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Particulate matters have detrimental effects on human health, environment and economic. This pollutant may emit from anthropogenic or natural sources. On global scale, main proportion of natural particulate matter release to the atmosphere because of wind erosion from arid and semi-arid regions. Recently, the amount of dust coming from Arabian countries has dramatically increased, especially dust storms that are affecting western and even central parts of Iran. This phenomenon has caused a lot of environmental problems. Dust source identification and trajectory simulation using numerical techniques are the main aims of this study. HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model dust module and trajectory simulation are utilized in this research and two case studies are investigated (in May and June 2010). The base of the HYSPLIT dust module is the PM10 dust storm emission algorithm for desert land use. This methodology is applied to estimate hotspots and trajectories. Due to the results, dust storms started on May 17th and June 7th because of high wind shear (>8.5 m/s) from the western Syrian Desert. The source region limited to 32.50 °N to 33.80 °N and 38.00 °E to 38.80 °E coordinates. Dust plumes lifted and dispersed towards the east and southeast of the sources and reached Ahvaz on May 18th and June 8th. The average of PM10 concentration in these dates reached 625 and 494 μgm3 on Ahvaz monitoring stations, respectively. Moreover, the results gained from the model for dust motion simulation are similar to the MODIS satellite images.

This study aims at providing a reduced‐order neural network model (RONNM) based on proper orthogonal decomposition (POD) for online prediction of the 5 days biochemical oxygen demand (BOD5). To achieve this goal, the POD method is used to reduce the input vectors to neural network (NN) model. Evaluation of the selected RONNM and its comparison with the model fed with all input vectors (NN model) shows that it reduces not only the output error but also computational cost. Results depict that Pearson correlation coefficient (R) and root mean square error for the best fitted RONNM are 0.94 and 7.75, respectively. Furthermore, accuracy analysis of the output results of models based on developed discrepancy ratio statistic reveals that RONNM is superior. Thus, developed methodology can be considered as an effective tool to train the water resources managers for online evaluation of water quality related to BOD5. © 2011 American Institute of Chemical Engineers Environ Prog, 32: 120–127, 2013.

In this paper, two categories of reduced-order modeling (ROM) of the shallow water equations (SWEs) based on the proper orthogonal decomposition (POD) are presented. First, the traditional Galerkin-projection POD/ROM is applied to the one-dimensional (1D) SWEs. The result indicates that although the Galerkin-projection POD/ROM is suitable for describing the physical properties of flows (during the POD basis functions’ construction time), it cannot predict that the dynamics of the shallow water flows properly as it was expected, especially with complex initial conditions. Then, the study is extended to applying the equation-free/Galerkin-free POD/ROM to both 1D and 2D SWEs. In the equation-free/Galerkin-free framework, the numerical simulation switches between a fine-scale model, which provides data for construction of the POD basis functions, and a coarse-scale model, which is designed for the coarse-grained computational study of complex, multiscale problems like SWEs. In the present work, the Beam & Warming and semi-implicit time integration schemes are applied to the 1D and 2D SWEs, respectively, as fine-scale models and the coefficients of a few POD basis functions (reduced-order model) are considered as a coarse-scale model. Projective integration is applied to the coarse-scale model in an equation-free framework with a time step grater than the one used for a fine-scale model. It is demonstrated that equation-free/Galerkin-free POD/ROM can resolve the dynamics of the complex shallow water flows. Moreover, the computational cost of the approach is less than the one for a fine-scale model.