Determining Discharge Characteristics and Limits of Heavy Metals and Metalloids for Wastewater Treatment Plants (WWTPs) in China Based on Statistical Methods

Abstract

Industrial wastewater and sewage are both important sources of heavy metals and metalloids in urban wastewater treatment plants (WWTPs). China has made great efforts to control heavy metal and metalloid pollution by setting discharge limits for WWTPs. There is, however, limited discharge data and no systematic methodology for the derivation of discharge limits. In this study, 14 heavy metals and metalloids (Hg, alkyl mercury, As, Cd, Cr, Cr⁶⁺, Pb, Ni, Be, Ag, Cu, Zn, Mn, Se) that are listed in the Discharge Standard of Pollutants for Municipal Wastewater Treatment Plant (GB 18918-2002) were selected for the analysis of discharge characteristics while using the supervised monitoring data from more than 800 WWTPs located in nine provinces in China. Of the 14 heavy metals and metalloids, all but alkyl mercury were detected in the discharge water. There was a high rate of detection of As, Cu, Mn, Se, and there were some samples that exceeded the standard concentrations of Cr, Cr⁶⁺, Pb, and Ni. Removal rates of Hg, As, Cd, Cr, Cr⁶⁺, Pb, Ni, Cu, Zn, Mn, and Se were higher than 40%, comparable to values from other countries. Hg and As were selected to analyze the influencing factors of effluent and derive discharge limits of WWTPs using a statistical method, because these two metals had more detected data than other metals. The study used supervised monitoring data from Zhejiang WWTPs with 99 for Hg and 112 for As. Based on the delta-lognormal distribution, the results showed that geographic location was significantly closely correlated with Hg (P = 0.027 < 0.05) and As (P ≈ 0 < 0.05) discharge concentrations, while size (for Hg P = 0.695 > 0.05, for As P = 0.088 > 0.05) and influent concentration (R² < 0.5) were not. Derived Hg and As discharge limits suggest that it is necessary to establish stricter discharge limits for WWTPs, which is more consistent with the real-world situation in China. The study here comprehensively researches the discharge characteristics of heavy metals and metalloids in effluent of WWTPs in China, and developed for the first time in China heavy metals and metalloids discharge limits based on statistical methods. The results may inform special discharge limit settings for WWTPs in China.

1. Introduction

Industrial wastewater and sewage are major sources of heavy metals and metalloids at urban wastewater treatment plants (WWTPs). The European Union (EU) 2001 report on Pollutants in Urban Waste Water and Sewage Sludge [1] indicated that washing, drug use, and daily care were the main sources of heavy metal emissions. Oliver et al. [2] studied the heavy metals concentration in influent of WWTPs in Canada, which showed that the plants that received more industrial wastewater had higher concentrations of heavy metals than the plants that received only sewage, and Li et al. [3] had the similar conclusion when studied the influent of WWTPs received more industrial wastewater in China. Strengthening the monitoring and control of heavy metals and metalloids in WWTPs can play a substantial role in improving environmental quality and reducing environmental risks.

Like those of most countries, China’s discharge standards have been designed to regulate the end-of-pipe wastewater discharges. The national standard “Integrated Wastewater Discharge Standard (GB 8978-1996)” [4] first established separate discharge limits for WWTPs. With development of environmental standard systems in the country, in 2002, the national discharge standard of WWTPs (GB 18918-2002) [5] was established, and it has played an important role in controlling water pollutants discharged from WWTPs. Because some of the WWTPs in China process industrial wastewater, GB 18918-2002 has set the limits for 14 heavy metals and metalloids to prevent environmental risks from both industrial wastewater and sewage, including total mercury (Hg), alkyl mercury, total arsenic (As), total cadmium (Cd), total chromium (Cr), hexavalent chromium (Cr⁶⁺), total nickel (Ni), total lead (Pb), total silver (Ag), total copper (Cu), total zinc (Zn), total beryllium (Be), total manganese (Mn), and total selenium (Se). However, the setting of limits for heavy metals and metalloids was based primarily on comparison to the limits for industrial wastewater control [6], without enough discharge data to analyze the difference to industrial wastewater. The limits setting also took some account of public health using times of surface water quality standards as the discharge limits considering the dilution [6] with a shortage of measured data to evaluate the real-world discharge level.

In recent years, China has made great efforts to collect discharge data, and these data, especially from WWTPs, have been used to reveal statistical distributions for pollutants discharge concentrations and evaluate the rationality of discharge standards [7,8,9]. However, these studies mainly concerned the conventional pollutants as chemical oxygen demand (COD) and ammonia nitrogen rather than heavy metals and metalloids or other toxic pollutants. Furthermore, there has been a lack of an overall perspective regarding heavy metals and metalloids discharge situations of WWTPs and a systematic method for deriving the discharge limits based on statistical approaches in China.

This study aims to comprehensively research the discharge characteristics of heavy metals and metalloids in effluent of WWTPs in China, and for the first time to derive discharge limits of heavy metals and metalloids based on measured data analysis. In the present study, 14 heavy metals and metalloids controlled in GB 18918-2002 were selected for analysis of the discharge characteristics, and the Hg and As with more detected data were selected to derive discharge limits of WWTPs using a statistical method. We also addressed factors that may affect Hg and As discharge concentrations from different WWTPs. The result of this study may provide guidance for revisions to standards for heavy metals and metalloids discharge in China.

2. Data Source

2.1. Data Source

In our study, supervised monitoring data carried out by local environmental protection authorities from Oct. 2016 to Mar. 2017 of concentrations of 14 heavy metals and metalloids discharged from more than 800 WWTPs located in nine provinces were used. These nine provinces were Jilin, Shanxi, Shandong, Zhejiang, Hubei, Hunan, Guangxi, Gansu, and Sichuan, which covered the northeast, northwest, north, east, south, and the southwest of China (Figure 1), including the higher heavy metal pollution level regions as Hunan, Guangxi, and Gansu [10]. The WWTPs from data sources of different sizes covered various ratios of industrial wastewater treated, classified as <50%, 50–70% (including 50%) and ≥70%. In this way, the selected WWTPs were representative of Chinese WWTPs for locations, sizes, and ratios of the received industrial wastewater.

Hg and As were selected to derive the discharge limits using statistical method because of higher ratios of detected data. When using the statistical method, there should be minimum data requirements of at least seven daily averages, and among these averages there should be at least three daily averages higher than the method detection limits [11]. Based on this principle, for the sake of more valid data, we adopted the supervised monitoring data from 2015 to 2017 of Hg and As discharged by WWTPs in Zhejiang Province as the data source, which were also representative of Chinese WWTPs. There were 99 WWTPs for Hg and 112 WWTPs for As conforming to the data principles.

2.2. Data Analysis

The data collected were daily average concentrations of 14 heavy metals and metalloids sampled once every 2 h for 24 h and mixed for analysis, while using the Environment Monitoring Analytical Method Standards in China (

Table 1. Environment monitoring analytical methods used for analysis of 14 heavy metals and metalloids.

No.	Pollutants	Method Source	Method Detection Limits (mg/L)
1	Hg	HJ 694-2014 [12]	0.00004
2	alkyl mercury	GB/T 14204-93 [13]	0.00001
3	As	HJ 694-2014 [12]	0.0003
4	Cd	GB 7471-87 [14]	0.001
5	Cr	GB 7466-87 [15]	0.004
6	Cr6+	GB 7467-87 [16]	0.004
7	Pb	GB/T 7470-1987 [17]	0.01
8	Ni	GB 11912-89 [18]	0.05
9	Be	HJ/T 59-2000 [19]	0.00002
10	Ag	HJ 700-2014 [20]	0.00004
11	Cu	HJ 485-2009 [21]	0.01
12	Zn	GB 7475-87 [22]	0.05
13	Mn	GB 11911-89 [23]	0.01
14	Se	GB 11902-89 [24]	0.00025

). We obtained the averages, maximums, minimums, mediums for each pollutant to study the discharge characteristics as rate of detection, rate of exceeding standards, and similar outcomes. In order to investigate the statistical distribution of heavy metals and metalloids discharged by WWTPs in China, the Hg and As with more available data and higher detected rate were selected for analysis while using Origin Pro 7.0 (OriginLab Corporation, Northampton, MA, USA). Based on the fitted statistical distribution, we used One-way analysis of variance to study the factors influencing discharge level by PASW Statistics 18.0 (IBM, New York, NY, USA), including locations, sizes, and influent concentrations level of WWTPs.

Based on the fitted statistical distribution of heavy metals and metalloids discharge concentrations, discharge limits were derived by Equation (1) while using the long-term average (LTA) multiplied by the variability factor (VF). This equation has been used by the U.S. EPA in developing limits for many industries, such as the Organic Chemicals, Plastics and Synthetic Fibers (OCPSF) industry [25]. In this equation, the average of concentrations and the fluctuations in the treatment system are both considered, which reflects a state that most treatment systems are capable of achieving.

$$\mathrm{Limits}=\mathrm{LTA}\times \mathrm{VF}$$

(1)

LTA is the target value that a plant’s treatment system should achieve on an average basis. In the present study, we used the average concentrations to derive the Hg and As LTAs for each WWTP when considering both the undetected and detected data [11,25]. VF is the ratio of strong effluent to the average level, which expresses the relationship between large values and average treatment performance levels that a well-designed and operated treatment system should be capable of achieving all the time [11]. The U.S. EPA used the 99th and 95th percentiles to express the daily maximum and monthly average VFs, respectively [11,25]. Because the concept of monthly average was not used in China, we adopted the daily maximum 99th percentile VF in our study.

3. Results and Discussion

3.1. The Heavy Metal and Metalloid Discharge Concentrations Level of WWTPs

As shown (

Table 2. Discharge concentrations of 14 heavy metals and metalloids in wastewater treatment plants (WWTPs) of China.

No.	Pollutant	Number of WWTPs	Number of Samples	Detection Rate	Data of Detection (mg/L)				Discharge Limits in GB 18918-2002 (mg/L)
					Max	Min	Average	Medium
1	Hg	872	1080	26.7%	0.00109	0.00004	0.00017	0.00011	0.001
2	alkyl mercury	438	498	0%	— 1	— 1	— 1	— 1	Non-detected
3	As	858	925	58.2%	0.064	0.0003	0.0023	0.0012	0.1
4	Cd	873	786	6.0%	0.009	0.001	0.004	0.004	0.01
5	Cr	864	761	31.3%	0.44	0.004	0.026	0.016	0.1
6	Cr6+	881	1120	15.9%	0.118	0.004	0.014	0.009	0.05
7	Pb	873	877	6.2%	0.11	0.01	0.04	0.03	0.1
8	Ni	122	124	14.5%	0.4	0.06	0.18	0.14	0.05
9	Be	32	43	18.6%	0.0001	0.00002	0.00004	0.00004	0.002
10	Ag	32	15	6.7%	0.00023	0.00023	0.00023	0.00023	0.1
11	Cu	104	50	44.0%	0.36	0.01	0.11	0.09	0.5
12	Zn	63	63	22.2%	0.37	0.05	0.14	0.11	1.0
13	Mn	37	63	41.3%	0.470	0.012	0.128	0.060	2.0
14	Se	24	43	41.9%	0.02530	0.00025	0.00444	0.00050	0.1

¹ “—” indicates that the data is not enough to get the results.

), there were 13 pollutants that were detected in the discharged water of WWTPs, except alkyl mercury. In relation to the detection rate, four pollutants were higher than 40%, including As, Cu, Mn, and Se, which indicated the higher exposure of these four pollutants in WWTPs of China. The detection rate of Cr, Hg and Zn were higher than 20%, which indicated the relative high exposure of the three pollutants also. In the point view of discharge concentrations, among the 13 detected pollutants, there were cases of over the standard for four pollutants, including Cr, Cr⁶⁺, Pb, and Ni, with the standard exceeding ratio being 0.9%, 0.4%, 0.1%, and 17.0%, respectively, which most occurred in the WWTPs received wastewater from electroplate plants. The ratios of average concentrations to discharge limits in GB 18918-2002 for other nine pollutants were between 0.02 and 0.4, which were 1–2 orders of magnitude lower than the limits and indicated the low level of discharge concentrations. The ratios of average concentrations to mediums were all ≥1.0 for 13 pollutants, which indicated that most of the concentrations were lower than the average. When comparing the discharge concentrations of WWTPs in China to other countries, they were equivalent (

Table 3. Comparison of discharge concentrations of 14 heavy metals and metalloids in WWTPs of China and other countries.

No.	Pollutants	Countries	Concentration (μg/L)	Reference	This Study (μg/L)
1	Hg	U.S. Brazil	0.023 ± 0.016 0–0.24	[26] [27]	0.04–1.09
2	alkyl mercury	U.S.	0.00153 ± 0.00093	[26]	Undetected
3	As	Italy	0.5–9.2	[28]	0.3–64
4	Cd	Italy Brazil Canada	0.1–1.6 0.04–0.11 1–20	[28] [27] [2]	1–9
5	Cr	Brazil Canada Poland	1.68–13.53 10–710 10 ± 10	[27] [2] [29]	4–440
6	Cr6+	U.S.	1 ± 0.6	[30]	4–118
7	Pb	Brazil Canada Poland	4.22–76.42 50–200 10 ± 10	[27] [2] [29]	10–110
8	Ni	Canada Poland	30–670 20 ± 10	[2] [29]	60–400
9	Be	Canada	Undetected	[2]	0.02–0.1
10	Ag	Canada Poland	<10 10	[2] [29]	0.23
11	Cu	Brazil Canada Poland	2.13–19.87 20–100 10 ± 20	[27] [2] [29]	10–360
12	Zn	Italy Brazil Canada Poland	24–238 22.80–76.25 40–560 50 ± 90	[28] [27] [2] [29]	50–370
13	Mn	Brazil Canada	35.55–73.41 20–220	[27] [2]	12–470
14	Se	Spain	0.133 ± 0.085	[31]	0.25–25.3

).

3.2. Removal Rate of Heavy Metals and Metalloids in WWTPs in China

When researching the removal rate of heavy metals and metalloids in WWTPs, the discharge concentrations of undetected data were calculated with the Equation (2). The removal rates of 14 heavy metals and metalloids are shown in

Table 4. Removal rates of heavy metals in WWTPs of China.

No.	Pollutant	Average Removal Rate
1	Hg	57.4%
2	alkyl mercury	— 1
3	As	47.3%
4	Cd	61.0%
5	Cr	69.8%
6	Cr6+	64.4%
7	Pb	66.4%
8	Ni	50.8%
9	Be	25.6%
10	Ag	— 1
11	Cu	53.1%
12	Zn	65.5%
13	Mn	42.8%
14	Se	63.3%

¹ “—” indicates that the data is not enough to get the results.

.

$$\mathrm{Discharge}\mathrm{concentration}\mathrm{of}\mathrm{undetected}\mathrm{data}=\frac{1}{2}\times \mathrm{MDL}$$

(2)

where:

• MDL—Method Detection Limit.

As noted in Table 4, the removal rates of Hg, Cd, Cr, Cr⁶⁺, Pb, Ni, Cu, Zn, and Se in the WWTPs are higher than 50% ranging from 50.8% to 69.8%, while the removal rate of As and Mn are 47.3% and 42.8%, respectively. The rate of Be is lower than 30%.

Removal rates of heavy metals and metalloids in China’s WWTPs were comparable to those reported in research findings in other countries. Obarska-Pempkowiak [32] showed that the removal rate of zinc in WWTPs was 50%, copper was 60%, lead was 79%, and cadmium was 80%. Olive et al. [2] found that the removal rate of aluminum, cadmium, chromium, copper, iron, lead, mercury, and zinc were all higher than 70% in Canadian WWTPs, but they reported lower values for manganese, nickel and strontium. Kulbat et al. [29] found that the rate of zinc removal was 78.1%–86.2%, copper was 78.1%–93.2%, chromium was 45.3%–66.7%, silver was 28.3%–66.7%, lead was 48%–83% in Polish WWTPs, and these research results indicated higher removal rate of heavy metals.

It is difficult to discuss the influence of treatment process to the removal rate of heavy metals and metalloids under any but the most controlled conditions. Most metals in wastewater are soluble, but if mixed with sewage, the changes in pH and the act of mixing with other organics can render many types of metals insoluble and convert them to sludge, leading to high removal rates in wastewater [2]. The removal rate is affected by many factors, including type of metal, concentration in the influent, interactions with microbes in the sewage treatment system, and the treatment processes that are employed by the WWTPs [29].

3.3. Statistical Distribution of Heavy Metal and Metalloid Discharge Concentrations

Among the 1080 Hg discharge figures that were collected from 872 WWTPs, 792 indicated that no mercury was detected and 288 indicated mercury had been detected. Among the 925 As discharge data from 858 WWTPs, 387 were not detected, while 537 were detected. While using the detected daily average data, as shown in Figure 2 and Figure 3, it the lognormal distribution was visible for both Hg and As detected according to the fitting lines with the logarithm of Hg or As concentrations on the horizontal x-axis against the normal probabilities on the vertical y-axis. This distribution was also verified while using the P-P graph test (Figure 4 and Figure 5) with the fitting lines of cumulative probability of monitoring data after logarithmic transformation against the expected cumulative probability. The lognormal distribution of effluent concentrations is also consistent with other research [7,33].

When considering some of the undetected data, the delta-lognormal distribution provides a reasonable and practical basis for further analysis of the heavy metal and metalloid data and determining the discharge limits, which is in accordance with the EPA regulations [11,25,34]. The delta-lognormal distribution consists of two parts, undetected numbers and detected numbers conforming to the lognormal distribution. The distribution function of delta-lognormal distribution can be expressed as [11,25,34]:

$$f(x)=\delta I(x_0)+(1-\delta )g(x)$$

(3)

where:

• $\delta$—the ratio of non-detected, $0\le \delta \le 1$; when $x_0=\mathrm{ND}$, $I(x_0)$= 1, or else $I(x_0)$= 0;
• $g(x)$—the function of lognormal distribution.

3.4. Factors Influencing Heavy Metal and Metalloid Discharge Concentrations

There are some data requirements for One-way analysis of variance, conforming to normal distribution, independence, and homogeneity variance. The logarithm of detected data have been shown to fit normal distribution, and because the data were all from independent samples, they met the independence requirement. The homogeneity of variance test was taken with the One-way analysis of variance, if any sample did not pass the test, the Kruskal-Wallis test was used for analysis.

3.4.1. Regional Differences

Discharge concentrations of heavy metals and metalloids in WWTPs from nine provinces were analyzed. Figure 6 indicates that the Hg concentrations from Shandong, Zhejiang, Guangxi, Gansu, and Shanxi WWTPs were relatively high with about 50% WWTPs in these regions being higher than 0.0001 mg/L. Figure 7 indicates that the As concentrations from Zhejiang, Hunan, Guangxi, Gansu, and Shanxi WWTPs were relatively high with average concentrations of As in these regions higher than 0.002 mg/L. According to China’s “Environment Statistical Annual Report 2015” [10] of China, Guangxi, and Gansu are top two regions in which more Hg was discharged in wastewater, because of large number of facilities that engage in nonferrous metallurgy, chemical raw materials, and chemical products producing. This was consistent with the results of data analysis.

The effect of geographical location on Hg and As discharge concentration levels in WWTPs was analyzed while using One-way analysis of variance with the independent variable being the logarithm of concentration data, and the variables being different regions. Because the data did not pass the homogeneity of variance test, Kruskal-Wallis test was used and the results showed there to be significant differences in Hg (P = 0.027 < 0.05) and As (P ≈ 0 < 0.05) discharge concentrations for different regions, with the Hunan Province showing significant differences from all other regions.

3.4.2. Scale Differences

The numbers of samples from WWTPs in nine provinces that consisted of the scale information were 95 for Hg and 187 for As. Among these data, the average concentrations of Hg and As for large-scale (treatment capacity ≥ 0.1 million m³/day), mid-scale (10 thousand m³/day ≤ treatment capacity < 0.1 million m³/day), and small-scale (treatment capacity < 10 thousand m³/day) of WWTPs are shown in

Table 5. Discharge concentrations of Hg and As for different scales of WWTPs.

Pollutant	Scale of WWTPs	Number of Detected Samples	Average Discharge Concentration(mg/L)
Hg	Large-scale	10	0.00013
	Mid-scale	75	0.00016
	Small-scale	10	0.00016
As	Large-scale	18	0.0063
	Mid-scale	148	0.0017
	Small-scale	21	0.0010

. We can find the Hg concentrations from different scale WWTPs are close, but As concentrations from large scale are higher. The latter may be attributable to 6 of the 18 large-scale WWTPs with high influent concentrations. The scale factor is not significant to Hg (P = 0.695 > 0.05) according to the One-way analysis of variance and not significant for As (P = 0.088 > 0.05) either, according to Kruskal-Wallis test.

3.4.3. Relationship between the Concentrations of Influent and Effluent

The relationship between the concentrations of influent and effluent was analyzed (Figure 8 and Figure 9). The results show that the linear relationship between the concentrations of influent and effluent is weak (R² < 0.5). Gbondo-Tugbawa et al. [26] also reported that there is no significant relationship between the mercury influent concentration and effluent.

Research has been performed on influent and effluent concentration data of Hg and As from WWTPs in Zhejiang Province. From the

Table 6. Average of standard deviations of influent and effluent for Hg and As.

Pollutants	Average of Standard Deviation (mg/L)
	Influent	Effluent
Hg	0.0115	0.0001
As	0.0115	0.0017

we can conclude that the standard deviation of Hg and As influent concentration is higher than the effluent concentration, which suggests that the biochemical treatment system could play a role in buffering the fluctuation of heavy metals and metalloids, such as Hg and As, and there was the same conclusion in Balogh et al. [35] and Li et al. [36] research, which could explain the weak linear relationship between influent and effluent concentrations.

3.5. Discharge Limits of Hg and As

3.5.1. Long-Time Averages

Based on the delta-lognormal distribution, the long-time averages were calculated, as follows [11,25,34]:

$${\mathrm{LTA}}_j=\delta D+(1-\delta )\frac{{\displaystyle \sum _{i=1}^n{X}_i}}{n}$$

(4)

where:

• ${\mathrm{LTA}}_j$—the long-time average of heavy metal or metalloid of ${\mathrm{WWTP}}_j$;
• $\delta$—the rate of below method detection limit;
• $D$—method detection limit;
• $X_i$—the concentration higher than method detection limit;
• $n$—the number of concentration data up the method detection limit.

According to the equation above, the long-time averages of Hg for 99 WWTPs and As for 112 WWTPs are shown in

Table 7. LTAs of Hg for 99 WWTPs.

No. of WWTPs	Number of Samples	Rate of Undetected	LTA (mg/L)	No. of WWTPs	Number of Samples	Rate of Undetected	LTA (mg/L)
1	38	76.3%	0.00007	51	18	66.7%	0.00011
2	34	76.5%	0.00010	51	18	44.4%	0.00005
3	23	47.8%	0.00041	51	7	42.9%	0.00007
4	25	44.0%	0.00008	51	18	77.8%	0.00005
5	22	27.3%	0.00008	51	18	72.2%	0.00005
6	21	47.6%	0.00012	51	17	23.5%	0.00014
7	21	38.1%	0.00007	51	15	40.0%	0.00014
8	16	25.0%	0.00005	51	15	60.0%	0.00011
9	23	65.2%	0.00007	51	10	70.0%	0.00004
10	23	65.2%	0.00008	51	15	46.7%	0.00019
11	24	66.7%	0.00009	51	15	53.3%	0.00011
12	14	71.4%	0.00008	51	14	42.9%	0.00008
13	21	19.0%	0.00027	51	11	0.0%	0.00032
14	10	30.0%	0.00008	51	13	69.2%	0.00006
15	22	81.8%	0.00005	51	13	0.0%	0.00008
16	8	12.5%	0.00033	51	12	41.7%	0.00009
17	22	54.5%	0.00006	51	12	58.3%	0.00008
18	13	15.4%	0.00010	51	12	75.0%	0.00008
19	10	40.0%	0.00011	51	10	40.0%	0.00006
20	22	63.6%	0.00005	51	10	20.0%	0.00028
21	11	27.3%	0.00009	51	10	20.0%	0.00038
22	22	22.7%	0.00008	51	10	40.0%	0.00015
23	15	13.3%	0.00045	51	8	50.0%	0.00005
24	22	45.5%	0.00008	51	9	0.0%	0.00052
25	21	47.6%	0.00006	51	9	11.1%	0.00076
26	21	61.9%	0.00007	51	9	0.0%	0.00026
27	18	77.8%	0.00005	51	9	22.2%	0.00009
28	21	23.8%	0.00007	51	9	0.0%	0.00016
29	8	0.0%	0.00008	51	7	0.0%	0.00024
30	18	0.0%	0.00025	51	9	0.0%	0.00015
31	18	72.2%	0.00005	51	9	0.0%	0.00016
32	21	38.1%	0.00011	51	9	66.7%	0.00009
33	21	33.3%	0.00010	51	9	0.0%	0.00014
34	21	19.0%	0.00011	51	9	11.1%	0.00025
35	21	23.8%	0.00007	51	9	22.2%	0.00006
36	18	0.0%	0.00025	51	8	25.0%	0.00006
37	17	0.0%	0.00022	51	8	0.0%	0.00017
38	11	0.0%	0.00034	51	7	0.0%	0.00021
39	18	0.0%	0.00027	51	9	22.2%	0.00009
40	21	57.1%	0.00008	51	8	50.0%	0.00006
41	21	57.1%	0.00011	51	8	0.0%	0.00029
42	21	9.5%	0.00019	51	9	22.2%	0.00005
43	21	61.9%	0.00005	51	9	11.1%	0.00026
44	21	57.1%	0.00005	51	9	33.3%	0.00025
45	21	52.4%	0.00018	51	9	55.6%	0.00022
46	20	60.0%	0.00006	51	8	50.0%	0.00010
47	21	76.2%	0.00006	51	7	28.6%	0.00005
48	18	5.6%	0.00018	51	8	62.5%	0.00013
49	18	77.8%	0.00006	51	7	28.6%	0.00006
50	18	33.3%	0.00081

and

Table 8. LTAs of As for 112 WWTPs.

No. of WWTPs	Number of Samples	Rate of Undetected	LTA (mg/L)	No. of WWTPs	Number of Samples	Rate of Undetected	LTA (mg/L)
1	38	18.4%	0.0008	57	18	0.0%	0.0099
2	36	8.3%	0.0010	58	17	0.0%	0.0018
3	30	13.3%	0.0040	59	13	0.0%	0.0008
4	25	64.0%	0.0005	60	18	27.8%	0.0010
5	24	70.8%	0.0004	61	10	0.0%	0.0024
6	23	13.0%	0.0007	62	18	0.0%	0.0080
7	24	70.8%	0.0004	63	18	0.0%	0.0008
8	23	26.1%	0.0010	64	18	22.2%	0.0019
9	23	0.0%	0.0029	65	16	0.0%	0.0011
10	24	58.3%	0.0019	66	16	6.3%	0.0006
11	24	58.3%	0.0005	67	17	58.8%	0.0007
12	23	4.3%	0.0023	68	11	45.5%	0.0009
13	23	52.2%	0.0009	69	15	0.0%	0.0012
14	23	4.3%	0.0011	70	15	26.7%	0.0009
15	22	0.0%	0.0012	71	9	33.3%	0.0005
16	22	36.4%	0.0007	72	14	71.4%	0.0004
17	11	0.0%	0.0019	73	13	0.0%	0.0031
18	22	9.1%	0.0016	74	13	0.0%	0.0011
19	22	22.7%	0.0009	75	9	11.1%	0.0009
20	17	0.0%	0.0011	76	12	75.0%	0.0005
21	22	4.5%	0.0010	77	12	8.3%	0.0013
22	22	68.2%	0.0006	78	12	50.0%	0.0014
23	22	0.0%	0.0009	79	12	50.0%	0.0010
24	22	63.6%	0.0004	80	10	0.0%	0.0016
25	22	72.7%	0.0017	81	10	60.0%	0.0004
26	20	0.0%	0.0033	82	9	55.6%	0.0009
27	22	0.0%	0.0015	83	9	0.0%	0.0010
28	16	6.3%	0.0040	84	9	0.0%	0.0013
29	22	72.7%	0.0003	85	7	0.0%	0.0012
30	22	4.5%	0.0250	86	9	0.0%	0.0041
31	22	9.1%	0.0012	87	9	0.0%	0.0063
32	15	0.0%	0.0012	88	9	0.0%	0.0018
33	18	0.0%	0.0013	89	8	0.0%	0.0018
34	21	9.5%	0.0014	90	9	55.6%	0.0005
35	15	80.0%	0.0003	91	9	11.1%	0.0007
36	11	9.1%	0.0019	92	9	0.0%	0.0022
37	21	23.8%	0.0028	93	9	0.0%	0.0016
38	21	71.4%	0.0003	94	7	57.1%	0.0003
39	20	0.0%	0.0018	95	7	0.0%	0.0050
40	21	0.0%	0.0018	96	9	0.0%	0.0010
41	21	47.6%	0.0008	97	7	0.0%	0.0010
42	20	85.0%	0.0003	98	7	0.0%	0.0093
43	21	9.5%	0.0021	99	9	0.0%	0.0035
44	21	0.0%	0.0008	100	8	0.0%	0.0010
45	21	0.0%	0.0010	101	9	11.1%	0.0013
46	15	20.0%	0.0010	102	8	25.0%	0.0014
47	21	0.0%	0.0019	103	9	33.3%	0.0078
48	21	0.0%	0.0064	104	9	0.0%	0.0013
49	21	0.0%	0.0014	105	9	0.0%	0.0022
50	13	0.0%	0.0029	106	9	11.1%	0.0015
51	20	5.0%	0.0008	107	8	37.5%	0.0017
52	18	5.6%	0.0013	108	8	50.0%	0.0006
53	14	0.0%	0.0011	109	8	0.0%	0.0072
54	15	0.0%	0.0014	110	7	57.1%	0.0006
55	10	10.0%	0.0009	111	7	0.0%	0.0011
56	19	0.0%	0.0015	112	7	0.0%	0.0020

. The LTAs for Hg are from 0.00004 to 0.00081 mg/L with an average of 0.00015 mg/L and medium of 0.00009 mg/L. The LTAs for As are from 0.0003 to 0.0250 mg/L with an average of 0.0021 mg/L and medium of 0.0012 mg/L.

We can conclude that the LTAs of Hg and As are close to the level of surface water quality limits Grade I~III in “Surface water environmental quality standards” (GB 3838-2002) [37], and also comparable to the results of other works. For example, research shows the influent concentration of Hg was about 0.001 mg/L, varying with time and location [38]. The average influent concentration of Hg of 3 WWTPs in Canada was 0.000061 mg/L, and the discharge concentrations were from 0.000003–0.000014 mg/L [39]. Some studies in China also showed that the Hg concentrations from sewage were 0.0002–0.0024 mg/L, and the discharge concentrations were 0.00001–0.000029 mg/L, which was much lower than the limits in GB 18918-2002 [5]. Lu et al. [40] studied on the As concentration of WWTPs in Chongqing Province, showing that the influent concentration was about 0.00105 mg/L, and the discharge concentration was about 0.0007 mg/L.

3.5.2. Daily Maximum Variability Factors

Based on the delta-lognormal distribution of Hg and As discharge concentrations, the daily maximum variability factors were calculated while using the following equations:

$$\mathrm{VF}(1)=\frac{{\widehat{P}}_{99}}{\widehat{E}(X)}$$

(5)

where:

When $\delta \ge 0.99$, ${\widehat{P}}_{99}=D$; When $\delta <0.99$, ${\widehat{P}}_{99}=\max (D,e^{\mu +z\sigma })$

$$\widehat{E}(x)=\widehat{\delta }D+(1-\widehat{\delta })e^{\widehat{\mu }+0.5{\widehat{\sigma }}^2}$$

(6)

and,

$$\widehat{\mu }=\frac{1}{n}{\displaystyle \sum _{i-1}^n{y}_i}$$

(7)

$${\widehat{\sigma }}^2={{\displaystyle \sum _{i=1}^n(y_i-\widehat{\mu })}}^2/(n-1)$$

(8)

$$z={\phi }^{-1}(\frac{0.99-\delta }{1-\delta })$$

(9)

where:

• $\mathrm{VF}\left(1\right)$—daily maximum variable factor;
• ${\widehat{P}}_{99}$—estimated 99th percentile of Hg or As discharge concentration at a given plant;
• $\widehat{E}(x)$—estimated expected value of Hg or As discharge concentration at a given plant;
• $y_i$—natural logarithm of Hg or As discharge concentration other than ND;
• $\varphi$—normal cumulated distribution function value.

Based on the method described above, the Hg daily maximum variable factors for 99 WWTPs and As daily maximum variable factors for 112 WWTPs are shown in

Table 9. VF(1)s and derived discharge limits of Hg for 99 WWTPs.

No. of WWTPs	VF(1)	Discharge Limit (mg/L)	No. of WWTPs	VF(1)	Discharge Limit (mg/L)
1	6.09	0.00042	51	8.40	0.00096
2	9.00	0.00087	52	2.23	0.00012
3	7.78	0.00317	53	3.80	0.00027
4	3.72	0.00030	54	2.72	0.00013
5	2.67	0.00020	55	2.81	0.00014
6	6.42	0.00078	56	3.86	0.00054
7	2.92	0.00020	57	5.05	0.00069
8	1.75	0.00009	58	6.23	0.00066
9	3.88	0.00026	59	1.55	0.00007
10	3.82	0.00032	60	7.41	0.00138
11	6.51	0.00060	61	6.16	0.00066
12	3.52	0.00027	62	3.62	0.00028
13	5.92	0.00158	63	3.00	0.00096
14	2.62	0.00021	64	3.97	0.00022
15	2.84	0.00013	65	2.19	0.00016
16	5.42	0.00177	66	5.83	0.00055
17	2.77	0.00016	67	3.68	0.00030
18	3.57	0.00034	68	6.34	0.00051
19	4.02	0.00044	69	2.93	0.00017
20	2.25	0.00011	70	3.20	0.00088
21	3.02	0.00026	71	6.01	0.00228
22	2.46	0.00020	72	5.41	0.00082
23	3.49	0.00158	73	2.28	0.00012
24	3.58	0.00029	74	3.52	0.00182
25	3.02	0.00018	75	5.42	0.00411
26	4.25	0.00031	76	3.33	0.00087
27	1.82	0.00008	77	2.07	0.00019
28	2.29	0.00017	78	3.12	0.00050
29	1.52	0.00012	79	4.77	0.00114
30	1.81	0.00045	80	2.01	0.00029
31	2.28	0.00011	81	2.27	0.00036
32	5.22	0.00058	82	8.83	0.00082
33	3.44	0.00035	83	2.45	0.00033
34	2.70	0.00030	84	2.18	0.00054
35	2.10	0.00015	85	2.07	0.00012
36	3.81	0.00095	86	2.44	0.00014
37	2.67	0.00059	87	3.74	0.00065
38	2.43	0.00084	88	4.26	0.00090
39	3.97	0.00105	89	2.63	0.00024
40	4.42	0.00035	90	2.32	0.00013
41	6.23	0.00071	91	2.09	0.00060
42	3.27	0.00061	92	1.59	0.00008
43	2.42	0.00013	93	2.65	0.00070
44	2.48	0.00013	94	4.34	0.00109
45	5.09	0.00091	95	8.71	0.00194
46	3.22	0.00019	96	3.78	0.00036
47	3.95	0.00022	97	1.34	0.00007
48	3.52	0.00062	98	8.49	0.00108
49	4.68	0.00026	99	1.85	0.00010
50	11.07	0.00902

and

Table 10. VF(1)s and derived discharge limits of As for 112 WWTPs.

No. of WWTPs	VF(1)	Discharge Limit (mg/L)	No. of WWTPs	VF(1)	Discharge Limit (mg/L)
1	3.15	0.0024	57	2.47	0.0244
2	3.00	0.0029	58	3.26	0.0059
3	7.68	0.0310	59	1.73	0.0013
4	4.20	0.0020	60	4.17	0.0040
5	2.34	0.0008	61	3.37	0.0081
6	2.42	0.0017	62	8.15	0.0654
7	2.99	0.0013	63	1.46	0.0012
8	4.07	0.0039	64	5.50	0.0107
9	2.99	0.0086	65	1.53	0.0017
10	8.43	0.0163	66	3.35	0.0021
11	4.23	0.0022	67	5.48	0.0036
12	5.66	0.0129	68	7.69	0.0073
13	3.47	0.0030	69	1.96	0.0024
14	3.76	0.0043	70	3.67	0.0032
15	1.93	0.0023	71	2.17	0.0012
16	5.40	0.0037	72	2.41	0.0009
17	2.63	0.0050	73	2.42	0.0075
18	2.68	0.0043	74	3.43	0.0038
19	4.35	0.0041	75	2.96	0.0027
20	3.36	0.0038	76	3.54	0.0017
21	3.97	0.0038	77	4.82	0.0062
22	7.17	0.0042	78	4.37	0.0061
23	2.56	0.0023	79	6.17	0.0059
24	2.18	0.0008	80	4.01	0.0064
25	16.05	0.0266	81	1.64	0.0006
26	4.99	0.0163	82	5.70	0.0054
27	4.04	0.0062	83	2.25	0.0022
28	5.87	0.0233	84	3.43	0.0045
29	1.64	0.0005	85	2.71	0.0031
30	5.92	0.1482	86	6.35	0.0263
31	3.41	0.0040	87	4.64	0.0291
32	2.76	0.0033	88	2.92	0.0053
33	1.86	0.0025	89	2.27	0.0041
34	3.30	0.0047	90	3.30	0.0015
35	1.44	0.0005	91	2.16	0.0016
36	5.61	0.0108	92	5.28	0.0114
37	2.61	0.0073	93	2.04	0.0033
38	1.81	0.0006	94	1.44	0.0005
39	2.62	0.0046	95	3.17	0.0158
40	3.58	0.0064	96	2.06	0.0021
41	3.75	0.0029	97	2.33	0.0023
42	2.53	0.0008	98	5.09	0.0473
43	5.57	0.0118	99	5.69	0.0201
44	1.54	0.0012	100	2.04	0.0020
45	2.07	0.0021	101	3.77	0.0051
46	5.45	0.0054	102	4.54	0.0062
47	2.50	0.0048	103	10.25	0.0799
48	3.93	0.0252	104	3.31	0.0043
49	3.98	0.0056	105	3.21	0.0072
50	3.86	0.0112	106	4.93	0.0075
51	2.43	0.0019	107	2.98	0.0050
52	3.50	0.0046	108	4.34	0.0028
53	3.00	0.0032	109	5.70	0.0410
54	2.34	0.0033	110	3.24	0.0020
55	4.02	0.0037	111	2.03	0.0021
56	3.48	0.0053	112	1.78	0.0036

. As shown in the tables, it can find that the Hg daily maximum variable factors are 1.34–11.07 with an average of 3.89 and medium of 3.33. The As daily maximum variable factors are 1.44–16.05 with an average of 3.77 and medium of 3.35.

3.5.3. Discharge Limits

We obtained the Hg and As discharge limits for each WWTP (Table 9 and Table 10) while using Equation (1). From these results, it can find that the Hg discharge limits are 0.00007–0.00902 mg/L with an average of 0.00067 mg/L and medium of 0.00035 mg/L. These values are lower than the limit (0.001 mg/L) in GB 18918-2002. Derived discharge limits for As are 0.0005–0.1482 mg/L with an average of 0.0092 mg/L and medium of 0.0041 mg/L, which are also lower than the limit (0.1 mg/L) in GB 18918-2002. When considering that the national discharge limits should be suitable for the entire country, we used the medium value of LTAs and average value of VF(1)s as the suggested Hg and As discharge limits [11] (

Table 11. Suggested Hg and As discharge limits of WWTPs in China.

Pollutant	Medium of LTAs (mg/L)	Average of VF(1)s	Suggested Discharge Limit (mg/L)
Hg	0.00009	3.89	0.00035
As	0.0012	3.77	0.0045

).

4. Conclusions

This study analyzed the discharge characteristics of 14 heavy metals and metalloids at WWTPs in China. The study used Hg and As as an example to derive the discharge limits based on statistical methods. In this study, 13 of the 14 heavy metals and metalloids, with the exception of alkyl mercury, were detected in the discharge water with high rates of detection of As, Cu, Mn, and Se. In many cases, Cr, Cr⁶⁺, Pb, and Ni exceeded standards. Removal rates of Hg, As, Cd, Cr, Cr⁶⁺, Pb, Ni, Cu, Zn, Mn, and Se exceeded 40%, which is consistent with values in other countries.

Results from this study suggest that the delta-lognormal distribution was suitable for the heavy metal and metalloid discharge concentrations of WWTPs. Based on the statistical distribution, the study found that geographic location was strongly correlated with Hg and As discharge concentrations, while size and influent concentration were not. The derived LTAs for Hg of 99 WWTPs and As of 112 WWTPs showed that they were much lower than the limits in GB 18918-2002. The derived VF(1)s of WWTPs revealed that the Hg and As fluctuations were not large, suggesting that WWTPs in China are relatively well-managed. Lastly, the derived Hg and As discharge limits suggest that it is necessary to establish 1–2 orders of magnitude lower discharge limits for WWTPs in China.