APCA Honors Six Individuals and Three Sections In 1987 Awards Program

Abstract

A growing interest in security and occupant exposure to contaminants revealed a need for fast and reliable identification of contaminant sources during incidental situations. To determine potential contaminant source positions in outdoor environments, current state-of-the-art modeling methods use computational ?uid dynamic simulations on parallel processors. In indoor environments, current tools match accidental contaminant distributions with cases from precomputed databases of possible concentration distributions. These methods require intensive computations in pre- and postprocessing. On the other hand, neural networks emerged as a tool for rapid concentration forecasting of outdoor environmental contaminants such as nitrogen oxides or sulfur dioxide. All of these modeling methods depend on the type of sensors used for real-time measurements of contaminant concentrations. A review of the existing sensor technologies revealed that no perfect sensor exists, but intensity of work in this area provides promising results in the near future. The main goal of the presented research study was to extend neural network modeling from the outdoor to the indoor identification of source positions, making this technology applicable to building indoor environments. The developed neural network Locator of Contaminant Sources was also used to optimize number and allocation of contaminant concentration sensors for real-time prediction of indoor contaminant source positions. Such prediction should take place within seconds after receiving real-time contaminant concentration sensor data. For the purpose of neural network training, a multizone program provided distributions of contaminant concentrations for known source positions throughout a test building. Trained networks had an output indicating contaminant source positions based on measured concentrations in different building zones. A validation case based on a real building layout and experimental data demonstrated the ability of this method to identify contaminant source positions. Future research intentions are focused on integration with real sensor networks and model improvements for much more complicated contamination scenarios.     

大气环境质量的人工神经网络决策模型

为了对环境质量进行综合评价，运用误差反向传播算法的人工神经网络方法建立了环境质量评价的Ｂ－Ｐ决策模型。用此模型研究实例的大气环境质量，结果表明Ｂ－Ｐ网络用于环境质量评价具有客观性和实用性。     

Fuzzy neural network for flow estimation in sewer systems during wet weather.

Estimation of the water flow from rainfall intensity during storm events is important in hydrology, sewer system control, and environmental protection. The runoff-producing behavior of a sewer system changes from one storm event to another because rainfall loss depends not only on rainfall intensities, but also on the state of the soil and vegetation, the general condition of the climate, and so on. As such, it would be difficult to obtain a precise flowrate estimation without sufficient a priori knowledge of these factors. To establish a model for flow estimation, one can also use statistical methods, such as the neural network STORMNET, software developed at Lyonnaise des Eaux, France, analyzing the relation between rainfall intensity and flowrate data of the known storm events registered in the past for a given sewer system. In this study, the authors propose a fuzzy neural network to estimate the flowrate from rainfall intensity. The fuzzy neural network combines four STORMNETs and fuzzy deduction to better estimate the flowrates. This study's system for flow estimation can be calibrated automatically by using known storm events; no data regarding the physical characteristics of the drainage basins are required. Compared with the neural network STORMNET, this method reduces the mean square error of the flow estimates by approximately 20%. Experimental results are reported herein.     

Remote Sensing Tools Assist in Environmental Forensics. Part I: Traditional Methods

This is part one of a two-part discussion, in which we will provide an overview of the use of aerial photography, topographic mapping and photogrammetry in environmental enforcement actions. The visualization of spatial relationships of natural and man-made features can focus the scope of environmental investigation, and provide a simple, yet quantitative, historical record of changes in conditions on a site. Aerial photography has been used in environmental remote sensing since the early part of the 20th century. Aerial photos are valuable tools for environmental assessment because they provide objective, detailed documentation of surface conditions at a specific time. Furthermore, they can generally be obtained even in cases where access on the ground is denied to investigators. From aerial photos, precise quantitative information can be collected using photogrammetry. Such measurement and positional data can be produced in digital format for input into a Geographic Information System (GIS) for computerized analysis and display. Other information derived from aerial photographs requires specialized photointerpretive skills and experience. These include the recognition of vegetation mortality, oil-spill damage, and the ecological quality of water bodies. The location, extent and historical change of hazardous waste sites can be documented on topographic maps. These maps are often created from aerial photographs, and display the extent and location of real-world features by symbolizing them. The major advantage of maps over aerial photos is that maps can show things that are not visible from the air, while omitting unnecessary and distracting information. Because maps are derived products, they may contain bias in content and presentation, and they must be backed up by careful documentation and quality assurance protocols.     

Remote Sensing Tools Assist in Environmental Forensics. Part I: Traditional Methods

This is part one of a two-part discussion, in which we will provide an overview of the use of aerial photography, topographic mapping and photogrammetry in environmental enforcement actions. The visualization of spatial relationships of natural and man-made features can focus the scope of environmental investigation, and provide a simple, yet quantitative, historical record of changes in conditions on a site. Aerial photography has been used in environmental remote sensing since the early part of the 20th century. Aerial photos are valuable tools for environmental assessment because they provide objective, detailed documentation of surface conditions at a specific time. Furthermore, they can generally be obtained even in cases where access on the ground is denied to investigators. From aerial photos, precise quantitative information can be collected using photogrammetry. Such measurement and positional data can be produced in digital format for input into a Geographic Information System (GIS) for computerized analysis and display. Other information derived from aerial photographs requires specialized photointerpretive skills and experience. These include the recognition of vegetation mortality, oil-spill damage, and the ecological quality of water bodies. The location, extent and historical change of hazardous waste sites can be documented on topographic maps. These maps are often created from aerial photographs, and display the extent and location of real-world features by symbolizing them. The major advantage of maps over aerial photos is that maps can show things that are not visible from the air, while omitting unnecessary and distracting information. Because maps are derived products, they may contain bias in content and presentation, and they must be backed up by careful documentation and quality assurance protocols.     

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.     

Patterns of PCDDs and PCDFs in human milk and food and their characterization by artificial neural networks

Artificial neural network (ANN) has been recently introduced as a tool for data analysis. In this study, Kohonen's self-organizing maps (SOMs), a special type of neural network, were applied to a set of PCDD/PCDF concentrations found in 54 human milk and 83 food samples, which were collected in a number of countries all over the world. Data were obtained from the scientific literature. The purpose of the study was to find a potential relationship between PCDD/PCDF congener profiles in human milk and the dietary habits of the different countries in which samples were collected. The comparison of the SOM component planes for human milk and foodstuffs indicates that those countries with a greater fish consumption show also higher PCDD/PCDF concentrations in human milk. SOMs enable both the visualization of sample units and the visualization of congener distribution.     

Co-occurrence patterns of some small-bodied freshwater fishes in southwestern France: implications for fish conservation and environmental management

We assessed the influence of environmental variables (elevation, stream order, distance from source, catchment area, slope, stream width, and fish species richness) on the co-occurrence patterns of the minnow, the stone loach, and the gudgeon at the stream system scale. A total of 474 sites were classified according to the seven variables using the Self-Organizing Map (neural network), and three clusters were detected (k-means algorithm). The frequency of the various fish co-occurrence patterns was calculated for each cluster, and general linear modeling was used to specify the conditions that predict the occurrence of each species. Piedmont streams were more likely to support coexisting gudgeon and minnow populations because of higher probabilities of occurrence for both species. The higher co-occurrence frequency for the three species together in headwater streams resulted from lower occurrence frequencies in gudgeon and minnow. Focusing on areas that favor the co-occurrence of species may enhance the effectiveness of conservation projects.     

Forecasting Daily Source Air Quality Using Multivariate Statistical Analysis and Radial Basis Function Networks

Abstract

It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.     

Breakthrough Curves Characterization and Identification of an Unknown Pollution Source in Groundwater System Using an Artificial Neural Network (ANN)

Contamination of groundwater constrains its uses and poses a serious threat to the environment. Once groundwater is contaminated, the cleanup may be difficult and expensive. Identification of unknown pollution sources is the first step toward adopting any remediation strategy. The proposed methodology exploits the capability of a universal function approximation by a feed-forward multilayer artificial neural network (ANN) to identify the sources in terms of its location, magnitudes, and duration of activity. The back-propagation algorithm is utilized for training the ANN to identify the source characteristics based on simulated concentration data at specified observation locations in the aquifer. Uniform random generation and the Latin hypercube sampling method of random generation are used to generate temporal varying source fluxes. These source fluxes are used in groundwater flow and the transport simulation model to generate necessary data for the ANN model-building processes. Breakthrough curves obtained for the specified pollution scenario are characterized by different methods. The characterized breakthrough curves parameters serve as inputs to ANN model. Unknown pollution source characteristics are outputs for ANN model. Experimentation is also performed with different number of training and testing patterns. In addition, the effects of measurement errors in concentration measurements values are used to show the robustness of ANN based methodology for source identification in case of erroneous data.     

基于Web GIS的饮用水水源地水质安全监控系统的研究

基于Web GIS的饮用水水质监控系统,利用Internet/Intranet技术、GIS技术、数据库技术和环境保护技术,建立了完善的水污染监测与管理网络体系,实现了水源地污染空间信息、属性信息的综合管理.首先介绍了此系统的需求目标,进一步阐述了系统的体系结构、数据库设计和主要功能.     

Evaluation of Absorption Sampling Devices

The information presented in this paper is directed to those with the responsibility of designing and operating air quality monitoring networks. An analytical model for location of monitor sites based upon maximizing a sum of coverage factors for each source is developed. An heuristic solution method from the facilities location analysis literature is used for solution of the model. Results of an example problem are presented and compared with the monitoring network currently In place. The model is shown to be a valuable addition to the methods available to the air quality monitor network designer. Needs for further research are pointed out.     

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.     

Evaluation of Virtual Source Beam Configurations for Rapid Tomographic Reconstruction of Gas and Vapor Concentrations in Workplaces

Abstract

Beam path average data from an Open Path Fourier Transform Infrared (OP-FTIR) spectrometer can be used to reconstruct two-dimensional concentration maps of the gas and vapor contaminants in workplaces and the environment using computed tomographic (CT) techniques. However, a practical limitation arises because in the past, multiple-source and detector units were required to produce a sufficient number of intersecting beam paths in order to reconstruct concentration maps. Such a system can be applied to actual field monitoring situations only with great expense and difficulty. A single monostatic OP-FTIR system capable of rapid beam movement can eliminate this deficiency. Instead of many source and detector units, a virtual source arrangement has been proposed using a number of flat mirrors and retroreflectors to obtain intersecting folded beam paths.

Three virtual source beam configurations generated for a single-beam steerable FTIR system were tested using 54 flat mirrors and four retroreflectors or 54 flat mirrors and 56 retroreflectors mounted along the perimeter walls of a typical 24- x 21-ft test room. The virtual source CT configurations were numerically evaluated using concentration maps created from tracer gas concentration distributions measured experimentally in a test chamber. Synthetic beam path integral data were calculated from the test maps and beam configurations. Computer simulations of different beam configurations were used to determine the effects of beam geometry. The effects of noise and peak-reducing artifacts were evaluated. The performance of the tomographic reconstruction strategy was tested as a function of concentration and concentration gradients.     

Neuro-fuzzy and neural network systems for air quality control

In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. The air quality control can be formalized as a two-objective nonlinear mathematical problem, integrating source–receptor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source–receptor models cannot be implemented through deterministic modelling systems, that would bring to a computationally unfeasible mathematical problem. In this paper we suggest to identify source–receptor statistical models (neural network and neuro-fuzzy) processing the simulations of a deterministic multi-phase modelling system (GAMES). The methodology has been applied to ozone and PM10 concentrations in Northern Italy. The results show that, despite a large advantage in terms of computational costs, the selected source–receptor models are able to accurately reproduce the simulation of the 3D modelling system.     

Forecasts using neural network versus Box-Jenkins methodology for ambient air quality monitoring data

This study explores ambient air quality forecasts using the conventional time-series approach and a neural network. Sulfur dioxide and ozone monitoring data collected from two background stations and an industrial station are used. Various learning methods and varied numbers of hidden layer processing units of the neural network model are tested. Results obtained from the time-series and neural network models are discussed and compared on the basis of their performance for 1-step-ahead and 24-step-ahead forecasts. Although both models perform well for 1-step-ahead prediction, some neural network results reveal a slightly better forecast without manually adjusting model parameters, according to the results. For a 24-step-ahead forecast, most neural network results are as good as or superior to those of the time-series model. With the advantages of self-learning, self-adaptation, and parallel processing, the neural network approach is a promising technique for developing an automated short-term ambient air quality forecast system.     

A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

Application of Aerial Photography and Photogrammetry in Environmental Forensic Investigations

Historical aerial photography can be a powerful tool in environmental forensic investigations. Historical aerial photography is available for many sites from the 1930s on. It is researched and obtained from both public and private sources. Most of the photography consists of vertical stereoscopic film annotated with the date of the photomission. A current photomission can be flown using airborne GPS for precise registration of the photomosaics. The photography is scanned at a very high resolution and registered in a coordinate system using a digital stereoplotter that removes terrain distortion and allows the precise measurement of objects. The digital stereoplotter is used to produce photomosaics and to interpret environmentally significant features in the photography. The accuracy of the environmental interpretations is dependent on the skill and experience of the interpreter as well as the resolution of the photography and quality of the equipment used. The photomosaics are then registered in a geographic information system along with the interpretations of environmentally significant features. In a similar manner, historic maps are scanned and registered into the same coordinate system. The interpreted images and maps form a significant part of the expert report. A computer projection system is used to show the interpreted images at trial.     

Surrogate Model-Based Simulation-Optimization Approach for Groundwater Source Identification Problems

This study investigates and discusses a time-efficient technology that contains a surrogate model within a simulation-optimization model to identify the characteristics of groundwater pollutant sources. In the proposed surrogate model, Latin hypercube sampling (a stratified sampling approach) and artificial neural network (commencing at the stress period when the concentration is within a certain range, and ending at the peak time) were utilized to reduce workload and costly computing time. The results of a comparison between the proposed surrogate model and the common artificial neural network model and non-surrogate model indicated that the proposed model is a time-efficient technology which could be used to solve groundwater source identification problems.