Saburo Ikeda

Design of air pollutant monitoring system by spatial sample stratification

Abstract This paper deals with the monitoring system for air pollution in an urban area, especially the optimum allocation of a given number of sensors. We discuss the available models for air pollution monitoring system in order to give an optimum allocation of sensors which represent local pollutant levels. A quantitative measure to allocate a given number of sensors is proposed on the basis of the Monte-Carlo variance reduction method. A two-dimensional region is divided into a given number of subdomains in such a way that one monitoring station is placed in each subdomain. It is proved that in a special case this division is equivalent to that made by the requirement of each subdomain having the same fluctuation of concentration in some defined sense. Next the relation between the number of sensors necessary in an urban area and the prespecified allowable error is derived. For illustration, our technique was applied to a highly industrialized city (Kurashiki) making use of a simplified simulation model.

A dynamic water quality model of Lake Biwa — a simulation study of the lake eutrophication

Abstract Lake Biwa is the largest lake in Japan and is the origin of the Yodo River which supplies drinking water to 12 × 10 6 people and industrial water to the economic center of western Japan. Recently, serious environmental problems, especially eutrophication, have become evident in Lake Biwa. This paper treats the eutrophication of Lake Biwa as a dynamic process related to nutrient, mainly nitrogen and phosphorus influx, and the effect this influx has on aquatic ecosystems, water quality and the internal nutrient cycle. For purposes of simplification, Lake Biwa has been treated as a five-block system consisting of three geographical blocks and two of which are divided into two layers. The main objectives of the research reported in this paper are to construct a dynamic eutrophication model of Lake Biwa and to provide basic information for the future exploration of water quality management.

Dynamics of the nitrogen cycle in a lake and its stability

Abstract Recent eutrophication of lakes or sea water has caused serious damage to the ecological balance in aquatic systems and to the quality of water for human use. The purpose of this study is to construct a dynamic model of the nitrogen cycle in a lake and to study the dynamic behavior of the various nitrogen forms such as organic, inorganic and plankton in connection with the mechanism of algal blooms in lakes. The dynamics of the system are represented by a system of non-linear differential equations which include the predator-prey relation between plankton types, and are discussed with the stability analysis of the critical points of differential equations. The effects of seasonal changes of environmental parameters are studied with computer simulations.

Fish population dynamics under nutrient enrichment — A case of the East Seto Inland Sea

Abstract The significant increase of pollutants in the sea and the slow exchange rate of sea water between the inland sea and the ocean have worsened the general environmental conditions in the Seto Inland Sea since the 1960s. In particular, the enormous amounts of nutrients such as nitrogen and phosphorus flowing into the sea seem to have had a wide range of effects on its fish population. In this paper, a theoretical model of fish population dynamics in the eutrophicated inland sea is proposed, based on the prey-predator relationship of the Lotka-Volterra type. This model is validated by checking the recent fishery statistics of the sea area. Specific attention is focused on the impact of nutrient enrichment on fish population dynamics.

Identification method in environmental systems and its application to water pollution

An identification algorithm is presented for a pollution source distributed along a river. The water quality is described by a couple of partial differential equations of the first order or a parabolic one. This problem, after transformation, results in the Fredholm integral equation of the first kind, which is non-well posed in the sense of Hadamard. The solution does not continuously depend on the observed data. The application of the regularization method proposed by Tikhonov (1963) yields a stable algorithm for the identification problem. Some simulation examples are given to illustrate the applicability of this method to environmental control systems based on the convective or dispersive phenomena.

Identification of distributed systems and the theory of the regularization

Abstract The identification problems, i.e., the problems of finding unknown parameters in distributed systems from the observations are very important in modern control theory. The solutions of these identification problems can be obtained by solving the equations of the first kind. However, the solutions are often unstable. In other words, they are not continuously dependent on the data. The regularization or Tihonovs regularization is known as one of the stabilizing algorithms to solve these non well-posed problems. In this paper is studied the regularization method for identification of distributed systems. Several approximation theorems are proved to solve the equations of the first kind. Then, identification problems are reduced to the minimization of quadratic cost functionals by virtue of these theorems. On the other hand, it is known that the statistical methods for identification such as the maximum likelihood lead to the minimization problems of certain quadratic functionals. Comparing these quadratic cost functionals, the relations between the regularization and the statistical methods are discussed. Further, numerical examples are given to show the effectiveness of this method.

MULTI-SPECIES OF PLANKTONS AND NUTRIENTS MODEL OF LAKE EUTROPHICATION - A SIMULATION STUDY IN LAKE BIWA -

ABSTRACT The main objectives of this study are to construct a dynamic eutrophication model of Lake Biwa which is the largest one in Japan and to obtain an quantitative relationship between the amount of nutrients and biomass of various species of planktons. This will provide basic information for the future management of water quality to prevent the further deterioration toward a dangerous direction of eutrophication. The lake is devided into two parts geographically and the larger one consists of two layers taking into account the thermal stratification during summer season. The major elements involved in the model are three types of phytoplanktons, zooplankton and nutrients (nitrogen and phosphorus) in both organic and inorganic forms. The particular feature of the study is that lots of experimental and field works have been done for identification of the model structure and parameters under the sponsorship of the Japan Society of Civil Engineers and the Ministry of Construction (Lake Biwa Office).

A measure of obtaining representative pointwise source and its availability for air pollution control

Abstract When we need to make a simplified model of the air pollution phenomena for planning use, it is necessary to choose an optimal appropriate form and dimension of the source vector in some defined sense. There are various kinds and sizes of pollution sources in an urban area. It is desirable to divide them into some groups and set them to representative pointwise sources, taking their distribution and emission intensities into consideration. This paper proposes a quantitative measure to divide sources into groups on the basis of the Monte-Carlo variance reduction method. We develop the concept of the representative pointwise source as a potential function of the air pollution which represents the actual types of pollution sources. The results are illustrated by a numerical example and the application to a real air-shed is presented. The availability of the new measure for the practical optimal location plan of monitoring stations referrred.

Non-linear prediction model of river flow by self-organization method

This paper presents two heuristic self-organization methods for construction of a non-linear prediction model of river flows. The self-organization algorithms have multi-layered structures of the perceptron type and provide the optimally complex non-linear equation of the input-output relation. The algorithms are applied to the river-flow prediction of Karasu JRiver and Katsura River in Japan. The performance of the prediction models by the self-organization methods is compared with that of the hydrological models. The numerical comparison shows that without any hydrological and geographical knowledge the prediction models presented here aro superior to the elaborate hydrological models.

Multidimensional Scaling Approach to Clustering Multivariate Data for Water-Quality Modeling

This paper is concerned with a statistical treatment of multivariate water-quality data to help regulatory and operational personnel engaged in monitoring, control, and managing problems of water pollution and eutrophication to obtain a comprehensive view of water quality in their own areas. Because of the variety of parameters observed as water-quality data, and the complexity and uncertainty involved in pollution and eutrophication mechanisms in aquatic environments, it is necessary to develop a methodology that is able to identify common and differing aspects of water quality in data from various sources. In particular, in order to build a water-quality model of the compartment type which is better able to identify regional characteristics, it is necessary to have an integrated index of regional water quality which makes it possible to divide a designated area into several compartments.