Continuous thermodynamics of phase equilibria using the beta distribution function and an equation of state

1993 ◽  
Vol 10 (2) ◽  
pp. 71-77 ◽  
Author(s):  
Jiho Park ◽  
Hwayong Kim
Keyword(s):  
Distribution Function ◽  
Equation Of State ◽  
Phase Equilibria ◽  
Beta Distribution ◽  
Beta Distribution Function

scholarly journals Method development estimating ambient oxidized mercury concentration from monitored mercury wet deposition

2013 ◽  
Vol 13 (22) ◽  
pp. 11287-11293 ◽  
Author(s):  
S. Chen ◽  
X. Qiu ◽  
L. Zhang ◽  
F. Yang ◽  
P. Blanchard
Keyword(s):  
Distribution Function ◽  
Beta Distribution ◽  
Wet Deposition ◽  
Method Development ◽  
Elemental Mercury ◽  
Atmospheric Mercury ◽  
Mercury Deposition ◽  
Gaseous Elemental Mercury ◽  
Spatial Coverage ◽  
Beta Distribution Function

Abstract. To quantify mercury dry deposition, the Atmospheric Mercury Network (AMNet) of the National Atmospheric Deposition Program (NADP) was established recently to monitor the speciated atmospheric mercury (i.e. gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM) and particulate-bound mercury (PBM)). However, the spatial coverage of AMNet is far less than the long-established Mercury Deposition Network (MDN) for wet deposition monitoring. The present study describes the first attempt linking ambient concentration of the oxidized mercury (GOM + PBM) with wet deposition aiming to estimate GOM + PBM roughly at locations and/or times where such measurement is not available but where wet deposition is monitored. The beta distribution function is used to describe the distribution of GOM + PBM and is used to predict GOM + PBM from monitored wet deposition. The mean, median, mode, standard deviation, and skewness of the fitted beta distribution parameters were generated using data collected in 2009 at multiple monitoring superstations. The established beta distribution function from the 2009 GOM + PBM data is used to construct a model that predicts GOM + PBM from wet deposition data. The model is validated using 2010 data at multiple stations, and the predicted monthly GOM + PBM concentrations agree reasonably well with measurements. The model has many potential applications after further improvements and validation using different data sets.

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