Effects of Ornamental Plant Density and Mineral/Plastic Media on the Removal of Domestic Wastewater Pollutants by Home Wetlands Technology.

Wastewater treatment (WWT) is a priority around the world; conventional treatments are not widely used in rural areas owing to the high operating and maintenance costs. In Mexico, for instance, only 40% of wastewater is treated. One sustainable option for WWT is through the use of constructed wetlands (CWs) technology, which may remove pollutants using cells filled with porous material and vegetation that works as a natural filter. Knowing the optimal material and density of plants used per square meter in CWs would allow improving their WWT effect. In this study, the effect of material media (plastic/mineral) and plant density on the removal of organic/inorganic pollutants was evaluated. Low (three plants), medium (six plants) and high (nine plants) densities were compared in a surface area of 0.3 m2 of ornamental plants (Alpinia purpurata, Canna hybrids and Hedychium coronarium) used in polycultures at the mesocosm level of household wetlands, planted on the two different substrates. Regarding the removal of contaminants, no significant differences were found between substrates (p ≥ 0.05), indicating the use of plastic residues (reusable) is an economical option compared to typical mineral materials. However, differences (p = 0.001) in removal of pollutants were found between different plant densities. For both substrates, the high density planted CWs were able to remove COD in a range of 86-90%, PO4-P 22-33%, NH4-N in 84-90%, NO3-N 25-28% and NO2-N 38-42%. At medium density, removals of 79-81%, 26-32, 80-82%, 24-26%, and 39-41%, were observed, whereas in CWs with low density, the detected removals were 65-68%, 20-26%, 79-80%, 24-26% and 31-40%, respectively. These results revealed that higher COD and ammonia were removed at high plant density than at medium or low densities. Other pollutants were removed similarly in all plant densities (22-42%), indicating the necessity of hybrid CWs to increase the elimination of PO4-P, NO3-N and NO2-N. Moreover, high density favored 10 to 20% more the removal of pollutants than other plant densities. In addition, in cells with high density of plants and smaller planting distance, the development of new plant shoots was limited. Thus, it is suggested that the appropriate distance for this type of polyculture plants should be from 40 to 50 cm in expansion to real-scale systems in order to take advantage of the harvesting of species in these and allow species of greater foliage, favoring its growth and new shoots with the appropriate distance to compensate, in the short time, the removal of nutrients.

Decision support system for NPK fertilization: a solution method for minimizing the impact on human health, climate change, ecosystem quality and resources.

Sugarcane cultivation requires correct fertilizer rates. However, when nutrients are not available, or there is over-fertilization, the yields are significantly reduced and the environmental burden increase. In this study, it is proposed a decision support system (DSS) for the correct NPK (nitrogen, phosphorus and potassium) fertilization. The DSS consists of two fuzzy models; the edaphic condition model (EDC-M) and the NPK fertilization model (NPK-M). The DSS using parameters from soil analysis and is based on the experience of two groups of experts to avoid the bias to the reality of a single group of professionals. The results of the DSS are compared with the results of soil analysis and those of the group of experts. One hundred and sixty tests were developed in the NPK-M. The N rate shows R 2=0.981 for the DSS and R 2=0.963 for soil analyzes. The P rate shows R 2=0.9702 for the DSS and R 2=0.9183 for the soil analyzes. The K rate shows R 2=0.9691 for the DSS and R 2=0.9663 for the soil analyzes. Environmental results indicate that the estimated rates with the DSS do reduce the environmental impact on the tests performed.

Analysis of the behavior for operation parameters in the anaerobic digestion process with thermal pretreatment, using fuzzy logic.

This article presents a study that identifies the variables with greatest impact on the biogas and methane production over a process with thermal pretreatment, to accelerate anaerobic digestion process in sewage sludge in a water treatment plant, for a poultry processing factory, by using fuzzy logic. The designed fuzzy logic model includes 688 inference rules, with a correlation of 99.3% between prediction data against experimental data, for the biogas variable; and 97% for the methane variable. The predictions of the fuzzy logic model were analyzed with response surface models, and it is concluded that the temperature and operating time variables are mutually determining in the biogas and methane production. Likewise, this research provides a methodology for the design of an expert decision support system that allows to evaluate and optimize a mesophilic anaerobic digestion process through a previous thermal treatment in order to improve the yields of biogas and methane in the treatment of effluent sludge from agroindustry. These results propose to diffuse logic as a reliable tool to make comparisons, and predictions for operation variables management on the treatment of residual sludge with thermal pretreatment on anaerobic digestion.

Life cycle assessment of cane sugar production: The environmental contribution to human health, climate change, ecosystem quality and resources in México.

The cane sugar industry in Mexico depends heavily on the supply of energy, fossil fuels and material resources for its proper operation. The overuse of these resources plus the technical and technological deficiency causes severe environmental consequences. This scientific work aims to analyze the environmental damage attributable to cane sugar production following the life cycle assessment (LCA) methodology. System boundaries include sugarcane growing and harvesting, sugarcane transportation, sugar milling and electricity cogeneration from bagasse. The associated emissions were acquired from the SimaPro-Ecoinvent database, the Roundtable on Sustainable Biofuels (RSB) and the Agroscope Reckenholz-Tänikon Research Station (ART). The life cycle impact assessment (LCIA) was carried out by SimaPro 8.3.0 software and the characterization method used was IMPACT 2002+. The results show that sugarcane growing and harvesting stage provides the most harmful environmental impacts (52%) followed by electricity cogeneration (25.7%), sugarcane transportation (12.1%) and finally, sugar milling (10.2%). Regarding the environmental contributions at the endpoint categories, the highest percentage of impacts is found in the Human health category (53%), followed by Climate change (21%), Ecosystem quality (16%) and Resources (10%). The LCA in cane sugar production can support the decision-making process to deal with this environmental problem.

Use of artificial neuronal networks for prediction of the control parameters in the process of anaerobic digestion with thermal pretreatment.

This article focuses on the analysis of the behavior patterns of the variables involved in the anaerobic digestion process. The objective is to predict the impact factor and the behavior pattern of the variables, i.e., temperature, pH, volatile solids (VS), total solids, volumetric load, and hydraulic residence time, considering that these are the control variables for the conservation of the different groups of anaerobic microorganisms. To conduct the research, samples of physicochemical sludge were taken from a water treatment plant in a poultry processing factory, and, then, the substrate was characterized, and a thermal pretreatment was used to accelerate the hydrolysis process. The anaerobic digestion process was analyzed in order to obtain experimental data of the control variables and observe their impact on the production of biogas. The results showed that the thermal pre-hydrolysis applied at 90°C for 90 min accelerated the hydrolysis phase, allowing a significant 52% increase in the volume of methane produced. An artificial neural network was developed, and it was trained with the database obtained by monitoring the anaerobic digestion process. The results obtained from the artificial neural network showed that there is an adjustment between the real values and the prediction of validation based on 60 samples with a 96.4% coefficient of determination, and it was observed that the variables with the major impact on the process were the loading rate and VS, with impact factors of 36% and 23%, respectively.

An Interactive System for Fine Motor Rehabilitation.

PURPOSE: One of the most important aspects in neuromotor rehabilitation is the need of feedback for patients. The rehabilitation system's efficiency relies on the therapist's judgment; the therapist tells the patient whether he/she is performing the exercises correctly. This process may be quite subjective, because it depends on the therapist's personal opinion. On the other hand, recent studies have shown that vibrotactile biofeedback can improve the effectiveness of interaction as it is a very helpful tool in the physiological process of neuromotor rehabilitation. DESIGN: We designed an interactive system focused on rehabilitation of the upper limbs using active markers and image processing, which consists of drawing activities in both augment and virtual reality. METHODS: The system gives the user a correction through multimodal stimuli feedback (vibrotactile, visual, and sound stimulus) and force measurement to let the patients know if they are not achieving the tasks' goals. FINDINGS: The developed system could be used by nursing assistants to better help patients. The purpose of this system was assisting patients with injuries in shoulders, elbows, or wrists, providing an audio-vibrotactile feedback as a factor of correction in the movements of the patient. To examine our system, 11 participants were asked to participate in an experiment where they performed activities focused to strengthen their fine motor movements. CONCLUSIONS AND CLINICAL RELEVANCE: Results showed show that patients' fine motor skills improved 10% on average by comparing their error rates throughout the sessions.

Development of an expert system as a diagnostic support of cervical cancer in atypical glandular cells, based on fuzzy logics and image interpretation.

Cervical cancer is the second largest cause of death among women worldwide. Nowadays, this disease is preventable and curable at low cost and low risk when an accurate diagnosis is done in due time, since it is the neoplasm with the highest prevention potential. This work describes the development of an expert system able to provide a diagnosis to cervical neoplasia (CN) precursor injuries through the integration of fuzzy logics and image interpretation techniques. The key contribution of this research focuses on atypical cases, specifically on atypical glandular cells (AGC). The expert system consists of 3 phases: (1) risk diagnosis which consists of the interpretation of a patient's clinical background and the risks for contracting CN according to specialists; (2) cytology images detection which consists of image interpretation (IM) and the Bethesda system for cytology interpretation, and (3) determination of cancer precursor injuries which consists of in retrieving the information from the prior phases and integrating the expert system by means of a fuzzy logics (FL) model. During the validation stage of the system, 21 already diagnosed cases were tested with a positive correlation in which 100% effectiveness was obtained. The main contribution of this work relies on the reduction of false positives and false negatives by providing a more accurate diagnosis for CN.