

      


                                                






                          Technical Support Document
                                       
Estimating the Benefit per Ton of Reducing PM2.5 Precursors from the Other Non-EGU Point Sources Sector






                     U.S. Environmental Protection Agency
                          Office of Air and Radiation
                 Office of Air Quality Planning and Standards
                       Research Triangle Park, NC 27711





	February 2012

                                       
                                       
                              CONTACT INFORMATION
                                       
      This document has been prepared by staff from the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. Questions related to this document should be addressed to Neal Fann or Charles Fulcher, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Health and Environmental Impacts Division, Risk and Benefits Group, Research Triangle Park, North Carolina 27711 (email: fann.neal@epa.gov, fulcher.charles@epa.gov). 


Overview

This Technical Support Document (TSD) describes our approach for estimating the avoided human health impacts, and monetized benefits, of reducing emissions of PM2.5 and PM2.5 precursors including NOx and SO2 from Other Non-EGU Point Sources using the results of source apportionment photochemical modeling. In this context, "Other" refers to sources that are not part of any other sector for which modeling was performed. We focus in particular on the aspects of our approach that represent a change from the benefit per ton calculation methodology described in Fann, Fulcher and Hubbell (2009) and subsequently applied in several EPA RIAs. We also describe the ways in which these new estimates can improve our characterization of the PM2.5-related health benefits from Other Non-EGU Point Sources and other sectors. We summarize the benefit per ton estimates for both the Other Non-EGU Point Sources sector that were used in the RIA for this rule, as well as 12 other emission sectors. 


Summary of Calculations

The procedure for calculating benefit per ton coefficients follows three steps:

   1. Use source apportionment photochemical modeling to predict the directly emitted PM2.5, nitrate and sulfate attributable to each of 13 emission sectors across the Continental U.S., including the Other Non-EGU Point Sources sector; see below for a summary of the sectors modeled.
      
   2. For each sector, estimate the health impacts, and the economic value of these impacts, associated with the directly emitted PM2.5, sulfate and nitrate PM2.5 using the environmental Benefits Mapping and Analysis Program (BenMAP v4.0.44).
      
   3. For each sector, divide the PM2.5-related health impacts attributable to each type of PM2.5, and the monetary value of these impacts, by the level of precursor emissions.









Figure 1: Conceptual overview of the steps for calculating benefit per-ton estimates


                                       

The example above depicts the total PM2.5 contribution from the pulp and paper sector, though we repeat this process for each of the 13 sectors, which include:

   1. Cement Kilns
   2. Pulp and Paper Facilities
   3. Refineries
   4. Coke Ovens
   5. Iron and Steel Foundries
   6. Integrated Iron and Steel Facilities
   7. Electric Arc Furnaces
   8. Taconite Mining
   9. Ferroalloy Production
   10. Residential Wood Combustion
   11. Other Nonpoint Sources
   12. Other Non-EGU Point Sources
   13. Electricity Generating Units

The "Other Nonpoint Sources" and "Other Non-EGU Point Sources" categories are agglomerations of sectors that were not modeled separately. When selecting a benefit per ton estimate for use with a sector not specifically modeled, it is necessary to determine which composite sector is the best match with respect to the source characteristics that would affect the level of benefits. These attributes include the proximity to receptor populations, the geographic distribution of sources, and the release parameters of the source. 

Readers interested in a full discussion of the air quality modeling performed for this rule may consult "Air Quality Modeling Technical Support Document: Source Sector Assessments" (U.S. EPA 2011). We project PM2.5 levels for sector to 2016. The appendix to this TSD includes plots of the PM2.5 levels attributed to each of these sectors for which we estimated benefit per-ton metrics.

The photochemical modeling used here also produced estimates of ozone levels attributable to each sector. However, the complex non-linear chemistry governing ozone formation prevented us from developing a complementary array of ozone benefit per ton values. This limitation notwithstanding, we anticipate that the ozone-related benefits associated with reducing emissions of NOx and VOC for many of these sectors would be substantial. 

Finally, it is important to note that while most VOCs emitted are oxidized to carbon dioxide (CO2) rather than to PM, a portion of VOC emission contributes to ambient PM2.5 levels as organic carbon aerosols (US EPA 2009). Therefore, reducing these emissions would reduce PM2.5 formation, human exposure to PM2.5, and the incidence of PM2.5-related health effects. However, we have not quantified a VOC benefit per ton estimate in this analysis. Analysis of organic carbon measurements suggest only a fraction of secondarily formed organic carbon aerosols are of anthropogenic origin. The current state of the science of secondary organic carbon aerosol formation indicates that anthropogenic VOC contribution to secondary organic carbon aerosol is often lower than the biogenic (natural) contribution. Photochemical models typically estimate secondary organic carbon from anthropogenic VOC emissions to be less than 0.1 ug/m[3]. For this reason, we have not reported a VOC benefit per ton estimate. 

Below we provide an expanded discussion of each of the latter two steps to the calculation -- estimating health impacts and economic value of PM2.5 attributable to each sector and calculating the benefit per ton coefficients. The discussion of these topics is not intended to be exhaustive, and readers interested in learning more about our approach to performing an air pollution health impact and benefits analysis may consult the Cross-State Air Pollution Rule (CSAPR)(US EPA 2011).


Estimating the number of PM2.5-related health impacts attributable to each sector

In this stage of the analysis we performed a Health Impact Assessment (HIA), which quantifies the changes in the incidence of adverse health impacts resulting from changes in human exposure to PM2.5 from each sector. HIAs are a well-established approach for estimating the retrospective or prospective change in adverse health impacts expected to result from population-level changes in exposure to pollutants (Levy et al. 2009). PC-based tools such as the environmental Benefits Mapping and Analysis Program (BenMAP) can systematize health impact analyses by applying a database of key input parameters, including health impact functions and population projections. Analysts have applied the HIA approach to estimate human health impacts resulting from hypothetical changes in pollutant levels (Davidson et al. 2007; Hubbell et al. 2004; Tagaris et al. 2009). 
The HIA approach used in this analysis involves three basic steps: (1) utilizing CAMx--generated projections of PM2.5 levels attributed to each sector; (2) determining the subsequent change in population-level exposure; (3) calculating health impacts by applying concentration-response relationships drawn from the epidemiological literature  to this change in population exposure (Hubbell et al. 2009). This procedure is operationalized within BenMAP using a health impact function. 
A typical health impact function looks as follows:

                        ∆y=yo ∙eβ∙∆x- 1∙Pop
                                       
where y0 is the baseline incidence rate for the health endpoint being quantified (for example, a health impact function quantifying changes in mortality would use the baseline, or background, mortality rate for the given population of interest); Pop is the population affected by the change in air quality, whose size and distribution we have projected to 2016 for this analysis; x is the change in air quality; and β is the effect coefficient drawn from the epidemiological study. Tools such as BenMAP can systematize the HIA calculation process, allowing users to draw upon a library of existing air quality monitoring data, population data and health impact functions. 
Figure 2 provides a simplified overview of this approach, using PM2.5-related premature mortality as an example, though the procedure is generally the same for other health endpoints. This sequence of steps is repeated for each of the 13 sectors. The PM2.5 health endpoints quantified and the health impact functions applied in this analysis are consistent with the CSAPR RIA. That RIA includes a detailed discussion of each of the data inputs, analytical assumptions and sources of uncertainty. In the interest of brevity, we do not repeat these here in detail. However, it is worth noting that we exclude the value of several important non-health endpoints, including recreational and residential visibility, climate-related impacts and ecological endpoints. Table 1 below summarizes the endpoints quantified in this benefit per ton TSD.

Figure 2: Illustration of BenMAP Approach
                                       
Baseline Air QualityPost-Policy Scenario  Air QualityIncremental Air QualityImprovementPM2.5 ReductionPopulationAges 18-65BackgroundIncidenceRateEffectEstimateMortality Reduction



Table 1: Human health effects of PM2.5 quantified and not quantified in this analysis

Category
                                Specific Effect
                          Effect Has Been Quantified
                           Effect Has Been Monetized
                    More Information (refers to CSAPR RIA)
Improved Human Health
Reduced incidence of premature mortality from exposure to PM2.5
Adult premature mortality based on cohort study estimates and expert elicitation estimates (age >25 or age >30)
                                      
                                      
Section 5.4

Infant mortality (age <1)
                                      
                                      
Section 5.4
Reduced incidence of morbidity from exposure to PM2.5
Non-fatal heart attacks (age > 18)
                                      
                                      
Section 5.4

Hospital admissions -- respiratory  (all ages)
                                      
                                      
Section 5.4

Hospital admissions -- cardiovascular (age >20)
                                      
                                      
Section 5.4

Emergency room visits for asthma (all ages)
                                      
                                      
Section 5.4

Acute bronchitis (age 8-12)
                                      
                                      
Section 5.4

Lower respiratory symptoms (age 7-14)
                                      
                                      
Section 5.4

Upper respiratory symptoms (asthmatics age 9-11)
                                      
                                      
Section 5.4

Asthma exacerbation (asthmatics age 6-18)
                                      
                                      
Section 5.4

Lost work days  (age 18-65)
                                      
                                      
Section 5.4

Minor restricted-activity days (age 18-65)
                                      
                                      
Section 5.4

Chronic Bronchitis (age >26)
                                      
                                      
Section 5.4

Emergency room visits for cardiovascular effects (all ages)
                                      --
                                      --
Section 5.4

Strokes and cerebrovascular disease (age 50-79)
                                      --
                                      --
Section 5.4

Other cardiovascular effects (e.g., other ages)
                                      --
                                      --
PM ISA[2]

Other respiratory effects (e.g., pulmonary function, non-asthma ER visits, non-bronchitis chronic diseases, other ages and populations)
                                      --
                                      --
PM ISA[2]

Reproductive and developmental effects (e.g., low birth weight, pre-term births, etc)
                                      --
                                      --
PM ISA[2,3]

Cancer, mutagenicity, and genotoxicity effects
                                      --
                                      --
PM ISA[2,3]
1 We assess these benefits qualitative due to time and resource limitations for this analysis.
[2] We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.
[3] We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over the strength of the association.



Estimating the economic value of health impacts attributable to each sector

After quantifying the change in adverse health impacts, the next step is to estimate the economic value of these avoided impacts. The appropriate economic value for a change in a health effect depends on whether the health effect is viewed ex ante (before the effect has occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population. The appropriate economic measure is therefore ex ante Willingness to Pay (WTP) for changes in risk. However, epidemiological studies generally provide estimates of the relative risks of a particular health effect avoided due to a reduction in air pollution. A convenient way to use this data in a consistent framework is to convert probabilities to units of avoided statistical incidences. This measure is calculated by dividing individual WTP for a risk reduction by the related observed change in risk. For example, suppose a measure is able to reduce the risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then the WTP for an avoided statistical premature mortality amounts to $1 million ($100/0.0001 change in risk). Using this approach, the size of the affected population is automatically taken into account by the number of incidences predicted by epidemiological studies applied to the relevant population. The same type of calculation can produce values for statistical incidences of other health endpoints. 
For some health effects, such as hospital admissions, WTP estimates are generally not available. In these cases, we use the cost of treating or mitigating the effect as a primary estimate. For example, for the valuation of hospital admissions we use the avoided medical costs as an estimate of the value of avoiding the health effects causing the admission. These cost of illness (COI) estimates generally (although not in every case) understate the true value of reductions in risk of a health effect. They tend to reflect the direct expenditures related to treatment but not the value of avoided pain and suffering from the health effect.
We express the economic value of the avoided impacts using constant year 2008 dollars, adjusted for growth in real income out to 2016 (the year of the air quality change) using projections provided by Standard and Poor's. Economic theory argues that WTP for most goods (such as environmental protection) will increase if real income increases. Many of the valuation studies used in this analysis were conducted in the late 1980s and early 1990s. Because real income has grown since the studies were conducted, people's willingness to pay for reductions in the risk of premature death and disease likely has grown as well. We did not adjust cost of illness-based values because they are based on current costs. For these two reasons, these cost of illness estimates may underestimate the economic value of avoided health impacts in 2016. As with the selection of health studies, the economic valuation estimates applied in this analysis are consistent with those used in the CSAPR RIA. 




Calculating the benefit per ton estimate

The final step is to divide the incidence of adverse health outcomes, and the economic value of those outcomes, attributable to the directly emitted PM2.5, nitrate and sulfate from each sector by the emissions of directly emitted PM2.5, NOx and SO2. The result is a suite of incidence per ton and $ benefit per ton estimates for each sector. Below we summarize the total $ per ton estimates for each of the 13 sectors (Table 1), as well as more detailed health impacts per ton for the Other Non-EGU Point Sources sector. 






















                                       
                   Pope et al. (2002) mortality estimate[A]

                   Laden et al. (2006) mortality estimate[A]
 Sector
                            Directly emitted PM2.5
                                      SO2
                                      NOx

                            Directly emitted PM2.5
                                      SO2
                                      NOx
Cement Kilns
                                   $360,000
                                    $43,000
                                    $5,700
                                       
                                   $890,000
                                   $110,000
                                    $14,000
Pulp & Paper Facilities
                                   $150,000
                                    $45,000
                                    $3,800
                                       
                                   $370,000
                                   $110,000
                                    $9,200
Refineries
                                   $330,000
                                    $69,000
                                    $6,800
                                       
                                   $810,000
                                   $170,000
                                    $17,000
Coke Ovens
                                   $480,000
                                    $52,000
                                    $11,000
                                       
                                  $1,200,000
                                   $130,000
                                    $26,000
Iron and Steel Foundries
                                   $520,000
                                   $420,000
                                    $17,000
                                       
                                  $1,300,000
                                  $1,000,000
                                    $41,000
Integrated Iron and Steel Facilities
                                   $520,000
                                    $90,000
                                    $14,000
                                       
                                  $1,300,000
                                   $220,000
                                    $34,000
Electric Arc Furnaces
                                   $440,000
                                    $81,000
                                    $9,700
                                       
                                  $1,100,000
                                   $200,000
                                    $24,000
Taconite Mining
                                    $85,000
                                    $34,000
                                    $6,200
                                       
                                   $210,000
                                    $84,000
                                    $15,000
Ferroalloys Production
                                   $280,000
                                    $44,000
                                    $4,500
                                       
                                   $680,000
                                   $110,000
                                    $11,000
Residential Wood Combustion
                                   $370,000
                                   $100,000
                                    $13,000
                                       
                                   $910,000
                                   $250,000
                                    $33,000
Other Nonpoint Sources
                                   $330,000
                                    $49,000
                                    $7,900
                                       
                                   $800,000
                                   $120,000
                                    $19,000
Other Non-EGU Point Sources
                                   $270,000
                                    $40,000
                                    $6,400
                                       
                                   $670,000
                                   $100,000
                                    $16,000
Electricity Generating Units
                                   $130,000
                                    $36,000
                                    $5,300
                                       
                                   $330,000
                                    $87,000
                                    $13,000
[A] Value represents sum of the value of avoided morbidity impacts and mortality impacts quantified using the PM2.5 mortality risk estimate noted. 
Estimates rounded to two significant figures in this table, but all calculations are performed with the unrounded estimates.

Results


Table 1: Summary of the total dollar value (mortality and morbidity) per ton of directly emitted PM2.5 and PM2.5 precursor reduced by each of 13 sectors in 2016 (2008$, 3% discount rate)


Table 2: Dollar value (mortality and morbidity) per ton of directly emitted PM2.5 and PM2.5 precursors reduced in 2016 for the Other Non-EGU Point Sources sector (2008$, 3% discount rate)



                               Pollutant emitted
 Mortality risk estimate
                                      NOx
                                      SO2
                            Directly emitted PM2.5
Risk estimates derived from the epidemiological literature[A]
                                       
                                       
                                       
  Pope et al. (2002)
                                    $6,400 
                                   $40,000 
                                   $270,000 
  Laden et al. (2006)
                                   $16,000 
                                   $100,000 
                                   $670,000 
[A] Value represents sum of the value of avoided morbidity impacts and mortality impacts quantified using the PM2.5 mortality risk estimate noted. Estimates are rounded to two significant digits in this table, but all calculations are performed with the unrounded estimates.
                                       


Table 3: Incidence per ton of avoided mortalities and morbidities from the Other Non-EGU Point Sources sector for directly emitted PM2.5 and PM2.5 precursors reduced in 2016 

 Health Endpoint
                               Pollutant emitted

                                      NOx
                                      SO2
                            Directly emitted PM2.5
Premature mortality
                                       
                                       
                                       
  Pope et al. (2002)
                                   0.000709
                                   0.004527
                                   0.030196
  Laden et al. (2006)
                                   0.001821
                                   0.011618
                                   0.077553
Morbidity
                                       
                                       
                                       
   Chronic bronchitis
                                   0.000507
                                   0.003073
                                   0.021082
   Respiratory emergency room visits
                                   0.000543
                                   0.003475
                                   0.023347
   Acute bronchitis
                                   0.001135
                                   0.006805
                                   0.047404
   Lower respiratory symptoms
                                   0.014437
                                   0.086648
                                   0.603231
   Upper respiratory symptoms
                                   0.010903
                                   0.065553
                                   0.455821
   Acute respiratory symptoms
                                   0.582557
                                   3.497060
                                   24.255076
   Work loss days
                                   0.098143
                                   0.588890
                                   4.092673
   Asthma exacerbation
                                   0.023960
                                   0.143794
                                   1.000293
   Non-fatal heart attacks
                                   0.000800
                                   0.005119
                                   0.033306
   Respiratory hospital admissions
                                   0.000135
                                   0.000900
                                   0.005704
   Cardiovascular hospital admissions
                                   0.000301
                                   0.001940
                                   0.012489

                                       
                                       
                                       


Lowest Measured Air Quality Level Exposure Assessment

Assessments quantifying PM2.5 related health impacts generally find that cases of avoided mortality represent the majority of the monetized benefits. For this reason, EPA has historically performed a series of analyses that characterize the uncertainty associated with the PM-mortality relationship and the economic value of reducing the risk of premature death (Mansfield et al. 2009; Roman et al. 2008; US EPA 2011). Here we focus on the level of uncertainty associated with the avoided premature deaths estimated to occur due to air quality improvements below the lowest levels of PM2.5 observed in the epidemiological studies used to quantify such risks. 

We include a "Lowest Measured air quality Level" (LML) assessment, which identifies the baseline (i.e. pre-rule) annual mean PM2.5 levels at which populations are exposed and the minimum observed air quality level of each long-term cohort study we use to quantify mortality impacts. In general, our confidence in the estimated number of premature deaths diminishes as we estimate these impacts in locations whose PM2.5 levels are below this minimum level. While an LML assessment provides some insight into the level of uncertainty in the estimated PM mortality benefits, EPA does not view this minimum value as a concentration threshold. The central benefits estimates reported in this RIA reflect a full range of modeled air quality concentrations. We maintain a high confidence in the PM2.5-related impacts estimated from air quality changes down to the LML of the two major cohort studies used to quantify PM-related premature deaths. That is -- our confidence in the estimated benefits above the LML should not imply an absence of confidence in benefits estimated below the LML.

Because we cannot estimate PM2.5 air quality changes for every policy scenario for which these source apportionment model results might be used, we cannot quantify the distribution of mortality impacts across baseline PM2.5 levels for each rule. As an alternative, we provide the percentage of the population exposed at baseline PM2.5 levels. It is important to note that baseline exposure is only one parameter in the health impact function, along with baseline incidence rates population, and change in air quality.  In other words, the percentage of the population exposed to air pollution below the LML is not the same as the percentage of the population experiencing health impacts as a result of a specific emission reduction policy.  A very large proportion of the population is exposed at or above the lowest LML of the cohort studies (Figures 3 and 4), increasing our confidence in the PM mortality analysis. Figure 3 shows a bar chart of the percentage of the population exposed to various air quality levels in the baseline. Figure 4 shows a cumulative distribution function of the same data. Both figures identify the LML for each of the major cohort studies. Using the Pope et al. (2002) study, the 77% of the population is exposed to annual mean PM2.5 levels at or above the LML of 7.5 ug/m[3]. Using the Laden et al.  (2006) study, 25% of the population is exposed above the LML of 10 ug/m[3]. 





Figure 3:Percentage of Adult Population According to Annual Mean PM2.5 Exposure in the Baseline





















Figure 4:Cumulative Distribution of Adult Population According to Annual Mean PM2.5 Exposure in the Baseline






Limitations

This analysis includes many data sources as inputs, including emission inventories, air quality data from models (with their associated parameters and inputs), population data, health effect estimates from epidemiology studies, and economic data for monetizing benefits. Each of these inputs may be uncertain and would affect the benefits estimate. When the uncertainties from each stage of the analysis are compounded, small uncertainties can have large effects on the total quantified benefits. This analysis does not include the type of detailed uncertainty assessment found in the PM NAAQS RIA (US EPA, 2006). However, the results of the Monte Carlo analyses of the health and welfare benefits presented in Chapter 5 of the PM RIA can provide some evidence of the uncertainty surrounding the benefits results presented in this analysis. 

In this analysis we assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM2.5 produced via transported precursors emitted from EGUs may differ significantly from direct PM2.5 released from other industrial sources. However, the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. We also assume that the health impact function for fine particles is linear down to the lowest air quality levels modeled in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM2.5, including both regions that are in attainment with fine particle standard.

It is also important to note that the monetized benefit-per-ton estimates used here reflect specific geographic patterns of emissions and specific air quality and benefits modeling assumptions. Great care should be taken in applying these estimates to emission reductions occurring in any specific location, as these are all based on national emission reduction assumptions and therefore represent an average benefits-per-ton over the entire United States. The benefits-per-ton for emission reductions in specific locations may be very different from the estimates presented here. The maps in the Appendix that follows provide an indication of the location of the facilities that were modeled as well as the associated PM2.5 levels. 




Appendix: Modeled annual mean PM2.5 levels attributable to 13 Sectors in 2016
















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