Contaminant Source Identification from Finite Sensor Data: Perron–Frobenius Operator and Bayesian Inference

Sensors in the built environment ensure safety and comfort by tracking contaminants in the occupied space. In the event of contaminant release, it is important to use the limited sensor data to rapidly and accurately identify the release location of the contaminant. Identification of the release location will enable subsequent remediation as well as evacuation decision-making. In previous work, we used an operator theoretic approach—based on the Perron–Frobenius (PF) operator—to estimate the contaminant concentration distribution in the domain given a finite amount of streaming sensor data. In the current work, the approach is extended to identify the most probable contaminant release location. The release location identification is framed as a Bayesian inference problem. The Bayesian inference approach requires considering multiple release location scenarios, which is done efficiently using the discrete PF operator. The discrete PF operator provides a fast, effective and accurate model for contaminant transport modeling. The utility of our PF-based Bayesian inference methodology is illustrated using single-point release scenarios in both two and three-dimensional cases. The method provides a fast, accurate, and efficient framework for real-time identification of contaminant source location.

Inexpensive Organic Materials and Their Applications towards Heavy Metal Attenuation in Waters from Southern Peru

There is interest in using locally available, low cost organic materials to attenuate heavy metals such as Cd, Cr, Cu, Hg, Ni, Pb, and Zn found in surface waters in Peru and other developing regions. Here we mesh Spanish language publications, archived theses, and prior globally available literature to provide a tabulated synthesis of organic materials that hold promise for this application in the developing world. In total, nearly 200 materials were grouped into source categories such as algae and seashells, bacteria and fungi, terrestrial plant-derived materials, and other agricultural and processing materials. This curation was complemented by an assessment of removal potential that can serve as a resource for future studies. We also identified a subset of Peruvian materials that hold particular promise for further investigation, including seashell-based mixed media, fungal blends, lignocellulose-based substrates including sawdust, corn and rice husks, and food residuals including peels from potatoes and avocadoes. Many studies reported percent removal and/or lacked consistent protocols for solid to liquid ratios and defined aqueous concentrations, which limits direct application. However, they hold value as an initial screening methodology informed by local knowledge and insights that could enable adoption for agriculture and other non-potable water reuse applications. While underlying removal mechanisms were presumed to rely on sorptive processes, this should be confirmed in promising materials with subsequent experimentation to quantify active sites and capacities by generating sorption isotherms with a focus on environmental conditions and specific contaminated water properties (pH, temperature, ionic strength, etc.). These organics also hold promise for the pairing of sorption to indirect microbial respiratory processes such as biogenic sulfide complexation. Conversely, there is a need to quantify unwanted contaminant release that could include soluble organic matter and nutrients. In addition to local availability and treatment efficacy, social, technical, economic, and environmental applicability of those materials for large-scale application must be considered to further refine material selection.