Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC.

This study presents an approach for obtaining realization sets of parameters for nitrogen removal in a pilot-scale waste stabilization pond (WSP) system. The proposed approach was designed for optimal parameterization, local sensitivity analysis, and global uncertainty analysis of a dynamic simulation model for the WSP by using the R software package Flexible Modeling Environment (R-FME) with the Markov chain Monte Carlo (MCMC) method. Additionally, generalized likelihood uncertainty estimation (GLUE) was integrated into the FME to evaluate the major parameters that affect the simulation outputs in the study WSP. Comprehensive modeling analysis was used to simulate and assess nine parameters and concentrations of ON-N, NH3-N and NO3-N. Results indicate that the integrated FME-GLUE-based model, with good Nash-Sutcliffe coefficients (0.53-0.69) and correlation coefficients (0.76-0.83), successfully simulates the concentrations of ON-N, NH3-N and NO3-N. Moreover, the Arrhenius constant was the only parameter sensitive to model performances of ON-N and NH3-N simulations. However, Nitrosomonas growth rate, the denitrification constant, and the maximum growth rate at 20 °C were sensitive to ON-N and NO3-N simulation, which was measured using global sensitivity.

Molecular techniques in ecohealth research toolkit: facilitating estimation of aggregate gastroenteritis burden in an irrigated periurban landscape.

Assessment of microbial hazards associated with certain environmental matrices, livelihood strategies, and food handling practices are constrained by time-consuming conventional microbiological techniques that lead to health risk assessments of narrow geographic or time scope, often targeting very few pathogens. Health risk assessment based on one or few indicator organisms underestimates true disease burden due a number of coexisting causative pathogens. Here, we employed molecular techniques in a survey of Cryptosporidium parvum, Giardia lamblia, Campylobacter jejuni, Escherichia coli O157:H7, Listeria monocytogenes, Salmonella spp., Shigella spp., Vibrio cholera, and Rotavirus A densities in canal water with respect to seasonality and spatial distribution of point-nonpoint pollution sources. Three irrigational canals stretching across nearly a 150-km(2) periurban landscape, traditionally used for agricultural irrigation but function as vital part of municipal wastewater stabilization in recent years, were investigated. Compiled stochastic data (pathogen concentration, susceptible populations) and literature-obtained deterministic data (pathogen dose-response model parameter values) were used in estimating waterborne gastroenteritis burden. Exposure scenarios include swimming or fishing, consuming canal water-irrigated vegetables, and ingesting or inhaling water aerosols while working in canal water-irrigated fields. Estimated annual gastroenteritis burden due individual pathogens among the sampling points was -10.6log(10) to -2.2log(10) DALYs. Aggregated annual gastroenteritis burden due all the target pathogens per sampling point was -3.1log(10) to -1.9log(10) DALYs, far exceeding WHO acceptable limit of -6.0log(10) DALYs. The present approach will facilitate the comprehensive collection of surface water microbiological baseline data and setting of benchmarks for interventions aimed at reducing microbial hazards in similar landscapes worldwide.

Infection risk assessment of diarrhea-related pathogens in a tropical canal network.

A quantitative microbial risk assessment (QMRA) of Cryptosporidium, Giardia and diarrhegenic Escherichia coli (DEC) infection was performed using Monte Carlo simulations to estimate the human health risks associated with the use of canal water for recreational purposes, unrestricted and restricted irrigation in a tropical peri-urban area. Three canals receiving municipal, agricultural, and, predominantly, industrial wastewater were investigated. Identification of pathogenic protozoans revealed the major presence of Cryptosporidium hominis and both assemblages A and B of Giardia lamblia. The highest individual infection risk estimate was found to be for Giardia in an exposure scenario involving the accidental ingestion of water when swimming during the rainy season, particularly in the most polluted section, downstream of a large wholesale market. The estimated annual risks of diarrheal disease due to infection by the protozoan parasites were up to 120-fold greater than the reported disease incidence in the vicinity of the studied district and the entire Thailand, suggesting a significant host resistance to disease beyond our model's assumptions. In contrast, annual disease risk estimates for DEC were in agreement with actual cases of diarrhea in the study area.