Supply curves for using powder river basin coal to reduce sulfur emissions

Supply curves were prepared for coal-fired power plants in the contiguous United States switching to Wyoming's Powder River Basin (PRB) low-sulfur coal. Up to 625 plants, representing approximately 44% of the nameplate capacity of all coal-fired plants, could switch. If all switched, more than dollars 8.8 billion additional capital would be required and the cost of electricity would increase by up to dollars 5.9 billion per year, depending on levels of plant derating. Coal switching would result in sulfur dioxide (SO2) emissions reduction of 4.5 million t/yr. Increase in cost of electricity would be in the range of 0.31-0.73 cents per kilowatt-hour. Average cost of S emissions reduction could be as high as dollars 1298 per t of SO2. Up to 367 plants, or 59% of selected plants with 32% of 44% nameplate capacity, could have marginal cost in excess of dollars 1000 per t of SO2. Up to 73 plants would appear to benefit from both a lowering of the annual cost and a lowering of SO2 emissions by switching to the PRB coal.     

automated data handling for small authorities

The body of information presented in this paper is directed to administrators of smaller authorities—defined, arbitrarily, as those with small staff and not embracing large metropolitan areas. As their activities increase, viable programs are likely to produce prodigious quantities of data, necessitating the use of modern computers for most efficient analysis.

Remote links with high-speed time sharing computers provide a means for smaller authorities to satisfy this need. The capabilities of a large computer can be brought into the local office to handle (a) administrative and enforcement problems, (b) technical data analysis, and (c) instructional needs.

A data handling system being developed for use by the Northwest Air Pollution Authority is described, together with an examination of its potential utility to the Authority as its activities increase.     

Fuel consumption, emissions estimation, and emissions cost estimates using global positioning data

The methodology laid out in this paper shows that typical operational data from vehicle fleets monitored by a global positioning system (GPS) can be used to estimate heavy-duty diesel vehicle (HDDV) emissions, thereby enabling waste managers and governing bodies to internalize the responsibility for socioenvironmental costs traditionally absorbed by external parties. Although municipal solid waste (MSW) collection vehicles are the subjects of this particular study, the methodology presented here can be applied to any fleet of vehicles monitored by GPS. This study indicates that MSW collection trucks may be considerably less fuel efficient in the field than published values for HDDV fuel efficiency suggest. The average fuel efficiency of one MSW collection truck was estimated as 0.90 +/- 0.44 km/L (2.12 +/- 1.03 mi/gal). This same truck would generate approximately 42 metric tons of CO2 equivalents/yr, which is comparable to the greenhouse gas emissions of a large sport utility vehicle driving six times the distance, in town, for a year. In terms of the impacts such emissions have, projections for the monetary cost of emissions are available but highly variable. They suggest that the external monetary costs of emissions range between 6 and 39% of the annual fuel costs for the studied MSW collection truck. The results of this study indicate a need for further research into valuation of the hidden, external costs of emissions, borne by local and global socioecological communities. The possible implications of this result include poorly advised fleet procurement decisions and underestimation of MSW collection fleet emissions.     

Road grade estimation for on-road vehicle emissions modeling using light detection and ranging data

Vehicle-specific power (VSP) is useful for explaining a substantial portion of variability in real-world vehicle emissions, such as those measured with portable emissions monitoring systems (PEMS). VSP is a function of vehicle speed, acceleration, and road grade. Road grade is shown to significantly affect estimates of both VSP and of real-world emissions via sensitivity analysis and analysis of empirical data. However, road grade is difficult to measure reliably using PEMS. Therefore, alternative methods for estimating road grade were identified and compared. A preferred method for estimating road grade was explored in more detail based on light detection and ranging (LIDAR) data. The method includes buffering LIDAR data onto roadway maps using a geographic information system tool, defining segments of roadway based on criteria pertaining to vertical curvature, quantification of roadway elevations within the buffered segments, and estimation of road grade and banking by fitting a plane to each segment. Factors influencing errors in road grade estimates are discussed. The method was evaluated by application to selected interstate highways and comparison to design drawing data. The development and application of LIDAR-based road grade data are demonstrated via a case study using PEMS data collected in the Research Triangle Park, NC, area. LIDAR data are shown to be reliable and accurate for road grade estimation for vehicle emissions modeling.     

Global estimation of CO emissions using three sets of satellite data for burned area

Using three sets of satellite data for burned areas together with the tree cover imagery and a biogeochemical component of the Integrated Science Assessment Model (ISAM) the global emissions of CO and associated uncertainties are estimated for the year 2000. The available fuel load (AFL) is calculated using the ISAM biogeochemical model, which accounts for the aboveground and surface fuel removed by land clearing for croplands and pasturelands, as well as the influence on fuel load of various ecosystem processes (such as stomatal conductance, evapotranspiration, plant photosynthesis and respiration, litter production, and soil organic carbon decomposition) and important feedback mechanisms (such as climate and fertilization feedback mechanism). The ISAM estimated global total AFL in the year 2000 was about 687 Pg AFL. All forest ecosystems account for about 90% of the global total AFL. The estimated global CO emissions based on three global burned area satellite data sets (GLOBSCAR, GBA, and Global Fire Emissions Database version 2 (GFEDv2)) for the year 2000 ranges between 320 and 390 Tg CO. Emissions from open fires are highest in tropical Africa, primarily due to forest cutting and burning. The estimated overall uncertainty in global CO emission is about ±65%, with the highest uncertainty occurring in North Africa and Middle East region (±99%). The results of this study suggest that the uncertainties in the calculated emissions stem primarily from the area burned data.     

Top-down estimate of mercury emissions in China using four-dimensional variational data assimilation

An inverse modeling method using the four-dimensional variational data assimilation approach is developed to provide a top-down estimate of mercury emission inventory in China. The mercury observations on board the C130 aircraft during the Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia) campaign in April 2001 are assimilated into a regional chemical transport model, STEM. Using a 340 Mg of elemental mercury emitted in 1999, the assimilation results in an increase in Hg⁰ emissions for China to 1140 Mg in 2001. This is an upper limit amount of the elemental mercury required in China. The average emission-scaling factor is ∼3.4 in China. The spatial changes in the mercury emissions after the assimilation are also evaluated. The largest changes are estimated on the China north-east coastal areas and the areas of north-center China. The influences of the observation and inventory uncertainties and the initial and boundary conditions on the emission estimates are discussed. Increasing the boundary conditions of Hg from 1.2 to 1.5 ng m⁻³, results in a top-down estimate of Hg⁰ emissions for China of 718 Mg, and leads the average scaling factor from 3.4 to 2.1.     

Assessment of emissions data for the Toronto region using aircraft-based measurements and an air quality model

An aircraft-based measurement campaign was conducted during the summer of 1996 in the vicinity of Toronto, Canada. The objective of the campaign was to assess the errors in a particular emission inventory used by three-dimensional air quality models. Measurements of NO₂ and hydrocarbons were made both upwind and downwind of Toronto, on days with strong synoptic-scale flow from a west to northerly direction. The chemical composition of the background airmass on these days was typical of unpolluted continental air. Measurements have been compared with the output from an on-line air quality model (MC2-AQ) run at 5 km resolution and suggest that emissions of NO_x from Toronto are well described in the emission database, though evidence that NO_x emissions are underestimated for suburban regions surrounding Toronto was found. In general, no significant underestimation of hydrocarbon emissions was found, though emissions of the model propane species, which includes acetylene and benzene, was underestimated by at least a factor of two.     

A comparative analysis of using trip-based versus link-based traffic data for regional mobile source emissions estimation

The current method used for calculating vehicle emissions integrates travel data and associated vehicle emission factors. Travel data from traditional travel demand models are normally link-based (e.g., volumes on roadway segments), while California emission factors are trip-based (i.e., average emission factors over an entire trip), creating a mismatch in the modeling interface. Using dynamic simulation for trip assignment, we present a new modeling framework that consistently provides both trip-based and link-based VMT-speed distributions. Using the Sacramento Metropolitan Area and Kern County in California, we demonstrate the feasibility of this new method and quantify the effects of using trip-based versus link-based travel data on regional peak period emission inventories. The comparison results indicate that for the base scenario in both studied regions, the link-based method generally results in higher emissions than the trip-based method. The sensitivities of the link and trip-based methods to road network variations also appear dissimilar. The link-based emissions are more sensitive to facility-related changes, while the trip-based emissions are more sensitive to demand-related changes. This suggests that greater care may need to be taken to specify the effects of this modeling interface issue within the transportation conformity process and subsequent mobile emissions analysis.     

Design and performance characterization strategy using modeling for biofiltration control of odorous hydrogen sulfide

Biofilter, dynamic modeling software characterizing contaminant removal via biofiltration, was used in the preliminary design of a biofilter to treat odorous hydrogen sulfide (H2S). Steady-state model simulations were run to generate performance plots for various influent concentrations, loadings, residence times, media sizes, and temperatures. Although elimination capacity and removal efficiency frequently are used to characterize biofilter performance, effluent concentration can be used to characterize performance when treating to a target effluent concentration. Model simulations illustrate that, at a given temperature, a biofilter cannot reduce H2S emissions below a minimum value, no matter how large the biofilter or how long the residence time. However, a higher biofilter temperature results in lower effluent H2S concentrations. Because dynamic model simulations show that shock loading can significantly increase the effluent concentration above values predicted by the steady-state model simulations, it is recommended that, to consistently meet treatment objectives, dynamic feed conditions should be considered. This study illustrates that modeling can serve as a valuable tool in the design and performance optimization of biofilters.     

The effect of diesel fuel sulfur content on particulate matter emissions for a nonroad diesel generator

The effect of sulfur content on diesel particulate matter (DPM) emissions was studied using a diesel generator (Generac Model SD080, rated at 80 kW) as the emission source to simulate nonroad diesel emissions. A load simulator was used to apply loads to the generator at 0, 25, 50, and 75 kW, respectively. Three diesel fuels containing 500, 2100, and 3700 ppm sulfur by weight were selected as generator fuels. The U.S. Environmental Protection Agency sampling Method 5 "Determination of Particulate Matter Emissions from Stationary Sources" together with Method 1A "Sample and Velocity Traverses for Stationary Sources with Small Stacks or Ducts" was adopted as a reference method for measurement of the exhaust gas flow rate and DPM mass concentration. The effects of various parameters on DPM concentration have been studied, such as fuel sulfur contents, engine loads, and fuel usage rates. The increase of average DPM concentrations from 3.9 mg/Nm3 (n cubic meter) at 0 kW to 36.8 mg/Nm3 at 75 kW is strongly correlated with the increase of applied loads and sulfur content in the diesel fuel, whereas the fuel consumption rates are only a function of applied loads. An empirical correlation for estimating DPM concentration is obtained when fuel sulfur content and engine loads are known for these types of generators: Y = Zm(alphaX + beta), where Y is the DPM concentration, mg/m3, Z is the fuel sulfur content, ppm(w) (limited to 500-3700 ppm(w)), X is the applied load, kW, m is the constant, 0.407, alpha and beta are the numerical coefficients, 0.0118 +/- 0.0028 (95% confidence interval) and 0.4535 +/- 0.1288 (95% confidence interval), respectively.     

Estimates of community exposure and health risk to sulfur dioxide from power plant emissions using short-term mobile and stationary ambient air monitoring

To estimate plausible health effects associated with peak sulfur dioxide (SO₂) levels from three coal-fired power plants in the Baltimore, Maryland, area, air monitoring was conducted between June and September 2013. Historically, the summer months are periods when emissions are highest. Monitoring included a 5-day mobile and a subsequent 61-day stationary monitoring study. In the stationary monitoring study, equipment was set up at four sites where models predicted and mobile monitoring data measured the highest average concentrations of SO₂. Continuous monitors recorded ambient concentrations each minute. The 1-min data were used to calculate 5-min and 1-hr moving averages for comparison with concentrations from clinical studies that elicited lung function decrement and respiratory symptoms among asthmatics. Maximum daily 5-min moving average concentrations from the mobile monitoring study ranged from 70 to 84 ppb (183–220 µg/m³), and maximum daily 1-hr moving average concentrations from the mobile monitoring study ranged from 15 to 24 ppb (39–63 µg/m³). Maximum 5-min moving average concentrations from stationary monitoring ranged from 39 to 229 ppb (102–600 µg/m³), and maximum daily 1-hr average concentrations ranged from 15 to 134 ppb (40–351 µg/m³). Estimated exposure concentrations measured in the vicinity of monitors were below the lowest levels that have demonstrated respiratory symptoms in human clinical studies for healthy exercising asthmatics. Based on 5-min and 1-hr monitoring, the exposure levels of SO₂ in the vicinity of the C.P. Crane, Brandon Shores, and H.A. Wagner power plants were not likely to elicit respiratory symptoms in healthy asthmatics.

Implications: Mobile and stationary air monitoring for SO₂ were conducted to quantify short-term exposure risk, to the surrounding community, from peak emissions of three coal-fired power plants in the Baltimore area. Concentrations were typically low, with only a few 5-min averages higher than levels indicated during clinical trials to induce changes in lung capacity for healthy asthmatics engaged in exercise outdoors.     

基于QAR数据的民航发动机尾气污染物排放量估算

对现有航空发动机尾气污染物排放估算模型进行总结与分析,重点对一阶逼近3.0尾气颗粒污染物（particulate matter,PM）估算方法和ICAO尾气气体污染物估算方法进行了详细的研究。借助QAR记录的发动机相关性能数据,利用ICAO公布的排放指数,用一阶逼近3.0方法来估算CFM56民航发动机一个LTO阶段尾气颗粒污染物的排放情况,用ICAO气体排放模型估算CFM56民航发动机一个LTO阶段NOx、CO、HC气体的排放情况。最终得到CFM56发动机一个典型航班LTO阶段的尾气污染物排放总体情况。实例计算得到CFM56发动机一个典型航班LTO阶段的排放情况为：NOx,5 683.61 g;CO,3 821.89 g;HC,208.946 g;PM,121.287 g。本文提出的单机尾气污染物排放量估算方法可推广应用到估算整个机队LTO阶段的污染物排放量,以及整个机队某个时间段的排放情况,对评估机场附近区域大气环境提供了重要参考数据。     

Applications of satellite remote sensing data for estimating biogenic emissions in southeastern Texas

The sensitivity of biogenic emission estimates and air quality model predictions to the characterization of land use/land cover (LULC) in southeastern Texas was examined using the Global Biosphere Emissions and Interactions System (GloBEIS) and the Comprehensive Air Quality Model with extensions (CAMx). A LULC database was recently developed for the region based on source imagery collected by the Landsat 7 Enhanced Thematic Mapper-Plus sensor between 1999 and 2003, and field data for land cover classification, species identification and quantification of biomass densities.  Biogenic emissions estimated from the new LULC data set showed good general agreement in their spatial distribution, but were approximately 40% lower than emissions from the LULC data set currently used by the State of Texas, primarily because of differences in the biomass estimates of key species such as Quercus. Predicted ozone mixing ratios using the biogenic emissions produced from the new LULC data set were as much as 26 ppb lower in some areas on some days, depending on meteorological conditions. Satellite data and image classification techniques provide useful tools for mapping and monitoring changes in LULC. However, field validation is necessary to link species and biomass densities to the classification system used for accurate biogenic emissions estimates, especially in areas such as riparian corridors that contain dense spatial coverage of key species.     

Evaluating peats for their capacities to remove odorous compounds from liquid swine manure using headspace "solid-phase microextraction".

This paper reports on research designed to investigate the capacities of different highly characterized peats to remove odorous compounds from liquid swine manure (LSM). Peat types representing a wide range of properties were tested in order to establish which chemical and physical properties might be most indicative of their capacities to remediate odors produced by LSM. Eight percent slurries (of peat/LSM) were measured for odor changes after 24 hours using odor panel and GC/MS-Solid-phase microextraction (GC/MS-SPME) analysis. The GC/MS-SPME and odor panel results indicated that, although all peats tested in this study were found to be effective at removing odor-causing compounds found in LSM, some peats tended to work better than others. Overall, the peats that were the most effective at removing odor-causing compounds tended to have lower bulk densities, ash contents, fulvic acids contents, and guaiacyl lignins contents, and higher water holding capacities, hydraulic conductivities, "total other lignins" contents, hydrogen contents, carbon contents, and total cellulose contents. GC/MS-SPME analysis was found to be a reasonably inexpensive and efficient way of conducting this type of research. It allows one to identify a large number of the odor-causing compounds found in LSM, and more importantly, to detect with some precision specific differences in the amounts of these compounds between peat types.     

Analytical method using gas chromatography and ion trap tandem mass spectrometry for the determination of S-triazines and their metabolites in the atmosphere

Gas chromatography-ion trap detector (GC-ITD) was used to detect atmospheric triazines and their degradation products in the gaseous and particulate phases. Because triazines and their metabolites are expected to be present at very low concentrations and enclosed in the complex atmospheric matrix, the analytical method used was both highly selective and sensitive. These two properties were obtained by associating chromatography with ion trap tandem mass spectrometry (GC-ITD (MS/MS)). To develop this method, a comparison between the two ionization modes (electron impact and positive-chemical-ionisation) in single-MS was first conducted to choose the parent ions of the five target analytes, i.e. atrazine, desethylatrazine, deisopropylatrazine, terbuthylazine and desethylterbuthylazine. Then, a MS/MS method was optimised by parameters such as the radio frequency storage level and the collision-induced dissociation excitation voltage. Finally, a last step enabled the development of a calibrating program based on the quantification of daughter ions. With this analytical procedure, the detection limits varied between 0.8 and 15 pg m(-3) depending on the compounds under study. This method was tested with success for four atmospheric samples collected in Strasbourg (France) in which four of the five target compounds were detected.     

Stationary sources of airborne lead: a comparison of emissions data for southern California

Estimates for the air releases of lead from stationary point sources are considered for the South Coast Air Basin of California. We have examined four databases published by U.S. Environmental Protection Agency, the California Air Resources Board, and the South Coast Air Quality Management District. Our analysis indicates that none of the databases includes every emitting facility in the South Coast Air Basin of California and that other discrepancies among the databases exist. Additionally, the data have been analyzed for temporal variation, and some of the California Air Resources Board data are not current. The South Coast Air Quality Management District inventory covers 12 times more facilities in 2001 than in 1996. From this analysis, we conclude that all four of the databases would benefit by sharing data, increasing transparency, analyzing uncertainty, and standardizing emission estimation methods.     

Monitoring change in mountainous dry-heath vegetation at a regional scale using multitemporal Landsat TM data

Vegetation cover-change analysis requires selection of an appropriate set of variables for measuring and characterizing change. Satellite sensors like Landsat TM offer the advantages of wide spatial coverage while providing land-cover information. This facilitates the monitoring of surface processes. This study discusses change detection in mountainous dry-heath communities in J?mtland County, Sweden, using satellite data. Landsat-5 TM and Landsat-7 ETM+ data from 1984, 1994 and 2000, respectively, were used. Different change detection methods were compared after the images had been radiometrically normalized, georeferenced and corrected for topographic effects. For detection of the classes change--no change the NDVI image differencing method was the most accurate with an overall accuracy of 94% (K = 0.87). Additional change information was extracted from an alternative method called NDVI regression analysis and vegetation change in 3 categories within mountainous dry-heath communities were detected. By applying a fuzzy set thresholding technique the overall accuracy was improved from of 65% (K = 0.45) to 74% (K = 0.59). The methods used generate a change product showing the location of changed areas in sensitive mountainous heath communities, and it also indicates the extent of the change (high, moderate and unchanged vegetation cover decrease). A total of 17% of the dry and extremely dry-heath vegetation within the study area has changed between 1984 and 2000. On average 4% of the studied heath communities have been classified as high change, i.e. have experienced "high vegetation cover decrease" during the period. The results show that the low alpine zone of the southern part of the study area shows the highest amount of "high vegetation cover decrease". The results also show that the main change occurred between 1994 and 2000.