greatest challenge lies in calculating efficiently the many sensitivity coefficients that are needed to relate the air quality concentrations that are input (and in our case are the “2050 desired” air quality concentrations) to the emissions fields that we seek (i.e., emissions in the year 2050 that are needed to produce the 2050 desired air quality). At the time of this writing, this work is progressing on schedule. Looking ahead to Year 3 of the project, we will relate the resultant emissions derived from the inverse model to land use types using the SMARTRAQ database (http://www.smartraq.org/ Exit ).