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Environmental Sciences » "Advanced Air Pollution", book edited by Farhad Nejadkoorki, ISBN 978-953-307-511-2, Published: August 17, 2011 under CC BY-NC-SA 3.0 license. © The Author(s).

Chapter 6

Evaluation of Hexamethylene Diisocyanate as an Indoor Air Pollutant and Biological Assessment of Hexamethylene Diamine in the Polyurethane Factories

By Seyedtaghi Mirmohammadi, M. Hakimi. Ibrahim and G. N. Saraji
DOI: 10.5772/17886

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Relationship between HDI concentration and psychrometric parameters (relative humidity and dry bulb temperature)
Figure 1. Relationship between HDI concentration and psychrometric parameters (relative humidity and dry bulb temperature)
HDA concentrations in different factories
Figure 2. HDA concentrations in different factories
Relationship between air HDI concentration and urinary HDA concentration
Figure 3. Relationship between air HDI concentration and urinary HDA concentration

Evaluation of Hexamethylene Diisocyanate as an Indoor Air Pollutant and Biological Assessment of Hexamethylene Diamine in the Polyurethane Factories

Seyedtaghi Mirmohammadi1, M. Hakimi Ibrahim2 and G. N. Saraji3

1. Introduction

Isocyanates are widely used in surface coatings, polyurethane foams, adhesives, resins, elastomers, binders, and sealants. In general, the types of exposures (inhale and dermal) encountered during the use of isocyanates (i.e., monomers, prepolymers, polyisocyanates, and oligomers) in workplace are related to vapor pressure of the individual chemical compounds (Bello et al., 2002; Tury et al., 2003). Isocyanates exist in many different physical forms in the workplace. The workers are potentially exposed to unreacted monomer, prepolymer, polyisocyanate, and/or oligomer species found in a given product formulation (Rosa et al., 1999). They can also be exposed to partially reacted isocyanate containing intermediates formed in the course of polyurethane production (Woskie et al., 2004; Tinnerberg and Sennbro, 2005). The second type of exposure might be more hazardous, as a number of isocyanates reactions are exothermic in nature and also evolve sufficient heat for the volatilization isocyanate compounds (NIOSH, 1994).

Isocyanate exposure is irritative to the skin, mucous membranes, eyes, and respiratory tract. The most common adverse health outcome associated with isocyanate exposure is asthma due to sensitization; less prevalent are contact dermatitis (both irritant and allergic forms) and hypersensitivity pneumonitis (Vandenplas et al., 1992). Hexamethylene diisocyanate (HDI) is a colorless compound or may be slightly yellow liquid and is not much heavier than water (Adam et al., 2002). This substance forms oily droplets in water and hydrolyses rapidly.

If isocyanates are inhaled, they are metabolised or broken down in body, eliminated and discharged through the urine. The metabolized form of Hexamethylene diisocyanate (HDI) is hexamethylene diamine (HDA) which is an organic compound and isocyanate-derived diamines from protein which is conjugated with urine (Dalene et al., 1994b, 1996; Williams et al., 1999; Dalene, 2004). HDA has chemical formula of H2N(CH2)6NH2. The molecule is a diamine, consisted of a hexamethylene hydrocarbon chain terminated with amine.

The level of isocyanate metabolites in urine is an indicator of how much isocyanate has been absorbed and how well the pollution controllers and the prevention units are working. The levels of HDA are reported as “µmol/mol creatinine”. The guidance standard value for HDA is at a level of 1 µmol/mol creatinine and each sample above the guidance standard value is an indication of exposure to contaminated environment (Williams et al., 1999 and 2004).

The specific goals of this chapter are to answer the following questions:

  • What are the determinants of HDI pollution level in the polyurethane factories?

  • Does any correlation exist between HDI concentration from air sampling and HDA concentration from biological sampling (worker’s urine sample) in the polyurethane factories?

  • What is the pollution condition in terms of HDI and HDA concentration in the different selected factories?

2. Materials and methods

2.1. Polyurethane factories

In this study, five HDI polyurethane factories were selected for air sampling and biological monitoring. These factories are situated in three provinces namely T (factories coded as H1 and H2), K (H3 and H4) and M (H5).

The main uses of HDI in the HDI factories (H1, H2, H3, H4 and H5) are for the production of adhesives, process regulators and paints and lacquers and varnishes, as well as for the production of polishing paints and adhesives. 5 HDI polyurethane factories were selected for this study; the number of workers working in factories H1, H2, H3, H4 and H5 were 265, 130, 320, 120 and 130, respectively.

The workers were exposed to HDI in the indoor environment. There were some workers who did not work full-time in the workplace; they worked as officers and sometimes came and went into workplaces and are identified as unexposed workers. In the HDI polyurethane factories all the exposed workers wear simple gloves and simple paper respirable mask (Model: 2600 Half-mask with elastic) when working at their workplaces in the factories.

2.2. Air sampling and analysis

There were two group workers in the polyurethane factories. The first group as office workers who had least exposure (n= 100). The second one were workers working inside factories (n= 400) and they were sufficiently exposed to HDI. Only five samples were collected from office spaces as blank samples for least exposed environment.

Sample handling and preparation include those steps taken to stabilize the sample or make the sample more compatible with the analytical procedure. Sample handling considerations actually began before sample collection. For air sampling in the field, sample pumps [low flow (SIBATA, MP302 Model) Japan] were attached to individual adsorbent tubes using 30 cm lengths of clear, inert polythene tubing suitable for low-level organic compounds. Each pump and tube assembly was calibrated using a bubble flow meter (SKC UK, UK) in order to measure and adjust the sampling flow-rate precisely. The pumps were adjusted to provide a flow-rate of 2 L/min ~ Samples (static and personal) were taken adjacent to an operation where HDI was being handled in the factory for the periods of 2 h. On completion of field sampling, the tubes were sealed with vials end-caps and returned to the analytical laboratory. Samples were chilled to 2°C in a laboratory refrigerator and analysed within 24 h.

The first step in the analysis of a solution is derivatization of isocyanates for the separation through high-performance liquid chromatography (HPLC) for their qualitative as well as quantitative analysis. All the air samples were analyzed in the laboratory for HDI using HPLC through standard method of analysis (NIOSH, 1994)

2.3. Biological sampling and analysis

The biological sampling was carried out by collecting the workers’ urine at the end of working shift; it was collected into polystyrene containers with citric acid and transferred to laboratory for GC Mass spectrometry analysis (Wu et al., 1990; Williams et al., 1999).

Due to the short half life (about 1.5 - 3 h) of HDA in urine, samples were collected at the end of the shift to detect any short term exposure as well as an estimation of the 8 h time weighted average exposure (Williams et al., 2004).

In this study the urine samples were collected and dispatched in a similar way, frozen and sent blind to the laboratory for analysis. Based on Williams’s method, subjects provided urine samples on the working day at the end of the working shift. The time of exposure and the personal protective equipment worn were recorded. For the factories securities, the samples obtained were labeled with code numbers and either the time or by sample number with other details like whether they were exposed worker, unexposed, or bystander.

2.4. Determinants of HDI pollution level

During air sampling for HDI concentration inside the factories, data for indoor relative humidity, indoor dry bulb temperature, altitude and dimension of factory were recorded. HDI was considered as dependent variable and the rest were independent variables. These independent indoor air variables were divided into two groups. Regression analysis procedure was used to state the statistical relationship between the variables and identify any meaningful relationship between those variables. Due to the fact that the number of independent variables was more than one, multiple linear regression analysis was used in the present analysis (Bahatin and Ibrahim, 2007). A multiple regression equation, which has four independent variables was used, and it can be expressed as follows:

Y=B0+B1Rh+B2Td+B3D+  B4Alt+ei


Y is the dependent variable (HDI concentration)

Rh, Td, D and Alt are independent variables (relative humidity, dry bulb temperature factory dimension and altitude,) as the predictors in this model.

B0, B1, B2, B3 and B4 are the model coefficients.

ei is the residual error

The values for the constant and the coefficients are determined using the least-squares method which minimizes the error as ‘e’ in the above regression equation. The significance level of the constant and coefficients are statistically tested using t-distribution. The R2 (coefficient of determination) determines the direction and significance level of relation between the variables in the mathematical model and shows how much the dependent variable is affected by the independent variables. All statistical analysis was performed using SPSS software Ver.16. In this study the alpha level is 0.05 (P ≤ 0.05) similar to other indoor air pollution studies (Marek et al., 1999; Molander et al., 2002; Dalene, 2004).

3. Results and discussion

3.1. Air sampling and indoor air variables

The sampling protocols described in NIOSH Method 5522 were used throughout the experiments. The psychrometric parameters (relative humidity and dry bulb temperature) and factory parameters (dimension of factory and altitude) were measured for all HDI polyurethane factories and work stations. A total of 300 air samples were collected randomly from working places inside the 5 factories for the exposed workers (20 samples per each HDI factory and for three times per working shift).

Table 1. shows the maximum, minimum and mean values of indoor air independent variables with respect to HDI concentration in the polyurethane factories. The lowest minimum HDI concentration in all the factories was 61 μg/m3 and also the highest of the maximum HDI concentration was 96 μg/m3. These values can be considered as high, when compared to NIOSH exposure limit of 35 μg/m3. The mean HDI concentration was 78.8 μg/m3, the mean indoor relative humidity was 37.7%, and the mean dry bulb temperature was 28.3C. The size of workplace for the five polyurethane factories ranged from 5,000 to 9,800 m3 and the altitude of factories were from as low as 22 to as high as 1200 m.

AverageH5H4H3H2H1Factories code
HDI concentration (µg/m³), Max
Relative Humidity (%), Max
Dry bulb temperature (˚C), Max
746098009000740061005000Dimension of factory ( m³)
88222890110012001200Altitude (m)

Table 1.

Values of indoor air variables in the HDI polyurethane factories

It has been summarized and stated in Table 1. that several factories show variable concentration of diisocyanates, with respect to relative humidities of each factory. The highest mean value HDI concentration was 82.2 µg/m³ which corresponds to the highest mean relative humidity of 40.5% inside of factory H1. Factories H1 and H2 were located in T province. Factory H2 had the second highest mean HDI concentration and also the second highest mean relative humidity (79.8 µg/m³ and 40%, respectively). Factory H5 had the lowest mean value HDI concentration which was 76.7 µg/m³. Factory H5 was situated in M province and this factory had the lowest mean relative humidity of 34%.

3.2. Relationship of HDI and indoor air variables

In this study, the data (n = 100) of psychrometric parameters (indoor relative humidity and dry bulb temperature) and factory parameters (factory dimension and altitude) were collected and used to predict HDI pollution level. Correlation analysis was carried out after checking the normality assumptions for both variables, all parameters are strongly correlated with HDI concentration where P < 0.05. The calculated R2 values for RH is 0.5461 (R2= 0.7392), Td is 0.77 (R2=0.882), D is 0.8767 (R2= -0.8872) and Alt is 0.7225 (R2= 0.852). Since all parameters are strongly correlated, all of them are put in the regression model. Table 2. showed the regression model summary where it can be seen that 83.7% of the HDI concentration can be attributed to any or all the independent variables (relative humidity, dry bulb temperature, dimension and altitude) (R2 = 0.837).

ModelrAdjusted r²

Table 2.

Regression model summary of HDI

Predictors: (Constant), Altitude (m), Dimension of factory (m³), Relative humidity (%), Dry bulb temperature (ºC)

The results of the summary imply that all or some of parameters (altitude, dimension of factory, relative humidity and dry bulb temperature) can be significant predictors of HDI concentration in the polyurethane workplaces.

Table 3. below reports an analysis of variance for HDI concentration in the polyurethane factories. From the table, it can be seen that F is 121.9 which is significant at P < 0.05. We can conclude that the regression model predicts the concentration level of HDI significantly well.

ModelMean SquareFP value

Table 3.

Regression model for HDI polyurethane factories factors

Predictors: (Constant), Altitude (m), Dimension of factory (m³), Relative humidity (%), Dry bulb temperature (˚C), Dependent Variable: HDI concentration (µg/m³)

Since the results of regression model test in Table 1.3 illustrate that the independent variables are significant predictors of HDI concentration, we can employ equation 1.1 to stand for the different psychrometric and factory parameters in order to measure the predictive regression correlation between the parameters and HDI concentration.

Table 4. shows the results of regression analysis between HDI concentration and polyurethane indoor air parameters. Both indoor relative humidity and dry bulb temperature can be seen to be significant predictors of HDI pollution (P< 0.05). Both of these parameters fall under the psychrometric parameters group. The other two parameters (factory dimension and altitude) are the factory parameters and both are not significant (P>0.05).

Relative humidity (%)0.3670.0715.1820.0001
Dry bulb temperature (˚C)1.1120.3992.7850.006
Dimension of factory (m³)-0.0010.001-1.4790.142
Altitude (m)0.0030.0021.4750.144

Table 4.

Result of regression analysis between HDI concentration and polyurethane indoor air parameters

Dependent Variable: HDI concentration (µg/m³)

The independent variables (relative humidity and dry bulb temperature) were reproduced for the model to find the regression coefficients for HDI pollution in the polyurethane factories. The coefficients with respect to the constant, relative humidity and the dry bulb temperature as well as the collinearity statistics are shown in Table 5.

ModelCoefficientstP valueCollinearity Statistics
Relative humidity (%)0.3680.0675.4540.00010.5921.689
Dry bulb temperature (ºC)1.4920.11912.5880.00010.5921.689

Table 5.

Collinearity statistical model coefficients

Dependent Variable: HDI concentration (µg/m³)

The Variance Inflation Factor (VIF) measures the impact of collinearity among the variables in a regression model. Myers (1990) suggests that a value of more than 10 means there is a concern to worry about collinearity. Menard (1995) suggests tolerance (or 1/VIF) below 0.2 indicates a potential collinearity problem. For the current work, the suggestion by Myers (1990) is used and it can be seen in Table 1.5 the VIF values are well below 10 and the tolerance is above 0.2 indeed. Therefore, it can be safely said that there is no collinearity within the current data.

The two factory predictor variables (dimension of factory and altitude) were found to be not significant in the model. They were eliminated for making a new regression model based on relative humidity and dry bulb temperature. By replacing Y, X1 and X2 with HDI, Rh (relative humidity) and Td (dry bulb temperature), respectively and eliminating X3 (factory dimension) and X4 (altitude) and also substituting the relevant coefficients from Table 5., Equation 2, can be written as follows:


Equation 2 implies that relative humidity and dry bulb temperature affect diisocyanates pollutant concentration in the work places. The background HDI concentration was about 27.77µg/m³ as indicated by the value of the constant in the regression equation.

3.3. HDI concentration determinants

Figure 1. shows the relationship between HDI concentration and psychrometric parameters (relative humidity and dry bulb temperature) in five polyurethane factories based on equation 1.2.


Figure 1.

Relationship between HDI concentration and psychrometric parameters (relative humidity and dry bulb temperature)

Both indoor air relative humidity and dry bulb temperature are significantly contributing to the variability of the HDI concentration (R2 = 0.837) and both factors also show a straight positive relationship with the HDI concentration. This means that as the indoor relative humidity or the dry bulb temperature increases, the HDI concentration also increases.

As for the effects of relative humidity on isocyanate concentration, Ludwig and Urban (1996) observed that reactions of isocyanate groups with OH groups during cross-linking are inversely proportional to the relative humidity of the environment. They explained that the presence of competing reactions between water and isocyanate, hinder the degree of cross-linking between them. Abram and Bowler (2005) studied the effect of relative humidity on the curing and dielectric properties of the polyurethane-based composites. They have found that a polyurethane factory at 87% relative humidity (RH) gave more noticeable effect as compared to one at 37.7% RH. The electromagnetic properties of the polyurea/polyurethane-based composites studied were found to be strongly influenced by the presence of water vapor during the curing process, as evidenced by the significant difference in the real relative permittivity of samples cured in different RH environments. This difference was caused primarily by water uptake into the polymer matrix. The RH alters the dielectric properties of the composite material due to its strong polar nature and high value of real relative permittivity. In this study, the RH was in the range of 31 to 52%. The working range for RH in this study is lower than that reported by Abram and Bowler (2005) for the noticeable effects (87% RH). Hence the effect of RH is expected to be not as significant as to the effects of temperature.

The mean isocyanate concentration in this study was 78.87µg/m3 (0.079mg/m3). This is comparable to an investigation conducted in southern Australian auto body shops which reported a geometric mean concentration of 0.07 mg/m3 isocyanates (range <0.01–3.5 mg/m3 NCO) (Pisaniello and Muriale, 1989). Swedish auto body shops exposures during spray painting showed higher values between 0.26 and 1.1 mg/m3 (Torling et al., 1990). A survey of Oregon auto body shops measured a geometric mean concentration of 0.35 mg/m3 isocyanate with a maximum of 4 mg/m3 isocyanate (Janko et al., 1992). Maître et al. (1996) have reported an arithmetic mean level of 0.33 mg/m3 isocyanate (range 0.05–0.65 mg/m3) in a French isocyanates spray factory. These findings suggest that the measurements in this research are comparable to other related literatures. However comparison of such results is quite difficult because of the sampling time, sampling method and manufacturing process were totally different (Maître et al., 1993).

Previous studies often involved isocyanate exposure levels in auto body shops (Pisaniello and Muriale, 1989; Torling et al., 1990; Woskie et al., 2004) and up to date little work has been done to evaluate the determinants of isocyanate concentration levels in factories producing polyurethane foams with respect to HDI and indoor air quality variables. The current work points to psychrometric factors (indoor air temperature and relative humidity) as important predictors of HDI within the factories. Woskie et al. (2004) have pointed out that for small, low volume auto body shops as a consistent predictor of higher exposures especially during the colder months when buildings were closed up and general ventilation was reduced. However in this research dimension or size of the factories along with the altitude were seen to be not significant as HDI pollution determinants. Large size factory has difficulty in interpretation. For instance, Woskie et al. (2004) in a work based on auto body shops pointed that ‘large shop’ might mean more activities or jobs per day or a larger less cramped work area with greater general dilution ventilation.

The current work admittedly has limitations but may be a useful initiative in estimating possible HDI pollution situation in the polyurethane workplaces based on indoor air temperature and relative humidity. It can hopefully help to provide a basis to prioratise future exposure evaluation and intervention efforts and reduce the workers to exposure to isocyanates which are well known as asthmagens and respiratory irritants.

3.4. Biological monitoring

The aims of the present investigation regarding biological monitoring are to: (i) show the relationship between air pollution and exposed worker health; (ii) characterize worker diisocyanate exposure and examine the relative negative impact of pollution and inhalation routes in HDI polyurethane factories.

3.4.1. Subject individual characteristics

Individual exposure data as well as information regarding each individual were extracted, pooled, and entered into SPSS, V. 16 software for statistical analysis. The sample size for biological monitoring was 50 workers. At the end of the shift, workers took a shower and changed clothes before going to the factory's medical service in order to give urine samples. All samples were collected in polystyrene bottles containing 10 g citric acid, and stored at 4 °C until analysis. A negative ion chemical ionization mass spectrometry was used to determine the urinary HDA.

Table 6. describes the mean age of workers, who work in the HDI polyurethane factories was 34.5 years. The mean duration of work history was 4.9 years and the mean weight of the workers was 69.6 kg.

Age (year)5034.487.4652447
Weight (kg)5069.6412.3485088
Work history (year)504.742.94213

Table 6.

Characteristics of subjects

Table 7. shows that 54% of total workers, working in the HDI factories were categorized as smokers and also the same percentage of HDI workers had some symptoms [sore eyes, running nose, sore throat, coughing, wheezing (asthma) and chest tightness] relevant to diisocyanates exposure.

Smoking27 – Smoker
23 - None
Symptoms of disease27- with
23- without

Table 7.

Frequency and percentage of smokers and symptoms of disease

Table 1.8 shows the age, weight and years of the services of the workers in the HDI factories. The information is presented in terms of the ranges, frequency and percentage.

Age (year)20-30
Weight (kg)40-60
Years of services (year)0-5

Table 8.

Statistical data for the age, weight and years of services of the workers

3.4.2. Urinary concentration of HDA in the factories

Table 9 shows the descriptive statistics of urinary hexamethylene diamine (HDA) in different factories. The maximum concentration of HDA measured from worker's urine in the HDI polyurethane factories was 4 µmol/mol creatinine and the mean value was in the range of 3.01 to 3.58 µmol/mol creatinine for all factories. The urine results for all of the workers indicated high exposure with respect to HDI, because of the lowest concentration of HDA in their urine was 2.33 µmol/mol creatinine.

Factory codeMeanSDMinimumMaximum

Table 9.

Descriptive statistics of hexamethylene diamine (HDA) in different factories (n= 10)


Figure 2.

HDA concentrations in different factories

Figure 2. indicates different HDA concentration compare to different factories because there are some factors that affected on workers exposure in the different work area, for example working process, space of work station, etc.

3.4.3. HDI and HDA relationship in the factories

Fifty male workers in the five polyurethane factories, in an age range of 20-50 years, participated in the present investigation. They were engaged in injection glue device, painting sprayer and varnishing. Based on the diisocyanates biomonitoring method (Rosenberg et al., 1986; Skarping et al., 1995; HSE, 2005), the urine samples were collected at the end of exposure. This is due to the urinary half-life is about 2 hours and results reflected after 2 – 4 h exposure. Table 1.10 shows the HDA values in the urine associated with HDI concentration for the 50 workers. Air samples taken near the gluing operation contained high concentration of HDI (83.1 to 92.7 μg/m3) as compared to the other operations. The workers at the gluing operations also showed high urinary HDA concentration of between 2.88 to 4 μmol/mol creatinine. Workers involved in other operations had lower HDA concentration in their urine and also they had lower values of HDI exposure. The lowest HDI exposure was in varnishing operation (62.24 μg/m3 ) where the HDA value was found to be also the lowest at 2.33 μmol/mol creatinine but the value is also above the guideline value of 1 μmol/mol creatinine (Williams et al., 1999).

Subject FrequencyOperationHDI (μg/m³)HDA (μmol/molC)
4Paint sprayer80.572.88
3Paint sprayer79.292.88
3Paint sprayer74.672.88
3Paint sprayer68.53
4Paint sprayer67.722.83
3Paint sprayer65.332.67
3Paint sprayer64.112.67

Table 10.

Exposure to HDI and excretion of HDA in workers urine (n=50)

Table 11. shows the correlated results between HDI concentration in the air and HDA in the urine sample taken from workers. It can be seen that HDI concentration in the air is directly related to HDA concentration in the urine of workers with a Pearson correlation coefficient at r = 0.857 and the significance value is less than 0.001. This means that, as the HDI concentration in the air in factories increase, the HDA level in the urine of workers also increase.

The regression model summary for HDI and HDA is shown in Table 12. It indicates that the value of R2 is 0.735. This means that the exposure to HDI pollution in the factories account for 73.5% of the level of HDA in the workers’ urine.

Table 12. shows the F-test in the regression analysis; it is a relationship test between HDI in air samples and HDA in urine samples. For these data F is 133.247, which is significant at P< 0.05. Thus the regression model predicts the exposure to HDI pollutant significantly well.

HDA ConcentrationHDI Concentration
Pearson CorrelationHDA Concentration.0.857
HDI Concentration0.8571
Sig. (2-tailed)HDA Concentration.0.0001
HDI Concentration0.0001.
NHDA Concentration5050
HDI Concentration5050

Table 11.

Correlation is significant at the 0.05 level.

Generalized correlation HDI concentration and HDA
ModelSum of SquaresFP value
Regression13.161133.247< 0.0001

Table 12.

Generalized regression model for HDI and HDA

(Constant), HDA Concentration, HDI Concentration

The regression model used in this study is a simple linear model in the form of

From Table 13, B0 is -0.62 and B1 is 0.051.

Since B0 is rather small in magnitude, the B0 value can be ignored but B1 is significant in model (P< 0.05). The predictive relationship for HDI and HDA can be suggested in a linear regression equation (R²= 0.735) as shown in Equation 4:


HDA is dependent variable

HDI is independent variable

ModelCoefficientsTP value
HDI Pollution0.0510.00411.5430.0001

Table 13.

Generalized regression model coefficients for HDI and HDA

Dependent Variable: HDA Concentration

Figure 3. shows the graph for the relationship between factory air HDI concentration and urine HDA concentration of the workers. The graph shows a linear relationship between HDI and HDA. No background HDA was detected in the workers and the graph validates HDA as an initial indicator of a preceding exposure of workers to HDI with R2 = 0.735.

In a related research conducted by Maitre et al. (1993) focusing on biological monitoring of occupational exposure among nine (9) workers; they were exposed to toluene diisocyanate (TDI) in TDI-based polyurethane production. The study has validated the use of urinary


Figure 3.

Relationship between air HDI concentration and urinary HDA concentration

toluene diamine (TDA) as an indicator of preceding exposure to TDI. Their TDI exposure level was 9.5 to 94 µg/m³, and TDA concentrations varied from 6.5 to 31.7 µmol/mol creatinine. TDI and TDA were found to be linearly related and no background TDA was detected. It was noted that the workers in the research carried out by Maitre et al. (1993) did not wear personal protective equipment and there was also no ventilation system. That possibly explains the higher values of TDA and TDI. The workers were also engaged in different jobs (injection molding, mold stripping, cutting and gluing foam). Some were directly exposed and some were indirectly exposed to isocyanates.

In glue injection, the concentration of exposure to HDI in the air was higher than the working area near the paint spraying area or varnishing. In the other study, a high levels exposure has also been found by Lesage et al. (2001) at isocyanate injection workplace. The concentrations of HDA detected in this study are less than those found in the other studies (Brorson et al., 1990; Maitre et al., 1993). Brorson et al. (1990) have reported a mean value of HDA by the end of the shift was 20 μmol/mol creatinine after 7.5 h exposure to 25 μg/m3 HDI. Whereas Maitre et al. (1993) have measured a value of 12 μmol/mol creatinine for HDI exposure at the same concentration from a separate survey of workers.

In a developing country, the above situations reinforced the reasons why Kakooei et al. (2006) had expressed the importance of studying indoor air pollution in such industries. For the imported technology and the machinery used without suitable application of adequate engineering controls and proper safe work practice together with the levels of training and awareness can cause great exposure to air pollutants and may result in more occupational health problems than in the developed countries.

In a similar study that has been conducted by Cocker (2007) the urine samples which were also analysed for the diamine metabolites of hexamethylene diisocyanate had detectable levels with 22 µmol/mol creatinine. This value was much higher than that the maximum urinary concentration of the current study (4 µmol/mol creatinine).

Comparison of obtained results with previous work was difficult; because of the sampling time, sampling method, and manufacturing process were totally different. The present study detected a straight correlation between the isocyanate concentration in the air and its biological results in the workers’ urine. The obtained biological results from the present conducted research was lesser than that of the results reported by Lesage et al. (2001) that was related to manufacturing of foam plants.

The obtained results from the analysis of worker's urine showed that all of the workers were exposed via isocyanates in their workplaces based on guidance value for HDA. This implies that the high level of metabolites concentration in the urine of the workers can be attributed to the polluted situation of the workplaces. The concentrations of diisocyanates metabolite detected in the polyurethane workers were higher than the guidance value (>1 µmol/mol creatinine). However, this result is low compared to work performed by Brorson et al. (1990) where it was reported that the mean value of isocyanates metabolite at the end of working shift was 20 µmol/mol creatinine. Similarly, Maitre et al. (1996) have measured a value of 12 µmol/mol creatinine for diisocyanates exposure at the same concentration from a survey of workers.

The result of present investigation showed that the HDA concentration was also less than the research conducted by Tiljander et al. (1989) in US polyurethane factories for methylene diamine (5-30 μg/l in urine) and it was similar to results that have been reported by Selden et al. (1992).

The results of present study showed that biological monitoring can be a useful tool to assess isocyanate exposure in this group of exposed workers in the polyurethane factories. This work is comparable to the biological monitoring work done by Williams et al. (1999) when assessing exposure from use of isocyanates in motor vehicle repair-shop. They have noted that urinary biological monitoring has the potential to measure a worker’s body burden received by all routes of exposure (oral, inhalation and dermal). However biological monitoring does not determine the route of exposure which means exposure could have been through any of the three methods although inhalation is the most likely route given the lack of published evidence for the absorption of isocyanates through the skin (Williams et al., 1999).

Tinnerberg and Sennbro (2005) have cautioned that both air and biological monitoring methods have limitations. They have discussed that the exposures are complex and different in various environments. In their studies, biological monitoring has been used as a standard method to survey the exposure to aromatic diisocyanates and air monitoring to assess peak exposures and to find emission sources. They have also found that by using biomarkers it was easier to collect and analyse more samples as the sampling was not as time-consuming as air monitoring. Analyzing urine may be simple however in this study, it was also noted that the workers were quite reluctant to give their urine possibly due to urinary analysis is often associated to drug screening tests.

3.4.4. HDI work-related disease risk assessment

Diisocyanate work-related disease risk assessment is defined as an estimate of daily human exposure to diisocyanates pollution (HSE, 2005). Risk assessment is derived whilst adequate data exist to identify the high risk factors in the polyurethane factories. Eventually, it is important to comprehend how HDI interact with humans, with other species and physical environment. The risk assessment regression model summary where about 89.5% of the HDI risk can be attributed by any or all the independent variables (age and weight of workers, smoking, years of services and HDI pollution level) (R2 = 0.895, Adjusted R²= 0.883).

A multiple linear regression analysis was performed to evaluate individual factors for developing asthma induced by diisocyanates (Table 14.). Two factors were statistically related to risk of work-related disease of diisocyanate at the 0.05 significance level. These were: (a) weight of exposed workers, (b) HDI pollution. None of them had experienced previous incidents of exposure to diisocyanates.

ModelCoefficientstP value.
HDI Pollution0.6310.1125.6120.0001
Years of services0.0460.0401.1340.263

Table 14.

Regression summary model for HDI work-related disease risk assessment

Dependent Variables: Symptoms of HDI work-related disease

Risk assessment of exposure to diisocyanates among 50 workers increased with HDI pollution with potentially higher exposure with trends in TWA concentrations with regard to individual parameters (workers’ weight and HDI pollution) (Redlich and Meryl, 2002).

For HDI work-related disease risk assessment were assessed for possible risk factors experiencing an exposure to diisocyanate through inhalation. The assessment was based on determining the total risk set at the time of each event and accumulating evidence across events in favor or against specific associations. The likelihood of HDI work related disease risk assessment was not statistically related to age, history of work, or current level of cigarette smoking and years of services in the factory. In this study significant associations were found for diisocyanates concentration (expressed as a TWA) and workers’ weight and HDI pollution.

There was a strong correlation between symptoms of HDI work-related disease and the two independent variables (workers’ weight and HDI pollution), as summarized in Table 15.

ModelSum of SquaresFP value
Regression1412.599117.938< 0.0001

Table 15.

Summarized model for isocyanates risk assessment

Predictors: (Constant), HDI pollution, workers’ weight. Dependent Variable: Symptoms of HDI work-related disease

Under ascertainment is less likely among long term workers at the factories because of the asthmatic people who continue to work in exposed areas often develop increasingly severe symptoms, but surprisingly, there is no significance between years of services and incidence of work-related disease for diisocyanates workers. The result of this study is similar to the other research conducted by Diem et al. (1982) and Weill et al. (1981). The present study detected the effects of weight of the workers on HDI work-related disease and the results were similar to the study conducted by Lange et al. (1998).

4. Conclusion

Both indoor air relative humidity and temperature significantly contribute to the variability of the HDI concentration (R2 = 0.837) and both factors also showed a linear relationship with the HDI concentration. The relationship between HDI and relative humidity and indoor air temperature was found through multiple linear regression according to equation 1.2.


It was also shown that urinary HDA is detectable following HDI exposure. Urinary HDA concentration of the workers was found to be linearly related to HDI concentration of the air in the factories as correlated by following relation:


The HDI work-related disease risk assessment of the present study showed that two personal and work condition variables were correlated with Symptoms of HDI work-related disease in the polyurethane factories (workers’ weight and HDI pollution).

Finally, this study has shown that air monitoring together with biological monitoring can be used to estimate the HDI pollution situation as well as the exposure to workers of polyurethane factories. Such a study at polyurethane factories having more than 100 workers in a factory is not known till to date. Future research should focus among others on HDI exposure in relation to outdoor psychrometric factors, ventilation or application of adequate engineering controls, respiratory protective equipment, behavioral issues, the levels of training and awareness, occupational health background as well as the usage of other possible biomarkers of HDI.


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