Air Quality Assessment and Forecast System: 

Near-Term Opportunity Plan 

PRE-PUBLICATION September 2006 

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TABLE OF CONTENTS 

1. PREFACE ................................................................................................................................................4

2. THE OPPORTUNITY................................................................................................................................4

2.1 GENERAL BENEFITS..............................................................................................................................5

2.2 WHY NOW? ..........................................................................................................................................6

2.3 PRODUCTS/SERVICES ...........................................................................................................................6

3. INTEGRATED OBSERVED-MODELED AIR QUALITY FIELDS ............................................................7

3.1 NEED AND RATIONALE ..........................................................................................................................7

3.2 GENERAL DESCRIPTION ........................................................................................................................7

3.3 BENEFITS .............................................................................................................................................7

3.4 PRIMARY EXISTING COMPONENTS .........................................................................................................8

3.5 PRIMARY PLANNED COMPONENTS.........................................................................................................8

3.6 GAPS ...................................................................................................................................................9

4. SYSTEMS FOR UTILIZING OBSERVATIONS TO IMPROVE AIR QUALITY FORECASTS ................9

4.1 NEED AND RATIONALE ..........................................................................................................................9

4.2 GENERAL DESCRIPTION ........................................................................................................................9

4.3 BENEFITS ...........................................................................................................................................10

4.4 PRIMARY COMPONENTS ......................................................................................................................10

5. ASSESSMENTS OF KEY AIR QUALITY PROCESSES.......................................................................11

5.1 NEED AND RATIONALE ........................................................................................................................11

5.2 GENERAL DESCRIPTION ......................................................................................................................11

5.3 BENEFITS ...........................................................................................................................................11

5.4 PRIMARY EXISTING SYSTEMS ..............................................................................................................12

5.5 PRIMARY ADDITIONAL PLANNED SYSTEMS...........................................................................................12

5.6 GAPS .................................................................................................................................................12

6. IMPROVED EMISSIONS INVENTORIES ..............................................................................................12

6.1 NEED AND RATIONALE ........................................................................................................................12

6.2 GENERAL DESCRIPTION ......................................................................................................................12

6.3 BENEFITS ...........................................................................................................................................13

6.4 EXISTING COMPONENTS ......................................................................................................................13

6.5 PLANNED COMPONENTS......................................................................................................................13

6.6 GAPS .................................................................................................................................................13

7. IMPROVED INTERNATIONAL TRANSPORT ASSESSMENTS...........................................................14

7.1 NEED AND RATIONALE ........................................................................................................................14

7.2 GENERAL DESCRIPTION ......................................................................................................................14

7.3 BENEFITS ...........................................................................................................................................15

7.4 PRIMARY EXISTING COMPONENTS .......................................................................................................15

7.5 PRIMARY PLANNED COMPONENTS.......................................................................................................15

7.6 GAPS .................................................................................................................................................15

8. RELATED ISSUE ...................................................................................................................................15

9. PROJECT EXTENSIONS AND LONGER-TERM CONSIDERATIONS ................................................16

APPENDIX A: SATELLITE SENSORS......................................................................................................17

APPENDIX B: EXAMPLE OF INTEGRATED USE OF FIELD STUDY DATA AND MODELS TO 

ESTIMATE EMISSIONS.............................................................................................................................18
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1. PREFACE 

This plan was prepared by the U.S. Group on Earth Observations1 and is one of six near-term opportunities identified in 

the Strategic Plan for the U.S. Integrated Earth Observation System.2

 Near-term opportunities in this context are identifying 

observing systems or integration of components that meet high-priority societal needs, and making improvements to 

update existing systems that can be completed within 5 years and have tangible, measurable results. The near-term 

opportunities are: 

 Improved Observations for Disaster Warnings (published September 2006); 

 Global Land Observation System (in development); 

 Sea Level Observation System (in development); 

 National Integrated Drought Information System (published September 2006); 

 Air Quality Assessment and Forecast System (published September 2006); and 

 Architecture and Data Management (in development). 

2. THE OPPORTUNITY 

In spite of substantial progress in air pollution abatement over the last two decades, air quality remains a priority public 

health concern. To address this issue, the IEOS Strategic Plan proposes an Air Quality Assessment and Forecast System 

that will improve data collection and provide more accurate and timely forecasts. This system will help the public avoid 

harmful exposures to air pollution and help decision-makers better manage air quality. These tools will improve 

environmental resource management by advancing the primary objectives articulated in the U.S. Group on Earth 

Observations (USGEO) strategic plan: 

 Improve the ability to forecast air quality across large parts of the country (and in other parts of the world) for 

which forecasts are not currently available; 

 Provide better information about emissions and transport mechanisms on regional to international scales; and 

 Provide important information to help the public avoid harmful exposures and to help air quality management 

better reduce and respond to air pollution episodes over the short and long terms. 

The Global Earth Observation System of Systems (GEOSS) offers the potential to focus the complementary strengths 

of a variety of observation and modeling platforms toward advancing air quality assessment and forecasting capabilities. 

This plan integrates existing and planned efforts from a number of agencies and builds on recommendations from 

multiple sources, including The Changing Atmosphere: An Integrated Global Atmospheric Chemistry Observation Theme for the 

IGOS Partnership and a 2004 joint EPA-NASA-NOAA Air Quality workshop. 

Five well-integrated products/services are described below that collectively advance air quality assessment and 

forecasting: 

 Integrated Observed-Modeled Air Quality Fields providing 

enhanced air quality characterizations through the fusion of air 

observation systems and predictive air quality models for air 

quality management, forecasting, science, and health effects 

research; 

 Systems for Utilizing Observations to Improve Air Quality 

Forecasts to enable delivery of accurate, high-resolution 

national forecast guidance for ozone and particulate matter; 

 Assessments of Key Air Quality Processes at hemispheric, 

national, regional, and local levels to support the development of 

well-informed air quality policies, plans, models, and 

applications; 

 Improved National Emissions Inventories to serve Federal, 

State, and local officials for policies, plans, and forecasts; and 

 Improved International Transport Assessments integrating 

global observations and models to inform international 

negotiations and decisions about transnational pollutant flows.

 

1 The United States Group on Earth Observations (USGEO) was established in March 2005 as a standing subcommittee of the National Science and 

Technology Council Committee on Environment and Natural Resources. 

2 http://usgeo.gov/docs/EOCStrategic_Plan.pdf 

Particulate matter smaller than 2.5 

micrometers in aerodynamic diameter 

(PM2.5) comes from direct (or primary) 

particle emissions and through 

reactions (or phase transformations) of 

nitrogen oxides, sulfur oxides, volatile 

organic compounds (VOCs) and 

semivolatile organic compounds 

(SVOCs). 

Ozone is a secondarily formed 

pollutant. Ozone is not directly emitted 

from a source; it is generated through 

reactions of precursors, which include 

VOCs and nitrogen oxides (NOx), in 

the presence of sunlight. 
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This near-term opportunity should focus on ozone and particulate matter smaller than 2.5 micrometers in aerodynamic 

diameter (PM2.5). These two ambient air pollutants are associated with a variety of respiratory and cardiovascular effects 

at current levels in some areas. Unless stated otherwise, the phrase “air quality” in this plan refers to ozone and PM2.5 

and their precursors. Outdoor air quality is the primary subject with recognition of its relation to indoor and personal 

exposures. 

This plan leverages existing observing and modeling systems, which are generally more mature for ozone than they are 

for PM2.5. While this near term plan focuses on ozone and PM2.5, the steps taken to address these pollutants establish the 

framework for managing information about hazardous air pollutants (HAPs) and persistent bioaccumulative toxins 

(PBTs), such as mercury. 

Ozone and PM2.5 share many common local and regional sources and precursors, including mobile and industrial sources 

and electric generating units, which emit volatile organic compounds (VOCs) and nitrogen and sulfur oxides, which are 

precursor gases to secondarily formed PM2.5 and ozone. Significant contributions to ozone precursors and PM also come 

from other source categories, including biogenic emissions from living trees and combustion through wildfires and 

controlled burns. 

2.1 GENERAL BENEFITS

America’s air quality has improved substantially over the last few decades. Since 1970, the aggregate total emissions for 

six pollutants [Carbon Monoxide (CO), Nitrogen Oxides (NOx), Sulfur Dioxide (SO2), Particulate Matter (PM), Volatile 

Organic Compounds (VOCs) and Lead (Pb)] have been cut from 301.5 million tons per year to 138.6 million tons per 

year, a decrease of 54 percent. Annual emissions statistics for these six pollutants are considered major indicators of the 

quality of the Nation’s air because of their impact on human health and the existence of their long-standing national 

standards. From 2001-2004, total emissions dropped 21 million tons, a 13.5 percent reduction. Total emissions have 

continued to decrease even as the U.S. economy has continued to grow. 

Despite this progress, reducing exposure to air pollutants 

remains a national public health priority. More than 158 

million U.S. residents live in areas that violate health-based 

national ambient air quality standards3. As an example of the 

magnitude of the issue, the U.S. EPA estimates that 

implementation of the Clean Air Interstate Rule designed to 

reduce PM2.5 exposures should result in avoiding 17,000 

premature deaths and 22,000 nonfatal heart attacks annually 

by 2015.4 Air quality also has a significant economic impact, as 

illustrated in the sidebar on the right. 

An IEOS could provide significant value to efforts to protect 

public health in the context of an evolving international 

economic and environmental system. As Figure 1 illustrates, 

the air quality assessment forecast systems benefit the Nation 

in two major ways. 

1. Improved information about air quality enables 

policy-makers and environmental managers to 

develop and evaluate more effective policies and 

plans. Optimization of risk-management strategies 

improves our tools for assuring public health and 

well-being, protecting critical ecosystems, and 

maintaining a vital economy. 

 

3 U.S. Environmental Protection Agency, “8-Hour Ozone Nonattainment Areas: As of March 2, 2006,” 

http://www.epa.gov/oar/oaqps/greenbk/gntc.html 4 U.S. Environmental Protection Agency, “Regulatory Impact Analysis for the Final Clean Air Interstate Rule,” EPA-452/R-05-002, 2005, 

http://www.epa.gov/cair/pdfs/finaltech08.pdf

Examples of Economic Impacts

Decisions based on air quality information have a 

significant economic impact on the Nation. For 

instance, using information provided by the U.S. 

Environmental Protection Agency (EPA) and 

several key assumptions, the Office of 

Management and Budget (OMB) reported that 

EPA air regulations in the period 1993-2003 

produced an estimated annual benefit to the 

Nation of $35-166 billion, and yet had an 

estimated annual cost of $16-18 billion4. 

Also, an air quality assessment in Houston in 

2000 concluded that unanticipated emissions of 

highly reactive volatile organic compounds were 

a key contributor to poor air quality in that 

region. That information allowed the State of 

Texas to develop a more focused plan for 

achieving good air quality, which estimates 

indicate will save the state more than $9 billion 

and 64,000 jobs by 20104 while still protecting 

public health.
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2. Enhanced air quality forecasts allow communities and individuals to more effectively mitigate 

pollution exposures. Mitigating exposures of all types—outdoors, indoors, and personal—when air quality is 

poor is especially important for individuals at risk for adverse health effects, including those with asthma, 

certain allergies, and cardiovascular or pulmonary diseases. 

The specific benefits of each product/service are described below. These products/services also provide secondary 

benefits to most GEOSS societal benefit areas including improvements to weather forecasting and understanding 

climate change, more effective management of agriculture and energy sectors, and better protection of oceans and 

ecosystems. 

Figure 1: Benefits of the air quality 

assessment and forecast system 

2.2 WHY NOW? 

Many of the more straightforward air quality problems have already been addressed through regulation enforcement, 

including ambient air standards for lead, nitrogen dioxide, sulfur dioxide, and carbon monoxide. However, more 

complicated issues require sophisticated data collection and integration, such as understanding the toxic components of 

particulate matter and long-range transport. Fortunately, scientific and technical advancements, including new observing 

and information technologies, and insights into atmospheric processes, have created opportunities to address these more 

difficult issues at the applications level. Many of these technologies and processes have been developed for research and 

applications other than air quality (e.g., status of stratospheric ozone, climate change, weather forecasting). An integrated 

earth observation system provides an opportunity to leverage these relevant and substantial resources for improving the 

effectiveness of the Nation’s planning and response systems. 

2.3 PRODUCTS/SERVICES

Figure 2 illustrates the mutually supporting nature of some of the plan’s products/services. 

Figure 2: Some of this plan’s products/services support other of the products/services. In this diagram, the product/service that is the source of an 

arrow supports the destination of the arrow. 

 

1. Integrated Air 

Quality Fields

2. Utilize Observations 

for Forecasts

3. AQ Process 

Assessments

5. International 

Transport Assessments

4. Emissions 

Inventories

AQ Assessment and

Forecast System

Improved Environmental

Management

Improved AQ Forecasts

Reduced Mortality &

Morbidity

More Robust Economy

Critical Ecosystems

Protected

Intermediate

Outcomes

Societal

Outcomes
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3. INTEGRATED OBSERVED-MODELED AIR QUALITY FIELDS 

3.1 NEED AND RATIONALE

Air quality characterization provides a key infrastructure component underlying virtually any assessment associated with 

air quality management practices, forecasting, research, and health effects phenomena. Currently, there is an excessive 

reliance on the Nation’s regulatory-based air monitoring networks for forecasting and research. However, the regulatory 

network typically provides point-based surface measurements, which may not be representative of a wide area. 

A more sound, scientifically-rigorous approach would maximize the spatial and temporal resolution of air quality 

characterizations by using all observational and modeling resources to fill in important gaps (space, time, and 

composition) that land-based monitors cannot adequately cover alone. Otherwise, analyses based on air quality data, 

such as judging the effects of regulations or investigating air quality-health relationships, are based on limited 

characterization fields, which cannot adequately account for vast populations and geographic areas, and are hampered by 

limited temporal and compositional (i.e., chemistry and physical properties) structure. Moreover, surface observations 

provide no information about pollutants aloft, which can have a significant impact on surface concentrations. 

Through the fusion of land-based systems, overhead measurements (e.g., satellite total column measurements of ozone 

and aerosols), and results from retrospective simulations, air quality managers can perform more robust assessments 

across broad spaces and timeframes covering realistic representations of populations and their exposures to air pollution. 

An integrated observation-modeling platform offers the possibility of advancing air quality characterizations across 

needed spatial, temporal and compositional dimensions. In addition to supporting air quality assessments, an integrated 

data infrastructure would support all of the other components of this plan. 

3.2 GENERAL DESCRIPTION

The integrated system will be capable of producing gridded and point estimates of concentrations of ozone, PM2.5, and 

precursor species at one-hour time resolution with accompanying information about uncertainties in the estimates. The 

capability will include prospective air quality fields as well as retrospective archived maps contingent on historical data 

availability. Air quality maps will be produced by integrating multiple sources of information. Results will be available in 

multiple forms, including binary fields and contour maps. 

3.3 BENEFITS

The integrated data infrastructure will: 

Support all phases of air quality management. A robust, integrated information base will strengthen the 

development of emission reduction strategies and air program policy. Continued availability of the information will 

drive air quality trends and deposition analyses, which support accountability reviews of existing policies and regulations. 

In turn, those analyses enable policy modifications and facilitate adjustments for unanticipated changes in atmospheric 

conditions. An integrated infrastructure with routine air quality information will allow decision-makers to assess air 

quality across a wide range of geographic areas and determine if mitigation strategies and regulations are accomplishing 

their intended air quality goals. As new policies and regulations are required, the infrastructure will provide baseline 

information. Air quality modeling science will reinforce the ability to adjust to unforeseen events and new scientific 

findings. 

Support population-based studies that explore the relationship between health outcomes and ambient air pollution 

exposures and support national air quality standards development. Such associations in turn will allow for a meaningful 

accountability of our Nation’s air pollution mitigation efforts based on real outcomes and provide a basis for continued 

assessment and revision of our national air quality standards. 

Identify gaps in data and observing systems to maximize information utilization. 
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3.4 PRIMARY EXISTING COMPONENTS 

Land-Based Ambient Point Measurements. Existing land-based ambient air quality point measurement networks 

managed by EPA, the Department of the Interior, states, and local air agencies. 

Deposition Measurements. Deposition of pollutants to land and watersheds is measured by the National Atmospheric 

Deposition Program (NADP) and the Mercury Deposition Network, which are supported by multiple organizations to 

understand long-term trends and to monitor primarily precipitation inputs to ecosystems. 

Air Quality Models. The Community Multiscale Air Quality (CMAQ) Model (EPA-NOAA and non-Federal partners) 

and the chemistry version of the Weather Research and Forecasting model (WRF/CHEM) (NOAA and non-Federal 

partners) provide state-of-the-art air quality simulation capabilities, which could be applied retrospectively to integrate 

observations. 

Tropospheric and Total Column Observations. Satellite systems operated by NASA (Aqua, Terra, Aura), NOAA 

(ozone from polar orbiting satellites and aerosols from geostationary satellites), and foreign entities (ENVISAT) provide 

total column depth observations of ozone, aerosols and other key gaseous species, including CO, CH4, NO2, H2CO, 

SO2, and CHOCHO (glyoxal). 

Vertical Gradient Systems. The measurement of ozone and water vapor by Airbus in-service aircraft observations 

from European airliners. Ozonesondes are launched weekly from several locations in the continental United States 

(NOAA, NASA). NASA, DOE, NOAA, and universities support LiDAR systems that profile aerosols using a vertically 

pointed laser and, at limited locations, ozone. NASA’s Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite 

Observation (CALIPSO) satellite measures vertical profiles of PM around the world. 

Approaches for Integrating Observations. Collaborations between atmospheric scientists and health researchers are 

underway to test the utility of fused, more geographically broad measurements of ambient air quality for analyses of 

morbidity and mortality than is provided by point-based air quality measurements. EPA and the Centers for Disease 

Control and Prevention (CDC) are collaborating to integrate local point data with continuous spatial coverage and 

gradients provided by models. The National Institute of Environmental Health Sciences (NIEHS), NOAA, and EPA are 

exploring the benefits of combining surface and satellite air quality observations for health studies. 

Data Management. Many separate data management systems are used for the observations described above. For 

example, routine surface observations of ozone and PM are available through EPA’s Air Quality System (AQS) and 

AIRNOW, and specialized measurement data from some large field studies are available through NARSTO’s Quality 

Systems Science Center. Current research efforts are beginning to demonstrate service-oriented architecture approaches 

that show promise to allow interoperability between systems with different data formats and access protocols. 

3.5 PRIMARY PLANNED COMPONENTS 

National Core (NCore) is a new ambient air-land-based monitoring structure that ties together several existing air 

monitoring networks and adds nearly 75 new colocated multiple pollutant measurement stations in major cities and key 

background and rural locations. Managed by the EPA, NCore will be phased in from 2006 through 2009 and will 

facilitate the integration of land-based observations with satellite observations and air quality models. 

Following up on successful prototype efforts to compare integrated air quality measures with public health tracking data, 

EPA and CDC are collaborating on an accessible system for state public health agencies. 

Satellite systems coming on line over the next 5 years (see Appendix A): 

 MetOp (2006) will provide ozone profiles; 

 NPP (2008) will provide aerosol column optical thickness measurements; and 

 Glory (2009) will collect data on the chemical, microphysical, and optical properties, and spatial and temporal 

distributions of aerosols. 
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3.6 GAPS

Several core elements are required to create an integrated air quality data system and realize its benefits. These include 

the following: 

 Data management support for accessing, assimilating, archiving, and distributing diverse information sources 

across Federal agencies. This will include the use of common data protocols and linkages to data analysis and 

visualization packages. To build upon existing systems, the integrated data management infrastructure should 

provide a virtual repository where data are actually stored in distributed servers in diverse formats. Data 

publishing, discovery, assistance in access, and services will be provided via a Web-based portal that exploits 

open, standards-based approaches to provide interoperability. This is consistent with the vision of the IEOS 

Architecture and Data Management Working Group whose guidance supports an integrated air quality data 

infrastructure. (EPA). 

 Routine air quality modeling, data interpolation, and observation-model integration that incorporates historical 

data, (EPA). Also, models should be capable of dynamically assimilating previous data. 

 Outreach/capacity building with the air quality and health communities, including regional planning 

organizations, state/local governments, academia, and the private sector. 

Certain strategic investments will yield dramatic increases in the efficiency of leveraging integrated observation systems. 

In priority order: 

 Land-based LiDAR network for characterizing vertical gradients of PM and ozone. Satellite data typically 

provide total column depth measurements that lack the necessary detail to determine, for instance, if a PM 

plume actually reaches the ground. Vertical profiling provides the binding ingredient that allows for the most 

effective integration of remote satellite observations with land-based point measurements. In addition, vertical 

profile information provides key insights into physical process understanding and supports evaluation of air 

quality models. A logical starting point for such a network is the Regional East Atmospheric LiDAR Mesonet 

(REALM). REALM is designed to monitor air quality in the vertical direction from multiple locations on the 

East Coast, but remains unfunded at this point. Initial proof-of-concept studies integrating LiDAR, land-based 

point measurements, and satellite-based observations have yielded successful results. 

 Based on lessons learned and benefits demonstrated by NASA research satellites, operational NO2 column 

measurements could be provided by GOME-2 on the European MetOp satellite. NO2 is an indicator of 

combustion and is an important precursor to ozone and particulate matter. To take advantage of this 

opportunity, development of a retrieval algorithm (based on prior work) and operational products would be 

required (NOAA). 

4. SYSTEMS FOR UTILIZING OBSERVATIONS TO IMPROVE AIR QUALITY 

FORECASTS 

4.1 NEED AND RATIONALE

A number of state and local governments issue air quality forecasts and alerts to try to reduce the severity of poor air 

quality episodes (e.g., by urging residents to decrease driving) and to help the public protect themselves (e.g., by staying 

indoors or reducing physical activity). Air quality forecasters routinely use surface air quality observations to inform their 

forecasts; however, this approach does not adequately incorporate transport and other processes affecting air quality. 

NOAA’s operational air quality forecast guidance provides prognostic information for forecasters, but the current 

operational model does not incorporate air quality observations. The air quality forecast system uses an emissions 

inventory and the chemical state of the atmosphere predicted by the last simulation as the chemical basis for predictions, 

which might not accurately represent the actual state of the atmosphere. Errors in one day’s forecasts of the chemical 

state of the atmosphere may be propagated to a second day’s forecasts. 

4.2 GENERAL DESCRIPTION

New systems that rely on observations to improve air quality forecasts would improve accuracy and better enable 

individuals to avoid exposures when air quality is poor. Three paths are envisioned to improve state and local air quality 

forecasts. The first path is to fuse real-time observations with surface data. The second path is to develop an operational 

data assimilation capability for air quality models (note: meteorological models routinely assimilate data, which 

significantly improves accuracy). The third path is to determine what enhancements to the air quality observing system 

would best enhance forecast accuracy. 
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4.3 BENEFITS

This work will improve the information available to air quality forecasters and their ability to utilize that information, 

leading to better air quality forecasts, more targeted mitigation efforts, and reduced exposures to poor air quality. 

4.4 PRIMARY COMPONENTS

Data Fusion and Visualization to Support Forecasting

While an integrated air quality data infrastructure will provide access to diverse observations, the fusion of selected data 

sets is required to provide effective support for forecasters, who must make decisions in a very limited time. 

Primary Existing Systems include: 

 Hazard Mapping System (HMS) (NOAA). The HMS provides subjectively integrated information about 

fire locations from multiple sources, including Geostationary Operational Environmental Satellite (GOES), 

Polar Orbiting Environmental Satellites (POES), Terra and Aqua, and the Defense Meteorological Satellite 

Program. The HMS also uses images from GOES to identify smoke plumes. 

 Infusing satellite Data into Environmental Applications (IDEA) (NASA, EPA, and NOAA). The IDEA 

project provides a prototype integrated view of fire locations derived from: GOES; aerosol optical depth 

(AOD) and cloud optical thickness from NASA’s Terra satellite; surface PM2.5 observations from the EPA’s 

AIRNOW system (which collects observations from international, Federal, state, and local partners); and 

850mb winds from NOAA’s North American Model. 

 BlueSky/RAINS (USDA and EPA). The locations of forest fire smoke plumes are predicted and displayed 

on a GIS-like Web site. 

Additional Planned Systems include: 

 The integrated IDEA prototype will become operational at NOAA. 

Utilization of Real-Time Air Quality Observations in Regional Air Quality Models

Observations offer great potential for improving air quality forecasts by enabling air quality models to more accurately 

represent actual conditions. Observations can be used in several ways. Information about episodic and unpredictable 

emissions sources, such as biomass burning, can fill gaps in emissions inputs into air quality models. Observations of 

ambient conditions along a model’s boundary can be used to improve information about pollutants such as 

intercontinental dust clouds flowing into the model’s domain. Finally, air quality observations within the model’s domain 

can be dynamically assimilated into the model to guide the initial state of a forecast to more closely match reality. In the 

future, improvements such as these will be incorporated into operational ozone and particulate matter forecast models. 

 

Primary Existing Systems include: 

 HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Smoke Forecast Guidance 

Demo Project (NOAA). Using biomass burning locations identified by the Hazard Mapping System, this 

prototype uses the USDA BlueSky fire emissions model and NOAA’s HYSPLIT transport model to predict 

smoke plume locations. 

 IDEA AOD Trajectories (NASA, EPA, and NOAA). The IDEA project also includes a prototype product 

that forecasts where areas of high PM may be transported. 

 Regional Air Quality Modeling System (RAQMS) (NASA). This research system assimilates global satellite 

observations of ozone into a global model to provide improved boundary conditions for regional air quality 

models. 

 STEM-II Model (NSF and others). This research air quality model assimilates surface air quality 

observations and has demonstrated that such assimilation can improve air quality forecasts, but this work is at 

an early stage. 

Gaps include: 

 Prototype Air Quality Data Assimilation System (NOAA in consultation with EPA). This will 

demonstrate the capabilities required to dynamically assimilate real-time air quality observations into 

operational regional air quality models. This will include enhanced observational data quality control, 

characterizing errors in observations and models, eliminating biases, and developing data assimilation 

algorithms and analysis software. 
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Data to Determine Requirements for Additional Observations to Support Air Quality Forecasting 

The existing air quality observation system has been designed to support air quality decision-making, compliance 

monitoring, and climate studies—not air quality forecasting. It is likely that new types of routine air quality monitoring 

would improve the accuracy of air quality forecast guidance, which can be translated into longer forecast lead times. A 

combination of field measurements to collect additional observations for a limited period over a wide region and 

modeling studies that evaluate the benefits of those measurements would provide the information required to guide 

additional observation investments for air quality forecasting. 

Gaps include: 

 Modeling Studies for Observing System Design (NOAA). Conduct modeling studies to determine which 

observations might most effectively contribute to improved forecasts. 

 Collect Sample Observations (NOAA). Guided by the modeling studies, collect enhanced air quality data 

over a limited geographic and temporal period to simulate what an enhanced operational network might 

provide. This work could be conducted in conjunction with one or more field studies. This data will be used to 

confirm the results from the modeling studies and/or contribute to a more comprehensive understanding of 

how observations could support air quality forecast models. 

5. ASSESSMENTS OF KEY AIR QUALITY PROCESSES 

5.1 NEED AND RATIONALE

Air pollution sources and atmospheric processes that form, transport, and remove ozone, PM2.5, and precursor species 

determine when and where air quality problems will occur. In many cases, uncertainties in the understanding of such 

scientific issues can lead to continued threats to public health or approaches that impose unnecessary burdens on the 

Nation (e.g., the Houston example). 

5.2 GENERAL DESCRIPTION

Through intensive field observations and follow-up analysis, agencies characterize the key processes that drive air quality 

problems. These scientific assessments enable air quality decision-makers to make well-informed decisions that protect 

public health while also maintaining a vital economy. To address different agency missions, studies are conducted at 

hemispheric, national, regional, and local scales. These studies lead to policy-relevant summaries of the factors that drive 

air quality problems. Typical scientific issues addressed by air quality assessments include key chemical processes, factors 

affecting radiation balance, long-range transport of pollutants, top-down emissions inventory verification, and model 

evaluation. 

While air quality and climate are often considered separately, they are different perspectives on one atmosphere—often 

with significant areas of overlap. As appropriate, multi-agency studies combine unique capabilities and mission-driven 

perspectives to provide a more holistic understanding of the atmosphere at less cost than would be possible with 

independent studies. For instance, NASA, NOAA, DOE, NSF, and a number of U.S. and international partners 

conducted the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) in 

2004. ICARTT studied air quality and climate issues across the U.S., with a focus on New England and the Atlantic. The 

insight into atmospheric processes developed during ICARTT will inform policy development and planning for 

international, national and regional air quality and climate issues. 

The observing systems deployed during regional assessments are typically state-of-the-science, providing much more 

information than permanent monitoring installations. However, these systems also require more hands-on intervention 

and specialized resources. Often, this work requires new instruments to measure parameters with higher sensitivity or 

frequency, which can lead to new techniques and equipment for routine monitoring. Field studies deploy observing 

systems in carefully planned suites that study processes from different perspectives. Integrated analysis of the data from 

those suites provides the information required to characterize and prioritize the importance of various physical and 

chemical processes, such as transport and emissions of pollutants and chemical reactions. 

5.3 BENEFITS

These assessments provide information that drives improvements in all aspects of air quality management and modeling. 

These air quality assessments enable air quality policy-makers and environmental managers to more effectively develop 

approaches to reduce ambient air pollution while also maintaining a vital economy. The scientific information from 

assessments of key air quality processes also provides critical information for improving air quality models used for 

forecasting and management assessments. 
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5.4 PRIMARY EXISTING SYSTEMS 

 WP-3 Orion aircraft for in situ air quality measurements of the lower part of the atmosphere (NOAA); 

 LiDAR aircraft for measurements in the lower troposphere (NOAA); 

 Light aircraft for in situ measurements of surface fluxes (NOAA); 

 Research Vessel Ronald H. Brown configured for studying air quality processes in marine and coastal 

environments (NOAA); 

 DC-8 aircraft for in situ and LiDAR measurements in the mid- and upper-troposphere (NASA); 

 WB-57 aircraft for in situ measurements in the mid- and upper-troposphere (NASA); 

 J-31 aircraft for incoming and outgoing radiation measurements (NASA); 

 G-1 aircraft for in situ measurements related to PM formation and effects (DOE); 

 Ozonesondes (NOAA, NASA); 

 Research wind profiler network for frequent characterizations of 3-D wind fields (NOAA); and 

 Surface flux network for measuring transfer of pollutants from the atmosphere to the surface (NOAA). 

5.5 PRIMARY ADDITIONAL PLANNED SYSTEMS

 Enhanced measurements of PM-related processes (NOAA) 

5.6 GAPS

 Extending the spatial extent and duration of regional assessments to better characterize regional transport and 

to characterize more types of meteorological conditions that drive poor air quality, with the goal of providing 

the comprehensive information required by air quality decision-makers (NOAA). 

6. IMPROVED EMISSIONS INVENTORIES 

The National Emission Inventory (NEI) is a national database of air emission information that is compiled by EPA and 

used for a number of critical environmental management and policy activities. Emissions inventories catalog the 

amounts of pollutants emitted into the atmosphere. Inventories typically include emissions from several sources, 

including biogenic (e.g., biomass burning, VOCs released from forests), industrial, and transportation. Considerable 

uncertainties exist in these inventories, which affect the accuracy and reliability of simulations for air quality forecasting 

and air quality management assessments. 

Observations support two general approaches to improve emissions estimates. The “bottom-up” approach uses 

observations to assess activities that cause emissions, such as identifying how much of a geographic area has been 

developed. The “top-down” approach uses observations of a primary (directly emitted) or secondary (formed by 

reactions in the atmosphere) pollutant to infer emissions. NARSTO has described a number of ways these approaches 

can be advanced and listed high priority inventories needing improvement, including biogenic emissions.5

6.1 NEED AND RATIONALE

Within the NEI, there is a critical need to improve the estimate of biogenic emissions, which include those from 

wildfires and other biomass burning. In particular, there is a need for a systematic framework within NEI to gather and 

calculate emissions from prescribed and wildland fires to produce an accurate and automated fire emissions inventory, 

including routine updating and reporting. In addition, measurements are required to better estimate other classes of 

emissions, such as mobile, that currently have large uncertainties. 

6.2 GENERAL DESCRIPTION

A systematic framework within or supported by NEI regarding biomass burning emissions must include detection of fire 

events, estimates of fire fuel type, the amount of fuel consumed, and ambient air quality observations. Satellite-based fire 

detection products need to be evaluated and improved to advance their application to improving inventories. Also, more 

frequent updates to land cover and fuel maps could reflect changing fuel conditions. A highly automated framework 

could reduce biomass burning emissions inventory updates from a 2-year time lag to near-real-time. This activity would 

span organizational and state boundaries. 

 

5 “Improving Emission Inventories for Effective Air Quality Management Across North America,” NARSTO, 

http://www.narsto.org/section.src?SID=8. 
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Much of the data, knowledge, and techniques are underutilized because tools to integrate the data are not readily 

available. GEOSS provides the opportunity for both bottom-up and top-down improvements in the inventory. As 

noted, the availability of specific fire information is a rich source of ‘bottom-up” information on activity levels that with, 

modest direct chemical measurements of fire composition, will add significant contributions. From a “top-down” 

perspective, components of the integrated observation and modeling system will support an effective “inverse 

modeling” inventory enhancement by identifying key disparities (spatially and temporally) between predictions and 

observations. This product/service will make significant use of information generated as part of the integrated air quality 

data and air quality processes assessment products/services. Appendix B provides an example of how these sources of 

information can be integrated to improve emissions estimates (in this case with application to international transport 

assessments). 

6.3 BENEFITS

Emission modeling utilizing all available data sources (including expanded observations) will help in lowering the 

uncertainty in retrospective and forecast models. This will improve air quality planning, regulatory decisions, and 

forecasts. The benefits also include timely allocation and fulfillment of requests for prescribed burning permits. 

6.4 EXISTING COMPONENTS 

 Monitoring and source characterization studies of all major categories of sources, including agricultural, landfill, 

and hazardous air pollutant emissions (EPA); 

 Vegetation and landcover change datasets, including leaf-area and vegetation indices (NASA). 

 Landfire data set providing high-resolution fuel information (USDA); 

 Fuels Characteristics Class derived in part from NASA data used to estimate fire emissions (EPA); 

 Analysis of emissions of nitrogen oxides and volatile organic compounds based on satellite observations 

(NASA); 

 Identify locations of large fires from satellite observations (includes use of GOES Imager and Moderate 

Resolution Imaging Spectroradiometer [MODIS]) (USDA, NOAA, NASA); 

 Identify burn scars (USDA); 

 Collect emission factors and radiant energy information for planned and actual fires (USDA, EPA); 

 Models to compute emissions from forest fires, such as BlueSky (USDA, EPA, NOAA); 

 Field monitoring and reporting of wildfires and agricultural burns (USDA, States); 

 Satellite-based monitoring of burn plumes and locations using MODIS instruments on Aqua and Terra, and 

GOES satellites (NASA, NOAA); 

 Monitoring of lower tropospheric gases (NOx, selected VOCs) and particulates from NASA and European 

satellites; 

 Inverse modeling, where observations are compared to model predictions based on varying emissions estimates 

to identify and/or address issues with inventories - for instance, CMAQ and surface PM observations are used 

to estimate seasonal ammonia emissions (EPA/NOAA); and 

 Space-borne LiDAR observations of PM from CALIPSO (NASA). 

6.5 PLANNED COMPONENTS

 Advanced fire/burn-area detection from satellite (USDA, NASA); 

 Routine gridded estimates of emissions from biomass burning (EPA, USDA); and 

 Characterization of particulate matter emissions and hazardous air pollutants from aircraft engines that power 

commercial transport aircraft (FAA, EPA, NASA). 

6.6 GAPS

Source oriented measurements of the chemical constituents from wildfires, and controlled burning efforts, including 

open agricultural burning operations. These measurements are necessary to produce emission factor estimates that vary 

across the type of burning operation (e.g., fuel/vegetation composition). The relative activity data (frequency, extent, 

duration, and location) provided by satellites is combined with emission factors to yield emission estimates. 

Additional size-segregated and speciated emission measurements of pollutants and their precursors from additional 

major source categories, including: forests and crops, large-scale petrochemical and industrial facilities, animal feed lots, 

off-road mobile sources like farm and construction equipment, and air and marine transport, and paved and unpaved 

roadways. 
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Inverse modeling assessment and improvement of emission inventories. The availability of the fused observation and 

air quality modeling datasets described in Section 1 can support emissions inventory improvement. What is lacking is a 

dedicated data analysis effort that specifically diagnoses the underlying causes leading to disparities between emissions 

and observations. 

7. IMPROVED INTERNATIONAL TRANSPORT ASSESSMENTS 

7.1 NEED AND RATIONALE

Air quality assessment and forecasting are linked and dependent on transport of pollutants across various scales (urban, 

regional, continental, and global). Ozone, PM, precursor species, and mercury all have potential for significant longrange transport both into and out of the United States. Such transport can be episodic, driven for instance by dust 

storms in Siberia and Africa, biomass burning in the Americas, or industrial/combustion emissions in Asia. In addition, 

recent observational and modeling studies indicate that intercontinental transport of air pollutants may persist over time 

and can have significant impact on air quality and climate. Assessment needs addressing long-range transport and 

climate-air quality interactions are founded on a set of common analytical tools (e.g., global scale modeling and emission 

inventories). Consequently, other efforts such as the International Polar Year 2007-2008 would realize peripheral 

benefits associated with GEOSS support for transport. 

While components of transport characterization are included in all previous products (Sections 1-4), international 

transport characterization by itself stands as a seminal (and logical) product emerging from the GEOSS framework, 

particularly from hemispherical-and global-scale perspectives. The ability to bring together a diversity of physical and 

chemical data sets across a variety of scales in combination with predictive models in the aggregate forms the basis for 

sound transport analyses. For instance, The Long Range Transboundary Air Pollution Convention was enacted in 1979 

and currently has 49 signatures. Eight protocols have been negotiated under the Convention, establishing a monitoring 

and modeling program and national obligations for emission reductions of SO2, NOx, VOC, NH3, metals, and Persistent 

Organic Pollutants (POPs). Work is currently in progress to provide information and the scientific basis for hemispheric 

transport of air pollutants, and the United States and European Commission are the task force leads. 

Imported transport of PM, ozone, and persistent bioaccumulative toxins such as mercury have the potential to become 

an increasingly important contributor to adverse U.S. air quality over the next two decades, given the growth projections 

of Asia and Central/South America. The relative influence also will grow as the United States continues to implement 

air quality regulations impacting emissions. In addition, EPA is reviewing the National Ambient Air Quality Standards 

for both ozone and particulate matter. 

Current international transport assessments build on strengths provided by large-scale meteorological and physical 

transport models and access to a limited set of observations amenable to large scale air quality assessment and 

prediction. Important information gaps remain in: 

 Air quality observations appropriate for characterizing transport across key import and export trajectories, and 

that separate pollutants aloft from those diffused down to ground level; 

 More effective linking between global- and regional-scale meteorological and air quality models that enable 

impacts of transport to be quantified over short and long time periods; and 

 Emission releases from emerging growth areas such as East and Southeast Asia, Africa, and Central/South 

America. 

Clearly, GEOSS presents an opportunity to address directly some of the important information gaps attendant with 

international transport assessments. 

7.2 GENERAL DESCRIPTION

The product envisioned is an improved integrated assessment capability that brings together observational sources and 

air quality models throughout the Northern Hemisphere and Equatorial regions. 

Integrated observational datasets combining, on a hemispherical scale, remote satellite observations, aircraft, and 

land-based systems. This may include both routine observations and the more comprehensive and detailed datasets 

produced by air quality assessments. This component differs from the major data integration product (Section 1) by 

having a global scope but limited temporal period (e.g., selected episodes or years) without the automated data 

management infrastructure to support continuous updates and distribution (which could be added later). 
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Improved model linkage that allows for the systematic and dynamic coupling across global-regional spatial scales and 

climate-air quality temporal scales. Currently, such coupling is usually unidirectional and difficult to implement, but the 

interactions between those scales are bidirectional and common. 

Dedicated inflow and outflow sentinel observation platforms characterizing the amount and types of pollutants that 

cross continental U.S. boundaries and that are brought down to ground level. 

International emissions inventory enhancements in those pollutant categories and geographical regions of greatest 

uncertainty and magnitude. Without such improvements, the contributions of tasks 1-3 by themselves will be 

insufficient to adequately evaluate model performance and future growth and related emission strategy scenarios. 

7.3 BENEFITS

 Stronger analytical framework and scientifically sound tools for assessing the impact of international transport 

on air quality; 

 Improved assessment framework for developing U.S. air program policy, (any analysis of internal policies will 

be contingent on an accurate understanding of long-range transport); and 

 Scientific knowledge base for future international agreements on hemispheric transport of air pollutants. 

7.4 PRIMARY EXISTING COMPONENTS 

 Global scale air quality model platforms (e.g., GEOS-CHEM, Model for OZone And Related chemical 

Tracers-2, and RAQMS) by multiple agencies; 

 Regional Scale air quality model platforms (e.g., CMAQ, WRF/CHEM) (EPA, NOAA); 

 Observation platforms listed in Section 1 (satellite, land-based and vertical profiling systems) supported by 

multiple agencies; 

 Global emissions inventories (EPA); and 

 Observations of selected long-lived species from light aircraft (NOAA). 

7.5 PRIMARY PLANNED COMPONENTS

 Improved linking and comparison of GEOS-CHEM and CMAQ (EPA) 

 Model performance evaluation against satellite, aircraft intensive, and surface-based observations (EPA). 

7.6 GAPS

 Sentinel land-based observation platforms dedicated to characterizing long range transport and mixing to the 

ground. Six monitoring stations capable of detecting Asian and African inflow to the United States and North 

American outflow to Europe. Parameters include CO, NO2, NOy, O3, SO2, PM2.5 aethalometer (black carbon), 

continuous sulfate, and organic carbon (EPA); 

 Mercury monitoring to supplement measurements for PM and ozone issues (EPA); 

 Vertically resolved measurements of key species (EPA); 

 An integrated data set composed from in situ and remotely sensed observations (EPA); 

 Improved global emissions inventory and future projections, especially for rapidly growing countries such as 

China and India (EPA); 

 Dynamic linking of global and regional models and model intercomparisons (EPA) 

 Assessment of current and future transport based on the integrated data set and improved emissions inventory 

and models (EPA); and 

 Vertically resolved NO and NO2 measurements (NOAA). 

8. RELATED ISSUE 

Transport and diffusion modeling for emergency response is related to traditional air quality issues in that the people are 

exposed to harmful material in the air, but the sources are often much more localized than for air quality issues, and 

events can be very irregular—typically involving an unusual or accidental release of material. Examples include an 

accidental release of industrial materials. Risk mitigation includes collection and distribution of observational data, 

incorporation of the data in predictive/diagnostic tools, and interpretation of the results for those who take mitigating 

action. There appear to be significant near-term opportunities for the IEOS to improve the Nation’s ability to deal with 

emergencies. For instance, see the Federal Research and Development Needs and Priorities for Atmospheric Transport 

and Diffusion Modeling (Office of the Federal Coordinator for Meteorological Services and Supporting Research, FCMR23-2004, September 2004). 
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9. PROJECT EXTENSIONS AND LONGER-TERM CONSIDERATIONS 

Most air pollution assessments share a common need for an underlying characterization of physical and chemical 

processes that drive pollutant movement and reactions. Consequently, the work discussed in this plan can lead to 

additional future benefits. Example applications include: 

Cross-media environmental assessments. Deposition of pollutants from the atmosphere to land and water surfaces is 

a major contributor to high mercury levels in fish (and people) and excess nutrients in estuaries and coastal regions. 

Improved understanding and modeling of air quality is a key component to assessments of such cross-media issues. 

Bio-aerosol transport. Pollens and spores can be transported long distances in the air. Improvements in characterizing 

and modeling transport of particulate matter can contribute to assessments of bio-aerosol issues, such as the spread of 

soybean rust. 

Redesigned air quality compliance system. The current point-based system of monitoring compliance with air 

quality regulations could be extended to use synthesized air quality surfaces harmonized with the physical/chemical 

environment to yield more realistic attainment and non-attainment boundaries. 

Improved observing systems. The near-term opportunities will improve our understanding of requirements and 

opportunities for air quality observations. This may lead to observing systems such as advanced satellites with broader 

coverage of chemical compounds; higher horizontal, vertical, and temporal resolution; and/or greater temporal 

continuity. 

Multi-scale exposure and health assessments. Integrating the work and results of air quality process assessments 

typically done at hemispheric to large urban scales with that of air quality composition and concentration studies done at 

regional to neighborhood scales can contribute to health effects studies. 

Ongoing research is required to develop the advanced capabilities required to prevent and respond to such air quality 

issues. The Air Quality Research Subcommittee of the Committee on Environment and Natural Resources and its 

member agencies work to enhance the effectiveness and productivity of U. S. air quality research and to improve the 

information interface between research and public policy on these issues. 
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APPENDIX A: SATELLITE SENSORS 

Sensor Name 

Vertical 

extent of 

measurement 

Horizontal 

resolution, 

domain 

Target Constituent/ 

Property for air 

quality 

Platform 

(launch) 

Current and past instruments 

GOME 

Global Ozone 

Monitoring 

Experiment 

TR and ST 

40 x 40 km2, 

40 x 320 km2 

swath 

Tropospheric 

columns for O3, NO2, 

HCHO, clouds and 

aerosols 

ESA ERS-2 

(1995) 

MODIS 

Moderate 

Resolution Imaging 

Spectroradiometer 

Surface to space 

0.25 - 1 km , 

2330 km wide 

swath 

Aerosol column 

optical thickness, 

aerosol type (sulfate, 

biomass burning) 

over land 

NASA Terra 

(1999) 

NASA Aqua 

(2002) 

AVHRR 

Advanced Very 

High Resolution 

Radiometer 

Surface to space 0.5-1km, 

~3km 

Fire locations, aerosol 

column thickness NOAA POES 

MISR 

Multi-angle 

Imaging 

SpectroRadiometer 

Surface to space 

275 m, 

360km wide

swath 

Aerosol properties 

(angular radiance 

dependence) 

NASA Terra 

(1999) 

SCHIAMACHY 

Scanning Imaging 

Absorption 

SpectroMeter for 

Atmospheric 

CHartographY 

ST & TR 30x60 km, 

960 km swath O3, NO2, S02, aerosols ESA ENVISAT 

(2002) 

OMI Ozone Monitoring 

Instrument 

ST profiles, TR 

columns 48 x 48 km2

Tropospheric 

columns for O3, S02, 

HCHO, N02, and 

aerosol 

NASA Aura 

(2004) 

TES Total Emission 

Spectrometer 

ST profiles, TR 

layers 26 x 42 km2

Tropospheric 

columns for 03, NOy, 

CO, SO2, CH4

NASA Aura 

(2004) 

CALIOP 

Cloud-Aerosol 

Lidar and Infrared 

Pathfinder Satellite 

Observations 

ST profiles, TR 

profiles 0.3 x 0.3 km2 Aerosol density and 

radiative properties CALIPSO (2006) 

Future instruments scheduled to be launched 

VIIRS 

Visible/Infrared 

Imager/Radiometer 

Suite 

Surface to space 400m, 

~3km swath 

Aerosol column 

optical thickness, 

aerosol type 

NPP (2008) 

APS Aerosol 

Polarimetric Sensor Surface to space 1-4 km, 10km swath 

Aerosols – optical 

depth, size 

Glory (2009; 

formulation) 

GOME-2 

Global Ozone 

Monitoring 

Experiment 

TR and ST 40km x 40km Ozone profiles 

MetOp (also does 

temp, humidity, 

and winds 
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APPENDIX B: EXAMPLE OF INTEGRATED USE OF FIELD STUDY DATA AND 

MODELS TO ESTIMATE EMISSIONS 

This figure summarizes the experimental design of the Transport and Chemical Evolution over the Pacific (TRACE-P) 

as an integrated chemical-transport model (CTM)-aircraft-satellite mission with application to analysis of Asian carbon 

monoxide (CO) sources. (CO is a byproduct of combustion.) A central objective of TRACE-P was to facilitate 

improved knowledge of Asian sources through combining top-down constraints from observations of Asian outflow 

together with the bottom-up inventories. A priori Asian emissions for the TRACE-P period, based on the best bottomup understanding of processes, were generated prior to the mission by Streets (anthropogenic emissions) and Logan 

(biomass burning emissions). These were used in chemical forecasts by five different CTMs during execution of the 

mission to guide the aircraft towards Asian outflow and therefore optimize the testing of the models. Validation of the 

Measurements of Pollution in the Troposphere (MOPITT) CO observations was conducted during the mission to 

provide a seamless aircraft-satellite CO data set. The top-down CO data from the satellite and aircraft mission were then 

used in an inverse model analysis to improve the a priori emission estimates. Major conclusions were that anthropogenic 

emissions from China are 50 percent too low (indicating sources that were not included in the emissions inventory), 

biomass burning emissions from Southeast Asia are 60 percent too high, and Japanese and Korean emissions are roughly 

correct.

NASA DATA IMPROVES EMISSION INVENTORIES

TRACE-P CO DATA A priori

bottom-up

sources

(customized 

for TRACE-P)

Fossil and biofuel

Daily biomass burning

(satellite fire counts)

Chemical Transport 

Model (CTM)

MOPITT CO 

inverse 

analysis

validation

chemical

forecasts

top-down

constraints

Correction to a priori sources:

+40% for China

Application to Carbon Monoxide

Streets et al. [2003] Heald et al. [2003]

Revision of 

bottom-up 

inventory

Identification of previously missed boilers

with low combustion efficiencies

NASA DATA IMPROVES EMISSION INVENTORIES

TRACE-P CO DATA A priori

bottom-up

sources

(customized 

for TRACE-P)

Fossil and biofuel

Daily biomass burning

(satellite fire counts)

Chemical Transport 

Model (CTM)

MOPITT CO 

inverse 

analysis

validation

chemical

forecasts

top-down

constraints

Correction to a priori sources:

+40% for China

Application to Carbon Monoxide

Streets et al. [2003] Heald et al. [2003]

Revision of 

bottom-up 

inventory

Identification of previously missed boilers

with low combustion efficiencies

A Report of the National Science and Technology Council 

September 2006