Observation-Based Quantification of the PM and Ozone at the US-Global Boundary

CAPITA, Washington UniversityRudolf B. Husar, PI

In Cooperation with EPA and other Organizations

Proposal for a Coop. Agreement, Response to EPA STAR RFA:

Sensitivity of U.S. air quality to climate change and future global impacts

Project Period: Nov. 2002-Oct 2005

Est. Budget: $500k, $165K/yr

Background and Goals

Project Background

• Intercontinental-scale transport of aerosols and other pollutants from major global source areas is becoming increasingly evident from real-time satellite and surface based sensors as well as from modeling studies. Recognizing these developments, EPA and other agencies are conducting several programs to assess the global-regional interaction of air pollutants.

• In the past, modeling was the main source of insights on global-s air pollution. The increasing quality and quantity of global/regional sensory data now allows the observation-based quantification of global-scale transport of aerosols and ozone. However, the analyses of the multitude of data and model simulations are not adequate to quantitatively estimate the impact of intercontinental transport on the short and long-term pollutant concentrations in the US.

Project Goals• The goal of this proposed STAR project is to use observational data in collaboration with

modeling, to quantitatively characterize the impacts of intercontinental pollutant transport on short- and long-term air quality with emphasis on PM and at the US boundaries.

Project Scope: Quantify PM and O3 at the US-Global Boundary

Figure 1. Schematics of the EPA Integrated Assessment Framework for the study of global-regional air quality interactions. This project focuses linkage 1 of the EPA Framework: Quantifying the magnitude of external impact on the US air quality based mainly on the observational evidence.

ProposedProject

ApproachGeneral: Integrate diverse data and combine data with models to estimate external impact

Project Subprojects:

1. Gather an Integrate Satellite and Surface Data

2. Complement and Verify Model Simulations

3. Estimate External Sources Impacts on PM and O3 at the US boundaries.

Subproject 1:

Gather an Integrate Satellite and Surface Data Focus on PM and Ozone; 1991; US Boundary domain

• Gather Data from Multiple Providers – Satellite Data.

– Optical Depth and Surface Visibility.

– PM Concentration Data.

• Adopt Tools and Methods for Data Integration– Enrich datasets with metadata for common formatting and coding

– Access data through simple common graphic interface (Voyager)

– Develop data transformation tools and procedures for data fusion

• Fuse Data to Derive New Parameters– Aerosol Type: TOMS+SeaWiFS

– Aerosol Elevation: Surface and Integral; Topo+SeaWiFS

– Flux: Vertical Integral + Horizontal Wind Speed

Adopt Tools for Data Integration and Processing

The main data integration and fusion tool will be the distributed Voyager software, for browsing of distributed multidimensional

datasets. Voyager is based on the new Web Services paradigm

The community-sported Voyager allows overlaying and exploration of datasets in linked spatial, temporal and other views. http://capita.wustl.edu/dvoy_services/dvoy.aspx

Superposition of daily SeaWiFS and TOMS satellite data, July 29, 2002

Subproject 2:

Complement, Verify and Combine Data with Models

• Complement Model Simulations– Estimate boundary conditions for Regional Models

– Estimate strengths of episodic sources (e.g. fires, dust storms)

• Verify Model Simulations for O3 and PM– Compare spatial pattern

– Compare temporal variations

• Combine Data and Modeling Approaches– Devise assimilation schemes of measured episodic emissions data

– Assimilation of concentration data into models

– Diagnostic modeling for source identification/apportionment

Data-Model Comparison

Sahara Dust

Data from surface and space sensors will be compared to dynamic model simulations. Of particular interest is the vertical

distribution as measured by the NASA GSFC Micro Pulse Lidar Network, MPLNet

Data-model comparison will include spatial pattern comparisons, as illustrated here using the NRL NAAPS global aerosol model for windblown dust. The nature and magnitude

of the deviations will be quantified.

Superposition of daily SeaWiFS and NAAPS Dust model, July 29, 2002

MPL Sampling Sites

Sahara Dust over Florida: GOES, TOMS, NAAPSJuly 29, 2002

GOES Sahara DustNAAPS Model

Sahara Dust

Subproject 3:

Estimate Impact of External Sources on PM and Ozone in the US

• Test methods of estimating external impact on US – Climatological estimates

– Combined chemical tracers and back-trajectory

– Full dynamic modeling (collaborative)

• Estimate external impacts on PM and O3 AQ in the US– Spatial pattern and flux of imported pollutant species

– Temporal (short-term, seasonal and secular) trend of imports

• Evaluate causes of pollutant imports and their variation– Industrial emissions and changes

– Non-industrial (biomass burning, dust storms)

– Estimate role of climate variables (e.g. drought) on imports.

Climatological O3 Concentrations at the Boundary

Seasonal Contributions of OzoneOzone at the boundaries of Eastern US

(Global Sources)

The O3 concentration at the boundaries can be established from routine monitoring network data (EPA AIRS). Evidently, the summer daytime maximum ozone at the ‘edges of E. US is 30-40 ppb, compared to >70 ppb in the urban-industrial region.

The O3 concentration at peripheral sites is 30-40 ppb, but location-dependent. The ‘tropospheric background’ contributed by natural and manmade sources generally shows a spring peak but over the Gulf states, it is bimodal.

Source Estimation: Chemical Tracer MethodPM2.5 concentration at the ‘edges’ ranges between 4-15 ug/m3 depending on location and season.

Sulfate, Sahara dust, and Central American smoke from Central America are the main contributions. In July, the dust is from Sahara; Spring and Fall dust is of local.

Big Bend(scale 0-15 ug/m3)

Everglades

Big Bend -Dust Everglades-Dust

The main aerosol components are organics, black carbon and sulfate and dust in July. Virtually all the fine dust is from Sahara.

Source Estimation: Back-trajectory Analysis

Airmass histories indicate the general direction of sources.

Big Bend, TX: There are large seasonal differences in the airmass transport directions. During winter spring, the flow is from W, NW; in the summer it is form, the east.

Sources of ‘clean’ and ‘dirty’ air. Low ozone concentrations are associated with air flow into the EUS (left); high ozone levels occur in air masses passing through the EUS center (right).

Seasonal and Secular Trends of Sahara Dust over the US

In W. Africa, the extinction coefficient (inverse visibility) was high in the 1980s due to prolonged drought. It has declined sine then.

Regional Sahara Dust events over the Southeast occur several times

each summer (as shown by Prospero, Cahill, others). Analysis

of the 20-year trend would elucidate the causality of DE USS

dust trends (e.g. drought)

Expected Project Results and Benefits

• Quantitative estimation of global (external) impact on US air quality. This will allow estimating the relative importance of external and homegrown sources.

• Better regional AQ modeling through improved boundary conditions and performance verification. This will permit more robust evaluations of emission scenarios.

• The improved analysis of observational evidence will better inform policy and decision makers. This, in turn, can lead to more effective actions for global/regional AQ management.

Project Success Factors:

CAPITA Experience, Cooperation, Tools

• CAPITA has conducted pioneering research on long range transport since the 1973 through the documentation of regional industrial haze episodes, global-scale dust and smoke events and through

• CAPITA has effectively cooperated with EPA researchers and policy analysts uninterrupted since 1973.

• The Project Team has developed and applied many tools to enable community-based analysis and data sharing. Tools development is supported by other project but this project will adapt and apply these tools (dev environments).