Obtaining Gradients of XCO2 in Atmosphere Using the Constrained Linear Least-Squares Technique and Multi-Wavelength IPDA LiDAR

Han, Ge; Shi, Tianqi; Ma, Xin; Xu, Hao; Zhang, Miao; Liu, Qi; Gong, Wei

doi:10.3390/rs12152395

Cited by 9 publications

(3 citation statements)

References 43 publications

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“…In addition, it will be further evaluated with larger sewage networks, more substances, and different sewage hydraulic conditions in further publications. A benchmark of different localization methods–such those described in [ 24 , 27 , 28 , 29 , 30 ]—for an estimation of the pollution source could be considered as an important step forward in this area, as well as adapting effective existing methods for detection of polluting sources in similar environments, such as in [ 37 , 38 ].…”

Section: Discussionmentioning

confidence: 99%

Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network

Buras

Solano

2021

Sensors

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Harsh pollutants that are illegally disposed in the sewer network may spread beyond the sewer network—e.g., through leakages leading to groundwater reservoirs—and may also impair the correct operation of wastewater treatment plants. Consequently, such pollutants pose serious threats to water bodies, to the natural environment and, therefore, to all life. In this article, we focus on the problem of identifying a wastewater pollutant and localizing its source point in the wastewater network, given a time-series of wastewater measurements collected by sensors positioned across the sewer network. We provide a solution to the problem by solving two linked sub-problems. The first sub-problem concerns the detection and identification of the flowing pollutants in wastewater, i.e., assessing whether a given time-series corresponds to a contamination event and determining what the polluting substance caused it. This problem is solved using random forest classifiers. The second sub-problem relates to the estimation of the distance between the point of measurement and the pollutant source, when considering the outcome of substance identification sub-problem. The XGBoost algorithm is used to predict the distance from the source to the sensor. Both of the models are trained using simulated electrical conductivity and pH measurements of wastewater in sewers of a european city sub-catchment area. Our experiments show that: (a) resulting precision and recall values of the solution to the identification sub-problem can be both as high as 96%, and that (b) the median of the error that is obtained for the estimation of the source location sub-problem can be as low as 6.30 m.

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Section: Discussionmentioning

confidence: 99%

Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network

Buras

Solano

2021

Sensors

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“…Several types of dual-wavelength (online and offline) IPDA lidar have been demonstrated previously for measuring XCO 2 from aircraft (Spiers et al, 2011;Menzies et al, 2014;Jacob et al, 2019;Dobler et al, 2013;Campbell et al, 2020;Refaat et al, 2016Refaat et al, , 2020Refaat et al, , 2021Amediek et al, 2017;Zhu et al, 2019Zhu et al, , 2020. A multi-wavelength IPDA lidar has also been reported.…”

Section: Introductionmentioning

confidence: 99%

Retrieval algorithm for the column CO<sub>2</sub> mixing ratio from pulsed multi-wavelength lidar measurements

et al. 2021

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Abstract. The retrieval algorithm for CO2 column mixing ratio from measurements of a pulsed multi-wavelength integrated path differential absorption (IPDA) lidar is described. The lidar samples the shape of the 1572.33 nm CO2 absorption line at multiple wavelengths. The algorithm uses a least-squares fit between the CO2 line shape computed from a layered atmosphere model and that sampled by the lidar. In addition to the column-average CO2 dry-air mole fraction (XCO2), several other parameters are also solved simultaneously from the fit. These include the Doppler shift at the received laser signal wavelength, the product of the surface reflectivity and atmospheric transmission, and a linear trend in the lidar receiver's spectral response. The algorithm can also be used to solve for the average water vapor mixing ratio, which produces a secondary absorption in the wings of the CO2 absorption line under humid conditions. The least-squares fit is linearized about the expected XCO2 value, which allows the use of a standard linear least-squares fitting method and software tools. The standard deviation of the retrieved XCO2 is obtained from the covariance matrix of the fit. The averaging kernel is also provided similarly to that used for passive trace-gas column measurements. Examples are presented of using the algorithm to retrieve XCO2 from measurements of the NASA Goddard airborne CO2 Sounder lidar that were made at constant altitude and during spiral-down profile maneuvers.

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“…The retrieval algorithms used in these IPDA lidar calculate the ratios of on-line to the off-line atmosphere transmission, convert them to differential absorption optical depths (DAOD), and then solve for XCO2 from the DAOD based on the atmosphere models. In the case of a multi-wavelength lidar, a series of DAODs are calculated and a least-squares fit is used to solve for XCO2 (Han et al, 2020).…”

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confidence: 99%

Retrieval Algorithm for the Column CO<sub>2</sub> Mixing Ratio from Pulsed Multi-Wavelength Lidar Measurements

Sun¹,

Abshire²,

Ramanathan³

et al. 2020

Preprint

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Abstract. The retrieval algorithm for the column mixing ratio of CO2 from the measurements of a pulsed multi-wavelength integrated path differential absorption (IPDA) lidar is described. The lidar samples the shape of the 1572.33 nm CO2 absorption line at 15 or 30 wavelengths. The algorithm uses a least-squares fit between the CO2 line shape computed from a layered atmosphere model to that sampled by the lidar. In addition to the column average CO2 dry air mole fraction (XCO2), several other parameters are also solved simultaneously from the fit. These include the Doppler shift in the received laser signal wavelengths, the product of the surface reflectivity and atmospheric transmission and a linear trend in the lidar receiver's spectral response. The algorithm can also be used to solve for the average water vapor mixing ratio, which causes a secondary absorption in the wings of the CO2 absorption line under high humidity conditions. The least-squares fit is linearized about the expected XCO2 value which allows the use of a standard linear least-squares fitting method and software tools. The standard deviation of the retrieved XCO2 is obtained from covariance matrix of the fit. An averaging kernel is defined similarly to that used for passive trace-gas sounding. Examples are presented of using the algorithm to retrieve XCO2 from the measurements from NASA Goddard's airborne CO2 Sounder lidar made at a constant altitude and during spiral-down maneuvers.

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Obtaining Gradients of XCO2 in Atmosphere Using the Constrained Linear Least-Squares Technique and Multi-Wavelength IPDA LiDAR

Cited by 9 publications

References 43 publications

Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network

Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network

Retrieval algorithm for the column CO<sub>2</sub> mixing ratio from pulsed multi-wavelength lidar measurements

Retrieval Algorithm for the Column CO<sub>2</sub> Mixing Ratio from Pulsed Multi-Wavelength Lidar Measurements

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Obtaining Gradients of XCO2 in Atmosphere Using the Constrained Linear Least-Squares Technique and Multi-Wavelength IPDA LiDAR

Cited by 9 publications

References 43 publications

Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network

Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network

Retrieval algorithm for the column CO&lt;sub&gt;2&lt;/sub&gt; mixing ratio from pulsed multi-wavelength lidar measurements

Retrieval Algorithm for the Column CO&lt;sub&gt;2&lt;/sub&gt; Mixing Ratio from Pulsed Multi-Wavelength Lidar Measurements

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Retrieval algorithm for the column CO<sub>2</sub> mixing ratio from pulsed multi-wavelength lidar measurements

Retrieval Algorithm for the Column CO<sub>2</sub> Mixing Ratio from Pulsed Multi-Wavelength Lidar Measurements