مجله آب و فاضلاب
پیاپی 130 (بهمن و اسفند 1399)

Release of dye-containing wastewater into ecosystems has posed serious risks to the environment and aquatic life because of toxicity and adverse effects on the water bodies. Malachite green is a basic dye that has very wide industrial applications, especially in the aquaculture industry. This study was carried out in order to remove the malachite green from aqueous solutions by thiolated graphene oxide in batch system. In the present work, the effects of experimental parameters such as adsorbent dosage, solution pH, initial dye concentration, thermodynamics and adsorption mechanism were comprehensively studied in batch system. In order to characterize the physical and chemical properties of the synthetized nanostructure and also to confirm the functionalization steps, different analyses including SEM and FT-IR were used. Batch studies showed that the experimental data fitted logically to applied isotherms, namely Langmuir (R2=0.991) and Freundlich (R2=0.983) models. Kinetic calculations confirmed that malachite green adsorption was described more accurately by pseudo-second order model compared to the pseudo-first order model. The study showed that thiolated graphene oxide is an effective adsorbent for malachite green removal from aqueous solution. Under controlled reaction conditions, Gibbs free energy (ΔG) varied from -1.46 to -3.25 kJ/mol, besides, the resulting ΔH° and ΔS° values were obtained 0.059 kJ/mol and 15.67 kJ/mol.K, respectively. So, it can be considered that the adsorption of malachite green onto the thiolated graphene oxide nanostructure is a physico-chemical and spontaneous process.

This paper summarizes the results of the comprehensive first flush characterization study performed on three highly urbanized highway sites in Los Angeles, California. The study was performed from 2000 through 2005 and a total of 97 storm events were monitored ranging from 0.5 to 137 mm with an average rainfall of about 25.5 mm. First flush characterization was performed based on contaminant concentration and mass loading. To determine the first flush effect, multiple grab samples were obtained throughout the storm event with an emphasis on collecting additional grab samples during the first hour of storm event. Topics presented and discussed in this paper include: (1) meaningful definition of first flush characterization, (2) strategic method of collecting first flush sampling, (3) uniform method and interpreting first flush results, and (4) the implication of first flush results for urban stormwater runoff management.

Microplastics have raised many concerns because of their potential negative impacts on the environment. Identifying the sources of microplastics release to the environment is a challenging subject. Synthetic textiles, such as machine woven carpets, are highly capable to propagate and emit microplastics, especially fibers. While the carpet cleaning service has experienced a fast growth due to urbanization and its impact on life style, there is a lack of information on the number and the size of fibers, which are released during the washing process. In this study, we characterized the microplastic fibers in the wastewater of two carpet-washing workshops in the cities of Ahwaz and Sari in Iran. Three replicates of 10 L-samples were taken from the wastewater of washing and drying stages. All samples were passed through sieves of 500, 300 and 37 μm. The residues were washed with 1 L distilled water and poured into clean glass bottles. Then, the samples were passed through 25 μm filter paper. The remained materials on the filter were examined using a stereo microscope. Shapiro-Wilk and Levin tests were applied to test the normality and homogeneity of data. One-way ANOVA test was used to investigate the differences in size of microplastic fibers and independent t-test to determine the difference between the total number of the released microplastic fibers and the ones at each stage in the cities. More than 3097 and 1824 microplastic fibers per square meter of carpet (equal to 81 and 48 microplastic fibers per liter of wastewater) were counted in the workshops in Ahwaz and Sari, respectively. The shares of microplastic fibers in the size of ≥500, 300-500 and 37-300 μm were 18.4%, 24.6%, 57% in Ahwaz and 14.4%, 28.8%, 56.8% in Sari, respectively. The number of released microplastic fibers per liter of wastewater of carpet washing workshops is much higher than the number of fibers in the raw wastewater entering Sari WWTP, which was 4.9-12 microplastic fibers per liter. The washing of machine woven carpets is an important emission source of microplastic fibers especially with the size of less than 300 micron. The number of released microplastic fibers depends on the type of washing and drying practices. It is expected its emission load will sharply increase in future due to the booming growth in demand for these kinds of reasonably priced floor covers.

Groundwater is an important source of freshwater the world over, especially in arid and semiarid regions. In recent years, groundwater overextraction has led to a serious drawdown in groundwater level in many aquifers. Hence, the projecting groundwater level is essential for the planning and management of water resources in a basin scale. This study aimed to project the mean groundwater level in Najafabad Plain in central Iran. Najafabad Plain is one of the most important aquifers in the Zayandeh-Rud River basin currently facing a negative hydrologic balance, which has been aggravated by the excessive agricultural demand that has adversely affected its groundwater level. For the purpose of the study, a multilayer perceptron Artificial Neural Network (ANNs) was developed. Recently, alternative algorithms have been used for training ANNs to overcome the disadvantages of the Back Propagation (BP) algorithm that is easily stuck in local minima and slow training convergence. In this regard, the Levenberg–Marquardt algorithm as the classical method and the Particle Swarm Optimization (PSO) as the evolutionary algorithm are adopted for training the feed forward ANNs and improving their performance. The obtained results from LM-NN were then compared with those from ANN-PSO model and observed information. Comparison of the results projected by the ANN-PSO and the observed mean groundwater levels using 58 piezometric wells with monthly time steps over a 20-year period showed that the ANN-PSO model is superior to LM in predicting groundwater level. As an illustration, for models run using nine hidden neurons for Nekouabad right zones the root mean square error (RMSE) of the testing dataset for ANN-PSO was the lowest (1.50) compared to those for LM-NN (1.76). Accordingly, the ANN-PSO models are able to be used as a reliable tool for evaluating different scenarios of the water resources management in the study aquifer. Finally, three management scenarios under two climate change scenarios, A2 and B1 (obtained from GCMs), were defined and the trained ANN-PSO was subsequently used to project the effects of each scenario on the groundwater level in the plain.

Although temporal and spatial severity of climate change remains uncertain, its occurrence and impacts on water resources is quite perceivable. Under any uncertain condition, such as climate change, proper and sustainable pollutant load allocation to receiving water bodies remains as a serious challenge. In the absence of statistical data and reliable probability distribution function for uncertain parameters, planners may use non-probabilistic approaches for tackling the imposed uncertainties. Among the common non-probabilistic approaches, the regret method is a robust and successfully used method for decision analysis. This paper presents an integrated approach for pollutant load allocation under uncertain climate condition. It integrates an efficient optimization algorithm and a physical quality simulation model in a regret-based decision analysis platform. The proposed system establishes a linkage between loads and receiving water conditions to maximize the dischargeable total maximum daily load (TMDL). Water quality responses of the receiving water body under different loads are estimated using QUAL2K simulation model. Maximization of total daily load under varying scenarios is carried out with the charged system search (CSS) algorithm. Effects on uncertainties in occurrence and severity of the assumed scenarios are analyzed in a non-probabilistic framework with minimizing the maximum and total regret (MMR, MTR), and the best scenario is proposed for implementation. Performance of the proposed approach is tested using the data from New River at the Salton outlet.

One of the most important results of hydraulic and hydrological modeling of the urban drainage network is hydrograph estimation. Annual journals on hydraulic and hydrological problems, especially in developing countries, are full of missing data, discrete and continuous gaps in most hydrological data, such as inlet flow and other series of flow data. This is due to reasons such as not registering statistics, deleting wrong statistics, or failure of measuring devices. The present paper attempts to investigate the flow rate of wastewater entering the sewage treatment plant (STP) for pumping station planning. The novelty of the research is using the Monte Carlo method, which is one of the simulators of the effect of the uncertainties in the timing and cost of the project, and Fourier series regression model to randomly generate data. These methods were used to simulate a 1-minute time scale of sewage flow data for Zahedan. Considering the construction of the treatment plant in the last decade, this is the first research with this approach in this treatment plant. The results indicate that, this method has been successful in estimating sewage flow data in the hourly and minute intervals. Finally, 270 days of flow data were obtained from a time interval of one minute with two methods of distribution: Lognormal function and a nonlinear Fourier series. Among these two methods, Fourier series’ accuracy was higher in terms of statistical indicators. In this simulation, RMSE, d, CI and EF values for Fourier regression model are 0.29, 0.99, 0.99, and 0.99, respectively.