Effects of Organic Carbon Origin on Hydrophobic Organic Contaminant Fate in the Baltic Sea

Abstract

The transport and fate of hydrophobic organic contaminants (HOCs) in the marine environment are closely linked to organic carbon (OC) cycling processes. We investigated the influence of marine versus terrestrial OC origin on HOC fluxes at two Baltic Sea coastal sites with different relative contributions of terrestrial and marine OC. Stronger sorption of the more than four-ring polycyclic aromatic hydrocarbons and penta-heptachlorinated polychlorinated biphenyls (PCBs) was observed at the marine OC-dominated site. The site-specific partition coefficients between sediment OC and water were 0.2−1.0 log units higher at the marine OC site, with the freely dissolved concentrations in the sediment pore-water 2−10 times lower, when compared with the terrestrial OC site. The stronger sorption at the site characterized with marine OC was most evident for the most hydrophobic PCBs, leading to reduced fluxes of these compounds from sediment to water. According to these results, future changes in OC cycling because of climate change, leading to increased input of terrestrial OC to the marine system, can have consequences for the availability and mobility of HOCs in aquatic systems and thereby also for the capacity of sediments to store HOCs.

Passive-Sampler-Derived PCB and OCP Concentrations in the Waters of the World─First Results from the AQUA-GAPS/MONET Network

“…24 In brief, for almost all target compounds, the detection frequencies were slightly higher in the SSP than in the LDPE: 87 versus 78% for Σ 7 PCBs, 90 vs 80% for ΣDDTs, 41 vs 16% for ΣHCHs, and 62 vs 40% for other OCs. 20 Detection frequencies in SSP ranged from 100% for PCB 52, HCB, o,p′-DDD, p,p′-DDD, and trans-chlordane to 2% for δ-HCH, with a median frequency of 79% for 36 target compounds. In LDPE, the median detection frequency was 60% ranging from 100% for HCB to 0 for δ-HCH and several other OCPs.…”

“…In modeling global PCBs in the oceans, Wagner et al noted that current data (2000–2015) for PCBs in the open oceans and coastal marine waters were sparse except for the Arctic Ocean. Regional seas such as the Baltic and the Mediterranean, as well as the Great Lakes, have better coverage using active − and passive sampling approaches , but involved multiple research groups and analytical techniques.…”

“…The data obtained from these deployments were used for a detailed comparison of the performance of the two passive sampling phases . In brief, for almost all target compounds, the detection frequencies were slightly higher in the SSP than in the LDPE: 87 versus 78% for Σ 7 PCBs, 90 vs 80% for ΣDDTs, 41 vs 16% for ΣHCHs, and 62 vs 40% for other OCs . Detection frequencies in SSP ranged from 100% for PCB 52, HCB, o , p ′-DDD, p , p ′-DDD, and trans -chlordane to 2% for δ-HCH, with a median frequency of 79% for 36 target compounds.…”

Fluorescent Visualization of Chemical Profiles across the Air–Water Interface

“…G (τ)) can be fitted with eq after low-pass filtering (Figures C and S12), to yield D ( z ) of pyrene with eq (Figure D) which remain largely constant at z ≫ d DL and z ≪ d SML in the gaseous and aqueous phases ((3.6 ± 0.06) × 10 –2 and (8.6 ± 3.2) × 10 –6 cm 2 s –1 ; Table S2). These values were comparable to those obtained previously . Furthermore, the value in the aqueous phase ((8.6 ± 3.2) × 10 –6 cm 2 s –1 ) is within the same order of magnitude of the theoretical value 2.1 × 10 –6 cm 2 s –1 assuming that the size of pyrene is 0.8 nm and the viscosity of water is 1 mPa s. D ( z ) follows an exponentially rising pattern along the z direction, which is D ( z ) = e az + b , revealing that D ( z = d SML ) is greater than D ( z ) in the bulk water (Table S2).…”

“…The chemical diffusion coefficient (D) near the air−water interface is often treated as a constant equal to that in bulk solution or the atmosphere. 41 However, the air−water interface may induce significant spatial heterogeneity in the diffusion dynamics of pyrene along the z direction. To assess the magnitude of this spatial heterogeneity, the spatialmodulated fluorescence correlation spectrum algorithm is applied to estimate the z-dependent apparent diffusion coefficient (D(z)) from time-dependent counts(z, t) with high sampling rates, that is, (Δt) −1 = 10 6 Hz in the gaseous phase and (Δt) −1 = 5 Hz in the aqueous phase.…”