pH-gradient real-time aeration control for nitritation community selection in a non-porous hollow fiber membrane biofilm reactor (MBfR) with dilute wastewater

Nitritation (ammonium to nitrite) as a pre-treatment of Anammox (anaerobic ammonium oxidation) is a key step for an energy-efficient nitrogen-removal alternative from dilute wastewaters, e.g. anaerobically-treated sewage, with which limited study has achieved sustainable nitritation at ambient temperature and short hydraulic retention times. To this end, pH-gradient real-time aeration control in an oxygen-based membrane biofilm reactor was observed at 20 °C in the sequencing batch mode. An optimum oxygen supply via diffusion for ammonium-oxidizing bacteria (AOB) was established, but nitrite-oxidizing bacteria (NOB) could be inhibited. The system achieved nitrite accumulation efficiencies varying from 88% to 94% with the aeration control. Mass balance and rate performance analyses indicate that this aeration control is able to supply an oxygen rate of 1.5 mol O₂ mol^?1 ammonium fed, the benchmark oxygenation rate based on stoichiometry for nitritation community selection. Microbial analyses confirmed AOB prevalence with NOB inhibition under this aeration control.     

Random parameter models for accident prediction on two-lane undivided highways in India

Introduction

Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation.

Method

The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models.

Results

The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations.

Conclusions

The paper is concluded with a discussion on modeling results and the limitations of the present study.