Does the Choice of NOEC or EC10 Affect the ... - ACS Publications

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Environmental Science & Technology

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RESEARCH ARTICLE

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Does the choice of NOEC or EC10 affect the hazardous concentration for 5% of the

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species?

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Yuichi Iwasaki*,† Kensuke Kotani‡, Shosaku Kashiwada†, and Shigeki Masunaga†

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†Research Center for Life and Environmental Sciences, Toyo University, 1-1-1 Izumino,

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Itakura, Oura, Gunma 374-0193, Japan

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‡Graduate School of Environment and Information Sciences, Yokohama National

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University, 79-7 Tokiwadai, Hodogaya, Yokohama 240-8501, Japan

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†Faculty of Environment and Information Sciences, Yokohama National University, 79-7

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Tokiwadai, Hodogaya, Yokohama 240-8501, Japan

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Corresponding Author

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*E-mail: [email protected]

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Tel: +81-276-82-9337

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Fax: +81-276-82-9337

ACS Paragon Plus Environment


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Abstract

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We evaluated if the choice of no observed effect concentration (NOEC) or 10% effect

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concentration (EC10) affects the hazardous concentrations for 5% of the species (HC5s)

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estimated from species sensitivity distributions (SSDs). By reviewing available literature

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reporting NOECs and re-analyzing original toxicity data to estimate EC10s, we developed

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two SSDs for five chemicals (zinc, lead, nonylphenol, 3,4-Dichlorobenzenamine, lindane)

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based separately on 9 to19 EC10s and NOECs. On average, point estimates of HC5s based

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on EC10s were 1.2 (range: 0.6–1.9) times higher than those based on NOECs. However,

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both EC10-based and NOEC-based HC5s estimated for five substances were of the same

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order of magnitude, and their 95% confidence intervals overlapped considerably. Thus,

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although EC10 was chosen a representative of ECx in this study, our results suggest that

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the choice of ECx (e.g., EC5, EC10, or EC20) or NOEC does not largely affect the

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resulting HC5s. Therefore, use of NOECs would be acceptable particularly in regulatory

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contexts, although the NOEC has important shortcomings and should be used with caution.

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Keywords

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EC5, EC20, No observed effect concentration, Species sensitivity distribution, ecological

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risk assessment


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INTRODUCTION

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No observed effect concentration/level (NOEC or NOEL), which is the highest

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concentration that does not cause a statistically-significant adverse effect in a toxicity test,

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is one of the commonly-used toxic measurements in ecological risk assessments (ERAs).

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However, because of substantial shortcomings, the NOEC and a related toxicity

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measurement (i.e., lowest observed effect concentration, LOEC) have been heavily

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criticized for more than 30 years1-3. Reasons include that (1) statistical significance (i.e.,

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estimation of NOEC and LOEC) depends on experimental design, data variability, sample

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size, effect size, and statistical analysis used (including the significance level chosen); (2)

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statistical insignificance does not guarantee ecological, biological, or ecotoxicological

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insignificance; and (3) the magnitude of the effect at the NOEC or LOEC is not explicitly

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defined. Indeed, several studies demonstrated that effect sizes at NOECs are mostly

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between 0 and 20%4, 5.

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Recently, Landis and Chapman1 called for the ban of using NOECs and LOECs

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and for more emphasis on concentration–response approaches. This editorial stimulated a

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line of discussion that both agrees and disagrees with the original call6-10. The most

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frequently-applied alternative is to estimate the x% effect concentration (ECx) based on the



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concentration-response relationship. Values of 5–20% have been commonly suggested or

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used as the x% (e.g.,

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EC10) will likely be carried out at least gradually, it is important to determine if and how it

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affects the outcome of ERAs.

11-13

). Because the replacement of the NOEC with an ECx (e.g.,

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Species sensitivity distributions (SSD) have been frequently applied to estimate

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the hazardous concentration for 5% of the species (HC5), which is used as a “safe”

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concentration (e.g., for environmental water quality criteria) and a predicted no effect

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concentration (PNEC) in ERAs14-16, mostly by applying a safe (or assessment) factor. The

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SSD is typically estimated by fitting a statistical distribution (e.g., a log-normal

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distribution) to multiple NOECs. Although the relationship between NOEC and ECx has

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been evaluated in a few studies4, 5, it is uncertain how the use of ECx instead of NOEC

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changes the resulting HC5.

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In this study, we evaluated how the choice of EC10 or NOEC affects the resulting

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HC5s. Although EC10 was operationally selected as a representative ECx, discussion on

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using other values such as EC5 and EC20 can be found below. To this end, we first

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reviewed published literature that employed the SSD approach using NOECs (rarely ECx)

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and re-analyzed the original toxicity data to quantify concentration-response relationships


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that allow us to estimate EC10. Then, we estimated two SSDs for five chemicals (zinc, lead,

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nonylphenol,

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[lindane]) based separately on EC10s and NOECs, and compared the resulting HC5s.

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Through this procedure, we also estimated the magnitude of effect (in this study, predicted

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percent reduction compared to controls) at NOECs and LOECs and the EC10/NOEC ratios

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to investigate relationships between NOECs an EC10s. Determining whether and how the

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use of EC10 changes the resulting HC5 provides essential knowledge for performing ERAs

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and setting environmental quality criteria. Even if there is little impact, such information

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can help optimize risk assessment processes.

3,4-dichlorobenzenamine

[3,4-DCA],

gamma-hexachlorocyclohexane

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METHODS

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Data

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We referred to 15 risk assessment documents that include ecological risk assessments

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performed by the Research Center for Chemical Risk Management, National Institute of

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Advanced Industrial Science and Technology, Japan (https://unit.aist.go.jp/riss/crm/

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mainmenu/e_1.html) and Versteeg et al.17 that estimated SSDs for 11 substances. From

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these sources, we chose five substances based on two criteria: (1) the original SSD was



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estimated based mostly on NOECs, and (2) EC10s can be determined for more than 4 to 8

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species, which is the minimum database size that is generally accepted for regulatory

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contexts18. Furthermore, only ecotoxicity tests for which the data could be obtained from

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original literature, and that included more than four concentration levels including controls

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were used to estimate EC10s. The numbers of toxicity tests analyzed for the five chemicals

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were zinc19 (17), lead20 (19), nonylphenol21 (9), 3,4-DCA17 (16), and lindane17 (13).

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From the original literature used for estimation of NOECs, measured

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concentrations (if unavailable, nominal concentrations) and response variables (e.g.,

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survival) were collected to estimate the corresponding EC10s. If requisite data were only

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available in figures, we obtained numerical data using GSYS2.2 (Japan Nuclear Reaction

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Data Centre (JCPRG) Digitizing software; http://www.jcprg.org/gsys/gsys-j.html). Note

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that if the raw data were unavailable and the EC10 was estimated and used in the original

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study, we used the reported EC10 for the present study. Although bioavailability affects a

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metals toxicity22 we did not consider bioavailability in this study because our aim was to

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compare HC5s based on EC10s and NOECs.

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Data analysis


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Concentration-response relationships were modeled with the “drc” package (version

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2.3-96) in R version 3.0.2

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models (log-logistic, Weibull-1, and Weibull-2 functions with 2–4 parameters), and the best

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model with the smallest value of Akaike’s information criterion (AIC) was selected. A

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smaller AIC value indicates a more parsimonious model that makes better prediction (see

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e.g., ref 25).

23, 24

. Individual toxicity datasets were fitted by a total of 9

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The best models selected for most of the datasets were log-logistic, Weibull-1, and

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Weibull-2 models with two and three parameters. The three-parameter log-logistic model

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(LL.3) is

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y=

d 1 + exp(b(log(x) − log(e)))

Equation 1,

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where x is the substance concentration, b is the slope at the concentration e, and d is the

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maximum response value. In the two-parameter log-logistic model (LL.2), the value of d is

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set to 1. Similarly, the three-parameter Weibull-1 model (W1.3) and three-parameter

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Weibull-2 model (W2.3) are

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y = d exp( − exp(b(log( x) − log(e))))

Equation 2

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y = d (1 − exp( − exp(b(log( x ) − log(e)))))

Equation 3

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respectively, and the value of d is 1 in the two-parameter Weibull-1 model (W1.2) and



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two-parameter Weibull-2 model (W2.2). Further details on other models can be found in

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Ritz et al.23 and Ritz24. By using the best models selected, for individual toxicity datasets,

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we estimated the EC5, EC10 and EC20 and the percent reductions at the NOEC and LOEC

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compared to the control responses.

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We then fit the NOECs and EC10s separately to a log-normal distribution (i.e.,

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estimated SSD), and estimated HC5s and their 95% confidence limits26. If there were

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multiple NOECs or EC10s for a species, the geometric mean was calculated and used for

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deriving the SSD.

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RESULTS AND DISCUSSION

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Details about the species, endpoints, test durations, response concentrations, and best-fit

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concentration-response models in the 74 toxicity data set for the five chemicals are

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provided in Tables S1–S5, Supporting Information. For each chemical, 9 to 19 NOECs

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were available, although one and three EC10s were used as surrogate NOECs for zinc and

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lead, respectively, because the NOEC and LOEC were not reported for those studies

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(Tables S1 and S2). Major endpoints were survival (34%), reproduction (23%), growth

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(18%), and population growth (14%; Tables S1–S5). Other endpoints were fish deformity,


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algal biomass, insect emergence, time to hatch, and survival time. The numbers of species

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ranged from 8 to 16, and fish (47%), crustaceans (28%), and algae (9%), which are

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common taxon groups in general toxicity tests, dominated the datasets.

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Percent reduction predicted at NOEC and LOEC

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Among the five chemicals, no significant difference in the estimated magnitude of effect

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(i.e., percent reduction) at the NOECs or LOECs was observed (Kruskal-Wallis test; p =

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0.31 and 0.72, respectively; Figure S1 in Supporting Information). After pooling all the

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data, the median (and range) of percent reductions estimated at the NOECs and LOECs

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were 5.3 (0.0–67.6) and 34.0 (2.7–99.8)%, respectively (n = 69; Figure 1). This result

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indicates that, on average, the NOEC approximately equals the EC5. The distribution of

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percent reductions at the NOECs was not uniform, with 50% of reductions at the NOECs

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being ≤5%, 70% of reductions being ≤10%, and 85% of reductions being ≤20%. It is rather

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surprising that, in approximately 15% of cases, the actual effects at NOECs were larger

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than 20%. In contrast, the percent reductions at the LOECs were distributed almost

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uniformly from 0 to 100%. Given the reductions at the NOECs, this is simply because any

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percent reductions can be observed at the LOECs depending on the shape of



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concentration-response curves. These results are generally consistent with previous studies4,

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5

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(algae, cladocerans, and zebrafish; n = 22) were 4.7% and 3.0%, respectively5, which is

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close to our estimate despite different toxicity datasets used.

. For example, the mean percent reductions that were observed and predicted at NOECs

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Relationship between NOEC and EC10

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The median (and range) of the distribution of EC10/NOEC ratios was 1.3 (0.1–6.5; Figure

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S2), indicating that EC10s were usually higher than NOECs, as with obtained results above.

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Among all the toxicity data, the EC10 was lower than the NOEC in approximately 30% of

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the cases (Figure 2). These results are similar to Isnard et al.5, who reported the median

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EC10/NOEC ratio was 1.32.

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Influences of using EC10 on species sensitivity distributions

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On average, HC5s estimated based on EC10s (hereafter referred to as the EC10-based

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HC5) were 1.2 times higher than those estimated based on NOECs ((range of ratios of

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(EC10 based HC5)/(NOEC based HC5): 0.6–1.9; Table 1 and Figure 3); only for lindane,

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the EC10-based HC5 (0.17 µg/L) was lower than the NOEC-based HC5 (0.27 µg/L).


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However, both EC10-based and NOEC-based HC5s estimated for five substances were of

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the same order of magnitude (Table 1). The smaller range of (EC10-based

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HC5)/(NOEC-based HC5) ratios is somewhat surprising given that the range of the

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EC10/NOEC ratios was relatively large (0.1–6.5). This is because HC5s are less affected

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by individual toxic values since SSDs are derived from multiple NOECs or EC10s and we

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fitted the log-normal distribution to the data (in other words, log-transformed values of

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NOECs and EC10s were used to derive SSDs). Overall, the close overlap of the 95%

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confidence intervals for EC10-based HC5s with those for NOEC-based HC5s suggests that

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the choice of EC10 or NOEC has little influence on the resulting HC5s.

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Implications and limitations

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This study provides empirical evidence that the choice of EC10 or NOEC does not largely

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affect the resulting HC5s estimated from SSDs. If EC5s or EC20s were used instead of

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NOECs to calculate HC5s, the resulting point estimates of HC5 would have decreased or

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increased marginally, but the confidence intervals of HC5s derived from EC5s and EC20s

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would have still overlapped those derived from NOECs. Indeed, because NOECs generally

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corresponded to EC5s in the current datasets, the EC5-based HC5 was closer to the



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NOEC-based HC5 (data not shown).

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Other factors affect SSDs and thus HC5s: (1) how to estimate the SSD (selection

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of statistical distributions, and use of bootstrap-based and regression-based estimations)14,

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27, 28

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proportions of multiple taxonomic groups included in an SSD (e.g., how to adjust the

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proportions of algae, invertebrates, and vertebrates)29, 30. By deriving chronic SSDs for 15

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substances, Duboudin et al.29 demonstrated that the latter two factors had more influence on

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estimation of HC5s than the first factor. Also, the selection of toxicity data used and acute

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versus chronic definitions can be very important31. Furthermore, there still remains

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considerable uncertainty in the extrapolation from results of simplified laboratory toxicity

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tests to field effects32. Therefore, we suggest that the overall influence of the choice of

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NOEC or EC10 on the estimation of HC5 is trivial.

, (2) how to deal with multiple toxicity data for a species29, and (3) how to determine

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We conclude that the use of NOECs would be practically acceptable for

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calculation of HC5s, particularly in regulatory contexts, since our results indicate that

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HC5s derived from EC10s or NOECs do not differ much. However, we emphasize that

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NOECs have important shortcomings as discussed elsewhere (see the Introduction section).

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We suggest that actual magnitudes of effect (e.g., percent reduction of survival,


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reproduction, etc.) be provided for NOECs in case of using or reporting them. This is

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essential for transparent ERAs, as the NOEC should not be thought of as the concentration

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where there are no effects. In addition, sensitive traits in terms of NOECs might not

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correspond to traits critical for population-level consequences33 that are determined

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through nonlinear interaction among effects on individual-level traits such as survival and

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reproduction. Thus, information on actual effect size at the NOEC is unquestionably

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important in interpretation of the test results. Note that the use of ECx has a similar issue

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and the x value must be carefully chosen7. Likewise, reporting uncertainty in estimates of

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ECx is also important (see e.g., Tables S1–5) to assess the reliability although such

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information for NOECs cannot be generally estimated. Finally, we recommend that

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concentration-response relationships be quantitatively modeled to the extent possible1, and

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that model parameters as well as raw data be reported so readers can generate ECx values

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such as the EC10.

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■ASSOCIATED CONTENT

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Supporting Information



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Table S1: Summary table for zinc. Table S2: Summary table for lead. Table S3: Summary

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table for nonylphenol. Table S4: Summary table for 3,4-dichlorobenzenamine.

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Table S5: Summary table for lindane. Figure S1: Percent reduction of endpoint response

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estimated at the no observed effect concentration (NOEC) and the lowest observed effect

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concentration (LOEC) for the five chemicals evaluated in this study. Figure S2:

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Relationships between the no observed effect concentration (NOEC) and the 10% effect

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concentration (EC10) for the five chemicals evaluated in this study. This material is

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available free of charge via the Internet at http://pubs.acs.org

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AUTHOR INFORMATION

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Corresponding Author

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*Dr. Yuichi Iwasaki; phone: +81-276-82-9337; e-mail: [email protected]

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Notes

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The authors declare no competing financial interest.

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ACKNOWLEDGMENT

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Useful comments and edits by J.S. Meyer and four anonymous reviewers are greatly


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appreciated. This study is partly supported by the New Project Fund for Risk Assessments,

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from the Ministry of Economy, Trade and Industry, Japan and a Grant-in-Aid for Strategic

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Research Base Project for Private Universities, which is funded by the Ministry of

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Education, Culture, Sport, Science, and Technology, Japan, 2014–2018 (Grant number

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S14111016). YI was funded by the Japan Society for the Promotion of Science (JSPS)

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Research Fellowship for Young Scientists.

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Figure captions

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Figure 1. Cumulative probability distribution of percent reductions (i.e., effect magnitudes)

339

observed at the no observed effect concentration (NOEC) and lowest-observed effect

340

concentration (LOEC). Data were pooled across the five chemicals evaluated in this study

341

(n = 69). Tests in which only EC10s were available or raw data was unavailable were

342

excluded.

343 344

Figure 2. Cumulative probability distribution of ratios of the 10% effect concentration

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(EC10) to the no observed effect concentration (NOEC). Data were pooled across the five

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chemicals evaluated in this study (n = 70). Tests in which only EC10s were available were

347

excluded.

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Figure 3. NOEC- and EC10-based species sensitivity distributions for zinc, lead,

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nonylphenol, 3,4-dichlorobenzenamine (3,4-DCA), and lindane.

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NOEC = no observed effect concentration, EC10 = 10% effect concentration

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Table 1. Estimated hazardous concentration for 5% of species (HC5) based on no observed

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effect concentrations (NOECs) and 10% effect concentrations (EC10s). NOEC-based

EC10-based

Substance HC5

95% CI

HC5

95% CI

Zinc

21.5

4.2–46.3

22.3

3.8–50.3

Lead

6.61

0.99–16.9

7.99

1.34–19.61

Nonylphenol

2.75

0.10–8.78

3.48

0.11–11.38

3,4-DCA

1.12

0.01–4.59

2.12

0.03–8.74

Lindane

0.27

0.00–1.31

0.17

0.00–0.90

355

CI = confidence interval.

356

3,4-DCA = 3,4-dichlorobenzenamine



1

Page 24 of 27

TOC/Abstract Art

Fraction affected

Zinc Question: Does the choice of NOEC or EC10 affect the hazardous concentration for 5% of the species?

Lead

NOEC-based EC10-based

Nonylphenol

3,4-DCA

2 3


Lindane

Page 25 of 27

Figure 1

Cumulative probability

4


NOEC

Percent reduction 5


LOEC


Figure 2

Cumulative probability

6

EC10/NOEC 7


Page 26 of 27

Page 27 of 27

8


Figure 3

Zinc

Lead

NOEC-based

Fraction affected

EC10-based

Nonylphenol

3,4-DCA

Concentration (μg/L) Lindane

Concentration (μg/L) 9


Does the Choice of NOEC or EC10 Affect the ... - ACS Publications

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