Introduction
With the increasing use of prescribed fire as a way of managing wildland
areas in the United States, predicting the potential impacts and assessing
risks are becoming more important. Of great concern are the effects of
smoke on air quality and visibility. Although few prescribed fires emit
enough to violate clean air standards, many people are sensitive to slight
amounts of smoke, especially if they already experience respiratory problems
like emphysema or asthma. Citizen complaints can cause active burning
programs to be delayed, redesigned, or even terminated. Also, smoke can
severely degrade visibility when combined with other pollutants or moisture.
Not only can this ruin scenic vistas, but the degraded visibility from
smoke has been known to cause severe traffic accidents.
Unfortunately, consistent and timely emission inventories from wildland
biomass burning are difficult to obtain and summarize for a national risk
assessment. Also, data on the timing and release rate of emissions, which
determine whether smoke will be lofted into the atmosphere or stay close
to the ground, are not routinely kept. Lacking useful emissions data,
it is assumed that a simple index of ventilation potential is sufficient
to help determine significant aspects of the risks to air quality and
visibility from biomass burning. Because ventilation potential is the
product of wind and mixing height, it can be determined easily. Also,
current and forecast values of the ventilation index are well known by
air quality regulators and are used for managing biomass smoke in many
parts of the country.
By developing ventilation potential as a spatial climate data base it
can be overlain with other elements of risk for a more complete assessment
of the impact of prescribed fire in wildland areas of the United States.
Certain aspects of ventilation climatology already are well known by air
pollution managers. For instance, low mixing heights and poor ventilation
are common in coastal areas of the United States where moist marine air
increases static stability. Poor ventilation also is common at night when
radiative cooling at the surface increases atmospheric stability. What
is not known, however, is the probability of poor ventilation on any given
day at any selected spot on the landscape. A long time series of high-resolution
spatial data can help determine such probabilities.
To develop probabilities of good and poor ventilation, we generated a
40-year time series at 0000 UTC and 1200 UTC each day. The generated values
of wind, mixing height, and ventilation index cover the entire United
States at a horizontal grid spacing of 2.5 minute latitude / longitude
(about 5 km), except Alaska where the grid spacing is fixed at 5 km x
5 km (Map
Projections). Complete descriptions of the methods used to generate
the data are found in the Technical
Background section.
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