Occurrence and environmental impact of industrial agglomeration on regional soil heavy metalloid accumulation: A case study of the Zhengzhou Economic and Technological Development Zone (ZETZ), China

Journal Pre-proof Occurrence and environmental impact of industrial agglomeration on regional soil heavy metalloid accumulation: a case study of the Zhengzhou Economic and Technological Development Zone (ZETZ), China

Yinan Chen, Jianhua Ma, Changhong Miao, Xinling Ruan PII:

S0959-6526(19)33546-2

DOI:

https://doi.org/10.1016/j.jclepro.2019.118676

Reference:

JCLP 118676

To appear in:

Journal of Cleaner Production

Received Date:

06 June 2019

Accepted Date:

30 September 2019

Please cite this article as: Yinan Chen, Jianhua Ma, Changhong Miao, Xinling Ruan, Occurrence and environmental impact of industrial agglomeration on regional soil heavy metalloid accumulation: a case study of the Zhengzhou Economic and Technological Development Zone (ZETZ), China, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.118676

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Journal Pre-proof •

Occurrence

and

environmental

impact

of

industrial

agglomeration on regional soil heavy metalloid accumulation: a case study of the Zhengzhou Economic and Technological Development Zone (ZETZ), China •

Yinan Chena, Jianhua Maa,b, Changhong Miaoa, Xinling Ruanb

•

E-mail address: [email protected]

•

Corresponding author: Jianhua Maa,b, Changhong Miaoa

•

a Key Research Institute of Yellow River Civilization and Sustainable Development,

Henan University, Kaifeng 475001, China

•

b

The College of Environment and Planning of Henan University, Kaifeng 475001, China

Journal Pre-proof Occurrence and environmental impact of industrial agglomeration on regional soil heavy metalloid accumulation: a case study of the Zhengzhou Economic and Technological Development Zone (ZETZ), China Highlights •

Spatial distribution patterns of heavy metalloid (HM) concentration and contamination in soils in an industrial agglomeration area were identified.

•

The relationships between industrial output value and soil HM accumulation were quantified based on the toxic equivalent mass of soil HM accumulation per capital output.

•

Comparative analysis of the relationship between industrial production and HM accumulation in soils in an industrial agglomeration area and non-agglomeration areas was performed.

Abstract To clarify whether industrial agglomeration affects soil heavy metalloid (HM) accumulation and pollution and whether industrial agglomeration results in less soil HM pollution than in areas without industrial agglomeration, an investigation of the HM concentration and pollution in soils in an industrial agglomeration area, the national Zhengzhou Economic and Technological Development Zone (ZETZ), was conducted. Determinations of the Hg, Pb, Cr, As, Zn, Cd, Ni, Co, Cu and Mn concentrations in soils were performed using inductively coupled plasma-mass spectrometry, inductively coupled plasma-atomic emission spectrometry and atomic fluorescence spectrometry (ICP-MS, ICP-AES). The contamination factor (CF) of different elements, and the comprehensive pollution load index (PLI) of multiple HMs were calculated. Furthermore, to quantify the relationships between industrial output and soil HM accumulation, the concept of the toxic equivalent mass of soil HM accumulation per capital output (TEM) is proposed. The key results are as follows: (1) The average concentrations for all the studied HMs were higher than the local background concentration (BC) values. The distribution of the soil HM concentrations showed significant spatial heterogeneity, and the regions with the highest soil HM concentrations were generally affected by local industrial activities. (2) The pollution load index (PLI) values indicate that the extent of the human impacts on the soil environment in the study area correspond

Journal Pre-proof to the slight pollution level as a whole. (3) The PLI values, the total accumulated mass of soil HMs over time per million industrial capital output (PMt), and the TEM values for the ZETZ, Kaifeng Carbon Factory (KCF), and Wanyang Lead Smelter (WLS) were subjected to comparative analyses, with the KCF and WLS showing markedly higher levels than the ZETZ. Accordingly, although slight HM accumulation occurred in the soil of the ZETZ, the industry-related accumulation of soil HMs in the ZETZ is significantly less than that in the KCF and WLS; thus, industrial agglomeration may be effective for reducing the intensity of HM accumulation. Keywords: Industrial agglomeration, soil heavy metalloid, accumulation, Zhengzhou economic and technological development zone (ZETZ) 1. Introduction The relationship between economic growth and environmental pollution in industrial agglomeration areas has long been an important and controversial issue (Cerkan G, 2009; Jiang et al., 2017; Xie et al., 2018;). In new economic geography theory, industrial agglomeration is an important way to achieve a win-win balance between economic development and environmental protection (Gao et al., 2011; Choy et al., 2013; Cheng et al., 2016). A large number of studies have concluded that industrial agglomeration can help to reduce environmental pollution (Yang et al., 2015; Hu et al., 2016; Chen et al., 2018; Liang et al., 2019; Zhou et al., 2019). Hence, facing the dual pressure of demands for economic development and environmental protection, the government of China has actively adopted various policies to vigorously promote the development and construction of industrial agglomeration areas (Huang et al., 2017; Wang et al., 2018a; Cai et al., 2019; Yang et al., 2019). For example, Henan Province strived to promote the construction of industrial agglomeration areas starting in 2008, and after a decade, 182 industrial agglomeration areas have been developed, with a total established area of more than 2100 km2 (HNDRC, 2017). The inward flow of capital from other provinces to Henan Province amounted to RMB 30 billion in 2003 and exceeded RMB 100 billion by 2008. From January to November 2014, a total of RMB 230.06 billion was transferred from other provinces to the industrial and information fields in Henan Province (NBSPRC, 2015; HNPBS, 2015; Wang, 2015). Currently, industrial

Journal Pre-proof agglomeration has gradually become an important means of coordinated regional economic development and plays a crucial role in new types of urbanization (He et al., 2014; Cheng et al., 2017; Li et al., 2017; Zhang et al., 2019; Song et al., 2019). However, contrary to the theoretical prediction, increasingly objective analyses indicate that regional industrialization is often accompanied by environmental pollution (Wang et al., 2016; Jing et al., 2017; Wu et al., 2018; Lei et al., 2018; Ding et al., 2019). Thus, to understand the gap between the above mentioned industrial agglomeration theory and reality, the question of whether industrial agglomeration is a driving force or hindrance to environmental governance urgently needs to be answered (Wang et al., 2016; Jing et al., 2017; Wu et al., 2018; Lei et al., 2018; Ding et al., 2019). In recent years, the literature on the relationship between industrial agglomeration and environmental quality has become fairly extensive, but conflicting findings have been reported. In general, the research results can be categorized into the following three points. First, some studies show that agglomeration aggravates environmental pollution due to increasing industrial production and pollutant emissions (Virkanen, 1998; Frank, 2001; Verhoef and Nijkamp, 2002; Ren et al., 2003; Duc et al., 2007). However, other studies concluded that industrial agglomeration can promote the improvement of labor productivity and pollutant treatment by increasing returns via scale effects (Krugman, 1998; Lu and Feng, 2014), knowledge/technology spillover and competition (Chen and Hu, 2008; Dong et al., 2012; Baomin et al., 2012), concentration of steps in the same industrial chain and industrial symbiosis (Ehrenfeld., 2003; Cheng, 2016), all of which subsequently help to promote regional economic growth (Lu and Feng, 2014). Moreover, some views argue that industrial agglomeration can thus have important effects on regional environmental pollution, but the effects are uncertain (Zeng and Zhao, 2009; Ni et al., 2011; He and Wang, 2012; Canfei et al., 2014; Li, 2014). In general, theoretical research on agglomeration's effects on environmental pollution is still lacking, and empirical studies have not drawn consistent conclusions. The existing literature suggests that the relationship between industrial agglomeration and environmental pollution still features the following shortcomings. First, when studying the effect of agglomeration on environmental pollution, two different notions, i.e., the amount of pollutant emissions and the actual pollution level of the environment, are not well distinguished. Most of the existing literature focuses on the impact of industrial agglomeration on air or water pollution; in particular, pollutant

Journal Pre-proof emission amounts are generally used as a measure of the environmental pollution (Hu et al., 2016; Wang et al., 2018b; Li et al., 2019). The reason is that both water quality data and air pollution data, especially pollutant emission data, can be obtained directly from existing statistical yearbooks or indirectly calculated from different types of material and energy consumption information. In fact, the discharge of pollutants and environmental pollution are different. As evidenced by a review of current research, few studies examine the effect of industrial agglomeration on soil heavy metalloid (HM) concentrations, largely because soil HM concentration data cannot be obtained indirectly. Such data need to be collected through field sampling and chemical analysis in the laboratory. Furthermore, existing research tends to explore the relationship between agglomeration and pollution according to cut-in points of marketization, labor productivity and direct foreign investment based on statistical data by adopting dynamic panels or Tobit models (Tone and Tsutsui, 2010; Otsuka et al., 2010; Cerina and Mureddu, 2014; Liu J, 2017; Chen D. K, 2018) but ignores the influence of industrial transfer behavior on the regional environment in the immigration locations and the positive economic benefits brought by industrial agglomeration in the region. Third, macroscopic scales are mostly used in studies of industrial pollution and are mainly based on data at the provincial level (Ding et al., 2009; Li and Zhang, 2011; Liu et al., 2014; Zheng et al., 2014), urban level (Gao et al., 2011; Cheng, 2016), and industrial agglomeration level (Zhao et al., 2014; Wang et al., 2014). Although the above perspectives can assess the relationship between industrial agglomeration and pollution at the overall scale, the estimated results often have a low degree of accuracy, and environmental pollution in industrial agglomeration areas has long been a black box issue. For example, most of the results in the above studies indicate that industrial agglomeration can ease environmental pollution in a region, but the intensification of localized pollution in the agglomeration region may be neglected. Therefore, examination of the specific impact of industrial activities on the regional environment at the micro-level is urgently needed. Soil HM contamination has been recognized as a major environmental concern related to the process of industrialization (Li, 2018). Excessive HMs in soil, which can affect human health, are durable and difficult to remove due to their long storage and residence times (Luo et al., 2012; Yang et al., 2014). With the rapid rise of Chinese industrialization, soil HM pollution has become an increasingly severe environmental problem. More than 30,000 tons of Cr and 800,000 tons of

Journal Pre-proof Pb have reportedly been released into the environment globally in the past 5 decades, most of which have accumulated in soil, resulting in serious HM pollution (Chen et al., 2016a). According to the national soil contamination survey in China between 2005 and 2013 (China's Ministry of Environmental Protection, 2014), the situation of soil HM pollution is very serious. Soils in the country exceed the acceptable standards for HMs (the Grade II environmental quality standard for soils in China (GB15618-1995)) by 16.1%. Generally, HM pollution in urban soils is serious, and especially high contents of soil HMs generally occur in many well-developed industrial areas. Thus, industrial development should be considered in terms of both economic growth and the relevant negative soil environmental impacts. In this study, we measured the contents of nine HMs in surface soil samples collected from the Zhengzhou Economic and Technologic Development Zone (ZETZ). ZETZ is the state-level Economic and Technological Development Zone, which is approved by the State Council and occupies the highest position of the existing Economic and Technological Development Zones in China. By attracting foreign capital, introducing advanced manufacturing industries and combining with the domestic industrial base, modern industrial parks have been gradually formed. This study aims to address the following questions: Does industrial agglomeration lead to regional soil HM accumulation? What is the relationship between industrial production and soil HM pollution? Does industrial agglomeration promote or alleviate regional soil HM accumulation? The paper is organized as follows. First, the accumulation and pollution characteristics of HMs in soils of the ZETZ were comprehensively analyzed. Then, the accumulation of HMs in soil from different types of industrial production areas is quantitatively investigated using the new concept of equivalent mass, which is proposed in order to compare the regional total accumulated amount of soil HMs per unit of output in different areas. Finally, comparative analysis of the soil HM pollution degree, the total accumulated mass of soil HMs over time per million industrial capital output (PMt) and the toxic equivalent mass of soil HM accumulation per capital output (TEM) values for an industrial agglomeration area (ZETZ) and non-agglomerated area was performed. The relevant conclusions are then summarized.

Journal Pre-proof 2. Materials and methods 2.1. Sample collection and analysis 2.1.1. Research area The ZETZ was established in 1993 and is located in the southeastern suburbs of Zhengzhou, Henan Province, China, with a planned area of 137 km2 (HNDRC, 2018). The local soil is mainly composed of Yellow River alluvial deposits (Niu and Kang, 2010). This comprehensive industrial zone has four main industries, namely, automobile production and parts manufacturing, equipment manufacturing, electronic information industries and modern logistics (MC-ZETZ, 2019).

Fig. 1 Location of the study area and distribution of sampling points

2.1.2. Soil sampling Based on the digital orthophoto map of the ZETZ with a scale of 1:2000 in 2013, surface soil samples were collected from densely distributed sites in a 200×200 m grid. Representativeness is the main principle of sample collection, and soils with relatively long accumulation histories were selected to avoid obvious point pollution areas, garbage dumps, newly accumulated soil, ridges and so on. A total of 446 surface soil samples (0–20 cm in depth) in the study area were collected in August 2015 (Fig. 1). In this study, the natural background level of soil HMs in this regional scale study was assessed with 30 control soil samples, which were collected in undeveloped regions 3 km to the east of the study area. Each sample is composed of 5 subsamples collected

Journal Pre-proof from the topsoil layer (0–20 cm) within the corresponding grid. Approximately 1.0 kg of soil was taken from each site, and the grass, leaves, and roots in the samples were discarded after gentle shaking. 2.1.3. Sample analysis and quality control The soil samples were air dried, manually ground with an agate mortar and pestle, passed through a 100 mesh (0.149 mm) nylon sieve and stored in closed polyethylene bags for chemical analysis. For the determination of Cd, Co, Cr, Cu, Mn, Ni, Pb and Zn contents, the soil samples were digested in a HCl-HNO3-HF-HClO4 solution (Wang et al., 2018). The concentrations of Co, Cr, Cu, Ni, Pb, Zn and Mn were determined using inductively coupled plasma atomic emission spectroscopy (ICP-AES, Shimadzu, Japan), and the concentrations of Cd and Pb were determined using inductively coupled plasma mass spectrometry (ICP-MS, Thermo Fisher Scientific, America). For determination of As and Hg, the samples were digested in a HSO4-HNO3-KMnO4 solution and were then analyzed with an atomic fluorescence spectrometer (Lu, 2000). Standard reference materials (GSS-2, GSS-8 and GSS-13) from the Center of National Standard Reference Material of China were analyzed as part of quality assurance and quality control (QA/QC) procedures. The recovery rates for all the HMs were between 90% and 110%, and the relative percent difference (RPD) for each pair of duplicates revealed RPD values of less than 20% in all cases. 2.2. Model building 2.2.1. Contamination assessment methods The pollution load index (PLI) was calculated to assess the contamination level of HMs in the surface soil in this study. The PLI was introduced by Tomlinson (1980) and is commonly used to quantitatively evaluate soil HM pollution levels. The PLI is calculated with the following formula: CFi = Ci/Coi PLI = n CF1 × CF2 × ⋯CFn

(1) (2)

where CFi is the contamination factor for metal i, Ci is the HM concentration in the sample, Coi is the background concentration (BC) of metal i, and PLI reflects the integrated pollution of multiple HMs in a sample. We adopted a pollution level subdivision scheme according to the situation

Journal Pre-proof because the pollution levels of Tomlinson are not detailed enough, and the classification standards in this study are as follows: PLI (CF)≤1, unpolluted; 13, heavy pollution (Chen et al., 2014; Li et al., 2015; Liu et al., 2016). 2.2.2. Soil HM accumulated mass Based on the calculation of soil carbon density (Dahms and Egli, 2016), this study explored the following accurate valuation method for the accumulated mass of exogenous HM input into regional soils (from anthropogenic sources): Mi = S ∙ D ∙ ρ ∙ (Ci - Cbi) ∙ 10

-3

(3)

where Mi is the accumulated mass (kg) of metal i in the soil of a region, S is the area (m2), D is the thickness of the topsoil (0.2 m), ρ is the bulk density of the regional soil (g·cm−3), Ci is the average concentration (mg/kg) of metal i in all soil samples collected from the region, Cbi is the background value of metal i, and 10−3 is the conversion factor (unitless). In this study, the soil bulk densities ranged from 1.13 to 1.76 g/cm3, with an average value of 1.67 g/cm3. Due to the spatial heterogeneity of the soil HM content distribution, the target area should be divided into several parts based on the pollution assessment results of PLI. The accumulated mass of the exogenous metal i in the topsoil sum over n parts (region) of the study area is as follows: m

∑M

Mti =

(4)

j

j=1

where Mti is the total accumulated mass of element i in the topsoil of the entire study area in kg, Mj is the accumulated mass of subregion j, and m is the number of subregions in the study area. 2.2.3. HM accumulated mass per unit of economic output The HM accumulated mass in the topsoil of the whole study area per unit of economic output (RMB yuan) was estimated using Eqs. (5) and (6): PMti =

Mti

(5)

TEO n

𝑇𝐸𝑂 =

∑EO

k

(6)

k=1

where PMti is the total accumulated mass per unit of economic output for metal i with the unit of kg·10−6 yuan (in 1990 prices), TEO is the total economic output from the start of the operation of

Journal Pre-proof the research object to the soil sampling, which is transformed into the comparable price in 1990 with the unit of 10−6 yuan; EOk is the economic output of year k; and n is the number of years from the factory operation time to the sampling time. 2.2.4. Total toxic equivalent mass of HMs accumulated per unit of economic output Due to the different toxicity coefficients of various HMs, it is obviously unscientific and inappropriate to perform comparative analysis with a simple summation of the amount of accumulated soil HMs. Therefore, a new concept, equivalent mass, is proposed in order to make the regional total accumulated amount of soil HMs per unit of output comparable. This concept is based on the toxicity coefficient (T) of the HMs (Zn = Mn = 1, Co = Cu = Pb = Ni = 5, As = 10, Hg = 40, Cr = 2 and Cd = 30) (Xv et al., 2008). The relative toxicity of the mass of total HMs, called the total equivalent mass (TEM), in a regional soil was calculated as follows: n

𝑇𝐸𝑀 =

∑EM

i

(7)

i=1

EMi = Ti × PMti

(8)

where EMi is the relative equivalent mass of metal i accumulated in topsoil per unit of economic output in units of kg·10−6 yuan, Ti is the biological toxicity factor of an individual metal, and n is the number of HMs. 3. Results 3.1. Concentrations of HMs in the soil in the ZETZ The descriptive statistical results are shown in Table 1 and Fig. 2. The mean concentrations for Mn, Zn, Cr, Pb, Ni, Cu, Co, As and Cd in the soil samples were 375.7, 46.08, 42.03, 23.34, 15.86, 14.80, 9.91, 5.82 and 0.33 mg/kg, respectively, and the mean concentration for Hg was 61.74 μg/kg. In Fig. 2, outliers can be seen in the box diagrams of all the studied HMs, especially As, Cd, Hg and Pb, thus indicating that most of the soil HMs have been more or less influenced by local human activities. Moreover, except for those of Co, Mn and Ni, the lengths of the upper and lower whiskers of the boxplots are unequal for most HM concentrations; in particular, the mean and median values of Hg and Cr differ greatly. These differences may be attributed to local human

Journal Pre-proof activities that have made large contributions to the concentration of Hg and Cr. The concentrations of the studied HMs in the surface soil samples collected from the control area, which were used to determine the local soil BC values, are also presented in Table 1. The mean concentrations of all HMs exceeded the corresponding soil BC levels. The concentrations of Hg, Pb, Cr, As, Zn, Cd, Ni, Co, Cu and Mn in the soils in the ZETZ were 1.62, 1.44, 1.40, 1.36, 1.33, 1.27, 1.24, 1.22, 1.21 and 1.20 times higher than BCs, respectively. Because the area of the ZETZ is small, the variations in soil HM concentrations should be relatively small. However, the coefficients of variation (CV) were 150%, 60%, 60%, 54%, 43%, 41%, 34%, 31%, 30% and 25% for Hg, Pb, Cu, Cd, As, Zn, Cr, Ni, Co and Mn, respectively. Hg recorded exceptionally high variation and wide concentration ranges, and Cu, Pb and Cd were also highly variable. Co, Mn, Ni, Cr, Zn and As showed moderate variation. Therefore, the spatial distributions of the HMs in the soil of the studied area are heterogeneous, which further indicates the existence of anthropogenic sources. Hg is the most heterogeneous element and is likely scattered in different parts of the studied area, such as the location of factories associated with the burning of fossil fuels, base-metal processing and some chemical industrial activities (Lis and Pasieczna, 1995; Tang Z et al., 2016). The Cu, Pb and Cd contents of the topsoil are known to increase due to pollution from various sources, including several types of industrial emissions (sewage waters, gasoline vehicles, iron–steel industries or agricultural and municipal wastes and industrial emissions) and domestic sludge (Norrish, 1975; Lis and Pasieczna, 1995; Kabata-Pendias, 2000). Table 1 Statistics of the HM concentrations in topsoil in the ZETZ (n=446) HMs

As

Cd

Co

Cr

Cu

Hg

Mn

Ni

Pb

Zn

Mean

5.82

0.33

9.91

42.03

14.80

61.74

375.7

15.86

23.34

46.08

Median

5.36

0.29

9.50

39.27

12.23

29.79

355.4

14.95

20.32

42.23

Min

0.017

0.017

4.86

16.00

1.40

2.47

204.8

6.65

10.08

16.35

Max

29.17

5.25

23.22

116.8

81.61

1110

688.2

41.69

235.9

163.8

SD

2.48

0.29

2.95

14.27

9.71

102.2

88.8

4.89

13.90

18.95

CV /%

42.65

54.21

29.8

33.97

59.63

150.5

24.76

30.80

59.58

41.13

4.29

0.26

BCs

8.12

30.06

12.18

38.00

311.7

12.77

16.24

34.70

Notes: The units of most HM concentrations are mg/kg, except Hg (μg/kg); SD: standard deviation; CV: coefficient of variation; BCs: Background concentrations.

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Fig. 2 Box-and-whisker plots of the HM concentrations in the ZETZ

3.2. Pollution assessment of soil HMs To further characterize HM pollution in soil samples, we calculated the CF and PLI values. The mean CF values of the HMs followed the order Pb > Cr > As > Ni > Mn > Co > Zn > Cd > Cu > Hg and ranged from 1.21 to 1.41 (Table 2), suggesting that the contamination levels for all the HMs corresponded to the slight pollution level. The proportions of soil samples with slight pollution levels for Pb, Cr, As, Ni, Mn, Co, Zn, Cd, Cu and Hg accounted for 83.9%, 74.7%, 70.0%, 69.8%, 68.6%, 68.2%, 62.1%, 50.9%, 37.2% and 18.6%, respectively. The proportions of soil samples with moderate to heavy pollution levels for Hg, Cu, Zn, Pb, As, Cd, Cr, Co, Ni and Mn were 18.40%, 13.20%, 10.30%, 9.86%, 9.64%, 9.20%, 8.52%, 4.93%, 4.49% and 3.36%, respectively. Pb was the HM present at the highest concentrations, which were generally associated with various local human activities, and many soil samples also featured high Hg concentrations due to local industrial activities. PLI has generally been considered a metric that quantitatively reflects comprehensive environmental pollution due to contamination. The PLI values for all the soil samples varied from 0.44 to 3.04, with an average of 1.20. These values are indicative of only slight pollution by the HMs under investigation. The percentages of soil samples with no HM pollution, slight HM pollution, moderate HM pollution and high HM pollution were 28.0%, 67.7%, 3.81% and 0.45%, respectively. Table 2 Statistics of CF and PLI values for the soil HMs in the study area (n=446) CF As

Cd

Co

Cr

Cu

Hg

Mn

Ni

Pb

Zn

PLI

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Min

0.01 0.06

0.60

0.53

0.12

0.07

0.60

0.52

0.62

0.47

0.44

Max

6.82 20.23

2.86

3.89

6.70

22.67

3.06

3.26

14.52 4.72

3.04

Mean

1.36 1.25

1.35

1.39

1.22

1.21

1.21

1.24

1.44

1.33

1.20

SD

0.58 0.85

0.40

0.47

0.80

2.00

0.37

0.38

0.86

0.55

0.37

55.34 41.41

31.11

CV/% 42.55 140.02 30.77 34.46 64.00

165.42

30.47 31.29

3.3. Spatial distribution of soil HM pollution The spatial distribution of HM concentrations is useful for identifying regions with high HM concentrations and for assessing possible sources of pollution. The semivariogram calculation was applied, and the experimental semivariogram of the soil HM concentrations could be fitted with an exponential model for PLI. The nugget value (C0), sill (C0+C), ratio of nugget to sill (C0+C), coefficients of determination (R2) and residual sum of squares (RSS) were 0.083, 0.175, 0.526, 0.907, and 4.88E-04. The values of R were significant at the 0.01 level, as determined by the F test, which showed that the semivariogram models reflect the spatial structural characteristics of the soil HMs. The estimated map of PLI for the studied HMs is presented in Fig. 3. The spatial difference of the soil HMs in the study area can be intuitively observed. There are four major high-value regions: northeast (NE), northwest (NW), middle-west (MW), and southwest (SW); additionally, there are two low-value regions: southeast (SE) and center (CR). The mean PLI values of the soil HMs in the six regions followed the order NE > NW > MW > SW > SE > CR. CR is the largest low-value region and is located in the north-central part of the study area. The other low-value region, SE, is sandwiched between the two high-value regions of MW and SW. The mean CF values of most elements in these six regions were in the range of 1 and 2 (slight pollution), except for Hg and Cu. The CF value of Hg was higher than 2 in MW and SE (moderate pollution) and lower than 1 in CR (no pollution). The CF value of Cu was lower than 1 in SE and CR. Generally, the pollution level of the soil HMs decreased from northwest to southeast due to the intensity of human activities. The area closer to the city center (NW) showed a higher PLI. In addition, the PLI values of the soil HMs around the factories were higher than those in the other land use regions. The contaminative status was comparatively serious in the regions with densely distributed factories. Relative to these higher pollution level regions, the region farther away from the factories, or with lower densities of factories, exhibited soils with lower HM pollution. The

Journal Pre-proof pollution level associated with the soil HM concentrations in NE was the highest based on field investigation and analysis, as well as according to regional yearbook records. The drivers of this HM contamination include the large number of pollution-generating factories in this area in the past. Although these factories have since been relocated, the soil HM pollution is still more serious in this region than in regions with many emerging factories (SE).

Fig. 3 Partitioning based on the PLI results of the soil HMs 4. Discussion Agglomeration and dispersion are two basic spatial forms for industrial layouts. In China, the rapid growth of industrial factories in scattered villages has been a specific phenomenon (Tian, 2015). In 2013, rural industrial gross output accounted for nearly half of the total output and played a significant role in China's economic growth (NBSC, 2013; Jiang et al., 2017). Nonetheless, the dispersed distribution of industrial factories is often accompanied by serious pollution and waste (Klapwijk, 1997), which have grievously hindered sustainable development. To highlight the environmental impact of industrial agglomeration on regional soil HM accumulation, we comparatively analyzed two types of industrial organization, industrial agglomeration areas and non-agglomeration areas, on the basis of the quantitative connection between industrial production and the accumulation of soil HMs and the soil HM TEM values. Thus, two non-agglomerated factories distributed independently and located far from the ZETZ were chosen: the Kaifeng Carbon Factory (KCF), located in the eastern suburb of Kaifeng City

Journal Pre-proof (Chen et al., 2016), and the Wanyang Lead Smelter (WLS), located in the northwestern suburb of Jiyuan City (Cheng et al., 2014; Liang Q, 2016). 4.1. Comparative analysis of the environmental effects of soil HM accumulation in industrial agglomeration areas and non-agglomeration areas 4.1.1. Basic data for the soil HM accumulation calculations. 4.1.1.1. General situations of the selected factories. The KCF began production formally in 2007, and the actual occupied area of the factory is over 56 ha. The company mainly manufactures ultrahigh-power graphite electrodes and nipples. The annual production capacity is 50,000 tons of large-diameter ultrahigh-power graphite electrodes and 55,000 tons of other carbon products. From the time of the start of the KCF production to the soil sampling, the industrial gross output of the factory over the years was 1381.20 million yuan (in 1990 values). Farmland is the main land use type surrounding the KCF, and the other land uses occupy small areas that are scattered throughout the farmland area. The WLS began production formally in 1995 and covers an area of 65 ha. As a typical indigenous township factory, it was founded and developed upon local mineral resources. The main products of the factory are electrolytic lead, alloy lead, gold, silver and sulfuric acid, and the annual output of electrolytic lead and recycled lead are more than 100,000 tons. The gross industrial output was 7 256.68 million yuan from the year that WLS was put into production to the time of soil sampling. At the time of sample collection in June 2008, the land use of the surrounding areas was mostly farmland.

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Fig. 4 Locations of the ZETZ, KCF and WLS

4.1.1.2. Identification of the scope of pollution from the study objects. Industrial point source pollution is mainly centered at places such as factories, industrial and mining chimneys and waste dumps, and diffusion occurs from these points in all directions. Here, the scope of pollution diffusion was defined according to the documented research results and the relevant standards formulated by the state. Additionally, to avoid overestimating the accumulated mass of pollutants around the research object and reduce the cross-effects of HMs discharged by other human activities, the accumulation of HMs in soils of the scattered regions that were strongly affected by other human activities was not estimated. ZETZ: Due to the spatial heterogeneity in the soil HM concentrations in the ZETZ, the study area was divided into six parts based on the kriging of the comprehensive PLI values. As shown in Fig. 3, due to the influence of pollution sources in the ZETZ, although the peak of soil HM pollution appears in the northern and western parts, the pollution halo zone did not fully fall in the study area; hence, this pattern indicates that the HM contamination in soils has already spread beyond the study area. The HMs enter the soil mainly through the flow of water and air dispersion. Based on the analysis of the geomorphological and climatic conditions, combined with the results of field investigation, the main pathway of HM pollution dispersion in the study area is the atmosphere. According to Kong (2010), a high-concentration zone of particulate matter and HM elements in the surface soil occurs within 3000 m of industrial pollution sources. Table 3 Coverage area of the soil HM pollution in the ZETZ/km2

Journal Pre-proof The ZETZ

Areaa

Areab

NW (n=66)

20.50

37.19

NE (n=80)

25.08

27.91

MW (n=64)

26.34

42.91

SW (n=56)

25.05

33.48

SE (n=66)

14.70

14.70

CR (n=103)

14.62

22.58

Total

126.3

178.8

a The actual area of the factory/region b Diffusion area of the soil HMs

Fig. 5 Sketch map for the pollution range of the ZETZ

KCF: The soil HM concentration data around KCF were from Chen et al. (2016b). According to the national sanitary protective distance standards of industrial factories, the health-protecting distance for the graphite carbon (electrode) products industry is 1000 m (GB 18068.4-2012). The pollution ranges to the north, east, west and south of the KCF were identified as 500 m, 500 m, 500 m and 1000 m, respectively. The identified pollution range of the KCF, which was calculated as 3.72 km2, is treated as the calculated area of soil HM accumulation (Fig. 5). The calculation of soil HM accumulation was combined with the sampling method, and after calculating the accumulated mass of soil HMs for each grid, all of the data were added.

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Fig. 6 Sketch map for the pollution range of the KCF

WLS: The data for the soil HM concentrations around the WLS were recorded by Cheng (2014) and Ran (2010). The data for the BCs of soil HMs at the location of the WLS were collected from Liang (2013). The delineation of the pollution range for the WLS was based on the research results of Zhao (2014); the maximum internal diameter of the Class I pollution zone for the lead-zinc smelter is 1400 m. Additionally, considering the diffusion characteristics of point source pollutants, we used the smelter chimney as the center and drew two circles with radii of 1000 m and 1900 m, respectively (Fig. 7). The distances of A1 and A2 essentially complied with the spatial change characteristics of HM pollutant diffusion. The area of the outer ring is 11.94 km2, the area of the inner ring is 3.46 km2, and the total area is 15.40 km2.

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Fig. 7 Sketch map for the pollution range of the WLS

4.1.1.3. Economic output of the study area. According to the indexes of gross industrial production over the years in the city or district of the factory, the chain index values were calculated, and the total industrial output values of the three research objects were then converted into 1990 values (Table 4). The industrial production data were collected from the Zhengzhou statistical yearbook (2013–2016), Jiyuan statistical yearbook (1997–2011), and Kaifeng statistical yearbook (1997–2011), and some data were obtained from field investigations. Table 4 Relevant parameters for the calculation of the total output value in 1990 values/million yuan The ZETZ Year

Indexa

The KCF

Chain

Output

index

valueb

Indexa

The WLS

Chain

Output

index

valueb

Indexa

Chain

Output

index

valueb

1996

–

–

–

–

–

–

118.8

1.32

7.58

1997

–

–

–

–

–

–

102

1.33

7.51

1998

–

–

–

–

–

–

109.2

1.27

7.86

1999

–

–

–

–

–

–

11.9

1.23

9.74

2000

–

–

–

–

–

–

112.8

1.26

68.66

2001

–

–

–

–

–

–

120.7

1.18

101.48

2002

100

1.82

785.37

–

–

–

118.8

1.31

94.27

2003

136.4

1.87

1078.19

–

–

–

118.9

1.40

121.72

2004

143.5

2.01

1481.90

–

–

–

119.9

1.44

277.14

2005

138.3

2.16

467.68

–

–

–

125.9

1.56

434.68

Journal Pre-proof 2006

103.5

2.27

423.57

–

–

–

119.6

1.71

585.18

2007

128.8

2.42

511.96

113

2.25

79.56

121.1

1.80

940.08

2008

131.1

2.59

719.70

113.2

2.51

123.00

117

2.05

1573.90

2009

121.5

2.55

818.42

112.1

2.48

159.45

115

1.83

1640.41

2010

148.4

2.76

1128.45

112.2

2.63

190.18

113.2

1.97

1386.47

2011

231.9

2.98

2455.92

112.9

2.70

173.90

–

–

–

2012

113.3

2.95

2860.93

111.1

2.73

269.93

–

–

–

2013

114.4

3.00

2772.49

110.8

2.79

240.41

–

–

–

2014

120.7

3.00

8534.64

110

2.79

144.77

–

–

–

2015

109.7

2.94

12392.59

–

–

–

TIOc

36431.81

1381.20

7256.68

a Gross industrial product index (the index of the previous year is 100) b The output value in 1990 values c The total industrial output values over the operating period

4.1.2. Comparison of soil HM pollution. Based on the soil BCs of each research site, the CF and PLI values of the soil HMs were calculated and compared, and the results are shown in Fig. 8. KCF: The mean CF values of the HMs in soils of the KCF showed the following trend in decreasing order: Cd (5.91) > Zn (2.85) > As (2.37) > Pb (1.92) > Hg (1.71) > Cu (1.68) > Cr (1.09) > Ni (1.02). All HMs were found at the slight pollution level. According to the CF values, Cd pollution was the most significant and occurred at the heavy pollution level, As and Zn were generally present at the moderate pollution level, and Ni, Cr, Cu, Hg and Pb were all present at the slightly polluted level. The PLI for the HMs in soils around KCF varied from 1.15 to 2.85, with an average of 1.91, indicating a slight pollution level. WLS: The decreasing order of mean CF values for the HMs in soils around the WLS was Cd (34.10) > Pb (8.81) > Cr (2.21) > Zn (1.42) > Cu (1.02) > Ni (0.94). Cd and Pb were present at the heavy pollution level, Cr was present at the moderate pollution level, Cu and Zn were present at the slight pollution level, and Ni was present at the safe level. The average PLI value was 2.95, indicating a moderate pollution level. The mean PLI values of soils in the two annular regions around the WLS were A2 (3.21) > A1 (2.69), which correspond to a slight pollution level and a moderate pollution level, respectively.

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Fig. 8 Average CF and PLI values of the soil HMs in the ZETZ, KCF and WLS

Due to differences in the types of products produced and production processes, the characteristics of HMs produced by factories and industrial agglomeration areas vary greatly. The comparison showed that the CF values of most soil HMs in the ZETZ were significantly lower than those in the KCF and WLS areas; however, the CF value of Cr in the ZETZ was higher than that in the KCF, the CF value of Cu in the ZETZ was higher than that in the WLS, and the CF value of Ni was higher than those in both the KCF and WLS (Fig. 8). Overall, the CF values varied greatly among the different metals in the KCF and WLS. The mean PLI values of the ZETZ, KCF and WLS were 1.25, 1.91 and 2.95, respectively, with the first two corresponding to slight pollution levels and the third corresponding to a moderate pollution level. Thus, the most severe soil HM pollution was in the WLS, followed by the KCF and ZETZ. 4.1.2.1. Comparative analysis of the TMt of soil HMs The total accumulated mass (TMt) values of the soil HMs at the three study sites were calculated according to formulas 3 and 4, and the results are as follows. In the ZETZ, the TMt of the HMs in soils of the MW, NW, SW, CR, NE and SE regions were 8.87 × 105, 6.00 × 105, 3.46 × 105, 3.01 × 105, 1.76 × 105 and 7.39 × 104 kg, respectively. The Mti of the HMs in soils decreased in the order of Zn (7.33 × 105 kg) > Cr (7.12 × 105 kg) > Pb (4.60 × 105 kg) > Ni (1.92 × 105 kg) > Cu (1.85 × 105 kg) > As (9.59 × 104 kg) > Cd (4.41 × 103 kg) > Hg (1.32 × 103 kg), which accounted for 30.74%, 29.85%, 19.31%, 8.07%, 7.77%, 4.02%, 0.18% and 0.056% of the TMt, respectively. The Qti values of Zn, Cr and Pb were the highest, together accounting for 79.90% of the TMt. Additionally, Cu, As, Cd and Hg accounted for 20.10% of the TMt.

Journal Pre-proof In the KCF, the TMt value of the HMs in soils was 1.71 × 105 kg, and the Mti values of the HMs in soils around the KCF followed the decreasing order of Zn (1.24 × 105 kg) > Pb (2.01 × 104 kg) > Cu (1.49 × 104 kg) > As (5.40 × 103 kg) > Cr (3.98 × 103 kg) > Cd (1.25 × 103 kg) > Ni (1.20 × 103 kg) > Hg (1.34 × 102 kg), which accounted for 72.53%, 11.74%, 8.73%, 3.16%, 2.33%, 0.73%, 0.70% and 0.078% of the TMt, respectively. In the WLS, the TMt value of the soil HMs was 7.19 × 105 kg, and the affected area of A1 was comparatively larger than that of A2. Consequently, the Mti values of the HMs in A1 (5.22 × 105 kg) were also higher than those in A2 (2.07 × 105 kg). The Mti values of the HMs in soils of the WLS followed the order of Pb (3.62 × 105) > Cr (2.85 × 105 kg) > Zn (6.38 × 104 kg) > Cd (1.82 × 104 kg) > Cu and Ni (0 kg). The Mti contributions to the TMt of those HMs accounted for 49.63%, 39.07%, 8.76%, 2.54%, 0% and 0%, respectively. The TMt values of the HMs in the soils in these three research areas followed the order of ZETZ > WLS > KCF, which was the same as the order of their pollution scopes in terms of area. The results are in agreement with the calculation formula suggesting that the TMt of the HMs in soils is directly associated with the coverage area of the soil HM pollution. The common feature of the three research areas is that Mti of Zn and Pb were the highest, which accounted for a relatively large proportion of the TMt of the HMs. 4.1.2.2. Comparative analysis of TPMti of the soil HMs To quantitatively and comparatively analyze the accumulation of HMs in soils caused by the development of agglomerated and non-agglomerated factories, the accumulated mass of the HMs discharged into soils in units of industrial output (kg·million yuan−1) was calculated according to formulas 3 and 4, and the results are shown in Table 5. In the ZETZ, the accumulated mass of the HMs in soils per million industrial capital outputs decreased in the order of Cr > Zn > Pb > Ni > Cu > As > Cd > Hg, which accounted for 31.45%, 29.76%, 18.70%, 7.81%, 7.75%, 4.28%, 0.19% and 0.06% of the TPMti, respectively, In the KCF, the PMti values of the HMs in soils decreased in the order of Zn > Pb > Cu > As > Cr > Cd > Ni > Hg and represented 72.53%, 11.74%, 8.73%, 3.16%, 2.33%, 0.73%, 0.70% and 0.08% of the TPMti, respectively. In the WLS, the PMti values for the soil HMs were Pb > Cr > Zn > Cd, and their contributions to the TPMti were 49.63%, 39.23%, 8.79% and 2.55%, respectively. Overall, the TPMt (kg·million yuan−1) of the HMs in soils of the three research areas

Journal Pre-proof followed the order of KCF (119.70) > WLS (100.42) > ZETZ (64.66). In the ZETZ, the PMt values of Cr, Zn and Pb were comparatively higher than those in the KCF and WLS. In contrast, compared with the values in the KCF and WLS, the PMt values for As, Cd, Hg and Pb in the ZETZ were significantly lower. The PMt value for Cr was higher in the ZETZ than in the KCF but lower than in the WLS. The PMt values for Cu and Zn were both higher than those in the WLS but significantly lower than those in the KCF. The PMt value for Ni in the ZETZ was higher than those in both the KCF and WLS. Thus, most of the PMti values of the HMs in soils in the ZETZ were significantly lower than those in the KCF and WLS, but the values for the individual metals were slightly higher. In the KCF, the PMt values of Zn, Pb and Cu in soils were significantly higher, especially for Zn, which was 4.96 and 10.14 times higher in KCF than in ZETZ and WLS, respectively. One reason for the high accumulated mass of these elements was the industrial production activities of the KCF. Another important reason is that zinc smelters located 2 km away may have also made certain contributions to the HM pollution. In the WLS, the PMt values of Pb, Cd and Cr were 3.94, 19.62 and 1.85 times higher than those in the ZETZ and 3.43, 2.83 and 13.62 times higher than those in the KCF. The soil environment around the WLS may be polluted by a large amount of Pb dust that is produced during the process of lead smelting, while the accumulated mass of the related element Cr was also greatly increased. Table 5 The PMti (kg·−6 yuan) and TPMt (kg·−6 yuan) of the three different study areas PMti

Items Factories

TPMta

TPMtb

20.12

67.59

64.66

14.52

89.73

123.71

119.70

49.84

8.79

100.42

100.42

As

Cd

Cr

Cu

Hg

Ni

Pb

Zn

ZETZ

2.89

0.13

21.26

5.24

0.034

5.28

12.64

KCF

3.91

0.90

2.88

10.80

0.097

0.87

WLS

–

2.55

39.23

0.00

–

0.00

‒ Not analyzed a Total equivalent mass of the eight measured metals per unit of economic output b Total equivalent mass for Cd, Cr, Cu, Ni, Pb and Zn per unit of economic output

4.1.2.3. Comparative analysis of the TEM value of the soil HMs Because the toxicity coefficients of the HMs vary greatly, the comparative analysis involving only the TEM is far from reflecting the objective reality. Consequently, the toxic equivalent mass of the HMs accumulated per unit of economic output (EMi and TEM) was also calculated

Journal Pre-proof according to formulas 7 and 8. Notably, due to the lack of data for As and Hg contents in the soils around the WLS, this study calculated EQi from the determined HMs, including Cd, Cr, Cu, Ni, Pb and Zn. The results are shown in Table 6. In the ZETZ, the EM values of the soil HM accumulated mass followed the decreasing order of As > Pb > Cr > Ni > Cu > Zn > Cd > Hg, and the contributions of EMi values to the TEM values were 92.73%, 2.50%, 1.68%, 1.05%, 1.04%, 0.80%, 0.15% and 0.57%, respectively. In the KCF, the decreasing order of EM values of the soil HM accumulated mass was As > Zn > Pb > Cu > Cd > Cr > Ni > Hg, which accounted for 92.49%, 2.62%, 2.12%, 1.60%, 0.79%, 0.17%, 0.13% and 0.11% of the TEM values, respectively. In the WLS, the decreasing order of EM values for the soil HM accumulated mass was Pb > Cr > Cd > Zn, and the contributions of the EMi values to the TEM values were 60.35%, 19.00%, 18.53% and 2.13%, respectively. The TEM values of the soil HMs (kg·yuan−6) for the three research objects exhibited the following decreasing order: WLS (413.00) > KCF (252.48) > ZETZ (182.19). In both the ZETZ and KCF, the EMAs values were the highest and contributed the greatest to the TEM, both due to the high toxicity coefficient and high accumulated mass. The EMPb values were also relatively high in all three study areas. The major toxic equivalent masses were As and Pb in soils of the ZETZ; As, Zn and Pb in soils of the KCF; and Pb, Cr and Cd in soils of the WLS. Table 6 Toxic equivalent mass of the soil HMs accumulated per unit of economic output (kg·10−6 yuan) EQi

Items Factories

TEQ a

TEQ b

20.12

2525.79

182.19

72.61

89.73

3426.17

253.48

249.22

8.79

‒

413.00

As

Cd

Cr

Cu

Hg

Ni

Pb

Zn

ZETZ

2342.15

3.77

42.52

26.19

1.45

26.40

63.20

KCF

3168.81

27.05

5.76

53.99

3.89

4.34

WLS

‒

76.52

78.46

0

‒

0

‒ Not analyzed a

Total equivalent mass of the eight measured metals per unit of economic output

b

Total equivalent mass of Cd, Cr, Cu, Ni, Pb and Zn per unit of economic output

Based on these studies, the results of comparative analysis showed that both the TPMt and the TEM of the soil HMs in ZETZ were always less than those of the soils in the WLS and KCF. Accordingly, the results showed that the rate of soil HM accumulation from industrial production was lower in the agglomerated area than in the non-agglomerated areas. Industrial agglomeration had a positive influence on the reduction of soil HM accumulation. On the one hand, benefits such as resource sharing, reducing, reusing and recycling; centralized treatment of pollutants; and

Journal Pre-proof spatial agglomeration of industrial activities may reduce industrial pollution intensity (Deng et al., 2018; Wu et al., 2018; Wang et al., 2018; Zhang et al., 2018; Zheng et al., 2018; Xiao et al., 2019). In contrast, most of the non-agglomerated factories are "indigenous" and mainly developed on the basis of local resource advantages, especially in the early stage of factory development, with factories often committed to pursuing economic interests without regard to the environmental costs (Ma, 2010; Cai et al., 2015; Chen et al., 2015; Nie et al., 2016; Ding et al., 2019). Alternatively, environmental protection has already received widespread attention at the national scale in the present situation. Therefore, local Chinese governments have adopted a series of actions to balance the relationship between the development of the economy and the protection of the environment. Actions such as regulating and controlling industrial and environmental policies have led to the rational allocation of resources and have encouraged the development of high-tech industries (Drucker and Feser, 2012; Zhang, 2015; Chen et al., 2018; Tian et al., 2018; Zhou et al., 2018; Shu and Xiong et al., 2019; Susur et al., 2019). 4.2. Disadvantages and shortcomings In the comparison, there are some limitations due to uncertainty in the factors influencing the regional soil HM pollution, such as the types of factories, the referenced soil background values, the possibility of superimposed pollution caused by the surrounding environment, and the HMs in soil introduced by the land use prior to construction of the factories. In particular, the industrial type for each factory had a significant impact on the research results. Here, the industry types of the selected non-agglomerated factories and the industrial agglomeration area are not entirely consistent. However, even so, this study has still reached robust conclusions despite the influence of other related variables. 5. Conclusions The relationship between economic growth and environmental pollution in industrial agglomeration areas is an important and controversial issue. This work indicates that in the study area, the spatial distribution of heavy metalloid in soils of the industrial agglomeration area ZETZ demonstrated different degrees of variation. Hg, Pb, Cr and As were the elements that were most

Journal Pre-proof affected by local human activities. The relationship between economic growth and environmental pollution in the industrial agglomeration area and areas around non-agglomerated factories were comparatively analyzed, and the values of the PLI, TPMt and TEM of the studied soil HMs around two non-agglomerated factories were obviously greater than those in the industrial agglomeration area ZETZ. The PLI value of the HMs in the soil of ZETZ was lower than that of both the KCF and WLS. All the HMs in soils in the ZETZ occurred at the slight pollution level, and Pb was the most accumulated element. In the KCF, the soil HMs with the most serious pollution levels were Cd (5.9), Zn (2.9) and As (2.4). In the WLS, the soil HMs with the most serious pollution levels were Cd (34.1), Pb (8.81) and Cr (2.21). The greatest contributors to the TEM were As and Pb in the ZETZ; As, Zn and Pb in the KCF; and Pb, Cr and Cd in the WLS. Accordingly, although the HMs in the soils of the industrial agglomeration area had accumulated to a certain extent, the accumulation of the soil HMs per unit of economic output in the ZETZ (64.66 kg·10−6 yuan) was significantly lower than that for the KCF (119.70 kg·10−6 yuan) and WLS (100.42 kg·10−6 yuan), which indicated that industrial agglomeration is effective in reducing the intensity of soil HM accumulation due to industrial gathering effects, such as technological progress and resource sharing. However, the characteristics of HM accumulation in soils around the industrial areas were directly affected by the local industrial production process.

Acknowledgements

This work was financially supported by the “the Key Project of Natural Science Foundation of China (No. 41430637)”, Henan Postdoctoral Sustentation Fund (2018), China and Key Research

Projects of Universities in Henan Province (17A790009). "National Natural Science Foundation of China Youth Foud (No. 41901206)".

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