17 August 2021 Application of imaging S-lidars: functional and diagnostic capabilities for remote air pollution detection
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Abstract

Imaging S-lidars have proven themselves in recent years as a new class of laser sensors for remote environmental monitoring and an alternative to traditional atmospheric lidars. Providing range-resolvable remote monitoring, these lidars use low-power CW lasers and advanced nanophotonics technologies to enable compact and cost-effective technological solutions. As a topical application, we have explored the potential of S-lidars to detect atmospheric pollution. We presented a generalized system structure adapted for such application field focusing on approaches to provide the necessary spatial selectivity. By adapting the universal lidar equation to S-lidar features, we have used a dimensionless parametric approach to provide a generalized description of this class of remote sensors. The possible wide variability of the ambient optical weather in the visible and near-infrared ranges was taken into account. It was shown how to apply the Q-criterion of spatial selectivity, we introduced for accounting the S-lidars specificity, to predict the borders of the operation range that can actually be covered by the sensor for reliable gaseous pollution detection. We have demonstrated how to estimate the possible narrowing of the range of concentration sensitivity with increasing requirements for spatial selectivity. The proposed methodology for analyzing the functional and diagnostic capabilities of S-lidars shows the presence of both undoubted advantages and some specific limitations of the achievable range of detectable gas concentrations. Following this methodology, it is possible to improve the validity of design solutions in a variety of applications of this promising class of lidars.

© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2021/$28.00 © 2021 SPIE
Ravil R. Agishev "Application of imaging S-lidars: functional and diagnostic capabilities for remote air pollution detection," Optical Engineering 60(8), 084104 (17 August 2021). https://doi.org/10.1117/1.OE.60.8.084104
Received: 17 April 2021; Accepted: 20 July 2021; Published: 17 August 2021
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
LIDAR

Air contamination

Diagnostics

Pollution detection

Sensors

Optical engineering

Remote sensing

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