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.     

Uncertainty in health risks due to anthropogenic primary fine particulate matter from different source types in Finland

The emission-exposure and exposure-response (toxicity) relationships are different for different emission source categories of anthropogenic primary fine particulate matter (PM_2.5). These variations have a potentially crucial importance in the integrated assessment, when determining cost-effective abatement strategies. We studied the importance of these variations by conducting a sensitivity analysis for an integrated assessment model. The model was developed to estimate the adverse health effects to the Finnish population attributable to primary PM_2.5 emissions from the whole of Europe. The primary PM_2.5 emissions in the whole of Europe and in more detail in Finland were evaluated using the inventory of the European Monitoring and Evaluation Programme (EMEP) and the Finnish Regional Emission Scenario model (FRES), respectively. The emission-exposure relationships for different primary PM_2.5 emission source categories in Finland have been previously evaluated and these values incorporated as intake fractions into the integrated assessment model. The primary PM_2.5 exposure-response functions and toxicity differences for the pollution originating from different source categories were estimated in an expert elicitation study performed by six European experts on air pollution health effects. The primary PM_2.5 emissions from Finnish and other European sources were estimated for the population of Finland in 2000 to be responsible for 209 (mean, 95% confidence interval 6–739) and 357 (mean, 95% CI 8–1482) premature deaths, respectively. The inclusion of emission-exposure and toxicity variation into the model increased the predicted relative importance of traffic related primary PM_2.5 emissions and correspondingly, decreased the predicted relative importance of other emission source categories. We conclude that the variations of emission-exposure relationship and toxicity between various source categories had significant impacts for the assessment on premature deaths caused by primary PM_2.5.     

Spatial patterns of mobile source particulate matter emissions-to-exposure relationships across the United States

Assessing the public health benefits from air pollution control measures is assisted by understanding the relationship between mobile source emissions and subsequent fine particulate matter (PM_2.5) exposure. Since this relationship varies by location, we characterized its magnitude and geographic distribution using the intake fraction (iF) concept. We considered emissions of primary PM_2.5 as well as particle precursors SO₂ and NO_x from each of 3080 counties in the US. We modeled the relationship between these emissions and total US population exposure to PM_2.5, making use of a source–receptor matrix developed for health risk assessment. For primary PM_2.5, we found a median iF of 1.2 per million, with a range of 0.12–25. Half of the total exposure was reached by a median distance of 150 km from the county where mobile source emissions originated, though this spatial extent varied across counties from within the county borders to 1800 km away. For secondary ammonium sulfate from SO₂ emissions, the median iF was 0.41 per million (range: 0.050–10), versus 0.068 per million for secondary ammonium nitrate from NO_x emissions (range: 0.00092–1.3). The median distance to half of the total exposure was greater for secondary PM_2.5 (450 km for sulfate, 390 km for nitrate). Regression analyses using exhaustive population predictors explained much of the variation in primary PM_2.5 iF (R²=0.83) as well as secondary sulfate and nitrate iF (R²=0.74 and 0.60), with greater near-source contribution for primary than for secondary PM_2.5. We conclude that long-range dispersion models with coarse geographic resolution are appropriate for risk assessments of secondary PM_2.5 or primary PM_2.5 emitted from mobile sources in rural areas, but that more resolved dispersion models are warranted for primary PM_2.5 in urban areas due to the substantial contribution of near-source populations.     

Intake fraction distributions for benzene from vehicles in the Helsinki metropolitan area

The intake fraction (iF) gives a measure of the portion of a source's emissions that is inhaled by an exposed population over a defined period of time. This study examines spatial and population-based iF distributions of a known human carcinogen, benzene, from a ubiquitous urban source, local vehicular traffic, in the Helsinki Metropolitan Area using three computational methods. The first method uses the EXPAND model (EXPosure to Air pollution, especially to Nitrogen Dioxide and particulate matter), which incorporates spatial and temporal information on population activity patterns as well as urban-scale and street canyon dispersion models to predict spatial population exposure distributions. The second method uses data from the personal monitoring study EXPOLIS (Air Pollution Exposure Distributions of Adult Urban Populations in Europe) to estimate the intake fractions for individuals in the study. The third method, a one-compartment box model provides estimates within an order-of-magnitude or better for non-reactive agents in an urban area. Population intake fractions are higher using the personal monitoring data method (median iF 30 per million, mean iF 39 per million) compared with the spatial model (annual mean iF 10 per million) and the box model (median iF 4 per million, mean iF 7 per million). In particular, this study presents detailed intake fraction distributions on several different levels (spatial, individual, and generic) for the same urban area.     

PM2.5 Assessment in 21 European Study Centers of ECRHS II: Method and First Winter Results

Abstract

The follow-up of a cohort of adults from 29 European centers of the former European Community Respiratory Health Survey (ECRHS) I (1989–1992) will examine the long-term effects of exposure to ambient air pollution on the incidence, course, and prognosis of respiratory diseases, in particular asthma and decline in lung function. The purpose of this article is to describe the methodology and the European-wide quality control program for the collection of particles with 50% cut-off size of 2.5 µm aerodynamic diameter (PM_2.5 ) in the ECRHS II and to present the PM_2.5 results from the winter period 2000–2001.

Because PM_2.5 is not routinely monitored in Europe, we measured PM_2.5 mass concentrations in 21 participating centers to estimate background exposure in these cities. A standardized protocol was developed using identical equipment in each center (U.S. Environmental Protection Agency Well Impactor Ninety-Six [WINS] and PQ167 from BGI, Inc.). Filters were weighed in a single central laboratory. Sampling was conducted for 7 days per month for a year.

Winter mean PM_2.5 mass concentrations (November 2000–February 2001) varied substantially, with Iceland reporting the lowest value (5 µg/m³) and northern Italy the highest (69 µg/m³). A standardized procedure appropriate for PM_2.5 exposure assessmnt in a multicenter study was developed. We expect ECRHS II to have sufficient variation in exposure to assess long-term effects of air pollution in this cohort. Any bias caused by variation in the characteristics of the chosen monitoring location (e.g., proximity to traffic sources) will be addressed in later analyses. Given the homogenous spatial distribution of PM_2.5 , however, concentrations measured near traffic are not expected to differ substantially from those measured at urban background sites.     

Compilation and application of a primary PM2.5 emissions inventory with high sectoral resolution in Japan

To elucidate the macro-structure of the PM_2.5 emissions generated by Japan's economic activities, this paper presents an emission inventory of primary particles of PM_2.5 with high sectoral resolution based on the Japanese Input–Output Tables, comprising some 400 sectors. These primary PM_2.5 emissions were estimated by multiplying the estimated energy consumption associated with each fuel type by a PM₁₀ emission factor incorporating the technological level of dust collection in each sector and the mass ratio of PM_2.5 to PM₁₀. Non-energy emissions from agricultural open burning were also determined. Total PM_2.5 emissions in 2000 were 252 kt, 49% of which were due to mobile emission sources. Changes in total PM_2.5 emissions between 1990 and 2000 were also calculated. This showed that a substantial increase in energy sector emissions due to rising coal consumption was offset by a sharp decline in emissions from road vehicles and shipping vessels, resulting in an overall decrease in total emissions. In addition, the emissions induced by economic demand in each sector were quantified by means of input–output analysis, which revealed that demand for construction, foods and communications and services constituted the principal causes of real domestic emissions. An assessment of sectoral contributions to PM_2.5 emissions that takes into account the effects of human exposure, expressed as external costs, suggests that the contribution of transportation is greater than indicated on the grounds of direct emissions alone.     

Particulate matter speciation profiles for light-duty gasoline vehicles in the United States

Representative profiles for particulate matter particles less than or equal to 2.5 µm (PM_2.5) are developed from the Kansas City Light-Duty Vehicle Emissions Study for use in the U.S. Environmental Protection Agency (EPA) vehicle emission model, the Motor Vehicle Emission Simulator (MOVES), and for inclusion in the EPA SPECIATE database for speciation profiles. The profiles are compatible with the inputs of current photochemical air quality models, including the Community Multiscale Air Quality Aerosol Module Version 6 (AE6). The composition of light-duty gasoline PM_2.5 emissions differs significantly between cold start and hot stabilized running emissions, and between older and newer vehicles, reflecting both impacts of aging/deterioration and changes in vehicle technology. Fleet-average PM_2.5 profiles are estimated for cold start and hot stabilized running emission processes. Fleet-average profiles are calculated to include emissions from deteriorated high-emitting vehicles that are expected to continue to contribute disproportionately to the fleet-wide PM_2.5 emissions into the future. The profiles are calculated using a weighted average of the PM_2.5 composition according to the contribution of PM_2.5 emissions from each class of vehicles in the on-road gasoline fleet in the Kansas City Metropolitan Statistical Area. The paper introduces methods to exclude insignificant measurements, correct for organic carbon positive artifact, and control for contamination from the testing infrastructure in developing speciation profiles. The uncertainty of the PM_2.5 species fraction in each profile is quantified using sampling survey analysis methods. The primary use of the profiles is to develop PM_2.5 emissions inventories for the United States, but the profiles may also be used in source apportionment, atmospheric modeling, and exposure assessment, and as a basis for light-duty gasoline emission profiles for countries with limited data.

Implications: PM_2.5 speciation profiles were developed from a large sample of light-duty gasoline vehicles tested in the Kansas City area. Separate PM_2.5 profiles represent cold start and hot stabilized running emission processes to distinguish important differences in chemical composition. Statistical analysis was used to construct profiles that represent PM_2.5 emissions from the U.S. vehicle fleet based on vehicles tested from the 2005 calendar year Kansas City metropolitan area. The profiles have been incorporated into the EPA MOVES emissions model, as well as the EPA SPECIATE database, to improve emission inventories and provide the PM_2.5 chemical characterization needed by CMAQv5.0 for atmospheric chemistry modeling.     

Intake-to-delivered-energy ratios for central station and distributed electricity generation in California

In previous work, we showed that the intake fraction (iF) for nonreactive primary air pollutants was 20 times higher in central tendency for small-scale, urban-sited distributed electricity generation (DG) sources than for large-scale, central station (CS) power plants in California [Heath, G.A., Granvold, P.W., Hoats, A.S., Nazaroff, W.W., 2006. Intake fraction assessment of the air pollutant exposure implications of a shift toward distributed electricity generation. Atmospheric Environment 40, 7164–7177]. The present paper builds on that study, exploring pollutant- and technology-specific aspects of population inhalation exposure from electricity generation. We compare California's existing CS-based system to one that is more reliant on DG units sited in urban areas. We use Gaussian plume modeling and a GIS-based exposure analysis to assess 25 existing CSs and 11 DG sources hypothetically located in the downtowns of California's most populous cities. We consider population intake of three pollutants—PM_2.5, NO_x and formaldehyde—directly emitted by five DG technologies—natural gas (NG)-fired turbines, NG internal combustion engines (ICE), NG microturbines, diesel ICEs, and fuel cells with on-site NG reformers. We also consider intake of these pollutants from existing CS facilities, most of which use large NG turbines, as well as from hypothetical facilities located at these same sites but meeting California's best-available control technology standards. After systematically exploring the sensitivity of iF to pollutant decay rate, the iFs for each of the three pollutants for all DG and CS cases are estimated. To efficiently compare the pollutant- and technology-specific exposure potential on an appropriate common basis, a new metric is introduced and evaluated: the intake-to-delivered-energy ratio (IDER). The IDER expresses the mass of pollutant inhaled by an exposed population owing to emissions from an electricity generation unit per quantity of electric energy delivered to the place of use. We find that the central tendency of IDER is much greater for almost every DG technology evaluated than for existing CS facilities in California.     

Evaluating multimedia/multipathway model intake fraction estimates using POP emission and monitoring data

This paper presents a structured evaluation of a novel multimedia chemical fate and multi-pathway human exposure model for Western Europe, IMPACT 2002, using data for PCDD/F congeners. PCDD/F congeners provide an illustration of the potential use of POPs (Persistent Organic Pollutant) data for the evaluation of such models. Based on available emission estimates, model predictions with and without spatial resolution are evaluated at three different stages against monitored data: at environmental contamination levels, food exposure concentration, and in terms of human intake fractions (iF): the fraction of an emission that is taken in by the population. The iF is approximately 3.5.10(-3) for emissions of dioxin in Western Europe. This iF compares well to the traditional non-spatial multi-media/-pathway model predictions of 3.9.10(-3) for the same region and to 2.10(-3) for the USA. Approximately 95% of the intake from Western European emissions occurs within the same region, 5% being transferred out of the region in terms of food contaminants and atmospheric advective transport.     

Measurements and modelling of PM2.5 concentrations near a major road in Kuopio,Finland

A particle measurement campaign was conducted in a suburban environment near a major road in Kuopio, Central Finland from 3 August to 9 September 1999. The mass concentrations of fine particles (PM_2.5) were measured simultaneously at distances of 12, 25, 52 and 87 m from the centre of a major road at a height of 1.8 m, using identical samplers. The concentration measurements were conducted during 16 daytime hours (from 6.00 a.m. to 10.00 p.m.) for 27 days. Traffic flows and relevant meteorological parameters were measured on-site; meteorological measurements from a nearby synoptic weather station were also utilised. We also suggest a preliminary model for predicting the concentrations of PM_2.5 and apply this model in order to analyse the measured data. The regionally and long-range transported contribution was evaluated on the basis of a semi-empirical mathematical model utilising as input values the daily sulphate, nitrate and ammonium measurements at the EMEP stations (Co-operative programme for monitoring and evaluation of the long-range transmission of air pollutants in Europe). The influence of primary vehicular emissions from the nearest roads was evaluated using a roadside emission and dispersion model, CAR-FMI, in combination with a meteorological pre-processing model, MPP-FMI. The contribution of non-exhaust particulate matter emissions (including resuspension of particulate matter from road surfaces) was estimated simply to be directly proportional to the concentrations originating from primary vehicular emissions. Comparison of the predicted results and measurements yields information on the relative importance of various source categories of the measured concentrations of PM_2.5. The regionally and long-range transported contribution, the primary and non-exhaust vehicular emissions, and other sources were estimated to contribute on average 41±6%, 33±6% and 26±7% of the observed PM_2.5 concentrations, respectively. The model presented could also be applied in other European cities for analysing the source contributions to measured fine particulate matter concentrations.     

Fine Particulate Matter Emissions Inventories: Comparisons of Emissions Estimates with Observations from Recent Field Programs

Abstract

Emissions inventories of fine particulate matter (PM_2.5) were compared with estimates of emissions based on data emerging from U.S. Environment Protection Agency Particulate Matter Supersites and other field programs. Six source categories for PM_2.5 emissions were reviewed: on-road mobile sources, nonroad mobile sources, cooking, biomass combustion, fugitive dust, and stationary sources. Ammonia emissions from all of the source categories were also examined. Regional emissions inventories of PM in the exhaust from on-road and nonroad sources were generally consistent with ambient observations, though uncertainties in some emission factors were twice as large as the emission factors. In contrast, emissions inventories of road dust were up to an order of magnitude larger than ambient observations, and estimated brake wear and tire dust emissions were half as large as ambient observations in urban areas. Although comprehensive nationwide emissions inventories of PM_2.5 from cooking sources and biomass burning are not yet available, observational data in urban areas suggest that cooking sources account for approximately 5–20% of total primary emissions (excluding dust), and biomass burning sources are highly dependent on region. Finally, relatively few observational data were available to assess the accuracy of emission estimates for stationary sources. Overall, the uncertainties in primary emissions for PM_2.5 are substantial. Similar uncertainties exist for ammonia emissions. Because of these uncertainties, the design of PM_2.5 control strategies should be based on inventories that have been refined by a combination of bottom-up and top-down methods.     

The Relationship between Cross-Sectional and Time Series Studies

Abstract

There is a dearth of information on dust emissions from sources that are unique to the U.S. Department of Defense testing and training activities. However, accurate emissions factors are needed for these sources so that military installations can prepare accurate particulate matter (PM) emission inventories. One such source, coarse and fine PM (PM₁₀ and PM_2.5) emissions from artillery backblast testing on improved gun positions, was characterized at the Yuma Proving Ground near Yuma, AZ, in October 2005. Fugitive emissions are created by the shockwave from artillery pieces, which ejects dust from the surface on which the artillery is resting. Other contributions of PM can be attributed to the combustion of the propellants. For a 155–mm howitzer firing a range of propellant charges or zones, amounts of emitted PM₁₀ ranged from ～19 g of PM₁₀ per firing event for a zone 1 charge to 92 g of PM₁₀ per firing event for a zone 5. The corresponding rates for PM_2.5 were ～9 g of PM_2.5 and 49 g of PM_2.5 per firing. The average measured emission rates for PM₁₀ and PM_2.5 appear to scale with the zone charge value. The measurements show that the estimated annual contributions of PM₁₀ (52.2 t) and PM_2.5 (28.5 t) from artillery backblast are insignificant in the context of the 2002 U.S. Environment Protection Agency (EPA) PM emission inventory. Using national–level activity data for artillery fire, the most conservative estimate is that backblast would contribute the equivalent of 5 x 10^–4% and 1.6 x 10^–3% of the annual total PM₁₀ and PM_2.5 fugitive dust contributions, respectively, based on 2002 EPA inventory data.     

Resolving the interactions between population density and air pollution emissions controls in the San Joaquin Valley,USA

The effectiveness of emissions control programs designed to reduce concentrations of airborne particulate matter with an aerodynamic diameter <2.5 μm (PM_2.5) in California's San Joaquin Valley was studied in the year 2030 under three growth scenarios: low, medium, and high population density. Base-case inventories for each choice of population density were created using a coupled emissions modeling system that simultaneously considered interactions between land use and transportation, area source, and point source emissions. The ambient PM_2.5 response to each combination of population density and emissions control was evaluated using a regional chemical transport model over a 3-week winter stagnation episode. Comparisons between scenarios were based on regional average and population-weighted PM_2.5 concentrations. In the absence of any emissions control program, population-weighted concentrations of PM_2.5 in the future San Joaquin Valley are lowest under growth scenarios that emphasize low population density. A complete ban on wood burning and a 90% reduction in emissions from food cooking operations and diesel engines must occur before medium- to high-density growth scenarios result in lower population-weighted concentrations of PM_2.5. These trends partly reflect the fact that existing downtown urban cores that naturally act as anchor points for new high-density growth in the San Joaquin Valley are located close to major transportation corridors for goods movement. Adding growth buffers around transportation corridors had little impact in the current analysis, since the 8-km resolution of the chemical transport model already provided an artificial buffer around major emissions sources.

Assuming that future emissions controls will greatly reduce or eliminate emissions from residential wood burning, food cooking, and diesel engines, the 2030 growth scenario using “as-planned” (medium) population density achieves the lowest population-weighted average PM_2.5 concentration in the future San Joaquin Valley during a severe winter stagnation event.

Implications: The San Joaquin Valley is one of the most heavily polluted air basins in the United States that are projected to experience strong population growth in the coming decades. The best plan to improve air quality in the region combines medium- or high-density population growth with rigorous emissions controls. In the absences of controls, high-density growth leads to increased population exposure to PM_2.5 compared with low-density growth scenarios (urban sprawl).     

Development and Preliminary Evaluation of a Particulate Matter Emission Factor Model for European Motor Vehicles

ABSTRACT

Although modeling of gaseous emissions from motor vehicles is now quite advanced, prediction of particulate emissions is still at an unsophisticated stage. Emission factors for gasoline vehicles are not reliably available, since gasoline vehicles are not included in the European Union (EU) emission test procedure. Regarding diesel vehicles, emission factors are available for different driving cycles but give little information about change of emissions with speed or engine load. We have developed size-specific speed-dependent emission factors for gasoline and diesel vehicles. Other vehicle-generated emission factors are also considered and the empirical equation for re-entrained road dust is modified to include humidity effects. A methodology is proposed to calculate modal (accelerating, cruising, or idling) emission factors. The emission factors cover particle size ranges up to 10 um, either from published data or from user-defined size distributions.

A particulate matter emission factor model (PMFAC), which incorporates virtually all the available information on particulate emissions for European motor vehicles, has been developed. PMFAC calculates the emission factors for five particle size ranges [i.e., total suspended particulates (TSP), PM₁₀, PM₅, PM₂₅, and PM₁] from both vehicle exhaust and nonexhaust emissions, such as tire wear, brake wear, and re-entrained road dust. The model can be used for an unlimited number of roads and lanes, and to calculate emission factors near an intersection in user-defined elements of the lane. PMFAC can be used for a variety of fleet structures. Hot emission factors at the user-defined speed can be calculated for individual vehicles, along with relative cold-to-hot emission factors. The model accounts for the proportions of distance driven with cold engines as a function of ambient temperature and road type (i.e., urban, rural, or motorway).

A preliminary evaluation of PMFAC with an available dispersion model to predict the airborne concentration in the urban environment is presented. The trial was on the A6 trunk road where it passes through Loughborough, a medium-size town in the English East Midlands. This evaluation for TSP and PM₁₀ was carried out for a range of traffic fleet compositions, speeds, and meteorological conditions. Given the limited basis of the evaluation, encouraging agreement was shown between predicted and measured concentrations.     

Elemental composition and reflectance of ambient fine particles at 21 European locations

We sampled fine particles (PM_2.5) over a 1-year period at 21 central urban monitoring sites in 20 cities of the European Community Respiratory Health Survey (ECRHS). Particle filters were then analysed for elemental composition using energy dispersive X-ray fluorescence spectrometry and reflectance (light absorption). Elemental analyses yielded valid results for 15 elements (Al, As, Br, Ca, Cl, Cu, Fe, K, Mn, Pb, S, Si, Ti, V, Zn).Annual and seasonal means of PM_2.5, reflectance, and elements show a wide range across Europe with the lowest levels found in Iceland and up to 80 times higher concentrations in Northern Italy. This pattern holds for most of the air pollution indicators. The mass concentration of S did constitute the largest fraction of the analysed elements of PM_2.5 in all locations. The crustal component varies from less than 10% up to 25% across these cities. Temporal correlations of daily values vary considerably from city to city, depending on the indicators compared. Nevertheless, correlations between estimates of long-term exposure, such as annual means, are generally high among indicators of PM_2.5 from anthropogenic sources, such as S, metals, and reflectance. This highlights the difficulty to disentangle effects of specific sources or PM constituents in future health effect analyses using annual averages.     

Trends in primary particulate matter emissions from Canadian agriculture

Particulate matter (PM) has long been recognized as an air pollutant due to its adverse health and environmental impacts. As emission of PM from agricultural operations is an emerging air quality issue, the Agricultural Particulate Matter Emissions Indicator (APMEI) has been developed to estimate the primary PM contribution to the atmosphere from agricultural operations on Census years and to assess the impact of practices adopted to mitigate these emissions at the soil landscape polygon scale as part of the agri-environmental indicator report series produced by Agriculture and Agri-Food Canada. In the APMEI, PM emissions from animal feeding operations, wind erosion, land preparation, crop harvest, fertilizer and chemical application, grain handling, and pollen were calculated and compared for the Census years of 1981–2006. In this study, we present the results for PM₁₀ and PM_2.5, which exclude chemical application and pollen sources as they only contribute to total suspended particles. In 2006, PM emissions from agricultural operations were estimated to be 652.6 kt for PM₁₀ and 158.1 kt for PM_2.5. PM emissions from wind erosion and land preparation account for most of PM emissions from agricultural operations in Canada, contributing 82% of PM₁₀ and 76% of PM_2.5 in 2006. Results from the APMEI show a strong reduction in PM emissions from agricultural operations between 1981 and 2006, with a decrease of 40% (442.8 kt) for PM₁₀ and 47% (137.7 kt) for PM_2.5. This emission reduction is mainly attributed to the adoption of conservation tillage and no-till practices and the reduction in the area of summerfallow land.

Implications: Increasing sustainability in agriculture often means adapting management practices to have a beneficial impact on the environment while maintaining or increasing production and economic benefits. We developed an inventory of primary PM emissions from agriculture in Canada to better quantify the apportionment, spatial distribution, and trends for Census years 1981–2006. We found major reductions of 40% in PM₁₀ and 47% in PM_2.5 emissions over the 25-yr period as a co-benefit of increasing carbon sequestration in agricultural soils. Indeed, farmers adopted conservation tillage/no-till practices, increased usage of cover crops, and reduced summerfallow, in order to increase soil organic matter and reduce carbon dioxide emissions, which also reduced primary PM emissions, although the agricultural production increased over the period.     

PM Emissions Emanating from Limited-Access Highways

ABSTRACT

Motor vehicle contributions to primary particulate matter (PM) emissions include exhaust, tire wear, brake and clutch wear, and resuspended road dust. Relatively few field studies have been conducted to quantify fleetaverage exhaust emissions for actual on-road conditions. Therefore, direct measurements of motor vehicle-related PM emissions are warranted. In this study, PM₁₀ and PM_2.5 mass concentrations were measured near two major highways in the St. Louis area over the period from February–April 1997. Samplers were deployed both upwind and downwind of the roadways to capture the transport and dispersion of PM with distance from the roadway. The observed microscale concentration fields were compared to estimates using the PART5 emission factor model together with the CALINE4 highway dispersion model. Traffic- induced PM mass concentrations observed downwind of the roadway were always less than PART5/CALINE4 predictions; average percent differences for observed traffic-induced mass concentrations compared to predicted values were ?34% for PM_2.5 and -70% for PM₁₀. In most cases, the observed PM concentration decay with increasing distance from the roadway was steeper than predicted by dispersion modeling. Motor vehicle-induced emission factors were reconstructed by fitting CALINE4 to the observed concentration data with the emission factor as the sole adjustable parameter. Reconstructed fleet-average motor vehicle emission factors for the urban interstate highway were 0.03–0.04 g/VMT for both PM_2.5 and PM₁₀, while the fleet-average emission factors for the rural interstate highway were 0.2 and 0.3 g/VMT for PM_2.5 and PM₁₀, respectively.     

Effect of SME biodiesel blends on PM2.5 emission from a heavy-duty engine

To explore the effect of biodiesel and sulfur content on PM_2.5 emissions, engine dynamometer tests were performed on a Euro II engine to compare the PM_2.5 emissions from four fuels: two petroleum diesel fuels with sulfur contents of 50 and 100 ppm respectively, and two B20 fuels in which soy methyl ester (SME) biodiesel was added to each of the above mentioned petroleum diesel fuels (v/v: 80%/20% for petroleum diesel and SME respectively). Gaseous pollutants and PM_2.5 emissions were sampled with an AVL AMA4000 and Model 130 High-Flow Impactor (MSP Corp). Measurements were made of the PM_2.5 mass, organic carbon (OC), elemental carbon (EC) and the water-soluble ion distribution. The results showed that PM_2.5 emissions decreased with lower sulfur content or blending with SME biodiesel, and the decrease would be more by applying both two methods together. Particles of approximately 0.13 μm contributed 48–83% of PM_2.5 emissions. The impact of sulfur content on this percentage was different for low and high engine speed. The majority of PM_2.5 was comprised of OC and EC, and the carbon emission rate had the same trend as PM_2.5. Since the EC abatement of B20 was larger than OC, the OC/EC ratio of B20 was always larger than that of petroleum diesel. For petroleum diesel, the OC/EC increased with sulfur content, which was not the case for B20. The SO₄^2? had highest emission rate in the water-soluble ions of PM.     

PM2.5 Emissions from Aluminum Smelters: Coefficients and Environmental Impact

ABSTRACT

From 2004 to 2009, aiming to better understand implications for its smelters, Rio Tinto Alcan conducted a detailed study of PM_2.5 and PM₁₀ (particulate matter [PM] ≤ 2.5 and 10 μm in aerodynamic diameter, respectively) in its facilities. This involved a two-level study: part 1, emission quantification; and part 2, assessment of aluminum smelter contribution to the surrounding environment. In the first part, U.S. Environmental Protection Agency Other Test Method (OTM) OTM27 and OTM28 are assessed as relevant and efficient methods for measuring fine particle emissions from aluminum smelter stacks. Rio Tinto Alcan has also developed a safe and robust method called CYCLEX to measure PM_2.5 and condensable particulate matter (CPM) at the roof vents of potrooms. This work aims to determine the PM_2.5 emission coefficients of 17, 55, and 417 g·t^?1 of aluminum produced (including CPM) in anode baking furnace exhaust (fume treatment center), at potroom scrubber stacks (gas treatment centers), and at potroom roof vents, respectively. Results indicate that roof vents are the primary PM_2.5 emitters (85% of all smelter emissions) and that 71% of all smelter PM_2.5 comes from CPM. In the second part, preliminary inorganic speciation studies are conducted by scanning electron microscopy–energy-dispersive X-ray analysis and by isotopic ratios to track smelter emissions to their surrounding environment. This paper releases the first speciation results for an aluminum smelter, and the preliminary isotopic ratio study indicates a 3% impact in terms of PM_2.5 emissions for a representative smelter in an urban area.

IMPLICATIONS Aluminum smelters tend to continuously improve their competitiveness by incrementally increasing production. In this context, assessing the effect of major contaminants is overriding, and ambient air modeling is often the preferred way to do so. Fine particles fit this category, and the primary aluminum industry needs to accurately know their emission factors to obtain representative modeling. Moreover, not all aluminum smelters have a method to measure PM_2.5 at roof vents, the primary emission outlets. Therefore, this paper describes the first-rate PM_2.5 measurement methods for aluminum smelter roof vents without down-comers. It also provides insight for environmental managers for tracking PM_2.5 emissions in plant surroundings.     

Source apportionment of PM2.5 at two Seattle chemical speciation sites

ABSTRACT

Positive Matrix Factorization analysis of PM_2.5 chemical speciation data collected from 2015–2017 at Washington State Department of Ecology’s urban NCore (Beacon Hill) and near-road (10th and Weller) sites found similar PM_2.5 sources at both sites. Identified factors were associated with gasoline exhaust, diesel exhaust, aged and fresh sea salt, crustal, nitrate-rich, sulfur-rich, unidentified urban, zinc-rich, residual fuel oil, and wood smoke. Factors associated with vehicle emissions were the highest contributing sources at both sites. Gasoline exhaust emissions comprised 26% and 21% of identified sources at Beacon Hill and 10th and Weller, respectively. Diesel exhaust emissions comprised 29% of identified sources at 10th and Weller but only 3% of identified sources at Beacon Hill. Correlation of the diesel exhaust factor with measured concentrations of black carbon and nitrogen oxides at 10th and Weller suggests a method to predict PM_2.5 from diesel exhaust without a full chemical speciation analysis. While most PM_2.5 sources exhibit minimal change over time, primary PM_2.5 from gasoline emissions is increasing on average 0.18 µg m^?3 per year in Seattle.