You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction
============

Petrochemical production is categorized as a high-risk industry. One of the important risks is the existence of high levels of gasoline vapors including benzene that during work shifts employees are exposed to. Employees in functional units are more vulnerable population because of their continuous contact with harmful substances such as benzene. Based on USEPA (United State Environmental Protection Agency) and IARC (International Agency for Research Cancer) reports, benzene has been classified to be a group A and Class 1 human ([@B1]).

Adverse health effects of benzene appear in two different type; short-term and long-term. Short-term effects are associated with high concentration and may involve headaches, dizziness, distraction and defects temporary memory and tremors. Whereas, exposure to benzene in a long-term is connected to intricate adverse health effects such as immunological, hemato-toxicity, Geno-toxicity, adverse effects on reproductive organs, and as well as various cancers ([@B1], [@B2]).

Therefore, a powerful method is required to predict existent exposure to harmful substances besides to evaluate probable adverse effects ([@B3]). Although several mathematical models have been carried out so far, there is high complexity related to chemical exposure in terms of human health. Due to the lack of information about chemical impacts on human particularly in long-term impacts, some unpredictable factors that called uncertainties can affect health risk assessment ([@B4], [@B5]). Health risk assessment through inhalation concerning benzene emission from equipment at the unit of benzene production was accomplished. This project is an example of using a hybrid approach that can incorporate uncertainties to assessment's process. Major variables affecting the absorption of chemicals moreover key parameters in the dispersion of chemicals can be reflected in a hybrid system which combining fuzzy logic and neural networks ([@B5], [@B6]).

In this research" average inhalation and absorption rates were used for assessment. However, breathing rates are affected by many individual characteristics, including age, sex, weight, health, and level of physical activity (running, jogging, etc.)" ([@B5], [@B9]).

Perhaps the first and distinguished use of fuzzy sets in health risk assessment was on the application of fuzzy logic in the environmental risk assessment ([@B10]). One more example which connected to human health risk assessment is the application of fuzzy sets in human health risk assessment. Fuzzy sets were employed to estimate carcinogenic risk caused by air pollution in Ten Russian cities ([@B11]).

Methods
=======

Study area
----------

The project was carried out since 2013 to 2014. The study area was in the northern part of the Persian Gulf with a distance of 10 miles from the coast. It was categorized as an intermediate petrochemical throughout the country. The plant mainly produced about one million tons annually of benzene, paraxylene, orto-xylene used to produce ethylbenzene, styrene, cyclohexane, and nitrobenzene. Benzene from refinery streams was typically produced from catalytic reformats pyrolysis, gasoline, and toluene de-alkylation. This study focused on tasks and activities those are exposed to potential contact with benzene during refinery.

Main workplaces related to workers exposed to benzene such as loading of tankers were listed in [Table 1](#T1){ref-type="table"}. Each workplace maybe had a different group of job and activities. In order to increase the information about jobs, activities, tasks, and places a questionnaire was prepared then accomplished through an interview with employees.

###### 

Main work places for workers

  **Work areas**     **Situation**   **Number of workers**   **Sex**
  ------------------ --------------- ----------------------- ---------
  Loading terminal   Out door        18                      M
  Process site       Out door        42                      M
  Tank units         Out door        17                      M
  Control room       In door         8                       M
  Laboratory         In door         6                       M

Sample collection
-----------------

The sampling procedure was based on absorption of benzene in an active charcoal tube (active sampling) ([@B12]). SKC model 222 pumps have been utilized for gas sampling. The Glass Tubes with a 6mm external diameter, 4 mm internal diameter and 70 mm height, containing activated charcoal holder with a restrictive orifice (separated by a 2-mm part of urethane foam) were installed. The pump was adjusted to work for 30 min at a flow rate of 100 ml/min ([@B12]).

Active sampling was accomplished in workdays. For 8 h the measurements were recorded on a daily source (08:00--16:00 Local Time). Four measuring procedures were performed lasting one week in the middle of each season ([@B12], [@B13]). Consequently, by the end of the week 70 samples were taken and during 15/2/2012 to 21/09/2013 totally 280 samples were collected. for detailed information stickers attached on each tube to tag sampling number, the time of end duration of sampling, the pump number, the humidity, the wet and dry temperature, and the date of sampling ([@B13]).

The human health risk Model
---------------------------

The human Lifetime Average Daily Dose (LADD) equation for a single chemical exposure proposed by U.S.EPA (2011) that related to cancer risk model (presuming that the inhalation is the only route of intake) is, as shown in [Eq. \[1\]](#FD1){ref-type="disp-formula"} ([@B14]): $$\text{LADD} = \text{CA}.\text{IR}.\text{EF}.\text{ED}/\text{BW}.\text{AT}$$

The Cancer Risk (CR) is calculated for exposure to benzene using [Eq. \[2\]](#FD2){ref-type="disp-formula"} ([@B15]): $$\text{Cancer}\,\text{Risk} = \text{LADD}\,\,\,\left( {\mu\text{g}/\text{kg}/\text{day}} \right) \times \text{SF}\,\left( {\mu\text{g}/\text{kg}/\text{day}} \right)^{- 1}$$

For [equation 1](#FD1){ref-type="disp-formula"}, CA is the concentration of a chemical in an exposure medium (μg/m^3^), IR represents the inhalation rate (m^3^/h), EF is the exposure frequency (number of working days per year), ED is the exposure duration (working years), BW is the body weight (kg), AT is averaging time (AT=70 yr 365 d/year for carcinogens), LADD shows lifetime average daily dose (μg/kg/day) \[[@B14]\] and SF in [equation 2](#FD2){ref-type="disp-formula"}, is the cancer slope factor of benzene (linear low-dose cancer potency factor) ([@B15]).

The quality of data in [equation 1](#FD1){ref-type="disp-formula"} is uncertain even if definite exposure-related measurements are available for variables ([@B16]). To explain more, some unpredictable parameters can change the results. As an illustration, movement of contaminants among environmental media changes remarkably the quality of data, therefore, uncertainty plays a crucial role involved in each variable as well as affected by physical and chemical factors ([@B11], [@B16]). The concentration of the air pollutant is derived as a function background concentration. These variables; wind speed, ambient temperature, humidity and rainfall cause fluctuation in concentration of air pollutants ([@B17]). Furthermore, the inhalation rate is also influenced by multiple individual aspects including age, body weight, and amounts of physical activity ([@B5], [@B11]).

Owing to these numerous variables, the trustworthy approach is required to make a better risk evaluation. Therefore, to overcome the problems of uncertainties the hybrid method was prepared ([@B5], [@B18]). The construction of the system is displayed in [Fig. 1](#F1){ref-type="fig"} ([@B5]). In this figure, each tablet represents a sub-system combined; they determine the chemical absorption rate through inhalation. The receptor description (Inhalation rate) and dose estimation sections use a set of fuzzy rules and obtain the average daily inhalation dose, based on fuzzy inference. The exposure prediction section consists of a designed neural network using a new back-propagation algorithm. This section created in order to calculate of real ambient concentration. In the following sections, details about the subdivisions will be explained ([@B5],[@B18]).

![Integrated system of risk assessment ([@B5])\
a. Artificial Neural Network\
b. Life Lifetime Average Daily Inhalation Dose](IJPH-45-1188-g001){#F1}

Using fuzzy logic for estimation of inhalation rate
---------------------------------------------------

Standard quantities technique in health issues has been often vague and unclear adjectives such as a little, too much...; so at the start it is necessary to quantify these adjectives and change them into fuzzy sets ([@B19], [@B20]). In this section, 36 fuzzy rules were used to determine the inhalation rate of adults. Age, body weight, and activity tend to be the significant reasons of daily energy expenditure in healthy people for as long as is also in energy balance ([@B20], [@B21]).

The triangular membership function is selected for age. Variables must be delineated with linguistic values rather than numerical values ([@B5], [@B21]). [Fig. 2](#F2){ref-type="fig"} shows one of the prepared fuzzy sets (fuzzy numbers) that related to age with three linguistic values. The horizontal vector indicates the size of the parameter and the vertical vector represents the degree of membership (degree of dependence) of each value ([@B5]).

![Membership functions of the variable "Age" ([@B5])](IJPH-45-1188-g002){#F2}

The membership function for age was set to range from 18 to 80 yr ([@B22]). Using census and demographic details, age (in years) was divided into three fuzzy linguistic sets: low, ranging from 18 to 30; medium, ranging from 25 to 64; and high, ranging from 59 to 80 ([@B5], [@B22]).

According to Brainard and Burmaster ([@B23]), body weight (45--110 kg) follows a normal distribution. It has been divided into three linguistic sets: low (from 45 to 68 kg), medium (from 56 to102 kg), and high (from 90 to110 kg). Fuzzy set was made for physical activity by means of four linguistic values and follows skewed (normal) distribution ([@B5]).

As for long-term physical activity levels (PALs), it is ranging from 1.2 to 2.5 times the BMR (Basal Metabolic Rate), where 1.2 represents the minimal intensity of activity and 2.5 shows a very physically active lifestyle. Aging is a parameter that can remarkably decline high-intensity activity ([@B21]). Moreover, seven linguistic values ranging from 5--25 m^3^/day with normal distribution have been provided to show inhalation rate, which is compatible with the human capability ([@B14]).

Artificial neural network for estimation of exposure to benzene
---------------------------------------------------------------

ANNs are working based on specified transfer function and made by artificial neurons ([@B17]). There are neuron networks so-called weight factors or simply weights. These networks are composed of neurons that interact with each other. Networks or connections transmit signals from other neurons. Signals contain either positive or negative weights. Using particular function ANNs can adjust the value of different weights properly.

In order to neural network learning, the data should be split into two subsets; training and testing sets. There is no specified rule to define the size of each subset. Hence, the data set for this project were randomly divided into a ratio of 3:1 between training and testing sets, respectively ([@B7], [@B10], [@B17]). As a result, available data (280 samples) arbitrarily fragmented into two subsets. One of those was training groups and included 75% of data. By developing the model this group predicted the concentration of benzene for the second group that contained the 25% of data ([@B10], [@B17]).

Parameters; wind speed, temperature, relative humidity, rainfall and measured concentration was chosen as model input data. The predicted benzene concentration was the output parameter. Details of measurement devices for each meteorological parameter are shown in [Table 2](#T2){ref-type="table"}. Proposed ANN designed in three layers. Input parameters located in the first layer in the form of five neurons. To gain best results experimentally founded to put 10 neurons in the second layer. The third layer involved just one neuron which is output parameter and shows actual benzene concentration. A typical three-layer neural network is shown in [Fig. 3](#F3){ref-type="fig"} ([@B17]).

![Selected three layer neural network ([@B17])](IJPH-45-1188-g003){#F3}

###### 

Details of the meteorological monitoring devices

  **Parameter**   **Reaction time**   **Procedure**
  --------------- ------------------- ---------------------------
  Wind speed      1s                  3-cup anemometer
  Temperature     10 s                Temperature sensor
  Humidity        15 s                Sensing element
  Rainfall        3 s                 Tipping bucket rain gauge

Before conducting the network preparation procedure, a training group (75% of the data) consisting of 210 cases had to be prepared from the environmental data done by the back propagation paradigm. Training group itself became divided into training set and testing set that this process conducted randomly. First one is the training set consisted of 90% of the data or 189 cases used to train the model. The second or final 10% contains 21 cases were left out as a test set ([@B7], [@B17], [@B24]).

Fuzzy inference calculation for LADD estimation
-----------------------------------------------

Output variables from the artificial neural network stage and fuzzy logic section contributed to the last part of the hybrid model and produced a chronic daily intake dose ([@B18]). The predicted benzene concentrations lie between 0 and 67 (μg/m^3^) considered very high. Although the global average is 0 to 15μg/m^3^ ([@B25]), the peak concentrations in the study area are high and the membership function for output and some fuzzy rules must be adjusted to range 0 and 67 (μg/m^3^). The process of adjustment was easily done that indicates a powerful feature of the dose assessment modeling system as well as the adaptability to different scenarios. In different circumstances, the user can change fuzzy rules according to the different situations ([@B6], [@B9]).

The outputs from the benzene prediction divided into six linguistic variables based on the frequency distribution of the data. The membership function of variable concentration is shown in [Fig. 4](#F4){ref-type="fig"} ([@B18]). The output from the receptor block was divided by the body weight to derive m^3^/d/kg then contributed as input to the dose estimation section ([@B5], [@B18]). The daily intake rate for the dose estimation is expressed by four linguistic fuzzy sets and is expressed in m^3^/d/kg ([@B18]). Finally, the life average daily intake dose defined as seven fuzzy linguistic values and ranging from 0 to 6 μg/kg/d.

![Input variable "predicted concentration (μg/m^3^)"](IJPH-45-1188-g004){#F4}

A sample fuzzy inference calculation for the dose estimation block is shown in [Fig. 5](#F5){ref-type="fig"}. Where IR denotes inhalation rate normalized for body weight (m^3^/day/kg); CC indicates chemical concentration (μg/m^3^); LADID = Lifetime Average Daily Inhalation Dose (μg/kg/d). As an illustration, if the inhalation rate were considered 0.35 m^3^/kg/d, the ambient benzene concentration is 15 μg/m^3^, lifetime average daily inhalation dose would be 1.63 μg/kg/day.

![Fuzzy inference for estimation of LADID (Matlab software; fuzzy toolbox)](IJPH-45-1188-g005){#F5}

![Comparison of observed and predicted concentration (Source: Author\'s calculations)](IJPH-45-1188-g006){#F6}

The output from this last module gives the overall dose through inhalation for each individual around the site, taking into account both the exposure factors and the receptor factors in a quantitative way, based on neural network and fuzzy inference. When the exposure is quantified, combined with Integrated Risk Information System (IRIS) toxicity factors, then as a final point the related risk can be estimated.

Results
=======

Artificial network validation and results
-----------------------------------------

In this section, Leave-One-Out Cross-Validation (LOOCV) was followed with the aim of training and testing the ANN model. Frequently, one sample is kept for testing while the rest is used for training up to all samples are finally tested ([@B26]). Before the proposed model is applied to the particular application, it must be trained using all available samples ([@B27]). The difference between the observed and the predicted values are shown in [Fig. 6](#F6){ref-type="fig"}.

The training of network continued until maximum correlation within the measured and predicted output was achieved ([Table 3](#T3){ref-type="table"}). Correlation expressed by R-squared that R^2^ is coefficient of multiple determinations and relative root mean square error (RMSE) ([@B26]). Correlation results are perfect when an R-squared value of 1, a very good fit is next to 1 and a very poor fit less than 0. On the other side, how much the value of RRMSE is smaller; the performance of the model is better.

###### 

ANN evaluation parameters (Source: author\'s calculations)

  **Pollutant**   **Structure of ANN**   **Evaluation parameters**   **Results**
  --------------- ---------------------- --------------------------- -------------
  Benzene         5-10-1                 Mean Absolute Error         5.9
                  5-10-1                 Minimum Absolute Error      0
                  5-10-1                 Maximum Absolute Error      13.1
                  5-10-1                 RMSE (μ g/m^3^)             4.8
                  5-10-1                 R^2^                        0.941

Predicted concentration results
-------------------------------

In [Fig. 7](#F7){ref-type="fig"}, the annually averaged results of passive sampling are presented. Exposure values in terminals were generally higher than another area. There is seasonal variation, especially in outdoor units. Ambient air temperature affects significantly the exposure levels, as a result, the high temperatures in the summer and spring explains the increased benzene concentrations in exposure values. When temperatures are very low (winter-time), the exposure levels are less than usual for an equivalent quantity of close benzene level ([Fig. 8](#F8){ref-type="fig"}). The presence of wind reduces exposure levels, especially to employees who are performing outdoor activities. No strong correlation found between wind speed and exposure levels of employees working in laboratory and control room.

![Annually average concentration (Source: Author\'s calculation)](IJPH-45-1188-g007){#F7}

![Seasonal comparison of ambient concentration of benzene (Source: Author\'s calculations)](IJPH-45-1188-g008){#F8}

Health risk assessment results
------------------------------

The measured benzene concentrations and the related exposure levels are not considered as an acute health risk; meanwhile, this is the possibility of leukemia existence after chronic occupational contact with benzene due to measured concentration. Long-term individual risk will be calculated by combining USEPA integrated risk information system (IRIS) toxicity features with results from exposure assessment section ([@B18]). The individual excess CR was calculated for exposure to benzene through inhalation is presented in [Table 4](#T4){ref-type="table"}. The best estimate for the variable distribution of risk in a population implied 33% of people exposed less than 1×10^−6^. Based on EPA Clean Air Act Risk Range, 1×10^−6^ risk range is considered as the most health protective end of the range ([@B27], [@B28]). Whereas 1×10^−5^ is the midpoint of risk range and 67% of the population is ranging from 1×10^−5^ to 9.8×10^−5^ cancer risk probability ([@B27], [@B28]).

###### 

Individual health risk assessment (based on average exposure obtained by passive sampling)

  **Average Life Time Cancer Risk Value**   **Frequency (Number of workers)**   **%**   **Average Life Time Cancer Risk Value**   **Frequency (Number of workers)**   **%**
  ----------------------------------------- ----------------------------------- ------- ----------------------------------------- ----------------------------------- -----------
  1.4×10^−6^                                3                                   3.3     2.7×10^−5^                                3                                   3.3
  3.0×10^−6^                                3                                   3.3     2.8×10^−5^                                4                                   4.4
  4.3×10^−6^                                2                                   2.2     2.9×10^−5^                                3                                   3.3
  5.2×10^−6^                                3                                   3.3     3×10^−5^                                  3                                   3.3
  6.1×10^−6^                                3                                   3.3     3.2×10^−5^                                3                                   3.3
  6.5×10^−6^                                2                                   2.2     3.4×10^−5^                                3                                   3.3
  6.8×10^−6^                                4                                   4.4     3.5×10^−5^                                5                                   5.5
  7.5×10^−6^                                2                                   2.2     3.6×10^−5^                                7                                   7.7
  8.1×10^−6^                                3                                   3.3     4.2×10^−5^                                3                                   3.3
  8.4×10^−6^                                2                                   2.2     6×10^−5^                                  3                                   3.3
  9.1×10^−6^                                3                                   3.3     7×10^−5^                                  2                                   2.2
  1.6×10^−5^                                3                                   3.3     8.2×10^−5^                                2                                   2.2
  2.1×10^−5^                                3                                   3.3     9.5×10^−5^                                1                                   1.1
  2.2×10^−5^                                5                                   5.5     9.8×10^−5^                                4                                   4.4
  2.4×10^−5^                                4                                   4.4     **Total**                                 **91**                              **100 %**

The average estimated risk for all work areas considering exposure to benzene is equal to 2.4×10^−5^, ranging from 1.5×10^−6^ to 6.9×10^−5^. The results counsel potential cancer risk for period exposure to benzene within the numerous areas but at different levels ([Fig. 9](#F9){ref-type="fig"}). These differences result from differences in the employee\'s type of activity, age, weight, and breathing rate that in common methods of risk assessment they did not pay attention to them.

![Potential cancer risk](IJPH-45-1188-g009){#F9}

Discussion
==========

The complete conception in establishing an integrated product is a dynamic model for risk estimation, able to evaluate probable interactions among the levels of benzene exposure through different task designs, those lead to different levels of benzene metabolic rate and subsequently to discount risk estimations ([@B29], [@B30]). In this model, risk factors that can influence inhalation rate were age, body weight, and physical activity of persons. These parameters considered as the input variable of fuzzy logic in order to estimate exposure level for the various situation of employees. The hybrid system also used Artificial Neural Network approach to predict actual atmospheric benzene concentration. ANN can effectively forecast the changes in measured atmospheric benzene concentration. Major variables affecting pollutant concentration were wind speed, ambient temperature, humidity, and rainfall.

The model is highly flexible so can easily accommodate any situation. Expert viewpoint is used as fuzzy rules and the rules can be changed according to the needs of the user. The produced composite model shows promise as a new tool for chemical exposure and health risk assessment, which it allows multiple uncertainties incorporate into health risk assessment.

Conclusion
==========

In this project, the new hybrid dose model has been able to replicate a measured exposure prospering believed to be more representative of actual intake dose than data from previously accepted methods.

Overall, according to aforementioned reasons this system can produce risk assessment data that appear realistic. It may also be extended to other risk management applications where multiple uncertainties exist.

Ethical considerations
======================

Ethical issues (Including plagiarism, Informed Consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, etc.) have been completely observed by the author.

The authors would like to thank the Editor and anonymous reviewers for their helpful comments and suggestions. No fund was received for conduction this study. The authors declare that there is no conflict of interests.