Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval.

Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for climate research. Here, we propose a novel pretrained deep learning framework that can extract information underlying each satellite pixel and use it to create new latent features that can be employed for improving retrieval accuracy in regions without in situ data. With the proposed model, we developed a new global fAOD (at 0.5 µm) data from 2001 to 2020, resulting in a 10% improvement in the overall correlation coefficient (R) during site-based independent validation and a 15% enhancement in non-AERONET site areas validation. Over the past two decades, there has been a noticeable downward trend in global fAOD (-1.39 × 10-3/year). Compared to the general deep-learning model, our method reduces the global trend's previously overestimated magnitude by 7% per year. China has experienced the most significant decline (-5.07 × 10-3/year), which is 3 times greater than the global trend. Conversely, India has shown a significant increase (7.86 × 10-4/year). This study bridges the gap between sparse in situ observations and abundant satellite measurements, thereby improving predictive models for global patterns of fAOD and other climate factors.

Substantial Underestimation of Fine-Mode Aerosol Loading from Wildfires and Its Radiative Effects in Current Satellite-Based Retrievals over the United States.

Wildfires generate abundant smoke primarily composed of fine-mode aerosols. However, accurately measuring the fine-mode aerosol optical depth (fAOD) is highly uncertain in most existing satellite-based aerosol products. Deep learning offers promise for inferring fAOD, but little has been done using multiangle satellite data. We developed an innovative angle-dependent deep-learning model (ADLM) that accounts for angular diversity in dual-angle observations. The model captures aerosol properties observed from dual angles in the contiguous United States and explores the potential of Greenhouse gases Observing Satellite-2's (GOSAT-2) measurements to retrieve fAOD at a 460 m spatial resolution. The ADLM demonstrates a strong performance through rigorous validation against ground-based data, revealing small biases. By comparison, the official fAOD product from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Multiangle Imaging Spectroradiometer (MISR) during wildfire events is underestimated by more than 40% over western USA. This leads to significant differences in estimates of aerosol radiative forcing (ARF) from wildfires. The ADLM shows more than 20% stronger ARF than the MODIS, VIIRS, and MISR estimates, highlighting a greater impact of wildfire fAOD on Earth's energy balance.

Application and Development of Cell Membrane Functionalized Biomimetic Nanoparticles in the Treatment of Acute Ischemic Stroke.

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.

Separating Daily 1 km PM2.5 Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data.

Fine particulate matter (PM2.5) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model simulations. We developed a four-dimensional spatiotemporal deep forest (4D-STDF) model to estimate daily PM2.5 chemical composition at a spatial resolution of 1 km in China since 2000 by integrating measurements of PM2.5 species from a high-density observation network, satellite PM2.5 retrievals, atmospheric reanalyses, and model simulations. Cross-validation results illustrate the reliability of sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and chloride (Cl-) estimates, with high coefficients of determination (CV-R2) with ground-based observations of 0.74, 0.75, 0.71, and 0.66, and average root-mean-square errors (RMSE) of 6.0, 6.6, 4.3, and 2.3 µg/m3, respectively. The three components of secondary inorganic aerosols (SIAs) account for 21% (SO42-), 20% (NO3-), and 14% (NH4+) of the total PM2.5 mass in eastern China; we observed significant reductions in the mass of inorganic components by 40-43% between 2013 and 2020, slowing down since 2018. Comparatively, the ratio of SIA to PM2.5 increased by 7% across eastern China except in Beijing and nearby areas, accelerating in recent years. SO42- has been the dominant SIA component in eastern China, although it was surpassed by NO3- in some areas, e.g., Beijing-Tianjin-Hebei region since 2016. SIA, accounting for nearly half (∼46%) of the PM2.5 mass, drove the explosive formation of winter haze episodes in the North China Plain. A sharp decline in SIA concentrations and an increase in SIA-to-PM2.5 ratios during the COVID-19 lockdown were also revealed, reflecting the enhanced atmospheric oxidation capacity and formation of secondary particles.

Reduced Graphene Oxide Coating LiFePO4 Composite Cathodes for Advanced Lithium-Ion Battery Applications.

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.

Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence.

Nitrogen dioxide (NO2) at the ground level poses a serious threat to environmental quality and public health. This study developed a novel, artificial intelligence approach by integrating spatiotemporally weighted information into the missing extra-trees and deep forest models to first fill the satellite data gaps and increase data availability by 49% and then derive daily 1 km surface NO2 concentrations over mainland China with full spatial coverage (100%) for the period 2019-2020 by combining surface NO2 measurements, satellite tropospheric NO2 columns derived from TROPOMI and OMI, atmospheric reanalysis, and model simulations. Our daily surface NO2 estimates have an average out-of-sample (out-of-city) cross-validation coefficient of determination of 0.93 (0.71) and root-mean-square error of 4.89 (9.95) µg/m3. The daily seamless high-resolution and high-quality dataset "ChinaHighNO2" allows us to examine spatial patterns at fine scales such as the urban-rural contrast. We observed systematic large differences between urban and rural areas (28% on average) in surface NO2, especially in provincial capitals. Strong holiday effects were found, with average declines of 22 and 14% during the Spring Festival and the National Day in China, respectively. Unlike North America and Europe, there is little difference between weekdays and weekends (within ±1 µg/m3). During the COVID-19 pandemic, surface NO2 concentrations decreased considerably and then gradually returned to normal levels around the 72nd day after the Lunar New Year in China, which is about 3 weeks longer than the tropospheric NO2 column, implying that the former can better represent the changes in NOx emissions.

Abnormally Shallow Boundary Layer Associated With Severe Air Pollution During the COVID-19 Lockdown in China.

After the 2020 Lunar New Year, the Chinese government implemented a strict nationwide lockdown to inhibit the spread of the Coronavirus Disease 2019 (COVID-19). Despite the abrupt decreases in gaseous emissions caused by record-low anthropogenic activities, severe haze pollution occurred in northern China during the COVID lockdown. This paradox has attracted the attention of both the public and the scientific community. By analyzing comprehensive measurements of air pollutants, planetary boundary layer (PBL) height, and surface meteorology, we show that the severe air pollution episode over northern China coincided with the abnormally low PBL height, which had reduced by 45%, triggering strong aerosol-PBL interactions. After dynamical processes initiated the temperature inversion, the Beijing metropolitan area experienced a period with continuously shallow PBLs during the lockdown. This unprecedented event provided an experiment showcasing the role of meteorology, in particular aerosol-PBL interactions in affecting air quality.

Reliable multiplex sequencing with rare index mis-assignment on DNB-based NGS platform.

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.

Satellite-Derived 1-km-Resolution PM1 Concentrations from 2014 to 2018 across China.

Particulate matter with aerodynamic diameters ≤1 µm (PM1) has a greater impact on the human health but has been less studied due to fewer ground observations. This study attempts to improve the retrieval accuracy and spatial resolution of satellite-based PM1 estimates using the new ground-based monitoring network in China. Therefore, a space-time extremely randomized trees (STET) model is first developed to estimate PM1 concentrations at a 1 km spatial resolution from 2014 to 2018 across mainland China. The STET model can derive daily PM1 concentrations with an average across-validation coefficient of determination of 0.77, a low root-mean-square error of 14.6 µg/m3, and a mean absolute error of 8.9 µg/m3. PM1 concentrations are generally low in most areas of China, except for the North China Plain and Sichuan Basin where intense human activities and poor natural conditions are prevalent, especially in winter. Moreover, PM1 pollution has greatly decreased over the past 5 years, benefiting from emission control in China. The STET model, incorporating the spatiotemporal information, shows superior performance in PM1 estimates relative to previous studies. This high-resolution and high-quality PM1 data set in China (i.e., ChinaHighPM1) can be greatly useful for air pollution studies in medium- or small-scale areas.

Satellite retrieval of cloud condensation nuclei concentrations by using clouds as CCN chambers.

Quantifying the aerosol/cloud-mediated radiative effect at a global scale requires simultaneous satellite retrievals of cloud condensation nuclei (CCN) concentrations and cloud base updraft velocities (Wb). Hitherto, the inability to do so has been a major cause of high uncertainty regarding anthropogenic aerosol/cloud-mediated radiative forcing. This can be addressed by the emerging capability of estimating CCN and Wb of boundary layer convective clouds from an operational polar orbiting weather satellite. Our methodology uses such clouds as an effective analog for CCN chambers. The cloud base supersaturation (S) is determined by Wb and the satellite-retrieved cloud base drop concentrations (Ndb), which is the same as CCN(S). Validation against ground-based CCN instruments at Oklahoma, at Manaus, and onboard a ship in the northeast Pacific showed a retrieval accuracy of ±25% to ±30% for individual satellite overpasses. The methodology is presently limited to boundary layer not raining convective clouds of at least 1 km depth that are not obscured by upper layer clouds, including semitransparent cirrus. The limitation for small solar backscattering angles of <25° restricts the satellite coverage to ∼25% of the world area in a single day.

Microphysical effects determine macrophysical response for aerosol impacts on deep convective clouds.

Deep convective clouds (DCCs) play a crucial role in the general circulation, energy, and hydrological cycle of our climate system. Aerosol particles can influence DCCs by altering cloud properties, precipitation regimes, and radiation balance. Previous studies reported both invigoration and suppression of DCCs by aerosols, but few were concerned with the whole life cycle of DCC. By conducting multiple monthlong cloud-resolving simulations with spectral-bin cloud microphysics that capture the observed macrophysical and microphysical properties of summer convective clouds and precipitation in the tropics and midlatitudes, this study provides a comprehensive view of how aerosols affect cloud cover, cloud top height, and radiative forcing. We found that although the widely accepted theory of DCC invigoration due to aerosol's thermodynamic effect (additional latent heat release from freezing of greater amount of cloud water) may work during the growing stage, it is microphysical effect influenced by aerosols that drives the dramatic increase in cloud cover, cloud top height, and cloud thickness at the mature and dissipation stages by inducing larger amounts of smaller but longer-lasting ice particles in the stratiform/anvils of DCCs, even when thermodynamic invigoration of convection is absent. The thermodynamic invigoration effect contributes up to ~27% of total increase in cloud cover. The overall aerosol indirect effect is an atmospheric radiative warming (3-5 W m(-2)) and a surface cooling (-5 to -8 W m(-2)). The modeling findings are confirmed by the analyses of ample measurements made at three sites of distinctly different environments.

Vertical distribution of aerosol optical properties based on aircraft measurements over the Loess Plateau in China.

Vertical distributions of aerosol optical properties based on aircraft measurements over the Loess Plateau were measured for the first time during a summertime aircraft campaign, 2013 in Shanxi, China. Data from four flights were analyzed. The vertical distributions of aerosol optical properties including aerosol scattering coefficients (σsc), absorption coefficients (σab), Angström exponent (α), single scattering albedo (ω), backscattering ratio (ßsc), aerosol mass scattering proficiency (Qsc) and aerosol surface scattering proficiency (Qsc(')) were obtained. The mean statistical values of σsc were 77.45 Mm(-1) (at 450 nm), 50.72 Mm(-1) (at 550n m), and 32.02 Mm(-1) (at 700 nm). The mean value of σab was 7.62 Mm(-1) (at 550 nm). The mean values of α, ßsc and ω were 1.93, 0.15, and 0.91, respectively. Aerosol concentration decreased with altitude. Most effective diameters (ED) of aerosols were less than 0.8 µm. The vertical profiles of σsc,, α, ßsc, Qsc and Qsc(') showed that the aerosol scattering properties at lower levels contributed the most to the total aerosol radiative forcing. Both α and ßsc had relatively large values, suggesting that most aerosols in the observational region were small particles. The mean values of σsc, α, ßsc, Qsc, Qsc('), σab and ω at different height ranges showed that most of the parameters decreased with altitude. The forty-eight hour backward trajectories of air masses during the observation days indicated that the majority of aerosols in the lower level contributed the most to the total aerosol loading, and most of these particles originated from local or regional pollution emissions.

Constraining effects of aerosol-cloud interaction by accounting for coupling between cloud and land surface.

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.

Cooperative simultaneous inversion of satellite-based real-time PM2.5 and ozone levels using an improved deep learning model with attention mechanism.

Ground-level fine particulate matter (PM2.5) and ozone (O3) are air pollutants that can pose severe health risks. Surface PM2.5 and O3 concentrations can be monitored from satellites, but most retrieval methods retrieve PM2.5 or O3 separately and disregard the shared information between the two air pollutants, for example due to common emission sources. Using surface observations across China spanning 2014-2021, we found a strong relationship between PM2.5 and O3 with distinct spatiotemporal characteristics. Thus, in this study, we propose a new deep learning model called the Simultaneous Ozone and PM2.5 inversion deep neural Network (SOPiNet), which allows for daily real-time monitoring and full coverage of PM2.5 and O3 simultaneously at a spatial resolution of 5 km. SOPiNet employs the multi-head attention mechanism to better capture the temporal variations in PM2.5 and O3 based on previous days' conditions. Applying SOPiNet to MODIS data over China in 2022, using 2019-2021 to construct the network, we found that simultaneous retrievals of PM2.5 and O3 improved the performance compared with retrieving them independently: the temporal R2 increased from 0.66 to 0.72 for PM2.5, and from 0.79 to 0.82 for O3. The results suggest that near-real time satellite-based air quality monitoring can be improved by simultaneous retrieval of different but related pollutants. The codes of SOPiNet and its user guide are freely available online at https://github.com/RegiusQuant/ESIDLM.

First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact.

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.

Long-term mortality burden trends attributed to black carbon and PM2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study.

BACKGROUND: Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate trends in black carbon and PM2·5 concentrations and their attributable mortality burden across the USA. METHODS: In this study, we derived daily concentrations of PM2·5 and its highly toxic black carbon component at a 1-km resolution in the USA from 2000 to 2020 via deep learning that integrated big data from satellites, models, and surface observations. We estimated the annual PM2·5-attributable and black carbon-attributable mortality burden at each 1-km2 grid using concentration-response functions collected from a national cohort study and a meta-analysis study, respectively. We investigated the spatiotemporal linear-regressed trends in PM2·5 and black carbon pollution and their associated premature deaths from 2000 to 2020, and the impact of wildfires on air quality and public health. FINDINGS: Our results showed that PM2·5 and black carbon estimates are reliable, with sample-based cross-validated coefficients of determination of 0·82 and 0·80, respectively, for daily estimates (0·97 and 0·95 for monthly estimates). Both PM2·5 and black carbon in the USA showed significantly decreasing trends overall during 2000 to 2020 (22% decrease for PM2·5 and 11% decrease for black carbon), leading to a reduction of around 4200 premature deaths per year (95% CI 2960-5050). However, since 2010, the decreasing trends of fine particles and premature deaths have reversed to increase in the western USA (55% increase in PM2·5, 86% increase in black carbon, and increase of 670 premature deaths [460-810]), while remaining mostly unchanged in the eastern USA. The western USA showed large interannual fluctuations that were attributable to the increasing incidence of wildfires. Furthermore, the black carbon-to-PM2·5 mass ratio increased annually by 2·4% across the USA, mainly due to increasing wildfire emissions in the western USA and more rapid reductions of other components in the eastern USA, suggesting a potential increase in the relative toxicity of PM2·5. 100% of populated areas in the USA have experienced at least one day of PM2·5 pollution exceeding the daily air quality guideline level of 15 µg/m3 during 2000-2020, with 99% experiencing at least 7 days and 85% experiencing at least 30 days. The recent widespread wildfires have greatly increased the daily exposure risks in the western USA, and have also impacted the midwestern USA due to the long-range transport of smoke. INTERPRETATION: Wildfires have become increasingly intensive and frequent in the western USA, resulting in a significant increase in smoke-related emissions in populated areas. This increase is likely to have contributed to a decline in air quality and an increase in attributable mortality. Reducing fire risk via effective policies besides mitigation of climate warming, such as wildfire prevention and management, forest restoration, and new revenue generation, could substantially improve air quality and public health in the coming decades. FUNDING: National Aeronautics and Space Administration (NASA) Applied Science programme, NASA MODIS maintenance programme, NASA MAIA satellite mission programme, NASA GMAO core fund, National Oceanic and Atmospheric Administration (NOAA) GEO-XO project, NOAA Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) programme, and NOAA Educational Partnership Program with Minority Serving Institutions.

Hydroxysafflower yellow A alleviates HK-2 cells injury in cold hypoxia/reoxygenation via mitochondrial apoptosis.

Cold storage for organ preservation in kidney transplantation is a core predisposing factor for delayed graft function and the long-term outcome of transplanted kidneys. Hydroxysafflor yellow A (HSYA) is the most effective water-soluble active monomer in Safflower with a strong property of inhibiting hypoxia and reoxygenation (H/R). However, the evidence concerning the effect of HSYA on H/R in kidney transplantation is limited. To investigate whether HSYA has a protective effect on cold H/R injury，we investigated the possible protective mechanism. Here, we incubated HK-2 cells to establish a cold H/R model and observed HSYA activation in an in vitro model of cold-storage rewarming which included the cell survival rate, cell morphology and ultrastructure, protein expression of Bcl-2, Bax, CytC, Apaf-1, and caspase-3, and status of mitochondrial permeability transformation pores (MPTPs). Our data showed that HSYA pretreatment increased the survival rate of the cells, alleviated mitochondrial damage, decreased the expression of apoptosis-related proteins and inhibited the openness of mitochondrial permeability transformation pores. Our findings suggested that HSYA may be a major predisposing mediator of mitochondrial apoptosis and renal tubular injury in cold storage-associated transplantation and may be an effective therapeutic target for improving graft function and graft survival.

The different sensitivities of aerosol optical properties to particle concentration, humidity, and hygroscopicity between the surface level and the upper boundary layer in Guangzhou, China.

This study investigates the impact of aerosol liquid water content (ALWC) and related factors, i.e., relative humidity (RH), aerosol mass concentration (PM2.5), and aerosol hygroscopicity, on aerosol optical properties, based on field measurements made in the Pearl River Delta (PRD) region of China at the surface (1 November 2019 to 21 January 2020) and in the upper boundary layer (the 532-m Guangzhou tower from 1 February to 21 March 2020). In general, temporal variations in the ambient aerosol backscattering coefficient (ßp) and ALWC followed each other. However, the surface ßp and 532-m ßp had generally opposite diurnal variation patterns, caused by dramatic differences in PM2.5 and ambient RH between the surface and the upper boundary layer. The ambient 532-m RH was systematically higher than the surface RH, with the latter having a much pronounced diurnal cycle than the former. The surface PM2.5 concentration was systematically higher than the PM2.5 concentration at 532 m, and their diurnal cycle patterns were overall opposite. These dramatic differences reveal that the atmospheric variables, i.e., ambient RH and the PM2.5 concentration in the upper boundary layer, cannot be directly represented by the same variables at the surface. Vertical variability should be considered. Clear differences in the sensitivities of aerosol light scattering to ambient RH, PM2.5, and aerosol hygroscopicity between the two levels were found and examined. Aerosol chemical composition played a minor role in causing the differences between the two levels. In particular, ßp was more sensitive to PM2.5 at the surface level but more to the ambient RH in the upper boundary layer. The larger contribution of aerosol loading to the variability in ßp at the surface implies that local emission controls can decrease ßp and further improve atmospheric visibility effectively at the surface during winter in the PRD region.

Insights into vertical differences of particle number size distributions in winter in Beijing, China.

Particle number size distribution (PNSD) is of importance for understanding the mechanisms of particle growth, haze formation and climate impacts. However, the measurements of PNSD aloft in megacities are very limited. Here we report the first simultaneous winter measurements of size-resolved particle number concentrations along with collocated gaseous species and aerosol composition at ground level and 260 m in Beijing. Our study showed that the vertical differences of particle number concentrations between ground level and aloft varied significantly as a function of particle size throughout the study. Further analysis illustrated the impacts of boundary dynamics and meteorological conditions on the vertical differences of PNSD. In particular, the temperature and relative humidity inversions were one of the most important factors by decoupling the boundary layer into different sources and processes. Positive matrix factorization analysis identified six sources of PNSD at both ground level and city aloft. The local source emissions dominantly contributed to Aitken-mode particles, and showed the largest vertical gradients in the city. Comparatively, the regional particles were highly correlated between ground level and city aloft, and the vertical differences were relatively stable throughout the day. Our results point towards a complex vertical evolution of PNSD due to the changes in boundary layer dynamics, meteorological conditions, sources, and processes in megacities.

Opportunistic experiments to constrain aerosol effective radiative forcing.

Aerosol-cloud interactions (ACIs) are considered to be the most uncertain driver of present-day radiative forcing due to human activities. The nonlinearity of cloud-state changes to aerosol perturbations make it challenging to attribute causality in observed relationships of aerosol radiative forcing. Using correlations to infer causality can be challenging when meteorological variability also drives both aerosol and cloud changes independently. Natural and anthropogenic aerosol perturbations from well-defined sources provide "opportunistic experiments" (also known as natural experiments) to investigate ACI in cases where causality may be more confidently inferred. These perturbations cover a wide range of locations and spatiotemporal scales, including point sources such as volcanic eruptions or industrial sources, plumes from biomass burning or forest fires, and tracks from individual ships or shipping corridors. We review the different experimental conditions and conduct a synthesis of the available satellite datasets and field campaigns to place these opportunistic experiments on a common footing, facilitating new insights and a clearer understanding of key uncertainties in aerosol radiative forcing. Cloud albedo perturbations are strongly sensitive to background meteorological conditions. Strong liquid water path increases due to aerosol perturbations are largely ruled out by averaging across experiments. Opportunistic experiments have significantly improved process-level understanding of ACI, but it remains unclear how reliably the relationships found can be scaled to the global level, thus demonstrating a need for deeper investigation in order to improve assessments of aerosol radiative forcing and climate change.