Monitoring and Ecological Risks of Heavy Metals of Urban Soils in Different Functional Areas Based on Plasma Technology

Heavy metal pollution in soil has become a global environmental problem in recent years. This study assessed heavy metals' pollution distribution, level and ecological risk in soils from different functional areas in Shihezi City, China. Heavy metals (Cr, Cu, Pb, Zn, Ni, and Cd) were measured using atmospheric pressure discharge plasma and inductively coupled plasma mass spectrometer. The mean concentration of all heavy metals in soil was higher than heavy metals' background values. The spatial distribution of Cr is the most different, and the distribution of Cu and Zn are similar in other functional regions. The single pollution index indicated that the heavy metals in industrial, traffic, and residential areas were enriched, and the pollution of Cd was more severe than others. The Nemerow pollution index showed that the near Manas River basin coast is alert (still clean), the industrial area is moderately polluted, and all other functional areas are lightly polluted. The potential ecological risk index demonstrated that only the nearshore Manas River Basin is at a mild ecological risk level, while all other functional areas are at a moderate ecological risk level. The determinate power of DEM, temperature, and precipitation were all over 65%, which meant that the topographic and climatic factors were the main factors affecting the change of heavy metal content. Secondly, socio-economic factors are important factors to promote the change of heavy metal content in soil. the relationship between soil heavy metal contamination and geoenvironmental factors using a geodetector model to identify possible heavy metal sources.

In recent years, with the rapid development of industrialization and urbanization, the number of sudden environmental pollution events caused by heavy metal pollution has increased dramatically. 1 Heavy metals enter the environment at high concentrations within a short period of time, posing a threat to human health and urban environmental quality. 2,3 Under Heavy metals eventually sink into the soil under the action of different pollution sources and exhibit complex spatial variability. 4 To a certain extent, the spatial variation characteristics of soil heavy metals can reflect the source and intensity of heavy metal pollution. Therefore, the study of the content of heavy metal elements in soil and their spatial distribution is a prerequisite for carrying out research related to the prevention and control of soil pollution and the early warning of soil environmental risks. 5 The detection techniques concerning heavy metals have also shown a trend toward automated, intelligent, integrated, and accurate rapid detection. The most widely used detection methods include inductively coupled plasma mass spectrometry/atomic emission spectrometry (ICP-MS/AES), atomic absorption spectrometry (AAS), atomic fluorescence spectrometry (AFS), laser-induced breakdown spectrometry (LIBS), etc. 6 Furthermore, atmospheric pressure discharge plasma technology (APDP) has a promising future for detecting heavy metals in soil because it eliminates the need for a costly vacuum system, simplifies experimental equipment, and lowers production costs. 7 Therefore, combining ICP-MS with APDP to detect heavy metal concentrations can improve the accuracy of results while also lowering the cost of testing samples.
In the practical application of conducting environmental pollution monitoring, the focus is no longer only on the specific content of pollutants at a particular location in space, but more on the spatial analysis of continuous areas. 8 At present, a large number of studies have been conducted on the spatial distribution 2 Scientific RepoRtS | characteristics of soil heavy metals for different scales and analytical perspectives, such as heavy metal migration direction, national, provincial, functional areas, and land use patterns. 9,10 In the horizontal direction, by extracting surface soil samples, it was found that the content of heavy metals generally showed a trend of decreasing in concentric circles in all directions with increasing distance from the point source. In the vertical direction, by sampling the soil in layers, the soil heavy metal content basically showed a trend of gradually decreasing from the surface layer downward. Scholars from various countries have studied the sources, characteristics, distribution, and risk assessment of heavy metals in the soils of forests, plains, wetlands, coastal zones, and around different functional areas and along roads. This is in addition to more studies on the spatial distribution of heavy metal pollution in cities, farmlands, and mining areas. [11][12][13] In Rizhao, China, Lv et al. 14 investigated the relationship between heavy metals and scales at various spatial scales. They discovered the environmental elements that influence spatial variability at different scales. Besides, for the analysis of the driving factors affecting the change of heavy metal content, the geodetector is able to iterate the multi-source spatial display data with economic statistics and other attribute analysis data, and is widely used to explore the multi-factor interaction influenced by nature, society, and the economy. 15 Urban soils, on the other hand, support a dense population and frequent industrial activities, with varying degrees of human activity in different functional sectors. As a result, increasing urban soil environmental quality and human habitat require a thorough examination of urban heavy metal pollution's geographic distribution, ecological risk, and pollution drivers. 16 Shihezi city's heavy industrial park is adjacent to agricultural land. It has a fragile ecological environment that has changed rapidly and significantly in recent years due to human activities and natural disturbances. In light of this, this study used ICP-MS and APDP techniques to examine heavy metal concentrations (Cr, Cu, Pb, Zn, Ni, Cd) in five functional areas in Shihezi, China. The main goals were to (1) investigate the distribution patterns of heavy metals in soils from the five functional areas; (2) to assess the extent of soil heavy metal contamination and the potential ecological risks; and (3) to investigate the relationship between soil heavy metal contamination and geoenvironmental factors using a geodetector model to identify possible heavy metal sources.

Materials and methods
Overview of the study area. The studied city, Shihezi, with an area of 6,007 km 2 , belongs to Xinjiang Uygur Autonomous Region, China. Shihezi is the central city of the economic belt on the northern slope of Tianshan Mountain. It is located at latitudes ranging from 43°26′ to 45°20′N and longitudes ranging from 84°58′ to 86°24′E (Fig. 1). Shihezi is the new military reclamation city's farm-based, industry-led, urban-rural, agricultural, and industrial integration. Shihezi's urban population reached 579,800 in 2018, with an urbanization rate of 83.2%, a regional Gross Domestic Product (GDP) of 53.778 billion yuan, and a per capita GDP of 82,584 yuan, according to the Shihezi Statistical Yearbook 2019. Shihezi experiences a long, cold winter and a short, hot summer. It has a typical temperate continental climate with annual mean temperatures ranging from 25.1 to 26.1°C and annual precipitation ranging from 125.0 to 207.7 mm. The city's topography is flat, and the soil is mostly grey desert soil, tidal soil, and meadow soil, with sparse native surface vegetation, primarily in the Gobi desert landscape. In recent years, new enterprises have been introduced on a continuous basis, and the combined effect of many factors has influenced the quality of Shihezi's ecological environment. www.nature.com/scientificreports www.nature.com/scientificreports/ 3 Scientific RepoRtS | Soil sampling and chemical analysis. The wind direction, terrain, functional area, and other aspects are completely considered based on the early stage's detailed study on the spot to ensure the scientificity and representativeness of the sampling site layout. The sampling sites were set up in 2021 according to the technical specification for soil environmental monitoring (HJ/t166-2004). Fig. 1 shows the distribution of all samples. Among them, 8 soil samples were collected around the industrial area, 5 in the traffic area, 5 in the residential area, 5 in the agricultural zone and 5 in the coastal area near the Manas River basin. The diagonal method yielded a representative sample (0~10 cm), each weighing around 1 kg. Twenty-eight samples were located by GPS, collected in polyethylene bags, and brought back to the laboratory. The soil sample is cleaned of impurities, air-dried, crushed, and sieved through a 100 mesh sieve before being stored for testing. In a digestion tank, a 0.2500 g soil sample is moistened with a small amount of experimental water. Add 6 ml HNO3, 3 ml HCl, and 2 ml HF in order to mix the sample and the digestion solution thoroughly. The digestion was carried out by a fully automatic microwave digestion apparatus. After the temperature in the tank was lowered to room temperature, the acid was driven by the acid-depleting apparatus and cooled to room temperature, and the final sample was fixed to 25 ml with ultrapure water. The acid used in the experiment is ultra-pure, and the water used in the experiment is also ultra-pure.

Determination of heavy metal content.
Determination of heavy metals by ICP-MS. ICP-MS is a well-established technique in element analysis that focuses on detecting trace heavy metals in various materials. 17 ICP-MS was used to determine Cr, Cu, Pb, Zn, Ni, and Cd levels in the soil. The accuracy and precision of the analytical method were tested using national-level soil standards (GBW series). The experiments and testing procedures were carried out in three parallel groups, and the average value was taken as the concentration of heavy metals in the soil samples. The calibration curves were corrected with the standard solution of heavy metals after five samples were analyzed, and the relative standard deviation (RSD) was less than 5%. The recovery rates of six heavy metal components ranged from 95% to 110%, indicating accurate measurement.
Determination of heavy metals by APDP. In the research of heavy metal detection, the traditional recognized effective methods are ICP-MS/AES, AAS, AFS, LIBS, etc. 18 Furthermore, the low-cost and portable APDP technique has been valued by scholars as an ideal method for detecting trace heavy elements. 19 APDP belongs to the non-thermal plasma (NTP) sourced by the gas discharge at atmospheric pressure. Plasma is a quasi-neutral gas composed of various particles, neutral atoms and molecules, charged particles (electrons and ions), sub-stable particles (excited atoms and molecules, free radicals) and photons. 20 In a plasma, heavy metal compounds or ions are atomized and excited to produce atomic emission spectral radiation, which is the principle of ICP-AES and LIBS for heavy metal detection. APDP is a technique that generates plasma at atmospheric pressure or even in an open-air environment. 21 Therefore, APDP for elemental analysis is possible.
In this study, electrolyte cathode discharge (ELCAD) was applied for heavy metal elemental detection, and its experimental setup is shown in Fig. 2. It consists of an ELCAD generator, a DC power supply, an electrical measurement system, and an optical detection system. 22 The anode electrode is usually made of a tungsten rod. The discharge plasma is excited by a DC power supply between the electrolyte cathode and the metal anode, where the heavy metals are atomized and excited to the excited state of the plasma. The atomic emission spectra of Cd, Cu, and Pb were detected, allowing heavy metal elements to be detected. Working curves and limits of detection (LOD) for elemental metal analysis were obtained using a variety of solutions and blanks for elemental metal concentrations (1,2,5,10,15,20,20,25, and 30 mg/L for Cd, Cu, and Pb, respectively) under optimal experimental conditions. Over 21 observations, the RSD of Cu emission intensity was around 2.6%. Cd, Cu, and Pb had LODs of 0.687, 0.109, and 0.969 mg/L, respectively. Single factor pollution index (Pi). The single factor pollution index method uses measured data and standard comparative classification to evaluate the degree of pollution for a particular pollutant in the soil. 23 The basic equation is as follows: In the formula, Pi is the environmental quality index of pollutant i in soil, Ci is the measured concentration of pollutant i (mg kg -1 ), Si is the evaluation standard of pollutant i (mg kg -1 ), and the background value of soil elements in Xinjiang is selected.
Nemerow pollution index(PN). The single factor pollution index method can only reflect the pollution degree of individual heavy metal elements. To fully reflect the pollution of the soil environment by various heavy metal elements, the Nemerow pollution index method can take into account the average value of the single factor pollution index and the highest value to highlight the role of the more polluting heavy metal pollutants. 24 It is defined based on the following formula: In the formula, PN is the comprehensive pollution index, (Pi)max is the maximum of the single factor pollution index, (Pi)ave is the average of the single factor pollution index. The grading criteria of the single factor pollution index method and the Nemerow pollution index method are shown in Tab. 1. 25 Potential ecological risk index (RI). The Swedish scientist Hakanson proposed an index system for assessing the potential ecological risk of soil heavy metal accumulation, which can link heavy metal content in soil with synergistic effects of multiple elements, ecological and environmental effects, and toxicology according to the nature and physicochemical environment of heavy metals, in order to improve the potential ecological risk assessment of soil heavy metals. 23 The RI is computed as follows: In the formula, RI is the potential ecological risk index of multiple heavy metals in soil, Ci is the measured value of a single element, Si uses the background value of soil environment in Xinjiang as the reference standard, is the toxicity coefficient of class i heavy metal elements (Cd=30；Cu = Ni = Pb = 5；Zn = 1；Cr = 2), is the potential ecological hazard coefficient of heavy metals. 26  Geographical detector. Geographical detectors are a statistical method that can detect both spatial heterogeneities and test the consistency of the spatial distribution of two variables, revealing the driving forces behind them. 15 Factor detectors in geographical detectors are able to determine the degree of influence of a factor on the variation of soil heavy metal content using the explanatory power of the factor. 28 This study used the six heavy metal contents in the study area as dependent variables and the driving factors as independent variables. The power determinant (PD) of each driving factor was obtained using a factor detector to explore the degree of influence of each driving force. The selection of the driving factors included natural factors and socio-economic factors. The natural factors include land-use type, Digital Elevation Model (DEM), soil type, soil texture, temperature, and rainfall; the socio-economic factors include population and GDP. ArcGIS 10.3 was used to extract the heavy metal content and driver values for the spatial location of each sampling point for analysis.
www.nature.com/scientificreports www.nature.com/scientificreports/ 5 Scientific RepoRtS | The data for the required driving factors were obtained from the Chinese Academy of Sciences' Data Center for Resource and Environmental Science (http://www.resdc.cn/). This study reclassifies land-use types according to the primary classification and data description of the Standard for Classification of Land Use Status (GBT 21010-2017), taking into account the characteristics of the natural environment and research needs of Shihezi city. Finally, the land was classified into six categories: arable land, forest land, grassland, water areas, construction land, and bare land. All the above data points were resampled to 1 km for the experiment, and the projection was uniformly transformed into Albers.

Results and discussion
Statistics on the concentration of heavy metals in soil. The statistics on the concentrations of heavy metals in the soil of Shihezi City are presented in Tab. 2. The mean values of Cr, Ni, Cu, Zn, Pb, and Cd content were 60.93, 29.04, 32.77, 75.11, 19.84, and 0.26 mg kg -1 , respectively. The average values of these six heavy metals did not exceed the secondary standards in the National Soil Environmental Quality Standards (GB 15618-1995). Still, the average values of the six elements were 1.24, 1.09, 1.23, 1.09, 1.02, and 2.17 times the background values of soil elements in Xinjiang, respectively. The exceedance rates of heavy metals at individual sampling sites were greater than 57%, with the exceedance rate of Cd content as high as 93%. This result is consistent with the study of Jia et al. 29 on regional heavy metal-related soil contamination, which all showed a high enrichment of Cd. It indicates that the city's economic development has influenced the soil's heavy metal content in Shihezi.
The effect of human activities on soil heavy metal content can be expressed using the coefficient of variation. The greater the value of the coefficient of variation, the stronger the degree of disturbance to the heavy metal content and the greater the difference in spatial distribution, and vice versa, the smaller. 30 For all six heavy metals, the magnitude of the coefficient of variation is in this order: Cr > Cd > Cu = Zn > Ni > Pb. According to Wilding's 30 definition of the degree of variability, the coefficients of variation for Pb and Ni are 28% and 29%, respectively, which are moderate (15% coefficient of variation, 36%). Cr, Cu, Cd, and Zn are all highly variable (coefficient of variation > 36%), with Cr in soil having the highest (56% coefficient of variation), indicating that it is seriously disrupted by human social behavior and has the greatest regional distribution variance. The variance in their distribution could be due to pollution emissions from industrial production, agricultural production activities, and traffic exhaust.  Table 2. Characteristics of statistical analysis of heavy metal content in soils.
The distribution of heavy metals in different functional areas. Heavy metal concentration data was loaded into SPSS software for a normal distribution test to match the requirements of the geochemical baseline and receptor models. Outliers were eliminated based on an interquartile range and histogram. According to the findings, the data for Cr, Ni, Cu, Zn, Pb, and Cd followed a normal distribution. Using ArcGIS software's inverse distance weight interpolation approach, Fig. 3 depicts the spatial distribution of six heavy metals in the soil surface layer of Shihezi City. Overall, the content of heavy metals in different functional areas varied significantly. The hot spots of heavy metal concentrations in soil were all found in the vicinity of traffic areas and industrial areas with intensive human activity in Shihezi, and the heavy metal concentrations decreased significantly with distance from the central area. This result is roughly the same as that studied by Yi et al. 33 regarding the distribution characteristics of soil heavy metals in different functional areas. The average contents of Cr and Cd in soil were industrial zone > transportation zone > residential zone > agricultural zone > near shore of the Manas River basin; the average contents of Ni were residential zone > industrial zone > agricultural zone > transportation zone > near shore of the Manas River basin; the average contents of Cu and Pb were transportation zone > residential zone > industrial zone > agricultural zone > near shore of the Manas River basin; the average contents of Zn were transportation zone > residential zone > agricultural zone > industrial area > near shore of the Manas River basin. The similarity of Cu and Zn in different functional areas indicates that both have similar pollution pathways caused by smelting, vehicle exhaust, and brake wear. 34 The highest Cd level is found in industrial regions, which is 4.5 times higher than the background value of Xinjiang soils. The residential area has the highest Ni level, which is 1.75 times the Xinjiang soil's background value. The greatest Cr, Cu, Zn, and Pb levels were found in the traffic region, which were 2.92, 2.53, 2.10, and 1.56 times higher than the background values of Xinjiang soils, respectively. This could be owing to the higher volume of traffic in this location, as well as the fact that Cr, Cu, Zn, and Pb in urban soils have been linked to transportation in studies. 35 6 Scientific RepoRtS | www.nature.com/scientificreports www.nature.com/scientificreports/ 7 Scientific RepoRtS | Assessment of soil heavy metal pollution in different functional areas. Using the background value of Xinjiang soil as the evaluation standard, the single factor pollution index method (Pi) and the Nemerow index method (PN) were used to evaluate the soil heavy metal content, and the pollution index of each functional area is shown in Fig. 4. According to the Pi and PN evaluation criteria (Tab. 1), the Pi of six heavy metal elements in industrial, transportation, and residential areas are all greater than 1, indicating that they are lightly polluted. It means that all of the heavy metals in these three functional regions have been enriched, and Cd has been severely polluted compared to other heavy metals. Only Cd and Zn are in light pollution along the near Manas River basin and agricultural areas, respectively, while all other heavy metals are in clean condition. In terms of PN, each functional region's PN is as follows: industrial area > transportation area > residential area > agricultural area > close shore of the Manas River basin. This result is similar to the ranking of PN values derived by Liu et al. 36 for soil heavy metals in different functional areas of Luoyang City. The PN of the Manas River Basin's near coast is less than 1, indicating it belongs to the alert line (still clean). The industrial area's PN is greater than 2, meaning moderate pollution. The PN of soil heavy metals in other functional areas is more than 1, indicating mild pollution. It shows that the environmental contamination issues caused by economic development have threatened Shihezi City's soil quality. The mean values of RI of the potential risk index of heavy metals in the soils of different functional areas are as follows: industrial area > transportation area > residential area > agricultural area > near the shore of the Manas River basin. Among them, only the nearshore of the Manas River Basin is at a mild ecological risk level, while all other functional areas are at a moderate ecological risk level. The relatively high mean RI values in the industrial and traffic areas may be due to the highest contents of soil Cd, Cu, and Pb in this area and the high toxicity coefficients of these three heavy metals, which need to be alerted. Secondly, the soil sampling sites in the traffic area are located in a busy traffic area, where the flow of people and vehicles is high, so the mean value of RI of heavy metals in the traffic area is higher.
The spatial characteristics of potential ecological risk levels under the synergistic effect of six heavy metals are shown in Fig. 5. Combining Fig. 3 and Fig. 5, it can be seen that the spatial distribution characteristics of potential ecological risk of soil in different functional areas are similar to the spatial distribution of soil heavy metal content, showing a gradual decrease from southwest to southeast. The highest ecological risk levels are found in industrial areas and busy commercial crossroads, whereas the lowest values are found primarily near the Manas River basin. Meanwhile, the regional distribution characteristics of the potential ecological risk are nearly identical to the spatial distribution trend of Cd, showing that Cd is the primary source of soil contamination in the research area. The main causes of the most serious Cd contamination and potential ecological risk in soils may be the high levels of Cd in soot and wastewater emitted from industrial areas. In addition to industrial pollution, fossil fuel combustion and transportation are also essential sources of Cd in urban soils.  Table 3. Evaluation of potential ecological risks in different functional areas of Shihezi city. Driving force analysis of heavy metals in soils. The variation of soil heavy metal content in Shihezi is influenced by a combination of natural, economic, and social factors, and the data for each geoenvironmental factor (Fig. 6) Table 4. PD values of various factors related to the evolution of heavy metal content in soils.

conclusion
Although the mean values of Cr, Ni, Cu, Zn, Pb, and Cd in Shihezi did not exceed the secondary standards set forth in the National Soil Environmental Quality Standards, they were 1.24, 1.09, 1.23, 1.09, 1.02, and 2.17 times higher than the background values of soil elements in Xinjiang, respectively. Cr is seriously disturbed by human social behavior in terms of coefficient of variation, and the spatial distribution varies significantly. Different land use types have an impact on the content of soil heavy metals in general. The concentration of soil heavy metals varies greatly among Shihezi city's functional regions, while Cu and Pb levels are consistent across them. The PN values for each functional area were as follows, in that order: industrial area > transportation area > residential area > agricultural area > close coast of Manas River basin. Cd levels were high in the industrial area; Cr, Cu, Zn, and Pb levels were high in the transportation area; and Ni levels were high in the residential area. In terms of the total potential ecological risk level, the characteristics of the spatial distribution of potential ecological risk in soils of different functional areas are similar to those of the spatial distribution of soil heavy metal content, all showing a gradual decrease from southwest to southeast. From , the average levels of soil heavy metals in Shihezi are Cd > Cu > Ni > Pb > Cr > Zn. The average values of RI of heavy metals in soils of different functional areas are industrial areas > transportation areas > residential areas > agricultural areas > near the shore of the Manas River basin, in order. Among them, only the nearshore of the Manas River Basin is at a mild ecological risk level, while all other functional areas are at a moderate ecological risk level. A combination of environmental, economic, and societal factors influenced the change in soil heavy metal content in Shihezi. The main elements impacting the change of soil heavy metal content are DEM, temperature, rainfall, and socio-economic factors. Soil type has no substantial effect on it.