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Evaluation and assessment of dust model results is of primary importance to get a better understanding of the models' performance, and therefore, enhancing the models' set up and structure. Besides some SDS-WAS dust models, two other high resolution WRF-Chem runs have been carried out for two dust episodes over the West Asia with alterations in the soil erodibility fields as one of the primary criteria of dust sources. The main aim of this article was to investigate the high resolution WRF-Chem modeling with the default and altered soil erosion, against the WMO SDS-WAS models. In this paper we investigated the application of WRF-Chem dust modeling for the region of interest (Iran), which cannot be seen entirely by the SDS-WAS models' domains. Comparisons of modelled dust surface concentrations with ground based measurements on 8 air quality stations show that the high resolution WRF-Chem could more or less lead to better predictions. For some cases, the results of the high resolution WRF-Chem unexpectedly presented a declined performance, which indicate that the improvements in the horizontal resolution and soil erodibility could not always lead to improved dust predictions, and more factors such as the model set-up and structure should be considered.

Presented herein is an analysis of an intensive dust intrusion of 8-11 February, 2015. The intrusion has been associated with the development of an intense front over the Middle East. During the episode, dust concentrations in Ahvaz air quality monitoring station in south-western Iran (Khuzestan region) reached over 10000 µg/m3, which was beyond the capacity of air quality sensors to be recorded. In this paper, the performance of WRF/Chem modelling system was evaluated for the west and southwestern Iran as well as the whole model domain. MADE and GOCART models as the main aerosol models implemented in WRF/Chem V3.6 were verified by ground observation data for the specific points. We have taken into account the advantages and disadvantages of MADE and GOCART aerosol models over the study period, by comparing the simulations' results for the specific points with the available dust concentration data. The time series of the area-average of modeled dust column mass density over the test area determined a coherent temporal variation with that of MERRA-2 model which shows the sensitivity of WRF/Chem modeling system for this area with most frequent dust storm events in the Middle East. The validation of WRF/Chem V3.6 modeling system results for the overall model domain is implemented using Hovmoller diagrams. The modeled latitude-average of dust column mass density for the whole model domain showed some discrepancies of dust transport pattern with the results of MERRA-2 model. Although there is a considerable difference between the modelled dust concentrations and the observations especially on the peak values, the temporal variations of the modelled dust concentration with MADE aerosol model is quite consistent with the observations. The results of MADE aerosol model for the dust concentration were more close to the observations of the points inside Khuzestan and surrounding low level plains with aeolian soils. The GOCART aerosol model had more reasonable simulation results for the points through the mountainous terrains of western Iran. It can be concluded that the structure of MADE and GOCART aerosol models can be revised with regard to their relative advantages in specific regions to minimize the error in modeling dust behavior. Scince this study has been employed on a dust episode, and considering that the gaseous air pollutants require the emissions data, the simulations of the gaseous aerosols has not been considered in this study. However, the results of the WRF/Chem model for the dust aerosols could be extended to the other aerosols, especially PM10 and PM2.5 particles. [ABSTRACT FROM AUTHOR]

Summary: Diode rectifiers are widely used in many applications, and therefore, there is a need for an accurate model to study the rectifiers. Because of the complexity and time consuming of the analytical model of rectifiers, the average value modeling has been introduced. This paper proposes a novel average value model (AVM) for the line‐commutated rectifier bridge supplied with an unbalanced power source. The unbalance condition, here, refers to the asymmetry of the three‐phase voltage magnitude and the three‐phase impedance, which is possible due to the occurrence of asymmetry in the sources with winding such as power sources, generators, and transformers. According to the three‐phase input current of the rectifier, one cycle is divided into some intervals, and the differential equation for each interval is provided. Then, utilizing these equations, the average value of the derivative of the load current is derived from which the average value of the load current and voltage can be calculated. The proposed AVM is then verified using simulation and experimental results for the diode rectifier bridge with the load of the brushless synchronous generator field winding. Besides, the model is investigated for different load and supply parameters. [ABSTRACT FROM AUTHOR]

One of the most important issues that power companies face when trying to reduce time and cost maintenance is condition monitoring. In electricity market worldwide, a significant amount of electrical energy is produced by synchronous machines. One type of these machines is brushless synchronous generators in which the rectifier bridge is mounted on rotating shafts. Since bridge terminals are not accessible in this type of generators, it is difficult to detect the possible faults on the rectifier bridge. Therefore, in this paper, a method is proposed to facilitate the rectifier fault detection. The proposed method is then evaluated by applying two conventional kinds of faults on rectifier bridges including one diode open-circuit and two diode open-circuit (one phase open-circuit of the armature winding in the auxiliary generator in experimental set). To extract suitable features for fault detection, the wavelet transform has been used on recorded audio signals. For classifying faulty and healthy states, K-Nearest Neighbours (KNN) supervised classification method was used. The results show a good accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2 ) is a major greenhouse gas. This study investigated the performance of three common algorithms, namely NIES, ACOS, and Remo Tec (SRFP). These algorithms were compared using GOSAT observation satellite data with reference data obtained from TCCON during the period 2009-2021. According to statistical evaluation, the SRFP and NIES algorithms achieved the lowest and highest correlation values of the 13 year (2009_2021) average of all sites, respectively. The average bias error values of NIES and ACOS was estimated to be less than that of SRFP approximately 0.5 ppm, while the bias within SRFP was of about 2 ppm. Comparing the RMSE and CRMS error values showed that the highest and lowest error values were related to the SRFP and NIES algorithms respectively, which were 0.37-1.67 and ppm 1.46-7.9. The researchers also compared them with monthly time changes based on ground measurements, and observed a time series of CO 2 concentration changes that well matched the trend of gas concentration values at ground stations obtained by NIES algorithm. The results showed that in most cases NIES was an effective algorithm to retrieve carbon dioxide gas concentrations, allowing the researchers to identify the main sources of greenhouse gas emissions in different areas. The clustering result in the study area showed that the continental CO 2 columnar concentration has a specific seasonal cycle, with the maximum and minimum values appearing in winter-early spring and spring-late summer, respectively. In conclusion, cluster analysis can classify the surface CO 2 column concentration values and determine the spatial distribution pattern of CO 2 ., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)