The Emissions and Dispersion Modeling System (EDMS): Its Development and Application at Airports and Airbases

A new complex source microcomputer model has been developed for use at civil airports and Air Force bases. This paper describes both the key features of this model and its application in evaluating the air quality impact of new construction projects at three airports: one in the United States and two in Canada.

The single EDMS model replaces the numerous models previously required to assess the air quality impact of pollution sources at airports. EDMS also employs a commercial data base to reduce the time and manpower required to accurately assess and document the air quality impact of airfield operations.

On July 20, 1993, the U.S. Environmental Protection Agency (EPA) issued the final rule (Federal Register, 7/20/93, page 38816) to add new models to the Guideline on Air Quality Models. At that time EDMS was incorporated into the Guideline as an Appendix A model.     

Environmental Condition Assessment of US Military Installations Using GIS Based Spatial Multi-Criteria Decision Analysis

Environment functions in various aspects including soil and water conservation, biodiversity and habitats, and landscape aesthetics. Comprehensive assessment of environmental condition is thus a great challenge. The issues include how to assess individual environmental components such as landscape aesthetics and integrate them into an indicator that can comprehensively quantify environmental condition. In this study, a geographic information systems based spatial multi-criteria decision analysis was used to integrate environmental variables and create the indicator. This approach was applied to Fort Riley Military installation in which land condition and its dynamics due to military training activities were assessed. The indicator was derived by integrating soil erosion, water quality, landscape fragmentation, landscape aesthetics, and noise based on the weights from the experts by assessing and ranking the environmental variables in terms of their importance. The results showed that landscape level indicator well quantified the overall environmental condition and its dynamics, while the indicator at level of patch that is defined as a homogeneous area that is different from its surroundings detailed the spatiotemporal variability of environmental condition. The environmental condition was mostly determined by soil erosion, then landscape fragmentation, water quality, landscape aesthetics, and noise. Overall, environmental condition at both landscape and patch levels greatly varied depending on the degree of ground and canopy disturbance and their spatial patterns due to military training activities and being related to slope. It was also determined the environment itself could be recovered quickly once military training was halt or reduced. Thus, this study provided an effective tool for the army land managers to monitor environmental dynamics and plan military training activities. Its limitation lies at that the obtained values of the indicator vary and are subjective to the experts' knowledge and experience. Thus, further advancing this approach is needed by developing a scientific method to derive the weights of environmental variables.     

Ethylene: An Urban Air Pollutant

Ethylene is an unusual air pollutant in that it is a plant hormone. Motor vehicle exhaust is a primary source. In the Washington, D. C, area, ethylene concentrations ranged from 700 ppb in the city center to 39 ppb in areas outside the circumferential beltway. Plants grown in these concentrations of ethylene, using controlled environment chambers, exhibited typical symptoms of ethylene toxicity: reduced growth, premature senescence, and reduced flowering and fruit production. When plants were grown in carbon-filtered ambient air, which was also filtered through KMnO₄ to remove ethylene by oxidation, growth, flowering, and fruit production increased. These observations demonstrate that ethylene air pollution is a continual source of stress for plant growth and development in an urban environment.     

Emissions into the Atmosphere Due to the Ceramic Salt Glazing Process

The techniques of Principal Component Analysis (PCA) and subsequent regression analysis were used in an attempt to describe local and upwind chemical and physical factors which affect the variability of SO₄ ^–2 concentrations observed in a rural area of the northeastern U.S. The data used in the analyses included upwind and local O₃ concentrations, temperature, relative humidity and other climatological information, SO₂, and meteorological information associated with backward trajectories. The investigation identified five principal components, three major (eigenvalues >1) and two minor (eigenvalues < one), which accounted for 52% (r = 0.72) of the variability in the SO₄ ^–2 regression model. These components can be described as representing local and upwind photochemistry, droplet growth, SO₂ emissions, and air mass characteristics. The study also indicated that in future studies it will be necessary to a priori select air pollution and meteorological variables for measurement to potentially increase the sensitivity of this type of receptor model.     

Comparison of Predicted and Measured Concentrations for 58 Alternative models of Plume Transport in Complex Terrain

The purpose of this study was to evaluate alternative prediction models for the SO₂ concentrations produced in the vicinity of the Ohio Edison Company Sammis Power Plant. The plant is situated in the northeastern portion of the Ohio River Valley in complex terrain. Comparisons of the 16 highest predicted and measured short-term SO₂ concentrations were conducted for a one year period for 58 alternative models. Several models were found to predict reasonably accurately the 16 highest measured 24-hour SO₂ concentrations. Each of these models requires an upward adjustment in the plume centerline location as the plume is transported downwind in rising terrain. These same models overpredict by substantial margins the 16 highest measured 3-hour SO₂ concentrations. Improvements in emissions inventory data and improvements in the prediction models used are believed necessary to increase prediction accuracy further.     

Magnetic ion exchange treatment of stabilized landfill leachate

Stabilized landfill leachate is characterized by a high concentration of non-biodegradable organic matter, which is similar in chemistry to dissolved organic matter (DOM) in the natural aquatic environment. Magnetic ion exchange (MIEX) resin treatment is well-studied in drinking water for removal of DOM from natural waters. There are fewer studies evaluating MIEX treatment of waste waters, and there is no previous work evaluating MIEX treatment of landfill leachate. This work systematically evaluated MIEX treatment of stabilized landfill leachate and evaluated the results in the context of previous studies of MIEX treatment of natural and waste waters. Five leachates from four landfills were evaluated as a function of MIEX resin dose, mixing time, and regeneration efficiency. MIEX resin removed DOM from landfill leachate, even in the presence of a reported high background concentration of inorganic ions. MIEX resin that was exhausted with leachate DOM was effectively regenerated with a concentrated NaCl solution, and regenerated MIEX resin performed similarly to virgin resin. For a majority of the leachates, the removal trend for MIEX resin was color > UV-absorbing substances > dissolved organic carbon ≈ COD > total nitrogen. Finally, MIEX resin removed a wider range of DOM from leachate than coagulation. The most important contribution of this work is that MIEX treatment of leachate followed very similar trends as MIEX treatment of natural waters, which will allow previous MIEX data to be used to estimate the treatment efficiency of other waste waters.     

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).     

Online sorting of recovered wood waste by automated XRF-technology: part II. Sorting efficiencies

Sorting of waste wood is an important process practiced at recycling facilities in order to detect and divert contaminants from recycled wood products. Contaminants of concern include arsenic, chromium and copper found in chemically preserved wood. The objective of this research was to evaluate the sorting efficiencies of both treated and untreated parts of the wood waste stream, and metal (As, Cr and Cu) mass recoveries by the use of automated X-ray fluorescence (XRF) systems. A full-scale system was used for experimentation. This unit consisted of an XRF-detection chamber mounted on the top of a conveyor and a pneumatic slide-way diverter which sorted wood into presumed treated and presumed untreated piles. A randomized block design was used to evaluate the operational conveyance parameters of the system, including wood feed rate and conveyor belt speed. Results indicated that online sorting efficiencies of waste wood by XRF technology were high based on number and weight of pieces (70-87% and 75-92% for treated wood and 66-97% and 68-96% for untreated wood, respectively). These sorting efficiencies achieved mass recovery for metals of 81-99% for As, 75-95% for Cu and 82-99% of Cr. The incorrect sorting of wood was attributed almost equally to deficiencies in the detection and conveyance/diversion systems. Even with its deficiencies, the system was capable of producing a recyclable portion that met residential soil quality levels established for Florida, for an infeed that contained 5% of treated wood.