The 2,4-dichlorophenoxyacetic acid (2,4-D) herbicide as an aromatic hydrocarbon is a dangerous and toxic organic pollutant among the agricultural pesticides. In this research, the performance of the biochar made from rice husk (BRH), granular activated carbon (GAC), and multi-walled carbon nanotubes (MWCNTs) was investigated for adsorption of 2,4-D in a fixed-bed column system. The influence of pH (2, 5, 7, 9), flow rate (0.5, 1, 1.5 mL min⁻¹), bed depth (3, 6, 9 cm), and influent 2,4-D concentration (50, 100, 150, 300 mg L⁻¹) was evaluated on adsorption process. The resulted breakthrough curves indicated that the higher removal efficiency of 2,4-D took place at the lower flow rate, lower influent 2,4-D concentration, higher bed depth, and lower pH. While in most cases the removal ability of GAC was better than other adsorbents, but generally, this study confirmed that the BRH as a cheap and sustainable material can be a viable alternative to GAC and MWCNTs for remediation and treatment scenarios, particularly in developing countries.

Drought as an exigent natural phenomenon, with high frequency in arid and semi-arid regions, leads to enormous damage to agriculture, economy, and environment. In this study, the seasonal Standardized Precipitation Index (SPI) drought index and time series models were employed to model and predict seasonal drought using climate data of 38 Iranian synoptic stations during 1967–2014. In order to model and predict seasonal drought ITSM (Interactive Time Series Modeling) statistical software was used. According to the calculated seasonal SPI, within the study area, drought severity classes 4 and 3 had the greatest occurrence frequency, while classes 6 and 7 had the least occurrence frequency. Results indicated that the best fitted models were Moving-Average or MA (5) Innovations and MA (5) Hannan-Rissenen, with 60.53 and 15.79 percentage, respectively. On the other hand, results of the prediction as well, indicated that drought class 4 with the highest percentages, was the most abundant class over the study area and drought class 7 was the least frequent class. According to results of trend analysis, without attention to significance of them, observed seasonal SPI data series (1967–2014), in 84.21% of synoptic stations had a negative trend, but this percentage changes to 86.84% when studying the combination of observed and predicted simultaneously (1967–2019).

Permanent monitoring of environmental issues demands efficient, accurate, and user-friendly pollutant prediction methods, particularly from operating variables. In this research, the efficiency of multiple polynomial regression in predicting the adsorption capacity of caffeine (q) from an experimental batch mode by multi-walled carbon nanotubes (MWCNTs) was investigated. The MWCNTs were specified by scanning electron microscope, Fourier transform infrared spectroscopy and point of zero charge. The results confirmed that the MWCNTs have a high capacity to uptake caffeine from the wastewater. Five parameters including pH, reaction time (t), adsorbent mass (M), temperature (T) and initial pollutant concentration (C) were selected as input model data and q as the output. The results indicated that multiple polynomial regression which employed C, M and t was the best model (normalized root mean square error = 0.0916 and R² = 0.996). The sensitivity analysis indicated that the predicted q is more sensitive to the C, followed by M, and t. The results indicated that the pH and temperature have no significant effect on the adsorption capacity of caffeine in batch mode experiments. The results displayed that estimations are slightly overestimated. This study demonstrated that the multiple polynomial regression could be an accurate and faster alternative to available difficult and time-consuming models for q prediction.