Chemometric Analysis of Surface Water Quality Data: Case Study of the Gorganrud River Basin, Iran

Canonical correlation analysis (CCA), principal component analysis (PCA), and principal factor analysis (PFA) have been adopted to provide ease of understanding: interpretation of a large complex data set in the Gorganrud River monitoring networks, evaluation of the temporal and spatial variations of water quality, and finally identification of monitoring stations and parameters which are most important in assessing annual variations of water quality in the river. In accomplishing the research, 11 surface water quality data related to both of physical and chemical parameters have been collected from seven monitoring stations from 1996 to 2002. In general, our results from CCA method indicated strong relationship between physical and chemical parameters in the Gorganrud River. In addition, analyzing data through the PCA and PFA techniques revealed that all monitoring stations are important in explaining the annual variation of data set. From the point of view of the degree of importance of parameters contributing to water quality variations, further investigations by running two scenarios (rotated factor correlation coefficient value equal to 0.95 and 0.90 for the first and second scenarios, respectively) showed that the important parameters in one season may not be important for another season. For example, unlike in summer, water temperature, total suspended solids, total phosphorous, and nitrate parameters were important, electrical conductivity, and turbidity parameters had been realized as important parameters in spring through the first scenario.     

Prediction of Crop Production using Drought indices at Different Time Scales and Climatic Factors to Manage Drought Risk1

Sadat Noori, S.M., A.M. Liaghat, and K. Ebrahimi, 2011. Prediction of Crop Production Using Drought Indices at Different Time Scales and Climatic Factors to Manage Drought Risk. Journal of the American Water Resources Association (JAWRA) 48(1): 1‐9. DOI: 10.1111/j.1752‐1688.2011.00586.x Abstract: Drought causes great damage to rainfed and irrigated farming. Therefore, prediction of crop production during the drought period is essential in order to manage drought risk. Thus, proceeding to agricultural drought risk management can be very useful. This study shows the results of early crop prediction using the combination of climate factors and drought indices at different time scales. The study region was Hamadan, a semiarid region in Iran. The methodology demonstrated here has allowed the prediction of production several months before harvest. Moreover, the predictive models constructed have explained 89% of the temporal variability of wheat production. This method could be very efficient for managing crop production. Moreover, having clear prediction, decision makers can plan better for overcoming drought impacts to reduce crop uncertainty for farmers in insurance companies.     

Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration

This study aims to predict daily carbon monoxide (CO) concentration in the atmosphere of Tehran by means of developed artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Forward selection (FS) and Gamma test (GT) methods are used for selecting input variables and developing hybrid models with ANN and ANFIS. From 12 input candidates, 7 and 9 variables are selected using FS and GT, respectively. Evaluation of developed hybrid models and its comparison with ANN and ANFIS models fed with all input variables shows that both FS and GT techniques reduce not only the output error, but also computational cost due to less inputs. FS–ANN and FS–ANFIS models are selected as the best models considering R², mean absolute error and also developed discrepancy ratio statistics. It is also shown that these two models are superior in predicting pollution episodes. Finally, uncertainty analysis based on Monte-Carlo simulation is carried out for FS–ANN and FS–ANFIS models which shows that FS–ANN model has less uncertainty; i.e. it is the best model which forecasts satisfactorily the trends in daily CO concentration levels.     

Microbial quality and physical–chemical characteristics of thermal springs

Microbial quality and physical–chemical properties of recreational spas were surveyed to investigate the health aspect of the spas’ water. A total of 195 samples were collected from pools and springs of the spas in five sites from Ardebil Province of Iran. The effects of an independent factor defined as ‘condition’ and its component sub-factors (i.e., sampling point, location, and sampling date) on microbial quality and physical–chemical properties of the spas were studied by applying path analysis. The influence of physical–chemical properties on microbial quality was also considered. The percentage of samples exceeding the ISIRI (Swimming pool water microbiological specifications (vol 9412), Institute of Standards and Industrial Research of Iran, Tehran, 2007) limits for Staphylococcus (spp.) was up to 55.8 in the springs and 87.8 in the pools, 58.1 and 99.2 for HPC, 90.7 and 97.8 for total coliform and fecal coliform, and 9.3 and 34.4 for Pseudomonas aeruginosa, respectively. There were significant differences between the pools and springs for both physical–chemical properties and microbial quality. From the path analysis, sampling point was the most effective sub-factor of ‘condition’ on both the physical–chemical properties and microbial quality. Among the physical–chemical properties, water color had the most enhancing or additive influence on microbial pollution, while EC indicated a reducing or subtractive effect.     

Comparative study between using natural and synthetic zeolites for the improvement of soil salinity and crop yield

The effects of natural zeolite (clinoptilolite, NZ) and synthetic zeolite (Y, SZ) on salinity and the presence of the harmful salts in the soil were compared on Raphanus sativus L. Nine soil treatments were studied: 1 (control), 2 (NaCl), 3 (Na₂SO₄), 4 (NZ), 5 (SZ), 6 (NZ?+?NaCl), 7 (SZ?+?NaCl), 8 (NZ?+?Na₂SO₄), and 9 (SZ?+?Na₂SO₄). Five radish seeds were planted in each pot. Each treatment was repeated 10 times. After 50 days under equal conditions some parameters consisting of the number of leaves (NL), total leaf area (TLA), total fresh weight (TFW), total dry weight (TDW), root fresh weight (RFW), air fresh weight (AFW), root dry weight (RDW) and air dry weight (ADW) were determined. Results showed that adding either NZ or SZ to salinic soil (treatments NZNaCl, NZNa₂SO₄, SZNaCl and SZNa₂SO₄) increased AFW, TFW and TDW. Using both NZ and SZ in normal soil increased the weight of plants by developing AFW. Further NZ was more effective on soil with NaCl than SZ.     

Determination of fluoride and its effects on flavonoids in some legumes

The effects of fluoride (Fl) emission, from an aluminum (Al) reduction plant in Iran, on the concentration of Fl in six legume species were studied. In addition, the influence of Fl on leaf flavonoids was investigated. Determination of Fl in collected plants (Alhagi camelorum Fisch., Cercis siliquastrum L., Glycyrrhiza glabra L. Medicago sativa L., Robinia peseudoacacia L., and Sophora alopecuroides L.) from the factory area was performed using a Fl ion-selective electrode. Identification of flavonoids in the samples was obtained by two-dimensional paper chromatography (2-D PC) and thin layer chromatography (TLC). Controls were obtained from plants located 10 km distance from the factory. Results showed high Fl concentration in all polluted samples. Phytochemical changes in polluted leaves were observed with appearance or disappearance of some flavonoids.