Integrated Technical–Economic–Environmental Evaluation of Available Technologies for Heavy Metal Wastewater Treatment Used in Lead–Zinc Smelting in the Yellow River Basin

Abstract

Evaluating the efficacy of available technology for pollutant treatment is critical for formulating environmental management policies and standards. To address the lack of systematic quantitative methods for evaluating available technology, we propose a method based on the Entropy Weight TOPSIS model which integrates technology, economic efficiency, environmental benefits, and operational feasibility. We applied this approach to evaluate six heavy metal wastewater treatment technologies used in the lead–zinc smelting industry in the Yellow River Basin of China. A total of 4 primary and 16 secondary evaluation indicators were identified. The data were mainly composed of supervised monitoring data collected by local environmental protection authorities and self-monitoring operation data collected from factories; moreover, 10 relevant experts were invited to assess the scoring indicators. The results showed that technical performance had the greatest contribution to the overall efficacy of the treatment technology (62.31% weight), followed by environmental benefits (14.24% weight), economic costs (12.08% weight), and operational feasibility (11.36% weight). The final scores and rankings of the six technologies evaluated showed that a sulfurization precipitation with two-stage lime neutralization and sedimentation technology received the highest score due to its balanced technical performance, economic cost, environmental benefits, and operational feasibility. Conversely, lime neutralization with flocculation precipitation technology ranked lowest due to its non-compliance with the emission limits in China, despite its low economic cost and carbon emission intensity. This study provides a quantitative methodological framework for evaluating available technology, emphasizing the balance of the technical, economic, and environmental benefits of the pollutant treatment technologies chosen and the relevant policies made.

1. Introduction

Available technology is one of the most important factors for most countries and international organizations in setting environmental management policies and emission limits. The United States Environmental Protection Agency (US EPA) has proposed the establishment of technology-based regulations [1] in the National Pollutant Discharge Elimination System for pollution source management. The US EPA divides water pollution prevention and control technologies into three categories: Best Practical Control Technology Currently Available (BPT), Best Conventional Pollutant Control Technology (BCT), and Best Available Technology Economically Achievable (BAT) [1]. The main factors considered for BPT evaluation are as follows: (1) the total cost of applying the control technology, (2) the age of the equipment and facilities, (3) the processes employed by the industry, (4) engineering aspects of the technology, (5) non-water quality environmental impacts, including energy requirements, and (6) other factors that the EPA deems appropriate [1]. BCT mainly targets five conventional pollutants: biochemical oxygen demand over 5 days (BOD₅), total suspended solids (TSS), fecal coliform, pH, oil, and grease. The US EPA establishes BCT limitations after consideration of a two-part “cost-reasonableness” test, which includes a publicly owned treatment works (POTW) cost-comparison test and an industry cost-effectiveness test [2]. BAT represents the best available economically achievable performance for plants that mainly target water pollutants other than conventional pollutants. Although the factors considered are similar to those of BPT, BAT represents the best available economically achievable performance for these plants. Regarding the method, the evaluation of BPT and BAT is primarily qualitative, while the two-part “cost-reasonability” test for BCT evaluation is quantitative.

The EU’s Directive 2010/75/EU on industrial emissions proposed a pollution prevention and control approach based on the “Best Available Techniques” (EU’s BAT) [3]. The EU’s BAT highlights the most effective and advanced stage in the development of activities and their methods of operation, providing the basis for emission limit values and other permit conditions designed to prevent and reduce emissions and the overall environmental impact [3]. “Available techniques” refers to those developed on a scale that allows implementation in the relevant industrial sector, under economically and technically viable conditions, taking into consideration the costs, advantages, and whether or not the techniques are used or produced inside the Member State in question, as long as they are reasonably accessible to the operator [3]. “Best” refers to the technique that is most effective at achieving a high level of protection of the environment as a whole [3]. EU’s BAT evaluation not only considers pollution reduction but also emphasizes the importance of low waste, reducing usage of hazardous substances, and energy efficiency.

The technological and economic feasibility are the main considerations that should be addressed in order for China to formulate pollutant emission standards. In 2010, China released the first “Guideline on Best Available Technologies of Pollution Prevention and Control for Coal fired Power Plant Industry”. In 2018, China released the “Development Guideline for Guidelines on Available Techniques of Pollution Prevention and Control, HJ 2300—2018” [4], in which “available techniques of pollution prevention and control” are defined as China’s environmental needs and economic level over a certain period of time, and are comprehensively adopted and applied on a large scale to ensure that pollutant emissions can stably meet national pollutant emission standards. In the process of technology evaluation, HJ 2300—2018 proposed to construct an indicator system, including indicators of the technology performance, economic cost, environmental benefits, and operational feasibility [4]; however, the quantitative evaluation methods are not specified.

In summary, the US, EU, and China have all established environmental management and emission standard formulation systems based on pollution prevention and control technology evaluation, forming an indicator system that comprehensively considers multiple factors, such as technology, economy, and the environment. However, qualitative analysis is often used in specific technology evaluation, and a systematic and quantitative evaluation method has not yet been created.

Some studies have explored quantitative methods for evaluating pollution prevention and control technologies. An environmental technology verification (ETV) method was used to investigate the verification of ultra-low air pollutant emission technologies in coal-fired power plants [5], but the method was based on engineering test data, which incurred high costs, and the verification testing was relatively time-consuming. Advanced and feasible techniques for air pollution and control in China’s ceramics industry were evaluated and selected [6], but the methods mainly considered the pollutant emission concentrations and ignored the factors of economic cost, energy consumption, and operational feasibility. Cost–benefit analysis (CBA) is one of the integrated methods used to assess the efficiency of wastewater treatment processes. Environmental benefits are compared with economic costs to obtain the net benefits used to assess the environmental–economic viability [7,8]. The method requires further monetization of environmental impacts, followed by a comparison with costs to obtain the net benefits [9]. Data envelopment analysis (DEA) was also used to assess the environmental–economic effectiveness by defining the inputs (e.g., costs and energy consumption) and outputs (e.g., the volume of wastewater treated) of the wastewater treatment process and measuring the technical efficiency based on a distance function [10,11]. However, basic DEA models assume either constant returns to scale or variable returns to scale, and these assumptions may not always reflect real-world technologies [12]. Multi-criteria decision analysis (MCDA) calculates a composite index to comprehensively evaluate the wastewater treatment process by assigning weights to indicators from different aspects. The methods of weighting include the Delphi method [13], expert survey method [14], principal component analysis (PCA) [15], analytic hierarchy process (AHP) [16,17,18], and fuzzy comprehensive evaluation (FCE) [19,20]. However, most of these methods are somewhat subjective, which can easily lead to incomplete evaluation results.

The technique for order preference by similarity to an ideal solution (TOPSIS), also known as the “ideal solution ranking method”, is a method that ranks multiple evaluation objects by comparing their similarity to the ideal solution and thus determines their relative superiority or inferiority. This method has the advantages of a flexible and convenient calculation process, as well as accurate and reasonable evaluation results [21]. The Entropy Weight method can effectively reflect the degree of difference in evaluation indicator data. The combination of the Entropy Weight method and TOPSIS model is a commonly used comprehensive evaluation method that effectively avoids the interference of subjective factors present in the traditional TOPSIS method. It makes full use of the original data and objectively reflects the development and changes among the influencing factors [22,23,24]. The Entropy Weight TOPSIS model has been applied in assessing water environment carrying capacity [25], land use performance [26], and the level of qualitative economic development [27]. In the present study, for the first time, we applied the Entropy Weight TOPSIS model to quantitatively evaluate heavy metal wastewater treatment technologies, addressing a critical gap in reconciling technical performance, economic costs, environmental benefits, and operational feasibility, with the goal of advancing the evidence-based selection of optimal treatment technology.

2. Study Scope

The Second National Pollution Source Census Bulletin of China has revealed that the discharge of heavy metals from wastewater in the nonferrous metal smelting industry accounts for about 13.8% of the wastewater pollution from the entire industrial sector, ranking third [28], while the wastewater generated from lead–zinc smelting industry amounts to about 60 million tons nationwide. In this study, the heavy metal wastewater treatment technology of the lead–zinc smelting industry in the Yellow River Basin was selected as the study object. The reserves of lead–zinc minerals in Gansu, Inner Mongolia, Shaanxi, and Henan provinces account for approximately 35.1% of China’s total reserves [29], and lead–zinc smelting is a key industry for the development of this region in the Yellow River Basin. According to the “China Nonferrous Metals Industry Yearbook 2022,” the total lead and zinc production of lead–zinc smelting factories in Gansu, Inner Mongolia, Shaanxi, and Henan province accounted for about 31.8% and 34.5% of the national total production, respectively [30].

The production processes adopted by lead–zinc smelting factories are shown in

Table 1. Production processes adopted by lead–zinc smelting factories in the Yellow River Basin in China.

Category	Production Process	Raw Material	Products
Primary smelting	Oxygen-rich bottom-/side-/top-blowing furnace with direct reduction of liquid high-lead slag via a side-blowing furnace	Raw ore, lead-containing waste	Lead bullion
	Sintering with a closed-blast furnace (imperial smelting process—ISP)	Raw ore	Lead bullion, spelter
	Hydrometallurgy of zinc	Raw ore, zinc oxide, zinc suboxide	Spelter, zinc sulfate
	Vertical zinc smelting	Raw ore, zinc oxide	Spelter
Secondary smelting	Oxygen-rich side-blowing furnace	Lead-containing waste	Lead bullion
	Reduction and recovery of zinc via a nitrification furnace	Zinc-containing waste	Zinc oxide, zinc suboxide
	Direct reduction process with rotary kiln	Zinc-containing waste	Zinc oxide, zinc suboxide

. The primary smelting technology for lead in upstream areas, such as Gansu and Inner Mongolia, mainly adopts the imperial smelting process (ISP), and the secondary smelting of zinc uses a direct reduction process with rotary kilns, which are generally at an intermediate level of technological development in China. The primary smelting technology of lead in Henan includes an oxygen-rich bottom-/side-/top-blowing furnace coupled with direct reduction of liquid high-lead-slag via a side-blowing furnace, and the secondary smelting of zinc mainly utilizes a nitrification furnace for the reduction and recovery of zinc, as such furnaces are at an advanced level of technological development worldwide.

Wastewater generated from the acid production process is the main source of heavy-metal-bearing wastewater from lead–zinc primary and secondary smelting (

Table 2. Typical characteristics of wastewater from the acid production process of lead–zinc smelting facilities before treatment.

Pollutants	Concentration (mg/L)	Data Source
pH	<7	[33,34]
COD	100~1000	[34]
TSS	100~1000	[34]
Pb	6.84~500	This study
Cd	4.7~400	This study
Hg	0.028~100	This study
As	0.02~4500	This study
Tl	0.03~13.28	This study

). In 2010, China issued the “Emission Standards of Pollutants for Lead Zinc Industry” (GB 25466—2010) [31], which set emission limits for heavy metals in wastewater generated from lead–zinc smelting and served as the basis for the legal discharge of pollutants. In 2020, China issued an amendment of GB 25466—2010 [32], which supplemented the emission limits for Tl in wastewater. The above standards stipulate that heavy metal wastewater must undergo treatment before being mixed with other wastewater or reused, and that it must meet the standards listed in

Table 3. Emission limits of five heavy metals in wastewater from the lead–zinc smelting industry.

No.	Pollutants	Emission Limits (mg/L) [31,32]
1	Pb	0.5
2	Cd	0.05
3	Hg	0.03
4	As	0.3
5	Tl	0.017

.

To meet the above emission limits, lead–zinc smelting factories in the Yellow River Basin have adopted various wastewater treatment technologies. In particular, after the release of Tl emission limits in 2020, some factories have upgraded their wastewater treatment facilities. According to an on-site investigation, six main types of heavy metal wastewater treatment technologies are used by lead–zinc smelting factories in the Yellow River Basin, as detailed in

Table 4. The six heavy metal wastewater treatment technologies.

Technology No.	Technology Description
1	Lime neutralization + Fe flocculation + sedimentation
2	Sulfurization precipitation + two-stage lime neutralization + sedimentation
3	Two-stage NaHS precipitation + lime neutralization + primary precipitation + electrochemical reaction + secondary sedimentation
4	Two stages of “lime neutralization + Fe flocculation + Biological agents a + Polyacrylamide (PAM) flocculation + sedimentation”
5	Lime neutralization + PAM flocculation + Tl removal agents b + primary precipitation + electrochemical reaction + secondary flocculation precipitation
6	Potassium permanganate oxidation + lime neutralization + sulfurization precipitation + Biological agents a + Tl removal agents b + sedimentation

^a “Biological agents” are designed by combining the metabolites of a complex functional bacterial community that is mainly composed of sulfur bacteria alongside other compounds. Through group grafting technology, biological agents containing a large number of functional groups such as hydroxyl, thiol, carboxyl, and amino groups are formed, and are complexed with heavy metal ions in wastewater to form stable heavy metal complexes [35,36]. ^b The term “Tl removal agents” refers to water-soluble polymers that convert Tl⁺ into colloidal Tl(OH)₃, which is then adsorbed by the highly efficient substance formed during the reaction process, forming a stable precipitate for separation from wastewater [37].

. These technologies are all well designed and applied to reduce the heavy metals in wastewater produced during lead–zinc smelting [31,32,35,36,37].

3. Methodology

3.1. Evaluation Index

The evaluation of pollution prevention and control technologies by countries and organizations, such as the US and EU, mainly includes two factors: pollution reduction and economic costs. Other environmental impacts, such as energy consumption caused by the implementation of the technology are also considered, as well as the feasibility of operation. In Appendix C of HJ 2300—2018 in China, reference indicators for pollution prevention and control technology investigation are proposed, with primary indicators including technical performance, economic costs, environmental benefits, and operational feasibility [4]. These indicators are generally consistent with the evaluation systems of the United States and the European Union. This study proposes a set of secondary indicators based on the primary indicators proposed in HJ 2300—2018, reflecting the technical, economic, environmental, and operational characteristics of lead–zinc smelting wastewater treatment. The discharge concentration and removal efficiency of heavy metals are the most important factors for evaluating technical performance, and the construction investment and operating costs constitute the main economic costs. Carbon emissions and environmental compliance reflect comprehensive environmental benefits other than pollution reduction. As an indicator of operational feasibility, the difficulty of operation mainly reflects the operation requirements for the operators and controllers of wastewater treatment processes and facilities, while the technology maturity takes into account factors such as technical stability and the popularity rate of the application. There would be some internal connection between the two indicators, but they also have their respective focuses. These indicators are the primary elements that constitute the comprehensive performance of the technologies (

Table 5. Evaluation index of heavy metal wastewater treatment technologies for the lead–zinc smelting industry.

Primary Indicators	Secondary Indicators		Unit	Attributes a
Technical performance	Discharge concentration of	Pb	mg/L	–
		Cd	mg/L	–
		Hg	mg/L	–
		As	mg/L	–
		Tl	mg/L	–
	Removal efficiency of	Pb	%	+
		Cd	%	+
		Hg	%	+
		As	%	+
		Tl	%	+
Economic costs	Construction investment		CNY/m3·d	–
	Operating cost		CNY/m3	–
Environmental benefits	Compliance with emission limits			+
	Carbon emission intensity		kgCO2/m3	–
Operational feasibility	Difficulty of operation			–
	Technology maturity			+

^a “+” refers to a positive indicator and “–” refers to a negative indicator.

).

3.2. Entropy Weight TOPSIS Model

The steps of the Entropy Weight TOPSIS calculation are as follows:

(1)

The initial evaluation matrix of the data is X:

$$X={{(x}_{ij})}_{m·n}=\left[\begin{array}{c}\begin{array}{ccc}{x}_{11}& x_{12}& \begin{array}{cc}\cdots & x_{1n}\end{array}\end{array}\\ \begin{array}{ccc}{x}_{21}& x_{22}& \begin{array}{cc}\cdots & x_{2n}\end{array}\end{array}\\ \begin{array}{ccc}⋮& ⋮& \begin{array}{cc}⋮& ⋮\end{array}\end{array}\\ \begin{array}{ccc}{x}_{m1}& b_{m2}& \begin{array}{cc}\cdots & x_{mn}\end{array}\end{array}\end{array}\right]$$

(1)

where the element index is denoted as $x_{ij}$, i represents the technology being evaluated (i = 1, 2, 3,…, m), and j denotes evaluation indicators (j = 1, 2, 3,…, n).

(2)

Use the range normalization method to standardize the indicator data to address the inconsistent units in the original data. S serves as the standardized evaluation matrix:

$$\mathrm{P}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e} \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{s}:s_{ij}={\displaystyle \frac{x_{ij}-min\left(x_j\right)}{\mathit{max}\left(x_j\right)-min\left(x_j\right)}}$$

(2)

$$\mathrm{N}\mathrm{e}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e} \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}:s_{ij}={\displaystyle \frac{\mathit{max}\left(x_j\right)-x_{ij}}{\mathit{max}\left(x_j\right)-min\left(x_j\right)}}$$

(3)

$$S={{(s}_{ij})}_{m·n}=\left[\begin{array}{c}\begin{array}{ccc}{s}_{11}& s_{12}& \begin{array}{cc}\cdots & s_{1n}\end{array}\end{array}\\ \begin{array}{ccc}{s}_{21}& s_{22}& \begin{array}{cc}\cdots & s_{2n}\end{array}\end{array}\\ \begin{array}{ccc}⋮& ⋮& \begin{array}{cc}⋮& ⋮\end{array}\end{array}\\ \begin{array}{ccc}{s}_{m1}& s_{m2}& \begin{array}{cc}\cdots & s_{mn}\end{array}\end{array}\end{array}\right]$$

(4)

(3)

Calculate the Entropy Weight. The greater the entropy, the higher the uncertainty of the data, the smaller the amount of information available, and the lower the weight; conversely, the smaller the entropy, the lower the uncertainty of the data, the greater the amount of information, and the higher the weight. The formula for calculating the Entropy Weight is as follows:

$$p_{ij}=s_{ij}/\sum _{i=1}^m{s}_{ij}$$

(5)

$$e_j=-{\displaystyle \frac{1}{\mathit{ln}m}}\sum _{i=1}^m(p_{ij}\mathit{ln}{p}_{ij})$$

(6)

$$w_j={\displaystyle \frac{1-e_j}{\sum _{j=1}^{n}1-e_j}}$$

(7)

where $p_{ij}$ is the proportion of the i-th technology in the j-th indicator, $e_j$ represents the information entropy of each indicator, and $w_j$ denotes the weight value.

(4)

Construct a decision matrix V:

$$V={\left(v_{ij}\right)}_{m·n}=\left[\begin{array}{c}\begin{array}{ccc}{w_{1}s}_{11}& w_2{s}_{12}& \begin{array}{cc}\cdots & w_n{s}_{1n}\end{array}\end{array}\\ \begin{array}{ccc}{w}_1{s}_{21}& {w_{2}s}_{22}& \begin{array}{cc}\cdots & w_n{s}_{2n}\end{array}\end{array}\\ \begin{array}{ccc}⋮& ⋮& \begin{array}{cc}⋮& ⋮\end{array}\end{array}\\ \begin{array}{ccc}{w_{1}s}_{m1}& w_2{s}_{m2}& \begin{array}{cc}\cdots & w_n{s}_{mn}\end{array}\end{array}\end{array}\right]$$

(8)

The selection of positive and negative ideal solutions considers the decision objectives and attribute characteristics. The calculation formula is as follows:

$${v_j}^{+}=\left\{\mathit{max}\left(v_{ij}\right)|i=\mathrm{1,2},\cdots ,m\right\}$$

(9)

$${v_j}^{-}=\left\{\mathit{min}\left(v_{ij}\right)|i=\mathrm{1,2},\cdots ,m\right\}$$

(10)

where ${v_j}^{+}$ represents the positive ideal solution for each indicator and ${v_j}^{-}$ denotes the negative ideal solution for each indicator.

(5)

Determine the Euclidean Distance for the technology evaluated in relation to the positive and negative ideal solutions. The specific formulas are as follows:

$$\left\{\begin{array}{c}{D_i}^{+}=\sqrt{{\displaystyle \sum _{j=1}^n}{(v_{ij}-{v_j}^{+})}^2}\\ {D_i}^{-}=\sqrt{{\displaystyle \sum _{j=1}^n}{(v_{ij}-{v_j}^{-})}^2}\end{array}\right.,i=\mathrm{1,2},\cdots ,m$$

(11)

where ${D_i}^{+}$ represents the distance to the optimal (positive ideal) solution and ${D_i}^{-}$ denotes the distance to the worst (negative ideal) solution.

(6)

Calculate the proximity of each evaluation object to the optimal solution:

$$F_i={\displaystyle \frac{{D_i}^{-}}{{D_i}^{+}+{D_i}^{-}}},i=\mathrm{1,2},\cdots ,m$$

(12)

where $F_i$ is the Proximity Value, which could to some extent reflect the superiority or inferiority of the evaluation object. The larger the $F_i$ value, the better the technology, while a smaller $F_i$ value is indicative of relatively inferior technology.

3.3. Data Source

In this study, monitoring data from local environmental protection authorities on the concentrations of five heavy metals discharged from 13 factories located in Gansu, Inner Mongolia, Shanxi, and Henan Provinces from 2021 to 2024 were used to analyze the discharge concentration levels. The data collected comprised the daily average concentrations of five heavy metals that were sampled four times in 24 h and mixed for analysis, according to the Environmental Monitoring Analytical Method Standards in China (

Table 6. Environmental monitoring analytical methods used for the analysis of five heavy metal concentrations.

No.	Pollutants	Method Source	Method Detection Limits (mg/L)
1	Pb	HJ 700—2014 [38]	0.00009
2	Cd	HJ 700—2014 [38]	0.00005
3	Hg	HJ 694—2014 [39]	0.00004
4	As	HJ 694—2014 [39]	0.0003
5	Tl	HJ 700—2014 [38]	0.00002

). We determined the detection rate, minimum and maximum concentration, and averages for each heavy metal to study the discharge concentration level.

To establish a quantitative evaluation system, we collected relevant data from six typical lead–zinc smelting factories in Gansu, Inner Mongolia, and Henan Provinces, covering the six types of heavy metal wastewater treatment technologies.

The discharge concentrations and removal efficiencies of five heavy metals are based on the monitoring data provided by the factories in compliance with the requirements of their pollutant discharge permit. The construction investment and operating costs of wastewater treatment were obtained from the factories’ statistics. The construction investment primarily involved civil engineering and equipment costs, while the operating costs mainly included fees for electricity, chemical agents, daily maintenance of equipment, monitoring of wastewater, disposal of solid waste generated from wastewater treatment, and workers’ salaries. The carbon emissions data were calculated based on the electricity consumption associated with wastewater treatment, with a carbon emissions factor of 0.5703 tCO₂/MW·h [40]. In terms of the indicator of compliance with emission limits, if the technology could not meet the emission limit for one or more pollutants, it was scored as 0, while if the technology met the standards requirement for all pollutants analyzed, it was scored as 1. The operational feasibility indicators were mainly obtained from expert scoring, in which 5 represents the highest score and 1 represents the lowest. We invited 10 experts—2 from universities, 3 from engineering research institutions, 3 from factories and 2 from environmental management departments—who were involved in wastewater treatment research or management. More experts from engineering research institutions and factories were chosen because of their greater familiarity with the practical situations of technical applications. None of the experts had ever worked for any of the six lead–zinc smelting factories selected in this research, and thus there were no conflicts of interest. During the scoring process, the experts listened to the introductions of six technologies from the six factories, including the basic information about the technology, process control requirements, stability of the effluent, etc., with Q&As and discussions, and provided scores for the six technologies independently. The average of 10 experts’ scores for each technology was taken as the final score for that technology.

4. Results

4.1. Heavy Metal Discharge Concentrations

More than 80% of lead–zinc smelting factories located in the Yellow River Basin do not discharge wastewater into surface water bodies. Instead, the wastewater is reused after the treatment. Regardless of whether the wastewater is discharged outside of the factory or reused within the factory, it must be treated and must meet the emission limits. As shown in

Table 7. Discharge concentrations of five heavy metals in wastewater after treatment.

No.	Pollutant	Number of Samples	Detection Rate	Data of Detection (mg/L)
				Min	Max	Average
1	Pb	29	91.3%	0.0001	0.11	0.0101
2	Cd	31	88.9%	0.0001	0.015	0.0043
3	Hg	49	74.4%	0.00004	0.015	0.0015
4	As	65	89.8%	0.0003	0.129	0.0107
5	Tl	15	44.4%	0.0003	0.064	0.0092

, the average concentration of the five heavy metals was one order of magnitude lower than the limits (Table 3). Due to the discharge control of Tl starting in 2020, some factories have not yet adopted targeted wastewater treatment processes, resulting in some exceeding the limit.

4.2. Performance of Heavy Metal Wastewater Treatment Technologies

Among the six technologies being evaluated, lime neutralization with flocculation sedimentation technology failed to meet the emission limits of GB 25466—2010 and its amendment in terms of the discharge concentrations of heavy metals (

Table 8. Removal efficiency of six heavy metal wastewater treatment technologies.

Technology No.	Indicators	Pb	Cd	Hg	As	Tl
1	Influent (mg/L)	67.66	39.41	0.58	8.32	6.14
	Effluent (mg/L)	40.69	26.03	0.01	0.087	0.064
	Removal efficiency (%)	39.86%	33.95%	98.28%	98.95%	98.96%
2	Influent (mg/L)	11.86	4.70	3.36	5.93	0.03
	Effluent (mg/L)	0.15	0.002	0.003	0.019	0.0093
	Removal efficiency (%)	98.74%	99.96%	99.91%	99.68%	69.00%
3	Influent (mg/L)	8.52	366.85	0.028	477.17	13.28
	Effluent (mg/L)	0.076	0.005	0.0061	0.022	0.0015
	Removal efficiency (%)	99.11%	99.99%	78.21%	99.99%	99.99%
4	Influent (mg/L)	500	150	100	4500	7.8
	Effluent (mg/L)	0.08	0.012	0.015	0.12	0.012
	Removal efficiency (%)	99.98%	99.99%	99.99%	99.99%	99.85%
5	Influent (mg/L)	7.79	378.85	0.031	506.17	9.28
	Effluent (mg/L)	0.11	0.007	0.008	0.078	0.0012
	Removal efficiency (%)	98.59%	99.99%	74.19%	99.98%	99.99%
6	Influent (mg/L)	6.84	6.13	9.94	0.02	0.17
	Effluent (mg/L)	0.041	0.005	0.004	0.007	0.003
	Removal efficiency (%)	99.40%	99.92%	99.96%	65.00%	98.24%

). Therefore, some factories have adopted potassium permanganate oxidation, sulfurization precipitation, and the addition of chemical agents in addition to lime neutralization with flocculation precipitation to achieve effective removal of heavy metals. From the perspective of removal efficiency, there were also differences among the six technologies, which were closely related to the characteristics of original wastewater quality and the operational feasibility.

The six heavy metal wastewater treatment technologies also differ in terms of construction investment and operation costs, carbon emission intensity, and operational feasibility indicators (Figure 1). For example, Technology 3 has moderate construction investment and operating costs, but a relatively high carbon emission intensity due to the high electricity consumption. Technologies 4 and 5 have higher operating costs per ton of water, due to the addition of chemicals, but their energy consumption and carbon emission intensity are not high in actual operation. It should be noted that Technology 1 is awarded the highest score in terms of difficulty of operation and the lowest score for technology maturity because of its failure to meet the emission limits for Pb, Cd, and Tl.

4.3. Evaluation of the Heavy Metal Wastewater Treatment Technologies

The weights of each indicator were calculated for each heavy metal wastewater treatment technology using the Entropy Weight TOPSIS method Equations (1)–(7). The results (

Table 9. Weight ratio of the primary and secondary indicators.

Primary Indicators	Secondary Indicators		Weight Ratio
Technical performance	Discharge concentration of	Pb	5.45%	31.65%	62.31%
		Cd	5.45%
		Hg	6.77%
		As	8.45%
		Tl	5.53%
	Removal efficiency of	Pb	5.45%	30.66%
		Cd	5.45%
		Hg	8.86%
		As	5.45%
		Tl	5.45%
Economic costs	Construction investment		5.63%	12.08%
	Operating cost		6.45%
Environmental benefits	Compliance with emission limits		5.45%	14.24%
	Carbon emission intensity		8.79%
Operational feasibility	Difficulty of operation		5.73%	11.36%
	Technology maturity		5.63%

) showed that the technical performance indicator contributed the highest proportion, followed by the environmental benefits indicator. Among the secondary indicators, the discharge concentration of As, removal efficiency of Hg, and the carbon emission intensity take greater proportions than others.

The ranking results of the technologies are described by the Proximity Value ($F_i)$ of each technology in relation to the optimal solution resulting from calculation by Equations (8)–(12). As shown in

Table 10. Evaluation values and ranking of six heavy metal wastewater treatment technologies.

Technology No.	Evaluation Values	Ranking
1	0.4973	6
2	0.7412	1
3	0.6297	3
4	0.6037	5
5	0.6188	4
6	0.6676	2

, Technology 2 presented the highest score and the highest ranking, while Technology 1 ranked last. Technology 2 met the emission limits for all five heavy metals, with a relatively good balance of removal efficiency, economic cost, environmental benefits, and operational feasibility. Technology 6 also exhibited a good performance because of its relatively low discharge concentrations of all five heavy metals and its low operating costs. Technology 1 ranked last because of its non-compliance with emission limits, despite its low economic costs and carbon emission intensity.

5. Discussion

Lead–zinc mines are usually associated with heavy metals such as Cd, Hg, As, and Tl. During smelting, some of these heavy metals enter the products, while others enter the flue gas, which is then converted into wastewater during flue gas purification. During the development of wastewater treatment technology, some studies showed that the combined use of sulfurization precipitation and lime precipitation can effectively remove heavy metals [31,32,41]. In this study, Technologies 2, 3, and 6, which utilized the sulfurization precipitation process, resulted in lower concentrations of heavy metals after wastewater treatment. Notably, in Technology 6, the removal efficiency of As was as high as 65%, even when the influent concentrations of As were low. The applicability of sulfurization precipitation in the treatment of lead–zinc smelting wastewater is demonstrated by the conclusion of this study that Technologies 2, 3, and 6 are superior to the other technologies. Regarding Tl removal in wastewater, Technologies 4, 5, and 6 all added targeted biological agents or Tl removal agents, and their removal efficiencies were above 98%. Although Technology 2 did not include a Tl removal process, due to the low concentration of Tl in its influent, two-stage lime precipitation can still satisfy the requirement to limit emissions, which is consistent with the findings of relevant studies [35,36,37]. However, the treatment performance of Technology 1 reflected that in the case of high influent Tl concentration, relying solely on lime precipitation was not sufficient to meet the requirements of emission limits.

In terms of economic cost, the construction investment and operating costs of Technologies 1, 2, and 3 were positively correlated. In contrast, Technologies 4 and 5 had a relatively low construction investment but high operational costs, while Technology 6 involved high construction investment but low operational costs. This is because Technologies 4 and 5 rely mainly on the addition of chemical agents to remove heavy metals from wastewater, thereby requiring fewer facilities, while Technology 6 requires oxidation tanks and sulfurization sedimentation tanks, increasing construction investment but achieving good heavy metal removal, thereby reducing the amount of chemicals required in subsequent processes. The high operating costs of Technology 4 would be the main reason for it being the less optimal choice.

The technology evaluation method used in this study also accounted for carbon emission intensity. This study showed that the relatively high weight of carbon emission intensity reflected the importance of this indicator in the overall evaluation process. Technologies 2, 3, and 6 had higher carbon emission intensities, which was proportional to their better wastewater treatment effect. However, although Technology 3 achieved a good wastewater treatment effect, its higher energy consumption and carbon emission intensity made it a less optimal choice. Some technology evaluation methods in China consider the emission concentration of pollutants the most important factor [7], without considering the energy consumption requirements of the technology, which is not entirely reasonable under the low-carbon transition requirements. By introducing carbon emission intensity as an evaluation indicator, this study aims to more intuitively reflect the synergistic effects of pollution control and carbon reduction.

In summary, compliance with emission limits is a basic requirement for wastewater treatment; therefore, it is easy to filter out Technology 1 despite its cheaper cost and lower carbon emission intensities. But for the other five technologies, the selection is more complicated because various factors should be considered and compared. The results of the Entropy Weight TOPSIS model calculation gave a clear indication that Technology 2 provided the best balance among efficiency of treatment, economic cost, and carbon emissions, which would help plant managers with their technological decisions and incentivize policymakers to improve the applications of technology in the lead–zinc smelting industry.

6. Conclusions

In this study, we constructed a quantitative method for evaluating available technologies for pollution prevention and control based on the Entropy Weight TOPSIS model. As a case study, technologies designed for heavy metal wastewater treatment were evaluated for the lead–zinc smelting industry in the Yellow River Basin. We proposed an evaluation indicator system using 4 primary indicators of technical performance, economic cost, environmental benefits, and operational feasibility, and 16 secondary indicators. The weight ratios showed that the technical performance indicator contributed the highest proportion, followed by the environmental benefits indicator. The final scores and rankings of the six technologies showed that lime neutralization with flocculation precipitation did not meet the emission limits for heavy metal pollutants in China, despite its low economic cost and carbon emission intensity. In contrast, sulfurization precipitation with two stages of lime neutralization and sedimentation technology received the highest score because of its balance of technical performance, economic cost, environmental benefits, and operational feasibility. This study expanded upon the use of the Entropy Weight TOPSIS model in technology evaluation, providing a more scientific and systematic approach to technology selection and environmental policy formulation.

In the next stage of this research, the technology evaluation based on the Entropy Weight TOPSIS model can be extended to the lead–zinc smelting industry nationwide, providing a scientific basis for the screening of best available pollution prevention and control technologies in the national lead–zinc smelting industry. It can also be applied in other industries to optimize and improve the indicator system according to the pollution prevention and control features specific to each industry to explore more scientific and applicable quantitative technology evaluation methods.