Developing intake fraction estimates with limited data: Comparison of methods in Mexico City

In order to estimate the health benefits of reducing mobile source emissions, analysts typically use detailed atmospheric models to estimate the change in population exposure that results from a given change in emissions. However, this may not be feasible in settings where data are limited or policy decisions are needed in the short term. Intake fraction (iF), defined as the fraction of emissions of a pollutant or its precursor that is inhaled by the population, is a metric that can be used to compare exposure assessment methods in a health benefits analysis context. To clarify the utility of rapid-assessment methods, we calculate particulate matter iFs for the Mexico City Metropolitan Area using five methods, some more resource intensive than others. First, we create two simple box models to describe dispersion of primary fine particulate matter (PM_2.5) in the Mexico City basin. Second, we extrapolate iFs for primary PM_2.5, ammonium sulfate, and ammonium nitrate from US values using a regression model. Third, we calculate iFs by assuming a linear relationship between emissions and population-weighted concentrations of primary PM_2.5, ammonium nitrate, and ammonium sulfate (a particle composition method). Finally, we estimate PM iFs from detailed atmospheric dispersion and chemistry models run for only a short period of time. Intake fractions vary by up to a factor of five, from 23 to 120 per million for primary PM_2.5. Estimates of 60, 7, and 0.7 per million for primary PM, secondary ammonium sulfate, and secondary ammonium nitrate, respectively, represent credible central estimates, with an approximate factor of two uncertainty surrounding each estimate. Our results emphasize that multiple rapid-assessment methods can provide meaningful estimates of iFs in resource-limited environments, and that formal uncertainty analysis, with special attention to model biases and uncertainty, would be important for health benefits analyses.     

Air quality impacts of distributed energy resources implemented in the northeastern United States

Emissions from the potential installation of distributed energy resources (DER) in the place of current utility-scale power generators have been introduced into an emissions inventory of the northeastern United States. A methodology for predicting future market penetration of DER that considers economics and emission factors was used to estimate the most likely implementation of DER. The methodology results in spatially and temporally resolved emission profiles of criteria pollutants that are subsequently introduced into a detailed atmospheric chemistry and transport model of the region. The DER technology determined by the methodology includes 62% reciprocating engines, 34% gas turbines, and 4% fuel cells and other emerging technologies. The introduction of DER leads to retirement of 2625 MW of existing power plants for which emissions are removed from the inventory. The air quality model predicts maximum differences in air pollutant concentrations that are located downwind from the central power plants that were removed from the domain. Maximum decreases in hourly peak ozone concentrations due to DER use are 10 ppb and are located over the state of New Jersey. Maximum decreases in 24-hr average fine particulate matter (PM2.5) concentrations reach 3 microg/m3 and are located off the coast of New Jersey and New York. The main contribution to decreased PM2.5 is the reduction of sulfate levels due to significant reductions in direct emissions of sulfur oxides (SO(x)) from the DER compared with the central power plants removed. The scenario presented here represents an accelerated DER penetration case with aggressive emission reductions due to removal of highly emitting power plants. Such scenario provides an upper bound for air quality benefits of DER implementation scenarios.     

Reduction of atmospheric emissions due to switching from fuel oil to natural gas at a power plant in a critical area in Central Mexico

ABSTRACT

A case study was conducted to evaluate the SO₂ emission reduction in a power plant in Central Mexico, as a result of the shifting of fuel oil to natural gas. Emissions of criteria pollutants, greenhouse gases, organic and inorganic toxics were estimated based on a 2010 report of hourly fuel oil consumption at the “Francisco Pérez Ríos” power plant in Tula, Mexico. For SO₂, the dispersion of these emissions was assessed with the CALPUFF dispersion model. Emissions reductions of > 99% for SO₂, PM and Pb, as well as reductions >50% for organic and inorganic toxics were observed when simulating the use of natural gas. Maximum annual (993 µg/m³) and monthly average SO₂ concentrations were simulated during the cold-dry period (152–1063 µg/m³), and warm-dry period (239–432 µg/m³). Dispersion model results and those from Mexico City’s air quality forecasting system showed that SO₂ emissions from the power plant affect the north of Mexico City in the cold-dry period. The evaluation of model estimates with 24 hr SO₂ measured concentrations at Tepeji del Rio suggests that the combination of observations and dispersion models are useful in assessing the reduction of SO₂ emissions due to shifting in fuels. Being SO₂ a major precursor of acid rain, high transported sulfate concentrations are of concern and low pH values have been reported in the south of Mexico City, indicating that secondary SO₂ products emitted in the power plant can be transported to Mexico City under specific atmospheric conditions.

Implications: Although the surroundings of a power plant located north of Mexico City receives most of the direct SO₂ impact from fuel oil emissions, the plume is dispersed and advected to the Mexico City metropolitan area, where its secondary products may cause acid rain. The use of cleaner fuels may assure significant SO₂ reductions in the plant emissions and consequent acid rain presence in nearby populated cities and should be compulsory in critical areas to comply with annual emission limits and health standards.     

An integrated simulation and optimization approach for managing human health risks of atmospheric pollutants by coal-fired power plants

This research developed a simulation-aided nonlinear programming model (SNPM). This model incorporated the consideration of pollutant dispersion modeling, and the management of coal blending and the related human health risks within a general modeling framework. In SNPM, the simulation effort (i.e., California puff [CALPUFF]) was used to forecast the fate of air pollutants for quantifying the health risk under various conditions, while the optimization studies were to identify the optimal coal blending strategies from a number of alternatives. To solve the model, a surrogate-based indirect search approach was proposed, where the support vector regression (SVR) was used to create a set of easy-to-use and rapid-response surrogates for identifying the function relationships between coal-blending operating conditions and health risks. Through replacing the CALPUFF and the corresponding hazard quotient equation with the surrogates, the computation efficiency could be improved. The developed SNPM was applied to minimize the human health risk associated with air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicated that it could be used for reducing the health risk of the public in the vicinity of the two power plants, identifying desired coal blending strategies for decision makers, and considering a proper balance between coal purchase cost and human health risk.

Implications:A simulation-aided nonlinear programming model (SNPM) is developed. It integrates the advantages of CALPUFF and nonlinear programming model. To solve the model, a surrogate-based indirect search approach based on the combination of support vector regression and genetic algorithm is proposed. SNPM is applied to reduce the health risk caused by air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicate that it is useful for generating coal blending schemes, reducing the health risk of the public, reflecting the trade-off between coal purchase cost and health risk.     

Study of the relationship of distant SO2 emissions to Dallas-Fort Worth winter haze

A study was conducted to estimate the changes in wintertime visual air quality in Dallas-Fort Worth (DFW) that might occur due to proposed reductions in SO2 emissions at two steam electric generating plants in eastern Texas, each over 100 km from the city. To provide information for designing subsequent investigations, the haze was characterized broadly during the first year of the study. Meteorological data acquired then demonstrated that, during haze episodes, emissions from only one of the two plants were likely to be transported directly to DFW. Therefore, the second year of the study was centered on just one of the power plants. Air quality was then characterized within the urban area and at rural locations that would be upwind and downwind of the plant during transport to DFW. An instrumented aircraft measured plume dispersion and the air surrounding the plume on selected days. A mathematical model was used to predict the change that would occur in airborne particulate matter concentrations in DFW if SO2 emissions were reduced to reflect the proposed limitations. The contribution of particles in the atmosphere to light extinction was estimated, and simulated photographs were produced to illustrate the visibility changes. The study concluded that the proposed emission reductions would, at most, subtly change perceived wintertime visibility.     

Study of the Relationship of Distant SO2 Emissions to Dallas-Fort Worth Winter Haze

ABSTRACT

A study was conducted to estimate the changes in wintertime visual air quality in Dallas-Fort Worth (DFW) that might occur due to proposed reductions in SO₂ emissions at two steam electric generating plants in eastern Texas, each over 100 km from the city. To provide information for designing subsequent investigations, the haze was characterized broadly during the first year of the study. Meteorological data acquired then demonstrated that, during haze episodes, emissions from only one of the two plants were likely to be transported directly to DFW. Therefore, the second year of the study was centered on just one of the power plants. Air quality was then characterized within the urban area and at rural locations that would be upwind and downwind of the plant during transport to DFW. An instrumented aircraft measured plume dispersion and the air surrounding the plume on selected days. A mathematical model was used to predict the change that would occur in airborne particulate matter concentrations in DFW if SO₂ emissions were reduced to reflect the proposed limitations. The contribution of particles in the atmosphere to light extinction was estimated, and simulated photographs were produced to illustrate the visibility changes. The study concluded that the proposed emission reductions would, at most, subtly change perceived wintertime visibility.     

Air quality impacts of power plant emissions in Beijing

The CALMET/CALPUFF modeling system was applied to estimate the air quality impacts of power plants in 2000 and 2008 in Beijing, and the intake fractions (IF) were calculated to see the public health risks posed. Results show that in 2000 the high emission contribution induced a relatively small contribution to average ambient concentration and a significant impact on the urban area (9.52 microg/m(3) of SO(2) and 5.29 microg/m(3) of NO(x)). The IF of SO(2), NO(x) and PM(10) are 7.4 x 10(-6), 7.4 x 10(-6) and 8.7 x 10(-5), respectively. Control measures such as fuel substitution, flue gas desulfurization, dust control improvement and flue gas denitration planned before 2008 will greatly mitigate the SO(2) and PM(10) pollution, especially alleviating the pressure on the urban area to reach the National Ambient Air Quality Standard (NAAQS). NO(x) pollution will be mitigated with 34% decrease in concentration but further controls are still needed.     

Application of a combined measurement and modeling method to quantify windblown dust emissions from the exposed playa at Mono Lake, California

Particulate matter < or =10 microm (PM10) emissions due to wind erosion can vary dramatically with changing surface conditions. Crust formation, mechanical disturbance, soil texture, moisture, and chemical content of the soil can affect the amount of dust emitted during a wind event. A refined method of quantifying windblown dust emissions was applied at Mono Lake, CA, to account for changing surface conditions. This method used a combination of real-time sand flux monitoring, ambient PM10 monitoring, and dispersion modeling to estimate dust emissions and their downwind impact. The method identified periods with high emissions and periods when the surface was stable (no sand flux), even though winds may have been high. A network of 25 Cox sand catchers (CSCs) was used to measure the mass of saltating particles to estimate sand flux rates across a 2-km2 area. Two electronic sensors (Sensits) were used to time-resolve the CSC sand mass to estimate hourly sand flux rates, and a perimeter tapered element oscillating microbalance (TEOM) monitor measured hourly PM10 concentrations. Hourly sand flux rates were related by dispersion modeling to hourly PM10 concentrations to back-calculate the ratio of vertical PM10 flux to horizontal sand flux (K-factors). Geometric mean K-factor values (K(f)) were found to change seasonally, ranging from 1.3 x 10(-5) to 5.1 x 10(-5) for sand flux measured at 15 cm above the surface (q15). Hourly PM10 emissions, F, were calculated by applying seasonal K-factors to sand flux measurements (F = K(f) x q15). The maximum hourly PM10 emission rate from the study area was 76 g/m2 x hr (10-m wind speed = 23.5 m/sec). Maximum daily PM10 emissions were estimated at 450 g/m2 x day, and annual emissions at 1095 g/m2 x yr. Hourly PM10 emissions were used by the U.S. Environmental Protection Agency (EPA) guideline AERMOD dispersion model to estimate downwind ambient impacts. Model predictions compared well with monitor concentrations, with hourly PM10 ranging from 16 to over 60,000 microg/m3 (slope = 0.89, R2 = 0.77).     

The sensitivity of PM2.5 source-receptor relationships to atmospheric chemistry and transport in a three-dimensional air quality model

Air quality model simulations constitute an effective approach to developing source-receptor relationships (so-called transfer coefficients in the risk analysis framework) because a significant fraction of particulate matter (particularly PM2.5) is secondary (i.e., formed in the atmosphere) and, therefore, depends on the atmospheric chemistry of the airshed. In this study, we have used a comprehensive three-dimensional air quality model for PM2.5 (SAQM-AERO) to compare three approaches to generating episodic transfer coefficients for several source regions in the Los Angeles Basin. First, transfer coefficients were developed by conducting PM2.5 SAQM-AERO simulations with reduced emissions of one of four precursors (i.e., primary PM, sulfur dioxide (SO2), oxides of nitrogen (NOx), and volatile organic compounds) from each source region. Next, we calculated transfer coefficients using two other methods: (1) a simplified chemistry for PM2.5 formation, and (2) simplifying assumptions on transport using information limited to basin-wide emission reductions. Transfer coefficients obtained with the simplified chemistry were similar to those obtained with the comprehensive model for VOC emission changes but differed for NOx and SOz emission changes. The differences were due to the parameterization of the rates of secondary PM formation in the simplified chemistry. In 90% of the cases, transfer coefficients estimated using only basin-wide information were within a factor of two of those obtained with the explicit source-receptor simulations conducted with the comprehensive model. The best agreement was obtained for VOC emission changes; poor agreement was obtained for primary PM2.5.     

Air Quality Impacts of Distributed Energy Resources Implemented in the Northeastern United States

Abstract

Emissions from the potential installation of distributed energy resources (DER) in the place of current utility-scale power generators have been introduced into an emissions inventory of the northeastern United States. A methodology for predicting future market penetration of DER that considers economics and emission factors was used to estimate the most likely implementation of DER. The methodology results in spatially and temporally resolved emission profiles of criteria pollutants that are subsequently introduced into a detailed atmospheric chemistry and transport model of the region. The DER technology determined by the methodology includes 62% reciprocating engines, 34% gas turbines, and 4% fuel cells and other emerging technologies. The introduction of DER leads to retirement of 2625 MW of existing power plants for which emissions are removed from the inventory. The air quality model predicts maximum differences in air pollutant concentrations that are located downwind from the central power plants that were removed from the domain. Maximum decreases in hourly peak ozone concentrations due to DER use are 10 ppb and are located over the state of New Jersey. Maximum decreases in 24-hr average fine particulate matter (PM_2.5) concentrations reach 3 μg/m3 and are located off the coast of New Jersey and New York. The main contribution to decreased PM_2.5 is the reduction of sulfate levels due to significant reductions in direct emissions of sulfur oxides (SO_x) from the DER compared with the central power plants removed. The scenario presented here represents an accelerated DER penetration case with aggressive emission reductions due to removal of highly emitting power plants. Such scenario provides an upper bound for air quality benefits of DER implementation scenarios.     

Development of a microscale emission factor model for particulate matter for predicting real-time motor vehicle emissions

The U.S. Environmental Protection Agency's National Exposure Research Laboratory is pursuing a project to improve the methodology for modeling human exposure to motor vehicle emissions. The overall project goal is to develop improved methods for modeling the source through the air pathway to human exposure in significant exposure microenvironments. Current particulate matter (PM) emission models, particle emission factor model (used in the United States, except California) and motor vehicle emission factor model (used in California only), are suitable only for county-scale modeling and emission inventories. There is a need to develop a site-specific real-time emission factor model for PM emissions to support human exposure studies near roadways. A microscale emission factor model for predicting site-specific real-time motor vehicle PM (MicroFacPM) emissions for total suspended PM, PM less than 10 microm aerodynamic diameter, and PM less than 2.5 microm aerodynamic diameter has been developed. The algorithm used to calculate emission factors in MicroFacPM is disaggregated, and emission factors are calculated from a real-time fleet, rather than from a fleet-wide average estimated by a vehicle-miles-traveled weighting of the emission factors for different vehicle classes. MicroFacPM requires input information necessary to characterize the site-specific real-time fleet being modeled. Other variables required include average vehicle speed, time and day of the year, ambient temperature, and relative humidity.     

Development of a GIS-based decision support system for urban air quality management in the city of Istanbul

A decision support system has been developed for urban air quality management in the metropolitan area of Istanbul. The system is based on CALMET/CALPUFF dispersion modeling system, digital maps, and related databases to estimate the emissions and spatial distribution of air pollutants with the help of a GIS software. The system estimates ambient air pollution levels at high temporal and spatial resolutions and enables mapping of emissions and air quality levels. Mapping and scenario results can be compared with air quality limits. Impact assessment of air pollution abatement measures can also be carried out.     

Natural emissions for regional modeling of background ozone and particulate matter and impacts on emissions control strategies

Natural emissions adopted in current regional air quality modeling are updated to better describe natural background ozone and PM concentrations for North America. The revised natural emissions include organosulfur from the ocean, NO from lightning, sea salt, biogenic secondary organic aerosol (SOA) precursors, and pre-industrial levels of background methane. The model algorithm for SOA formation was also revised. Natural background ozone concentrations increase by up to 4 ppb in annual average over the southeastern US and Gulf of Mexico due to added NO from lightning while the revised biogenic emissions produced less ozone in the central and western US. Natural PM_2.5 concentrations generally increased with the revised natural emissions. Future year (2018) simulations were conducted for several anthropogenic emission reduction scenarios to assess the impact of the revised natural emissions on anthropogenic emission control strategies. Overall, the revised natural emissions did not significantly alter the ozone responses to the emissions reductions in 2018. With revised natural emissions, ozone concentrations were slightly less sensitive to reducing NOx in the southeastern US than with the current natural emissions due to higher NO from lightning. The revised natural emissions have little impact on modeled PM_2.5 responses to anthropogenic emission reductions. However, there are substantial uncertainties in current representations of natural sources in air quality models and we recommend that further study is needed to refine these representations.     

The sensitivity of the CHIMERE model to emissions reduction scenarios on air quality in Northern Italy

The sensitivity of the CHIMERE model to emission reduction scenarios on particulate matter PM2.5 and ozone (O₃) in Northern Italy is studied. The emissions of NO_x, PM2.5 SO₂, VOC or NH₃ were reduced by 50% for different source sectors for the Lombardy region, together with 5 additional scenarios to estimate the effect of local measures on improving the air quality for the Po valley area. Firstly, we evaluate the model performance by comparing calculated surface aerosol concentrations for the standard case (no emission reductions) with observations for January and June 2005. Calculated monthly mean PM10 concentrations are in general underestimated. For June, modelled PM10 concentrations slightly overestimate the measurements. Calculated monthly mean SO₄, NO₃^?, NH₄⁺ concentrations are in good agreement with the observations for January and June. Secondly, the model sensitivity of emission reduction scenarios on PM2.5 and O₃ calculated concentrations for the Po valley area is evaluated. The most effective scenarios to abate PM2.5 concentration are based on the SNAP2 (non-industrial combustion plants) and SNAP7 (road traffic) sectors, for which the NO_x and PM2.5 emissions are reduced by 50%. The number of days that the 2015 PM2.5 limit value of 25 μg m^?3 in Milan is exceeded by reducing primary PM2.5 and NO_x emissions for SNAP2 and 7 by 50%, does not change in January when compared to the standard case for the Milan area. It appears that 40% of the PM2.5 concentration in the greater Milan area is caused by the emissions surrounding the Lombardy region and from the model boundary conditions.This study also showed that a more effective pollutant reduction (emissions) per ton of pollutant reduced (concentrations) for the greater Milan area is obtained by reducing the primary PM2.5 emissions for SNAP7 by 50%. The most effective scenario on PM2.5 decrease for which precursor emissions are reduced is achieved by reducing SO₂ emissions by 50% for SNAP7.Our study showed that during summer time, the largest reductions in O₃ concentrations are achieved for SNAP7 emission reductions, when volatile organic compounds (VOCs) are reduced by 50%.     

Evaluation of particulate matter abatement strategies for almond harvest

Almond harvest accounts for substantial PM10 (particulate matter [PM] < or =10 microm in nominal aerodynamic diameter) emissions in California each harvest season. This paper evaluates the effects of using reduced-pass sweepers and lower harvester separation fan speeds (930 rpm) on lowering PM emissions from almond harvesting operations. In-canopy measurements of PM concentrations were collected along with PM concentration measurements at the orchard boundary; these were used in conjunction with on-site meteorological data and inverse dispersion modeling to back-calculate emission rates from the measured concentrations. The harvester discharge plume was measured as a function of visible plume opacity during conditioning operations. Reduced-pass sweeping showed the potential for reducing PM emissions, but results were confounded because of differences in orchard maturity and irrigation methods. Fuel consumption and sweeping time per unit area were reduced when comparing a reduced-pass sweeper to a conventional sweeper. Reducing the separation fan speed from 1080 to 930 rpm led to reductions in PM emissions. In general, foreign matter levels within harvested product were nominally affected by separation fan speed in the south (less mature) orchard; however, in samples conditioned using the lower fan speed from the north (more mature) orchard, these levels were unacceptable.     

The benefits of lower ozone due to air pollution emission reductions (2002–2011) in the Eastern United States during extreme heat

ABSTRACT

Using the Community Multiscale Air Quality (CMAQ) model and the Benefits Mapping and Analysis Program – Community Edition (BenMAP-CE) tool, we estimate the benefits of anthropogenic emission reductions between 2002 and 2011 in the Eastern United States (US) with respect to surface ozone concentrations and ozone-related health and economic impacts, during a month of extreme heat, July 2011. Based on CMAQ simulations using emissions appropriate for 2002 and 2011, we estimate that emission reductions since 2002 likely prevented 10– 15 ozone exceedance days (using the 2011 maximum 8-hr average ozone standard of 75 ppbv) throughout the Ohio River Valley and 5– 10 ozone exceedance days throughout the Washington, DC – Baltimore, MD metropolitan area during this extremely hot month. CMAQ results were fed into the BenMAP-CE tool to determine the health and health-related economic benefits of anthropogenic emission reductions between 2002 and 2011. We estimate that the concomitant health benefits from the ozone reductions were significant for this anomalous month: 160–800 mortalities (95% confidence interval (CI): 70–1,010) were avoided in July 2011 in the Eastern U.S, saving an estimated $1.3–$6.6 billion (CI: $174 million–$15.5 billion). Additionally, we estimate that emission reductions resulted in 950 (CI: 90–2,350) less hospital admissions from respiratory symptoms, 370 (CI: 180–580) less hospital admissions for pneumonia, 570 (CI: 0–1650) less Emergency Room (ER) visits from asthma symptoms, 922,020 (CI: 469,960–1,370,050) less minor restricted activity days (MRADs), and 430,240 (CI: ?280,350–963,190) less symptoms of asthma exacerbation during July 2011.

Implications: We estimate the benefits of air pollution emission reductions on surface ozone concentrations and ozone-related impacts on human health and the economy between 2002 and 2011 during an extremely hot month, July 2011, in the eastern United States (US) using the CMAQ and BenMAP-CE models. Results suggest that, during July 2011, emission reductions prevented 10-15 ozone exceedance days in the Ohio River Valley and 5-10 ozone exceedance days in the Mid Atlantic; saved 160-800 lives in the Eastern US, saving $1.3 - $6.5 billion; and resulted in 950 less hospital admissions for respiratory symptoms, 370 less hospital admissions for pneumonia, 570 less Emergency Room visits for asthma symptoms, 922,020 less minor restricted activity days, and 430,240 less symptoms of asthma exacerbation.     

Impact of biogenic emission uncertainties on the simulated response of ozone and fine particulate matter to anthropogenic emission reductions

The role of emissions of volatile organic compounds and nitric oxide from biogenic sources is becoming increasingly important in regulatory air quality modeling as levels of anthropogenic emissions continue to decrease and stricter health-based air quality standards are being adopted. However, considerable uncertainties still exist in the current estimation methodologies for biogenic emissions. The impact of these uncertainties on ozone and fine particulate matter (PM2.5) levels for the eastern United States was studied, focusing on biogenic emissions estimates from two commonly used biogenic emission models, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the Biogenic Emissions Inventory System (BEIS). Photochemical grid modeling simulations were performed for two scenarios: one reflecting present day conditions and the other reflecting a hypothetical future year with reductions in emissions of anthropogenic oxides of nitrogen (NOx). For ozone, the use of MEGAN emissions resulted in a higher ozone response to hypothetical anthropogenic NOx emission reductions compared with BEIS. Applying the current U.S. Environmental Protection Agency guidance on regulatory air quality modeling in conjunction with typical maximum ozone concentrations, the differences in estimated future year ozone design values (DVF) stemming from differences in biogenic emissions estimates were on the order of 4 parts per billion (ppb), corresponding to approximately 5% of the daily maximum 8-hr ozone National Ambient Air Quality Standard (NAAQS) of 75 ppb. For PM2.5, the differences were 0.1-0.25 microg/m3 in the summer total organic mass component of DVFs, corresponding to approximately 1-2% of the value of the annual PM2.5 NAAQS of 15 microg/m3. Spatial variations in the ozone and PM2.5 differences also reveal that the impacts of different biogenic emission estimates on ozone and PM2.5 levels are dependent on ambient levels of anthropogenic emissions.     

Emission Impacts of Electric Vehicles

Alternative vehicular fuels are proposed as a strategy to reduce urban air pollution. In this paper, we analyze the emission Impacts of electric vehicles In California for two target years, 1995 and 2010. We consider a range of assumptions regarding electricity consumption of electric vehicles, emission control technologies for power plants, and the mix of primary energy sources for electricity generation. We find that, relative to continued use of gasoline-powered vehicles, the use of electric vehicles would dramatically and unequivocally reduce carbon monoxide and hydrocarbons. Under most conditions, nitrogen oxide emissions would decrease moderately. Sulfur oxide and particulate emissions would Increase or slightly decrease. Because other areas of the United States tend to use more coal in electricity generation and have less stringent emission controls on power plants, electric vehicles may have less emission reduction benefits outside California.     

Updating the conceptual model for fine particle mass emissions from combustion systems

Atmospheric transformations determine the contribution of emissions from combustion systems to fine particulate matter (PM) mass. For example, combustion systems emit vapors that condense onto existing particles or form new particles as the emissions are cooled and diluted. Upon entering the atmosphere, emissions are exposed to atmospheric oxidants and sunlight, which causes them to evolve chemically and physically, generating secondary PM. This review discusses these transformations, focusing on organic PM. Organic PM emissions are semi-volatile at atmospheric conditions and thus their partitioning varies continuously with changing temperature and concentration. Because organics contribute a large portion of the PM mass emitted by most combustion sources, these emissions cannot be represented using a traditional, static emission factor. Instead, knowledge of the volatility distribution of emissions is required to explicitly account for changes in gas-particle partitioning. This requires updating how PM emissions from combustion systems are measured and simulated from combustion systems. Secondary PM production often greatly exceeds the direct or primary PM emissions; therefore, secondary PM must be included in any assessment of the contribution of combustion systems to ambient PM concentrations. Low-volatility organic vapors emitted by combustion systems appear to be very important secondary PM precursors that are poorly accounted for in inventories and models. The review concludes by discussing the implications that the dynamic nature of these PM emissions have on source testing for emission inventory development and regulatory purposes. This discussion highlights important linkages between primary and secondary PM, which could lead to simplified certification test procedures while capturing the emission components that contribute most to atmospheric PM mass.