Estimating Escherichia coli loads in streams based on various physical, chemical, and biological factors.

Microbes have been identified as a major contaminant of water resources. Escherichia coli (E. coli) is a commonly used indicator organism. It is well recognized that the fate of E. coli in surface water systems is governed by multiple physical, chemical, and biological factors. The aim of this work is to provide insight into the physical, chemical, and biological factors along with their interactions that are critical in the estimation of E. coli loads in surface streams. There are various models to predict E. coli loads in streams, but they tend to be system or site specific or overly complex without enhancing our understanding of these factors. Hence, based on available data, a Bayesian Neural Network (BNN) is presented for estimating E. coli loads based on physical, chemical, and biological factors in streams. The BNN has the dual advantage of overcoming the absence of quality data (with regards to consistency in data) and determination of mechanistic model parameters by employing a probabilistic framework. This study evaluates whether the BNN model can be an effective alternative tool to mechanistic models for E. coli loads estimation in streams. For this purpose, a comparison with a traditional model (LOADEST, USGS) is conducted. The models are compared for estimated E. coli loads based on available water quality data in Plum Creek, Texas. All the model efficiency measures suggest that overall E. coli loads estimations by the BNN model are better than the E. coli loads estimations by the LOADEST model on all the three occasions (three-fold cross validation). Thirteen factors were used for estimating E. coli loads with the exhaustive feature selection technique, which indicated that six of thirteen factors are important for estimating E. coli loads. Physical factors included temperature and dissolved oxygen; chemical factors include phosphate and ammonia; biological factors include suspended solids and chlorophyll. The results highlight that the LOADEST model estimates E. coli loads better in the smaller ranges, whereas the BNN model estimates E. coli loads better in the higher ranges. Hence, the BNN model can be used to design targeted monitoring programs and implement regulatory standards through TMDL programs.

Performance of wastewater subsurface drip emitters at low and normal pressure.

Subsurface drip distribution is an important on-site wastewater treatment technique widely used with various soil types and restricted site conditions. This study evaluated the performance of five subsurface wastewater drip products under eight pressures, ranging from 0 to 310 kPa. Results showed that Netafim Bioline pressure-compensating emitters (Netafim Irrigation Inc., Fresno, California) had an application uniformity coefficient of 95% and a coefficient of variance (C(v)) of 4.9%. The average uniformity coefficient of Geoflow Wasteflow products (Geoflow USA, Charlotte, North Carolina) was 94.4%, with a C(v) value of 6.8%. Flowrate and pressure relationships were developed by analyzing low and normal operational pressure ranges, and R-square values ranged from 1.000 to 0.301. Geoflow pressure-compensating products were non-pressure-compensating emitters under low pressure. Netafim pressure-compensating emitters were partially pressure-compensating under low pressures. In normal operational pressure ranges, both Geoflow and Netafim products were fully pressure-compensating. Netafim pressure-compensating products were characterized as pressure-compensating over the full range of operational pressures.

Uniformity of domestic wastewater effluent application in a subsurface drip distribution system.

An on-site wastewater treatment project with two separate drip fields was operated for 6 years and received no maintenance. The two drip fields (with different design configurations) contained pressure-compensating emitters (PC) and non-pressure-compensating emitters (NPC), respectively, and received wastewater with an average 5-day biochemical oxygen demand concentration of 23 mg/L. Flowrates of the PC emitters reduced from rated average of 3.50 to 1.00 L/h, and the average flowrate of the NPC emitters reduced from 2.00 to 1.53 L/h. The statistical uniformities were 48 and 71%, and the uniformity coefficients were 70 and 86% for PC and NPC emitters, respectively. Significant, but incomplete, recovery was achieved with field-flushing and consecutive shock-chlorination treatments of 500 and 1000 mg/L.