Evaluation of solid sorbents for sampling nitroglycerin,ethylene glycol dinitrate and isopropyl nitrate in work-room air

Activated charcoal, Amberlite XAD-2, and Amberlite XAD-7 have been evaluated for the solid sampling of airborne nitroglycerin, ethylene glycol dinitrate and isopropyl nitrate. With the use of diethyl ether for solvent desorption, XAD-2 was found superior for sampling nitroglycerin and ethylene glycol dinitrate. Both XAD-7 and activated charcoal gave high recoveries for isopropyl nitrate; charcoal had a greater capacity than XAD-7 for this compound, however.     

Evaluation of solid sorbents for sampling aliphatic and aromatic nitro compounds in work-room air

Amberlite XAD-2 and XAD-7 porous polymers were evaluated for the solid sampling of aliphatic and aromatic nitro compounds in work-room air. With the use of solvent desorption, XAD-2 gave 80–100% recovery of nitroaromatics, and XAD-7 85–100% recovery of nitroaliphatics. The compounds studied were nitromethane, nitroethane, 2-nitropropane, nitrobenzene, $\text{m}$-dinitrobenzene and 2,4,6-trinitrotoluene. All six compounds gave very poor recoveries from activated charcoal.     

A Preliminary Assessment of Montréal Process Indicators of Forest Fragmentation for the United States

As part of the U.S. 2003 National Report on Sustainable Forests, four metrics of forest fragmentation--patch size, edge amount, inter-patch distance, and patch contrast--were measured within 137744 non-overlapping 5625 ha analysis units on land-cover maps derived from satellite imagery for the 48 conterminous States. The perimeter of a typical forest patch is about 100 m from the perimeter of its nearest neighbor, except when there is not much forest, in which case that distance is 200 to 300 m. A typical analysis unit has from 10 to 40% as much forest edge as it could possibly have, given the amount of forest present. Most analysis units contain a large number of patches that are less than one hectare in size, and about 10% contain one or more 2000 to 5000 ha patches. Forest often defines the background landscape, and patch contrast is generally either very high or very low in eastern regions and intermediate in western regions. Many research needs were identified by this experimental analysis of available data and metrics.     

Synthesis of HNO3 from Organic Matter and its Influence on Nutrient Replenishment in Forest Soils

Chemical analyses during a decade of bulk precipitation, throughfall, humus water and soil water in forest plots ranging from sand to silt of Pleistocene origin in Flanders (N-Belgium) prove that previous and present weathering is predominantly due to synthesis of HNO3 from soil organic matter. The HNO3 reacts with silicates and possibly PO4(3-) species, releasing Al, Fe, nutrient base cations and H2PO4-, and is transformed into NO3-. In all soils solubilized Al3+ is predominantly associated with NO3- and with some organic bases in the coarse-textured soils with undisturbed or previously plowed spodic B horizon. The amounts of ions leached, especially Al3+ and NO3-, are much higher in the sandy than in the silty soils as a result of a stronger neutralization of acidity in the silty soils. Nutrients, leached from the soil, have to be replenished in some way for a sustained forest growth. N- and S-species must come from the atmosphere. Basic cations and P-species have to be supplied out of the solid phase. The concentrations of NO3- in the soil water show that the liberation of these elements from the solid phase is performed by HNO3. Especially in the coarse-textured soils the need for NO3- is high. Therefore, instead of being a real hazard for the forest ecological system, supplementary addition of HNO3 or NH3 can be beneficial.     

An online air pollution forecasting system using neural networks

In this work, an online air pollution forecasting system for Greater Istanbul Area is developed. The system predicts three air pollution indicator (SO(2), PM(10) and CO) levels for the next three days (+1, +2, and +3 days) using neural networks. AirPolTool, a user-friendly website (http://airpol.fatih.edu.tr), publishes +1, +2, and +3 days predictions of air pollutants updated twice a day. Experiments presented in this paper show that quite accurate predictions of air pollutant indicator levels are possible with a simple neural network. It is shown that further optimizations of the model can be achieved using different input parameters and different experimental setups. Firstly, +1, +2, and +3 days' pollution levels are predicted independently using same training data, then +2 and +3 days are predicted cumulatively using previously days predicted values. Better prediction results are obtained in the cumulative method. Secondly, the size of training data base used in the model is optimized. The best modeling performance with minimum error rate is achieved using 3-15 past days in the training data set. Finally, the effect of the day of week as an input parameter is investigated. Better forecasts with higher accuracy are observed using the day of week as an input parameter.