Search Results (227)

This study investigates a typical ozone pollution episode in Hong Kong from May 29 to 31, 2023. Based on the observations of a Differential Absorption Lidar (DIAL) system, both ozone and aerosols accumulated below 1.5 km during the pollution episode. Ozone exhibited distinct formation and accumulation characteristics, with concentrations exceeding 200 μg m⁻³. Aerosols presented evident features of regional transport and local coupling, with extinction coefficients surpassing 1.1 km⁻¹. During late spring to early summer, the northward extension of the Western Pacific Subtropical High (WPSH) established favorable conditions for ozone production. This background was amplified by Typhoon Mawar, whose peripheral circulation channeled pollutants from the Pearl River Delta into Hong Kong through horizontal and vertical pathways, significantly worsening near-surface air quality. The episode was eventually mitigated, as enhanced vertical mixing facilitated the dispersion and removal of accumulated pollutants. These results highlight the critical role of meteorological–chemical interactions in shaping this ozone pollution episode. 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

Understanding aerosol chemical components’ roles in light extinction is critical for air quality management and climate mitigation. This study compared PM₁ optical properties and chemical compositions in Shanghai (southern China) and Dezhou (northern China) during winter using high-resolution aerosol mass spectrometers and optical instruments. Results showed PM₁ scattering coefficients (10.9–549.8 Mm⁻¹) in Shanghai were dominated by traffic-related organic aerosols (OA) (45.2%), with ammonium sulfate and nitrate contributing 60.5% of extinction. In Dezhou, higher scattering coefficients (3.5–2635.1 Mm⁻¹) were driven by heating/biomass burning, with OA accounting for 57.8% and ammonium nitrate 27.2%. Mass scattering efficiencies (MSEs) in Dezhou were significantly higher (sulfate: 10.75 m²/g; nitrate: 10.15 m²/g; OA: 4.9 m²/g) than those in Shanghai (4.2/3.85/3.00 m²/g). Pollution episodes revealed distinct mechanisms: high-humidity OA accumulation for Shanghai vs. nitrate-organic synergy for Dezhou. The IMPROVE model systematically underestimated scattering coefficients, emphasizing the need for region-specific parameterization. OA was identified as the primary scattering contributor in both cities, though inorganic species became critical under high-pollution conditions. These findings suggest targeted strategies: reducing VOC emissions in southern China and controlling NOx in northern industrial areas to improve winter visibility and air quality. Full article

Low-cost optical sensors for particulate matter (PM) monitoring, such as the SDS011, are widely used due to their affordability and ease of deployment. However, their accuracy strongly depends on aerosol properties and environmental conditions, necessitating reliable calibration. This study presents a theoretical and laboratory evaluation of a practical calibration method based on visibility sensors, which measure atmospheric light extinction and are readily available at many meteorological stations. Experiments were conducted in a controlled aerosol chamber, using SDS011 sensors, visibility sensors (FD70 and SWS250), and gravimetric samplers. The mass extinction coefficient was determined through parallel measurements of visibility and mass concentration, enabling conversion of optical signals into accurate PM values. The calibrated SDS011 sensors demonstrated consistent response with a stable normalization factor (dependent on aerosol type, wavelength, and particle size), allowing their deployment as a spatially distributed sensor network. Comparison with manufacturer calibration revealed substantial deviations due to differences in aerosol optical properties, highlighting the importance of application-specific calibration. The visibility-based approach enables real-time, continuous calibration of low-cost sensors with minimal equipment, offering a scalable solution for PM monitoring in resource-limited or remote environments. The method’s robustness under varying environmental conditions remains to be explored. Nevertheless, the results establish visibility-based calibration as a reliable and accessible framework for enhancing the accuracy of low-cost PM sensing technologies. The method enables scalable calibration with a single gravimetric reference and is suited for future field deployment in resource-limited settings, following additional validation under real atmospheric conditions. Full article

This work presents a proof of concept for a short-range high spectral resolution lidar (SR-HSRL) optimized for aerosol characterization in the first kilometer of the atmosphere. The system is based on a compact, high-repetition-rate diode-based fiber laser with a 300 MHz linewidth and 5 ns pulse duration, coupled with an iodine absorption cell. A central challenge in the instrument’s development was identifying a laser source that offered both sufficient spectral resolution for HSRL retrievals and nanosecond pulse durations for high spatiotemporal resolution, while also being compact, tunable, and cost-effective. To address this, we developed a methodology for complete spectral and temporal laser characterization. A two-day field campaign conducted in July 2024 in Tsukuba, Japan, validated the system’s performance. Despite the relatively broad laser linewidth, we successfully retrieved aerosol backscatter coefficient profiles from 50 to 1000 m, with a spatial resolution of 7.5 m and a temporal resolution of 6 s. The results demonstrate the feasibility of using SR-HSRL for detailed studies of aerosol layers, cloud interfaces, and aerosol–cloud interactions. Future developments will focus on extending the technique to ultra-short-range applications (<100 m) from ground-based and mobile platforms, to retrieve aerosol extinction coefficients and lidar ratios to improve the characterization of near-source aerosol properties and their radiative impacts. 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

Studying the vertical and horizontal distribution of particulate matter at the hectometer scale in the atmosphere is essential for understanding its sources, transportation, and transmission and its impact on human health. In this study, a method was developed based on hyperspectral instrumentation to obtain both vertical and horizontal distributions of aerosol extinction by employing multiple azimuth angles, selecting optimized elevation angles, and reducing the acquisition time of individual spectra. This method employed observations from different azimuth angles to represent particulate matter concentrations in various directions. The correlation coefficient between the hyperspectral observations and in-situ measurement was 0.627. Observations indicated that the aerosol extinction profile followed an exponential decay, with most aerosols confined below 1 km, implying a likely origin from local near-surface emissions. The horizontal distribution indicated that the northeastern urban areas and the eastern rural areas were the primary regions with high concentrations of particulate matter. The observational evidence suggests the presence of two potential emission sources within the study area. Moreover, health risk results indicated that even within the same town, differences of particulate matter concentration and population density could lead to varying health exposure risks. For instance, in the 200° and 210° directions, which represent adjacent urban areas less than 1 km apart, the number of PM_2.5-related illness cases in the 210° direction was 20.83% higher than that in the 200° direction. Full article

The Barcelona Raman LIDAR (BRL) will provide continuous monitoring of the aerosol extinction profile along the line of sight of the Cherenkov Telescope Array Observatory (CTAO). It will be located at its Northern site (CTAO-N) on the Observatorio del Roque de Los Muchachos. This article presents the performance of the pathfinder Barcelona Raman LIDAR (pBRL), a prototype instrument for the final BRL. Power budget simulations were carried out for the pBRL operating under various conditions, including clear nights, moon conditions, and dust intrusions. The LIDAR PreProcessing (LPP) software suite is presented, which includes several new statistical methods for background subtraction, signal gluing, ground layer and cloud detection and inversion, based on two elastic and one Raman lines. Preliminary test campaigns were conducted, first close to Barcelona and later at CTAO-N, albeit during moonlit nights only. The pBRL, under these non-optimal conditions, achieves maximum ranges up to about 35 km, range resolution of about 50 m for strongly absorbing dust layers, and 500 m for optically thin clouds with the Raman channel only, leading to similar resolutions for the LIDAR ratios and Ångström exponents. Given the reasonable agreement between the extinction coefficients obtained from the Raman and elastic lines independently, an accuracy of aerosol optical depth retrieval in the order of 0.05 can be assumed with the current setup. The results show that the pBRL can provide valuable scientific results on aerosol characteristics and structure, although not all performance requirements could be validated under the conditions found at the two test sites. Several moderate hardware improvements are planned for its final upgraded version, such as gated PMTs for the elastic channels and a reduced-power laser with a higher repetition rate, to ensure that the data acquisition system is not saturated and therefore not affected by residual ringing. Full article

Lidar has been used for many years to study the optical properties of aerosols, but estimating the boundary values requires solving the lidar elastic scattering equation, which remains a challenge. The boundary values are often determined by fitting to uniform regions of the atmosphere. This method typically excludes low signal-to-noise ratio (SNR) signals because it classifies them as non-uniform, reducing the effective detection range of the lidar. On the other hand, directly fitting low SNR signals to estimate the boundary values can introduce significant errors. The method is based on maximizing the lidar detection distance and determines the boundary value using a new estimation algorithm with the averaging of multiple fitted results in the low SNR region to reduce the impact of noise. Simulations demonstrate that the new method reduces the relative error in the boundary value estimation by approximately 5% and improves the accuracy of the extinction coefficient profile inversion compared with the method of directly fitting all-sample signals. Field comparison experiments with forward-scattering sensors further verify that the algorithm improves the retrieval accuracy by 17.3% under extremely low signal-to-noise ratio (SNR) conditions, while performing comparably to the traditional method in high SNR homogeneous atmospheres. Additionally, based on the scanned lidar signals, the algorithm can provide detailed information on the spatial distribution of sea fog and offer valuable insights for an in-depth understanding of the physical evolution of sea fog. Full article

Visibility is a fundamental meteorological parameter critical for surface transportation, aviation, maritime navigation, and weather process investigation. Scattering visibility meters are extensively utilised for their simple design and rapid response; however, their measurement principle is inherently limited, as they only quantify the scattering coefficient without assessing the absorption coefficient, potentially causing measurement errors. The World Meteorological Organisation (WMO) posits that the atmospheric absorption coefficient is usually relatively small and can be neglected, justifying the approximation of the extinction coefficient by the scattering coefficient. However, as black carbon is the predominant light-absorbing component in the atmosphere, an increase in its mass concentration markedly alters the atmospheric absorption coefficient, considerably impacting the accuracy of scattering-based visibility meters. Based on Mie scattering theory and incorporating both field observations and laboratory data, we systematically examined the effects of black carbon and its interactions with other aerosol components on the measurement errors of scattering visibility meters. Our findings revealed that the impact of black carbon on measurement errors is substantial, and under certain conditions, particularly pronounced. This influence is not only dependent on the mass concentration of black carbon but also closely associated with aerosol size distribution, mixing state, and the characteristics of other scattering aerosols. Due to the spatiotemporal variability of these factors, the impact of black carbon on visibility errors is uncertain. Therefore, during the calibration of scattering-based visibility meters, the effects of black carbon and its associated factors must be considered to enhance measurement accuracy. We propose calibration recommendations for scattering-based visibility meters aimed at reducing measurement errors and improving the accuracy of visibility assessments. Full article

In this study, the effective radius of aerosol particles was experimentally retrieved using a self-developed dual-wavelength atmospheric aerosol lidar. A single-valued lookup table was first established, based on the OPAC database and the Gamma size distribution model, to define the relationship between the extinction coefficient ratio and the effective radius of atmospheric aerosol particles. The extinction coefficients corresponding to the 355 nm and 1064 nm wavelengths were then calculated using the echo signals retrieved horizontally by the lidar, in conjunction with the Mie scattering lidar equation. Subsequently, the lookup table was used to retrieve the real-time effective radius of aerosol particles by inputting the extinction coefficient ratio of the two wavelengths. Finally, the retrieval results were compared with the effective radii measured by an optical particle spectrometer, which had been corrected for relative humidity. An analysis over six months showed a coefficient of determination (R²) greater than 0.83. The results demonstrated that the dual-wavelength lidar exhibits a stable performance, the retrieval method is valid, and the detection results are accurate and reliable. Full article

Accurate identification of dust emission sources is crucial for simulating dust aerosols in atmospheric chemical models. Therefore, a dynamically updated dust source function (DSF) was developed within the dust emission scheme of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) to simulate an East Asian dust storm event from 13 to 16 March 2021. Utilizing satellite-derived input of vegetation cover, snow cover, soil texture, and land use, the DSF was updated to better identify dust source areas over bare soils and sparsely vegetated regions in western China and central-western Mongolia. With the updated DSF, simulated dust emissions increase significantly over western China and Mongolia. The dust aerosol simulations demonstrate substantial improvements in near-surface PM₁₀ concentrations, a better agreement with remotely sensed dust aerosol optical depth (DOD), and a more accurate representation of the vertical distribution of dust extinction coefficients compared to observations. This study highlights the importance of integrating real-time data to accurately characterize dust emission sources, thereby improving atmospheric environment simulations. Full article

This paper presents the technical design of the pathfinder Barcelona Raman LIDAR (pBRL) for the northern site of the Cherenkov Telescope Array Observatory (CTAO-N) located at the Roque de los Muchachos Observatory (ORM). The pBRL is developed for continuous atmospheric characterization, essential for correcting high-energy gamma-ray observations captured by Imaging Atmospheric Cherenkov Telescopes (IACTs). The LIDAR consists of a steerable telescope with a 1.8 m parabolic mirror and a pulsed Nd:YAG laser with frequency doubling and tripling. It emits at wavelengths of 355 nm and 532 nm to measure aerosol scattering and extinction through two elastic and Raman channels. Built upon a former Cherenkov Light Ultraviolet Experiment (CLUE) telescope, the pBRL’s design includes a Newtonian mirror configuration, a coaxial laser beam, a near-range system, a liquid light guide and a custom-made polychromator. During a one-year test at the ORM, the stability of the LIDAR and semi-remote-controlled operations were tested. This pathfinder leads the way to designing a final version of a CTAO Raman LIDAR which will provide real-time atmospheric monitoring and, as such, ensure the necessary accuracy of scientific data collected by the CTAO-N telescope array. 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

In this work, we revise the retrieval of extinction coefficient profiles from Raman Lidar. This is an ill-posed problem, and we show that methods like Levenberg–Marquardt or Tikhonov–Phillips can be applied. We test these methods for a synthetic Lidar profile (known solution) with different noise realizations. Further, we apply these methods to three different cases of data from the Arctic: under daylight (Arctic Haze), under daylight with a high and vertically extended aerosol layer, and at nighttime with high extinction. We show that our methods work and allow a trustful derivation of extinction up to clearly higher altitudes (at about half a signal-to-noise ratio) compared with the traditional, non-regularized Ansmann solution. However, these new methods are not trivial and require a choice of parameters, which depend on the noise of the data. As the Lidar signal quality quickly decreases with range, a separation of the profile into several sub-intervals seems beneficial. Full article