Simulating wildfire patterns using a small-world network model

This paper presents the development and validation results of a weighted small-world network model designed to simulate fire patterns in real heterogeneous landscapes. Fire spread is simulated on a gridded landscape, a mosaic in which each cell represents an area of the land surface. In this model, the interaction between burning and non-burning cells (here, due to flame radiation) may extend well beyond nearest neighbors, and depends on local conditions of wind, topography, and vegetation. An approach based on the coupling of the solid flame model with the Monte Carlo method is used to predict the radiative heat flux from the flame generated by the burning of each combustible cell to its neighbors. The weighting procedure takes into account latency (a combustible cell will only ignite when it has accumulated enough energy along time) and flaming persistence of burning cells. The model is applied to very different fire scenarios: a historical Mediterranean fire that occurred in southeastern France in 2005 and experimental fires conducted in arid savanna fuels in South Africa in 1992. Model results are found to be in agreement with real fire patterns, in terms both of rate of spread, and of the area and shape of the burn. This work also shows that the fractal properties of fire patterns predicted by the model are similar to those observed from satellite images of three other Mediterranean fire scars.     

Organic aerosol source apportionment from highly time-resolved molecular composition measurements

Organic molecular composition measurements with 3.5 min time resolution were performed with the photoionization aerosol mass spectrometer (PIAMS) over an 18-day period in October–November 2007 in Wilmington, Delaware, USA. Mass spectra were obtained for a total of 6244 time periods, and the signal intensities of 60 specific m/z ratios corresponding to key organic molecular species were modeled by positive matrix factorization (PMF). Six factors were identified that could be tentatively linked to specific sources (diesel exhaust, car emissions/road dust, meat cooking) or types of compounds (alkanes/alkanoic acids, phthalates, PAHs). Owing to the inherent high time resolution of PIAMS, the temporal (diurnal) and wind direction dependencies of these factors could be examined in detail to assess the impacts of point sources and atmospheric processes. Time-resolved EC/OC and gas-phase data (O₃, NO_x, CO) were also obtained during the measurement period to help distinguish primary (POC) and secondary (SOC) organic carbon. The total organic carbon (TOC) concentration averaged 2.6 μg m^?3 during the measurement period and most (>90%) was classified as primary. Of this, approximately one-third could be assigned as combustion POC and the other two-thirds as non-combustion POC. The PMF results were combined with EC/OC data for source apportionment. The diesel and car/road dust factors together represented about two-thirds of TOC, while the alkane/alkanoic acid and meat cooking factors contributed most of the remaining one-third. The phthalate and PAH factors contributed very little, only a few percent of the total. The diesel factor correlated most strongly to combustion POC, while the sum of the remaining factors correlated well with non-combustion POC.     

Source characterization and identification by real-time single particle mass spectrometry

A Real-Time Single Particle Mass Spectrometer, RSMS-3, was deployed to Wilmington, Delaware to study regional and local contributions to fine and ultra-fine urban particulate matter (PM). Approximately two-thirds of PM₁ consisted of internally mixed secondary aerosol. The remaining one-third was externally mixed including biomass burning (13%), fossil fuel combustion (7%) and various industrial sources (13%). In this last group, particle classes containing specific combinations of transition and/or heavy metals gave wind-rose plots consistent with specific point sources. For example, particles containing V and Ni were detected from different wind directions than those containing V and Fe. Samples from two industrial emission stacks, a steel manufacturing facility 10 km away and a coal-fired electrical power generation facility 5 km away, were analyzed and compared to the ambient data set. In each case, a direct correlation was found: a Pb–Zn–K–Na class for the steel manufacturing facility and an Fe–La/Ce class for the power generation facility. The ambient particle classes showed additional small signals from secondary components indicating atmospheric processing. Ambient particle classes containing only a subset of these elements, such as Zn only, Fe only and Pb–K only, were nonspecific, that is, the wind-rose plots were more diffuse and the particles could not be mapped to individual sources. The merits of stack sampling as an aid to interpreting single particle data sets are discussed.