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Keywords:

  • Intake fraction;
  • Seasonal differentiation;
  • Uncertainty;
  • Life cycle assessment;
  • IMPACT 2002;
  • USEtox

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. Acknowledgements
  8. SUPPLEMENTAL DATA
  9. REFERENCES
  10. Supporting Information

The intake fraction (iF) is the fraction of an emitted mass of chemical that is ultimately taken in by an entire population, and it is used as an indicator of human health potential impacts related to environmental chemical persistence and bioaccumulation in the food chain. In chemical screening applications, the iF can be predicted using multimedia and multipathway fate and exposure models. One of the sources of iF uncertainty is the natural seasonal variability of the input parameters used in the models, i.e., the physicochemical properties of the pollutant and the landscape and exposure parameters. The objective of this article is to determine the relevance of including seasonal differentiation when assessing iFs in life cycle assessment. This was done by calculating and comparing seasonal iFs with each other and with iFs at 25° C, for both Canadian and global contexts. Two Canadian seasonal models based on the IMPACT 2002 predictive tool, and 2 models for the global context based on the USEtox consensus model were developed to calculate summer and winter iFs. Emissions into air and water and a set of 35 representative organic chemicals were considered. Partition coefficients for seasonal conditions were calculated using an integration of the van't Hoff equation. First-order degradation rate constants were calculated assuming that the rate constant doubles with each 10° C increase in temperature. For Canadian air emissions, results indicated that iFs for winter emissions could be up to 1 to 2 orders of magnitude higher than summer iFs or iFs calculated at 25° C. For Canadian water emissions, results showed that iFs for both summer and winter conditions were, in general, closer to each other with outliers within 1 order of magnitude to iFs calculated at 25° C. Results also indicated that seasonal variability was of lesser importance when assessing iFs within a global context. Because the ranking between chemicals was maintained, it can be concluded that seasonal variability is not relevant within a comparative context. However, this difference might be significant when comparing the magnitude of human toxicity impacts versus other impact categories contributing to human health damages. Integr Environ Assess Manag 2012; 8: 749–759. © 2012 SETAC


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. Acknowledgements
  8. SUPPLEMENTAL DATA
  9. REFERENCES
  10. Supporting Information

Life cycle assessment (LCA) is a tool used to assess the potential environmental impacts that a product, service, or process has on the environment, over its entire life cycle (Hauschild 2005). Potential impacts are calculated multiplying the mass of emitted pollutants by their respective characterization factors (CFs), which express the contribution of each inventory item to a specific environmental problem (Hertwich et al. 2002). Therefore, there are different sets of CFs specific to each impact category (climate change, ecotoxicity, etc.). These CFs are uncertain because of model and scenario uncertainty as well as parameter uncertainty and natural variability (Huijbregts 1998a, 1998b). The latter arises from the natural spatial and temporal variability of the fate and exposure parameters used to predict CFs (e.g., temperature, soil properties, rainfall rate, etc.). The uncertainty associated with CFs that are developed in a generic manner, i.e., without spatial or temporal differentiation, can determine whether a difference between 2 emission scenarios is significant or not and, therefore, affect the decision of LCA (Potting and Hauschild 2006). Indeed, the location and time of emissions can strongly affect the fate of contaminants and their potential impacts on receptors.

The importance of spatial differentiation of CFs depending on emission location scenarios has been studied extensively. For instance, spatially differentiated characterization factors for the human toxicity impact category have been developed (MacLeod et al. 2004; Pennington et al. 2005; Humbert et al. 2009; Manneh et al. 2010). Spatial differentiation has also been studied for other impact categories such as acidification (Potting et al. 1998) and land use (Saad 2011).

Temporal differentiation issues have received less attention. Indeed, LCA traditionally calculates CFs at steady-state conditions, where landscape and exposure parameters represent the average annual conditions within a given region. Temporal differentiation within LCA focuses on 3 aspects. The first aspect deals with the calculation of the potential impacts with a dynamic model over a given time horizon. Levasseur et al. (2010) developed an approach by computing a dynamic life cycle inventory for the global warming impact category, to consider the temporal profile of emissions and then to calculate time-dependent CFs. The second aspect of temporal differentiation focuses on the variation of emission rates, which has a direct influence on the modification of a threshold effect and on recipients' sensitivity. Potting and Hauschild (2005) have studied the variation of CFs for different emission years. In particular, they have calculated and compared acidification factors for the reference years 1990 and 2010. The variation noted was caused by the difference in the level of economic activity and emission control measures. The third aspect of temporal differentiation is the application of a scenario analysis based on the variation of fate and exposure parameters with, for example, the season of emission. These variations can be important. For instance, in Canada, the average temperature can vary from −18° C in the winter to +12° C in the summer (Ressources Naturelles du Canada 2007; Environnement Canada 2010), causing variations in temperature-dependent environmental properties of chemicals, such as the octanol–water and air–water partition coefficients. Shah and Ries (2009) have studied spatial and monthly variations in regional impacts, more specifically for the photochemical oxidant formation (NOx) impact category in the United States. They have developed monthly characterization factors and found that a temporal variability of 2 orders of magnitude existed in both the fate and exposure level CFs for NOx.

The application of a scenario analysis based on the seasonal variation of fate and exposure parameters has, however, been neglected so far. Human toxicity is one of the impact categories considered in LCA. Human toxicity CFs can be expressed as the product of an intake fraction (iF) and an effect factor (Rosenbaum et al. 2008). The iF represents the mass fraction of a chemical release that will ultimately be taken in by the entire population and is a combination of the fate of and the exposure to a specific contaminant within a given evaluative environment (Bennett, Margni, et al. 2002; Bennett, McKone, et al. 2002). It is calculated using multimedia and multipathway fate and exposure models, which are evaluative screening tools developed to predict the environmental behavior of hundreds of pollutants and their related potential impacts on human health and ecosystems. Examples of such models are CalTOX (Exposure and Risk Assessment Group 2003), Globox (Sleeswijk 2006), IMPACT 2002 (Pennington et al. 2005), and USEtox (Hauschild et al. 2008; Rosenbaum et al. 2008). Intake fractions are traditionally calculated using these multimedia models solved at steady-state conditions. The seasonal variability of iFs has not yet been assessed within such fate and exposure models.

This article focuses on studying this seasonal variability of iFs within the IMPACT 2002 and USEtox models. Because spatial variability for Canadian emission scenarios has already been studied within IMPACT 2002 (Manneh et al. 2010), it is now of interest to study the seasonal variability on the same model and emission scenarios. This allows comparing spatial versus seasonal variability in a consistent way and determining which one is the most important. IMPACT 2002 assumes an annual temperature of 25° C, which overestimates the average annual temperature for Canada. Moreover, it is expected to observe a high variability between winter and summer Canadian temperatures. USEtox is a model that benefits from a large scientific consensus for LCA and comparative risk assessment applications. It represents an average continent nested within a global box model. The selection of the USEtox model is hence an interesting choice to verify if seasonal differentiation is relevant within a global context.

More specifically, this article aims at 1) developing and comparing summer and winter iFs with each other and with iFs calculated at 25° C within the IMPACT 2002 Canadian model and USEtox consensus model, and 2) evaluating the relevance of seasonal differentiation of iFs in LCA. The variability of iFs obtained from these multimedia and multipathways fate and exposure models could eventually be used to reduce the uncertainty of the assessment whenever seasonal information of emission inventories is available.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. Acknowledgements
  8. SUPPLEMENTAL DATA
  9. REFERENCES
  10. Supporting Information

Description of the multimedia and multipathway fate and exposure models IMPACT 2002 and USEtox

The IMPACT 2002 model (Pennington et al. 2005) was originally developed for Western Europe, but the Canadian nonspatial version of the model was used for this study (Manneh et al. 2010). The fate assessment includes degradation and intermedia transfer through the environmental compartments air, water, sediment, vegetation (stem, leaf, and root), and soil (surface, root, and vadose). Exposure is assessed via inhalation and ingestion (drinking water consumption and intake via agricultural products, animal products, and fishes). USEtox is a scientific consensus multimedia and multipathway fate and exposure model including both continental and global scales. Fate and exposure is assessed in a similar manner as IMPACT 2002. Further details on the algorithms used in IMPACT 2002 and USEtox can be found in Pennington et al. (2005) and Rosenbaum et al. (2008).

Chemicals and emission scenarios considered

Thirty-five organic substances (Table 1) were considered for air and water emission scenarios. These substances form a set of nondissociating and nonamphiphilic organic chemicals covering all relevant combinations in terms of environmental partitioning and exposure routes, overall persistence, long-range transport and feedback fraction (Margni et al. 2002; Margni 2003). For modeling purposes, PCBs are grouped under one chemical. Physicochemical properties of PCBs are different. However, they all fall under the same class of environmental partitioning and exposure routes, which justifies their combination.

Table 1. Physicochemical properties of chemicals considered at 25° C and for summer and winter seasons for the Canadian nonspatial version of IMPACT 2002
Chemical #Chemical namelogKOW (25° C) (−)ΔH (kJ/mol)SlogKOW (s) (−)logKOW (w) (−)H (25° C) (Pa.m3/mol)dlnH/d(1/T) (K)SH(s) (Pa.m3/mol)H(w) (Pa.m3/mol)t0,5a (25° C) (h)t0,5a (s) (h)t0,5a(w) (h)t0,5w (25° C) (h)t0,5w (s) (h)t0,5w(w) (h)
  1. s = summer; S = sources; w = winter.

  2. Properties at 25° C were taken from Mackay et al. (2006). Sources used to determine the ΔH and dlnH/d(1/T) factors: 1 = Dewulf et al. (1999); 2 = assumed excess enthalpy in octanol; 3 = assumed excess enthalpy in octanol and water; 4 = Bahadur et al. (1997); 5 = Sander (1999); 6 = National Institute of Standards and Technology (2008).

1Tetrachloroethylene2.92.612.92.81.7E + 03510058.2E + 029.3E + 015.5E + 021.1E + 038.8E + 035.5E + 021.1E + 038.8E + 03
2Carbon tetrachloride2.61.312.62.63.2E + 03440051.7E + 032.6E + 021.7E + 043.4E + 042.7E + 051.7E + 033.4E + 032.7E + 04
31,3-Butadiene2.01021.91.77.4E + 03410054.0E + 037.0E + 025.0E + 001.0E + 018.0E + 011.7E + 023.4E + 022.7E + 03
4Methomyl0.61020.50.31.9E − 08638267.3E − 094.8E − 105.5E + 021.1E + 038.8E + 035.5E + 031.1E + 048.8E + 04
5Acephate−0.9102−0.9−1.25.1E − 11500052.4E − 112.9E − 127.6E + 001.5E + 011.2E + 021.3E + 032.5E + 032.0E + 04
6Formaldehyde0.41020.30.03.2E − 02640051.2E − 028.1E − 045.0E + 001.0E + 018.0E + 015.5E + 011.1E + 028.8E + 02
7PCBS7.1−1937.27.73.5E + 01670051.3E + 017.4E − 013.9E + 027.7E + 026.2E + 039.0E + 021.8E + 031.4E + 04
8Di(n-octyl) phthalate8.11028.07.81.2E − 01560055.1E − 024.7E − 032.7E + 015.4E + 014.3E + 023.4E + 026.7E + 025.4E + 03
9Benzene, hexabromo-6.11026.05.82.8E + 00530051.3E + 001.4E − 012.2E + 044.5E + 043.6E + 051.4E + 032.9E + 032.3E + 04
10Cypermethrin6.61026.56.31.9E − 0511 00053.8E − 063.5E − 081.0E + 012.1E + 011.7E + 021.2E + 022.4E + 021.9E + 03
11Mirex6.91026.86.61.3E − 0111 00052.5E − 022.3E − 041.7E + 023.4E + 022.7E + 031.7E + 023.4E + 022.7E + 03
12Trifluralin5.31025.35.02.7E + 0011 00055.3E − 014.8E − 031.7E + 023.4E + 022.7E + 031.7E + 033.4E + 032.7E + 04
13Dicofol5.01024.94.75.7E − 05400053.1E − 055.7E − 067.0E + 011.4E + 021.1E + 039.0E + 021.8E + 031.4E + 04
14p-Dichlorobenzene3.4−17.143.53.93.0E + 02270052.0E + 026.3E + 015.5E + 021.1E + 038.8E + 031.7E + 033.4E + 032.7E + 04
15Aldrin3.01022.92.71.1E + 0111 00052.1E + 002.0E − 025.0E + 001.0E + 018.0E + 011.7E + 043.4E + 042.7E + 05
161,1,2,2-Tetrachloroethane2.41022.32.12.6E + 01500051.2E + 011.5E + 001.7E + 043.4E + 042.7E + 051.7E + 033.4E + 032.7E + 04
17Captan2.31022.22.07.3E − 01310054.6E − 011.2E − 011.7E + 013.4E + 012.7E + 021.7E + 013.4E + 012.7E + 02
18Pronamide3.41023.43.15.4E − 0111 00051.1E − 019.8E − 041.4E + 032.7E + 032.2E + 049.8E + 022.0E + 031.6E + 04
19Anthracene4.5−1234.64.94.3E + 00400052.4E + 004.3E − 015.5E + 011.1E + 028.8E + 025.5E + 021.1E + 038.8E + 03
20Lindane3.7−1433.84.13.4E − 01550051.5E − 011.5E − 021.7E + 023.4E + 022.7E + 031.7E + 043.4E + 042.7E + 05
21Dimethyl phthalate2.11022.01.81.7E − 01570057.5E − 026.6E − 031.7E + 023.4E + 022.7E + 031.7E + 023.4E + 022.7E + 03
22Methanol−0.8183−0.9−1.34.5E − 01560062.0E − 011.8E − 021.7E + 023.4E + 022.7E + 035.5E + 011.1E + 028.8E + 02
231,2-Dichloroethane1.53.341.51.41.2E + 02390056.6E + 011.2E + 011.7E + 033.4E + 032.7E + 041.7E + 033.4E + 032.7E + 04
24Ethyl acetate0.71020.70.41.4E + 01570066.0E + 005.3E − 015.5E + 011.1E + 028.8E + 025.5E + 011.1E + 028.8E + 02
25N-nitrosodiethylamine0.51020.40.21.8E − 0110 00054.0E − 025.6E − 045.0E + 001.0E + 018.0E + 011.7E + 013.4E + 012.7E + 02
26Thiram1.71021.71.48.0E − 03400054.4E − 038.0E − 041.7E + 023.4E + 022.7E + 031.7E + 023.4E + 022.7E + 03
27Propoxur1.51021.41.24.5E − 0511 00058.8E − 068.1E − 085.0E + 001.0E + 018.0E + 015.5E + 021.1E + 038.8E + 03
28Folpet2.91022.82.53.9E − 0411 00057.6E − 056.9E − 072.7E + 015.4E + 014.3E + 021.4E + 042.8E + 042.2E + 05
29Benomyl2.31022.22.01.9E − 0911 00053.8E − 103.5E − 125.0E + 001.0E + 018.0E + 011.7E + 023.4E + 022.7E + 03
30Hexachlorobutadiene4.81024.74.52.4E + 03470051.2E + 031.6E + 021.7E + 043.4E + 042.8E + 051.7E + 033.4E + 032.7E + 04
31Hexachlorocyclopentadiene5.01025.04.72.2E + 03150051.8E + 039.3E + 025.0E + 009.9E + 007.9E + 018.7E + 011.7E + 021.4E + 03
32Heptachlor epoxide5.01024.94.71.5E + 0211 00052.9E + 012.7E − 013.3E + 016.6E + 015.3E + 027.0E + 031.4E + 041.1E + 05
33Hexachlorobenzene5.5−24.445.76.27.8E + 01580053.3E + 012.8E + 001.7E + 043.4E + 042.7E + 055.5E + 041.1E + 058.8E + 05
34Heptachlor5.31025.25.02.2E + 0111 00054.3E + 003.9E − 025.5E + 011.1E + 028.8E + 025.5E + 021.1E + 038.8E + 03
352,3,7,8-TCDD6.81026.76.52.5E + 0011 00054.9E − 014.5E − 031.7E + 023.4E + 022.7E + 035.5E + 021.1E + 038.8E + 03

Development of the winter and summer Canadian models based on IMPACT 2002

Two nonspatial versions specific to the Canadian context were created for the winter and summer seasons. To predict the iF, the IMPACT 2002 model makes use of 11 physicochemical properties and 105 landscape and exposure parameters. However, it was found that these fate and exposure parameters do not share the same importance when it comes to the calculated iF (Manneh et al. 2010). Only parameters to which the iF was the most sensitive and that presented the highest contribution to its uncertainty were varied along with the summer and winter conditions. For emissions into air, Manneh et al. (2010) found that the rainfall rate, the half-lives in air and water, the soil area, and the octanol–water partition coefficient (KOW) were the most important model parameters. Whereas, for emissions into water, the Henry's constant, the half-lives in air and water, and the KOW were identified as the most important.

Seasonal temperature and precipitation data were provided from Environment Canada and Natural Resources of Canada (Ressources Naturelles du Canada 2007; Environnement Canada 2010). These data indicated an average Canadian temperature for the summer and winter seasons of +12° C and −18° C, respectively. The rainfall rate was 210 mm/y during the summer and 134 mm/y during the winter.

Physicochemical properties at 25° C were obtained from Mackay et al. (2006). The KOW and Henry's constant for the summer and winter conditions were predicted by performing an integration of the van't Hoff equation (Schwarzenbach et al. 2002), as shown by Equations 1 and 2, respectively.

  • equation image(1)

equation image is the predicted octanol–water partition coefficient for summer and winter. equation image is the octanol–water partition coefficient at the temperature of 298.15 K. equation image is the standard enthalpy change of partition between the water and octanol phases (J/mol), defined as: equation image where equation image and equation image are the excess enthalpies in octanol and water, respectively (J/mol). R is the universal gas constant (J/mol.K). Tsummer/winter is the summer or winter temperature (K).

  • equation image(2)

Hsummer/winter is the predicted Henry's constant for the summer and winter seasons (Pa.m3/mol). H298.15 is the Henry's constant at the temperature of 298.15 K (Pa.m3/mol). equation image is a constant (K), defined as equation image, where ΔHaw is the standard enthalpy change of partition between the air and water phases (J/mol). R is the universal gas constant (J/mol.K). Tsummer/winter is the summer or winter temperature (K).

For KOW, the chemical-related standard enthalpy change of partition between the water and octanol phases (ΔHOW) was taken from literature data for some of the chemicals considered (refer to Table 1). For other substances for which only data on the excess enthalpy in water were available, the excess enthalpy in octanol was assumed to be equal to 10 kJ/mol and the standard enthalpy change was calculated as the difference between the 2 excess enthalpy values. This is considered to be a reasonable approximation because, for most organic compounds, the absolute value of the excess enthalpy in octanol does not exceed (+) or (−) 10 kJ/mol (Schwarzenbach et al. 2002). When no data was available for the excess enthalpies in octanol and water, the first was assumed to be 10 kJ/mol whereas the second was assumed to be equal to 0. This was another justified assumption because for many compounds, the excess enthalpy in water was found to have a small absolute value. For instance, its value was −3.6 kJ/mol for carbon tetrachloride and −1.6 for 1,2-dichloroethane (Okochi et al. 2004).

As for the Henry's constant (H), it was also predicted using the integration of the van't Hoff equation (Eqn. 2). The ratio of the derivative of the natural logarithm of the Henry's constant to the derivative of the inverse of temperature was obtained mainly from Sander (1999).

For the degradation rates in air and water, IMPACT 2002 assumes a first-order kinetic reaction. Rate constants for a temperature of 25° C were calculated using the available half-life values at this temperature (t0.5a for air and t0.5w for water, respectively) from Mackay et al. (2006). Usually, one can use the Arrhenius equation to determine the dependence of the rate constant on temperature. However, as data was not available for the chemicals considered, rate constants for summer and winter were calculated assuming that the rate constant doubles with every 10° C increase in temperature (Fogler 2006). This assumption was verified by sensitivity analyses, as mentioned later on in the article. Half-life values for the 2 seasons were then deduced.

Table 1 presents the physicochemical properties for the default temperature of 25° C as well as for the summer and winter seasons for the Canadian nonspatial version of IMPACT 2002.

Development of the summer and winter USEtox models

Two models for the summer and winter seasons were created based on the USEtox consensus multimedia model and included environmental data at the global scale. The average world global temperature was found to be 15.6° C for summer (National Oceanic and Atmospheric Administration 2008) and 12.1° C for winter (National Oceanic and Atmospheric Administration 2010). As for precipitation data, maps taken from the Global Precipitation and Climatology Center indicated that, even though continental precipitation data could change seasonally, the average global precipitation did not vary significantly (Global Precipitation and Climatology Center 2011). Therefore, the precipitation data was assumed to be the same as the annual one, i.e., 1000 mm/y. In addition to the KOW, Henry's constant, and half-lives in air and water, the vapor pressure was also calculated for these specific summer and winter temperatures. The latter was determined using the available USEtox data at 25° C and the heat of vaporization (National Institute of Standards and Technology 2008). The list of chemicals considered and their physicochemical properties for the global summer and winter temperatures can be found in Supplemental Data.

The USEtox consensus model assumes a global annual temperature of 12° C but uses physicochemical properties for a temperature of 25° C. Therefore, a 25° C model was created, assuming a temperature of 25° C with physicochemical properties calculated at that temperature, to ensure consistency.

Calculation of summer and winter iFs

Once fate and exposure parameters were calculated for the summer and winter seasons, 2 versions of both the IMPACT 2002 and USEtox models were created for these seasons. Summer and winter iFs were then determined for the set of organic chemicals considered and for air and water emission scenarios, respectively. Intake fractions were also determined for the same set of chemicals but using the default temperature of 25° C for IMPACT 2002 and USEtox.

Sensitivity analyses

Sensitivity analyses were carried out on the IMPACT 2002 summer and winter models to test a few key assumptions. First, the excess enthalpy in octanol was assumed to be −10 kJ/mol for the chemicals for which only data on the excess enthalpy in water were available, instead of +10 kJ/mol. This is a justified approach because for most organic chemicals, the value of the excess enthalpy in octanol is (+) or (−) 10 kJ/mol (Schwarzenbach et al. 2002). Second, the excess enthalpy in water was assumed to be +25 kJ/mol for the chemicals for which no data was available, instead of zero. This choice is justified by the fact that for some organic chemicals, such as lindane, this value was found to be equal to 24 (Schwarzenbach et al. 2002). Third, the degradation rate constants were assumed to increase by a factor of 1.25 and 4 for every 10° C increase in temperature, instead of doubling every 10° C. These factors were chosen to be higher than 1 because the rate constant increases with temperature. They were selected to see if the difference between the summer and winter rate constants would change significantly compared to the assumed factor of 2. A fifth sensitivity scenario was added to verify if results would be altered when considering seasonal agricultural and animal production. In this case, iFs were calculated using the same summer and winter models, but including seasonal rather than annual production data. Finally, the last sensitivity analysis scenario was included to verify if half-life values in air calculated on the basis of summer and winter hydroxyl radicals concentrations would alter the results obtained. Seasonal concentrations of these radicals were taken from Bousquet et al. (2005).

RESULTS AND DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. Acknowledgements
  8. SUPPLEMENTAL DATA
  9. REFERENCES
  10. Supporting Information

Seasonal variability of iFs within the Canadian IMPACT 2002 model

Summer and winter iFs were developed for the set of organic chemicals considered and for air and water emissions. Figure 1 represents the plots of these seasonal iFs versus the iFs calculated at a temperature of 25° C.

thumbnail image

Figure 1. Summer and winter iFs in comparison with iFs calculated at 25° C for emissions into air (a) and water (b) (IMPACT 2002 model).

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For air emissions, iFs varied between 3.1 × 10−8 to 1.5 × 10−3. For the summer, winter and 25° C models, the lowest iFs were obtained for 1,3-butadiene and hexachlorocyclopentadiene, whereas the highest iFs were obtained for hexabromobenzene and hexachlorobenzene. As indicated in Figure 1a, and for most of the chemicals considered, summer iFs were similar to iFs calculated at 25° C, whereas winter iFs were up to 1 or 2 orders of magnitude higher. The highest difference between winter and summer iFs was noted for emissions of chemicals such as benomyl and acephate. These chemicals are ingestion dominant and have a low persistence in the air compartment. As it can be observed in Table 1, the KOW was somewhat poorly dependent on the temperature, confirming the findings by Schwarzenbach et al. (2002). However, as the temperature decreases during the winter, the rate of degradation of these chemicals decreases and hence their half-life in air increases, increasing their persistence in these environmental compartments and thus their iF. A smaller difference was observed for chemicals such as hexachlorobutadiene and carbon tetrachloride. The dominant exposure pathway for these substances is inhalation. Their iFs is controlled by the air–water partition coefficient, hence the Henry's constant, as well as the half-life in air. As it can be seen from Table 1, the Henry's constant diminishes with decreasing temperature, whereas the half-life in air increases.

For water emissions, iFs varied between 1.7 × 10−9 to 1.4 × 10−04. The lowest iFs were obtained for N-nitrosodiethylamine and captan, whereas the highest iFs were obtained for carbon tetrachloride and hexachlorobenzene. Figure 1b indicates that, for most cases, the summer and winter iFs were similar to each other and to the iFs calculated at 25° C. There were, however, few cases where differences up to 1 order of magnitude were observed between winter iFs and iFs calculated at 25° C. For instance, this was the case for 1,3-butadiene. Both ingestion and inhalation play a role in the total iF for this chemical. When the temperature decreases, the half-life of 1,3-butadiene in air and water increases. This increase in environmental persistence causes higher iFs via both exposure pathways. For other chemicals, such as carbon tetrachloride, the iF for the winter season was lower than the one calculated at 25° C. The dominant exposure pathway for carbon tetrachloride is inhalation. As it can be seen from Table 1, the Henry's constant for this chemical for the winter season is 1 order of magnitude lower than the 1 at 25° C. Therefore, when emitted to water during the winter, this chemical tends to reduce its partition into air and hence to lower the iF via inhalation.

Results obtained indicate that the ranking among chemicals remained the same for air and water Canadian emission scenarios (Figure 1). Because the ranking was the same, the difference observed is not relevant in an LCA context. However, this difference might be significant when comparing the magnitude of human toxicity impacts versus other impacts contributing to human health damages (e.g., respiratory effects or photochemical oxidation), depending on the seasonal variability of their respective parameters.

Seasonal variability of iFs within the USEtox model for the global context

Intake fractions were developed with the USEtox model for the summer and winter global conditions. Figure 2 compares summer and winter iFs with each other and with iFs calculated at 25° C. In general, this comparison showed for air and water emissions that the difference between seasonal iFs and the iFs calculated at 25° C was lower than for Canada. This can be explained by the relatively low variation in the seasonal temperature and precipitation rate at the global level. Similarly as per the Canadian condition, the ranking is maintained, implying that seasonal differentiation is not relevant in a comparative context.

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Figure 2. Summer and winter iFs in comparison with iFs calculated at 25° C for emissions into air (a) and water (b) (USEtox model).

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Sensitivity analyses

The results of the sensitivity analyses are presented in Table 2 for air emissions. Results for water emissions can be found in Supplemental Information. Regarding the KOW calculation, results indicated for the first 2 sensitivity analyses S1 and S2 that the ratio of summer to 25° C iFs did not change significantly. Furthermore, even though the ratio of the winter to 25° C iFs did vary slightly, it remained in the same order of magnitude as the base scenario. These findings can be explained by the somewhat weak dependence of the KOW on the temperature change. Furthermore, the ranking of the chemicals was, in general, maintained with the sensitivity analyses performed.

Table 2. Summer and winter iFs compared to iFs calculated at 25° C for different sensitivity analyses scenarios S1 to S6 (air emissions).
Summer iFs/iFs at 25° CWinter iFs/iFs at 25° C
#Base#S1#S2#S3#S4#S5#S6#Base#S1#S2#S3#S4#S5#S6
  1. # = chemical number (see Table 1); Base = base scenario; S1 = excess enthalpy in octanol of −10 kJ/mol; S2 = excess enthalpy in water of +25 kJ/mol; S3 = rate constant increase factor of 1.25 for each 10° C increase in temperature; S4 = rate constant increase factor of 4 for each 10° C increase in temperature; S5 = scenario with seasonal agricultural and animal production; S6 = scenario where half-lives in air based on seasonal concentrations of hydroxyl radicals.

166.5E − 01166.5E − 01166.2E − 01166.3E − 01166.6E − 01166.4E − 01166.4E − 01305.1E − 0125.5E − 01164.7E − 01234.4E − 01305.2E − 01305.1E − 01234.2E − 01
91.0E + 00121.1E + 00121.1E + 00239.1E − 0191.0E + 00277.8E − 01239.1E − 0125.5E − 01236.0E − 0125.5E − 01304.6E − 0125.6E − 01125.3E − 01304.4E − 01
121.1E + 0091.1E + 00231.1E + 0091.0E + 00121.2E + 00128.0E − 0191.0E + 00236.0E − 01166.7E − 01236.0E − 0124.7E − 01236.5E − 0125.5E − 0124.5E − 01
231.1E + 00231.1E + 0091.1E + 00121.0E + 00231.4E + 00288.8E − 01301.0E + 00166.7E − 01307.9E − 01309.4E − 01166.3E − 01166.7E − 01235.9E − 01166.2E − 01
111.3E + 00131.3E + 00131.3E + 00301.0E + 00331.6E + 0098.8E − 01121.0E + 00129.0E − 0178.6E − 0171.1E + 00127.9E − 01129.3E − 01166.4E − 01128.4E − 01
131.3E + 00301.4E + 00301.4E + 00351.0E + 00111.6E + 00139.5E − 0121.0E + 0099.5E − 0191.0E + 0091.1E + 0099.4E − 0199.5E − 0196.4E − 0199.4E − 01
301.3E + 0021.4E + 0021.4E + 0021.0E + 00131.6E + 00111.1E + 00351.1E + 0071.1E + 00121.2E + 00121.3E + 0079.4E − 0171.1E + 0078.9E − 01279.9E − 01
21.4E + 00111.5E + 0081.5E + 00131.1E + 0071.7E + 00231.1E + 00131.1E + 00201.8E + 00201.8E + 00201.8E + 00111.1E + 00201.9E + 00209.9E − 0171.0E + 00
281.5E + 0081.5E + 00281.5E + 00111.1E + 00201.8E + 00261.1E + 00291.1E + 00132.0E + 00192.6E + 00112.4E + 00271.3E + 00132.4E + 00341.4E + 00281.4E + 00
331.5E + 00281.5E + 00331.5E + 00291.1E + 00301.8E + 0041.2E + 00241.2E + 00112.1E + 00342.8E + 00192.5E + 00131.3E + 00342.5E + 00191.5E + 0011.5E + 00
81.5E + 00331.5E + 00111.6E + 0081.1E + 0022.0E + 00201.3E + 0051.2E + 00342.3E + 0082.9E + 0082.7E + 00351.3E + 00192.6E + 00131.5E + 00131.5E + 00
71.7E + 0071.6E + 00271.7E + 00101.1E + 00282.1E + 00301.3E + 0041.2E + 00192.5E + 00133.0E + 00343.2E + 00201.7E + 00114.1E + 00111.9E + 00141.6E + 00
351.7E + 00271.7E + 0071.7E + 0051.1E + 0082.3E + 0081.4E + 0081.2E + 00143.9E + 00113.0E + 00133.5E + 00281.8E + 00334.3E + 00262.6E + 0041.6E + 00
201.7E + 0041.7E + 00201.7E + 00241.2E + 00192.3E + 0061.4E + 00111.2E + 00334.3E + 00143.9E + 00263.6E + 0011.8E + 00144.7E + 00183.4E + 00241.6E + 00
271.7E + 00261.7E + 0041.7E + 0041.2E + 00342.5E + 00351.4E + 00281.2E + 0014.4E + 00264.1E + 00283.7E + 00341.8E + 0015.8E + 00333.8E + 00351.7E + 00
41.7E + 00201.8E + 00261.7E + 00281.2E + 00182.7E + 00191.4E + 00141.2E + 0085.8E + 00334.3E + 00143.9E + 00141.9E + 00187.7E + 00143.9E + 00261.7E + 00
261.7E + 00241.8E + 00241.8E + 00141.2E + 00272.7E + 0021.4E + 00271.2E + 00266.0E + 00284.4E + 00334.3E + 00262.0E + 0089.4E + 0064.1E + 00201.7E + 00
241.8E + 00351.8E + 00191.8E + 00271.2E + 00262.8E + 00291.5E + 0011.2E + 00356.3E + 0014.4E + 0014.4E + 00242.0E + 00321.1E + 0144.3E + 00341.8E + 00
191.8E + 00141.9E + 00141.9E + 0011.2E + 0042.9E + 0071.5E + 00261.2E + 0046.6E + 0046.5E + 0046.5E + 0042.0E + 0041.1E + 0114.4E + 00111.9E + 00
141.9E + 00192.0E + 00351.9E + 00261.2E + 00243.1E + 00331.5E + 00311.3E + 00247.3E + 00247.4E + 00247.4E + 0082.0E + 00261.3E + 01354.9E + 00192.0E + 00
12.0E + 0012.0E + 0012.0E + 00311.3E + 00143.3E + 00341.5E + 0031.3E + 00187.7E + 00187.7E + 00188.1E + 00192.1E + 00281.5E + 01285.2E + 00312.2E + 00
342.0E + 00342.1E + 00342.1E + 0031.3E + 00353.3E + 0051.6E + 00101.4E + 00288.0E + 00351.0E + 01359.5E + 00102.3E + 00351.7E + 0185.5E + 0032.3E + 00
222.2E + 00222.2E + 00222.2E + 00171.4E + 00223.8E + 00241.8E + 00171.4E + 00329.8E + 00321.1E + 01321.2E + 01313.1E + 00243.3E + 01326.4E + 00292.4E + 00
102.2E + 00172.3E + 00292.5E + 00191.5E + 0013.8E + 00141.9E + 00191.5E + 0061.5E + 0161.5E + 0161.4E + 0133.3E + 00176.1E + 01246.8E + 0052.5E + 00
172.3E + 00312.5E + 0032.5E + 00221.5E + 00174.4E + 00171.9E + 00221.5E + 00171.8E + 01171.8E + 01211.6E + 01293.4E + 00216.6E + 01271.1E + 0183.1E + 00
312.4E + 00292.5E + 00312.5E + 00331.5E + 0064.6E + 00221.9E + 00331.5E + 00271.8E + 01271.9E + 01171.7E + 0153.4E + 00108.4E + 01171.5E + 01173.4E + 00
32.5E + 0032.5E + 0052.5E + 00201.5E + 00214.9E + 0012.0E + 00201.5E + 00102.2E + 01213.1E + 01272.0E + 01334.3E + 00151.1E + 02251.8E + 0164.0E + 00
292.5E + 0052.5E + 0062.7E + 0071.6E + 00315.9E + 00102.0E + 0071.6E + 00213.1E + 01313.6E + 01313.8E + 01174.4E + 0061.5E + 02102.1E + 01334.3E + 00
52.5E + 00182.6E + 00172.7E + 00341.6E + 00106.0E + 00312.2E + 00341.6E + 00313.3E + 0133.8E + 0133.8E + 0164.6E + 00221.6E + 02212.3E + 01106.0E + 00
182.6E + 00102.7E + 00102.9E + 0061.8E + 00326.2E + 00182.2E + 0061.8E + 0033.8E + 01154.2E + 01154.1E + 01327.1E + 00272.9E + 0253.0E + 01326.5E + 00
62.7E + 0062.7E + 00213.4E + 00152.2E + 0036.4E + 0032.5E + 00152.2E + 0054.8E + 0154.8E + 01294.8E + 01187.5E + 00314.3E + 02313.2E + 01187.5E + 00
213.1E + 00213.1E + 00183.5E + 00212.2E + 0057.7E + 00252.5E + 00212.2E + 00154.9E + 01295.1E + 0154.8E + 01219.2E + 00256.2E + 02153.4E + 01217.8E + 00
323.6E + 00324.2E + 00254.3E + 00322.3E + 00157.9E + 00212.7E + 00322.3E + 00255.3E + 01255.3E + 01255.2E + 01251.2E + 01296.7E + 02293.4E + 01259.8E + 00
153.7E + 00254.3E + 00324.3E + 00182.6E + 00297.9E + 00152.7E + 00182.6E + 00295.7E + 01105.8E + 01225.8E + 01221.2E + 0137.2E + 0233.8E + 01229.8E + 00
254.2E + 00154.3E + 00154.4E + 00252.6E + 00258.4E + 00323.0E + 00252.6E + 00225.8E + 01225.8E + 01107.7E + 01151.3E + 0157.2E + 02224.7E + 01151.1E + 01

Sensitivity analyses S3 and S4 were carried out to test the assumption that the rate constant doubles with every 10° C increase in temperature. Results, also presented in Table 2, showed that the ranking of the chemicals also remained similar when the sensitivity analyses were carried out on the output results. They indicated for the changes made that winter and summer iFs were still, in general, higher than the iFs calculated at 25° C, even for a change by a factor of 1.25 in the rate constant for every 10° C increase. As this factor increased (e.g., 4), the ratio of the winter iFs to iFs calculated at 25° C became higher (up to 2 orders of magnitude could be observed between winter iFs and iFs calculated at 25° C). These results indicated that the factor assumed did play a significant role in the results obtained. Future research could, therefore, focus on better evaluating the dependence of the rate constant on the temperature for the chemicals of concern. This could be done, for instance, by gathering more data on the rate constant as a function of temperature and, hence, by an evaluation of the Arrhenius constants.

As for sensitivity scenario 5, it showed that chemical ranking and iFs calculated with the models including seasonal agricultural and animal production were similar to the ones calculated by the models assuming average yearly production. Sensitivity scenario 6 indicated that, when basing calculations of seasonal half-lives in air on summer and winter OH concentrations, results obtained from the base case scenario were not altered, as is the case for the ranking of chemicals.

Seasonal variability in comparison with spatial variability for Canadian emission scenarios

The uncertainty of iFs can be caused by the natural spatial and seasonal variability of the fate and exposure parameters used in a multimedia and multipathways model. Manneh et al. (2010) studied the spatial variability of iFs for several Canadian emission scenarios and found that this variability could be as high as 10 orders of magnitude in the case of some emissions into water (e.g., benomyl and acephate), when using the model based on the subwatersheds spatial resolution. Compared to the findings in this article, it can be said that, although the seasonal variability, especially for winter, can be important for some emissions, the spatial variability of iFs was in general more significant for Canadian emission scenarios. This is because the seasonal variability of fate and exposure parameters was lower than their spatial variability.

Outlook

This research focused on the application of a scenario analysis based on the variation of fate and exposure parameters with the summer and winter seasons, for the LCA context. The seasonal variability calculated was up to 1 or 2 orders of magnitude for some chemicals when emitted to air. However, the fate and exposure models IMPACT 2002 and USEtox used to calculate the seasonal iFs are based on the steady-state assumption. This is a limitation of the study, especially in the case of chemicals that are persistent in the environment, for instance carbon tetrachloride or tetrachloroethylene. As future work, one could remedy to this limitation by using a dynamic model. This could result in a lesser variability between seasons for persistent chemicals.

In addition, the influence of ice and snow on the fate of the contaminants was neglected, although the average temperature during the Canadian winter reaches below the freezing point. Future research could therefore focus on the inclusion of the influence of snow in the models that were used, to verify if similar winter iFs would be obtained. This could be done by incorporating existing snow algorithms into the models used for iF calculation (Wania 1997; Daly and Wania 2004; Roth et al. 2004).

CONCLUSIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. Acknowledgements
  8. SUPPLEMENTAL DATA
  9. REFERENCES
  10. Supporting Information

This article addressed the relevance of seasonal differentiation when assessing human health iFs in the context of LCA. It showed that, for some chemical emissions, using a multimedia model with the temperature of 25° C could underestimate the iFs up to 1 to 2 orders of magnitude in regions such as Canada where extreme temperature changes are observed between the winter and summer conditions. Despite this difference, the seasonal variability is not relevant within a comparative context, because the ranking between chemicals was maintained. However, this difference might be significant when comparing the relevance of toxic impacts with other human health oriented impact categories such as respiratory effects, photochemical oxidation, etc., depending on the magnitude of their seasonal variability. Finally, compared to the findings of other research work on spatial variability, it can be concluded that the latter was in general more important than seasonal variability.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. Acknowledgements
  8. SUPPLEMENTAL DATA
  9. REFERENCES
  10. Supporting Information

The authors thank Mr. Robert Whitewood from Environment Canada for providing data on summer and winter temperatures and precipitations. The CIRAIG thanks the following industrial partners for their financial contributions: ArcelorMittal, Bell Canada, Cascades, Eco Entreprises Québec, RECYC-QUÉBEC, Groupe EDF, Gaz de France, Hydro-Québec, Johnson & Johnson, Mouvement des caisses Desjardins, Rio Tinto Alcan, RONA, SAQ, Total, and Veolia Environment.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. Acknowledgements
  8. SUPPLEMENTAL DATA
  9. REFERENCES
  10. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. Acknowledgements
  8. SUPPLEMENTAL DATA
  9. REFERENCES
  10. Supporting Information

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