Estimates of the Health Risk Reductions Associated with Attainment of Alternative Particulate Matter Standards in Two U.S. Cities

As part of its periodic re-evaluation of particulate matter (PM) standards, the U.S. Environmental Protection Agency estimated the health risk reductions associated with attainment of alternative PM standards in two locations in the United States with relatively complete air quality data: Philadelphia and Los Angeles. PM standards at the time of the analysis were defined for particles of aerodynamic diameter less than or equal to 10 microm, denoted as PM-10. The risk analyses estimated the risk reductions that would be associated with changing from attainment of the PM-10 standards then in place to attainment of alternative standards using an indicator measuring fine particles, defined as those particles of aerodynamic diameter less than or equal to 2.5 microm and denoted as PM-2.5. Annual average PM-2.5 standards of 12.5, 15, and 20 microg/m3 were considered in various combinations with daily PM-2.5 standards of 50 and 65 microg/m3. Attainment of a standard or set of standards was simulated by a proportional rollback of "as is" daily PM concentrations to daily PM concentrations that would just meet the standard(s). The predicted reductions in the incidence of health effects varied from zero, for those alternative standards already being met, to substantial reductions of over 88% of all PM-associated incidence (e.g., in mortality associated with long-term exposures in Los Angeles, under attainment of an annual standard of 12.5 microg/m3). Sensitivity analyses and integrated uncertainty analyses assessed the multiple-source uncertainty surrounding estimates of risk reduction.     

Caveats for Causal Interpretations of Linear Regression Coefficients for Fine Particulate (PM2.5) Air Pollution Health Effects

Recent linear regression analyses have concluded that decreasing levels of fine particulate matter (PM2.5) air pollution have increased life expectancy in the United States. These findings have left unresolved questions about the causal relation between reductions in PM2.5 levels and changes in cause‐specific (especially, cardiovascular disease, CVD) mortality risks. Their robustness (e.g., sensitivity to deletion of a single data point) has also been questioned. We investigate these issues in the National Mortality and Morbidity Air Pollution Study database. Comparing changes in PM2.5 levels and cause‐specific mortality rates for elderly people in 24 cities between two periods separated by a decade (1987–1989 and 1999–2000) shows that reductions in PM2.5 were significantly associated with increases in respiratory mortality rates and with decreases in CVD mortality rates. CVD and all‐cause mortality risks fell equally for all months of the year over this period, but average PM2.5 levels increased significantly for winter months. This casts doubts on the causal interpretation that declines in PM2.5 over the decade caused reduced short‐term mortality risks. Nonlinear regression suggests that reduced or negative marginal health benefits are associated with reductions of PM2.5 below 1999–2000 levels (about 15 μg/m³). Such nonlinear relations imply that risk communication statements that project a constant incremental reduction in mortality risks per unit reduction in PM2.5 do not adequately reflect the realistic possibility of nonlinear exposure‐response relations and diminishing returns to further exposure reductions.     

Integrating risk assessment and life cycle assessment: a case study of insulation.

Increasing residential insulation can decrease energy consumption and provide public health benefits, given changes in emissions from fuel combustion, but also has cost implications and ancillary risks and benefits. Risk assessment or life cycle assessment can be used to calculate the net impacts and determine whether more stringent energy codes or other conservation policies would be warranted, but few analyses have combined the critical elements of both methodologies In this article, we present the first portion of a combined analysis, with the goal of estimating the net public health impacts of increasing residential insulation for new housing from current practice to the latest International Energy Conservation Code (IECC 2000). We model state-by-state residential energy savings and evaluate particulate matter less than 2.5 microm in diameter (PM2.5), NOx, and SO2 emission reductions. We use past dispersion modeling results to estimate reductions in exposure, and we apply concentration-response functions for premature mortality and selected morbidity outcomes using current epidemiological knowledge of effects of PM2.5 (primary and secondary). We find that an insulation policy shift would save 3 x 10(14) British thermal units or BTU (3 x 10(17) J) over a 10-year period, resulting in reduced emissions of 1,000 tons of PM2.5, 30,000 tons of NOx, and 40,000 tons of SO2. These emission reductions yield an estimated 60 fewer fatalities during this period, with the geographic distribution of health benefits differing from the distribution of energy savings because of differences in energy sources, population patterns, and meteorology. We discuss the methodology to be used to integrate life cycle calculations, which can ultimately yield estimates that can be compared with costs to determine the influence of external costs on benefit-cost calculations.     

Empirical Bayes and Adjusted Estimates Approach to Estimating the Relation of Mortality to Exposure of PM10

Forward Links to Citing Articles Retraction . Risk Analysis 25: 6, 1683
In the framework of the APHEIS program (Air Pollution and Health: A European Information System), a health impact assessment of air pollution in 26 European cities was performed for particles of an aerodynamic diameter less than or equal to 10 μm (PM₁₀). For short-term effects, it was based on overall estimates from the APHEA-2 project (Air Pollution and Health: A European Approach). These city-specific risk assessments require city-specific concentration-response functions, raising the question of which concentration-response is most appropriate. Estimates from city-specific models are more specific, but have greater uncertainty than those provided from multicity analyses. We compared several estimates derived from the city-specific analyses in cities that were part of the APHEA-2 project, as well as in a city that was not included in APHEA-2 but was part of the APHEIS project. These estimates were: the estimates from a local regression model, the adjusted estimates based on two significant effect modifiers identified through meta-regression models, and the city-specific empirical Bayes (shrunken) estimates and their underlying distribution. The shrunken and adjusted estimates were used to improve the estimation of city-specific concentration-response function. From these different estimates, attributable numbers of deaths per year were calculated. The advantages and limits of the different approaches are discussed through real data and in a simulation study.     

Estimating the National Public Health Burden Associated with Exposure to Ambient PM2.5 and Ozone

Ground‐level ozone (O₃) and fine particulate matter (PM_2.5) are associated with increased risk of mortality. We quantify the burden of modeled 2005 concentrations of O₃ and PM_2.5 on health in the United States. We use the photochemical Community Multiscale Air Quality (CMAQ) model in conjunction with ambient monitored data to create fused surfaces of summer season average 8‐hour ozone and annual mean PM_2.5 levels at a 12 km grid resolution across the continental United States. Employing spatially resolved demographic and concentration data, we assess the spatial and age distribution of air‐pollution‐related mortality and morbidity. For both PM_2.5 and O₃ we also estimate: the percentage of total deaths due to each pollutant; the reduction in life years and life expectancy; and the deaths avoided according to hypothetical air quality improvements. Using PM_2.5 and O₃ mortality risk coefficients drawn from the long‐term American Cancer Society (ACS) cohort study and National Mortality and Morbidity Air Pollution Study (NMMAPS), respectively, we estimate 130,000 PM_2.5‐related deaths and 4,700 ozone‐related deaths to result from 2005 air quality levels. Among populations aged 65–99, we estimate nearly 1.1 million life years lost from PM_2.5 exposure and approximately 36,000 life years lost from ozone exposure. Among the 10 most populous counties, the percentage of deaths attributable to PM_2.5 and ozone ranges from 3.5% in San Jose to 10% in Los Angeles. These results show that despite significant improvements in air quality in recent decades, recent levels of PM_2.5 and ozone still pose a nontrivial risk to public health.     

Exposure Misclassification and Threshold Concentrations in Time Series Analyses of Air Pollution Health Effects

Linear, no-threshold relationships are typically reported for time series studies of air pollution and mortality. Since regulatory standards and economic valuations typically assume some threshold level, we evaluated the fundamental question of the impact of exposure misclassification on the persistence of underlying personal-level thresholds when personal data are aggregated to the population level in the assessment of exposure-response relationships. As an example, we measured personal exposures to two particle metrics, PM2.5 and sulfate (SO4(2-)), for a sample of lung disease patients and compared these with exposures estimated from ambient measurements Previous work has shown that ambient:personal correlations for PM2.5 are much lower than for SO4(2-), suggesting that ambient PM2.5 measurements misclassify exposures to PM2.5. We then developed a method by which the measured:estimated exposure relationships for these patients were used to simulate personal exposures for a larger population and then to estimate individual-level mortality risks under different threshold assumptions. These individual risks were combined to obtain the population risk of death, thereby exhibiting the prominence (and the value) of the threshold in the relationship between risk and estimated exposure. Our results indicated that for poorly classified exposures (PM2.5 in this example) population-level thresholds were apparent at lower ambient concentrations than specified common personal thresholds, while for well-classified exposures (e.g., SO4(2-)), the apparent thresholds were similar to these underlying personal thresholds. These results demonstrate that surrogate metrics that are not highly correlated with personal exposures obscure the presence of thresholds in epidemiological studies of larger populations, while exposure indicators that are highly correlated with personal exposures can accurately reflect underlying personal thresholds.     

Vehicle Emission Unit Risk Factors for Transportation Risk Assessments

When the transportation risk posed by shipments of hazardous chemical and radioactive materials is being assessed, it is necessary to evaluate the risks associated with both vehicle emissions and cargo-related risks. Diesel exhaust and fugitive dust emissions from vehicles transporting hazardous shipments lead to increased air pollution, which increases the risk of latent fatalities in the affected population along the transport route. The estimated risk from these vehicle-related sources can often be as large or larger than the estimated risk associated with the material being transported. In this paper, data from the U.S. Environmental Protection Agency's Motor Vehicle-Related Air Toxics Study are first used to develop latent cancer fatality estimates per kilometer of travel in rural and urban areas for all diesel truck classes. These unit risk factors are based on studies investigating the carcinogenic nature of diesel exhaust. With the same methodology, the current per-kilometer latent fatality risk factor used in transportation risk assessments for heavy diesel trucks in urban areas is revised and the analysis expanded to provide risk factors for rural areas and all diesel truck classes. These latter fatality estimates may include, but are not limited to, cancer fatalities and are based primarily on the most recent epidemiological data available on mortality rates associated with ambient air PM-10 concentrations.     

Health Effects Caused by Primary Fine Particulate Matter (PM2.5) Emitted from Buses in the Helsinki Metropolitan Area, Finland

Fine particle (PM(2.5)) emissions from traffic have been associated with premature mortality. The current work compares PM(2.5)-induced mortality in alternative public bus transportation strategies as being considered by the Helsinki Metropolitan Area Council, Finland. The current bus fleet and transportation volume is compared to four alternative hypothetical bus fleet strategies for the year 2020: (1) the current bus fleet for 2020 traffic volume, (2) modern diesel buses without particle traps, (3) diesel buses with particle traps, and (4) buses using natural gas engines. The average population PM(2.5) exposure level attributable to the bus emissions was determined for the 1996-1997 situation using PM(2.5) exposure measurements including elemental composition from the EXPOLIS-Helsinki study and similar element-based source apportionment of ambient PM(2.5) concentrations observed in the ULTRA study. Average population exposure to particles originating from the bus traffic in the year 2020 is assumed to be proportional to the bus emissions in each strategy. Associated mortality was calculated using dose-response relationships from two large cohort studies on PM(2.5) mortality from the United States. Estimated number of deaths per year (90% confidence intervals in parenthesis) associated with primary PM(2.5) emissions from buses in Helsinki Metropolitan Area in 2020 were 18 (0-55), 9 (0-27), 4 (0-14), and 3 (0-8) for the strategies 1-4, respectively. The relative differences in the associated mortalities for the alternative strategies are substantial, but the number of deaths in the lowest alternative, the gas buses, is only marginally lower than what would be achieved by diesel engines equipped with particle trap technology. The dose-response relationship and the emission factors were identified as the main sources of uncertainty in the model.     

Assessment of Interindividual and Geographic Variability in Human Exposure to Fine Particulate Matter in Environmental Tobacco Smoke

Environmental tobacco smoke (ETS) is a major contributor to indoor human exposures to fine particulate matter of 2.5 μm or smaller (PM_2.5). The Stochastic Human Exposure and Dose Simulation for Particulate Matter (SHEDS‐PM) Model developed by the U.S. Environmental Protection Agency estimates distributions of outdoor and indoor PM_2.5 exposure for a specified population based on ambient concentrations and indoor emissions sources. A critical assessment was conducted of the methodology and data used in SHEDS‐PM for estimation of indoor exposure to ETS. For the residential microenvironment, SHEDS uses a mass‐balance approach, which is comparable to best practices. The default inputs in SHEDS‐PM were reviewed and more recent and extensive data sources were identified. Sensitivity analysis was used to determine which inputs should be prioritized for updating. Data regarding the proportion of smokers and “other smokers” and cigarette emission rate were found to be important. SHEDS‐PM does not currently account for in‐vehicle ETS exposure; however, in‐vehicle ETS‐related PM_2.5 levels can exceed those in residential microenvironments by a factor of 10 or more. Therefore, a mass‐balance‐based methodology for estimating in‐vehicle ETS PM_2.5 concentration is evaluated. Recommendations are made regarding updating of input data and algorithms related to ETS exposure in the SHEDS‐PM model. Interindividual variability for ETS exposure was quantified. Geographic variability in ETS exposure was quantified based on the varying prevalence of smokers in five selected locations in the United States.     

Development of the PEARLS model (Particulate Exposure from Ambient to Regional Lung by Subgroup) and use of Monte Carlo simulation to predict internal exposure to 2.5 in Toronto.

Air pollution is a current and growing concern for Canadians, and there is evidence that ambient levels that meet current exposure standards may be associated with mortality and morbidity in Toronto, Canada. Evaluating exposure is an important step in understanding the relationship between particulate matter (PM) exposure and health outcomes. This report describes the PEARLS model (Particulate Exposure from Ambient to Regional Lung by Subgroup), which predicts exposure distributions for 11 age-gender population subgroups in Toronto to PM2.5 (PM with a median aerodynamic diameter of 2.5 microm or less) using Monte Carlo simulation techniques. The model uses physiological and activity pattern characteristics of each subgroup to determine region-specific lung exposure to PM2.5, which is defined as the mass of PM2.5 deposited per unit time to each of five lung regions (two extrathoracic, bronchial, bronchiolar, and alveolar). The modeling results predict that children, toddlers, and infants have the broadest distributions of exposure, and the greatest chance of experiencing extreme exposures in the alveolar region of the lung. Importance analysis indicates that the most influential model variables are air exchange rate into indoor environments, time spent outdoors, and time spent at high activity levels. Additionally, a "critical point" was defined and introduced to the PEARLS to investigate the effects of possible threshold-pathogenic phenomena on subgroup exposure patterns. The analysis indicates that the subgroups initially predicted to be most highly exposed were likely to have the highest proportion of their population exposed above the critical point. Substantial exposures above the critical point were predicted in all subgroups for ambient concentrations of PM2.5 commonly observed in Toronto after continuous exposure of 24 hours or more.     

Baseline Risk and Preference for Reductions in Risk-to-Life

The typical model of individual attitudes toward risk-to-life suggests that an individual's willingness to pay for a reduction in mortality risk increases with the baseline risk. The higher-baseline hypothesis has been the subject of several empirical tests but results have so far been mixed. Using survey evidence, we present a situation in which subjects do prefer to reduce risks for which the baseline is higher. This finding is robust to several alternative explanations. Survey responses reflect subjects'concerns about government effectiveness in risk reduction, environmental effects associated with the various hazards, and other idiosyncratic elements of the risks; however, these concerns appear to occur in addition to, not in lieu of, the preference to reduce higher risks.     

Enhancing the Characterization of Epistemic Uncertainties in PM2.5 Risk Analyses

The Environmental Benefits Mapping and Analysis Program (BenMAP) is a software tool developed by the U.S. Environmental Protection Agency (EPA) that is widely used inside and outside of EPA to produce quantitative estimates of public health risks from fine particulate matter (PM_2.5). This article discusses the purpose and appropriate role of a risk analysis tool to support risk management deliberations, and evaluates the functions of BenMAP in this context. It highlights the importance in quantitative risk analyses of characterization of epistemic uncertainty, or outright lack of knowledge, about the true risk relationships being quantified. This article describes and quantitatively illustrates sensitivities of PM_2.5 risk estimates to several key forms of epistemic uncertainty that pervade those calculations: the risk coefficient, shape of the risk function, and the relative toxicity of individual PM_2.5 constituents. It also summarizes findings from a review of U.S.‐based epidemiological evidence regarding the PM_2.5 risk coefficient for mortality from long‐term exposure. That review shows that the set of risk coefficients embedded in BenMAP substantially understates the range in the literature. We conclude that BenMAP would more usefully fulfill its role as a risk analysis support tool if its functions were extended to better enable and prompt its users to characterize the epistemic uncertainties in their risk calculations. This requires expanded automatic sensitivity analysis functions and more recognition of the full range of uncertainty in risk coefficients.     

Increased risk of emergency department presentations for bronchiolitis in infants exposed to air pollution

Air pollution has been linked to an increased risk of several respiratory diseases in children, especially respiratory tract infections. The present study aims to evaluate the association between pediatric emergency department (PED) presentations for bronchiolitis and air pollution. PED presentations due to bronchiolitis in children aged less than 1 year were retrospectively collected from 2007 to 2018 in Padova, Italy, together with daily environmental data. A conditional logistic regression based on a time-stratified case-crossover design was performed to evaluate the association between PED presentations and exposure to NO₂, PM2.5, and PM10. Models were adjusted for temperature, relative humidity, atmospheric pressure, and public holidays. Delayed effects in time were evaluated using distributed lag non-linear models. Odds ratio for lagged exposure from 0 to 14 days were obtained. Overall, 2251 children presented to the PED for bronchiolitis. Infants’ exposure to higher concentrations of PM10 and PM2.5 in the 5 days before the presentation to the PED increased the risk of accessing the PED by more than 10%, whereas high concentrations of NO₂ between 2 and 12 days before the PED presentation were associated with an increased risk of up to 30%. The association between pollutants and infants who required hospitalization was even greater. A cumulative effect of NO₂ among the 2 weeks preceding the presentation was also observed. In summary, PM and NO₂ concentrations are associated with PED presentations and hospitalizations for bronchiolitis. Exposure of infants to air pollution could damage the respiratory tract mucosa, facilitating viral infections and exacerbating symptoms.     

The Impact of Portfolio Location Uncertainty on Probabilistic Seismic Risk Analysis

Probabilistic seismic risk analysis is a well‐established method in the insurance industry for modeling portfolio losses from earthquake events. In this context, precise exposure locations are often unknown. However, so far, location uncertainty has not been in the focus of a large amount of research. In this article, we propose a novel framework for treatment of location uncertainty. As a case study, a large number of synthetic portfolios resembling typical real‐world cases were created. We investigate the effect of portfolio characteristics such as value distribution, portfolio size, or proportion of risk items with unknown coordinates on the variability of loss frequency estimations. The results indicate that due to loss aggregation effects and spatial hazard variability, location uncertainty in isolation and in conjunction with ground motion uncertainty can induce significant variability to probabilistic loss results, especially for portfolios with a small number of risks. After quantifying its effect, we conclude that location uncertainty should not be neglected when assessing probabilistic seismic risk, but should be treated stochastically and the resulting variability should be visualized and interpreted carefully.     

Air Pollution and Mortality: Quantification and Valuation of Years of Life Lost

To analyze the loss of life expectancy (LLE) due to air pollution and the associated social cost, a dynamic model was developed that took into account the decrease of risk after the termination of an exposure to pollution. A key parameter was the time constant for the decrease of risk, for which estimates from studies of smoking were used. A sensitivity analysis showed that the precise value of the time constant(s) was not critical for the resulting LLE. An interesting aspect of the model was that the relation between population total LLE and PM2.5 concentration was numerically almost indistinguishable from a straight line, even though the functional dependence was nonlinear. This essentially linear behavior implies that the detailed history of a change in concentration does not matter, except for the effects of discounting. This model was used to correct the data of the largest study of chronic mortality for variations in past exposure, performed by Pope et al. in 1995; the correction factor was shown to depend on assumptions about the relative toxicity of the components of PM2.5. In the European Union, an increment of 1 microg/m3 of PM2.5 for 1 year implies an average LLE of 0.22 days per person. With regard to the social cost of an air pollution pulse, it was found that for typical discount rates (3% to 8% real) the cost was reduced by a factor of about 0.4 to 0.6 relative to the case with zero discount rate, if the value of a life year was taken as given; if the value of a life year was calculated from the "value of statistical life" by assuming the latter as a series of discounted annual values, the cost varied by at most +/-20% relative to the case with zero discount rate. To assess the uncertainties, this study also examined how the LLE depended on the demographics (mortality and age pyramid) of a population, and how it would change if the relative risk varied with age, in the manner suggested by smoking studies. These points were found to have a relatively small effect (compared to the epidemiological uncertainties) on the calculated LLE.     

Different Roles and Viewpoints of Scientific Experts in Advising on Environmental Health Risks

Environmental health risks are often complex, largescale, and uncertain. The uncertainties inherent in these problems permit differences among experts in the appraisal of risks. This raises the question of whether different expert roles exist and, if so, how this affects the policy advice that is given. Here, we present a pilot study of the different roles and viewpoints that can be discerned among scientific experts in the Netherlands. Q methodology was used to empirically explore existing theoretical treatises on different expert roles. In total, 26 electromag­netic field (EMF) experts and 21 particulate matter (PM) experts participated. The responses were analyzed separately for EMF and PM respondents using Q factor analysis. In both the EMF and PM domain, three different expert roles were identified. This suggests that particular expert roles depend on the specific environmental health risk. The results indicate that different expert roles exist among scientists who provide policy advice on environmental health risks. This empirical study adds new data and insights to the literature on expert roles. The results of this study are relevant for the selection and composition of expert committees and the interpretation of expert advice.     

Comparing Estimates of the Effects of Air Pollution on Human Mortality Obtained Using Different Regression Methodologies

Studies using regression techniques report their results using a variety of statistics. Evaluation of the consistency of findings, such as in a metaanalysis, requires calculating the statistical estimates of the effect reported in each study in a comparable manner. In this paper, we consider multiple linear regression, multiple Poisson regression, and logistic regression estimates. We present results that are needed to calculate, on a common basis, the slope of the regression function at a specified value, the elasticity function of the regression function at a specified value, the relative risk at a specified value, and the odds ratio at a specified value. We apply these results to studies of the association of daily mortality in an area to the daily air pollution level of ozone and PM₁₀. We calculate the estimated slope of the number of deaths per billion population associated with an increase of 1 ppb of ozone level in studies of daily mortality in three urban areas. These studies, in Los Angeles, New York, and St. Louis, produced very comparable results on a common basis, especially when compared to the coefficients as reported. We also calculated the estimated elasticity function of the daily mortality and daily PM₁₀ level for eight areas and found that the elasticities varied within a factor of roughly two, much less than the variability in the coefficients as reported.     

Uncertain Benefits Estimates for Reductions in Fine Particle Concentrations

The Environmental Protection Agency's (EPA's) estimates of the benefits of improved air quality, especially from reduced mortality associated with reductions in fine particle concentrations, constitute the largest category of benefits from all federal regulation over the last decade. EPA develops such estimates, however, using an approach little changed since a 2002 report by the National Research Council (NRC), which was critical of EPA's methods and recommended a more comprehensive uncertainty analysis incorporating probability distributions for major sources of uncertainty. Consistent with the NRC's 2002 recommendations, we explore alternative assumptions and probability distributions for the major variables used to calculate the value of mortality benefits. For metropolitan Philadelphia, we show that uncertainty in air quality improvements and in baseline mortality have only modest effects on the distribution of estimated benefits. We analyze the effects of alternative assumptions regarding the value of reducing mortality risk, whether the toxicity is above or below the average for fine particles, and whether there is a threshold in the concentration‐response relationship, and show these assumptions all have large effects on the distribution of benefits.     

Diesel Engine Exhaust and Lung Cancer Mortality: Time‐Related Factors in Exposure and Risk

To develop a quantitative exposure‐response relationship between concentrations and durations of inhaled diesel engine exhaust (DEE) and increases in lung cancer risks, we examined the role of temporal factors in modifying the estimated effects of exposure to DEE on lung cancer mortality and characterized risk by mine type in the Diesel Exhaust in Miners Study (DEMS) cohort, which followed 12,315 workers through December 1997. We analyzed the data using parametric functions based on concepts of multistage carcinogenesis to directly estimate the hazard functions associated with estimated exposure to a surrogate marker of DEE, respirable elemental carbon (REC). The REC‐associated risk of lung cancer mortality in DEMS is driven by increased risk in only one of four mine types (limestone), with statistically significant heterogeneity by mine type and no significant exposure‐response relationship after removal of the limestone mine workers. Temporal factors, such as duration of exposure, play an important role in determining the risk of lung cancer mortality following exposure to REC, and the relative risk declines after exposure to REC stops. There is evidence of effect modification of risk by attained age. The modifying impact of temporal factors and effect modification by age should be addressed in any quantitative risk assessment (QRA) of DEE. Until there is a better understanding of why the risk appears to be confined to a single mine type, data from DEMS cannot reliably be used for QRA.     

Business Interruption Impacts of a Terrorist Attack on the Electric Power System of Los Angeles: Customer Resilience to a Total Blackout

Regional economies are highly dependent on electricity, thus making their power supply systems attractive terrorist targets. We estimate the largest category of economic losses from electricity outages-business interruption-in the context of a total blackout of electricity in Los Angeles. We advance the state of the art in the estimation of the two factors that strongly influence the losses: indirect effects and resilience. The results indicate that indirect effects in the context of general equilibrium analysis are moderate in size. The stronger factor, and one that pushes in the opposite direction, is resilience. Our analysis indicates that electricity customers have the ability to mute the potential shock to their business operations by as much as 86%. Moreover, market resilience lowers the losses, in part through the dampening of general equilibrium effects.