Rice, Hammitt, and Evans publish results from a probabilistic model describing the possible monetary benefits of reducing methyl mercury. The article, published in Environmental Science & Technology (volume 44, issue 13) estimates an additional $860 million with a 10% reduction in exposure to the US population.

http://pubs.acs.org/doi/abs/10.1021/es903359u

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For a period of more than 10 years from 1994 to the present, Dr. Wayne Ott of the Statistics Department, Stanford University measured indoor and outdoor particulate polycyclic aromatic hydrocarbons (PPAH) levels in his residential neighborhood in Redwood City, CA.

To predict the proximity effect, I have applied the "Markov I" stochastic model of Nicas (2001) to characterize the dispersion of air pollution from a "puff" release in a room that has dimensions of 10.7 x 7 x 2.6 meters (L x W x H). The model treats the dispersion of emitted particles as a Markov Chain process, where each particle moves by a series of "random walks" due to turbulent diffusion. The value for the turbulent diffusion coefficient, D, was set at 0.04 m2/sec.

In applying the model, I used 6 receptor positions located 0.5, 1, 1.5, 2, 2.5, and 3 meters away from the source in the horizontal direction. The air exchange rate for the simulation was 0.5 ach, which is typical for a residential location.

Attached to this post are two plots showing the results of the simulation:

1. A "Concentration versus Time" plot showing the concentration time series at each of the receptor points (note the y axis is on a log scale) for a period of 15 minutes after the release.

2. A "Average Concentration versus Distance" plot showing the proximity effect of 1-minute average concentration as a function of distance from the source. The 1-min average was take during the minute just after the release occurred.

These plots are in broad agreement with prior work showing that (A) air pollution from a "puff" release generally mixes thoroughly in the room of release within 5 to 10 minutes after release, and (B) that the proximity effect is generally an f(x) = 1/x function of Concentration vs. Distance.

This modeling approach can be expanded to include advection (air currents), obstructions, inlet and outlet flows, particle deposition, and reflection.

Note: Also attached to this post is the R source code used to run the Nicas model.