[Effects of a breath-triggered modulation of positive airway pressure ventilation on the AHI from patients with obstructive sleep apnoea]. / Effekte einer Atemzyklus-gesteuerten Druckmodulation (Flexline) auf den AHI bei Uberdrucktherapie von Patienten mit einem OSAS.

BACKGROUND: Positive airway pressure (CPAP) is the treatment of choice for patients with obstructive sleep apnoea syndrome (OSAS). Especially in relation to side effects, it is not clear whether it is better to administer a constant positive pressure during the respiratory cycle or to vary it while breathing. The Flexline maybe improves patients' adherence by increasing the pressure in the early inspiration and lowering it in early expiration. METHODS: The pressure characteristics of the Flexline were examined in healthy persons. Patients with OSAS were titrated with CPAP or with the Flexline in random order under PSG control in the sleep laboratory. The apnoea/hypopnoea index (AHI) per pressure level was determined. PATIENTS: 20 patients with the new diagnosis of OSAS, first time treated with CPAP (age 56.6 +/- 11.5 years, BMI 28.4 +/- 3.2 kg/m (2), AHI 44.1 +/- 18.7, SO2 min 77.7 +/- 8.4 % and ESS 8.6 +/- 5.1). RESULTS: Constant CPAP and the mean pressure in the modus Flexline are similar. The differences in pressure between inspiration and expiration in the Flexline are dependent on the breathing frequency and the primarily chosen pressure level. Reduction of upper airway obstructions is similar with both types of therapy (AHI: CPAP: 9.1 +/- 7.4, Flexline: 9.1 +/- 7.4) without influencing sleep quality (arousal index: CPAP: 21.7 +/- 8.4, Flexline: 20.1 +/- 9.8). CONCLUSIONS: The Flexline is equally efficient as CPAP in the treatment of OSAS.

Comparison of the pollutant loads in dry and wet weather runoff in a southern California urban watershed.

This research compares the relative contributions of potential contaminants discharged in dry weather flow (DWF) and wet weather flow (WWF) from the highly urbanized Ballona Creek watershed (BCW) in southern California using empirical and deterministic models. These models were used to compare the loading of the following pollutants: total suspended solids, biochemical oxygen demand, total nitrogen, total inorganic nitrogen, total Kjeldahl nitrogen, total phosphorus, copper, lead, arsenic, nickel, cadmium, and chromium. The results indicate DWF contributes approximately 10-30% of the total annual flow discharged from Ballona Creek. The annual DWF volume was fairly consistent; the variation in DWF percentage contribution was dependent on the highly variable volume of WWF. The relative contribution to the annual pollutant load varied considerably between each pollutant. In general, the DWF load was found to be significant, especially in years with lower precipitation totals. The results from this investigation have identified the relative relationship between DWF and WWF loads in the BCW and will aid in the decision-making process during the development of an integrated DWF-WWF management plan and allocation of water pollution control funds between DWF and WWF management.

Evaluation of land use/land cover datasets for urban watershed modeling.

Land use/land cover (LULC) data are a vital component for nonpoint source pollution modeling. Most watershed hydrology and pollutant loading models use, in some capacity, LULC information to generate runoff and pollutant loading estimates. Simple equation methods predict runoff and pollutant loads using runoff coefficients or pollutant export coefficients that are often correlated to LULC type. Complex models use input variables and parameters to represent watershed characteristics and pollutant buildup and washoff rates as a function of LULC type. Whether using simple or complex models an accurate LULC dataset with an appropriate spatial resolution and level of detail is paramount for reliable predictions. The study presented in this paper compared and evaluated several LULC dataset sources for application in urban environmental modeling. The commonly used USGS LULC datasets have coarser spatial resolution and lower levels of classification than other LULC datasets. In addition, the USGS datasets do not accurately represent the land use in areas that have undergone significant land use change during the past two decades. We performed a watershed modeling analysis of three urban catchments in Los Angeles, California, USA to investigate the relative difference in average annual runoff volumes and total suspended solids (TSS) loads when using the USGS LULC dataset versus using a more detailed and current LULC dataset. When the two LULC datasets were aggregated to the same land use categories, the relative differences in predicted average annual runoff volumes and TSS loads from the three catchments were 8 to 14% and 13 to 40%, respectively. The relative differences did not have a predictable relationship with catchment size.

Evaluation of an artificial neural network rainfall disaggregation model.

Previous research produced an artificial neural network (ANN) temporal rainfall disaggregation model. After proper training the model can disaggregate hourly rainfall records into sub-hourly time increments. In this paper we present results from continued evaluations of the performance of the ANN model specifically examining how the errors in the disaggregated rainfall hyetograph translate to errors in the prediction of the runoff hydrograph. Using a rainfall-runoff model of a hypothetical watershed we compare the runoff hydrographs produced by the ANN-predicted 15-minute increment rainfall pattern to runoff hydrographs produced by (1) the observed 15-minute increment rainfall pattern, (2) the observed hourly-increment rainfall pattern, and (3) the 15-minute increment rainfall pattern produced by a disaggregation model based on geometric similarity. For 98 test storms the peak discharges produced by the ANN model rainfall pattern had a median under-prediction of 16.6%. This relative error was less than the median under-prediction in peak discharge when using the observed 15-minute rainfall patterns aggregated to hourly increments (40.8%), and when using rainfall patterns produced by the geometric similarity rainfall disaggregation model (21.9%).