The Influence of Instrumental Line Shape Degradation on Gas Retrievals and Observation of Greenhouse Gases in Maoming, China

The instrument line shape (ILS), as a very important parameter, has a significant influence on the inversion of trace gas concentration. Different levels of ILS degradation for H2O, CO2, CH4, and CO gases were investigated, and the influence of ILS on the inversion of column-averaged dry air mole fractions (DMFs) was assessed. Our results indicate that the averages of XH2O, XCH4, and XCO with modulation efficiency (ME) amplitude values have a positive correlation, the correlation coefficients are 0.9925, 0.9968, and 0.9981 respectively, whereas the relationship between the average of XCO2 and ME is a negative correlation with 0.986 correlation coefficient. For a typical ILS degradation, a decrease of 5% in the modulation efficiency amplitude value results in the average of XCO2 changing by 0.744%, XCH4 and XH2O are less sensitive species, with average values of −0.206% and −0.464%, whereas XCO shows the strongest intraday variability with an average value of −0.238%. However, with a decrease of 2‰ in the phase error (PE) value, the average of XCO changed by −0.150%, XCO2 and XH2O almost coincided with the same average value of −0.141%, whereas XCH4 was the least sensitive species with an average value of −0.133%. At the same time, we measured the ILS for EM27/SUN spectrometers—the mean values of modulation efficiency amplitudes and phase errors were 0.9611 and 0.00593. Compared with standard values, the modulation efficiency amplitudes and the phase error deviations were 2.450% and 0.433%. During the observation period, the daily average of XCO2 ranged from 415.09 to 421.78 ppm. XCH4 ranged from 1.96 to 2.02 ppm with a mean of 1.982 ppm, and the daily average of XCO ranged from 0.118 to 0.157 ppm with a mean of 0.137 ppm. For the relationship between XCO2 and XCH4, the linear regression line shows a good correlation with the correlation coefficient R2 ≥ 0.5. Especially, for the correlation coefficient R2 = 0.82 on 8 October, our studies found a weak correlation in the variation of CO2 and CO during the observations. The correlation coefficient R2 ≥ 0.5 was only found on 30 September and 3 October. The trajectories dram at a height of 10 km give a hint of trace gas transport from the bay of western India, Bengal, and the Arabian Sea, whereas for the trajectories dram boundary layer height, trace gases were transported from southwest and east of China. These results provide a theoretical basis to understand the time and space distribution and the changes of greenhouse gas in the atmosphere as well as providing a theoretical basis for calculations of atmospheric radiation transmission.


Introduction
The continuing increase of atmospheric greenhouse gas is the main driver of global warming, particularly carbon dioxide (CO 2 ), methane (CH 4 ), and carbon monoxide (CO). In recent years, many countries have put great efforts into the observation of concentrations The phase error was close to zero for the whole time series with a mean value of 0.0019 ± 0.0018 by studying ILS of thirty EM27/SUN.
In the first part of this study, we quantitatively calculated the impact of ILS degradation on XH2O, XCO2, XCH4, and XCO and analyzed the average gas concentration changes with modulation efficiency amplitude and phase error. We performed lab-air observations of water gas signatures for the determination of instrumental line shape characteristics and obtained the values of ME and PE. Moreover, the change trend of greenhouse gas concentration in the Maoming area was analyzed, while at the same time we analyzed the correlation between XCH4, XCO, and XCO2.

Measurement Sit and Instruments
Measurements of the trace gases were made using the EM27/SUN spectrometer for XH2O, XCO2, XCH4, and XCO. The instruments were located at Bohe Marine Meteorological Observatory (21.453° N, 111.315° E, 0.02 km above sea level), adjacent to the South China Sea, located in the south Binhai New area of Maoming city (Figure 1 left). The coastline is very straight and has no terrain blocking, which is very conducive to the observation of various marine meteorological elements. We installed the instrument, consisting of the EM27/SUN spectrometer and solar tracker. A detailed description of the spectrometer can be found in Gisi et al. (2012), in the following we give only a short overview. To reach high stability with regard to thermal influences and vibrations, the EM27/SUN features a RockSolidTM pendulum interferometer with two cube corner mirrors and a CaF2 beam splitter. The instrument achieves 1.8 cm optical path difference (OPD) with a maximum spectral resolution of 0.5 cm −1 , as shown in Figure 1 (right). Measurements are recorded with an InGaAs detector operated at ambient temperature. Due to spectral coverage from 5000 to 11,000 cm −1 , the spectral bandwidth enables the detection of O2, H2O, CO2, CH4, and CO. The detector signal is DC coupled and thereby supports the correction of variable atmospheric transmission [12].

Data Processing and Methods
This section provides a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.
Atmospheric CO2, CH4, and CO are measured with a ground-based Fourier transform spectrometer that records the near infrared spectrum. We recorded double-sided interferograms with 0.5 cm −1 resolution. With 10 scans, one measurement takes about 58 s. In To reach high stability with regard to thermal influences and vibrations, the EM27/SUN features a RockSolidTM pendulum interferometer with two cube corner mirrors and a CaF 2 beam splitter. The instrument achieves 1.8 cm optical path difference (OPD) with a maximum spectral resolution of 0.5 cm −1 , as shown in Figure 1 (right). Measurements are recorded with an InGaAs detector operated at ambient temperature. Due to spectral coverage from 5000 to 11,000 cm −1 , the spectral bandwidth enables the detection of O 2 , H 2 O, CO 2 , CH 4, and CO. The detector signal is DC coupled and thereby supports the correction of variable atmospheric transmission [12].

Data Processing and Methods
This section provides a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.
Atmospheric CO 2 , CH 4, and CO are measured with a ground-based Fourier transform spectrometer that records the near infrared spectrum. We recorded double-sided interferograms with 0.5 cm −1 resolution. With 10 scans, one measurement takes about 58 s. In order to ensure data quality, a pre-processing is performed. To suppress the negative sidelobes, sometimes a numerical apodisation is applied. This is especially important for low resolution instruments. On the downside an apodisation decreases the spectral resolution and amplifies the correlation between measured values from different spectral positions. A good compromise between acceptable resolution, degradation, and suppressing the negative sidelobes is the Norton-Beer medium function [13]. Furthermore, a DC correction is performed. In addition, a quality filter discards interferograms with intensity Atmosphere 2021, 12, 863 4 of 15 fluctuations above 10% and intensities below 10% of the maximal modulation amplitude. To ensure high-precision greenhouse gas concentration, not only the altitude, latitude, and longitude, a priori temperature, humidity, pressure, and other parameters need to be considered, but also the real-time meteorological parameters of the site, such as surface temperature, surface pressure, and the instrumental characteristics of the measurement device (ILS), etc.
Precise knowledge of instrumental line shape (ILS) is of utmost importance to gain correct information from measurement signals. Instrumental line shape is the Fourier transform of the weighting applied to the interferogram. It consists of two parts and affects the accuracy of the final inversion results. Due to inherent self-apodization of the spectrometer, which is present also in an ideal instrument, one part describes the modulation loss. This contribution can easily be calculated utilizing the OPD and FOV of the spectrometer. The other part of the ILS results from misalignments and optical aberrations of the spectrometer and can be described by a modulation efficiency amplitude (ME) and a phase error (PE) as a function of the OPD. The theoretical ideal ILS, is a convolution of sinc and rectangular functions, representing the finite length of the interferogram and the finite circular field of view of the spectrometer [14], defined as: Here, σ is the wavenumber, σ 0 is the central wavenumber, L is the optical path difference, and θ is the angular radius of the circular internal FOV of the spectrometer. The ILS of a real spectrometer is equivalent to complex modulation efficiency in the interferogram, many problems lead to smooth variation of the complex modulation efficiency as for example, misalignments and optical aberrations of the spectrometer. The phase corrected interferogram generated by a spectral line is of the form [15]: where IFG(x) is the interferogram, Mod_amp(x) is the modulation efficiency amplitude (ME), and Mod_phas(x) is the phase error (PE). The spectral resolution is defined as: MOPD means the maximum optical path difference.
In this work, we analyzed spectra utilizing the PROFFIT retrieval fitting algorithm [10], which is in wide use and has been validated for retrieving dry-air mole fractions (DMF) of trace gases [8,[16][17][18]. The PROFFIT is a non-linear least-squares fitting algorithm. The atmospheric forward model is used to calculate synthetic spectra. We fitted the atmospheric spectra by scaling of a priori trace gas profiles with low resolution of the EM27/SUN as well as a priori vertical profiles for temperature, pressure, and water gas from the National Centers for Environmental Prediction (NCEP). Then an inverse method compares the synthetic spectra with the measured spectra. Typically, an inversion calculation starts with a forward model → F ( → x ), includes the instrumental characteristics of the measurement device and underlying physics which relates the measured quantities and the target variable. The approximation of the physics of the measurement process: Atmosphere 2021, 12, 863

of 15
Here, → y represents the measurement with the corresponding measurement error → ε y , → x is state vector, include pressure, temperature, or the volume mixing ratio. The assumption of discrete values for these parameters, the linearization of the forward model: Here, → x 0 is the reference state, K is the Jacobian matrix. The fitting residual is defined as follows: where y meas is the measured spectrum, y is the synthetic spectrum, S −1 y is the covariance matrix of inversion state parameters. The volume mixing ratio of O 2 in the atmospheric altitude range up to 100 km is nearly constant at approximately 0.2095. The calculation of the ratio of the target gas and the column amount of O 2 retrieved from the same spectrum to remove the effects of surface pressure variation, the column-averaged dry air mole fraction (DMF) is defined as (9), using the column abundance of O 2 as a reference to reduce the systematic errors: where column gas and column O 2 are the column abundance of the retrieval gas and O 2 respectively. Details of the retrieval method are given in Inverse Methods for Atmospheric Sounding: Theory and Practice [18]. The spectra windows for retrieval of the columnaveraged dry air mole fraction (DMF) CO 2 , CH 4 , CO, and O 2 are listed in Table 1. To make the measurements comparable to WMO scale, in post processing in our work, the calibration factors applied for XCO 2 , XCH 4, and XCO were 0.9869, 0.9898, and 0.925, respectively [11]. Figure 2 shows a typical measured atmospheric spectrum, the mode spectrum, and residuals.

Results Measurements and Discussion
The ME amplitude is connected to the width of the ILS, while the PE quantifies the degree of ILS asymmetry. The modulation efficiency amplitude (ME) is unity and the phase error (PE) is zero with an ideal ILS. However, if the spectrometer is not calibrated, the ME amplitude and PE would deviate from unity and zero. These parameters have to be calculated from laboratory measurement.

Influence of Instrumental Line Shape on Greenhouse Gas Inversion
In order to quantitatively analyze the impact of ILS on inversion, we analyzed the concentration changes of XH 2 O, XCO 2 , XCH 4 and XCO, when the ME deviate from ±5%, ±10%, ±15%, ±20% compared to the ideal value unity, and the PE deviate from ±2‰, ±4‰, ±6‰, ±8‰, ±10‰ compared to the ideal value zero. We took the retrievals with an ideal ILS as the reference. The difference is defined as: Taking the standard instrument as reference, the influence of the modulation efficiency amplitudes and phase errors on greenhouse gas inversion are shown in Figure 3. In general, the mean of XH 2 O, XCH 4, and XCO with ME values has a positive correlation. The correlation coefficients are 0.9925, 0.9968, and 0.9981 respectively. However, the relationship between the mean of XCO 2 and ME is the opposite, the mean of XCO 2 decreases as the ME loss increases. It has a negative correlation with ME loss value, the correlation coefficient is 0.986. This is opposed to prior studies reporting an increase of XCO 2 and decrease of XCH 4 for an increase of the modulation efficiency [19]. The reason may be that the total amount of the gas column depends on the airmass while the interference molecules in the inversion window of each gas molecule are different. This is in agreement with the findings from Frey, M dissertation (2018), who reported that XCO 2 decreases with increasing ILS, whereas XCH 4 increases [11]. The Xgas (XH 2 O, XCO 2 , XCH 4 and XCO)/ME loss slopes are 271.3, −59.33, 0.192, and 0.0069. The time series of relative difference of these species in terms of the total column are displayed in Figure 3c. A decrease of 5% in the ME value results in the highest amount of XCO 2 with an average value of 0.744%. While XCH 4 and XH 2 O are the less sensitive species, with average values of −0.206% and −0.464%. Among all these species, XCO shows the strongest intraday variability with an average value of −0.238%, ranging from −0.088% to −0.831%. The absorption of CO with the selected spectral window is much weaker than the other gases because of the superimposition from the nearby strong interfering lines of CH 4 and H 2 O.
The means of XH 2 O, XCO 2 , XCH 4, and XCO with different PE loss have a similar tendency, a negative correlation with PE loss (Figure 3 left). The correlation coefficients are higher than 0.999. The Xgas (XH 2 O, XCO 2 , XCH 4 and XCO)/PE loss slopes are −4451.08, −295.22, −1.35, and −0.107. The time series of the relative difference of these species in terms of total column shows a similar intraday variability (Figure 4d). The highest XCO has an average value of −0.150% with a decrease of 2‰ in the PE value, while XCO 2 and XH 2 O almost coincide with a same average value of −0.141%. Among all these species, XCH 4 are the least sensitive species with an average value of −0.133%. Figure 4a,b shows ∆XH 2 O, ∆XCO 2 , ∆XCH 4, and ∆XCO with ME and PE increasing or decreasing by the same value. The values of ∆XCO 2 are different when ME increases and decreases by the same value. Similarly, the values of ∆XCH 4 are different when ME increases and decreases by the same value. The reason for the asymmetry may be that the interference molecules in the retrieval band of each gas molecule are different, and the modulation degree of the instrument on the interferogram is also different, while ∆XH 2 O and ∆XCO are little different when ME increases and decreases by the same value. However, the values of ∆XH 2 O, ∆XCO 2 , ∆XCH 4, and ∆XCO are almost the same when PE increases and decreases by the same value. these species in terms of the total column are displayed in Figure 3c. A decrease of 5% in the ME value results in the highest amount of XCO2 with an average value of 0.744%. While XCH4 and XH2O are the less sensitive species, with average values of −0.206% and −0.464%. Among all these species, XCO shows the strongest intraday variability with an average value of −0.238%, ranging from −0.088% to −0.831%. The absorption of CO with the selected spectral window is much weaker than the other gases because of the superimposition from the nearby strong interfering lines of CH4 and H2O.      the same value. The reason for the asymmetry may be that the interference molecules in the retrieval band of each gas molecule are different, and the modulation degree of the instrument on the interferogram is also different, while ΔXH2O and ΔXCO are little different when ME increases and decreases by the same value. However, the values of ΔXH2O, ΔXCO2, ΔXCH4, and ΔXCO are almost the same when PE increases and decreases by the same value.  Table 2 shows the difference of XH2O, XCO2, XCH4, and XCO with ±1% in modulation efficiency and ±0.01 in phase error. For a typical ILS degradation of 1% in modulation efficiency, the column-averaged dry air mole fraction of H2O, CO2, CH4, and CO changed by 0.0492, 0.1151, 0.1042, 0.0523%, respectively, while an increase of 0.01in phase error leads to a decrease of 0.697% on XH2O, 0.689% on XCO2, 0.652% on XCH4, and 0.737% on XCO.   Figure 4. The e difference of XH 2 O, XCO 2 , XCH 4, and XCO with ME loss (a) and PE loss (b) with standard instrument, time series of relative difference of XH 2 O, XCO 2 , XCH 4, and XCO due to a 5% decrease of the ME values (c) and 2‰ rad PE values (d).

Instrumental Line Shape Monitoring
For the EM27/SUN spectrometer, the standard procedure to derive the ILS are laboratory measurements. Several meters of lab air measurements using a collimated standard 50 W halogen light bulb as source (Figure 5 left) and a stabilized digital laboratory DC power (11 V) supply were used and the water vapor lines were evaluated in the spectral region between 7000 and 7400 cm −1 . As the water column inside the spectrometer could not be neglected, the instrument was vented. The ILS retrievals are performed using LIN-FIT 14.5 [15]. As the ILS characteristics were close to nominal, we used the two parameters ILS model.
Due to the heat of the lamp affecting a non-negligible section of the open path, the distance between instrument and lamp should not be chosen as too small. Furthermore, the image of the lamp on the field stop is evenly illuminated and exceeds the diameter of the field stop. The resulting ILS values are presented in Tables 3 and 4, the modulation efficiency (ME) amplitudes and phase errors (PE) are shown in Figure 5 (right). Due to the venting, the mixing ratio of H 2 O inside the spectrometer is the same as outside, thus the plan of the simple analysis assumes a uniform path between lamp and detector. The modulation efficiency (ME) amplitudes and phase errors are relatively close at distances of 401, 519, and 605 cm. The mean value of the modulation efficiency (ME) amplitudes and phase errors (PE) are 0.9611and 0.00593. Compared with standard values (ME = 0.9855, PE = 0.0016), the modulation efficiency (ME) amplitude and the phase error (PE) deviations are 2.450% and 0.433%. The ILS results show the alignment and stability of the instrument over the whole period. For the trace gas retrieval, we used the mean value of the measurement. atory measurements. Several meters of lab air measurements using a collimated standard 50 W halogen light bulb as source (Figure 5 left) and a stabilized digital laboratory DC power (11 V) supply were used and the water vapor lines were evaluated in the spectral region between 7000 and 7400 cm −1 . As the water column inside the spectrometer could not be neglected, the instrument was vented. The ILS retrievals are performed using LIN-FIT 14.5 [15]. As the ILS characteristics were close to nominal, we used the two parameters ILS model.  Tables 3 and 4, the modulation efficiency (ME) amplitudes and phase errors (PE) are shown in Figure 5 (right). Due to the venting, the mixing ratio of H2O inside the spectrometer is the same as outside, thus the plan of the simple analysis assumes a uniform path between lamp and detector. The modulation efficiency (ME) amplitudes and phase errors are relatively close at distances of 401, 519, and 605 cm. The mean value of the modulation efficiency (ME) amplitudes and phase errors (PE) are 0.9611and 0.00593. Compared with standard values (ME = 0.9855, PE = 0.0016), the modulation efficiency (ME) amplitude and the phase error (PE) deviations are 2.450% and 0.433%. The ILS results show the alignment and stability of the instrument over the whole period. For the trace gas retrieval, we used the mean value of the measurement.

Variation of XCO 2 , XCH 4 and XCO
The direct absorption spectra were collected under clear-sky weather conditions from 30th September to 8 October 2018. The time series of the column-averaged dry air mole fraction of CO 2 , CH 4 and CO were retrieved. Because of instrument mechanical failure or adverse weather conditions, the data were not continuous, and all datasets were calculated by Equation (9). The WMO calibration factors applied for XCO 2 , XCH 4, and XCO were 0.9869, 0.9898, and 0.925, respectively.
The diurnal variations of XCO 2 , XCH 4, and XCO are illustrated in Figure 6. The time series of XCO 2 , XCH 4, and XCO show a significant diurnal variation. The XCO 2 and XCH 4 have similar daily variation, these reached a maximum at 15:00 p.m., then dropped until sunset. The similar daily variation indicates that XCO 2 and XCH 4 have a good correlation, detailed analysis in Section 4.4. The daily variation of XCO slowly varies, it reaches a minimum at 16:00 p.m., then climbs until sunset. Time series of daily averages of XCO 2 , XCH 4 and XCO are plotted in Figure 7. The daily average of XCO 2 ranged from 415.09 to 421.78 ppm during the campaign. XCH 4 ranged from 1.96 to 2.02 ppm with a mean of 1.982 ppm, showing higher variation than XCO 2 . The concentration of XCH 4 shows a positive correlation with the temperature. This might be due to the warmer weather increasing the activity of methanogens [20], resulting in higher atmospheric XCH 4 . The daily average of XCO shows a similar variation tendency to XCH 4 . The highest XCO concentration was observed at the beginning of the measurements. The daily average of XCO ranges from 0.118 to 0.157 ppm with a mean of 0.137 ppm.

The Correlation between XCH 4 , XCO, and XCO 2
The sources of the CO 2 emissions are fossil fuel use and biological respiration. Photosynthesis is the sink of CO 2 . The main sources of CH 4 are biogenic and artificial. The biogenic source comes from landfills and wetlands, oceans and forest, and artificial sources include fossil fuel burning, waste treatment, and geological sources [21]. In the paper of Denman, CH 4 produced by gas and oil production, industry, landfills, and waste treatment accounts for 15% to 40% of global anthropogenic CH 4 emissions. In addition, bacteria decompose organic carbon, converting it to CO 2 and CH 4 . CO comes from incomplete combustion, the main sources are biomass burning and fossil fuel [22]. Our site is located on the coastline on the south side of the Liantou peninsula. The coastline is northeastsouthwest. It is adjacent to the South China Sea and the north is a sparsely populated hilly area. Due to air-sea exchange, the photosynthesis of algae and the breathing of animals, the oceans are the sources and sinks of CO 2 and CH 4 .
A correlation study was carried out between XCH 4 , XCO, and XCO 2 for the entire study period. The diurnal variations of XCH 4 , XCO, and XCO 2 are highly correlated ( Figure 8). Fang et al. (2015) suggest that a correlation coefficient value higher than 0.50 indicates a similar source of CO 2 and CH 4 [23]. The correlations were determined by linear regression of the data shown in Figure 7. Our study also reveals a strong positive correlation observed between XCO 2 and XCH 4 . For the relationship between XCO 2 and XCH 4 , the linear regression line shows a good correlation with the correlation coefficient R 2 ≥ 0.5 (except on 2 and 3 October)-especially, the correlation coefficient R 2 = 0.82 on 8 October. The strong correlation between CO 2 and CH 4 indicates that atmospheric CO 2 and CH 4 are generated from common sources.
CO is a product of inefficient combustion that has often been used as a tracer of CO 2 from combustion [24]. The correlation slope of XCO to XCO 2 provides a characteristic signature of source regions and source type [25]. In the study of Wunch et al. (2009), the slope of the correlations of XCO to XCO 2 was 11 ± 2 ppb ppm −1 in the South Coast Air Basin around Los Angeles [26] . Wang wei et al. (2017) calculated the correlation slope of CO to CO 2 at the Hefei site as 5.66 ppb ppm −1 on 25 October 2014 [27]. However, our studies found a weak correlation in the variation of CO 2 and CO (Figure 8b) during the observations. The correlation coefficient R 2 ≥ 0.5 was only on 30th September and 3 October; the weak correlation between CO 2 and CO shows that there is a low influence of combustion emissions on CO 2 . The correlation slopes of CO to CO 2 were 1.94 ppb ppm −1 and 3.69 ppb ppm −1 on 30th September and 3 October, respectively. The correlation coefficient was much smaller on the other days, suggesting that CO 2 is dominated by the biosphere. In particular, the overall respiration component from a densely populated urban area may be significant relative to combustion because respiratory CO 2 emissions by urban residents are collocated with urban combustion sources. Our studies found a strong correlation in the variation of CO 2 and CH 4 (Figure 8a) during the observations. The correlation coefficient of 6 days was greater than 0.5 with a maximum of 0.77. It shows that CO 2 and CH 4 have the same source.
The HYSPLIT (the Hybrid Single-Particle Lagrangian Integrated Trajectory) model developed by the NOAA (the National Oceanic and Atmospheric Administration), based on the characteristics of the Lagrangian trajectory, can simulate the track of airflow and clearly indicates the source of the flow. It has been applied to studies on weather and climate. The HYSPLIT model was used to analyze the trajectories of air masses [28]. The calculated trajectories are helpful for resolving the evolution of airflow along the transport pathway.
The concentration of trace gases can be modulated not only by local emission but also by long transport from other regions. During the observation period, the diurnal variations of XCH 4 and XCO 2 were highly correlated. We computed HYSPLIT backward trajectories for the Maoming area. The trajectories used the GDAS model data on a 0.5 degree latitude longitude spatial resolution. The levels of 1 km, 5 km, and 10 km were taken as the initial height. The time interval was 6 h for output. Figure 9 shows backward air trajectories produced by HYSPLIT between 30 September and 8 October 2018. Figure 9a shows the 72 h backward movement trajectories on 3 October. The airflows in Figure 8a are mainly affected by long-range transport from India, Yunnan, and Jiangxi Province. Trajectories for 5 km and 10 km heights indicate movement of air masses from western India and the Yunnan province of China, whereas the trajectories dram at a height of 1 km give hint of trace gas transport from the Jiangxi province. Figure 9b shows the 72 h backward movement trajectories on 6 October. Trajectories at 5 km and 10 km heights indicate movement of air masses from the Bay of Bengal, whereas the trajectories dram at a height of 1 km give a hint of trace gas transport from Jiangsu province. Figure 9c shows the 72 h backward movement trajectories on 9 October. The trajectories dram at a height of 10 km gives a hint of trace gas transport from the Arabian Sea, whereas trajectories for 1 km and 5 km heights indicate movement of air masses from Myanmar and Jieyang City. From 30 September to 6 October, the upper atmosphere was mainly transported by external sources and during the whole observation period, the lower atmosphere was mainly affected by local sources. This shows that the high correlation between CO 2 and CH 4 in Figure 8 is due to the transportation from external sources. CO is a product of inefficient combustion that has often been used as a tracer of CO2 from combustion [24]. The correlation slope of XCO to XCO2 provides a characteristic signature of source regions and source type [25]. In the study of Wunch  of 10 km gives a hint of trace gas transport from the Arabian Sea, whereas trajectories for 1 km and 5 km heights indicate movement of air masses from Myanmar and Jieyang City. From 30 September to 6 October, the upper atmosphere was mainly transported by external sources and during the whole observation period, the lower atmosphere was mainly affected by local sources. This shows that the high correlation between CO2 and CH4 in Figure 8 is due to the transportation from external sources.

Conclusions
In this paper, a solar observatory was deployed at Maoming China to collect nearinfrared solar spectra. We assessed the influence of instrumental line shape degradation on the retrievals of the greenhouse gases. The study concluded that the influence of instrumental line shape degradation can be expressed as the modulation efficiency amplitude influence and the phase error influence. The modulation efficiency amplitude influence is the most important compared to the phase error influence. The means of XH 2 O, XCH 4, and XCO with ME values have a positive correlation. The correlation coefficients are 0.9925, 0.9968, and 0.9981 respectively. However, the relationship between the mean of XCO 2 and ME is opposite, with a correlation coefficients of 0.986. For a typical ILS degradation of 1% in modulation efficiency, the column-averaged dry air mole fraction of H 2 O, CO 2 , CH 4, and CO changed by 0.0492, 0.1151, 0.1042, 0.0523%, respectively, while an increase of 0.01 in phase error led to a decrease of 0.697% on XH 2 O, 0.689% on XCO 2 , 0.652% on XCH 4 and 0.737% on XCO.
The column-averaged dry air mole fraction of CO 2 , CH 4 and CO were successfully retrieved from low-resolution ground-based FTS (EM27/SUN) measurements. The daily average of XCO 2 ranged from 415.09 to 421.78 ppm during the campaign. XCH 4 ranged from 1.96 to 2.02 ppm with a mean of 1.982 ppm, showing higher variation than XCO 2 . The daily average of XCO ranged from 0.118 to 0.157 ppm with a mean of 0.137 ppm. At the same time, we analyzed CH 4 and CO correlation with CO 2 . Our study revealed a strong positive correlation observed between XCO 2 and XCH 4 with the correlation coefficient R 2 ≥ 0.5. The strong correlation between CO 2 and CH 4 indicates that atmospheric CO 2 and CH 4 are generated from common sources. However, there was a weak correlation between CO and CO 2 and the correlation slopes of CO to CO 2 were 1.94 ppb ppm −1 and 3.69 ppb ppm −1 on 30 September and 3 October, respectively; the CO 2 is dominated by the biosphere. The results of backward movement trajectories, indicate that the airflows are mainly affected by long-range transport from the Arabian Sea, India, Myanmar, Yunnan and Jiangxi Province. In the near future, more long-term in situ measurements are needed.

Data Availability Statement:
The data presented in this study are available on request from the corresponding author.