A Bayesian statistical approach to inferring particle dynamics from in-situ pump POC and chloropigment data from the Mediterranean Sea

摘要

Concentrations of chloropigments and particulate organic carbon (POC) in large-volume in-situ pump samples from the Mediterranean Sea were used to estimate rate constants of processes that control the fate of particles, and specifically chloropigments, in the water column. Here we introduce a Bayesian statistical inversion method that combines the data with a new box model and has the capacity to infer rate constants for POC respiration/dissolution, chlorophyll and pheopigment degradation, and particle aggregation and disaggregation. We use first-order kinetics to model disaggregation, and use both first-order and second-order kinetics to model aggregation. Using these methods, the estimated small-particle (1-70 mu m) POC respiration rate constant was 2.44(-1.00)(+1.69) yr(-1) (0.41 yr). The estimated disaggregation and second-order aggregation rate constants were 85.6(-36.4)(+63.4) yr(-1) (1.17 x 10(-2) yr) and 2.78(-1.17)(+2.01)mu M-1 yr(-1), respectively. Using the optimal rate constants and the corresponding particle concentrations, disaggregation is similar to 4.2 times faster than the small-size POC dissolution rate, which indicates that disaggregation is a dominant processes at the time of sampling. More importantly, by comparing our results with those of previous studies, we conclude that sampling methods have less influence than tracers themselves on inferring particle dynamic rate constants. We previously introduced a somewhat similar approach to modeling SV sediment trap data, but large volume pumps are a much more common sample collection method in oceanographic surveys than SV sediment traps, and thus our new model should have a wider applicability.