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.     

Concentrations of particulate matter emitted from large cattle feedlots in Kansas

Particulate matter (PM) emitted from cattle feedlots are thought to affect air quality in rural communities, yet little is known about factors controlling their emissions. The concentrations of PM (i.e., PM2.5, PM10, and total suspended particulates or TSP) upwind and downwind at two large cattle feedlots (KS1, KS2) in Kansas were measured with gravimetric samplers from May 2006 to October 2009 (at KS1) and from September 2007 to April 2008 (at KS2). The mean downwind and net (i.e., downwind - upwind) mass concentrations of PM2.5, PM10, and TSP varied seasonally, indicating the need for multiple-day, seasonal sampling. The downwind and net concentrations were closely related to the moisture content of the pen surface. The PM2.5/PM10 and PM2.5/TSP ratios at the downwind sampling location were also related to the moisture content of the pen surface, humidity, and temperature. Measurement of the particle size distribution downwind of the feedlot with a cascade impactor showed geometric mean diameter ranging from 7 to 18 microm, indicating that particles that were emitted from the feedlots were generally large in size.     

石家庄市土壤扬尘排放量估算及分布特征

以2013年为基准年,通过卫星遥感影像解译方法估算了石家庄市土壤扬尘源颗粒物排放量,并给出分辨率为3 km×3 km空间分布图。结果表明,石家庄市土壤扬尘源主要包括河滩、荒地、裸岩和耕地4种类型。冬季土壤扬尘源面积为3 918.19 km2,夏季土壤扬尘源面积为1 115.98 km2,仅为冬季的28.6%。对于总悬浮颗粒物（TSP）、可吸入颗粒物（PM10）和细颗粒物（PM2.5）,石家庄市4种土壤扬尘源类型的排放系数均为荒地 > 河滩 >裸岩 > 耕地。2013年石家庄市土壤扬尘TSP、PM10和PM2.5的排放量分别为12 569、3 771和947 t,与其他季节相比,春季裸地扬尘排放量最大,分别占总排放量的93.9%、93.9%和94.3%。土壤扬尘高排放区主要分布在石家庄市北部的行唐县、灵寿县和平山县区域,3个区域合计占石家庄市土壤扬尘TSP、PM10和PM2.5总排放量的47.5%、47.5%和48.1%。研究结果将为石家庄市颗粒物污染控制方法的建立提供必要的基础数据。     

A highly resolved temporal and spatial air pollutant emission inventory for the Pearl River Delta region, China and its uncertainty assessment

A highly resolved temporal and spatial Pearl River Delta (PRD) regional emission inventory for the year 2006 was developed with the use of best available domestic emission factors and activity data. The inventory covers major emission sources in the region and a bottom–up approach was adopted to compile the inventory for those sources where possible. The results show that the estimates for SO₂, NO_x, CO, PM₁₀, PM_2.5 and VOC emissions in the PRD region for the year 2006 are 711.4 kt, 891.9 kt, 3840.6 kt, 418.4 kt, 204.6 kt, and 1180.1 kt, respectively. About 91.4% of SO₂ emissions were from power plant and industrial sources, and 87.2% of NO_x emissions were from power plant and mobile sources. The industrial, mobile and power plant sources are major contributors to PM₁₀ and PM_2.5 emissions, accounting for 97.7% of the total PM₁₀ and 97.2% of PM_2.5 emissions, respectively. Mobile, biogenic and VOC product-related sources are responsible for 90.5% of the total VOC emissions. The emissions are spatially allocated onto grid cells with a resolution of 3 km × 3 km, showing that anthropogenic air pollutant emissions are mainly distributed over PRD central-southern city cluster areas. The preliminary temporal profiles were established for the power plant, industrial and on-road mobile sources. There is relatively low uncertainty in SO₂ emission estimates with a range of −16% to +21% from power plant sources, medium to high uncertainty for the NO_x emissions, and high uncertainties in the VOC, PM_2.5, PM₁₀ and CO emissions.     

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 microm (PM2.5) 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 PM2.5 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 PM2.5 concentrations. In the absence of any emissions control program, population-weighted concentrations of PM2.5 in the future San Joaquin Valley are lowest undergrowth 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 PM2.5. 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 PM2.5 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 PM2.5 compared with low-density growth scenarios (urban sprawl).     

Fine particulate matter emissions inventories: comparisons of emissions estimates with observations from recent field programs

Emissions inventories of fine particulate matter (PM2.5) 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 PM2.5 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 PM2.5 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 PM2.s are substantial. Similar uncertainties exist for ammonia emissions. Because of these uncertainties, the design of PM2.5 control strategies should be based on inventories that have been refined by a combination of bottom-up and top-down methods.     

Response of Fine Particulate Matter to Emission Changes of Oxides of Nitrogen and Anthropogenic Volatile Organic Compounds in the Eastern United States

Abstract

A three-dimensional chemical transport model (Particulate Matter Comprehensive Air Quality Model with Extensions [PMCAMx]) is used to investigate changes in fine particle (PM_2.5) concentrations in response to 50% emissions changes of oxides of nitrogen (NO_x) and anthropogenic volatile organic compounds (VOCs) during July 2001 and January 2002 in the eastern United States. The reduction of NO_x emissions by 50% during the summer results in lower average oxidant levels and lowers PM_2.5 (8% on average), mainly because of reductions of sulfate (9–11%), nitrate (45–58%), and ammonium (7–11%). The organic particulate matter (PM) slightly decreases in rural areas, whereas it increases in cities by a few percent when NO_x is reduced. Reduction of NO_x during winter causes an increase of the oxidant levels and a rather complicated response of the PM components, leading to small net changes. Sulfate increases (8–17%), nitrate decreases (18– 42%), organic PM slightly increases, and ammonium either increases or decreases a little. The reduction of VOC emissions during the summer causes on average a small increase of the oxidant levels and a marginal increase in PM_2.5. This small net change is due to increases in the inorganic components and decreases of the organic ones. Reduction of VOC emissions during winter results in a decrease of the oxidant levels and a 5–10% reduction of PM_2.5 because of reductions in nitrate (4–19%), ammonium (4–10%), organic PM (12–14%), and small reductions in sulfate. Although sulfur dioxide (SO₂) reduction is the single most effective approach for sulfate control, the coupled decrease of SO₂ and NO_x emissions in both seasons is more effective in reducing total PM_2.5 mass than the SO₂ reduction alone.     

Remote sensing-based estimates of annual and seasonal emissions from crop residue burning in the contiguous United States

Crop residue burning is an extensive agricultural practice in the contiguous United States (CONUS). This analysis presents the results of a remote sensing-based study of crop residue burning emissions in the CONUS for the time period 2003-2007 for the atmospheric species of carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), nitrogen dioxide (NO2, sulfur dioxide (SO2), PM2.5 (particulate matter [PM] < or = 2.5 microm in aerodynamic diameter), and PM10 (PM < or = 10 microm in aerodynamic diameter). Cropland burned area and associated crop types were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) products. Emission factors, fuel load, and combustion completeness estimates were derived from the scientific literature, governmental reports, and expert knowledge. Emissions were calculated using the bottom-up approach in which emissions are the product of burned area, fuel load, and combustion completeness for each specific crop type. On average, annual crop residue burning in the CONUS emitted 6.1 Tg of CO2, 8.9 Gg of CH4, 232.4 Gg of CO, 10.6 Gg of NO2, 4.4 Gg of SO2, 20.9 Gg of PM2.5, and 28.5 Gg of PM10. These emissions remained fairly consistent, with an average interannual variability of crop residue burning emissions of +/- 10%. The states with the highest emissions were Arkansas, California, Florida, Idaho, Texas, and Washington. Most emissions were clustered in the southeastern United States, the Great Plains, and the Pacific Northwest. Air quality and carbon emissions were concentrated in the spring, summer, and fall, with an exception because of winter harvesting of sugarcane in Florida, Louisiana, and Texas. Sugarcane, wheat, and rice residues accounted for approximately 70% of all crop residue burning and associated emissions. Estimates of CO and CH4 from agricultural waste burning by the U.S. Environmental Protection Agency were 73 and 78% higher than the CO and CH4 emission estimates from this analysis, respectively. This analysis also showed that crop residue burning emissions are a minor source of CH4 emissions (< 1%) compared with the CH4 emissions from other agricultural sources, specifically enteric fermentation, manure management, and rice cultivation.     

Trace gases and particulate matter emissions from wildfires and agricultural burning in Northeastern Mexico during the 2000 fire season

An inventory of air pollutants emitted from forest and agricultural fires in Northeastern Mexico for the period of January to August of 2000 is presented. The emissions estimates were calculated using an emissions factor methodology. The inventory accounts for the emission of carbon monoxide (CO), methane, nonmethane hydrocarbons, ammonia, nitrogen oxides, and particulate matter (PM). Particulate matter emissions include estimates for fine PM and coarse PM. A total of 2479 wildfires were identified in the domain for the period of interest, which represented approximately 810,000 acres burned and 621,130 short tons emitted (81% being CO). The main source of information used to locate and estimate the extent of the fires came from satellite imagery. A geographic information system was used to determine the type of vegetation burned by each fire. More than 54% of the total area burned during the period of study was land on the State of Tamaulipas. However, >58% of the estimated emissions came from the State of Coahuila. This was because of the mix of vegetation types burned in each state. With respect to the temporal distribution, 76.9% of the fires occurred during the months of April and May consuming almost 78% of the total area burned during the period of study. Analysis of wind forward trajectories of air masses passing through the burned areas and 850-mb wind reanalyses indicate possible transboundary transport of the emissions from Mexico to the United States during the occurrence of the major wildfires identified.     

Particulate emissions from construction activities

Although it has long been recognized that road and building construction activity constitutes an important source of particulate matter (PM) emissions throughout the United States, until recently only limited research has been directed to its characterization. This paper presents the results of PM10 and PM2.5 (particles < or = 10 microm and < or = 2.5 microm in aerodynamic diameter, respectively) emission factor development from the onsite testing of component operations at actual construction sites during the period 1998-2001. Much of the testing effort was directed at earthmoving operations with scrapers, because earthmoving is the most important contributor of PM emissions across the construction industry. Other sources tested were truck loading and dumping of crushed rock and mud and dirt carryout from construction site access points onto adjacent public paved roads. Also tested were the effects of watering for control of scraper travel routes and the use of paved and graveled aprons at construction site access points for reducing mud and dirt carryout. The PM10 emissions from earthmoving were found to be up to an order of magnitude greater than predicted by AP-42 emission factors drawn from other industries. As expected, the observed PM2.5:PM10 emission factor ratios reflected the relative importance of the vehicle exhaust and the resuspended dust components of each type of construction activity. An unexpected finding was that PM2.5 emissions from mud and dirt carryout were much less than anticipated. Finally, the control efficiency of watering of scraper travel routes was found to closely follow a bilinear moisture model.     

Trace Gases and Particulate Matter Emissions from Wildfires and Agricultural Burning in Northeastern Mexico during the 2000 Fire Season

Abstract

An inventory of air pollutants emitted from forest and agricultural fires in Northeastern Mexico for the period of January to August of 2000 is presented. The emissions estimates were calculated using an emissions factor methodology. The inventory accounts for the emission of carbon monoxide (CO), methane, nonmethane hydrocarbons, ammonia, nitrogen oxides, and particulate matter (PM). Particulate matter emissions include estimates for fine PM and coarse PM. A total of 2479 wildfires were identified in the domain for the period of interest, which represented ~810,000 acres burned and 621,130 short tons emitted (81% being CO). The main source of information used to locate and estimate the extent of the fires came from satellite imagery. A geographic information system was used to determine the type of vegetation burned by each fire. More than 54% of the total area burned during the period of study was land on the State of Tamaulipas. However, >58% of the estimated emissions came from the State of Coahuila. This was because of the mix of vegetation types burned in each state. With respect to the temporal distribution, 76.9% of the fires occurred during the months of April and May consuming almost 78% of the total area burned during the period of study. Analysis of wind forward trajectories of air masses passing through the burned areas and 850-mb wind reanalyses indicate possible transboundary transport of the emissions from Mexico to the United States during the occurrence of the major wildfires identified.     

Temporal,spatial characteristics and uncertainty of biogenic VOC emissions in the Pearl River Delta region,China

Using the Global Biosphere Emissions and Interactions System model (GloBEIS), 3 × 3 km gridded and hourly biogenic volatile organic compound (BVOC) emissions in the Pearl River Delta (PRD) were estimated for the year 2006. The study used newly available land cover database, observed meteorological data, and recent measurements of emission rates for tree species in China. The results show that the total BVOC emission in the PRD region in 2006 was 296 kt (2.2 × 10¹¹ gC), of which isoprene contributes about 25% (73 kt, 6.4 × 10¹⁰ gC), monoterpenes about 34% (102 kt, 8.9 × 10¹⁰ gC), and other VOCs (OVOC) about 41% (121 kt, 6.8 × 10¹⁰ gC). BVOC emissions in the PRD region exhibit a marked seasonal pattern with the peak emission in July and the lowest emission in January, and are mainly distributed over the outlying areas of the PRD region, where the economy and land use are less developed. The uncertainties in BVOC emission estimates were quantified using Monte Carlo simulation; the results indicate high uncertainties in isoprene emission estimates, with a relative error of ?82 to +177%, ranging from 12.4 to 186.4 kt; ?41 to +58% uncertainty for monoterpenes emissions, ranging from 67.7 to 181.9 kt; and ?26 to +30% uncertainty in OVOC emissions, ranging from 88.8 to 156.2 kt on the 95% confidence intervals. The key uncertainty sources include emission factors and the model empirical coefficients α, C_T1, C_L, and E_opt for estimating isoprene emission, and emission factors and foliar density for estimating monoterpenes and OVOC emissions. This implies that determining these empirical coefficient values properly and conducting more field measurements of emission rates of tree species are key approaches for reducing uncertainties in BVOC emission estimates. Improving future BVOC emission inventory work in the PRD region requires giving priority to research on shrub land, coniferous forests, and irrigated cropland and pasture.     

Hot filter/impinger and dilution sampling for fine particulate matter characterization from ferrous metal casting processes

A study using two stack-sampling methodologies for collecting particulate matter (PM) emissions was conducted using a hot filter followed by a cold impinger sampling train and a dilution sampler. Samples were collected from ferrous iron metal casting processes that included pouring molten iron into a sand mold containing an organic binder, metal cooling, removal of the sand from the cooled casting (shakeout), and postshakeout cooling. The shakeout process contributed more to PM emissions than the metal pouring and cooling processes. Particulate matter less than 2.5 microm in aerodynamic diameter (PM2.5) mass emissions for the entire casting cycle ranged from 3.4 to 4.7 lb/t of metal for the hot filter/impinger method and from 0.8 to 1.8 lb/t of metal for the dilution method. Most of the difference was due to PM captured by the impingers, much of which was probably dissolved gases rather than condensable vapors. Of the PM fraction captured by the impingers, 96-98% was organic in nature. The impinger PM fraction contributed 32-38% to the total suspended particle mass and caused a factor of 2-4 positive bias for PM2.5 emissions. For the pouring and cooling processes only, the factor increased to over seven times.     

In-stack condensible particulate matter measurements and issues

Particulate matter (PM) emitted from fossil fuel-fired units can be classified as either filterable or condensible PM. Condensible PM typically is not measured because federal and most state regulations do not require sources to do so. To determine the magnitude of condensible PM emissions relative to filterable PM emissions and to better understand condensible PM measurement issues, a review and analysis of actual U.S. Environmental Protection Agency (EPA) Method 202 (for in-stack condensible PM10) and EPA Method 201/201A (for in-stack filterable PM10) results were conducted. Methods 202 and 201/201A results for several coal-burning boilers showed that the condensible PM, on average, comprises approximately three-fourths (76%) of the total PM10 stack emissions. Methods 202 and 201/201A results for oil- and natural gas-fired boilers showed that the condensible PM, on average, comprises 50% of the total PM10 stack emissions. Methods 202 and 201/201A results for oil-, natural gas-, and kerosene-fired combustion turbines showed that the condensible PM, on average, comprises 69% of the total PM10 stack emissions. Based on these limited measurements, condensible PM can make a significant contribution to total PM10 emissions for fossil fuel-fired units. A positive bias (indicating more condensible PM than is actually emitted) may exist in the measured data due to the conversion of dissolved sulfur dioxide to sulfate compounds in the sampling procedure. In addition, these Method 202 results confirm that condensible PM, on average, is composed mostly of inorganic matter, regardless of the type of fuel burned.     

Methods of characterizing the distribution of exhaust emissions from light-duty, gasoline-powered motor vehicles in the U.S. fleet

Mobile sources significantly contribute to ambient concentrations of airborne particulate matter (PM). Source apportionment studies for PM10 (PM < or = 10 microm in aerodynamic diameter) and PM2.5 (PM < or = 2.5 microm in aerodynamic diameter) indicate that mobile sources can be responsible for over half of the ambient PM measured in an urban area. Recent source apportionment studies attempted to differentiate between contributions from gasoline and diesel motor vehicle combustion. Several source apportionment studies conducted in the United States suggested that gasoline combustion from mobile sources contributed more to ambient PM than diesel combustion. However, existing emission inventories for the United States indicated that diesels contribute more than gasoline vehicles to ambient PM concentrations. A comprehensive testing program was initiated in the Kansas City metropolitan area to measure PM emissions in the light-duty, gasoline-powered, on-road mobile source fleet to provide data for PM inventory and emissions modeling. The vehicle recruitment design produced a sample that could represent the regional fleet, and by extension, the national fleet. All vehicles were recruited from a stratified sample on the basis of vehicle class (car, truck) and model-year group. The pool of available vehicles was drawn primarily from a sample of vehicle owners designed to represent the selected demographic and geographic characteristics of the Kansas City population. Emissions testing utilized a portable, light-duty chassis dynamometer with vehicles tested using the LA-92 driving cycle, on-board emissions measurement systems, and remote sensing devices. Particulate mass emissions were the focus of the study, with continuous and integrated samples collected. In addition, sample analyses included criteria gases (carbon monoxide, carbon dioxide, nitric oxide/nitrogen dioxide, hydrocarbons), air toxics (speciated volatile organic compounds), and PM constituents (elemental/organic carbon, metals, semi-volatile organic compounds). Results indicated that PM emissions from the in-use fleet varied by up to 3 orders of magnitude, with emissions generally increasing for older model-year vehicles. The study also identified a strong influence of ambient temperature on vehicle PM mass emissions, with rates increasing with decreasing temperatures.     

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 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%.     

Hot Filter/Impinger and Dilution Sampling for Fine Particulate Matter Characterization from Ferrous Metal Casting Processes

Abstract

A study using two stack-sampling methodologies for collecting particulate matter (PM) emissions was conducted using a hot filter followed by a cold impinger sampling train and a dilution sampler. Samples were collected from ferrous iron metal casting processes that included pouring molten iron into a sand mold containing an organic binder, metal cooling, removal of the sand from the cooled casting (shakeout), and postshakeout cooling. The shakeout process contributed more to PM emissions than the metal pouring and cooling processes. Particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5) mass emissions for the entire casting cycle ranged from 3.4 to 4.7 lb/t of metal for the hot filter/impinger method and from 0.8 to 1.8 lb/t of metal for the dilution method. Most of the difference was due to PM captured by the impingers, much of which was probably dissolved gases rather than condensable vapors. Of the PM fraction captured by the impingers, 96–98% was organic in nature. The impinger PM fraction contributed 32–38% to the total suspended particle mass and caused a factor of 2–4 positive bias for PM_2.5 emissions. For the pouring and cooling processes only, the factor increased to over seven times.     

Application of a microscale emission factor model for particulate matter to calculate vehicle-generated contributions to fine particulate emissions

This paper discusses the evaluation and application of a new generation of particulate matter (PM) emission factor model (MicroFacPM). MicroFacPM that was evaluated in Tuscarora Mountain Tunnel, Pennsylvania Turnpike, PA shows good agreement between measured and modeled emissions. MicroFacPM application is presented to the vehicle traffic on the main approach road to the Ambassador Bridge, which is one of the most important international border entry points in North America, connecting Detroit, MI, with Windsor, Ontario, Canada. An increase in border security has forced heavy-duty diesel vehicles to line up for several kilometers through the city of Windsor causing concern about elevated concentrations of ambient PM. MicroFacPM has been developed to model vehicle-generated PM (fine [PM2.5] and coarse < or = 10 microm [PM10]) from the on-road vehicle fleet, which in this case includes traffic at very low speeds (10 km/h). The Windsor case study gives vehicle generated PM2.5 sources and their breakdown by vehicle age and class. It shows that the primary sources of vehicle-generated PM2.5 emissions are the late-model heavy-duty diesel vehicles. We also applied CALINE4 and AERMOD in conjunction with MicroFacPM, using Canadian traffic and climate conditions, to describe the vehicle-generated PM2.5 dispersion near this roadway during the month of May in 2003.     

Monitoring of PM10 and PM2.5 around primary particulate anthropogenic emission sources

Investigations on the monitoring of ambient air levels of atmospheric particulates were developed around a large source of primary anthropogenic particulate emissions: the industrial ceramic area in the province of Castelló (Eastern Spain). Although these primary particulate emissions have a coarse grain-size distribution, the atmospheric transport dominated by the breeze circulation accounts for a grain-size segregation, which results in ambient air particles occurring mainly in the 2.5–10 μm range. The chemical composition of the ceramic particulate emissions is very similar to the crustal end-member but the use of high Al, Ti and Fe as tracer elements as well as a peculiar grain-size distribution in the insoluble major phases allow us to identify the ceramic input in the bulk particulate matter. PM2.5 instead of PM10 monitoring may avoid the interference of crustal particles without a major reduction in the secondary anthropogenic load, with the exception of nitrate. However, a methodology based in PM2.5 measurement alone is not adequate for monitoring the impact of primary particulate emissions (such as ceramic emissions) on air quality, since the major ambient air particles derived from these emissions are mainly in the range of 2.5–10 μm. Consequently, in areas characterised by major secondary particulate emissions, PM2.5 monitoring should detect anthropogenic particulate pollutants without crustal particulate interference, whereas PM10 measurements should be used in areas with major primary anthropogenic particulate emissions.