Performance Evaluation of Integral and Analytical Plume Rise Algorithms

The purpose of this study was to evaluate the performance of current regulatory algorithms for predicting plume rise for refinerytype sources (short stacks and a wide range of source conditions) and the performance of new or alternate algorithms which may provide better estimates. To meet the objectives, five plume rise algorithms were statistically evaluated against ten field and laboratory plume rise data bases. Two forms of the Briggs plume rise equations were tested because they are almost exclusively used in current EPA regulatory models. Two modified Briggs equations were tested to assess how simple modifications can Improve the accuracy of the estimates. The fifth algorithm was a numerical solution to the basic equations for conservation of mass, momentum, and energy often referred to as an Integral plume rise algorithm. This algorithm was selected because It handles the wide range of source and atmospheric boundary-layer conditions that affect trajectories of plumes from refinery stacks.

Ten independent plume rise data bases were assembled that covered a wide range of source and meteorological conditions. From the data bases, a total of 107 different data sets were obtained and each data set included plume rise observations versus downwind distance for one source and meteorological condition. Each model was run for each data set and the root-mean-square and mean error between model and observation was computed for use in statistically evaluating model performance.

The statistical evaluation of the algorithms showed that the rms error (considering all data bases) for the Integral plume rise algorithm was approximately 30 percent less than the errors for all other algorithms tested. This difference was significant at the 95 percent confidence level. The results suggest that improved plume rise estimates in regulatory models applied to refineries and other appropriate sources could be achieved to reduce costs and improve ambient air quality estimates through the use of an integral plume rise algorithm.     

基于人工神经网络和遗传算法研究锰过氧化酶脱色甲基橙

基于锰过氧化物酶(MnP)氧化脱色偶氮类染料的原理,实验研究MnP对甲基橙的脱色工艺,采用人工神经网络(ANN)和遗传算法(GA)建立脱色模型并优化工艺。建立的ANN模型的误差、相关系数、均方根误差和绝对平均偏差分别为0.0009、0.9971、1.21和6.82,模型有效且能够用于预测和工艺优化。采用GA对ANN模型进行数值寻优,得到的最佳工艺条件为酶液量0.6 mL,Mn2+浓度4 mmol/L,H2O2浓度0.49 mmol/L。该条件下脱色率达到(90.74±0.59)%。ANN耦合GA有效地建立了锰过氧化物酶脱色甲基橙的模型,并优化了工艺参数,为甲基橙脱色的研究提供一定参考。     

Comparison of different stomatal conductance algorithms for ozone flux modelling

A multiplicative and a semi-mechanistic, BWB-type [Ball, J.T., Woodrow, I.E., Berry, J.A., 1987. A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. In: Biggens, J. (Ed.), Progress in Photosynthesis Research, vol. IV. Martinus Nijhoff, Dordrecht, pp. 221-224.] algorithm for calculating stomatal conductance (g(s)) at the leaf level have been parameterised for two crop and two tree species to test their use in regional scale ozone deposition modelling. The algorithms were tested against measured, site-specific data for durum wheat, grapevine, beech and birch of different European provenances. A direct comparison of both algorithms showed a similar performance in predicting hourly means and daily time-courses of g(s), whereas the multiplicative algorithm outperformed the BWB-type algorithm in modelling seasonal time-courses due to the inclusion of a phenology function. The re-parameterisation of the algorithms for local conditions in order to validate ozone deposition modelling on a European scale reveals the higher input requirements of the BWB-type algorithm as compared to the multiplicative algorithm because of the need of the former to model net photosynthesis (A(n)).     

Development of the IM147: an alternative inspection/maintenance mass-emission transient test to address vehicle preconditioning concerns

A series of studies was performed to develop an alternative to the U.S. Environmental Protection Agency's gold standard IM240 mass-based emission test. The new IM147 test was based on the second phase of the IM240 that consists of 147 sec of transient vehicle operation. Paired IM240/IM147 tests were conducted on vehicles ranging from 1981 to 1996 to determine IM147 cutpoints and excess emissions were identified. Additionally, an optimized test procedure was developed that combined possible triplicate IM147s with improved drive trace quality control, fast-pass, and retest methods. The optimized procedure was found to provide improved vehicle preconditioning with a relatively minor decrease in excess emissions identification. Resulting identification rates ranged from 96 to 100% for hydrocarbons (HC), 93-100% for CO, and 93-100% for NOx, depending on cutpoint selection, while false failures caused by lack of vehicle preconditioning were reduced to essentially zero. Significant vehicle throughput improvements were achieved through the development of software algorithms involving modal fast-pass and retest procedures. Modal drive trace variation limits also were developed to improve test accuracy. The combination of the algorithms reduced average IM147 test times by nearly 60%.     

Neural networks and periodic components used in air quality forecasting

Forecasting of air quality parameters is one topic of air quality research today due to the health effects caused by airborne pollutants in urban areas. The work presented here aims at comparing two principally different neural network methods that have been considered as potential tools in that area and assessing them in relation to regression with periodic components. Self-organizing maps (SOM) represent a form of competitive learning in which a neural network learns the structure of the data. Multi-layer perceptrons (MLPs) have been shown to be able to learn complex relationships between input and output variables. In addition, the effect of removing periodic components is evaluated with respect to neural networks. The methods were evaluated using hourly time series of NO₂ and basic meteorological variables collected in the city of Stockholm in 1994–1998. The estimated values for forecasting were calculated in three ways: using the periodic components alone, applying neural network methods to the residual values after removing the periodic components, and applying only neural networks to the original data. The results showed that the best forecast estimates can be achieved by directly applying a MLP network to the original data, and thus, that a combination of the periodic regression method and neural algorithms does not give any advantage over a direct application of neural algorithms.     

Combining data-intelligent algorithms for the assessment and predictive modeling of groundwater resources quality in parts of southeastern Nigeria

Environmental Science and Pollution Research - Machine learning algorithms have proven useful in the estimation, classification, and prediction of water quality parameters. Similarly, indexical...     

Biogenic volatile organic compound emissions (BVOCs). II. Landscape flux potentials from three continental sites in the U.S

Landscape flux potentials for biogenic volatile organic compounds (BVOCs) were derived for three ecosystems in the continental U.S. (Fernbank Forest, Atlanta, GA; Willow Creek, Rhinelander, WI; Temple Ridge, CO). Analytical data from branch enclosure measurements were combined with ecological survey data for plant species composition and biomass. Other quantitative flux measurements at the leaf and landscape level were incorporated to scale the results from the enclosure measurements to the landscape level. Flux estimates were derived by using a one week ambient temperature and light record (30 min time resolution) and adjusting all emission rates to these conditions with temperature and light correction algorithms.     

Estimating PM2.5 over southern Sweden using space-borne optical measurements

In the present study Bremen aerosol retrieval (BAER) columnar aerosol optical thickness (AOT) data, according to moderate resolution imaging spectroradiometer (MODIS) and medium resolution imaging sensor (MERIS) level 1 calibrated satellite data, have been compared with AOT data obtained with the MODIS and MERIS retrieval algorithms (NASA and ESA, respectively) and by AErosol RObotic NETwork (AERONET). Relatively good agreement is found between these different instruments and algorithms. The R² and relative RMSD were 0.86 and 31% for MODIS when comparing with AERONET and 0.92 and 21% for MERIS. The aerosols investigated were influenced by low relative humidity. During this period, a relatively large range of aerosol loadings were detected; from continental background aerosol to particles emitted from agricultural fires. In this study, empirical relationships between BAER columnar AOT and ground-measured PM_2.5 have been estimated. Linear relationships, with R² values of 0.58 and 0.59, were obtained according to MERIS and MODIS data, respectively. The slopes of the regression of AOT versus PM_2.5 are lower than previous studies, but this could easily be explained by considering the effect of hygroscopic growth. The present AOT–PM_2.5 relationship has been applied on MERIS full resolution data over the urban area of Stockholm and the results have been compared with particle mass concentrations from dispersion model calculations. It seems that the satellite data with the 300 m resolution can resolve the expected increased concentrations due to emissions along the main highways close to the city. Significant uncertainties in the spatial distribution of PM_2.5 across land/ocean boundaries were particularly evident when analyzing the high resolution satellite data.     

Field calibration of surface: a model of agricultural chemicals in surface waters

Agricultural chemicals sporadically occur at detectable levels in the surface waters of intensively farmed watersheds. HSPF, a previously released model of agricultural chemicals in surface water, had been used to predict concentrations which were much higher (10 X) than those actually observed during monitoring studies. A new model, SURFACE, is described here which is much simpler than HSPF and gives better predictions of surface water concentrations. SURFACE uses PRZM, an EPA model, to calculate edge-of-field runoff losses and simple hydraulic routing algorithms to determine concentrations at the bottom of large river basins. In water systems sampled during 1985 and 1986, SURFACE predictions of annualized mean concentrations for alachlor, atrazine, cyanazine and metolachlor were within 0.09 ppb half of the time.     

内陆水体水质监测与评价中的遥感应用

本文综述了水质监测与评价中遥感应用研究的发展与现状。主要讨论了水质遥感监测的原理、遥感手段、常用方法和几种具体水质参数的遥感研究方法与进展。最后提出了此领域的发展方向展望     

Evaluation of the reactive and optics model of emissions (ROME)

The reactive and optics model of emissions (ROME) is a reactive plume visibility model that simulates the potential atmospheric impacts of stack emissions. We present here an evaluation of the ability of ROME to simulate several plume physical and chemical variables, using an experimental data base that consists of a total of 40 case studies from four field programs. The evaluation variables include plume height, horizontal width, NO_x and SO₂ maximum concentrations, NO₂/NO_x concentration ratio at the plume centerline, and plume-to-sky radiance ratios. Three algorithms used to simulate plume dispersion in ROME were compared: (1) the empirical Pasquill–Gifford–Turner (PGT) scheme, (2) a first-order closure (FOC) algorithm and (3) a second-order closure (SOC) algorithm that simulates the instantaneous plume dimensions.The plume height results show a correlation of 0.82 between simulated and measured values and a gross error that is 13% of the mean measured value. For plume horizontal dispersion, the second-order closure algorithm produces a moderate correlation (0.54) and a small bias (5% of the mean measured value) in comparison with the field data. Although the PGT scheme also demonstrates moderate correlation with the measurements, it produces a negative bias by significantly underestimating plume horizontal dispersion. The first-order closure algorithm overestimates plume width and shows the least correlation (with the measurements) of the three dispersion algorithms.For the NYSEG data set where coordinated measurements of stack emissions, meteorology at plume height and plume characteristics were available, the SOC algorithm provides better correlations for NO_x concentrations, NO₂/NO_x ratios and plume visibility than the FOC and PGT algorithms. For plume visibility, the SOC algorithm shows a correlation of 0.96 at 405 nm, the wavelength where the plume was visible, and it simulates no visible plume at the other wavelengths (550 and 700 nm).A comparison of ROME simulations with those of the plume visibility model PLUVUE II shows that ROME, with the SOC algorithm, performs better for all variables.     

The persistence and disappearance by washoff and dryfall of methoxychlor from soybean foliage—a preliminary study

Abstract

The persistence and disappearance (washoff or dryfall) of methoxychlor [2,2‐bis(p‐methoxypheny1)‐1,1,1‐trichloroethane] from mature soybean [Glycine max (L.) Merrill] foliage was investigated in a small field plot study under natural rainfall conditions in 1977 and 1978. Residue analyses were conducted using whole plant samples‐ Methoxychlor washoff rate was 8±4% of the amount on plants (prior to rain) per centimeter of rainfall, regardless of time after application. Total seasonal washoff for 1978 accounted for 33.5% of the applied pesticide; however, 30.5% of the total loss was removed by washoff on the second day after application. Dryfall or dislodgeable residue accounted for less than 1% of the amount applied. The amount of dryfall was significantly greater in plots entered by workers than in those where entry was avoided. More than 19% of the applied methoxychlor was lost as a result of through‐fall to the ground during application to the plots. Statistical analyses indicated that within‐sample variation for mechanical chopping of plant samples was significantly smaller at the 5% level than for a hand chopping method. Results from this study will be useful in defining research objectives for the development of algorithms to describe the behavior of foliar‐applied compounds. Such algorithms are necessary for estimating runoff losses of insecticides to water bodies.     

An interlaboratory round robin study on the analysis of toxaphene in a cod liver oil standard reference material

An inter-laboratory round robin analysis of toxaphene in a National Institute of Standards Cod Liver Oil #1588 was carried out. Analysis was performed by GC-ECD, electron capture negative ion GCMS (ECNI GCMS) and electron impact GC/MS in high resolution single ion monitoring mode (EI-HRSIM). Results of the analyses by GC-ECD, ECNI and HRSIM were 4.22±2.45 ppm, 3.95±1.57 ppm, and 2.35±0.06 ppm respectively. Due to the wide inter-laboratory variation in workup, GC conditions, detection method, and quantitation algorithms used, no one set of factors stood out as the cause of the variation in results.     

Field calibration of surface: A model of agricultural chemicals in surface waters

Abstract

Agricultural chemicals sporadically occur at detectable levels in the surface waters of intensively farmed watersheds. HSPF, a previously released model of agricultural chemicals in surface water, had been used to predict concentrations which were much higher (10 X) than those actually observed during monitoring studies. A new model, SURFACE, is described here which is much simpler than HSPF and gives better predictions of surface water concentrations. SURFACE uses PRZM, an EPA model, to calculate edge‐of‐field runoff losses and simple hydraulic routing algorithms to determine concentrations at the bottom of large river basins. In water systems sampled during 1985 and 1986, SURFACE predictions of annualized mean concentrations for alachlor, atrazine, cyanazine and metolachlor were within 0.09 ppb half of the time.     

A neural-fuzzy approach to classify the ecological status in surface waters

A methodology based on a hybrid approach that combines fuzzy inference systems and artificial neural networks has been used to classify ecological status in surface waters. This methodology has been proposed to deal efficiently with the non-linearity and highly subjective nature of variables involved in this serious problem. Ecological status has been assessed with biological, hydro-morphological, and physicochemical indicators. A data set collected from 378 sampling sites in the Ebro river basin has been used to train and validate the hybrid model. Up to 97.6% of sampling sites have been correctly classified with neural-fuzzy models. Such performance resulted very competitive when compared with other classification algorithms. With non-parametric classification-regression trees and probabilistic neural networks, the predictive capacities were 90.7% and 97.0%, respectively. The proposed methodology can support decision-makers in evaluation and classification of ecological status, as required by the EU Water Framework Directive.     

Rapid Screening and Identification of Airborne Carcinogens of Greatest Concern in California

This paper describes a method for establishing a priority list of airborne carcinogens within a state Jurisdiction. In this case it was necessary to identify, from among hundreds of potential candidates, the five to ten materials of greatest potential concern in California as airborne carcinogens.

Because no previous inventory of carcinogens in California existed, published lists, rankings, and assessments of national scope were used to identify candidates. By systematic manipulation and comparison of these data sources, 47 materials of some notoriety were chosen for closer scrutiny. This selection was pared to 22 candidates largely by eliminating those which had very little production and use in California. (Substances primarily used as pesticides were excluded from the scope of this study.) The remaining candidates were then ranked by additive . and multiplicative algorithms and by a panel of experts. The results of these rankings were combined to produce a single selection of 11 priority candidates. In alphabetical order, they are arsenic, asbestos, benzene, cadmium, carbon tetrachloride, chloroform, ethylene dibromide, ethylene dichloride, N-nitrosoamines, perchloroethylene, and polycyclic aromatic hydrocarbons. In continuing studies, a baseline emissions inventory is being prepared, and a source testing program is being designed.     

Sensitivity Analysis of Dispersion Models for Point and Area Sources

The body of information presented in this paper is directed to air quality managers in industry and government contemplating modeling emissions from complex sources under the bubble concept.

Point and area source algorithms of PAL, RAM, and ISC-ST were analyzed to show the effect of various input assumptions on model output. Several important parameters were varied individually; receptor grid spacing, emission release height, area source size and source type. Each of these parameters was varied over a range of values while all other modeling parameters, both physical and meteorological, were held constant. The outputs of each model are plotted for easy comparison.

Results indicate that it would be inappropriate to make certain assumptions regarding source characteristics without knowing the behavior of each model. The graphs show how the model predictions can vary for different input parameters when applied to point and area sources. The paper presents general rules of thumb for evaluating model results for many applications such as the bubble concept, emissions banking, offsets, and new source reviews. The results serve as a guide in selecting and using models for both point and area sources.     

A model for pulse jet fabric filters

A new model for pulse jet fabric filtration is proposed. In contrast to the earlier model of Ravin and Humphries, which was formulated on the steady state assumption, the present study is aimed at developing a predictive capability for both transient and steady state operations, taking into account the compression effect of filter cakes. The model's relative simplicity allows frequent updating of the model parameter values, thus improving the accuracy of predictions. As a result, the model is particularly useful in developing control algorithms and designing controllers of pulse jet fabric filtration systems.     

Overview of Petroleum Environmental Research Forum (PERF) dense gas dispersion modeling project

This paper provides a background for and an overview of the results of a comprehensive study of transport and dispersion of dense gas plumes over rough surfaces typical of industrial sites. The Petroleum Environmental Research Forum (PERF) 93-16 project involved model development and evaluations using observations from three wind tunnels and from the Kit Fox field experiment. Detailed discussions of the results of the research are given in the other papers in this special issue. The wind tunnel experiments produced data showing that the resulting best-fit vertical entrainment formula was close to (i.e., within about 30%) the vertical entrainment formulas already in use by current models, which were derived primarily from observations over smooth surfaces. Observations from the Kit Fox field experiment demonstrated the validity of the entrainment curves derived from the wind tunnel data. The Kit Fox data were also used to evaluate algorithms for along-wind dispersion and cloud advection speeds for short-duration releases typical of an industrial site, and to evaluate the HEGADAS dense gas dispersion model.