Bayesian Hierarchical Modeling and Inference for Mechanistic Systems in Industrial Hygiene

by   Soumyakanti Pan, et al.

A series of experiments in stationary and moving passenger rail cars were conducted to measure removal rates of particles in the size ranges of SARS-CoV-2 viral aerosols, and the air changes per hour provided by existing and modified air handling systems. Such methods for exposure assessments are customarily based on mechanistic models derived from physical laws of particle movement that are deterministic and do not account for measurement errors inherent in data collection. The resulting analysis compromises on reliably learning about mechanistic factors such as ventilation rates, aerosol generation rates and filtration efficiencies from field measurements. This manuscript develops a Bayesian state space modeling framework that synthesizes information from the mechanistic system as well as the field data. We derive a stochastic model from finite difference approximations of differential equations explaining particle concentrations. Our inferential framework trains the mechanistic system using the field measurements from the chamber experiments and delivers reliable estimates of the underlying physical process with fully model-based uncertainty quantification. Our application falls within the realm of Bayesian “melding” of mechanistic and statistical models and is of significant relevance to environmental hygienists and public health researchers working on assessing performance of aerosol removal rates for rail car fleets.


Bayesian State Space Modeling of Physical Processes in Industrial Hygiene

Exposure assessment models are deterministic models derived from physica...

Local field reconstruction from rotating coil measurements in particle accelerator magnets

In this paper a general approach to reconstruct three dimensional field ...

Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification

Data-driven approaches coupled with physical knowledge are powerful tech...

Modeling ventilation in a low-income house in Dhaka, Bangladesh

According to UNICEF, pneumonia is the leading cause of death in children...

A Bayesian Non-linear State Space Copula Model to Predict Air Pollution in Beijing

Air pollution is a serious issue that currently affects many industrial ...

Nauticle: a general-purpose particle-based simulation tool

Nauticle is a general-purpose numerical solver pursuing the easy adoptio...

Accounting for location uncertainty in distance sampling data

Ecologists use distance sampling to estimate the abundance of plants and...

Please sign up or login with your details

Forgot password? Click here to reset