Search Results (368)

Multi-wavelength Raman–Mie polarization lidar provides vertically resolved measurements of aerosol scattering, particle shape, and wavelength-dependent response. We analyzed quality-controlled clear-sky observations from June 2025 to March 2026 to identify aerosol optical regimes at inland Beijing Nanjiao and coastal Beihai. The feature space comprised ${\log }_{10}\left({\beta }_{532}\right)$, ${\delta }_{p,532}$, and ${\mathrm{AE}}_{\beta ,355/532}$, representing scattering intensity, particle nonsphericity, and size-sensitive spectral response. K-means was applied independently at each site. At Beijing Nanjiao, a high-depolarization, strong-scattering, and low-${\mathrm{AE}}_{\beta ,355/532}$ regime was concentrated in the lowest observed layer, whereas lower-loading regimes occurred more frequently aloft. Its dust AOD and dust fraction were descriptively 37.5% and 19.2% above the site means, although the dust-related inter-regime differences were not significant after FDR correction. Beihai was dominated by low-depolarization regimes separated mainly by scattering intensity and wavelength response. Its strongest-scattering regime showed total, sea salt, OC, and sulfate AOD enhancements of 33.0%, 15.5%, 24.5%, and 41.9%, respectively; the inter-regime differences were significant for total and sulfate AODs but not for sea salt AOD. MERRA-2 aerosol diagnostics and trajectory analyses provided auxiliary regional context for interpreting the lidar-defined optical states and were not used as clustering inputs or direct chemical validation. These results demonstrate that combined polarization and multi-wavelength lidar sensing can distinguish vertically varying aerosol optical states that are obscured in surface or column-integrated observations. Full article

Oxygen-related absorption features provide fundamental constraints for passive atmospheric remote sensing in the reflected-solar spectrum. Because molecular oxygen is well-mixed in the dry atmosphere, O₂ absorption links measured radiance to atmospheric mass, pressure, and effective photon path length, while O₂-O₂ (O₄) collision-induced absorption provides complementary sensitivity to lower-tropospheric photon paths. This review synthesizes the spectroscopic basis, radiative-transfer mechanisms, satellite implementations, retrieval algorithms, and atmospheric applications of O₂ and O₄ measurements from the ultraviolet to the shortwave infrared. Particular emphasis is placed on the O₂ B-band near 687 nm, the O₂ A-band near 760 nm, O₄ bands in the UV–visible range, and the O₂ band near 1.27 µm. These features support retrievals of cloud fraction, cloud pressure, optical centroid pressure, aerosol layer height, surface pressure, dry-air column abundance, and light-path corrections for greenhouse gas observations. We review major algorithmic approaches, including cloud-as-reflecting-boundary models, cloud-as-layer models, DOAS-based retrievals, optimal-estimation frameworks, photon path-length distribution methods, and machine learning or hybrid techniques. Key applications include cloud climatology, aerosol vertical characterization, air mass factor correction, XCO₂ and XCH₄ retrievals, carbon-cycle studies, and multi-mission data integration. Remaining challenges include spectroscopic uncertainty, aerosol and cloud scattering degeneracy, surface bidirectional reflectance, three-dimensional radiative-transfer effects, wavelength-dependent path mismatch, and inconsistent uncertainty characterization. Future progress will depend on improved spectroscopy, active–passive validation, multi-angle polarimetry, physically constrained machine learning, and harmonized multi-mission retrieval frameworks. Full article

Mass concentrations of particulate matter are a fundamental metric for air quality and health impact assessment; however, they are insufficient for accurately characterizing exposure. They do not capture particle size distribution or number concentration. Therefore, aerosol assessment should include particle number concentration (PNC), which better represents toxicologically relevant fractions and enables more precise source identification. The aim of this study was to conduct a comprehensive evaluation of particle number concentration (PNC) measurements in the 0.3–10 µm size range obtained using three low-cost Sensirion SPS30 particle sensors under field conditions in an urban environment. The analyses included an assessment of agreement between the SPS30 sensors, an evaluation of their measurement performance against the OPS 3330 optical particle spectrometer, and the development of calibration models. The SPS30 sensors showed high inter-device repeatability for PNC in the 0.3–1.0 µm range (CV_d < 2%). However, measurement performance declined with increasing particle size, with the index of agreement (IOA) decreasing from 0.8 (0.3–0.5 µm) to −0.5 (2.5–10 µm). Sensor accuracy was influenced by meteorological conditions: relative humidity primarily affected short-term variability (precision and dynamic agreement), while temperature controlled systematic bias. Although incorporating these variables into advanced calibration models improved performance, SPS30 sensors remained unsuitable for PNC measurements in the 2.5–10 µm range, exhibiting systematic errors of ~25% even after nonlinear correction. The findings support the responsible use of low-cost particle sensors for supplementary air quality monitoring, contributing to accessible environmental data and sustainable urban air quality management. Full article

Inhalable ribonucleic acid (RNA) nanomedicines are emerging as versatile therapeutics for respiratory diseases and pulmonary metastases, enabling localized delivery of messenger RNA (mRNA), small interfering RNA (siRNA), antisense oligonucleotides, microRNA (miRNA) mimics, self-amplifying RNA, and genome-editing systems. This review synthesizes the available evidence and argues that the field has moved beyond asking whether RNA can reach the lungs. The more consequential translational question is whether RNA cargo, nanocarrier, excipients, manufacturing process, inhalation device, and pulmonary target cell can be integrated into a reproducible therapeutic product. Current research demonstrates progress in disease-corrective mRNA expression, silencing of inflammatory and fibrotic pathways, mucosal vaccination, antiviral therapy, and localized cancer treatment, alongside advances in ionizable lipid nanoparticles, lipid–polymer hybrids, chitosan and polyethyleneimine (PEI) polyplexes, dendrimers, peptide carriers, biomimetic systems, and dry-powder formulations. Translational maturity, however, remains uneven. Many studies demonstrate carrier feasibility, reporter expression, or preclinical activity, whereas fewer establish device-compatible aerosolization, preservation of RNA integrity during processing, traversal of pulmonary barriers, target-cell engagement, repeat-dose tolerability, and clinically meaningful benefit. Development should therefore be target-defined, analytically gated, device-specific, and outcome-centered. Inhalable RNA nanomedicines are best understood as integrated pulmonary products whose success depends on preserving RNA function throughout manufacturing, aerosolization, post-deposition barrier navigation, intracellular delivery, and disease-relevant pharmacodynamic activity. Full article

Atmospheric correction of high-spatial-resolution (HSR) optical satellite imagery is strongly affected by the adjacency effect (AE). Conventional Atmospheric Point Spread Function (APSF)-based AE correction methods are often based on simple local averaging or distance-weighted background approximations. These are often insufficient for highly heterogeneous HSR scenes and become computationally expensive when the AE’s influence range spans far more pixels. To address these issues, this study proposes a fast AE correction algorithm with adaptive local surface constraints. The method first introduces a surface-atmosphere coupling correction based on effective reflectance. It then constructs downward- and upward-weighting kernels and incorporates local reflectance constraints into the estimation of environmental reflectance to better characterize AE intensity in HSR scenes. Since environmental reflectance estimation is retained in a kernel-weighted form, the infinite-domain integration is reformulated as a finite-window computation with truncation compensation and accelerated via fast Fourier transform (FFT) convolution, followed by a few iterations for reflectance retrieval. Validation with GaoFen-2 (GF-2) panchromatic imagery shows that, at an aerosol optical depth of about 0.4, the proposed method achieves the best performance among the compared methods, with a mean absolute error (MAE) below 0.006 relative to in situ measurements, sharpness and contrast increases of approximately $99.0\%$ and $97.9\%$, respectively, and a National Imagery Interpretability Rating Scale (NIIRS) increase of more than 0.3. For a $1024\times 1024$ image with a 501-pixel AE window diameter, the running time is below 4 s, substantially lower than that of previous APSF-based AE correction methods. The FFT implementation also avoids the quadratic dependence on window size in direct spatial convolution. Additional experiments on multiple GF-2 and Gao Fen Duo Mo scenes show that the proposed method provides stable AE correction and achieves higher image quality and visual interpretability than the compared methods in HSR imagery. Full article

Accurate, large-scale maritime traffic monitoring supports marine spatial planning, fishery regulation, and ecological conservation. Medium-resolution optical satellite imagery, such as Sentinel-2A/B, provides a cost-effective complement to incomplete Automatic Identification System (AIS) data. Detecting small vessels in coastal waters remains challenging due to target size, complex backgrounds, and class imbalance. This study presents a robust end-to-end framework for small vessel detection and traffic density mapping using optical remote sensing imagery. A high-quality dataset of 8123 manually annotated vessels was constructed from 14 Sentinel-2 scenes across three marine environments in Fujian, China. An overlapping sliding-window cropping strategy, area truncation filtering, and negative sample preservation improved training efficiency and balance. Experiments compared top-of-atmosphere reflectance with surface reflectance (L2R) from the ACOLITE atmospheric correction (AC) algorithm, showing L2R mitigates aerosol scattering and nearly doubles vessel edge sharpness. YOLO-based detectors were evaluated, with YOLO26m achieving the best localization: F1-score 0.8461, mAP50 0.8979, mAP50–95 0.5536. Using this framework, 1 × 1 km traffic heatmaps for the Fujian coast in 2025 captured seasonal variations influenced by logistics and fishery moratoriums. Results demonstrate that integrating atmospherically corrected imagery with optimized deep learning strategies enhances sub-pixel ship detection, offering a scalable solution for intelligent maritime governance. Full article

Recently, numerous studies have exploited satellite Aerosol Optical Depth (AOD) to estimate near-surface particulate matter (PM) concentrations, with the aim of overcoming the limited spatial and temporal coverage of ground-based air quality monitoring networks. Despite significant progress, the relationship between AOD and PM remains highly uncertain, mainly due to the inadequate representation of local aerosol microphysical properties and of hygroscopic growth effects. In particular, satellite AOD is retrieved at ambient relative humidity, whereas standard PM measurements are performed under dry conditions. This study proposes a physics-informed, semi-empirical approach that overcomes these limitations by directly relating satellite AOD to PM measured at ambient humidity. Co-located measurements, from a Light Optical Aerosol Counter (LOAC) in the urban area of Bologna (Po Valley, Italy) during 2023, are used. This study is designed as a pilot application to evaluate the physical consistency of the proposed framework under well-characterised observational conditions, including spatial co-location, temporal matching to satellite overpasses, and exclusion of precipitation and desert dust events. The LOAC provides particle number size distribution and particle-type classification, which are used to estimate key aerosol properties controlling the AOD–PM theoretical relationship, including the Effective Radius, Extinction Efficiency, and aerosol Mass Density. These quantities, together with Mixing Layer Height, are combined within a theoretical framework linking PM and AOD, allowing for the derivation of a physically based scaling coefficient without relying on empirical hygroscopic growth corrections. The results show that using ambient PM_2.5 alone already yields a moderate linear correlation with AOD normalized by Mixing Layer Height (Pearson’s R = 0.56) whereas no meaningful correlation is found when using standard dry PM_2.5. When aerosol microphysical properties derived from LOAC measurements are incorporated, the correlation substantially improves (R = 0.76), with regression slopes close to unity and reduced errors, independently of the season. These results demonstrate that explicitly accounting for aerosol size and optical properties enhances the physical consistency and robustness of satellite-based PM estimates. The proposed framework also provides a pathway to indirectly derive aerosol hygroscopic growth factors by coupling ambient PM estimates from satellite observations with conventional dry PM measurements. This opens new perspectives for characterizing aerosol–humidity interactions from space and for improving air quality monitoring in regions lacking of dense in situ networks. Full article

Accurate surface downward shortwave radiation (SDSR) is essential for solar resource assessment, photovoltaic applications, and land–atmosphere studies. Although ERA5 is widely used in radiation-related research, its SDSR estimates over China still show considerable uncertainties under complex topographic and climatic conditions. Using hourly observations from the 162-station China Meteorological Administration (CMA) radiation network during April 2024–March 2025, of which 160 stations were retained after quality control, this study systematically evaluated ERA5 SDSR and developed a physics-guided Light Gradient Boosting Machine (LightGBM) correction framework. Raw ERA5 exhibits a strong systematic positive bias (PBIAS = 57.40%, ME = 124.2 W/m²) together with a pronounced nonlinear structural bias, characterized by overestimation under low-radiation conditions and underestimation under high-radiation conditions. The largest errors occur in the Southern Monsoon region in summer and the Northwest Arid region in spring, indicating the combined effects of cloud extinction, aerosol attenuation, and terrain-related representativeness differences. To address these mechanisms, the correction model incorporates physically relevant predictors from ERA5 and Copernicus Atmosphere Monitoring Service (CAMS), including cloud microphysical variables, aerosol optical depth, solar geometry, and elevation. SHapley Additive exPlanations (SHAP) analysis shows that the learned correction behavior is broadly consistent with known radiative-transfer processes. On the independent station hold-out test set, the correction increases the Pearson correlation coefficient from 0.8680 to 0.8967 and reduces RMSE from 173.1 to 100.8 W/m², while substantially suppressing the strong positive bias of raw ERA5. Additional robustness tests, including season-blocked validation, interpolation-sensitivity analysis, ablation experiments, and multi-model comparison, further support the stability of the framework. External benchmarking against FY-4B and Himawari also shows that the corrected ERA5 substantially narrows the gap relative to independent geostationary satellite products. Overall, the proposed framework provides an effective and physically interpretable approach for improving ERA5 SDSR over China. Full article

Estimating the joint probability density of seasonal geophysical variables like Aerosol Optical Depth (AOD) and Day of Year (DOY) presents three unresolved challenges. The first of these challenges is the periodic nature of the temporal axis. The second is the physically distinct scales of the two variables. The third is the marginal inconsistency introduced by smoothing operations. Existing methods for estimating probability density do not address each of these challenges simultaneously. Here we introduce CADS (the Circular-Adaptive Density Smoother), a computationally efficient algorithm for estimating the joint probability density of two seasonal geophysical variables that simultaneously addresses each of these three challenges. CADS is evaluated on two synthetic datasets and one real observational dataset of AERONET measurements from NASA Ames ($n=74,653$) using five-fold cross-validation. CADS achieves the highest mean log-likelihood relative to other methods for estimating probability density, and it is approximately 3000 times faster than kernel density estimation. An ablation study confirms that each component of CADS contributes independently to its high performance. Finally, a novel metric for evaluating the geometric correctness of the treatment of the circular boundary of the DOY variable is introduced. Full article

Ammonia (NH₃) is an important alkaline gas and a key precursor to secondary inorganic aerosol. In the Fen River valley, coking plants are concentrated due to transportation advantages, while NH₃ emissions from coking processes have received limited attention despite their potential importance. In this study, atmospheric NH₃ was sampled by OGAWA samplers in a typical coal coking industrial park in Taiyuan during autumn and winter of 2024–2025, and its nitrogen isotopic composition was used for source apportionment. The results showed that the NH₃ concentration in the industrial park was 27.4 ± 3.8 μg m⁻³, significantly higher than that in the urban area (9.3 ± 4.2 μg m⁻³) and higher than winter levels reported for North China cities. The δ¹⁵N-NH₃ was −29.7 ± 1.6‰ and increased to −14.7 ± 1.6‰ after correcting for passive sampling bias. Source apportionment further indicated that NH₃ in the industrial park was dominated by non-agricultural sources (80.7%), with ammonia slip as the largest contributor (34.2 ± 20.1%), followed by coal combustion (25.8 ± 16.5%), traffic emissions (20.7 ± 11.6%) and agricultural sources (19.3 ± 11.6%). Therefore, some measures should be taken to reduce the NH₃ emissions from ammonia slip and traffic during autumn and winter. Full article

Background: The aim of this study was to assess in vitro nebulized drug delivery during invasive and non-invasive ventilation, comparing jet nebulizers (JN) and vibrating mesh nebulizers (VMN) across various pediatric ventilation models. Methods: Drug delivery performance was compared between a continuous output JN (Aquineb) and VMN (Aerogen Solo A-VMN). The non-invasive model simulated a spontaneously breathing 9-month-old child using an anatomically correct upper airway model and breathing simulator. The invasive model used a mechanical ventilator with heated humidifier in a pediatric breathing circuit with an endotracheal tube. Nebulizers were driven with supplemental oxygen at manufacturer-recommended rates and positioned at approved locations. Absolute inhaled dose, delivery rate and residual volume were assessed using face mask, mechanical ventilation, high-flow nasal therapy and blow-by delivery methods. Dose was quantified using spectrophotometric analysis. Results: During spontaneous breathing, A-VMN delivered almost double the dose of the evaluated JN (p < 0.001), with a significantly faster delivery rate (p < 0.001) and lower residual volume (p < 0.0001). During mechanical ventilation, A-VMN demonstrated a greater than 3-fold increase in delivered dose (p < 0.0001) and faster delivery (p < 0.0001), with reduced residual volume (p < 0.001). During high-flow nasal therapy, delivery via nasal cannula was affected by gas flow rate for both devices, with A-VMN consistently delivering greater doses. A-VMN delivered significantly greater salbutamol doses during blow-by delivery. Conclusions: VMN demonstrated significantly superior dose delivery, faster delivery rates and reduced residual volumes compared to the evaluated JN across all tested pediatric respiratory support modalities. These in vitro findings provide important performance data for evidence-based device selection and warrant clinical investigation to determine potential therapeutic benefits in pediatric populations requiring aerosol therapy during respiratory support. Full article

This study focuses on modelling the spatial dependence of airborne particle contamination using Response Surface Methodology (RSM), with consideration of its implications for technical cleanliness and employee health. The analysis is based on two measurement campaigns conducted in an industrial production hall, where particle concentrations were recorded across multiple size fractions using a TROTEC PC220 device. The results demonstrate that RSM effectively captures nonlinear relationships and spatial gradients, enabling the identification of local extrema and contamination hotspots. Statistical analysis confirmed a significant influence of spatial coordinates on particle concentration across all fractions, with finer particles exhibiting stronger spatial dependence, consistent with aerosol behaviour in indoor environments. Quadratic model terms revealed stable hotspot regions persisting even after corrective measures, indicating persistent contamination sources or structural factors. Residual analysis suggested additional unmodeled local sources or transport mechanisms. Based on the integration of RSM and multi-fraction analysis, a mechanistic contamination model (source–transport–receptor framework with deposition processes) is proposed, linking particle behaviour with surface contamination and potential human exposure. The approach enables data-driven, localised contamination control and supports optimisation of technical cleanliness and occupational health conditions. Full article

Air pollution remains a major global environmental risk, and exposure to fine particulate matter (PM_2.5) is associated with adverse health outcomes even at low concentrations. Meteorological conditions influence PM_2.5 variability, and precipitation is often expected to reduce particle loads through wet removal. However, humid and wet conditions may coincide with elevated PM_2.5 under specific atmospheric and compositional conditions. Here, we investigate long-term relationships between precipitation regimes and PM_2.5 concentrations in the Metropolitan Region of Belém (Eastern Amazonia) over the period 1980–2024. We combined PM_2.5 from the MERRA-2 reanalysis (including a bias-corrected product) with in situ precipitation records, and classified precipitation conditions using the Standardized Precipitation Index (SPI). We find statistically significant positive long-term tendencies in both precipitation and PM_2.5. Stratified analyses show that PM_2.5 concentrations are significantly higher under wet conditions, with a weak but significant positive relationship between SPI and PM_2.5 (r = 0.23 for the full period; r = 0.24 for the wet class, p-value < 0.01). These findings indicate that increased precipitation in a strong humid tropical urban environment does not necessarily lead to improved air quality. Instead, wet conditions may favor processes such as hygroscopic growth and secondary aerosol formation, contributing to higher PM_2.5 concentrations on a monthly scale. Overall, this study highlights the importance of considering precipitation regimes and associated atmospheric processes when assessing air quality in tropical urban environments. Full article

Fast radiative transfer models are essential for the efficient processing of hyperspectral satellite data in trace gas retrievals, as full multi-stream radiative transfer simulations are computationally demanding. We present FALCAS (Fast Atmospheric Line-by-line Code with Aerosol and Cloud Scattering), a surrogate forward model combining line-by-line radiative transfer with the virtual isotropic scattering layer approximation adopted from FOCAL. FALCAS retains much of the accuracy of full multi-stream calculations while enabling rapid simulations. Previously validated against synthetic spectra from a discrete ordinate radiative transfer model, FALCAS is here applied to real measurements from the TROPOspheric Monitoring Instrument (TROPOMI) to retrieve total column water vapour (TCWV) in the shortwave infrared band around 2.3 μm. Retrieval results are compared to the operational TROPOMI Level-2 TCWV from the CH₄ product. As this comparison is performed against an operational product from the same instrument, it represents an intercomparison rather than an evaluation against an independent reference dataset. FALCAS retrievals show a Pearson correlation coefficient greater than 0.99 with the operational data, and after empirical bias correction, the mean absolute bias across all regions is 1.45 mol m⁻² (0.12% relative) and the mean RMSE is 39.24 mol m⁻² (3.85% relative). These results demonstrate that FALCAS shows strong agreement with the operational TROPOMI Level-2 TCWV product, offering substantial computational advantages for large-scale processing. Full article

Polarized side-scattering techniques are widely used in aerosol detection, oceanographic optics, and biomedical sensing due to their high sensitivity to weak optical signals in low-scattering coefficient media. Conventional polarized Monte Carlo methods face significant challenges in such regimes due to geometric mismatch, where photon exit positions deviate substantially from the detector plane. This study addresses the geometric mismatch issue in polarized Monte Carlo simulations for side scattering in low-scattering media (scattering coefficient ${\mu }_s=$ 1 cm⁻¹), where photon exit positions often deviate from the detector plane. We propose a novel algorithm incorporating backward ray tracing with geometric projection correction to enhance simulation accuracy. Experimental validation was conducted using 532 nm laser illumination on both 500 nm polystyrene microspheres (${\mu }_s=$ 0.21 cm⁻¹) and 5 nm TiO₂ nanoparticles (${\mu }_s=$ 1.06 × 10⁻⁶–1.06 × 10⁻⁵ cm⁻¹). The results demonstrate excellent agreement between simulations and experiments, confirming the algorithm’s capability to accurately capture the polarization characteristics of side-scattered light. This work provides a high-fidelity simulation tool for designing optical sensors in low-scattering media and holds direct applicability in nanoparticle concentration sensing and aerosol monitoring. Full article