# Presentation: 759723

## Mediated Modeling of CO2 Emissions & Air Quality Issues in an Urban Airshed

**Craig B. Forster****1     ****Jim I. Mills****1****     Philip C. Emmi****1**
**Jessica Durfee****2  ****  Tarla Rai Peterson****2     ****Richard Valentine****3   **
**1****University of Utah, *****College of Architecture+Planning***
**2****University of Utah, *****Department of Communication***
***3******Salt Lake Valley Health Department***

**Notes:**
Mediated modeling is a participatory process designed to engage community participants in developing an improved (and shared) understanding of a system of interest, or concern, to them. This approach has been used to good advantage in water resources, and other, applications to enable community groups to more clearly see the interconnected factors that must be accounted for when assessing policy alternatives and decision-making.  The system of interest here is one where personal choice and policy decisions affect the rate of air pollutant and CO2 emitted by burning fossil fuels for  transportation, home heating, electricity production and industrial processes.  An important goal is to use a model constructed with community input to help recognize how some policies or choices might lead to unintended consequences; despite the initial expectation of only beneficial results as viewed from the narrow context being considered prior to the mediated modeling workshop series.  

The fundamental ideas and structure inherent in the model developed in the mediated modeling approach described here are applicable to any urban airshed system.  Although the participatory process outlined in this presentation was carried out in the Salt Lake Valley, Utah; the model developed here can be readily ‘retuned’ to fit the specific circumstances and interests of other urban communities.           

## Outline

**Community Engagement Goals**
**Urban Airshed System**
**Mediated Modeling**
**Example: Salt Lake Valley Airshed Project**
**Stakeholder/Decision-Maker Workshop Series**
**Urban Airshed System Model**
**Summary**

**Notes:**
This presentation outlines how the mediated modeling approach was used to bring together the ideas of a campus-community research team and a group of urban decision-makers, airshed managers and air quality advocates in a model that computes emissions for a range of multi-decade, alternative future scenarios.   Unlike the community engagement processes aimed at school children, or the general population, we convened a group of workshop participants who are most likely to influence – today - the policies and decisions that will impact tomorrow’s urban CO2 and air pollutant emissions in the Salt Lake Valley.  These people are currently affecting the way that cities are growing and are making important decisions regarding the future infrastructure (roads, communities, etc.) that will be available to current school children when they are adults.  Although the workshop participants already have mental models that they use to make decisions that affect the urban airshed, the breadth of their understanding across both time and space varies strongly from person to person.  It is important to note that the mediated modeling approach leaves the workshop participants with a much deeper understanding of the inner workings and assumptions in the model than would normally be the case for the more detailed airshed models typically used in formulating state implementation plans and permitting new facilities.       

## Reach those making decisions and setting policy affecting CO2 emissions and air quality
Municipal planners
State air quality division
Developers
Air quality advocacy groups
Health department
Energy corporations

**Reach those making decisions and setting policy affecting CO****2**** emissions and air quality**
**Municipal planners**
**State air quality division**
**Developers**
**Air quality advocacy groups**
**Health department**
**Energy corporations**

**Improve understanding of linkages, feedbacks and delays associated with airshed processes and social institutions**

**Increase awareness of ways that CO****2**** emissions can be minimized as air pollutant emissions are minimized**

**Notes:**
We invited  120 people to a 5-workshop series who directly, or indirectly, work with or make decisions that affect urban CO2 and air pollutant emissions.   Planners and developers affect housing densities and road networks that can lead to greater, or lesser, vehicle miles traveled.   State agencies are responsible for monitoring emissions, issuing emissions permits, enforcing emissions regulations and notifying the public when unhealthy conditions occur.  Air quality advocacy groups and local health departments are attempting to find ways to maintain and improve public health while emissions are increasing as the metropolitan area grows.  

We wanted each workshop participant to leave the process with a better understanding of the complex interactions between the growing numbers of people, jobs and vehicles that produce CO2 and air pollutant emissions in ways that are affected by the personal choices and public policy decisions they are making today and in the future.  

We wanted the participants, who are strongly motivated to minimize air pollutant emissions, to recognize the dual benefits that can be obtained in reducing CO2 emissions if fuel consumption is minimized through increased fuel efficiency – rather than focusing only on capturing or controlling exhaust emissions.  In Utah, the way that CO2 emissions derived from human activity lead to global warming has yet to become an issue of common concern.  Thus, we also wanted to raise awareness of the global warming issue among the workshop participants.          

## Urban System

**Urban System**

**Urban Airshed System**

**Notes:**
NOTE:  THIS DIAGRAM IS BEST VIEWED BY UNFOLDING IN THE SLIDESHOW MODE
This figure attempts to capture, in very simplified form, the complex interacting set of urban processes that comprise the Srban System as it is surrounded by both the Urban Airshed and the Global Atmosphere. 

The urban airshed system shown here is a generic view of any urban airshed where the urban system (imagine all the activities that go on in a city) is surrounded by the local volume of air that forms its airshed and is, in turn, surrounded by the global atmosphere.  Human activity in the city leads to changes in the local weather patterns that create an urban climate the differs from what would have been found before the city was established.   Meanwhile, CO2 and other urban greenhouse emissions affect the global climate that, in turn, influences the local urban climate.  For example, urban heat islands created around mega-cities reflect a change in urban climate caused by energy use in the city and the greater absorption of solar radiation by dark roofs and pavement.  At the same time, greenhouse gas emissions by metropolitan areas lead to global warming that contributes to further warming of the urban climate.  

In a rapidly growing city, land is developed (often with low density housing) around the outer edge of the city then roads must be built to service the new communities.   As the roads are constructed, the reduced traffic congestion attracts more people to new developments located progressively further away from the city center.  This is termed an urban sprawl feedback process because the growth of more roads attracts more people to the urban periphery which in turn leads to more roads in an attempt to reduce traffic congestion while also attracting more development at the urban periphery.   As this process unfolds, growing numbers of people and jobs lead to more homes, businesses and vehicles that burn fossil fuels for heating, electricity and transportation.  The resulting air pollutant emissions affect human health and local urban climate while CO2 emissions lead to global warming.   It is important to note that, although warmer global and urban climate may lead to reduced need for wintertime heating, greater electricity production will be required to meet the increased demand for summertime cooling through air conditioning.  The urban forest grows with the growth of the city and provides shade that helps cool the city while sequestering CO2 and emitting volatile organic carbons.  

The system shown here represents a complex, interacting set of urban processes that are best viewed as they operate together so that an optimal mix of policies can be developed for managing the urban airsheds of the future.  Focusing only on one part of the system (e.g., capturing or treating exhaust emissions which have little impact on CO2 emissions) may leave other benefits unrealized (e.g., reducing fuel consumption by increased energy/fuel efficiency reduces both air quality and CO2 emissions).       

## Mediated Modeling

**Collaborative team learning experience**
**All are involved in system model building**
**Mental models are made explicit, examined and tested**
**Model helps confront “beliefs” and “facts”**
**Shared level of understanding**
**Broad and deep consensus**
**Facilitated by user-friendly system modeling language (e.g., *****STELLA*****®****)**

***van***** *****den Belt (2004)***

**Notes:**
van den Belt (2004) in her book titled “Mediated Modeling” outlines the principles of this approach and provides a series of case histories where the approach has been applied to a variety of issues.  A principal goal is to engage non-modelers in an exercise where their own mental models – as modified through shared learning in the group workshop context – are incorporated in a relatively simple model of a complex system of shared concern (in this case, the urban airshed system).  By requiring all aspects of the system to be inspected from various perspectives, the participants must distinguish between their own beliefs; and facts brought to the table by the research team and the other participants.  Using a user-friendly system modeling language enables the participants to articulate, in story-form, they way they think the system operates while the research team concurrently develops a numerical model that fits the views of the group as a whole.  Without a rigorous inspection of all aspects of the system, it is impossible to meaningfully compare the possible outcomes of alternative choices and policies.   Developing a numerical model provides a quantitative framework for making comparisons and establishing what aspects of the system must be considered in order to optimize management plans.   Because the participants are directly involved in the model development process, they are keenly aware of the assumptions and limitations inherent in the model and are able to keep them in mind as they review the model results.       

## Scoping Model as preliminary to more rigorous prediction

Accessible to Stakeholders & Decision-makers

Enable Exploration of Mental Models

Build Consensus & Understanding

Recognize System Behavior as Sum of Component Behaviors 

**Accessible to Stakeholders & Decision-makers**
**Enable Exploration of Mental Models**
**Build Consensus & Understanding**
**Recognize System Behavior as Sum of Component Behaviors **

**System Dynamics****Modeling Goals**

**Notes:**
System dynamics modeling provides a vehicle for reconnaissance (or scoping) explorations of a system before developing more rigorous predictive models.  In many cases, however, more rigorous models do not exist and will likely not exist for some time to come.  Yet, decisions must be made in the interim.  System dynamicists often talk about taking a ‘high-level’ or ’30,000 feet elevation’ view of a system to indicate that not all details of the real system will be represented in the model – but, all the key aspects of the real system will be represented. Imagine the view of a metropolitan area that you have when you fly overhead at high altitude.   You see the character of buildings and roads within the boundaries of the urban-affected area, but you don’t see detailed patterns of vehicle traffic, people or characteristics of individual homes and businesses.  Thus, the goal is to lump, or aggregate, the effects of the details into a numerical model that enables one to compare how different urban growth futures might affect both urban emissions and other factors of interest and concern to future residents.  Representing the mental models of each workshop participant in a model aids in building consensus and shared understanding that would not otherwise be possible in the absence of a clear target (the model) for discussion.  Using this approach requires only that participants come to the table with their own mental models and a willingness to revise those models through a shared learning process as the numerical model is constructed.  Formal modeling skills and background are not required.  A key tenet of the system dynamics approach is to recognize that changes in one part of the system may cause unexpected changes in other parts of the system that can only be discovered by enabling all parts of the system to interact together.  When sub-components of the system are viewed in isolation, their relationship to other parts of the system are not considered.         

## Utah’s Salt Lake Valley

**Notes:**
The Salt Lake Valley is the core of Utah’s principal metropolitan area; ‘the Wasatch Front’.  Even though Utah has only 2.3 million residents it is one of the most urbanized states in the nation because most (about 2 million) of Utah’s residents live within the 75-mile long region extending along the mountain front both north and south of the Salt Lake Valley.  

## Salt Lake Valley Airshed

**Notes:**
The Salt Lake Valley is bounded on three sides by mountains that lead to natural wintertime inversions and summertime weather conditions that trap air quality pollutants in the valley.   As a consequence, the Salt Lake Valley can have some of the worst, short-term, air quality conditions in the nation.  

## Wintertime Inversion

*Mature cloud-topped cold pool from Little Cottonwood Canyon*

- Photo: Craig Clements

**Notes:**
This is a high-elevation view of the top of a wintertime inversion layer that fills about 1000 feet of the Salt Lake Valley with fog.  Emissions generated by the city are trapped in the fog layer leading to increasing concentrations of air pollutants and CO2 as the number of days increases during each inversion period.     

***By: Rich Peterson***

**Winter**

**Notes:**
Fog that forms during wintertime inversions obscures the surrounding mountains.  Meanwhile, photochemical smog that forms during warm summer days can be trapped in the valley and obscure the surrounding mountains. 

## Project Team

**Project Team**

***Departments of:***
***Communication***
***Psychology***
***Family & Preventative Medicine***
***Architecture+Planning***
***Biology***
***Meteorology***
***Chemical & Fuels Engineering***
***Mechanical Engineering***

**Notes:**
The project team includes both university researchers and community partners across a broad range of disciplines (from education, advocacy, psychology and communication to science, urban planning and engineering).  This breadth of perspective led to a conducive environment for shared learning, collection of data, and brainstorming.    

## Salt Lake Valley

**Salt Lake Valley**

***From G.F. Rogers (1982)***

**Notes:**
In 1865, the Salt Lake Valley had been settled for almost 20 years and the population was growing rapidly in an arid sage-brush grassland with few trees; except those found along creeks and rivers.   140 years later, the Valley contains almost 1 million people and an extensive urban forest supported by irrigation water.  During this century-and a-half history, fossil fuel burning in the Salt Lake Valley and elsewhere has caused a rapid increase the CO2 concentrations that are contributing to global warming.  During the same period, the Salt Lake Valley has seen several cycles of worsening, then improving, air quality.   Growth in coal-fired heating and industrial emissions of the early 1900’s often filled the valley with smog which declined as coal was replaced by natural gas.  Meanwhile, growth in transportation and industrial emissions during the mid 1900’s that led to non-compliance for particulate and ozone pollutants that was mitigated by improving exhaust and smokestack technologies in the 1980’s and 1990’s.   Current trends to more stringent air quality standards, however, may lead the valley back into future periods of non-attainment.      

## Past and Future Airshed Emissions?

**2060**

**Time**

**Population**
**(millions)**

**Past and Future Airshed Emissions?**

***Now***

**4.5**

**0**

**Notes:**
This graph shows 200 years of historic and projected population growth (blue curve) in the urbanized metro region of Utah.  Rapid population growth supported by a high natural fertility and in-migration will lead to a tripling of population by 2060.  Over the same period, per capita CO2 emissions (black curve) have grown in the past (particularly during the mid-to-late 1900s and are expected to grow in the future.   Although an optimistic lessening in the projected growth of CO2 emissions is shown in the future section of the graph, the solid red curve shows a rapid increase in total future emissions from the urban area.  Even if policy and technology changes enable us to stabilize per capita CO2 emissions at their current level (Alternative Scenario 1), rapid population growth still yields total future CO2 emissions in 2060 more than triple those of the present time.          

## Past and Future Airshed Emissions?

**2060**

**Time**

**Population**
**(millions)**

**Past and Future Airshed Emissions?**

***Now***

**4.5**

**0**

**Notes:**
If we want to significantly reduce the total emissions rate (from solid red line to dashed red line) to only double those of the present time then we must reduce per capita CO2 emissions to 1970 levels (Alternative Scenario 2 is achieved by reducing per capita emissions along the dashed black line rather than following the solid black line). 

A key question is .... “How can such significant CO2 emissions reductions be accomplished”?  What mix of urban planning strategies, technology fixes, public policies and personal choice can help yield per capita emissions rates that are less than those of present – while undergoing rapid population growth?

Using a mediated modeling approach to build a numerical model of the urban airshed system enables community members and the research team to collectively identify and explore alternative futures and their consequences that best reflect the interests and concerns of the community.     

## Urban Airshed Processes

**Urban Airshed Processes**

**Notes:**
Urban airshed processes included in the mediated modeling discussions involve the factors that influence CO2 emissions during urbanization, the role of the urban forest in the carbon cycle and transport of CO2 in the urban airshed.   We are monitoring gas fluxes and meteorological conditions to provide improved observational and theoretical support for the ideas contained in the urban airshed system dynamics model.   

## 5-Workshop Series

**Systems Thinking & Collaborative Discussion**
**Historic Timeline of emissions-related issues 1850 to 2004**
**Overview of urban growth, CO****2**** emissions, air quality **
**“****Ice Cream Game” an experiential systems thinking activity**

**2.****	****Build Qualitative Model Maps**

**3.****	****Build Quantitative Model Components**

**4.****	****Review and Explore Urban Airshed System Model**

**Review and Explore *****Revised *****Urban Airshed Model (optional) **

**Notes:**
15 to 35 (attendence varied from workshop to workshop) of the original 50 people who were invited attended each of 4, ~6-hour mediated modeling workshops.  The 5th workshop was optional, but still attracted 12 people to work with the model and interface that had been modified in response to their comments during the 4th, model testing workshop.   The workshop sequence was designed to develop the system thinking skills needed to contribute to model development while also eliciting the stories and mental models that participants use to explain their understanding of emissions in the Salt Lake Valley urban airshed.  Concurrently, the core modeling team constructed and revised the model to fit the overarching stories and mental models that evolved through group discussions.       

**Workshop Participants****Qualitative Model Map**

**Notes:**
This is the qualitative model map created on butcher paper by one of three participant teams during the 3rd workshop.  In this map, population plays a key role in the future of the urban airshed and is connected to a variety of concerns/factors; including climate change, household activity, work, energy and recreation.   Not shown here, the other two maps have a number of features in common; however, they feature air quality as the key feature of the map.   

## Use Participant Input in Urban Ecosystem Model

**Notes:**
This is a modeler’s representation of the quantitative model map distilled by the modeling team from the qualitative model maps produced by the workshop participants.   Here the principal features of the participant maps are presented in an organized form that reflects the connections that must be made between different model components.   The arrows represent how one factor affects the next.  For example, in this system we assume that climate affects natural gas use for wintertime heating, but not vice versa.   Meanwhile urban growth and urban form (high-density vs low-density housing) affect the transportation sector through the need for transportation strategies (highways vs transit) that in turn affects the form of the urban area.    

## Preliminary (programmers) Interface

**Notes:**
This preliminary model interface identifies the various aspects of the model that can be adjusted by the user.  Moving more deeply into the interface would reveal sets of graphs and ‘sliders’ that can be easily modified by the user to implement different scenario alternatives.  This model was presented at the 4th workshop where the participants worked in groups to explore how well the model represented their own mental models and to assess ways that emissions might be reduced.   

## Interface was too complicated for easy exploration

Wanted a simpler interface to explain concepts to management

System is more complex than realized

Air quality is of greatest concern

**Wanted a simpler interface to explain concepts to management**
**System is more complex than realized**
**Air quality is of greatest concern**

**Participant Feedback**

**Notes:**
At the end of the 4th workshop, feedback from the participants was collected in order to further refine the model and its interface.  

## Revised Interface

**Notes:**
This is the much-revised interface presented at the 5th workshop where the participants continued to explore how different policy options might affect CO2 and air pollutant emissions.   At this high-level interface several scenarios were provided with pre-mixed policies that seemed to be those that might be of greatest interest.  At the same time, however, the earlier ‘programmers’ interface was retained to enable the users to ‘drill down’ for more detailed adjustment of their simulations.   

One key strategy is to dampen the sprawl feedback process in order to reduce emissions by reducing vehicle miles traveled through higher-density urban planning strategies.  Other, technology fix strategies involve improving fuel efficiency (e.g., heating furnaces, electricity production, vehicle gas mileage, etc.) and/or reducing air pollutant emissions through improved air pollution controls.  Finally, we wanted the participants to gain a better understanding of how uncertainty in estimating the future might affect the outcomes of their simulations.   Thus, we provide opportunities to explore plausible future variations in: 1) population growth rates, 2) delays that might occur between the time that decisions are made and the time when policies are implemented, and  3) climate change futures and their impact on fossil fuel burning due to changes in future urban temperatures.       

## Revised Interface

**Revised Interface**

**Notes:**
The graphing window of the interface provides opportunities to: 1) compare results obtained for one parameter (e.g., CO2 emissions or NOx emissions) for sequential runs, and 2) compare a suite of parameters outcomes for a single simulation (e.g., population, household density, daily vehicle miles traveled, CO2 emissions and NOx emissions).   

## Reference Case

**Reference Case**

**1980**

**1990**

**2000**

**2010**

**2020**

**2030**

**Year**

**Population**** **

**Notes:**
This reference case shows a 20-year year history and 30-year future projection of a suite of urban growth characteristics.   Note that urban land area and road land miles are rising more rapidly than population because continued growth in this ‘business-as-usual’ case is assumed to involve mostly low-density housing development at the periphery of the urban area rather than infilling high-density development. 

## Damping Sprawl Feedback

**Stabilize previous decline in population density**** by building all new development at 5.5 people per acre**

**Start in 2005**

**Reduce traffic congestion ****by accelerating roadbuilding from a 5-year to a 3-year cycle and increasing road capacity by 10% through intelligent traffic systems**

**Reduce vehicle miles traveled**** ****by single occupancy vehicles**** by increasing transit use from 3% to 20% of vehicle miles traveled**

**Notes:**
A scenario where the sprawl feedback process is dampened involves setting the model to run with the input changes shown above.  The simulation result is compared against the results of the previous reference case.  

## Damping Sprawl Feedback(dashed lines are Reference Case)

**1980**

**Urban Land Area**

**Road Lane Miles**

**1990**

**2000**

**2010**

**2020**

**2030**

**0**

**6**

**Year**

**people/acre**

**Population Density**** **

**Population**** **

**millions of people**

**0**

**2.5**

**millions of acres**

**0**

**0.8**

**1000s of miles**

**0**

**20**

***Implement***
***2005***

***Damping Sprawl Feedback******(dashed lines are Reference Case)***

**Notes:**
The dashed lines represent the reference ‘business-as-usual’ scenario while the solid lines show the outcome of the sprawl dampening scenario.   Holding the population density of all new development to 5.5 people per acre starting in 2005 leads to reduced urban land area while increased transit use combined with reduced land area leads to much reduced road lane miles.  The reduced road lane miles leads to the reduced vehicle miles travelled and lower fuel consumption that yield slower growth in CO2 and air pollutant emissions.    

## Emissions Implications (NOx)

**1980**

**1990**

**2000**

**2010**

**2020**

**2030**

**0**

**1**

**2**

**3**

**Year**

***Implement***

**NOx Emissions Ratio**
**(relative to 2000)**

**Notes:**
Implementing three alternative scenarios reveals how the system model can illustrate which policies might have greater, or lesser, impact on air quality emissions outcomes.  Here an emissions ratio is used to more readily illustrate how future emissions might grow relative to emissions rates of 2000.  It is reasonable to assume that a doubling of emissions will lead to worse air quality, however, it is difficult to anticipate the actual NOx concentrations that might be encountered.     

## Emissions Implications (CO2)

**1980**

**1990**

**2000**

**2010**

**2020**

**2030**

**0**

**20**

**Year**

***Implement***

**Reference**

**Total CO****2**** Emissions**
**(million tons per year)**

**Notes:**
Implementing three alternative scenarios reveals how the system model can illustrate which policies might have greater, or lesser, impact on air quality emissions outcomes.  Here the absolute rate of CO2 emissions is plotted to reflect current concern regarding the tons per year of CO2 that will be contributed to the global atmosphere.  

## Workshop Participants

**Weber Sustainability Consulting**
**TreeUtah**
**Wasatch Clean Air Coalition**
**Sierra Club**
**Future Moves Coalition**
**League of Women Voters**

**Envision Utah**
**Mountainland Assoc’n. Govt’s.**
**Wasatch Front Regional Council**

**Hill Air Force Base Enviro. Compl.**
**CH****2****M-Hill**

**Utah Division of Air Quality**
**Utah Governor’s Science Advisor**
**Utah Dept. of Transportation**
**Utah Transit Authority**

**Salt Lake Valley Health Dept.**
**Salt Lake County Public Works**

**Salt Lake Clean Cities Coalition**
**Salt Lake City Corporation**
**Draper City Planning**
**Sandy City Community Development**
**City of Murray Community Development**

**US Magnesium Corp**
**Air & Waste Management Assoc.**
**Kennecott Utah Copper**
**Murray City Power**
**Chevron Products Co.**

**Notes:**
We want to thank this diverse group workshop participants for taking time from their busy lives to work with us on this project.  The research team learned much from their contribution and the final product is much more likely to be useful to others interested in managing emissions in urban airsheds.    

## Expanded view of urban airshed system
Global context
Historical airshed context
Complexity of airshed system
Systems perspective
Trade-offs and alternatives
Good shared learning opportunity 

**Global context**
**Historical airshed context**
**Complexity of airshed system**
**Systems perspective**
**Trade-offs and alternatives**
**Good shared learning opportunity**

**Participant Experience**

**Urban Airshed**

**Urban System**

**Global Atmosphere**

**Sprawl Feedback**

**Urban Climate**

**Global Climate **

**Notes:**
Although we are currently processing more formal evaluations, the above comments reflect the idea that the participants found the workshop experience to be worthwhile and positive.  We are convinced that our principal goals for the community engagement process were met.     

## Summary

**System dynamics models for “scoping” help to engage participants in mediated modeling process**
**“****Scoping” models set scene before engaging with more rigorous, predictive models**
**Participants take ownership of model**
**Participants introduced to CO****2**** emissions issues through their interest in air quality**
**Participants gained appreciation of complexity, feedbacks, uncertainty and delays in urban airshed systems**
**Project team has better understanding of participant interests**

**Notes:**
The mediated modeling approach described here can be repeated in any urban setting using the foundation model structure developed for the Salt Lake Valley.  Data and insight collected from a similar workshop series would lead to a model tuned to the new urban context while leading the workshop participants through a shared learning experience similar to that described here.  Even if not modified, the Salt Lake Valley model should provide a good example for initiating valuable shared learning experiences; even without a model  specifically tuned to the participants’ own urban context. 

## Utah Trace-gas Emissions Study (UTES)
[Salt Lake Valley Airshed Project]

**[Salt Lake Valley Airshed Project]**

***Funded by: NSF Biocomplexity in the Environment Program***

***http://slvairshed.utah.edu***

**Notes:**
This project was funded by the National Science Foundation (Award ATM 0215768) for research in biocomplexity science aimed at understanding CO2, H2O and Volatile Organic Carbon cycling in an urban airshed.  Working with the workshop participants and our community partners led us to consider air quality issues to a much greater degree than originally intended.  Please don’t hesitate to contact me with comments or questions.  forster@arch.utah.edu