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We present an overview of the methodology for bioassays and other techniques used in phytoremediation studies under controlled conditions, as practised at the Laboratory for Basic Sciences in Horticulture of Warsaw Agricultural University. These relate both to urban and industrial areas and concentrate on the major pollutants heavy metals (HMs), polycyclic aromatic carbohydrates (PAHs) and de-icing salt (NaCl). Depending on the specific goals of particular experiments, the following bioassays are employed: seed germinability, seedling vigour, assessment of biomass production and partitioning, and accumulation and distribution of pollutants. In studies of the physiological, biochemical and molecular plant responses to pollutants, plant gas exchange is measured by infrared gas analysis, plant water status by a dew point voltmeter (assessing water and osmotic potentials and relative water content), wax deposition by chloroform washing, changes in total glutathione and low molecular weight organic acid levels by HPLC, the expression of genes encoding metallothionein by PCR and DNA sequencing, and gene expression profiling by micro-array/DNA chip). Some of these will be presented briefly, together with the results of some case studies. [ABSTRACT FROM AUTHOR]

Plants are among the most tolerant of organisms to pollution, which emphasises their utility for the emerging science of environmental biotechnology. Many botanical families, in particular the Brassicaceae, Poaceae, Fabaceae, Asteraceae, Salicaceae, Chenopodiaceae, and Careophylaceae include multiple species showing phytoremediation potential, and other families (Cyperaceae, Amaranthaceae, Cannabaceae, Cannaceae, Typhaceae and Pontederiaceae) contain promising individual species: Each species enjoys certain advantages, but suffers some limitations for application as phytoremediants. Careful selection of the appropriate family and genotype to match the particular pollutant and environment is crucial for successful phytoremediation. [ABSTRACT FROM AUTHOR]

The chapter summarizes research carried out within the last 15 years on the role of mycorrhizal fungi in phytoremediation of zinc wastes located in southern Poland. [ABSTRACT FROM AUTHOR]

The control issues, as applied to structures, include precise positioning or tracking. It is expected that the positioning and tracking requirements should be satisfied for structures with natural frequencies within the controller bandwidth and within the disturbance spectra. LQG (Linear system, Quadratic cost, Gaussian noise) controllers can typically meet these conditions and they are often used for tracking and disturbance rejection purposes. A good insight into the problems of analysis and design of LQG controllers can be obtained from the books by Kwakernaak and Sivan [91], Maciejowski [104], Anderson and Moore [3], Furuta, Sano, and Atherton [41], Lin [100], Skogestad and Postlethwaite [129], Dorato, Abdallah, and Cerone [28], Burl [13], and Fairman [34]. [ABSTRACT FROM AUTHOR]

In the LQG controller design we assumed that the control inputs were collocated with disturbances, and that the control outputs were collocated with the performance. This assumption imposes significant limits on the LQG controller possibilities and applications. The locations of control inputs do not always coincide with the disturbance locations, and the locations of controlled outputs are not necessarily collocated with the location where the system performance is evaluated. This was discussed earlier, when the generalized structure was introduced. The H2 and H∞ controllers address the controller design problem in its general configuration of non-collocated disturbance and control inputs, and noncollocated performance and control outputs. Many books and papers have been published addressing different aspects of H∞ controller design, and [12], [30], [94], [99], [100], [104], [122], and [129] explain the basic issues of the method. The H∞ method addresses a wide range of the control problems, combining the frequency- and time-domain approaches. The design is an optimal one in the sense of minimization of the H∞ nonn of the closed-loop transfer function. The H∞ model includes colored measurement and process noise. It also addresses the issues of robustness due to model uncertainties, and is applicable to the single-input-single-output systems as well as to the multiple-input-multiple-output systems. [ABSTRACT FROM AUTHOR]

Collocated controllers have their sensors collocated with actuators. They are a special case of the dissipative controllers, which are designed based on the passivity principle. In this book we select the collocated controllers as a first step to controller design, since they are simple, always stable, and some of their properties are similar to the more advanced controllers described later in this book. A good introduction to the collocated control of structures—but from a different point of view—can be found in the book by Preumont [120]. [ABSTRACT FROM AUTHOR]

The LQG and H∞ controllers, analyzed later in this book, are model-based ones, i.e., such that the plant model (used as an estimator) is a part of the controller. In this case the performance of the closed-loop system depends on the accuracy of the plant model. The accuracy is defined as a discrepancy between the dynamics of the actual plant and its model. For this reason, analytical models of a plant obtained, for example, from the finite-element model, are inaccurate and are acceptable in the simulation stages only. In implementation the test data are used to determine the accurate plant model—in a procedure known as system identification. [ABSTRACT FROM AUTHOR]

In some structural tests it is desirable to isolate (i.e., excite and measure) a single mode. Such a technique considerably simplifies the determination of modal parameters, see [116]. This was first achieved by using the force appropriation method, also called the Asher method, see [107], or phase separation method, see [21]. In this method a spatial distribution and the amplitudes of a harmonic input force are chosen to excite a single structural mode. Modal actuators or sensors in a different fonnulation were presented in [38], [93], [75], and [114] with application to structural acoustic problems. In this chapter we present two techniques to determine gains and locations of actuators or sensors to excite and sense a target mode or a set of targeted modes. [ABSTRACT FROM AUTHOR]

A typical actuator and sensor location problem for structural dynamics testing can be described as a structural test plan. The plan is based on the available information on the structure itself, on disturbances acting on the structure, and on the required structural performance. The preliminary information on structural properties is typically obtained from a structural finite-element model. The disturbance information includes disturbance location and disturbance spectral contents. The structure performance is commonly evaluated through the displacements or accelerations of selected structural locations. The actuator and sensor placement problem was investigated by many researchers, see, for example, [1], [7], [24], [47], [55], [86], [89], [90], [96], [97], [101], [103], [105], [106], [127], and a review article [131]. [ABSTRACT FROM AUTHOR]

Model reduction is a part of dynamic analysis, testing planning, and the control design of structures. Typically, a model with a large number of degrees of freedom, such as one developed for static analysis, causes numerical difficulties in dynamic analysis, to say nothing of the high computational cost. In system identification, on the other hand, the order of the identified system is determined by the reduction of the initially oversized model that includes a noise model. Finally, in structural control design the complexity and performance of a model-based controller depends on the order of the structural model. In all cases the reduction is a crucial part of the analysis and design. Thus, the reduced-order system solves the above problems if it acquires the essential properties of the full-order model. [ABSTRACT FROM AUTHOR]

System norms serve as a measure of intensity of its response to standard excitations, such as unit impulse, or white noise of unit standard deviation. The standardized response allows comparing different systems. Three system norms, H2, H∞, and Hankel are used in this book. We show that for flexible structures the H2 norm has an additive property: it is a root-mean-square (rms) sum of the norms of individual modes. We also show that the H∞ and Hankel norms are determined from the corresponding modal norms, by selecting the largest one. All three norms of a mode with multiple inputs (or outputs) can be decomposed into the rms sum of norms of a mode with a single input (or output). Later in this book these two properties allow for the development of unique and efficient model reduction methods and actuator/sensor placement procedures. [ABSTRACT FROM AUTHOR]

Controllability and observability are structural properties that carry information useful for structural testing and control, yet they are not fully utilized by structural engineers. The usefulness can be found by reviewing the definitions of the controllability and observability of a structure. A structure is controllable if the installed actuators excite all its structural modes. It is observable if the installed sensors detect the motions of all the modes. This information, although essential in many applications (e.g, in the placement of sensors and actuators), is too limited. It answers the question of mode excitation or detection in terms of yes or no. The more quantitative answer is supplied by the controllability and observability grammians, which represent a degree of controllability and observability of each mode. [ABSTRACT FROM AUTHOR]

Models described in the previous chapter include typical structural models, which are continuous-time, stable, and with proportional damping. In this chapter we consider models that are not typical in the above sense but, nevertheless, often used in engineering practice. Thus, we will consider models with rigid-body modes (which are unstable), models with nonproportional damping, discrete-time structural models, models with acceleration measurements, and generalized structural models. The latter include two kinds of inputs: controlled (or test) inputs and disturbance inputs, and also two kinds of outputs: measured outputs and outputs where the system performance is evaluated. [ABSTRACT FROM AUTHOR]

In this and the following chapter we explain structural models that describe standard —or more common—structures. The standard models include structures that are stable, linear, continuous-time, and with proportional damping. [ABSTRACT FROM AUTHOR]

Flexible structures in motion have specific features that are not a secret to a structural engineer. One of them is resonance—strong amplification of the motion at a specific frequency, called natural frequency. There are several frequencies that structures resonate at. A structure movement at these frequencies is harmonic, or sinusoidal, and remains at the same pattern of deformation. This pattern is called a mode shape, or mode. The modes are not coupled, and being independent they can be excited separately. More interesting, the total structural response is a sum of responses of individual modes. Another feature—structural poles—are complex conjugate. Their real parts (representing modal damping) are typically small, and their distance from the origin is the natural frequency of a structure. [ABSTRACT FROM AUTHOR]