Open Access
Add to BibSonomy
Abstract
It is a challenge to retrieve atmospheric sulphur hexafluoride (SF6) with high resolution solar spectra because it has only one single retrieval micro window and has interfered with many factors in the retrieval. Optical misalignment is one of the key factors that affect the accuracy of SF6 retrieval. In this study, we first present a long term time series of the SF6 total column over Hefei, China, between January 2017 and December 2020, retrieved by mid-infrared (MIR) solar spectra recorded by ground-based high-resolution Fourier transform infrared spectroscopy (FTIR). The sensitivities of the total column, root mean square of fitting residual (RMS), total error budgets, degrees of freedom for signal (DOFs), and vertical mixing ratio (VMR) profile with respect to different levels of optical misalignment for SF6 retrieval were assessed. The SF6 total column is sensitive to optical misalignment. In order to avoid inconsistencies in the total column due to optical misalignment, we use the true instrumental line shape (ILS) derived from regular low-pressure HBr cell measurements to retrieve the time series of SF6. The total column of SF6 over Hefei presents strong seasonal dependent features. The maximum monthly average value of (3.57 ± 0.21) × 1014 molecules*cm-2 in summer is (7.60 ± 3.50) × 1013 molecules*cm-2 (21.29 ± 9.80) % higher than the minimum monthly average value of (2.81 ± 0.14) × 1014 molecules*cm-2 in winter. The annual average SF6 total columns in 2017-2020 are (3.02 ± 0.17), (3.50 ± 0.18), (3.25 ± 0.18), and (3.08 ± 0.16) × 1014 molecules*cm-2, respectively, which are close to each other. It indicates that SF6 total column over Hefei is stable in the past four years. Our study can improve the current understanding for ground-based high-resolution remote sensing of SF6 and also contribute to generate new reliable remote sensing data in this sparsely monitored region for investigations of climate change, global warming, and air pollution.
© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
1. Introduction
Sulphur hexafluoride (SF6) is labelled as an important greenhouse gas in the Kyoto protocol and the United Nations Framework Convention on Climate Change (UNFCCC) [1,2]. The global warming potential of SF6 is 22800 times (100-year time horizon) that of carbon dioxide (CO2) [3]. SF6 is mainly generated from anthropogenic emissions such as chemical industry, voltage electrical equipment, semi-conductors and magnesium manufacturing [4,5], while the natural sources of SF6 are very low [6]. Previous studies have estimated that the Northern Hemisphere (NH) accounts for 94% of the total global SF6 emissions [7]. The only known sink of SF6 is destruction in the mesosphere [1]. As a long-lived and stable trace gas (with a life time of about 850 years) [8], SF6 is an ideal tracer to estimate the lifetime of other atmospheric gases and to investigate the atmospheric transport [9–11]. Furthermore, understanding the spatial and temporal variabilities of SF6 is of great significance for investigating global climate change. SF6 concentration showed a stable and linear growth trend in a long-term history. In recent years, the global averaged near-surface SF6 volume mixing ratio (VMR) has reached up to 7.6 pptv, with an annual increase of 0.3 pptv/year [12].
Some scientists have used the atmospheric monitoring technology such as balloon, remote sensing, to observe spatial-temporal distribution of SF6. Rigby et al. [13] present SF6 mole fractions measured by Advanced Global Atmospheric Gases Experiment (AGAGE) gas chromatographic-mass spectrometric (GC-MS) systems since 1973 in the Northern Hemisphere and 1978 in the Southern Hemisphere. They found that SF6 mole fraction continued to increase with 7.4 ± 0.6 Gg/yr during 1973-2008, and reached the maximum value in 2008. Geller et al. [14] measure SF6 by gas chromatography with electron capture detection (GC/ECD). Patra et al. [9] using balloon-borne instrument to describe the vertical distribution of SF6 over India on 1994. Stiller et al. [11] obtain global distributions of SF6 from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat during September 2002 to March 2004. Rinsland et al. [15] retrieved SF6 concentrations in the upper troposphere and lower stratosphere by the Atmospheric Trace Molecule Spectroscopy instrument (ATMOS). Zhou et al. [2] observed SF6 annual trend with 0.265 ± 0.013 pptv/year-1 by the Fourier Transform infrared spectroscopy (FTIR) during the period of 2004 to 2016 on Reunion Island. Up to now, both spatial and temporal coverages of SF6 time series are still limited.
Ground-based high resolution FTIR spectrometers within the Network for the Detection of Atmospheric Composition Change (NDACC, http://www.ndacc.org, last access: 11 July 2021) are capable to retrieve the total column of SF6. These FTIR instruments are highly precise and stable devices, their optical alignment can change abruptly as a consequence of operator intervention or drift slowly due to mechanical degradation over time [16–22]. The optical misalignment can degrade the instrumental line shape (ILS) of the FTIR instrument, and may cause large influences on SF6 retrieval because it only has a single retrieval micro window but interfered with many interfering gases. How the optical misalignment induced ILS degradation influence SF6 retrieval and how much optical misalignment is acceptable if an ideal line shape is assumed are still not fully quantified.
In this study, we first present time series of SF6 total columns retrieved from high-resolution mid-infrared spectra recorded with the ground-based FTIR spectrometers at the Hefei station, eastern China. In order to obtain consistent observation data, we investigated the influence of ILS degradation on the retrieval of SF6. We also investigated the seasonality and interannual variability of SF6 over Hefei, eastern China. These data are significant for investigation of greenhouse gas emission reduction, climate change and the lifetime of air.
2. Methodology
2.1 Site description and instrumentation
The FTIR observation site is located on the Science Island (117°10′E, 31°54′N, 30 m a.s.l.) in the suburbs of Hefei in highly industrialized and densely populated eastern China [23–25]. This station is not yet affiliated to the NDACC network but its observation routine follows the NDACC standard convention since 2015 [20,21,25–27]. The FTIR observatory is a cross-disciplinary observation platform developed by the Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences (AIOFM-CAS) in collaboration with University of Science and Technology of China (USTC) and other NDACC partners such as University of Wollongong, Australia and University of Bremen, Germany. The observatory is a part of the Atmospheric Environmental Observation and Simulation (AEOS) infrastructure, which is a National Mage-project of Science Research of China and includes many kinds of environmental instruments, simulators and platforms for intensive atmospheric science research. Hefei is populated with seven million people and the city center is located to the southeast of this station. In the other directions, the observation station is surrounded by cultivated lands or wetlands. The regional landscape around the station is mostly flat with a few hills. The observatory is the only qualified station in China that has a long term FTIR observations of key atmospheric constituents. Observations at the Hefei site have been widely used for evaluating satellite observation, chemical model simulation, air quality and the long-range transport of atmospheric pollutants arise from anthropogenic and natural emissions in this important region [23,26–29].
The observatory is including a high-resolution FTIR spectrometer (IFS125HR, Bruker GmbH, Germany), a solar tracker (Tracker-A Solar 547, Bruker GmbH, Germany) and a meteorological station (ZENO3200, Coastal Environmental Systems, Inc., Seattle, USA). The spectrometer is placed inside a room, whereas the solar tracker and the weather station are installed directly on the building roof. The solar tracker tracks the sun by adjusting two aluminized folding mirrors and guides the sunlight into the FTS spectrometer. Taking the advantage of the Cam-tracker mode, the solar tracker tracks the sun with a precision of ±0.1 mrad [1]. The meteorological station mounted near the dome includes sensors for air temperature (± 0.30°C), relative humidity (± 3.0%), air pressure (± 0.1hpa), wind speed (± 0.50 m/s), wind direction (± 5.0°), solar radiation (± 5.0% during daylight), and the presence of rain. These meteorological parameters are used for improving spectral retrievals.
We started to collect the middle infrared spectra used for SF6 retrievals since January 2017. These SF6 spectra cover a spectral range of 500 to 1,500 cm-1 and are saved with a spectral resolution of 0.005cm-1. The FTIR instrument is equipped with a KBr beam splitter, a filter centered at 947 cm-1, and a MCT detector. In order to adapt the incident radiation, the entrance field stop size ranges from 0.80 to 1.5 mm. The number of SF6 spectra within each sunny day varied from 1 to 20, with a median value of 4. In total, there were 3389 qualified spectra between January 2017 and December 2020.
2.2 Influence of optical misalignment
We used the latest version of the SFIT4 (version 0.9.4.4) algorithm to retrieve the total column and profile of SF6 (http://www.ndacc.org/, last access on 23 November 2020). The basic principle of SFIT4 is the use of an Optimal Estimation Method (OEM) to fit the calculated-to-measured spectra with an iterative Gauss–Newton scheme [30]. The SFIT4 includes a nonlinear least-squares spectral fitting subroutine that iteratively generates forward-modelled spectra until the best fit to the measured spectrum is achieved. This iteration process can be expressed as,
For a perfectly aligned spectrometer, the ILS might be close to the theoretical limit, and the measured spectra can be well reproduced by the synthetic spectra calculated with an ideal ILS. However, if an FTIR spectrometer is subject to misalignment, the resulting ILS degradation could affect the calculated spectra and therefore cause the retrieval to reach convergence with the wrong atmospheric status. The xa and Sa are independent of the ILS because they are a priori parameters. The Sε and Sb depend on the ILS, since the ILS is part of the forward model calculation and can introduce correlations among the different spectral points, causing non-diagonal matrices of Sε and Sb. The Jacobians also depend on the ILS because they represent the sensitivity of the spectra to the atmospheric status, and the analyzed spectra depend on the ILS.
If the number of iterations is j, and Sε, Sb, and Kb turn into $\mathbf{S}_\varepsilon ^{\prime} $, $\mathbf{S}_b ^{\prime} $, and $\mathbf{K}_b ^{\prime} $, respectively, when using an error ILS, the resulting biases for x, the DOFs, and the total error can be expressed as
2.3 Retrieval strategy
Input settings implemented in SFIT4 for SF6 retrievals are summarized in Table 1. The a prior profiles of water vapor (H2O), pressure and temperature are interpolated from the National Centers for Environmental Protection/National Center for Atmospheric Research (NCEP/NCAR) 6-hourly reanalysis [32]. The a priori profiles of all gases except H2O are extracted from the monthly averages of WACCM (Whole Atmosphere Community Climate Model) version 6 simulations from 1980 to 2020. All spectroscopic line parameters for all gases are provided by HIRTRAN 2012 database [33]. The SF6 VMR profile was retrieved in a broad window (MW) of 946.5-949.0 cm-1. The absorption of SF6 in this retrieval window is interfered with the absorptions of C2H4, O3, H2O, and CO2. In order reduce these cross-absorption interferences, the profile of H2O and total columns of C2H4, CO2, and O3 were retrieved simultaneously with the retrieval of SF6. No de-weighting SNR (signal to noise ratio) is used in the MW for SF6.The diagonal element of the measurement noise covariance matrix ${S_\mathrm{\varepsilon }}$ is set to the square inverse of the fitting spectrum SNR, and its non-diagonal element is set to zero. We set the diagonal elements of a priori profile covariance matrices ${S_a}$ to standard deviations of the WACCM model running from 1980 to 2020, and set its non-diagonal elements to zero. In evaluation of the optical alignment, different levels of ILS deviations are incorporated into the SFIT4 forward model. In evaluation of the variability of SF6, the SF6 time series are retrieved with the real ILS deduced from regular low-pressure HBr cell measurements.
Table 1. Summary of the retrieval parameters used for SF6 retrieval.
2.4 Simulation of optical misalignment
We used the program ALIGN60 to simulate optical misalignment in a high resolution FTIR spectrometer [21]. The ALIGN60 is a raytracing model for FTIR spectrometers following the classical Michelson design, assuming one fixed and one movable arm, and using cube corners instead of plane mirrors. It calculates the resulting phase distortions in the recombined beam and from these deduces the variable intensity observed by the detector. The ALIGN60 takes into account the lateral shear error of the movable retro-reflector as a function of the OPD (optical path difference), a decenter of the field stop with respect to the optical axis, an unsharp boundary line, or deformation of the field stop image (possibly caused by a defocused collimator), and vignetting effects with increasing OPD. It can generate trustworthy results with respect to all types of misalignments [34].
Depending on the location of optical misalignment occurs in the OPD, the FTIR spectrometer will result in many types of ILS degradations. All these ILS degradations can be regarded as positive degradation if its amplitude is larger than unity or negative degradation if it is less than unity [34]. Sun et al. [21] compared the influences of different types of ILS degradation on the total columns of many gases, and found that, for positive ILS degradation, the optical misalignment occurs around the zero or maximum OPD (hereafter P-type misalignment) causes the maximal influence, while for negative ILS degradation, the optical misalignment occurs around the middle OPD (hereafter N-type misalignment) causes the maximal influence. In order to simulate different levels of P-type misalignment, we add different levels of cosine and sine bending force to the moveable arm, which causes a chordal increase in misalignment within the first half of OPD and causes a chordal decrease in misalignment within the second half of OPD. In order to simulate different levels of N-type misalignment, we add a constant shear force plus different levels of cosine and sine bending force to the movable arm, which causes a chordal decrease in misalignment within the first half of OPD and causes a chordal increase in misalignment within the second half of OPD. This study uses all these simulated ILS degradations to investigate their influences on SF6 retrievals at the Hefei station.
3. Results and discussions
3.1 Characterization and error budget
Figure 1 presents the a priori and retrieved profiles (a), the cumulative sum of DOFs (b), and the averaging kernels and their area (c) for SF6 retrieval at the Hefei station on December 10, 2020. Ground-based FTIR SF6 observations at the Hefei station have a sensitivity of larger than 0.5 between 5 to about 20 km altitude, indicating that the SF6 retrievals are mainly sensitive to the middle troposphere and lower stratosphere. This also means that the retrieved profile information within 5 - 20 km altitude comes for more than 50% from the measurement, while the a priori signal impacts the retrieval by more than 50% outside this range. Especially, all retrieved profile information above ∼30 km is basically from the a priori signal. The typical DOFS obtained at the Hefei station over the total atmosphere for SF6 is 0.93, meaning that we can get only one piece of trustworthy information on the vertical profile. As a result, this study only discusses the total column rather than profile of SF6. As a tropospheric gas, the shape of the retrieved profile is heavily weighted toward the lower troposphere. The total column of SF6 mainly reflects the variability of SF6 in the troposphere.
Fig. 1. (a) The a priori and retrieved volume mixing ratio (VMR) profiles of SF6, (b) cumulative sum of the degrees of freedom for signal (DOFs), (c) the averaging kernel (AVK) matrix A and their area for randomly selected SF6 retrieval with MIR spectra suite at Hefei, China.
The formalism proposed by Rodgers [30] was used to calculate the retrieval error budget. The error components are separated into systematic and random errors depending on whether they are constant over consecutive measurements, or vary randomly. The random errors include z-shift uncertainty, retrieval parameters uncertainties, interfering species uncertainty, temperature uncertainty and measurement error. Systematic errors include solar zenith angle (SZA) uncertainty, background curvature uncertainty, optical path difference uncertainty, field of view uncertainty, solar line shift uncertainty, solar line strength uncertainty, and smoothing error. We summarize the random, systematic and total error budget for SF6 total column retrieval in Table 2. The input covariance matrix for temperature is estimated using the standard deviation of an ensemble of NCEP to the Sonde temperature profiles near Hefei, leading to 2 K to 5 K in the troposphere and 3 K to 7 K in the stratosphere. For each interfering species, the associated covariance matrix was obtained with the WACCM v6 climatology. The input covariance matrix for measurement error is based on the inverse square of the SNR of each spectrum. The input uncertainties for background curvature, optical path difference, the field of view, and interferogram phase, solar line strength, and shift uncertainties are estimated to be 0.1%. For the SF6 spectroscopic parameters, we use 5% for line intensity, pressure, and temperature-broadening coefficients. For each individual retrieval parameter and the smoothing error, the input covariance matrix is prescribed from the optimal estimation retrieval output.
Table 2. Error budgets and DOFs for the SF6 retrievals at Hefei
For SF6 retrieval at the Hefei station, we found that temperature uncertainty (4.49%) and background curvature uncertainty (2.62%) dominate the random and systematic errors, respectively. Total random and systematic errors calculated as square root sum of the squares of each error component are estimated to be 3.36% and 4.66%, respectively. Total retrieval error is thus estimated to be 5.75%.
3.2 Influence of optical misalignment on SF6 retrieval
By including different levels of optical misalignments into the ALIGN60, we simulated seven levels of positive (ranging from 4% to 28%) and negative (ranging from –4% to –28%) ILS degradations. The corresponding Haidinger fringes at the maximum misalignment position for positive and negative ILS degradations are shown in Figs. S1 and S2, respectively. For both type of misalignment, the larger the ILS degradation, the larger the degree of decenter of Haidinger fringes. We incorporated them into the SFIT forward model in sequence and calculated the fractional difference (D %) in the total column, DOFs, the root mean square (RMS) of the residual (difference between measured and calculated spectra after the fit), and total error budgets for SF6 relative to the retrieval with the reference ILS (i.e., with no optical misalignment). We first performed a case study by randomly selecting only one typical spectrum from all measurements on sunny days, and then we evaluated the consistency of the resulting deduction with one year of measurements. The spectrum selected for case study was collected on December 12, 2020 with SZA of 36.7°. The spectra used in the consistency evaluation were collected from January 2020 to December 2020. For all spectra used in this study, the retrievals with the reference ILS fulfill the following criteria:
- 1) RMSs in all fitting windows are less than 3%.
- 2) The OEM retrievals are converged within iterative 10 times.
- 3) The concentrations of SF6, C2H4, O3, H2O, and CO2 at altitude grids of the retrieved profiles are positive.
- 4) The solar intensity variation (SIV) is less than 10%. The SIV within the duration of a spectrum is the ratio of the standard deviation to the average of the measured solar intensities.
These above criteria removed the imperfect spectra collected with sampling errors or contaminated by clouds or unpredictable objects in unfavorable weather conditions. The fractional difference (D%) is calculated through,
where X represents either the profile, total column, DOFs, RMS, or total error budgets of SF6. Xref is the same as X but for the reference ILS.3.2.1 Sensitivity study
The sensitivities of total column, DOFs, RMS and total error budgets with respect to different levels of ILS degradation are present in Fig. 2. Figure 3 is the sensitivities of SF6 profile to different levels of positive and negative ILS degradations, respectively. Overall, optical misalignment induced ILS degradation affect the total column, RMS, DOFs, total error budgets and profile of SF6 retrieval at the Hefei station. Positive and negative ILSs have opposite effects on these quantities. Positive and negative ILS degradations cause increases and decreases in these quantities, respectively. The total column, RMS, DOFs, and total error budgets vary linearly over the level of ILS degradation. The larger the ILS degradation, the larger the influence. For the same level of ILS degradation, the influence of positive ILS degradation is larger than that of the negative ILS degradation.
Fig. 2. Sensitivity of total column, DOFs, RMS and total error budgets with respect to ILS degradation. P_Tclmn and N_Tclmn represent the sensitivities of total column with respect to positive and negative ILS degradations, respectively. P_DOFs, P_RMS, and P_Terr are the same as P_Tclmn, but for DOFs, RMS, and total error budgets, respectively. N_DOFs, N_RMS, and N_Terr are the same as N_Tclmn but for DOFs, RMS, and total error budgets, respectively.
Fig. 3. Sensitivity of profile with respect to positive and negative ILS degradation. Fourteen levels of degradations ranging from -28% to 28% are presented. “Ideal” represents the reference ILS, i.e., no degradation is implemented.
The ILS degradation has large influence on the total column, RMS, and total error budgets of SF6 retrieval, of which it has the largest influence on RMS. In contrast, the influence of ILS degradation on DOFs is negligible. With the level of ILS degradation varies over 0 to 28%, the influences of positive ILS on total column, RMS, and total error budgets vary over 0-11.7%, 0-25.4%, 0-9.1%, respectively, while the influences of negative ILS on these quantities are within 3%. Regardless of positive or negative ILS degradations, their influences on DOFs are less than 1%.
The influence of ILS degradation on profile mainly occurs in the altitude range that show high retrieval sensitivity between 0-20 km. The higher the altitude the smaller the influence. For the altitude range above 20 km, both positive and negative ILS degradations induced influences are small. With the level of ILS degradation varies over 0 to 28%, the influence of positive ILS on the profile between 0-20 km varies over 0-14.7%, and for negative ILS the values are -1.8% to 0. Regardless of positive or negative ILS degradations, their influences on the profile above 20 km are less than 1%.
3.2.2 Consistency evaluation
We use the spectra recorded at the Hefei station from January 2020 to December 2020 to assess the consistency of above study. These spectra spanned a large range of gas concentrations, solar zenith angles (SZAs), and meteorological conditions such as atmospheric humidity, temperatures, surface pressures, wind speeds, and wind directions. All retrievals fulfill the criteria listed in section 3.2 are included in this study. Since the positive ILS degradation has much larger influence on SF6 retrieval than the negative ILS degradation. Here we only select positive ILS degradation for consistency evaluation. A simulated positive ILS degradation of 8% is used all retrievals. The results are compared to the retrievals deduced from the reference ILS.
With an 8% positive ILS degradation, time series of fractional differences in the total column, RMS, total error budgets, and DOFs for SF6 retrievals between January 2020 and December 2020 are shown in Fig. 4. The results show that the fractional differences in total column and total error budgets are season dependent. The fractional differences in total column (total error budgets) in summer are larger (smaller) than those in winter. The case study shows that the deviations of both total column and total error budgets are less than 4% for an 8% positive ILS degradation. However, most of the deviations for total column are larger than 5% and for total error budgets are larger than 10%. In contrast, the influences of ILS degradation on DOFs and RMS shows good consistency throughout the year. Most of the deviations for RMS are less than 2% and for DOFs are less than 1%, which are in good agreement with results deduced from case study. Inconsistencies in total column may result in pseudo conclusions from climate studies with the retrieved time series of SF6. In order to avoid this problem, we regularly use a low-pressure HBr cell to diagnose the misalignment of the spectrometer and to realign the optical path of the FTIR instrument when indicated. Furthermore, for evaluation of the variability of SF6 in subsequent study, the SF6 time series are retrieved with the real ILS deduced from regular low-pressure HBr cell measurements.
Fig. 4. Time series of fractional differences in the total column, RMS, total error budgets, and DOFs for SF6 retrievals between January 2020 and December 2020, where ILS with a maximum ILS degradation of 8% is used.
3.3 Seasonality and inter-annual variability
We first present time series of SF6 total columns over Hefei, China recorded by FITR from January 2017 to December 2020 in Fig. 5(a), while the monthly average is presented in Fig. 5(b). The bootstrap resampling method proposed by Gardiner et al. [35] with a second Fourier series is used to fit the time series of SF6 total column in order to obtain the seasonal characteristics of SF6. The method can be described as follows:
Fig. 5. (a) The time series of SF6 total column over Hefei, China retrieved by MIR spectral recorded by FTIR during January 2017 to December 2020. The red fitted curve represents the seasonal variations of the SF6 total column, which is obtained by bootstrap resampling method fitting. (b) The monthly average of SF6 total column over Hefei, China.
Fig. 6. (a) Seasonal variations of FTIR SF6 total column from 2015 to 2020 over Hefei, eastern China. The blue line and the shadows represent monthly mean values of SF6 total column and the 1-σ standard variations, respectively. (b) Fractional differences of FTIR SF6 total column time series relative to their seasonal mean values. Vertical error bars represent retrieval uncertainties.
The seasonality variations of SF6 total column can be reproduced well by the bootstrap resampling model. The Pearson correlation coefficient (R) between results of observations and bootstrap resampling model is 0.71. It indicates that SF6 total column over Hefei present strong seasonal dependent features. The seasonal variations and fractional differences of SF6 total column from 2017 to 2020 is presented in Fig. 6.
The maximum in SF6 total column occur in July with the value of (3.57 ± 0.21) × 1014 molecules*cm-2 during summer, while the minimum in SF6 total column is (2.81 ± 0.14) × 1014 molecules*cm-2 in February during winter. The SF6 total columns in January are, on average, (7.60 ± 3.50) × 1013 molecules*cm-2 (21.29 ± 9.80) % higher than those in September. The seasonal characteristics of the SF6 total column in Hefei are mainly characterized as the following features: (1) High levels of SF6 total column occur in summer and early autumn, while low levels occur in early spring and late winter. (2) The variations in the summer to autumn are larger than those in winter to spring. (3) The SF6 total columns roughly increases in the first half of the year, while decreases in the second half of the year. The annual average SF6 total column is (3.02 ± 0.17) × 1014 molecules*cm-2, (3.50 ± 0.18) × 1014 molecules*cm-2, (3.25 ± 0.18) × 1014 molecules*cm-2, and (3.08 ± 0.16) × 1014 molecules*cm-2 in 2017-2020, respectively. As commonly observed, the seasonal enhancement of SF6 spans a large range of -39.08% to 77.41% depending on the season and measurement time. The annual average maximum value of SF6 column concentration appeared in 2018, at (3.50 ± 0.18) × 1014 molecules*cm-2, while the minimum value appeared in 2017, at (3.02 ± 0.17) × 1014 molecules*cm-2. The maximum value is larger than the minimum value by (4.74 ± 3.51) × 1013 molecules*cm-2 (13.55% ± 10.02%). This indicates that the SF6 total column over eastern, China is stable in the past four years.
4. Conclusions
In this study, we first presented time series of SF6 total column by FTIR in the polluted atmosphere over Hefei, China during January 2017 to December 2020. We investigated the influence of ILS degradations on the retrieval of SF6 total column. The total column, RMS, DOFs, and total error budgets vary linearly over the level of ILS degradation. The larger the ILS degradation, the larger the influence. For the same level of ILS degradation, the influence of positive ILS degradation is larger than that of the negative ILS degradation. The inconsistencies of the SF6 total column due to ILS degradation may lead to pseudo conclusions in the retrieved time series of SF6 for climate studies. Therefore, we used the real ILS reduced from regular low-pressure HBr cell measurements to retrieved the time series of SF6 total columns.
Based on the good retrieval results, we study the annual and seasonal variations of SF6 total column over Hefei. The maximum SF6 total column occurred in July with the value of (3.57 ± 0.21) × 1014 molecules*cm-2, which was (7.60 ± 3.50) × 1013 molecules*cm-2 (21.29% ± 9.80%) higher than the minimum with the value of (2.81 ± 0.14) × 1014 molecules*cm-2 that occurred in January. The SF6 total column showed a strong seasonal variation, with high values in summer and low values in winter. The annual average SF6 total columns in 2017 to 2020 are (3.02 ± 0.17), (3.50 ± 0.18), (3.25 ± 0.18), and (3.08 ± 0.16) × 1014 molecules*cm-2, respectively. These annual mean values are close with each other. This indicates that the SF6 total column over Hefei is stable in the past four years.
Our study provides the first dataset of SF6 total column recorded by ground-based high-resolution FTIR over Hefei, China. To our knowledge, this is the first continuous, long time series data set of SF6 total column over China. This study can enhance the understanding of ground-based high-resolution remote sensing techniques for SF6 and its evolution and contribute to form new reliable remote sensing data in this sparsely monitored region for research on climate change, global warming and air pollution.
Funding
Youth Innovation Promotion Association (No.2019434); the Sino-German Mobility programme (M-0036).
Acknowledgments
The processing environment of SFIT4 and some plot programs are provided by National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA. The NDACC networks is acknowledged for supplying the SFIT software and advice. The ALIGN60 software was provided by Frank Hase, Karlsruhe Institute of Technology (KIT), Institute for Meteorology and Climate Research (IMK-ASF), Germany.
Disclosures
The authors declare no conflicts of interest.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
Supplemental document
See Supplement 1 for supporting content.
References
1. B. D. Hall, G. S. Dutton, D. J. Mondeel, J. D. Nance, M. Rigby, J. H. Butler, F. L. Moore, D. F. Hurst, and J. W. Elkins, “Improving measurements of SF6 for the study of atmospheric transport and emissions,” Atmos. Meas. Tech. 4(11), 2441–2451 (2011). [CrossRef]
2. M. Zhou, B. Langerock, C. Vigouroux, P. Wang, C. Hermans, G. Stiller, K. A. Walker, G. Dutton, E. Mahieu, and M. De Mazière, “Ground-based FTIR retrievals of SF6 on Reunion Island,” Atmos. Meas. Tech. 11(2), 651–662 (2018). [CrossRef]
3. T. Kovács, W. Feng, A. Totterdill, J. M. C. Plane, S. Dhomse, J. C. Gómez-Martín, G. P. Stiller, F. J. Haenel, C. Smith, P. M. Forster, R. R. García, D. R. Marsh, and M. P. Chipperfield, “Determination of the atmospheric lifetime and global warming potential of sulfur hexafluoride using a three-dimensional model,” Atmos. Chem. Phys. 17(2), 883–898 (2017). [CrossRef]
4. M. Maiss and C. A. M. Brenninkmeijer, “Atmospheric SF6: Trends, Sources, and Prospects,” Environ. Sci. Technol. 32(20), 3077–3086 (1998). [CrossRef]
5. L. Niemeyer and F. Y. Chu, “SF6 and the atmosphere,” IEEE Trans. Elect. Insul. 27(1), 184–187 (1992). [CrossRef]
6. D. A. Deeds, M. K. Vollmer, J. T. Kulongoski, B. R. Miller, J. Mühle, C. M. Harth, J. A. Izbicki, D. R. Hilton, and R. F. Weiss, “Evidence for crustal degassing of CF4 and SF6 in Mojave Desert groundwaters,” Geochimica et Cosmochimica Acta 72(4), 999–1013 (2008). [CrossRef]
7. M. Maiss, L. P. Steele, R. J. Francey, P. J. Fraser, R. L. Langenfelds, N. B. A. Trivett, and I. Levin, “Sulfur hexafluoride—A powerful new atmospheric tracer,” Atmospheric Environment 30(10-11), 1621–1629 (1996). [CrossRef]
8. E. A. Ray, F. L. Moore, J. W. Elkins, K. H. Rosenlof, J. C. Laube, T. Röckmann, D. R. Marsh, and A. E. Andrews, “Quantification of the SF6 lifetime based on mesospheric loss measured in the stratospheric polar vortex,” J. Geophys. Res. Atmos 122(8), 4626–4638 (2017). [CrossRef]
9. P. K. Patra, S. Lal, B. H. Subbaraya, C. H. Jackman, and P. Rajaratnam, “Observed vertical profile of sulphur hexafluoride (SF6) and its atmospheric applications,” J. Geophys. Res. 102(D7), 8855–8859 (1997). [CrossRef]
10. P. K. Patra, M. Takigawa, G. S. Dutton, K. Uhse, K. Ishijima, B. R. Lintner, K. Miyazaki, and J. W. Elkins, “Transport mechanisms for synoptic, seasonal and interannual SF6 variations and “age” of air in troposphere,” Atmos. Chem. Phys. 9(4), 1209–1225 (2009). [CrossRef]
11. G. P. Stiller, T. von Clarmann, M. Höpfner, N. Glatthor, U. Grabowski, S. Kellmann, A. Kleinert, A. Linden, M. Milz, T. Reddmann, T. Steck, H. Fischer, B. Funke, M. López-Puertas, and A. Engel, “Global distribution of mean age of stratospheric air from MIPAS SF6 measurements,” Atmos. Chem. Phys. 8(3), 677–695 (2008). [CrossRef]
12. WMO, “Scientific Assessment of Ozone Depletion,” 2014, World Meteorological Organization, Global Ozone Research and Monitoring Project Report No. 55, 416 pp. (2014).
13. M. Rigby, J. Mühle, B. R. Miller, R. G. Prinn, P. B. Krummel, L. P. Steele, P. J. Fraser, P. K. Salameh, C. M. Harth, R. F. Weiss, B. R. Greally, S. O’Doherty, P. G. Simmonds, M. K. Vollmer, S. Reimann, J. Kim, K. R. Kim, H. J. Wang, J. G. J. Olivier, E. J. Dlugokencky, G. S. Dutton, B. D. Hall, and J. W. Elkins, “History of atmospheric SF6 from 1973 to 2008,” Atmos. Chem. Phys. 10(21), 10305–10320 (2010). [CrossRef]
14. L. S. Geller, J. W. Elkins, J. M. Lobert, A. D. Clarke, D. F. Hurst, J. H. Butler, and R. C. Myers, “Tropospheric SF6: Observed latitudinal distribution and trends, derived emissions and interhemispheric exchange time,” Geophys. Res. Lett. 24(6), 675–678 (1997). [CrossRef]
15. C. P. Rinsland, L. R. Brown, and C. B. Farmer, “Infrared spectroscopic detection of sulfur hexafluoride (sf6) in the lower stratosphere and upper troposphere,” J. Geophys. Res. 95(D5), 5577–5585 (1990). [CrossRef]
16. P. Duchatelet, P. Demoulin, F. Hase, R. Ruhnke, W. Feng, M. P. Chipperfield, P. F. Bernath, C. D. Boone, K. A. Walker, and E. Mahieu, “Hydrogen fluoride total and partial column time series above the Jungfraujoch from long-term FTIR measurements: Impact of the line-shape model, characterization of the error budget and seasonal cycle, and comparison with satellite and model data,” J. Geophys. Res. 115(D22), D22306 (2010). [CrossRef]
17. D. G. Feist, S. G. Arnold, F. Hase, and D. Ponge, “Rugged optical mirrors for Fourier transform spectrometers operated in harsh environments,” Atmos. Meas. Tech. 9(5), 2381–2391 (2016). [CrossRef]
18. F. Hase, “Improved instrumental line shape monitoring for the ground-based, high-resolution FTIR spectrometers of the Network for the Detection of Atmospheric Composition Change,” Atmos. Meas. Tech. 5(3), 603–610 (2012). [CrossRef]
19. S. C. Olsen and J. T. Randerson, “Differences between surface and column atmospheric CO2 and implications for carbon cycle research,” J. Geophys. Res. 109(D2), D02301 (2004). [CrossRef]
20. Y. Sun, C. Liu, K. Chan, W. Wang, C. Shan, Q. Hu, and J. Liu, “The Influence of Instrumental Line Shape Degradation on the Partial Columns of O3, CO, CH4 and N2O Derived from High-Resolution FTIR Spectrometry,” Remote Sensing 10(12), 2041 (2018). [CrossRef]
21. Y. Sun, M. Palm, C. Liu, F. Hase, D. Griffith, C. Weinzierl, C. Petri, W. Wang, and J. Notholt, “The influence of instrumental line shape degradation on NDACC gas retrievals: total column and profile,” Atmos. Meas. Tech. 11(5), 2879–2896 (2018). [CrossRef]
22. Y. Sun, M. Palm, C. Weinzierl, C. Petri, J. Notholt, Y. Wang, and C. Liu, “Technical note: Sensitivity of instrumental line shape monitoring for the ground-based high-resolution FTIR spectrometer with respect to different optical attenuators,” Atmos. Meas. Tech. 10(3), 989–997 (2017). [CrossRef]
23. Y. Sun, H. Yin, C. Liu, E. Mahieu, J. Notholt, Y. Té, X. Lu, M. Palm, W. Wang, C. Shan, Q. Hu, M. Qin, Y. Tian, and B. Zheng, “The reduction in C2H6 from 2015 to 2020 over Hefei, eastern China, points to air quality improvement in China,” Atmos. Chem. Phys. 21(15), 11759–11779 (2021). [CrossRef]
24. Y. W. Sun, C. Liu, M. Palm, C. Vigouroux, J. Notholt, Q. H. Hui, N. Jones, W. Wang, W. J. Su, W. Q. Zhang, C. G. Shan, Y. Tian, X. W. Xu, M. De Maziere, M. Q. Zhou, and J. G. Liu, “Ozone seasonal evolution and photochemical production regime in the polluted troposphere in eastern China derived from high-resolution Fourier transform spectrometry (FTS) observations,” Atmos. Chem. Phys. 18(19), 14569–14583 (2018). [CrossRef]
25. H. Yin, Y. Sun, C. Liu, W. Wang, C. Shan, and L. Zha, “Remote Sensing of Atmospheric Hydrogen Fluoride (HF) over Hefei, China with Ground-Based High-Resolution Fourier Transform Infrared (FTIR) Spectrometry,” Remote Sensing 13(4), 791 (2021). [CrossRef]
26. H. Yin, Y. Sun, C. Liu, X. Lu, D. Smale, T. Blumenstock, T. Nagahama, W. Wang, Y. Tian, Q. Hu, C. Shan, H. Zhang, and J. Liu, “Ground-based FTIR observation of hydrogen chloride (HCl) over Hefei, China, and comparisons with GEOS-Chem model data and other ground-based FTIR stations data,” Opt. Express 28(6), 8041–8055 (2020). [CrossRef]
27. H. Yin, Y. Sun, C. Liu, L. Zhang, X. Lu, W. Wang, C. Shan, Q. Hu, Y. Tian, C. Zhang, W. Su, H. Zhang, M. Palm, J. Notholt, and J. Liu, “FTIR time series of stratospheric NO2 over Hefei, China, and comparisons with OMI and GEOS-Chem model data,” Opt. Express 27(16), A1225–A1240 (2019). [CrossRef]
28. Y. Sun, C. Liu, L. Zhang, M. Palm, J. Notholt, H. Yin, C. Vigouroux, E. Lutsch, W. Wang, C. Shan, T. Blumenstock, T. Nagahama, I. Morino, E. Mahieu, K. Strong, B. Langerock, M. De Mazière, Q. Hu, H. Zhang, C. Petri, and J. Liu, “Fourier transform infrared time series of tropospheric HCN in eastern China: seasonality, interannual variability, and source attribution,” Atmos. Chem. Phys. 20(9), 5437–5456 (2020). [CrossRef]
29. Y. Sun, H. Yin, C. Liu, L. Zhang, Y. Cheng, M. Palm, J. Notholt, X. Lu, C. Vigouroux, B. Zheng, W. Wang, N. Jones, C. Shan, M. Qin, Y. Tian, Q. Hu, F. Meng, and J. Liu, “Mapping the drivers of formaldehyde (HCHO) variability from 2015 to 2019 over eastern China: insights from Fourier transform infrared observation and GEOS-Chem model simulation,” Atmos. Chem. Phys. 21(8), 6365–6387 (2021). [CrossRef]
30. C. Rodgers, “Inverse Methods for Atmospheric Sounding - Theory and Practice,” (World Scientific, 2000).
31. S. P. A. Davis, M. C. Brault, and J. W., “Fourier transform spectrometry,” Academic Press, (2001).
32. E. Kalnay, M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, R. Jenne, and D. Joseph, “The NCEP/NCAR 40-year reanalysis project,” Bull. Amer. Meteor. Soc. 77(3), 437–471 (1996). [CrossRef]
33. L. S. Rothman, I. E. Gordon, A. Barbe, D. C. Benner, P. E. Bernath, M. Birk, V. Boudon, L. R. Brown, A. Campargue, J. P. Champion, K. Chance, L. H. Coudert, V. Dana, V. M. Devi, S. Fally, J. M. Flaud, R. R. Gamache, A. Goldman, D. Jacquemart, I. Kleiner, N. Lacome, W. J. Lafferty, J. Y. Mandin, S. T. Massie, S. N. Mikhailenko, C. E. Miller, N. Moazzen-Ahmadi, O. V. Naumenko, A. V. Nikitin, J. Orphal, V. I. Perevalov, A. Perrin, A. Predoi-Cross, C. P. Rinsland, M. Rotger, M. Simeckova, M. A. H. Smith, K. Sung, S. A. Tashkun, J. Tennyson, R. A. Toth, A. C. Vandaele, and J. Vander Auwera, “The HITRAN 2008 molecular spectroscopic database,” J Quant Spectrosc Ra 110(9-10), 533–572 (2009). [CrossRef]
34. F. Hase, T. Blumenstock, and C. Paton-Walsh, “Analysis of the instrumental line shape of high-resolution Fourier transform IR spectrometers with gas cell measurements and new retrieval software,” Appl. Opt. 38(15), 3417–3422 (1999). [CrossRef]
35. T. Gardiner, A. Forbes, M. de Maziere, C. Vigouroux, E. Mahieu, P. Demoulin, V. Velazco, J. Notholt, T. Blumenstock, F. Hase, I. Kramer, R. Sussmann, W. Stremme, J. Mellqvist, A. Strandberg, K. Ellingsen, and M. Gauss, “Trend analysis of greenhouse gases over Europe measured by a network of ground-based remote FTIR instruments,” Atmos. Chem. Phys. 8(22), 6719–6727 (2008). [CrossRef]
