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Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

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Estimating inputs for dispersion modeling in mobile platform applications.

Ranga Rajan Thiruvenkatachari¹, Yifan Ding¹, Javier González-Rocha¹

¹Department of Mechanical Engineering, University of California, Riverside, CA 92521, USA.

The Science of the Total Environment

|April 8, 2023

Summary

This summary is machine-generated.

Mobile monitoring platforms estimate air pollutant emissions using dispersion models. This study introduces a method using simpler sensors to provide accurate meteorological inputs, crucial for mobile air quality studies.

Keywords:

Bead thermistor Heat flux Manure lagoons Methane emissions Micrometeorology Mobile monitoring Temperature fluctuations

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Area of Science:

Environmental Science
Atmospheric Chemistry
Air Quality Monitoring

Background:

Mobile monitoring platforms (MMP) are widely used for air quality studies, particularly for estimating area source emissions.
Accurate meteorological data, like kinematic heat flux and surface friction velocity, are essential for dispersion models used in these studies.
Traditional methods using 3-D sonic anemometers are incompatible with the mobility requirements of MMPs.

Purpose of the Study:

To develop and validate an alternative method for estimating essential meteorological inputs for dispersion models using simpler instrumentation suitable for MMPs.
To assess the accuracy of this new method by comparing emission estimates with those derived from 3-D sonic anemometer measurements.

Main Methods:

Developed a method using horizontal wind speed and temperature fluctuation measurements at a single height to estimate meteorological parameters.
Evaluated the method by comparing methane emissions from a dairy manure lagoon using modeled versus 3-D sonic anemometer-derived meteorological inputs.
Adapted the method for MMP applications using a 2-D sonic anemometer and a bead thermistor.

Main Results:

Emission estimates derived from the modeled meteorological inputs closely matched those obtained using 3-D sonic anemometers.
The adapted method using a 2-D sonic anemometer and bead thermistor on an MMP also yielded comparable results to 3-D sonic anemometers.
This demonstrates the feasibility of using simplified instrumentation for mobile air quality monitoring.

Conclusions:

A novel method using readily available sensors can accurately provide meteorological inputs for dispersion modeling in air quality studies.
This approach enhances the practicality and mobility of air quality monitoring platforms.
The findings support the use of simplified instrumentation for mobile emission estimations from area sources.