De Schamphelaere, K.

Demeestere, K.

Huysman, S.

Janssen, C.

Moeris, S.

Van Langenhove, H.

Vanhaecke, L.

Vanryckeghem, F.

English

Open Marine Archive

Using no observed effects to identify main contributing 
micropollutants in mixture toxicity assessment 
Samuel Moeris1, Francis Vanryckeghem2, Steve Huysman3, Kristof Demeestere2, Lynn 
Vanhaecke3, Herman Van Langenhove2, Colin Janssen1 and Karel De Schamphelaere1 
1Department of Applied Ecology and Environmental Biology, Laboratory of Environmental Toxicology and 
Aquatic Ecology (GhEnToxLab), Ghent University, Coupure Links 653, Builidng F, 9000 Ghent, Belgium 
2Department of Sustainable Organic Chemistry and Technology, Research Group Environmental Organic 
Chemistry and Technology (EnVOC), Ghent University, Coupure Links 653, Building B, 9000 Ghent, Belgium 
3Department of Veterinary Public Health and Food Safety, Laboratory for Chemical Analysis (LCA), 
Salisburylaan 133, Hoogbauw, 9820 Merelbeke, Belgium 
 
E-mail contact: samuel.moeris@ugent.be   
1. Introduction 
In current environmental risk assessment, researchers assess effects caused by single substances towards 
single species and extrapolate for realistic conditions where organisms are usually exposed to complex 
micropollutant mixtures. The use of passive sampling opens new possibilities to work with such mixtures and 
transfer them into biotest systems by either applying passive dosing (for equilibrium based samplers) or 
extract spiking (for integrative samplers). The advantages and disadvantages of both methods were 
described in detail elsewhere [1]. 
Only few ecotoxicological studies tested environmentally realistic contaminant mixtures e.g. using either 
passive dosing [2] or extract spiking [3]. Interestingly exposure of marine diatoms to realistic mixture 
concentration levels ranged from 50 % growth stimulation to 100 % growth inhibition [2, 3] and the 
authors concluded that “exposure to low levels of persistent organic pollutants may threaten sensitive 
genotypes and benefit healthy populations” [3]. This argument could be adapted to stimulation effects stating 
that “stimulation effects favouring the growth of one species may limit the growth of concurring species”. 
Our initial research objective was to investigate whether or not environmentally realistic contaminant 
mixtures have effects on marine phytoplankton and how eventual effects could be explained. Based on our 
findings we further formulated the hypothesis that no observed effects can be used to identify main 
contributing micropollutants to observed growth stimulation effects caused by environmentally 
realistic contaminant mixtures. 
2. Materials and methods 
In the presented research we used extracts of Speedisk™ passive samplers deployed in and outside of the 
harbour of Zeebrugge (Belgium) for 8 weeks to spike 72 h growth inhibition tests with the marine diatom 
Phaeodactylum tricornutum following ISO 10253. The different growth inhibition tests were performed over a 
time of 16 months with tests 0, 8 and 16 months after extraction and thus considerably different extract 
storage time.  
In detail, Speedisks™ passive samplers were rinsed with seawater on site after retrieval and further rinsed in 
the lab by applying 10 mL HPLC grade water. The samplers were eluted with 10 mL of a mix of methanol: 
acetonitrile (50:50 v/v) and the elution solvent fully evaporated under nitrogen. The precipitate was re-
dissolved in 1 mL of a methanol: HPLC water mix (10:90 v/v) + 0.1 % formic acid + 0.01 % EDTA. Re-
dissolved extract were stored at -20 °C in the dark until chemical analysis or biotesting. For each location 3 
replicate Speedisk™ extracts were analysed and tested. 
For biotesting a concentration series with 10 concentration treatments following dilution factor 5 was 
prepared. P. tricornutum was cultured following ISO 10253 and 4 days prior to the actual test, a pre-culture 
with a cell density of 10,000 cells/mL was inoculated in 100 mL erlenmeyer flasks. At test start 100 µL of 
each concentration treatment were added to 50 mL of fresh algae test medium in triplicates. Further a 
dilution series of extracts of non-deployed Speedisks™ (procedural blanks, beside test 1) as well as 6 flasks 
with pure test medium (controls) and another 6 flasks with test medium spiked with the pure solvent used for 
extraction (solvent controls) were included. All flasks were inoculated with 10,000 cells/mL from the pre-
culture and kept randomly under continuous white light (7,500 – 9,000 lux) at 22 ± 1 °C for 72 h. Algae cells 
were randomly counted every 24 h using a particle counter (Beckman Z™ series Coulter Counter®). 
3. Results and discussion 
3.1. Algae growth inhibition experiments 
The algae growth inhibition experiments resulted in growth stimulation of P. tricornutum for both sampling 
locations for the extracts stored for 0 (test 1) and 8 (test 2) months. The extracts stored for 16 (test 3) months 
did not show any effects on the growth of P. tricornutum as seen in Figure 1. Whenever stimulation effects 
were observed, these were constantly significant over the 3 to 5 highest concentration treatments for test 2 
and 1, respectively.  
 
Figure 1 Concentration-response testing of the Speedisk™ extract after different extract storage times (0, 8 and 16 months). 
Test concentrations range from 0.001 µL to 2 µL extract per mL medium. Shown is the % of growth stimulation vs. the log 
extract equivalent concentration (log C EEQ). Blank (red triangles) Speedisks™ were tested in duplicates, harbour (green 
circles) and sea (blue squares) samples in triplicates. Error bars represent the standard deviation (SD) and the significance 
levels indicate significant differences of the growth rates as compared to the controls (ANOVA followed by Dunnett’s Multiple 
Comparison Test; significance levels: * = P ≤ 0.05 and ** = P ≤ 0.01). 
3.2. Chemical analysis 
Chemical analysis of the Speedisk™ extracts revealed the presence of 36 ± 5 out of a list of 89 pesticides, 
pharmaceuticals and personal care products (PPPCPs) in the harbor of Zeebrugge and 29 ± 15 PPPCPs 
outside of the harbor. Table 1 shows the concentrations of part of the analysed compounds. 
The tested summed PPPCP concentrations were 125 ± 16 ng/L and 116 ± 42 ng/L for the harbor and the 
sea extracts, respectively. These concentrations correlate well with summed PPPCP concentration levels 
analysed in grab samples taken at the moment of passive sampler deployment at both sampling locations. 
Table 1 Example of 8 compounds analysed in the Speedisk extracts. Shown are the concentrations in 1 mL re-dissolved 
extract in ng/L for the Speedisk™ triplicates as well as their mean and standard deviation (SD). Empty cells indicate no 
detection of the compound in the respective extract. PE = Pesticides, PH = Pharmaceuticals 
Compound Compound group 
 Concentration Harbour Zeebrugge [ng/L] Concentration Sea Zeebrugge [ng/L] 
Harbor 1 Harbor 2 Harbor 3 Mean SD Sea 1 Sea 2 Sea 3 Mean SD 
Acetamiprid PE 
 
97 104     
   
  
Acyclovir PH 1659 
 
915   5451 1139 508 2366 2690 
Alachlor PE 
    
    
   
  
Alprazolam PH 
    
    129 
  
  
Amantadine PH 3490 3618 3655 3588 87 13809 11432 12074 12438 1230 
Amitriptyline PH 
    
    
   
  
Atenolol PH 3080 2973 2965 3006 64 12908 6403 6218 8510 3810 
Atrazine PE 1239 
 
1183     2517 
  
  
4. Conclusions 
At this moment we are analyzing both Speedisk™ extracts and test media to compare the contaminant 
concentrations in the different tests. Once the results are available (december 2017) we will perform principal 
component analysis to identify main contributing compounds to the observed effects for both targeted and 
non-targeted analysis. 
Our results showed growth stimulation effects on P. tricornutum for Speedisk™ ecxtracts stored 0 and 8 
months while the effects disappeared after 16 months. We therefore hypothesised that the main contributing 
micropollutants to the observed effects must have degraded during the extract storage. Stimulation effects 
favouring specific phytoplankton species might lead to shifts in community compositions and might have 
impacts on marine ecosystems. We therefore emphazise the implementation of environmentally realistic 
contaminant mixture toxicity assessment into environmental risk assessment. 
Table 1 could look like this 
5. References 
1. Jahnke, A., et al., Combining Passive Sampling with Toxicological Characterization of Complex 
Mixtures of Pollutants from the Aquatic Environment. Adv Biochem Eng Biotechnol, 2016. 
2. Claessens, M., et al., Passive sampling reversed: Coupling passive field sampling with passive lab 
dosing to assess the ecotoxicity of mixtures present in the marine environment. Marine Pollution 
Bulletin, 2015. 93(1-2): p. 9-19. 
3. Echeveste, P., et al., Toxicity of natural mixtures of organic pollutants in temperate and polar marine 
phytoplankton. Science of the Total Environment, 2016. 571: p. 34-41. 
Acknowledgement - The authors thank the Belgian Science Policy (BELSPO) for funding the NewSTHEPS 
project (BR/143/A2/NEWSTHEPS; www.newstheps.be). 
Further financial support from the Hercules Foundation (Flemish Government; AUGE/11/016) and from the 
Ghent University Special Research Fund (01B07512) is acknowledged for the UHPLC-Q-Exactive™ and the 
automated SPE equipment, respectively. 
 
 


Using no observed effects to identify main contributing micropollutants in mixture toxicity assessment

http://www.vliz.be/imisdocs/publications/324391.pdf

Using no observed effects to identify main contributing micropollutants in mixture toxicity assessment

Abstract

Similar works

Full text

Available Versions

Open Marine Archive