A Statistical Model of Atmospheric Transport of Contamination over A Long Distance Yahui Zhuang

U

Development of the Atmospheric Transport Model Identify the controlling processes Determine statistical relationships among the processes Formulate the model mathematically Demonstrate the model simulations for a hypothetical source Summary

Processes Controlling Long-Term Atmospheric Transport 3. Resuspension caused by wind erosion 4. Soil fixation due to physical, chemical and biological interactions, and downward leaching 1. Time-averaged regional wind rose (wind speed and direction) 2. Statistics on wet and dry synoptic regions and the associated wet and dry deposition velocities Processes Controlling Long-Term Atmospheric Transport

Physical processes included in the model Total Air Concentration Primary X Resuspended X r Ground Concentration X g Deposition (V w +V d ) X Resuspension RX Soil fixation a Physical processes included in the model

Parameters and their most likely values used in the atmospheric transport model Description t d (46 h) Expected length of time from an arbitrary moment in a dry period until precipitation begins w (7 h) Expected length of time from an arbitrary moment in a wet period until dry period begins l (10 -5 s) Dry scavenging rate -4 Wet scavenging rate R (10 -9 Resuspension rate a (3x10 -8 Soil fixation rate

Environmental Compartments ? Dry Air Wet Air Ground Soil Environmental Compartments

U Q Concentration = Release rate x Residence time per unit volume

ò Model Formulation C Q P n( ) ( r = t dt , | Q(r0) release rate ¥ ò t dt , | Q(r0) release rate Pn(r,t|r0) probability density function Model Formulation

Pn(r,t|r0) = Gn(t|r0)Dn(r|r0) Gn(t|r0) Gain or loss probability Dn(r|r0) Distribution function

Modeling the gain or loss probability w g s 1/ t l R a Dry air Wet air Surface Soil

Modeling the gain and loss probability functions dG 1 dt G RG R d w s g = + - t l a ( ) Modeling the gain and loss probability functions

Temporal evolutions of the gain and loss probabilities Time (hour) 20 40 60 80 100 Probabilities 10 -6 -5 -4 -2 -1 -3 G s (t) a g =G d (t)+ G w Temporal evolutions of the gain and loss probabilities Initial condition: Gd(0)= 1, Gw(0) = Gg(0) = Gs(0) = 0

Temporal variations of gain and loss probabilities Time (year) 20 40 60 80 100 Probabilities 10 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 G d (t)+ G w (t) s g a (t)= Initial condition: Gg(0) = 1, Gd(0) = Gw(0) = Gs(0) = 0

Modeling the distribution functions 1. Air D(r|r0) = PV(r|r0) PH (r|r0) PV(r|r0) = 1 PH (r|r0) = f(u, q)/2pr where f(u, q) describes relative frequency with which the wind blows from q direction at a speed u.

Modeling the distribution functions dC dt P V C R g d w a = + - ( ) s where t /( and are the local probabilities of dry and wet states, respectively. 2. ground and soil

Variations of concentrations with time at 10 km from the source Release time (years) 20 40 60 80 100 Normalized concentrations 10 -8 -7 -6 -5 -4 -3 -2 air ground soil Variations of concentrations with time at 10 km from the source

Normalized air concentration Ca/Q [a km-3] West - East (km) -100 -80 -60 -40 -20 20 40 60 80 100 South - North (km) 6E-10 7E-10 4E-9 2E-9 1E-9 N Normalized air concentration Ca/Q [a km-3]

Normalized ground concentration Cg/Q [a km-2] West - East (km) -100 -80 -60 -40 -20 20 40 60 80 100 South - North (km) 8e-8 1e-7 6e-7 4e-7 2e-7 6e-8 4e-8 Normalized ground concentration Cg/Q [a km-2] T = 100 years

Normalized soil concentrations Cs/Q [a km-2] South - East (km) -100 -80 -60 -40 -20 20 40 60 80 100 3e-5 5e-4 7e-5 5e-5 4e-5 2e-5 West - East (km) South - North (km) 0.003 0.05 0.01 0.007 0.005 (a) (b) T=100 years T=1000 years

Summary · A statistical long-term atmospheric transport model for assessing environmental impact has been developed The model emphasizes the role of the atmosphere in redistributing contamination between the air and the underlying surfaces It explicitly accounts for dispersion, deposition and resuspension of contaminated tracer material, using a set of statistical parameters The model is time-dependent, and is best suited for long-term environmental assessments of air and ground contamination