A geochemical equilibrium modeling approach to assessing soil acidification impacts due to depositions of industrial air emissions

Soil acidification impacts arising from depositions of industrial air emissions may become a serious environmental concern. Currently, in the literature quantitative mechanistic modeling and the experimental acid neutralizing capacity (ANC) approach and a qualitative evaluation approach classifying soils into various levels of sensitivity to acid additions have been reported to assess the long-term soil acidification impacts due to industrial air emissions. Another alternative quantitative approach proposed by this study is the geochemical modeling approach that can be used to similate an ANC curve based on relevant soil chemistry data by calculating the equilibrium distributions of chemical species in the soil solution according to the specified geochemical processes. The purpose of this syudy was essentially to illustrate the potential applications and practical utility of the proposed geochemical modeling approach to assessing soil acidification impacts due to industrial air emissions. The application of the geochemical modeling approach was illustrated by comparisons of the experimental and simulated ANC curves for a calcareous and a noncalcareous soil representing insensitive and sensitive soil cases, respectively. Results obtained from these comparisons reveal that, in terms of producing the ANC curve for the soil solution, the geochemical modeling approach seems to perform well and produce more reliable results for calcareous soil than for noncalcareous soil. However, the approach can also be used for noncalcareous soils when the air emission rates are low and may need further testing with additional measured data for a wide range of soils other than those presented in this study

Patients With Kidney Cancer

To develop a preoperative prognostic model in order to predict recurrence-free survival in patients with nonmetastatic kidney cancer.A multi-institutional data base of 1889 patients who underwent surgical resection between 1987 and 2007 for kidney cancer was retrospectively analyzed. Preoperative variables were defined as age, gender, presentation, size, presence of radiological lymph nodes and clinical stage. Univariate and multivariate analyses of the variables were performed using the Cox proportional hazards regression model. A model was developed with preoperative variables as predictors of recurrence after nephrectomy. Internal validation was performed by Harrells concordance index.The median follow-up was 23.6 months (1222 months). During the follow-up, 258 patients (13.7) developed cancer recurrence. The median follow-up for patients who did not develop recurrence was 25 months. The median time from surgery to recurrence was 13 months. The 5-year freedom from recurrence probability was 78.6. All variables except age were associated with freedom from recurrence in multivariate analyses (P 0.05). Age was marginally significant in the univariate analysis. All variables were included in the predictive model. The calculated c-index was 0.747.This preoperative model utilizes easy to obtain clinical variables and predicts the likelihood of development of recurrent disease in patients with kidney tumors

Monitoring of drinking water distribution system by SCADA in Antalya City, Turkey

Antalya water and wastewater administration has recently completed SCADA (Supervisory Control And Data Acquisition) system. The system enabled the on-line continuous monitoring of many water quantity and quality parameters such as flow rate, pressure, temperature, pH, turbidity, electrical conductivity, and free residual chlorine. Additionally, water levels in the distribution reservoirs, water pumps, energy consumption and the closing valves are monitored and controlled by the SCADA system. Beside the on-line continuous monitoring, field sampling and lab analyses of other water quality parameters such as total organic carbon, THM, bromide, iron, nitrogen and phosphorous groups, and coliform bacteria were conducted. The results of field sampling agreed with the on-line monitoring values. The SCADA system proved to be very useful for reducing water losses, improving water quality, reducing energy consumption and improving the reliability of the system

Assessment of predictive models for chlorophyll-a concentration of a tropical lake

<p>Abstract</p> <p>Background</p> <p>This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes.</p> <p>Results</p> <p>Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task.</p> <p>Conclusions</p> <p>Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR.</p

A three-dimensional water quality-macrophyte interaction model for shallow lakes

A dynamic three-dimensional water quality model for macrophyte-dominated shallow lakes was proposed and tested. The proposed model is capable of simulating macrophytes and its interactions with water quality constituents such as dissolved oxygen (DO), organic nitrogen, ammonia, nitrate, organic phosphorus, orthophosphate, biochemical oxygen demand, phytoplankton and the sediment layer in shallow lakes. An existing two-dimensional hydrodynamic model has been utilized in conjunction with the water quality model to simulate water levels, velocities and flow rates. The modelled macrophyte processes are photosynthesis, respiration, mortality and excretion. Hourly simulation of photosynthesis process has been realized. The hourly simulations need special attention to predict diurnal variations of DO in macrophyte dominated lakes. The proposed water quality simulation model was subjected to calibration, verification and prediction processes using the data collected from Mogan Lake, Turkey. Mogan Lake exhibits wide variations of macrophyte biomass seasonally. The lake also exhibits highly variable DO levels both seasonally and diurnally. Statistical error quantification methods have been utilized to test the goodness of fit between the water quality model predictions and field measurements. Good agreement has been achieved between model predictions and measurements. Moreover, uncertainty analysis has been carried out for macrophyte and DO constituents. The analysis showed that the magnitude of the saturated growth rate of macrophyte is the most sensitive model parameter both for macrophyte and DO. The proposed water quality simulation model gave some promising initial results as a management tool to predict the expected reductions for the undesired consequences of eutrophication problem

EFFECT OF PRETREATMENTS ON THE SEMICONTINUOUS ANAEROBIC-DIGESTION OF SUNFLOWER HEADS

The effects of hydraulic retention time and alkali treatment on methane production rate from the semicontinuous anaerobic digestion of 2 % sunflower-head/water mixtures were investigated. The experiments were carried out in laboratory-scale fermenters, fed with 1 liter of untreated, 2 g NaOH/100 g total solids (TS), and 5 g NaOH/100 g TS alkali-treated sunflower-head/water mixtures, respectively, and maintained at 55-degrees-C Digestion experiments were performed for hydraulic retention times of 8, 10, and 15 days. The amount and composition of produced gas were measured until steady state was attained in each run. The steady-state methane production rates were found to decrease with hydraulic retention time and increase with alkali dosage used for pretreatment

BIOGAS PRODUCTION FROM AGRICULTURAL WASTES - SEMICONTINUOUS ANAEROBIC-DIGESTION OF SUNFLOWER HEADS

Data for semicontinuous anaerobic digestion of sunflower heads were obtained in fermentors of 11 of effective volume, maintained at 55-degrees-C Experiments were carried out with a 2% sunflower head-water mixture for hydraulic retention times of 15, 10, and 8 days. Biogas quantities and compositions were measured each day until steady state was achieved. The steady state was achieved after three times of each hydraulic retention time (HRT). Gas productions of 0.180 m3/m3.dig.day with 62% CH4, 0.280 m3/m3.dig.day with 58% CH4, and 0.375 m3/m3.dig.day with 48% CH4 were obtained, for 15, 10, and 8 days of HRT respectively