JWSS - Journal of Water and Soil Science

Many studies have been done on various properties of dust and one of the most important characteristics of dust is the ability to carry different microorganisms from the source points. The aim of this study was to investigate the bacterial and fungal community of dust and to identify its dominance species in a single event of intense dust storm, in the northern regions of Sistan and Blauchestan Province (Sistan plain). Dust samples were accordingly collected by Siphon dust samplers after one of the most intense dust storms in 28-31 August, 2015, from 5 cities in the northern regions of Sistan and Blauchestan Province; after that, the microbial community of dust was determined by culturing in petri dish and its dominant bacterial and fungal species were identified. The results showed maximum aerobic and anaerobic bacteria population was observed in the Hirmand city dust (1875000 CFU/g and 156667 CFU/gr, respectively). The maximum aerobic fungi population was observed in the Zabol city (833 CFU/g) and the maximum anaerobic fungus population was observed in Zahak city (2167 CFU/g). The most frequent type of bacteria was Bacillus sp, which was followed by Streptomyces pactum. The most frequent type of fungi in this research was Penicillium sp and the second one was Aspergillus. The results showed the high and variated microbial community, especially pathogenic fungi associated with dust in this region.

The agricultural sector as one of the most important sectors of water consumption has great importance for the sustainability of the country's water resources systems. The objective of this study was to estimate the river water abstraction (RWA) for agricultural consumption in the study area of Nobaran in the Namak Lake basin. The RWA was estimated using variables related to morphological, hydrological, and land use factors, as well as a combination of their variables collected through field sampling. Data mining methods such as adaptive-network-based fuzzy inference systems (ANFIS), group method of data handling (GMDH), radial basis function (RBF), and regression trees (Rtree) were also used to estimate the RWA variables. In the current study, the GMDH₂₄ model with a combined scenario including the variables of river width, river depth, minimum flow, maximum flow, average flow, crop, and the garden cultivated area was adopted as the best model to estimate the RWA variable. The RMSE value for the combined scenario of the GMDH₂₄ model was found to be 0.046 for estimating RWA in the Nobaran study area. The results showed that the performance of the GMDH₂₄ model for estimating RWA for maximum values is very acceptable and promising. Therefore, modeling and identifying various variables that affect the optimal RWA rate for agricultural purposes fulfills the objectives of integrated water resources management (IWRM).