A regression-based approach for estimating primary and secondary particulate matter intake fractions

Location-specific co-benefits of carbon emissions reduction from coal-fired power plants in China

Climate policies that achieve air quality co-benefits can better align developing countries' national interests with global climate mitigation. Since the effects of air pollutants are highly dependent on source locations, spatially nuanced policies are crucial to maximizing the achievement of co-benefits. Using the coal power industry as a case study, this study presents an interdisciplinary approach to assessing facility level co-benefits at every specific source location in China. We find that co-benefits range from US$51-$278 per ton CO₂ reduction nationwide and are highly heterogeneous spatially, with "hotspot" regions that should be the priority of emissions reduction policies, and that provinces should use different techno-economic strategies to reduce emissions. The location-specific co-benefit value plus a carbon price serves as a unified environmental indicator that enables policy makers to more accurately understand the social costs of electricity generation from coal burning and provides a scientific framework for geographically nuanced policymaking.

Using Low-Cost Air Quality Sensor Networks to Improve the Spatial and Temporal Resolution of Concentration Maps

We present an approach to analyzing fine particulate matter (PM2.5) data from a network of "low cost air quality monitors" (LCAQM) to obtain a finely resolved concentration map. In the approach, based on a dispersion model, we first identify the probable locations of the sources, and then estimate the magnitudes of the emissions from these sources by fitting model estimates of concentrations to corresponding measurements. The emissions are then used to estimate concentrations on a grid covering the domain of interest. The residuals between model estimates at the monitor locations and the measured concentrations are then interpolated to the grid points using Kriging. We illustrate this approach by applying it to a network of 20 LCAQMs located in the Imperial Valley of Southern California. Estimating the underlying mean concentration field with a dispersion model provides a more realistic estimate of the spatial distribution of PM2.5 concentrations than that from the Kriging observations directly.

Intra-urban variability of the intake fraction from multiple emission sources

BACKGROUND

Ambient air pollution and associated adverse health effects are among most acute environmental problems in many cities worldwide. The intake fraction (iF) approach can be applied for evaluating the health benefits of reducing emissions, especially when rapid decisions are needed. Intake fraction is a metric that represents emission-to-intake relationship and characterizes abatement of exposure potential attributed to specific emission sources.

AIM

In this study, the spatial variability of iF in Warsaw agglomeration, Poland, is discussed.

METHODS

The iF analysis is based on the earlier air quality modeling results, that include the main pollutants characterizing an urban atmospheric environment (SO₂, NOx, PM₁₀, PM_2.5, CO, C₆H₆, B(a)P, heavy metals). The annual mean concentrations were computed by the CALPUFF modeling system (spatial resolution 0.5 × 0.5 km²) on the basis of the emission and meteorological data from year 2012. The emission field comprised 24 high (power generation) and 3880 low (industry) point sources, 7285 mobile (transport) sources, and 6962 area (housing) sources.

RESULTS

The aggregated iFs values are computed for each emission class and the related polluting compounds. Intra-urban variability maps of iFs are attributed to an emission sources by emission category and pollutant.

CONCLUSIONS

Intake fraction is shown as a decision support tool for implementing the cost-effective emission policy and reducing the health risk of air pollution.

Individual and population intake fractions of diesel particulate matter (DPM) in bus stop microenvironments

Diesel particulate matter (DPM) is associated with adverse human health effects. This study aims to investigate the relationship between DPM exposure and emissions by estimating the individual intake fraction (iFi) and population intake fraction (iFp) of DPM. Daily average concentrations of particulate matter at two bus stops during rush hours were measured, and then they were apportioned to DPM due to heavy-duty diesel bus emissions using Chemical Mass Balance Model. The DPM emissions of diesel buses for different driving conditions (idling, creeping and traveling) were estimated on the basis of field observations and published emission factors. The median iFi of DPM was 0.67 and 1.39 per million for commuters standing at the bus stop and pedestrians/cyclists passing through the bus stop during rush hours, respectively. The median iFp of DPM was 94 per million. Estimations of iFi and iFp of DPM are potentially significant for exposure assessment and risk management.

A CFD modeling study in an urban street canyon for ultrafine particles and population exposure: The intake fraction approach

Air quality in street canyons is of major importance, since the highest pollution levels are often encountered in these microenvironments. The canyon effect (reduced natural ventilation) makes them "hot spots" for particulate pollution contributing to adverse health effects for the exposed population. In this study we tried to characterize the influence of UFP (ultrafine particle) emissions from traffic on population exposure in an urban street canyon, by applying the intake fraction (iF) approach. One month long measurements of UFP levels have been monitored and used for the need of this study. We applied a three dimensional computational fluid dynamic (CFD) model based on real measurements for the simulation of UFP levels. We used infiltration factors, evaluated on a daily basis for the under study area, to estimate the indoor UFP levels. As a result the intake fraction for the pedestrians, residents and office workers is in the range of (1E-5)-(1E-4). The street canyon is mostly residential justifying partially the higher value of intake fraction for residents (1E-4). The above iF value is on the same order of magnitude with the corresponding one evaluated in a relative street canyon study. The total iF value in this microenvironment is one order of magnitude higher than ours, explained partially by the different use and activities. Two specific applications of iF to assess prioritization among emission sources and environmental justice issues are also examined. We ran a scenario with diesel and gasoline cars and diesel fueled vehicle seems to be a target source to improve overall iF. Our application focus on a small residential area, typical of urban central Athens, in order to evaluate high resolution iF. The significance of source-exposure relationship study in a micro scale is emphasized by recent research.

Life cycle impact assessment modeling for particulate matter: A new approach based on physico-chemical particle properties

Particulate matter (PM) causes severe damage to human health globally. Airborne PM is a mixture of solid and liquid droplets suspended in air. It consists of organic and inorganic components, and the particles of concern range in size from a few nanometers to approximately 10μm. The complexity of PM is considered to be the reason for the poor understanding of PM and may also be the reason why PM in environmental impact assessment is poorly defined. Currently, life cycle impact assessment is unable to differentiate highly toxic soot particles from relatively harmless sea salt. The aim of this article is to present a new impact assessment for PM where the impact of PM is modeled based on particle physico-chemical properties. With the new method, 2781 characterization factors that account for particle mass, particle number concentration, particle size, chemical composition and solubility were calculated. Because particle sizes vary over four orders of magnitudes, a sound assessment of PM requires that the exposure model includes deposition of particles in the lungs and that the fate model includes coagulation as a removal mechanism for ultrafine particles. The effects model combines effects from particle size, solubility and chemical composition. The first results from case studies suggest that PM that stems from emissions generally assumed to be highly toxic (e.g. biomass combustion and fossil fuel combustion) might lead to results that are similar compared with an assessment of PM using established methods. However, if harmless PM emissions are emitted, established methods enormously overestimate the damage. The new impact assessment allows a high resolution of the damage allocatable to different size fractions or chemical components. This feature supports a more efficient optimization of processes and products when combating air pollution.

Comparing models for quantitative risk assessment: an application to the European Registry of foreign body injuries in children

Risk Assessment is the systematic study of decisions subject to uncertain consequences. An increasing interest has been focused on modeling techniques like Bayesian Networks since their capability of (1) combining in the probabilistic framework different type of evidence including both expert judgments and objective data; (2) overturning previous beliefs in the light of the new information being received and (3) making predictions even with incomplete data. In this work, we proposed a comparison among Bayesian Networks and other classical Quantitative Risk Assessment techniques such as Neural Networks, Classification Trees, Random Forests and Logistic Regression models. Hybrid approaches, combining both Classification Trees and Bayesian Networks, were also considered. Among Bayesian Networks, a clear distinction between purely data-driven approach and combination of expert knowledge with objective data is made. The aim of this paper consists in evaluating among this models which best can be applied, in the framework of Quantitative Risk Assessment, to assess the safety of children who are exposed to the risk of inhalation/insertion/aspiration of consumer products. The issue of preventing injuries in children is of paramount importance, in particular where product design is involved: quantifying the risk associated to product characteristics can be of great usefulness in addressing the product safety design regulation. Data of the European Registry of Foreign Bodies Injuries formed the starting evidence for risk assessment. Results showed that Bayesian Networks appeared to have both the ease of interpretability and accuracy in making prediction, even if simpler models like logistic regression still performed well.

A methodology for estimating health benefits of electricity generation using renewable technologies

At Copenhagen, the developed countries agreed to provide up to $100 bn per year to finance climate change mitigation and adaptation by developing countries. Projects aimed at cutting greenhouse gas (GHG) emissions will need to be evaluated against dual criteria: from the viewpoint of the developed countries they must cut emissions of GHGs at reasonable cost, while host countries will assess their contribution to development, or simply their overall economic benefits. Co-benefits of some types of project will also be of interest to host countries: for example some projects will contribute to reducing air pollution, thus improving the health of the local population. This paper uses a simple damage function methodology to quantify some of the health co-benefits of replacing coal-fired generation with wind or small hydro in China. We estimate the monetary value of these co-benefits and find that it is probably small compared to the added costs. We have not made a full cost-benefit analysis of renewable energy in China as some likely co-benefits are omitted from our calculations. Our results are subject to considerable uncertainty however, after careful consideration of their likely accuracy and comparisons with other studies, we believe that they provide a good first cut estimate of co-benefits and are sufficiently robust to stand as a guide for policy makers. In addition to these empirical results, a key contribution made by the paper is to demonstrate a simple and reasonably accurate methodology for health benefits estimation that applies the most recent academic research in the field to the solution of an increasingly important problem.

Intake fraction for particulate matter: recommendations for life cycle impact assessment

Particulate matter (PM) is a significant contributor to death and disease globally. This paper summarizes the work of an international expert group on the integration of human exposure to PM into life cycle impact assessment (LCIA), within the UNEP/SETAC Life Cycle Initiative. We review literature-derived intake fraction values (the fraction of emissions that are inhaled), based on emission release height and "archetypal" environment (indoor versus outdoor; urban, rural, or remote locations). Recommended intake fraction values are provided for primary PM(10-2.5) (coarse particles), primary PM(2.5) (fine particles), and secondary PM(2.5) from SO(2), NO(x), and NH(3). Intake fraction values vary by orders of magnitude among conditions considered. For outdoor primary PM(2.5), representative intake fraction values (units: milligrams inhaled per kilogram emitted) for urban, rural, and remote areas, respectively, are 44, 3.8, and 0.1 for ground-level emissions, versus 26, 2.6, and 0.1 for an emission-weighted stack height. For outdoor secondary PM, source location and source characteristics typically have only a minor influence on the magnitude of the intake fraction (exception: intake fraction values can be an order of magnitude lower for remote-location emission than for other locations). Outdoor secondary PM(2.5) intake fractions averaged over respective locations and stack heights are 0.89 (from SO(2)), 0.18 (NO(x)), and 1.7 (NH(3)). Estimated average intake fractions are greater for primary PM(10-2.5) than for primary PM(2.5) (21 versus 15), owing in part to differences in average emission height (lower, and therefore closer to people, for PM(10-2.5) than PM(2.5)). For indoor emissions, typical intake fraction values are ∼1000-7000. This paper aims to provide as complete and consistent an archetype framework as possible, given current understanding of each pollutant. Values presented here facilitate incorporating regional impacts into LCIA for human health damage from PM.

Evaluation of the public health impacts of traffic congestion: a health risk assessment

BACKGROUND

Traffic congestion is a significant issue in urban areas in the United States and around the world. Previous analyses have estimated the economic costs of congestion, related to fuel and time wasted, but few have quantified the public health impacts or determined how these impacts compare in magnitude to the economic costs. Moreover, the relative magnitudes of economic and public health impacts of congestion would be expected to vary significantly across urban areas, as a function of road infrastructure, population density, and atmospheric conditions influencing pollutant formation, but this variability has not been explored.

METHODS

In this study, we evaluate the public health impacts of ambient exposures to fine particulate matter (PM2.5) concentrations associated with a business-as-usual scenario of predicted traffic congestion. We evaluate 83 individual urban areas using traffic demand models to estimate the degree of congestion in each area from 2000 to 2030. We link traffic volume and speed data with the MOBILE6 model to characterize emissions of PM2.5 and particle precursors attributable to congestion, and we use a source-receptor matrix to evaluate the impact of these emissions on ambient PM2.5 concentrations. Marginal concentration changes are related to a concentration-response function for mortality, with a value of statistical life approach used to monetize the impacts.

RESULTS

We estimate that the monetized value of PM2.5-related mortality attributable to congestion in these 83 cities in 2000 was approximately $31 billion (2007 dollars), as compared with a value of time and fuel wasted of $60 billion. In future years, the economic impacts grow (to over $100 billion in 2030) while the public health impacts decrease to $13 billion in 2020 before increasing to $17 billion in 2030, given increasing population and congestion but lower emissions per vehicle. Across cities and years, the public health impacts range from more than an order of magnitude less to in excess of the economic impacts.

CONCLUSIONS

Our analyses indicate that the public health impacts of congestion may be significant enough in magnitude, at least in some urban areas, to be considered in future evaluations of the benefits of policies to mitigate congestion.

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 (SO(2)) 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 SO(2), $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 SO(2) 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.

Exposure to air pollution: an intake fraction application in Turin province

An application of the intake fraction methodology was carried out in the southwestern area of the city of Turin, Italy, one of Europe's more polluted areas. The results from the case study were compared to similar analysis published in the literature and the intake Fraction (iF) formulation was critically revised and evaluated. These findings imply that there is potential for improving the evaluation of exposure to transport-related air pollution based on the use of the iF.

The influence of geographic location on population exposure to emissions from power plants throughout China

This analysis seeks to evaluate the influence of emission source location on population exposure in China to fine particles and sulfur dioxide. We use the concept of intake fraction, defined as the fraction of material or its precursor released from a source that is eventually inhaled or ingested by a population. We select 29 power-plant sites throughout China and estimate annual average intake fractions at each site, using identical source characteristics to isolate the influence of geographic location. In addition, we develop regression models to interpret the intake fraction values and allow for extrapolation to other sites. To model the concentration increase due to emissions from selected power plants, we used a detailed long-range atmospheric dispersion model, CALPUFF. Primary fine particles have the highest average intake fraction (1 x 0(-5)), followed by sulfur dioxide (5 x 10(-6)), sulfate from sulfur dioxide (4 x 10(-6)), and nitrate from nitrogen oxides (4 x 10(-6)). For all pollutants, the intake fractions span approximately an order of magnitude across sites. In the regression analysis, the independent variables are meteorological proxies (such as climate region and precipitation) and population at various distances from the source. We find that population terms can explain a substantial percentage of variability in the intake fraction for all pollutants (R(2) between 0.86 and 0.95 across pollutants), with a significant modifying influence of meteorological regime. Near-source population is more important for primary coarse particles while population at medium to long distance is more important for primary fine particles and secondary particles. A significant portion of intake fraction (especially for secondary particles and primary fine particles) occurs beyond 500 km of the source, emphasizing the need for detailed long-range dispersion modeling. These findings demonstrate that intake fractions for power plants in China can be estimated with reasonable precision and summarized using simple regression models. The results should be useful for informing future decisions about power-plant locations and controls.

Intake fractions of industrial air pollutants in China: estimation and application

Intake fractions, an emissions-intake relationship for primary pollutants, are defined and are estimated in order to make simple estimates of health damages from air pollution. The sulfur dioxide (SO2) and total suspended particles (TSP) intake fractions for five cities of China are estimated for the four main polluting industries-electric power generation, mineral (mostly cement) products industry, chemical process industry and metallurgical industry (mainly iron and steel smelting). The Industrial Source Complex Long Term (ISTLT3) model is used to simulate the spatial distribution of incremental ambient concentrations due to emissions from a large sample of site-specific sources. Detailed population distribution information is used for each city. The average intake fractions within 50 km of these sources are 4.4x10(-6) for TSP, and 4.2x10(-6) for SO2, with standard deviations of 8.15x10(-6) and 9.16x10(-6), respectively. They vary over a wide range, from 10(-7) to 10(-5). Although the electric power generation has been the focus of much of the air pollution research in China, our results show that it has the lowest average intake fraction for a local range among the four industries, which highlights the importance of pollutant emissions from other industrial sources. Sensitivity analyses show how the intake fractions are affected by the source and pollutant characteristics, the most important parameter being the size of the domain. However, the intake fraction estimates are robust enough to be useful for evaluating the local impacts on human health of primary SO2 and TSP emissions. An application of intake fractions is given to demonstrate how this approach provides a rapid population risk estimate if the dose-response function is linear without threshold, and hence can help in prioritizing pollution control efforts.

The public health benefits of insulation retrofits in existing housing in the United States

BACKGROUND

Methodological limitations make it difficult to quantify the public health benefits of energy efficiency programs. To address this issue, we developed a risk-based model to estimate the health benefits associated with marginal energy usage reductions and applied the model to a hypothetical case study of insulation retrofits in single-family homes in the United States.

METHODS

We modeled energy savings with a regression model that extrapolated findings from an energy simulation program. Reductions of fine particulate matter (PM2.5) emissions and particle precursors (SO2 and NOx) were quantified using fuel-specific emission factors and marginal electricity analyses. Estimates of population exposure per unit emissions, varying by location and source type, were extrapolated from past dispersion model runs. Concentration-response functions for morbidity and mortality from PM2.5 were derived from the epidemiological literature, and economic values were assigned to health outcomes based on willingness to pay studies.

RESULTS

In total, the insulation retrofits would save 800 TBTU (8 x 10(14) British Thermal Units) per year across 46 million homes, resulting in 3,100 fewer tons of PM2.5, 100,000 fewer tons of NOx, and 190,000 fewer tons of SO2 per year. These emission reductions are associated with outcomes including 240 fewer deaths, 6,500 fewer asthma attacks, and 110,000 fewer restricted activity days per year. At a state level, the health benefits per unit energy savings vary by an order of magnitude, illustrating that multiple factors (including population patterns and energy sources) influence health benefit estimates. The health benefits correspond to 1.3 billion dollars per year in externalities averted, compared with 5.9 billion dollars per year in economic savings.

CONCLUSION

In spite of significant uncertainties related to the interpretation of PM2.5 health effects and other dimensions of the model, our analysis demonstrates that a risk-based methodology is viable for national-level energy efficiency programs.

Exposure efficiency: an idea whose time has come?

Exposure efficiency, the fraction of material released from a source that is eventually inhaled or ingested, is arguably the simplest of all possible descriptions of the link between pollutant emissions and population exposures. This paper, prepared in late 1999 for the SGOMSEC Workshop, notes that several groups of researchers independently developed the concept of exposure efficiency in the late 1980s and early 1990s but argues that the potential importance of exposure efficiency in risk analysis and life cycle assessment has only recently been appreciated. The paper reviews the history of the concept; discusses and summarizes previous estimates of exposure efficiency for particulate matter and other air pollutants; presents new values for fine particulate matter emitted from power plants and mobile sources in the United States; and illustrates how preliminary estimates of exposure efficiency might be developed. The authors assert that in order for the concept of exposure efficiency to achieve its full potential exposure efficiency estimates for a wide variety of pollutants and sources must be developed and that both the results and methods must be made widely available and accessible to the community of risk assessors and life cycle analysts.

Life cycle impact assessment: a challenge for risk analysts

Modern technology, together with an advanced economy, can provide a good or service in myriad ways, giving us choices on what to produce and how to produce it. To make those choices more intelligently, society needs to know not only the market price of each alternative, but the associated health and environmental consequences. A fair comparison requires evaluating the consequences across the whole "life cycle"--from the extraction of raw materials and processing to manufacture/construction, use, and end-of-life--of each alternative. Focusing on only one stage (e.g., manufacture) of the life cycle is often misleading. Unfortunately, analysts and researchers still have only rudimentary tools to quantify the materials and energy inputs and the resulting damage to health and the environment. Life cycle assessment (LCA) provides an overall framework for identifying and evaluating these implications. Since the 1960s, considerable progress has been made in developing methods for LCA, especially in characterizing, qualitatively and quantitatively, environmental discharges. However, few of these analyses have attempted to assess the quantitative impact on the environment and health of material inputs and environmental discharges Risk analysis and LCA are connected closely. While risk analysis has characterized and quantified the health risks of exposure to a toxicant, the policy implications have not been clear. Inferring that an occupational or public health exposure carries a nontrivial risk is only the first step in formulating a policy response. A broader framework, including LCA, is needed to see which response is likely to lower the risk without creating high risks elsewhere. Even more important, LCA has floundered at the stage of translating an inventory of environmental discharges into estimates of impact on health and the environment. Without the impact analysis, policymakers must revert to some simple rule, such as that all discharges, regardless of which chemical, which medium, and where they are discharged, are equally toxic. Thus, risk analysts should seek LCA guidance in translating a risk analysis into policy conclusions or even advice to those at risk. LCA needs the help of RA to go beyond simplistic assumptions about the implications of a discharge inventory. We demonstrate the need and rationale for LCA, present a brief history of LCA, present examples of the application of this tool, note the limitations of LCA models, and present several methods for incorporating risk assessment into LCA. However, we warn the reader not to expect too much. A comprehensive comparison of the health and environmental implications of alternatives is beyond the state of the art. LCA is currently not able to provide risk analysts with detailed information on the chemical form and location of the environmental discharges that would allow detailed estimation of the risks to individuals due to toxicants. For example, a challenge for risk analysts is to estimate health and other risks where the location and chemical speciation are not characterized precisely. Providing valuable information to decisionmakers requires advances in both LCA and risk analysis. These two disciplines should be closely linked, since each has much to contribute to the other.