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Based on the macro-invertebrate community and physico-chemical indices, water quality of the Tajan River in 6 stations along 80 km of the river length was assessed. Monthly sampling was conducted for one year (September 2011 to August 2012), using a surber with 30.5 × 30.5 cm dimensions and a 60μ mesh net for the benthic invertebrates sampling. The identified macro-invertebrates belonged to 31 families, 12 orders and 5 classes, the highest and the lowest abundance of which belonged to Psychodidae and Chironomidae, respectively. Based on Hilsenhoff Family Biotic Index, the research stations were classified into 4 qualitative categories, viz. excellent, good, fair, and fairly poor. Stations 1 and 5, respectively, showed the highest and lowest HFBI index. The highest and the lowest values for the Shannon–Weiner index were recorded in station 1 and station 2, respectively. The results of physicochemical parameters indicated  reduced dissolved oxygen in the stations 2 and 5, and increased TDS in station 2 and 1 and increased nitrite and ammonium in station 3. Results of Spearman correlation coefficients between biotic indices and physicochemical variables revealed significant relationship between DO, TDS, EC, PO₄ and water flow with HFBI and Shannon-Weiner indices. This study showed, generally pollution in stations 2 and 5 has reduced water quality in these station's.

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Aim: Landslide classification using a fractal model at the Tajan river basin in northern Iran is the study intended as a new approach based on 142 landslide information data set.
Materials & Methods: The obtained results were interpreted using the Depth–Number (Dp–N) fractal model and a fairly wide set of information available for each landslide class, consisting of Digital Elevation Model (DEM), rainfall, landuse, geology (lithology and fault) and drainage network data.
Findings: The log–log plot shows five classes for depth (weakly, moderately, highly, strongly, and extremely magnitude) which shows that the extremely magnitude landslides have depths higher than 19.95 m in the NE, middle, western, and SE parts of the Tajan basin. The strong (5-19.95 m) and high (2.4-5 m) magnitude landslides happened in the northern, NE, western and NW parts. The results, which were matched up to landuse, drainage network, DEM, and fault allocation patterns revealed an affirmative correlation between landslide classes and the particulars in the area. In addition, the coefficient of determination, R2, for each population shows that the classification has been done correctly using the Dp-N fractal model. Amounts of P-value obtained from paired samples t-test and ANOVA showed that the separated categories are in incongruity with each other and are significantly different (sig=0.000).
Conclusion: Results show that separating the populations of landslides based on a parameter as magnitude and the difference between the populations’ magnitude of landslides should be considered in landslide susceptibility zonation.