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Abstract Surface visibility (SV), a key indicator of atmospheric transparency, is used widely in the fields of environmental monitoring, transportation, and aviation. However, the sparse distribution and limited number of SV monitoring sites make it difficult to fulfill the urgent need for spatiotemporally seamless fine-scale monitoring. Here, we developed the operational real-time SV retrieval (RT-SVR) framework for China that incorporates information from multiple data sources, including Chinese Land Data Assimilation System meteorological data, in situ observations, and other ancillary data. Seamless hourly SV data with 6.25-km spatial resolution are available in real time via the operational RT-SVR model, which was built using a two-layer stacked ensemble approach that combines multiple machine learning algorithms and a deep learning module. Sample-based cross-validation of the RT-SVR model on approximately 41.3 million data pairs revealed strong robustness and high accuracy, with a Pearson correlation coefficient (R) value of 0.95 and a root mean square error (RMSE) of 3.17 km. An additional hindcast-validation experiment, performed with continuous observations obtained over one year (approximately 20.8 million data pairs), demonstrated the powerful generalization capabilities of the RT-SVR model, albeit with slight degradation in performance (R = 0.85, RMSE = 5.28 km). The seamless hourly SV data with real-time update capability enable tracking of the generation, development, and dissipation of various low-SV events (e.g., fog, haze, and dust storms) in China. The developed framework might also prove useful for quantitative retrieval of aerosol-related parameters (e.g., PM2.5, PM10, and aerosol optical depth).

Once challenged by the SARS-CoV-2 virus, the human host immune system triggers a dynamic process against infection. We constructed a mathematical model to describe host innate and adaptive immune response to viral challenge. Based on the dynamic properties of viral load and immune response, we classified the resulting dynamics into four modes, reflecting increasing severity of COVID-19 disease. We found the numerical product of immune system's ability to clear the virus and to kill the infected cells, namely immune efficacy, to be predictive of disease severity. We also investigated vaccine-induced protection against SARS-CoV-2 infection. Results suggested that immune efficacy based on memory T cells and neutralizing antibody titers could be used to predict population vaccine protection rates. Finally, we analyzed infection dynamics of SARS-CoV-2 variants within the construct of our mathematical model. Overall, our results provide a systematic framework for understanding the dynamics of host response upon challenge by SARS-CoV-2 infection, and this framework can be used to predict vaccine protection and perform clinical diagnosis.

Our study reframes our understanding of stratosphere–troposphere interactions, traditionally thought to be confined within individual hemispheres, by introducing a novel cross-hemispheric link. We demonstrate that strong boreal winter Arctic stratospheric polar vortex (APV) boosts the transmission of upper tropospheric waves from Northern Hemisphere’s mid-high latitudes to the equator. Facilitated by the tropical central and eastern Pacific’s ‘westerly bridge’, these waves reach Southern Hemisphere’s mid-high latitudes. The entire process shows a ‘semicircular road’. Waves reaching the Southern Hemisphere affect the circulation through wave-flow interaction, causing a southward swing of the Southern Hemispheric westerly jet center. This displacement weakens the subtropical jet and strengthens the polar jet, resulting in increased subtropical precipitation and decreased mid-latitude precipitation in the Southern Hemisphere during austral summer. Correspondingly, a weak APV may lead to the opposite result. Our findings underscore APV’s broader impact on the tropospheric atmosphere, extending beyond prior knowledge.

Using 51 years (1960–2010) of observations from meteorological stations in the Sichuan-Chongqing region, including atmospheric visibility as a proxy for aerosol concentration, relative humidity, air temperature, wind speed and FNL reanalysis data (1°×1°) of air temperature, pressure and wind, and the altitude of each station, a linear trend and multivariate fitting approach was used to explore the effects of the large topography on the atmospheric environment in the Sichuan-Chongqing region. The region mainly consists of two areas: Sichuan Basin (SCB) and Western Sichuan Plateau (WSP; eastern part of the Tibetan Plateau). Visibility was relatively low in the SCB and high in the WSP, indicating the high and low levels of aerosols respectively in the SCB and the WSP. Additionally, visibility and wind speed were positively correlated within the basin (altitude below 750 m), while negatively correlated at stations above 1,500 m, such as on the WSP, indicating that the topography had an influence on the atmospheric environment of the basin. On the one hand, the vertical structures of the wind fields and the vertical profiles of latitudinal deviations in wind speed and air temperature in the basin show that the unique large topography causes a “harbour” effect on the leeward-slope of the WSP, with the SCB being a weak wind region, while the descending air currents in the upper westerlies of the basin form a huge “vault” of air. On the other hand, topographic effects can make the basin more susceptible to the formation of inversion structures near the surface and at high altitudes, thus stabilising the atmosphere. The topographic effects, which is not conducive to horizontal diffusion and convective transport of pollutants, were the most significant in winter, followed by autumn and spring.

Abstract Anthropogenic aerosol (AA) forcing has been shown as a critical driver of climate change over Asia since the mid-20th century. Here we show that almost all Coupled Model Intercomparison Project Phase 6 (CMIP6) models fail to capture the observed dipole pattern of aerosol optical depth (AOD) trends over Asia during 2006–2014, last decade of CMIP6 historical simulation, due to an opposite trend over eastern China compared with observations. The incorrect AOD trend over China is attributed to problematic AA emissions adopted by CMIP6. There are obvious differences in simulated regional aerosol radiative forcing and temperature responses over Asia when using two different emissions inventories (one adopted by CMIP6; the other from Peking university, a more trustworthy inventory) to driving a global aerosol-climate model separately. We further show that some widely adopted CMIP6 pathways (after 2015) also significantly underestimate the more recent decline in AA emissions over China. These flaws may bring about errors to the CMIP6-based regional climate attribution over Asia for the last two decades and projection for the next few decades, previously anticipated to inform a wide range of impact analysis.

Seasonal and diurnal characteristics of the vertical profiles of aerosol properties are essential for detecting the regional transport and the climatic radiative effects of aerosol particles. We have studied the seasonal and diurnal characteristics of the vertical distribution of aerosols in urban Beijing from 2017 to 2021 based on long-term Raman–Mie LiDAR observations. The influence of the vertical distribution of aerosols, the meteorological conditions within the boundary layer, the optical–radiometric properties of aerosols, and their interconnections, were investigated during a heavy haze pollution event in Beijing from 8 to 15 February 2020 using both meteorological and sun photometer data. The aerosol extinction coefficient was highest in summer (0.4 km−1), followed by winter (0.35 km−1), and roughly equal in spring and autumn (0.3 km−1). The aerosol extinction coefficient showed clear daily variations and was different in different seasons as a result of the variation in the height of the boundary layer. During the haze pollution event, the particulate matter mainly consisted of scattered spherical fine particles and the accumulation time of pollutants measured via the AOD440nm and PM2.5 mass concentration was different as a result of the hygroscopic growth of the aerosol particles. This growth increased scattering and led to an increase in the aerosol optical depth. The vertical transport of particulate matter also contributed to the increase in the aerosol optical depth.

The aerosol optical property products of Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis dataset have been extensively investigated on a global or regional scale. However, the understanding of MERRA-2 aerosol component products on an extensive temporal and spatial scale is inadequate. Recently, the aerosol component products have been derived from the observations of Polarization and Directionality of the Earth’s Reflectances/Polarization and Anisotropy of Reflectance for Atmospheric Science coupled with observations from a Lidar (POLDER/PARASOL). This study presents a quantitative evaluation of the MERRA-2 reanalysis dust and black carbon (BC) column concentration using independent satellite-based aerosol component concentration retrievals. Both GRASP/Component and MERRA-2 reanalysis products can capture well the temporal variation in dust column concentration over the dust emission resource and downwind dust-dominated regions with the correlation coefficient (R) varying from 0.80 to 0.98. MERRA-2 reanalysis dust products present higher column concentration than GRASP/Component dust retrievals with relative differences of about 20~70%, except in the Taklamakan Desert and Bay of Bengal, where the relative differences can be negative. The differences in dust column concentration over the African dust regions are larger than that over the Asian dust regions. Similar temporal variations in BC column concentration are characterized by both GRASP/Component BC retrievals and MERRA-2 BC products with R of about 0.70~0.90, except in the North China Plain region. We should pay more caution with the regional applicability of MERRA-2 component products when large differences and high correlation coefficients are obtained simultaneously. The results are favorable for identifying the behavior of MERRA-2 reanalysis component estimation in a new view and demonstrate a practical application of the satellite-based component retrievals, which could make more contributions to the improvement of model estimation in the near future.

Located in East Asia, the North China Plain (NCP) has a severe air pollution problem. In this study, variations in visibility, particulate matter (PM), gaseous pollutants, vertical meteorological parameters, and different types of aerosols and their optical properties were evaluated during an air pollution episode that occurred from 10 to 15 January 2019 in Tianjin over the NCP. The visibility was

An assessment and verification of the Langley calibration method of the Sun photometer at Mt Foyeding (MFYD) Observatory in Beijing was performed. We explored whether the Langley plot calibration is practicable for this mountainous site by analyzing the aerosol climatology and carrying out a case study. Then, the aerosol optical depth (AOD) results were verified under the reference of AERONET AOD. The results showed that satisfactory atmospheric conditions are present on winter mornings, characterized by a smaller average AOD (~0.09–0.14) and a lower range ratio (~36.97–63.38%) than in the afternoons and over a whole day. The six days selected as the case study all showed stable atmospheric conditions characterized by daily average triplets of V0λ at all wavelengths was V0λ value was derived from 31 days of observation and the deviations from the reference V0λ were about −1.69, −1.29, −0.81, −0.42, −0.34, −0.22, −0.63 and −0.36% at 340, 380, 440, 500, 675, 870, 1020 and 1640 nm, respectively. The regression analysis of the AOD validation showed a perfect AOD performance, with 100% of the retrievals lying within the expected error (0.05 ± 10%) from 380 to 1640 nm and 99.99% for the 340 nm band. Good AOD agreement (correlation coefficients > 0.998) and residual standard deviation values ranging from ~0.006 to 0.011 were observed, with the relative mean bias varying from 0.999 to 1.066. The mean biases were concentrated within ±0.02 for the ultraviolet bands and within ±0.01 for the other bands; therefore, the results of this preliminary assessment and verification indicated that the Langley plots method is suitable for photometer calibration at the MFYD Observatory.

Aerosols can affect vertical thermal structure during heavily polluted episodes (HPEs). Here, we selected four typical HPEs in 2018, which were further subdivided into dust and haze events. The vertical distribution of aerosols extinction coefficient (EC) and variations in columnar optical properties were investigated based on sun-photometer and Lidar observation at an urban site in Beijing. The vertical characteristics in shortwave radiative heating rate (HR) of aerosols were studied using NASA/Goddard radiative transfer model along with observational data. In the haze episode, EC layer is less than 1.5 km and shows strong scattering, with single-scattering albedo (SSA440nm) of ~0.97. The heating effects are observed at the middle and upper atmosphere, and slight heating effects are found at the lower layer. The mean HR within 1.5 km can be up to 16.3 K day−1 with EC of 1.27 km−1, whereas the HR within 0.5 km is only 1.3 K day−1. In the dust episode, dust aerosols present the absorption with SSA440nm of ~0.88, which would heat the lower atmosphere to promote vertical turbulence, and the height of EC layer can be up to 2.0–3.5 km. In addition, the strong heating effects of dust layer produced cooling effects near the surface. Therefore, the accurate measurement of aerosols optical properties in HPEs is of great significance for modeling aerosols direct radiative effects.

With increasing public concerns on air pollution in China, there is a demand for long-term continuous PM2.5 datasets. However, it was not until the end of 2012 that China established a national PM2.5 observation network. Before that, satellite-retrieved aerosol optical depth (AOD) was frequently used as a primary predictor to estimate surface PM2.5. Nevertheless, satellite-retrieved AOD often encounter incomplete daily coverage due to its sampling frequency and interferences from cloud, which greatly affect the representation of these AOD-based PM2.5. Here, we constructed a virtual ground-based PM2.5 observation network at 1180 meteorological sites across China using the Extreme Gradient Boosting (XGBoost) model with high-density meteorological observations as major predictors. Cross-validation of the XGBoost model showed strong robustness and high accuracy in its estimation of the daily (monthly) PM2.5 across China in 2018, with R2, root-mean-square error (RMSE) and mean absolute error values of 0.79 (0.92), 15.75 μg/m3 (6.75 μg/m3) and 9.89 μg/m3 (4.53 μg/m3), respectively. Meanwhile, we find that surface visibility plays the dominant role in terms of the relative importance of variables in the XGBoost model, accounting for 39.3% of the overall importance.We then use meteorological and PM2.5 data in the year 2017 to assess the predictive capability of the model. Results showed that the XGBoost model is capable to accurately hindcast historical PM2.5 at monthly (R2 = 0.80, RMSE = 14.75 μg/m3), seasonal (R2 = 0.86, RMSE = 12.28 μg/m3), and annual (R2 = 0.81, RMSE = 10.10 μg/m3) mean levels. In general, the newly constructed virtual PM2.5 observation network based on high-density surface meteorological observations using the Extreme Gradient Boosting model shows great potential in reconstructing historical PM2.5 at ~1000 meteorological sites across China. It will be of benefit to filling gaps in AOD-based PM2.5 data, as well as to other environmental studies including epidemiology.

Abstract Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest‐growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high‐density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF‐estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root‐mean‐square error of 2.34 MJ/m2, and mean bias of −0.04 MJ/m2. The geographical distributions of R values, root‐mean‐square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high‐resolution DGSR network, which can not only be used to effectively evaluate the long‐term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy.

This study used the MERRA-2 reanalysis dataset and ground-based and satellite observational data to comprehensively analyze a typical dust storm event in east Asia on 2−7 May 2017 which engulfed most of China as well as ocean and Japan, and explore the accuracy and comprehensiveness of the MERRA-2 dataset in the analysis of dust processes. The results of comparison show that the description of the spatiotemporal distribution and evolution of the dust aerosols in the dust event using the MERRA-2 data is consistent with the data of AERONET, National Urban Air Quality Real-time Publishing Platform and Hamawari-8. Gobi Deserts was the most influential source area of this dust event with the highest emissions reaching 1.9 × 106 μg/m3. The vertical motion of the atmosphere can lift dust from the source area above 500 hPa. There were low-pressure troughs at 500 and 850 hPa and the winds behind and in front of the trough led to the high-altitude, long-distance transport of dust. Dust gradually affected the northwest China, north China, northeast China, and even the ocean and Japan on 2−7 May. This study demonstrates that although there is some uncertainty about the source of dust emission in the MERRA-2 model, the data accurately simulated the evolution of the dust event and analyze it comprehensively, while the accuracy of simulating the long-term evolution of dust requires further evaluation.

Water vapor (WV) has a vital effect on global climate change. Using satellite data observed by AURA/MLS and ERA-Interim reanalysis datasets, the spatial distributions and temporal variations of WV were analyzed. It was found that high WV content in the UTLS over the southern Tibetan Plateau is more apparent in summer, due to monsoon-induced strong upward motions. The WV content showed the opposite distribution at 100 hPa, though, during spring and winter. And a different distribution at 121 hPa indicated that the difference in WV content between the northern and southern plateau occurs between 121 and 100 hPa in spring and between 147 and 121 hPa in winter. In the UTLS, it diminishes rapidly with increase in altitude in these two seasons, and it shows a “V” structure in winter. There has been a weak increasing trend in WV at 100 hPa, but a downtrend at 147 and 215 hPa, during the past 12 years. At the latter two heights, the WV content in summer has been much higher than in other seasons. Furthermore, WV variation showed a rough wave structure in spring and autumn at 215 hPa. The variation of WV over the Tibetan Plateau is helpful in understanding the stratosphere-troposphere exchange (STE) and climate change.

This study analyzed the long-term variations and trends of haze pollution and its relationships with emission and meteorological factors using the haze days (HDs) data derived from surface observation stations in Sichuan-Chongqing (SCC) region during 1980–2016. The results showed that the multi-year mean number of HDs were 68.7 and 4.9 days for the Sichuan-Basin (SCB) and the rest of SCC region, respectively. The seasonally averaged HDs over SCB reached its maximum in winter (34.7 days), followed by autumn (17.0 days) and spring (11.6 days), and with the minimum observed in summer (5.5 days). The inter-annual variations of HDs in 18 main cities revealed that Zigong, Neijiang, and Yibin, which are located in the southern of SCB, have been the most polluted areas over the SCC region in the past decades. A notable increasing trend in annual HDs over the majority of SCC region was found during 1980–1995, then the trend sharply reversed during 1996–2005, while it increased, fluctuating at some cities after 2006. Seasonally, the increased trend in spring and autumn seems to be the strongest during 1980–1995, whereas the decreased trend in spring and winter was stronger than other seasons during 1996–2005. In addition, a remarkable increasing trend was found in winter since 2006. Using correlation analysis between HDs and emission and meteorological factors during different periods, we found that the variability of local precipitation days (PDs), planetary boundary layer height (PBLH), near-surface wind speed (WS), and relatively humidity (RH) play different roles in influencing the haze pollution change during different historical periods. The joint effect of sharp increase of anthropogenic emissions, reduced PDs and WS intensified the haze pollution in SCB during 1980–1995. In contrast, decreased HDs during 1996–2005 are mainly attributable to the reduction of PM2.5 emission and the increase of PDs (especially in winter). In addition, the decrease of PDs is likely to be responsible for the unexpected increase in winter HDs over SCB in the last decade.

This research systematically studied heavy rain that occurred on 5 August 2014 over the southeastern Tibetan Plateau (31°N–35°N, 96°E–103°E) using orbital data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR), the TRMM Multi-satellite Precipitation Analysis (TMPA) products, and the European Centre for Medium-range Weather Forecast (ECMWF) Re-Analysis Interim reanalysis data (ERA-Interim). The data studied included spatial and temporal distributions of the precipitation; horizontal distributions and vertical structures of the precipitation system; convective storm top altitudes and types of rain; mean rainfall profiles; the influence of water vapor content before and after the rainfall; and the atmospheric circulation background. The results suggest that most precipitation on the Tibetan Plateau occurs in the southeast, and that the maximum near-surface precipitation rate for this event was more than 100 mm·h−1. The convection was so powerful that the convective storm top altitude surpassed 16 km. Furthermore, the water vapor content caused obvious changes in the upper troposphere and lower stratosphere (UTLS) area. The mean rainfall profile can be roughly divided into four layers and showed that the maximum rainfall rate appeared at about 5.5 km. Deep weak precipitation provided the largest contribution to the total precipitation, while the highest average precipitation rate was from deep strong convective precipitation. The atmospheric circulation situation is conducive to the formation of strong convective weather, and the terrain is also an external factor affecting precipitation for this event.

An extreme haze event occurred in the Beijing area from 17 to 23 December 2015. Ground-based measurements and satellite observations during this event were used to further our understanding of the formation process of haze pollution and aerosol optical properties. The results suggest that high relative humidity, poor diffusion conditions (low wind speed and stable stratification) and favorable secondary transformation conditions under the hygroscopic growth of aerosol and high emissions led to this serious haze episode. During the haze period, the daily average value was 1.15 and 0.42 for aerosol optical depth (AOD500nm) and columnar water-vapor (CWV, in cm), respectively. On 19 December, the correlation coefficient between CWV and AOD500nm was 0.91, indicating the effect of hygroscopic growth of fine-mode articles. The daily average values for Ångström exponent, fine-mode fraction, aerosol absorption optical depth, and Ångström absorption exponent were 1.19, 0.81, 0.11 and 1.47, respectively, which suggests that fine aerosol particles were dominant in the atmosphere and fine-mode particles were the dominant contributor to atmospheric extinction during the haze period. Moreover, it also reflects that there were more absorbing aerosol particles during the haze period. Compared with other polluted periods with a bimodal distribution, there was an obvious trimodal distribution on 19 December. There were three peaks at radii of about 0.1 μm, 0.5–0.8 μm and 4 μm. Satellite observations show that there was an obvious aerosol layer in the Beijing area during the haze period, concentrated at ground level to within 2 km in the upper layers. The types of aerosol were mainly composed of mixed pollution aerosols.

A special period in Beijing from 6 August to 17 September 2015, during which the World Athletics Championships and Victory Day military parade took place, and which involved measures to restrict traffic and reduce factory emissions, was selected to analyze the aerosol optical properties and the impact of meteorological conditions on pollution levels. The study was based on AERONET observational and retrieval data, particulate matter measurements (TEOM 1405), meteorological data, and then the HYSPLIT model was used to analyze the pollution sources. The study period was divided into three sub-periods according to the different stages of implementation of the control measures, and the main conclusions can be summarized as follows. During the period in which the restrictive measures were applied, the air quality improved significantly, with the average value of the AOD being 0.34 ± 0.20, about 69% less than before. Meanwhile, the average Ångström exponent was about 9.5% higher than before, with an average value of 1.38 ± 0.25, indicating that the main pollutants were fine particles. Single scattering albedo decreased as wavelength increased, being higher than in the other two stages (mean value of 0.944 ± 0.045). This showed that the strong scattering capacity and absorption aerosol optical depth was at its lowest, at about 0.008 ± 0.009. The peaks of aerosol volume concentration in the fine and coarse mode were significantly reduced. Meteorological conditions also had a certain effect on the aerosol optical properties, with the blowing of clean and dry wind and the occurrence of precipitation contributing to the overall improvement in air quality.

In situ observation of the aerosol optical properties is important to the validations of satellite and modeling results; however, the operational measurements can be affected by some objective factors. An experiment study has been performed in order to analyze the error in retrievals of aerosol optical properties from sunphotometer measurements caused by a variety of in situ objective factors. The standard instrument relative error analysis method was used to determine the relative error of aerosol optical depth (AOD) and Ångström exponent (AE) under the effects of five factors: spider web inside the collimator (F1); collimator bending (F2); dust inside the optical head (F3); incrustation scale inside the optical head (F4); and dust and incrustation scale inside the optical head (F5). The results showed that the five factors caused error for AOD retrieved at 1020, 870, 670 and 440 nm, with the maximum error occurring at 870 nm due to the more sensitive measurement signals. The error ranges of AOD derived from the direct solar measurements in the four bands were −0.34%–8.77%, −6.22%–9.68%, −0.05%–2.52%, −0.96%–3.48% and 5.42%–13.38% for F1, F2, F3, F4 and F5, respectively. The maximum error occurred under the influence of F5 with an average error value of 10%, while the minimum occurred owing to F3 with an average error value of 1%. All of the AEs retrieved from the experimental instruments were smaller than that from the reference instrument. The AE error values were 15.19%, 25.57%, 4.56%, 4.41% and 8.83% for F1, F2, F3, F4 and F5, respectively. The average AE retrieval error value was 11.7%.

Purpose: Brain metastases (BMs) are common in Small Cell Lung Cancer (SCLC), but the prognosis is very poor. Currently, there is no standard of care on what constitutes optimal treatment, and there is no consensus regarding maintenance therapy in SCLC. Case description: We report the case of a 55-year-old man with advanced SCLC. After the initial diagnosis, he received routine chemotherapy and chest radiotherapy but developed brain metastases with 2 lesions seven months later. We used an effective combination therapy consisting of the antiangiogenic inhibitor, Anlotinib and whole-brain radiotherapy. We then administered anti-PD-L1 immunotherapy Atezolizumab in combination with Anlotinib as long-term maintenance therapy. Twelve months later, there was a progression in one of the brain metastases. The patient underwent further stereotactic radiotherapy (SRT) for the lesion. However, after four months of treatment with SRT, the lesion began to gradually grow in size. The patient underwent surgical resection of the lesion, which confirmed radioactive brain necrosis. After a full 3-year course of anti-PD-L1 therapy, the patient discontinued immunotherapy and was administered only Anlotinib as maintenance. At the time of writing up this report, the patient was alive and the overall survival reached 41 months after the onset of BM. Conclusion: This indicated a potential synergistic effect of combined immunotherapy and antiangiogenic targeted therapy with local radiotherapy in patients with BM-SCLC and can provide directions for future clinical decisions. [ABSTRACT FROM AUTHOR]