Abstract

Heavy metal (HM) in industrial wastewater has been one of the serious environmental issues in China for a long time. This paper analyzes the distribution of HMs and governance input efficiency in industrial wastewater based on the archival data of China Statistical Yearbook on Environment from 2001 to 2014. The empirical analysis shows that the concentrations of Hg, Cd, Pb, As, and Cr(VI) generally decreased from 2001 to 2014. The emissions of Hg, Cd, Pb, and As are mostly concentrated in the central provinces (i.e., Hunan, Hubei, Jiangxi), the southern provinces (i.e., Guangxi and Guangdong), and the northern provinces (i.e., Gansu and Inner Mongolia). The distribution pattern is closely related to local industry due to resources dependence, such as mining and processing of non-ferrous metal ores, smelting and pressing of ferrous or non-ferrous metals. Cr(VI) is mainly located in the eastern coastal provinces, including Zhejiang and Jiangsu, and caused by manufacturing industries such as automobile, metal products, leather, fur, feather and related products, and footware. Furthermore, we find that the annual expenditure on and the capacity to deal with industrial wastewater play significant negative effects on reducing HM concentrations in industrial wastewater.

INTRODUCTION

Heavy metal pollution in water is mainly caused by industrial wastewater. With rapid industrialization, the environmental pollution due to heavy metals in industrial wastewater has led to more and more serious problems in China. There have been noticeable incidents in these years, such as blood lead poisoning events in Changxin, Zhejiang province (2004), excessive urinary arsenic in Hechi (in 2008), cadmium pollution incidents in Longjiang and Guangxi provinces (2012), mercury pollution events in Wanshan, Guizhou province (2014), and excessive cadmium events in Xinyu, Jiangxi province (2016). Heavy metals are generally defined as those metals which have a specific density of more than 5 g/cm3, such as mercury (Hg), cadmium (Cd), lead (Pb), arsenic (As), hexavalent chromium (Cr(VI)), nickel (Ni), zinc (Zn), and copper (Cu) (Järup 2003; Huang et al. 2016; Li et al. 2017a). Heavy metal can bring about serious pollution hazard, especially the heavy metals Hg, Cd, Pb, As, and Cr (Li et al. 2013). Their characteristics include their slight solubility, persistence (Wang et al. 2014), toxicity, bioaccumulation of food chain, amplification (Klimmek et al. 2001; Watanabe et al. 2003), and their compound pollution effect (Gupta et al. 2008; Klavins et al. 2009), which can greatly harm human health.

In order to control heavy metal pollution in industrial wastewater, China has invested a great deal of manpower and material resources in environmental treatment. In 2005, China invested in 69,231 sets of equipment and operating costs of 27.67 billion yuan for industrial wastewater treatment. The investment had increased to 83,227 sets and 68.53 billion yuan in 2015. In recent years, although expenditure has been increasing, the governance input efficiency has not been tested empirically. This paper attempts to discuss the distribution pattern of heavy metals and governance input efficiency in industrial wastewater, which is important for ecological, social sustainable development and human health.

Many researchers have explored some of the relevant issues related to heavy metal emissions in industrial wastewater. The related research primarily focuses on distribution patterns, ecological environment, human health, the sources of pollution, and governance. The studies on the spatial distributions of heavy metals are mainly from a regional or national perspective. For example, OuYang et al. (2004) showed that channels of the Pearl River Delta and Shenzhen River have relatively high concentrations. Relatively lower concentrations of heavy metals are found in Xijiang River and Beijiang River. Fan & Luo (2013) and Hu et al. (2014) explored the spatial distribution pattern of heavy metal pollution in China. Wang & Nie (2017) measured the spatial distribution characteristics of Cu and Pb contaminants in a river network of Daye, China. The results show that although the distributions of heavy metals (Cu and Pb) are not uniform, there is a significant spatial correlation among the heavy metal emissions in the rivers of Daye.

The rapid increase of heavy metal emissions in industrial wastewater leads to serious environmental problems. He et al. (2003), and Huang et al. (2013) found that heavy metal pollution causes serious harm to the waterbody in the Yellow River. Affected by the contaminants in industrial wastewater, the waterbody of the Three Gorges Reservoir area has been polluted differently (Gao et al. 2016). Li & Huo (2000), Ho & Hui (2001), Gao & Chen (2012), and Zeng et al. (2015) studied the problems of heavy metal pollution in the Haihe River, Pearl River, Bohai River, and Xiangjiang River, respectively, and found that these rivers had been polluted in various degrees by different heavy metals. Pollution in water will not only cause huge economic losses, but also harm to regional human health. For example, lead pollution can affect the neural and digestive system and be fatal in severe cases (Zhu 2014). Furthermore, excessive lead in blood can damage the hematopoietic system to cause anemia. Moreover, it damages the developing brain tissue and nervous system, thereby affecting children's intellectual development, and the consequence is irreversible (Yang et al. 2013). Heavy metals will be accumulated in the human body over a long time, affecting the baby through the mother, and then seriously damaging the health of several generations (Li et al. 2017b).

The treatment of heavy metal pollution is critically important for further research on governance strategy. Some researchers considered the corresponding governance problems to control the sources of heavy metal pollution. Dean et al. (1972) and Diao et al. (2004) believed that the main sources of heavy metal pollution in water environments are chemical, mining, metal smelting and processing, electroplating, shipbuilding, and other industries. Wang et al. (2015) found that heavy metal emissions primarily came from petrochemistry, leather industry, and metallurgy in the Haihe River Basin. Wu & Jiang (2012) analyzed the emission characteristics of heavy metal contaminant in Chinese industrial wastewater in 2007, and it was suggested that China should constrain the manufacture of metal products, non-ferrous metal smelting, leather, and raw chemical materials as the four main prevention and control industries.

Some researchers have explored the governance technology to control heavy metal pollution. In recent years, researchers have developed many more effective technologies that can not only reduce the discharge of wastewater, but also improve the treatment efficiency of wastewater, such as adsorption, membrane separation, photocatalytic process, etc. (Leung et al. 2000). Photocatalytic process is an innovative and promising technique for efficient destruction of pollutants in water (Skubal et al. 2002). Membrane separation has been increasingly used recently for the treatment of wastewater due to its convenient operation (Kurniawan et al. 2006). Barakat (2011) reviewed the recent developments and technical applicability of various treatments for the removal of heavy metals from industrial wastewater. Zhang et al. (2015) and Wang et al. (2016) argued that ion exchange and adsorption resins, polyacrylamide superabsorbent polymers can effectively remove heavy metals pollution in wastewater respectively.

There are also some researchers focusing on governance rule of heavy metals, for example, administrative regulations, governing patterns, and policies. Kathuria (2006) believed that administrative regulation was an important way to control water environment pollution. Zeng et al. (2015) found that improving the capacity of controlling pollution in the Xiangjiang River basin was inefficient without the support of the governmental fiscal system mechanism. Meng (2007) proposed the principles of ‘by categories, by regions, by classes and by phases’, which could apply to pollution prevention and cure in the water environment of China. To control heavy metal pollution in water, technical routes of the environmental protection system should be corrected and independent laws should be enacted specifically for the five kinds of heavy metals to ensure implementation of the technical route of source control of excessive discharge (Fu 2012).

Through the prior literature review, we know that previous research on the distribution of heavy metals in industrial wastewater has mainly focused on the regional perspective, and there are few studies that extend to the national perspective. With regard to the governance effectiveness of heavy metal pollution control, the former studies are primarily from the perspectives of pollution source, technical treatment, and macroeconomic policy. The studies of governance input efficiency are less involved from the quantitative perspective. This paper examines the data of the China Statistical Yearbook on Environment from 2001 to 2014 in order to explore the distribution pattern and governance input efficiency of heavy metal emission in Chinese industrial wastewater. As the data of Taiwan, Hongkong, Macao, Tibet, and Hainan are incomplete, we deleted these areas and collect related data from the other 29 provinces. The remainder of this article is organized as follows. The next section provides the variation trend and analyzes the status quo of five major heavy metal pollutions in recent years. This is followed by a section that further explores the governance input effect of heavy metal contents in Chinese industrial wastewater. Concluding remarks and corresponding countermeasures to address heavy metal pollution appear in the final section.

The variation trend and distribution pattern of heavy metal content in industrial wastewater

The variation trend of heavy metal content in industrial wastewater

In order to investigate the variation tendency and status quo of heavy metal content in industrial wastewater, this paper researched the five heavy metal contents of Hg, Cd, Pb, As, and Cr(VI) based on data of the China Statistical Yearbook on Environment. The changing trend was obtained from the five types of heavy metal concentrations in industrial wastewater (Figure 1).

Figure 1

Emission trends of five types of heavy metal contents in Chinese industrial wastewater from 2001 to 2014.

Figure 1

Emission trends of five types of heavy metal contents in Chinese industrial wastewater from 2001 to 2014.

The concentrations of Pb and As in Chinese industrial wastewater show an obvious decreasing trend from 2001 to 2014. The concentrations of Cr(VI) and Cd also show a downward trend as a whole. Due to the low concentration of Hg, there is not a significant downward trend. In addition, overall, their decreasing order is Pb, As, Cr(VI), Cd, and Hg before 2011. After 2012, their order changes to As, Pb, Cr(VI), Cd, and Hg. The result demonstrates that the emission of heavy metals in Chinese industrial wastewater is slow and has been declining for many years, but there is still an increasing trend for some heavy metals in some specific years. Compared with other heavy metals, the increasing trend of As is more obvious after 2011. Although the treatment of heavy metal pollution in industrial wastewater has produced some achievements, incidents of heavy metal pollution have been publicized by the media very often, and these incidents have a significant influence on the ecological environment and public health. Therefore, the treatment of heavy metal pollution cannot be ignored. According to the data of heavy metal discharge from different provinces from 2001 to 2014, this paper applies statistical methods to explore regional and industrial heavy metal emissions in industrial wastewater, which account for a proportion of the total emissions, and discusses the development trend of heavy metals emission in industrial wastewater, so as to determine the key control areas and industries.

The distribution pattern of heavy metal in industrial wastewater

The distribution of five heavy metals according to province are shown below.

Hg: As shown in Figure 2, in 2001, the discharge amount of Hg in Hunan was the highest in the whole country. The emissions of Hg were mainly concentrated in Hunan and Liaoning, with 40% of emissions in Hunan and over 30% in Liaoning, and the total proportion being more than 70%. At the same time, Guangxi, Chongqing, Gansu, and Inner Mongolia each contains more than 3%. Additionally, Hebei, Fujian, Henan, Guangdong, Sichuan, and Yunnan account for over 1% but less than 2%. Up to 2014, the emission percentage of Hunan decreased to 20% and Liaoning significantly reduced to less than 1%. The prominent discharge provinces are Hunan, Gansu, Guangxi, and Jiangxi, which account for more than 54% in total, each with more than 10%. Shanxi, Inner Mongolia, Shanghai, Fujian, Shandong, Henan, Hubei, Guangdong, Sichuan, Guizhou, Yunnan, Shaanxi, Qinghai, and Xinjiang provinces each account for 1–6% which totals 40% approximately. In summary, the emission percentages of Hunan and Liaoning tended to decrease; however, the number of provinces with over 1% increased obviously, and thus a diffused trend was displayed from 2001 to 2004.

Figure 2

Comparison chart of Hg emission in provincial industrial wastewater between 2001 and 2014.

Figure 2

Comparison chart of Hg emission in provincial industrial wastewater between 2001 and 2014.

Cd:Figure 3 shows that the discharge amount of Cd is concentrated in Hunan, Guangxi, Gansu, and Liaoning, which contain 80% in total, and the emission proportion in each province is more than 10% in 2001. The percentages of Anhui, Jiangxi, Guangdong, Sichuan, and Yunnan range from 1% to 4%, a total amount of 10%. From 2001 to 2014, the discharge rate of Hunan increases from 19.68% to 37.89%. In the central and southern areas, the main discharge areas spread from Hunan and Guangxi to Jiangxi, Hubei, Fujian, and Yunnan. In the northwest, Gansu is the major emission region and spreads to Inner Mongolia, Shaanxi, and Qinghai. In 2014, Hunan, Jiangxi, Hubei, Shandong, and Guangxi are the prominent discharge areas containing 65% in total, and Inner Mongolia, Henan, Yunnan, Gansu and Fujian each account for about 4%, which is 22% in total. The emission rates of Zhejiang, Guangdong, Guizhou, Shaanxi, and Qinghai are more than 1% each. It is worth pointing out that due to the adjustment of industry structure, the emission rate in Liaoning reduced dramatically from 26.51% in 2001 to 0.14% in 2014.

Figure 3

Comparison chart of Cd emission in provincial industrial wastewater between 2001 and 2014.

Figure 3

Comparison chart of Cd emission in provincial industrial wastewater between 2001 and 2014.

Pb: In 2001, as shown in Figure 4, the Pb discharge in industrial wastewater is major in Hunan, Gansu, Guangxi, Guangdong, Yunnan, and Liaoning, accounting for 80%, with 30%, 15%, and 12%, respectively, for Hunan, Gansu, and Guangxi while Guangdong, Yunnan, and Liaoning account for between 5% and 10%. The discharge of Inner Mongolia, Jiangsu, Jiangxi, Anhui, Fujian, Sichuan, Guizhou, and Shaanxi is 1–5% for each province and totals 16%. In 2014, Hunan, Inner Mongolia, Jiangxi, Hubei, Guangxi, Yunnan, Gansu, and Fujian, with 78%, are the major emission regions. The emission rate of Hunan is 30% and for Inner Mongolia, Hubei, Jiangxi, Guangxi, Yunnan, Gansu, and Fujian the range is 5–10%.

Figure 4

Comparison chart of Pb emission in provincial industrial wastewater between 2001 and 2014.

Figure 4

Comparison chart of Pb emission in provincial industrial wastewater between 2001 and 2014.

Compared to emissions in 2001, the percentage for Hunan in 2014 remains at the same level, but it reduces in Gansu, Guangxi, Guangdong, Yunnan, and Liaoning. Hubei, Jiangxi, and Fujian which are all located around Hunan have increased and the amount for Inner Mongolia rises from 3% to 10%.

As:Figure 5 shows, in 2001, the As discharge in industrial wastewater is major in Hunan, Gansu, Yunnan, and Liaoning, containing 70% in total, and every province is over 10%. Hunan ranks first among all provinces. The emissions of Hebei, Jiangsu, Anhui, Jiangxi, Hubei, Guangdong, Guangxi, and Sichuan range from 1% to 5% and total 18%. In 2014, the discharge is concentrated in Hunan, Inner Mongolia, and Hubei, totaling 57%, and each province is greater than 10%. The emission rates of Yunnan, Jiangxi, and Guangxi are 4–10% and for Fujian, Gansu, Shandong, Anhui, Sichuan, and Qinghai are 1–4%. The percentages for Gansu, Yunnan, and Liaoning decrease from 18.10%, 15.58%, and 11.14% in 2001 to 2.98%, 6.7%, and 0.06% in 2014, respectively. However, Hunan, Hubei, and Inner Mongolia rise from 25.06%, 2.87%, and 0.86% to 32.62%, 11.34%, and 14.25%, respectively, which implies the emissions of As spread from Hunan to Hubei, Jiangxi, and Guangxi during 2001–2014.

Figure 5

Comparison chart of As emission in provincial industrial wastewater between 2001 and 2014.

Figure 5

Comparison chart of As emission in provincial industrial wastewater between 2001 and 2014.

Cr(VI):Figure 6 indicates that the discharge areas of Cr(VI) are discrete. In 2001, the emissions of Cr(VI) are concentrated in the central and western provinces which contain Hunan, Hubei, Sichuan, Henan, and Chongqing. Each of these provinces accounts for over 3% and totals 43%. The eastern coastal regions which contain Zhejiang, Jiangsu, Guangdong, Hebei, and Fujian account for over 3% each and total 34%. Liaoning, Jilin, Shanghai, Jiangxi, Shandong, Guangxi, Shaanxi, Gansu, and Xinjiang range from 1% to 3%. In 2014, the emission rate of Hubei is 30%, ranking first, with Zhejiang and Jiangsu on 13% and 10%, respectively. The emission percentage of Hunan reduces from 18.21% to 3.59% while it increases in Guangdong, Hebei, and Fujian. In 2014, the emissions of Cr(VI) are centered in Hubei as well as in the eastern coastal provinces of Zhejiang, Jiangsu, Guangdong, Hebei, and Fujian. Every province accounts for over 6% and totals 46%. Shanghai, Jiangxi, Shandong, Henan, Chongqing, Sichuan, Gansu, and Xinjiang range from 1% to 3%. In general, the major emission provinces transfer from the central and western provinces to the eastern coastal provinces in 2001–2014.

Figure 6

Comparison chart of Cr(VI) emission in provincial industrial wastewater between 2001 and 2014.

Figure 6

Comparison chart of Cr(VI) emission in provincial industrial wastewater between 2001 and 2014.

The industrial distribution patterns of heavy metals

In order to further study the industrial distribution patterns of five types of heavy metals in industrial wastewater, the top three industries for the five kinds of heavy metals from 2001 to 2014 are shown below. These account for the proportions of heavy metal emissions in the whole industry.

Hg:Table 1 indicates that the top three industries for Hg emissions in industrial wastewater are mining and processing of non-ferrous metal ores, smelting and pressing of non-ferrous metals, and manufacture of raw chemical materials and chemical products from 2001 to 2007, with a total proportion of more than 80% in most years. In addition, Table 1 also shows that smelting and pressing of non-ferrous metals, manufacture of raw chemical materials and chemical products accounting for the total proportion in national industry is approximately more than 70%. In 2008–2010, the share of smelting and pressing of ferrous metals is greater – about 30%. Hg emissions of the top three industries are mining and processing of non-ferrous metal ores, manufacture of raw chemical materials and chemical products, and smelting and pressing of ferrous metals in 2008 and 2009, with the proportion being more than 70% in total. In 2010, the industries which accounted for 77% of mining and processing of non-ferrous metal ores, smelting and pressing of non-ferrous and ferrous metals are the top three industries for Hg emissions. Hg emissions are still concentrated in mining and processing of non-ferrous metal ores, smelting and pressing of non-ferrous metals, manufacture of raw chemical materials and chemical products from 2011 to 2014, accounting for 73%. Due to mining and processing of non-ferrous metal ores reducing emissions slowly, its proportion has increased from 3.04% in 2001 to 31.8% in 2014. However, the emissions of smelting and pressing of non-ferrous metals reduced rapidly from 50.13% in 2001 to 32.1% in 2014.

Table 1

Hg emission proportions of top three industries in national industrial wastewater from 2001 to 2014 (%)

Sector20012002200320042005200620072008200920102011201220132014
Mining and processing of non-ferrous metal ores 3.04 3.26 3.47 6.84 5.24 5.88 19.01 30.46 35.11 17.67 22.39 27.1 32.4 31.8 
Smelting and pressing of non-ferrous metals 50.13 50.69 43.45 28.34 31.04 23.77 17.93   31.61 23.86 16.1 28.8 32.1 
Manufacture of raw chemical materials and chemical products 22.29 40.41 34.89 52.46 51.67 59.89 50.58 14.45 18.24  28.88 41.8 27.9 20.6 
Smelting and pressing of ferrous metals        30.60 28.91 28.56     
Sector20012002200320042005200620072008200920102011201220132014
Mining and processing of non-ferrous metal ores 3.04 3.26 3.47 6.84 5.24 5.88 19.01 30.46 35.11 17.67 22.39 27.1 32.4 31.8 
Smelting and pressing of non-ferrous metals 50.13 50.69 43.45 28.34 31.04 23.77 17.93   31.61 23.86 16.1 28.8 32.1 
Manufacture of raw chemical materials and chemical products 22.29 40.41 34.89 52.46 51.67 59.89 50.58 14.45 18.24  28.88 41.8 27.9 20.6 
Smelting and pressing of ferrous metals        30.60 28.91 28.56     

Cd:Table 2 shows that the top three industries of Cd emissions in industrial wastewater are mining and processing of non-ferrous metal ores, smelting and pressing of non-ferrous metals, and manufacture of raw chemical materials and chemical products. Cd emissions of smelting and pressing of non-ferrous metals account for a higher proportion of the whole industry, more than 60% in most years. It is the highest proportion in 2001, which is 82.38%, but it continues to decline after 2001. Its proportion declines to 40.69%, which is the lowest number. Then, it gradually picks up to 70.7% in 2014. For the mining and processing of non-ferrous metal ores, Cd emissions accounting for the proportion of the whole industry fluctuate frequently in 2001–2010. It rises from 5.86% in 2001 to 39.47% in 2010. Moreover, its proportion of 47% exceeds even the 41.16% of smelting and pressing of non-ferrous metals, but it gradually reduces after 2010 and falls to 13.6% in 2014. Finally, the share of manufacture of raw chemical materials and chemical products remains stable in 2001–2009, with less volatility and less than 7%, but it reaches a maximum of 12% in 2010 and then reduces to 6.8% in 2014.

Table 2

Cd emission proportions of top three industries in national industrial wastewater from 2001 to 2014 (%)

Sector20012002200320042005200620072008200920102011201220132014
Mining and processing of non-ferrous metal ores 5.86 7.70 10.62 16.33 18.64 26.53 47.04 32.76 38.75 39.47 24.59 9.7 12.6 13.6 
Smelting and pressing of non-ferrous metals 82.38 79.49 77 69.56 66.92 62.06 41.16 56.89 48.39 40.69 58.55 76.4 69.5 70.7 
Manufacture of raw chemical materials and chemical products 3.26 2.46 4.81 6.82 6.93 4.53 4.10 4.02 3.04 12.53 9.27 9.3 6.8 
Sector20012002200320042005200620072008200920102011201220132014
Mining and processing of non-ferrous metal ores 5.86 7.70 10.62 16.33 18.64 26.53 47.04 32.76 38.75 39.47 24.59 9.7 12.6 13.6 
Smelting and pressing of non-ferrous metals 82.38 79.49 77 69.56 66.92 62.06 41.16 56.89 48.39 40.69 58.55 76.4 69.5 70.7 
Manufacture of raw chemical materials and chemical products 3.26 2.46 4.81 6.82 6.93 4.53 4.10 4.02 3.04 12.53 9.27 9.3 6.8 

Pb:Table 3 shows that the top three industries of Pb emissions in industrial wastewater from 2001 to 2010 are mining and processing of non-ferrous metal ores, smelting and pressing of non-ferrous and ferrous metals. Of these, Pb emissions of mining and processing of non-ferrous metal ores, smelting and pressing of non-ferrous metals have a large proportion that is more than 70% in total in most years. The proportion of mining and processing of non-ferrous metal ores increases gradually during 2001–2009, reaching a maximum in 2009 at 63.35%, and then it drops to 36.6% in 2014. The proportion of Pb emissions from smelting and pressing of non-ferrous metals is 47.92% in 2001, and after that it declines year by year. In 2007, it drops to 11.03%, and then gradually increases to 42% in 2014. In addition, smelting and pressing of ferrous metals emissions are greater in 2001–2010, but the proportion is less than 20% in most years. Since 2010, the proportion of manufacture of raw chemical materials and chemical products has increased, over 7%, which is more than the proportion of smelting and pressing of ferrous metals. Therefore, the three industries with the highest Pb emissions from 2011 to 2014 are mining and processing of non-ferrous metal ores, smelting and pressing of non-ferrous metals, and manufacture of raw chemical materials and chemical products.

Table 3

Pb emission proportions of top three industries in national industrial wastewater from 2001 to 2014 (%)

Sector20012002200320042005200620072008200920102011201220132014
Mining and processing of non-ferrous metal ores 26.01 25.53 29.51 33.63 32.23 53.78 66.63 59.13 63.35 61.6 44.2 26.8 34.2 36.6 
Smelting and pressing of non-ferrous metals 47.92 44.25 40.53 27.65 36.37 21.51 11.03 17.65 14.44 20.27 36.59 52.9 43.7 42 
Manufacture of raw chemical materials and chemical products           7.38 8.80 11.4 7.3 
Smelting and pressing of ferrous metals 13.43 18.67 18.68 24.91 17.32 11.03 10.73 9.34 8.39 5.96     
Sector20012002200320042005200620072008200920102011201220132014
Mining and processing of non-ferrous metal ores 26.01 25.53 29.51 33.63 32.23 53.78 66.63 59.13 63.35 61.6 44.2 26.8 34.2 36.6 
Smelting and pressing of non-ferrous metals 47.92 44.25 40.53 27.65 36.37 21.51 11.03 17.65 14.44 20.27 36.59 52.9 43.7 42 
Manufacture of raw chemical materials and chemical products           7.38 8.80 11.4 7.3 
Smelting and pressing of ferrous metals 13.43 18.67 18.68 24.91 17.32 11.03 10.73 9.34 8.39 5.96     

As: According to Table 4, we know that mining and processing of non-ferrous metal ores, manufacture of raw chemical materials and chemical products, and smelting and pressing of non-ferrous metals are the top three industries for As emissions in industrial wastewater. In 2001–2014, the total proportion of As emissions in the three industries is more than 90%, but their volatility trends are significant. The proportion of mining and processing of non-ferrous metal ores declines from 17.66% in 2001 to 4.23% in 2005, and then gradually increases in 2007–2009, to more than 40% and it is 32.70% in 2014. Manufacture of raw chemical materials and chemical products rises gradually from 19.97% in 2001, reaching a maximum of 40.96% in 2010. After 2010, due to the Twelfth Five-Year Plan, As emissions in industrial wastewater reduce gradually, and are down to 30.5% in 2014. The As emissions of smelting and pressing of non-ferrous metals are in first place from 2001 to 2006, but begin to decline in 2006, falling to 12.76%, which is the minimum, in 2007. Then the emissions gradually pick up, accounting for 23.5% in 2014.

Table 4

As emission proportions of top three industries in national industrial wastewater from 2001 to 2014 (%)

Sector20012002200320042005200620072008200920102011201220132014
Mining and processing of non-ferrous metal ores 17.66 13.64 19.01 13.87 4.23 10.83 45.57 55.76 58.02 36.33 30.17 24.0 28.2 32.7 
Smelting and pressing of non-ferrous metals 49.13 41.51 37.64 38.21 63.93 49.16 12.76 19.37 18.17 19.91 24.06 28.2 22.7 23.5 
Manufacture of raw chemical materials and chemical products 19.97 33.14 30.93 33.13 22.35 34.24 37.68 22.87 21.79 40.96 37.73 34.5 34.4 30.5 
Sector20012002200320042005200620072008200920102011201220132014
Mining and processing of non-ferrous metal ores 17.66 13.64 19.01 13.87 4.23 10.83 45.57 55.76 58.02 36.33 30.17 24.0 28.2 32.7 
Smelting and pressing of non-ferrous metals 49.13 41.51 37.64 38.21 63.93 49.16 12.76 19.37 18.17 19.91 24.06 28.2 22.7 23.5 
Manufacture of raw chemical materials and chemical products 19.97 33.14 30.93 33.13 22.35 34.24 37.68 22.87 21.79 40.96 37.73 34.5 34.4 30.5 

Cr(VI):Table 5 shows that the distribution of Cr(VI) emissions is more dispersed. Cr(VI) emission in the manufacture of metal products accounted for the proportion of the industry ranked first in 2001–2014, all more than 20%. The share of the manufacture of metal products increases from 24.93% to 63.5% in 2001–2012 and falls to 45.7% in 2014. Smelting and pressing of ferrous metal emissions are greater in 2002–2003, 2006–2012, and 2014, and the proportion overall has a downward trend. It gradually decreases from 14.93% in 2002 to 6.9% in 2014. The manufacture of leather, fur, feather and related product emissions are greater in 2001–2002 and 2004–2010, and the proportion is the largest in 2005, more than 20%, but gradually declines after 2005. Smelting and pressing of non-ferrous metals accounts for a larger proportion in 2003–2005, up to 27.62% in 2004. After that, emissions are very low, which means the effect of reduction is significant. In addition, the discharge in the manufacture of automobiles is a large volume in 2011–2014, more than 13%, so the emissions in the manufacture of automobile should be reduced.

Table 5

Cr(VI) emission proportions of top three industries in national industrial wastewater from 2001 to 2014 (%)

Sector20012002200320042005200620072008200920102011201220132014
Manufacture of metal products 24.93 26.89 29.14 20.98 29.27 31.7 30.51 25.81 36.94 39.71 51.61 63.5 63 45.7 
Smelting and pressing of ferrous metals  14.93 10.75   9.82 9.21 9.51 13.26 7.58 6.54 5.5  6.9 
Manufacture of leather, fur, feather, and related products and footware 13.69 9.68  16.8 20.28 12.55 11.13 8.93 11.33 13.21     
Manufacture of automobile           13.86 14.5 14.3 23.2 
Smelting and pressing of non-ferrous metals   18.58 27.62 11.56        4.6  
Manufacture of raw chemical materials and chemical products 15.26              
Sector20012002200320042005200620072008200920102011201220132014
Manufacture of metal products 24.93 26.89 29.14 20.98 29.27 31.7 30.51 25.81 36.94 39.71 51.61 63.5 63 45.7 
Smelting and pressing of ferrous metals  14.93 10.75   9.82 9.21 9.51 13.26 7.58 6.54 5.5  6.9 
Manufacture of leather, fur, feather, and related products and footware 13.69 9.68  16.8 20.28 12.55 11.13 8.93 11.33 13.21     
Manufacture of automobile           13.86 14.5 14.3 23.2 
Smelting and pressing of non-ferrous metals   18.58 27.62 11.56        4.6  
Manufacture of raw chemical materials and chemical products 15.26              

The analysis of spatial and industrial distribution

According to the spatial distribution, in 2001, the heavy metal pollution is concentrated in Hunan, Liaoning, Gansu, Guangxi, and Yunnan. Additionally, Hg emission is major in Hunan and Liaoning, Cd emission is major in Hunan, Guangxi, Gansu, and Liaoning, Pb emission is major in Hunan, Gansu, Guangxi, Guangdong, Yunnan, and Liaoning, As emission is major in Hunan, Gansu, Yunnan, and Liaoning, and Cr(VI) emission is major in Hunan, Hebei, Sichuan, and Zhejiang, Jiangsu. Until 2014, the prominent pollution areas of industrial wastewater are Hunan, Hubei, Jiangxi, Inner Mongolia, Guangxi, and Gansu. Moreover, Hunan, Gansu, Guangxi, and Jiangxi are major regions of Hg emission, Hunan, Jiangxi, Hubei, Shandong, and Guangxi are major regions of Cd emission, while Hunan, Inner Mongolia, Jiangxi, Hubei, Guangxi, Yunnan, Gansu, and Fujian are major regions of Pb emission. Hunan, Inner Mongolia, and Hubei are major regions of As emission, and Cr(VI) emission is major in Hunan, Hebei, Sichuan, Zhejiang, and Jiangsu. From 2001 to 2014, the five heavy metals in industrial wastewater show a diffused trend. Hg pollution in Hunan and Liaoning has reduced while that in Gansu, Guangxi, Jiangxi, Shanxi, and Inner Mongolia has increased. The Cd and Pb pollution in Guangxi, Gansu, and Liaoning has reduced while the Cd pollution in Hunan, Jiangxi, Hubei, Shandong, and Inner Mongolia has increased, but the Pb pollution in Hunan has remained at the same level while that of Jiangxi, Inner Mongolia, Hubei, and Fujian has got worse. As pollution in Yunnan and Gansu, Liaoning has been reduced while that in Hunan, Hubei, Fujian, and Inner Mongolia has increased. Cr(VI) pollution in Hunan and Sichuan, Chongqing has reduced while it has become aggravated in Hubei and the coastal regions of Guangdong, Fujian, and Hebei. It is worth pointing out that Hunan is always the largest pollution province among all the provinces and the worst heavy metal pollution area from 2001 to 2014 in China. Liaoning is also a severe pollution area for the five heavy metals' pollution in 2001, but due to the promotion of industrial structure, the effect of reduction in Liaoning is significant, and the pollution has reduced dramatically in 2014. Additionally, the pollution of Cr(VI) spreads gradually from the central and western to the eastern coastal regions.

From the industrial distribution pattern of heavy metal pollution, we know that Hg, Cd, Pb, and As emissions are concentrated in the mining and processing of non-ferrous metal ores, smelting and pressing of non-ferrous metals, and manufacture of raw chemical materials and chemical products. The distribution of Cr(VI) pollution is relatively scattered, and mainly concentrated in the manufacture of metal products, the manufacture of leather, fur, feather and related products, smelting and pressing of ferrous metals and the manufacture of automobiles. From 2001 to 2014, the Hg, Cd, Pb, and As emissions in mining and processing of non-ferrous metal ores show an upward trend, but smelting and pressing of non-ferrous metals present a downward trend; moreover, the emissions from smelting and pressing of non-ferrous metals are larger than the mining and processing of non-ferrous metal ores in most years. This shows that Hg, Cd, Pb, and As pollution caused by smelting and pressing of non-ferrous metals is very serious, especially Cd pollution. Reduction effects show that Hg, Cd, Pb, and As pollution of mining and processing of non-ferrous metal ores is worse than smelting and pressing of non-ferrous metals, moreover, the As emission of manufacturing of raw chemical materials and chemical products lags behind the Hg, Cd, and Pb emissions. Cr(VI) pollution in the manufacture of metal products is the most serious, and Cr(VI) emission is increasing. Thus, we should focus on controlling the emission of Cr(VI) in the manufacture of metal products.

Governance input efficiency of heavy metal emissions in Chinese industrial wastewater

From the previous analysis, we find that Chinese heavy metal pollution is widely distributed, and its governance problem has attracted a great deal of attention (Kathuria 2006; Meng 2007; Fu 2012). China has invested much manpower and material resources in controlling heavy metal pollution in industrial wastewater for a long time, but the governance input effect is controversial. This section attempts to study the governance input effect from the perspective of empirical analysis.

The spatial correlation of heavy metal emissions in industrial wastewater

We find that there are obvious trends in regional concentration and spread from the spatial distribution patterns of heavy metals. There may be spatial effects of heavy metal pollution in different regions. It is necessary to explore the distribution features of Chinese heavy metal contained in industrial wastewate. We use Moran's I Index to measure it. The formula for calculating Moran's I Index is as follows:  
formula
where , is the total concentration of five heavy metals in industrial wastewater of province i, n is the number of provinces, is a spatial weight matrix. This article uses the spatial weight matrix of k-nearest neighbor. The number of neighbors is four. The value of Moran's I Index is from −1 to 1. It is positively correlated between 0 and 1. The closer to 1, the closer the relationship in different spatial units. The value of 0 means no spatial correlation among regions. It is negatively correlated between −1 and 0. The closer to −1, the greater the differences in different spatial units.

From the changing trend of Moran's I Index between 2000 and 2014 (Table 6), we know that the values of Moran's I Index are positive in the majority of years; moreover, the significance test shows that there is an obvious spatial autocorrelation of the heavy metal contents in Chinese industrial wastewater. This indicates that the spatial correlation may be an influencing factor of the concentration disparity of heavy metal in Chinese industrial wastewater.

Table 6

Spatial autocorrelation test in Chinese provinces from 2000 to 2014

YearMoran's IP-value
2001 −0.043 0.461 
2002 0.188** 0.028 
2003 0.173** 0.035 
2004 0.198** 0.022 
2005 0.034** 0.033 
2006 0.192** 0.019 
2007 0.190** 0.029 
2008 0.172** 0.018 
2009 0.139** 0.030 
2010 −0.007 0.227 
2011 0.104* 0.093 
2012 0.124* 0.067 
2013 0.099* 0.087 
2014 0.099* 0.096 
YearMoran's IP-value
2001 −0.043 0.461 
2002 0.188** 0.028 
2003 0.173** 0.035 
2004 0.198** 0.022 
2005 0.034** 0.033 
2006 0.192** 0.019 
2007 0.190** 0.029 
2008 0.172** 0.018 
2009 0.139** 0.030 
2010 −0.007 0.227 
2011 0.104* 0.093 
2012 0.124* 0.067 
2013 0.099* 0.087 
2014 0.099* 0.096 

** and * stand for significance at 5% and 10% levels, respectively.

The spatial econometric models for the governance input effect of heavy metal pollution in industrial wastewater

The above analysis shows that the concentrations of heavy metals in industrial wastewater have slow or steep downward trends from 2001 to 2014, which indicates that the Chinese environmental protection departments attach importance to the treatment of heavy metal pollution in industrial wastewater. However, pollution events of heavy metal have frequently happened in China, and the governance input effects of heavy metals have caused wide discussion in society. In the following section, we employ the spatial econometric model to further explore the governance input effects of heavy metal pollution in industrial wastewater.

  1. Ordinary panel model:  
    formula
    where i denotes the province, t denotes the year (the same applies below). is the total concentrations of five heavy metals in industrial wastewater. is the column vector of the number, annual expenditure, and capacity of industrial wastewater treatment facilities. is the parameter to be estimated. is the random disturbance term. From the above analysis, we know that there is a certain spatial correlation among provinces in China, so we should introduce the space-related information into ordinary panel model to obtain more reliable estimated results. Since the spatial effects may exist in the lagged and error terms, we use an exploratory spatial autocorrelation model for the analysis.
  2. Spatial panel autocorrelation model:  
    formula
    where is the coefficient in the regression residuals, w is the spatial weight matrix of , is the spatial autoregressive parameter, is the error term, is random disturbance term. Other parameters are the same as above. The spatial autocorrelation model includes not only the spatial lag factor of the dependent variable, but also the spatial disturbance and population correlations. If is 0, it becomes the spatial lag model. If is 0, it is the spatial error model.

Table 7 presents the regression results of the ordinary panel model, the spatial panel lag, and error models. In this paper, the panel data are examined by Hausman test before the spatial panel models are used to estimate the five heavy metal contents of industrial wastewater in 29 provinces from 2001 to 2014. The Hausman test values of ordinary panel model, spatial panel lag, and error models are 31.81, 27.41, and 8.68, respectively, and all of them are significant at 5% level. Therefore, we should reject the null hypothesis of random effects model and choose the fixed effects model. As shown in Table 7, the ordinary panel fixed effects model shows that the regression coefficient of annual expenditure of industrial wastewater treatment facilities is significant at 1% level, but the estimated values of the number and capacity of industrial wastewater treatment facilities are insignificant at 10% level. Moreover, the LM test value is 136.03 and it is significant at 5% level, which indicates that there is spatial correlation among the provincial heavy metal emissions in industrial wastewater. Thus, we need to introduce the spatial correlation into the model. When the spatial correlation is introduced into the ordinary panel fixed effects model, the values of and are significant at 1% level, which illustrates that the spatial models are more suitable than the traditional model. In addition, the results of the spatial dependent test show that the LM tests for the spatial error and lag models all pass the 1% significance test, and the robustness tests of them also pass 1% significance test, which indicates that the spatial error and lag models are suitable for analyzing the governance input effects of heavy metal pollution in Chinese industrial wastewater. In view of this, we use the spatial panel autocorrelation fixed effects model to further analyze the governance input effects of heavy metal pollution in industrial wastewater.

Table 7

Estimated results of regression model parameters for the concentrations of heavy metals in Chinese industrial wastewater from 2000 to 2014

Estimation methodOrdinary panel model
Spatial panel lag model
Spatial panel error model
Fixed effectsRandom effectsFixed effectsRandom effectsFixed effectsRandom effects
Intercept  4.42 × 10−3  1.71 × 10−3  7.39 × 10−3 
Fac 1.92 × 10−6 −9.55 × 10−7 2.45 × 10−6 7.16 × 10−7 3.08 × 10−6** 2.04 × 10−6** 
Cap −9.72 × 10−7 −1.17 × 10−6 −2.190 × 10−6 −2.070 × 10−6 −2.03 × 10−6 −1.94 × 10−6 
Fee 4.77 × 10−8*** 4.08 × 10−8*** 1.43 × 10−8 1.07 × 10−8 −2.36 × 10−8 −2.82 × 10−8*** 
   0.47*** 0.48***   
     0.56*** 0.56*** 
Adjusted  0.02 0.04     
12.98*** 6.03***     
LR Test   151.31*** 67.45*** 144.42*** 75.61*** 
Hausman 31.81***  27.41***  8.68***  
LM Test 136.03***  129.12***  102.81***  
R-LM Test   34.61***  8.31***  
Estimation methodOrdinary panel model
Spatial panel lag model
Spatial panel error model
Fixed effectsRandom effectsFixed effectsRandom effectsFixed effectsRandom effects
Intercept  4.42 × 10−3  1.71 × 10−3  7.39 × 10−3 
Fac 1.92 × 10−6 −9.55 × 10−7 2.45 × 10−6 7.16 × 10−7 3.08 × 10−6** 2.04 × 10−6** 
Cap −9.72 × 10−7 −1.17 × 10−6 −2.190 × 10−6 −2.070 × 10−6 −2.03 × 10−6 −1.94 × 10−6 
Fee 4.77 × 10−8*** 4.08 × 10−8*** 1.43 × 10−8 1.07 × 10−8 −2.36 × 10−8 −2.82 × 10−8*** 
   0.47*** 0.48***   
     0.56*** 0.56*** 
Adjusted  0.02 0.04     
12.98*** 6.03***     
LR Test   151.31*** 67.45*** 144.42*** 75.61*** 
Hausman 31.81***  27.41***  8.68***  
LM Test 136.03***  129.12***  102.81***  
R-LM Test   34.61***  8.31***  

Note: Fac is the number of industrial wastewater treatment facilities (set). Cap is the capacity of industrial wastewater treatment facilities (10,000 tons/day). Fee is the annual expenditure of industrial wastewater treatment facilities (10,000 yuan). Anselin & Florax (1995) proposed the following discriminated rules, since we could not know in advance the spatial lag model (SLM) and the spatial error model (SEM) which conformed to reality according to prior experience. If the LM-lag is statistically more significant than the LM-error, and at the same time the Robust LM-lag is significant, but the Robust LM-error is insignificant, the spatial lag model should be used. Conversely, if the LM-error is more statistically significant than the LM-lag, the Robust LM-error is significant, and the Robust LM-lag is insignificant, the spatial error model should be adopted.

*** and ** stand for significance at 1% and 5% levels, respectively.

According to Table 8, the estimated values of capacity and annual expenditure of industrial wastewater treatment facilities are −2.81 × 10−6 and −3.19 × 10−8, respectively, and they are significant at 10% level. The coefficient of the number of industrial wastewater treatment facilities is 2.98 × 10−6, and it is significant at 1% level. It indicates that the more powerful the capacity and costs of annual expenditure of industrial wastewater treatment facilities, the lower the total concentrations of five kinds of heavy metals in industrial wastewater. However, we cannot blindly increase the number of industrial wastewater treatment facilities.

Table 8

Estimated results of the spatial panel autocorrelation model for the concentrations of heavy metals in Chinese industrial wastewater from 2000 to 2014

ModelSpatial panel autocorrelation model
Fac 2.98 × 10−6*** 
Cap −2.81 × 10−6
Fee −3.19 × 10−8*** 
 0.84*** 
 −0.79*** 
ModelSpatial panel autocorrelation model
Fac 2.98 × 10−6*** 
Cap −2.81 × 10−6
Fee −3.19 × 10−8*** 
 0.84*** 
 −0.79*** 

*** and * stand for significance at 1% and 10% levels, respectively.

CONCLUSION

This paper discussed the changing trend, distribution pattern, and governance input efficiency of heavy metal concentrations in Chinese industrial wastewater, based on the contents of five key heavy metals, annual expenditure, and capacity of industrial wastewater treatment facilities from 2001 to 2014. The main conclusions are as follows:

  1. From the changing trend of concentrations of heavy metals in Chinese industrial wastewater from 2001 to 2014, we know that the five kinds of heavy metal concentrations show a slow and significant decline trend in total.

  2. Heavy metal emissions show a diffused trend from the perspective of spatial distribution, and different kinds of heavy metals exhibit different distribution patterns from 2001 to 2014.

  3. From the view of industrial distribution pattern, different kinds of heavy metal emissions display concentrated and diffused characteristics.

  4. The test results of Moran's I Index from 2001 to 2014 indicate that there is a spatial correlation of concentrations of heavy metals in Chinese industrial wastewater. Thus, we introduced the spatial factors into the model, by Hausman and LM test, and finally used the spatial panel autocorrelation model to analyze. We find that improving the capacity and increasing the annual expenditure are more effective than adding to the amount of treatment equipment for controlling heavy metal pollution in industrial wastewater.

In addition, we tentatively highlight further implications for policy-makers and researchers. (1) In the treatment of five kinds of heavy metal pollution in industrial wastewater, we should focus on the serious areas and industries of heavy metal pollution, such as the central provinces including Hunan, Hubei, and Jiangxi, and the southern provinces, Guangxi, Guangdong, the northern provinces, Gansu, Inner Mongolia where the Hg, Cd, Pb, As emissions are mainly concentrated, especially in Hunan province, also, the eastern coastal areas of Zhejiang, Jiangsu and other provinces with Cr(VI) emissions. At the same time, controlling the key industries of heavy metal emissions will be helpful, including mining and processing of non-ferrous metal ores, smelting and pressing of ferrous or non-ferrous metals, manufacture of metal products, manufacture of leather, fur, feather and related products and footware. (2) Based on the spatial and industrial distribution of heavy metals, we find that pollution in the central provinces and the north provinces are closely connected with local resources-dependent industries such as mining, smelting and processing ferrous or non-ferrous metal ores. The pollution in the eastern coastal provinces is highly related to manufacturing industries such as metal products, transport or electrical machinery, communication equipment, automobiles, etc. (3) We should focus on enhancing the capacity and increasing the annual expenditure of treatment facilities so as to reduce the heavy metal concentrations in Chinese industrial wastewater.

ACKNOWLEDGEMENTS

This study was funded by the National Natural Science Foundation of China (71672192) and the Humanities and Social Sciences Foundation of Ministry of Education of China (17YJCZH081).

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