The effects of aeration rate on generated compost quality, using aerated static pile method

To determine the most appropriate composting process in an active municipal solid waste system, an experiment was carried out using a nested design method with three aeration rates. During each aeration rate, parameters such as temperature, pH, EC, carbon-to-nitrogen ratio, NO(3)-N, nitrogen, potassium and phosphorous were measured and the efficiency of different composting processes was evaluated. The result of this study showed that the lower and medium aeration rates had a significant impact on nitrogen, carbon-to-nitrogen ratio and temperature profile, while higher aeration rates led to higher EC values. Furthermore, the thermophilic phase lasted 13, 9 and 4 weeks for the aeration rates of 0.4, 0.6 and 0.9 L min(-1)kg(-1), respectively. Accordingly, it was concluded that starting at a rate of 0.6 L min(-1)kg(-1) during first 2 months (about 9 weeks) of the process and continuing at a rate of 0.4 L min(-1)kg(-1)until the end of composting process would result in lower energy consumption.     

Phosphorus removal and recovery: state of the science and challenges

Environmental Science and Pollution Research - Phosphorus is one of the main nutrients required for all life. Phosphorus as phosphate form plays an important role in different cellular processes....     

Synthesis of vinasse-dolomite nanocomposite biochar via a novel developed functionalization method to recover phosphate as a potential fertilizer substitute

• Nanocomposites were prepared by adding dolomite to vinasse at different ratio. • Textural and morphological features of adsorbents were studied in detail. • CCD based RSM was used for investigation of P ion removal by nanocomposite. • The q_m based on Langmuir model for modified vinasse biochar was 178.57 mg/g. • P loaded nanocomposite improved plant growth and could be utilized as P-fertilizer. The effectiveness of phosphate (P) removal from aqueous solutions was investigated by novel low-cost biochars synthesized from vinasse and functionalized with calcined dolomite. The vinasse-derived biochar, synthesized via pyrolysis at different temperatures, showed easy preparation and a large surface area. The novel vinasse biochar nanocomposites were prepared by adding dolomite to the vinasse biochars with different weight percentages (10, 20 and 30%). The characteristics of the prepared materials were identified for further understanding of the inherent adsorption mechanism between P ions and vinasse biochars. Vinasse-dolomite nanocomposite was very effective in the adsorption of P species from aqueous media. The effect of the operational factors on Vinasse-dolomite nanocomposite was explored by applying response surface methodology (RSM). According to RSM results, the optimum condition was achieved to be contact time 90 (min), 250 (mg/L) of P concentration and pH 7. Thermodynamic isotherm and kinetic studies were applied on experimental data to understand the adsorption behavior. The Vinasse-dolomite nanocomposite revealed preferential P species adsorption in the presence of co-existing anions. The P species could be recovered by 1.0 M HCl where the efficiency was not affected up to the fifth cycle. The P-loaded Vinasse-dolomite nanocomposite was successfully tested on a plant; it significantly improved its growth and proved its potency as a P-based fertilizer substitute.     

Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NO_x), nitrogen dioxide (NO₂), nitrogen monoxide (NO), ozone (O₃), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM₁₀) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R ² of more than 0.82 between modeled and observed hourly pollutant levels for CO, NO_x, NO₂, NO, and PM₁₀. However, predicted O₃ levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.     

Marine dredged sediments as new materials resource for road construction

Large volumes of sediments are dredged each year in Europe in order to maintain harbour activities. With the new European Union directives, harbour managers are encouraged to find environmentally sound solutions for these materials. This paper investigates the potential uses of Dunkirk marine dredged sediment as a new material resource for road building. The mineralogical composition of sediments is evaluated using X-ray diffraction and microscopy analysis. Since sediments contain a high amount of water, a dewatering treatment has been used. Different suitable mixtures, checking specific geotechnical criteria as required in French standards, are identified. The mixtures are then optimized for an economical reuse. The mechanical tests conducted on these mixtures are compaction, bearing capacity, compression and tensile tests. The experimental results show the feasibility of the beneficial use of Dunkirk marine dredged sand and sediments as a new material for the construction of foundation and base layers for roads. Further research is now needed to prove the resistance of this new material to various environmental impacts (e.g., frost damage).     

Portland cement solidified construction waste using MSW leachate and phosphate admixtures

Journal of Material Cycles and Waste Management - Many dumping sites all over the world release leachate into the environment. The primary goal of this study was to convert raw municipal...