M. Amini, M. Afyuni, H. Khademi,
Volume 10, Issue 4 (1-2007)
Abstract
Heavy metals including cadmium (Cd) and lead (Pb) are entering agricultural soils from different routes and mainly due to human activities. Accumulated Cd and Pb in the soil would eventually enter the human and animal food chains and pose threat to their health. Therefore, evaluating heavy metal accumulation is necessary to prevent soil and environmental pollutions and should be considered by researchers as well as policy makers. This study was conducted to model the accumulation rates of Cd and Pb in the agro-ecosystems of Isfahan, Mobarakeh, Lenjan, Borkhar, Najafabad, Khomeinishahr and Felavarjan. Cadmium and lead accumulation rates in the agro-ecosystems were computed using a stochastic mass balance model which uses Latin Hypercube sampling in combination with Monte-Carlo simulation procedure. Agricultural information including crop types, crop area and yield, the type and the number of livestock, application rate of mineral fertilizers, compost and sewage sludge and also metal concentration in plant and amendments were used to quantify Cd and Pb accumulation rates. Modeling Cd and Pb accumulation rates indicated that the metals are accumulating in the agricultural lands in the studied townships. The largest Cd (18 g ha-1 yr-1) and Pb (260 g ha-1 yr-1) accumulation rates were found in the township of Isfahan but the minimum accumulation rates were found in township of Lenjan for Cd (3 g ha-1 yr-1) and Mobarakeh for Pb (10 g ha-1 yr-1). The major input route to agricultural soils is phosphate fertilizers for Cd but for Pb is manure on the regional scale. High application rates of sewage sludge and compost in agricultural lands in the township of Isfahan could result in considerable amounts of Cd and Pb entering the soils of this region.
H. Faghih, J. Behmanesh, K. Khalili,
Volume 22, Issue 1 (6-2018)
Abstract
Precipitation is one of the most important components of water balance in any region and the development of efficient models for estimating its spatiotemporal distribution is of considerable importance. The goal of the present research was to investigate the efficiency of the first order multiple-site auto regressive model in the estimation of spatiotemporal precipitation in Kurdistan, Iran. For this purpose, synoptic stations which had long time data were selected. To determine the model parameters, data covering 21 years r (1992-2012) were employed. These parameters were obtained by computing the lag zero and lag one correlation between the annual precipitation time series of stations. In this method, the region precipitation in a year (t) was estimated based on its precipitation in the previous year (t-1). To evaluate the model, annual precipitation in the studied area was estimated using the developed model for the years 2013 and 2014; then, the obtained data were compared with the observed data. The results showed that the used model had a suitable accuracy in estimating the annual precipitation in the studied area. The percentages of the model in estimating the region's annual precipitation for the years 2013 and 2014 was obtained to be 7.9% and 17.3%, respectively. Also, the correlation coefficient between the estimated and observed data was significant at the significance level of one percent (R=0.978). Furthermore, the model performance was suitable in terms of data generation; so the statistical properties of the generated and historical data were similar and their difference was not significant. Therefore, due to the suitable efficiency of the model in estimating and generating the annual precipitation, its application could be recommended to help the better management of water resources in the studied region.
