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 Research Paper in the Documentation section.
