Differential Absorption Spectroscopy Research Articles

The global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2

Abstract. Remote sensing based on satellites can provide long-term, consistent, and global coverage of NO2 (an important atmospheric air pollutant) as well as other trace gases. However, satellites often miss data due to factors including but not limited to clouds, surface features, and aerosols. Moreover, as one of the longest continuous observational platforms of NO2, the Ozone Monitoring Instrument (OMI) has suffered from missing data over certain rows since 2007, significantly reducing its spatial coverage. This work uses the OMI-based tropospheric NO2 (OMNO2) product as well as a NO2 product from the Global Ozone Monitoring Experiment-2 (GOME-2) in combination with machine learning (eXtreme Gradient Boosting – XGBoost) and spatial interpolation (data-interpolating empirical orthogonal function – DINEOF) methods to produce the 16-year global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2; https://doi.org/10.5281/zenodo.10968462; Qin et al., 2024), which increases the average global spatial coverage of NO2 from 39.5 % to 99.1 %. The HSTCM-NO2 dataset is validated using upward-looking observations of NO2 (multi-axis differential optical absorption spectroscopy – MAX-DOAS), other satellites (the Tropospheric Monitoring Instrument – TROPOMI), and reanalysis products. The comparisons show that HSTCM-NO2 maintains a good correlation with the magnitudes of other observational datasets, except for under heavily polluted conditions (> 6 × 1015 molec.cm-2). This work also introduces a new validation technique to validate coherent spatial and temporal signals (empirical orthogonal function – EOF) and confirms that HSTCM-NO2 is not only consistent with the original OMNO2 data but in some parts of the world also effectively fills in missing gaps and yields a superior result when analyzing long-range atmospheric transport of NO2. The few differences are also reported to be related to areas in which the original OMNO2 signal was very low, which has been shown elsewhere but not from this perspective, further confirming that applying a minimum cutoff to retrieved NO2 data is essential. The reconstructed data product can effectively extend the utilization value of the original OMNO2 data, and the data quality of HSTCM-NO2 can meet the needs of scientific research.

NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations

Abstract. We introduce NitroNet, a deep learning model for the prediction of tropospheric NO2 profiles from satellite column measurements. NitroNet is a neural network trained on synthetic NO2 profiles from the regional chemistry and transport model WRF-Chem, which was operated on a European domain for the month of May 2019. This WRF-Chem simulation was constrained by in situ and satellite measurements, which were used to optimize important simulation parameters (e.g. the boundary layer scheme). The NitroNet model receives NO2 vertical column densities (VCDs) from the TROPOspheric Monitoring Instrument (TROPOMI) and ancillary variables (meteorology, emissions, etc.) as input, from which it reproduces NO2 concentration profiles. Training of the neural network is conducted on a filtered dataset, meaning that NO2 profiles showing strong disagreement (>20 %) with colocated TROPOMI column measurements are discarded. We present a first evaluation of NitroNet over a variety of geographical and temporal domains (Europe, the US West Coast, India, and China) and different seasons. For this purpose, we validate the NO2 profiles predicted by NitroNet against satellite, in situ, and MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) measurements. The training data were previously validated against the same datasets. During summertime, NitroNet shows small biases and strong correlations with all three datasets: a bias of +6.7 % and R=0.95 for TROPOMI NO2 VCDs, a bias of −10.5 % and R=0.75 for AirBase surface concentrations, and a bias of −34.3 % to +99.6 % with R=0.83–0.99 for MAX-DOAS measurements. In comparison to TROPOMI satellite data, NitroNet even shows significantly lower errors and stronger correlation than a direct comparison with WRF-Chem numerical results. During wintertime considerable low biases arise because the summertime/late-spring training data are not fully representative of all atmospheric wintertime characteristics (e.g. longer NO2 lifetimes). Nonetheless, the wintertime performance of NitroNet is surprisingly good and comparable to that of classic regional chemistry and transport models. NitroNet can demonstrably be used outside the geographic and temporal domain of the training data with only slight performance reductions. What makes NitroNet unique when compared to similar existing deep learning models is the inclusion of synthetic model data, which offers important benefits: due to the lack of NO2 profile measurements, models trained on empirical datasets are limited to the prediction of surface concentrations learned from in situ measurements. NitroNet, however, can predict full tropospheric NO2 profiles. Furthermore, in situ measurements of NO2 are known to suffer from biases, often larger than +20 %, due to cross-sensitivities to photooxidants, which other models trained on empirical data inevitably reproduce.

The CH3D Absorption Spectrum Near 1.58 μm: Extended Line Lists and Rovibrational Assignments

Monodeuterated methane (CH3D) contributes greatly to absorption in the 1.58 μm methane transparency window. The spectrum is dominated by the 3ν2 band near 6430 cm−1, which is observed in natural methane and used for a number of planetary applications, such as the determination of the D/H ratio. In this work, we analyze the CH3D spectrum recorded by high-sensitivity differential absorption spectroscopy in the 6099–6530 cm−1 region, both at room temperature and at 81 K. Following a first contribution to this topic by Lu et al., the room-temperature line list is elaborated (11,189 lines) and combined with the previous 81 K list (8962 lines) in order to derive about 4800 empirical lower-state energy values from the ratio of the line intensities measured at 81 K and 294 K (2T-method). Relying on the position and intensity agreements with the TheoReTS variational line list, about 2890 transitions are rovibrationally assigned to twenty bands, with fifteen of them being newly reported. Variational positions deviate from measurements by up to 2 cm−1, and the band intensities are found to be in good agreement with measurements. All the reported assignments are confirmed by Ground-State Combination Difference (GSCD) relations; i.e., all the upper-state energies (about 1370 in total) have coinciding determinations through several transitions (up to 8). The energy values, determined with a typical uncertainty of 10−3 cm−1, are compared to their empirical and variational counterparts. The intensity sum of the transitions assigned between 6190 and 6530 cm−1 represents 76.9 and 90.0% of the total experimental intensities at 294 K and 81 K, respectively.

First evaluation of the GEMS glyoxal products against TROPOMI and ground-based measurements

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) on board the GEO-KOMPSAT-2B satellite is the first geostationary satellite launched to monitor the environment. GEMS conducts hourly measurements during the day over eastern and southeastern Asia. This work presents glyoxal (CHOCHO) vertical column densities (VCDs) retrieved from GEMS, with optimal settings for glyoxal retrieval based on sensitivity tests involving reference spectrum sampling and fitting window selection. We evaluated GEMS glyoxal VCDs by comparing them to the TROPOspheric Monitoring Instrument (TROPOMI) and multi-axis differential optical absorption spectroscopy (MAX-DOAS) ground-based observations. On average, GEMS and TROPOMI VCDs show a spatial correlation coefficient of 0.63, increasing to 0.87 for northeastern Asia. While GEMS and TROPOMI demonstrate similar monthly variations in the Indochinese Peninsula regions (R > 0.67), variations differ in other areas. Specifically, GEMS VCDs are higher in the winter and either lower or comparable to TROPOMI and MAX-DOAS VCDs in the summer across northeastern Asia. We attributed the discrepancies in the monthly variation to a polluted reference spectrum and high NO2 concentrations. When we correct GEMS glyoxal VCDs as a function of NO2 SCDs, the monthly correlation coefficients substantially increase from 0.16–0.40 to 0.45–0.72 in high NO2 regions. When averaged hourly, GEMS and MAX-DOAS VCDs exhibit similar diurnal variations, especially at stations in Japan (Chiba, Kasuga, and Fukue).

Preliminary Global NO2 Retrieval from EMI-II Onboard GF5B/DQ1 and Comparison to TROPOMI

The Environmental Trace Gases Monitoring Instrument (EMI-II) onboard the Chinese GaoFen-5B (GF5B) and DaQi-1 (DQ1) satellites is the successor of the previous EMI onboard the Chinese GaoFen-5 (GF5) satellite, and has a higher spatial resolution and a better signal-to-noise ratio. The GF5B and DQ1 were launched in September 2021 and April 2022, respectively. As part of China’s ultraviolet-visible hyperspectral satellite instrument series, the EMI-II aims to conduct network observations of pollution gases globally in the morning and early afternoon. In this study, NO2 data were retrieved from the EMI-II payloads on the GF5B and DQ1 satellites using the Differential Optical Absorption Spectroscopy (DOAS) algorithm. The two satellites were consistently compared, and the results showed strong consistency on various spatial and temporal scales (R2 > 0.8). In four representative regions worldwide, NO2 data from the EMI-II exhibited good spatial consistency with those from the TROPOMI. The correlation coefficient (R2) of the total vertical column density (VCD) between the EMI-II and TROPOMI exceeded 0.85, and that of the tropospheric NO2 VCD exceeded 0.57. Compared with single-satellite observations, the dual-satellite network of the GF5B and DQ1 can effectively increase the observation frequency. On a daily scale, dual-satellite observations can reduce the impact of cloud coverage by 6–8% compared to single-satellite observations, and there are two valid observations of nearly 50% of the world’s regions. Additionally, the differences between the two satellites can reflect the NO2 diurnal variations, which demonstrates the potential for studying pollutant gas diurnal variations.

Validation of GEMS tropospheric NO2 columns and their diurnal variation with ground-based DOAS measurements

Abstract. Instruments for air quality observations on geostationary satellites provide multiple observations per day and allow for the analysis of the diurnal variation in important air pollutants such as nitrogen dioxide (NO2). The South Korean instrument GEMS (Geostationary Environmental Monitoring Spectrometer), launched in February 2020, is the first geostationary instrument that is able to observe the diurnal variation in NO2. The measurements have a spatial resolution of 3.5 km × 8 km and cover a large part of Asia. This study compares 1 year of tropospheric NO2 vertical column density (VCD) observations from the operational GEMS L2 product, the scientific GEMS IUP-UB (Institute of Environmental Physics at the University of Bremen) product, the operational TROPOspheric Monitoring Instrument (TROPOMI) product, and ground-based differential optical absorption spectroscopy (DOAS) measurements in South Korea. The GEMS L2 tropospheric NO2 VCDs overestimate the ground-based tropospheric NO2 VCDs with a median relative difference of +61 % and a correlation coefficient of 0.76. The median relative difference is −2 % for the GEMS IUP-UB product and −16 % for the TROPOMI product, with correlation coefficients of 0.83 and 0.89, respectively. The scatter in the GEMS products can be reduced when observations are limited to the TROPOMI overpass time. Diurnal variations in tropospheric NO2 VCDs differ by the pollution level of the analyzed site but with good agreement between the GEMS IUP-UB and ground-based observations. Low-pollution sites show weak or almost no diurnal variation. In summer, the polluted sites show a minimum around noon, indicating the large influence of photochemical loss. Most variation is seen in spring and autumn, with increasing NO2 in the morning, a maximum close to noon, and a decrease towards the afternoon. Winter observations show rather flat or slightly decreasing NO2 throughout the day. Winter observations under low-wind-speed conditions at high-pollution sites show enhancements of NO2 throughout the day. This indicates that under calm conditions, dilution and the less effective chemical loss in winter do not balance the accumulating emissions. Diurnal variation observed at a low-pollution site follows seasonal wind patterns. A weekday–weekend effect analysis shows good agreement between the different products. However, the GEMS L2 product, while agreeing with the other data sets on weekdays, shows significantly less reduction on weekends. The influence of the stratospheric contribution and the surface reflectivity product on the satellite tropospheric NO2 VCD products is investigated. While the TM5 model's stratospheric VCDs, used in the TROPOMI product, are too high, resulting in tropospheric NO2 VCDs that are too low and even negative, when used in the GEMS IUP-UB retrieval, the GEMS L2 stratospheric VCD is too low. Surface reflectivity comparisons indicate that the GEMS L2 reflectivity makes a large contribution to the observed overestimation and scatter.

Ground-Based MAX-DOAS Observations for Spatiotemporal Distribution and Transport of Atmospheric Water Vapor in Beijing

Understanding the spatiotemporal distribution and transport of atmospheric water vapor in urban areas is crucial for improving mesoscale models and weather and climate predictions. This study employs Multi-Axis Differential Optical Absorption Spectroscopy to monitor the dynamic distribution and transport flux of water vapor in Beijing within the tropospheric layer (0–4 km) from June 2021 to May 2022. The seasonal peaks in precipitable water occur in August, reaching 39.13 mm, with noticeable declines in winter. Water vapor was primarily distributed below 2.0 km and generally decreases with increasing altitude. The largest water vapor transport flux occurs in the southeast–northwest direction, whereas the smallest occurs in the southwest–northeast direction. The maximum flux, observed at about 1.2 km in the southeast–northwest direction during summer, reaches 31.77 g/m2/s (transported towards the southeast). Before continuous rainfall events, water vapor transport, originating primarily from the southeast, concentrates below 1 km. Backward trajectory analysis indicates that during the rainy months, there was a higher proportion of southeasterly winds, especially at lower altitudes, with air masses from the southeast at 500 m accounting for 69.11%. This study shows the capabilities of MAX-DOAS for remote sensing water vapor and offers data support for enhancing weather forecasting and understanding urban climatic dynamics.

Large eddy simulation of the dispersion of short duration emissions: Implications for the metrological evaluation of remote sensing devices for on-road emissions monitoring

Remote sensing techniques have emerged as valuable tools for characterizing pollutant emissions from large vehicle fleets and identifying high emitter single vehicles in real driving conditions. Nevertheless, the use of these systems for official emission control purposes by public administrations is an issue because the remote sensing devices must obtain official metrological certification, which currently lacks an international technical standard. The fluid dynamic study that we present demonstrates the promising potential of using pulsed synthetic reference plumes of known chemical composition in order to simulate exhaust emissions produced by combustion engine vehicles in a repetitive and controlled way. This scheme would facilitate the implementation of these complex metrological certification tests and drastically reduce the potential costs associated to these certifications and the emission of gases. In this paper, the atmospheric dispersion of the synthetic puff-like plumes after being released from a vehicle has been studied through fluid dynamic simulations, in order to identify their optimal usage conditions as reference materials. The simulations have allowed to study the evolution of two types of reference short plumes (puffs generated at 2 and 6 bars) from a vehicle at static and dynamic conditions. Results show that, in spite of the fast dispersion of these puffs, it is possible to accurately determine their chemical composition by optical techniques, for instance, by differential absorption spectroscopy. This opens the way for designing advanced and robust metrological evaluation procedures that could be the basis of a future technical standard for the certification of optical remote sensors of traffic emissions. This would allow future deployment of those certificated remote sensors on roads, contributing to a sustainable mobility and effective air pollution management strategies.

The Tula Industrial Area Field Experiment: Quantitative Measurements of Formaldehyde, Sulfur Dioxide, and Nitrogen Dioxide Emissions Using Mobile Differential Optical Absorption Spectroscopy Instruments

The Tula industrial area in Central Mexico comprises, among other industries, a refinery and a thermoelectric power plant. It is well known for its constant emissions of gases into the atmosphere and considered an important area where pollutants released into the atmosphere have an influence on local and regional air quality. During March and April 2017, a field campaign was conducted with the objective of quantifying formaldehyde (HCHO), sulfur dioxide (SO2), and nitrogen dioxide (NO2) emissions from this industrial area using mobile differential optical absorption spectroscopy (DOAS) instruments. Calculated average emissions of the Francisco Perez Rios Power Plant and the Miguel Hidalgo Refinery were 3.14 ± 2.13 tons per day of HCHO, 362.08 ± 300.14 tons per day of SO2, and 24.76 ± 12.82 tons per day of NO2. From the measurements conducted, the spatial distribution patterns of SO2, NO2, and HCHO were reconstructed, showing a dispersion pattern of SO2 and NO2 towards the southwest of the industrial complex, impacting agricultural and urban areas. Occasionally, and usually during the morning hours, SO2 and NO2 were dispersed towards the north or northeast of the industrial complex. In the case of HCHO, dispersion was observed towards the south and southeast of the industrial complex. The far-reaching implications of this study are that for the first time, formaldehyde emissions were quantified. In addition, a follow-up study was conducted regarding nitrogen dioxide and sulfur dioxide emissions from the Tula Industrial area.

Troposphere–stratosphere-integrated bromine monoxide (BrO) profile retrieval over the central Pacific Ocean

Abstract. Bromine monoxide (BrO) is relevant to atmospheric oxidative capacity, affecting the lifetime of greenhouse gases (i.e., methane, dimethylsulfide) and mercury oxidation. However, measurements of BrO radical vertical profiles are rare, and BrO is highly variable. As a result, the few available aircraft observations in different regions of the atmosphere are not easily reconciled. Autonomous multi-axis differential optical absorption spectroscopy (MAX-DOAS) instruments placed at remote mountaintop observatories (MT-DOAS) present a cost-effective alternative to aircraft, with the potential to probe the climate-relevant yet understudied free troposphere more routinely. Here, we describe an innovative full-atmosphere BrO and formaldehyde (HCHO) profile retrieval algorithm using MT-DOAS measurements at Mauna Loa Observatory (MLO – 19.536° N, 155.577° W; 3401 m a.s.l.). The retrieval is based on time-dependent optimal estimation and simultaneously inverts 190+ individual BrO (and formaldehyde, HCHO) SCDs (slant column densities; SCD = dSCD + SCDRef) from solar stray light spectra measured in the zenith and off-axis geometries at high and low solar zenith angles (92° > SZA > 30°) to derive BrO concentration profiles from 1.9 to 35 km with 7.5 degrees of freedom (DoFs). Two case study days are characterized by the absence (26 April 2017, base case) and presence of a Rossby-wave-breaking double tropopause (29 April 2017, RW-DT case). Stratospheric-BrO vertical columns are nearly identical on both days (VCD = (1.5 ± 0.2) × 1013 molec. cm−2), and the stratospheric-BrO profile peaks at a lower altitude during the RW-DT (1.6–2.0 DoFs). Tropospheric-BrO VCDs increase from (0.70 ± 0.14) × 1013 molec. cm−2 (base case) to (1.00 ± 0.14) × 1013 molec. cm−2 (RW-DT) owing to a 3-fold increase in BrO in the upper troposphere (1.7–1.9 DoFs). BrO at MLO increases from (0.23 ± 0.03) pptv (base case) to (0.46 ± 0.03) pptv (RW-DT) and is characterized by an added time resolution (∼ 3.8 DoFs). Up to (0.9 ± 0.1) pptv BrO is observed above MLO in the lower free troposphere in the absence of the double tropopause. We validate the retrieval using aircraft BrO profiles and in situ HCHO measurements aboard the NSF/NCAR GV aircraft above MLO (11 January 2014) that establish BrO peaks around 2.4 pptv above 13 km in the upper troposphere–lower stratosphere (UTLS) during a similar RW-DT event (0.83 × 1013 molec. cm2 tropospheric-BrO VCD above 2 km). The tropospheric-BrO profile measured using MT-DOAS (RW-DT case) and using the aircraft agree well (after averaging-kernel smoothing). Furthermore, these tropospheric-BrO profiles over the central Pacific Ocean are found to closely resemble those over the eastern Pacific Ocean (2–14 km) and are in contrast to those over the western Pacific Ocean, where a C-shaped tropospheric-BrO profile shape has been observed.

Retrieval and Comparison of Multi-Satellite Polar Ozone Data from the EMI Series Instruments

The Environmental Trace Gases Monitoring Instrument (EMI) series are second-generation Chinese spectrometers on board the GaoFen-5 (GF-5) and DaQi-1 (DQ-1) satellites. In this study, a comparative analysis of EMI series data was conducted to determine the daily trend of ozone concentration changes owing to different transit times and to improve the overall quality and reliability of EMI series datasets. The daily EMI total ozone column (TOC) obtained using the Differential Optical Absorption Spectroscopy (DOAS) method were compared to vertical column density (VCD) gathered by the TROPOspheric Monitoring Instrument (TROPOMI). The results from October to November 2023 indicated a fine correlation (R = 0.98) between the daily EMI series data and a fine correlation (R ≥ 0.95) and spatial distribution closely resembling that of the TROPOMI TOCs. Furthermore, the EMI series data fusion results were highly correlated with TROPOMI TOCs (R = 0.99). Since the EMI series instruments had two different overpass times and the volume of available data at same pixel was increased by approximately three-fold, the temporal and spatial resolution was improved a lot. The results indicated that, compared to a single sensor, the EMI series DOAS TOCs generated more accurate and stable global TOC results and also enabled looking at the changes in the intraday TOCs. These outcomes highlight the potential of the EMI instruments for reliably monitoring the ozone variations in polar regions.

Simultaneous measurement of temperature and C2H4 concentration in hydrocarbon flames using interference-immune differential absorption spectroscopy at 3.3 μm

In diagnostic applications based on tunable diode laser absorption spectroscopy, the measurement of target substances can be influenced by factors such as background thermal radiation in the combustion environment, extinction caused by solid or liquid particles, and other interfering absorptions. In this work, we developed a differential absorption diagnostic technique based on wavelength pairs, utilizing an interband cascade laser near 3.3 μm to simultaneously measure temperature and C2H4 concentration in hydrocarbon flames. Based on a detailed study of the C2H4 spectrum in this region and considering the optimal standard for spectral lines, two wavelength pairs were selected. The temperature is determined by the ratio of the absorption cross-sections of two wavelength pairs, and the C2H4 concentration is inferred based on the wavelength pair with higher differential absorption. In the initial stage, the system’s accuracy was verified in high-temperature static conditions (T = 300–800 K, P = 1 atm), and continuous time series measurements demonstrated the system’s stability. The limit of detection achieved by Allan-Werle variance analysis is 2.5 ppm at the optimal average time of 100 s. Subsequently, measurements were taken in a hydrocarbon flame. The obtained results indicate an average deviation of 1.021 % between the measured temperature in the flame and the reference value, with a standard deviation of 1.381 % for concentration measurement. All the measurements show that the system can be potentially applied to combustion diagnosis.

Comparative Study on the Vertical Column Concentration Inversion Algorithm of Tropospheric Trace Gas Based on the MAX-DOAS Measurement Spectrum

The tropospheric vertical column concentration (VCDtrop) of NO2, SO2, and HCHO was retrieved, respectively, by employing the geometric method (Geomtry), simplified model method (Model), and look-up table method (Table) with the observation spectra of the multi-axis differential absorption spectroscopy instrument (MAX-DOAS). The correlation and relative differences of the inversion results obtained by these three algorithms, as well as the changes in quantiles, were explored. The comparative analysis reveals that the more concentrated the vertical distribution height of gas components is in the near-surface layer, the better the conformity of the VCDtrop retrieved by different algorithms. However, the increase in relative differences is also related to the diurnal variation of gas components. The influence of aerosols on the inversion of the VCDtrop is greater than the change in the vertical distribution height of the gas component itself. The near-surface concentration and distribution height of gas components are the internal factors that give rise to relative differences in the inversion of the VCDtrop by different algorithms, while aerosols are one of the extremely important external reasons. The VCDtrop inverted by Geomtry without considering the influence of aerosols is generally larger except for NO2. Model sets up aerosols in accordance with the height and meteorological conditions of the atmospheric environment. Table can invert the aerosol profile in real time. Compared with Model, it shows a significant improvement in the refined setting of aerosols. Moreover, while obtaining the vertical distribution of aerosols, it can invert the diurnal variation of the VCDtrop. The VCDtrop inverted by Table is the smallest, and the relative difference with Model is on average about 10% smaller. The relative difference of the VCDtrop for the same height (aerosol optical thickness) quantile is 7–15% (about 25% lower on average). When comparing the inversion results of Table with the Ozone Monitoring Instrument (OMI) satellite product, the MAX-DOAS inversion results of NO2, SO2, and HCHO are all larger than the OMI product. This is related to the different observation methods of the MAX-DOAS and OMI and the configuration between the aerosol layer and the distribution height of gas components.

Detection of ultra-low concentration NH3, SO2, and NO using UV-DOAS combined with multidimensional spectral fusion

Since nitric oxide (NO), sulfur dioxide (SO2), and ammonia (NH3) in flue gas cause severe air pollution, accurate measurements of their concentrations are crucial for optimizing the efficiency of flue gas denitrification in coal-fired power plants and protecting the atmosphere. Due to their large absorption cross sections in the ultraviolet (UV) band, NH3, SO2, and NO in flue gas can be detected by UV Differential Optical Absorption Spectroscopy (UV-DOAS) techniques. However, spectral overlap and noise caused by short optical path limitations in the measurement are the main obstacles to achieve concentration inversion. Here, we propose a multidimensional spectral fusion method one-dimensional convolutional neural network combined with two-dimensional convolutional neural network (1D-2D-CNN) with the ability to invert concentration of NH3, SO2 and NO at ppb level with 0.5 m optical-length. To separate the absorption features, one-dimensional spectra are converted into two-dimensional time-frequency plots by continuous wavelet transform (CWT). After denoising the spectra by wavelet thresholding, one-dimensional and two-dimensional spectral features are extracted by 1D-2D-CNN model and fused for inversion. The experimental results show that the proposed system can effectively eliminate interferences from NH3, SO2, and NO, significantly improving the concentration inversion accuracy. The detection limits for NH3, SO2 and NO in the mixture are 0.95 ppb∙m, 6.31 ppb∙m, and 2.53 ppb∙m, respectively, demonstrating that our approach outperforms other reported methods for flue gas analysis. The proposed system is expected to be applied to ammonia slip monitoring of Selective Catalytic Reduction (SCR) in power plants.

Ground-based MAX-DOAS observations of tropospheric formaldehyde and nitrogen dioxide: Insights into ozone formation sensitivity

Tropospheric profiles of HCHO, NO2, and O3 are important for analyzing ozone formation mechanism. In this study, ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS), light detection and ranging (LIDAR), and in-situ measurements were simultaneously performed to diagnose ozone formation sensitivity at the Heshan Observatory in the Pearl River Delta (PRD) region from September to end October 2019. The profiles of tropospheric HCHO and NO2 were retrieved from MAX-DOAS measurements using an optimal estimation method. The retrieved surface HCHO and NO2 results were validated with 2,4-dinitrophenylhydrazine (DNPH) and Thermo 42i measurements, and the correlation coefficients (R) were 0.78 and 0.81, respectively. The retrieved tropospheric vertical column densities (VCDs) of HCHO and NO2 were compared with TROPOMI measurements, and the correlation coefficients (R) were 0.68 and 0.87, respectively. In addition, MAX-DOAS and LIDAR measurements were combined to diagnose a typical planetary boundary layer (PBL) ozone pollution episode from September 28 to October 10, 2019; this episode was analyzed using HCHO/NO2 ratio as an indicator and was found to be dominated by the VOC-sensitive regime. Moreover, the regime transition of ozone formation sensitivity was calculated using the surface HCHO/NO2 ratio and increased O3 from the MAX-DOAS and Thermo 49i measurements, with transition thresholds of 1.43 and 1.78, respectively. Based on this definition, the ozone formation sensitivity at Heshan Observatory varied from VOC-sensitive (< 0.2 km and > 0.8 km) to NOx-sensitive (0.3–0.7 km) to VOC-NOx-sensitive (0.2–0.3 km and 0.7–0.8 km). The results improve our understanding of ozone formation sensitivity in the PRD region.

Quantitative Detection of C₆F₁₂O/CO₂ Gas Mixture Based on UV Differential Absorption Spectrometry Combined With Variational Mode Decomposition

Quantitative Detection of C₆F₁₂O/CO₂ Gas Mixture Based on UV Differential Absorption Spectrometry Combined With Variational Mode Decomposition

Ppb-Level Ammonia Sensor for Exhaled Breath Diagnosis Based on UV-DOAS Combined with Spectral Reconstruction Fitting Neural Network.

Ammonia (NH3) in exhaled breath (EB) has been a biomarker for kidney function, and accurate measurement of NH3 is essential for early screening of kidney disease. In this work, we report an optical sensor that combines ultraviolet differential optical absorption spectroscopy (UV-DOAS) and spectral reconstruction fitting neural network (SRFNN) for detecting NH3 in EB. UV-DOAS is introduced to eliminate interference from slow change absorption in the EB spectrum while spectral reconstruction fitting is proposed for the first time to map the original spectra onto the sine function spectra by the principle of least absolute deviations. The sine function spectra are then fitted by the least-squares method to eliminate noise signals and the interference of exhaled nitric oxide. Finally, the neural network is built to enable the detection of NH3 in EB at parts per billion (ppb) level. The laboratory results show that the detection range is 9.50-12425.82 ppb, the mean absolute percentage error (MAPE) is 0.83%, and the detection accuracy is 0.42%. Experimental results prove that the sensor can detect breath NH3 and identify EB in simulated patients and healthy people. Our sensor will serve as a new and effective system for detecting breath NH3 with high accuracy and stability in the medical field.

A convolutional neural networks method for tropospheric ozone vertical distribution retrieval from Multi-AXis Differential Optical Absorption Spectroscopy measurements

The vertical distribution of tropospheric ozone (O3) is crucial for understanding atmospheric physicochemical processes. A Convolutional Neural Networks (CNN) method for the retrieval of tropospheric O3 vertical distribution from ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements to tackle the issue of stratospheric O3 absorption interference faced by MAX-DOAS in obtaining tropospheric O3 profiles. Firstly, a hybrid model, named PCA-F_Regression-SVR, is developed to screen features sensitive to O3 inversion based on the MAX-DOAS spectra and EAC4 reanalysis O3 profiles, which incorporates Principal Component Analysis (PCA), F_Regression function, and Support Vector Regression (SVR) algorithm. Thus, these screened features for ancillary inversion include the profiles of temperature, specific humidity, fraction of cloud coverage, eastward and northward wind, the profiles of SO2, NO2, and HCHO, as well as season and time features to serve as sensitive factors. Secondly, the preprocessed MAX-DOAS spectra dataset and the sensitive factor dataset are utilized as input, while the O3 profiles of the EAC4 reanalysis dataset incorporating the surface O3 concentrations are employed as output for constructing the CNN model. The Mean Absolute Percentage Error (MAPE) decreases from 26 % to approximately 19 %. Finally, the CNN model is applied for inversion and comparison of tropospheric O3 profiles using independent input data. The CNN model effectively reproduces the O3 profiles of the EAC4 dataset, showing a Gaussian-like spatial distribution with peaks primarily around 950 hPa (550 m). Since the reanalysis data used for model training has been smoothed, the CNN model is insensitive to extreme values. This behavior can be attributed to the MAPE loss function, which evaluates Absolute Percentage Errors (APEs) of O₃ concentration at all altitudes, resulting in varying retrieval accuracy across different altitudes while maintaining overall MAPE control. Temporally, the CNN model tends to overestimate surface O3 in summer by around 20 μg/m3, primarily due to the influence of the temperature feature in the sensitivity factor dataset. In conclusion, leveraging MAX-DOAS spectra enables the retrieval of tropospheric O3 vertical distribution through the established CNN model.

Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote-sensing observations

Abstract. Formaldehyde (HCHO), a precursor to tropospheric ozone, is an important tracer of volatile organic compounds (VOCs) in the atmosphere. Two years (2019–2020) of HCHO simulations obtained from the global chemistry transport model CHASER at a horizontal resolution of 2.8° × 2.8° have been evaluated using the Tropospheric Monitoring Instrument (TROPOMI) and multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations. In situ measurements from the Atmospheric Tomography Mission (ATom) in 2018 were used to evaluate the HCHO simulations for 2018. CHASER reproduced the TROPOMI-observed global HCHO spatial distribution with a spatial correlation (r) of 0.93 and a negative bias of 7 %. The model showed a good capability to reproduce the observed magnitude of the HCHO seasonality in different regions, including the background conditions. The discrepancies between the model and satellite in the Asian regions were related mainly to the underestimated and missing anthropogenic emission inventories. The maximum difference between two HCHO simulations based on two different nitrogen oxide (NOx) emission inventories was 20 %. TROPOMI's finer spatial resolution than that of the Ozone Monitoring Instrument (OMI) sensor reduced the global model–satellite root-mean-square error (RMSE) by 20 %. The OMI- and TROPOMI-observed seasonal variations in HCHO abundances were consistent. The simulated seasonality showed better agreement with TROPOMI in most regions. The simulated HCHO and isoprene profiles correlated strongly (R=0.81) with the ATom observations. However, CHASER overestimated HCHO mixing ratios over dense vegetation areas in South America and the remote Pacific region (background condition), mainly within the planetary boundary layer (&lt; 2 km). The simulated seasonal variations in the HCHO columns showed good agreement (R&gt;0.70) with the MAX-DOAS observations and agreed within the 1σ standard deviation of the observed values. However, the temporal correlation (R∼0.40) was moderate on a daily scale. CHASER underestimated the HCHO levels at all sites, and the peak occurrences in the observed and simulated HCHO seasonality differed. The coarseness of the model's resolution could potentially lead to such discrepancies. Sensitivity studies showed that anthropogenic emissions were the highest contributor (up to ∼ 35 %) to the wintertime regional HCHO levels.

Online testing of extinction method of high-pressure natural gas oil mist based on light extinction

Abstract This paper proposes a theoretical model and scheme for accurately measuring droplet concentration in high-pressure natural gas environments, aiming for precise, online measurement of lubricating oil mist. The model integrates droplet behavior, light extinction properties of the medium, and pressure, using the light extinction method. It applies the Debye Series Expansion of Mie scattering to improve the light extinction coefficient for mediums such as lubricating oil particles. Differential Optical Absorption Spectroscopy is used to calculate the extinction index of natural gas at different pressures. The study innovates by replacing the traditional parallel light path with a non-parallel path that focuses the light beam, countering beam diameter expansion due to gas pressure. Empirical testing shows the method’s accuracy in online oil mist testing under 10 MPa pressure, with less than 5% error compared to offline methods. This work paves the way for future high-pressure gas particle monitoring, particularly in the oil and natural gas sector.