Search Results (61)

The Italian peninsula is, as shown by satellite and ground-based measurements, a pollution hotspot. In recent years, ground-based MAX-DOAS commercial systems have been installed in the Po Valley and the area surrounding Rome to monitor NO₂ tropospheric column densities and validate coincident satellite (e.g., TROPOMI) products. Three of the instruments are located in the Po Valley at San Pietro Capofiume (Bologna), Bologna city, and Mount Cimone (Modena), and one is located in Tor Vergata (Rome). The chosen system is the SkySpec-2D from Airyx. All the recorded spectra are saved in the FRM4DOAS format and processed with QDOAS software to obtain slant column densities (SCDs) of NO₂, O₄, and other trace gases. The MAX-DOAS SCD sequences are then analysed with the DEAP code to retrieve tropospheric profiles of NO₂ and aerosol extinction, while zenith-sky SCDs are used to retrieve NO₂ total columns. A dedicated campaign, involving the network instruments, has been conducted in the Po Valley to compare the performance of the individual instruments in the network with respect to the one that participated in the CINDI-3 campaign (Cabauw, The Netherlands). The results of the intercomparison campaign indicated that all instruments showed comparable performance. As an example of obtainable products, one year (from September 2024 to August 2025) of NO₂ tropospheric columns, as well as their comparison with TROPOMI measurements, is presented, highlighting the potential of this network for both air quality studies and satellite validation. Due to Italy’s location in the highly complex Mediterranean hotspot region, these data represent an important contribution to satellite validation efforts, particularly in view of upcoming missions such as Copernicus Sentinel-4, Sentinel-5, and the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) constellation. We found a negative TROPOMI bias relative to SkySpec-2D for NO₂ tropospheric columns ranging from −13% in San Pietro Capofiume, to −25% in Bologna and −44% in Rome Tor Vergata. The comparison between NO₂ total columns from TROPOMI and SkySpec-2D at Mount Cimone shows generally good agreement, with TROPOMI being 15% higher. Full article

Exposure to Particulate Matter (PM) is linked to respiratory and cardiovascular diseases, certain types of cancer, and accounts for approximately seven million premature deaths globally. While governments and organizations have implemented various strategies for Air Quality (AQ) such as the deployment of Air Quality Monitoring Networks (AQMN), these networks often suffer from limited spatial coverage and involve high installation and maintenance costs. Consequently, the implementation of networks based on Low-Cost Sensors (LCS) has emerged as a viable alternative. Nevertheless, LCS systems have certain drawbacks, such as lower reading precision, which can be mitigated through specific calibration models and methods. This paper presents the results and conclusions derived from simultaneous PM₁₀ and PM_2.5 monitoring comparisons between LCS nodes and a T640X reference sensor. Additionally, Relative Humidity (RH), temperature, and absorption flow measurements were collected via an Automet meteorological station. The monitoring equipment was installed at the Faculty of Environment of the Universidad Distrital in Bogotá. The LCS calibration process began with data preprocessing, which involved filtering, segmentation, and the application of FastDTW. Subsequently, calibration was performed using a variety of models, including two statistical approaches, three Machine Learning algorithms, and one Deep Learning model. The findings highlight the critical importance of applying FastDTW during preprocessing and the necessity of incorporating RH, temperature, and absorption flow factors to enhance accuracy. Furthermore, the study concludes that Random Forest and XGBoost offered the highest performance among the methods evaluated. While satellites map city-wide patterns and MAX-DOAS enables hourly source attribution, our calibrated LCS network supplies continuous, street-scale data at low CAPEX/OPEX—forming a practical backbone for sustained micro-scale monitoring in Bogotá. Full article

Monitoring the horizontal distribution of PM_2.5 within urban areas is of great significance, not only for environmental management but also for providing essential data to understand the distribution, formation, transport, and transformation of PM_2.5 within cities. This study proposes a novel approach—the Spectral Analysis-based PM_2.5 Estimation Machine Learning (SAPML) model. This method uses a machine learning model trained with features derived from multi-azimuth and multi-elevation MAX-DOAS observations, specifically the oxygen dimer (O₄) differential slant column densities (O₄ dSCDs), and labels provided by near-surface ground measurements corresponding to each azimuthal direction, to estimate near-surface PM_2.5 concentrations. This approach does not rely on meteorological data and enables multi-directional near-surface PM_2.5 monitoring using only a single independent instrument. SAPML bypasses the intermediate retrieval of aerosol extinction coefficients and directly estimates PM_2.5 concentrations from spectral analysis results, thereby avoiding the accumulation of errors. Using O₄ dSCD data from multiple MAX-DOAS stations for model training eliminates inter-station conversion differences, allowing a single model to be applied across multiple sites. Station-based k-fold cross-validation yielded an average Pearson correlation coefficient (R) of 0.782, demonstrating the robustness and transferability of the method across major regions in China. Among the machine learning algorithms evaluated, Extreme Gradient Boosting (XGBoost) exhibited the best performance. Feature optimization based on importance ranking reduced data collection time by approximately 30%, while the correlation coefficient (R) of the estimation results decreased by only about 1.3%. The trained SAPML model was further applied to two MAX-DOAS stations in Hefei, HF-HD, and HFC, successfully resolving the near-surface PM_2.5 spatial distribution at both sites. The results revealed clear intra-urban heterogeneity, with higher PM_2.5 concentrations observed in the western industrial park area. During the same observation period, an east-to-west PM_2.5 pollution transport event was captured: PM_2.5 increases were first detected in the upwind direction at HF-HD, followed by the downwind direction at the same station, and finally at the downwind station HFC. These results indicate that the SAPML model is an effective approach for monitoring intra-urban PM_2.5 distributions. Full article

The vertical profile of PM_2.5 is important for understanding its secondary formation, transport, and deposition at high altitudes; it also provides important data support for studying the causes and sources of PM_2.5 near the ground. Based on machine learning methods, this study fully utilized simultaneous Multi-Axis Differential Optical Absorption Spectroscopy measurements of multiple air pollutants in the atmosphere and employed the measured vertical profiles of aerosol extinction—as well as the vertical profiles of precursors such as NO₂ and SO₂—to evaluate the vertical distribution of PM_2.5 concentration. Three machine learning models (eXtreme Gradient Boosting, Random Forest, and back-propagation neural network) were evaluated using Multi-Axis Differential Optical Absorption Spectroscopy instruments in four typical cities in China: Beijing, Lanzhou, Guangzhou, and Hefei. According to the comparison between estimated PM_2.5 and in situ measurements on the ground surface in the four cities, the eXtreme Gradient Boosting model has the best estimation performance, with the Pearson correlation coefficient reaching 0.91. In addition, the in situ instrument mounted on the meteorological observation tower in Beijing was used to validate the estimated PM_2.5 profile, and the Pearson correlation coefficient at each height was greater than 0.7. The average PM_2.5 vertical profiles in the four typical cities all show an exponential pattern. In Beijing and Guangzhou, PM_2.5 can diffuse to high altitudes between 500 and 1000 m; in Lanzhou, it can diffuse to around 1500 m, while it is primarily distributed between the near surface and 500 m in Hefei. Based on the vertical distribution of PM_2.5 mass concentration in Beijing, a high-altitude PM_2.5 pollutant transport event was identified from January 19th to 21st, 2021, which was not detected by ground-based in situ instruments. During this process, PM_2.5 was transported from the 200 to 1500 m altitude level and then sank to the near surface, causing the concentration on the ground surface to continuously increase. The sinking process contributes to approximately 7% of the ground surface PM_2.5 every hour. Full article

This work presents a comprehensive investigation of tropospheric NO₂ measurements using a portable ground-based Pandora spectrometer, incorporating an independently designed and implemented calibration and retrieval process (P-CAR v1.0). We designed and optimized a region-specific algorithm for retrieving tropospheric NO₂ column densities in China. The measurement process began with establishing a spectral calibration system for processing the Pandora’s raw observations, followed by enhancing the differential optical absorption spectroscopy (DOAS) algorithm to retrieve both the slant column densities (SCDs) and tropospheric vertical column densities (VCDs) of NO₂. To validate our retrieval products, comparative analyses were conducted against co-located MAX-DOAS measurements. The results demonstrate excellent agreement between Pandora-retrieved tropospheric NO₂ and MAX-DOAS observations, with correlation coefficients exceeding 0.96 for both hourly and daily mean VCDs and fitting slopes greater than 0.90. Furthermore, the validation extended to multi-satellite observations from the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI), exhibiting pronounced consistency, as evidenced by the correlation coefficients all surpassing 0.90 for the hourly mean values. These findings confirm the high accuracy and reliability of NO₂ retrievals from the portable Pandora instrument, significantly boosting its potential for atmospheric monitoring and application. Full article

The acoustically actuated antenna technology enables a significant reduction in antenna dimension, facilitating miniaturization of ground-penetrating radar systems in the very high-frequency (VHF) band. However, the current acoustically actuated antennas suffer from narrow bandwidth and low range resolution. To address this issue, this paper proposed a three-dimensional (3D) localization method for underground targets, which combined two-dimensional (2D) array direction-of-arrival (DOA) estimation with continuous spatial sampling without relying on range resolution. By leveraging the small dimension of acoustically actuated antennas, a 2D uniform linear array was formed to obtain the target’s angle using DOA estimation. Based on the variation pattern of 2D angles in continuous spatial sampling, the genetic algorithm was employed to estimate the 3D coordinates of underground targets. The numerical simulation results indicated that the root mean square error (RMSE) of the proposed 3D localization method is 1.68 cm, which outperforms conventional methods that utilize wideband frequency-modulated pulse signals with hyperbolic vertex detection in theoretical localization accuracy, while also demonstrating good robustness. The gprMax electromagnetic simulation results further confirmed that this method can effectively localize multiple targets in ideal homogeneous underground media. Full article

Pollutants information can be retrieved from visible (VIS) and ultraviolet (UV) diffuse solar spectra exploiting Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments. In May 2021, the Italian research institute CNR-ISAC acquired and deployed a MAX-DOAS system SkySpec-2D. It is located in the “Giorgio Fea” observatory in San Pietro Capofiume (SPC), in the middle of the Po Valley, where it has constantly acquired zenith and off-axis diffuse solar spectra since the 1st October 2021. This work presents the retrieved tropospheric NO₂ and aerosol extinction profiles (and their columns) derived from the MAX-DOAS measurements using the newly developed DEAP retrieval code. The code has been validated both using synthetic differential Slant Column Densities (dSCDs) from the Fiducial Reference Measurements for Ground-Based DOAS Air-Quality Observations (FRM4DOAS) project and real measured data. For this purpose, DEAP results are compared with the ones obtained with three state-of-the-art retrieval codes. In addition, an inter-comparison with satellite products from Sentinel-5P TROPOMI, for the tropospheric NO₂ Vertical Column Densities (VCDs), and MODIS-MAIAC for the tropospheric Aerosol Optical Depth (AOD), is performed. We find a bias of −0.6 × 10¹⁵ molec/cm² with a standard deviation of 1.8 × 10¹⁵ molec/cm² with respect to Sentinel-5P TROPOMI for NO₂ tropospheric VCDs and of 0.04 ± 0.08 for AOD with respect to MODIS-MAIAC data. The retrieved data show that the SPC measurement site is representative of the background pollution conditions of the Po Valley. For this reason, it is a good candidate for satellite validation and scientific studies over the Po Valley. Full article

Diagnosing ozone (O₃) formation sensitivity using tropospheric observations of O₃ and its precursors is important for formulating O₃ pollution control strategies. Photochemical reactions producing O₃ occur at the earth’s surface and in the elevated layers, indicating the importance of diagnosing O₃ formation sensitivity at different layers. Synchronous measurements of tropospheric O₃ and its precursors nitrogen dioxide (NO₂) and formaldehyde (HCHO) were performed in urban Hefei to diagnose O₃ formation sensitivity at different atmospheric layers using multi-axis differential optical absorption spectroscopy observations. The retrieved surface NO₂ and O₃ were validated with in situ measurements (correlation coefficients (R) = 0.81 and 0.80), and the retrieved NO₂ and HCHO vertical column densities (VCDs) were consistent with TROPOMI results (R = 0.81 and 0.77). The regime transitions of O₃ formation sensitivity at different layers were derived using HCHO/NO₂ ratios and O₃ profiles, with contributions of VOC-limited, VOC-NO_x-limited, and NO_x-limited regimes of 74.19%, 7.33%, and 18.48%, respectively. In addition, the surface O₃ formation sensitivity between HCHO/NO₂ ratios and O₃ (or increased O₃, ΔO₃) had similar regime transitions of 2.21–2.46 and 2.39–2.71, respectively. Moreover, the O₃ formation sensitivity of the lower planetary boundary layer on polluted and non-polluted days was analyzed. On non-polluted days, the contributions of the VOC-limited regime were predominant in the lower planetary boundary layer, whereas those of the NO_x-limited regime were predominant in the elevated layers during polluted days. These results will help us understand the evolution of O₃ formation sensitivity and formulate O₃ mitigation strategies in the Yangtze River Delta region. Full article

This study employed dual-azimuth scanning MAX-DOAS to monitor vertical column densities of NO₂ and HCHO in Shanghai during the summer and winter of 2023, and compared the results with Sentinel-5P TROPOMI data. Dual-azimuth scanning revealed a generally consistent trend in gas concentrations (r > 0.95), but concentrations at 90° were higher than those at 0°, especially near the surface. This suggests that averaging multiple azimuth angles is necessary to better represent regional pollution levels. During the observation period, diurnal patterns revealed that NO₂ exhibited a “double peak” in the morning and evening, which was more pronounced in the summer, while HCHO peaked between 13:00 and 15:00. Comparisons with the TROPOMI data demonstrated overall good agreement. However, the probability of TROPOMI’s NO₂ and HCHO measurements being lower than those of MAX-DOAS was 80% and 62.5%, respectively. Furthermore, TROPOMI tended to overestimate at high concentrations, with overestimation reaching 41.14% for NO₂ when exceeding 9.54 × 10¹⁵ molecules/cm² and 25.93% for HCHO when exceeding 1.26 × 10¹⁶ molecules/cm². Sensitivity analysis of the sampling distance (0–40 km) between TROPOMI samples and the ground-based site, and the sampling time (±5 to ±60 min) relative to the TROPOMI overpass, revealed that using a sampling distance of 15–25 km for NO₂ and 10–20 km for HCHO, along with appropriately shortening sampling times in the winter and extending them in the summer, can effectively enhance the consistency between satellite and ground-based observations. These findings not only reveal the spatiotemporal distribution characteristics of regional pollutants but optimize the sampling time and distance parameters for satellite–ground observation validation, providing data support for improving and enhancing the accuracy of satellite retrieval algorithms. Full article

Multi-axis differential absorption spectroscopy (MAX-DOAS) has become an important tool for detecting trace gases in optical remote sensing. At present, the temporal resolution of the system using the traditional motor-rotated elevation telescope is extremely low. We focus on studying the atmospheric radiation transmission of fast synchronous MAX-DOAS (FS MAX-DOAS), which has greatly improved the temporal resolution on the ground and on mobile platforms and the influence of related parameters on the atmospheric mass factor (AMF), which is used to guide the design and experiments of the new system. The optimal elevation angle combination, the spectral resolution, and the specific effects of relevant parameters on the AMF during profile inversion by the new system were analyzed, and the feasibility of the new system for mobile MAX-DOAS was evaluated. The inversion results of the measured spectra collected by the system show that FS MAX-DOAS can meet the requirements of both ground and mobile platform observation scenarios. The results of our sensitivity study are of great significance for guiding experiments. Full article

The UV–Visible Working Group of the Network for the Detection of Atmospheric Composition Changes (NDACC) focuses on the monitoring of air-quality-related stratospheric and tropospheric trace gases in support of trend analysis, satellite validation and model studies. Tropospheric measurements are based on MAX-DOAS-type instruments that progressively emerged in the years 2010 onward. In the interest of improving the overall consistency of the NDACC MAX-DOAS network and facilitating its further extension to the benefit of satellite validation, the ESA initiated, in late 2016, the FRM4DOAS project, which aimed to set up the first centralised data processing system for MAX-DOAS-type instruments. Developed by a consortium of European scientists with proven expertise in measurements, data extraction algorithms and software design specialities, the system has now reached pre-operational status and has demonstrated its ability to deliver a set of quality-controlled atmospheric composition data products with a latency of one day. The processing system has been designed using a highly modular approach, making it easy to integrate new tools or processing updates. It incorporates advanced algorithms selected by community consensus for the retrieval of total ozone, lower tropospheric and stratospheric NO₂ vertical profiles and formaldehyde profiles. The ozone and NO₂ products are currently generated from a total of 22 stations and delivered daily to the NDACC rapid delivery (RD) repository, with an additional mirroring to the ESA Validation Data Centre (EVDC). Although it is still operated in a pre-operational/demonstrational mode, FRM4DOAS was already used for several validation and science studies, and it was also deployed in support of field campaigns for the validation of the TROPOMI and GEMS satellite missions. It recently went through a CEOS-FRM self-assessment process aiming at assessing the level of maturity of the service in terms of instrumentation, operations, data sampling, metrology and verification. Based on this evaluation, it falls under class C, which is a good rating but also implies that further improvements are needed to reach full compliance with FRM standards, i.e., class A. Full article

Formaldehyde (HCHO), a key volatile organic compound (VOC) in the atmosphere, plays a crucial role in driving photochemical processes. Satellite-based observations of column concentrations of HCHO and other gaseous pollutants (e.g., NO₂) have generally been used in previous studies to elucidate the mechanisms behind secondary organic aerosol (SOA) and ozone (O₃) formation. This study aimed to investigate the characteristics of HCHO by retrieving its vertical profile over Nanjing during the warm season (May–June 2022) and analyzing the diurnal variation in vertical distribution and potential source regions on non-polluted (MDA8 O₃ < 160 μg m⁻³, NO₃P) and O₃-polluted (MDA8 O₃ ≥ 160 μg m⁻³, O₃P) days. Under both conditions, HCHO was primarily concentrated below 1.5 km altitude, with average vertical profiles displaying similar Boltzmann-like distributions. However, HCHO concentrations on O₃P days were 1.2–1.6 times higher than those on non-polluted days at the same altitude below 1.5 km. Maximum HCHO concentrations occurred in the afternoon, while the peak value in the 0.1–0.4 km layers was reached around noon (~11:00 a.m.). The variation rates (VR) of HCHO in the 0.3–1.2 km altitudes had a maximum on O₃P days (approximately 0.33 ppbv h⁻¹), and were significantly higher (p < 0.01) than the VR observed on NO₃P days (0.14–0.20 ppbv h⁻¹). The analysis of footprints showed that HCHO concentrations were jointly influenced by the upstream region and the surroundings of the study site. The study results improve the understanding of the vertical distribution and potential source regions of HCHO. Full article

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. Full article

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. Full article