Transient modeling of trickling filters for biological ammonia removal

A dynamic model for nitrifying trickling filters is developed, based on material balances in the biofilm and the bulk liquid. The model predicts the profile of ammonia as a function of the operating parameters (volumetric flow rate and feed ammonia concentration) and the biofilm thickness as a function of filter depth and time. This revised version was published online in July 2006 with corrections to the Cover Date.     

Comparison of Nitrate Levels in Raw Water and Finished Water from Historical Monitoring Data on Iowa Municipal Drinking Water Supplies

Nitrate contamination of water sources is a concern where large amounts of nitrogen fertilizers are regularly applied to soils. Ingested nitrate from dietary sources and drinking water can be converted to nitrite and ultimately to N-nitroso compounds, many of which are known carcinogens. Epidemiologic studies of drinking water nitrate and cancer report mixed findings; a criticism is the use of nitrate concentrations from retrospective drinking water data to assign exposure levels. Residential point-of-use nitrate data are scarce; gaps in historical data for municipal supply finished water hamper exposure classification efforts. We used generalized linear regression models to estimate and compare historical raw water and finished water nitrate levels (1960s--1990s) in single source Iowa municipal supplies to determine whether raw water monitoring data could supplement finished water data to improve exposure assessment. Comparison of raw water and finished water samples (same sampling date) showed a significant difference in nitrate levels in municipalities using rivers; municipalities using other surface water or alluvial groundwater had no difference in nitrate levels. A regional aggregation of alluvial groundwater municipalities was constructed based on results from a previous study showing regional differences in nitrate contamination of private wells; results from this analysis were mixed, dependent upon region and decade. These analyses demonstrate using historical raw water nitrate monitoring data to supplement finished water data for exposure assessment is appropriate for individual Iowa municipal supplies using alluvial groundwater, lakes or reservoirs. Using alluvial raw water data on a regional basis is dependent on region and decade.     

Effects of Flooding on Lead and Cadmium Speciation in Sediments from a Drinking Water Reservoir

Římov water reservoir on the river Malše is the main source of drinking water for the town of České Budějovice and for the majority of inhabitants in the South Bohemian region, Czech Republic. Changes in cadmium and lead contents in bottom sediments before and after an extensive flood on the river Malše in August, 2002 were therefore determined. A five-step sequential extraction procedure was used in order to obtain more detailed information about the influence of the flood on heavy metal retention. In order to determine the mobility of lead and cadmium, the mobility factor (MF) for these heavy metals was calculated. The mobility factor of cadmium showed a significant decrease in the upper parts of the sediment profiles after the flood (e.g., from 59.4% to 49.1%) caused by a release of cadmium especially from the exchangeable fraction. There were no significant changes in the lead mobility factor after the flood, but a decrease of lead concentration in the exchangeable fraction was observed. Presented results show that the flood led to a leaching of the heavy metals present in bottom sediments into the environment.     

Evaluating response surface designs for uncertainty analysis and prescriptive applications of a large-scale water quality model

Environmental models are often too large and cumbersome for effective use in regulatory decision making or in the characterization of uncertainty. This paper describes and compares four response surfaces that could complement a large-scale water quality model, the U.S. National Water Pollution Control Assessment Model (NWPCAM), in simulation and regulatory decision support applications. Results show that a physically based reduced-form model that exploits the mathematical structure of the underlying water quality model is a better predictor of policy-relevant outputs than the polynomial expansions that are frequently used in response surface studies.     

Comparison of a Subjective and a Physical Approach for Identification of Priority Areas for Soil and Water Management in a Watershed – A Case Study of Nagwan Watershed in Hazaribagh District of Jharkhand, India

The present investigation was an attempt to compare the within-watershed prioritization capabilities of a physical model based SDSS with the SYI and RPI model based subjective-SDSS, conventionally devised for between-watershed prioritizations, by All India Soil Survey and Land Use Planning Division of Ministry of Agriculture, Government of India. Application of these two approaches on a test watershed situated in Damodar-Barakar catchment in India, the second most seriously eroded area in the world, demonstrated that the proposed physical model based SDSS was capable of realistically and logically mimicking the sub-watershed-scaled water and soil losses thereby suggesting its immense application potential for priority area identification within the test watershed. In contrast to the proposed physical method, the subjective approaches, which assigned totally reverse priorities to about 67–93% of the test-sub-watersheds, were observed to be incapable of realistically assessing the impact of topography and varied land use and soil types in the test watershed on their sub-watershed scaled run-off and soil loss generating potential. Besides, the physical approach could be used for assessing the annual dynamics of the total water and sediment yields, under prevailing resource management systems in the test watershed with good to moderately good correlation coefficients of 0.83 and 0.65; model efficiency coefficients of 0.54 and 0.70; mean relative errors of –4.28% and –17.97% and root mean square prediction errors of 71.8 mm and 9.63 t/ha, respectively.