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Abstract In this study, we evaluated the performance of machine learning (ML) models (XGBoost) in predicting low‐cloud fraction (LCF), compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each having a different cloud scheme. ML models show a substantial enhancement in predicting LCF regarding root mean squared errors and correlation coefficients. The good performance is consistent across the full spectrums of atmospheric stability and large‐scale vertical velocity. Employing an explainable ML approach, we revealed the importance of including the amount of available moisture in ML models for representing spatiotemporal variations in LCF in the midlatitudes. Also, ML models demonstrated marked improvement in capturing the LCF variations during the stratocumulus‐to‐cumulus transition (SCT). This study suggests ML models' great potential to address the longstanding issues of “too few” low clouds and “too rapid” SCT in global climate models.

Abstract To enhance our understanding of cloud simulations over land, this study provides the first assessment of coupling between cloud and land surface in the Large‐Eddy Simulation (LES) Atmospheric Radiation Measurement Symbiotic Simulation and Observation (LASSO) activity for the shallow convection scenario. The analysis of observation data reveals a diurnal cycle of cloud‐land coupling, which co‐varies with surface fluxes. However, coupled (or decoupled) cumulus clouds are inadequately simulated, manifesting as a too‐high (or low) occurrence frequency during the afternoon. This discrepancy is mirrored by the overestimated cloud liquid water path and cloud‐top height. These overestimations are linked to the overpredicted boundary‐layer development and the easier trigger of shallow convection misrepresented in LES runs. Our study underscores the need to improve the representations of boundary‐layer processes and cloud‐land interactions within LES to better simulate shallow clouds in the future.

Abstract Here we retrieve global daily 1 km gapless PM2.5 concentrations via machine learning and big data, revealing its spatiotemporal variability at an exceptionally detailed level everywhere every day from 2017 to 2022, valuable for air quality monitoring, climate change, and public health studies. We find that 96%, 82%, and 53% of Earth’s populated areas are exposed to unhealthy air for at least one day, one week, and one month in 2022, respectively. Strong disparities in exposure risks and duration are exhibited between developed and developing countries, urban and rural areas, and different parts of cities. Wave-like dramatic changes in air quality are clearly seen around the world before, during, and after the COVID-19 lockdowns, as is the mortality burden linked to fluctuating air pollution events. Encouragingly, only approximately one-third of all countries return to pre-pandemic pollution levels. Many nature-induced air pollution episodes are also revealed, such as biomass burning.

In this study, we investigate the planetary boundary layer height (PBLH) using micro-pulse lidar (MPL) and microwave radiometer (MWR) methods, examining its relationship with the mass concentration of particles less than 2.5 µm in aerodynamic diameter (PM2.5) and its chemical compositions. Long-term PBLH retrieval results are presented derived from the MPL and the MWR, including its seasonal and diurnal variations, showing a superior performance regarding the MPL in terms of reliability and consistency with PM2.5. Also examined are the relationships between the two types of PBLHs and PM2.5. Unlike the PBLH derived from the MPL, the PBLH derived from the MWR does not have a negative correlation under severe pollution conditions. Furthermore, this study explores the effects of the PBLH on different aerosol chemical compositions, with the most pronounced impact observed on primary aerosols and relatively minimal influence on secondary aerosols, especially secondary organics during spring. This study underscores disparities in PBLH retrievals by different instruments during long-term observations and unveils distinct relationships between the PBLH and aerosol chemical compositions. Moreover, it highlights the greater influence of the PBLH on primary pollutants, laying the groundwork for future research in this field.

The Planetary Boundary Layer Height (PBLH) significantly impacts weather, climate, and air quality. Understanding the global diurnal variation of the PBLH is particularly challenging due to the necessity of extensive observations and suitable retrieval algorithms that can adapt to diverse thermodynamic and dynamic conditions. This study utilized data from the Cloud-Aerosol Transport System (CATS) to analyze the diurnal variation of PBLH in both continental and marine regions. By leveraging CATS data and a modified version of the Different Thermo-Dynamics Stability (DTDS) algorithm, along with machine learning denoising, the study determined the diurnal variation of the PBLH in continental mid-latitude and marine regions. The CATS DTDS-PBLH closely matches ground-based lidar and radiosonde measurements at the continental sites, with correlation coefficients above 0.6 and well-aligned diurnal variability, although slightly overestimated at nighttime. In contrast, PBLH at the marine site was consistently overestimated due to the viewing geometry of CATS and complex cloud structures. The study emphasizes the importance of integrating meteorological data with lidar signals for accurate and robust PBLH estimations, which are essential for effective boundary layer assessment from satellite observations.

Abstract Understanding interactions between low clouds and land surface fluxes is critical to comprehending Earth's energy balance, yet their relationships remain elusive, with discrepancies between observations and modeling. Leveraging long‐term field observations over the Southern Great Plains, this investigation revealed that cloud‐land interactions are closely connected to cloud‐land coupling regimes. Observational evidence supports a dual‐mode interaction: coupled stratiform clouds predominate in low sensible heat scenarios, while coupled cumulus clouds dominate in high sensible heat scenarios. Reanalysis data sets, MERRA‐2 and ERA‐5, obscure this dichotomy owing to a shortfall in representing boundary layer clouds, especially in capturing the initiation of coupled cumulus in high sensible heat scenarios. ERA‐5 demonstrates a relatively closer alignment with observational data, particularly in capturing relationships between cloud frequency and latent heat, markedly outperforming MERRA‐2. Our study underscores the necessity of distinguishing different cloud coupling regimes, essential to the understanding of their interactions for advancing land‐atmosphere interactions.

Ischemic stroke is a serious neurological disease involving multiple complex physiological processes, including vascular obstruction, brain tissue ischemia, impaired energy metabolism, cell death, impaired ion pump function, and inflammatory response. In recent years, there has been significant interest in cell membrane-functionalized biomimetic nanoparticles as a novel therapeutic approach. This review comprehensively explores the mechanisms and importance of using these nanoparticles to treat acute ischemic stroke with a special emphasis on their potential for actively targeting therapies through cell membranes. We provide an overview of the pathophysiology of ischemic stroke and present advances in the study of biomimetic nanoparticles, emphasizing their potential for drug delivery and precision-targeted therapy. This paper focuses on bio-nanoparticles encapsulated in bionic cell membranes to target ischemic stroke treatment. It highlights the mechanism of action and research progress regarding different types of cell membrane-functionalized bi-onic nanoparticles such as erythrocytes, neutrophils, platelets, exosomes, macrophages, and neural stem cells in treating ischemic stroke while emphasizing their potential to improve brain tissue’s ischemic state and attenuate neurological damage and dysfunction. Through an in-depth exploration of the potential benefits provided by cell membrane-functionalized biomimetic nanoparticles to improve brain tissue’s ischemic state while reducing neurological injury and dysfunction, this study also provides comprehensive research on neural stem cells’ potential along with that of cell membrane-functionalized biomimetic nanoparticles to ameliorate neurological injury and dysfunction. However, it is undeniable that there are still some challenges and limitations in terms of biocompatibility, safety, and practical applications for clinical translation.

Abstract This study leveraged a Lagrangian framework to examine the evolution of stratocumulus clouds under cold and warm advections (CADV and WADV) in the Community Earth System Model 2 (CESM2) against observations. We found that CESM2 simulates a too rapid decline in low‐cloud fraction (LCF) and cloud liquid water path (CLWP) under CADV conditions, while it better aligns closely with observed LCF under WADV conditions but overestimates the increase in CLWP. Employing an explainable machine learning approach, we found that too rapid decreases in LCF and CLWP under CADV conditions are related to overestimated drying effects induced by sea surface temperature, whereas the substantial increase in CLWP under WADV conditions is associated with the overestimated moistening effects due to free‐tropospheric moisture and surface winds. Our findings suggest that overestimated drying effects of sea surface temperature on cloud properties might be one of crucial causes for the high equilibrium climate sensitivity in CESM2.

Abstract Early diagnosis of spinal tuberculosis in young children is difficult due to atypical clinical features. We report a case of lumbar tuberculosis with abscess of the psoas major muscle in a 4‐year‐old child. Spinal reconstruction was challenging due to the involvement of multiple vertebral levels. The child complained of lumbar and back pain. Preoperative magnetic resonance imaging (MRI) revealed bone destruction of the L1–L3 vertebral body and abscess formation of the psoas major muscle. The child underwent excision of the vertebral lesions and a fresh iliac bone graft from his mother through an anterior approach and posterior fixation with T12–L1 and L4–L5 pedicle screws. At the 15‐month follow‐up, the patient had good clinico‐radiologic outcomes with evidence of bony fusion. No functional impairment of lumbar movements was found through physical examination during the follow‐up period. The report highlights the diagnostic dilemma of early childhood spinal tuberculosis due to lesions seen both on radiology and during surgery and discusses challenges in management of the disease and the relevant literature. The mother's iliac bone graft we describe can be a viable option in very young children with severe vertebral bone destruction requiring reconstruction by surgical procedure.

In this paper, multi-source observation, such as aircraft, ground-based remote sensing, and satellite-retrieved data, has been utilized to compare and analyze the vertical characteristics of aerosol optical properties and the planetary boundary layer height (HPBL) over the North China Plain (NCP) region during May–June 2016. Aircraft observations show the vertical profiles of aerosol absorption coefficients (σabs), scattering coefficients (σsca), and extinction coefficients (σext) gradually decrease with altitude, with their maximum values near HPBL. The vertical profiles of σext depended most on the vertical distribution of measured σsca, indicating a significant contribution of scattering aerosols. In addition, the prominent characteristic of the inverse relationship between σext and moisture profile could serve as a reference for predicting air quality in the NCP region. The lower layer pollution during the field experiment was likely caused by the accumulation of fine-mode aerosols, characterized by the vertical distribution of the Ångström exponent and the Aerosol Robotic Network (AERONET) products. Typically, HPBL derived from aircraft and surface Micro Pulse Lidar (MPL) was approximate, while the predicted HPBL by meteorological data indicates an underestimation of ~192 m. Aerosol optical depth (AOD) calculated from aircraft and ground-based remote sensing (such as MPL and AERONET) experienced a strong correlation, and both of them exhibited a similar tendency. However, the AOD retrieved from satellites was significantly larger than that from aircraft and ground-based remote sensing. Overall, the inversion algorithm, cloud identification algorithm, representativeness of the space, and time of the observation may lead to an overestimation or underestimation of AOD under certain circumstances. This study may serve as a re-evaluation of AOD retrieved from multi-source observations and provide a reference to uncover the actual atmospheric environment in the NCP regions.

Recently, the application of LiFePO4 (LFP) batteries in electric vehicles has attracted extensive attention from researchers. This work presents a composite of LFP particles trapped in reduced graphene oxide (rGO) nanosheets obtained through the high-temperature reduction strategy. The obtained LiFePO4/rGO composites indicate spherical morphology and uniform particles. As to the structure mode of the composite, LFP distributes in the interlayer structure of rGO, and the rGO evenly covers the surface of the particles. The LFP/rGO cathodes demonstrate a reversible specific capacity of 165 mA h g−1 and high coulombic efficiency at 0.2 C, excellent rate capacity (up to 10 C), outstanding long-term cycling stability (98%) after 1000 cycles at 5 C. The combined high electron conductivity of the layered rGO coating and uniform LFP particles contribute to the remarkable electrochemical performance of the LFP/rGO composite. The unique LFP/rGO cathode provides a potential application in high-power lithium-ion batteries.

Abstract Aerosol-boundary layer interactions play an important role in affecting atmospheric thermodynamics and air pollution. As a key factor in dictating the development of the boundary layer, the entrainment process in the context of aerosol-boundary layer interactions is still poorly understood. Using comprehensive field observations made at a superstation in Beijing, we gain insight into the response of the entrainment process to aerosols. We found that high aerosol loading can significantly suppress the entrainment rate, breaking the conventional linear relationship between sensible heat fluxes and entrainment fluxes. Related to aerosol vertical distributions, aerosol heating effects can alter vertical heat fluxes, leading to a strong interaction between aerosols and the entrainment process in the upper boundary layer. Such aerosol-entrainment coupling can inhibit boundary layer development and explains the great sensitivity of observed entrainment rates to aerosols than can traditional calculations. The notable impact of aerosols on the entrainment process raises holistic thinking about the dynamic framework of the boundary layer in a polluted atmosphere, which may have a significant bearing on the dispersion of air pollutants and the land-atmosphere coupling.

Abstract An initially well‐mixed stratocumulus deck can remain overcast for several tens of hours under warm‐advection conditions, although moisture supply is cut off from the ocean due to surface‐atmosphere decoupling (stabilization of the surface‐atmosphere interface). In this study, a set of idealized large‐eddy simulations were performed to investigate the physical mechanism of how warm‐air advection impacts the evolution of a pre‐existing stratocumulus deck. To mimic warm‐air advection, we decrease the sea surface temperature linearly over time in a doubly periodic domain. Given the same initial conditions, the stratocumulus deck is more persistent when experiencing warm‐air advection than cold‐air advection. This persistence is caused by reduced cloud‐top entrainment drying due to decoupling, a process more influential than the decoupling‐induced cutoff of moisture supply. This mechanism is more notable when the free troposphere becomes more humid. The relevance of the mechanism to previous observations of less low‐level cloudiness under warm‐advection conditions is discussed.

Abstract Due to surface heating, the morning boundary layer transits from stable to neutral or convective conditions, exerting critical influences on low tropospheric thermodynamics. Low clouds closely interact with the boundary layer development, yet their interactions bear considerable uncertainties. Our study reveals that cloud‐surface coupling alters the morning transition from stable to unstable boundary layer and thus notably affects the diurnal variation of the boundary layer. Specifically, due to the reduction in surface fluxes, decoupled clouds can delay the process of eroding nocturnal inversion by 0.8‐hr and even prevent the transition of the boundary layer from happening for 12% of decoupled cases, keeping the boundary layer in a stable state during the noontime. On the other hand, when clouds are coupled with the surface, cloud‐top radiative cooling can directly cool the upper boundary layer to facilitate sub‐cloud convection, leading to an unstable boundary layer in the earlier morning.

Abstract Retinal image analysis has become an intuitive and standard aided diagnostic technique for eye diseases. The good image quality is essential support for doctors to provide timely and accurate disease diagnosis. This paper proposes an end‐to‐end learning based method for evaluating the retinal image quality. First, blood vessels of the input image are segmented by U‐Net, and the fundus image is divided into two parts: blood vessels and background. Then, we design a dual branch network module which extracts global features that influence the image quality and suppress the interference of blood vessels and local textures to achieve better performance. The proposed module can be embedded in various advanced network structures. The experimental results show the more efficient convergence rate for the network with the module. The best network accuracy rate is 85.83%, the AUC is 0.9296, and the F1‐score is 0.7967 on the collected local dataset. Additionally, the model generalization is tested on the public DRIMDB dataset. The accuracy, AUC, and F1‐score reach 97.89%, 0.9978, and 0.9688, respectively. Compared with the state‐of‐the‐art networks, the performance of the proposed method is proven to be accurate and effective for retinal image quality assessment.

Abstract Plant growth and crop harvest are impacted by both climate change and air pollution. However, their relative importance in crop yields remains elusive, especially in heavily polluted regions. Here we develop crop yield prediction models, based on a large volume of historical crop data, as well as climate and pollution records in China since 1980. A long‐term surface ozone concentration data set is developed from a machine‐learning model and various observations. An assessment of four climate and pollution factors reveals the critical role of particulate and ozone pollution in regulating interannual variations of crop yields in China. During 2010–2018, we find that the particulate pollution mitigation outweighs the negative impacts of concurrent climate change, resulting in 0.5%–1.9% net yield increases nationwide, despite of the ozone increases in the North China Plain. Looking to the future, the impacts of climate change, particularly from surface temperature increase, will dominate over pollution factors and profoundly reduce future maize and rice yields by 0.6 to 2.8% 10 yr−1 by 2050. Our findings call for attention on the threat to future global food security from the absence of pollution mitigation and the persistence of global warming.

In this study, the impact of artificial precipitation enhancement operation on the wet scavenging of air pollutants is investigated based on an aircraft precipitation enhancement operation in the Yangtze River Delta region of China on 1 November 2020. Multiple measurement data are used, including the regional meteorological and environmental data and the comprehensive field observation data at the Nanjing University of Information Science and Technology (NUIST) campus site. The results show that the affected areas of this operation cover part of Anhui and Jiangsu provinces. The precipitation ratio between the target and contrast areas is always greater than 1, indicating the effectiveness of the seeding operation of silver iodide for precipitation enhancement. The regional analysis of air pollutants suggests that the enhanced precipitation can prevent the increase of air pollutants, especially for the particulate matter. The impact of enhanced precipitation on regional trace gases is complex because they are easily affected by the primary emissions. More data analysis at NUIST indicates that two cloud layers appear before the precipitation and merge into one thick cloud layer during the precipitation likely due to the impact of catalysis operation on the upper cloud. After the precipitation, the number concentration of aerosols with a diameter lower than 300 nm reduces significantly, the mass concentration of particulate matter with an aerodynamic diameter less than 2.5 μm decreases by about 39%, and the mass concentrations of nitrogen dioxide, sulfur dioxide, and carbon monoxide also decrease in different degrees. These results indicate that it is effective to reduce air pollutants by artificial precipitation enhancement. More studies are needed in the future.

PM2.5 refers to the total mass concentration of tiny particulates in the atmosphere near the surface, obtained by means of in situ observations and satellite remote sensing. Given the highly limited number of ground observation stations of inhomogeneous distribution and an ill-posed remote sensing approach, increasing efforts have been devoted to the application of machine-learning (ML) models to both ground and satellite data. A key satellite-derived parameter, aerosol optical thickness (AOD), has been most commonly used as a proxy of PM2.5, although their correlation is fraught with large uncertainties. A critical question that has been overlooked concerns how much AOD helps to improve the retrieval of PM2.5 relative to its uncertainty incurred concurrently. The question is addressed here by taking advantage of high-density PM2.5 stations in eastern China to evaluate the contributions of AOD, determined as the difference in the accuracy of PM2.5 retrievals with and without AOD for varying densities of PM2.5 stations, using four popular ML models (i.e., Random Forest, Extra-trees, XGBoost, and LightGBM). Our results reveal that as the density of monitoring stations decreases, both the feature importance and permutation importance of satellite AOD demonstrate a consistent upward trend (p < 0.05). Furthermore, the ML models without AOD exhibit faster declines in overall accuracy and predictive ability compared with the models with AOD assessed using the sample-based and station-based (spatial) independent cross-validation approaches. Overall, a 10% reduction in the number of stations results in an increase of 0.7–1.2% and 0.6–1.2% in uncertainty in estimated and predicted accuracies, respectively. These findings attest to the indispensable role of satellite AOD in the PM2.5 retrieval process through ML because it can significantly mitigate the negative impact of the sparse distribution of monitoring sites. This role becomes more important as the number of PM2.5 stations decreases.

Abstract Early detection and treatment of skin cancer can considerably reduce the patient mortality rates. Convolutional neural network (CNN) has been widely applied in the field of computer aided diagnosis. However, for skin lesions, the inconsistent size of lesion regions in dermatoscope images hinders the convolutional neural network precise discrimination. To solve this problem, multiscale ensemble of convolutional neural networks called MECNN is proposed, which involves three branches with different lesion scales as the model input. The first branch locates the lesion region outline by identifying the largest local response point. Then, MECNN reduces the search area of the lesion region and divides the outline into two scales used as the input for the other two branches. A global loss function is defined to control the learning objectives of the three branches and MECNN fuses the branches output as the final classification result. The proposed model is evaluated on the public HAM10000 dataset and achieves a higher classification accuracy than the comparative state‐of‐the‐art methods.

Abstract The retinal vessel is the only microvascular network that can be directly and non‐invasively observed in humans. Cardiovascular and cerebrovascular diseases, such as diabetes, hypertension, can lead to structural changes of the retinal microvascular network. Therefore, it is of great significance to study effective retinal vessel segmentation methods and assist doctors in early diagnoses with quantitative results for vascular networks. In this study, we propose a novel convolutional neural network named feature pyramid U‐Net (FPU‐Net) that extracts multiscale representations by constructing two feature pyramids both on the encoder and the decoder of U‐Net. In this representation, objects features with different size like micro‐vessels and pathology will be fused for better vessel segmentation. The experimental results show that compared with state‐of‐the‐art methods, FPU‐Net is superior in terms of accuracy, sensitivity, F1‐score, and area under the curve and capable of stronger domain generalisation across different datasets.

Abstract The role of new particle formation (NPF) events and their contribution to haze formation through subsequent growth in polluted megacities is still controversial. To improve the understanding of the sources, meteorological conditions, and chemistry behind air pollution, we performed simultaneous measurements of aerosol composition and particle number size distributions at ground level and at 260 m in central Beijing, China, during a total of 4 months in 2015–2017. Our measurements show a pronounced decoupling of gas-to-particle conversion between the two heights, leading to different haze processes in terms of particle size distributions and chemical compositions. The development of haze was initiated by the growth of freshly formed particles at both heights, whereas the more severe haze at ground level was connected directly to local primary particles and gaseous precursors leading to higher particle growth rates. The particle growth creates a feedback loop, in which a further development of haze increases the atmospheric stability, which in turn strengthens the persisting apparent decoupling between the two heights and increases the severity of haze at ground level. Moreover, we complemented our field observations with model analyses, which suggest that the growth of NPF-originated particles accounted up to ∼60% of the accumulation mode particles in the Beijing–Tianjin–Hebei area during haze conditions. The results suggest that a reduction in anthropogenic gaseous precursors, suppressing particle growth, is a critical step for alleviating haze although the number concentration of freshly formed particles (3–40 nm) via NPF does not reduce after emission controls.

Based on aircraft measurements of aerosols and continental cumulus clouds made over the Loess Plateau of China (Xinzhou, Shanxi Province) on 30 July 2020, this study focuses on the vertical profiles of microphysical properties of aerosols and cumulus clouds, and use them to study aerosol-cloud interactions. During the study period, the boundary layer was stable with a height ∼1,500 m above sea level. Aerosols in the boundary layer mainly came from local emissions, while aerosols above this layer were mostly dust aerosols transported over long distances. Vertical profiles of aerosols and cloud condensation nuclei were obtained, and aerosol activation ratios at different supersaturation (SS) levels ranged from 0.16 to 0.32 at 0.2% SS and 0.70 to 0.85 at 0.8% SS. A thick cumulus cloud in the development stage was observed from the bottom to the top with the horizontal dimension of 10 km by 7 km, the cloud-base height of 2,450 m (15.8°C), and the cloud-top height of 5,400 m (−3°C). The maximum updraft velocity near the cloud top was 13.45 m s−1, and the maximum downdraft velocity occuring in the upper-middle part of the cloud was 4.44 ms−1. The temperature inside the cloud was higher than the outside, with their difference being positively correlated with the cloud water content. The temperature lapse rate inside the cloud was about −6.5°C km−1. The liquid water content and droplet effective radius (Re) increased with increasing height. The cloud droplet number concentration (Nc) increased first then decreased, peaking in the middle and lower part of the cloud, the average values of Nc and Re were 767.9 cm−3 and 5.17 μm, respectively. The cloud droplet spectrum had a multi-peak distribution, with the first appearing at ∼4.5 μm. SS in the cloud first increased then decreased with height. The maximum SS is ∼0.7% appearing at ∼3,800 m. The conversion rate of intra-cloud aerosols to cloud droplets was between 0.2 and 0.54, with the ratio increasing gradually with increasing height. The cloud droplet spectral dispersion and Nc were positively correlated. The aerosol indirect effect (AIE) was estimated to be 0.245 and 0.16, based on Nc and Re, respectively. The cloud droplet dispersion mainly attenuated the AIE, up to ∼34.7%.

Air quality has greatly improved in China owing to the strict control policy enforced during the last decade. This study investigated the impact of particulate pollution control on aerosol hygroscopicity and cloud condensation nuclei (CCN) activity in North China based on several data sources. The mass concentration of particles with an aerodynamic diameter smaller than 2.5 µ m (PM _2.5 ) decreased by one third from the summer of 2014 to the summer of 2020 in Xinzhou (XZ). The mass fractions of aerosol chemical components in PM _2.5 also clearly changed, showing an increase in hydrophilic inorganics and a decrease in hydrophobic organics and black carbon from 2014 to 2020. Measurements of the particle number size distribution in XZ indicate that the occurrence frequency of new particle formation (NPF) events decreased significantly from 2014 to 2020, leading to a reduction in the generation of daytime ultrafine particles. The weakened NPF and increasing influence of morning and evening peak emissions modified the diurnal variations of the number concentration of condensation nuclei ( N _CN ) and CCN ( N _CCN ). The aerosol activation ratio was always higher in the summer of 2014 than in the summer of 2020. These results demonstrate that particulate pollution control can decrease N _CN and N _CCN but enhance aerosol hygroscopicity and activation ability.

The urban heat island (UHI) refers to the land surface temperature (LST) difference between urban areas and their undeveloped or underdeveloped surroundings. It is a measure of the thermal influence of the urban built-up area expansion (UBAE), a topic that has been extensively studied. However, the impact of UBAE on the LST differences between urban areas and rural areas (UHIU−R) and between urban areas and emerging urban areas (UHIU−S) in different seasons has seldom been investigated. Here, the UHIU−S and UHIU−R in 34 major metropolitan regions across China, and their spatiotemporal variations based on long-term space-borne observations during the period 2001–2020 were analyzed. The UBAE quantified by the difference in landscape metrics of built-up areas between 2020 and 2000 and their impact on UHI was further analyzed. The UBAE is impacted by the level of economic development and topography. The UBAE of cities located in more developed regions was more significant than that in less developed regions. Coastal cities experienced the most obvious UBAE, followed by plain and hilly cities. The UBAE in mountainous regions was the weakest. On an annual basis, UHIU−R was larger than UHIU−S, decreasing more slowly with UBAE than UHIU−S. In different seasons, the UHIU−S and UHIU−R were larger, more clearly varying temporally with UBAE in summer than in winter, and their temporal variations were significantly correlated with UBAE in summer but not in winter. The seasonal difference in UHIU−R was larger than that of UHIU−S. Both the UHIU−S and UHIU−R in coastal cities were the lowest in summer, decreasing the fastest with UBAE, while those in mountain cities decreased the slowest. The change in the density of built-up lands was the primary driver affecting the temporal variations in UHIU−S and UHIU−R during UBAE, followed by changes in proportion and shape, while the impact of the speed of expansion was the smallest, all of which were more obvious in summer than in winter. The decreased density of built-up lands can reduce UHI. These findings provide a new perspective for a deeper understanding of the effect of urban expansion on LST in different seasons.

Abstract The dust generated in East Asia influences the western North America (WNA) through its trans‐Pacific transport. This study investigates the distinct contribution between two main dust sources in East Asia on this remote influence based on Modern‐Era Retrospective analysis for Research and Applications version 2 data set. Results show that the dust generated in Gobi desert (GD) exerts a larger influence on the WNA compared to those in Taklimakan desert (TD). This difference is attributed to the different terrain and background winds in GD and TD. The GD is relatively flat and dominated by westerlies throughout the troposphere, which facilitates the trans‐Pacific transport of dust to WNA. However, the TD is located in the Tarim Basin and dominated by easterly wind in the lower troposphere. The uplifted dust is largely redeposited in TD. Moreover, the influence of GD on dust in WNA experiences decadal change around 1999, which is related to intrinsic change of dust loading in GD.

Abstract Background Massively-parallel-sequencing, coupled with sample multiplexing, has made genetic tests broadly affordable. However, intractable index mis-assignments (commonly exceeds 1%) were repeatedly reported on some widely used sequencing platforms. Results Here, we investigated this quality issue on BGI sequencers using three library preparation methods: whole genome sequencing (WGS) with PCR, PCR-free WGS, and two-step targeted PCR. BGI’s sequencers utilize a unique DNA nanoball (DNB) technology which uses rolling circle replication for DNA-nanoball preparation; this linear amplification is PCR free and can avoid error accumulation. We demonstrated that single index mis-assignment from free indexed oligos occurs at a rate of one in 36 million reads, suggesting virtually no index hopping during DNB creation and arraying. Furthermore, the DNB-based NGS libraries have achieved an unprecedentedly low sample-to-sample mis-assignment rate of 0.0001 to 0.0004% under recommended procedures. Conclusions Single indexing with DNB technology provides a simple but effective method for sensitive genetic assays with large sample numbers.

COVID-19 has a tremendous impact on both human life and the environment due to the unprecedented large-scale shutdown of economic activities at the beginning of 2020. While it was widely expected to see a dramatic reduction in air pollution, reality appears to be much more complex due to the joint influences of emissions and meteorology in dictating air pollution. By analyzing ample meteorological and environmental observational data, this study attempts to evaluate the contribution of an economic lockdown or at a well-below normal level across China to air pollution during the COVID-19 pandemic in the Beijing-Tianjin-Hebei region. Besides the unprecedented emission reductions that helped to improve air quality, multiple other factors came into play, such as high humidity and low wind speed that are favorable for haze formation. After separating long-term trends, seasonal signals, holiday effects, and meteorological contributions concerning climatology, we estimated that the relative contributions of human activities to changes in particulate matter with a diameter of less than 2.5 μm and nitrogen dioxide during the epidemic were −17.13 μg/m3 and −0.03 μg/m3, respectively, with negative quantities denoting reductions to air pollution. Furthermore, comparing the changes in PM2.5 and NO2 concentrations after lockdown revealed that for short-term control measures, meteorological factors mainly affected pollutant particles.

Respirable particles with aerodynamic diameters ≤ 10 µm (PM10) have important impacts on the atmospheric environment and human health. Available PM10 datasets have coarse spatial resolutions, limiting their applications, especially at the city level. A tree-based ensemble learning model, which accounts for spatiotemporal information (i.e., space-time extremely randomized trees, denoted as the STET model), is designed to estimate near-surface PM10 concentrations. The 1-km resolution Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product and auxiliary factors, including meteorology, land-use cover, surface elevation, population distribution, and pollutant emissions, are used in the STET model to generate the high-resolution (1 km) and high-quality PM10 dataset for China (i.e., ChinaHighPM10) from 2015 to 2019. The product has an out-of-sample (out-of-station) cross-validation coefficient of determination (CV-R2) of 0.86 (0.82) and a root-mean-square error (RMSE) of 24.28 (27.07) μg/m3, outperforming most widely used models from previous related studies. High levels of PM10 concentration occurred in northwest China (e.g., the Tarim Basin) and the Northern China Plain. Overall, PM10 concentrations had a significant declining trend of 5.81 μg/m3 per year (p

Meteorologically observed air temperature (Ta) is limited due to low density and uneven distribution that leads to uncertain accuracy. Therefore, remote sensing data have been widely used to estimate near-surface Ta on various temporal scales due to their spatially continuous characteristics. However, few studies have focused on instantaneous Ta when satellites overpass. This study aims to produce both daily and instantaneous Ta datasets at 1 km resolution for the Jingjinji area, China during 2018–2019, using machine learning methods based on remote sensing data, dense meteorological observation station data, and auxiliary data (such as elevation and normalized difference vegetation index). Newly released Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 surface Downward Shortwave Radiation (DSR) was introduced to improve the accuracy of Ta estimation. Five machine learning algorithms were implemented and compared so that the optimal one could be selected. The random forest (RF) algorithm outperformed the others (such as decision tree, feedforward neural network, generalized linear model) and RF obtained the highest accuracy in model validation with a daily root mean square error (RMSE) of 1.29 °C, mean absolute error (MAE) of 0.94 °C, daytime instantaneous RMSE of 1.88 °C, MAE of 1.35 °C, nighttime instantaneous RMSE of 2.47 °C, and MAE of 1.83 °C. The corresponding R2 was 0.99 for daily average, 0.98 for daytime instantaneous, and 0.95 for nighttime instantaneous. Analysis showed that land surface temperature (LST) was the most important factor contributing to model accuracy, followed by solar declination and DSR, which implied that DSR should be prioritized when estimating Ta. Particularly, these results outperformed most models presented in previous studies. These findings suggested that RF could be used to estimate daily instantaneous Ta at unprecedented accuracy and temporal scale with proper training and very dense station data. The estimated dataset could be very useful for local climate and ecology studies, as well as for nature resources exploration.

Particulate matter with a mass concentration of particles with a diameter less than 2.5 μm (PM2.5) is a key air quality parameter. A real-time knowledge of PM2.5 is highly valuable for lowering the risk of detrimental impacts on human health. To achieve this goal, we developed a new deep learning model-EntityDenseNet to retrieve ground-level PM2.5 concentrations from Himawari-8, a geostationary satellite providing high temporal resolution data. In contrast to the traditional machine learning model, the new model has the capability to automatically extract PM2.5 spatio-temporal characteristics. Validation across mainland China demonstrates that hourly, daily and monthly PM2.5 retrievals contain the root-mean-square errors of 26.85, 25.3, and 15.34 μg/m3, respectively. In addition to a higher accuracy achievement when compared with various machine learning inversion methods (backpropagation neural network, extreme gradient boosting, light gradient boosting machine, and random forest), EntityDenseNet can “peek inside the black box” to extract the spatio-temporal features of PM2.5. This model can show, for example, that PM2.5 levels in the coastal city of Tianjin were more influenced by air from Hebei than Beijing. Further, EntityDenseNet can still extract the seasonal characteristics that demonstrate that PM2.5 is more closely related within three month groups over mainland China: (1) December, January and February, (2) March, April and May, (3) July, August and September, even without meteorological information. EntityDenseNet has the ability to obtain high temporal resolution satellite-based PM2.5 data over China in real-time. This could act as an important tool to improve our understanding of PM2.5 spatio-temporal features.

Although the characteristics of biomass burning events and the ambient ecosystem determine emitted smoke composition, the conditions that modulate the partitioning of black carbon (BC) and brown carbon (BrC) formation are not well understood, nor are the spatial or temporal frequency of factors driving smoke particle evolution, such as hydration, coagulation, and oxidation, all of which impact smoke radiative forcing. In situ data from surface observation sites and aircraft field campaigns offer deep insight into the optical, chemical, and microphysical traits of biomass burning (BB) smoke aerosols, such as single scattering albedo (SSA) and size distribution, but cannot by themselves provide robust statistical characterization of both emitted and evolved particles. Data from the NASA Earth Observing System’s Multi-Angle Imaging SpectroRadiometer (MISR) instrument can provide at least a partial picture of BB particle properties and their evolution downwind, once properly validated. Here we use in situ data from the joint NOAA/NASA 2019 Fire Influence on Regional to Global Environments Experiment-Air Quality (FIREX-AQ) field campaign to assess the strengths and limitations of MISR-derived constraints on particle size, shape, light-absorption, and its spectral slope, as well as plume height and associated wind vectors. Based on the satellite observations, we also offer inferences about aging mechanisms effecting downwind particle evolution, such as gravitational settling, oxidation, secondary particle formation, and the combination of particle aggregation and condensational growth. This work builds upon our previous study, adding confidence to our interpretation of the remote-sensing data based on an expanded suite of in situ measurements for validation. The satellite and in situ measurements offer similar characterizations of particle property evolution as a function of smoke age for the 06 August Williams Flats Fire, and most of the key differences in particle size and absorption can be attributed to differences in sampling and changes in the plume geometry between sampling times. Whereas the aircraft data provide validation for the MISR retrievals, the satellite data offer a spatially continuous mapping of particle properties over the plume, which helps identify trends in particle property downwind evolution that are ambiguous in the sparsely sampled aircraft transects. The MISR data record is more than two decades long, offering future opportunities to study regional wildfire plume behavior statistically, where aircraft data are limited or entirely lacking.

Urbanization and air pollution are major anthropogenic impacts on Earth’s environment, weather, and climate. Each has been studied extensively, but their interactions have not. Urbanization leads to a dramatic variation in the spatial distribution of air pollution (fine particles) by altering surface properties and boundary-layer micrometeorology, but it remains unclear, especially between the centers and suburbs of metropolitan regions. Here, we investigated the spatial variation, or inhomogeneity, of air quality in urban and rural areas of 35 major metropolitan regions across China using four different long-term observational datasets from both ground-based and space-borne observations during the period 2001–2015. In general, air pollution in summer in urban areas is more serious than in rural areas. However, it is more homogeneously polluted, and also more severely polluted in winter than that in summer. Four factors are found to play roles in the spatial inhomogeneity of air pollution between urban and rural areas and their seasonal differences: (1) the urban–rural difference in emissions in summer is slightly larger than in winter; (2) urban structures have a more obvious association with the spatial distribution of aerosols in summer; (3) the wind speed, topography, and different reductions in the planetary boundary layer height from clean to polluted conditions have different effects on the density of pollutants in different seasons; and (4) relative humidity can play an important role in affecting the spatial inhomogeneity of air pollution despite the large uncertainties.

Water uptake by aerosol particles alters its light-scattering characteristics significantly. However, the hygroscopicities of different aerosol particles are not the same due to their different chemical and physical properties. Such differences are explored by making use of extensive measurements concerning aerosol optical and microphysical properties made during a field experiment from December 2018 to March 2019 in Beijing. The aerosol hygroscopic growth was captured by the aerosol optical characteristics obtained from micropulse lidar, aerosol chemical composition, and aerosol particle size distribution information from ground monitoring, together with conventional meteorological measurements. Aerosol hygroscopicity behaves rather distinctly for mineral dust coarse-mode aerosol (Case I) and non-dust fine-mode aerosol (Case II) in terms of the hygroscopic enhancement factor, f β ( R H , λ 532 ) , calculated for the same humidity range. The two types of aerosols were identified by applying the polarization lidar photometer networking method (POLIPHON). The hygroscopicity for non-dust aerosol was much higher than that for dust conditions with the f β ( R H , λ 532 ) being around 1.4 and 3.1, respectively, at the relative humidity of 86% for the two cases identified in this study. To study the effect of dust particles on the hygroscopicity of the overall atmospheric aerosol, the two types of aerosols were identified and separated by applying the polarization lidar photometer networking method in Case I. The hygroscopic enhancement factor of separated non-dust fine-mode particles in Case I had been significantly strengthened, getting closer to that of the total aerosol in Case II. These results were verified by the hygroscopicity parameter, κ (Case I non-dust particles: 0.357 ± 0.024; Case II total: 0.344 ± 0.026), based on the chemical components obtained by an aerosol chemical speciation instrument, both of which showed strong hygroscopicity. It was found that non-dust fine-mode aerosol contributes more during hygroscopic growth and that non-hygroscopic mineral dust aerosol may reduce the total hygroscopicity per unit volume in Beijing.

Aerosols can alter the macro- and micro-physical properties of deep convective clouds (DCCs) and their radiative forcing (CRF). This study presents what is arguably the first long-term estimate of the aerosol-mediated changes in CRF (AMCRF) for deep cloud systems derived from decade-long continuous ground-based and satellite observations, model simulations, and reanalysis data. Measurements were made at the US Department of Energy's Atmospheric Radiation Measurement Program's Southern Great Plains (SGP) site. Satellite retrievals are from the Geostationary Operational Environmental Satellite. Increases in aerosol loading were accompanied by the thickening of DCC cores and the expansion and thinning of anvils, due presumably to the aerosol invigoration effect (AIV) and the aerosol microphysical effect. Meteorological variables dictating these cloud processes were investigated. Consistent with previous findings, the AIV is most significant when the atmosphere is moist and unstable with weak wind shear. Such aerosol-mediated systematic changes in DCC core thickness and anvil size alter CRF at the top of atmosphere (TOA) and at the surface. Using extensive observations, ~300 DCC systems were identified over a 10 years period at the SGP site (2000–2011) and analyzed. Daily mean AMCRF at the TOA and at the surface are 29.3 W m−2 and 22.2 W m−2, respectively. This net warming effect due to changes in DCC microphysics offsets the cooling resulting from the first aerosol indirect effect.

Large-scale measurements of cloud condensation nuclei (CCN) are difficult to obtain on a routine basis, whereas aerosol optical quantities are more readily available. This study investigates the relationship between CCN and aerosol optical quantities for some distinct aerosol types using extensive observational data collected at multiple Atmospheric Radiation Measurement (ARM) Climate Research Facility (CRF) sites around the world. The influences of relative humidity (RH), aerosol hygroscopicity (fRH) and single scattering albedo (SSA) on the relationship are analyzed. Better relationships are found between aerosol optical depth (AOD) and CCN at the Southern Great Plains (US), Ganges Valley (India) and Black Forest sites (Germany) than those at the Graciosa Island (the Azores) and Niamey (Niger) sites, where sea salt and dust aerosols dominate, respectively. In general, the correlation between AOD and CCN decreases as the wavelength of the AOD measurement increases, suggesting that AOD at a shorter wavelength is a better proxy for CCN. The correlation is significantly improved if aerosol index (AI) is used together with AOD. The highest correlation exists between CCN and aerosol scattering coefficients (σsp) and scattering AI measured in situ. The CCN–AOD (AI) relationship deteriorates with increasing RH. If RH exceeds 75%, the relationship where AOD is used as a proxy for CCN becomes invalid, whereas a tight σsp–CCN relationship exists for dry particles. Aerosol hygroscopicity has a weak impact on the σsp–CCN relationship. Particles with low SSA are generally associated with higher CCN concentrations, suggesting that SSA affects the relationship between CCN concentration and aerosol optical quantities. It may thus be used as a constraint to reduce uncertainties in the relationship. A significant increase in σsp and decrease in CCN with increasing SSA is observed, leading to a significant decrease in their ratio (CCN / σsp) with increasing SSA. Parameterized relationships are developed for estimating CCN, which account for RH, particle size, and SSA.

Satellite-derived aerosol optical depths (AODs) have been widely used to estimate surface fine particulate matter (PM2.5) concentrations over areas that do not have PM2.5 monitoring sites. To date, most studies have focused on estimating daily PM2.5 concentrations using polar-orbiting satellite data (e.g., from the Moderate Resolution Imaging Spectroradiometer), which are inadequate for understanding the evolution of PM2.5 distributions. This study estimates hourly PM2.5 concentrations from Himawari AOD and meteorological parameters using an ensemble learning model. We analyzed the spatial agglomeration patterns of the estimated PM2.5 concentrations over central East China. The estimated PM2.5 concentrations agree well with ground-based data with an overall cross-validated coefficient of determination of 0.86 and a root-mean-square error of 17.3 μg m−3. Satellite-estimated PM2.5 concentrations over central East China display a north-to-south decreasing gradient with the highest concentration in winter and the lowest concentration in summer. Diurnally, concentrations are higher in the morning and lower in the afternoon. PM2.5 concentrations exhibit a significant spatial agglomeration effect in central East China. The errors in AOD do not necessarily affect the retrieval accuracy of PM2.5 proportionally, especially if the error is systematic. High-frequency spatiotemporal PM2.5 variations can improve our understanding of the formation and transportation processes of regional pollution episodes.

Aerosols may modify cloud properties and precipitation via a variety of mechanisms with varying and contradicting consequences. Using a large ensemble of satellite data acquired by the Moderate Resolution Imaging Spectroradiometer onboard the Earth Observing System's Aqua platform, the CloudSat cloud profiling radar and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite over the tropical oceans, we identified two distinct correlations of clouds and precipitation with aerosol loading. Cloud-top temperatures are significantly negatively correlated with increasing aerosol index (AI) over oceans and aerosol optical depth (AOT) over land for deep mixed-phase clouds with liquid droplets near the warm bases and ice crystals near the cold tops; no significant changes were found for uniformly liquid clouds. Precipitation rates are positively correlated with the AI for mixed-phase clouds, but negatively correlated for liquid clouds. These distinct correlations might be a manifestation of two potential mechanisms: the invigoration effect (which enhances convection and precipitation) and the microphysical effect (which suppresses precipitation). We note that the highly limited information garnered from satellite products cannot unequivocally support the causal relationships between cloud-top temperature/precipitation rate and aerosol loading. But if aerosols are indeed the causes for the observed relationships, they may change the overall distribution of precipitation, leading to a more extreme and unfavorable rainfall pattern of suppressing light rains and fostering heavy rains.

Aerosol-cloud interactions (ACIs) are vital for regulating Earth's climate by influencing energy and water cycles. Yet, effects of ACI bear large uncertainties, evidenced by systematic discrepancies between observed and modeled estimates. This study quantifies a major bias in ACI determinations, stemming from conventional surface or space measurements that fail to capture aerosol at the cloud level unless the cloud is coupled with land surface. We introduce an advanced approach to determine radiative forcing of ACI by accounting for cloud-surface coupling. By integrating field observations, satellite data, and model simulations, this approach reveals a drastic alteration in aerosol vertical transport and ACI effects caused by cloud coupling. In coupled regimes, aerosols enhance cloud droplet number concentration across the boundary layer more homogeneously than in decoupled conditions, under which aerosols from the free atmosphere predominantly affect cloud properties, leading to marked cooling effects. Our findings spotlight cloud-surface coupling as a key factor for ACI quantification, hinting at potential underassessments in traditional estimates. [ABSTRACT FROM AUTHOR]