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.

Contaminant Source Identification in Aquifers: A Critical View

Forty years and 157 papers later, research on contaminant source identification has grown exponentially in number but seems to be stalled concerning advancement towards the problem solution and its field application. This paper presents a historical evolution of the subject, highlighting its major advances. It also shows how the subject has grown in sophistication regarding the solution of the core problem (the source identification), forgetting that, from a practical point of view, such identification is worthless unless it is accompanied by a joint identification of the other uncertain parameters that characterize flow and transport in aquifers.

Prompt location of indoor instantaneous air contaminant source through multi-zone model-based probability method by utilizing airflow data from coarse-grid CFD model

In order to ensure indoor air quality safety, locating the airborne contaminant sources accurately and quickly is extremely important so that timely measures can be taken to undermine the spread of pollutant and even eliminate the negative effects. Previous studies have shown that the multi-zone model can greatly reduce the inverse calculation time. However, the multi-zone model cannot describe the details of the indoor velocity field, which limits its application in complex multi-zone or large space buildings. On the premise of the accuracy and computational speed, based on the joint probability method, this study adopted the coarse-grid CFD method to speed up the process of acquiring the indoor airflow field, together with the multi-zone model method, to perform the inverse calculation of indoor airborne contaminant source location. In the backward calculation process, we conducted the ‘transpose' of the velocity field to obtain adjoint matrix, instead of computing ‘negative' of the velocity vector to save the calculation time. A two-dimensional ventilation model was utilized to validate the method, which proved the accuracy and time-saving potential of it. This study provides theoretical and practical prospect for the real-time inverse calculation of locating the indoor airborne contaminant sources.

Using the Backward Probability Method in contaminant source identification with a finite-duration source loading in a river

Violation of industries in discharging their effluents into rivers leads to river pollution, which endangers the environment and human health. Appropriate tools are needed to deal with violations and protect rivers. The Backward Probability Method (BPM) is one of the most recommended tools identifying the release time and location of the pollutant source. However, the BPM generally was developed for groundwater and spill injection. Since most industries inject their effluents with a constant rate for a finite-duration, the use of prevailing models will have some errors. In this study, a numerical model was developed that could simulate a source with either a finite-duration or spill injection. This model is verified for two hypothetical cases and one real case. The results show that the model can accurately identify the release time and location of the pollutant source.