A Simple Neural Network for Estimating Emission Rates of Hydrogen Sulfide and Ammonia from Single Point Sources

Abstract

Neural networks have shown tremendous promise in modeling complex problems. This work describes the development and validation of a neural network for the purpose of estimating point source emission rates of hazardous gases. This neural network approach has been developed and tested using experimental data obtained for two specific air pollutants of concern in West Texas, hydrogen sulfide and ammonia. The prediction of the network is within 20% of the measured emission rates for these two gases at distances of less than 50 m. The emission rate estimations for ground level releases were derived as a function of seven variables: downwind distance, crosswind distance, wind speed, downwind concentration, atmospheric stability, ambient temperature, and relative humidity. A backpropagation algorithm was used to develop the neural network and is also discussed here. The experimental data were collected at the Wind Engineering Research Field Site located at Texas Tech University in Lubbock, Texas. Based on the results of this study, the use of neural networks provides an attractive and highly effective tool to model atmospheric dispersion, in which a large number of variables interact in a nonlinear manner.     

The Application of the Karl Fischer Oven for the Determination of Water in Consumer Products

Abstract

The direct Karl Fischer (KF) titration method has known interferences for measuring water content. In addition, in analyzing some paints, KF can fail to produce an accurate analysis. The California Air Resources Board (GARB) staff has developed a KF procedure that can be used to determine the water content of consumer products. The procedure uses an oven accessory to the titration system, and is based on a distillation method developed by the California Polytechnical University at San Luis Obispo (Cal Poly). Samples are diluted in l-methoxy-2-propanol (MPA), and an aliquot is injected into an enclosed oven system, where the MPA/water azeotrope is swept directly into the KF titration vessel. The technique is accurate and precise and, thus far, proves to be a fast and reliable method for analysis.     

Feasibility of Applying a Stable Isotopic Tracer for Direct Determination of Dry Particulate Deposition to Soybean Plants

ABSTRACT

A stable rare-earth isotopic tracer was used to measure the deposition of KNO₃ particles on soybean leaves by direct measurement of the tracer on the plant surfaces by thermal-ionization mass spectrometry. Submicrometer particles, made from a solution containing 3 |mg mL^-1 neodymium isotope (¹⁴⁸Nd, 87.9%) and 1,000 mg mL^-1 KNO₃, were dispersed with a two-fluid nozzle and released upwind of a soybean field. Total suspended- and size-fractionated-aerosol particles were collected on an open-face filter and in a micro-orifice impactor, respectively, at a distance of 40 m from the release point. Soybean leaves exposed to the plume were collected at distances ranging from 25 to 100 m. As little as 5.5 pg of the tracer (i.e., excess ¹⁴⁸Nd) was detected in soybean leaves at signal-to-noise ratios ranging from 7,500 to 240,000, in the presence of 200 to 2,700 pg of naturally occurring Nd. The dry-particle deposition velocity, determined from the ratio of the aerial concentration and directly deposited aerosol (geometric mass mean diameter, 0.20 mm) flux, and its corresponding analytical uncertainty were 0.30 cm sec^-1 and 2.5%, respectively.     

Statistical Considerations in Determining the Health Significance of Constituents of Airborne Particulate Matter

ABSTRACT

Because of the U.S. Environmental Protection Agency’s (EPA) new ambient air quality standard for fine particles, the need is likely to continue for more detailed scientific investigation of various types of particles and their effects on human health. Epidemiology studies have become the method of choice for investigating health responses to such particles and to other air pollutants in community settings. Health effects have been associated with virtually all of the gaseous criteria pollutants and with the major constituents of airborne particulate matter (PM), including all size fractions less than about 20 gm, inorganic ions, carbonaceous particles, metals, crustal material, and biological aerosols. In many of the more recent studies, multiple pollutants or agents (including weather variables) have been significantly associated with health responses, and various methods have been used to suggest which ones might be the most important. In an ideal situation, classical least-squares regression methods are capable of performing this task. However, in the real world, where most of the pollutants are correlated with one another and have varying degrees of measurement precision and accuracy, such regression results can be misleading. This paper presents some guidelines for dealing with such collinearity and model comparison problems in both single- and multiple-pollutant regressions. These techniques rely on mean effect (attributable risk) rather than statistical significance per se as the preferred indicator of importance for the pollution variables.     

An Intelligent Emissions Controller for Fuel Lean Gas Reburn in Coal-Fired Power Plants

ABSTRACT

The application of artificial intelligence techniques for performance optimization of the fuel lean gas reburn (FLGR) system is investigated. A multilayer, feedforward artificial neural network is applied to model static nonlinear relationships between the distribution of injected natural gas into the upper region of the furnace of a coal-fired boiler and the corresponding oxides of nitrogen (NO_x) emissions exiting the furnace. Based on this model, optimal distributions of injected gas are determined such that the largest NO_x reduction is achieved for each value of total injected gas. This optimization is accomplished through the development of a new optimization method based on neural networks. This new optimal control algorithm, which can be used as an alternative generic tool for solving multidimensional nonlinear constrained optimization problems, is described and its results are successfully validated against an off-the-shelf tool for solving mathematical programming problems. Encouraging results obtained using plant data from one of Commonwealth Edison's coal-fired electric power plants demonstrate the feasibility of the overall approach.

Preliminary results show that the use of this intelligent controller will also enable the determination of the most cost-effective operating conditions of the FLGR system by considering, along with the optimal distribution of the injected gas, the cost differential between natural gas and coal and the open-market price of NO_x emission credits. Further study, however, is necessary, including the construction of a more comprehensive database, needed to develop high-fidelity process models and to add carbon monoxide (CO) emissions to the model of the gas reburn system.     

Aerosol Filtration by Sorbent Beds

Fixed beds of sorbent media are used for the evaluation of poiynuclear aromatic hydrocarbons (PAH) present in air. Twostage sampling and separate extraction and analyses of PAH associated with aerosol particles and those present in the vapor state are usually performed. The ability of commonly used sorbents to retain particulate matter introduces a potential for reducing the time and cost of PAH evaluation procedures.

The filtration efficiency of three sorbent media, Florisil, XAD-2, and polyurethanefoam (PUF), for particles in 0.1 to 1 µm size range was studied using airflow rates from 4 to 2501 /mm through a PS 1 sorbent cartridge. Theoretical considerations were used to identify the principal filtration mechanisms and to assess the predictability of the aerosol filtration performance of sorbent filters. The results of this study indicate XAD-2 to be an efficient filtration medium owing to the electrostatic enhancement of capturing and retaining aerosol particles.

As a result of theoretical considerations, Brownian diffusion and inertial deposition were found to be major filtration mechanisms accompanied by electrostatic effects. While the efficiency of the diffusional deposition mechanism was reasonably well predicted with available theories, modeling of submicron particle impaction at higher fluid velocities appeared to be inadequate. Further developments are suggested to improve our understanding of filtration phenomena in sorbent beds under high flow rate conditions.     

Influence of age,season, body condition and geographical area on concentrations of chlorinated and brominated contaminants in wild mink (Neovison vison) in Sweden

The wild mink has gained acceptance as a sentinel species in environmental monitoring. However, only limited data are available in the literature on factors driving variability in concentrations of organic pollutants in this species. This study characterizes the differences in contaminant concentrations in subcutaneous fat of male mink from four different areas in Sweden and demonstrates how age, season and body condition influence concentrations of polychlorinated biphenyl (PCB) congeners, polybrominated diphenyl ether (PBDE) congeners (including methoxylated forms, MeO-PBDEs), as well as the pesticides dichlorodiphenyldichloroethylene (DDE), chlordane and hexachlorobenzene (HCB). The data were statistically treated using multiple regression and principal component analysis. The ∑PCB concentration and concentrations of PCB congeners 138, 156, 157, 180, 170/190, 189, 194, 206, 209 as well as PBDE 153/154 varied with age. Season had an influence on ∑PCB, PBDE 47 and PBDE 153/154 concentrations, as well as concentrations of most PCB congeners, with the exception of PCB 101, 110, 141 and 182/187. Lean mink had higher concentrations of most PCBs and PBDEs than mink with larger fat depots. The analyzed pesticides (DDE, oxychlordane, HCB) showed no systematic variation with season, age or body condition. The concentrations of MeO-PBDEs were generally low and 6MeO-PBDE 47 was the most commonly detected MeO-PBDE in mink from marine, brackish and freshwater areas. The results indicate that age, season and body condition are factors that may influence the concentrations of PCBs and PBDEs, and it is thus recommended to take these factors into account when analyzing mink exposure data.     

Multi-year, seasonal genotypic surveys of coral-algal symbioses reveal prevalent stability or post-bleaching reversion

This report documents the extent to which coral colonies show fluctuations in their associations with different endosymbiotic dinoflagellates. The genetic identity of Symbiodinium from six coral species [Acropora palmata (Lamarck), A. cervicornis (Lamarck), Siderastrea siderea (Ellis and Solander), Montastrea faveolata (Ellis and Solander), M. annularis (Ellis and Solander), and M. franksi (Gregory)] was examined seasonally over five years (1998 and 2000–2004) in the Bahamas and Florida Keys at shallow (1 to 4 m) fore-reef/patch reef sites and at deeper fore-reef (12–15 m) locations. Symbionts were identified genetically using denaturing gradient gel electrophoresis (DGGE) fingerprinting of the internal transcribed spacer region 2 (ITS2) of ribosomal RNA gene loci. Repetitive sampling from most labeled colonies from the Bahamas and the Florida Keys showed little to no change in their dominant symbiont. In contrast, certain colonies of M. annularis and M. franksi from the Florida Keys exhibited shifts in their associations attributed to recovery from the stresses of the 1997–1998 El Niño southern oscillation (ENSO) event. Over several years, a putatively stress-tolerant clade D type of Symbiodinium was progressively replaced in these colonies by symbionts typically found in M. annularis and M. franksi in Florida and at other Caribbean locations. Greater environmental fluctuations in Florida may explain the observed changes among some of the symbioses. Furthermore, symbiotic associations were more heterogeneous at shallow sites, relative to deep sites. The exposure to greater environmental variability near the surface may explain the higher symbiont diversity found within and between host colonies.