Identifying the key parameters or components mainly contributing to the acute toxicity of wastewater would be helpful to quickly and conveniently reflect their biological toxicity. In this study, the components/parameters and biological toxicity of 64 effluent samples collected from two factories producing konjac and glass were analyzed. It was found that the two types of wastewaters were not effectively dealt with. Moreover, the acute biological toxicity evaluated by the bioluminescence inhibition to Vibrio fischeri revealed that ∼90% of the effluents were marked as toxic with bioluminescence inhibition higher than 50%. By applying a grey relational analysis (GRA) method to investigate the influential priorities of the effluent characteristics on biological toxicity, the results demonstrated that the top four influential factors on the bioluminescence inhibition were as follows: TN ≈ SO42− > Cl > As ≈ Hg (for konjac-manufacturing effluents) and Zn > SO42− ≈ TN > As > Cl (for glass-producing effluents). These results would be useful for fast recognizing the biological toxicity features of industrial effluents via evaluating the most influential parameters, and helpful for reducing the biological acute toxicity to protect the downstream wastewater treatment plant from abrupt collapse.

  • Components/parameters and biological toxicity of effluent samples collected from two factories were analyzed.

  • ∼ 90% of the effluents were marked as toxic.

  • A grey relational analysis (GRA) was adopted to investigate the influential priorities of the effluent characteristics on biological toxicity.

  • Top four influential factors on the bioluminescence inhibition were obtained.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Water as a limited and vulnerable resource is essential to human survival and social development. With the fast urbanization and population explosion, massive quantities of wastewater have been generated all over the world. Especially, the large amount of industrial wastewater discharged from various chemical industries has the potential to threaten the natural ecology and environment (Oller et al. 2011; Yang et al. 2021). For example, a variety of manufacturing facilities and products are included in the glass industry, resulting in a major issue with efficiently treating its wastewater. Impurities such as glass splinters and silica particles, oil and/or other lubricants used in the cutting process, dissolved salts and water treatment chemicals used in the cooling-water system, and heavy metals as impurities in some raw materials in cullet and in fuels were likely to be contained in the effluents (Gholipour et al. 2020). Konjac is a distinctive kind of food primarily produced in the Sichuan, Shanxi, Yunnan, and Hubei Provinces of China. Different from other common food wastewater characterized by high chemical oxygen demand (COD) and N, some heavy metals are also speculated to exist in the wastewater of konjac-producing factories.

As for the convenience of government regulation, industrial parks are commonly used in both developed and developing countries all over the world (Hu et al. 2019a; Boysen et al. 2020; Hilbig et al. 2020). According to the data from 2018, China hosts 2,543 industrial parks, which have generated more than 50% of the gross industrial output value of the whole country (Hu et al. 2019b). Therefore, controlling the discharge of industrial parks' wastewater and their effective treatment are crucial to water pollution control in China. In order to deal with wastewater seasonal fluctuation and the occurrence of illegal discharge, a centralized two-stage wastewater treatment model, namely pretreatment within enterprises and further treatment in a centralized wastewater treatment plant (WWTP), is usually implemented in industrial parks (Liu & Tang 2018; Cong et al. 2021). Biological treatments have been commonly applied in WWTP for industrial wastewater decontamination, so effluents with high acute toxicity would potentially result in the collapse of the biological treatment processes. As such, evaluation of the biological toxicity of industrial wastewater effluents is necessary.

Bioassays quantifying the physiological or behavioral changes of living organisms due to metabolic disruption induced by toxic compounds have gradually advanced as an efficient tool for biological toxicity estimation and environmental risk assessment in recent years (Abbas et al. 2018). Multicellular organisms such as algae, plants and their seeds, mussels, crustaceans and fish (like rainbow trout and fathead minnow), prokaryotic (bacteria) and eukaryotic cells have been adopted to conduct the assays (Abbas et al. 2018). In general, the acute toxicity of industrial wastewater evaluated by the luminescence inhibition rate of luminescent bacteria like Vibrio fischeri (V. fischeri) is more favored due to its high throughput and sensitivity, cost-effectiveness, and simple operation (Abbas et al. 2018; Sun et al. 2021; Xue et al. 2021). For instance, Liu et al. (2002) explored the acute toxicity of various types of industrial effluents by V. fischeri with a result of the toxicity ranking as electroplating effluents > acrylonitrile-manufacturing effluents > paper effluents > tannery effluents. It should be noted that the natural bioluminescence bacteria in some cases are very sensitive to industrial wastewater as a result of ineffective reflection to normal operating conditions of activated sludge (Philp et al. 2003). However, the traditional activated sludge respiration inhibition tests are not adequate in this situation because these tests are time-consuming (Ren & Frymier 2005). Moreover, the samples tested in this study were not the original industrial wastewater, but the effluents treated by the inner wastewater treatment technology with less toxicity. Additionally, a bioluminescence bacteria test has been adopted by the Chinese government for evaluating the toxicity of some complex industrial wastewater like pharmaceutical wastewater, dyeing wastewater, papermaking wastewater, etc. (Hu et al. 2011). Taking these factors into consideration, V. fisheri was appropriate for predicting the biological toxicity of effluents collected from industries. However, the bioassays exhibit high costs and prolonged reproductive periods, which are not beneficial for timely and in situ detection. In comparison, the analysis of chemical components and parameters is easier and more time efficient. Identifying the key parameters or components mainly contributing to the acute toxicity of wastewater would be helpful to quickly and conveniently reflect their biological toxicity.

A grey relational analysis (GRA) is broadly used to analyze the priority of various factors affecting the research objectives in available studies of the environmental field. You et al. (2017) used the GRA to analyze the relationship between the achievement of air pollution elimination and air pollution reduction by evaluating air quality trends in Japan and demonstrated SO2 as the most critical index for air pollutants. Li et al. (2016) applied the GRA to identify the most important factor to control the treatment efficiency of various effluent quality indexes by polyferric chloride coagulation. Response surface methodology (RSM) coupled with the GRA method was used to evaluate the effects of substrate concentration, temperature, NH4+-N concentration and aeration rate on the production of soluble microbial products in activated sludge reactors with the result of the influential priorities: temperature > substrate concentration > aeration rate > NH4+-N concentration (Xu et al. 2011). It is expected that the GRA would be helpful to quantitatively evaluate the significance of various components on the acute toxicity of industrial wastewater, which requires further exploration.

It should be noted that the compositions of the effluent strongly depend on the type of industry and the effectiveness of the internal WWTP (Rodriguez-Vidal et al. 2022). Moreover, one aim of this research is to provide technical support for industrial parks containing various industries. Therefore, 64 effluent samples from two factories producing konjac and glass were collected for components and acute toxicity analysis in this study. The characterization of glass production and effluents was conducted by monitoring their acute toxicity, COD, N and P concentration, pH, and heavy metals and inorganic anion concentration. The glass-producing industry investigated here contains processes for producing float glass (two lines), Low-E coated glass (one line), armored glass (seven lines), hollow glass (two lines), sandwich glass (one line), and high-quality silica sand (one line). Afterwards, the GRA was used to quantitatively evaluate the significance of these parameters on the biological acute toxicity. The results would be useful for fast and in situ assessment of the toxicity of wastewater and providing guidance for designing or adopting appropriate technology to reduce the biological acute toxicity of the effluents.

Chemicals and wastewater samples

Nessler's reagent used for NH4+-N determination was obtained from Macklin (Shanghai, China). Methanol and formic acid at HPLC grade for LC–MS–MS analysis were purchased from Aladdin Co., Ltd (Shanghai, China). The agents for COD test (LH-DE-500) were bought from Lianhua Technology Co., Ltd (Beijing, China). Other chemicals at analytical reagent used for NH4+-N, total phosphorus (TP), and heavy metals measurements in this study, including ascorbic acid (≥99.7%), potassium persulfate (≥99.5%), ammonium molybdate (≥99.0%), sulfourea (≥99.0%), C4H4KO7Sb·1/2H2O (≥99.5%), NaKC4H4O6·4H2O (≥99.0%), H2SO4, HCl, HNO3, and KH₂PO4 were bought from Sinopharm Chemical Reagent Co., Ltd (Shanghai, China) and used as received unless otherwise stated. All solutions were prepared with 18 MΩ·cm Milli-Q water (Millipore). A portable water toxicity kit containing V. fischeri (NRRL B-11177) with IC50 of Zn2+ being 2.0 mg/L was used to evaluate the acute toxicity of industrial wastewater samples, and was obtained from Yangtze Delta Region Institute of Tsinghua University (Zhejiang, China).

Wastewater samples collection

The industrial wastewater samples were collected from the wastewater outlet of the konjac-manufacturing factory and glass-producing factory in Chengdu at every 3 h within 4 days. An anaerobic/oxic (A/O) treatment equipment is implanted in this konjac-manufacturing factory to treat the wastewater, while the original wastewater produced by the glass production process is simply treated by neutralization and precipitation processes. For analysis, 32 samples taken from the wastewater outlet of each factory were collected and stored under 4 °C before passing the filtrate through 0.45 μm PVDF syringe filters for subsequent analysis.

Analytical methods

pH of the collected samples was, respectively, tested by a PHS-3C pH meter. COD was analyzed according to manufacturer guidance on a COD testing set (Lianhua, China). Determination of NH4+-N in water was conducted by Nessler's reagent spectrophotometry on a UV–Vis spectrometer (Unico 2355, China). Total nitrogen (TN) in wastewater samples was detected using a Shimadzu TOC-5000A analyzer (Japan) equipped with the TNM-L unit (De Laat et al. 2011). After the survey of the downstream WWTP, Zn, Pb, Cd, Cr, Hg, and As were detected in the influent of WWTP with high frequency; therefore, the concentrations of these heavy metals were evaluated. It should be noted that the samples used for metals analysis were digested in advance for the removal of organics. Concentrations of Hg and As were analyzed using an AFS-9X atomic fluorescence spectrometer, while the contents of Zn, Pb, Cd, and Cr were quantified by an atomic absorption spectrophotometer (AA-6880, Shimadzu, Japan). The amounts of sulfate, chloride, and fluoride in the industrial wastewater were determined by ion chromatography (CS2000, China).

Acute toxicity analysis of wastewater samples

The acute toxicity of the wastewater samples was assessed by the luminescence inhibition of V. fischeri following the manufacture's instruction (Jiao et al. 2008; An et al. 2020). Briefly, prior to toxicity assessment, 1 mL of bacteria was reactivated in lyophilized powder vials for 10 min and diluted to 40 mL using 2% NaCl. 190 μL of wastewater sample and 10 μL of reactivated bacteria were added to a 96-well reaction plate. Bioluminescence was recorded on a BioTek Synergy H1microplate reader (US) after 15 min of exposure and inhibition of bioluminescence was calculated by using 2% NaCl solution as a control.

Grey relational analysis

The GRA method was used to evaluate the influential degrees of COD, TN, TP, inorganic anions, and metal ions on the biological toxicity (bioluminescence inhibition ratio here) of the two wastewater effluents. The details of the GRA calculation process were stated as follows (Xu et al. 2011; Fang et al. 2014; Li et al. 2016).

First, the obtained data containing the parent sequence (the value of biological toxicity) and the other sub sequences (such as COD, TN, TP, and inorganic anions) were averaged. Then, due to the different physical meanings of various factors in the system, dimensionless data processing was generally necessary for this GRA procedure (Equation (1)). Subsequently, calculating the correlation coefficients between each parameter in each sub sequence and the corresponding parameter of the parent sequence were calculated as displayed in Equation (2). Finally, according to Equation (3), the gray correlation degree of each sub sequence can be obtained (the grey correlation degree is the average value of the correlation coefficient of each column). In general, a greater grey correlation degree indicates a larger influence on the parent sequence, thus it can be determined which factor has the greatest impact on biological toxicity according to the degree of the obtained correlation values.
formula
(1)
formula
(2)
formula
(3)
where i and k represent sub sequence and sampling time point of each water sample, respectively. The min(i)min(k)Δi(k) and max(i)max(k)Δi(k) are the minimum difference of two levels and the maximum difference of two levels, respectively. ρ is the resolution coefficient which is generally between 0 and 1, and it is 0.3 in this study.

In this way, influential priorities of the factors on biological toxicity of the two kinds of industrial effluents could be identified.

Conventional physicochemical characteristics of industrial wastewater effluents

The pH of the 64 wastewater effluent samples was in the range of permissible limit of Chinese environmental protection agency, i.e. 6.0–9.0 (GB3838-2002). The COD, NH4+-N, TN, and TP of the original wastewater of the konjac-manufacturing factory before the A/O treatment were analyzed to be 6,122.5 ± 137.88, 2.18 ± 0.10, 238.0 ± 1.34, and 94.2 ± 3.45 mg/L, respectively. As illustrated in Figure 1, the maximum COD, TN, and TP detected in the konjac effluent samples were 1,320.5 ± 26.87, 65.9 ± 0.42, and 37.3 ± 0.19 mg/L, respectively. In detail, the proportion of samples with COD higher than 500 mg/L (the third standard of the integrated wastewater discharge standard (GB8978-1996)) is 18.75%, with TN higher than 15 mg/L it is about 60%, and with TP higher than 0.5 mg/L it is more than 78%. It should be noted that the TP in the last three samples separately was 7.2 ± 0.29, 37.3 ± 0.19, 35.5 ± 0.39 mg/L, whereas the CODs of these three samples were lower than 200 mg/L, indicating the inefficient utilization of organics by phosphorus-accumulating organisms. These results demonstrate the low efficiency or inefficient operation of the A/O technology to treat the wastewater of the konjac-manufacturing process. The COD, TP, and NH4+-N of most glass-producing effluents (Figure 2) were relatively low and within the maximum permissible limit of the third standard of the integrated wastewater discharge standard (GB8978-1996).
Figure 1

Physicochemical characteristic results of effluents taken from konjac-manufacturing factories.

Figure 1

Physicochemical characteristic results of effluents taken from konjac-manufacturing factories.

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Figure 2

Physicochemical characteristic results of effluents taken from glass-producing factories.

Figure 2

Physicochemical characteristic results of effluents taken from glass-producing factories.

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Particularly, the samples were collected from the wastewater outlet of these two factories at every 3 h for 4 days considering the actual discharge situation of industrial wastewater. The influent of the pretreatment A/O process in the konjac-producing factory was the mixture of konjac-producing wastewater and domestic wastewater, which would be fluctuated with the operation condition during every period and result in varied A/O treatment efficiencies at different periods. The water samples of glass-producing factories were the mixture of effluents from different lines. As mentioned above, 14 process lines for producing seven products were contained in the factory, which would greatly influence the wastewater quality. Therefore, the parameters of these samples fluctuated to a great extent.

Inorganic ions and heavy metals

The concentrations of chloride and sulfate were detected. As can be seen from Figure 3(a), an average of 450.3 mg/L of chloride was detected in the effluents of the konjac-manufacturing factory, with the maximum concentrations achieving 1,635.2 ± 21.35 mg/L. The chloride concentrations of 62.5% samples were in the range of 200–300 mg/L, while 12.5% samples contained more than 1,000 mg/L of chloride. As illustrated in Figure 3(c), the sulfate concentrations of 32 konjac effluent samples ranged from 174.4 ± 10.95 to 1,788.0 ± 19.27 mg/L with an average of 573.5 mg/L. Similarly, the concentrations of chloride and sulfate in the effluents of glass-producing factories are also at a high level with the average values separately being 380.8 mg/L (Figure 3(b)) and 822.2 mg/L (Figure 3(d)). It should be noted that the maximum concentration of chloride and sulfate in glass-producing effluents achieved 1,662.7 ± 31.67 and 5,072.6 ± 23.21 mg/L, respectively, but occurred only once, which could have resulted from some accidents and are unrepresentative.
Figure 3

Concentrations of Cl and SO42− in the (a, c) konjac-manufacturing and (b, d) glass-producing effluents. The red line in the figures represents the average value of each parameter. Please refer to the online version of this paper to see this figure in color: http://dx.doi.org/10.2166/wrd.2022.045.

Figure 3

Concentrations of Cl and SO42− in the (a, c) konjac-manufacturing and (b, d) glass-producing effluents. The red line in the figures represents the average value of each parameter. Please refer to the online version of this paper to see this figure in color: http://dx.doi.org/10.2166/wrd.2022.045.

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According to the information provided by the downstream WWTP, Zn, Pb, Cr, Cd, Hg, and As might exist in the effluents of the two factories. Therefore, concentrations of the six heavy metals were detected in the 64 samples. For the effluents of two factories, Zn, Hg, and As were the frequently detected heavy metals, whereas the other three elements occasionally occurred in the effluents with concentrations up to dozens of μg/L (Figure 4). Due to the higher permissible limit of Zn, its presence in the 64 samples was acceptable. However, the dosages of Hg and As could reach hundreds of μg/L and even more than 1 mg/L, which extensively exceed the range of permissible limit of Chinese environmental protection agency.
Figure 4

Concentrations of heavy metals detected in industrial effluents taken from (a) konjac-manufacturing and (b) glass-producing factories.

Figure 4

Concentrations of heavy metals detected in industrial effluents taken from (a) konjac-manufacturing and (b) glass-producing factories.

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Acute biological toxicity

In most cases, the acute biological toxicity test result for V. fischeri should be expressed as effective concentrations of wastewater causing 50% inhibition of bioluminescence to V. fischeri (EC50) (Yu et al. 2014; Abbas et al. 2018; Sigurnjak Bures et al. 2021), however, due to the complicated composition of the industrial effluents (Figures 14), inhibition rates of bioluminescence after exposure to the samples were used in this study to represent the acute biological toxicity. This is also widely accepted for wastewater biological toxicity evaluation (Fang et al. 2019; Lai et al. 2021). The toxicity degrees were divided into four classes according to their inhibition rates of bioluminescence, namely not harmful (0–30%), harmful (30–50%), toxic (50–80%), and very toxic (80–100%). The biological toxicity results of the 64 samples are depicted in Figure 5. No samples were in the ‘not harmful’ section, and only 3–5 samples were determined as ‘harmful’. More seriously, about 71.8 and 18.8% of konjac-manufacturing effluents were marked as ‘toxic’ and ‘very toxic’, respectively. Although the samples of ‘toxic’ occupied about 62.5% of the glass-producing effluents samples, 21.9% effluents were located in the ‘very toxic’ section. These results further suggest the low efficiency of the wastewater treatment technology adopted by the two factories.
Figure 5

The acute biological toxicity of 64 effluent samples collected from two industries as evaluated by the bioluminescence inhibition ratio (IR) to Vibrio fischeri.

Figure 5

The acute biological toxicity of 64 effluent samples collected from two industries as evaluated by the bioluminescence inhibition ratio (IR) to Vibrio fischeri.

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Grey relational analysis

As presented above, most effluents of the two factories were toxic and detrimental to the operation of the downstream WWTP. As such, it is necessary to prioritize the compositions and parameters of effluents toward their contribution to acute biological toxicity, and identify the most important factor. It should be noted that although the designed wastewater discharges of these two factories (620 and 80 m3/d) were far less than the daily influent quantity of the downstream WWTP, attention should be paid to the intentional and abrupt discharge of massive wastewater during the night, which would cause a serious shock to the operation of the WWTP. Therefore, the GRA method was applied to comprehensively evaluate the influential degrees of COD, TN, TP, inorganic ions, and heavy metals on the bioluminescence inhibition of effluents to V. fischeri. Notably, since the NH4+-N concentrations of the whole samples were less than 3.0 mg/L, and pH values were in the ranges that are suitable for microorganisms’ growth, these three parameters were not taken into consideration.

The grey relational grades γ of these factors for the acute biological toxicity were calculated with results summarized in Table 1. It should be noted that γ of ‘inorganic ions’ and ‘heavy metals’ in Table 1 were evaluated using the sum of three inorganic ions and six heavy metal concentrations, respectively. For konjac-manufacturing effluents, TN (γ = 0.897) showed the most significant effect on bioluminescence inhibition, followed by inorganic ions (γ = 0.892) and heavy metals (γ = 0.887). This is acceptable because TN represents the amount of nitrogen-containing compounds in the effluents, the majority of which are toxic to microorganisms and hardly degradable by most wastewater biological treatment processes (Xing et al. 2012; Yu 2014; Hu et al. 2022). Interestingly, COD has been frequently reported to significantly and positively correlate with the biological acute toxicity indicators (Yu et al. 2014; Xue et al. 2021; Hu et al. 2022), but it exhibited a least-significant effect indicative by γ of 0.814 in this study. For inorganic ions, the influential priorities were in the order of SO42− (γ = 0.896) > Cl (γ = 0.889), probably attributed to the higher content of SO42− in most effluents (Figure 2) and the possible transformation of SO42− into toxic H2S (Yu 2014). The influential priorities of six heavy metals are in the following order: As (γ = 0.861) ≈ Hg (γ = 0.860) > Zn (γ = 0.849) > Cr (γ = 0.842) > Cd (γ = 0.841) > Pb (γ = 0.790), which can be explained from the following aspects: (1) As and Hg are both highly toxic to organisms and they were detected in the whole effluents of konjac-manufacturing effluents with high concentrations (Figure 3); (2) although the concentrations of Hg are significantly lower than As in most samples, the biological toxicity of Hg (EC50 = 33.8 ± 1.99 μg/L) is much higher than As (EC50 = 821 ± 187 μg/L) (Hsieh et al. 2004), finally resulting in the equal contribution to bioluminescence inhibition.

Table 1

The grey relational grades γ of parameters and compositions for the acute biological toxicity of two industrial effluents obtained by the GRA

CODTNTPClSO42−Inorganic ionsZnPbCrCdHgAsHeavy metals
Konjac-manufacturing effluents 0.814 0.897 0.863 0.889 0.896 0.892 0.849 0.790 0.842 0.841 0.860 0.861 0.887 
Glass-producing effluent 0.841 0.923 0.878 0.910 0.924 0.917 0.937 0.798 0.801 0.829 0.843 0.920 0.927 
CODTNTPClSO42−Inorganic ionsZnPbCrCdHgAsHeavy metals
Konjac-manufacturing effluents 0.814 0.897 0.863 0.889 0.896 0.892 0.849 0.790 0.842 0.841 0.860 0.861 0.887 
Glass-producing effluent 0.841 0.923 0.878 0.910 0.924 0.917 0.937 0.798 0.801 0.829 0.843 0.920 0.927 

As depicted in Table 1, the top five influential factors on the bioluminescence inhibition of glass-producing effluents were as follows: Zn (γ = 0.937) > SO42− (γ = 0.924) ≈ TN (γ = 0.923) > As (γ = 0.920) > Cl (γ = 0.910), which is clearly different from the results of konjac-manufacturing effluents. It has been well-reported that all the heavy metals investigated in this study have toxic effects on living organisms resulting from disruption of the cell membrane structures, DNA damage, and enzyme inactivation (Yang et al. 2016; He et al. 2017; Rasheed et al. 2018). As outlined previously (Hsieh et al. 2004; Fulladosa et al. 2005), the toxicity of Zn is obviously higher than As. For example, Hsieh et al. (2004) evaluated the toxicity of 13 priority pollutant metals (As, Cd, Cr, Cu, Pb, Be, Ag, Hg, Zn, Se, Ni, Sb, and Tl) through Microtox® chronic toxicity test using V. fischeri and found that the concentration causing a 50% inhibition of growth and survival for Zn and As were 73.8 ± 7.85 and 821 ± 187 μg/L, respectively. Hence, the relatively high detection frequencies and concentrations of Zn in 32 samples finally resulted in Zn as the most significant factor affecting the acute biological toxicity of glass-producing effluents.

It can be concluded from the above results that the influential priority of the 13 factors on the bioluminescence inhibition of two industrial effluents is highly dependent on their characteristics. The konjac-manufacturing process of a food industry is characterized by producing wastewater with high TN, so TN ranked the highest in influencing the toxicity of wastewater effluents. Glass industrial wastewater is generated in large quantities from the washing, cooling, cullet separation, and grinding process. Impurities such as glass splinters and silica particles remain in the wastewater, resulting in turbidity formation. Glass industry wastewater commonly contains heavy metals present as minor impurities in some raw materials like cullet and fuels, dissolved salts, and water treatment chemicals used in the cooling-water system (Gholipour et al. 2020). As such, Zn and sulfate were calculated to be the top two factors influencing the bioluminescence inhibition of glass-producing effluents.

In this study, the characteristics and acute biological toxicity of 64 effluent samples collected from two industries were analyzed, and the relationships between the characteristics and biological toxicity of each kind of wastewater were comprehensively investigated. Results showed that Zn, Hg, and As were the frequently detected heavy metals in all samples with concentrations ranging from dozens to thousands of μg/L, whereas Cr, Cd, and Pb occasionally occurred in the effluents with concentrations up to dozens of μg/L. All the samples exhibited > 30% of bioluminescence inhibition ratio to V. fischeri. Furthermore, it is found that although the wastewater of the konjac-manufacturing process of a food industry is characterized by high COD, the GRA disclosed that TN ranked first in influencing the toxicity of wastewater effluents, followed by inorganic ions and heavy metals. Glass industry wastewater commonly contains high concentrations of heavy metals and dissolved salts, therefore heavy metals, especially Zn, As, and sulfate, had strong effects on the bioluminescence inhibition of glass-producing effluents. It can be concluded that the GRA is a promising method to pick out the influential priority of the factors governing the acute biological toxicity of varied industrial wastewaters. Successful identification of the key parameters or components mainly contributing to the acute toxicity of wastewater would be helpful to quickly and conveniently reflect their biological toxicity, and provide guidance for designing or adopting appropriate pretreatment technology to precisely control the most influential components and reduce the biological acute toxicity of the effluents.

The authors wish to thank the National Key Research and Development Program of China (2019YFC0408502) and Sichuan Science and Technology Program (2019YFS0501 and 2018SZ0291) for financially supporting this study.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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