How synergistic or antagonistic effects may influence the mutual hazard ranking of chemicals

Lars Carlsen; Lars Carlsen

doi:10.3934/environsci.2015.2.241

AIMS Environmental Science

2015, Volume 2, Issue 2: 241-252. doi: 10.3934/environsci.2015.2.241

Previous Article Next Article

Research article

How synergistic or antagonistic effects may influence the mutual hazard ranking of chemicals

Lars Carlsen ^{1,2
,
,}

1.
Awareness Center Linkøpingvej 35, Trekroner, DK-4000 Roskilde, Denmark;
2.
Center of Physical Chemical Methods of Research and Analysis, al-Farabi Kazakh National University, 96A Tole Bi st., 050012, Almaty, Kazakhstan

Received: 02 December 2014 Accepted: 31 March 2015 Published: 12 April 2015

The presence of various agents, including humic materials, nanomaterials, microplastics, or simply specific chemical compounds, may cause changes in the apparent persistence, bioaccumulation, and/or toxicity (PBT) of a chemical compound leading to an either increased or decreased PBT characteristics and thus an increased or decreased hazard evaluation. In the present paper, a series chloro-containing obsolete pesticides is studied as an illustrative example. Partial order methodology is used to quantify how changed P, B, or T characteristics of methoxychlor (MEC) influences the measure of the hazard of MEC, relative to the other 11 compounds in the series investigated. Not surprisingly, an increase in one of the three indicators (P, B, or T) lead to an increased average order and thus an increased relative hazard as a result of a synergistic effect. A decrease in one of the indicator values analogously causes a decreased average order/relative hazard through an antagonistic effect; the effect, however, being less pronounced. It is further seen that the effect of changing the apparent value of the three indicators is different. Thus, persistence apparently is more important that bioaccumulation which again appears more important than toxicity, which is in agreement with previous work. The results are discussed with reference to the European chemicals framework on registration, evaluation and authorization of chemicals (REACH) framework.
- synergistic effects,
- antagonistic effects,
- partial order ranking,
- Hasse diagram,
- average order,
- chemical hazard
Citation: Lars Carlsen. How synergistic or antagonistic effects may influence the mutual hazard ranking of chemicals[J]. AIMS Environmental Science, 2015, 2(2): 241-252. doi: 10.3934/environsci.2015.2.241

Related Papers:

Abstract

The presence of various agents, including humic materials, nanomaterials, microplastics, or simply specific chemical compounds, may cause changes in the apparent persistence, bioaccumulation, and/or toxicity (PBT) of a chemical compound leading to an either increased or decreased PBT characteristics and thus an increased or decreased hazard evaluation. In the present paper, a series chloro-containing obsolete pesticides is studied as an illustrative example. Partial order methodology is used to quantify how changed P, B, or T characteristics of methoxychlor (MEC) influences the measure of the hazard of MEC, relative to the other 11 compounds in the series investigated. Not surprisingly, an increase in one of the three indicators (P, B, or T) lead to an increased average order and thus an increased relative hazard as a result of a synergistic effect. A decrease in one of the indicator values analogously causes a decreased average order/relative hazard through an antagonistic effect; the effect, however, being less pronounced. It is further seen that the effect of changing the apparent value of the three indicators is different. Thus, persistence apparently is more important that bioaccumulation which again appears more important than toxicity, which is in agreement with previous work. The results are discussed with reference to the European chemicals framework on registration, evaluation and authorization of chemicals (REACH) framework.

References

[1]	Sailaukhanuly Y, Zhakupbekova A, Amutova F, et al. (2013) On the Ranking of Chemicals based on their PBT Characteristics: Comparison of Different Ranking Methodologies Using Selected POPs as an Illustrative Example. Chemosphere 90: 112-117. doi: 10.1016/j.chemosphere.2012.08.015
[2]	Steinberg ChEW, Haitzer M, Geyer HJ (2001) Biokonzentrations studien in Abhängigkeit von Huminstoffen, In: Streß in limnischen Ökosystemen. Neu Ansätze in der ökotoxikologischen Bewartung von Binnengewässern, Steinberg, Ch.E.W., Brüggemenn, R., Kümmerer-Liess, K., Pflugmacher, St and Zauge, G.-P. (eds), Parey Buchverlag, Berlin, 2001, section 2.2, pp. 16-25.
[3]	Beat the microbead (2014) Microplastics: scientific evidence, http://beatthemicrobead.org/en/science (accessed Nov, 2014).
[4]	Syberg K (2009) Mixture toxicity and European chemical regulation: a interdisciplinary study, PhD thesis, Roskilde University, 88pp.
[5]	Stockholm Convention (2008) http://chm.pops.int/Home/tabid/2121/language/en-GB/Default.aspx (accessed Nov. 2014).
[6]	Mallouk TE, Yang P (2009) Chemistry at the Nano-Bio Interface. J Am Chem Soc 131: 7937-7939. doi: 10.1021/ja9038104
[7]	EPA (2013) Estimation Program Interface (EPI) Suite, ver. 4.11 http://www.epa.gov/oppt/exposure/pubs/episuite.htm (accessed Nov. 2014).
[8]	Walker JD, Carlsen L (2002) QSARs for identifying and prioritizing substances with persistence and bioaccumulation potential, SAR QSAR. Environ Res 13: 713-726.
[9]	Koch V (2008) PBT Assessment & Category Approach, http://reach.setac.eu/embed/presentations_reach/18_KOCH_PBT_Assessment.pdf (accessed Nov. 2014).
[10]	Bruggemann R, Carlsen L (2012) Multi-criteria decision analyses. Viewing MCDA in terms of both process and aggregation methods: Some thoughts, motivated by the paper of Huang, Keisler, and Linkov. Sci Tot Environ 425: 293-295.
[11]	Bruggemann R, Carlsen L (2006) Partial Order in Environmental Sciences and Chemistry. Springer, Berlin.
[12]	Bruggemann R, Patil GP (2011) Ranking and prioritization for multi-indicator systems—Introduction to partial order applications. Springer, New York.
[13]	Bruggemann R, Annoni P (2014) Average heights in partially ordered sets. MATCH Commun Math Comput Chem 71: 117-142.
[14]	De Loof K, De Meyer H, De Baets B (2006) Exploiting the Lattice of Ideals Representation of a Poset. Fundamenta Informaticae 71: 309-321.
[15]	Morton J, Pachter L, Shiu A, et al. (2009) Convex Rank Tests and Semigraphoids. SIAM J Discrete Math 23: 1117-1134. doi: 10.1137/080715822
[16]	Wienand O (2006) lcell, http://bio.math.berkeley.edu/ranktests/lcell/ (accessed Nov 2014).
[17]	Bruggemann R, Carlsen L (2011) An Improved Estimation of Averaged Ranks of Partially Orders. MATCH Commun Math Comput Chem 65: 383-414.
[18]	Bruggemann R, Carlsen L, Voigt K, et al. (2014) PyHasse Software for Partial Order Analysis. In R. Bruggemann, L. Carlsen, J. Wittmann (Eds.), Multi-Indicator Systems and Modelling in Partial Order (pp. 389–423). Springer, New York.
[19]	EC (2006) Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC; Article 57d,e (http://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1415770830954&uri=CELEX:32006R1907); Accessed Nov. 2014)
[20]	Munda G (2008) Social Multi-Criteria Evaluation for a Sustainable Economy. Springer-Verlag, Berlin.

Reader Comments

Your name:*

Email:*
© 2015 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)