Development of Raman Lidar for Remote Sensing of CO₂ Leakage at an Artificial Carbon Capture and Storage Site

Abstract

We developed a Raman lidar system that can remotely detect CO₂ leakage and its volume mixing ratio (VMR). The system consists of a laser, a telescope, an optical receiver, and detectors. Indoor CO₂ cell measurements show that the accuracy of the Raman lidar is 99.89%. Field measurements were carried out over a four-day period in November 2017 at the Eumsong Environmental Impact Evaluation Test Facility (EIT), Korea, where a CO₂ leak was located 0.2 km from the Raman lidar. The results show good agreement between CO₂ VMR measured by the Raman lidar system (CO₂ VMR_{Raman LIDAR}) and that measured by in situ instruments (CO₂ VMR_In-situ). The correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and percentage difference between CO₂ VMR_In-situ and CO₂ VMR_{Raman LIDAR} are 0.81, 0.27%, 0.37%, and 4.92%, respectively. The results indicate that Raman lidar is an effective tool in detecting CO₂ leakage and in measuring CO₂ VMR remotely.

1. Introduction

The average temperature of Earth has increased by 0.8 °C since the beginning of the twentieth century, garnering worldwide interest [1]. This global warming is reported to be caused by an increase in the concentrations of greenhouse gases (e.g., CO₂, CH₄, N₂O, O₃, and CFCs) produced by human activities such as fossil fuel combustion, industrial processes, and deforestation [2,3].

As concern about increasing greenhouse gas concentrations has increased, many countries have begun to regulate carbon emissions [4] according to the Kyoto Protocol [5]. Carbon capture and storage (CCS) is considered a promising technology to reduce atmospheric CO₂ [3,6]. However, leakage of CO₂ from large-scale CCS can affect the CO₂ reduction efficiency and have serious impacts on the surrounding ecosystem, such as reduced soil pH, damage to plant and microbial communities, and groundwater contamination [7]. Previous studies [8,9] have monitored CO₂ leaking from CCS sites. Hui et al. [8] assessed CO₂ leakage from a storage facility in Xuzhou city, Jiangsu province, China, using multiple in situ instruments and wireless sensor networks. Elio et al. [9] reported a technique for measuring CO₂ soil flux at an artificial CO₂ injection site in Hontomin, Spain. While these in situ instruments have a high CO₂ measurement accuracy, many instruments are required to monitor large CCS sites. It may be difficult to detect a small, continuous CO₂ leak if in situ instruments are sparsely distributed at a large CCS site. In contrast, remote sensing techniques can be used to monitor CO₂ leakages over a large CCS site using a single instrument. However, in compiling a map of CO₂ distribution via two-dimensional horizontal scanning, some spots might be missed by Raman lidar because of the nature of the terrain or the presence of obstacles.

The detection limit (LDL), accuracy, and cost of the in situ instrument used by Jung et al. [10] were 0 ppm, 1.5% of reading, and about $35,000, respectively, while those of the instrument used by Jun et al. [11] were 0 ppm, 1% of reading, and about $5500, respectively. There is no commercially available Raman lidar designed to measure CO₂. Only one previous study [12] has reported tropospheric CO₂ measurement using Raman lidar, although the authors provided no information on the LDL, precision, or cost of the lidar. Atmospheric CO₂ measurements have been reported down to 40 ppmv for 20,000 shots at altitudes up to 1.5 km. Since a lidar system with a 50 mJ laser and repetition rate of 20 Hz costs about $40,000 [12], the cost of the lidar is thought to be higher than this figure.

In the present study, we developed a Raman lidar technique capable of detecting and remotely sensing the mixing ratio information of CO₂ leakage from the surface. The remainder of this paper is organized as follows. The Raman lidar hardware system and the algorithms used to retrieve the CO₂ volume mixing ratio (CO₂ VMR) are described in Section 2, and the indoor and outdoor CO₂ measurement performance of the system is reported in Section 2 and Section 3. Finally, the discussion, and conclusions are given in Section 4 and Section 5, respectively.

2. Materials and Methods

We used the elastic scattering and the Raman lidar equation to retrieve CO₂ mixing ratios. The lidar equation applied to CO₂ Raman lidar is as follows [13]:

$$\mathrm{P}\left(\mathsf{\lambda },\mathrm{z}\right)=E\left(\mathsf{\lambda }\right)\times \exp \left(-{{\displaystyle \int }}_0^z\alpha \left(\mathsf{\lambda },\mathrm{z}\right)\mathrm{d}{z}^{\prime }\right)\times \beta \left(\mathsf{\lambda },\mathrm{z}\right)\times \exp \left(-{{\displaystyle \int }}_0^z\alpha \left(\mathsf{\lambda },\mathrm{z}\right)\mathrm{d}{z}^{\prime }\right)\frac{S_{area}}{z^2}\times \mathsf{\xi }\left(\mathrm{z}\right)\times {\eta }_{\lambda },$$

(1)

where $\mathrm{P}\left(\mathsf{\lambda },\mathrm{z}\right)$ is the returned backscatter signal from distance $\mathrm{z}$ at the laser wavelength $\mathsf{\lambda }$, $E\left(\mathsf{\lambda }\right)$ is the initial laser energy, $\alpha$ is the total (=aerosol + molecular) extinction coefficient, $\beta$ is the total (=aerosol + molecular) backscatter coefficient, $S_{area}$ is the telescope receiver area, $\mathsf{\xi }$ describes the overlap function, and ${\eta }_{\lambda }$ is the receiver efficiency that reflects how many of the incoming photons are detected. Raman scattering refers to a process of inelastic scattering of light by molecules such as CO₂ in which the wavelength of the incident light is changed [14]. The Nd:YAG laser of wavelength 355 nm used in this study is shifted to a wavelength of 371.6 nm caused by the vibrational Raman process of CO₂ molecules. For reference, the Raman scattering wavelength of N₂ molecules in dry air in the lower troposphere is 386.7 nm. By using Equation (1), the backscattered signal from a Raman CO₂ channel at 371.6 nm, $P_{C{O}_2}$, can be expressed as

$$P_{C{O}_2}\left(\mathrm{z}\right)=\frac{k_{C{O}_2}}{z^2}{\sigma }_{C{O}_2}\left(\pi \right)n_{C{O}_2}\left(z\right)q\left({\lambda }_{C{O}_2}, z_0, z\right)\exp \left(-{{\displaystyle \int }}_0^z\alpha \left(\mathsf{\lambda },\mathrm{z}\right)\mathrm{d}{z}^{\prime }\right),$$

(2)

where $k_{C{O}_2}$ is a proportionality constant for the system optical efficiency, the telescope receiver area, photomultiplier tube (PMT) efficiency, and the laser output energy. ${\sigma }_{C{O}_2}$ is the backscattered cross-section for CO₂ caused by Raman scattering, $n_{C{O}_2}$ is the number density for CO₂ as a function of distance $\mathrm{z}$, and $\alpha \left(\mathsf{\lambda }\right)$ is the volume extinction coefficient at wavelength $\mathsf{\lambda }$.

$$P_{N_2}\left(\mathrm{z}\right)=\frac{k_{N_2}}{z^2}{\sigma }_{N_2}\left(\pi \right)n_{N_2}\left(z\right)q\left({\lambda }_{N_2}, z_0, z\right)\exp \left(-{{\displaystyle \int }}_0^z\alpha \left(\mathsf{\lambda },\mathrm{z}\right)\mathrm{d}{z}^{\prime }\right),$$

(3)

where $k_{N_2}$ is a proportionality constant for the system optical efficiency, the telescope receiver area, photomultiplier tube (PMT) efficiency, and the laser output energy. ${\sigma }_{N_2}$ is the backscattered cross-section for N₂ caused by Raman scattering, $n_{N_2}$ is the number density for N₂ as a function of distance $\mathrm{z}$, and $\alpha \left(\mathsf{\lambda }\right)$ is the volume extinction coefficient at wavelength $\mathsf{\lambda }$.

$$\exp \left(-{{\displaystyle \int }}_0^z\alpha \left(\lambda ,z\right)d{z}^{\prime }\right)=q\left({\lambda }_0, z_0, z\right),$$

(4)

where $q\left({\lambda }_0, z_0, z\right)$ is the atmospheric transmittance from Raman lidar at distance $z_0$ to $\mathrm{z}$ at wavelength 355 nm. We used Raman signals of N₂ as a reference to calculate the CO₂ mixing ratio. The CO₂ mixing ratio is the mass of CO₂ divided by the mass of dry air in a given volume [12]. Since N₂ is present at a constant rate in dry air, the N₂ Raman signal, $P_{N_2}$, is used as a measure of dry air [13]. The CO₂ mixing ratio can be derived from the normalization signal, which is the quotient of the CO₂ Raman signal divided by the N₂ Raman signal. The normalization signal can be expressed as

$$\mathrm{Normalization}\mathrm{signal}=\frac{P_{C{O}_2}\left(\mathrm{z}\right)}{P_{N_2}\left(\mathrm{z}\right)}.$$

(5)

The backscatter signals of CO₂ and N₂ were used to calculate the normalization signal. The Raman scattering signals of CO₂ and N₂ are obtained at different wavelengths (371.7 and 386.7 nm, respectively), and thus it has been assumed that there is no wavelength dependence with respect to extinction and consequently to atmospheric transmittance.

Figure 1 shows a flow chart of the process used to calculate the CO₂ volume mixing ratio using the Raman lidar system. First, the normalized ratio is calculated using the CO₂ and N₂ inelastic signals caused by Raman scattering, as measured by the Raman lidar system.

In the right part of Figure 1, the regression coefficients are determined using the calculated normalized ratio and the CO₂ VMR measured by the in situ instrument. The regression coefficients are then used in the calibration equation. The surface CO₂ mixing ratios are calculated using a regression between the calculated normalized ratio and the CO₂ VMR measured by an in situ instrument.

2.1. Raman Lidar Setup

The Raman lidar system consists of the third harmonic of a Nd:YAG laser of wavelength 355 nm with 80 mJ energy and 20 Hz repetition rate, a telescope, an optical receiver, and detectors. The pulsed laser is emitted horizontally to the ground from the Nd:YAG laser. Since the laser is emitted horizontally, the diameter of the laser beam was expanded, using a beam expander, by a factor of five times to ensure eye safety. The eye safety distance of our Raman lidar system is 208 m. The Raman lidar is applied co-axially with respect to the optical system to detect CO₂ near the location of the lidar, with an overlap distance of 5–10 m. To observe CO₂ mixing ratios, the Raman lidar system measures backscatter signals caused by CO₂ Raman, N₂ Raman, and Rayleigh–Mie scattering. These signals are detected and recorded as a function of distance and wavelength. To achieve real-time monitoring, a three-channel signal collection system was designed to collect the three types of backscatter signals simultaneously (Figure 2).

A Schmidt–Cassegrain telescope with a diameter of 15.24 cm is used as an optical receiver. First, the scattered light is collected by the telescope. After passing through a pinhole, it is narrowed by the collimating lens (L1). A dichroic beam splitter (D.M1) reflects the light at wavelengths above 355 nm. Since the Raman scattering signal is weaker than Rayleigh–Mie scattering [12], a notch filter (F1) is used which reflects light at 355 nm and transmits it at all other wavelengths, thus reflecting the elastic signals caused by Rayleigh–Mie (total) scattering and detecting the inelastic signal caused by Raman scattering. The CO₂ filter (F2) transmits light with a wavelength of 371.7 nm to detect the Raman scattering signals of CO₂ and reflects light at all other wavelengths. The CO₂ filter (F3) transmits light at 386.7 nm and reflects all other wavelengths to detect the Raman scattering signals of N₂ needed to calculate the CO₂ VMR. The 355 nm filter (F4) transmits light at 355 nm to detect the elastic scattering signal used in aligning the lidar signal. The lenses L2, L3 and L4 are used to focus the CO₂ Raman, N₂ Raman, and elastic signals into the PMT. The signals detected by the three-channel collecting system are analyzed to determine the CO₂ mixing ratio.

Table 1. Components of the Raman lidar system.

Component	Specification
Laser	Nd:YAG laser Wavelength: 355 nm Pulse Energy: 80 mJ Beam divergence: <1.5 mrad (full angle) Repetition rate: 20 Hz Pulse duration: 9 ns
F1	Notch filter, 355 nm Spectral Range: 350–1100 nm Nominal Transmission: 1.0%
F2	CO2 Raman (371.7 nm) CWL: 371.7 nm FWHM: 0.5 nm Tabs > 70% @ 371.7 nm (Typical T > 85%) OD > 7 @ 354.7 nm OD > 6 from 200–1200 nm out-of-band
F3	N2 Raman (386.7 nm) FWHM < 0.6 nm OD7 Transmission: 386.7 nm > 80% T Absolute
F4	355 nm filter CWL: 355 nm FWHM: 10 nm OD > 5 Transmission > 90%
PMT1	CO2 Channel R9880U-210
PMT2	N2 Channel R9880U-210
PMT3	355 nm Channel R9880U-210
Telescope	6-In. Schmidt Cassegrain
Beam expander	5X, fixed beam-expander
Size of the Raman lidar system	1100 × 850 × 550 mm (W × H × D)
Weight of the Raman lidar system	70 kg
Power demand of the Raman lidar system	1000 W

summarizes the main components of the CO₂ Raman lidar system.

2.2. Indoor CO₂ Cell Measurement

Indoor CO₂ cell measurements were carried out to quantify the accuracy of the lower detection limit of the Raman Lidar system. Figure 3 shows a schematic diagram of the indoor CO₂ cell setup using the Raman lidar. The cell measurement setup consists of a CO₂ gas vessel, a mass flow controller (MFC), a CO₂ gas cell, a vacuum pump, and the Raman lidar system. CO₂ in the CO₂ gas vessel is injected into the CO₂ cell using the MFC, which controls the CO₂ mixing ratio in the cell. A vacuum pump is connected to the cell to release CO₂ gas from the cell into the air.

To measure the VMR of CO₂ gas using Raman lidar, the inside of the CO₂ cell is evacuated and CO₂ gas is injected. The CO₂ VMR of the cell is set to range from 10% to 100%, as the lowest measurement unit of the vacuum gauge used in this study is 1%. The inelastic signals of CO₂ and N₂ caused by Raman scattering are subsequently measured by the Raman lidar and are hereafter referred to as the normalized ratio.

3. Results

3.1. Indoor CO₂ Cell Measurement

Figure 4 shows the correlation between the normalized ratio measured by CO₂ Raman lidar and the CO₂ VMRs from the CO₂ cell. The x-axis represents the CO₂ VMR inside the CO₂ cell (CO₂ VMR_CELL) and the y-axis represents the normalized ratio, calculated from the inelastic signals of CO₂ and N₂ caused by Raman scattering (see Equation (5)). The correlation coefficient (R) between the CO₂ VMR_{Raman LIDAR} and the normalized ratio is 1, showing excellent agreement. The deviation from the regression line observed at CO₂ VMR of 10% in Figure 4 is due to the inaccuracy of the vacuum gauge used to set the CO₂ concentration in the vacuum cell. The vacuum gauge has a concentration error of 1%. Next, the CO₂ VMR in the cell is retrieved using the normalized ratio and the regression equation obtained in Figure 4 based on the method displayed in Figure 1.

Figure 5 shows the correlation between CO₂ VMR_CELL and CO₂ VMR as measured by the Raman lidar system (CO₂ VMR_{Raman LIDAR}). CO₂ VMR_{Raman LIDAR} obtained from CO₂ Raman Lidar shows a good agreement with CO₂ VMR_CELL. Both R and the slope between CO₂ VMR_CELL and CO₂ VMR_{Raman LIDAR} are 1.00. An R value close to 1 indicates more stable and consistent laser power and higher repeatability of the detector than an R value lower than 1, because CO₂ is the only variable that changes during the training and retrieval of indoor cell measurements of CO₂. If the R value departs from 1, then the output power of the laser is not consistent or the repeatability of the detector is not sufficient for CO₂ retrieval. The CO₂ in the cell was measured 20 times by Raman lidar under constant conditions. We calculated the error of the value measured by Raman lidar when the CO₂ VMR in the cell was the same as that during CO₂ measurements. The CO₂ measurement accuracy of our Raman lidar is 99.89% based on the indoor CO₂ cell test.

3.2. Field Test

To examine the Raman lidar system’s capability in measuring spatially resolved CO₂ and N₂ Raman signals remotely, it was used to detect signals at a distance of 900 m from the lidar. The test was carried out on 30 October 2017, at a study site in Daejeon, South Korea, as shown in Figure 6a. We detected the Raman signals of CO₂ and N₂ for 1 h during the night. The effective spatial resolution of the Raman lidar was 250 ns (37.5 m). CO₂ and N₂ inelastic signals caused by Raman scattering were measured between the lidar system and a mountain location. During the daytime, the background signal from sunlight is stronger than the Raman scattering signal, so measurements were made only at night. Figure 6b shows the Raman signals of CO₂ and N₂ as a function of distance from the lidar system and the motorway. The increased CO₂ Raman signals found at 400 and 750 m from the lidar system represent the locations of a local motorway (A) and the Gapcheondosi Expressway (B), respectively. The enhanced Raman signals of CO₂ at locations A and B in Figure 6b imply that the lidar system is capable of detecting spatially resolved Raman signals of CO₂ emitted from motor vehicles. The Raman lidar obtained clear and stable Raman scattering signals of N₂ and CO₂ at a distance of 900 m from the lidar system.

CO₂-Leakage Measurement Campaign

To examine the CO₂-leakage measurement capability of the lidar system, measurements were carried out using both the CO₂ Raman lidar system and an in situ CO₂ probe (VAISALA, GMP343). The measurements took place between 20 and 24 November 2017, at the Eumseong Environmental Impact Evaluation Test Facility (EIT) on Seepage of Geologically Stored CO₂, an artificial CO₂ gas-leakage site (36°96′N, 127°47′E). As shown in Figure 7, CO₂ is injected at 0.5 m below the surface and left to leak back out. CO₂ gas was injected into the ground at 12 L/min for 12 h on 22 November, 2017. The Raman lidar system is located 200 m from the in situ CO₂ measurement device, which is located near the CO₂ injection location. The Raman lidar line-of-sight is located 0.5 m above the surface of the CO₂ injection inlet. The effective spatial resolution of the Raman lidar is 250 ns (37.5 m) and the beam diameter is about 29.98 cm at the target. The in situ instrument measured the CO₂ VMR at a distance of 1 m from the Raman lidar line-of-sight. A linear regression equation between the normalized ratio and CO₂ VMR measured by the in situ instrument is derived based on the measurements on 24 November, 2017. Six hours after stopping the CO₂ gas injection, the measurement of CO₂ on the surface was started using the CO₂ Raman lidar. The change in Raman scattering signals with respect to the change in the CO₂ mixing ratio on the surface was measured as CO₂ gas was being injected into the ground at 12 L/min. During the campaign period, the in situ instrument with its inlet located 0.5 m from the surface often failed to measure CO₂ leakage near the surface. This is thought to be associated with low CO₂ concentrations even at 0.5 m from the surface, probably due to a negligible exit velocity of CO₂ molecules. To evaluate the performance via comparison with the in situ measurement, the point of view of the lidar system was focused at the in situ inlet location to increase the lidar’s sensitivity to CO₂ molecules.

Figure 8 shows the time series of CO₂ VMR_In-situ measured by the in situ instrument (blue line) and CO₂ VMR_{Raman LIDAR} measured by the Raman lidar system (red lines) at the Eumseong EIT site during the field campaign. The CO₂ gas was artificially injected into the ground and left to leak from the surface. An increasing trend in both CO₂ VMR_In-situ and CO₂ VMR_{Raman LIDAR} is expected with continued CO₂ injection. In Figure 8, the CO₂ VMR measured by the CO₂ Raman lidar increases steadily over time, while the CO₂ VMR measured by the in situ instrument shows an increasing trend with large variability over a short time interval. The reason for this difference between the steadily increasing CO₂ VMR_{Raman LIDAR} and the increasing CO₂ VMR_In-situ trend with large fluctuations could be associated with differences in measurement coverage. The spatial effective resolution of the Raman lidar is 37.5 m, which is different from the in situ instrument inlet area with a diameter of 1.5 cm. It is clearly shown that CO₂ VMR_{Raman LIDAR} tends to be lower than CO₂ VMR_In-situ obtained from the CO₂ leakage spot in Figure 9, which implies a certain influence of the ambient CO₂ VMR on the CO₂ VMR_{Raman LIDAR} since the ambient CO₂ VMR is likely to be lower than the CO₂ VMR of the leakage and also does not change rapidly for a short time period [15]. In the present study, if the CO₂ concentration of the leak is lower than the concentration of CO₂ leaking from the field campaign in the case of a small leak, our current Raman lidar system with a spatial resolution of 37.5 m is barely able to detect the CO₂ leak due to the long spatial resolution of the system. Therefore, the spatial resolution of the lidar system is an important factor in improving the CO₂ precision and detection limit, especially for small leaks. A high-speed photon counter, which provides fine spatial resolution, is required to enhance the precision and detection limit. The LDL and precision of the Raman lidar are also thought to affect the overall efficiency and capability of the lidar to detect such small variations in low CO₂ levels. The trend difference in Figure 8 between CO₂ VMR_In-situ and CO₂ VMR_{Raman LIDAR} under windy conditions could be associated with the wind direction between a CO₂ leak and the measurement location of each instrument, as the closest distance between the Raman lidar line-of-sight and the in situ instrument is 1 m. However, the effect of wind direction and speed could not be quantified due to the unavailability of wind data during the campaign period.

Figure 9 shows the results of linear regression for CO₂ VMR_In-situ and CO₂ VMR_{Raman LIDAR} at the Eumseong EIT site, revealing a correlation. The R value and slope between CO₂ VMR_In-situ and CO₂ VMR_{Raman LIDAR} are 0.81 and 0.15, respectively. The mean absolute error (MAE), the root mean square error (RMSE) and the percentage difference between CO₂ VMR_In-situ and CO₂ VMR_{Raman LIDAR} are 0.27%, 0.37%, and 4.92%, respectively. The errors due to a decrease in backscattered Raman signals that follow a Poisson distribution are 4.9% (112.5 m), 5.4% (150.0 m), or 6.3% (187.5 m). The uncertainty due to the regression coefficients, as calculated from the residuals between the regression line and measured values, is 5.5%. Thus, the total error in CO₂ VMR_{Raman LIDAR}, which is calculated by error propagation, is estimated to be 7.4% (112.5 m), 7.7% (150.0 m), or 8.4% (187.5 m).

4. Discussion

Hui et al. [8] monitored CO₂ leakage from a geological CO₂ storage site in Xuzhou, Jiangsu, China, using multiple in situ instruments and wireless sensor networks, and Elio et al. [9] applied a technique for measuring CO₂ soil flux at a CO₂ injection site at Hontomin, Spain. While these in situ instruments have high CO₂ measurement accuracy, a large number of such instruments are needed to monitor large CCS sites. To enhance monitoring efficiency over a large area, remote sensing techniques are desired. Therefore, as part of efforts to develop remote sensing techniques for CO₂ measurements at the Earth’s surface, we developed the first compact, lightweight, and portable Raman lidar system for remote sensing of surface CO₂ leakage. To examine the performance of the lidar, it was used to detect strong Raman signals of CO₂ at two roads located 400 and 750 m from the instrument. In the case of measurements at an artificial CO₂ leakage site, the CO₂ VMRs at 0.5 m from the surface and 200 m from the Raman lidar were measured successfully and show a good correlation with data collected by an in situ instrument. It is also necessary to understand the longest remote sensing distance with the highest possible accuracy. In order to quantify the longest remote sensing distance of our Raman lidar system, it should be measured in the area without any obstacles on the line of sight. To obtain a relative accuracy from the comparison between CO₂ VMRs obtained from in situ instrument and the lidar, multiple in situ sensors need to be deployed at several distances on the line of the lidar sight.

During the campaign period, the CO₂ VMRs were detected successfully only near the surface, probably due to the low CO₂ exit velocity from the ground. In the case of such low velocities, the altitude of the lidar line of sight needs to be lowered to measure CO₂, since this compound is likely to exist near the surface. However, lowering the measurement altitude may cause noise such as fluorescence effect in the backscatter signals if the laser encounters obstacles near the surface or the ground itself, arising from the lowered altitude of the line of sight. Our current CO₂ retrieval algorithm needs to be improved to account for fluorescence effects. In addition, Elio et al. [9] proposed a CCS area of 3 km × 3 km that requires a Raman lidar scan that can provide sufficiently high spatial resolution to detect CO₂ leakage from a small site. In the present study, a laser with 80 mJ power was used in the lidar. A laser with stronger power and a more efficient receiver is needed to generate the Raman signal of CO₂ for sites far from the laser and to detect weak CO₂ Raman signals. In addition, in the case of elevated terrain along the lidar line of sight, the measurement distance on the line of sight is reduced, meaning that a lidar located at a single spot cannot complete two-dimensional horizontal scanning over a large CCS site with elevated terrain.

5. Conclusions

We developed a Raman lidar system that remotely detects CO₂ leakage and CO₂ VMR. The system consists of a laser, a telescope, an optical receiver, and detectors. In terms of indoor CO₂ cell measurement, CO₂ Raman lidar shows very high accuracy. Field measurements were carried out using Raman lider at the Eumseong EIT site where a CO₂ leak is located 0.2 km from the Raman lidar system. There is good agreement between CO₂ VMR_{Raman LIDAR} as measured by the Raman lidar system and CO₂ VMR_In-situ as measured by an in situ instrument at the Eumseong EIT site. The R value and percentage difference between CO₂ VMR_In-situ and CO₂ VMR_{Raman LIDAR} are 0.81 and 4.92%, respectively. Discrepancies between the CO₂ values measured by Raman lidar and by the in situ instrument could be due to differences between measurement coverage and the measurement geometry of the two approaches. In the case of low CO₂ exit velocities, it is necessary to measure the CO₂ by lowering the altitude of both lidar and in situ instruments. In the case of lidar, lowering the measurement altitude may cause noise signals such as fluorescence to be included in the backscatter signals when the laser hits the ground due to the terrain. Therefore, the lidar algorithm and measurement methods need to be studied further. The CO₂ VMR measurement resolution and detection limit also requires further study at low levels of CO₂. In a future experiment, wind data will be used to assess the optimal measurement height for the detection and measurement of CO₂ leaks.