Characterizing the burden of disease of particulate matter for life cycle impact assessment

Abstract

Fine particulate air pollution (PM_2.5) is a major environmental contributor to human burden of disease and therefore an important component of life cycle impact assessments. An accurate PM_2.5 characterization factor, i.e., the impact per kilogram of PM_2.5 emitted, is critical to estimating “cradle-to-grave” human health impacts of products and processes. We developed and assessed new characterization factors (disability-adjusted life years (DALY)/kg_{PM2.5 emitted}), or the products of dose-response factors (deaths/kg_{PM2.5 inhaled}), severity factors (DALY/death), and intake fractions (kg_{PM2.5 inhaled}/kg_{PM2.5 emitted}). In contrast to previous health burden estimates, we calculated age-specific concentration- and dose-response factors using baseline data, from 63 US metropolitan areas, consistent with the US study population used to derive the relative risk. We also calculated severity factors using 2010 Global Burden of Disease data. Multiplying the revised PM_2.5 dose responses, severity factors, and intake fractions yielded new PM_2.5 characterization factors that are higher than previous factors for primary PM_2.5 but lower for secondary PM_2.5 due to NO_x. Multiplying the concentration-response and severity factors by 2005 ambient PM_2.5 concentrations yielded an annual US burden of 2,000,000 DALY, slightly lower than previous US estimates. The annual US health burden estimated from PM emissions and characterization factors was 2.2 times higher.

Appendices

Appendix A: Additional materials and methods

Concentration- and dose-response factors

Data

The RRs in the ACS Study (Pope et al. 2002) accounted for confounding by several individual risk factors (age, sex, race, smoking, education, marital status, body mass, alcohol consumption, occupational exposure, and diet) and spatial autocorrelation. The ACS Study evaluated differences in mortality associated with chronic (multi-year) PM_2.5 exposure, but some of the short-term effects of PM_2.5 are likely captured in this study type. Although time series studies of mortality and morbidity associated with only short-term exposure to PM_2.5 have been conducted, characterization factors based on these have been estimated to be two to four orders of magnitude lower than the characterization factor for mortality due to chronic exposure in past calculations (van Zelm et al. 2008). Therefore, short-term effects are not addressed separately here.

To elaborate on the point that RRs are not very “portable” or generalizable from one population to another (Steenland and Armstrong 2006), RRs estimate health effects relative to the baseline levels of that health effect. For example, a country may have a higher rate of mortality than the US among individuals aged 55–59, due to causes other than outdoor pollutants, such as tobacco smoke. Therefore, the fraction of deaths attributable to PM would be overestimated in that country if a US-based study were used for the calculation. Thus, in estimating absolute increases in a particular health effect per unit of pollutant, it is better to be consistent between the study population used to derive the RR and the corresponding health effect data. Because the RRs were derived from the ACS Study cohort, to calculate a PM_2.5-attributable fraction, we obtained mortality and population data for US SMSAs by age group. We used this US-based data because the distribution of population factors that may modify the association between PM_2.5 and mortality, such as tobacco smoking, would be more likely comparable with those in the ACS study population than data from another country. Also, because the ACS Study population was 94 % white, dose-response factors were calculated using mortality rates among whites only to ensure consistency.

US mortality data were obtained from the US Centers for Disease Control and Prevention’s National Center for Health Statistics (National Center for Health Statistics 2010). After 1988, one-year-age-specific mortality data was made available only for counties and cities with populations greater than 100,000 persons (Data Release Policy, http://www.cdc.gov/nchs/nvss/dvs_data_release.htm), so counts of deaths by five-year age group and cause of death for each SMSA were calculated for the years 1982–1988, within the follow-up period for the ACS Study (1982–1998). The ACS Study only enrolled individuals aged 30 years and older, so only mortality data for decedents 30 years and older were considered. Annual mortality rates for each cause of death were calculated by dividing the deaths by annual population estimates for each age group, which were obtained from the US Census (Intercensal Population Estimates by Age, Sex, and Race: 1980–1989 2009). The seven years were then averaged by five-year age group, cause of death and SMSA.

Calculation

The concentration–response factors (CRF, PM_2.5-associated annual mortality rate per μg/m³ PM_2.5 inhaled) for mortality, for each cause of death (cardiopulmonary disease, lung cancer, and all causes) and age group, were defined as the population-weighted increase in mortality rate attributed to PM_2.5 in the US SMSAs divided by the average PM_2.5 concentration:

$$ {\mathrm{C}\mathrm{RF}}_i=\frac{{\mathrm{MR}}_{\mathrm{PM}2.5, i}}{{\mathrm{C}}_i}\cdot {10}^9 $$

(6)

where MR _PM2.5,i is the PM_2.5-associated annual mortality rate for metropolitan area i in deaths/person/year, and C _i is the PM_2.5 concentration (in μg/m³ = 10⁻⁹ kg/m³) in area i.

From Cox proportional hazards models (and other log-linear models commonly used in epidemiology studies), the RR (unitless) for each unit increase in PM_2.5 concentration (C in μg/m³) is equivalent to e ^β, where β is the increase in ln(deaths) per 1 μg/m³ increase in PM_2.5. Considering that in the range of applicable PM_2.5 concentrations and RRs in the US, the association between mortality and PM is approximately linear, the attributable fraction for metropolitan area i, or the proportion of total cases attributable to PM_2.5 in that metropolitan area, is

$$ {\mathrm{AF}}_{\mathrm{PM}2.5, i}=\frac{{\mathrm{MR}}_{\mathrm{PM}2.5, i}}{{\mathrm{MR}}_{\mathrm{total}, i}}=1-\frac{1}{e^{\beta {C}_i}}\approx \frac{\left( RR-1\right){\mathrm{C}}_i}{\left( RR-1\right){\mathrm{C}}_i+1} $$

(7)

The concentration-response factor for metropolitan area i therefore becomes

$$ {\mathrm{C}\mathrm{RF}}_i=\frac{M{ R}_{\mathrm{total}, i}\cdot {\mathrm{AF}}_{\mathrm{PM}2.5, i}}{{\mathrm{C}}_i}\cdot {10}^9=\frac{{\mathrm{MR}}_{\mathrm{total}, i}\cdot \left(\mathrm{RR}-1\right)}{\left(\mathrm{RR}-1\right){\mathrm{C}}_i+1}\cdot {10}^9 $$

(8)

PM_2.5 concentration and mortality rate vary by location within the US, but the RRs presented in the ACS Study were not specific to any one metropolitan area. Therefore, the recommended concentration-response factor (for each cause of death and age group) was calculated as a population-weighted average of the concentration-response factors of individual metropolitan areas. This can also be represented as the increase in risk multiplied by a population-weighted non-PM mortality rate (last term of Eq. 9):

$$ \mathrm{CRF}=\left(\mathrm{RR}-1\right){\displaystyle \sum_{i=1}^{63\;\mathrm{SMSAs}}\left[\frac{{\mathrm{MR}}_{\mathrm{total}, i}}{\left( RR-1\right){\mathrm{C}}_i+1}\cdot \frac{{\mathrm{P}\mathrm{OP}}_i}{{\displaystyle \sum_{i=1}^{63\;\mathrm{SMSAs}}\mathrm{PO}{\mathrm{P}}_i}}\right]=\left( RR-1\right){\displaystyle \sum_{i=1}^{63\;\mathrm{SMSAs}}\left[{\mathrm{MR}}_{\mathrm{non}\hbox{-} \mathrm{PM}, i}\cdot \frac{{\mathrm{P}\mathrm{OP}}_i}{{\displaystyle \sum_{i=1}^{63\;\mathrm{SMSAs}}{\mathrm{P}\mathrm{OP}}_i}}\right]}} $$

(9)

where POP _i is the population size of metropolitan area i in persons.

Severity factors and effect factors

The human health burden of disease due to the emission of an atmospheric pollutant can be expressed using disability-adjusted life years (DALY) (Murray and Lopez 1996). DALY are the sum of years of life lost (YLL) and years of life lost due to disability (YLDs) for a disease. YLDs are the product of the incidence, duration, and weight factor (on a scale of 0 (perfect health) to 1 (death)) for that disease (Murray and Lopez 1996). Severity factors relate the cases of death attributed to PM, determined by the above-described dose-response, to the corresponding number of DALY. Severity factors are expressed in terms of DALY/death, where “death” in the denominator refers to the PM-attributed cases of cardiopulmonary or lung cancer mortality calculated using the DRFs.

We used DALY and YLL which do not include age weights or 3 % discounting; these have been taken as the standard for LCIA (Crettaz et al. 2002; Hofstetter 1998; Pennington et al. 2002; van Zelm et al. 2008). Users interested in a value-of-statistical-life quantity (VSL) may convert the PM_2.5-associated mortality rate to a VSL.

Effect factors for secondary PM_2.5 were assumed to be equivalent to effect factors from primary PM_2.5 since the effect factor was derived from monitors capturing a mixture of primary and secondary PM_2.5.

Characterization factors—impact per kilogram emitted

The human health impact per kilogram of a given atmospheric emission, called the characterization factor (CF, DALY kg_emitted ⁻¹), is the product of four parameters:

$$ \mathrm{CF}=\mathrm{SF}\cdot \mathrm{DRF}\cdot \mathrm{XF}\cdot \mathrm{FF}=\mathrm{EF}\cdot \mathrm{iF} $$

(10)

The fate factor (FF, kg_air per [kg_emitted year⁻¹]) relates the emission rate (kg_emitted year⁻¹) to the mass in the exposure medium (kg_air); the exposure factor determines the change in intake rate per change in mass in the environment (XF, [kg_inhaled year⁻¹] per kg_air), and the dose-response factor indicates the change in morbidity or mortality attributable to a change in intake (DRF, cases per kg_inhaled). The emitted pollutant can be a single chemical or a group of chemicals, and it can be a primary pollutant or a contributor to a secondary pollutant (Rosenbaum et al. 2007). The product of SF and DRF is the effect factor (EF, DALY kg_inhaled ⁻¹) and the product of XF and FF is the intake fraction (iF, kg_inhaled per kg_emitted). The intake fraction for primary pollutants indicates the fraction of the emission taken in (inhaled) by the overall population (Bennett et al. 2002). The intake fraction for secondary pollutants is the inhaled mass of the pollutant attributable to a specific precursor per mass emission of the precursor.

Since coarse (between 2.5 and 10 μm in aerodynamic diameter, PM_10–2.5) particles are likely removed faster from the atmosphere than fine particles (iF(PM_10–2.5) < iF(PM_2.5 (Lai et al. 2000; Liu and Nazaroff 2003)) and the effect factor of coarse particles is lower (EF(PM_10–2.5) << EF(PM_2.5) (Brunekreef and Forsberg 2005; Cooke et al. 2007; Dockery et al. 1993; European Commission 2005; Hofstetter 1998; U.S. Environmental Protection Agency 2010)), the overall characterization factor is therefore dominated by PM_2.5:

$$ \mathrm{CF}\left({\mathrm{PM}}_{10}\right)=\mathrm{iF}\left({\mathrm{PM}}_{2.5}\right)\cdot \mathrm{EF}\left({\mathrm{PM}}_{2.5}\right)\cdot {f}_{\mathrm{PM}2.5}+\mathrm{iF}\left({\mathrm{PM}}_{10-2.5}\right)\cdot \mathrm{EF}\left({\mathrm{PM}}_{10-2.5}\right)\left(1-{f}_{\mathrm{PM}2.5}\right)\approx \mathrm{iF}\left({\mathrm{PM}}_{2.5}\right)\cdot \mathrm{EF}\left({\mathrm{PM}}_{2.5}\right)\cdot {f}_{\mathrm{PM}2.5}=\mathrm{CF}\left({\mathrm{PM}}_{2.5}\right)\cdot {f}_{\mathrm{PM}2.5} $$

(11)

where f _PM2.5 is the fraction of PM₁₀ which is emitted as PM_2.5.

Burden of disease—impact per year

Estimate using ambient concentrations

PM_2.5 concentrations for each county in the nation were estimated using Voronoi neighborhood averaging of 2005 ambient monitor data from the EPA’s BenMAP 3.0 software which estimates health benefits from reductions in air pollutants (Abt Associates Inc. 2008).

Estimate using emissions inventory

Stack-height specific characterization factors were assigned to each emissions source according to Table S2 in Humbert et al. (2011). Emissions with uncategorized stack heights were assigned to the low stack height category. The characterization factors were weighted according to the proportion of the population that was considered urban vs. rural in the US 2000 Census. The characterization factors for remote sources were applied in counties with population densities less than 10 persons/km².

Appendix B: age- or location-specific data and results

Table 4 Mortality risk ratios (RRs) associated with PM_2.5 exposure derived from the ACS Study (Pope et al. 2002) (Fig. 4) by cause of death and age group

Table 5 Population-weighted means for annual total mortality rates, non-PM mortality rates, and PM_2.5-attributable mortality rates per 100,000 population and attributable fractions by each mortality-cause category and age group (n = 63 SMSAs)

Table 6 Population-weighted means for dose-response and concentration–response factors and final severity and effect factors for specific age groups

Table 7 Emission-weighted average world region-specific characterization factors for primary PM_2.5

Appendix C: alternate severity and effect factors

Estimates of disability due to PM in Appendix C Table 8 do not make the assumption that morbidity due to PM_2.5 is equivalent to morbidity due to other causes of that disease. Most of these estimates are small compared to the estimate of chronic mortality with the exception of disability due to chronic bronchitis. Künzli et al. (2000) estimated a high burden of chronic bronchitis due to PM using both incidence rates and risk ratios from the Seventh-Day Adventist Cohort Study (Abbey et al. 1993). The incidence rates of chronic bronchitis presented in the Seventh-Day Adventist Cohort Study (approximately 6 per 1,000 annually among non-smokers among individuals over the age of 25) (Abbey et al. 1995) are much higher than the COPD incidence estimated for industrialized nations for the WHO (approximately 2 per 1,000 annually among all individuals over the age of 30) (Lopez et al. 2006; Shibuya et al. 2001) considering that most COPD is attributed to smoking (Hnizdo et al. 2002; Salvi and Barnes 2009). Hofstetter (1998) proposes a very conservative disability weight to assign to chronic bronchitis—0.05 per incident case (over a 40-year duration)—compared to that used by the WHO in the 2000 Global Burden of Disease (0.17 for mild/moderate COPD and 0.53 for severe COPD) (Mathers et al. 2006a). In Appendix Table 8, we applied the more conservative severity factor to the Seventh-Day Adventist Cohort Study effect estimate associated with the high chronic bronchitis incidence rate among non-smokers. The 41 additional YLDs due to PM estimated in the Appendix Table 8 effect factor are higher than the 4 YLDs estimated in Table 1 which used our simplified severity factor calculation. The burden of chronic bronchitis due to PM may be higher than we account for in our simplified severity factor calculation, but the uncertainty in directly attempting to estimate the PM-associated burden of chronic bronchitis from the Seventh-Day Adventist Cohort Study is large, so we do not use the effect factors in Appendix Table 8 in our final characterization factor.

Table 8 Alternate evaluations of dose-response, severity and effect factors of PM₁₀, and conversion to PM_2.5