Assessment of Impact Distances for Particulate Matter Dispersion: A stochastic approach

S.M. GODOY; P.L.MORES; A.S.M.SANTACRUZ; N.J.SCENNA

RELIABILITY ENGINEERING & SYSTEMS SAFETY

Elsevier Ltd

Lugar: Amsterdam; Año: 2009 vol. 94 p. 1658 - 1665

0951-8320

It is known that pollutants can be dispersed from the emission sources by the wind, or settled on the ground. Particle size, stack height, topography and meteorological conditions strongly affect particulate matter   (PM) dispersion. In this work, an impact distance calculation methodology considering different particulate sizes is presented. A Gaussian-type dispersion model for PM that handles size particles larger than 0.1 mm is used. The model considers primary particles and continuous emissions. PM  concentration distribution at e very affected geographical point defined by a grid is computed. Stochastic uncertainty caused by the natural variability of atmospheric parameters is taken in to consideration in the dispersion model by applying a Monte Carlo methodology. The prototype package (STRRAP) that takes into account the stochastic behavior of atmospheric variables, developed for risk assessment and safe distances calculation [Godoy SM, SantaCruz ASM, Scenna NJ. STRRAPSYSTEM–A software for hazardous materials risk assessment and safe distances calculation. Reliability Engineering and System Safety 2007; 92(7): 847–57] is enlarged for the analysis of the PM air dispersion. STRRAP computes distances from the source to every affected receptor in each trial and generates the impact distance distribution for each particulate size. In addition, a representative impact distance value to delimit the affected area can be obtained. Fuel oil stack effluents dispersion in Rosario city is simulated as a case study. Mass concentration distributions and impact distances are computed for the range of interest in environmental air quality evaluations (PM2.5–PM10).