Effects of climate change on surface water management of Seyhan basin,Turkey

The goal of this study was to set-up the basis for climate change adaptation of water resources management policies in Seyhan River basin. The first priority was to identify the balances between water resources and water users with respect to existing and planned projects. In this respect various aspects of Seyhan basin were evaluated, including evaluation of existing water resources, determination of water demand of existing and planned projects, and water resources supply-demand characteristics. The global climate change model was downscaled to the basin scale, the results were associated with hydrometeorological monitoring network and finally the impact of climate change on surface water resources and demands were determined for specific projection years. Water resources management scenarios were developed to evaluate adaptation alternatives to climate change scenarios at the basin level. It was determined that even though there was no water stress in Seyhan basin in 2010, many parts of the basin were expected to suffer significant shortages over the coming years.     

Estimation of transport parameters of phenolic compounds and inorganic contaminants through composite landfill liners using one-dimensional mass transport model

One-dimensional (1D) advection–dispersion transport modeling was conducted as a conceptual approach for the estimation of the transport parameters of fourteen different phenolic compounds (phenol, 2-CP, 2-MP, 3-MP, 4-MP, 2-NP, 4-NP, 2,4-DNP, 2,4-DCP, 2,6-DCP, 2,4,5-TCP, 2,4,6-TCP, 2,3,4,6-TeCP, PCP) and three different inorganic contaminants (Cu, Zn, Fe) migrating downward through the several liner systems. Four identical pilot-scale landfill reactors (0.25 m³) with different composite liners (R1: 0.10 + 0.10 m of compacted clay liner (CCL), L_e = 0.20 m, k_e = 1 × 10⁻⁸ m/s, R2: 0.002-m-thick damaged high-density polyethylene (HDPE) geomembrane overlying 0.10 + 0.10 m of CCL, L_e = 0.20 m, k_e = 1 × 10⁻⁸ m/s, R3: 0.002-m-thick damaged HDPE geomembrane overlying a 0.02-m-thick bentonite layer encapsulated between 0.10 + 0.10 m CCL, L_e = 0.22 m, k_e = 1 × 10⁻⁸ m/s, R4: 0.002-m-thick damaged HDPE geomembrane overlying a 0.02-m-thick zeolite layer encapsulated between 0.10 + 0.10 m CCL, L_e = 0.22 m, k_e = 4.24 × 10⁻⁷ m/s) were simultaneously run for a period of about 540 days to investigate the nature of diffusive and advective transport of the selected organic and inorganic contaminants. The results of 1D transport model showed that the highest molecular diffusion coefficients, ranging from 4.77 × 10⁻¹⁰ to 10.67 × 10⁻¹⁰ m²/s, were estimated for phenol (R4), 2-MP (R1), 2,4-DNP (R2), 2,4-DCP (R1), 2,6-DCP (R2), 2,4,5-TCP (R2) and 2,3,4,6-TeCP (R1). For all reactors, dispersion coefficients of Cu, ranging from 3.47 × 10⁻⁶ m²/s to 5.37 × 10⁻² m²/s, was determined to be higher than others obtained for Zn and Fe. Average molecular diffusion coefficients of phenolic compounds were estimated to be about 5.64 × 10⁻¹⁰ m²/s, 5.37 × 10⁻¹⁰ m²/s, 2.69 × 10⁻¹⁰ m²/s and 3.29 × 10⁻¹⁰ m²/s for R1, R2, R3 and R4 systems, respectively. The findings of this study clearly indicated that about 35–50% of transport of phenolic compounds to the groundwater is believed to be prevented with the use of zeolite and bentonite materials in landfill liner systems.     

Alum-based sludge (AbS) recycling for turbidity removal in drinking water treatment: an insight into statistical,technical, and health-related standpoints

This study aimed to evaluate the feasibility of recycling alum-based sludge (AbS) generated from drinking water treatment facility for turbidity removal. A response surface methodology (RSM)-based modeling and factor analysis were first implemented for assessing the optimal conditions of four independent factors, such as initial turbidity concentration, humic acid (HA) concentration, pH, and AbS dose on the water turbidity removal via the use of AbS as a coagulant agent. The optimum values of the four main variables were determined as initial turbidity concentration?=?59.65 NTU, pH?=?5.56, AbS dose?=?19.71 g/L, and HA concentration?=?12.28 mg/L, and at the optimum conditions, the percentage of turbidity removal was obtained as 94.81 (±?1.01)% for real water. At the optimum conditions of AbS usage as a coagulant for real water samples, monitoring of water quality parameters of the process indicated no health-related concerns in terms of hardness (all types), alkalinity, pH, residual aluminum, and even bacteriological (fecal and total coliforms) contamination. The results indicated a potential for AbS recycling in the treatment plant as a coagulant agent, although some requirements should be fulfilled before full-scale application.     

Secondary collisions and injury severity: A joint analysis using structural equation models

Objective: This study aims to investigate the contributing factors to secondary collisions and the effects of secondary collisions on injury severity levels. Manhattan, which is the most densely populated urban area of New York City, is used as a case study. In Manhattan, about 7.5% of crash events become involved with secondary collisions and as high as 9.3% of those secondary collisions lead to incapacitating and fatal injuries.

Methods: Structural equation models (SEMs) are proposed to jointly model the presence of secondary collisions and injury severity levels and adjust for the endogeneity effects. The structural relationship among secondary collisions, injury severity, and contributing factors such as speeding, alcohol, fatigue, brake defects, limited view, and rain are fully explored using SEMs. In addition, to assess the temporal effects, we use time as a moderator in the proposed SEM framework.

Results: Due to its better performance compared with other models, the SEM with no constraint is used to investigate the contributing factors to secondary collisions. Thirteen explanatory variables are found to contribute to the presence of secondary collisions, including alcohol, drugs, inattention, inexperience, sleep, control disregarded, speeding, fatigue, defective brakes, pedestrian involved, defective pavement, limited view, and rain. Regarding the temporal effects, results indicate that it is more likely to sustain secondary collisions and severe injuries at night.

Conclusions: This study fully investigates the contributing factors to secondary collisions and estimates the safety effects of secondary collisions after adjusting for the endogeneity effects and shows the advantage of using SEMs in exploring the structural relationship between risk factors and safety indicators. Understanding the causes and impacts of secondary collisions can help transportation agencies and automobile manufacturers develop effective injury prevention countermeasures.     

Assessment of indoor and outdoor particulate air pollution at an urban background site in Iran

The relationship between indoor and outdoor particulate air pollution was investigated at an urban background site on the Payambar Azam Campus of Mazandaran University of Medical Sciences in Sari, Northern Iran. The concentration of particulate matter sized with a diameter less than 1 μm (PM_1.0), 2.5 μm (PM_2.5), and 10 μm (PM₁₀) was evaluated at 5 outdoor and 12 indoor locations. Indoor sites included classrooms, corridors, and office sites in four university buildings. Outdoor PM concentrations were characterized at five locations around the university campus. Indoor and outdoor PM measurements (1-min resolution) were conducted in parallel during weekday mornings and afternoons. No difference found between indoor PM₁₀ (50.1 ± 32.1 μg/m³) and outdoor PM₁₀ concentrations (46.5 ± 26.0 μg/m³), indoor PM_2.5 (22.6 ± 17.4 μg/m³) and outdoor PM_2.5 concentration (22.2 ± 15.4 μg/m³), or indoor PM_1.0 (14.5 ± 13.4 μg/m³) and outdoor mean PM_1.0 concentrations (14.2 ± 12.3 μg/m³). Despite these similar concentrations, no correlations were found between outdoor and indoor PM levels. The present findings are not only of importance for the potential health effects of particulate air pollution on people who spend their daytime over a period of several hours in closed and confined spaces located at a university campus but also can inform regulatory about the improvement of indoor air quality, especially in developing countries.     

Stochastic modeling approaches based on neural network and linear–nonlinear regression techniques for the determination of single droplet collection efficiency of countercurrent spray towers

This paper presents a new mathematical model and a two-layer neural network approach to predict the single droplet collection efficiency (SDCE), η _d, of countercurrent spray towers. SDCE values were calculated using MATLAB^® algorithm for 205 different artificial scenarios given in a large range of operating conditions. Theoretical results were compared with outputs obtained from a two-layer neural network and DataFit^® scientific software. The predicted model developed from linear–nonlinear regression analysis and network outputs agreed with the theoretical data, and all predictions proved to be satisfactory with a correlation coefficient of about 0.921 and 0.99, respectively. By using the proposed model, iterations between Reynolds number (Re), drag coefficient (C _D) and terminal velocity values (v _T) were neglected for a large range of operating conditions. SDCE values were also obtained speedily and practically for five main operating inputs used in the model.     

Appraisal of methane production and anaerobic fermentation kinetics of livestock manures using artificial neural networks and sinusoidal growth functions

This study aimed to perform a comparative analysis of the performance of five models (Gompertz, logistic, Richards, the first-order, artificial neural networks) in predicting methane production rate from anaerobic digestion of livestock manures. The input variables were fermentation time, digestion temperature, biogas temperature, ambient temperature, pH, and specific biogas production rate. The physicochemical compositions of cow manure and sheep manure showed that volatile solid (VS) contents were close to each other in manure compositions (77.6% and 64.7%, respectively), while the potential of methane production from cow manure (673.44 mL CH₄/g VS) was greater than that from sheep manure (320.32 mL CH₄/g VS). The determination coefficients (R²) for logistic function, Gompertz, Richards, the first-order, and ANN models were obtained as 0.968, 0.967, 0.975, 0.825, and 0.995 for the cow manure, respectively. In case of the sheep manure, the R² values obtained from these models were 0.976, 0.979, 0.981, 0.968 and 0.991, respectively. Although the determination coefficients of all models were in satisfactory agreement with the experimental data, the ANN model showed competitive lower RMSE values of 0.111 and 0.164 for cow and sheep manure data sets, respectively, indicating its superior performance than other models.