A preliminary study of the application of some predictive modeling techniques to assess atmospheric mercury emissions from terrestrial surfaces.
Predictive modeling techniques are applied to investigate their potential usefulness in providing first order estimates on atmospheric emission flux of gaseous soil mercury and in identifying those parameters most critical in controlling such emissions. Predicted data by simulation and statistical techniques are compared to previously published observational data. Results showed that simulation techniques using air/soil coupling may provide a plausible description of mercury flux trends with a RMSE of 24.4ngm(-2)h(-1) and a mean absolute error of 10.2ngm(-2)h(-1) or 11.9%. From the statistical models, two linear models showed the lowest predictive abilities (R2=0.76 and 0.84, respectively) while the Generalized Additive model showed the closest agreement between estimated and observational data (R2=0.93). Predicted values from a Neural Network model and the Locally Weighted Smoother model showed also very good agreement to measured values of mercury flux (R2=0.92). A Regression Tree model demonstrated also a satisfactory predictability with a value of R2=0.90. Sensitivities and statistical analyses showed that surface soil mercury concentrations, solar radiation and, to a lesser degree, temperature are important parameters in predicting airborne Hg flux from terrestrial soils. These findings are compatible with results from recent experimental studies. Considering the uncertainties associated with mercury cycling and natural emissions, it is concluded, that predictions based on simple modeling techniques seem quite appropriate at present; they can be useful tools in evaluating the role of terrestrial emission sources as part of mercury modeling in local and regional airsheds.
Tsiros
Ioannis X
IX
Department of General Sciences, Agricultural University of Athens, Greece. itsiros@otenet.gr
Dimopoulos
Ioannis F
IF
D000393:Air Pollutants / Q000032:analysis*; D005544:Forecasting; D005740:Gases; D008628:Mercury / Q000032:analysis*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012987:Soil; D014835:Volatilization
D000393:Air Pollutants; D005740:Gases; D012987:Soil; D008628:Mercury