About
See a history of modifications to AIRPACT: AIRPACT Changelog
AIRPACT is a computerized system for predicting air quality (AQ) for the immediate future of one to three days for the entirely of the states of ID, OR and WA, and surrounding parts of Canada, MT, WY, UT, NV and CA. AIRPACT predicts air quality by calculating the chemistry and physics of air pollutants as determined by pollutant emissions within the context of the background, natural air chemistry and predicted meteorology. Meteorology has a direct effect on air pollution, with variables such as wind speed, temperature and precipitation affecting transport and dilution, chemical reaction rates, and the removal of pollutants through rain-out, respectively.
Pollutant emissions are another (along with meteorology) key factor affecting air quality. Emissions represent the amount of pollutants released into the atmosphere from different sources. These are calculated referring to detailed spatial databases of land use, traffic volumes, industrial emissions and natural emissions from vegetation and soils, all adjusted as appropriate by date, time of day and predicted temperature and solar (uv) light intensity. The emissions data are collected from federal, state, and local agencies and compiled for use in AIRPACT. AIRPACT's project name, the Air Information Report for Public Awareness and Community Tracking, reflects the goal of providing meaningful information about the quality of the air (or the level of air pollutants) to the public from a variety of sources, including both model results and monitor observations.
Table. Models used in the AIRPACT system (a partial list)
| WRF | Weather Research and Forecasting Model | WRF has been created and maintained by the meteorological community and is widely used both to simulate past weather and to forecast weather. |
| MCIP | Meteorology-Chemistry Interface Processor | MCIP reformats WRF results and derives additional variables. |
| SMOKE | Sparse Matrix Operator Kernel Emissions | SMOKE imports emissions inventory data and produces model ready emissions on hourly time steps, mapped to the modeling domain and grid. |
| CMAQ | Community Multi-scale Model for Air Quality | CMAQ calculates the evolving air quality, resolved in space and time, by computing the chemistry and physics of air pollutants, accounting for emissions, transport, vertical mixing, dilution, rain-out and deposition. |
The key components of the AIRPACT modeling system include WRF, SMOKE, and CMAQ. WRF is a numerical weather model that predicts future weather in terms of wind speed and direction, temperature, precipitation, and related terms for each hour and each grid cell in the forecast period. WRF predictions are transferred every day from the University of Washington to Washington State University. The emissions data are processed each day using SMOKE, which provides hourly pollutant emissions for air quality modelling. The weather and emissions data, along with boundary chemistry conditions, and initial chemistry conditions throughout the domain, are read by the air quality model, CMAQ, which calculates air pollutant concentrations in the atmosphere throughout the domain for each hour. The WRF and CMAQ models each explicitly account for variations in terrain and landcover; they also use global models to account for incoming weather systems and the long-range transport of pollutants from outside the AIRPACT domain.
Air quality refers to the extent that air pollution has degraded our air, both in terms of human health risk from breathing air pollutants and in terms of other non-health effects such as crop damage, material degradation, and visibility degradation of scenic vistas. Air quality is generally evaluated in terms of criteria pollutants, which are gaseous and particulate contaminants regulated under the Clean Air Act and amendments thereof.
Criteria pollutants include the following gas phase chemicals:
- O3, ozone, a super-oxidant formed chemically in the atmosphere (but not emitted directly from any sources), attacks both living tissue and materials such as rubber, plastics, fabrics, paint and metals;
- NOx, nitrogen oxides NO and NO2, which are toxic and also contribute to ozone formation and photochemical smog;
- CO, carbon monoxide, a toxic gas emitted from all combustion sources;
- SO2, sulfur dioxide, a toxic gas produced in large quantities at smelters and coal-fired power plants; also known for causing acid rain;
Pollutants also include the following atmospheric particulate matter:
- PM2.5, particles of solid and/or liquid with diameter < 2.5 micron (µm), often associated with smoke; and
- PM10, particles of solid and/or liquid with diameter < 10 micron (µm), often associated with dust.
Volatile organic compounds (VOCs) such as benzene from automobiles or isoprene, released naturally by some
tree species, are not regulated as criteria pollutants, but some VOCs are known as hazardous air pollutants (HAPs)
for impacts on human health. VOCs are also critical contributors to the formation of ozone and PM.
Ammonia (NH3) is also an important air pollutant, largely emitted from animal waste and fertilizer use, which can chemically convert into particulate matter. Exposure to high concentrations of ammonia in air causes immediate burning of the nose, throat and respiratory tract.
All air pollutants listed above are included and treated in the CMAQ air quality model.
AIRPACT uses an EPA developed model called the Community Model for Air Quality (CMAQ) that calculates air quality for a region by treating the region as a three-dimensional grid of cells of regular size. Thus, AIRPACT5 treats the Pacific and Inland Northwest region as a gridded volume of 285 columns (West to East) by 258 rows (South to North), in 37 vertical layers. The cells are 4 km by 4 km in horizontal extent but the cell vertical level depths vary, primarily by layer, being shallowest near the surface and growing deeper aloft. The problem of predicting the region's air quality thus becomes the problem of computing the air quality within a model grid cell volume, many times over; for AIRPACT5, solutions are required for 2.7 million cells per time step. For each grid cell volume, air quality is solved by calculating changes in air chemistry from the beginning of a time step (say t=0) until the end of that step (t=1) accounting for the contribution of several different factors for each chemical and particulate of concern:
- Emissions of fresh pollutant into the cell volume
- Creation and destruction of molecules by chemical reactions
- Growth or shrinkage of particles by physical and chemical reactions
- Transport into and out of the cell across N, S, E and W cell walls by wind
- Transport across top and bottom walls (cell floor and ceiling) by vertical mixing
- Removal by deposition to land or water surfaces or by precipitation 'wash-out'
Thus, treating each grid cell volume in turn, marching forward in time allows for the calculation of air quality hour by hour.
Using AIRPACT for Air-Quality Forecasting Support
Webinar Recordings by Joe Vaughan
WSU/VCEA/CEE/LAR
These recordings are based on a presentation at the NW-AIRQUEST meeting of June, 2020, on navigating and understanding the AIRPACT5 webpages, and reference:
Training Recordings:
| LENGTH mm:ss |
ABOUT | COMMENTS | CORRECTIONS |
|---|---|---|---|
| 0:20 | Introduction | ||
| 2:44 | Topics | This maps topics covered to slides in the ppt (above) | |
| 1:27 | What AIRPACT is | ||
| 1:09 | Domain | Grid cells are spaced at 4 km and cells are 4 km by 4 km. This is not a 4-km 'resolution', although this is a common usage. | Lines don't outline grid cells |
| 2:47 | AIRPACT Framework | Orientation to system design | |
| 5:26 | Products on website | Orientation to webpages | |
| 3:11 | Live tour of homepage | Begins "Live Demonstration" | Says "Next..." but first a digression |
| 2:44 | Digression on use of AIRPACT5 for AQ forecasting | Any use of AIRPACT for forecasting must take performance into account. | |
| 7:58 | Smoke forecast & map navigation | Example on navigating the surface layer animations | Gridlines button displays lines connecting cell centers, not outlining cells. |
| 7:55 | Boundary Conditions, 2D plots for O3, CO, and PM2.5 along vertical domain boundaries | Preceding topics involved 2D surface layer; this captures vertical dimension. In combination with wind data, this can be useful for considering the significance of long-range and trans-boundary transport. | We only display 3 species (O3, CO, PM2.5); while BCON contains 59 species |
| 4:09 | Curtain Plots | See comments in row above | We only display 3 species (O3, CO, PM2.5); while BCON contains 59 species |
| 18:35 | Performance Plots, two ways | Performance is critical to interpreting AIRPACT results for forecast use | |
| 8:52 | Kalman Filter Bias Correction and Machine Learning | All topics above describe deterministic results (they use recent observations). These products are different. | |
| 21:53 | The AIRPACT5 Change Log | ||
| 25:37 | Orientation to Air-Quality Modeling |
Federal (both USA and Canada), state, provincial, local and tribal AQ authorities jointly strive to protect human health and other values by protecting air quality; AIRPACT has been developed with support, guidance and cooperation from all these, particularly from US EPA Region 10, the WA Department of Ecology, the Oregon Department of Environmental Quality, the Idaho Department of Environmental Quality, and the Puget Sound Clean Air Agency.
AIRPACT has been implemented, over a succession of versions, at the WSU Laboratory for Atmospheric Research, by faculty and graduate students. Funding for the initial implementation was from an EPA grant; after the charter of NW-AIRQUEST AIRPACT development continued under annual contracts.
For more information, contact: