1 INTRODUCTION

Sulfometuron-methyl (SM) is a non-selective sulfonylurea herbicide, with a broad herbicidal spectrum, strong inner-absorption conductivity, and a long period of action. At present, SM is not only used to control annual and perennial broad-leaved weeds and grasses in non-agricultural places (i.e., forestry, non-crop industrial places, or unmodified turf), but also adopted to kill weeds in agricultural areas (Kang, 2012). SM is often used alone, or in combination with diuron and hexazinone in sugarcane plantations in Brazil for weed control (Crusciol et al., 2017; Da Silva Teófilo et al., 2020). A previous study has shown that only 10%–30% of herbicides are absorbed by target plants or soil particles, and most thereof will enter groundwater or surface runoff through rainfall or irrigation, eventually being transported to coastal waters (Yang et al., 2019). SM has been detected in surface water and effluent water from sewage plants in some regions of the United States (at a concentration below 0.03 μg/L) (Baker et al., 2022), while Yi et al. (2004) detected 0.16–2.60-μg/L SM in groundwater contaminated areas of Xi'an, China. In recent years, SM has been applied to kill Spartina alterniflora, an invasive plant affecting the coastal waters of China (Wang, 2017). This results in the direct introduction of SM into the seawater, which is likely to be harmful to non-target marine organisms (Zhang et al., 2017). Previous research has shown that SM has high acute toxicity to freshwater microalgae and plants (no marine species have been reported). The median effective concentrations (EC₅₀) of SM on Pseudokirchneriella subcapitata, Anabaena flosaquae, and Lemna gibba were 0.004 3 mg/L (5 d), 0.073 mg/L (5 d), and 0.000 45 mg/L (14 d), respectively (USEPA, 2022). The acute toxicity of SM to marine crustaceans and fish has also been reported. The 96-h median lethal concentrations (LC₅₀) of Americamysis bahia and Cyprinodon variegatus were 44.8 and 45 mg/L respectively (USEPA, 2022). Therefore, it is necessary to evaluate the marine ecological risk of SM used in sea areas for control of S. alterniflora.

At present, the ecological risk of herbicides is evaluated from mostly single-species toxicity data, but the results may lack objectivity because this cannot reflect the fact that multiple species in the ecosystem are affected by pollution simultaneously. The species sensitivity distribution (SSD) method developed in the late 1970s can be adopted to evaluate the harm of pollutants at the ecosystem level by considering the differences of sensitivity to one specific contaminant among species. By doing so, the uncertainty resulting from the extrapolation of toxicity data based on one species to other species can be reduced. The principle underpinning this method is simple, and its ecological significance is clear. Therefore, in the past decade, it has become a commonly used method for risk assessment around the world (Du et al., 2013; He et al., 2019; Van Den Brink et al., 2019).

An SSD approach assumes that the sensitivity of various organisms in the ecosystem to pollutants can be characterized by toxicity data such as LC₅₀, EC₅₀, and no-observed effect concentration (NOEC), and may follow a certain distribution (such as a log-normal distribution, log-logistic distribution). Furthermore, the available toxicological data are regarded as a sample of the sensitivity distribution of ecosystem, and then SSD curves are constructed for estimating the hazardous concentration for 5% of species (HC₅) and the potentially affected fraction (PAF) of species. Both parameters (HC₅ and PAF) are often used to estimate the potential ecological risk for species in a certain environment (Barron and Wharton, 2005).

Microalgae, acting as the dominant primary producers in marine ecosystems, play a key role in marine productivity and they form the base of oceanic food webs (Duan et al., 2017). Microalgae are often used as test species for chemical toxicology research and environmental risk assessment because of their high sensitivity to pollutants (Weyers and Vollmer, 2000). However, according to the ECOTOX database (USEPA, 2022) of the U.S. Environmental Protection Agency (USEPA), EC₅₀ data of SM on marine microalgae are extremely scarce, and there is only one NOEC datum involved in diatom Skeletonema costatum. Also, in the Pan Pesticide Database, no data pertaining to the acute toxicity of SM to marine microalgae are quoted (PAN, 2022). This generates a fundamental problem when attempting to apply the SSD approach to evaluation of the ecological risk for SM in a marine environment. In the present study, growth inhibition tests were conducted to obtain the 96-h EC₅₀ values of SM to six marine microalgae, and then these values were combined with the toxicity data of SM to other organisms reported in the ECOTOX database and other literature. In this way, SSD curves were constructed. Furthermore, the HC₅ of SM and its PAF values at different concentrations on marine organisms were calculated. To the best of our knowledge, this is the first report to suggest an HC₅ value for SM using the SSD method and this will contribute to the management of this herbicide, based on ecological risk assessment, especially in sea areas for S. alterniflora control with SM spraying.

2 MATERIAL AND METHOD 2.1 Algae, acclimatization, and chemicals

Six species of marine microalgae including two diatoms (Phaeodactylum tricornutum and S. costatum), two green algae (Dunaliella salina and Chlorella Pacifica), and two golden algae (Diacronema viridis and Isochrysis galbana), were selected. All of them are provided by the Algal Culture Collection at Ocean University of China (OUC). Among them, S. costatum is the test species designated in the Ecological Effects Test Guidelines OCSPP 850.4500: Algal toxicity formulated by USEPA (USEPA, 2012). P. tricornutum is the test species designated in EN ISO 10253 (EN International Organization for Standardization (EN ISO), 2016). Dunaliella salina is a good model organism for studying abiotic stress, which can also produce commercially important compounds (Soto, 2015; He et al., 2020). The other microalgae are usually used as maricultural bait for fish, mollusks, and crustaceans due to their rapid propagation and nutrient-rich nature, and they are also commonly used in toxicity studies (Chen et al., 2004; Garrido et al., 2019; Thiagarajan et al., 2019).

Each species was acclimatized for more than two weeks in F/2 medium (Guillard, 1975) prepared using 0.45-μm membrane filtered natural seawater (pH 7.86± 0.02, salinity 30) collected from Shilaoren, Qingdao, China, at 20 ℃ under a 14-h꞉10-h illumination/dark regime at 60-μmol photons/(m²·s) irradiance.

SM (declared purity: 98.2%) was purchased from Dr. Ehrenstorfer (Germany). Dimethyl sulfoxide (DMSO) (purity ³99.5%) was obtained from Sinopharm Chemical Reagent Co., Ltd. (China). Stock solutions of SM ranging from 800 to 20 000 mg/L were prepared by dissolving SM in DMSO (as a cosolvent). The stock solution was then diluted with F/2 medium to the designed concentrations. The maximum concentration of DMSO in the exposure system varied with algae species: for D. salina, C. Pacifica, P. viridis, and I. galbana, it did not exceed 0.1% (v/v); but for S. costatum and P. tricornutum, it was no more than 1% (v/v). A pre-test showed that DMSO could not significantly affect the growth of corresponding microalgae (P>0.05) when its concentrations were less than these limits.

2.2 Algal growth inhibition test

According to OCSPP 850.4500 (USEPA, 2012), the exposure levels for five species of algae (Table 1) in the growth inhibition test were arranged using an equal logarithmic interval method based on results from a range-finding test. For each level, 50-mL medium containing SM under a certain concentration was added to 100-mL Erlenmeyer flasks, followed by inoculation with pre-cultured algal cells in exponential phase with an initial cell density of 1×10⁴ cells/mL (media without SM were used as a control). All treatments and controls were conducted in triplicate and incubated aseptically in an incubator (GXZ-280B, Ningbo Jiangnan Instrument Factory, China) for 96 h under the conditions in accordance with the pre-cultured state. Suspension cultures were shaken by hand twice daily to avoid adherent growth on the flask wall.

The diatom P. tricornutum was a different case: a preliminary test showed that SM has no inhibitory effect on it at a concentration of up to 100 mg/L. Based on OECD Guidelines for the Testing of Chemicals: Freshwater Alga and Cyanobacteria, Growth Inhibition Test (OECD, 2011), in this case, a limit test involving a comparison of responses between a control group and a treatment group (i.e., SM at a concentration of 100 mg/L or equal to its solubility), must be performed. Therefore, 100 mg/L was selected as the concentration of treatment group because the solubility of SM in seawater has not been reported at time of writing. Like other microalgae, the medium without SM but inoculated with microalgae was used as the control group. Both the control group and the treatment group were repeated six times according to OECD 201 guidelines (OECD, 2011). The culture conditions were as described above.

For each alga in every control group and every treatment group, 1 mL of algal culture was taken from each replicate after 24, 48, 72, and 96 h of cultivation. The cell density (cells/mL) was counted in a flow cytometry device (BD Accuri C6 Plus, BD, USA).

Microalgal growth curves were plotted against time, and the area (A) under the growth curve based on cell density was calculated using Eq.1 (USEPA, 2012):

where b₀, b₁, b₂, and b_n are the observed cell densities at test initiation (t₀), time (days) of the first measurement (t₁), the second measurement (t₂), and the n^th measurement (t_n) after initiation of the test, respectively.

The percent inhibition (% I) for each treatment group at 96 h was calculated using the following equation:

where A_C and A_T represent the areas under the growth curve in the control group and treatment group, respectively.

For each microalga in Table 1 except P. tricornutum, the linear regression equation between the logarithmic concentrations (X) of SM and the probability unit values (Y) converted from growth inhibition rates (%I) was established, and the 96-h EC₅₀ values were equal to the SM concentration at the probability unit of 5.0 (USEPA, 2012).

For P. tricornutum, if there was no significant difference (P>0.05) in algal cell density between the treatment group and the control group during the limit test, its 96-h EC₅₀ to the microalgae would be expressed as ">100 mg/L" based on OECD 201 (OECD, 2011).

The specific growth rate (r), expressed as /d, was calculated as follows.

where, b₂ and b₁ denote the algal cell densities (cells/ mL) at times t₂ and t₁, respectively.

2.3 Ecological risk analysis based on the SSD model 2.3.1 Collection of available toxicity data

The SSD model was established using acute toxicity data such as LC₅₀ and EC₅₀ or chronic data such as NOEC. According to our literature search, there are few data on the chronic toxicity of SM to marine organisms reported. Therefore, the L(E)C50 data of SM to marine organisms were employed to perform SSD analysis in this study. The available data come from three sources: (ⅰ) ECOTOX database of USEPA (2022); (ⅱ) the published literature; (ⅲ) the toxicity data determined in this study. All these data were measured under static exposure for 48 or 96 h. Toxicity data pertaining to 10 marine organisms (Tables 2 & 3, except for P. tricornutum) were obtained.

2.3.2 SSD curve

Firstly, all the acute toxicity data listed in Tables 2 & 3 were sorted in ascending order, and the cumulative probability for each species (i.e., the ratio of its number to (n+1), where n denotes the amount of data) was calculated. Then, the log-normal distribution model, recommended by the United States and some European countries, was used for the construction of SSD. This model has been widely applied in the ecological risk assessment of pollutants such as nonylphenol and polycyclic aromatic hydrocarbons (Qin et al., 2013; Gao et al., 2014). The formula, shown in Eq.4 (Feng, 2020), was used to provide a best-fit dataset (cumulative probability (%) with the SM concentration) using SSD Toolbox 1.0 software, and the fitting curve was then plotted.

where y is the cumulative probability and x represents a toxicity value, μ and σ represent the two parameters of the model, i.e., the sample mean and standard deviation, and erf denotes the error function.

2.3.3 HC₅ and PAF calculations

The SSD can be used for a risk assessment in two ways: forward use and reverse use (Schoeman, 2007). The first aims at calculating the PAF for a certain concentration of the compound of interest. In the second one, HC₅, the hazardous concentration affecting 5% of species, was calculated from the cumulative probability (Xu et al., 2015). In other words, HC₅ is the SM concentration corresponding to the cumulative probability 5% on the SSD curves (Eq.5). In this study, the values of PAF and HC₅ were calculated using the SSD Toolbox 1.0 and MATLAB^TM Compiler Runtime R2018b (9.5) software.

where p% is the cumulative probability and HC_p represents hazardous concentration for p% of species.

2.4 Data statistics and analysis

For each treatment group and the control group, the algal cell density of microalgae was expressed as mean±standard deviation of three or six repeated measurements. The data were statistically analyzed using IBM SPSS Statistics 24 software, and Tukey's test in one-way analysis of variance (ANOVA) was adopted to evaluate the significant differences among treatment groups. For the limit test, the significant difference between the treatment group and the control group was determined by Student's t-test, and the level of statistical significance was set to P < 0.05.

3 RESULT 3.1 Effects of SM on the growth of five marine microalgae

The 96-h growth curves of five marine microalgae exposed to SM at different concentrations are illustrated in Fig. 1. Microalgae in the control group showed a good exponential growth trend, while their growth in the treatment groups was subjected to different degrees of inhibition. Based on these data, the 96-h EC₅₀ values of SM for five microalgae were calculated (Table 3). Among them, green algae, D. salina and C. pacifica, demonstrated a higher sensitivity to SM, with 96-h EC₅₀ values of 0.11 and 0.13 mg/L, respectively. Diatom S. costatum was more resistant to SM, with a 96-h EC₅₀ value of 148.99 mg/L. The sensitivity of Chrysophyta microalgae was between the above two phyla, with 96-h EC₅₀ values of 14.24 and 21.48 mg/L for D. viridis and I. galbana, respectively. This difference is also demonstrated in Fig. 2. The specific growth rates (r) of both D. salina and C. pacifica decreased significantly when the SM concentration was less than 1 mg/L, and were only 14.11% and 7.46% of the control group at concentrations of 0.8 and 0.9 mg/L respectively. On the contrary, the r values for S. costatum, I. galbana, and D. viridis remained at 98.53%, 90.10%, and 83.16% of that of the control group, respectively, even when the concentrations of SM reached 100, 18, and 7 mg/L.

3.2 Effect of SM on the growth of P. tricornutum

During the 96-h limit test, the growth of P. tricornutum in the treatment group with SM of 100 mg/L did not show a significant difference (P>0.05) compared with the control group in the same period (Fig. 3); therefore, the 96-h EC₅₀ of SM to this species was expressed as ">100 mg/L".

3.3 SSD curve fitting and calculation of HC₅ and PAF

The L(E)C₅₀ for 10 species ranged from 0.11 to 374.45 mg/L (Tables 2 & 3). The SSD curve obtained from these data are shown in Fig. 4, and the HC₅ of SM to marine species was calculated to be 0.077 mg/L. The PAF values of SM at different concentrations for marine organisms are listed in Table 4, indicating that more species would be affected by the acute toxicity of SM with the increase of the SM concentration. When the SM concentration increased to 1, 10, and 100 mg/L, the proportion of affected species would reach 19%, 43%, and 70%, respectively.

4 DISCUSSION

SM is weakly acidic, therefore, at low pH, it has higher bioconcentration and toxicity in freshwater microalgae, as reported by Fahl et al. (1995). In the present study, it was found that the growth of marine microalgae, except P. tricornutum, could be inhibited by SM in the medium prepared with seawater at pH 7.86. This means that it will also cause potential harm to non-target organisms in marine ecosystems when SM is applied in coastal waters for eliminating S. alterniflora. According to the Guidelines for the Hazard Evaluation of New Chemical Substances (HJ/T 154-2004, 2004) issued by the State Environmental Protection Administration of China, SM is highly toxic to marine green algae D. salina and C. pacifica (EC₅₀≤1.0 mg/L); is moderately toxic to Chrysophyta species D. viridis and I. galbana (10 mg/L < EC₅₀≤100 mg/L); and has low toxicity to diatoms S. costatum and P. tricornutum (EC₅₀>100 mg/L).

It is speculated that compared to diatoms and golden algae, the higher sensitivity of green algae is related to the great inhibition of their acetolactate synthase (ALS, EC 4.1.3.18) activity by SM. This is mainly due to the following reasons: (ⅰ) ALS (also known as acetohydroxy acid synthase, AHAS, EC 2.2.1.6) is indispensable for plant growth (Wang et al., 2019), because it is not only able to catalyze the synthesis of branched-chain amino acids but also the key enzyme for chlorophyll synthesis (Lonhienne et al., 2020); (ⅱ) ALS is the sole target site in plants including green algae (Chaleff and Mauvais, 1984; Landstein et al., 1990), and the inhibition of its activity has been considered to be one of the primary causes of death in susceptible plants (Wang et al., 2019); (ⅲ) Previous research indicated that sulfonylurea herbicide decreased ALS activity consequently inhibiting the synthesis of branched chain amino acids, which in turn resulted in the overall inhibition of growth in susceptible plants (Tanaka and Yoshikawa, 1998); and (ⅳ) The resistance of plant was due to reduced sensitivity of the ALS enzyme to sulfonylurea herbicide (Wang et al., 2019).

Compared with green algae, two species of marine diatoms showed higher tolerance to SM (96-h EC₅₀=148.99 mg/L or >100.00 mg/L), which might be related to their siliceous cell walls forming a barrier to prevent the cellular internalization of SM (Piccapietra et al., 2012). This difference in tolerance was also reported in previous research on the toxicity of SM to freshwater microalgae. The 120-h NOEC value of SM to diatom Navicula pelliculosa (0.37 mg/L) is three orders of magnitude greater than that of green algae Pseudokirchneriella subcapitata (120-h NOEC=0.000 63 mg/L) (USEPA, 2022). In addition, SM is less toxic to marine diatoms than other herbicides. For example, bentazon has a 72-h EC₅₀ value of 24.00 mg/L to S. costatum (Macedo et al., 2008); diuron, atrazine, and hexazinone show 72-h EC₅₀ values of 7.8, 130, and 27 μg/L for Navicula sp., and 8, 50, and 10 μg/L for Nephrolimus pyriformis, respectively (Magnusson et al., 2008).

For freshwater microalgae, the acute toxicity of SM to two species (P. subcapitata and A. flosaquae) has been reported in the literature. Their 120-h EC₅₀ values are 0.004 3 and 0.073 mg/L respectively (USEPA, 2022), which are one to two orders of magnitude lower than the minimum 96-h EC₅₀ of marine microalgae in the present study (0.11 mg/L). This finding indicates that freshwater microalgae are much more sensitive to SM compared with their marine counterparts. In fact, the sensitivity of freshwater microalgae and seawater microalgae to other organic pollutants is also quite different. For example, the sensitivity of marine microalgae to acrylic acid is generally less than that of freshwater microalgae (Sverdrup et al., 2001). Phenol shows a higher toxicity to four marine microalgae (96-h EC₅₀ values ranged from 27.32 to 92.97 mg/L) than those of freshwater microalgae (96-h EC₅₀ values ranged from 126.85 to 389.8 mg/L) (Duan et al., 2017). This means that, for a given pollutant, if the toxicity data from freshwater species were used to evaluate its marine ecological risk by conducting SSD analysis, there would be a larger deviation in the evaluation results. Therefore, in this study, it is necessary to determine the acute toxicity of SM to marine microalgae for evaluating the marine ecological risk of this herbicide.

Since its launch in the 1980s, the SSD has become the most widely used method for studying the effects of chemical contaminants on water quality and/or for ecological risk assessment purposes (Fox et al., 2021). At present, this method has been applied in the European Risk Management Scheme (EFSA Panel on Plant Protection Products and their Residues (PPR), 2013) and used as the basis for establishing national environmental water quality standards in the United States (TenBrook et al., 2009).

When establishing the SSD, the sample size affects the reliability of specific parameter (e.g., HC₅) estimation. Parameter values derived from samples with fewer toxicity data are of little practical use due to their very high level of uncertainty (Aldenberg and Luttik, 2002). There is no universal protocol for establishing the minimum sample size. For instance, USEPA requires the minimum number (n) of data to be 8 from eight taxa, while the European Union requires n=10 from eight taxa, Australia and New Zealand require n=5 from five taxa, and The Netherlands requires n=4 from four taxa (Del Signore et al., 2016; Vidal et al., 2019). Zhao and Chen (2016) found that a minimum sample size of 10 to 23 was required to establish a stable SSD model. Furthermore, it has been suggested that the species for SSD analysis should be preferentially selected according to the specific mode of action of chemicals. For toxicity research involving herbicide chemicals, primary producers should be the first choice (Vidal et al., 2019). In this study, 10 marine species were used for SSD analysis, covering four trophic levels as follows: microalgae (five species), crustaceans (one species), fish (two species), and mollusks (two species).

The HC 5 value of SM obtained in this study was 0.077 mg/L, implying that 5% of marine organisms could be adversely affected by SM at this level. Its predicted no-effect concentrations (PNEC) value can be derived by the assessment factor (AF) method (Adam et al., 2015). When applying an AF of 5 (maximal assessment factor to calculate the PNEC based on SSD) (European Commission, 2003) to the calculated HC 5 value for SM, a PNEC of 0.015 mg/L could be obtained. Currently, there have been no reports of measured concentrations available for SM in seawater, so, we conducted a theoretical estimation based on the application rate of SM in tidal flat of Ningde City, Fujian Province in China. Wang (2017) reported that the average application amount of SM (in the form of 75% wettable powder) is 13.5 kg/hm² for removing S. alterniflora in tidal flats in Xiapu County. This implies that the minimum dosage of SM (calculated at 100% purity) is 1.012 5 g/m². In addition, a survey reports that, in China, the average water depth is 2 m in the coastal areas where S. alterniflora grows (Liu, 2018). Based on this information, assuming that SM is completely mixed with seawater, the initial concentration of SM in seawater is estimated to be about 0.51 mg/L after it is sprayed to remove S. alterniflora. This concentration may cause acute injury or death of 14% of marine species in the spraying area. A recent study by our group (Shao et al., 2022) has indicated that the natural attenuation rate constant and half-life of SM in seawater are 0.069 4/d and 9.99 days, respectively. Therefore, it will take about 27 days for the SM concentration to be reduced to a level less than HC 5 (0.077 mg/L). Thus, we can predict that non-target marine organisms will be poisoned for a long time in an SM-spraying area. Therefore, to avoid severe damage to the marine ecosystem, the amount of SM sprayed into the sea should be reduced as much as possible on the premise of effectively inhibiting the growth of S. alterniflora.

Finally, it should be noted that the sensitivity of marine microalgae to SM is species-specific (Table 3). Except for P. tricornutum, the 96-h EC₅₀ values for SM on five microalgae ranged from 0.11 to 148.99 mg/L (a 1 354-fold range). This finding showed that the toxicity data of a few microalgae alone could not fully reflect the impact of SM on this group of marine organisms. Therefore, in the future, it is necessary to ascertain the toxicity of SM to more species of marine organisms for obtaining more comprehensive ecological risk assessment conclusions.

5 CONCLUSION

In this study, the acute toxicity of SM to six species of marine microalgae was detected. Green algae exhibited higher sensitivity, followed by golden algae, and diatoms were the most tolerant. According to the currently available 96-h EC₅₀ data, marine microalgae were more tolerant to SM compared with their freshwater counterparts. The values of HC 5 and PAF for SM for marine ecosystem were also estimated using the SSD approach. Based on SM application dose in the distribution regions of S. alterniflora in Fujian Province, China, SM entering the sea by spraying was expected to do harm to those sensitive species in about one month.

6 DATA AVAILABILITY STATEMENT

The data generated, or analyzed, during the current study are available from the corresponding author on reasonable request.