Inland waters are substantial sources of atmospheric carbon, but relevant data are rare in Asian monsoon regions including Korea. Emissions of CO2 to the atmosphere depend largely on the partial pressure of CO2 (pCO2) in water; however, measured pCO2 data are scarce and calculated pCO2 can show large uncertainty. This study had three objectives: 1) to examine the spatial variability of pCO2 in diverse surface water systems in Korea; 2) to compare pCO2 calculated using pH-total alkalinity (Alk) and pH-dissolved inorganic carbon (DIC) with pCO2 measured by an in situ submersible nondispersive infrared detector; and 3) to characterize the major environmental variables determining the variation of pCO2 based on physical, chemical, and biological data collected concomitantly. Of 30 samples, 80% were found supersaturated in CO2 with respect to the overlying atmosphere. Calculated pCO2 using pH-Alk and pH-DIC showed weak prediction capability and large variations with respect to measured pCO2. Error analysis indicated that calculated pCO2 is highly sensitive to the accuracy of pH measurements, particularly at low pH. Stepwise multiple linear regression (MLR) and random forest (RF) techniques were implemented to develop the most parsimonious model based on 10 potential predictor variables (pH, Alk, DIC, Uw, Cond, Turb, COD, DOC, TOC, Chla) by optimizing model performance. The RF model showed better performance than the MLR model, and the most parsimonious RF model (pH, Turb, Uw, Chla) improved pCO2 prediction capability considerably compared with the simple calculation approach, reducing the RMSE from 527-544 to 105μatm at the study sites.
Keywords: CO(2) supersaturation; Random forests; Variable selection; pCO(2) prediction model.
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