Development of a Statistical Model for Estimating Spatial and Temporal Ambient Ozone Patterns in the Sierra Nevada, California

Statistical approaches for modeling spatially and temporally explicit data are discussed for 79 passive sampler sites and 9 active monitors distributed across the Sierra Nevada, California. A generalized additive regression model was used to estimate spatial patterns and relationships between predicted ozone exposure and explanatory variables, and to predict exposure at nonmonitored sites. The fitted model was also used to estimate probability maps for season average ozone levels exceeding critical (or subcritical) levels in the Sierra Nevada region. The explanatory variables — elevation, maximum daily temperature, and precipitation and ozone level at closest active monitor — were significant in the model. There was also a significant mostly east-west spatial trend. The between-site variability had the same magnitude as the error variability. This seems to indicate that there still exist important site features not captured by the variables used in the analysis and that may improve the accuracy of the predictive model in future studies. The fitted model using robust techniques had an overall R2 value of 0.58. The mean standard deviation for a predicted value was 6.68 ppb

Climate Change, Growth, and California Wildfire

Large wildfire occurrence and burned area are modeled using hydroclimate and landsurface characteristics under a range of future climate and development scenarios. The range of uncertainty for future wildfire regimes is analyzed over two emissions pathways (the Special Report on Emissions Scenarios [SRES] A2 and B1 scenarios); three global climate models (Centre National de Recherches Météorologiques CM3, Geophysical Fluid Dynamics Laboratory CM21 and National Center for Atmospheric Research PCM2); a mid‐range scenario for future population growth and development footprint; two model specifications related to the uncertainty over the speed and timing with which vegetation characteristics will shift their spatial distributions in response to trends in climate and disturbance; and two thresholds for defining the wildland‐urban interface relative to housing density. Results were assessed for three 30‐year time periods centered on 2020, 2050, and 2085, relative to a 30‐year reference period centered on 1975. Substantial increases in wildfire are anticipated for most scenarios, although the range of outcomes is large and increases with time. The increase in wildfire area burned associated with the higher emissions pathway (SRES A2) is substantial, with increases statewide ranging from 57 percent to 169 percent by 2085, and increases exceeding 100 percent in most of the forest areas of Northern California in every SRES A2 scenario by 2085. The spatial patterns associated with increased fire occurrence vary according to the speed with which the distribution of vegetation types shifts on the landscape in response to climate and disturbance, with greater increases in fire area burned tending to occur in coastal southern California, the Monterey Bay area and northern California Coast ranges in scenarios where vegetation types shift more rapidly.National Oceanic and Atmospheric Administration (NOAA) Regional Integrated Science and Assessment Program for California, United StatesCalifornia Climate Change Center/[CEC-500-2009-046-F]//Estados UnidosUnited States Department of Agriculture (USDA) Forest Service Pacific Southwest Research Station///Estados UnidosNational Oceanic and Atmospheric Administration (NOAA) Regional Integrated Science and Assessment Program for California///Estados UnidosUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI

Risk assessment: a forest fire example

The concern of this paper is obtaining baseline values for the number of forest fires as a function of time and location and other explanatories. A model is developed and applied to a large data set from Federal lands in the state of Oregon. To proceed the data are grouped into small spatial-temporal cells (voxels). Fires are rare so there are many of these voxels with no fires. In fact there are so many such cells that in the analyses presented a sample is taken to make the work manageable. The paper sets down a likelihood for the sampled data and fits a generalized additive model involving location, elevation and day of the year as explanatories

Determining the Impact of Wildland Fires on Ground Level Ambient Ozone Levels in California

Wildland fire smoke is visible and detectable with remote sensing technology. Using this technology to assess ground level pollutants and the impacts to human health and exposure is more difficult. We found the presence of satellite derived smoke plumes for more than a couple of hours in the previous three days has significant impact on the chances of ground level ozone values exceeding the norm. While the magnitude of the impact will depend on characteristics of fires such as size, location, time in transport, or ozone precursors produced by the fire, we demonstrate that information on satellite derived smoke plumes together with site specific regression models provide useful information for supporting causal relationship between smoke from fire and ozone exceedances of the norm. Our results indicated that fire seasons increasing the median ozone level by 15 ppb. However, they seem to have little impact on the metric used for regulatory compliance, in particular at urban sites, except possibly during the 2008 forest fires in California