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 ( 1 ) . 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 ( 1 , 2 ) . therefore , a powerful method is required to predict existent exposure to harmful substances besides to evaluate probable adverse effects ( 3 ) . 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 ( 4 , 5 ) . 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 ( 5 , 6 ) . in this research however , breathing rates are affected by many individual characteristics , including age , sex , weight , health , and level of physical activity ( running , jogging , etc . ) ( 5 , 9 ) . 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 ( 10 ) . 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 ( 11 ) . the study area was in the northern part of the persian gulf with a distance of 10 miles from the coast . 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 . each workplace maybe had a different group of job and activities . in order to increase the information about jobs , activities , tasks , and main work places for workers the sampling procedure was based on absorption of benzene in an active charcoal tube ( active sampling ) ( 12 ) . skc model 222 pumps have been utilized for gas sampling . the glass tubes with a 6 mm 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 ( 12 ) . four measuring procedures were performed lasting one week in the middle of each season ( 12 , 13 ) . 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 ( 13 ) . 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 . ( 14 ) : 
 ladd = ca.ir.ef.ed / bw.at 
  the cancer risk ( cr ) is calculated for exposure to benzene using eq . ( 15 ) : 
 cancer  risk = ladd  ( g / kg / day)sf  ( g / kg / day)1 for equation 1 , ca is the concentration of a chemical in an exposure medium ( g / m ) , ir represents the inhalation rate ( m / 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 ( g / kg / day )   and sf in equation 2 , is the cancer slope factor of benzene ( linear low - dose cancer potency factor ) ( 15 ) . the quality of data in equation 1 is uncertain even if definite exposure - related measurements are available for variables ( 16 ) . 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 ( 11 , 16 ) . these variables ; wind speed , ambient temperature , humidity and rainfall cause fluctuation in concentration of air pollutants ( 17 ) . furthermore , the inhalation rate is also influenced by multiple individual aspects including age , body weight , and amounts of physical activity ( 5 , 11 ) . 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 ( 5 , 18 ) . 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 ( 5,18 ) . integrated system of risk assessment ( 5 ) a. artificial neural network b. life lifetime average daily inhalation dose 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 ( 19 , 20 ) . in this section 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 ( 20 , 21 ) . variables must be delineated with linguistic values rather than numerical values ( 5 , 21 ) . 2 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 ( 5 ) . membership functions of the variable  age  ( 5 ) the membership function for age was set to range from 18 to 80 yr ( 22 ) . 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 ( 5 , 22 ) . according to brainard and burmaster ( 23 ) , body weight ( 45110 kg ) 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 ( 5 ) . 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 ( 21 ) . moreover , seven linguistic values ranging from 525 m / day with normal distribution have been provided to show inhalation rate , which is compatible with the human capability ( 14 ) . anns are working based on specified transfer function and made by artificial neurons ( 17 ) . using particular function anns can adjust the value of different weights properly . in order to neural network learning hence , the data set for this project were randomly divided into a ratio of 3:1 between training and testing sets , respectively ( 7 , 10 , 17 ) . 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 ( 10 , 17 ) . parameters ; wind speed , temperature , relative humidity , rainfall and measured concentration was chosen as model input data . the predicted benzene concentration was the output parameter . 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 ( 17 ) . selected three layer neural network ( 17 ) details of the meteorological monitoring devices 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 ( 7 , 17 , 24 ) . 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 ( 18 ) . the predicted benzene concentrations lie between 0 and 67 ( g / m ) considered very high . ( 25 ) , 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 ( g / m ) . 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 ( 6 , 9 ) . 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 ( 18 ) . the output from the receptor block was divided by the body weight to derive m / d / kg then contributed as input to the dose estimation section ( 5 , 18 ) . the daily intake rate for the dose estimation is expressed by four linguistic fuzzy sets and is expressed in m / d / kg ( 18 ) . finally , the life average daily intake dose defined as seven fuzzy linguistic values and ranging from 0 to 6 g / kg / d . input variable  predicted concentration ( g / m )  a sample fuzzy inference calculation for the dose estimation block is shown in fig . 5 . where ir denotes inhalation rate normalized for body weight ( m / day / kg ) ; cc indicates chemical concentration ( g / m ) ; ladid = lifetime average daily inhalation dose ( g / kg / d ) . as an illustration , if the inhalation rate were considered 0.35 m / kg / d , the ambient benzene concentration is 15 g / m , lifetime average daily inhalation dose would be 1.63 g / kg / day . fuzzy inference for estimation of ladid ( matlab software ; fuzzy toolbox ) comparison of observed and predicted concentration ( source : author 's calculations ) 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 . the study area was in the northern part of the persian gulf with a distance of 10 miles from the coast . 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 . each workplace maybe had a different group of job and activities . in order to increase the information about jobs , activities , tasks , and the sampling procedure was based on absorption of benzene in an active charcoal tube ( active sampling ) ( 12 ) . skc model 222 pumps have been utilized for gas sampling . the glass tubes with a 6 mm 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 ( 12 ) . four measuring procedures were performed lasting one week in the middle of each season ( 12 , 13 ) . 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 ( 13 ) . 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 . ( 14 ) : 
 ladd = ca.ir.ef.ed / bw.at 
  the cancer risk ( cr ) is calculated for exposure to benzene using eq . ( 15 ) : 
 cancer  risk = ladd  ( g / kg / day)sf  ( g / kg / day)1 for equation 1 , ca is the concentration of a chemical in an exposure medium ( g / m ) , ir represents the inhalation rate ( m / 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 ( g / kg / day )   and sf in equation 2 , is the cancer slope factor of benzene ( linear low - dose cancer potency factor ) ( 15 ) . the quality of data in equation 1 is uncertain even if definite exposure - related measurements are available for variables ( 16 ) . 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 ( 11 , 16 ) . these variables ; wind speed , ambient temperature , humidity and rainfall cause fluctuation in concentration of air pollutants ( 17 ) . furthermore , the inhalation rate is also influenced by multiple individual aspects including age , body weight , and amounts of physical activity ( 5 , 11 ) . 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 ( 5 , 18 ) . the construction of the system is displayed in fig . 1 ( 5 ) . 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 ( 5,18 ) . integrated system of risk assessment ( 5 ) a. artificial neural network b. life lifetime average daily inhalation dose 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 ( 19 , 20 ) . in this section 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 ( 20 , 21 ) . variables must be delineated with linguistic values rather than numerical values ( 5 , 21 ) . 2 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 ( 5 ) . membership functions of the variable  age  ( 5 ) the membership function for age was set to range from 18 to 80 yr ( 22 ) . 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 ( 5 , 22 ) . according to brainard and burmaster ( 23 ) , body weight ( 45110 kg ) 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 ( 5 ) . 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 ( 21 ) . moreover , seven linguistic values ranging from 525 m / day with normal distribution have been provided to show inhalation rate , which is compatible with the human capability ( 14 ) . anns are working based on specified transfer function and made by artificial neurons ( 17 ) . using particular function anns can adjust the value of different weights properly . in order to neural network learning hence , the data set for this project were randomly divided into a ratio of 3:1 between training and testing sets , respectively ( 7 , 10 , 17 ) . 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 ( 10 , 17 ) . parameters ; wind speed , temperature , relative humidity , rainfall and measured concentration was chosen as model input data . 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 ( 17 ) . selected three layer neural network ( 17 ) details of the meteorological monitoring devices 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 ( 7 , 17 , 24 ) . 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 ( 18 ) . the predicted benzene concentrations lie between 0 and 67 ( g / m ) considered very high . ( 25 ) , 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 ( g / m ) . 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 ( 6 , 9 ) . 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 ( 18 ) . the output from the receptor block was divided by the body weight to derive m / d / kg then contributed as input to the dose estimation section ( 5 , 18 ) . the daily intake rate for the dose estimation is expressed by four linguistic fuzzy sets and is expressed in m / d / kg ( 18 ) . finally , the life average daily intake dose defined as seven fuzzy linguistic values and ranging from 0 to 6 g / kg / d . input variable  predicted concentration ( g / m )  a sample fuzzy inference calculation for the dose estimation block is shown in fig . 5 . where ir denotes inhalation rate normalized for body weight ( m / day / kg ) ; cc indicates chemical concentration ( g / m ) ; ladid = lifetime average daily inhalation dose ( g / kg / d ) . as an illustration , m / kg / d , the ambient benzene concentration is 15 g / m , lifetime average daily inhalation dose would be 1.63 g / kg / day . fuzzy inference for estimation of ladid ( matlab software ; fuzzy toolbox ) comparison of observed and predicted concentration ( source : author 's calculations ) 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 . 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 ( 26 ) . before the proposed model is applied to the particular application , it must be trained using all available samples ( 27 ) . the training of network continued until maximum correlation within the measured and predicted output was achieved ( table 3 ) . correlation expressed by r - squared that r is coefficient of multiple determinations and relative root mean square error ( rmse ) ( 26 ) . 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 . 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) . 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 ) seasonal comparison of ambient concentration of benzene ( source : author 's calculations ) 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 ( 18 ) . the individual excess cr was calculated for exposure to benzene through inhalation is presented in table 4 . the best estimate for the variable distribution of risk in a population implied 33% of people exposed less than 110 . based on epa clean air act risk range , 110 risk range is considered as the most health protective end of the range ( 27 , 28 ) . whereas 110 is the midpoint of risk range and 67% of the population is ranging from 110 to 9.810 cancer risk probability ( 27 , 28 ) . individual health risk assessment ( based on average exposure obtained by passive sampling ) the average estimated risk for all work areas considering exposure to benzene is equal to 2.410 , ranging from 1.510 to 6.910 . the results counsel potential cancer risk for period exposure to benzene within the numerous areas but at different levels ( 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 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 ( 26 ) . before the proposed model is applied to the particular application , it must be trained using all available samples ( 27 ) . the training of network continued until maximum correlation within the measured and predicted output was achieved ( table 3 ) . correlation expressed by r - squared that r is coefficient of multiple determinations and relative root mean square error ( rmse ) ( 26 ) . 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 . 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) . 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 ) seasonal comparison of ambient concentration of benzene ( source : author 's calculations ) 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 ( 18 ) . the individual excess cr was calculated for exposure to benzene through inhalation is presented in table 4 . the best estimate for the variable distribution of risk in a population implied 33% of people exposed less than 110 . based on epa clean air act risk range , 110 risk range is considered as the most health protective end of the range ( 27 , 28 ) . whereas 110 is the midpoint of risk range and 67% of the population is ranging from 110 to 9.810 cancer risk probability ( 27 , 28 ) . individual health risk assessment ( based on average exposure obtained by passive sampling ) the average estimated risk for all work areas considering exposure to benzene is equal to 2.410 , ranging from 1.510 to 6.910 . the results counsel potential cancer risk for period exposure to benzene within the numerous areas but at different levels ( 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 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 ( 29 , 30 ) . 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 . major variables affecting pollutant concentration were wind speed , ambient temperature , humidity , and rainfall . 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 . 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 . 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 .