Environmental impact of mining and beneficiation of copper sulphate mine based on life cycle assessment

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The present study analyzes the life cycle environmental impact of copper concentrate production, along with selection of a typical copper sulphate mine in China. Life cycle assessment (LCA) was conducted using SimaPro with ReCiPe 2016 method. The midpoint and endpoint results were performed with uncertainty information based on Monte Carlo calculation. Normalization of midpoint results revealed that impact from the marine ecotoxicity category was the largest contributor to the total environmental impact, followed by freshwater ecotoxicity, human carcinogenic toxicity, human non-carcinogenic and terrestrial ecotoxicity. The mining activity, backfilling activity and electricity generation were proved to be the dominant factors. In addition, main processes and substances to the identified key categories were also classified. Specifically, the cement production in the backfilling process, blasting activity, on-site emission and electricity generation were regarded as the critical processes. Copper to air and zinc emission to water were considered as the critical substances. The sensitivity analysis indicated that controlling on-site emissions and reducing pollution from cement production were the most effective measure to solve the environmental problems caused by the concentrate production process. Finally, the corresponding technical and management measures were proposed to facilitate the development of cleaner metal industry. Life cycle assessment Copper concentration production On-site emission Tailing pollution Backfill materials Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Copper is popularly used in the manufacture of electronic equipment, wiring, alloys as well as building materials (Beylot and Villeneuve, 2015 ), paving the way for modern civilization. Mining, beneficiation, and metallurgy are the three indispensable stages in the copper production process. Raw ore, concentrate, and metallic copper are the products of each stage, respectively. Due to the convenience of transportation and storage, copper concentrate occupies an important position in the world copper market. China is a major producer and consumer of copper concentrates. In the period 2001–2017, China's copper concentrate production increased by about one million tons, accounting for approximately 19% of the total global copper concentrate growth (CSY, 2018 ; He, 2018 ). The production of copper concentrate has contributed tremendously to China's rapid development, but it also brought a series of environmental pollution problems. The dust and toxic gases generated during the blasting process induce severely adverse effects on mining personnel and the surrounding ecosystem. The mining and beneficiation process consumes a lot of energy and power (Hong et al., 2018a ), and produces crushed stones made of unwanted minerals, and a mixture of processing fluids from mining and beneficiation processes (Beylot and Villeneuve, 2017 ), thus contributing to severe environment problems, especially for marine fauna and flora(Gu et al., 2012b ; Farjana et al., 2019a ; Sun et al., 2019 ). In addition, improper treatments of tailings may lead to severely heavy metal pollution problems (Jahed Armaghani et al., 2016 ). In rural areas of China, mining activities have proven to be one of the most serious sources of soil heavy metal pollution (Dong et al., 2011 ; Yang et al., 2018 ). Therefore, systematically assessing the environmental impact of the copper concentrate production process is essential to reduce energy consumption and alleviate environmental pollution, as well as provide practical suggestions on the shortage of copper resources and sustainable development. Life cycle assessment has become intensively applied in mineral processing industries by quantifying the impacts of all inputs and outputs within the production process ( Asif Z et al. , 2016; Guinée et al. , 2011; Norgate et al., 2007a ). In the last 15 years, remarkable research has been published involving aluminum (Farjana et al. , 2019; Tan and Khoo, 2005 ), iron (Ferreira and Leite, 2015 ; Gan and Griffin, 2018 ), gold (Farjana et al. , 2019d; Norgate and Haque, 2012 ), lead (Yang et al., 2019 ), zinc (Qi et al., 2017 ), coal (Wang et al. , 2019), etc. Copper has also attracted increasingly attention all over the world, and a number of researches have focused on the environmental pollution caused by the production process based on the life cycle assessment method. The environmental impacts of the refined and reclaimed copper were analyzed based on LCA. These studies covered the environmental impacts of the energy consumption, carbon intensity, different smelting technologies and tailings treatment on copper manufacture, which have propelled clean production in the copper industry (Chen et al., 2019 ; Rubin et al., 2014 ; Song et al., 2017 ). However, due to the limitations of the times and the development of disciplines, these investigations have been unable to well reflect the problems existing in current copper production. For example, in many studies, only special impact category results were identified, other serious hot spot pollution issues were ignored (Memary et al., 2012 ; Norgate et al., 2007b ; Northey et al., 2013 ). In addition, there were few studies on the copper mines in China for the lack of relevant data (Ekman Nilsson et al., 2017 ; Haque and Norgate, 2014 ). Chen et al. compared the environmental impact of the China's refined and reclaimed copper production, but there was a lack of complete uncertainty information in the study and the uncertainty data obtained were relatively large, leading to deviations in the research findings(Hong et al. , 2018b). In a majority of the past studies, only refined metals were considered, ignoring that a production process combining mining and beneficiation is generally adopted in China and concentrates are important intermediate products (Chen et al. 2019). In addition, in the past research, the life cycle assessment of co-production mine production often overlooked the environmental impact of by-product production, which affected the accuracy of the environmental analysis results (Northey et al. 2013). Actually, copper sulfide ore is the main occurrence form of China's copper resources, while the sulfur concentrate is always produced as well in the beneficiation process of copper concentrate production because of the interests of mining companies. Therefore, it is highly required to performan overall environmental impact analysis of copper concentrates and by-product sulfur concentrates. To address the foregoing problems, this study assessed the environmental impacts of concentrate production based on copper sulphate mines in China. First, the ore mining and beneficiation process were analyzed through LCA method with the economic allocation of all co-products. Second, midpoint results of 18 environmental impact categories and endpoint results of 3 impact categories were examined with uncertainty information. Moreover, the entire concentrate production process was divided into four groups and 10 sub-processes, and then main processes and substances to the key impact categories were classified in contribution analysis. Eventually sensitivity analysis was conducted to provide more effective and practical policy insights for effective decision making in the copper sulphate mine production, and even the entire metallurgical industry. 2 Materials And Methods 2.1 Background introduction The data source of this study was from the project feasibility assessment plan of a typical copper sulfide mine in Jiangxi Province, China. Like most mining enterprises in China, this mine adopts a combination of mining and beneficiation process with concentrates as their final products, so it is typical to explore the environmental impact of the concentrate production process. Upon drilling and blasting, the copper sulfide ore was broken underground, and then sent to the site of the mineral processing industry through lifting and transportation system for further crushing and grinding. The grinding products were transported to the next stage for flotation, copper concentrate and copper tailings were received through the copper flotation process initially, and subsequently the by-products high-sulfur and low-sulfur concentrates were obtained from the graded copper tailings. The grade of copper in this mine was 0.883% and that of the sulfur was 8.447%. The mine also contained a few other metals of gold 0.140g / t and silver 10.658g / t. However, owing to the low grade of gold and silver, the complex extraction process accompanied with the high cost, they were not exported as products. After a series of mining and dressing processes, three products were finally obtained: copper concentrate, high-sulfur concentrate and low-sulfur concentrate as byproducts. 2.2 LCA of copper sulphide ore mining and beneficiation 2.2.1 Functional unit and system boundary Functional unit can provide a quantified reference for related inputs and outputs of an investigated system (ISO, 2006 ), which should be identified first in life cycle assessment. In this study, the mining process of one tonne of copper-sulfur raw ore was selected as the functional unit. One tonne raw ore would be approximately transformed into 0.03 tonne of copper concentrate, 0.14 tonne of high-sulfur concentrate and 0.02 tonne of low-sulfur concentrate through the mining and beneficiation process. Economic and quality distributions are the two most chosen distribution methods. Owing to the large amount of production and relatively lower economic value of sulfide concentrates, it is reasonable to select economic allocation for it can effectively express the relative significance of products. Therefore, economic allocation was applied in our study to analyze the environmental pollution of the production process of the copper concentrate and its by-product sulfur concentrates. The calculation results are given in Table 1 . While mining one tonne of copper-sulfur ore, the copper concentrate obtained accounted for 95.78% of the environmental allocation, the high-sulfur and the low-sulfur concentrate obtained accounted for 3.79% and 0.43%, respectively. Table 1 Economic allocation of concentrates. Product Yield (t) Unite price (USD) Yield proportion Total price (USD) Economic allocation(Environ-mental allocation) Copper concentrate 8.87E4 4598.05 16.71% 4.08E8 95.78% High sulfur concentrate 3.82E5 42.24 71.96% 1.61E8 3.79% Low sulfur concentrate 6.01E4 30.17 11.33% 1.81E6 0.43% System boundaries were established by applying a cradle-to-gate approach as shown in Fig. 1 . The end-of-life product stages or environmental emissions for the concentration ore were not included. Raw materials and energy consumption, road transportation, direct emissions, and waste disposal were considered for each process. The raw materials were transported from the urban area eight kilometers away from the mining area. Due to the short distance between the mining and beneficiation sites, internal transportation mainly considered the distance of 1.5 kilometers from the mining area to the waste rock dump. After a series of mining and dressing processes, copper (23% grade), low-sulfur (grade 35%) and high-sulfur (grade 45%) concentrates were finally obtained. The one tonne raw ore of the mining enterprise could produce 0.03 tonne copper concentrate, 0.14 tonne high sulfur concentrate, 0.02 tonne low sulfur concentrate and 0.845 tonne tailings (about 24% of the tailings go to the waste rock dump, 56% of the tailings were mixed with cement for backfilling, and the remaining 20% entered the tailings pond) after processes. Additionally, the ore loss rate in the mining and beneficiation process was around 6%-8%. 2.2.2 LCIA methodology SimaPro is one of the leading software programs utilized for life cycle assessment studies worldwide, providing easy access and integrity with renowned and validated databases like EcoInvent, USGS, and AusLCI, containing numerous datasets of mining and mineral processing industries (Klpffer, 1997 ), and the newest available software version was employed in this study. A life cycle impact assessment (LCIA) was conducted at both midpoint and endpoint level by employing the ReCiPe 2016 method (updated and extended version of ReCiPe 2008), which is one of the most commonly used indicator approaches in the LCA analysis (Goedkoop, 2009b ; Schryver et al., 2009 ). The characterization factors used were representative for the global scale, instead of the European scale as it was done in ReCiPe 2008. ReCiPe 2016 consists of both midpoint (problem oriented) and endpoint (damage oriented) impact categories, available for three different perspectives: individualist (I), hierarchist (H), and egalitarian (E). Midpoint categories include global warming, stratospheric ozone depletion, lionizing radiation, ozone formation (human health), fine particulate matter formation, ozone formation (terrestrial ecosystems), terrestrial acidification, freshwater eutrophication, marine eutrophication, terrestrial ecotoxicity, freshwater ecotoxicity, marine ecotoxicity, human carcinogenic toxicity, human non-carcinogenic toxicity, land use, mineral resource scarcity, fossil resource scarcity and water consumption. Endpoint categories include human health, ecosystems and resources. Figure 2 shows the relations between the midpoint impact category and the endpoint area of production (Huijbregts et al., 2020 ), The dotted line means there is no constant mid-to-endpoint factor for fossil resources. Normalization was conducted to analyze the share of different impact categories to the overall environmental impact and achieve comparable midpoint results (Hong et al. 2018a). More details on ReCiPe 2016 method are available on the website of the Institute of Environmental Science in Leiden University of the Netherlands (IESLUN, 2018 ). Uncertainty analysis was conducted based on Monte-Carlo calculation, using 1000 runs simulation. 2.2.3 Life cycle inventory and data sources All data input and output were within the function unit of the mining and beneficiation process of one tonne copper sulfide ore. The foreground data, mainly including the consumption of raw materials and energy in each process, external and internal transportation, on-site emissions and waste disposal, weree collected from a typical copper sulfide mine in China. The background data as the production of generic materials, energy, transport and waste management, were obtained by the Ecoinvent 3.5 databases, one of the most advanced life cycle inventory database covering the basic data of China and other countries (Duce et al., 2016 ; Centre, 2015). It should be pointed that for the lack of China-related information, data based on global national production conditions or western country production conditions were selected. In addition, the uncertainty analysis of the data was required, lognormal distribution was always assumed in Ecoinvent to present the measurements. The typical feature of the lognormal distribution is that the square of the geometric standard deviation covers a 95% confidence interval. For example, if the square of the set standard deviation is 1.2, it means that about 95% of the measured values are distributed between the mean value divided by 1.2 and the mean value multiplied by 1.2. Ecoinvent uses the pedigree matrix to estimate the geometric standard deviation, which was developed by Weidema. In recent years, Ecoinvent 3 pedigree matrix has updated to the most advanced version that is most suitable for the current situation (Bo, 1998 ; Frischknecht et al., 2005 ; Tao et al., 2019 ). Each data presented was evaluated based on five criteria and basic uncertainty factor (depending on the type of data). The formula can be expressed in the following form. $$GS{D^2}=\exp \sqrt {{{\left[ {\ln ({U_1})} \right]}^2}+{{\left[ {\ln ({U_2})} \right]}^2}+{{\left[ {\ln ({U_3})} \right]}^2}+{{\left[ {\ln ({U_4})} \right]}^2}+{{\left[ {\ln ({U_5})} \right]}^2}+{{\left[ {\ln ({U_b})} \right]}^2}}$$ 1 The factors U 1 -U 5 refer to the scores in the reliability, completeness, temporal correlation, geographical correlation and further technology, respectively. The factor Ub refers to the basic uncertainty factor. Table 2 presents the life cycle inventory and the calculation results of uncertainty data. Table 2 Life cycle inventory of mining and beneficiation of one tonne copper sulfide ore. Inventory Substance Amount Unit \({\text{G}\text{S}\text{D}}^{2}\) Element in ore Copper 8.83 kg 1.16 Sulfur 84.47 kg 1.16 Gold 0.14 g 1.16 Silver 10.66 g 1.16 Raw material and energy consumption Explosive 0.89 kg 1.16 Land occupation 13.00×10 4 m 2 1.58 Water 4.04 t 1.19 Electricity 67.35 kwh 1.20 Diesel 0.29 kg 1.24 Cleft timber 0.17 kg 1.24 Lime 7.50 kg 1.24 Alloy 1.57 g 1.24 Steel 0.19 kg 1.24 Cement 0.07 t 1.16 Sand 0.08 t 1.16 Sodium hydroxide 51.94 g 2.10 Carbon disulfide 98.69 g 2.10 Ethanol 59.45 g 2.10 Terpenic oil 68 g 2.10 Emission to air Carbon dioxide 0.51 kg 2.15 Carbon monoxide 43.39 g 5.94 Nitrogen oxides 344.94 g 2.37 Sulfur dioxide 662.40 mg 2.10 Particulates, < 10 um 1980 mg 2.77 Emission to water COD 4.05 mg 1.58 BOD 1.35 mg 1.58 Suspended solid(SS) 48.82 g 1.58 Copper 964.40 mg 5.07 Lead 668 mg 5.07 Zinc 964.20 mg 5.07 Arsenic 213.76 mg 5.07 Cadmium 167.50 mg 1.58 Chromium 13.36 mg 5.07 Ammonia nitrogen 540 mg 1.58 Emission to soil Tailings 845 kg 1.16 Hazardous waste 0.13 kg 1.53 Transportation Lorry(freight,16-32metric ton) 12.50 tkm 2.06 Products Copper concentrates 0.03 t 1.16 High-sulfur concentrate 0.14 t 1.16 Low-sulfur concentrate 0.02 t 1.16 3 Results And Discussion 3.1 LCIA midpoint results Table 3 shows the LCIA midpoint results for one tonne copper concentrate production, calculated by ReCiPe 2016 method. The mining and beneficiation process of one tonne copper sulfide ore is listed as a basic functional unit. 0.03 tonnes copper concentrate, 0.14 tonnes high-sulfur concentrate and 0.02 tonnes low-sulfur concentrate obtained from one tonne raw ore were performed based on the economic allocation principle. Uncertainty analysis results were presented as geometric square deviation ( \({\text{G}\text{S}\text{D}}^{2}\) ) for each impact category. The confidence interval of \({\text{G}\text{S}\text{D}}^{2}\) was set at 95%, which means 95% of the uncertain results obtained by 1000 Monte Carlo Simulation were within the range of dividing and multiplying the midpoint result value by \({\text{G}\text{S}\text{D}}^{2}\) (Tao, Zhang, Wang, Cao and Jiang, 2019 ). For the global warming category of copper concentrate, the midpoint value was 3407.31 kg CO 2 eq and the \({\text{G}\text{S}\text{D}}^{2}\) value was 1.10, so the 95% confidence interval ranged between 3097.55 CO 2 eq and 3748.04 CO 2 eq. Table 3 LCIA midpoint results. Category One tonne copper sulfide ore One tonne copper concentrate Unit \({\text{G}\text{S}\text{D}}^{2}\) 0.03 tonnes copper concentrate 0.14 tonnes high-sulfur concentrate 0.02 tonnes Low-sulfur concentrate Global warming 115.85 4.58 0.52 3407.31 kg CO 2 eq 1.10 Stratospheric ozone depletion 8.44E-05 3.34E-06 3.79E-07 2.48E-03 kg CFC11 eq 1.10 Ionizing radiation 0.79 3.11E-02 3.53E-03 23.18 kBq Co-60 eq 1.10 Ozone formation, Human health 0.55 2.16E-02 2.45E-03 16.18 kg NOx eq 1.08 Fine particulate matter formation 0.17 6.82E-03 7.74 E-04 5.13 kg PM 2.5 eq 1.07 Ozone formation, Terrestrial ecosystems 0.57 2.20E-02 2.49E-03 16.40 kg NOx eq 1.08 Terrestrial acidification 0.50 1.97E-02 2.24E-03 14.62 kg SO 2 eq 1.07 Freshwater eutrophication 1.55E-02 6.14E-04 6.97E-05 0.46 kg P eq 1.08 Marine eutrophication 1.30E-03 5.15E-05 5.85E-06 3.84E-02 kg N eq 1.08 Terrestrial ecotoxicity 118.17 4.68 0.53 3475.71 kg 1,4-DCB 1.14 Freshwater ecotoxicity 1.25 4.96E-02 5.63E-03 36.92 kg 1,4-DCB 1.30 Marine ecotoxicity 1.76 6.97E-02 7.91E-03 51.93 kg 1,4-DCB 1.29 Human carcinogenic toxicity 2.38 9.44E-02 1.07 E-02 70.22 kg 1,4-DCB 1.08 Human non-carcinogenic toxicity 33.65 1.33 0.15 988.71 kg 1,4-DCB 1.34 Land use 1.76 6.97 E-02 7.91 E-03 51.73 m 2 a crop eq 1.12 Mineral resource scarcity 10.93 0.43 4.91E-02 321.61 kg Cu eq 1.12 Fossil resource scarcity 16.89 0.67 7.58 E-02 496.52 kg oil eq 1.08 Water consumption 115.85 4.58 0.52 38.68 m 3 1.13 In addition, in order to solve the incompatibility of units and compare the results, the normalization of midpoint results was performed. The normalization indicates to what extent an impact category indicator result has a relatively high or a relatively low value as compared to a reference. It can be seen from Fig. 3 that the impact on marine ecotoxicity contributed the largest to the overall environmental impact, the freshwater ecotoxicity, human toxicity (both carcinogenic and non-carcinogenic), and terrestrial ecotoxicity also exhibited significant contributions, while the environmental impacts from other categories were negligible. 3.2 LCIA endpoint results Table 4 illustrates the LCIA endpoint results. Uncertainty information was presented \({\text{G}\text{S}\text{D}}^{2}\) for each of the three impact categories. For example, in terms of copper concentrate, the potential impact on human health was 6.95E-03 DALY (Disability-adjusted life years) and the corresponding \({\text{G}\text{S}\text{D}}^{2}\) was 1.08. Such findings revealed that impact on human health category varied between 6.44E-03 DALY to 7.51E-03 DALY, with a 95% confidence interval. In addition, the dominant contributors of the three endpoint impact categories are defined in Table 4 . Electricity generation, cement consumption and blasting were the main causes to human health and ecosystems, while for resources category, copper ore mining took almost half of the environmental responsibility, accounting for 41.4%, cement consumption and electricity generation contributed 29.5% and 15.5%, respectively. Table 4 LCIA endpoint results for one tonne copper concentrate production Categories Amount Unit Dominant contributors \({\text{G}\text{S}\text{D}}^{2}\) Human Health 6.95E-03 DALY Electricity (40.2%) +Cement (37%) + Blasting (16.7%) 1.08 Ecosystems 1.61E-05 species.yr Cement (39.6%) +Electricity (35.2%) +Blasting (18.3%) 1.08 Resources 172.62 USD2013 Ore mining (41.4%) +Cement (29.5%) +Electricity (15.5%) 1.08 3.3 Dominant contributor analysis 3.3.1 Contributions of main groups To analyze and compare the environmental impact of different groups for copper concentrate production, the mining and beneficiation of copper sulfide ore process is divided into the following four groups: extraction process, copper and sulfur flotation process, tailings treatment and auxiliary systems. The auxiliary systems include ventilation, compression, drainage, road transportation and consumption of living areas. Figure 4 presents the analysis results, and the extraction process accounts for more than half of the shares in stratospheric ozone depletion, ozone formation( human health), ozone formation (terrestrial ecosystems) and mineral resource scarcity, especially for stratospheric ozone depletion (80%) and mineral resource scarcity (96%). The flotation process was the main cause of fine particulate matter formation, freshwater eutrophication, marine eutrophication, human carcinogenic toxicity, land use and fossil resource scarcity. Tailings treatment had the greatest impact on ionizing radiation and terrestrial ecotoxicity. The auxiliary system was responsible for 74% of the water consumption due to the provision of domestic water. For terrestrial acidification, the extraction and flotation processes were the main contributions, while for global warming, flotation process and tailings treatment were the main contributions, accounting for 41% and 45%, respectively. In addition, for the freshwater ecotoxicity and marine ecotoxicity as well as human non-carcinogenic toxicity, the excavation process, flotation process and tailings treatment, each takes about one-third of the environmental burden. 3.3.2 Contributions of main sub-processes In order to obtain clearer environmental impact analysis results and guide the cleaner production of copper sulfide mines, the foregoing four groups were further subdivided into 10 processes: mining, backfilling, milling, tailings dam, chemicals consumption, electricity, transportation, living consumption and others. Figure 5 indicated that all impact categories could be mainly attributed to the mining activity, backfilling activity and electricity generation, except for water consumption and ionizing radiation. Electricity and backfilling activity dominantly affected the role in global warming, human carcinogenic toxicity, fossil resource scarcity, freshwater eutrophication, marine eutrophication. Meanwhile, mining activity was the main cause for ozone formation (both human health and terrestrial ecosystems). In particular, mining activity took more than 80% of the environmental burden for stratospheric ozone depletion and mineral resource scarcity. Although tailings dam, chemicals consumption and milling activity did not account for a large proportion of environmental impacts, they h influenced all 18 environmental impact categories, which were most evident in terrestrial ecotoxicity, freshwater ecotoxicity, marine ecotoxicity and human non-carcinogenic toxicity. Additionally, chemicals consumption and backfilling were the significant reasons of ionizing radiation. Land use were mainly caused by mining activity, backfilling activity, chemicals consumption and electricity, and water use in living areas was the most dominant contributor to water consumption. 3.3.3 Process contributor to the key categories As shown by the normalized LCIA midpoint results in Fig. 3 , the marine ecotoxicity, freshwater ecotoxicity, human carcinogenic toxicity, human non-carcinogenic and terrestrial ecotoxicity were the prominent categories of the overall environmental impacts. In addition, global warming was taking into account as a hot environmental issue in recent years. Thus, the above six environmental impact categories were defined as key environmental impact categories. To evaluate the process roles on these key categories, process contribution analysis was performed within the system boundary, the results were presented in Fig. 6 . It was found that the cement production, electricity generation, blasting pollution, transportation, on-site emissions and steel production were the main process contributors to these key categories and cement production, electricity generation and blasting pollution contribute to all six key categories. Cement production was the main reason of global warming, terrestrial ecotoxicity and human non-carcinogenic, accounting for 53.56%, 37.68%, 28.90%, respectively. While for human carcinogenic toxicity, marine ecotoxicity and freshwater ecotoxicity, electricity generation played a significant role, accounting for 52.41%, 30.56%, 30,42%, respectively. In addition, on-site emissions, mainly from mining dust, wastewater discharge during mining and beneficiation process and heavy metal pollution in tailings, had a greater impact on the marine ecotoxicity, freshwater ecotoxicity, and human non-carcinogenic. It should be noted that steel production, mainly from the consumption of steel balls in the dressing and grinding process, exhibited a certain impact on human carcinogenic toxicity, accounting for 11.79%. 3.3.4 Substance contributor to key categories To further clarify the specific pollutants, Fig. 7 illustrates the contributions of major substances to the key categories. The dominant contributors to climate change were carbon dioxide, which is mainly released from coal-burning electricity generation and diesel combustion to air. Copper to air and zinc to air contribute 68.6% and 9.9% to the terrestrial ecotoxicity respectively, and others like nickel to air, vanadium to air and mercury to air demonstrated a minor impact. The contribution of chromium emission to water is the highest for human carcinogenic toxicity, while for human non-carcinogenic toxicity, zinc emission to water was the most significant factor, accounting for 87.7%. For the impact of freshwater ecotoxicity and marine ecotoxicity, the main contributor was zinc emission, followed by copper emission to water, vanadium emission to water and chromium to water. In summary, the substances above were identified as specific pollutants that should be strictly controlled by improving the energy efficiency and reducing on-site emissions during ore mining and beneficiation process of copper sulfide mine. 3.4 Sensitivity analysis To examine the most effective method of reducing the environmental pollution in copper concentrate production, sensitivity analysis of main process contributors on the identified key categories was conducted. The variation coefficient of the input value of the dominant process was all set at 5%, the assumptions were changed and then the LCA was recalculated. Table 5 presents the analysis results. Tailings pollution had the highest variation in the global warming, terrestrial ecotoxicity, freshwater ecotoxicity, marine ecotoxicity and human non-carcinogenic toxicity. Particularly, the cement production in tailings treatment plays a key role. For example, a 5% reduction in tailings pollution will result in an environmental benefit of 2.82% in the global warming category while a 5% reduction in cement consumption can bring 2.68% environmental benefit. Similarly, in mining activity, reducing blasting pollution is an effective measure in diminishing the environmental pollution, analogy results could be obtained from Table 5 . Especially, the effect of the variation in electricity consumption was the highest on human carcinogenic toxicity in which a 5% electricity reduction could decrease 5.05E-02 kg 1,4-DCB. In addition, the effect of changes in on-site emissions also greatly influenced all impact categories. By contrast, the variation in chemicals consumption had a minor impact. It should be pointed out that the sensitivity calculation was based on the functional unit of one tonne copper sulphate ore mining and beneficiation process. Table 5 Sensitivity analysis of process contributors on the identified key categories Category Chemicals consumption Electricity generation On-site emissions Mining activity Tailings pollution Total Blasting pollution Total Cement production Global warming 0.20% 1.56% 2.68% 0.32% 0.19% 2.82% 2.68% Terrestrial ecotoxicity 0.60% 0.69% 1.88% 0.77% 0.55% 2.72% 1.88% Freshwater ecotoxicity 0.45% 1.15% 1.95% 1.30% 0.44% 2.01% 1.26% Marine ecotoxicity 0.46% 1.16% 1.93% 1.28% 0.45% 2.01% 1.29% Human carcinogenic toxicity 0.35% 2.03% 1.16% 0.90% 0.45% 1.36% 1.16% Human non-carcinogenic toxicity 0.51% 1.06% 1.99% 1.31% 0.50% 2.04% 1.44% 4 Process Optimization As reported in Fig. 6 in Section 3.3.3 , for tailings pollution, cement used in tailings treatment has made great contributions to the global warming, ecotoxicity, and human toxicity. During the production process of the copper concentrate in this mine, part of the tailings entered the waste rock dump, part entered the tailing pond, and the others were mixed with cement for backfilling. It was found revealed that through purification and recycling, the waste rock dump and the tailing pond had a minor environmental impact, the use of cement in the backfilling process contributed to the most environmental pollution burden. In fact, cement paste backfilling (CPB) is a major method of processing tailings and is widely used in mines across China. In a majority of the past studies, the performance and price of backfill materials have been given priority, and the environmental impact has always been neglected. The backfill materials are usually made of the Portland cement, solid waste (such as waste rock, fly ash, tailing and slag) and water composition (Zhou et al., 2020 a). However, in recent years, the research on filling pastes has been continuously improved. In the backfilling process, new backfill materials have been more widely selected ta substitute for cement to mitigate environmental pollution. For example, recycled building materials (broken bricks, the recycled concrete aggregates and recycled asphalt pavement) were utilized as substitutes for backfill materials (Rahman et al., 2014 ). Deng et al. developed a novel CPB using the gangue rocks, fly ash, quicklime and ordinary Portland cement which was much more environmental-friendly (Deng et al., 2017 ). In order to further quantitatively analyze the environmental impact of different backfill materials, the existing databases in the Ecoinvent database in SimaPro were utilized. Based on the actual practice, one tonne of Portland cement used in the backfilling process was respectively replaced with the following four backfilling materials in the folloeing five scenarios: scenario a–cement (alternative constituents 6–20%), scenario b–cement (alternative constituents 21–35%), scenario c—ordinary Porland, scenario d–cement (pozzolana and fly ash 11–35%) and scenario e–cement (pozzolana and fly ash 36–55%). The environmental impacts analysis on key categories of different backfill materials is shown in Fig. 8 . The scenario e has the greatest impact on the five environmental impact categories, especially in the global warming, which reduces the environmental impact by approximately 40.39%. Replaced with cement (alternative constituents 6–20%) has little effect on the total environment. The replacement effects of cement (alternative constituents 21–35%) and cement (pozzolana and fly ash 11–35%) are similar. Among them, the environmental impact reduced by approximately 22.06% in Global warming. Therefore, the application of these new backfill materials provides new ideas for cleaner mine production in backfilling process (Zhou et al, 2020 ). In addition, the environmental impact of electricity generation cannot be ignored, mainly related to the air pollution generated by the coal-based power generation process (Parker et al., 2016 ). The sensitivity analysis in Section 3.4 indicated that a 5% reduction in electricity consumption resulted in an environmental benefit of 1.56% in the global warming category and 3.09% in the human toxicity category. In the past 5 years, although there has been a trend of declining, the coal-based power generation has retained at about 70% of the China's total energy production. In order to achieve a further detailed quantitative analysis, the following four scenario assumptions were performed based on the China’s power structure,(CEC, 2016 ) the proportion of coal-fired power generation was replaced by hydropower (scenario 1), gas-fired power (scenario 2), nuclear power (scenario 3) and wind power (scenario 4), respectively. Table 6 presents the environmental impacts on the identified key categories of the four scenarios. As a whole, when the proportion of coal-fired power generation decreased, the overall environmental burden diminished. When coal-fired power generation was replaced by nuclear power, the environmental pollution on the global warming can be reduced by about 86.57%. The impact on human carcinogenic toxicity and freshwater ecotoxicity would be reduced by 84.08% and 71.88%, respectively, as the coal-based electricity generation was substituted by the gas-fired power. Therefore, its crucial to improve energy efficiency and adjust energy structure for achieving cleaner electricity generation. Table 6 Environmental impacts on the key categories of the four scenarios. Environmental categories Unit Basic scenario Scenario 1 Scenario 2 Scenario 3 Scenario 4 Value Value CR(%) Value CR(%) Value CR(%) Value CR(%) Global warming kg CO 2 eq 0.24 8.56E-02 64.63 0.143 40.91 3.25E-02 86.57 3.58E-02 85.21 Terrestrial ecotoxicity kg 1,4-DCB 0.12 4.61E-02 60.93 3.82E-02 67.63 5.45E-02 53.81 5.95E-02 49.58 Freshwater ecotoxicity kg 1,4-DCB 2.08E-03 8.13E-04 60.91 5.85E-04 71.88 4.87E-04 76.59 1.93E-03 7.21 Marine ecotoxicity kg 1,4-DCB 2.93E-03 1.13E-03 61.43 8.34E-04 71.54 6.80E-04 76.79 2.45E-03 16.38 Human carcinogenic toxicity kg 1,4-DCB 6.66E-03 2.44E-03 63.36 1.06E-03 84.08 1.12E-03 83.18 2.21E-03 66.82 Human non-carcinogenic toxicity kg 1,4-DCB 5.09E-02 1.88E-02 63.06 1.49E-02 70.73 1.09E-02 78.59 1.99E-02 60.90 Note: CR denotes changing rate. 5 Conclusions The normalization of midpoint results indicated that the marine ecotoxicity, freshwater ecotoxicity, human carcinogenic toxicity, human non-carcinogenic and terrestrial ecotoxicity were the major environmental impact categories, while the others were relatively negligible. In addition, it was concluded from contribution analysis that the mining activities, tailings pollution, and on-site emissions were the dominant contributions to the overall environmental impact. The further detailed analysis identified that the environmental impact of the blasting process in mining activities was the largest attribution. The adoption of cement in the backfilling activity of tailings treatment was the main source of tailings pollution (environmental pollution caused by the upstream cement production process was included). For on-site emissions, copper to air is the main substance affecting terrestrial ecotoxicity, zinc and chromium emission to water are the main substances influencing the freshwater ecotoxicity, marine ecotoxicity and human toxicity. In addition, the impact of chemical consumption on ecotoxicity could not be ignored, and the electricity generation was also regarded as the key process contribution due to its great environmental impact. Finally, the sensitivity analysis was performed by taking the input value of the key process as an independent variable. The analysis results revealed that adjusting electricity structure and reducing the pollution from backfill materials were crucial to solving environmental problems owing to the copper-sulfur mining and beneficiation process. The findings obtained from this research provide realistic policy suggestions for effectively decision-making in the copper-sulfur mine production and the entire metallurgical industry. Moreover, the LCI results are useful for improving the LCI database of copper-sulfur mine production. Declarations Author Contributions The study conception and design were proposed by Ming Tao. Material preparation and data collection were performed by Rui Zhao and Ying Shi. The first draft of the manuscript was written by Kemi Nie and all authors commented on previous versions of the manuscript. Ming Tao and Wenzhuo Cao approved the final manuscript. Funding The research presented in this paper was supported by the National Natural Science Foundation of China (Grant numbers: 12072376). Data availability If any researchers need the original data of this manuscript, the authors agree to provide relevant information. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing Interests The authors declare no competing interests. References Asif Z, Chen Z (2016) Environmental management in North American mining sector[J]. Environ Sci Pollut R 23(1). 10.1007/s11356-015-5651-8 Beylot A, Villeneuve J (2015) Assessing the national economic importance of metals: An Input–Output approach to the case of copper in France. Resour Policy 44:161–165. https:// doi.org/10. 1016/j.resourpol.2015.02.007 Beylot A, Villeneuve J (2017) Accounting for the environmental impacts of sulfidic tailings storage in the Life Cycle Assessment of copper production: A case study. J Clean Prod. 153(JUN.1),139–145. https://doi.org/10.1016/j.jclepro.2017.03.129 Bo PW (1998) Multi-user test of the data quality matrix for product life cycle inventory data. Int J Life Cycle Assess 5(3):259–265. https://doi.org/10.1007/BF02979832 CEC (2016) China Electricity Council. Beijing,China: https://www.cec.org.cn/ Ecoinvent (2015) Swiss Centre for Life Cycle Inventories. https://v34. ecoquery.ecoinvent. org/Account/LogOn?ReturnUrl¼%2fHome%2fIndex. (Accessed 22 April 2019) Chen J, Wang Z, Wu Y, Li L, Li B, Pan DA, Zuo T (2019) Environmental benefits of secondary copper from primary copper based on life cycle assessment in China. Resour Conserv Recycl 146:35–44. https://doi.org/10.1016/j.resconrec.2019.03.020 CSY (2018) China Statistical Yearbook. Beijing,China: China Statistic Press. http:// www . stats.gov.cn/tjsj/ndsj/2018/indexch.htm Deng X, Zhang J, Klein B, Zhou N, DeWit B (2017) Experimental characterization of the influence of solid components on the rheological and mechanical properties of cemented paste backfill. Int J Miner Process 168:116–125. https://doi.org/10.1016/j.minpro.2017.09.019 Dong J, Yang Q, Sun L, Zeng Q, Liu S, Pan J, Liu X (2011) Assessing the concentration and potential dietary risk of heavy metals in vegetables at a Pb/Zn mine site, China. Environ Earth Sci 64(5):1317–1321. https://doi.org/10.1007/s12665-011-0992-1 Duce AD, Gauch M, Althaus HJ (2016) Electric passenger car transport and passenger car life cycle inventories in ecoinvent version 3. Int J Life Cycle Assess 21:1314–1326. https:/. /doi.org/ 10 Ekman Nilsson A, Macias Aragonés M, Arroyo Torralvo F, Dunon V, Angel H, Komnitsas K, Willquist K (2017) A Review of the Carbon Footprint of Cu and Zn Production from Primary and Secondary Sources. Minerals-Basel 7(9):168. https://doi.org/10.3390/min7090168 Farjana SH, Huda N, Mahmud MAP (2019a) Life cycle analysis of copper-gold-lead-silver-zinc beneficiation process. Sci Total Environ 659:41–52. https://doi.org/10.1016/j.scitotenv.2018.12.318 Farjana SH, Huda N, Mahmud MAP (2019b) Impacts of aluminum production: A cradle to gate investigation using life-cycle assessment. Sci Total Environ 663:958–970. https://doi.org/ 10. 1016/ j.scitotenv.2019.01.400 Farjana SH, Huda N, Mahmud MAP, Lang C (2019c) Impact analysis of gold silver refining processes through life-cycle assessment. J Clean Prod. 228,867–881. https://doi.org/10. 1016/ j.jclepro. 2019.0 4. 166 Ferreira H, Leite MGP (2015) A Life Cycle Assessment study of iron ore mining. J Clean Prod 108:1081–1091. https://doi.org/10.1016/j.jclepro.2015.05.140 Frischknecht R, Jungbluth N, Althaus H, Doka G, Dones R, Heck T, Hellweg S, Hischier R, Nemecek T, Rebitzer G, Spielmann M (2005) The ecoinvent Database: Overview and Methodological Framework (7 pp). Int J Life Cycle Assess 10(1):3–9. https://doi.org/10.1065/lca2004.10.181.1 Gan Y, Griffin WM (2018) Analysis of life-cycle GHG emissions for iron ore mining and processing in China—Uncertainty and trends. Resour Policy 58:90–96. https://doi.org/10. 1016/j. resourpol. 2018.03.015 Goedkoop M (2009b) ReCiPe 2008: A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level. Spatial Planning and the Environment, Ministry of Housing, Spatial Planning and the Environment Gu Y, Wang Z, Lu S, Jiang S, Mu D, Shu Y, Heijungs JB, Huppes R, Zamagni G, Masoni A, Buonamici P, Ekvall R, Rydberg T (2012b) T, 2011. Life Cycle Assessment: Past, Present, and Future. Environ Sci Technol 45(1),90–96. https://doi.org/10.1021/es101316v Haque N, Norgate T (2014) The greenhouse gas footprint of in-situ leaching of uranium, gold and copper in Australia. J Clean Prod 84:382–390. https://doi.org/10.1016/j.jclepro.2013.09.033 He X (2018) Copper market analysis and outlook. China Metal Bulletin 12:1–4 CNKI:SUN:JSTB.0.2018-12-001 Hong J, Chen Y, Liu J, Ma X, Qi C, Ye L (2018a) Life cycle assessment of copper production: a case study in China. Int J Life Cycle Assess 23(9):1814–1824. https://doi.org/10.1007/s11367-017-1405-9 Hong J, Yu Z, Fu X, Hong J (2019) Life cycle environmental and economic assessment of coal seam gas-based electricity generation. Int J Life Cycle Assess 24(10):1828–1839. https:// doi.org/ 10.1007/s11367-019-01599-6 Huijbregts MAJ, Steinmann ZJN, Elshout PMF, Stam G, Verones F, Vieira M, Zijp M, Hollander A, van Zelm R (2020) Correction to: ReCiPe2016: a harmonised life cycle impact assessment method at midpoint and endpoint level. Int J Life Cycle Assess 25(8):1635. .https:/ /doi.org/10. 1007/s 11367-020-01761-5 IESLUN (2018) Institute of Environmental Sciences in Leiden University of the Institute of Environmental Sciences in Leiden University of the Netherlands, https:// www. universiteitleiden.nl/ en/ science/environmental sciences. (Accessed 22 April 2019) ISO I (2006) ISO/DIS 14040. Environmental Management - Life Cycle Assessment - Principles and Framework Jahed Armaghani D, Tonnizam Mohamad E, Hajihassani M, Alavi Nezhad Khalil Abad SV, Marto A, Moghaddam MR (2016) Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng Comput-Germany 32(1):109–121. .https:// doi.org/10. 1007/s00366-015-0402-5 Jain P, Powell JT, Smith JL, Townsend TG, Tolaymat T (2014) Life-Cycle Inventory and Impact Evaluation of Mining Municipal Solid Waste Landfills. Environ Sci Technol 48(5):2920–2927. https://doi.org/10.1021/es404382s Klpffer W (1997) Life Cycle Assessment: From the beginning to the current state[J]. Environ Sci Pollut R 4(4):223–228. 10.1007/BF02986351 Memary R, Giurco D, Mudd G, Mason L (2012) Life cycle assessment: a time-series analysis of copper. J Clean Prod 33:97–108. https://doi.org/10.1016/j.jclepro.2012.04.025 Norgate TE, Jahanshahi S, Rankin WJ (2007a) Assessing the environmental impact of metal production processes. J Clean Prod 15(8–9):838–848. https://doi.org/10.1016/j.jclepro.2006.06.018 Norgate TE, Jahanshahi S, Rankin WJ (2007b) Assessing the environmental impact of metal production processes. J Clean Prod 15(8–9):838–848. https://doi.org/10.1016/j.jclepro.2006.06.018 Norgate T, Haque N (2012) Using life cycle assessment to evaluate some environmental impacts of gold production. J Clean Prod 29–30:53–63. https://doi.org/10.1016/j.jclepro.2012.01.042 Northey S, Haque N, Mudd G (2013) Using sustainability reporting to assess the environmental footprint of copper mining. J Clean Prod 40:118–128. https://doi.org/10.1016/j.jclepro.2012.09.027 Parker DJ, MNaughton CS, Sparks GA (2016) Life Cycle Greenhouse Gas Emissions from Uranium Mining and Milling in Canada. Environ Sci Technol 50(17):9746–9753. https:// doi.org/10. 1021/acs. est.5b06072 Qi C, Ye L, Ma X, Yang D, Hong J (2017) Life cycle assessment of the hydrometallurgical zinc production chain in China. J Clean Prod 156:451–458. https://doi.org/10.1016/j.jclepro.2017.04.084 Rahman MA, Imteaz M, Arulrajah A, Disfani MM (2014) Suitability of recycled construction and demolition aggregates as alternative pipe backfilling materials. J Clean Prod 66:75–84. https://doi.org/10.1016/j.jclepro.2013.11.005 Rubin RS, Castro, M A S D D, Schalch V, Ometto AR (2014) Utilization of Life Cycle Assessment methodology to compare two strategies for recovery of copper from printed circuit board scrap. J Clean Prod 64:297–305. https://doi.org/10.1016/j.jclepro.2013.07.051 Schryver AMD, Brakkee KW, Goedkoop MJ, Huijbregts MAJ (2009) Characterization Factors for Global Warming in Life Cycle Assessment Based on Damages to Humans and Ecosystems. Environ Sci Technol 43(6):1689–1695. https://doi.org/10.1021/es800456m Song X, Pettersen JB, Pedersen KB, Røberg S (2017) Comparative life cycle assessment of tailings management and energy scenarios for a copper ore mine: A case study in Northern Norway. J Clean Prod 164:892–904. https://doi.org/10.1016/j.jclepro.2017.07.021 Sun W, Skidmore AK, Wang T, Zhang X (2019) Heavy metal pollution at mine sites estimated from reflectance spectroscopy following correction for skewed data. Environ Pollut 252:1117–1124. https://doi.org/10.1016/j.envpol.2019.06.021 Tan RBH, Khoo HH (2005) An LCA study of a primary aluminum supply chain. J Clean Prod 13(6):607–618. https://doi.org/10.1016/j.jclepro.2003.12.022 Tao M, Zhang X, Wang S, Cao W, Jiang Y (2019) Life cycle assessment on lead–zinc ore mining and beneficiation in China. J Clean Prod. https://doi.org/10.1016/j.jclepro.2019.117833 . 237,117833 Wang Q, Liu W, Yuan X, Zheng X, Zuo J (2016) Future of lignite resources: a life cycle analysis[J]. Environ Sci Pollut R 23(24):1–12. https://doi.org/10.1007/s11356-016-7642-9 Yang D, Yin Y, Ma X, Zhang R, Zhai Y, Shen X, Hong J (2019) Environmental improvement of lead refining: a case study of water footprint assessment in Jiangxi Province, China. Int J Life Cycle Assess 24(8):1533–1542. https://doi.org/10.1007/s11367-018-01578-3 Yang Y, Meng Z, Jiao W (2018) Hydrological and pollution processes in mining area of Fenhe River Basin in China. Environ Pollut 234:743–750. https://doi.org/10.1016/j.envpol.2017.12.018 Zhou N, Zhang J, Ouyang S, Deng X, Dong C, Du E (2020) Feasibility study and performance optimization of sand-based cemented paste backfill materials. J Clean Prod. https://doi.org/10.1016/j.jclepro.2020.120798 . 259,120798 Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor Revision 19 Apr, 2022 Reviews received at journal 14 Mar, 2022 Reviewers invited by journal 11 Mar, 2022 Editor invited by journal 25 Feb, 2022 Editor assigned by journal 16 Feb, 2022 First submitted to journal 23 Jan, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1288761","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":90000774,"identity":"03165093-01f1-4a4c-ba76-9c9369ff150f","order_by":0,"name":"Ming 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2","display":"","copyAsset":false,"role":"figure","size":44463,"visible":true,"origin":"","legend":"\u003cp\u003e\tThe relations between the midpoint impact category and the endpoint area of production.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1288761/v1/c37eaa0a59e4d4ff4ddb30cf.png"},{"id":19250107,"identity":"a11e4868-0f8b-48c2-9d8c-adb7ae454b4a","added_by":"auto","created_at":"2022-03-15 15:58:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":23810,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized LCIA midpoint results for one tonne copper concentrate 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categories\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-1288761/v1/aad581b9b04b0682e89a1b7d.png"},{"id":19250113,"identity":"c7875b69-3488-4f20-a06e-cc3cd8a19eb8","added_by":"auto","created_at":"2022-03-15 15:58:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":20799,"visible":true,"origin":"","legend":"\u003cp\u003eProcess contributors to key categories\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-1288761/v1/f2315de60e1636d43de6a1b2.png"},{"id":19250109,"identity":"4ff0e279-bf26-400c-9d31-c0bfac79671a","added_by":"auto","created_at":"2022-03-15 15:58:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":53331,"visible":true,"origin":"","legend":"\u003cp\u003e\tSubstance contributors to key 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16:04:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":784239,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1288761/v1/197dff68-b1f1-4409-b8da-e6ee831e65f7.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eEnvironmental impact of mining and beneficiation of copper sulphate mine based on life cycle assessment \u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eCopper is popularly used in the manufacture of electronic equipment, wiring, alloys as well as building materials (Beylot and Villeneuve, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), paving the way for modern civilization. Mining, beneficiation, and metallurgy are the three indispensable stages in the copper production process. Raw ore, concentrate, and metallic copper are the products of each stage, respectively. Due to the convenience of transportation and storage, copper concentrate occupies an important position in the world copper market. China is a major producer and consumer of copper concentrates. In the period 2001\u0026ndash;2017, China's copper concentrate production increased by about one million tons, accounting for approximately 19% of the total global copper concentrate growth (CSY, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; He, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The production of copper concentrate has contributed tremendously to China's rapid development, but it also brought a series of environmental pollution problems. The dust and toxic gases generated during the blasting process induce severely adverse effects on mining personnel and the surrounding ecosystem. The mining and beneficiation process consumes a lot of energy and power (Hong et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e), and produces crushed stones made of unwanted minerals, and a mixture of processing fluids from mining and beneficiation processes (Beylot and Villeneuve, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), thus contributing to severe environment problems, especially for marine fauna and flora(Gu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e; Farjana et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, improper treatments of tailings may lead to severely heavy metal pollution problems (Jahed Armaghani et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In rural areas of China, mining activities have proven to be one of the most serious sources of soil heavy metal pollution (Dong et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, systematically assessing the environmental impact of the copper concentrate production process is essential to reduce energy consumption and alleviate environmental pollution, as well as provide practical suggestions on the shortage of copper resources and sustainable development.\u003c/p\u003e \u003cp\u003eLife cycle assessment has become intensively applied in mineral processing industries by quantifying the impacts of all inputs and outputs within the production process ( Asif Z \u003cem\u003eet al.\u003c/em\u003e, 2016; Guin\u0026eacute;e \u003cem\u003eet al.\u003c/em\u003e, 2011; Norgate et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007a\u003c/span\u003e). In the last 15 years, remarkable research has been published involving aluminum (Farjana \u003cem\u003eet al.\u003c/em\u003e, 2019; Tan and Khoo, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), iron (Ferreira and Leite, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gan and Griffin, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), gold (Farjana \u003cem\u003eet al.\u003c/em\u003e, 2019d; Norgate and Haque, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), lead (Yang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), zinc (Qi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), coal (Wang \u003cem\u003eet al.\u003c/em\u003e, 2019), etc. Copper has also attracted increasingly attention all over the world, and a number of researches have focused on the environmental pollution caused by the production process based on the life cycle assessment method. The environmental impacts of the refined and reclaimed copper were analyzed based on LCA. These studies covered the environmental impacts of the energy consumption, carbon intensity, different smelting technologies and tailings treatment on copper manufacture, which have propelled clean production in the copper industry (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rubin et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, due to the limitations of the times and the development of disciplines, these investigations have been unable to well reflect the problems existing in current copper production. For example, in many studies, only special impact category results were identified, other serious hot spot pollution issues were ignored (Memary et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Norgate et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007b\u003c/span\u003e; Northey et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, there were few studies on the copper mines in China for the lack of relevant data (Ekman Nilsson et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Haque and Norgate, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Chen et al. compared the environmental impact of the China's refined and reclaimed copper production, but there was a lack of complete uncertainty information in the study and the uncertainty data obtained were relatively large, leading to deviations in the research findings(Hong \u003cem\u003eet al.\u003c/em\u003e, 2018b). In a majority of the past studies, only refined metals were considered, ignoring that a production process combining mining and beneficiation is generally adopted in China and concentrates are important intermediate products (Chen \u003cem\u003eet al.\u003c/em\u003e2019). In addition, in the past research, the life cycle assessment of co-production mine production often overlooked the environmental impact of by-product production, which affected the accuracy of the environmental analysis results (Northey \u003cem\u003eet al.\u003c/em\u003e2013). Actually, copper sulfide ore is the main occurrence form of China's copper resources, while the sulfur concentrate is always produced as well in the beneficiation process of copper concentrate production because of the interests of mining companies. Therefore, it is highly required to performan overall environmental impact analysis of copper concentrates and by-product sulfur concentrates.\u003c/p\u003e \u003cp\u003eTo address the foregoing problems, this study assessed the environmental impacts of concentrate production based on copper sulphate mines in China. First, the ore mining and beneficiation process were analyzed through LCA method with the economic allocation of all co-products. Second, midpoint results of 18 environmental impact categories and endpoint results of 3 impact categories were examined with uncertainty information. Moreover, the entire concentrate production process was divided into four groups and 10 sub-processes, and then main processes and substances to the key impact categories were classified in contribution analysis. Eventually sensitivity analysis was conducted to provide more effective and practical policy insights for effective decision making in the copper sulphate mine production, and even the entire metallurgical industry.\u003c/p\u003e"},{"header":"2 Materials And Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Background introduction\u003c/h2\u003e\n \u003cp\u003eThe data source of this study was from the project feasibility assessment plan of a typical copper sulfide mine in Jiangxi Province, China. Like most mining enterprises in China, this mine adopts a combination of mining and beneficiation process with concentrates as their final products, so it is typical to explore the environmental impact of the concentrate production process. Upon drilling and blasting, the copper sulfide ore was broken underground, and then sent to the site of the mineral processing industry through lifting and transportation system for further crushing and grinding. The grinding products were transported to the next stage for flotation, copper concentrate and copper tailings were received through the copper flotation process initially, and subsequently the by-products high-sulfur and low-sulfur concentrates were obtained from the graded copper tailings.\u003c/p\u003e\n \u003cp\u003eThe grade of copper in this mine was 0.883% and that of the sulfur was 8.447%. The mine also contained a few other metals of gold 0.140g / t and silver 10.658g / t. However, owing to the low grade of gold and silver, the complex extraction process accompanied with the high cost, they were not exported as products. After a series of mining and dressing processes, three products were finally obtained: copper concentrate, high-sulfur concentrate and low-sulfur concentrate as byproducts.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 LCA of copper sulphide ore mining and beneficiation\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec5\"\u003e\n \u003ch2\u003e2.2.1 Functional unit and system boundary\u003c/h2\u003e\n \u003cp\u003eFunctional unit can provide a quantified reference for related inputs and outputs of an investigated system (ISO, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), which should be identified first in life cycle assessment. In this study, the mining process of one tonne of copper-sulfur raw ore was selected as the functional unit. One tonne raw ore would be approximately transformed into 0.03 tonne of copper concentrate, 0.14 tonne of high-sulfur concentrate and 0.02 tonne of low-sulfur concentrate through the mining and beneficiation process. Economic and quality distributions are the two most chosen distribution methods. Owing to the large amount of production and relatively lower economic value of sulfide concentrates, it is reasonable to select economic allocation for it can effectively express the relative significance of products. Therefore, economic allocation was applied in our study to analyze the environmental pollution of the production process of the copper concentrate and its by-product sulfur concentrates.\u003c/p\u003e\n \u003cp\u003eThe calculation results are given in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. While mining one tonne of copper-sulfur ore, the copper concentrate obtained accounted for 95.78% of the environmental allocation, the high-sulfur and the low-sulfur concentrate obtained accounted for 3.79% and 0.43%, respectively.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEconomic allocation of concentrates.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProduct\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYield\u003c/p\u003e\n \u003cp\u003e(t)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnite price (USD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYield proportion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal price\u003c/p\u003e\n \u003cp\u003e(USD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEconomic\u003c/p\u003e\n \u003cp\u003eallocation(Environ-mental allocation)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopper concentrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.87E4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4598.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.08E8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh sulfur concentrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.82E5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61E8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow sulfur concentrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.01E4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81E6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eSystem boundaries were established by applying a cradle-to-gate approach as shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The end-of-life product stages or environmental emissions for the concentration ore were not included. Raw materials and energy consumption, road transportation, direct emissions, and waste disposal were considered for each process. The raw materials were transported from the urban area eight kilometers away from the mining area. Due to the short distance between the mining and beneficiation sites, internal transportation mainly considered the distance of 1.5 kilometers from the mining area to the waste rock dump.\u003c/p\u003e\n \u003cp\u003eAfter a series of mining and dressing processes, copper (23% grade), low-sulfur (grade 35%) and high-sulfur (grade 45%) concentrates were finally obtained. The one tonne raw ore of the mining enterprise could produce 0.03 tonne copper concentrate, 0.14 tonne high sulfur concentrate, 0.02 tonne low sulfur concentrate and 0.845 tonne tailings (about 24% of the tailings go to the waste rock dump, 56% of the tailings were mixed with cement for backfilling, and the remaining 20% entered the tailings pond) after processes. Additionally, the ore loss rate in the mining and beneficiation process was around 6%-8%.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec6\"\u003e\n \u003ch2\u003e2.2.2 LCIA methodology\u003c/h2\u003e\n \u003cp\u003eSimaPro is one of the leading software programs utilized for life cycle assessment studies worldwide, providing easy access and integrity with renowned and validated databases like EcoInvent, USGS, and AusLCI, containing numerous datasets of mining and mineral processing industries (Klpffer, \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e), and the newest available software version was employed in this study. A life cycle impact assessment (LCIA) was conducted at both midpoint and endpoint level by employing the ReCiPe 2016 method (updated and extended version of ReCiPe 2008), which is one of the most commonly used indicator approaches in the LCA analysis (Goedkoop, \u003cspan class=\"CitationRef\"\u003e2009b\u003c/span\u003e; Schryver et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). The characterization factors used were representative for the global scale, instead of the European scale as it was done in ReCiPe 2008. ReCiPe 2016 consists of both midpoint (problem oriented) and endpoint (damage oriented) impact categories, available for three different perspectives: individualist (I), hierarchist (H), and egalitarian (E). Midpoint categories include global warming, stratospheric ozone depletion, lionizing radiation, ozone formation (human health), fine particulate matter formation, ozone formation (terrestrial ecosystems), terrestrial acidification, freshwater eutrophication, marine eutrophication, terrestrial ecotoxicity, freshwater ecotoxicity, marine ecotoxicity, human carcinogenic toxicity, human non-carcinogenic toxicity, land use, mineral resource scarcity, fossil resource scarcity and water consumption. Endpoint categories include human health, ecosystems and resources. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the relations between the midpoint impact category and the endpoint area of production (Huijbregts et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), The dotted line means there is no constant mid-to-endpoint factor for fossil resources. Normalization was conducted to analyze the share of different impact categories to the overall environmental impact and achieve comparable midpoint results (Hong \u003cem\u003eet al.\u003c/em\u003e2018a). More details on ReCiPe 2016 method are available on the website of the Institute of Environmental Science in Leiden University of the Netherlands (IESLUN, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Uncertainty analysis was conducted based on Monte-Carlo calculation, using 1000 runs simulation.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec7\"\u003e\n \u003ch2\u003e2.2.3 Life cycle inventory and data sources\u003c/h2\u003e\n \u003cp\u003eAll data input and output were within the function unit of the mining and beneficiation process of one tonne copper sulfide ore. The foreground data, mainly including the consumption of raw materials and energy in each process, external and internal transportation, on-site emissions and waste disposal, weree collected from a typical copper sulfide mine in China. The background data as the production of generic materials, energy, transport and waste management, were obtained by the Ecoinvent 3.5 databases, one of the most advanced life cycle inventory database covering the basic data of China and other countries (Duce et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Centre, 2015). It should be pointed that for the lack of China-related information, data based on global national production conditions or western country production conditions were selected. In addition, the uncertainty analysis of the data was required, lognormal distribution was always assumed in Ecoinvent to present the measurements. The typical feature of the lognormal distribution is that the square of the geometric standard deviation covers a 95% confidence interval. For example, if the square of the set standard deviation is 1.2, it means that about 95% of the measured values are distributed between the mean value divided by 1.2 and the mean value multiplied by 1.2. Ecoinvent uses the pedigree matrix to estimate the geometric standard deviation, which was developed by Weidema. In recent years, Ecoinvent 3 pedigree matrix has updated to the most advanced version that is most suitable for the current situation (Bo, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e; Frischknecht et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Tao et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Each data presented was evaluated based on five criteria and basic uncertainty factor (depending on the type of data). The formula can be expressed in the following form.\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ1\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$GS{D^2}=\\exp \\sqrt {{{\\left[ {\\ln ({U_1})} \\right]}^2}+{{\\left[ {\\ln ({U_2})} \\right]}^2}+{{\\left[ {\\ln ({U_3})} \\right]}^2}+{{\\left[ {\\ln ({U_4})} \\right]}^2}+{{\\left[ {\\ln ({U_5})} \\right]}^2}+{{\\left[ {\\ln ({U_b})} \\right]}^2}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe factors U\u003csub\u003e1\u003c/sub\u003e-U\u003csub\u003e5\u003c/sub\u003e refer to the scores in the reliability, completeness, temporal correlation, geographical correlation and further technology, respectively. The factor Ub refers to the basic uncertainty factor. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the life cycle inventory and the calculation results of uncertainty data.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLife cycle inventory of mining and beneficiation of one tonne copper sulfide ore.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInventory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubstance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAmount\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{G}\\text{S}\\text{D}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eElement in ore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSulfur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e84.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSilver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"15\"\u003e\n \u003cp\u003eRaw material and energy consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExplosive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand occupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13.00\u0026times;10\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003em\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectricity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e67.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekwh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiesel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCleft timber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlloy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium hydroxide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e51.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbon disulfide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e98.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthanol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e59.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTerpenic oil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eEmission to air\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCarbon dioxide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCarbon monoxide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNitrogen oxides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSulfur dioxide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e662.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eParticulates, \u0026lt; 10 um\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003eEmission to water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSuspended solid(SS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCopper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e964.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eZinc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e964.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eArsenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCadmium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChromium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAmmonia nitrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEmission to soil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTailings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHazardous waste\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransportation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLorry(freight,16-32metric ton)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etkm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eProducts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCopper concentrates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHigh-sulfur concentrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLow-sulfur concentrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3 Results And Discussion","content":"\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e3.1 LCIA midpoint results\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the LCIA midpoint results for one tonne copper concentrate production, calculated by ReCiPe 2016 method. The mining and beneficiation process of one tonne copper sulfide ore is listed as a basic functional unit. 0.03 tonnes copper concentrate, 0.14 tonnes high-sulfur concentrate and 0.02 tonnes low-sulfur concentrate obtained from one tonne raw ore were performed based on the economic allocation principle. Uncertainty analysis results were presented as geometric square deviation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{G}\\text{S}\\text{D}}^{2}\\)\u003c/span\u003e\u003c/span\u003e) for each impact category. The confidence interval of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{G}\\text{S}\\text{D}}^{2}\\)\u003c/span\u003e\u003c/span\u003e was set at 95%, which means 95% of the uncertain results obtained by 1000 Monte Carlo Simulation were within the range of dividing and multiplying the midpoint result value by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{G}\\text{S}\\text{D}}^{2}\\)\u003c/span\u003e\u003c/span\u003e (Tao, Zhang, Wang, Cao and Jiang, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). For the global warming category of copper concentrate, the midpoint value was 3407.31 kg CO\u003csub\u003e2\u003c/sub\u003eeq and the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{G}\\text{S}\\text{D}}^{2}\\)\u003c/span\u003e\u003c/span\u003e value was 1.10, so the 95% confidence interval ranged between 3097.55 CO\u003csub\u003e2\u003c/sub\u003eeq and 3748.04 CO\u003csub\u003e2\u003c/sub\u003eeq.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLCIA midpoint results.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eOne tonne copper sulfide ore\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOne tonne copper concentrate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{G}\\text{S}\\text{D}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.03 tonnes copper concentrate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.14 tonnes high-sulfur concentrate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.02 tonnes Low-sulfur concentrate\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal warming\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3407.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg CO\u003csub\u003e2\u003c/sub\u003e eq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStratospheric ozone depletion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.44E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.34E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.79E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg CFC11 eq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIonizing radiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.11E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.53E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekBq Co-60 eq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOzone formation, Human health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.16E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg NOx eq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFine particulate matter formation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.82E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.74 E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg PM\u003csub\u003e2.5\u003c/sub\u003e eq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOzone formation, Terrestrial ecosystems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.20E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.49E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg NOx eq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTerrestrial acidification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.97E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.24E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg SO\u003csub\u003e2\u003c/sub\u003e eq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreshwater eutrophication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.14E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.97E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg P eq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarine eutrophication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.15E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.85E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.84E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg N eq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTerrestrial ecotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3475.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg 1,4-DCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreshwater ecotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.96E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.63E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg 1,4-DCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarine ecotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.97E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.91E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg 1,4-DCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman carcinogenic toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.44E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg 1,4-DCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman non-carcinogenic toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e988.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg 1,4-DCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.97 E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.91 E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003em\u003csup\u003e2\u003c/sup\u003ea crop eq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMineral resource scarcity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.91E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg Cu eq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFossil resource scarcity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.58 E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e496.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg oil eq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003em\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn addition, in order to solve the incompatibility of units and compare the results, the normalization of midpoint results was performed. The normalization indicates to what extent an impact category indicator result has a relatively high or a relatively low value as compared to a reference. It can be seen from Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e that the impact on marine ecotoxicity contributed the largest to the overall environmental impact, the freshwater ecotoxicity, human toxicity (both carcinogenic and non-carcinogenic), and terrestrial ecotoxicity also exhibited significant contributions, while the environmental impacts from other categories were negligible.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003e3.2 LCIA endpoint results\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the LCIA endpoint results. Uncertainty information was presented \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{G}\\text{S}\\text{D}}^{2}\\)\u003c/span\u003e\u003c/span\u003e for each of the three impact categories. For example, in terms of copper concentrate, the potential impact on human health was 6.95E-03 DALY (Disability-adjusted life years) and the corresponding \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{G}\\text{S}\\text{D}}^{2}\\)\u003c/span\u003e\u003c/span\u003e was 1.08. Such findings revealed that impact on human health category varied between 6.44E-03 DALY to 7.51E-03 DALY, with a 95% confidence interval. In addition, the dominant contributors of the three endpoint impact categories are defined in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Electricity generation, cement consumption and blasting were the main causes to human health and ecosystems, while for resources category, copper ore mining took almost half of the environmental responsibility, accounting for 41.4%, cement consumption and electricity generation contributed 29.5% and 15.5%, respectively.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLCIA endpoint results for one tonne copper concentrate production\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 10.0977%;\"\u003e\n \u003cp\u003eAmount\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDominant contributors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 36.9707%;\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{G}\\text{S}\\text{D}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.0977%;\"\u003e\n \u003cp\u003e6.95E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDALY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectricity (40.2%)\u003c/p\u003e\n \u003cp\u003e+Cement (37%)\u003c/p\u003e\n \u003cp\u003e+ Blasting (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 36.9707%;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEcosystems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.0977%;\"\u003e\n \u003cp\u003e1.61E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003especies.yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCement (39.6%)\u003c/p\u003e\n \u003cp\u003e+Electricity (35.2%)\u003c/p\u003e\n \u003cp\u003e+Blasting (18.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 36.9707%;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.0977%;\"\u003e\n \u003cp\u003e172.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSD2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOre mining (41.4%)\u003c/p\u003e\n \u003cp\u003e+Cement (29.5%)\u003c/p\u003e\n \u003cp\u003e+Electricity (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 36.9707%;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003e3.3 Dominant contributor analysis\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec12\"\u003e\n \u003ch2\u003e3.3.1 Contributions of main groups\u003c/h2\u003e\n \u003cp\u003eTo analyze and compare the environmental impact of different groups for copper concentrate production, the mining and beneficiation of copper sulfide ore process is divided into the following four groups: extraction process, copper and sulfur flotation process, tailings treatment and auxiliary systems. The auxiliary systems include ventilation, compression, drainage, road transportation and consumption of living areas. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the analysis results, and the extraction process accounts for more than half of the shares in stratospheric ozone depletion, ozone formation( human health), ozone formation (terrestrial ecosystems) and mineral resource scarcity, especially for stratospheric ozone depletion (80%) and mineral resource scarcity (96%). The flotation process was the main cause of fine particulate matter formation, freshwater eutrophication, marine eutrophication, human carcinogenic toxicity, land use and fossil resource scarcity. Tailings treatment had the greatest impact on ionizing radiation and terrestrial ecotoxicity. The auxiliary system was responsible for 74% of the water consumption due to the provision of domestic water. For terrestrial acidification, the extraction and flotation processes were the main contributions, while for global warming, flotation process and tailings treatment were the main contributions, accounting for 41% and 45%, respectively. In addition, for the freshwater ecotoxicity and marine ecotoxicity as well as human non-carcinogenic toxicity, the excavation process, flotation process and tailings treatment, each takes about one-third of the environmental burden.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec13\"\u003e\n \u003ch2\u003e3.3.2 Contributions of main sub-processes\u003c/h2\u003e\n \u003cp\u003eIn order to obtain clearer environmental impact analysis results and guide the cleaner production of copper sulfide mines, the foregoing four groups were further subdivided into 10 processes: mining, backfilling, milling, tailings dam, chemicals consumption, electricity, transportation, living consumption and others. Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e indicated that all impact categories could be mainly attributed to the mining activity, backfilling activity and electricity generation, except for water consumption and ionizing radiation. Electricity and backfilling activity dominantly affected the role in global warming, human carcinogenic toxicity, fossil resource scarcity, freshwater eutrophication, marine eutrophication. Meanwhile, mining activity was the main cause for ozone formation (both human health and terrestrial ecosystems). In particular, mining activity took more than 80% of the environmental burden for stratospheric ozone depletion and mineral resource scarcity. Although tailings dam, chemicals consumption and milling activity did not account for a large proportion of environmental impacts, they h influenced all 18 environmental impact categories, which were most evident in terrestrial ecotoxicity, freshwater ecotoxicity, marine ecotoxicity and human non-carcinogenic toxicity. Additionally, chemicals consumption and backfilling were the significant reasons of ionizing radiation. Land use were mainly caused by mining activity, backfilling activity, chemicals consumption and electricity, and water use in living areas was the most dominant contributor to water consumption.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec14\"\u003e\n \u003ch2\u003e3.3.3 Process contributor to the key categories\u003c/h2\u003e\n \u003cp\u003eAs shown by the normalized LCIA midpoint results in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the marine ecotoxicity, freshwater ecotoxicity, human carcinogenic toxicity, human non-carcinogenic and terrestrial ecotoxicity were the prominent categories of the overall environmental impacts. In addition, global warming was taking into account as a hot environmental issue in recent years. Thus, the above six environmental impact categories were defined as key environmental impact categories. To evaluate the process roles on these key categories, process contribution analysis was performed within the system boundary, the results were presented in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. It was found that the cement production, electricity generation, blasting pollution, transportation, on-site emissions and steel production were the main process contributors to these key categories and cement production, electricity generation and blasting pollution contribute to all six key categories. Cement production was the main reason of global warming, terrestrial ecotoxicity and human non-carcinogenic, accounting for 53.56%, 37.68%, 28.90%, respectively. While for human carcinogenic toxicity, marine ecotoxicity and freshwater ecotoxicity, electricity generation played a significant role, accounting for 52.41%, 30.56%, 30,42%, respectively. In addition, on-site emissions, mainly from mining dust, wastewater discharge during mining and beneficiation process and heavy metal pollution in tailings, had a greater impact on the marine ecotoxicity, freshwater ecotoxicity, and human non-carcinogenic. It should be noted that steel production, mainly from the consumption of steel balls in the dressing and grinding process, exhibited a certain impact on human carcinogenic toxicity, accounting for 11.79%.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec15\"\u003e\n \u003ch2\u003e3.3.4 Substance contributor to key categories\u003c/h2\u003e\n \u003cp\u003eTo further clarify the specific pollutants, Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the contributions of major substances to the key categories. The dominant contributors to climate change were carbon dioxide, which is mainly released from coal-burning electricity generation and diesel combustion to air. Copper to air and zinc to air contribute 68.6% and 9.9% to the terrestrial ecotoxicity respectively, and others like nickel to air, vanadium to air and mercury to air demonstrated a minor impact. The contribution of chromium emission to water is the highest for human carcinogenic toxicity, while for human non-carcinogenic toxicity, zinc emission to water was the most significant factor, accounting for 87.7%. For the impact of freshwater ecotoxicity and marine ecotoxicity, the main contributor was zinc emission, followed by copper emission to water, vanadium emission to water and chromium to water. In summary, the substances above were identified as specific pollutants that should be strictly controlled by improving the energy efficiency and reducing on-site emissions during ore mining and beneficiation process of copper sulfide mine.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec16\"\u003e\n \u003ch2\u003e3.4 Sensitivity analysis\u003c/h2\u003e\n \u003cp\u003eTo examine the most effective method of reducing the environmental pollution in copper concentrate production, sensitivity analysis of main process contributors on the identified key categories was conducted. The variation coefficient of the input value of the dominant process was all set at 5%, the assumptions were changed and then the LCA was recalculated. Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the analysis results. Tailings pollution had the highest variation in the global warming, terrestrial ecotoxicity, freshwater ecotoxicity, marine ecotoxicity and human non-carcinogenic toxicity. Particularly, the cement production in tailings treatment plays a key role. For example, a 5% reduction in tailings pollution will result in an environmental benefit of 2.82% in the global warming category while a 5% reduction in cement consumption can bring 2.68% environmental benefit. Similarly, in mining activity, reducing blasting pollution is an effective measure in diminishing the environmental pollution, analogy results could be obtained from Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Especially, the effect of the variation in electricity consumption was the highest on human carcinogenic toxicity in which a 5% electricity reduction could decrease 5.05E-02 kg 1,4-DCB. In addition, the effect of changes in on-site emissions also greatly influenced all impact categories. By contrast, the variation in chemicals consumption had a minor impact. It should be pointed out that the sensitivity calculation was based on the functional unit of one tonne copper sulphate ore mining and beneficiation process.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSensitivity analysis of process contributors on the identified key categories\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eChemicals consumption\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eElectricity generation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOn-site emissions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMining activity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTailings pollution\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBlasting pollution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCement production\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal warming\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTerrestrial ecotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreshwater ecotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarine ecotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman carcinogenic toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman non-carcinogenic toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Process Optimization","content":"\u003cp\u003eAs reported in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e in Section \u003cspan class=\"InternalRef\"\u003e3.3.3\u003c/span\u003e, for tailings pollution, cement used in tailings treatment has made great contributions to the global warming, ecotoxicity, and human toxicity. During the production process of the copper concentrate in this mine, part of the tailings entered the waste rock dump, part entered the tailing pond, and the others were mixed with cement for backfilling. It was found revealed that through purification and recycling, the waste rock dump and the tailing pond had a minor environmental impact, the use of cement in the backfilling process contributed to the most environmental pollution burden. In fact, cement paste backfilling (CPB) is a major method of processing tailings and is widely used in mines across China. In a majority of the past studies, the performance and price of backfill materials have been given priority, and the environmental impact has always been neglected. The backfill materials are usually made of the Portland cement, solid waste (such as waste rock, fly ash, tailing and slag) and water composition (Zhou et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003ea). However, in recent years, the research on filling pastes has been continuously improved. In the backfilling process, new backfill materials have been more widely selected ta substitute for cement to mitigate environmental pollution. For example, recycled building materials (broken bricks, the recycled concrete aggregates and recycled asphalt pavement) were utilized as substitutes for backfill materials (Rahman et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Deng et al. developed a novel CPB using the gangue rocks, fly ash, quicklime and ordinary Portland cement which was much more environmental-friendly (Deng et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). In order to further quantitatively analyze the environmental impact of different backfill materials, the existing databases in the Ecoinvent database in SimaPro were utilized. Based on the actual practice, one tonne of Portland cement used in the backfilling process was respectively replaced with the following four backfilling materials in the folloeing five scenarios: scenario a\u0026ndash;cement (alternative constituents 6\u0026ndash;20%), scenario b\u0026ndash;cement (alternative constituents 21\u0026ndash;35%), scenario c\u0026mdash;ordinary Porland, scenario d\u0026ndash;cement (pozzolana and fly ash 11\u0026ndash;35%) and scenario e\u0026ndash;cement (pozzolana and fly ash 36\u0026ndash;55%). The environmental impacts analysis on key categories of different backfill materials is shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. The scenario e has the greatest impact on the five environmental impact categories, especially in the global warming, which reduces the environmental impact by approximately 40.39%. Replaced with cement (alternative constituents 6\u0026ndash;20%) has little effect on the total environment. The replacement effects of cement (alternative constituents 21\u0026ndash;35%) and cement (pozzolana and fly ash 11\u0026ndash;35%) are similar. Among them, the environmental impact reduced by approximately 22.06% in Global warming. Therefore, the application of these new backfill materials provides new ideas for cleaner mine production in backfilling process (Zhou et al, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn addition, the environmental impact of electricity generation cannot be ignored, mainly related to the air pollution generated by the coal-based power generation process (Parker et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The sensitivity analysis in Section \u003cspan class=\"InternalRef\"\u003e3.4\u003c/span\u003e indicated that a 5% reduction in electricity consumption resulted in an environmental benefit of 1.56% in the global warming category and 3.09% in the human toxicity category. In the past 5 years, although there has been a trend of declining, the coal-based power generation has retained at about 70% of the China\u0026apos;s total energy production. In order to achieve a further detailed quantitative analysis, the following four scenario assumptions were performed based on the China\u0026rsquo;s power structure,(CEC, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) the proportion of coal-fired power generation was replaced by hydropower (scenario 1), gas-fired power (scenario 2), nuclear power (scenario 3) and wind power (scenario 4), respectively. Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e presents the environmental impacts on the identified key categories of the four scenarios. As a whole, when the proportion of coal-fired power generation decreased, the overall environmental burden diminished. When coal-fired power generation was replaced by nuclear power, the environmental pollution on the global warming can be reduced by about 86.57%. The impact on human carcinogenic toxicity and freshwater ecotoxicity would be reduced by 84.08% and 71.88%, respectively, as the coal-based electricity generation was substituted by the gas-fired power. Therefore, its crucial to improve energy efficiency and adjust energy structure for achieving cleaner electricity generation.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab6\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEnvironmental impacts on the key categories of the four scenarios.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEnvironmental categories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBasic scenario\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eScenario 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eScenario 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eScenario 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eScenario 4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCR(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCR(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCR(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCR(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal warming\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg CO\u003csub\u003e2\u003c/sub\u003e eq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.56E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.25E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.58E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTerrestrial ecotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg 1,4-DCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.61E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.82E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.45E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.95E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreshwater ecotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg 1,4-DCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.08E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.13E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.85E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.87E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarine ecotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg 1,4-DCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.93E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.34E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.80E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman carcinogenic toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg 1,4-DCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.66E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.44E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman non-carcinogenic toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ekg 1,4-DCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.09E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eNote: CR denotes changing rate.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThe normalization of midpoint results indicated that the marine ecotoxicity, freshwater ecotoxicity, human carcinogenic toxicity, human non-carcinogenic and terrestrial ecotoxicity were the major environmental impact categories, while the others were relatively negligible. In addition, it was concluded from contribution analysis that the mining activities, tailings pollution, and on-site emissions were the dominant contributions to the overall environmental impact. The further detailed analysis identified that the environmental impact of the blasting process in mining activities was the largest attribution. The adoption of cement in the backfilling activity of tailings treatment was the main source of tailings pollution (environmental pollution caused by the upstream cement production process was included). For on-site emissions, copper to air is the main substance affecting terrestrial ecotoxicity, zinc and chromium emission to water are the main substances influencing the freshwater ecotoxicity, marine ecotoxicity and human toxicity. In addition, the impact of chemical consumption on ecotoxicity could not be ignored, and the electricity generation was also regarded as the key process contribution due to its great environmental impact. Finally, the sensitivity analysis was performed by taking the input value of the key process as an independent variable. The analysis results revealed that adjusting electricity structure and reducing the pollution from backfill materials were crucial to solving environmental problems owing to the copper-sulfur mining and beneficiation process.\u003c/p\u003e \u003cp\u003eThe findings obtained from this research provide realistic policy suggestions for effectively decision-making in the copper-sulfur mine production and the entire metallurgical industry. Moreover, the LCI results are useful for improving the LCI database of copper-sulfur mine production.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e The study conception and design were proposed by Ming Tao. Material preparation and data collection were performed by Rui Zhao and Ying Shi. The first draft of the manuscript was written by Kemi Nie and all authors commented on previous versions of the manuscript. Ming Tao and Wenzhuo Cao approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e The research presented in this paper was supported by the National Natural Science Foundation of China (Grant numbers: 12072376).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e If any researchers need the original data of this manuscript, the authors agree to provide relevant information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAsif Z, Chen Z (2016) Environmental management in North American mining sector[J]. Environ Sci Pollut R 23(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11356-015-5651-8\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeylot A, Villeneuve J (2015) Assessing the national economic importance of metals: An Input\u0026ndash;Output approach to the case of copper in France. Resour Policy 44:161\u0026ndash;165. https:// doi.org/10. 1016/j.resourpol.2015.02.007\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeylot A, Villeneuve J (2017) Accounting for the environmental impacts of sulfidic tailings storage in the Life Cycle Assessment of copper production: A case study. J Clean Prod. 153(JUN.1),139\u0026ndash;145. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2017.03.129\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBo PW (1998) Multi-user test of the data quality matrix for product life cycle inventory data. Int J Life Cycle Assess 5(3):259\u0026ndash;265. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF02979832\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCEC (2016) China Electricity Council. Beijing,China: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cec.org.cn/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEcoinvent (2015) Swiss Centre for Life Cycle Inventories. https://v34. ecoquery.ecoinvent. org/Account/LogOn?ReturnUrl\u0026frac14;%2fHome%2fIndex. (Accessed 22 April 2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Wang Z, Wu Y, Li L, Li B, Pan DA, Zuo T (2019) Environmental benefits of secondary copper from primary copper based on life cycle assessment in China. Resour Conserv Recycl 146:35\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.resconrec.2019.03.020\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCSY (2018) China Statistical Yearbook. Beijing,China: China Statistic Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp:// www\u003c/span\u003e\u003c/span\u003e. stats.gov.cn/tjsj/ndsj/2018/indexch.htm\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng X, Zhang J, Klein B, Zhou N, DeWit B (2017) Experimental characterization of the influence of solid components on the rheological and mechanical properties of cemented paste backfill. Int J Miner Process 168:116\u0026ndash;125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.minpro.2017.09.019\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong J, Yang Q, Sun L, Zeng Q, Liu S, Pan J, Liu X (2011) Assessing the concentration and potential dietary risk of heavy metals in vegetables at a Pb/Zn mine site, China. Environ Earth Sci 64(5):1317\u0026ndash;1321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-011-0992-1\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuce AD, Gauch M, Althaus HJ (2016) Electric passenger car transport and passenger car life cycle inventories in ecoinvent version 3. Int J Life Cycle Assess 21:1314\u0026ndash;1326. https:/. /doi.org/ 10\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEkman Nilsson A, Macias Aragon\u0026eacute;s M, Arroyo Torralvo F, Dunon V, Angel H, Komnitsas K, Willquist K (2017) A Review of the Carbon Footprint of Cu and Zn Production from Primary and Secondary Sources. Minerals-Basel 7(9):168. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/min7090168\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarjana SH, Huda N, Mahmud MAP (2019a) Life cycle analysis of copper-gold-lead-silver-zinc beneficiation process. Sci Total Environ 659:41\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2018.12.318\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarjana SH, Huda N, Mahmud MAP (2019b) Impacts of aluminum production: A cradle to gate investigation using life-cycle assessment. Sci Total Environ 663:958\u0026ndash;970. https://doi.org/ 10. 1016/ j.scitotenv.2019.01.400\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarjana SH, Huda N, Mahmud MAP, Lang C (2019c) Impact analysis of gold silver refining processes through life-cycle assessment. J Clean Prod. 228,867\u0026ndash;881. https://doi.org/10. 1016/ j.jclepro. 2019.0 4. 166\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira H, Leite MGP (2015) A Life Cycle Assessment study of iron ore mining. J Clean Prod 108:1081\u0026ndash;1091. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2015.05.140\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrischknecht R, Jungbluth N, Althaus H, Doka G, Dones R, Heck T, Hellweg S, Hischier R, Nemecek T, Rebitzer G, Spielmann M (2005) The ecoinvent Database: Overview and Methodological Framework (7 pp). Int J Life Cycle Assess 10(1):3\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1065/lca2004.10.181.1\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGan Y, Griffin WM (2018) Analysis of life-cycle GHG emissions for iron ore mining and processing in China\u0026mdash;Uncertainty and trends. Resour Policy 58:90\u0026ndash;96. https://doi.org/10. 1016/j. resourpol. 2018.03.015\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoedkoop M (2009b) ReCiPe 2008: A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level. Spatial Planning and the Environment, Ministry of Housing, Spatial Planning and the Environment\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu Y, Wang Z, Lu S, Jiang S, Mu D, Shu Y, Heijungs JB, Huppes R, Zamagni G, Masoni A, Buonamici P, Ekvall R, Rydberg T (2012b) T, 2011. Life Cycle Assessment: Past, Present, and Future. Environ Sci Technol 45(1),90\u0026ndash;96. https://doi.org/10.1021/es101316v\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaque N, Norgate T (2014) The greenhouse gas footprint of in-situ leaching of uranium, gold and copper in Australia. J Clean Prod 84:382\u0026ndash;390. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2013.09.033\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe X (2018) Copper market analysis and outlook. China Metal Bulletin 12:1\u0026ndash;4 CNKI:SUN:JSTB.0.2018-12-001\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong J, Chen Y, Liu J, Ma X, Qi C, Ye L (2018a) Life cycle assessment of copper production: a case study in China. Int J Life Cycle Assess 23(9):1814\u0026ndash;1824. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11367-017-1405-9\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong J, Yu Z, Fu X, Hong J (2019) Life cycle environmental and economic assessment of coal seam gas-based electricity generation. Int J Life Cycle Assess 24(10):1828\u0026ndash;1839. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps:// doi.org/ 10.1007/s11367-019-01599-6\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuijbregts MAJ, Steinmann ZJN, Elshout PMF, Stam G, Verones F, Vieira M, Zijp M, Hollander A, van Zelm R (2020) Correction to: ReCiPe2016: a harmonised life cycle impact assessment method at midpoint and endpoint level. Int J Life Cycle Assess 25(8):1635. .https:/ /doi.org/10. 1007/s 11367-020-01761-5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIESLUN (2018) Institute of Environmental Sciences in Leiden University of the Institute of Environmental Sciences in Leiden University of the Netherlands, https:// www. universiteitleiden.nl/ en/ science/environmental sciences. (Accessed 22 April 2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eISO I (2006) ISO/DIS 14040. Environmental Management - Life Cycle Assessment - Principles and Framework\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJahed Armaghani D, Tonnizam Mohamad E, Hajihassani M, Alavi Nezhad Khalil Abad SV, Marto A, Moghaddam MR (2016) Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng Comput-Germany 32(1):109\u0026ndash;121. .https:// doi.org/10. 1007/s00366-015-0402-5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJain P, Powell JT, Smith JL, Townsend TG, Tolaymat T (2014) Life-Cycle Inventory and Impact Evaluation of Mining Municipal Solid Waste Landfills. Environ Sci Technol 48(5):2920\u0026ndash;2927. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/es404382s\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlpffer W (1997) Life Cycle Assessment: From the beginning to the current state[J]. Environ Sci Pollut R 4(4):223\u0026ndash;228. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/BF02986351\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMemary R, Giurco D, Mudd G, Mason L (2012) Life cycle assessment: a time-series analysis of copper. J Clean Prod 33:97\u0026ndash;108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2012.04.025\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorgate TE, Jahanshahi S, Rankin WJ (2007a) Assessing the environmental impact of metal production processes. J Clean Prod 15(8\u0026ndash;9):838\u0026ndash;848. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2006.06.018\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorgate TE, Jahanshahi S, Rankin WJ (2007b) Assessing the environmental impact of metal production processes. J Clean Prod 15(8\u0026ndash;9):838\u0026ndash;848. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2006.06.018\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorgate T, Haque N (2012) Using life cycle assessment to evaluate some environmental impacts of gold production. J Clean Prod 29\u0026ndash;30:53\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2012.01.042\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorthey S, Haque N, Mudd G (2013) Using sustainability reporting to assess the environmental footprint of copper mining. J Clean Prod 40:118\u0026ndash;128. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2012.09.027\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParker DJ, MNaughton CS, Sparks GA (2016) Life Cycle Greenhouse Gas Emissions from Uranium Mining and Milling in Canada. Environ Sci Technol 50(17):9746\u0026ndash;9753. https:// doi.org/10. 1021/acs. est.5b06072\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi C, Ye L, Ma X, Yang D, Hong J (2017) Life cycle assessment of the hydrometallurgical zinc production chain in China. J Clean Prod 156:451\u0026ndash;458. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2017.04.084\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman MA, Imteaz M, Arulrajah A, Disfani MM (2014) Suitability of recycled construction and demolition aggregates as alternative pipe backfilling materials. J Clean Prod 66:75\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2013.11.005\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubin RS, Castro, M A S D D, Schalch V, Ometto AR (2014) Utilization of Life Cycle Assessment methodology to compare two strategies for recovery of copper from printed circuit board scrap. J Clean Prod 64:297\u0026ndash;305. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2013.07.051\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchryver AMD, Brakkee KW, Goedkoop MJ, Huijbregts MAJ (2009) Characterization Factors for Global Warming in Life Cycle Assessment Based on Damages to Humans and Ecosystems. Environ Sci Technol 43(6):1689\u0026ndash;1695. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/es800456m\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong X, Pettersen JB, Pedersen KB, R\u0026oslash;berg S (2017) Comparative life cycle assessment of tailings management and energy scenarios for a copper ore mine: A case study in Northern Norway. J Clean Prod 164:892\u0026ndash;904. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2017.07.021\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun W, Skidmore AK, Wang T, Zhang X (2019) Heavy metal pollution at mine sites estimated from reflectance spectroscopy following correction for skewed data. Environ Pollut 252:1117\u0026ndash;1124. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envpol.2019.06.021\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan RBH, Khoo HH (2005) An LCA study of a primary aluminum supply chain. J Clean Prod 13(6):607\u0026ndash;618. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2003.12.022\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao M, Zhang X, Wang S, Cao W, Jiang Y (2019) Life cycle assessment on lead\u0026ndash;zinc ore mining and beneficiation in China. J Clean Prod. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2019.117833\u003c/span\u003e\u003c/span\u003e. 237,117833\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, Liu W, Yuan X, Zheng X, Zuo J (2016) Future of lignite resources: a life cycle analysis[J]. Environ Sci Pollut R 23(24):1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-016-7642-9\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang D, Yin Y, Ma X, Zhang R, Zhai Y, Shen X, Hong J (2019) Environmental improvement of lead refining: a case study of water footprint assessment in Jiangxi Province, China. Int J Life Cycle Assess 24(8):1533\u0026ndash;1542. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11367-018-01578-3\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Meng Z, Jiao W (2018) Hydrological and pollution processes in mining area of Fenhe River Basin in China. Environ Pollut 234:743\u0026ndash;750. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envpol.2017.12.018\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou N, Zhang J, Ouyang S, Deng X, Dong C, Du E (2020) Feasibility study and performance optimization of sand-based cemented paste backfill materials. J Clean Prod. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2020.120798\u003c/span\u003e\u003c/span\u003e. 259,120798\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Life cycle assessment, Copper concentration production, On-site emission, Tailing pollution, Backfill materials","lastPublishedDoi":"10.21203/rs.3.rs-1288761/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1288761/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChina is a major producer of copper concentrate as its smelting capacity continues to expand dramatically. The present study analyzes the life cycle environmental impact of copper concentrate production, along with selection of a typical copper sulphate mine in China. Life cycle assessment (LCA) was conducted using SimaPro with ReCiPe 2016 method. The midpoint and endpoint results were performed with uncertainty information based on Monte Carlo calculation. Normalization of midpoint results revealed that impact from the marine ecotoxicity category was the largest contributor to the total environmental impact, followed by freshwater ecotoxicity, human carcinogenic toxicity, human non-carcinogenic and terrestrial ecotoxicity. The mining activity, backfilling activity and electricity generation were proved to be the dominant factors. In addition, main processes and substances to the identified key categories were also classified. Specifically, the cement production in the backfilling process, blasting activity, on-site emission and electricity generation were regarded as the critical processes. Copper to air and zinc emission to water were considered as the critical substances. The sensitivity analysis indicated that controlling on-site emissions and reducing pollution from cement production were the most effective measure to solve the environmental problems caused by the concentrate production process. Finally, the corresponding technical and management measures were proposed to facilitate the development of cleaner metal industry.\u003c/p\u003e","manuscriptTitle":"Environmental impact of mining and beneficiation of copper sulphate mine based on life cycle assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-03-15 15:58:49","doi":"10.21203/rs.3.rs-1288761/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revision","date":"2022-04-20T03:24:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-03-14T12:50:03+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-03-11T08:15:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2022-02-25T20:49:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-02-17T04:46:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2022-01-23T08:40:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fcd93005-b309-4e33-9878-d904bf122038","owner":[],"postedDate":"March 15th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-06-03T04:50:56+00:00","versionOfRecord":[],"versionCreatedAt":"2022-03-15 15:58:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1288761","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1288761","identity":"rs-1288761","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}