Uncertainty and Variability in Health-Related Damages from Coal-Fired Power Plants in the United States

The health‐related damages associated with emissions from coal‐fired power plants can vary greatly across facilities as a function of plant, site, and population characteristics, but the degree of variability and the contributing factors have not been formally evaluated. In this study, we modeled the monetized damages associated with 407 coal‐fired power plants in the United States, focusing on premature mortality from fine particulate matter (PM_2.5). We applied a reduced‐form chemistry‐transport model accounting for primary PM_2.5 emissions and the influence of sulfur dioxide (SO₂) and nitrogen oxide (NO_x) emissions on secondary particulate formation. Outputs were linked with a concentration‐response function for PM_2.5‐related mortality that incorporated nonlinearities and model uncertainty. We valued mortality with a value of statistical life approach, characterizing and propagating uncertainties in all model elements. At the median of the plant‐specific uncertainty distributions, damages across plants ranged from $30,000 to $500,000 per ton of PM_2.5, $6,000 to $50,000 per ton of SO₂, $500 to $15,000 per ton of NO_x, and $0.02 to $1.57 per kilowatt‐hour of electricity generated. Variability in damages per ton of emissions was almost entirely explained by population exposure per unit emissions (intake fraction), which itself was related to atmospheric conditions and the population size at various distances from the power plant. Variability in damages per kilowatt‐hour was highly correlated with SO₂ emissions, related to fuel and control technology characteristics, but was also correlated with atmospheric conditions and population size at various distances. Our findings emphasize that control strategies that consider variability in damages across facilities would yield more efficient outcomes.     

Major Factors Influencing the Health Impacts from Controlling Air Pollutants with Nonlinear Chemistry: An Application to China

Predicting the human‐health effects of reducing atmospheric emissions of nitrogen oxide (NO_x) emissions from power plants, motor vehicles, and other sources is complex because of nonlinearity in the relevant atmospheric processes. We estimate the health impacts of changes in fine particulate matter (PM_2.5) and ozone concentrations that result from control of NO_x emissions alone and in conjunction with other pollutants in and outside the mega‐city of Shanghai, China. The Community Multiscale Air Quality (CMAQ) Modeling System is applied to model the effects on atmospheric concentrations of emissions from different economic sectors and geographic locations. Health impacts are quantified by combining concentration‐response functions from the epidemiological literature with pollutant concentration and population distributions. We find that the health benefits per ton of emission reduction are more sensitive to the location (i.e., inside vs. outside of Shanghai) than to the sectors that are controlled. For eastern China, we predict between 1 and 20 fewer premature deaths per year per 1,000 tons of NO_x emission reductions, valued at $300–$6,000 per ton. Health benefits are sensitive to seasonal variation in emission controls. Policies to control NO_x emissions need to consider emission location, season, and simultaneous control of other pollutants to avoid unintended consequences.     

The Effects of Urban Form on Ambient Air Pollution and Public Health Risk: A Case Study in Raleigh,North Carolina

Since motor vehicles are a major air pollution source, urban designs that decrease private automobile use could improve air quality and decrease air pollution health risks. Yet, the relationships among urban form, air quality, and health are complex and not fully understood. To explore these relationships, we model the effects of three alternative development scenarios on annual average fine particulate matter (PM_2.5) concentrations in ambient air and associated health risks from PM_2.5 exposure in North Carolina's Raleigh‐Durham‐Chapel Hill area. We integrate transportation demand, land‐use regression, and health risk assessment models to predict air quality and health impacts for three development scenarios: current conditions, compact development, and sprawling development. Compact development slightly decreases (?0.2%) point estimates of regional annual average PM_2.5 concentrations, while sprawling development slightly increases (+1%) concentrations. However, point estimates of health impacts are in opposite directions: compact development increases (+39%) and sprawling development decreases (?33%) PM_2.5‐attributable mortality. Furthermore, compactness increases local variation in PM_2.5 concentrations and increases the severity of local air pollution hotspots. Hence, this research suggests that while compact development may improve air quality from a regional perspective, it may also increase the concentration of PM_2.5 in local hotspots and increase population exposure to PM_2.5. Health effects may be magnified if compact neighborhoods and PM_2.5 hotspots are spatially co‐located. We conclude that compactness alone is an insufficient means of reducing the public health impacts of transportation emissions in automobile‐dependent regions. Rather, additional measures are needed to decrease automobile dependence and the health risks of transportation emissions.     

Finely Resolved On‐Road PM2.5 and Estimated Premature Mortality in Central North Carolina

To quantify the on‐road PM_2.5‐related premature mortality at a national scale, previous approaches to estimate concentrations at a 12‐km × 12‐km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on‐road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on‐road PM_2.5‐related premature mortality in central North Carolina with two different concentration estimation approaches—(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36‐km × 36‐km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space–time interpolation technique to provide annual average PM_2.5 concentrations at a Census‐block level (～105,000 Census blocks). The hybrid modeling approach estimated 24% more on‐road PM_2.5‐related premature mortality than CMAQ. The major difference is from the primary on‐road PM_2.5 where the hybrid approach estimated 2.5 times more primary on‐road PM_2.5‐related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on‐road PM_2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near‐road primary PM_2.5 and suggesting that previous studies may have underestimated premature mortality due to PM_2.5 from traffic‐related emissions.     

Returns to scale vs. damages to scale in data envelopment analysis: An impact of U.S. clean air act on coal-fired power plants

This study proposes a use of Data Envelopment Analysis (DEA) to measure Returns to Scale (RTS) of US coal-fired power plants. The power plants produce not only desirable outputs (e.g., electricity) but also undesirable outputs (e.g., CO₂ and NO_x) as a result of their plant operations. Therefore, the proposed use of DEA also measures a new concept, or “DTS: Damages to Scale” (corresponding to RTS for undesirable outputs). Both scale measures discussed in this study are a quick-and-easy approach for assessing RTS and DTS, but not an exact method, because it does not consider a direct linkage between the two measures. This study applies the proposed approach to examine the legal validity of U.S. Clean Air Act (CAA). We find that the CAA has been legally effective and influential on the operation of coal-fired power plants in the United States because their plant operations belong to increasing RTS on a desirable output and increasing DTS on three undesirable outputs. The increasing DTS indicates a need for managerial improvement and/or engineering innovation such as advanced clean coal technology. This empirical result implies that U.S. federal and state governments need to expand the legal scope of CAA to the emission control of CO₂ because the current CAA excludes the regulation on CO₂ emission that is now considered as a main source of the global warming and climate change.     

Current and Future Particulate‐Matter‐Related Mortality Risks in the United States from Aviation Emissions During Landing and Takeoff

Demand for air travel is projected to increase in the upcoming years, with a corresponding influence on emissions, air quality, and public health. The trajectory of health impacts would be influenced by not just emissions growth, but also changes in nonaviation ambient concentrations that influence secondary fine particulate matter (PM_2.5) formation, population growth and aging, and potential shifts in PM_2.5 concentration‐response functions (CRFs). However, studies to date have not systematically evaluated the individual and joint contributions of these factors to health risk trajectories. In this study, we simulated emissions during landing and takeoff from aircraft at 99 airports across the United States for 2005 and for a 2025 flight activity projection scenario. We applied the Community Multiscale Air Quality (CMAQ) model with the Speciated Modeled Attainment Test (SMAT) to determine the contributions of these emissions to ambient concentrations, including scenarios with 2025 aircraft emissions and 2005 nonaviation air quality. We combined CMAQ outputs with PM_2.5 mortality CRFs and population projections, and evaluated the influence of changing emissions, nonaviation concentrations, and population factors. Given these scenarios, aviation‐related health impacts would increase by a factor of 6.1 from 2005 to 2025, with a factor of 2.1 attributable to emissions, a factor of 1.3 attributable to population factors, and a factor of 2.3 attributable to changing nonaviation concentrations which enhance secondary PM_2.5 formation. Our study emphasizes that the public health burden of aviation emissions would be significantly influenced by the joint effects of flight activity increases, nonaviation concentration changes, and population growth and aging.     

Inference on Regressions with Interval Data on a Regressor or Outcome

This paper examines inference on regressions when interval data are available on one variable, the other variables being measured precisely. Let a population be characterized by a distribution P(y, x, v, v₀, v₁), where y∈R¹, x∈R^k, and the real variables (v, v₀, v₁) satisfy v₀≤v≤v₁. Let a random sample be drawn from P and the realizations of (y, x, v₀, v₁) be observed, but not those of v. The problem of interest may be to infer E(y|x, v) or E(v|x). This analysis maintains Interval (I), Monotonicity (M), and Mean Independence (MI) assumptions: (I) P(v₀≤v≤v₁)=1; (M) E(y|x, v) is monotone in v; (MI) E(y|x, v, v₀, v₁)=E(y|x, v). No restrictions are imposed on the distribution of the unobserved values of v within the observed intervals [v₀, v₁]. It is found that the IMMI Assumptions alone imply simple nonparametric bounds on E(y|x, v) and E(v|x). These assumptions invoked when y is binary and combined with a semiparametric binary regression model yield an identification region for the parameters that may be estimated consistently by a modified maximum score (MMS) method. The IMMI assumptions combined with a parametric model for E(y|x, v) or E(v|x) yield an identification region that may be estimated consistently by a modified minimum‐distance (MMD) method. Monte Carlo methods are used to characterize the finite‐sample performance of these estimators. Empirical case studies are performed using interval wealth data in the Health and Retirement Study and interval income data in the Current Population Survey.     

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.     

Assortative Matching and Search

In Becker's (1973) neoclassical marriage market model, matching is positively assortaive if types are complements: i.e., match output f(x, y) is supermodular in x and y. We reprise this famous result assuming time‐intensive partner search and transferable output. We prove existence of a search equilibrium with a continuum of types, and then characterize matching. After showing that Becker's conditions on match output no longer suffice for assortative matching, we find sufficient conditions valid for any search frictions and type distribution: supermodularity not only of output f, but also of log f_x and log f_xy. Symmetric submodularity conditions imply negatively assortative matching. Examples show these conditions are necessary.     

Distance graphs on R n with 1-norm

Suppose S is a subset of a metric space X with metric d. For each subset D⊆{d(x,y):x,y∈S,x≠y}, the distance graph G(S,D) is the graph with vertex set S and edge set E(S,D)={xy:x,y∈S,d(x,y)∈D}. The current paper studies distance graphs on the n-space R ₁ ⁿ with 1-norm. In particular, most attention is paid to the subset Z ₁ ⁿ of all lattice points of R ₁ ⁿ . The results obtained include the degrees of vertices, components, and chromatic numbers of these graphs. Dedicated to Professor Frank K. Hwang on the occasion of his 65th birthday. Supported in part by the National Science Council under grant NSC-94-2115-M-002-015. Taida Institue for Mathematical Sciences, National Taiwan University, Taipei 10617, Taiwan. National Center for Theoretical Sciences, Taipei Office.     

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.     

Near automorphisms of trees with small total relative displacements

For a permutation f of the vertex set V(G) of a connected graph G, let δ _f (x,y)=|d(x,y)−d(f(x),f(y))|. Define the displacement δ _f (G) of G with respect to f to be the sum of δ _f (x,y) over all unordered pairs {x,y} of distinct vertices of G. Let π(G) denote the smallest positive value of δ _f (G) among the n! permutations f of V(G). In this note, we determine all trees T with π(T)=2 or 4. Dedicated to Professor Frank K. Hwang on the occasion of his 65th birthday.     

Miscommunicating Risk,Uncertainty, and Causation: Fine Particulate Air Pollution and Mortality Risk as an Example

A recent paper in this journal (Fann et al., 2012) estimated that “about 80,000 premature mortalities would be avoided by lowering PM_2.5 levels to 5 μg/m³ nationwide” and that 2005 levels of PM_2.5 cause about 130,000 premature mortalities per year among people over age 29, with a 95% confidence interval of 51,000 to 200,000 premature mortalities per year.⁽¹⁾ These conclusions depend entirely on misinterpreting statistical coefficients describing the association between PM_2.5 and mortality rates in selected studies and models as if they were known to be valid causal coefficients. But they are not, and both the expert opinions of EPA researchers and analysis of data suggest that a true value of zero for the PM_2.5 mortality causal coefficient is not excluded by available data. Presenting continuous confidence intervals that exclude the discrete possibility of zero misrepresents what is currently known (and not known) about the hypothesized causal relation between changes in PM_2.5 levels and changes in mortality rates, suggesting greater certainty about projected health benefits than is justified.     

Weighing the Risks of Nuclear Energy and Climate Change: Trust in Different Information Sources,Perceived Risks,and Willingness to Pay for Alternatives to Nuclear Power

We examined how individuals perceive nuclear energy in the context of climate change mitigation and how their perceptions are associated with trust in different risk information sources. We analyzed the interrelationships between trust, perceived risk of nuclear power, climate change concern, perception of nuclear energy as an acceptable way to mitigate climate change, and willingness to pay (WTP) for alternatives to nuclear power. A nationwide survey (N = 967) collected in Finland was analyzed with structural equation modeling. The associations between trust and perceived risk of nuclear power, climate change concern, and perception of nuclear power as a way to mitigate climate change varied by the type of information source. Political party support and other background variables were associated with trust in different information sources. The effect of trust in information sources on WTP was mediated by perceived risks and benefits. The results will increase our understanding of how individuals perceive nuclear energy as a way to cut CO₂ emissions and the role of trust in different information sources in shaping nuclear risk perceptions and energy choices.     

Setting Risk‐Informed Environmental Standards for Bacillus Anthracis Spores

In many cases, human health risk from biological agents is associated with aerosol exposures. Because air concentrations decline rapidly after a release, it may be necessary to use concentrations found in other environmental media to infer future or past aerosol exposures. This article presents an approach for linking environmental concentrations of Bacillus. anthracis (B. anthracis) spores on walls, floors, ventilation system filters, and in human nasal passages with human health risk from exposure to B. anthracis spores. This approach is then used to calculate example values of risk‐informed concentration standards for both retrospective risk mitigation (e.g., prophylactic antibiotics) and prospective risk mitigation (e.g., environmental clean up and reoccupancy). A large number of assumptions are required to calculate these values, and the resulting values have large uncertainties associated with them. The values calculated here suggest that documenting compliance with risks in the range of 10^?4 to 10^?6 would be challenging for small diameter (respirable) spore particles. For less stringent risk targets and for releases of larger diameter particles (which are less respirable and hence less hazardous), environmental sampling would be more promising.     

燃煤机组退役与技术升级的减排及经济效益研究——基于鲁棒电源规划的方法

火电行业是中国推行节能减排战略所重点关注的行业之一。为了降低高污染燃煤机组对环境的不利影响，中国近年来出台了一系列的能源政策，鼓励淘汰高污染的燃煤机组或者对其进行超低排放改造的技术升级。本文研究了燃煤机组主动退役及超低排放改造升级的环境及经济的双重效益。基于电源投资规划鲁棒优化的方法，考虑了受不确定的环保税、超低排放机组上网电价的补贴及电能需求影响下的发电机组最优配置问题，构建了含两阶段决策的三层鲁棒优化模型。该模型是一类具有多个不确定场景耦合特征的大规模优化问题。为了求解的便利，采用列与约束生成（column and constraint generation，CCG）算法进行求解。算例分析结果显示，当考虑了燃煤机组主动退役及超低排放改造升级的决策时，本文构建模型所提供的最优电源结构方案能够有效地降低各类污染物的排放，同时提升发电企业的经济性。     

Intake Fraction Variability Between Air Pollution Emission Sources Inside an Urban Area

The cost‐effective mitigation of adverse health effects caused by air pollution requires information on the contribution of different emission sources to exposure. In urban areas the exposure potential of different sources may vary significantly depending on emission height, population density, and other factors. In this study, we quantified this intraurban variability by predicting intake fraction (iF) for 3,066 emission sources in Warsaw, Poland. iF describes the fraction of the pollutant that is inhaled by people in the study area. We considered the following seven pollutants: particulate matter (PM), nitrogen oxides (NO_x), sulfur dioxide (SO₂), benzo[a] pyrene (BaP), nickel (Ni), cadmium (Cd), and lead (Pb). Emissions for these pollutants were grouped into four emission source categories (Mobile, Area, High Point, and Other Point sources). The dispersion of the pollutants was predicted with the CALPUFF dispersion model using the year 2005 emission rate data and meteorological records. The resulting annual average concentrations were combined with population data to predict the contribution of each individual source to population exposure. The iFs for different pollutant‐source category combinations varied between 51 per million (PM from Mobile sources) and 0.013 per million (sulfate PM from High Point sources). The intraurban iF variability for Mobile sources primary PM emission was from 4 per million to 100 per million with the emission‐weighted iF of 44 per million. These results propose that exposure due to intraurban air pollution emissions could be decreased more effectively by specifically targeting sources with high exposure potency rather than all sources.     

ORGANIZATIONAL ENVIRONMENT,DIFFERENTIATION AND PERCEIVED ENVIRONMENTAL UNCERTAINTY1

Four propositions are derived from an a priori model which relates environmental characteristics, organizational differentiation, and perceived environmental uncertainty. The four propositions are: (P₁) the greater the magnitude of each of several task environmental dimensions confronting an organization, the greater the degree of organizational differentiation, (P₂) the greater the degree of task environment change, the greater the degree of organizational differentiation, (P₃) the greater the size of the organization, the greater the organizational differentiation, and (P₄) the greater the organizational differentiation, the less the degree of perceived environmental uncertainty seen by top administrators. These propositions were tested on 23 Employment Service District Offices with objective environmental and organizational differentiation measures and with self report indices of perceived environmental uncertainty. Zero-order correlational and regression analyses supported propositions P₁, P₃, and P₄. Based on the empirical findings, a revised model of environment, differentiation and uncertainty was developed. Suggestions for future research and theorizing are offered.     

Health and well-being of older workers: comparing their associations with effort–reward imbalance and Pressure,Disorganisation and Regulatory Failure

Work organisation has well-established associations with health. This study compares the associations of Pressure, Disorganisation and Regulatory Failure (PDR) and effort–reward imbalance (ERI) with health and well-being among older workers. Participants were 714 Australian workers aged 45–65 (56.3% female), with a mean age of 54.6 years (SD = 5.0) and a mean of 34.7 working hours per week (SD = 13.9). Hierarchical regression analyses tested the strengths of the associations of both ERI and PDR with work–life conflict and mental health. Independent variables were entered in blocks: demographic characteristics (age and gender), working hours and then ERI or PDR (measured using the four subscales: financial pressure, reward pressure, disorganisation and regulatory failure). Compared to ERI, the PDR subscales collectively accounted for slightly less variance in work–life conflict and slightly more variance in mental health. The PDR subscales also accounted for extra variance in both dependent variables when ERI was included in the model. These findings indicate that PDR is a promising construct that includes elements of work organisation not addressed by ERI.     

Trajectories of effort-reward imbalance in Swedish workers: Differences in demographic and work-related factors and associations with health

ABSTRACT

The aim of the study was to identify trajectories of effort-reward imbalance (ERI), to examine these with respect to demographic (age, gender, socio-economic position) and work-related (employment contract, work hours, shift work, sector) factors, and to investigate associations with different health indicators (self-rated health, depressive symptoms, migraine, sickness absence). The study used four waves of data (N?=?6702), collected biennially within the Swedish Longitudinal Occupational Survey of Health (SLOSH). Using latent class growth modelling, we identified four trajectories: a stable low imbalance trajectory, which comprised 90% of all participants, and three change trajectories including a decreasing trajectory (4% of the participants), an inverted U-shaped trajectory and an increasing imbalance trajectory, both in 3% of the participants. Results indicate that a sizeable proportion of Swedish employees’ experience imbalance between efforts and rewards at work. The most favourable trajectory comprised relatively more men and was characterised by better work-related characteristics than the less favourable ERI trajectories. All change trajectories were dominated by women and employees in the public sector. Health developments followed ERI trajectories, such that less favourable trajectories associated with impaired health and more favourable trajectories associated with better health. Sickness absence increased among all ERI trajectories, most so for the decreasing and increasing ERI trajectory.