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

Greenhouse gas monitoring using an IPDA lidar based on a dual-comb spectrometer

We present the development of a multi-spectral, integrated-path differential absorption (IPDA) lidar based on a dual-comb spectrometer for greenhouse gas monitoring. The system uses the lidar returns from topographic targets and does not require retroreflectors. The two frequency combs are generated by electro-optic modulation of a single continuous-wave laser diode. One of the combs is pulsed, amplified, and transmitted into the atmosphere, while the other acts as a local oscillator for coherent detection. We discuss the physical principles of the measurement, outline a performance model including speckle effects, and detail the fiber-based lidar architecture and signal processing. A maximum likelihood algorithm is used to estimate simultaneously the gas concentration and the central frequency of the comb, allowing the system to work without frequency locking. H2O (at 1544 nm) and CO2 (at 1572 nm) concentrations are monitored with a precision of 3% and 5%, respectively, using a non-cooperative target at 700 m. In addition, the measured water vapor concentrations are in excellent agreement with in-situ measurements obtained from nearby weather stations. To our knowledge, this is the first complete experimental demonstration and performance assessment of greenhouse gas monitoring with a dual-comb spectrometer using lidar echoes from topographic targets.

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

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.