High Spatial-temporal Resolution Research Articles

Photoresponsive Hydrogels for Tissue Engineering.

Hydrophilic and biocompatible hydrogels are widely applied as ideal scaffolds in tissue engineering. The "smart" gelation material can alter its structural, physiochemical, and functional features in answer to various endo/exogenous stimuli to better biomimic the endogenous extracellular matrix for the engineering of cells and tissues. Light irradiation owns a high spatial-temporal resolution, complete biorthogonal reactivity, and fine-tunability and can thus induce physiochemical reactions within the matrix of photoresponsive hydrogels with good precision, efficiency, and safety. Both gel structure (e.g., geometry, porosity, and dimension) and performance (like conductivity and thermogenic or mechanical properties) can hence be programmed on-demand to yield the biochemical and biophysical signals regulating the morphology, growth, motility, and phenotype of engineered cells and tissues. Here we summarize the strategies and mechanisms for encoding light-reactivity into a hydrogel and demonstrate how fantastically such responsive gels change their structure and properties with light irradiation as desired and thus improve their applications in tissue engineering including cargo delivery, dynamic three-dimensional cell culture, and tissue repair and regeneration, aiming to provide a basis for more and better translation of photoresponsive hydrogels in the clinic.

Low-cost UAV coordinated carbon observation network: Carbon dioxide measurement with multiple UAVs

Improving the mitigation of global greenhouse effects requires drastic reductions in carbon emissions. Effective carbon management at different spatial scales is therefore crucial to a more sustainable path of economic development. Regional and local carbon management currently rely on the traditional bottom-up method of emission reports, which are known for their opaque and complex procedures. Novel top-down carbon monitoring methods including space-based, aerial and ground-based observations are tantalising alternatives to more reliable data sources of carbon emissions as well as sinks. Unmanned Aerial Vehicles (UAVs) based carbon monitoring is an upcoming field because of its lower costs, higher spatial-temporal resolution and operability at a wider range of weather conditions compared to satellite observations. UAVs equipped with Non-dispersive Infrared (NDIR) carbon dioxide (CO2) sensors can measure in the air with faster response and can intelligently navigate themselves within areas of greater interest. With the help of lighter NDIR payloads and multiple UAVs flying in synchrony, larger cross sections or areas for maximising information acquisition of emission plumes can be covered. In this article, we first introduce the design of a miniaturised NDIR payload, which is compatible with a lightweight UAV to achieve a single system weighing less than 4 kg. Then, we test our design via comparisons against another NDIR payload to determine its accuracy (−0.82 ppm and −0.44 ppm) and precision (0.12 ppm and −0.03 ppm) in measurements of two vertical concentration profiles. Finally, we deployed four UAVs on two major anthropogenic emission source monitoring campaigns with coordinated flight patterns. To fully understand the benefit of multi-UAV carbon monitoring, we attempted two flight patterns and computed power plant emission rates via the cross-sectional flux method. While all flights successfully captured the concentration enhancements downwind of the power plants, the single-sectional flight pattern measured an emission rate of 10ktCO2/day which is 44% less than that reported monthly and the multi-sectional flight pattern yielded relatively varied results (8.5ktCO2/day on average, which is 54% less) for the respective sections due to wind underestimations, plume section undersampling and wind fluctuations.

High Spatial Resolution Temperature Sensing Determined by Sampling Spacing With RDTS System

High Spatial Resolution Temperature Sensing Determined by Sampling Spacing With RDTS System

Rapid determination of seismic influence field based on mobile communication big data-A case study of the Luding Ms 6.8 earthquake in Sichuan, China.

Smartphone location data provide the most direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate the distribution of disasters with high temporal-spatial resolution. On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China. We quantitatively analyzed the Ms 6.8 earthquake from both temporal and geographic dimensions by combining 1,806,100 smartphone location records and 4,856 spatial grid locations collected through communication big data with the smartphone data under 24-hour continuous positioning. In this study, the deviation of multidimensional mobile terminal location data is estimated, and a methodology to estimate the distribution of out-of-service communication base stations in the disaster area by excluding micro error data users is explored. Finally, the mathematical relationship between the seismic intensity and the corresponding out-of-service rate of communication base stations is established, which provides a new technical concept and means for the rapid assessment of post-earthquake disaster distribution.

Catalytic NIR chemiluminescence sensor with enhanced persistence and intensity for in vivo imaging

Chemiluminescence (CL) is a self-illumination phenomenon that involves the emission of light from chemical reactions, and it provides favorable spatial and temporal information on biological processes. However, it is still a great challenge to construct effective CL sensors that equip strong CL intensity, long emission wavelength, and persistent luminescence for deep tissue imaging. Here, we report a liposome encapsulated polymer dots (Pdots)-based system using catalytic CL substrates (L-012) as energy donor and fluorescent polymers and dyes (NIR 695) as energy acceptors for efficient Near-infrared (NIR) CL in vivo imaging. Thanks to the modulation of paired donor and acceptor distance and the slow diffusion of biomarker by liposome, the Pdots show a NIR emission wavelength (λ em, max = 720 nm), long CL duration (>24 h), and a high chemiluminescence resonance energy transfer efficiency (46.5 %). Furthermore, the liposome encapsulated Pdots possess excellent biocompatibility, sensitive response to H2O2, and persistent whole-body NIR CL imaging in the drug-induced inflammation and the peritoneal metastatic tumor mouse model. In a word, this NIR-II CL nanoplatform with long-lasting emission and high spatial-temporal resolution will be a concise strategy in deep tissue imaging and clinical diagnostics.

Temperature and strain gradient monitoring of the buoyancy material curing reaction with a submillimeter spatial resolution fs-FBG array

Millimeter-scale temperature and strain gradient measurements are crucial for understanding the mechanical properties of buoyancy materials. In this paper, we successfully fabricated ultrashort and high-density fiber Bragg grating (FBG) array based on femtosecond laser point-by-point writing technology. By optimizing the energy and number of femtosecond laser pulse, and introducing secondary tilt apodization technology, submillimeter-spatial-resolution FBG arrays were prepared. To the best of our knowledge, this is the first application of femtosecond FBG array for high spatial resolution temperature and strain gradient in situ monitoring of buoyancy materials. FBGs exhibit excellent anti-chirp performance and achieve submillimeter spatial resolution curing monitoring under temperature and strain monitoring gradients of 2.6 °C/mm and 231 με/mm, respectively.

Nanoactuator for Neuronal Optoporation.

Light-driven modulation of neuronal activity at high spatial-temporal resolution is becoming of high interest in neuroscience. In addition to optogenetics, nongenetic membrane-targeted nanomachines that alter the electrical state of the neuronal membranes are in demand. Here, we engineered and characterized a photoswitchable conjugated compound (BV-1) that spontaneously partitions into the neuronal membrane and undergoes a charge transfer upon light stimulation. The activity of primary neurons is not affected in the dark, whereas millisecond light pulses of cyan light induce a progressive decrease in membrane resistance and an increase in inward current matched to a progressive depolarization and action potential firing. We found that illumination of BV-1 induces oxidation of membrane phospholipids, which is necessary for the electrophysiological effects and is associated with decreased membrane tension and increased membrane fluidity. Time-resolved atomic force microscopy and molecular dynamics simulations performed on planar lipid bilayers revealed that the underlying mechanism is a light-driven formation of pore-like structures across the plasma membrane. Such a phenomenon decreases membrane resistance and increases permeability to monovalent cations, namely, Na+, mimicking the effects of antifungal polyenes. The same effect on membrane resistance was also observed in nonexcitable cells. When sustained light stimulations are applied, neuronal swelling and death occur. The light-controlled pore-forming properties of BV-1 allow performing "on-demand" light-induced membrane poration to rapidly shift from cell-attached to perforated whole-cell patch-clamp configuration. Administration of BV-1 to ex vivo retinal explants or in vivo primary visual cortex elicited neuronal firing in response to short trains of light stimuli, followed by activity silencing upon prolonged light stimulations. BV-1 represents a versatile molecular nanomachine whose properties can be exploited to induce either photostimulation or space-specific cell death, depending on the pattern and duration of light stimulation.

Locating and Grading of Lidar-Observed Aircraft Wake Vortex Based on Convolutional Neural Networks

Aircraft wake vortices are serious threats to aviation safety. The Pulsed Coherent Doppler Lidar (PCDL) has been widely used in the observation of aircraft wake vortices due to its advantages of high spatial-temporal resolution and high precision. However, the post-processing algorithms require significant computing resources, which cannot achieve the real-time detection of a wake vortex (WV). This paper presents an improved Convolutional Neural Network (CNN) method for WV locating and grading based on PCDL data to avoid the influence of unstable ambient wind fields on the localization and classification results of WV. Typical WV cases are selected for analysis, and the WV locating and grading models are validated on different test sets. The consistency of the analytical algorithm and the CNN algorithm is verified. The results indicate that the improved CNN method achieves satisfactory recognition accuracy with higher efficiency and better robustness, especially in the case of strong turbulence, where the CNN method recognizes the wake vortex while the analytical method cannot. The improved CNN method is expected to be applied to optimize the current aircraft spacing criteria, which is promising in terms of aviation safety and economic benefit improvement.

Three-dimensional dipole orientation mapping with high temporal-spatial resolution using polarization modulation

Fluorescence polarization microscopy is widely used in biology for molecular orientation properties. However, due to the limited temporal resolution of single-molecule orientation localization microscopy and the limited orientation dimension of polarization modulation techniques, achieving simultaneous high temporal-spatial resolution mapping of the three-dimensional (3D) orientation of fluorescent dipoles remains an outstanding problem. Here, we present a super-resolution 3D orientation mapping (3DOM) microscope that resolves 3D orientation by extracting phase information of the six polarization modulation components in reciprocal space. 3DOM achieves an azimuthal precision of 2° and a polar precision of 3° with spatial resolution of up to 128 nm in the experiments. We validate that 3DOM not only reveals the heterogeneity of the milk fat globule membrane, but also elucidates the 3D structure of biological filaments, including the 3D spatial conformation of λ-DNA and the structural disorder of actin filaments. Furthermore, 3DOM images the dipole dynamics of microtubules labeled with green fluorescent protein in live U2OS cells, reporting dynamic 3D orientation variations. Given its easy integration into existing wide-field microscopes, we expect the 3DOM microscope to provide a multi-view versatile strategy for investigating molecular structure and dynamics in biological macromolecules across multiple spatial and temporal scales.

Development of an autonomous on-site dissolved inorganic carbon analyzer using conductometric detection

BackgroundThe increase in anthropogenic CO2 concentrations in the Earth's atmosphere since the industrial revolution has resulted in an increased uptake of CO2 by the oceans, leading to ocean acidification. Dissolved Inorganic Carbon (DIC) is one of the key variables to characterize the seawater carbonate system. High quality DIC observations at a high spatial-temporal resolution is required to improve our understanding of the marine carbonate system. To meet the requirements, autonomous DIC analyzers are needed which offer a high sampling frequency, are cost-effective and have a low reagent and power consumption. ResultsWe present the development and validation of a novel analyzer for autonomous measurements of DIC in seawater using conductometric detection. The analyzer employs a gas diffusion sequential injection approach in a “Tube In A Tube” configuration that facilitates diffusion of gaseous CO2 from an acidified sample through a gas permeable membrane into a stream of an alkaline solution. The change in conductivity in the alkaline medium is proportional to the DIC concentration of the sample and is measured using a detection cell constructed of 4 hollow brass electrodes. Physical and chemical optimizations of the analyzer yielded a sampling frequency of 4 samples h−1 using sub mL reagent volumes for each measurement. Temperature and salinity effects on DIC measurements were mathematically corrected to increase accuracy. Analytical precision of ±4.9 μmol kg−1 and ±9.7 μmol kg−1 were achieved from measurements of a DIC reference material in the laboratory and during a field deployment in the southwest Baltic Sea, respectively. SignificanceThis study describes a simple, cost-effective, autonomous, on-site benchtop DIC analyzer capable of measuring DIC in seawater at a high temporal resolution as a step towards an underwater DIC sensor. The analyzer is able to measure a wide range of DIC concentrations in both fresh and marine waters. The achieved accuracy and precision offer an excellent opportunity to employ the analyzer for ocean acidification studies and CO2 leakage detection in the context of Carbon Capture and Storage operations.

Laser speckle contrast imaging versus microvascular Doppler sonography in aneurysm surgery: A prospective study

ObjectiveThis study aimed to compare microvascular Doppler sonography (MDS) and laser speckle contrast imaging (LSCI) for assessing vessel patency and aneurysm occlusion during microsurgical clipping of intracranial aneurysms. MethodsMDS and LSCI were used after clip placement during six neurovascular procedures including six patients, and agreement between the two techniques was assessed. LSCI was performed in parallel or right after MDS evaluation. The Doppler response was assessed through listening while flow in the LSCI videos was evaluated by three blinded neurovascular surgeons after the surgery. Statistical analysis determined the agreement between the techniques in assessing flow in 18 regions of interest (ROIs). ResultsAgreement between MDS and LSCI in assessing vessel patency was observed in 87 % of the ROIs. LSCI accurately identified flow in 93.3 % of assessable ROIs, with no false positive or negative measurements. Three ROIs were not assessable with LSCI due to motion artifacts or poor image quality. No complications were observed. ConclusionsLSCI demonstrated high agreement with MDS in assessing vessel patency during microsurgical clipping of intracranial aneurysms. It provided continuous, real-time, full-field imaging with high spatial resolution and temporal resolution. While MDS allowed evaluation of deep vascular regions, LSCI complemented it by offering unlimited assessment of surrounding vessels.

Climatological Observation and Model Simulation of Near-Surface Hourly Maximum Gust Wind in Northern China

Abstract While previous work on the climatology of Northern China has focused on mean wind speed, wind gusts have received comparatively less attention but are equally important to various users. In this paper, an observed hourly maximum gust wind speeds (HMGS) dataset across North China has been created by using time series from 174 meteorological stations. The dataset offers superior quality, high spatiotemporal near-surface HMGS series for North China spanning from 2015 to 2022. The objective of this study is first to improve our understanding of the spatiotemporal gusts climatology in North China by analyzing the observed gust data. Second, we aim to supplement the observational data by using gust analysis and forecast data with a high spatial–temporal resolution from model simulations. The spatial characteristics of the seasonal cycle of the simulated analysis of mean HMGS and the performance in predicting gusts based on the geographical locations and elevations of the validation stations were investigated by comparing it with the observations. Results indicate the following: 1) Wind direction and intensity are affected by the terrain and climate conditions of different weather stations. Stations situated along the Bohai Bay coastal region and at higher-elevation areas of North China exhibit a higher mean HMGS than those located in the coastal and inland plains. 2) The probability density function curves for wind speed and wind direction exhibit notable variations across different elevation intervals. The contribution of moderate and strong gust wind speeds increases gradually with increasing altitude, while the gust directions in mountainous areas exhibit relatively consistent patterns due to the increased exposure to synoptic-scale forcing at higher elevations. 3) The nowcasting prediction system analysis of mean HMGS provides a higher horizontal resolution that is capable of capturing the contrasts between land and sea, as well as the influence of high HMGS associated with large-scale circulations in high-elevation regions. Significance Statement The purpose of this study is to better understand the spatiotemporal gust climatology in North China and the performance of the model-simulated gust analysis and forecast data. This is important because gusts conditions differ due to varying topographic and climatic conditions of different weather stations. Our results provide a valuable insight into the climatological variations of HMGS, their drivers, and identify the deficiencies in the model-simulation gusts.

High-resolution vision in pelagic polychaetes

High-resolution object vision - the ability to separate, classify, and interact with specific objects in the environment against the visual background - has only been conclusively shown to have evolved in three of the thirty-five animal phyla: chordates, arthropods, and mollusks (cephalopods)1. However, alciopid polychaetes (Phyllodocidae, Alciopini), which possess a pair of bulbous camera-type eyes, have also been hypothesized to achieve high acuity. In this study, we examined three species of night-active pelagic alciopids from the Mediterranean Sea. Our optical, morphological, and electrophysiological investigations show that their eyes have high spatial acuity and temporal resolution, supporting the notion that they are capable of active, high-resolution object vision. These results encourage interesting hypotheses about the visual ecology of these enigmatic polychaetes.

High-Resolution Temperature Evolution Maps of Bangladesh via Data-Driven Learning

As a developing country with an agricultural economy as a pillar, Bangladesh is highly vulnerable to adverse effects of climate change, so the generation of high-resolution temperature maps is of great value for Bangladesh to achieve agricultural sustainable development. However, Bangladesh’s weak economy and sparse meteorological stations make it difficult to obtain such maps. In this study, by mining internal features and links inside observed data, we developed an efficient data-driven downscaling technique to generate high spatial-resolution temperature distribution maps of Bangladesh directly from observed temperature data at 34 meteorological stations with irregular distribution. Based on these high-resolution historical temperature maps, we further explored a data-driven forecast technique to generate high-resolution temperature maps of Bangladesh for the period 2025–2035. Since the proposed techniques are very low-cost and fully mine internal links inside irregular-distributed observations, they can support relevant departments of Bangladesh to formulate policies to mitigate and adapt to climate change in a timely manner.

High spatial resolution temperature variation detection in one direction with a fiber solenoid and RDTS system

Limited by the large bottom width of the pulsed light and the large sampling spacing of the data acquisition card in conventional Raman distributed temperature sensing (RDTS) systems, short straight fibers cannot acquire temperature measurements with high spatial resolution and high temperature accuracy in a single temperature variation region (TVR). In contrast, winding sensing fiber on a screwed tube as a fiber solenoid for temperature sensing can indirectly improve the spatial resolution of the system. However, as the radius of the screwed tube decreases, the ability of the screwed tube to improve the spatial resolution of the system will deteriorate. To break this limitation, we innovatively propose a signal processing scheme to first construct a three-dimensional surface of the sensing length of screwed tube-temperature-area value, then obtain the length of the TVR and the area value of the temperature rise curve generated from a single TVR, and finally demodulate the temperature of the TVR in the surface. The experimental results show that the proposed scheme improves the spatial resolution of the conventional screwed tube from 8 cm to 1 cm, and the average temperature accuracy of the three TVRs with the interval and length of the TVRs in the order of centimeters is ± 0.63 °C. The screwed tube can be used for axial temperature distribution measurements of liquids as well as solids, and the proposed method can further expand the application prospects of the RDTS system for continuous measurement of multiple TVRs with lengths and intervals of centimeters in one direction without changing the internal hardware.

Calibration method of particulate matter sensor based on density peaks clustering combined with stacking algorithm

More and more low-cost sensors (LCS) were used to obtain monitoring data with higher temporal-spatial resolution, as air quality has become more concerned in recent decades. However, due to its working principle, relatively simple internal structure, and complex external environmental conditions, the accuracy of LCS’s measurement data has always been questioned. Therefore, it is necessary to develop calibration method for LCS to improve the reliability of the data. This study proposed a calibration method for particulate matter LCS using a K-nearest Neighbor Fuzzy Density Peaks Clustering combined with Stacking Ensemble Learning (KFDPC-Stacking). Experiments were conducted using the LCS network in Zhengzhou, China to verify the effectiveness of the developed calibration model. The results show that the developed model had higher accuracy in data calibration compared to other calibration models based on Machine Learning techniques. The transferability of the model had been validated in multiple areas of Zhengzhou, and the results indicated that the calibration model could be applied directly in comparable environments. Additionally, the data of LCS in Zhengzhou City within one year after calibration were analyzed, and the suggested calibration interval for such sensors was determined, which could provide information and supports for the subsequent LCS research and calibration.

Growing Importance of Micro-Meteorology in the New Power System: Review, Analysis and Case Study

With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides review including the relevant micro-meteorological techniques such asbservation, assimilation andumerical techniques, as well as artificial intelligence, presenting a relatively complete overview ofhe most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systemss analyzed inix different forms of power generationnd three typical scenariosf different stages in theower system, as well as integrated energy systemsnd disaster prevention and reduction. Finally, aase study in China is provided. This case takes wind power prediction as an example in aower system to compare the performance whenpplying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial–temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to theower system to some extent.

Genetically encoded sensors for invivo detection of neurochemicals relevant to depression.

Depressive disorders are a common and debilitating form of mental illness with significant impacts on individuals and society. Despite the high prevalence, the underlying causes and mechanisms of depressive disorders are still poorly understood. Neurochemical systems, including serotonin, norepinephrine, and dopamine, have been implicated in the development and perpetuation of depressive symptoms. Current treatments for depression target these neuromodulator systems, but there is a need for a better understanding of their role in order to develop more effective treatments. Monitoring neurochemical dynamics during depressive symptoms is crucial for gaining a better a understanding of their involvement in depressive disorders. Genetically encoded sensors have emerged recently that offer high spatial-temporal resolution and the ability to monitor neurochemical dynamics in real time. This review explores the neurochemical systems involved in depression and discusses the applications and limitations of current monitoring tools for neurochemical dynamics. It also highlights the potential of genetically encoded sensors for better characterizing neurochemical dynamics in depression-related behaviors. Furthermore, potential improvements to current sensors are discussed in order to meet the requirements of depression research.

Validating IMERG data for diurnal rainfall analysis across the Indonesian maritime continent using gauge observations

The availability of surface rainfall data with high spatial-temporal resolution is needed to understand the diurnal rainfall characteristics in the Indonesian Maritime Continent (IMC) to improve the accuracy of weather and climate models in this region. This study validates the accuracy of the Final Run product of IMERG data version 06B (V06B) and version 07A (V07A), which have a resolution of 0.1°–30 min for diurnal rainfall analysis over IMC. Validation was conducted for precipitation amount (PA), precipitation frequency (PF), and precipitation intensity (PI), by recording 302 automatic rain gauges (RG) every 10 min from January 2014 to September 2021. IMERG V06B and V07A perform well in observing diurnal PA and PF but struggle in observing diurnal PI compared to RG observations. This has been determined by the correlation coefficient (CC) values of IMERG V06B (V07) data, which are 0.76 (0.72) for PA, 0.77 (0.76) for PF, and 0.21 (0.13) for PI. The IMERG data align to an extent with RG observations for the PA value, having a small relative bias (RB). The results also display a systematic PF and PI values error. The high false alarm ratio (FAR) of IMERG data suggests rain detection errors, leading to overestimated PF values. Additionally, the underestimation of PI values is due to the limitation of IMERG data in observing extreme and small-scale rain events. About 86.10% (78.14%) and 81.45% (81.78) of IMERG V06B (IMERG V07A) data show a peak time difference of <3h for PA and PF when compared with RG observations. Overall, IMERG V06B performs better than V07, possibly due to inaccurate orbits of GPROF data, removal of SAPHIR satellite observations, and inter-calibration issues with CORRA and GPCP data. Continuous monitoring and data improvements are necessary to improve the accuracy and reliability of IMERG data in detecting diurnal rainfall patterns in the IMC region.

Validation of initial observation from the first spaceborne high-spectral-resolution lidar with a ground-based lidar network

Abstract. On 16 April 2022, China successfully launched the world's first spaceborne high-spectral-resolution lidar (HSRL), which is called the Aerosol and Carbon Detection Lidar (ACDL), on board the Atmospheric Environment Monitoring Satellite known as Daqi-1 (DQ-1). The ACDL is expected to precisely detect the three-dimensional distribution of aerosol and cloud globally with high spatial–temporal resolutions. To assess the performance of the newly launched satellite lidar, the ACDL-retrieved observations were compared with ground-based lidar measurements of atmospheric aerosol and cloud over northwest China from May to July 2022 using the Belt and Road lidar network (BR-lidarnet) initiated by Lanzhou University in China and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar observations. A total of six cases in the daytime and nighttime, including clear days, dust events, and cloudy conditions, were selected for further analysis. Moreover, profiles of the total attenuated backscatter coefficient (TABC) and the volume depolarization ratio (VDR) at 532 nm measured by the ACDL, the CALIPSO lidar, and ground-based lidar are compared in detail. Comparison is made between the 532 nm extinction coefficient and lidar ratio obtained from ACDL HSRL retrieval and the Raman retrieval results obtained from BR-lidarnet. The achieved results revealed that the ACDL observations were in good agreement with the ground-based lidar measurements during dust events with a relative deviation of about −10.5 ± 25.4 % for the TABC and −6.0 ± 38.5 % for the VDR. Additionally, the heights of the cloud top and bottom from these two measurements were well matched and comparable. Compared with the observation of CALIPSO, the ACDL also shows high consistency. This study proves that the ACDL provides reliable observations of aerosol and cloud in the presence of various climatic conditions, which helps to further evaluate the impacts of aerosol on climate and the environment, as well as on the ecosystem in the future.