Aerosol Types Research Articles

Enhancing Fine Aerosol Simulations in the Remote Atmosphere with Machine Learning

Global aerosol models often underestimate the mass concentration of aerosols in the remote troposphere, as evidenced by aircraft measurements. This study leveraged data from the NASA Atmospheric Tomography Mission (ATom), which provides remote aerosol concentrations, to refine algorithms for simulating these concentrations. Using the GEOS-Chem model, we simulate five fine aerosol types and enhance the simulation results using five machine-learning algorithms: Random Forest, XGBoost, SVM, KNN, and LightGBM, and compare the performance of these algorithms. Additionally, we evaluate the refinement effect of algorithms based on decision trees on a validation dataset. The results demonstrate that GEOS-Chem generally underestimated aerosol mass concentration. Among the tested algorithms, algorithms based on decision trees, particularly the Random Forest algorithm and the LightGBM algorithm, exhibited a superior performance, significantly improving prediction accuracy and computational efficiency in both the training and testing phases, as well as on the validation dataset.

Particulate emissions from cooking: emission factors, emission dynamics, and mass spectrometric analysis for different cooking methods

Abstract. Since most people, especially in developed countries, spend most of their time indoors, they are heavily exposed to indoor aerosols, which can potentially lead to adverse health effects. A major source of indoor aerosols are cooking activities, which release large quantities of particulate emissions (in terms of both number and mass), often with complex compositions. To investigate the characteristics of cooking emissions and what influences these emissions, we conducted a comprehensive study by cooking 19 dishes with different ingredients and cooking methods. The emissions were monitored in real time with several online instruments that measured both physical and chemical particle properties as well as trace gas concentrations. The same instrumentation was used to study the influence of cooking emissions on the ambient aerosol load at two German Christmas markets. In contrast to previous studies, which often focus on individual aspects or emission variables, this broad and coherent approach allows a comparison of the influence of different parameters (e.g., ingredients, cooking method, cooking temperature, cooking activities) on the emissions. We found an influence of cooking emissions on six variables: number concentration of smaller (particle diameter dp > 5 nm) and larger (dp > 250 nm) particles, particulate matter (PM: PM1, PM2.5, PM10), black carbon (BC), PAHs (polycyclic aromatic hydrocarbons), and organic aerosol mass concentration. In general, similar emission characteristics were observed for dishes with the same cooking method, mainly due to similar cooking temperature and use of oil. The temporal dynamics in the emissions of the aforementioned variables, as well as the sizes of the emitted particles, were mainly influenced by the cooking temperature and the activities during cooking. Emissions were quantified using emission factors, with the highest values for grilled dishes, 1 to 2 orders of magnitude lower for oil-based cooking (baking, stir-frying, deep-frying), and the lowest for boiled dishes. For the identification of cooking emissions with the Aerodyne aerosol mass spectrometer (AMS), and more generally for the identification of new AMS markers for individual organic aerosol types, we propose a new plot type that takes into account the mass spectral variability for individual aerosol types. Combining our results and those of previous studies for the quantification of cooking-related organic aerosols with the AMS, we recommend the use of relative ionization efficiency values higher than the default value for organics (RIEOrg = 1.4): 2.17 ± 0.48 for rapeseed-oil-based cooking and 5.16 ± 0.77 for soybean-oil-based cooking.

Accelerated surface brightening in China: The decisive role of reduced anthropogenic aerosol emissions

A profound comprehension of the key determinants behind the fluctuations in surface solar radiation (SSR) over decadal timescales is essential for advancing the fields of energy meteorology, climatology, and solar resources. While there is a general agreement among prior research on the significant impact of aerosols on SSR, a granular understanding of the specific influence that varying aerosol types exert on SSR remains elusive. This investigation delves into the respective impacts of aerosols and cloud radiative effects across China, utilizing data from the Clouds and the Earth's Radiant Energy System (CERES). It further dissects the distinct impacts of different aerosol types on the decadal-scale variations in SSR, employing spectral aerosol optical depths derived from the MODIS aerosol products. The findings reveal a predominantly positive trend in SSR, ranging from 1.5 to 2.5 W/m2/yr between the years 2011 and 2022 for the majority of China's regions. However, certain areas within Northeast and Western China exhibit a decline in SSR exceeding −1 W/m2/yr. Notably, in Eastern China, a downward trend in aerosol radiative effect is observed, with a pronounced reduction of approximately −2 W/m2/yr noted specifically during the summer in Northern China. The study infers that the reduction in urban industrial emissions and biomass burning aerosols is linked to the acceleration of the brightening phenomenon in China. Our results underscore that stringent regulation of anthropogenic emissions could yield dual benefits: environmental amelioration and an upsurge in solar resource. These insights merit serious contemplation in the formulation of future policy and decision-making frameworks.

Three-dimensional distribution of aerosols of multiple types at daily scale using TROPOMI spaceborne observations

We present new satellite-based daily observations of the 3D distribution of aerosols from most common types and origins, both over land and ocean, derived from TROpospheric Ozone Monitoring Instrument (TROPOMI) measurements. This is done using the so-called AEROS5P (AEROsol Sentinel 5 Precursor) approach which retrieves vertical profiles of aerosol extinction from passive measurements of high-resolution spectral reflectance in the visible and near infrared for cloud-free conditions. Previous work demonstrated the potential of this approach, but it was initially applicable only to biomass burning aerosols with limited spatial coverage over ocean. The new methodology presented here generalizes the method for the most common aerosol types and improves spatial coverage. We take advantage of an operational aerosol type product derived from Visible Infrared Imaging Radiometer Suite (VIIRS) observations, combined with those from TROPOMI, to choose a priori aerosol set of intensive properties (particle size and refractive index). AEROS5P shows good agreement in aerosol optical depth, when compared to observations from AErosol RObotic NETwork and other widely used satellite measurements. Additionally, the vertical distribution of aerosol plumes of different types derived from AEROS5P agrees well with lidar coincident observations from the Advanced Topographic Laser Altimeter System spaceborne sensor and ground-based measurements. We utilize AEROS5P to analyze the three-dimensional distribution of aerosols for four different scenarios. It traces the three-dimensional pathways of smoke from Canadian wildfires reaching the Eastern USA in July 2023, desert dust from the Namibian deserts over the southeast Atlantic Ocean confined near the surface during the same month, fine aerosol pollution over Western Europe during June 2019, and an extreme dust event mixed with fine aerosol pollution over Northern China in March 2021. These observations provide valuable insights into aerosol transport patterns, surface-level air quality impacts, and can potentially be used to assess the radiative effects at various atmospheric layers.

Usefulness and limitations of various detector systems for estimation of 131I thyroid activity following an RN event

Following a radiological or nuclear (RN) event, rapid measurement of131I in members of the public is of utmost importance, and much equipment is needed for a high throughput. In this study, three gamma cameras (GCs), two thyroid uptake meters (TUMs) and one whole-body counter (WBC) were calibrated for activity measurements of131I in the thyroid. Minimum detectable activity was derived for the GCs, the TUMs and the WBC giving that a committed effective dose (CED) in the interval 2.0-85μSv, 13-700μSv and 0.52-6.4μSv, and thyroid absorbed doses in the interval 0.075-2.1 mGy, 0.48-17 mGy, and 0.020-0.15 mGy, respectively, can be assessed for children, adolescents, and adults. These numbers are based on 10 min measurement, performed at 1, 3 and 7 d after intake, and the CED includes intake by ingestion and inhalation of aerosols Type F, with an activity median aerodynamic diameter of 1μm. For a fractional signal loss of 63% due to dead time, a CED up to 2.0, 84 and 3.6 Sv and thyroid absorbed dose up to 47 Gy, 2000 Gy and 88 Gy for the three systems, respectively, can be assessed for children and intake by ingestion as a worst-case scenario in terms of CED, measured 7 d after intake. This study demonstrates the potential and limitations of using equipment readily available at larger hospitals for estimation of131I content in thyroid, which could increase the measurement capability following an RN event.

Aerosol type classification and its temporal distribution in Kanpur using ground-based remote sensing

Based on AERONET version 3 level 2 inversion products, we classify aerosol types and investigate their temporal distribution in the atmosphere using particle linear depolarization ratio (PLDR) and single scattering albedo (SSA) at the wavelength of 1020 nm over Kanpur. It is for the first time over the North Indian region the work has been emphasized. The remarkable findings over Kanpur station indicate that SSA and coarse-mode particles in the atmosphere increased with increasing PLDR at 440, 675, 870, and 1020 nm wavelengths. It is observed in the 2-dimensional histogram that the rate of occurrence of aerosols is high when the fine mode fraction (FMF) is high and the dust ratio (Rd) is low. The atmosphere of Kanpur is partially influenced by dust-dominated mixture (DDM), pollution-dominated mixture (PDM), and pure dust (PD) with 53% whereas, the rest of the dust-free pollution aerosols are 47%. The annual mean occurrence rate for different aerosol types is 5% for Strongly Absorbing (SA), 20% for Moderately Absorbing (MA), 19% for Weakly Absorbing (WA), 3% for Non-Absorbing (NA), 27% for DDM, 22% for PDM, and 4% for PD, ranging from January 2001 to January 2022. There is a variation in the distribution of various types of pollution particles, which is influenced by the changing seasons. The rate of occurrence of dust-free pollution aerosols is 47%, mostly observed throughout the post-monsoon and winter seasons. The PLDR values in the atmosphere of Kanpur are almost balanced equally because it is affected by both (dust and dust-free) pollution aerosols and the changes can be seen due to the frequent occurrence of dust storms and anthropogenic activities.

High-accuracy full-coverage PM2.5 retrieval from 2014 to 2023 over China based on satellite remote sensing and hierarchical deep learning model

ABSTRACT Obtaining precise ground-level fine particulate matter (PM2.5) information is significant for human health. Spatial PM2.5 maps can be obtained by remote sensing technology, but considerable uncertainty exists when suffering from high pollution with complicated aerosol types. To address this issue, we propose using a hierarchical machine learning model to retrieve high-accuracy and daily full-coverage PM2.5 concentrations from 2014 to 2023 in China. Our hierarchical model was validated by the sample-based 10 cross-validation . Results suggest that our model performs better in terms of RMSE of 12.12 µg/m3, MAE of 8.14 µg/m3 and R2 of 0.95 than traditional model with RMSE of 18.18 µg/m3, MAE of 12.21 µg/m3 and R2 of 0.89, showing 27.49–37.41% improvements for RMSE, 21.85–39.26% improvements for MAE and 8.31–15.39% improvements for R2 at three-folds samples. On longer time scales, our model also shows better results than previous studies. Additionally, for high-pollution provinces, our model can capture PM2.5 trends more preciously than the traditional model. Under severe haze, our hierarchical model can also rightly reflect PM2.5 changes. Overall, due to the hierarchical strategy, our ML-based model can obtain daily full-coverage PM2.5 maps in China with high accuracy and can be applied for follow-up studies.

Estimation of Aerosol Characteristics from Broadband Solar Radiation Measurements Carried Out in Southern Algeria

Aerosols in the atmosphere significantly reduce the solar radiation reaching the Earth’s surface through scattering and absorption processes. Knowing their properties becomes essential when we are interested in measuring solar radiation at a given location on the ground. The commonly used parameters that characterize their effects are the Aerosol Optical Depth τ, the Angstrom exponent α, and the Angstrom coefficient β. One method for estimating these parameters is to fit ground-based measurements of clear-sky direct solar radiation using a model on which it depends. However, the choice of model depends on its suitability to the atmospheric conditions of the site considered. Eleven empirical solar radiation models depending on α and β were thus chosen and tested with solar radiation measurements recorded between 2005 and 2014 in Tamanrasset in southern Algeria. The results obtained were compared to measurements made with the AERONET solar photometer on the same site during the same period. Among the 11 models chosen, the best performing ones are REST2 and CPCR2. They proved to be the best suited to estimate β with approximately the same RMSE of 0.05 and a correlation coefficient R with respect to AERONET of 0.95. The results also highlighted good performances of these models for the estimation of τ with an RMSE of 0.05 and 0.04, and an R of 0.95 and 0.96, respectively. The values of α obtained from the fitting of these models were, however, less good, with R around 0.38. Additional treatments based on a Recurrent Neural Network (RNN) were necessary to improve its estimation. They provided promising results showing a significant improvement in α estimates with R reaching 0.7 when referring to AERONET data. Furthermore, this parameter made it possible to identify different types of aerosols in Tamanrasset such as the presence of maritime, dust, and mixed aerosols representing, respectively, 31.21%, 3.25%, and 65.54%, proportions calculated over the entire period studied. The seasonal analysis showed that maritime aerosols are predominant in the winter in Tamanrasset but decrease with the seasons to reach a minimum in the summer (JJA). Dust aerosols appear in February and persist mainly in the spring (MAM) and summer (JJA), then disappear in September. These results are also consistent with those obtained from AERONET.

Influence of columnar versus vertical distribution of aerosol properties on the modulation of shortwave radiative effects

Quantifying the interaction of atmospheric aerosols with incoming solar radiation remains a challenge owing to the limitations associated with measuring aerosol optical properties. This study investigates how the distribution of aerosol properties, whether columnar or vertical, affects the aerosol radiative forcing (ARF) and heating rates (HRs) across different atmospheric layers under cloud-free conditions in the shortwave region. We also assess the atmospheric parameters, namely, pressure, temperature, water vapour density, and ozone density, from in-situ measurements, reanalysis data, and a standard tropical atmosphere to understand their impact on ARF and HR estimates across seasons. Our findings show that aerosol absorption is highest during monsoon, while it is lowest in the winter. Significant atmospheric warming due to aerosols resulted from the substantial cooling at the surface. Columnar properties of aerosols measured at limited or multiple wavelengths yield similar ARF and HR estimates, provided spectral dependency is considered using the Angstrom exponent across seasons. However, the vertical profiles of aerosol extinction, together with a constant single scattering albedo (SSA) along the atmospheric column versus an actual SSA profile, led to notable differences in ARF and HRs, specifically in pre-monsoon and monsoon periods. Free tropospheric aerosol absorption is underestimated when using columnar properties compared to vertical distribution, while boundary layer absorption is overestimated (> 10 Wm-2). The heterogeneity in aerosol types across atmospheric layers significantly influenced aerosol absorption, highlighting the importance of accurate vertical distribution information. HR profiles obtained with vertical distribution reflect the structure of aerosol extinction, whereas those estimated with columnar properties result in smoother profiles that fail to capture altitude gradients. Aerosol-induced HRs are higher within the boundary layer and free troposphere in the monsoon season for all scenarios of defined aerosol properties. These findings underscore the need for actual vertical profile measurements of aerosol properties to quantify aerosol radiation interaction.

Submicron Organic Aerosol Types in the Summertime Arctic: Mixing State, Geographic Distribution, and Drivers

AbstractDuring the 2017 summertime Arctic cruise observation campaigns, we measured over 290,000 individual submicron particles and clustered them into two inorganic classes (dominated by sea salt, accounting for 38.6% by number fraction) and five organic classes (dominated by natural and anthropogenic organics, 61.4%), presenting a distinct difference in geographic distribution. In the high Arctic and marginal ice zone (81.1–84.6°N) compared with the low Arctic (Chukchi Sea, Svalbard, and Iceland, <80°N), ocean‐derived organic aerosols were more prevalent (73.6% vs. 37.1%). Specifically, we found sharp contrasts in the geographic distributions of OC‐Ca (organics internally mixed with calcium, 29.0% vs. 9.4%) and OC‐S (organics internally mixed with sulfate, 3.2% vs. 21.4%). Utilizing an explainable machine learning technique, we inferred that OC‐Ca was driven by wind‐blown sea ice and/or sea ice floes and/or bubble bursting within sea ice leads under low wind speed conditions in the high Arctic, while OC‐S tended to associate with elemental carbon, sulfate, and higher temperatures, potentially originating from combustion emissions at low latitude regions.

In chamber calibration and performance evaluation of air quality low-cost sensors

Assessing individual exposure to PM2.5 (particulate matter of aerodynamic diameter lesser than 2.5 μm) requires precise monitoring of PM2.5 concentrations at specific geographical and temporal scales. This demand is met globally by low-cost particulate matter sensors, although calibrating them is difficult. In this study, four low-cost PM sensors, Sharp GP2Y1010AU0F, Honeywell HPMA115S0-XXX, Plantower PMSA003-A, and Sensirion SPS30, were calibrated and tested using various aerosols. The calibration method has three steps: individual (considering each sensor independently to a single aerosol type; n = 1), combined (all sensors for a specific model together for a specific aerosol type; n = 4), and generic (all sensors for a given model together to all aerosols; n = 16). Sensor responses are processed using linear, quadratic, power-law, and artificial neural network (ANN) algorithms in each calibration stage. Performance metrics, including coefficient of determination (R2), mean absolute percentage error (MAPE), root mean square error (RMSE), and percentage coefficient of variation (% CV), were utilized for assessment. Amongst all the four tested sensors, the Sensirion SPS30 sensors gave the best performance with a minimum R2 value of 0.911 when calibrated with a generic ANN calibration algorithm. Also, the MAPE was less than 10 %, and the RMSE was less than 7 % when exposed to different particles. Sensirion SPS30 showed the lowest inter-sensor variability with % CV less than 6 %. Sensors identified monodisperse polystyrene latex (PSL) particle size in the investigation. Regardless of exposure to 0.3, 0.46, 0.60, or 1.0 μm PSL, the reported number size distribution for the PMSA003 sensor remained consistent and did not align with the results from Grimm. As the PSL size rose, the SPS30 size distribution changed towards larger particle sizes, although it did not always match Grimm data. As the PSL size increased, the sensor's PM1, PM2.5, and PM10 mass proportions altered.

Observation and Classification of Low-Altitude Haze Aerosols Using Fluorescence–Raman–Mie Polarization Lidar in Beijing during Spring 2024

Haze aerosols have a profound impact on air quality and pose serious health risks to the public. Due to its geographical location, Beijing experienced haze events in the spring of 2024. Lidar is an active remote sensing technology with a high spatiotemporal resolution and the ability to classify aerosols, and it is essential for effective haze monitoring. This study utilizes fluorescence–Raman–Mie polarization lidar with an emission wavelength of 355 nm, employing the δp-Gf method based on the particle depolarization ratio at 355 nm (δp355) and the fluorescence capacity (Gf), and combines meteorological data and backward-trajectory analysis to observe and classify low-altitude haze aerosols in Beijing during the spring of 2024. Notably, a mining dust event with strong fluorescence backscatter was detected. The haze aerosols were categorized into three types: pollution aerosols, desert dust, and mining dust. Their optical properties were summarized and compared. Desert dust showed a particle depolarization ratio range of 0.23–0.39 and a fluorescence capacity range from 0.18 × 10−4 to 0.63 × 10−4. Pollution aerosols had a larger fluorescence capacity but a lower depolarization ratio compared to desert dust, with a fluorescence capacity ranging from 0.55 × 10−4 to 1.10 × 10−4 and a depolarization ratio ranging from 0.10 to 0.17. Mining dust shared similar depolarization characteristics with desert dust but had a larger fluorescence capacity, ranging from 0.71 × 10−4 to 1.23 × 10−4, with a depolarization ratio range of 0.30–0.39. This study validates the effectiveness of the δp355-Gf method in classifying low-altitude haze aerosols in Beijing. Additionally, it offers a new perspective for more detailed dust classification using lidar. Furthermore, utilizing the δp355-Gf classification method and the δp355-Gf distributions of three typical aerosol samples, we developed a set of equations for the analysis of mixed aerosols. This method facilitates the separation and fraction analysis of aerosol components under various mixing scenarios. It enables the characterization of variations in the three types of haze aerosols at different altitudes and times, offering valuable insights into the interactions between desert dust, mining dust, and pollution aerosols in Beijing.

Influence of soot aerosol properties on the counting efficiency of instruments used for the periodic technical inspection of diesel vehicles

Abstract. In this work, we investigated the influence of different types of soot aerosol on the counting efficiency (CE) of instruments employed for the periodic technical inspection (PTI) of diesel vehicles. Such instruments report particle number (PN) concentration. Combustion aerosols were generated by a prototype bigCAST, a miniCAST 5201 BC, a miniCAST 6204 C, and a miniature inverted soot generator (MISG). For comparison purposes, diesel soot was generated by a Euro 5b diesel test vehicle with by-passed diesel particulate filter (DPF). The size-dependent counting efficiency profile of six PN–PTI instruments was determined with each one of the aforementioned test aerosols. The results showed that the type of soot aerosol affected the response of the PN–PTI sensors in an individualised manner. Consequently, it was difficult to identify trends and draw conclusive results about which laboratory-generated soot is the best proxy for diesel soot. Deviations in the counting efficiency remained typically within 0.25 units when using laboratory-generated soot compared to Euro 5b diesel soot of similar mobility diameter (∼ 50–60 nm). Soot with a mobility diameter of ∼ 100 nm generated by the MISG, the lowest size we could achieve, resulted in most cases in similar counting efficiencies as those generated by the different CAST generators at the same particle size, showing that MISG may be a satisfactory – and affordable – option for PN–PTI verification; however, further optimisation will be needed for low-cost soot generators to comply with European PN–PTI verification requirements.

First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia

Abstract. Aerosol optical properties have been provided by the Geostationary Environment Monitoring Spectrometer (GEMS), the world's first geostationary-Earth-orbit (GEO) satellite instrument designed for air quality monitoring. This study describes improvements made to the GEMS aerosol retrieval (AERAOD) algorithm, including spectral binning, surface reflectance estimation, cloud masking, and post-processing, along with validation results. These enhancements aim to provide more accurate and reliable aerosol-monitoring results for Asia. The adoption of spectral binning in the lookup table (LUT) approach reduces random errors and enhances the stability of satellite measurements. In addition, we introduced a new high-resolution database for surface reflectance estimation based on the minimum-reflectance method, which was adapted to the GEMS pixel resolution. Monthly background aerosol optical depth (BAOD) values were used to estimate hourly GEMS surface reflectance consistently. Advanced cloud-removal techniques have been implemented to significantly improve the effectiveness of cloud detection and enhance aerosol retrieval quality. An innovative post-processing correction method based on machine learning has been introduced to address artificial diurnal biases in aerosol optical depth (AOD) observations. In this study, we investigated selected aerosol events, highlighting the capability of GEMS in monitoring and providing insights into hourly aerosol optical properties during various atmospheric events. The performance of the GEMS AERAOD products was validated against the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data for the period from November 2021 to October 2022. GEMS AOD at 443 nm demonstrated a strong correlation with AERONET AOD at 443 nm (R = 0.792). However, it exhibited biased patterns, including the underestimation of high AOD values and overestimation of low-AOD conditions. Different aerosol types (highly absorbing fine aerosols, dust aerosols, and non-absorbing aerosols) exhibited distinct validation results. The retrievals of GEMS single-scattering albedo (SSA) at 443 nm agreed well with the AERONET SSA at 440 nm within reasonable error ranges, with variations observed among aerosol types. For GEMS AOD at 443 nm exceeding 0.4 (1.0), 42.76 % (56.61 %) and 67.25 % (85.70 %) of GEMS SSA data points fell within the ±0.03 and ±0.05 error bounds, respectively. Model-enforced post-processing correction improved GEMS AOD and SSA performance, thereby reducing the diurnal variation in the biases. The validation of the retrievals of GEMS aerosol layer height (ALH) against the CALIOP data demonstrates good agreement, with a mean bias of −0.225 km and 55.29 % (71.70 %) of data points falling within ±1 km (1.5 km).

Comparisons of aerosol types and optical characters over Shouxian Area China observed from ground- and space-based systems.

This study evaluates the performance of moderate-resolution Imaging spectroradiometer (MODIS) in aerosol optical depth(AOD) and Ångström exponent(AE) retrievals under high aerosol loading conditions across various aerosol types, utilizing ground-based and space-borne aerosol measurements in Shouxian, China. The intercomparison reveals cloud-aerosol LiDAR with orthogonal polarization's (CALIOP) efficacy in detecting significant aerosol layers and the refinement of sunphotometer-based aerosol type classification through CALIPSO, achieving approximately 80% accuracy. Analysis of 2016-2017 data indicates substantial aerosol presence in Shouxian, with monthly mean AODs ranging from 0.35 to 0.72 at 550 nm, significantly above the global average. The predominant aerosol types were mixed-type (54.8%), desert dust (21.2%), urban/industrial(15.5%), biomass-burning aerosol (6.4%), and continental aerosol (12.1%), with frequent observations of elevated long-range transported aerosol layers. MODIS AOD retrievals generally align with sunphotometer measurements but exhibit higher biases, especially with increasing AOD magnitudes. However, there is a notable difference between MODIS and sunphotometer aerosol AE measurements, with MODIS accurately assessing BBA but showing varied performance across other aerosol types. The combination of AOD and AE of the DD aerosol type is the most accurate. Further analysis showed that MODIS AOD biases and AE biases are negatively correlated, these negative bias correlations show strong aerosol type sensitivities. Monthly analysis of MODIS and sunphotometer comparisons highlights varying performance, particularly during normalized difference vegetation index (NDVI) transitions, suggesting that local vegetation cycles and associated surface spectral reflectance changes significantly impact MODIS aerosol retrieval accuracy under high aerosol loading conditions.

Retrieval and analysis of the composition of an aerosol mixture through Mie–Raman–fluorescence lidar observations

Abstract. In the atmosphere, aerosols can originate from numerous sources, leading to the mixing of different particle types. This paper introduces an approach to the partitioning of aerosol mixtures in terms of backscattering coefficients. The method utilizes data collected from the Mie–Raman–fluorescence lidar, with the primary input information being the aerosol backscattering coefficient (β), particle depolarization ratio (δ), and fluorescence capacity (GF). The fluorescence capacity is defined as the ratio of the fluorescence backscattering coefficient to the particle backscattering coefficient at the laser wavelength. By solving a system of equations that model these three properties (β, δ and GF), it is possible to characterize a three-component aerosol mixture. Specifically, the paper assesses the contributions of smoke, urban, and dust aerosols to the overall backscattering coefficient at 532 nm. It is important to note that aerosol properties (δ and GF) may exhibit variations even within a specified aerosol type. To estimate the associated uncertainty, we employ the Monte Carlo technique, which assumes that GF and δ are random values uniformly distributed within predefined intervals. In each Monte Carlo run, a solution is obtained. Rather than relying on a singular solution, an average is computed across the whole set of solutions, and their dispersion serves as a metric for method uncertainty. This methodology was tested using observations conducted at the ATOLL (ATmospheric Observation at liLLe) observatory, Laboratoire d'Optique Atmosphérique, University of Lille, France.

Simulation Analysis on the Characteristics of Aerosol Particles to Inhibit the Infrared Radiation of Exhaust Plumes.

Aerosol infrared stealth technology is a highly effective method to reduce the intensity of infrared radiation by releasing aerosol particles around the hot exhaust plume. This paper uses a Computational Fluid Dynamics (CFD) two-phase flow model to simulate the exhaust plume fields of three kinds of engine nozzles containing aerosol particles. The Planck-weighted narrow spectral band gas model and the Reverse Monte Carlo method are used for infrared radiation transfer calculations to analyze the influencing factors and laws for the suppression of the infrared radiation properties of exhaust plumes by four typical aerosol particles. The simulation calculation results show that the radiation suppression efficiency of aerosol particles on the exhaust plume reaches its maximum value at a detection angle (ϕ) of 0° and decreases with increasing ϕ, reaching its minimum value at 90°. Reducing the aerosol particle size and increasing the aerosol mass flux can enhance the suppression effect. In the exhaust plume studied in this paper, the radiation suppression effect is best when the particle size is 1 μm and the mass flux is 0.08 kg/s. In addition, the inhibition of aerosol particles varies among different materials, with graphite having the best inhibition effect, followed by H2O, MgO, and SiO2. Solid particles will increase the radiation intensity and change the spectral radiation characteristics of the exhaust plume at detection angles close to the vertical nozzle axis due to the scattering effect. Finally, this paper analyzed the suppression effects of three standard nozzle configurations under the same aerosol particle condition and found that the S-bend nozzle provides better suppression.

Characterization of aerosol composition: Insights from SEM-EDX analysis and CALIPSO overpasses

The effect of atmospheric aerosols on the earth’s radiation budget is primarily through their direct effects. Understanding aerosols’ morphology and elemental composition is crucial in this context. For two years, from January 2017 to December 2018, aerosol samples (PM10) were collected on quartz filter papers using a high-volume sampler (PM10) in Chennai, a semi-urban coastal environment located at 12.81°N, 80.03°E. These filter papers were stored under ambient laboratory conditions and were subsequently subjected to ultrasonication using ethanol as the solvent to separate the aerosols from the filters. The extracted samples were then analyzed using scanning electron microscopy (SEM) for the morphology and energy dispersive X-ray analysis (EDX) for elemental composition. The EDX analysis of the samples revealed the presence of various elements such as carbon (C), oxygen (O), aluminum (Al), silicon (Si), sulfur (S), potassium (K), iron (Fe), magnesium (Mg), calcium (Ca), vanadium (V), bismuth (Bi), sodium (Na), chlorine (Cl), phosphorus (P), and technetium (Tc). High concentrations of carbon, oxygen, and aluminum–silicate particles indicated the presence of carbonaceous aerosols and wind-blown dust particles. Concentration-weighted trajectory (CWT) analysis was carried out to assess the transport pathways of aerosols in the region. CALIPSO overpasses during the study period were selected, and the Vertical Feature Mask (VFM) identifiers for different aerosol types were compared with the local experimental results. The findings show that CALIPSO possess a reasonably effective capability in detecting and characterizing the local aerosol types. CALIPSO detected Dusty marine, Clean marine, Elevated smoke and Polluted dust in the study area across all four seasons. These findings closely match the characteristics of such aerosol types obtained from SEM/EDX analysis, HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) modeling, and the Angstrom exponent derived from sunphotometer readings.

Comparison of Health Complaints, Occupational Risks, and Occupational Health Practices of Healthcare Workers According to Professions and Departments in the Hospital.

It is essential to protect the health of healthcare workers who constitute a large part of the workforce worldwide and whose importance has become more evident after the recent pandemic. There are numerous occupational hazards for healthcare workers in hospitals. The study aims to assess the exposure hazards of healthcare workers and their health complaints, as well as their awareness, knowledge, opinions, and attitudes towards occupational health and safety (OHS), considering workers' professions and departments in a public hospital. This cross-sectional study conducted a survey among healthcare workers (n=608) who worked at Yozgat City Hospital, Yozgat, Turkey. The majority of workers were nurses (43.4%, N=264/608). Latex exposure (63.7%, N=387/608) and noise (55.8%, N=339/608) were the most common exposed hazards, and the risk varies depending on their professions and job descriptions. However, the risk perception of workers was priorities of infectious diseases (48.5%, N=292/602) and violence (27.4%, N=165/602). Musculoskeletal system problems (71.9%, n=439/608) were observed very frequently in workers. Additionally, 9.2% (N=56/608) of workers were diagnosed with an occupational disease. The unit and profession most commonly diagnosed with occupational diseases were the laboratory (22.5%, N=9/40) and midwives (19.4%, N=14/72), respectively. The frequency of workers who stated that they had a work accident at least once in their lives was 31.9% (N=194/608), and higher frequencies belonged to nurses, health officers, and midwives. Additionally, the emergency department was the riskier unit. The study conducted relationship analyses between exposure to various occupational agents at different exposure frequencies and various health complaints. The relationships of occupational hazards such as chemotherapeutics, anesthetic gases, aerosol type drugs, sterilization and disinfection agents, xylene, toluene, formaldehyde, and surgical smoke with health complaints such as liver, dermal diseases, respiratory problems, and varicose veins have been determined. The hospital workers had a high rate of injuries to sharp objects and musculoskeletal systems. Remarkably, operating rooms and emergency rooms were found to be riskier in terms of work accidents. More than half of healthcare workers may delay using personal protective equipment (PPE) due to excessive workload. Further studies are needed on the effects of more specific occupational chemicals and diseases, such as varicose veins, fertility, and neurological problems. Frequent risk assessments, effective training, workload reduction, and biomarker monitoring are crucial for hospital workplace safety.

Transport of the Hunga volcanic aerosols inferred from Himawari-8/9 limb measurements

Abstract. The Hunga volcano (21.545° S, 178.393° E; also known as Hunga Tonga-Hunga Ha′apai) erupted on 15 January 2022, producing copious amounts of aerosols that reached high into the stratosphere, exceeding 30 km and settling into layers a few kilometres deep between 22 and 28 km. The Advanced Himawari Imager (AHI) on board the geostationary Himawari-8/9 platform at 140.7° E was able to monitor the eruption at 10 min intervals and 0.25 to 4 km2 spatial resolution within 16 spectral channels ranging from visible to infrared wavelengths and over a latitude–longitude field of view of ∼ ±75°. Here a new use of these data is proposed where the limb region of the field of view is exploited to detect aerosol layers extending vertically into the atmosphere. The analyses provide vertical profiles of scattered visible light and are compared to CALIOP space lidar measurements. Hunga aerosols are detected using the ratio of near-infrared reflectances at 1.61 and 2.25 µmm in the western limb from 22 January and in the eastern limb from 31 January 2022 up until the present time (December 2023). Between January and April 2022, the average zonal velocity is estimated to be ∼ −25 m s−1 (westwards) and the meridional velocity to be ∼ 0.2 m s−1 (northwards). The latitudinal spread is characterized by a gradual northerly movement of the main layer situated between 22 and 28 km in the first 60 d, and stagnation or slight southerly spread thereafter. There is a shallow maximum of the lower stratospheric aerosol between 10 and 20° S, and the aerosol loading during 2023 is elevated compared with the 3 months prior to the eruption. The Southern Hemisphere (0–30° S) tropical lower stratospheric aerosol e-folding time is estimated to be ∼ 12 months, but the decay is not uniform and has high variability. The current methodology does not provide quantitative estimates of the amount or type of aerosol, but based on the spectral properties of water and ice clouds the analysis suggests there is a strong liquid water content in the aerosol layers.