Novel algorithm for tomographic reconstruction of atmospheric chemicals with sparse sampling

Area of Science:

• Environmental Science
• Chemical Engineering
• Imaging Technology

Background:

• Accurate air monitoring is crucial for assessing chemical exposures and source emissions.
• Optical remote sensing (ORS) combined with computed tomography (CT) offers high-resolution 2D chemical concentration mapping.
• The mathematical algorithm for reconstruction is key to the accuracy of ORS-CT methods.

Purpose of the Study:

• To evaluate a novel reconstruction algorithm for ORS-CT air monitoring.
• To assess the effectiveness of the Maximum Likelihood Expectation Maximization (MLEM) with grid-translation and multi-grid (GT-MG) techniques.
• To compare the GT-MG method against existing reconstruction techniques.

Main Methods:

• Numerical studies and computer simulations were conducted.
• A simulated ORS system with four instruments and 40 path-integrated measurements was used.
• 120 test maps of varying complexity were utilized to evaluate the GT-MG algorithm.

Main Results:

• The novel GT-MG reconstruction method demonstrated significant improvements over previous methods.
• Peak exposure errors were reduced by up to 85% using the GT-MG approach.
• Artifacts in the reconstructed chemical concentration maps were dramatically minimized.

Conclusions:

• The GT-MG algorithm represents a substantial advancement in ORS-CT based air monitoring.
• This method enhances the accuracy and reliability of reconstructing chemical exposures and source emissions.
• The findings support the adoption of the GT-MG method for improved environmental monitoring applications.