**Source Analysis and Human Health Risk Assessment Based on Entropy Weight Method Modification of PM2.5 Heavy Metal in an Industrial Area in the Northeast of China**

**Siyu Sun 1, Na Zheng 1,2,\*, Sujing Wang 1, Yunyang Li 2,3, Shengnan Hou 2,3, Xue Song 2, Shanshan Du 1, Qirui An 1, Pengyang Li 1, Xiaoqian Li 1, Xiuyi Hua 1 and Deming Dong 1**

**Abstract:** In this study, PM2.5 was analyzed for heavy metals at two sites in industrial northeast China to determine their sources and human health risks during heating and non-heating periods. A positive matrix factorization (PMF) model determined sources, and US Environmental Protection Agency (USEPA) and entropy weight methods were used to assess human health risk. PM2.5 heavy metal concentrations were higher in the heating period than in the non-heating period. In the heating period, coal combustion (59.64%) was the primary heavy metal source at Huagong Hospitals, and the contribution rates of industrial emissions and traffic emissions were 21.06% and 19.30%, respectively. Industrial emissions (42.14%) were the primary source at Xinqu Park, and the contribution rates of coal combustion and traffic emissions were 34.03% and 23.83%, respectively. During the non-heating period, coal combustion (45.29%) and industrial emissions 45.29% and 44.59%, respectively, were the primary sources at Huagong Hospital, and the traffic emissions were 10.12%. Industrial emissions (43.64%) were the primary sources at Xinqu Park, where the coal combustion and traffic emissions were 25.35% and 31.00%, respectively. In the heating period, PM2.5 heavy metals at Xinqu Park had noncarcinogenic and carcinogenic risks, and the hazard index of children (5.74) was higher than that of adult males (5.28) and females (4.49). However, adult males and females had the highest lifetime carcinogenic risk (1.38 × 10−3 and 1.17 × 10−3) than children (3.00 × 10−4). The traditional USEPA and entropy weight methods both produced reasonable results. However, when there is a difference between the two methods, the entropy weight method is recommended to assess noncarcinogenic health risks.

**Keywords:** particulate matter; source apportionment analysis; health risk assessment; Huludao

#### **1. Introduction**

According to the latest results of the Global Burden of Disease Study, particulate matter (PM) has become the sixth leading cause of death [1,2], and air pollution is responsible for 1.5% of the total mortality worldwide [3]. Many epidemiological studies demonstrate that when inhaled and deposited in the lungs, PM2.5 (particle diameter less than 2.5 μm) can cause respiratory and cardiovascular disease in humans [4–6]. Thus, exposure to high concentrations of PM2.5 leads to increases in morbidity and mortality. There are many sources of PM2.5, including industrial emissions, coal combustion, traffic emissions,

**Citation:** Sun, S.; Zheng, N.; Wang, S.; Li, Y.; Hou, S.; Song, X.; Du, S.; An, Q.; Li, P.; Li, X.; et al. Source Analysis and Human Health Risk Assessment Based on Entropy Weight Method Modification of PM2.5 Heavy Metal in an Industrial Area in the Northeast of China. *Atmosphere* **2021**, *12*, 852. https://doi.org/10.3390/ atmos12070852

Academic Editors: Ashok Luhar, Omar Ramírez Hernández and Dmitry Vlasov

Received: 26 May 2021 Accepted: 28 June 2021 Published: 30 June 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

rocks, and soil weathering [7–9]. The contributions to PM2.5 from different sources depend on geographic location, climatic conditions, economic structure, and populations [10,11]. When industrial production is reduced, PM2.5 pollution is also reduced in many cities in China. However, industrial emissions and associated PM2.5 levels remain a cause for concern in China [10]. The increased burning of coal and biomass in the winter, particularly in northern China, is the main source of PM2.5 [12,13], in contrast to southern China [14,15]. In industrial cities in northern China, the dual effects of emissions from industry and coal combustion need to be monitored and evaluated. Heavy metals in PM2.5 can harm multiple human physiological systems and organs, and therefore, effects on human health are a concern [16–18]. Moreover, the characteristics of heavy metals in atmospheric particulates vary depending on the source [19–21]. Studies have shown that the concentration of PM2.5 in Zibo, which is dominated by petrochemical industry, is relatively high in the spring, with K+ and Mg2+ mainly representing the source of fugitive dust; in winter, the concentration of F- and NO3 - is relatively high, which represents coal burning; Cd and Ni, the elements of industrial emissions, do not change significantly throughout the year; research on industrial cities such as Baoding and the plateau city of Kunming also shows that the concentration of As and Hg, the elements of coal combustion, is higher in winter; Cd and Pb produced by zinc smelting are important sources of pollution in Huludao City [8,22–24].

To assess the impact of PM2.5 on human health, the human health risk assessment method of the US Environmental Protection Agency (USEPA) is often selected [25–29]. Most analyses of PM2.5 sources and human health risk assessment calculate human health risk without considering the effects of different sources, and the default parameters of models assume that factors do not interact or that interactions can be ignored [27,30,31]. However, because of factors such as humidity, wind speed and direction, temperature, and human activity, there may be different combinations of pollution sources with different levels of contribution and concentrations [32]. The USEPA method can be improved by coupling the source of the pollution analysis model with the human health risk assessment model; however, such models only analyze data for a specific time [33], and there is no approach for comprehensive analysis of long time series data. Heavy metals may bioaccumulate, and animal models indicate that the concentrations of heavy metals and the extent of their effects are nonlinear [34]. Moreover, the cellular toxicity of multiple heavy metals is not merely the sum of the toxicity of single heavy metals, and combinations of different heavy metals can produce synergistic or antagonistic effects [35,36]. Therefore, the health risks of different heavy metals to a population should not be simply determined by the addition of effects. To determine the effects of different combinations of pollution sources on human health risk in a long time series, the possible compound effects of multiple heavy metals on human health need to be considered. In this article, multiple heavy metals were regarded as a system, and the entropy weight method was introduced to supplement human health risk assessment.

Entropy was proposed by the German physicist Clausius to describe random thermal motion in thermodynamics [37]. Thereafter, Boltzmann developed entropy theory and applied it to information theory, in which entropy is defined as a random variable [37] and the acquisition of entropy indicates a loss of information. Therefore, with an increase in the entropy value, less information is provided by a factor, and it is less important in a comprehensive evaluation [38]. To reduce the uncertainty of factors in an evaluation model, the information entropy model was established, which calculates a factor's entropy weight [37,39]. The biggest advantage of the entropy method is to use the data itself to calculate the weight index instead of human factors, thereby improving the objectivity of the comprehensive evaluation results [40]. The entropy weight method has been validated in many types of assessments, including environmental pollution assessment, vulnerability distribution in coastal aquifers, ecological risk assessment, and comprehensive water quality assessment [39,41,42]. In such studies, the weightings between the indicators in the index and the weightings of the subsystems in the evaluation system are applied reasonably, demonstrating the suitability of the weighting method in evaluation systems. Thus, this approach should also be applicable to human health risk assessment of atmospheric PM.

To assess heavy metal pollution in Huludao City, Liaoning Province, China, environmental pollution study has focused on soils, indoor and outdoor dust, sediments, fresh water, insects, and food, and has included heavy metal accumulation, distribution, and degree of pollution, as well as human health risk assessment [22,43–46]. In this research, the concentrations of heavy metals in atmospheric PM2.5 were determined, and the health risks were assessed at two sites in Huludao City. To assess human health risk, the entropy weight method was compared with the traditional USEPA method. The objectives of the study were the following: (1) to identify changes in PM2.5 and the heavy metal concentrations during heating and non-heating periods; (2) to determine the main sources of heavy metals and their contributions in the different periods; (3) to assess the human health risks of heavy metals in PM2.5 (carcinogenic and noncarcinogenic risks); and (4) to compare different methods that estimate the human health risk over the long-term.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The research was conducted at two sites in Huludao City, an industrial city in northeast China. The city is in the southwestern part of Liaoning Province adjacent to the Liaodong Gulf of the Bohai Sea (Figure 1). The region has a north temperate continental monsoon climate. The west monsoon is dominant in winter, and the east monsoon prevails in summer. The average annual wind speed is 3 m/s. The main industries in Huludao City are petrochemicals, nonferrous metals, mechanical shipbuilding, and energy and power. The city has the largest zinc smelter in Asia, the Huludao Zinc Plant (HZP). As a result of the smelting operations, heavy metals are discharged into the environment and pollute the atmosphere, soil, and rivers [22,43]. Industrial activities and coal combustion are responsible for an approximate twofold increase in pollution in the study area. One sampling site was at Huagong Hospital, which is in the Lianshan District of Huludao City. Metal mining is the main industry in the region, and the city has a large proportion of secondary and tertiary industries. The other sampling site was at Xinqu Park, which is in the Longgang District of Huludao City. The forested land in this area accounted for 50.6% of the total land area, and it is a relatively new urban area dominated by secondary and tertiary industries.

#### *2.2. Sample Collection and Preparation*

The distribution of sampling sites is shown in Figure 1. At the Huagong Hospital site, the PM2.5 mass concentrations and the atmospheric particle samples were collected and measured continuously using an atmospheric particulate monitor (APM; Dasibi 7201, Beijing Zhongshengtyco Environmental Science and Technology Development Co. Ltd., Beijing, China). The duration of sample collection was 1 h, and the airflow was 16.7 L min−1. At the Xinqu Park site, the PM2.5 mass concentrations and the atmospheric particulate samples were collected separately by using two APM systems (i-5030 and i-FH62C14, Thermo Fisher, Franklin, MA, USA). The duration for both sample collections was 6 h, and the airflow was 16.7 L min−1. The PM2.5 was sampled in the study areas from April 2015 to March 2016. According to the "Huludao City Heating Management Measures" (2011), November to March is the heating period, and April to October is the non-heating period [47]. The PM2.5 was sampled at two points in both Huagong Hospital and Xinqu Park, and a total of 96 valid samples were collected.

**Figure 1.** Study area. S1: Xinqu Park sampling area; S2: Huagong Hospital sampling area.

#### *2.3. Chemical Analyses*

The PM2.5 samples were collected on 47 mm diameter quartz fiber filters. A sample was weighed after constant temperature (25 ◦C) and relative humidity (30%) for 24 h, the filter membrane was cut in half and transferred to a Teflon digestion vessel. Then, HNO3, HF, and HClO4 (1:1:5) were added to the vessel. The sample mixture was heated for 3 h at 200 ◦C [48]. After digestion, the sample was cooled to room temperature, and the solution was quantitatively transferred to a 50 mL volumetric flask, which was filled to the mark. The sample digests were analyzed using inductively coupled plasma mass spectrometry (Thermo Fisher, Germany) to determine the concentrations of nickel (Ni), cadmium (Cd), lead (Pb), copper (Cu), and arsenic (As) on the filters [31].

For quality assurance/quality control, 10 blank filters and river sediment standard (Fluvial sediments: GBW08301) were digested and analyzed as described above to determine heavy metal detection limits and recovery rates. The average concentrations in the blank filters were the following (μg m−3): 0.021 ± 0.006 Ni; 0.088 ± 0.006 Cu; 0.249 ± 0.034 As; 0.003 ± 0.002 Cd; and 0.037 ± 0.020 Pb. The detection limits were the following (μg m−3): 0.016 Ni, 0.018 Cu, 0.098 As, 0.006 Cd, and 0.057 Pb. According to the river sediment tests, the average recovery rates of heavy metals ranged from 89.83% to 115.15%. The calculated values of the atmospheric PM2.5 samples used in this article were obtained by subtracting the blank filter value from the test value.

#### *2.4. Positive Matrix Factorization Model (PMF)*

A positive matrix factorization (PMF) model allocates sources based on internal correlation of the data and determines the main contributing factors according to the size of the contribution and the actual local conditions [19,49]. The eigenvalues of each component are analyzed, and the source type represented by each factor is determined [19,49]. The PMF model has been widely used in the analysis of sources of heavy metals, aerosols, and organic matter in atmospheric PM and has performed well in separating different combustion sources [19,20,50]. The goal of PMF is to solve the chemical mass balance between the measured species concentrations and the source profiles [19,49]. In addition, minimization of the objective function of the PMF model enables derivation of the factor contributions and the profiles. The species method-specific limit of detection limit (MDL) is calculated to determine the uncertainty of an element [50–52]. In this study, the PMF model used 96 samples as the input items. For the model, the MDL was 2.82 times the standard deviation of the 10 blank filters. The uncertainty was calculated according to Equations (1) and (2). When the concentration was lower than the MDL, Equation (1) was used to calculate the uncertainty, and when the concentration was higher than the MDL, Equation (2) was used, with the error function the relative standard deviation. According

to the PMF5.0 user guide, a signal-to-noise ratio lower than 0.5 is defined as "Bad", a ratio higher than 0.5 and lower than 1 is "Weak", and a ratio higher than 1 is "Strong". According to the running result of the model objective function, the residual result between −3 and +3 was selected, and the minimum objective function was the output result [53,54].

$$dInc = \frac{5}{6} \times MDL \tag{1}$$

$$\text{Lnc} = \sqrt{\left(Error \, Fraction \times concentration\right)^2 + \left( 0.5 \times MDL \right)^2} \tag{2}$$

#### *2.5. Health Risk Assessment*

Heavy metals are readily accumulated in human organs such as the liver and kidney and are difficult to degrade and excrete from the body [55]. Heavy metals in atmospheric particulates will enter the human body via respiration, skin absorption, and oral intake, and pose risks to human health [56,57]. Currently, the USEPA's human health risk assessment model is widely used to estimate heavy metal exposure levels. The model is based on the exposure mode, exposure time, concentration factor, and the average daily inhaled dose of non-carcinogenic metal (*ADDinh*, mg·kg−1·day−1) and the average daily inhaled dose of metal carcinogen for lifetime exposure (*LADDinh*, mg·kg−1·day−1) in order to calculate the risk index for the human health, as given by Equations (3)–(5) [29,58]. In this study, a risk assessment for heavy metal (Ni, Cu, As, Cd, and Pb) exposure based on intake of PM2.5 was carried out. The metals were divided into two categories, in which all the heavy metals were considered non-carcinogenic, and Ni, As, and Cd were considered carcinogenic [59].

The basic statistical data and abbreviations used in the study are as follows:

$$ADD\_{inh\ i}(LADD\_{inh\ i}) = \mathcal{C} \times \frac{InhR \times EF \times ED}{BW \times AT\_n(AT\_\circ)} \tag{3}$$

*C*: concentration of heavy metals in PM2.5, mg/m3. *InhR*: inhalation rate, 19.20 m3/day for adult males, 14.17 m3/day for adult females, 5.00 m3/day for children [29,58]. *EF*: exposure frequency, 350 day/year in this study [56]. *ED*: exposure duration, 30 years for adults, and 6 years for children [29,58]. *BW*: average body weight, 62.70 kg for adult males, 54.40 kg for adult females, 15.00 kg for children [30,60]. ATn: averaging time for non-carcinogens, *ED* × 365 days [30,60]. *ATc*: averaging time for carcinogens, 70 × 365 days [29,58]. Age of children, 0–16 years; adults, 17–70 years [56].

The hazard quotient (*HQ*) represents the risk due to non-carcinogens, and the incremental lifetime cancer risk (*ILCR*) for carcinogens was calculated using the following equations [22,29,56,58]:

$$HQ\_i = \frac{ADD\_{\text{inh}\,\,i}}{RfD\_i} \tag{4}$$

$$LCRR\_i = LADD\_{\text{inh }i} \times CSF\_i \tag{5}$$

*RfD*: reference dose; Ni 2.00 × 10−2 mg·kg−1·day−1; Cu 4.00 × 10−2 mg·kg−1·day−1; As 3.00 × 10−4 mg·kg−1·day−1; Cd 1.00 × 10−3 mg·kg−1·day−1; Pb 3.50 × 10−3 mg·kg−1· day−1 [29,58]. *CSF*: carcinogens slope factor; Ni 0.84 mg·kg−1·day−1; As 1.51 mg·kg−1·day−1; Cd 6.30 mg·kg−1·day−1 [29,58].

As in previous studies [8,29,54], and without considering the interaction between heavy metals, the hazard index (*HI*) was calculated by superimposing the HQ value of each heavy metal, and the *ILCRtotal* was calculated by superimposing the ILCR value of each heavy metal, as shown in Equations (6) and (7).

$$HI = \sum\_{i} HQ\_{i} \tag{6}$$

$$ILCR\_{total} = \sum\_{i} ILCR\_{i} \tag{7}$$

The human health risk index, however, calculated by the above method does not take into account the impact of different pollution sources. Although the ME2 model can also calculate health risks from different sources, it does not consider the long-term effects of the factors on the population, especially heavy metal pollutants [33,61]. Therefore, this study introduces the entropy weight method into the human health risk assessment to evaluate the impact of factors which pose a long-term effect. First, we establish relevant feature matrices based on the samples and the evaluation parameters:

$$\mathbf{A} = \begin{bmatrix} a\_{11} & a\_{12} & \cdots & a\_{1n} \\ a\_{21} & a\_{22} & \cdots & a\_{2n} \\ \vdots & \vdots & \vdots & \vdots \\ a\_{m1} & a\_{m2} & \cdots & a\_{mn} \end{bmatrix} \tag{8}$$

where A represents the relevant feature matrices, m is the number of considered features (in this paper m is the number of heavy metals), and *n* is the number of samples [39,62]. The parameter index amount, *j*, in sample *i* is subsequently (*yij*) calculated by Equation (9); the information entropy (*Hi*) is calculated by Equation (10); and the entropy weight, Ei, is calculated by Equation (11) [62].

$$y\_{i\bar{j}} = a\_{i\bar{j}} / \sum\_{i=1}^{m} a\_{i\bar{j}} \quad i = 1, 2, \dots, m; j = 1, 2, \dots, n. \tag{9}$$

$$H\_i = -\frac{1}{\ln n} \sum\_{j=1}^n y\_{ij} \ln y\_{ij} \text{ } i = 1, 2, \dots, \text{ } \text{ $m$ :} \text{ } j = 1, 2, \dots \text{ } \text{ }, n \tag{10}$$

$$E\_i = \frac{1 - H\_i}{m - \sum\_{i=1}^{m} H\_i} \mathbf{i} = 1, 2, \cdots, m. \tag{11}$$

To show the impact of different pollution sources on the human health risk, the entropy weight method was used to calculate the weight value of the *HQ* value and the *ILCR* for each heavy metal, and the weighted correction of the HI' value and the *ILCRtotal*' for human health risk, as shown in Equations (12) and (13):

$$HI' = \sum\_{i} HQ\_{i} \cdot E\_{i} \tag{12}$$

$$ILCR\_{total}^{\prime} = \sum\_{i} ILCR\_{i} \cdot E\_{i} \tag{13}$$

where *Ei* represents the weight of different heavy metal HQ values or ILCR values in the method for calculation (see Equations (9)–(11)). If *HQ* and *HI* exceed 1 or if *ILCR* and *ILCRtotal* exceed 10−4, then there is a chance that non-carcinogenic or carcinogenic effects might occur. If the *ILCR* and *ILCRtotal* values exceed 10−6, but do not exceed 10−4, there is a potential carcinogenic risk for the heavy metals [22,55].

#### *2.6. Data Analysis*

SPSS Statistics 25 was used for statistical analysis and Origin 2017 for plotting. Excel in office 2016 calculates the coefficient of variation (CV).

#### **3. Results and Discussion**

#### *3.1. Particulate Matter Concentrations*

The average PM2.5 concentrations were 45.31 μg m−3 in Xinqu Park and 57.36 μg m−3 in Huagong Hospital. The concentrations were higher during the heating period (62.63 μg m−3 in Xinqu Park; 68.36 μg m−3 in Huagong Hospital) than in the non-heating period (33.91 μg m−3 in Xinqu Park; 52.05 μg m−3 in Huagong Hospital). This finding is consistent with the results of several studies [48,63]. The concentrations in the study area were relatively low compared with those in Beijing (89.60 to 196.3 μg m−3) [48], Shanghai (103.1 μg m−3) [63], and other large cities. In addition, the concentrations in Chengdu (41.45 to 115.4 μg m−3) [64], a city of greater size and population density than Huludao, were generally higher. The highest PM2.5 concentrations in Huludao were similar to those in the tourist city of Tai'an (63.00 μg m−3) [65] but were lower than those in the industrial city of Zhuzhou (81.00 to 201.3 μg m−3) [27]. Existing studies have shown that only considering the level of social and economic development, when the share of urban secondary industry is low, PM2.5 pollution is lighter, and the polycentric and scattered population distribution will increase the concentration of PM2.5 in Chinese cities [10,11].

The concentration of PM2.5 at Huagong Hospital was higher than that at Xinqu Park. However, the PM2.5 concentration (33.91 μg m−3) at Xinqu Park only met the ambient air quality standards (35.00 μg m−3) during the non-heating period. High concentrations of PM2.5 may increase human health risks [60]. A nonparametric test indicated the PM2.5 concentration in Huagong Hospital and Xinqu Park was significantly different (*p* < 0.05). The concentration was also significantly different between the two sampling sites during the heating and the non-heating periods (*p* < 0.05). These results indicated that the main sources of PM2.5 at the two sampling sites were different. This difference might be because Xinqu Park is downwind of the HZP, whereas Huagong Hospital is in the old part of the city northwest of the HZP and is relatively less affected by plant emissions.

#### *3.2. Heavy Metal Content of PM2.5*

Figure 2 shows heavy metal concentrations in PM2.5 in the study area. Except for As and Pb at Huagong Hospital, the concentration of heavy metals in the heating period was higher than that in the non-heating period. The concentration of heavy metals in PM2.5 at Xinqu Park was higher than that at Huagong Hospital during the heating period, and the concentrations of Ni and Cd at Xinqu Park were higher than those at Huagong Hospital during the non-heating period. Differences in the locations and sources of the two sampling sites might have affected the concentrations of heavy metals. In previous studies, the concentration of heavy metals is highest in winter and lowest in summer [13,64]. The heavy metal content of PM2.5 in the industrial city of Changzhou in southern China in autumn and winter was higher than that in the present study. In Changzhou, traffic and industrial emissions are the main sources of pollution, with coal combustion sources the smallest contributor [30]. By contrast, in Chifeng City in northern China, the main sources of heavy metal pollution are coal combustion and vehicle emissions [66]. The heavy metal concentrations of PM2.5 in Chifeng City are slightly lower than those in the present study, and the highest concentrations occur in winter and the lowest concentrations occur in summer [66].

Nonparametric tests on the heavy metal concentrations in the different sampling periods detected differences between heavy metals (*p* < 0.05), suggesting there were multiple sources of heavy metals. During the heating period, the concentrations of As, Pb, and Cu were relatively high. Arsenic is an important indicator of coal combustion, and Pb is an important pollutant from zinc smelting and may also be a product of coal combustion [64,67]. Therefore, during the heating period, heavy metal concentrations in PM2.5 might be most affected by coal combustion and industrial production. In the non-heating period, the concentrations of As, Pb, and Cu were higher at Huagong Hospital than at Xinqu Park, whereas the concentrations of Ni and Pb were higher at Xinqu Park than at Huagong Hospital. Nickel is primarily derived from the burning of fossil fuels, the wear of auto parts, and ship emissions [64,67,68]. Therefore, during the non-heating period, the differences in the concentrations of heavy metals between the two sampling points might be due to the different locations and sources.

**Figure 2.** Heavy metal concentrations of PM2.5 in Huagong Hospital and Xinqu Park. Note: (**a**) represents April to June; (**b**) represents July to September; (**c**) represents October to De-cember; (**d**) represents January to March.

#### *3.3. Source Apportionment Analysis*

#### 3.3.1. Identification of Factors

The USEPA PMF5.0 model was used to classify the sources of heavy metals in PM2.5 at Huagong Hospital and Xinqu Park. Figure 3 shows the major air pollution sources and their contributions. The first factor had high concentrations of As (44.30–84.53%), Pb (36.61–47.86%), and Cd (6.50–66.20%). Many studies show that As and Pb are important indicators of coal combustion, especially As [64,69,70]. The combustion of coal also releases many trace elements, including Pb and Cd [58,71], and thus, those metals in PM2.5 indicated that coal combustion might be the source. The second factor had high concentrations of Cd (22.53–57.46%), Pb (33.95–50.60%), and Cu (13.87–74.23%). The metals Cd, Cu, and Pb are typically associated with smelting and nonferrous metal industries [64,72–74], Cd and Pb are important pollution signs in zinc smelting [40–42], and therefore, the emission signature was attributed to industrial emissions. The third factor had relatively high concentrations of Ni (12.41–79.05%), Cd (11.27–36.04%), and Pb (12.79–22.62%). Nickel is primarily derived from the combustion of fossil fuels but is also an important indicator of ship emissions and is closely linked with road dust derived from worn vehicle components (e.g., brake linings and tire wear) and vehicle exhaust (oil, diesel combustion) [66,68,75]. The study area is adjacent to Liaodong Bay, and ship emissions might be a source of heavy metals in PM2.5, which were classified as traffic emissions. In this article, the traffic emissions are the total emissions generated by vehicle and ship emissions. The study area is in northeast China and is a typical metallurgical processing area. The HZP produces 3.3 × 105 tonnes of zinc per year, as well as Cd [46] and a variety of heavy metals [76]. Thus, industrial emissions and coal combustion were the main sources of PM2.5 in the study area.

**Figure 3.** Source profiles and percentage contributions of heavy metals in PM2.5 by PMF.

3.3.2. Source Apportionment for the Non-Heating Period

Figure 4 shows the source contributions from the PMF model. In the non-heating period, the main source of heavy metals in PM2.5 at Huagong Hospital was industrial emissions (45.29%), followed by coal combustion (44.59%) and traffic emissions (10.12%). At Xinqu Park, the main source was industrial emissions (43.64%), followed by traffic emissions (31.00%) and coal combustion (25.35%). According to the model results, industrial emissions were the main source of PM2.5 in the study area. The different secondary sources for the two sampling points might be related to their locations and meteorological factors. During the non-heating period, the study area is dominated by southerly and southwesterly winds. Therefore, the sampling points in Xinqu Park were likely affected by emissions from ships in Liaodong Bay, and the contribution from traffic emissions was relatively high. The results in this study are consistent with those for Chengdu and Tai'an in southern China [64,65]. However, industrial emissions had a relatively high contribution in the study area, likely because zinc smelting was the dominant industry. Chengdu is a provincial capital city, and Tai'an is a tourist destination [64,65]. The contribution of industrial emissions during the non-heating period (summer) was higher than that of coal burning, which is in contrast to results from the industrial city of Chifeng in northern China [66]. The contrast may be due to coal-fired power generation and coke plants in Chifeng City, which increase the contribution of coal combustion during the non-heating period [66], compared with that in the study area.

**Figure 4.** Contribution of PM2.5 heavy metals from different source. Note: (**A**) represents non-heating period of Huagong Hospital in 2015; (**B**) represents heating period of Huagong Hospital in 2015; (**C**) represents non-heating period of Xinqu Park in 2015; (**D**) represents heating period of Xinqu Park in 2015.

#### 3.3.3. Source Apportionment for the Heating Period

The PMF model identified the main sources of PM2.5 in the study area (Figure 4). During the heating period, the main source of heavy metals in PM2.5 at Huagong Hospital was coal combustion (59.64%), followed by industrial emissions (21.06%) and traffic emissions (19.30%), whereas at Xinqu Park, industrial emissions (42.14%) dominated, followed by coal combustion (34.03%) and traffic emissions (23.83%). Thus, there were differences in the main sources of PM2.5 heavy metals at the two sampling sites. The differences might be because Huagong Hospital is in the old urban area of the city where the population is relatively dense. Coal-fired heating might also affect the site, in addition to industrial production, particularly considering the wind direction. During the heating period, the wind direction is mainly northerly and southwesterly, and because of the dilution/dispersal effect, the wind direction might have had a disproportionate effect on pollutant dispersal [77]. Coal-fired heating might have had less effect on Xinqu Park. In Liaoning Province, for example, coal consumption for industrial processes reached 175 million tonnes in 2015, including the burning of 140 million tonnes of coal for fuel [78]. In Chengdu, air conditioners are heavily used in the cold period, and that usage creates an increased demand for coal in thermal power generation; hence, the contribution of PM2.5 associated with coal combustion increases in winter [64].

Overall, industrial emissions were the main source of PM2.5 in the study area, followed by coal combustion and then traffic emissions. These results are the same as those for Tai'an and Chengdu [64,65], whereas the differences between Chifeng City and the present study are only because the research area is an industrial city dominated by zinc smelting [66]. At Huagong Hospital, coal-fired heating increased the contribution of coal combustion to heavy metals in PM2.5. A similar conclusion was also reached in a PM10 source analysis in northeast China [79]. According to the data of the Department of Ecology and Environment of Liaoning Province (2015), the contributions to PM2.5 from industrial emissions are much higher than those from traffic emissions. Because contributions from pollution sources change in time and space, there are different combinations of pollutants, and they likely have different health risks to populations.

#### *3.4. Health Risk Assessment*

The human health risk assessment index only represents the assessment results for a particular period. However, because of the bioaccumulation of heavy metals, the assessment results of individual sampling periods cannot fully represent the human health risks in a study area. Therefore, in this paper, the average value of heavy metal concentrations during the sampling period was used to represent the human health risk associated with heavy metals in PM2.5. In this study, *HQ* was used to evaluate the noncarcinogenic risk, and the ILCR was used to evaluate the carcinogenic risk. The different types of risk were analyzed separately for the heating and non-heating periods. The total carcinogenic risk and the total noncarcinogenic risk were calculated by the traditional USEPA method and the entropy weight method; the calculation results are shown in Figure 5.

**Figure 5.** Risk indices of the two methods. Note: (**A**) represents non-heating period in Huagong Hospital; (**B**) represents heating period in Huagong Hospital; (**C**) represents non-heating period in Xinqu Park; (**D**) represents heating period in Xinqu Park. Children: 0–16 years; males: 17–70 years; females: 17–70 years. EWM: entropy weight method.

#### 3.4.1. Non-Carcinogenic Risk Assessment

The lowest noncarcinogenic risk index in the non-heating period was for adult women at Xinqu Park (2.14 × 10−2 (USEPA) and 1.78 × 10−2 (entropy weight method)), whereas the highest noncarcinogenic risk index was for children at Huagong Hospital (0.469 (USEPA) and 0.788 (entropy weight method)) (Figure 5). In the non-heating period, the hazard index was less than 1, indicating that heavy metals in PM2.5 in the study area did not pose a noncarcinogenic human health risk. During the heating period, adult women at Huagong Hospital had the lowest noncarcinogenic risk (5.67 × 10−2 (USEPA) and 1.79 × 10−2 (entropy weight method)), whereas children at Xinqu Park had the highest noncarcinogenic risk index (5.90 (USEPA) and 5.74 (entropy weight method)). The HI value of Xinqu Park was greater than 1, indicating that heavy metals posed a noncarcinogenic risk to human health in the area. Similar results are also reported for Tianjin City and Sistan in Iran [80,81]. Increased human health risks associated with increased heavy metal concentrations have been reported previously [16,82]. For the different populations at the two sampling locations within the same sampling period, the noncarcinogenic risk index was ranked as children > adult males > adult females. Thus, children likely faced the greatest noncarcinogenic risk from heavy metal pollution. This finding is consistent with studies in which the noncarcinogenic risk for children is higher than that for adults. In addition, the exposure parameters of weight and breathing rate, among others, are higher for adult males than for adult females [8,11,31]. A nonparametric test of the population HI determined by the two methods in the different periods showed that the results were

significantly different at Huagong Hospital (*p* < 0.05). However, there was no significant difference between the results of the two methods at Xinqu Park (*p* > 0.05). This difference might be because the main sources of heavy metals in PM2.5 in the heating and non-heating periods at Huagong Hospital were different, as were the main contributing heavy metals. Therefore, the weight of the HQ value of each heavy metal calculated by the entropy weight method was different. To compare the difference in the results of the two methods, the coefficient of variation (CV) of the HI value during the period was calculated. At Huagong Hospital, the CV values of the entropy weight method were slightly higher (1.185 for the non-heating period and 0.524 for the heating period) than those of the USEPA method (1.182 for the non-heating period and 0.453 for the heating period). At Xinqu Park, the CV values of the entropy weight method were lower (0.561 for the non-heating period and 1.419 for the heating period) than those of the USEPA method (0.563 for the non-heating period and 1.437 for the heating period). Thus, there was little difference between the results of the two methods at Xinqu Park, and the values calculated by the entropy weight method had a lower degree of dispersion. In evaluating the effects of long-term changes in heavy metal concentrations in PM2.5 on human health risks, the calculation results of entropy weight method might be more representative.

#### 3.4.2. Carcinogenic Risk Assessment

Figure 5 shows the carcinogenic risk assessment results for the study area. The lowest carcinogenic risk index for the non-heating period was for children at Xinqu Park (3.31 × 10−6 (USEPA) and 3.73 × 10−6 (entropy weight method)), whereas the highest carcinogenic risk index was for adult males at Huagong Hospital (8.30 × 10−5 (USEPA) and 1.55 × 10−4 (entropy weight method)). Therefore, according to the entropy weight method, the heavy metals in PM2.5 in the non-heating period were a potential carcinogenic risk in the study area or a carcinogenic risk to adults at Huagong Hospital (1.55 × 10−4 for males and 1.32 × 10−4 for females). The lowest carcinogenic risk index for the heating period was for children at Huagong Hospital (3.17 × 10−6 (USEPA) and 1.33 × 10−6 (entropy weight method)), whereas the highest carcinogenic risk index was for adult males at Xinqu Park (1.38 × 10−3 (USEPA) and 1.37 × 10−3 (entropy weight method)). During the heating period, the heavy metals in PM2.5 were a potential carcinogenic risk to people at Huagong Hospital and a carcinogenic risk to people at Xinqu Park. A nonparametric test of the population HI of the two methods in different periods showed that the two methods produced different results at Huagong Hospital (*p* < 0.05) but not at Xinqu Park (*p* > 0.05). At Huagong Hospital, the CV values of the entropy weight method were slightly higher (1.185 for the non-heating period and 0.889 for the heating period) than those of the USEPA method (1.160 for the non-heating period and 0.518 for the heating period). At Xinqu Park, the CV values of the entropy weight method were lower (0.742 for the non-heating period and 1.396 for the heating period) than those of the USEPA method (0.607 for the non-heating period and 1.396 for the heating period). The results of the two methods at Xinqu Park were similar, although the result of the USEPA method had a smaller degree of dispersion. For ILCR, the traditional USEPA method might be more applicable. At Xinqu Park, the carcinogenic risk during the heating period was higher than that in the non-heating period, a conclusion also reached in a recent study [8]. However, at Huagong Hospital, the risk of carcinogenesis was high during the non-heating period. For the different population groups, the risk of carcinogenesis was higher in adults than that in children, and the health risks of adult males were higher than those of adult females. Studies of the Pearl River delta region and Baoding City reached conclusions consistent with those of this study [8,83]. With respect to the study area, industrial emissions and coal combustion were the main sources of carcinogenic risk for the population, and heavy metals caused the highest carcinogenic risk to adult males and the lowest carcinogenic risk to children.

#### **4. Conclusions**

In this study, PM2.5 concentrations were quantified, the sources were identified, and health risks were assessed in an industrial area of northeast China. The concentrations of PM2.5 in the heating period were higher than those in the non-heating period. In addition, the PM2.5 concentrations in the old city at Huagong Hospital were higher than those at Xinqu Park. The PM2.5 concentrations of various metals differed. In general, the heavy metal concentrations in the heating period were higher than those in the non-heating period. Nonparametric tests detected differences between concentrations of heavy metals in PM2.5, indicating there were different sources of heavy metals in the study area. The PMF model identified industrial emissions as the main source of heavy metals in PM2.5, followed by coal combustion and then traffic emissions. Compared with the sources of emissions at Xinqu Park, an increased demand for coal combustion for heating during the winter led to elevated concentrations of heavy metals in PM2.5 at Huagong Hospital, demonstrating coal combustion was the most important source of pollution. In the heating period, coal combustion (59.64%) was primary heavy metal source at Huagong Hospitals, and the contribution rates of industrial emissions and traffic emissions were 21.06% and 19.30%, respectively. Industrial emissions (42.14%) were the primary source at Xinqu Park, and the contribution rates of coal combustion and traffic emissions were 34.03% and 23.83%, respectively. During the non-heating period, coal combustion (45.29%) and industrial emissions (44.59%) were the primary sources at Huagong Hospital, and the traffic emissions were 10.12%. Industrial emissions (43.64%) were the primary sources at Xinqu Park, the coal combustion and traffic emissions were 25.35% and 31.00%, respectively. Differences in the contributions of heavy metals in PM2.5 from different pollution sources affected the risks to human health. Children had the highest noncarcinogenic risk and adult females had the lowest noncarcinogenic risk, whereas adult males had the highest carcinogenic risk and children had the lowest carcinogenic risk. Furthermore, during the heating period, there were noncarcinogenic and carcinogenic risks to the public only at Xinqu Park, whereas during the non-heating period, there was potential carcinogenic risk for people in Huagong Hospitals and Xinqu Park. Compared with the traditional USEPA method, the results of the entropy weight method were also reasonable. However, when there is a difference between the two methods, the entropy weight method is recommended to assess noncarcinogenic health risks, because the result has smaller dispersion and is more representative. In contrast, the USEPA method is recommended for ILCR.

 All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Natural Science Foundation of China (Grant Nos. 41722110 and 41571474), Leading Talents and Team Project of Scientific and Technological Innovation for Young and Middle-aged Groups of Jilin Province<PHONE_NUMBER>5RQ), and Major Special Program of Scientific and Technological of Jilin Province<PHONE_NUMBER>3SF).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We want to thank the editor and anonymous reviewers for their valuable comments and suggestions to this paper.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


*Edited by John Kanayochukwu Nduka and Sevgi Akarsu*

Life expectancy is often associated with economic, educational, and public health development. These factors were previously thought to be the exclusive privilege of the developed countries of the world, but the outbreak of COVID-19 seemingly evened the global playing field. COVID-19, the Ebola virus, and HIV and AIDS were considered "rare diseases" at the time of their occurrence and their outbreaks led to new thinking about public health issues. Technological advancements in telecommunication, aviation, industrial processing, agriculture, military armaments, and more, along with their impacts on climate and environment, have led to increasing levels of contaminants, posing a major public health problem. Exposure to toxins and chemicals via processed foods, work/occupation, or the environment is increasingly causing rare diseases that are difficult to diagnose and treat, especially in undeveloped areas of the world with inadequate health facilities and diagnostic skills. This book examines some of these rare diseases. It includes nine chapters that discuss conditions such as Behcet's disease, malignant mesothelioma, dentofacial anomalies, neuropathic pruritus, lipoatrophia semicircularis, and short QT syndrome.

Published in London, UK © 2023 IntechOpen © nightman1965 / iStock

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## **Irreversible Electroporation in Pancreatic Cancer**

Melanie Holzgang, Benjamin Eigl, Suna Erdem, Beat Gloor and Mathias Worni Melanie Holzgang, Benjamin Eigl, Suna Erdem, Beat Gloor and Mathias Worni

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.75737

#### **Abstract**

Pancreatic cancer is the deadliest of the gastrointestinal tract with 5-year survival rates of less than 5%. Given common asymptomatic early disease course, most patients (50%) present with an already metastatic disease, while only 20% can undergo potentially curative resection. The remaining 30% present with locally advanced disease, defined as extended vascular encasement, where the risk of surgical therapy often outweighs its benefits. Traditional thermal local ablative modalities (RFA, MWA, or cryotherapy) have the disadvantage that they are not applicable in proximity to vital vascular structures, which are abundant in the peripancreatic region. Irreversible electroporation (IRE) is an emerging non-thermal alternative that induces apoptosis of tumor cells by the delivery of short repetitive impulses of high-voltage electric current. Given its mostly non-thermal modality, IRE is not hampered by a heat-sink effect and is applicable with little risk around vascular structures, bile and pancreatic ducts. Recent research suggests that local tumor destruction through IRE improves overall survival, progression-free survival and quality of life in patients with locally advanced pancreatic cancer.

**Keywords:** locally advanced pancreatic cancer, borderline resectable pancreatic cancer, irreversible electroporation, local tumor destruction, apoptosis, overall survival

### **1. Introduction**

Pancreatic adenocarcinoma is a prevalent disease with 53,760 newly diagnosed patients in the United States in 2017 [1]. Despite the rapidly growing medical progress in the twenty-first century and extensive efforts in cancer research, pancreatic adenocarcinoma remains a highly aggressive malignancy with 5-year survival rate still not exceeding 5% [1, 2]. By extrapolating annual incidence rates, pancreatic adenocarcinoma is estimated to rise to the second leading

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

cause of cancer-related death in the United States by 2020 [3]. Unfortunately, only a minority of patients presented early during the disease course, and screening programs have been crowned by little success so far [4]. It is globally accepted that early detection of the tumor provides the only chance for cure, given that treatment modalities other than surgical resection are inherently palliative. Several clinical staging systems for patients with pancreatic adenocarcinoma exist, while all of them comply with the only potentially curative treatment option, that is, surgical resection. Thanks to more elaborate imaging techniques now widely available (high-resolution CT, MRI, endoscopic ultrasound), the tumor-vessel relationships can be determined with high precision rendering pretreatment staging increasingly accurate. Among others, one of the largely used and accepted staging definitions has been established by the National Comprehensive Cancer Network (NCCN) [5]. This classification groups pancreatic adenocarcinoma into four categories such as resectable, borderline resectable, locally advanced and metastatic disease [6].

Definition according to the National Comprehensive Cancer Network (NCCN) [6]:

*Resectable tumor*: no distant metastases, no radiographic evidence of superior mesenteric vein (SMV) and/or portal vein (PV) abutment, distortion, tumor thrombus, or venous encasement. Clear fat planes around the celiac axis, hepatic artery and superior mesenteric artery (SMA).

*Borderline resectable*: no distant metastases, encasement of the SMV/PV but without encasement of the nearby arteries or short segment venous occlusion resulting from either tumor thrombus or encasement but with suitable vessel proximal and distal to the area of vessel involvement, allowing for safe resection and reconstruction. Gastroduodenal artery encasement up to the hepatic artery, without extension to the celiac axis. Tumor abutment of the SMA not to exceed >180° of the circumference of the vessel wall (**Figure 1**).

*Locally advanced (unresectable)*: tumor involvement or occlusion of the SMV or PV, which precludes reconstruction of vessels or greater than 180° tumor contact with either SMA, celiac artery or any involvement of first jejunal branch of SMA or Aorta (**Figure 2**).

**Figure 1.** Borderline resectable pancreatic cancer. Tumor with contact to the SMV (black arrow).

**Figure 2.** Locally advanced pancreatic cancer. Encasement of the superior mesenteric artery (black arrow).

**Figure 3.** Metastatic pancreatic cancer. Liver metastasis (black arrow).

*Metastatic disease*: presence of distant metastases (e.g. hepatic, peritoneal, or other) (**Figure 3**).

At the time of diagnosis, only approximately 20% of patients present with a resectable or borderline resectable disease. If treated by surgical resection with negative histological margins (R0) followed by adjuvant chemotherapy, this patient group can achieve a 5-year survival rate of about 20% [2, 5]. While 50% of patients initially present with metastatic disease, the remnant 30% show a locally advanced stage with involvement of vital adjacent structures as defined earlier. While the diagnosis of resectable and metastatic disease usually is simple, the distinction between borderline resectable and locally advanced disease can be challenging. In patients with borderline resectable disease, the involvement of major vessels is less extensive, and a macroscopic negative resection margin (e.g., with segmental/partial resection of venous structures) is potentially achievable without adding major morbidity [7]. In contrast, patients with locally advanced pancreatic cancer are often characterized by arterial vessel involvement. A resection with negative margins in these cases is technically rarely feasible

and often implies arterial resection associated with high perioperative morbidity and mortality [8–11]. A timely meta-analysis by Mollberg et al. assessed the impact of arterial resection on perioperative outcomes among patients undergoing pancreatic resection. They showed that perioperative morbidity and mortality was, with 53 and 12%, significantly higher for patients with arterial resection compared to around 25–30 and 6% reported for pancreatoduodenectomies, not requiring arterial reconstruction, respectively [12–14]. In summary, given the high perioperative risk and its limited impact on survival, arterial resection can at present not be justified as a standard procedure in the treatment of pancreatic cancer [2, 10]. Accordingly, the current treatment recommendations for patients with locally advanced pancreatic cancer are chemo- and/or radiotherapy, which achieve a median overall survival of 9–13 months on average [2]. There is notably a difference in the standard treatment regimens according to the geographic location; most patients in the USA with locally advanced pancreatic cancer are currently undergoing combined chemoradiotherapy, whereas patients in the Europe are usually treated with chemotherapy alone. Recent advances in chemotherapy allow to downstage certain patients with locally advanced disease, making a surgical resection of some tumors possible [15, 16]. Patients treated with neoadjuvant chemotherapy followed by surgical resection can achieve similar survival rates like patients diagnosed in a resectable or borderline resectable state. It is estimated that in select patient cohorts, up to one-third of patients initially judged as non-resectable can be converted into a resectable state by neoadjuvant chemotherapy. However, the neoadjuvant regimen is still debated and is not internationally accepted yet as standard of care [17, 18].

Given that patients with locally advanced disease have a poor prognosis despite multimodal therapy, additional treatment alternatives are desperately needed. In the group of locally advanced pancreatic cancer, the tumor is confined to the location of origin without evidence of distant spread—rendering local therapy an attractive additional treatment option. As such, loco-regional therapies including radiofrequency ablation (RFA), microwave ablation (MWA) and irreversible electroporation (IRE) have gained increased attention in the treatment of locally advanced pancreatic adenocarcinoma over the last years [19, 20].

## **2. Local ablative strategies**

#### **2.1. Thermal local ablative strategies**

Radiofrequency (RFA) and microwave ablation (MWA) have been used in an attempt to achieve local control among patients with locally advanced pancreatic cancer. Both treatments generate thermal energy by a high-frequency alternating current, which is delivered to the cancerous tissue by one or more needle electrodes. The created high local temperature at the tip of the electrodes leads to cell death by coagulative necrosis and protein denaturation [21, 22]. RFA has been used with success in the setting of unresectable tumors in multiple solid organs (liver, lung, kidney, brain, breast, prostate, bone, adrenal glands, spleen) [22]. Over the last years, it has also been deployed in the palliative setting of locally advanced pancreatic cancer. However, RFA in patients with pancreatic adenocarcinoma has not been widely accepted because of considerable morbidity and mortality rates [19]. The high complication rate was thought to be due to thermal injury to the multiple delicate structures (bile duct, pancreatic duct, duodenum, vital vessels)

surrounding the pancreas. By adjusting the administered temperature from 105 to 90°C for 5 min' length, complications in recent patient cohorts treated by RFA were substantially reduced [20]. Still, gastrointestinal hemorrhages, acute pancreatitis, pancreatic/biliary fistulas, duodenal injury and portal vein thrombosis are regularly reported in the literature [19]. A systematic review from 2014 cited an RFA-related morbidity ranging from 10 to 37% and an RFA-related mortality from 0 to 19% [23]. Another important downside of thermal ablative therapies is the so-called "heat-sink effect." During the ablation process, adjacent blood vessels are "cooling the tissue down" leading to an insufficient temperature in the immediate proximity of the vessels, where therefore efficient cell death cannot be induced [24]. Given the anatomical complexity of the pancreatic region and bearing the abovementioned aspects in mind, it is self-evident that the application of thermal ablative therapy in locally advanced pancreatic cancer is delicate. It is at this state unknown whether RFA should be combined with chemo- and/or radiotherapy as a standard treatment. A retrospective analysis of patients with locally advanced pancreatic cancer with short induction chemotherapy and RFA compared to a patient group with RFA did not show a difference in early disease progression or overall survival [25]. While there is no evidence from randomized controlled trials regarding the oncological outcome of RFA in locally advanced disease, several case series show a significantly increased median overall survival in patients where RFA was part of the treatment concept [26, 27].

MWA is less prone to the heat-sink effect compared to RFA and is therefore more suitable for application closer to large vessels. Similar to RFA, no randomized data using MWA in locally advanced pancreatic cancer are available [28]. Given the heterogeneous reports of MWA and RFA, direct comparisons between the two techniques in regard of long-term survival are currently not available. However, based on published evidence, MWA seems to lead to less postoperative pain and decreased ablation time with similar results in morbidity and mortality compared to RFA [29]. However, at present, MWA is still studied less extensively than RFA [30, 31].

#### **2.2. Irreversible electroporation**

#### *2.2.1. Introduction to IRE*

Irreversible electroporation (IRE) is an emerging ablative modality that gained enormous interest over the last years. In contrast to the abovementioned thermal ablative strategies, IRE leads to cell death mainly through a nonthermal technique. In IRE, high voltage (maximum of 3000 V) electrical pulses of 70–90 μs duration are applied through a minimum of two electrodes positioned next to or into the target neoplastic tissue. The thus created electrical field leads to a disruption of the cell membrane's lipophilic bilayer by formation of nanoscale micropores. This damage to the cell membrane eventually leads to a collapse of intracellular homeostasis and an activation of apoptotic pathways, finally resulting in cell death. The distinct advantage of this technique compared to thermal ablative strategies is the preservation of structural components like collagen and elastin as thermal damage does only occur in the very close proximity to the ablation needles depending on pulse length, exposure of the needle tips, delivered energy, distance between the electrodes and underlying tissue. Another advantage of IRE compared to thermal ablative modalities is its nonexisting "heat sink effect," which means that the efficiency of IRE will not be reduced in proximity to large vessels [21, 32]. For the above-cited reasons, IRE is a very attractive local ablation method in pancreatic cancer, given the inherent proximity of the pancreas to vital vascular structures as well as the bile and pancreatic duct.

However, IRE cannot be applied under any circumstances given that several contraindications for its use exist. The presence of metallic material in close proximity to the placed IRE needles (e.g., metallic biliary stent that is not removable) is a relative contraindication for IRE, given that the conductivity of the metal could potentiate the minimal thermal effect of it. Even more importantly, the presence of metal can distract, respectively, and derivate the electrical current used in IRE, rendering prediction of the ablation zone impossible. Hence the effect of IRE is potentially dangerous [33, 34]. Also, a tumor size >5 cm is generally seen as a contraindication, given that the volume of the ablation zone of a tumor exceeding this size is technically difficult to achieve [21]. Additionally, IRE is contraindicated in patients with certain cardiac arrhythmias, and patients with pacemakers should be evaluated by a cardiologist prior to IRE, as the high-voltage electric current applied can itself provocate potentially serious arrhythmias [32]. To avoid such complications in ablations at the level of the pancreas, the electrical pulses are applied during the complete refractory phase of the heart (50 ms after the R wave). To achieve the coordination of the IRE pulses and the heart rhythm of the patient, the IRE device is synchronized with the patient's ECG. Furthermore, application of IRE is not recommended in patients having a history of epilepsy or recent myocardial infarction. No data exist about the use of IRE in pregnancy.

#### *2.2.2. IRE in locally advanced pancreatic cancer*

IRE has first been established as a complementary local therapy in conjunction with chemotherapy for patients with locally advanced pancreatic cancer, which is not amenable to surgical resection [30]. In situations where surgical resection seems too risky (e.g., a tumor encapsulating the superior mesenteric artery), IRE has shown to be a safe and valuable treatment alternative. Standalone IRE without surgical resection of the primary tumor is called "in situ" IRE. Its primary aim is to achieve maximal local tumor control. As in thermal ablative strategies, there is currently no randomized data available that look at oncological outcomes of (radio-) chemotherapy and IRE compared to (radio-) chemotherapy alone. However, there is encouraging evidence that suggests a relevant improvement of overall survival in patients with in situ IRE after induction chemotherapy/(radio-) chemotherapy [2, 35]. A propensity-matched score analysis by Martin et al. showed a survival benefit of induction chemotherapy and/or radiation followed by IRE compared to (radio-) chemotherapy alone. The additional treatment with IRE showed a prolongation of local progression free survival from 6 to 14 months, distant progression free survival from 9 to 15 months and overall survival from 13 to 20 months [35]. Another study analyzing 200 patients with locally advanced pancreatic cancer, either undergoing in situ IRE or margin accentuation IRE after an induction chemotherapy/(radio-)chemotherapy showed an encouraging median overall survival of 24.9 months and local recurrence rates of only 3% [36]. These results indicate that local tumor control with IRE is achievable and has a significant positive effect on patients with locally advanced pancreatic cancer. However, the interpretation of data on long-term oncological outcomes after IRE is still difficult, given that the studies available are of substantial heterogeneity and mostly lacking direct control groups. Additionally, most studies were not primarily designed to demonstrate oncological efficacy of the procedure but rather aimed to demonstrate safety and efficacy of the IRE procedure itself [2]. Some authors emphasize the possible impact of neoadjuvant chemotherapy over the direct effect of IRE given that the specific impact of IRE has not yet been demonstrated. However, Gillen et al. found a median overall survival of 22 months in patients with locally advanced pancreatic cancer treated with neoadjuvant chemotherapy and if possible subsequent pancreatic resection [17]. These survival outcomes are still slightly worse than the ones of the 200 patients documented by Martin et al. in his cohort undergoing in situ IRE/margin accentuation IRE with pancreatic resection. IRE thus seems to add some additional benefit that systemic chemotherapy cannot provide, most probably by its local field of action. This observation has led to the hypothesis that the resection margin in pancreatic cancer deserves further investigation, as IRE might contribute to better overall survival by achieving more "true" R0 resections (see Chapter 4) [37]. Additional studies focusing on overall survival are certainly needed to further investigate the potential of IRE in improving the outcomes of patients with locally advanced pancreatic cancer.

#### **2.3. Induction therapy in locally advanced pancreatic cancer before in situ IRE**

At present, in situ IRE is mainly recommended in combination with upfront chemotherapy or (radio-) chemotherapy for at least 3 months. This does not only allow a "test for time" to get familiar with the biology of the patients underlying tumor, but also avoids local treatment with in situ IRE among patients with metastatic disease. Several induction treatment regimens have been suggested while so far no specific data are available, which favor one regimen over the other. Gemcitabine-based chemotherapy and/or radiotherapy is an option; however, more recent studies show beneficial results with the more aggressive FOLFIRINOX regimen as initial therapy [38–40]. Given the significant toxicity of FOLFIRINOX, a modified regimen has been suggested, where the 5-FU bolus is left out [15]. An alternative chemotherapy regimen often used in advanced pancreatic cancer and also in the setting of induction therapy before IRE is the combination of gemcitabine and Nab-paclitaxel [41]. Further studies assessing the best inductive treatment before IRE are needed before any general recommendation can be given.

Whatever induction therapy is used, it must be followed by restaging investigations. While there is no standard algorithm recommended, we perform a 3-phase contrast enhanced pancreas protocol computer tomography including the chest, to exclude pulmonary metastases and to plan the IRE procedure in detail. High quality CT-scans allow for sound judgment of tumor vessel relationships. In addition, given that diffusion MRI of the liver has shown to outperform CT-scans in regard of detection of liver metastases, all patients will undergo this imaging tool prior to surgical exploration [42].

## **3. Technique of IRE**

#### **3.1. General considerations**

As mentioned earlier, all eligible patients for in situ IRE with locally advanced pancreatic cancer have to complete at least 3 months of neoadjuvant (radio-) chemotherapy, mainly to avoid local IRE treatment in patients with metastatic disease. This said, restaging after finishing induction treatment is crucial and should be performed with major diligence. Noteworthy is the usually absent "radiographic response" in pancreatic imaging after neoadjuvant

therapy—consensus is therefore to proceed to in situ IRE unless imaging shows local disease progression or newly observed distant metastases [2, 35, 36].

In case restaging confirms the presence of locally advanced pancreatic adenocarcinoma, every patient should be discussed at an interdisciplinary tumor board including medical oncologists, radiation oncologists, radiologists, pathologists and surgical oncologists. If tumor response is achieved and the lesion can be downstaged to borderline resectable disease, patients should be considered for surgical resection with margin-accentuation IRE (see part 4). In cases of stable disease without development of distant metastases and a maximal tumor diameter of <5 cm, patients can be planned for in situ IRE.

#### **3.2. Practice of IRE**

#### *3.2.1. Open approach*

Technically, there are different ways to apply IRE to a target lesion. Practice in our institution is at the moment the "classical" open abdominal approach. An upper midline or transverse incision is performed followed by a meticulous abdominal exploration looking for occult metastatic disease. IRE needles are then placed under ultrasound guidance covering the suspected tumor area. At least two unipolar probes are needed to deliver the high-voltage current. Parallel orientation of the needles is of utmost importance, with ideally a distance of about 2 cm between each needle pair (**Figures 4** and **5**).

A maximum of six probes can be inserted at the same time [43]. During the IRE procedure itself, full neuromuscular relaxation has to be guaranteed as the high voltages transmitted by the electrodes can produce significant muscular contractions [44]. If successful ablation was performed at one site, needle pull-back can be repeated as many times as needed with performance of the treatment at another level in order to cover the full desired ablation volume. Early imaging documentation of the success of IRE treatment is not possible, given the unspecific postoperative changes. As such, control imaging by CT-scan is not recommended before 3 months after IRE, as the images can be altered by ongoing edema following electroporation [45].

**Figure 4.** (a and b) Parallel placement of two needles at the distance of 2 cm for an IRE treatment around the common hepatic artery.

**Figure 5.** Intraoperative needle positioning under ultrasound guidance. In this example the two needle tips (red arrows) are placed to the left and right of the superior mesenteric artery (\*).

#### *3.2.2. Minimal-invasive approach*

IRE may also be applied in the setting of minimally invasive surgery under laparoscopic ultrasound guidance [46].

Additionally, surgical interventions like hepaticojejunostomy or gastroenterostomy, which have the potential to improve the quality of life in patients suffering from locally advanced pancreatic adenocarcinoma, can be performed during the same intervention in patients receiving IRE by either an open or a laparoscopic approach.

#### *3.2.3. Percutaneous approach*

Several groups have gained experience in the percutaneous application of IRE supported by different imaging modalities. Narayanan et al. reported a series of 50 patients with CT-guided percutaneous IRE. The procedure was technically feasible in all patients. A median overall survival of 27 months from the time of diagnosis and 14 months from the time of IRE was reported, which is comparable to the oncological outcomes observed in open IRE [47]. Another group around Mansson performed IRE under ultrasound guidance. Of the 24 patients, all treatments were completed using ultrasound guidance only [48]. The case series presented are small, but the data suggest that the percutaneous approach is technically feasible and generally safe [47, 48]. A potential drawback of the percutaneous approach is the lack of visual assessment of the peritoneal cavity. Small liver/peritoneal lesions can be missed and patients with potential metastatic disease might be "locally overtreated," given that at present no data for application of IRE in metastatic settings exist.

#### **3.3. Potential complications of IRE**

Despite its nonthermal technique, also IRE is associated with potential complications. In the so far largest population from Martin et al. consisting of 200 patients treated with IRE, a total rate of 37% adverse events were recorded along with a mortality rate of 2%. The most common adverse events reported were pancreatic leak, pancreatitis and duodenal ulcer formation. Also, less frequently vascular complications (such as hepatic arterial thrombosis or mesenteric/portal vein thrombosis) and liver dysfunction/failure have been observed [36]. A group from Scandinavia analyzed the so-far gained IRE experience in a recent review including 10 studies comprising 446 patients in total (304 patients treated with open IRE and 142 patients treated percutaneously). A total of nine fatalities (2%) were recorded, while overall complication rate was summarized to be 35% [37]. It has to be kept in mind that complications after open IRE are challenging to interpret, as in many cases, patients had major gastrointestinal surgery in addition to their IRE treatment. However, whereas most complications seemed self-limited, there have been several reports on severe complications in open IRE such as portal vein thrombosis, pancreatic fistula and pancreatitis. Overall complications following percutaneous IRE vary from 0 to 20% in the different study groups. In the abovementioned population of 50 patients from Narayanan et al., most patients described postinterventional abdominal pain, 10 patients (20%) were reported to have a severe complication, but no IRErelated deaths occurred [47].

## **4. Navigated IRE**

An additional, novel technology is the so-called navigated IRE. One of the most critical and difficult steps of IRE is the correct positioning of the needles in accurate depth and perfect parallelism. If IRE is performed in an open fashion, the placement of the needles is normally controlled under ultrasound guidance. However, given the complex anatomical situation around the pancreas, 3D reconstructions based on preoperative imaging can provide the surgeon with a better topologic understanding of the patient's specific anatomy. Those reconstructions can nowadays be transferred to planning tools and even be used intraoperatively as navigational help. It has been shown that the ability to plan procedures on these image data and visualize them during the surgery holds significant value as different surgical strategies can be evaluated on the 3D models preoperatively and can be used as additional patientspecific information throughout the surgery (**Figure 6**) [49, 50].

In 2005, Grenacher et al. discussed the role of computer-assisted surgery (CAS) in the field of liver and pancreas surgery [51]. Up to then, CAS was well established in surgical procedures related to orthopedics and neurosurgery, but the advantage of transferring the knowledge to soft tissue applications was insufficiently studied. However, advances in computer science nowadays enable intraoperative navigation in hepato-pancreato-biliary surgery. Using the CAScination® system, the real world can be linked to tThe virtual scene, either by using landmarks on the surface of the organs or by using ultrasound to mark internal structures like

**Figure 6.** 3D reconstruction showing the arteries (red), veins (blue), tumor of the pancreatic body (yellow), duodenum (green) and the pancreas (light green).

**Figure 7.** Demonstration of equipment for navigated IRE: touch screen (red arrow); infra red detection device (black arrow); metal spheres required for real-time instrument tracking consisting of instrument and ultrasound (white arrow).

tumors or bifurcations of vessels [52]. The surgical instruments are then equipped with markers, which can be detected by an optical tracking system in real time (**Figure 7**).

A specific solution for IRE treatment of the pancreas has been implemented, which provides the surgeon with the possibility to preoperatively verify the needle placement based on given constraints like parallelism and spacing between the needles [53]. The aim of this novel technique in IRE would be the placement of the needles under live CAS-guidance according to the preoperatively defined plan. Nevertheless, further improvements of the intraoperative navigation

**Figure 8.** Preoperative 3D planning of an in situ IRE with 4 needles in a patient with locally advanced pancreatic cancer.

tools are required before they can be implemented to clinical routine and will be tackled by our team in the near future (**Figure 8**).

## **5. Additional indications: margin accentuation IRE**

In recent years, the indications of IRE have been expanded to the so-called "margin accentuation" IRE, typically in patients with borderline resectable pancreatic cancer. In this patient group, IRE is used as an adjunct to surgery intraoperatively, aiming to achieve a higher percentage of negative margin resections [32]. It is well known that margin-negative resection is a strong indicator for better overall survival in pancreatic cancer. However, isolated local recurrences are observed in 35–80% of patients after intended R0 resection, raising the hypothesis that R1 resections are underestimated [54, 55]. A comprehensive work-up done by Esposito et al. confirmed that almost 80% of the patients had a true R1 resection, if a thorough examination is performed by the pathologist [56]. It is commonly accepted that R1 resections are associated with worse outcome as compared to R0 resections. In addition, there are different R0 definitions used in the current literature: no microscopic tumor at the or within 1 mm of the resection margin [57]. Hence, the role of R1 resections is not yet entirely clear—some advocate that R1 margins have a negative impact on the overall survival, whereas others state that R1 margins do influence local recurrence rates, without having a significant effect on survival [58–61]. Margin-accentuation IRE has been implemented in multiple pancreatic centers aiming to achieve a higher "true" R0 percentage and to therefore potentially increase overall survival and decrease local recurrences. At the present time, there are no clear recommendations of when margin accentuation IRE should be performed, because there are no clear radiological signs of when a microscopic positive resection margin has to be expected. Given the true R1-resection rate of up to 80%, one might argue that every patient with suspected or proven pancreatic cancer should have a margin accentuation IRE, if the additional procedure risk is limited and operation time is not significantly prolonged. However, as long as no data are published on the benefit of margin accentuation IRE over surgical resection only, the indication remains somewhat arbitrary. Further research is definitely needed to assess the independent effect of margin accentuation IRE on local recurrence rates and overall survival.

## **6. Conclusions**

Pancreatic cancer remains a highly lethal disease. Especially patients with locally advanced pancreatic cancer usually face a discouraging prognosis with limited treatment options. The local ablative therapy with IRE is a valuable additional treatment modality, which, looking at present evidence, seems to have the potential to improve disease-specific and overall survival among patients with this dreadful disease. IRE is technically feasible and generally safe in its open and minimal-invasive access. It can either be applied as in situ IRE in unresectable cases or as a complementary treatment to surgery in borderline resectable patients in order to improve the percentage of true R0 resections. Despite being now an accepted and increasingly applied therapy, there are still a lot of open questions regarding the use of IRE. Future efforts should aim toward the establishment of standard treatment protocols for IRE, in order to make its potential benefit available to more patients suffering from pancreatic adenocarcinoma.

## **Conflict of interest**

The authors declare no conflict of interest.

## **Notes/thanks/other declarations**

None.

## **Author details**

Melanie Holzgang, Benjamin Eigl, Suna Erdem, Beat Gloor and Mathias Worni\*

\*Address all correspondence to<EMAIL_ADDRESS>
Department of Visceral Surgery and Medicine, University Clinic of Bern, Inselspital, Bern, Switzerland

## **References**

FOLFIRINOX for locally advanced unresectable (LAPC) and borderline resectable (BRPC) adenocarcinoma of the pancreas. Annals of Surgical Oncology. 2015;**22**:1153-1159


**Contents**

**Jose M. Le ´on-P ´erez, Mindy K. Shoss, Aristides I. Ferreira and Gabriele Giorgi** Emerging Issues in Occupational Health Psychology Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 11621, doi:10.3390/ijerph182111621 . **1 Nicola Magnavita, Paolo Maurizio Soave, Walter Ricciardi and Massimo Antonelli** Occupational Stress and Mental Health among Anesthetists during the COVID-19 Pandemic Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 8245, doi:10.3390/ijerph17218245 . . . **5 Martin Sanchez-Gomez, Gabriele Giorgi, Georgia Libera Finstad, Flavio Urbini, Giulia Foti, Nicola Mucci, Salvatore Zaffina and Jos´e M. Le ´on-Perez** COVID-19 Pandemic as a Traumatic Event and Its Associations with Fear and Mental Health: A Cognitive-Activation Approach Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 7422, doi:10.3390/ijerph18147422 . . . **19 Georgia Libera Finstad, Gabriele Giorgi, Lucrezia Ginevra Lulli, Caterina Pandolfi, Giulia Foti, Jos ´e M. Le ´on-Perez, Francisco J. Cantero-S´anchez and Nicola Mucci** Resilience, Coping Strategies and Posttraumatic Growth in the Workplace Following COVID-19: A Narrative Review on the Positive Aspects of Trauma Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 9453, doi:10.3390/ijerph18189453 . . . **33 Ward van Zoonen, Anu Sivunen, Kirsimarja Blomqvist, Thomas Olsson, Annina Ropponen, Kaisa Henttonen and Matti Vartiainen** Factors Influencing Adjustment to Remote Work: Employees' Initial Responses to the COVID-19 Pandemic Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 6966, doi:10.3390/ijerph18136966 . . . **57 Carla Estrada-Mu ˜noz, Alejandro Vega-Mu ˜noz, Dante Castillo, Sheyla M ¨uller-P´erez and Joan Boada-Grau** Technostress of Chilean Teachers in the Context of the COVID-19 Pandemic and Teleworking Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 5458, doi:10.3390/ijerph18105458 . . . **77 Julija Gecaite-Stonciene, Adomas Bunevicius, Julius Burkauskas, Julija Brozaitiene, Julius Neverauskas, Narseta Mickuviene and Nijole Kazukauskiene** Validation of the Multidimensional Fatigue Inventory with Coronary Artery Disease Patients Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 8003, doi:10.3390/ijerph17218003 . . . **91 Aristides I. Ferreira, Amalia R. P´erez-Nebra, Eva Ellen Costa, Maria Luisa A. Aguiar, Adriane Zambonato, Carla G. Costa, Jo˜ao G. Modesto and Paula da Costa Ferreira** Presenteeism and Productivity: The Role of Biomarkers and Hormones Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 5014, doi:10.3390/ijerph18095014 . . . **109 Ieva Urbanaviciute, Koorosh Massoudi, Cecilia Toscanelli and Hans De Witte** On the Dynamics of the Psychosocial Work Environment and Employee Well-Being: A Latent Transition Approach Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 4744, doi:10.3390/ijerph18094744 . . . **119**

## **Oliver Weigelt, Antje Schmitt, Christine J. Syrek and Sandra Ohly**

Exploring the Engaged Worker over Time—A Week-Level Study of How Positive and Negative Work Events Affect Work Engagement Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 6699, doi:10.3390/ijerph18136699 . . . **141**

## **Isabell Koinig and Sandra Diehl**

Healthy Leadership and Workplace Health Promotion as a Pre-Requisite for Organizational Health

Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 9260, doi:10.3390/ijerph18179260 . . . **169**

#### **Wenqing Tian, Huatian Wang and Sonja Rispens**

How and When Job Crafting Relates to Employee Creativity: The Important Roles of Work Engagement and Perceived Work Group Status Diversity Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 291, doi:10.3390/ijerph18010291 . . . **189**

## **Jean-S ´ebastien Boudrias, Francesco Montani and Christian Vandenberghe**

How and When Does Psychological Wellbeing Contribute to Proactive Performance? The Role of Social Resources and Job Characteristics Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 2492, doi:10.3390/ijerph18052492 . . . **207**

#### **Xiulan Cheng, Ying Ma, Jiaqi Li, Yonghui Cai, Ling Li and Jiao Zhang**

Mindfulness and Psychological Distress in Kindergarten Teachers: The Mediating Role of Emotional Intelligence

Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 8212, doi:10.3390/ijerph17218212 . . . **231**

#### **Silu Chen, Wanxing Jiang, Xin Li and Han Gao**

Effect of Employees' Perceived Green HRM on Their Workplace Green Behaviors in Oil and Mining Industries: Based on Cognitive-Affective System Theory Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 4056, doi:10.3390/ijerph18084056 . . . **243**

#### **Xiao Deng, Xi Guo, Yenchun Jim Wu and Min Chen**

Perceived Environmental Dynamism Promotes Entrepreneurial Team Member's Innovation: Explanations Based on the Uncertainty Reduction Theory Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 2033, doi:10.3390/ijerph18042033 . . . **259**

#### **Silu Chen, Yu Zhang, Lili Liang and Tao Shen**

Does Paradoxical Leadership Facilitate Leaders' Task Performance? A Perspective of Self-Regulation Theory

Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 3505, doi:10.3390/ijerph18073505 . . . **271**

#### **Inmaculada Mateo-Rodr´ıguez, Emily Caitlin Lily Knox, Coral Oliver-Hern´andez, Antonio Daponte-Codina and on behalf of the esTAR Group**

Mediational Occupational Risk Factors Pertaining to Work Ability According to Age, Gender and Professional Job Type

Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 877, doi:10.3390/ijerph18030877 . . . **287**

**Miriam Benitez, Jose M. Leon-Perez, Alejandro Orgamb´ıdez and Francisco J. Medina** Interpersonal Conflicts in the Unit Impact the Service Quality Rated by Customers: The Mediating Role of Work-Unit Well-Being Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 8137, doi:10.3390/ijerph18158137 . . . **297**

**Gabriela Petereit-Haack, Ulrich Bolm-Audorff, Karla Romero Starke and Andreas Seidler** Occupational Risk for Post-Traumatic Stress Disorder and Trauma-Related Depression: A Systematic Review with Meta-Analysis

Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 9369, doi:10.3390/ijerph17249369 . . . **311**

**Anne Richter, Marta Roczniewska, Carina Loeb, Christiane R. Stempel and Thomas Rigotti** The Cross-Level Moderation Effect of Resource-Providing Leadership on the Demands—Work Ability Relationship

Reprinted from: *Int. J. Environ. Res. Public Health* **2021**, *18*, 9084, doi:10.3390/ijerph18179084 . . . **333**

#### **Luo Lu, Shu-Fang Kao, Ting-Ting Chang and Cary L. Cooper**

Gender Diversity and Work–Life Conflict in Changing Times Reprinted from: *Int. J. Environ. Res. Public Health* **2020**, *17*, 9009, doi:10.3390/ijerph17239009 . . . **347**

## *Editorial* **Emerging Issues in Occupational Health Psychology**

**Jose M. León-Pérez 1,\* , Mindy K. Shoss 2,3, Aristides I. Ferreira 4 and Gabriele Giorgi 5**

The world of work is changing dramatically due to continuous technological advancements and globalization (the so-called industry 4.0), the health crisis due to the COVID-19 outbreak, and the ongoing global economic crisis, all of which have forced adaptation to new ways of organizing work that have been slowly incorporated during the last few decades (e.g., home working and teleworking), climate change demands, and social movements such as those trying to offer more sustainable alternatives to the current economic model of neoliberalism and the demand-driven manufacturing system, among other factors. Moreover, several studies have shown that workers' health and well-being can be at higher risk when organizations face relevant changes and economic turbulence [1].

In that sense, after a peer-review process involving international experts, the 21 papers accepted in this Special Issue are reviews and empirical contributions that highlight the emergence of new psychosocial risks for employees' health and well-being, which are challenging the existing theoretical models and evidence-based practices in Occupational Health Psychology (OHP).

Undoubtedly, the COVID-19 pandemic has posed some challenges. As usually occurs when an unexpected catastrophe happens, researchers are exploring the short- and long-term consequences on health and well-being, comparing with previous situations or another similar health-related crisis. In this sense, most studies have focused on the negative aspects of the COVID-19 pandemic. For example, Nicola Magnavita and colleagues conceptualize the COVID-19 pandemic crisis as a macro stressor that is negatively affecting the mental health of workers, particularly those in the healthcare sector [2]. Similarly, Martin Sanchez-Gomez and colleagues considered that the COVID-19 pandemic is a traumatic event that is associated with feelings of fear and impaired mental health [3]. Drawing on the Cognitive Activation Theory of Stress [4], they conducted two studies comprising more than 1100 participants in total. Their data supported the idea that the COVID-19 pandemic is associated with intrusive thoughts that keep people in a hyperactivated state (i.e., hyperarousal) which, in turn, diminishes mental health and increases negative emotions such as fear of social activities and being infected. On the other hand, taking a more positive approach, Georgia Libera Finstad and colleagues [5] reviewed previous studies that have explored resilience and growth at work after being exposed to a traumatic event such as the COVID-19 pandemic.

In a similar vein, other studies have focused on the changes in the work design and working procedures that have been triggered by the COVID-19 pandemic, especially regarding remote work or telework. For example, Ward van Zoonen and colleagues explored the factors influencing adjustment to remote work during the beginning of the COVID-19 related lockdown in a sample of 5452 Finnish employees [6]. Their findings identified the interplay between both environmental and contextual factors in predicting adjustment to

**Citation:** León-Pérez, J.M.; Shoss, M.K.; Ferreira, A.I.; Giorgi, G. Emerging Issues in Occupational Health Psychology. *Int. J. Environ. Res. Public Health* **2021**, *18*, 11621. https://doi.org/10.3390/ ijerph182111621

Received: 27 October 2021 Accepted: 2 November 2021 Published: 5 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

remote work. Additionally, Carla Estrada-Muñoz and colleagues [7] concluded that the lack of technological skills can endanger a risk of technostress in Chilean teachers.

Another issue that some studies in this Special Issue have covered is the development of scales to measure factors of psychosocial risks at work in particular groups such as the fatigue inventory adapted to coronary artery disease workers by Julija Gecaite-Stonciene and colleagues [8]. Indeed, people with cardiovascular disease have a higher risk of experiencing fatigue at work and, therefore, normative data in this population are needed. Regarding methodological issues, the study conducted by Aristides I. Ferreira and colleagues [9] addresses one important issue in advancing the field of OHP: combining self-report measures with physiological measures. As one of the typical limitations that studies include is the lack of physiological measures compared to self-reported measures, they discuss studies integrating physiological and self-reported measures and offer interesting further research avenues. Particularly, they reviewed the role of biomarkers and hormones in the relationship between presenteeism (another emerging topic in OHP) and performance.

Furthermore, from a methodological point of view, OHP should incorporate more complex designs beyond cross-sectional survey studies to capture dynamic processes in a changing environment. For example, Ieva Urbanaviciute et al. [10] conducted a study with two measurement points in a sample of 959 employees working in Switzerland. They used a latent transition approach to explore dynamics between psychosocial work environment and employee well-being. Additionally, Oliver Weigelt et al. [11] reported the results of a weekly diary study that explores how positive and negative events affect engagement over time. According to theoretical frameworks that advocate for analyzing how certain events can produce behavioral change in organizational contexts [12,13], they focus on the effects that exposure to discrete events at work have on workers' engagement through the emotions associated with such events. Their results revealed "that positive events accumulate to feed continuously high levels of work engagement over periods of several months" (p. 23). Finally, following a qualitative approach, Isabell Koinig and Sandra Diehl [14] report the key role of healthy leadership and the adoption of workplace health promotion practices, as part of the organizational culture, to develop healthy organizations.

Following a similar positive approach, some authors have explored how personal and social resources can increase employee well-being and performance. For example, Wenqing Tian et al. [15] analyzed some mechanisms that can shed some light on the relationship between job crafting and creativity at the individual level. Additionally, Jean-Sébastien Boudrias et al. [16], in line with the happy-productive worker hypothesis, explore the boundary conditions for the association between employee well-being and proactive performance. Finally, Xiulan Cheng et al. [17] examine how emotional intelligence mediates the relationship between mindfulness and psychological distress in a sample of kindergarten teachers.

In line with the United Nations' goals for a sustainable and healthy development [18], several studies have analyzed how certain features of individuals, teams, and organizations are associated with more sustainable and productive behaviors in complex environments. For example, Silu Chen et al. [19] reported how more sustainable and green HR practices can promote workers' green behaviors in industries with high environmental impact; Xiao Deng et al. [20] examined how more dynamic environments are associated with higher entrepreneurial innovation; and Silu Chen et al. [21] investigated how paradoxical leadership (i.e., using strategies that simultaneously balance and satisfy both structural and individual needs) is related to task performance.

Finally, from a stressor–stress–strain perspective, several studies have explored the association of diverse work-related factors with key outcome variables in OHP. Mateo-Rodríguez et al. [22] focused on occupational risk factors for work ability depending on sociodemographic information, which is crucial for identifying exposure risk groups and therefore implementing more accurate and/or tailored measures. Miriam Benítez et al. [23] conceived intragroup conflict as an interpersonal stressor that can negatively affect well-

being at the team level and, in turn, decrease the quality of service the unit provides (reported by customers). Gabriela Petereit-Haack et al. [24] conducted a systematic review and meta-analysis of the occupational risks that are related to post-traumatic stress disorder and trauma-related depression. Anne Richter et al. [25], in line with the differentiation between challenge versus hindrance stressors, challenge more traditional stress models and explore how job demands can being associated with positive outcomes. Additionally, Lu et al. [26] revealed the importance of examining the work and family interface by introducing different aspects of gender that capture better nontraditional gender identities.

In sum, the studies included in this Special Issue come from several disciplines and cultural contexts, involving authors from more than 15 different countries. These studies use strong and innovative theoretical approaches to provide evidence regarding the importance of working characteristics and resources to promote healthier and more sustainable environments in which employees can be happy and productive. Moreover, their findings offer several clues for implementing measures and developing healthier organizations in an uncertain and changing environment, particularly in the post-pandemic era. We hope the readers can benefit from the insights of these papers and that their findings can attract the attention of the scientific community in order to pursue further investigation into emerging issues in OHP.

**Author Contributions:** J.M.L.-P. writing: original draft preparation. M.K.S., A.I.F. and G.G. writing: review and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

International Journal of *Environmental Research and Public Health*