Aqueous secondary organic aerosol (aqSOA) contributes substantially to organic aerosol (OA), affecting air quality, human health, and climate. However, the molecular composition and processing of aqSOA in clouds remain unclear due to limited online field measurements. We measured molecular composition of OA online (time resolution 20 s) and tracked its processing at a mountain site in southeastern China, using an Extractive ElectroSpray Ionization inlet coupled with a Time-of-Flight Mass Spectrometer (EESI-ToF-MS). We identified 2084 molecular formulas and compared OA composition from three sample types: cloud droplet residuals (CDR), interstitial aerosol particles (INT), and cloud-free aerosol particles (CF) in representative cloud episodes. CHO class was the dominant constituent, followed by CHON class. In most cloud episodes, the fraction of CHO was lower in CDR than that in INT and CF, while the fraction of CHON was higher, which may result from the uptake of organonitrates or nitration in cloud water. Compounds in CDR had more carbon number and higher molecular weight than CF, which is attributed to accretion reactions in cloud water. We identified 39 significantly enriched compounds in CDR compared with CF, which could be potentially used as aqSOA tracers formed via cloud processing. This study also reveals rapid changes in aqSOA composition, which highlight the necessity for high time resolution measurements to capture the processing of aqSOA in clouds. Overall, this study provides clear information on processing of aqSOA in clouds and highlights the importance of accretion reactions, which have implications on the composition and physicochemical properties of SOA.

Recently, deep learning (DL) techniques have been routinely applied to improve chemical weather forecasts of fine particulate matter (PM2.5) based on chemical transport models (CTMs), usually taking the form of bias correction. However, their opaque nature hinders diagnostic analysis of their improvements to identify the key atmospheric processes responsible for CTM forecast errors. Here we propose leveraging interpretability methods as diagnostic tools to identify candidate targets for CTM refinements. We connect a convolutional neural network (CNN) to a CTM, and apply the Deep Learning Important FeaTures (DeepLIFT) interpretability method for diagnosis. The CNN learns the mapping from gridded surface forecast fields of PM2.5, ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ammonia (NH3) to PM2.5 concentration observations across 180 monitoring sites in the Yangtze River Delta (YRD) of China. Trained on a two-year dataset (2017-2018), our CTM-CNN hybrid achieves 16.8-34.7% error reductions compared to the standalone CTM for PM2.5 forecast cases. The DeepLIFT method quantifies comparable contributions from O3 (25.9%), SO2 (22.2%), and NO2 (17.6%), with PM2.5 as the dominant contributor (38.5%) and NH3 exhibiting a modest influence (−4.2%). Interpretability analysis suggests CTM refinements targeting SO2-sensitive and NO2-NH3-coupled secondary PM2.5 formation in the ammonia-poor YRD region, and on O3-related mechanisms affecting secondary PM2.5 formation in coastal areas. We position this integration study as a step towards more general, iterative, and bidirectional hybrid modeling frameworks, where the merits and potentials of both CTMs and DL techniques can be fully explored to ultimately break conventional limits in medium-term regional PM2.5 forecasting.

The atmospheric aqueous phase represents a critical chemical reactor, where day-night alternation establishes distinct physicochemical regimes that transform the composition and impacts of organic aerosol. This study investigates how these contrasting regimes in cloud water govern the molecular characteristics of water-soluble organic matter (WSOM) and its subsequent health implications. Analysis of cloud water samples from Mt. Damaojian, southeastern China, revealed that nocturnal samples exhibited higher liquid water content, greater acidity, and a pronounced shift in chemical dominance from WSOM to secondary inorganic ions compared with daytime. Molecular analysis by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) in both ESI ± modes identified CHO and CHON compounds dominated across both periods, but with a significant nocturnal enrichment of CHON species. The chemical signature of daytime WSOM exhibited a higher OSc and a proportion of Lipids reflecting dominant photo-oxidation processes. In contrast, nighttime samples showed higher N/C and a greater abundance of Protein1 (low oxidized protein), consistent with nitration and condensation reactions under dark conditions. Additionally, in vitro exposure experiments indicate that cloud-processed WSOM significantly inhibit cell viability, induce apoptosis, and trigger inflammation. Nighttime samples exhibited stronger cytotoxicity, likely due to their higher levels of nitrogen-containing and aromatic compounds. By integrating organic chemistry and toxicology, this work provides novel insights into the day-night dichotomy in cloud-processed WSOM composition and its corresponding biological impacts.

Elucidating the molecular-level evolution of organic aerosols (OA) is fundamental to understanding atmospheric chemistry and its climatic impacts, yet current analytical techniques are often constrained by thermal decomposition and fragmentation artifacts. In this study, we deployed a novel Vaporization Inlet for Aerosols coupled with a Vocus Proton Transfer Reaction Time-of-Flight Mass Spectrometer (VIA-PTR) during a wintertime field campaign in urban Beijing. The VIA-PTR exhibited high fidelity in molecular characterization, showing minimal thermal decomposition of dicarboxylic acid standards and robust agreement with co-located aerosol mass spectrometry for total OA (R2 = 0.88). On average, the VIA-PTR quantified 35% of the total OA mass, with mass closure reaching up to 60% during polluted episodes. Molecular analysis revealed that the particle-phase OA composition was dominated by low-molecular-weight, moderately oxygenated CHO compounds (~80%), such as small organic acids and ketones primarily driven by photochemical production. The system’s high temporal resolution enabled the detection of a transient, 10-minute biomass burning plume, identifying specific molecular signatures including catechol and guaiacol derivatives. Furthermore, positive matrix factorization (PMF) resolved six molecular-level OA factors, providing superior source insights compared to conventional bulk OA analysis. While the measured particle-phase fraction (Fp) of OA increased concurrently with pollution intensity and molecular oxygen content, traditional equilibrium partitioning models significantly underestimated the Fp of small oxygenated molecules. Our results highlight the VIA-PTR as a robust platform for real-time OA molecular speciation and underscore the need for improved gas-particle partitioning frameworks that incorporate molecular structure and complex pollution regimes in urban environments.

Accurate precipitation forecasting on the Qinghai-Tibet Plateau (QTP) remains a significant challenge due to complex terrain and nonlinear atmospheric dynamics. This study evaluates an XGBoost-SHAP framework for 24 h precipitation forecasting at Maqu Station, leveraging multi-source observations from 2020 to 2022. Vertical profile analyses via microwave radiometer (MWR) indicate that moisture is predominantly confined to altitudes below 4 km (AGL), with Integrated Water Vapor (IWV) and Liquid Water Path (LWP) typically varying between 0-15 mm and 0-2.5 mm, respectively. The optimized XGBoost model achieves an annual R2 of 0.872 and a Root Mean Square Error (RMSE) of 1.609 mm, showing improved statistical consistency compared with standard Random Forest baselines. While the framework maintains robust performance for winter stratiform precipitation (RMSE = 0.32 mm), predictive variance increases during summer convective periods (RMSE = 3.26 mm). SHAP diagnostic analysis identifies Dew Point Temperature (DPT) as a consistent year-round predictor. Feature sensitivity analysis further reveals shifting seasonal driving mechanisms: spring precipitation appears sensitive to mid-tropospheric geopotential height, whereas summer forecasts are more strongly modulated by 500 hPa specific humidity and lower-level water vapor density. Overall, the XGBoost-SHAP framework serves as a transparent and physically plausible diagnostic tool for examining seasonal moisture-dynamic coupling. While these site-specific results are encouraging, they represent a localized empirical baseline; further cross-site validation is required to assess regional generalizability.

To what extent the new particle formation (NPF) contributed to the cloud condensation nuclei (CCN) remained unclear, especially at the boundary layer top (BLT) in polluted atmosphere. Based on measurements at a mountain-top background site in southeastern China during spring 2024, this study systematically investigates the nucleation mechanism and subsequent growth dynamics of NPF events under contrasting air masses, and quantifies their role as a source of CCN. Eight NPF events were observed, and three of them occurred in the polluted conditions (NPF-P) which associated with regional transportation while the rest five events appeared in the clean conditions (NPF-C). The average formation rate (J2.5: 2.4 cm-3s-1 vs. 0.7 cm-3s-1) and growth rate (GR: 6.8 nm h-1 vs. 5.5 nm h-1) were significantly higher in NPF-P events than in NPF-C events, alongside elevated concentrations of sulfuric acid and ammonia. The correlation between log J3 and [H2SO4], as well as theoretical simulations with the MALTE_BOX model, indicates that the enhanced nucleation in polluted conditions can be attributed to the participation of ammonia in stabilizing sulfuric acid-based clusters. In addition, much higher CCN enhancement factor was observed in NPF-P (EFCCN: 1.6 vs. 0.7 in NPF-C) due to the regional transported of anthropogenic pollutants from the urban cluster regions and their secondary transformation under enhanced atmospheric oxidation capacity. Furthermore, the duration of NPF-to-CCN conversion was quantified using a "Time Window (tau)", revealing that polluted conditions accelerated the conversion by 17.0 % (tau = 16.4 h vs. 19.8 h). Nitrate played an important role in maintaining a rapid particle growth rate, thereby shortening tau and enhancing CCN production from NPF - a process that can ultimately influence cloud microphysical properties by increasing the potential cloud droplet number concentration. These findings reveal that polluted air masses enhance both the efficiency and speed of CCN production at the BLT through elevated atmospheric oxidation capacity.

Aerosol-cloud interactions exert substantial influences on atmospheric chemistry and regional climate, yet process-resolved characterizations of chemical and microphysical evolution within cloud droplets remain limited. Here, two intensive campaigns were conducted at the high-altitude Shanghuang station in southeastern China during autumn 2023 and spring 2024, capturing nocturnal orographic and long-persistence stratiform cloud events. Using two complementary cloud-droplet sampling systems, the ground-based counterflow virtual impactor and a custom-designed aerosol-cloud sampling inlet system, along with aerosol chemical speciation and cloud microphysical measurements, we resolved the composition of interstitial (INT), residual (RES) and ambient (AMB) particles. Organic aerosols (OA) dominated particle mass across both seasons, while inorganic species (nitrate, sulfate, ammonium) exhibited high scavenging efficiencies (>= 65%-70%) and strong enrichment in RES particles. Organic components showed seasonally contrasting partitioning patterns, with differences between INT and RES particles indicating variability in aqueous-phase processing. Air-mass analysis further revealed pronounced source-dependent variability, with polluted westerly inflow leading to the highest particle loadings and most aged organic signatures. Linking chemistry with microphysics, we found that secondary organic aerosol (SOA) formation is favored in smaller droplets, whereas primary organic aerosol is preferentially incorporated into larger droplets through collision-coalescence. The contrasting evolution of oxygen-to-carbon ratio in RES and INT particles as a function of OA/Delta CO indicates distinct oxidation pathways for activated and non-activated aerosols. These results demonstrate that droplet size and cloud dynamics jointly regulate aerosol processing and that in-cloud oxidation pathways differ between particle types. This study provides process-level constraints for improving the representation of aqueous-phase SOA formation and aerosol-cloud interactions in atmospheric models, particularly in regions influenced by complex terrain and variable cloud regimes.

In this study, observational data from the Atmospheric Boundary Layer Eco-Environment Shanghuang Observatory (ABLES) are used to investigate the physical principles driving flux transmission over diverse terrains. We extracted pure turbulent and submesoscale signals using an automated approach. We statistically examined the effects of coherent structures and turbulent anisotropy on flux transit. By adding the aspect and organization ratios to the conventional flux–variance similarity relationship, we created a new generalized similarity framework. The findings show that the degree of anisotropy can be accurately measured using the velocity aspect ratio. Momentum and sensible heat transport tend to be distinct under strongly anisotropic or isotropic conditions. The differences between sensible heat and latent heat in the flux transport exhibit strong and weak correlations. Coherence can be characterized by quantifying the percentage of organized flow structures via quadrant analysis. There is a sigmoid function link between the slope-normal transport efficiency of flux and their corresponding organization ratios. The clustering results of both parameters can be used to divide the similarity relationships into several layers independently. The systematic aberrations of the conventional Monin–Obukhov similarity theory (MOST) can be successfully corrected by the generalized flux–variance similarity relationship constructed on this basis. This generalized similarity relationship performs better than the conventional MOST in terms of the overall ability to characterize turbulent properties over complicated underlying surfaces, as confirmed by modified scoring measures. This work expands the use of MOST under complicated surface and flow conditions and offers a theoretical foundation for optimizing model parameterization strategies.

China’s atmospheric environment modeling has advanced rapidly in response to intensifying air pollution challenges, emerging scientific needs, and growing international engagement. This review synthesizes advances across the historical evolution of model systems, key innovations in mechanisms and technologies, and emerging strategic directions. We trace the development from early offline models to fully coupled meteorology–chemistry systems, culminating in high-resolution, multi-pollutant platforms increasingly integrated with artificial intelligence. These models have improved the representation of key processes such as heterogeneous chemistry, secondary aerosol formation, and ozone photochemistry, and have enhanced forecasting capacity through ensemble approaches, data assimilation, and decision-support applications. However, significant challenges remain, including the incomplete simulation of multiphase and feedback processes under compound extremes, limited computational scalability for high-resolution and ensemble use, and fragmented integration of multi-source observations. To address these challenges, this review highlights four priorities: (1) incorporate machine learning into mechanistic modeling; (2) advance open-source and internationally aligned platforms; (3) develop flexible numerical schemes for multi-scale coupling; (4) embed atmospheric chemistry into Earth system models. China’s experience illustrates not only a national transformation from model adaptation to innovation but also provides transferable insights for the global modeling community.

The effective density (ρeff) is a key parameter of black carbon-containing (BCc) particles and is related to their morphologies, deposition processes, and optical properties. In this study, a tandem system was established and used to determine the ρeff of ambient BCc particles. The results showed that the ρeff distribution of ambient BCc particles exhibited a bimodal pattern with a left peak located at 0.69 g cm−3 and a right peak at 1.45 g cm−3. The average ρeff of BCc particles over the entire observation period was 1.38 g cm−3. The ρeff of BCc particles showed a clear diurnal pattern with a relatively stable distribution at night and large variations during the daytime. The ρeff value was demonstrated to be a good indicator of BCc particle morphology. BCc particles became more regular with increasing ρeff related to the increasing coating thickness. More coating led to morphological restructuring of BCc particles. The restructuring could be more efficient under high relative humidity conditions. The observed data were further used in a dry deposition scheme, and it was found that the dry deposition velocity of fresh emitted BCc could be largely influenced by its irregular shape. This study reveals the presence of a significant amount of low-density/irregularly shaped black carbon in the environment with rapid morphological changes occurring during the daytime and highlights the need to consider morphological influences in future research on the physicochemical properties of BCc.

This study investigates the spatiotemporal dynamics of urban greenhouse gases amidst energy transition, aiming to assess the emission reduction effectiveness of Beijing's "coal-to-gas" and "coal-to-electricity" policies and to pinpoint the key sources arising from the subsequent structural shifts in the emission profile. We analyzed greenhouse gas concentrations from 1993 to 2024 using gas chromatography. Vertical profile data across the 8-240 m atmospheric layer from 2022 to 2024 were obtained using a pod-based measurement system installed on a 325-m meteorological tower. The results show that the CO2 growth rate initially increased and then decreased during the 1993-2024 period, and the difference in CO2 concentration between the ground and upper air decreased by 20-56% compared to the historical maxima. These trends demonstrate that the energy transition has effectively mitigated the increase in CO2 concentration. A concurrent deceleration in the growth rates of CH4 and N2O was also observed, albeit with less pronounced changes in their vertical gradients. This mitigation is largely linked to the shift from landfill disposal to waste incineration and the promotion of new energy vehicles. In contrast, SF6 emissions increased steadily from 2008 to 2024, primarily driven by growing electricity demand and the expansion of the semiconductor industry. The recent eight-year average growth rate reached 0.52 ppt yr-1. These findings underscore the power and semiconductor sectors as critical domains for targeted SF6 emission control.

The isotopic composition of atmospheric species provides fundamental insights into their sources, sinks, and chemical processes. Conventional end-member mixing models, however, cannot capture progressive isotopic evolution in open systems where mixing and reaction proceed simultaneously. This limitation hinders a comprehensive understanding of the isotope effect and its atmospheric applications. Here, we develop an isotope-enabled chemical transport model (CTM) that tracks four sulfur isotopologues (32SO2, 34SO2, 32SO42−, 34SO42−) through emissions, transport, chemistry, and deposition. An iterative time-splitting method reduces the numerical bias from applying the Rayleigh equation in the open atmosphere. The model reproduces the 34S enrichment of sulfate relative to SO2 and captures the spatial and seasonal patterns of the sulfur isotope effect across eastern China (simulated Δδ34S_SO42− / SO2=6.11 ‰ ±1.85 ‰; observed =3.43 ‰ ±1.11 ‰). Further, the agreement between simulated (with δ34S_SO2=0 ‰ emission assumption) and observed sulfate isotopic compositions, combined with the documented higher δ34S values of coal at 1 ‰–10 ‰ across eastern China, implies a systematic 34S depletion in emitted SO2 relative to fuels. This highlights the importance of considering isotopic fractionation during combustion, flue gas desulfurization and chemical processes for accurate source apportionment. The isotope-enabled model provides a new approach for constraining the sulfur budget.

A multiple-site filter-sampling observation study was conducted in a coastal industrial city (Rizhao, 35 degrees 10 ' 59 '' N, 119 degrees 23 ' 57 '' E) to understand the main components, formation mechanisms, and potential sources of particulate matter. The average (+/-sigma) mass concentration of PM2.5 across all the sites was 42 (+/- 27) mu g/m3, with high variability (6-202 mu g/m3). Water-soluble inorganic ions (WSIIs) were the major contributors (54%-60%) to PM2.5, with mean values for sulfate (13 mu g/m3), nitrate (6 mu g/m3), and ammonium (7 mu g/m3) (SNA). Cl- and K+ were the two components that accounted for large proportions of the water-soluble inorganic ions (WSIIs) in addition to SNA. Organic carbon (OC) and element carbon (EC) accounted for 19% and 9% of the total PM2.5, respectively. The highest concentrations of Cl- (1.9 mu g/m3) and Fe (1.3 mu g/m3) at the steel mill site imply that the site was heavily impacted by coal combustion and industrial burning. The residential area site presented the highest K+ concentration (0.86 mu g/m3), which was attributed to the strong influence of biomass burning. The highest char-EC/soot-EC ratios (2.4-6.1) occurred in winter, indicating that Rizhao was more affected by fuel combustion. The positive matrix factorization model (PMF) showed that secondary aerosols and vehicle emissions dominated the observation period. With increasing pollution in winter, the contributions of combustion sources and secondary aerosol sources increased significantly. Among all the sites, the steel mill site had the highest contribution of primary source emissions (67%), with emissions from combustion sources being particularly prominent.

This study reports the temporal dynamics of the net ecosystem exchange of carbon dioxide (NEE), the importance of various meteorological factors influencing NEE, and the response characteristics of NEE to meteorological changes at different temporal scales (half-hourly, daily and monthly) on the basis of eddy covariance and meteorological data collected from forest ecosystems in southeastern China between June 2022 and May 2024, thereby employing the random forest regression algorithm. The results show that the forest ecosystem in the study area provides a clear carbon sink function, with an estimated daily NEE period of approximately 11–12 h. Notably, this ecosystem functions as a carbon sink almost year-round, with the lowest NEE in summer, followed by spring and autumn, whereas winter shows minimal NEE, resulting in an annual total NEE of −2983.34 g CO2·m−2·a−1. Furthermore, the NEE varies temporally and is influenced by several meteorological factors with varying degrees of impact. Specifically, sub-daily fluctuations are driven primarily by the light intensity, whereas the effect of light decreases at longer time scales, allowing other meteorological factors to exert increased influence. The complex and nonlinear responses of NEE to these meteorological factors often feature thresholds and optimal values. This study contributes to our comprehensive understanding of NEE behavior and provides reference data for improving forest carbon cycle models and developing regional forest management measures.

Planetary boundary layers (PBL) characterized by thermal, dynamic, and material types and their spatiotemporal structures were simultaneously detected with ground‐based remote sensing instruments in summer by parcel method, Richardson number (Ri), and photoextinction coefficients at Wuhai City. The research site is located at the exit of the valley combined with desert, lake, and the Yellow River, which result in the diurnal variation and inconsistency of three types of PBL. Convective boundary layer (CBL) reaches 3,500 m in daytime while the top of stable boundary layer (SBL) reaches 1,500 m at night. The depth of surface layer, mixing layer (ML), residual layer (RL), entrainment zone (EZ), and temperature inversion are closely correlated with diurnal temperature variation. Substantial variability in CBL and SBL heights up to hundreds of meters was observed in high spatiotemporal resolution. Dynamic PBL representing dynamic stability from Ri fluctuates around 1,000 m during nighttime and rises to about 1,500 m during daytime. Material PBL, representing the distribution of atmospheric substances driven by solar heating, is classified into residual height of material boundary layer and accumulation height of material boundary layer to describe the capacity of dispersion and accumulation affected by CBL and SBL, respectively. Material PBL demonstrates similar trends to the thermal PBL in the daytime ranging from 1,500 to 3,500 m and shows similar variation with dynamic PBL in the nighttime due to orographic winds. This study emphasizes the differences among various kinds of PBL and importance of high resolution in meso‐micro scale study of PBL researches.

In the context of global warming, the increasing frequency of extreme weather events, meteorological disasters, and regional pollution events highlights the urgent need for targeted atmospheric observations in ecologically sensitive regions. The atmospheric boundary layer top remains a critical yet under-observed interface in climate and environmental research. To respond to this need, the Atmospheric Boundary Layer Eco-Environment Shanghuang Observatory (ABLES) was established in 2023 by the Institute of Atmospheric Physics, Chinese Academy of Sciences in Jinhua, southeastern China. As a high-altitude, state-of-the-art research platform, ABLES addresses the critical need for comprehensive atmospheric and environmental observations in East Asia. The observatory facilitates interdisciplinary research focusing on three core areas: (1) cross-sphere transport of pollutants between atmospheric layers and its environmental and climatic impacts; (2) physical and chemical interactions between clouds and aerosols under extreme weather conditions; and (3) multiscale feedback mechanisms between climate change and ecosystems. ABLES is also aimed at revealing long-term patterns in greenhouse gases, ozone depleting substances, and cloud properties, and to elaborate the formation mechanisms of severe weather events such as thunderstorms and freezing rain. The findings at ABLES will support advances in weather forecasting, air quality modeling, and adaptive strategies for climate resilience. As part of China’s growing environmental monitoring network, ABLES has been collaborating with various research organizations, serving as a cooperative research platform for technological development and evidence-based environmental policymaking.