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Abstract
High-resolution solar absorption spectra were continuously collected by a ground-based Fourier transform infrared (FTIR) spectrometer to retrieve the total column of carbon monoxide (CO), hydrogen cyanide (HCN), ethane (C2H6), acetylene (C2H2), and formaldehyde (H2CO). The time series and variation characteristics of these gases were analyzed. The biomass combustion process is identified by using the correlations between the monthly mean deviations of HCN, C2H6, C2H2 and H2CO versus CO and satellite fire point data. The months with high correlation coefficients (R > 0.8) and peaks of fire point number are considered to be with biomass combustion occurrence. The emissions of HCN, C2H6, C2H2 and H2CO in Anhui were estimated using the enhancement ratios of gases to CO in these months when biomass combustion was the main driving factor of gas concentration change. The study proved the ability of FTIR system in inferring the period during biomass combustion and estimating emissions of the trace gases concerning biomass combustion.
© 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
Biomass combustion, including forest fires, grassland fires, agricultural straw burning, and domestic biomass fuel combustion, is a significant source of atmospheric carbon and pollution emissions, which adversely affects air quality and human health [1,2]. Concentrations of carbon monoxide (CO), hydrogen cyanide (HCN), ethane (C2H6), acetylene (C2H2), and formaldehyde (H2CO) can indicate the emissions from biomass combustion [3,4]. Continuous measurements of the trace gases in atmosphere are of great significance for analyzing biomass combustion.
CO in atmosphere is from biomass combustion, fossil fuel combustion, as well as the oxidation of methane (CH4) and other non-methane hydrocarbons (NMHCs) [5]. It is primarily removed through reactions with hydroxyl radicals (OH) and partially through absorption by soil microorganisms, with an average lifetime of about two months [6,7]. CO is a tracer gas to present the long-distance transport and emissions of biomass combustion [8,9]. HCN in atmosphere primarily stems from biomass combustion, biological emissions, and biofuel combustion [10]. It is mainly removed through reactions with OH and absorption by oceans, and the average lifetime of HCN is 2-4 months [11,12]. C2H2 and C2H6 are mainly produced by fossil fuel combustion, biofuel combustion and biomass combustion, and removed by reaction with OH [13,14]. The lifetime of C2H6 is approximately two months, while C2H2 has a shorter lifetime of 2-4 weeks [15,16]. For atmospheric H2CO, open-air biomass burning is an important source, and H2CO is also the main intermediate product of isoprene degradation, having a significant impact on tropospheric ozone generation [17,18].
The correlations between the trace gases can be used to analyze the source of the gases. Zhao et al. utilized correlations between gases, combined with trajectory calculations, global fire maps, and satellite smoke images to analyze gas sources, and found that the elevated CO, C2H6, C2H2, and HCN in northern Japan mainly resulted from biomass burning in eastern Siberia [19]. Yoshihiro et al observed a high correlation between the deviations of species relative to their seasonal mean values (ΔCO, ΔHCN, ΔC2H6, ΔC2H2) in Moshiri and Rikubetsu in northern Japan, and the trajectory analysis confirmed that Siberian biomass burning was the main driving force of the increase of these species [20].
Ground-based high resolution Fourier transform infrared (FTIR) spectroscopy is important in climate research among various atmospheric monitoring techniques. FTIR technology allows simultaneous retrieval of atmospheric concentrations of various trace gases [21,22]. Viatte et al. used the vertical integration measurement of trace gases provided by the FTIR spectrometer to derive the emission ratios of HCN and C2H6 relative to CO and convert them into emission factors [23]. In addition, FTIR technology can be used to identify wildfire pollution events, biomass combustion emission transport and the impact of biomass combustion emissions on atmospheric gas concentration [24–26]. Zhao et al. determined the major contribution of Asian and Siberian biomass burning sources to the measured concentrations of CO, HCN, and C2H6 from ground-based FTIR measurements in Rikubetsu (43° N, 143° E), Japan [27,28].
In this study, we used the solar absorption spectra observed by the FTIR spectrometer to retrieve the total column of CO, HCN, C2H6, C2H2 and H2CO from 2015 to 2021. The effects of biomass burning emissions on the concentration variations of CO, HCN, C2H6, C2H2 and H2CO in Hefei area were studied. The second section describes the FTIR instrument, the retrieval method, retrieval strategies and error analysis. Section 3 analyzes variations of the time series of CO, HCN, C2H6, C2H2 and H2CO total columns and the correlations between the other four gases and CO. The correlation between the gases and satellite fire map data are used to identify the biomass combustion process. Then the emissions of HCN, C2H6, C2H2 and H2CO during the months when biomass burning occurred were calculated, based on the CO emissions in the emission inventory and the enhancement ratios of HCN, C2H6, C2H2 and H2CO relative to CO. Finally, the conclusions are drawn in section 4.
2. FTIR measurements
2.1 Instrument
A ground-based high-resolution FTIR system (Fig. 1) was installed at the Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (31.91° N, 117.17° E, altitude 29 m), located at the Hefei City, China, in January 2014. The observing site, known as the Hefei site, is presented in Fig. 2. The observing system comprises a high-resolution FTIR (Bruker 125HR Fourier transform infrared) instrument and a solar tracker (A547N, Bruker Optics, Germany) [29]. The FTIR instrument collects near-infrared and mid-infrared solar absorption spectra alternatively during clear days. The FTIR instrument uses KBr beam splitter and InSb, MCT detector to collect the mid-infrared spectra (600∼4500 cm-1) with a spectral resolution of 0.005 cm-1. The solar tracker, located on the rooftop of the laboratory building, directs the solar beam into the FTIR instrument with a tracking precision of 0.1° [30]. Adjacent to the solar tracker, a meteorological station (Zeno, Coastal Environmental Systems, USA) has been collecting various data since 2015, including surface pressure, surface temperature, relative humidity, wind speed, wind direction, solar radiation, rainfall or snowfall, and leaf wetness [31,32]. The system started to collect solar absorption spectra in July 2015. The observed absorption line shapes are affected by the instrument line shape (ILS). The ILS function of the FTIR instrument is monitored by low-pressure HCl and HBr cell measurement to maintain good alignment of the FTIR instrument [33]. The total column of CO, HCN, C2H6, C2H2 and H2CO can be retrieved by the mid-infrared solar spectra. To increase the signal-to-noise ratio (SNR) of the spectra used for retrieval of specific target gases, seven optical filters recommended by NDACC-IRWG are mounted in the wheel in front of the detectors. Figure 3 shows three types of observed spectra (tc, sc, and rc, where t, s and r represent use of three different filters, c represents use of an InSb detector) used to retrieve the target gases. The locations of micro-windows for retrieval of target gases are marked in Fig. 3. Table 1 lists the main features of the observed spectra.
Fig. 1. The FTIR instrument system at the Hefei site: FTIR spectrometer (Bruker IFS125HR) (a), solar tracker (b).
Table 1. The characteristics of the spectra for retrieval of CO, HCN, C2H6, C2H2 and H2CO at the Hefei site
2.2 Retrieval method
The SFIT4 retrieval code (version 0.9.4.4) is used to retrieve the total columns and vertical profiles of CO, HCN, C2H6, C2H2 and H2CO. The retrieval is based on the optimal estimation theory from Rodgers [34]. In this retrieval procedure, the solar absorption spectrum measured is presented as the simulation results adding the error:
$Y$ is the measurement vector. $F({\textrm{x},\textrm{b}} )$ is the simulation value of forward model, the unknowns are represented by a state vector x and a parameter vector b, which characterize the atmospheric state and the auxiliary and instrumental parameters, respectively. The errors between measurement and model results are represented by $\mathrm{\varepsilon }$. A cost function is used to constrain the calculation result, as the Eq. (1) is a non-linear function.Since remote sensing FTIR instruments have limited vertical resolution, it is important to properly describe the relationship between the retrieved results and the actual atmospheric state. This relationship is theoretically captured by the averaging kernel (AVK). The retrieved parameters can be expressed as a function of the AVK.
where ${x_r}$, ${x_a}$ and ${x_t}$ are the retrieved, a priori and true state vector, respectively;$A$ is the AVK, representing the sensitivity of the retrieval results to the true statues; $\varepsilon $ is the retrieval uncertainty. Each element of the AVK matrix represents the sensitivity of the retrieved parameter at a specific layer to the true state vector at different layers. The trace of the AVK matrix, which is known as the DOFs, is used to indicate how many independent layers can be retrieved. The vertical information of all layers obtained by retrieval is integrated to obtain the total column concentration. Overall, the AVK matrix, A, and the DOFs provide valuable insights into the sensitivity and resolution capabilities of the retrieval.Based on the retrieved profile, we can calculate the total column (${C_r}$):
2.3 Retrieval strategies
The vertical profiles are retrieved by fitting spectra in one or more narrow spectral windows. The spectral windows of the target gases are listed in Table 2. The spectral windows are chosen based on the NDACC-IRWG recommendation and previous studies [19,39–41]. The spectral windows are also slightly modified to improve the fitting. Interfering species are retrieved as an independent gas. For CO, C2H2 and H2CO, the OEM was used for retrieval, while for HCN and C2H6, the Tikhonov-type regularization method was used for retrieval. Considering the minimum retrieval uncertainty and DOFs larger than 1, $\alpha $ values are set as 1000, 7000 for HCN and C2H6, respectively. A priori information is very important in profile retrieval. The a priori profiles of atmospheric temperature, pressure and water vapor are based on the NCEP 6-hourly reanalysis data [42]. The a priori profiles of target gases and interfering gases are taken from the Whole Atmosphere Community Climate Model (WACCMv6) monthly mean values between 1980 and 2020 [43]. The atmospheric profile was separated as 48 layers from the surface to the top of the atmosphere (120 km). The spectroscopic line parameters are provided by HITRAN 2008 of all gases [44]. The typical spectral fitting results of the target species are shown in Fig. S1 in Supplement 1.
Table 2. The retrieval parameters for CO, HCN, C2H6, C2H2 and H2CO
2.4 Error analysis
The error analysis was performed according to the method of Rodgers [34,45]. The error budget is calculated by separating the measurement noise error, the smoothing error (representing the limited vertical resolution of the retrieval) and the forward model parameter error (due to errors of temperature profiles, spectroscopic and retrieval parameters, interfering species uncertainty and solar zenith angle error) [41,46,47]. The interference error was examined [46,48,49]. The interference error combines the error of the interference species in the spectral region of interest and the uncertainty caused by the retrieval parameters (such as instrument line shape and zero-level shift error). The uncertainty of the measurement noise is set 0.4%. For the uncertainty of atmospheric temperature profile, the systematic error is about 2 K for the vertical range from 0 to 30 km, 5–9 K above 30 km, and the temperature random error is 5 K for the whole atmosphere. The uncertainty of solar zenith angle (SZA) is set 0.025°. The system and random uncertainties of zero-level shift are estimated to be 1.0%. The uncertainty of instrument line shape (ILS) is 2% [32]. The Spectroscopy uncertainties of CO, HCN, C2H6, C2H2 and H2CO were set as 2%, 10%, 3%, 5% and 10%, respectively [36]. The uncertainties of the parameters for these species are shown in Table 3. The results of error analysis are summarized in Table 4.
Table 3. Error components used for error estimation. The second column gives the uncertainty of each component and the third column gives the partitioning of this uncertainty between statistical and systematic sources
Table 4. The errors estimated for the total columns of target species. The symbol “-” means that the value is less than 0.01
3. Results and discussion
3.1 Time series of atmospheric CO, HCN, C2H6, C2H2 and H2CO
The time series of the total column of CO, HCN, C2H6, C2H2 and H2CO observed from 2015 to 2021 at the Hefei site are depicted in Figs. 4–7. In order to clearly show the seasonal variation and long-term variation of the gases, the Fourier series of the gases containing the first-order polynomial and the third-order harmonic term are fitted. The fitting formula is as follows:
Fig. 4. Time series of CO total columns and the monthly variation of CO total columns. Left panels: Time series of CO total columns from FTIR measurements at Hefei. The yellow points are the individual measurements, the black points are the daily mean values, the error bars are the standard deviation of the daily mean values; the blue solid line and the red dotted line are the fitting curve of the individual measurements and the annual trend, respectively; N represents the total number of the individual measurements. Right panels: the monthly variation of CO total columns. The black line in the middle of the box represents the median of the data, and the blue dots in the box represent the average values; The bottom and upper boundaries of the box represent the 25% (Q1) and 75% (Q3) percentile of the data around the median value, respectively; the error bars extend no more than 1.5× IQR (IQR = Q3 - Q1) from the edges of the box, and the red cross is the outlier.
3.1.1 Variation of CO
As shown in Fig. 4, CO shows an obvious seasonal variation, and the seasonal amplitude is 8.2 × 1017 molecules·cm-2, and the seasonal variability is 29%. The seasonal amplitude is the difference between the maximum and minimum values of the monthly mean, and the seasonal variability is the seasonal amplitude divided by the annual mean. Atmospheric CO column concentrations are high in spring and winter, and low in summer. From February to July, the concentration of CO in the atmosphere gradually decreases, reaching the minimum value in July. From August to January of the following year, the concentration of CO gradually increases, reaching a seasonal peak in February. Monthly mean of CO total column had a maximum in winter and minimum in summer. The seasonal cycle of CO is related to the photochemical destruction by OH, and the concentration of OH in summer and autumn is higher than that in winter and spring [7,29,50]. According to anthropogenic emission inventories such as the Emissions Database for Global Atmospheric Research (EDGAR) v5.0 [51] and the Regional Emission Inventory in Asia (REAS) v3.2 [52], CO emissions in North China were high in winter and low in summer, which also explains the seasonal variation of atmospheric CO. For the Northern Hemisphere ‘s Paris and Jungfraujoch stations, the maximum value of CO observed from the FTIR measurement appeared in March to April, and the minimum value appeared in September to October [53]. The different CO seasonal variation at the two site is due to their different sources and sinks.
Atmospheric CO gradually decreased from 2015 to 2021. The annual variation rate of the CO total column is −1.85 ± 0.056% yr−1 during 2015 to 2021. A comprehensive record of satellite observations of the total column CO from 2000 to 2011 also observed a significant decrease of CO in the Northern Hemisphere [54].
3.1.2 Variation of HCN
HCN is a good tracer for biomass combustion, and its lifetime is about 2 ∼ 4 months [55]. Studies show that tropical biomass burning emissions can explain most of the variation of HCN in the upper troposphere and the lower stratosphere, and the rest is due to atmospheric transport and HCN chemistry [56]. Laboratory and field measurements support that biomass burning is the main source of atmospheric HCN, but its magnitude is still uncertain [10].
As shown in Fig. 5, the gap for HCN total column data from June to September, is caused by high humidity in summer, when the interference from water vapor influenced the HCN retrieval, so some data with poor quality were filtered out. However, it can be seen that atmospheric HCN in Hefei shows obvious seasonal variation. The total column of HCN is high in summer and low in winter. The maximum observed monthly mean value appeared in June, and the minimum value appeared in January. The seasonal maxima of HCN are largely due to the influence of biomass burnings [57]. The seasonal amplitude is 2.22 × 1015 molecules·cm-2 and the corresponding seasonal variations were 33%. Atmospheric HCN decreased sharply during the observation period, and the annual variation rate of HCN is −2.31 ± 0.087% yr−1.
3.1.3 Variation of C2H6 and C2H2
As shown in Fig. 6(a) and 6(b), the total columns of C2H6 and C2H2 are high in winter, spring and low in summer, fall. The highest monthly mean value of C2H6 was in March, and the lowest value was in July. The peak-to-peak amplitude of C2H6 is 1.41 × 1016 molecules·cm-2, about 54% relative to its annual average. Atmospheric C2H2 has two peaks, which appear in winter and summer. The monthly mean highest value of C2H2 appeared in February, and the lowest value was in June. The peak amplitude of the seasonal variation of C2H2 was 1.77 × 1015 molecules·cm-2, about 26% relative to its annual average. Atmospheric C2H6 decreased gradually during the observation period, while atmospheric C2H2 decreased rapidly throughout the observation period. The annual variation rate is −1.80 ± 0.054% yr−1 and −2.45 ± 0.098% yr−1 for C2H6 and C2H2, respectively.
The total column abundances of C2H6 and C2H2 observed at two Japan sites (Moshiri and Rikubetsu) from 1997 to 2005 showed the maximum in spring and the minimum in summer and autumn, and the second summer peak of C2H2 was also observed at the two sites [20]. The measurements at the Bremen site showed that the daily average of the total columns of C2H2 and C2H6 reached the maximum in February to March and the minimum in July to September [58]. The peak-to-peak amplitudes of the fitted seasonal variations at the Xianghe site are 0.38 × 1015 molecules·cm-2 and 1.16 × 1016 molecules·cm-2 for C2H2 and C2H6, respectively, corresponding to 42% and 38% of their mean values [38]. The seasonal variations of C2H2 and C2H6 in Xianghe were similar to those at the two Japanese sites, with high columns during January to April and low columns during June to September. [19,38]. So the measurements at the Hefei site show similar seasonal variation with that at the two Japan sites, the Bremen site and Xianghe site.
3.1.4 Variation of H2CO
H2CO is mainly formed by the oxidation of methane and other hydrocarbons emitted into the atmosphere by plants, animals, human activities and biomass burning. Shen et al used the GEOS-Chem model to quantify the source contribution of H2CO in China, pointing out that H2CO is mainly derived from anthropogenic and biological sources [59]. Secondary H2CO formation in biomass burning plumes has been proposed [60]. H2CO reacts rapidly with OH, NO3, Cl and Br, resulting in a lifetime of several hours [61].
As shown in Fig. 7, H2CO has obvious seasonal variation. The total column of H2CO is high in summer and low in winter. The highest monthly mean value of H2CO appeared in July, and the lowest value appeared in December or January. The seasonal amplitude is 1.88× 1016 molecules·cm-2, and the corresponding seasonal variations was 135%. Atmospheric H2CO decreased slowly during the observation period. The annual variation rate of H2CO is −0.23 ± 0.001% yr−1, respectively. The concentration of CH4 is higher in summer in Hefei area [62], and H2CO is produced by the oxidation of CH4. Also, plants emit more isoprene in summer, and isoprene is considered to be another important source of H2CO [63]. This may be the reason of the seasonal variation of H2CO. In short, all target species showed a negative variation trend from 2015 to 2021, which is caused by the action of control of air pollution in recent years in China [64,65].
3.2 Correlation of HCN, C2H6, C2H2 and H2CO with CO
CO is treated as a tracer gas here, and the correlations between the other four target gases and CO are investigated. In order to reduce the influence of the atmospheric background and remove the components of the seasonal cycle, the monthly mean was removed from the individual total column to obtain the monthly mean deviation [20,38]. The monthly mean deviations of CO, HCN, C2H6, C2H2 and H2CO (ΔCO, ΔHCN, ΔC2H6, ΔC2H2 and ΔH2CO) columns at the Hefei site are shown in Fig. 8. ΔCO, ΔHCN, ΔC2H6, ΔC2H2 and ΔH2CO are uniformly distributed around the zero at the y axis.
The individual ΔCO values are matched with the other four Δgas values measured on the same day, based on the closest measurement time. The Pearson correlation coefficient (R) is calculated to measure the strength of the linear relationship between ΔCO and the matched Δgas as follows:
Good correlations between other gases and CO indicate that these gases are subjected to common production and dilution processes [19]. HCN is often used as a tracer for biomass burning [24,56]. High correlation between HCN and other gases indicates that the enhancement of the gases is caused by biomass burning [20,57]. Many HCN data in summer are missing, so our study didn’t use HCN as the tracer directly. We identify biomass burning events by using the correlations between the monthly mean deviation of the four other gases and CO. Furthermore, the enhancement ratios of the four other gases relative to CO was used to quantify the emission of the gases later. The value of R between ΔHCN, ΔC2H6, ΔC2H2, ΔH2CO and ΔCO is 0.58, 0.60, 0.71 and 0.58 from 2015 to 2021, respectively, as shown in Fig. 9. The slope represents the enhancement ratio of the target gas to CO. The enhancement ratio of ΔHCN, ΔC2H6, ΔC2H2 and ΔH2CO relative to ΔCO is 1.09 × 10−3, 4.88 × 10−3, 2.04 × 10−3 and 5.65 × 10−3, respectively. H2CO has the largest enhancement ratio among all the four gases.
Fig. 8. The monthly mean deviation of the total columns of CO, HCN, C2H6, C2H2 and H2CO at the Hefei site from July 2015 to December 2021.
Fig. 9. The correlation plot between ΔHCN, ΔC2H6, ΔC2H2, ΔH2CO and ΔCO. The dots are colored by the density. The red line is the linear fit; R is the Pearson correlation coefficient; N is the co-located number.
To examine the correlations between ΔHCN, ΔC2H6, ΔC2H2, ΔH2CO and ΔCO in different seasons, Fig. 10 represents the correlation plot in the four seasons. The spring season in Anhui spans March, April, and May (MAM), summer covers June, July, and August (JJA), autumn encompasses September, October, and November (SON), and winter extends from December to the following February (DJF). It is important to note that due to rainy season in Hefei during summer, FTIR measurements got limited data in this season. The correlation coefficient R value is relatively high in autumn and relatively low in winter for the four gases. The R values between ΔHCN, ΔC2H6, ΔC2H2, ΔH2CO and ΔCO in autumn were 0.65, 0.65, 0.75, and 0.60, respectively, and the R values in winter were 0.61, 0.58, 0.66, and 0.58, respectively. This pattern is different from that observed at the Xianghe site [38]. The R value of the Xianghe site is relatively high in spring, autumn and winter and relatively low in summer. The difference in R values in four seasons between the Hefei site and the Xianghe site is attributed to different sources of the target gases at the two site.
Furthermore, we used the fire points in Anhui province observed by satellite to know the biomass combustion information. As shown in Fig. 11, the number of satellite fire points was recorded in four seasons in Anhui province during from 2015 to 2021. The fire atlas data were downloaded from Fire Information for Resource Management System [66]. Satellite fire points in Anhui in four seasons are relatively more in autumn and relatively less in winter. The seasonal variation of the number of fire points proves that biomass combustion occurred more in autumn and less in winter. This pattern is consistent with that shown by the correlation between the target gases and CO.
Fig. 11. The number of fire points in four seasons of Anhui from 2015 to 2021, and R values between ΔHCN, ΔC2H6, ΔC2H2, ΔH2CO and ΔCO in four seasons. All data of fire points are from the FIRMS fire atlas.
3.3 Identification of biomass combustion process occurred
In order to identify the influence of biomass combustion on the variation of CO, HCN, C2H6, C2H2, and H2CO column, the correlation of HCN, C2H6, C2H2, and H2CO with CO was analyzed monthly in Hefei area from 2015 to 2020. The results are shown in Table 5. As shown in Table 5, the month with all R values greater than 0.8 is October 2015, February 2016, May and December 2017, April and October 2018, November and December 2019, and April 2020. It is reasonable to refer that the enhancement of the target gases in these months was caused by biomass combustion emissions [19,20,24,56,57].
Table 5. Monthly correlation R value between HCN, C2H6, C2H2, H2CO and CO from 2015 to 2020 at the Hefei site. The R values with Gray shadow represent all the R values greater than 0.8 in one month.
Studies found that the correlation between ΔHCN and ΔCO was high in the months when the number of forest fires in Siberia and Eastern Europe was larger than the average values during 1999 to 2001 [20]. Similarly, there is a high correlation between the other four gases and CO in the months with more fire points in Anhui area. Figure 12 shows the occurrence frequency of fires in Anhui from 2015 to 2021. The number of fires in October 2015, February 2016, May and December 2017, April and October 2018, November and December 2019, and April 2020 (months with all R values greater than 0.8) have two common characteristics: Firstly, the number of fires peaked in these months, secondly, the number of fires in these months are 30% higher than the annual average. So the fire point number supported the deductions we made about the months with occurrence of biomass combustion.
Fig. 12. The number of fire points per month in Anhui from 2015 to 2021. All data are from the FIRMS fire atlas.
In June 2015, August 2016 and August 2020, the number of fires peaked and the number of fires exceeds 30% of the annual average. However, due to lack of data, some R values were missing, and the calculated R values are also greater than 0.8. So these months were likely with biomass combustion occurrence. In October 2019, the number of fires exceeded 30% of the annual average, but all R values were not greater than 0.8and close to 0.8, so this month was likely with biomass combustion occurrence. In December 2020, all R values of the four gases with CO were high, but the number of fires in this month is not prominent, so this month is not treated as the one with biomass combustion occurrence, as It is likely that CO, HCN, C2H6, C2H2, and H2CO have other common emission sources, rather than biomass combustion.
3.4 Emission estimation
The scatter plot of monthly mean deviation of HCN, C2H6, C2H2, and H2CO relative to that of CO in the months when biomass combustion emissions affect the Hefei site, is shown in Fig. 13. The enhancement ratio is determined by the slope of the regression line. Table 6 lists all these slopes and compares them with previous studies. The ΔHCN/ΔCO enhancement ratio is 1.0-3.1 × 10−3 (0.10-0.31%). Similarly, the ΔC2H6/ΔCO enhancement ratio is 3.6-35.8 × 10−3 (0.36-3.58%), the ΔC2H2/ΔCO enhancement ratio is 1.8-2.9 × 10−3 (0.18-0.29%), and the ΔH2CO/ΔCO enhancement ratio is 5.1-10.2 × 10−3 (0.51-1.02%).
Fig. 13. Scatter plots between ΔHCN, ΔC2H6, ΔC2H2, ΔH2CO and ΔCO in the month with R more than 0.8. Solid lines are linear fitting.
Table 6. The regression line slope and comparison with previous studiesa
The value of HCN/CO measured in laboratory by Yokelson et al was 0.18-7.1 × 10−3 (0.018–0.71%) and its mean value was 6.5 × 10−3 (0.65%), and the mean value of C2H6/CO was 4.5 × 10−3 (0.45%) and that of C2H2/CO was 1.7 × 10−3 (0.17%) [67]. The ΔHCN/ΔCO enhancement ratio of 1.0-3.1 × 10−3 (0.10-0.31%) at the Hefei site falls the range of the HCN/CO ratios, measured in the laboratory with 0.4-2.6 × 10−3 (0.04-0.26%) in the work of Holzinger et al [68]. Zhao et al. reported a ΔHCN/ΔCO ratio of 1.60 × 10−3 (0.16%) from the data observed at Moshiri and Rikubetsu in 1998 with the FTIR instrument [19]. The enhancement ratio of ΔHCN/ΔCO measured in the free troposphere over the Pacific Ocean was 0.34 ± 0.15% [69]. Likewise, the enhancement ratios were 2.4-6.6 × 10−3 (0.24-0.66%) for ΔHCN/ΔCO, 8.7-17.0 × 10−3 (0.87-1.70%) for ΔC2H6/ΔCO and 2.2–7.0 × 10−3 (0.22–0.70%) for ΔC2H2/ΔCO by Nagahamaa et al at Moshiri and Rikubetsu in northern Japan from 1997 to 2005 [20]. Vigouroux et al obtained the enhancement ratios of ΔHCN/ΔCO, ΔC2H6/ΔCO and ΔC2H2/ΔCO from the FTIR measurements at Reunion Island in 2012, with the value of 0.47 ± 0.03%, 0.78 ± 0.02% and 0.20 ± 0.01%, [41]. The ΔHCN/ΔCO, ΔC2H6/ΔCO, ΔC2H2/ΔCO and ΔH2CO/ΔCO obtained by Zhou et al in Xianghe during 2018 to 2021 were 0.5-0.9 × 10−3 (0.05-0.09%), 3.6-5.2 × 10−3 (0.36-0.52%), 2.0-2.7 × 10−3 (0.20-0.27%) and 3.7-6.8 × 10−3 (0.37-0.68%), respectively [38]. The enhancement ratio in our study is larger than the values in zhou et al [38], but close to the value of Moshiri and Rikubetsu area [20].
The ratios of Δgas to ΔCO in Hefei (Fig. 13) can directly be linked to the emission ratios.
Once the emission of CO is known, the emission of HCN, C2H6, C2H2 or H2CO can be estimated based on Eq. (6). Multi-resolution Emission Inventory for China (MEIC) v1.4 [70] are applied to calculate the CO emission [71,72]. Table 7 lists the CO emissions derived from MEIC v1.4 data in Anhui province, and the corresponding HCN, C2H6, C2H2 and H2CO emissions calculated from the measurements using Eq.7. CO emissions in Anhui range from 3.11 to 6.72 × 105 t/month. Therefore, the emission of HCN, C2H6, C2H2 and H2CO is estimated to be 0.35-1.18 × 103 t/month, 1.42-7.20 × 103 t/month (except December 2017), 0.65-1.59 × 103 t/month and 1.85-5.01 × 103 t/month, respectively. The emission of C2H6 is very high in December 2017, with the value of 19.67× 103 t, and the reason is unknown.
Table 7. The emissions of CO in the MEIC v1.4. HCN, C2H6, C2H2 and H2CO emissions are derived from the FTIR measurements at Hefei together with the CO emissions. The unit is t
4. Conclusions
Atmospheric CO, HCN, C2H6, C2H2 and H2CO total columns were observed from the mid-infrared solar absorption spectra in Hefei from July 2015 to December 2021. The time series and variation trend of the gases are analyzed. Then the correlation between HCN, C2H6, C2H2 and H2CO and CO are used to identification of the period with biomass combustion occurrence.
The seasonal variation of CO shows that the monthly mean of the total column was the largest in winter and the smallest in summer. The total columns of H2CO and HCN were high in summer and low in winter. The total columns of C2H6 and C2H2 were high in winter and spring, and low in summer and autumn. There were two peaks of C2H2 in winter and summer, respectively. The seasonal variation of CO, HCN, C2H6, C2H2 and H2CO were 29%, 33%, 54%, 26%, and 135%, respectively. The decrease rate of CO, HCN, C2H6, C2H2 and H2CO were −1.85 ± 0.056% yr−1, −2.31 ± 0.087% yr−1, −1.80 ± 0.054% yr−1, −2.45 ± 0.098% yr−1 and −0.23 ± 0.001% yr−1, respectively.
In order to identify biomass combustion emissions, we investigated the correlation between ΔHCN, ΔC2H6, ΔC2H2, ΔH2CO and ΔCO. The months with all R values greater than 0.8 and high number of fire points observed by satellite are considered to be with biomass combustion occurrence, so October 2015, February 2016, May and December 2017, April and October 2018, November and December 2019, April 2020 meet the criteria. The enhancement ratios of HCN, C2H6, C2H2 and H2CO to CO in these months affected by biomass burning emissions were calculated to be 1.0-3.1 × 10−3 (0.10-0.31%), 3.6-35.8 × 10−3 (0.36-3.58%), 1.8-2.9 × 10−3 (0.18-0.29%) and 5.1-10.2 × 10−3 (0.51-1.02%). It is further calculated that the emissions of CO, HCN, C2H6, C2H2 and H2CO in Anhui are 3.11-6.72 × 105 t/month, 0.35-1.18 × 103 t/month, 1.42-7.20 × 103 t/month (except December 2017), 0.65-1.59 × 103 t/month and 1.85-5.01 × 103 t/month, respectively. The results are relatively consistent with those in previous studies.
The study proved the ability of our FTIR system in measurements of vertical profiles and long-term total columns of the trace gases concerning biomass burning. The column centration measurements of CO, HCN, C2H6, C2H2, and H2CO in Hefei can be used to estimate the emission of the gases during biomass burning. So the FTIR measurements provide a valuable tool to study the biomass combustion.
Funding
National Key Technology R&D Program of China (2022YFB3904805); National Natural Science Foundation of China (42305139); University Natural Science Research Project of Anhui Province (2022AH051794); Major Projects of High Resolution Earth Observation Systems of National Science and Technology (05-Y30B01-9001-19/20-3); State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex (No.SCAPC202110); National Key Project for Causes and Control of Heavy Air Pollution (DQGG0102, DQGG0205); Strategic Priority Research Program of the Chinese Academy of Sciences (XDA23020301).
Acknowledgments
We gratefully acknowledge Nicholas Jones, University of Wollongong, and Omaira E. García, State Meteorological Agency of Spain, for guidance on the spectroscopy retrievals. We are grateful to the NDACC networks for providing information and advice on the SFIT (version 0.9.4.4) software.
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.
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