Article pubs.acs.org/est
Embodied Energy of Construction Materials: Integrating Human and Capital Energy into an IO-Based Hybrid Model Manish K. Dixit,*,† Charles H. Culp,‡ and Jose L. Fernandez-Solis‡ †
Sam Houston State University, Huntsville, Texas 77340, United States Texas A&M University, College Station, Texas 77843, United States
‡
S Supporting Information *
ABSTRACT: Buildings alone consume approximately 40% of the annual global energy and contribute indirectly to the increasing concentration of atmospheric carbon. The total life cycle energy use of a building is composed of embodied and operating energy. Embodied energy includes all energy required to manufacture and transport building materials, and construct, maintain, and demolish a building. For a systemic energy and carbon assessment of buildings, it is critical to use a whole life cycle approach, which takes into account the embodied as well as operating energy. Whereas the calculation of a building’s operating energy is straightforward, there is a lack of a complete embodied energy calculation method. Although an input−output-based (IO-based) hybrid method could provide a complete and consistent embodied energy calculation, there are unresolved issues, such as an overdependence on price data and exclusion of the energy of human labor and capital inputs. This paper proposes a method for calculating and integrating the energy of labor and capital input into an IO-based hybrid method. The results demonstrate that the IO-based hybrid method can provide relatively complete results. Also, to avoid errors, the total amount of human and capital energy should not be excluded from the calculation. energy.9,13,15 The total life cycle energy use of a building is the sum of life cycle embodied and operating energy.16 The calculation of a building’s operating energy is relatively straightforward compared to embodied energy, which lacks consistent, complete, and accurate data.9,17−19 This lack of an established embodied energy database also impedes the application of a life cycle-based environmental evaluation to the building design and construction industry. Among key challenges to establishing a complete and consistent embodied energy database is the lack of a globally accepted embodied energy calculation method.9 There are various embodied energy calculation methods that use process, input−output (IO), or hybrid data.11−13 Although each method has advantages and disadvantages, an IO-based hybrid analysis is currently considered the most appropriate method. However, some issues with the reliability of its results have been highlighted in the literature.11−13 In the past, improvements to the IO-based hybrid analysis enhanced its reliability, as noted by Treloar,11 Joshi,20 and Crawford.12 Despite these efforts, there remains potential for further improvement.12,13,21 Issues such as the overdependence on product price data, sector aggregation, and the lack of an
1. INTRODUCTION As a result of rising consumption of fossil fuel-based energy sources, the amount of carbon emissions in the atmosphere has increased radically.1−3 Most of this emission originates from fossil fuel combustion in transportation, construction, manufacturing, and residential and commercial operations.4−6 The global construction industry, on the average, consumes approximately 40% of the world’s energy each year and contributes to carbon emission significantly.7−9 According to the USEPA,10 buildings alone contribute to nearly 40% of the United States’ annual carbon emissions by consuming electricity and natural gas in their operation. A building consumes energy in two ways: (1) through the use of construction materials, products, and processes during its construction, maintenance, and demolition, and (2) in its operation during the occupancy phase.11−13 Each material or product installed in a building has consumed some amount of energy when it was manufactured and delivered to end users. Similarly, each process of construction, fabrication, transportation, and administration during the building’s life cycle also consumes energy.11,12,14,15 The total energy consumed by a building over its life cycle through the use of materials, products, and processes is called its life cycle embodied energy. Once the building is complete and occupied, the total energy expended in air-conditioning, heating, lighting, and powering building appliances is termed operating © 2015 American Chemical Society
Received: Revised: Accepted: Published: 1936
August 12, 2014 December 31, 2014 January 5, 2015 January 5, 2015 DOI: 10.1021/es503896v Environ. Sci. Technol. 2015, 49, 1936−1945
Article
Environmental Science & Technology
provide the flow of goods and services in monetary units among various industry sectors.11,12,21 The national IO accounts are published periodically at a summary or detailed level.39 For calculating embodied energy, a square matrix of direct requirement coefficients is used, which presents the input directly required to produce a unit of output for a particular industry sector. The direct requirements are accompanied by indirect requirements.40 For example, when an industry sector “C” increases its output by $1, its input providing sectors (e.g., “M” and “E”) must increase their output proportionally. This stage of indirect impacts is labeled stage one. To meet the increased output, sectors “M” and “E” exert pressure on their input supplying sectors to increase their output resulting in stage 2 indirect inputs. Similarly, there are stage three indirect inputs and so on. Therefore, the impact of raising industry output by $1 can be felt throughout the economy.40 This economy-wide impact represents indirect requirements.40 A detailed description of the method can be found in Treloar11 and Dixit.41 One major advantage of an IO-based calculation is that it covers most energy inputs to provide a relatively complete calculation.12,15,21 However, its results are for an aggregated industry sector that manufactures a wide range of products. In the IO method, all products would have the same energy intensity, which may not be true.11,12,15 There are two types of hybrid analyses: (1) process-based and (2) IO-based. In a process-based hybrid analysis, the basic framework remains process-based. To improve its completeness, IO data are inserted into the framework.36,38 For instance, to calculate the embodied energy of a house, the actual quantities of materials used are collected from the bill of quantities. These material quantities are then multiplied by their respective IObased embodied energy intensities to calculate the total embodied energy of the house.11,12 In an IO-based hybrid analysis, the process data of direct energy use are collected and inserted into an IO-based framework.11,12,21 The process data replace comparable IO-data in the framework.11 The basic assumption is that including more process data results in a more reliable model. The replacement of IO data with process data can be accomplished in many ways. For instance, if energy use data are available for all industry sectors, they can be incorporated directly into the IO model.42 If direct energy data are available only for a few sectors, then incorporating them into the IO model may cause unwanted indirect impacts.11 Treloar11 proposed a method for integrating energy use data into the economic model. This method involves the identification and extraction of direct energy paths from the IO model in order to integrate the studyspecific process data to avoid any unwanted indirect effects.11 According to Treloar,11 the incompleteness or error in typical embodied energy calculation and analysis is approximately 20% and, thus, no method provides accurate results. However, an IObased hybrid analysis is considered more complete and consistent than other existing methods.8 2.3. Embodied Energy Calculation: Major Issues. 2.3.1. Incompleteness and Inconsistency. Among the key parameters causing incompleteness and inconsistency are system boundary definition and data quality.13,19 A system boundary demarcates the energy inputs that are included in an embodied energy calculation.14,43−45 In most studies, the system boundary is defined subjectively since there are no system boundary definition guidelines.14,46 The results of such studies are not comparable and, therefore, cannot be used.17,18 Data quality is another parameter responsible for the incompleteness and inconsistency of embodied energy values.14 Studies often use
approach to integrate capital and labor inputs still need to be addressed.9,12,21 In this paper, we propose an approach to calculate the human and capital energy and integrate them into an IO-based hybrid framework developed for the United States’ economy. The aim of this paper is to enable a life cycle carbon assessment of a building by calculating the embodied energy of construction materials in a complete manner (covering the energy embodied in labor and capital inputs). We also analyze the results to investigate whether the energy embodied in human and capital inputs is insignificant.
2. LITERATURE REVIEW 2.1. Embodied Energy Model for a Building. The total energy embodied in a building over its service life is composed of its initial embodied energy (IEE), recurrent embodied energy (REE), and demolition energy (DE) as shown in Figure S1 (Supporting Information, SI).13,14,22 When a building is constructed, energy is consumed directly in processes such as construction, transportation, and administration. At this stage, energy is also consumed indirectly through the use of construction materials and pieces of building equipment installed in the building. The total energy embodied in constructing and delivering the building for occupancy is termed IEE.16,23,24 A major fraction of IEE comes from building material use.25−27 When building materials are extracted, processed, manufactured, packaged, and delivered, they have already consumed some energy.28−30 For instance, commonly used building materials, such as cement, steel, aluminum, and insulation, are highly energy intensive.31,32 The total energy consumed in extracting, processing, manufacturing, and delivering a building material is called its embodied energy.12,13,15 Once the building is occupied, it is maintained, refurbished, and some of its components are replaced. All such activities involve the use of construction materials and processes.33 The sum of all energy expended in maintenance, repair, refurbishment, and replacement activities is REE.7,19,23 The amount of REE depends on material and equipment selection and the service life of a building.25,34 For instance, selecting a durable product with a low maintenance requirement would lower the value of REE.16,23 Because REE is time dependent, the longer the service life of a component, the larger the value of life cycle REE for a building.16,26 When the building reaches the end of its service life, it is demolished and demolition waste is sorted and hauled for recycling, reuse, or disposal. The total energy consumed at this end-of-life stage is known as DE.14,22,27 For a comprehensive carbon assessment of buildings, it is critical that a net embodied energy value is calculated by taking into account any recovery through reuse and recycle, particularly at the endof-life phase.35 2.2. Embodied Energy Calculation Methods. There are three commonly used methods for calculating embodied energy: (1) process-based, (2) IO-based, and (3) hybrid analyses. Each of the three methods has limitations based upon the type and availability of data it uses. Because the three methods also differ in the extent to which their system boundaries cover the energy inputs, their results are not comparable.9,19 A process-based embodied energy calculation is a bottom-up approach, which starts with the building material as a final product and goes backward (upstream) to include as many direct and indirect energy inputs as possible.11−13,36−38 Most direct energy use data comes from the material manufacturers. An IO-based embodied energy calculation utilizes the national IO accounts, which 1937
DOI: 10.1021/es503896v Environ. Sci. Technol. 2015, 49, 1936−1945
Article
Environmental Science & Technology
approach to desegregate industry sectors in order to calculate product-specific energy intensities. 2.3.6. Human and Capital Inputs. Another major issue with the IO-based hybrid calculation is its inability to account for inputs such as human labor and capital investment.14,50 Current embodied energy methods fail to include the human and capital energy component of total embodied energy.50−54 Studies (Langston and Langston;52 Pulselli et al.55) have emphasized a need to incorporate human energy into embodied energy analysis. However, this is often not accomplished due to the lack of a clear human energy calculation method.56 There needs to be a system to apportion the household consumption that is counted toward human labor.50,57 In 1978, Costanza58 quantified the energy embodied in labor and government services by making the household sector endogenous to the conventional IO model. However, Cleveland50 argued that Costanza58 allocated entire household income to employment, which may not be accurate. Cleveland50 identified three energy components that should be accounted for when quantifying human energy: (1) the calorific value of food consumed by workers, (2) the embodied energy of food, and (3) fuel purchased by workers. To apportion human energy toward employment, Cleveland50 suggested comparing an earner’s expenditure to that of a nonearner. Each industry sector of an economy requires considerable investment in capital goods in addition to labor. Capital goods include all building and nonbuilding structures such as administration buildings, warehouses, workshops, electrical yards, loading and unloading areas, etc.59 The construction of these structures is energy intensive and this energy use should be treated as capital energy.14,60 Industry sectors also make purchases of a range of equipment, software, and automobiles, which also consume energy during their manufacturing or construction, delivery, and installation.12,50,58 Because the detailed IO accounts are not published annually, some capital expenditures that occur in a year other than the year of the detailed IO accounts may not show up in the IO table and may remain excluded from the calculation.11,12,15
data that are either nonrepresentative or secondary. A detailed discussion can be found in Dixit et al.13 2.3.2. Lack of a Standardized Comprehensive Calculation Method. The second big challenge to creating a consistent and complete embodied energy database is the lack of a globally accepted calculation method that provides specific and complete results.9,19,47 A method’s accuracy relates to the specificity of the calculation. If a calculation approach provides embodied energy values specific to the product under study, the calculation is considered more accurate than one that provides aggregated results. Completeness is governed by the extent to which most energy inputs are covered by the system boundary.14 Some energy inputs, such as energy of labor, capital equipment, and services, if not included in the calculation, could cause serious incompleteness.12,14 For instance, the choice between processbased and IO-based methods depends on how well a method provides complete and study-specific results. 2.3.3. Process Data Integration. The reliability of the IObased embodied energy results can be improved by integrating process data of energy use. Studies such as Treloar11 and Crawford12 improved the IO-based hybrid method incrementally. Treloar11 suggested systematically extracting IO data from an IO model and replacing them with process data of actual energy use. Treloar11 also highlighted the problem of double counting of energy sources in an IO-based method and recommended keeping all energy and nonenergy inputs to energy providing sectors at zero, using primary energy factors (PEFs) instead. A PEF represents the total primary energy consumed and lost in delivering end-use energy such as electricity. The PEF should be calculated by accounting for all direct and indirect energy use.48,49 In Treloar,11 it is assumed that the process data of direct energy use are calculated in a complete manner; incompleteness may leave gaps in the calculation. Also, it is assumed that PEF values incorporate all direct and indirect energy use of energy source generation and distribution. If the values of PEFs are obtained from a secondary source, they need to be used with caution. Crawford12 identified an issue with Treloar’s approach and proposed that, instead of extracting a direct energy path, the total energy path should be removed. However, when the total energy paths are removed and replaced with process data of direct energy use, there may remain some incompleteness. Acquaye21 analyzed the proposed approaches and opted for direct energy path extraction. 2.3.4. Use of Price Data in IO-Based Analysis. Most IO-based methods involve the use of price data to convert direct and indirect energy flows from monetary to energy units. If energy prices are inflated or deflated, a serious error in the calculation may occur.11 Furthermore, the energy intensities obtained from IO-based methods are in energy units/monetary unit (e.g., MBtu/$) and, therefore, require product prices to convert them to energy units/unit of mass or volume.12,15 This excessive dependence on price data can be problematic, particularly if reliable sector-specific prices are not available.21 The improvements suggested by Trealor11 and Crawford12 still involve the use of prices multiple times which could introduce large errors even if the prices are slightly off the actual values.21 2.3.5. Sector Aggregation. Because an IO framework is used in an IO-based hybrid method, the embodied energy intensities are obtained for an aggregated sector producing a wide range of products including the material under study. Such results for an aggregated sector lack specificity.9,11 In addition, manufacturing technology differs across products and using aggregated intensities may not be accurate. Joshi20 recommended an
3. RESEARCH GOAL AND OBJECTIVES This paper aims at improving the completeness of an IO-based hybrid method. The main goal of this study is to propose a human and capital energy calculation approach, integrate it into an improved IO-based hybrid method, and calculate embodied energy of commonly used construction materials. Additionally, we calculate embodied energy intensities in energy units/$ output, thereby avoiding the use of energy prices. We also calculate embodied energy values using the traditional IO method to compare results. We also quantify the contribution of human and capital energy to the total embodied energy. The research goal is accomplished by achieving the following objectives: • Construct an IO model for the United States’ economy • Gather and refine the data of actual energy use by various industry sectors and integrate them into the IO model to develop an IO-based hybrid model • Calculate human and capital energy values and insert them into the IO-based hybrid model • Calculate and compare embodied energy using IO and IObased hybrid methods with and without human and capital energy 1938
DOI: 10.1021/es503896v Environ. Sci. Technol. 2015, 49, 1936−1945
Article
Environmental Science & Technology
employment (according to BLS63). Finally, the value of TEE per hour per employee was calculated. The total number of employees was sourced from the USCB,66,68,69 USBLS,70 USDA (NASS71), and USDOC.72 A more detailed explanation of calculation can be referred from Dixit.40 4.3.1. Apportioning Personal Consumption. The transportation energy data for 2002 were sourced from the USDOT.73 Not all personal transportation is used for work-related purposes. The Federal Highway Administration (USDOT) published data on work-related trips for the years 1983, 1990, 1995, 2001, and 2009 (Santos et al.74), which showed the percentage of workrelated trips consistently between 22 and 30%. On the basis of these data, we apportioned 27% of the total personal trips toward employment. Similarly, all consumer expenditure cannot be assigned to the industry of employment. The USBLS provided the consumer expenditure for single and more than 2 consumers under the category of no earner and one earner (BLS75). Excluding the food and utilities expenses, the difference between the earner and nonearner for single and multiple consumers was in the range of 26−35%. An average of 30% of the total personal consumption expenditure (excluding food, utilities, and transportation) was allocated to the industry of employment as suggested by Cleveland.50 The personal consumption expenditure data were sourced from the USBEA’s 2002 Benchmark Input-Output Accounts. To account for the energy embodied in personal consumption items, the 2002 Benchmark Input-Output direct and total requirements tables were used.76 The residential energy consumption data for electricity, natural gas, and petroleum were sourced from the 2002 Annual Energy Review to account for the household energy use. 4.4. Capital Energy Calculation. Although the USBEA publishes capital flow data as supporting information to the benchmark accounts, the latest data were from 1997, and hence, could not be utilized for quantifying capital inputs in 2002. Data relating to capital expenditures of various industries were collected from diverse sources such as the USDOC,59 USCB,77 USBEA,78,79 USDA,80 and USCB.81 IO-based hybrid energy intensities were used to calculate average energy intensities of capital products. The energy intensities were averaged on the basis of relative share of capital inputs in the total net change in the fixed assets’ value at the end of 2002. For instance, capital inputs under the structures category included a wide range of residential (e.g., single and multifamily) and nonresidential buildings (e.g., healthcare, commercial, and manufacturing buildings) belonging to different industry sectors, and energy intensities of those were averaged using their % share in total capital inputs in buildings. Likewise, the equipment category included a variety of equipment such as computers, printing and metalwork machinery, and electrical and mechanical equipment, etc., which were used to calculate an average energy intensity. Tables S1 and S2 in the SI show the breakdown of the structure and equipment categories with their average energy intensities and percent share in the total capital expenditure.
4. RESEARCH METHODS 4.1. Hybrid IO Model. Although numerous approaches exist for integrating process data into an IO model, we used the approach proposed by Carter et al.42 In this approach, the energy inputs in monetary units are replaced with the process data of actual energy use in energy units. This way, the calculated values of direct and total requirement coefficients are obtained in energy unit/$, omitting the need to use energy prices. We used the 2002 U.S. Benchmark Accounts for constructing the IO model. A detailed description of the model development can be found in Section 1 of the SI. We used MBtu/lb as the functional unit to make the calculation and analysis simpler. However, these values can be translated into the units of volume using appropriate material densities. Because of varying material density, the functional properties of each material per unit of mass would be different. Consequently, the material requirements in the unit of volume would also vary. The calculated values can be converted from English units (kBtu/lb) to SI units (MJ/kg) by multiplying with 2.326. 4.2. Primary Energy Factor (PEF) Calculation. To avoid the double counting of energy inputs, we adopted the approach proposed by Treloar11 and calculated the primary energy factors (PEFs) for all five energy-providing sectors: (1) oil and gas extraction, (2) natural gas distribution, (3) coal mining, (4), petroleum refineries, and (5) electric power generation, transmission, and distribution. A detailed description of PEF calculation is available in Section 2 of the SI. A more detailed explanation of PEF calculation methods can also be referred from Dixit et al.16 4.3. Human Energy Calculation. We used the approach suggested by FAO61 to calculate the total energy expenditure (TEE) of an average human body. Three types of data were sourced to quantify TEE of an average working employee in the United States. First, the average range of weight and age by gender was gathered from BLS62,63 and Borrud et al.64 to calculate the basal metabolic rate (BMR) values. Next, an hourly activity schedule of a typical day of an employee was developed. The number of total employees was sourced from USBEA65 and USCB.66 Finally, the physical activity ratio (PAR) values of the activities of employee were quantified to calculate an average physical activity level (PAL). The activity hours and PAR values were based on FAO61 and the American Time Use Survey Data for 2003 from the United States Bureau of Labor Statistics (USBLS).67 The TEE was calculated as TEE = BMR × PAL The value of BMR is dependent on the age and body weight of a person and was calculated as follows: 61 Males, age 18−30 years BMR = 0.063 × Wtbd + 2.896 Males, age 30−60 years BMR = 0.048 × Wtbd + 3.653
5. FINDINGS 5.1. Human and Capital Energy. The total energy embodied in household expenditure of an average employee in the United States in 2002 was calculated as 45.7 MBtu (excluding work-related personal transport and food). The total energy consumed by an average employee was found to be 78.8 MBtu. This energy use included the energy of food (4%), consumables (31%), residential energy use (27%), and all work-related personal transportation (38%). On the basis of these results, the
Females, age 18−30 years BMR = 0.062 × Wtbd + 2.036
Females, age 30−60 years BMR = 0.034 × Wtbd + 3.538
where Wtbd is the body weight of a worker in kg. The TEE values were calculated for both male and female employees separately and were then averaged using their percent share in the total 1939
DOI: 10.1021/es503896v Environ. Sci. Technol. 2015, 49, 1936−1945
Article
Environmental Science & Technology Table 1. Embodied Energy of Materials under Study conventional IO-based method
IO-based hybrid + human and capital energy
IO-based hybrid method
study material
embodied energy (A) (kBtu/lb)
(B − A)/A (%)
embodied energy (B) (kBtu/lb)
(C − B)/B (%)
embodied energy (C) (kBtu/lb)
carpet (3/8 in. thick), level loop wood lumber hardwood plywood & veneer softwood plywood & veneer paints & coatings adhesives plastic pipes & fittings polystyrene foam insulation bricks clay wall & floor tiles (1/4 in. thick) vitrified clay sewer pipes flat glass cement concrete gypsum, bldg. products lime stone mineral wool insulation virgin steel primary aluminum copper
235.25 2.19 11.54 3.01 28.99 56.16 42.23 104.84 2.07 18.99 8.39 10.60 1.91 0.46 9.05 1.67 1.31 11.83 10.41 29.19 18.76
−3 11 21 21 −21 −61 11 0 −24 −24 −24 −3 64 20 12 12 −7 1 −3 172 31
228.21 2.42 13.95 3.64 22.82 21.64 46.86 104.70 1.57 14.38 6.36 10.29 3.13 0.54 10.12 1.87 1.22 11.90 10.11 79.30 24.67
6 11 9 9 6 6 4 5 6 6 6 3 3 7 3 3 17 6 3 1 4
242.10 2.70 15.19 3.97 24.12 23.00 48.74 110.12 1.66 15.20 6.72 10.62 3.23 0.58 10.38 1.92 1.43 12.60 10.39 80.17 25.77
Table 2. Share of Each Energy Source in Total Embodied Energy % of various energy sources in total embodied energy study material
oil and gas
coal
electricity
natural gas
petroleum
human energy
capital energy
carpet (3/8 in. thick), level loop wood lumber hardwood plywood & veneer softwood plywood & veneer paints & coatings adhesives plastic pipes & fittings polystyrene foam insulation bricks clay wall & floor tiles (1/4 in. thick) vitrified clay sewer pipes glass cement concrete gypsum, bldg. products lime stone mineral wool insulation virgin steel primary aluminum copper
1.60 1.35 1.20 1.20 2.76 2.53 3.27 2.56 0.31 0.31 0.31 0.28 0.22 0.41 0.30 0.30 0.72 0.52 0.18 3.54 0.13
4.21 0.81 1.12 1.12 4.45 4.66 2.12 3.07 2.14 2.14 2.14 1.18 38.30 21.05 22.49 22.49 3.76 3.82 26.43 0.48 6.29
36.35 27.33 32.94 32.94 23.87 26.02 23.26 24.34 23.20 23.20 23.20 28.36 28.16 24.28 18.50 18.50 33.64 39.01 38.07 64.69 51.09
23.11 10.46 14.01 14.01 21.67 21.49 20.15 24.61 51.91 51.91 51.91 55.97 6.03 14.61 26.56 26.56 17.11 35.55 24.70 9.17 26.96
29.00 49.76 42.58 42.58 41.88 39.37 47.34 40.48 17.02 17.02 17.02 11.10 24.37 32.98 29.65 29.65 30.27 15.54 7.91 21.04 11.24
3.55 5.85 5.13 5.13 2.75 3.13 1.84 2.62 2.96 2.96 2.96 1.47 0.71 3.12 1.21 1.21 9.27 2.45 1.52 0.58 2.19
2.18 4.44 3.01 3.01 2.62 2.80 2.02 2.31 2.46 2.46 2.46 1.65 2.21 3.54 1.28 1.28 5.23 3.10 1.19 0.50 2.09
sectors. Tables S3 and S4 in the SI list the top 9 sectors with higher human and capital energy use. 5.2. Embodied Energy of Construction Materials. Table 1 lists the calculated values of embodied energy of 21 commonly used construction materials. As shown in Table 1, the embodied energy values of adhesives, cement, aluminum, and copper calculated by the conventional IO-based method were quite different from those quantified using an IO-based hybrid approach. Surprisingly, the IO-based hybrid embodied energy of aluminum and cement was over 2.5 and 1.5 times their IO-
total energy embodied in human inputs was equal to approximately 11 quadrillion Btu. The total energy embodied in capital inputs such as structures, equipment, and software incurred during 2002 was calculated as 11.5 quadrillion Btu. The share of capital structures and equipment/software in total capital energy was approximately 46% and 54%, respectively. Interestingly, all primary energy supplying sectors had more capital energy under the category of structures or buildings, whereas the equipment and software category dominated the capital energy use of secondary energy 1940
DOI: 10.1021/es503896v Environ. Sci. Technol. 2015, 49, 1936−1945
Article
Environmental Science & Technology
Figure 1. IO-Based hybrid embodied energy comparison.
than the fossil fuel-based energy sources. Carpet was the most human (8.6 kBtu/lb) and capital energy (5.3 kBtu/lb) intensive material. Human and capital energy contributed to over 9% and 5% of the total embodied energy of stone, respectively. Figure 3 illustrates the percentage of direct and indirect energy in the total energy of construction materials. Manufacturing construction materials such as carpets, paints and coatings, adhesives, plastic products, concrete, and copper consumed more indirect than direct energy (72−92% indirect energy). Other materials, such as bricks, clay tiles, clay pipes, glass, cement, lime, and gypsum, used relatively lower indirect energy (11−27%). The embodied energy of all wood-based construction materials included approximately 46−51% of indirect energy.
based values, respectively. There was minimal difference in IObased hybrid and IO-based embodied energy values of polystyrene foam, mineral wool, and concrete. The inclusion of human and capital energy inputs in the embodied energy calculation increased the IO-based hybrid embodied energy values by 1−17%. Among the most human and capital energy intensive construction materials were stone, wood, and plywood products. The least human and capital energy use was for aluminum production. Among the top three energy-intensive materials were carpet, polystyrene foam, and aluminum. Among the materials with low embodied energy were concrete, stone, brick, and lime. Table 2 provides the relative share of each energy source in the total embodied energy of construction materials. A clear dominance of electricity, natural gas, and petroleum was seen when the total embodied energy was broken down by energy source. Among the most electricity-consuming materials were carpet (88 kBtu/lb) and aluminum (52 kBtu/lb). Industry sectors producing products such as plastic fittings and pipes and polystyrene insulation use petroleum products as feedstock materials. These sectors were among the most petroleumconsuming sectors. Surprisingly, carpet remained the most fossilfuel consuming material. Energy intensities broken down by energy sources are illustrated in Figures 1 and 2, which show the results of IO-based hybrid and conventional IO-based methods, respectively. Interestingly, the IO-based electricity intensity of aluminum was approximately 1/12th of its IO-based hybrid value. Human and capital energy was found to be much smaller
6. DISCUSSION The energy embodied in labor and capital inputs was quantified using approaches suggested by published studies. However, there were a number of assumptions made when apportioning personal consumption expenditure toward employment and disaggregating capital expenditure in the categories of structures, equipment, and software. More reliable data are needed to reliably compute human and capital inputs. Overall, human energy contributed to approximately 1−9% of the total embodied energy of the studied construction materials. Although this percentage seems insignificant and confirms the assertion that human energy could be negligible, for a building with excessive use of stone and wood-based materials, the total human 1941
DOI: 10.1021/es503896v Environ. Sci. Technol. 2015, 49, 1936−1945
Article
Environmental Science & Technology
Figure 2. Conventional IO-based embodied energy comparison.
Figure 3. Share of direct and indirect component in total embodied energy.
capital energy could contribute from 1 to 14% of total embodied energy. Excluding the energy of labor and capital investment from embodied energy calculation could cause incompleteness in
energy embodied could be much higher. Similarly, capital energy was calculated as 1−5%, which was insignificant compared to other fossil fuel-based energy sources. Collectively, human and 1942
DOI: 10.1021/es503896v Environ. Sci. Technol. 2015, 49, 1936−1945
Article
Environmental Science & Technology
approach to disaggregate an industry sector, which can be utilized to compute more product-specific energy intensities. The results of this study indicate that even if a process-based analysis is carried out covering one upstream stage, a significant error may occur due to the incompleteness of a truncated system boundary. Although the incompleteness inherent in a processbased analysis can be improved, covering a system boundary similar to an IO analysis seems impractical. On the other hand, the results of an IO-based hybrid analysis can be made more study-specific by using the process of sector disaggregation (Joshi20). However, the results should be interpreted and used with caution, as there are issues with IO analyses that still need to be addressed. For instance, using unreliable product price data contributes to the uncertainty of the results of IO-based methods. In addition, the assumptions of proportionality and homogeneity inherent in an IO table adds to this uncertainty. IO data are conventionally published late and using such old data without proper adjustment may yield misleading results. To avoid the use of price data, the total monetary output of a commodity in physical units, if available, can be used to convert embodied energy intensities to embodied energy (per unit of mass or volume). The total energy embodied in a commodity can be divided by the total output in physical units to calculate embodied energy. In addition, the process of sector disaggregation can help address some of the issues relating to the proportionality and homogeneity assumptions. The life cycle embodied energy of a building and its constituent materials can be a comprehensive indicator of the building’s life cycle carbon contribution. Mostly, embodied energy has been calculated as an aggregated energy value without knowing the individual contribution of energy sources such as coal, natural gas, oil, and electricity. In such a case, determining carbon impacts becomes difficult, since an average carbon emission coefficient is used. However, if embodied energy intensities are computed for each energy source, a more detailed carbon emission evaluation can be performed. We calculated aggregated as well as disaggregated embodied energy values that showed the composition of energy sources. Embodied energy intensities shown in terms of various energy sources also help distinguish materials that are more coal, oil, or electricity intensive. The manufacturing process of such materials then could be improved to reduce the use of carbon-intensive energy sources. In this paper, we demonstrated that the embodied energy of construction materials can be quantified in a complete manner by including capital and human energy and by avoiding the excessive use of energy prices.
the range of 1−17% at a material level (see Table 1). This incompleteness could be more at a building level depending upon the type and quantity of materials used. Whereas this study focuses on the United States, the results could be much different if calculated for countries such as India and China with more labor-intensive economies. The total embodied energy calculation results of the IO-based hybrid method were quite different from the conventional IObased values, particularly for industry sectors with higher electricity use. One main reason for this may be the use of PEF (4.12 times for electricity) for converting secondary energy to primary energy, which also takes into account transmission and distribution losses. Also, the calculated values of PEFs for coal, natural gas, and refined petroleum included energy use and losses that may not show up in annual IO accounts, as no monetary transaction occurs for such onsite energy use or loss. The embodied energy results demonstrated that carpet was the most energy-intensive material, particularly in the fossil fuel category. This was due to the heavy use of synthetic materials in its production, such as nylon, polyester, PVC, and adhesives having a higher embodied energy. As discussed in the earlier section, materials such as carpets, polystyrene, plastic pipes and fittings, paints and coatings, and adhesives showed a higher petroleum product use (10−70 kBtu/lb) probably due to their use of it as feedstock material. Such high feedstock energy use was visible in a higher percentage of indirect energy (72−92%) of these materials as seen in Figure 3. Materials that require relatively less energy-intensive ingredients such as clay, sand, and limestone, showed relatively lower percentage of indirect energy use (11− 21%). For instance, clay products that are made of clay, a relatively less energy-intensive material, held a lower indirect energy (16%). Similarly, glass and cement showed 21% and 11% of indirect energy, respectively, due to the use of ingredients with less embodied energy such as silica and limestone. The embodied energy results for cement and concrete were interesting. Because cement, a material with higher embodied energy, is one of the main constituent materials of concrete, the indirect energy of concrete was approximately 87%. However, the indirect energy represented less than 11% of the total embodied energy of cement. Furthermore, the IO-based embodied energy of materials such as cement and aluminum, which mainly included coal and electricity usage, increased significantly when calculated with IO-based hybrid method probably due to the use of PEF for electricity. In addition, most aluminum production plants installed near hydropower plants buy electricity at a considerably low price. The same may be true with most cement plants, which may be buying coal at a cheaper price. The IO-based embodied energy of adhesives was significantly higher than IO-based hybrid values probably due to a larger consumption of secondary energy sources such as petroleum products. Because the problem of double counting is more pronounced in the case of secondary fuels, the IO-based embodied energy value for adhesives is significantly higher than the IO-based value. As shown in Table 2, some construction materials such as hardwood and softwood plywood and lumber share the same embodied energy intensity because they come from the same industry sector. Similarly, because gypsum and lime products are grouped together, their energy intensities are the same. The same is true for all clay products including bricks, clay floor and wall tiles, and clay sewer pipes. More research is needed to disaggregate the output of an industry sector into the product under study and other products. Joshi20 has proposed an
■
ASSOCIATED CONTENT
S Supporting Information *
Additional text and tables as mentioned in the text. This material is available free of charge via the Internet at http://pubs.acs.org.
■
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
■
REFERENCES
(1) Holdren, J. P.; Ehrlich, P. R. Human population and the global environment: Population growth, rising per capita material consumption, and disruptive technologies have made civilization a global ecological force. Am. Sci. 1974, 62 (3), 282−292.
1943
DOI: 10.1021/es503896v Environ. Sci. Technol. 2015, 49, 1936−1945
Article
Environmental Science & Technology (2) Wackernagel, M.; Lewan, L.; Hansson, C. B. Evaluating the use of natural capital with the ecological footprint: Applications in Sweden and subregions. Ambio 1999, 604−612. (3) Bruce, N. Towards a sustainable balance of population and resources: Australia to 2050. J. Futures Stud. 2012, 16 (4), 107−116. (4) IPCC. Climate Change 2007, The Physical Science Basis; Working Group 1: Contribution to the fourth assessment; Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: New York, 2007. (5) Alcott, B. Population matters in ecological economics. Ecol. Econ. 2012, 80, 109−120. (6) USEPA. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990−2009; EPA 430-R-11-005; U.S. Environmental Protection Agency: Washington, DC, 2011. (7) Ding, G. Development of a multi-criteria approach for the measurement of sustainable performance for built projects and facilities. PhD Thesis, University of Technology, Sydney, 2004. (8) Langston, Y. L.; Langston, C. A. Reliability of building embodied energy modeling: An analysis of 30 Melbourne case studies. Constr. Manage. Econ. 2008, 26 (2), 147−160. (9) Dixit, M. K.; Fernández-Solís, J. L.; Lavy, S.; Culp, C. H. Need for an embodied energy measurement protocol for buildings: A review paper. Renewable Sustainable Energy Rev. 2012, 16 (6), 3730−3743. (10) USEPA. Buildings and their Impact on the Environment: A Statistical Summary; U.S. Environmental Protection Agency: Washington, DC, 2009; http://www.epa.gov/greenbuilding/pubs/gbstats.pdf. (11) Treloar, G. J. A comprehensive embodied energy analysis framework. Ph.D. Thesis, Deakin University, Victoria, Australia, 1998. (12) Crawford, R. H. Using input-output data in life cycle inventory analysis. Ph.D. Thesis, Deakin University, Victoria, Australia, 2004. (13) Dixit, M. K.; Fernández-Solís, J.; Lavy, S.; Culp, C. H. Identification of parameters for embodied energy measurement: A literature review. Energy Build. 2010, 42 (8), 1238−1247. (14) Dixit, M. K.; Culp, C. H.; Fernández-Solís, J. L. System boundary for embodied energy in buildings: A conceptual model for definition. Renewable Sustainable Energy Rev. 2013, 21, 153−164. (15) Langston, Y. L. Embodied energy modeling of individual buildings in Melbourne, the inherent energy-cost relationship. Ph.D. Thesis, Deakin University, Victoria, Australia, 2006. (16) Dixit, M. K.; Culp, C. H.; Lavy, S.; Fernández-Solís, J. L. Recurrent embodied energy in life cycle of built facilities. Facilities 2014, 32 (3/4), 160−181. (17) Fernández, J. Materials and construction for low-energy buildings in China. In Sustainable Urban Housing: Principles and Case Studies for Low-Energy Design in China; Glicksman, L., Lin, J., Eds.; Springer: Dordrecht, the Netherlands, 2006. (18) Burnett, J. Forestry Commission Scotland, Greenhouse Gas Emissions Comparison, Carbon Benefits of Timber in Construction; Edinburgh Centre for Carbon Management Ltd.: Edinburgh, UK, 2006. (19) Khasreen, M. M.; Banfill, P. F. G.; Menzies, G. F. Life cycle assessment and the environment impact of buildings: A review. Sustainability 2009, 1 (3), 674−701. (20) Joshi, S. V. Comprehensive product life-cycle analysis using input output techniques. Ph.D. Thesis, Carnegie Mellon University, Pittsburgh, PA, USA, 1998. (21) Acquaye, A. A stochastic hybrid embodied energy and CO 2_eq intensity analysis of building and construction processes in Ireland. Ph.D. Thesis, Dublin Institute of Technology, Dublin, 2010. (22) Cole, R. J.; Wong, K. S. Minimizing environmental impact of highrise residential buildings. In Proceedings of Housing for Millions: The Challenge Ahead; Housing Authority: Hong Kong, 1996; pp 262−265. (23) Cole, R. J. Determining permissible degree of inaccuracy in life cycle assessment protocols. In Proceedings of the Air & Waste Management Association’s 89th Annual Meeting and Exhibition, Nashville, TN, USA, 23−28 June, 1996. (24) Ramesh, T.; Prakash, R.; Shukla, K. K. Life cycle energy analysis of buildings: An overview. Energy Build. 2010, 42 (10), 1592−1600.
(25) Chen, T. Y.; Burnett, J.; Chau, C. K. Analysis of embodied energy use in the residential building of Hong Kong. Energy 2001, 26 (4), 323− 340. (26) Scheuer, C.; Keoleian, G. A.; Reppe, P. Life cycle energy and environmental performance of a new university building: Modeling changes and design implications. Energy Build. 2003, 35 (10), 1049− 1064. (27) Vukotic, L.; Fenner, R. A.; Symons, K. Assessing embodied energy of building structural elements. Eng. Sustain. 2010, 163 (ES3), 147−158. (28) Trusty, W. The Environmental Side of Sustainability: Using Life Cycle Assessment to Assess True Performance; NRCC-48691; National Research Council of Canada: Ottawa, ON, Canada, 2006. (29) Sartori, I.; Hestnes, A. G. Energy use in the life cycle of conventional and low-energy buildings: A review article. Energy Build. 2007, 39 (3), 249−257. (30) Ardente, F.; Beccali, M.; Cellura, M.; Mistretta, M. Building energy performance: A LCA case study of kenaf-fibres insulation board. Energy Build. 2008, 40 (1), 1−10. (31) Pullen S. A tool for depicting the embodied energy of the Adelaide urban environment. In Proceedings of Australian Institute of Building Surveyors International Transitions Conference, Adelaide, March 2007. (32) Hammond, G. P.; Jones, C. I. Embodied carbon: The concealed impact of residential construction. Global Warming-Green Energy Technol. 2010, 367−384. (33) Utama, A.; Gheewala, S. H. Indonesian residential high rise buildings: A life cycle energy assessment. Energy Build. 2009, 41 (11), 1263−1268. (34) Chau, C. K.; Yik, F. W. H.; Hui, W. K.; Liu, H. C.; Yu, H. K. Environmental impacts of building materials and building services components for commercial buildings in Hong Kong. J. Clean. Prod. 2007, 15 (18), 1840−1851. (35) Tingley, D. D.; Davison, B. Developing an LCA methodology to account for the environmental benefits of design for deconstruction. Build. Environ. 2012, 57, 387−395. (36) Alcorn, J. A.; Baird, G. Use of a Hybrid Energy Analysis Method for Evaluating the Embodied Energy of Building Materials; Center for Building Performance and Research, Victoria University of Wellington: New Zealand, 1996. (37) Crawford, R. H.; Treloar, G. J. An assessment of the energy and water embodied in commercial building construction. In Australian Life Cycle Assessment Conference (4th: 2005: Novotel, Sydney, NSW); August 2005. (38) Lenzen, M. Errors in conventional and input-output based life cycle inventories. J. Ind. Ecol. 2000, 4 (4), 127−148. (39) Horowitz, K. J.; Planting, M. A. Concepts and Methods of the InputOutput Accounts; U.S. Bureau of Economic Analysis: Washington, DC, 2009. (40) Miller, R. E.; Blair, P. D. Input-Output Analysis, Foundations and Extensions; Cambridge University Press: New York, 2009. (41) Dixit, M. K. Embodied Energy Calculation: Method and guidelines for a building and its constituent materials. Ph.D. Thesis, Texas A&M University, College Station, TX, 2013. (42) Carter, A. J.; Peet, N. J.; Baines, J. T. Direct and Indirect Energy Requirements of the New Zealand Economy; New Zealand Energy Research and Development Committee: Auckland, New Zealand, 1981. (43) Crawford, R. H.; Treloar, G. J. Validation of the use of Australian input output data for building embodied energy simulation. In Eighth International IBPSA conference, Eindhoven, Netherlands, August 2003. (44) IFIAS. IFIAS Workshop Report: Energy Analysis and Economics; International Federation of Institutes for Advanced Studies: Lidingo, Sweden, 22−27 June 1975; pp 151−204. (45) Peuportier, B. L. P. Life cycle assessment applied to the comparative evaluation of single family houses in the French context. Energy Build. 2001, 33 (5), 443−450. (46) Suh, S.; Lenzen, M.; Treloar, G. J.; Hondo, H.; Horvath, A.; Huppes, G. System boundary selection in life cycle inventories using hybrid approaches. Environ. Sci. Technol. 2004, 38 (3), 657−664. 1944
DOI: 10.1021/es503896v Environ. Sci. Technol. 2015, 49, 1936−1945
Article
Environmental Science & Technology
(67) BLS. Consumer Expenditure in 2002; Report 974; U.S. Department of Labor, Bureau of Labor Statistics: Washington, DC, 2004; http:// www.bls.gov/cex/csxann02.pdf. (68) USCB. Statistical Abstract of the United States: 2006, Natural Resources; U.S. Census Bureau: Washington, DC, 2006; https://www. census.gov/prod/2005pubs/06statab/natresor.pdf. (69) USCB. 2002 Economic Census, Construction, Industry Series; U.S. Census Bureau: Washington, DC, 2005; http://www.census.gov/econ/ census02/guide/INDRPT23.HTM. (70) BLS. Industry Output and Employment, 2002; U.S. Department of Labor, Bureau of Labor Statistics: Washington, DC, 2012; http://stats. bls.gov/emp/ep_data_industry_out_and_emp.htm. (71) NASS. 2002 Census of Agriculture, United States Summary and Data. Vol. 1, Geographic Area Series Part 51; AC-02-A-51; U.S. Department of Agriculture: Washington, DC, June, 2004. (72) USDOC. Statistics for Industry Groups and Industries: 2003, M03 (AS)-1 (RV); Annual Survey of Manufactures; U.S. Department of Commerce, Economics and Statistics Division, United States Census Bureau: Washington, DC, 2005. (73) USDOT. National Transportation Statistics; Bureau of Transportation Statistics, U.S. Department of Transportation: Washington, DC, 2012. (74) Santos, A.; McGuckin, N.; Nakamoto, H. Y.; Gray, D.; Liss, S. Summary of Travel Trends: 2009 National Household Travel Survey, U.S. Department of Transportation Trends in Travel Behavior, 1969−2009; Federal Highway Administration, U.S. Department of Transportation: Washington, DC, 2011. (75) BLS. American Time Use Survey Data, 2003; U.S. Department of Labor, Bureau of Labor Statistics: Washington, DC, 2004; http://www. bls.gov/news.release/archives/atus_09142004.pdf. (76) USBEA. 2002 Benchmark Input-Output Data, 2002 Data Files; U.S. Bureau of Economic Analysis: Washington, DC, 2008; http:// www.bea.gov/industry/io_benchmark.htm#2002data. (77) USCB. Annual Capital Expenditure Survey; U.S. Census Bureau: Washington, DC, 2002; http://www.census.gov/econ/aces/xls/2002/. (78) USBEA. Fixed Assets and Consumer Durable Goods for 1997−2010; U.S. Bureau of Economic Analysis: Washington, DC, 2011; http:// www.bea.gov/scb/pdf/2011/09%20September/0911_fixed-assets.pdf. (79) USBEA. Fixed Assets Accounts Tables; U.S. Bureau of Economic Analysis: Washington, DC, 2012; http://www.bea.gov/iTable/iTable. cfm?ReqID=10&step=1#reqid=10&step=1&isuri=1. (80) USDA. Farm Production Expenditures, 2003 Summary, July 2004; U.S. Department of Agriculture: Washington, DC, 2004. (81) USCB. Utilities: Subject Series − Misc. Subjects: Construction Activity by Utilities for the United States: 2002; 2002 Economic Census; U.S. Census Bureau: Washington, DC, 2002.
(47) Optis, M.; Wild, P. Inadequate documentation in published life cycle energy reports on buildings. Int. J. Life Cycle Assess. 2010, 15 (7), 644−651. (48) Deru, M.; Torcellini, P. Source Energy and Emission Factors for Energy Use in Buildings; NREL/TP-550-38617; National Renewable Energy Laboratory: Golden, CO, USA, 2007. (49) Molenbroek, E.; Stricker, E.; Boermans, T. Primary Energy Factors for Electricity in Buildings, Toward a Flexible Electricity Supply. Ecofys: Utrecht, The Netherlands; 2011. (50) Cleveland, C. J.; Costanza, R. Net energy analysis. Encyclopedia of Earth; 2008; http://www.www.eoearth.org/article/Net_energy_ analysis. (51) Ulgiati, S.; Ascione, M.; Bargigli, S.; Cherubini, F.; Federici, M.; Franzese, P. P.; Raugei, M.; Viglia, S.; Zucaro, A. Multi-method and multi-scale analysis of energy and resource conversion and use. In Energy Options Impact on Regional Security; Barbir, F., Ulgiati, S., Eds.; Springer, The Netherlands, 2010; pp 1−36. (52) Langston, Y. L.; Langston, C. A. Building energy and cost performance: An analysis of 30 Melbourne Case Studies. Aust. J. Constr. Econ. Build. 2007, 7 (1), 1−18. (53) Huberman, N.; Pearlmutter, D. A life-cycle energy analysis of building materials in Negev desert. Energy Build. 2008, 40 (5), 837−848. (54) Alshboul, A. A.; Alzoubi, H. H. Analysis of embodied energy requirements for natural dimensioned stone production in Jordan. Arch. Sci. Rev. 2008, 51 (2), 124−132. (55) Pulselli, R. M.; Simoncini, E.; Marchettini, N. Energy and emergy based cost−benefit evaluation of building envelopes relative to geographical location and climate. Build. Environ. 2009, 44 (5), 920− 928. (56) Dias, W. P. S.; Pooliyadda, S. P. Quality based energy contents and carbon coefficients for building materials: A systems approach. Energy 2004, 29 (4), 561−580. (57) Deng, W.; Prasad, D.; Osmond, P.; Li, F. T. Quantifying life cycle energy and carbon footprints of China’s residential small district. J. Green Build. 2011, 6 (4), 96−111. (58) Costanza, R. Energy Costs of Goods and Services in 1967: Including Solar Energy Inputs and Labor and Government Service Feedbacks; Energy Research Group; Center for Advanced Computation, University of Illinois at Urbana-Champaign: Urbana-Champaign, IL, 1978. (59) USDOC. Annual Survey of Manufactures: General Statistics: Capital Expenditures for Plant and Equipment by Industry Groups and Industries: 2003 and 2002; U.S. Department of Commerce, Economics and Statistics Division, U.S. Census Bureau: Washington, DC, 2003; http://www2.census.gov/econ2003/AM/sector31/AM0331GS105. zip. (60) Murphy, D. J.; Hall, C. A.; Dale, M.; Cleveland, C. Order from chaos: A preliminary protocol for determining the EROI of fuels. Sustainability 2011, 3 (10), 1888−1907. (61) FAO. Human Energy Requirements; Report of a Joint FAO/ WHO/UNU Expert Consultation; Food and Agriculture Organization of the United Nations: Rome, 17−24 October, 2001. (62) BLS. Databases, Tables and Calculators by Subject; U.S. Bureau of Labor Statistics: Washington, DC, 2002; http://data.bls.gov/pdq/ SurveyOutputServlet. (63) BLS. Highlights of Women’s Earning in 2002; Report 972; U.S. Department of Labor, Bureau of Labor Statistics: Washington, DC, 2003; http://www.bls.gov/cps/cpswom2002.pdf. (64) Borrud, L. G.; Flegal, K. M.; Looker, A. C.; Everhart, J. E. et al. Body Composition Data for Individuals 8 Years of Age and Older: U.S. Population, 1999−2004; Vital Health Stat, Series 11, Number 250; National Center for Health Statistics: Hyattsville, MD, 2010. (65) USBEA. Fulltime and Part-Time Employees by Industry; U.S. Department of Commerce, Bureau of Economic Analysis: Washington, DC, 2005; http://www.bea.gov/histdata/NIyear.asp. (66) USCB. American Community Survey Data; U.S. Census Bureau: Washington, DC., 2002; http://www2.census.gov/acs/downloads/ Core_Tables/2002/. 1945
DOI: 10.1021/es503896v Environ. Sci. Technol. 2015, 49, 1936−1945
