Considerable efforts have been devoted to the development of dynamic origin-destination (OD) estimation models, which are a key step to realizing self-adaptive traffic control systems for urban traffic management. However, most of the models proposed to date estimate OD flows based on a single traffic data source, and their performance is limited by the coverage and accuracy of traffic sensors. The inherent difficulty in estimating the dynamic traffic assignment matrix means that dynamic OD estimation remains a challenge for real-life applications. This paper proposes the use of a Kalman filter for dynamic OD estimation using multi-source sensor data. The dynamic characteristic of changing OD flow over time is analyzed, and the problem of dynamic OD estimation is converted to a problem of estimating OD structural deviation. The resulting dynamic relationship between traffic volume and OD structural deviation is then used to establish the Kalman filter model. An improved traffic assignment approach is developed and embedded into the measurement equation of the Kalman filter model to enable dynamic updating of the traffic assignment matrix. A dual self-adaptive mechanism based on the Kalman filter is used to calibrate the model. The proposed method was implemented on a real-life traffic network in the downtown area of Kunshan City, China. The results show that the proposed method is more accurate than, and outperforms, the traditional link-volume-based and turning-movement-based methods.

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Harmful algal bloom (HAB), often cyanobacteria in fresh water, is a frequent and worldwide occurrence especially as eutrophication of water supplies increases [1]. Environmental and health problems from these water blooms, such as deterioration of water quality, potentially decrease biodiversity [2] and affecting human health through food chains [3], have been documented in many regions due to the production of cyanotoxins [4]. Therefore, control and elimination of cyanobacterial blooms is crucial in the management and mitigation of aquatic ecosystems.A variety of methods have been proposed for removing and/or inhibiting cyanobacterial blooms, such as allelopathy (allelochemicals) from plants [5][6][7] Pol. J. Environ. Stud. Vol. 24, No. 1 (2015), [397][398][399][400][401][402] Original . These results demonstrated that the growth inhibitory activities of allelochemicals on unicellular M. aeruginosa cannot instead of the efficacies for controlling Microcystis bloom completely. And according to the different sensitivities of Microcystis species, the colonial Microcystis strains or natural Microcystis bloom will be proposed as the target organism when searching for Microcystis bloom control.