Ambient air pollution remains a leading environmental risk factor globally. Over the past decade, China has achieved marked reductions in particulate matter (PM) following the 2013 Air Pollution Prevention and Control Action Plan; however, surface ozone (O3) has increased in multiple urban agglomerations, signalling a transition from single-pollutant to complex multi-pollutant regimes. At the western terminus of the Fenwei Plain—a national key region designated in 2018—systematic, county-level assessments of long-term air-quality evolution are lacking for Baoji City. This study presents a nine-year (2017–2025) analysis of six criteria pollutants (SO2, NO2, CO, O3, PM2.5, and PM10) across all 12 administrative divisions (four districts and eight counties) of Baoji, using continuous monthly data from 16 national and provincial monitoring stations (n = 108 months). Spearman rank correlation, Mann–Kendall trend tests, and Sen’s slope estimators were applied to (i) quantify the significance of inter-annual concentration trends, (ii) characterise the spatial decoupling between particulate matter and ozone at the county level, and (iii) document the timing and magnitude of the regime shift in dominant pollution types. CO, NO2, PM10, and PM2.5 exhibited statistically significant downward trends at >90 % of monitoring sites (p < 0.05), with cumulative reduction rates exceeding 45 %, 46 %, 51 %, and 53 %, respectively. Annual mean SO2, NO2, and CO continuously met the National Grade-II Standard from 2020 onward, while PM2.5 achieved stable compliance from 2023 to 2025. The 90th percentile of daily maximum 8-hour O3 (O3-8h-90th) remained below the Grade-II limit of 160 μg/m3 throughout the study period, despite a phased rebound during 2022–2024. Spatially, PM2.5 and PM10 followed a west-low / centre-east-high distribution with a continuously shrinking regional gradient, whereas O3 maintained a stable east-high / south-low pattern, reflecting persistent photochemical pressure in the eastern plain counties. The proportion of O3 as the primary pollutant rose from 15 % in 2017 to 28 % in 2025, marking a regime shift from PM-dominated to O3–PM2.5 co-dominated air pollution. These findings provide empirical evidence for synergistic pollution–carbon governance and inform seasonally differentiated, location-specific control strategies in Baoji and analogous industrial basin cities.

The Qinling–Daba Mountains serve as a crucial ecological barrier in China’s national ecological security framework. Nonetheless, the designation of conservation areas and zoning management remain insufficiently refined and inadequately aligned with ecological processes, limiting effective preservation and high-quality regional development. Utilizing high-resolution remote sensing and multi-source spatial datasets (2000–2023), we performed a comprehensive evaluation of ecosystem patterns, ecosystem quality, and essential ecosystem services, and established a zoning framework that integrates ecosystem status, functional roles, and geographic constraints by superimposing key ecological function zones and biodiversity priority areas. Results indicate that (1) forest, built-up land, and wetlands increased by 1343.18 km2, 1725.36 km2, and 488.48 km2, respectively; landscape dynamics exhibited stable forest connectivity, farmland evolving from contiguous blocks to a more dispersed arrangement, and intensified clustering and expansion of urban land. Ecosystem quality increased overall: regions with growing EQI represented 48.07%, whereas 5.48% showed negative trends. Enhancements were mostly seen in the Qinling–Daba mountainous region, while reductions were more prevalent in the environmentally vulnerable northwestern area. Regions designated as “very important” for water and soil conservation constituted 17.90% and 10.75% of the study area, respectively, mostly aligning with mountainous areas characterized by dense vegetation and generally favorable hydrothermal conditions. By integrating qualitative and functional change indicators with spatial limitations, we identified three functional management categories: Ecological Enhancement (EI), Ecological Stability (ES), and Ecological Degradation Risk (ED), which were further separated into 18 zoning units. This research offers a practical spatial framework for targeted restoration and management, risk assessment, and efficient distribution of conservation resources to improve ecosystem resilience and governance accuracy.

The soil microbial functional community is a better predictor of ecosystem processes under global change. However, the responses of the soil microbial functional community structure to short-term nitrogen (N) and phosphorus (P) additions remain unclear. Here, we carried out an experiment exploring the effects of N and P additions on soil fungal and bacterial functional diversity and community composition in an alpine steppe on the Tibetan Plateau. The results showed that soil fungal functional α-diversity significantly decreased under N or P addition, whereas soil bacterial functional α-diversity did not change. Furthermore, soil bacterial functional group aerobic nitrite oxidation increased significantly under combined N and P additions, but soil bacterial and fungal community composition and β-diversity did not change. Further analysis illustrated that soil available nitrogen was the main factor leading to the changes in soil fungal functional α-diversity and soil bacterial functional groups under N and P additions. Therefore, we should give importance to the potential influence in view of future increasing N and P deposition on the soil microbial functional community in the alpine grassland on the Tibetan Plateau, which will be beneficial for the prediction of soil biogeochemical cycles and guiding ecosystem management.

Addressing climate change is central to and a prerequisite for the achievement of sustainable development goals (SDGs). Cities, as key drivers of carbon reduction, urgently require a planning and analysis framework tailored to their low-carbon sustainable development objectives. In this paper, the block is taken as the basic spatial unit, an XGBoost model is used to identify block functional types, and carbon emissions and sinks are estimated. Variations in net carbon emissions and their spatial-synergy mechanisms are analysed and, on this basis, spatial synergistic carbon reduction zones are established. The results indicate that neighbourhoods featuring a high concentration of productive and daily activities, such as industry and commerce, significantly overlap with areas responsible for high carbon emissions. In contrast, neighbourhoods that include agricultural and green blocks are closely related to areas with high carbon sinks. The specific adjacency relationships between neighbouring blocks can have carbon effects that transcend the attributes of individual blocks. Additionally, structural differences in the carbon emissions of blocks result from the combined influence of internal functional metabolic constraints and the interactive spatial networks of the surrounding neighbourhood. This research reveals that spatial synergistic carbon reduction zoning rooted in functional guidance at the urban block scale and major function-oriented zoning constraints in China can help identify key areas for governance and regions in which policies are misaligned, thereby establishing a better balance between regional development equity and the requirements of the low-carbon transition. This study thus helps advance the implementation of spatial synergistic carbon reductions.

Knowledge of the molecular characteristics of organic aerosols is essential for evaluating their atmospheric processes and associated environmental and health effects. However, little is known regarding the molecular characteristics of organic aerosols in coal resource-based cities. Herein, the molecular characteristics of watersoluble organic matter (WSOM) in wintertime PM2.5 during haze and heavy haze days in a typical coal resource-based city (Taiyuan, China) were analyzed using Fourier-transform ion cyclotron resonance mass spectrometry. A total of 5106 formulas were assigned, with m/z values predominantly concentrated in the range of 150-400 Da. The proportion of CHOS is higher than that in other cities, and a series of C7H6(CH2)0-8O5S formulas exhibited high intensities, most of which could be traced to coal combustion sources. Distinct differences in the chemical composition of WSOM were observed between haze and heavy haze days. On heavy haze days, WSOM showed a higher degree of unsaturation and aromaticity, and significantly lower molecular volatility. The relative abundance of sulfur-containing organic compounds increased significantly on heavy haze days compared to haze days (30.4 % vs. 25.0 %) and was much higher than that observed in other cities. Additionally, CHO, CHON, and CHOS formulas consistently exhibited higher oxygen content on heavy haze days, likely due to atmospheric oxidation processes. Moreover, oxygen addition, methylation, and carboxylic acid reactions were identified as the primary possible pathways driving the transformation of primary organic aerosol into secondary organic aerosol under both haze and heavy haze conditions. These findings extend our current understanding of WSOM in coal resource-based urban environments.

The stability of soil microbiomes is critical for ecosystem functioning under climate change, yet its assessment is confounded by the overlooked problem of observational temporal scale dependency. Here, we introduce a multi-temporal window framework to resolve this problem. Applied to a decade-long warming experiment, our approach reveals that the perceived stability of bacterial and fungal communities nonlinearly decays with observational temporal scale, and short windows systematically overestimate it. Crucially, we document a temporal scale-driven mechanistic shift. Community stability shifts from species resistance to compensatory asynchrony once the window exceeds a threshold. This transition occurs over a broader temporal scale range for fungi than for bacteria. Our work establishes that microbiome stability is an intrinsically temporal scale-dependent property and provides a scalable, bioinformatic-friendly framework that challenges conventional single-temporal-scale assessments. This paradigm is critical for accurately predicting the fate of soil carbon and other microbiome-governed functions in a warming world.

Anthropogenic activities have deteriorated habitat quality worldwide. Previous studies have mostly evaluated the human impact on habitat quality changes from the land-use perspective, failing to examine how global and regional habitat changes within different production, living, and ecological spaces. This study evaluates the spatiotemporal characteristics and regional heterogeneities of global habitat quality changes during 2000-2020 from the production-living-ecological perspective. Results show that global habitat quality values are on the decline over time, with the pronounced decline in high habitat quality regions. Production and living spaces have increased by 5.10 % and 22.00 % respectively, and the outflow of ecological space into production space, rather than living space, is the primary cause of declining habitat quality worldwide. Heterogeneity analysis show that greater habitat quality decline occurs in tropical Africa and South America with low-income level, mainly due to excessive deforestation, while regions in Europe with high-income level show less habitat quality decline. This study concludes that anthropogenic production activities, especially in the Global South, have largely reduced global habitat quality. We call for legally strengthening indigenous land rights and enforcing deforestation-free supply chains for agricultural commodities to protect natural habitats in these hotspots.

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.

Manufacturing is a significant source of global carbon emissions, and changes in its spatial location lead to the redistribution and spillover effects of carbon emissions across various geographical regions. In this study, the Yangtze River Delta (YRD) in China is used as a case study, and a super-stochastic block model (super-SBM), deviation share analysis, and spatial Durbin model are integrated to examine the spatial heterogeneity of the impact of manufacturing spatial agglomeration on carbon emission efficiency (CEE). The results show that manufacturing exhibits a systematic diffusion path from the core to the periphery, which is significantly spatially coupled with the ‘high core, low periphery’ spatial pattern of CEE. An analysis of the impact mechanism reveals that the effect of manufacturing relocation on CEE has distinct phased and spatially heterogeneous characteristics. Before 2019, industrial relocation promoted efficiency improvement in core cities and inhibited efficiency improvement in peripheral cities; however, after 2019, with the continued diffusion of industries in core cities and the enhanced technological absorptive capacity of peripheral regions, this relationship shifted. Technological innovation and environmental regulation can effectively moderate this process to help alleviate the ‘high-carbon lock-in’ risk faced by peripheral regions during industrial relocation. The innovation of this study lies in its construction of a theoretical analysis framework that reveals the differentiated impact of manufacturing relocation on the CEE of cities at different levels. This can provide a foundation for the formulation of coordinated policies on industrial transfer and carbon emission reduction between regions.

Uncertainties remain regarding how climate change and human activities affect the aboveground net primary productivity (ANPP) of grassland ecosystems, particularly the differential responses of distinct plant functional groups. Here, we investigate the alpine grasslands of the Qinghai-Xizang Plateau from 2000 to 2022, focusing on the fresh ANPP of the whole plant community and three key functional groups (sedges, graminoids, and forbs). Our objectives are to identify the spatiotemporal trends of ANPP and to determine the dominant drivers (climate vs. human activities) of these changes. Under the combined effects of climate change and human activities, the spatially averaged relative changes in fresh ANPP were -1.15% for the whole community, + 5.67% for sedges, -1.55% for graminoids, and -2.26% for forbs. Regional patterns varied, with some areas showing increasing or decreasing trends over time, while others exhibited no significant change. Climate change dominated 20.79% of the grassland area, human activities dominated 54.26%, and the two drivers jointly dominated 24.95%. When the ANPP of the three functional groups were considered together, the area where all three groups jointly regulated grassland fresh ANPP accounted for the largest proportion (39.12%), followed by areas dominated by forbs (35.43%), sedges (18.27%), and graminoids (7.18%). These results reveal pronounced geographical heterogeneity in the trends of fresh ANPP, both for the whole community and for individual functional groups. Human activities exert a larger controlling influence than climate change over the observed ANPP changes. Moreover, the contribution of each functional group to community ANPP varies spatially. Our findings provide a scientific basis for understanding grassland ecosystem functioning and for developing targeted conservation and management strategies.

Under the "dual-carbon" target, it is essential for regional sustainable development to achieve synergy between economic growth and carbon reduction. This study constructs a comprehensive indicator system of growth performance and carbon reduction performance. It systematically explores the synergistic effect of economic growth and carbon reduction in Chinese provinces by applying methods such as the entropy weight method and the coupling coordination model. The study reveals that: ① From 2011 to 2022, there was a continuous improvement in economic growth performance, which showed a spatial pattern of decreasing from the eastern coastal regions to the inland areas. ② Between 2011 and 2022, the carbon reduction performance fluctuated, reaching its lowest point in 2014 and peaking in 2017. Spatially, a pattern of "higher in the southeast and lower in the northwest" was observed, with Hebei, Shanxi, Shaanxi, Gansu, and Qinghai as the dividing line. ③ The average coupling degree between economic growth and carbon reduction continued to rise and remained relatively high. The coupling coordination degree showed an upward trend with fluctuations. However, there were provincial differences, forming a spatial pattern of "higher in the southeast and lower in the northwest," delineated by Qinghai-Gansu-Ningxia-Shanxi. Meanwhile, provinces with better coupling coordination, such as those along the Yangtze Economic Belt and the eastern coast, formed a "T"-shaped spatial pattern. ④ Regarding driving factors, the influence of transport structure on the coupling coordination degree was increasing, while that of technological input was relatively stable at a high level. In contrast, the influence of the government's economic governance capacity, environmental governance capacity, urbanization rate, and labor density showed a declining trend. The impact of each factor on the coupling coordination degree exhibited significant spatial-temporal differences, and their interactions jointly affected regional synergy.

Urban thermal environmental risks significantly threaten ecosystems and public health.Using the main urban area and Changsha County—typical"furnace"zones in China—as a case study,this research integrates multi-source data and applies morphological spatial pattern analysis(MSPA),patch-level land use simulation(PLUS),and circuit theory to simulate the spatiotemporal evolution of urban heat islands in 2040 and identify heat corridors and key nodes.The results showed that:① From 2013 to 2022,the heat island area was 610.57,596.97,630.31,and 641.00 km2,respectively.Heat islands were mainly concentrated in the central region,with continuous expansion observed in the northwest and southeast.Heat corridors were denser in the west and sparser in the east,with more pinch points than barriers.② By 2040,heat sources are expected to expand in the northwest,southwest,and southeast;corridors in the northwest extend outward,with new ones emerging elsewhere.③ Boundaries to conserve urban cold sources(BCUCS)can optimize the spatial configuration of heat islands and reduce thermal transmission paths.④ A"one-corridor,two-zone,three-focus"strategy is proposed based on the thermal spatial network.These findings provide a scientific basis for mitigating thermal risks and guiding climate-adaptive urban development.

Against the backdrop of China's vigorous promotion of green and low-carbon development, this study empirically examines the impact of ESG greenwashing on corporate financial sustainable development performance, using a sample of Chinese A-share-listed companies from 2018 to 2023. Empirical results indicate that ESG greenwashing significantly undermines corporate financial sustainable development performance. Furthermore, accounting conservatism mediates the relationship between ESG greenwashing and corporate financial sustainable development performance, whereas negative external media coverage moderates it. This research provides robust theoretical and empirical support for standardizing corporate ESG practices and advancing the achievement of green sustainable development objectives.

Supercell storms dominate tornadoes in Taihu Lake region. Discriminating tornadic and non-tornadic supercells is a critical challenge for operational severe weather forecasting. Previous studies lack systematic comparisons and specific convective conceptual models of polarimetric evolution. This study has established convective conceptual models using multi-source observations from 2020 to 2024. We have compared environmental and storm structural features to address supercell characteristics and verify key physical processes with a representative tornado case. Results showed that tornadic supercells had lower convective available potential energy (CAPE), lower lifting condensation level (LCL) and stronger low-level vertical wind shear before the tornadoes happened. These storms generate mesocyclones with lower base heights and more concentrated intense rotation. Classic tornadic supercells feature hail dominated microphysics and regular polarimetric evolution at different life stages. High precipitation tornadic supercells are controlled by warm rain coalescence with distinct polarimetric signatures. These models advanced understanding of subtropical supercell dynamics and supported operational tornado warning in the Taihu Lake region.

To better understand the in-cloud enrichment of secondary inorganic ions, the chemical composition of individual particles during cloud events and cloud-free periods, as well as cloud water and PM2.5 samples, were measured from August to September 2023 at Mt. Damaojian (1128 m a.s.l.) in southeastern China. Our results demonstrated that cloud processes promoted the aqueous-phase formation of ammonium, nitrate, and sulfate, but the degree of sulfate enrichment was lower than that observed for ammonium and nitrate. Furthermore, the average hourly relative peak area (RPA) of secondary inorganic ions revealed their selective enrichment in cloud droplets. The RPA of ammonium, nitrate, and sulfate in cloud droplet residual (RES) particles were 5.11, 3.05, and 1.43 times greater than those in cloud interstitial (INT) particles during two consecutive long-lasting cloud events, respectively. Both ammonium and nitrate showed ubiquitous enrichment across all particle types, including K-rich, Black carbon (BC), Metal, and Sea salt (SS) particles, although with distinct degrees of enrichment. Ammonium exhibited the highest degree of enrichment in the SS particles, whereas nitrate showed the most pronounced enhancement in the BC particles. Notably, the RPA of nitrate in all four types of particles exhibited a significant positive correlation with the liquid water content (LWC), reinforcing the role of aqueous-phase processing. Unlike ubiquitous ammonium and nitrate, sulfate enrichment was restricted to K-rich and Metal particles. The different enrichments of ammonium, nitrate, and sulfate underscore particle-specific influences on their in-cloud formation. These findings extend our current understanding of atmospheric chemistry in clouds.

The application of ground-based microwave radiometers (GMWRs), which provide high-quality and continuous vertical atmospheric observations, has traditionally focused on the indirect assimilation of retrieved profiles. This study advanced this application by developing a direct assimilation capability for GMWR radiance observations within the Weather Research and Forecasting Data Assimilation (WRFDA) system, along with a bias correction scheme based on the random forest technique. The proposed bias correction scheme effectively reduced the observation-minus-background (O-B) biases and standard deviations by 0.83 K (97.1 %) and 1.63 K (64.6 %), respectively. A series of 10 d experiments demonstrated that assimilating GMWR radiances improves both the initial conditions and the forecasts, with additional benefits from higher assimilation frequencies. In the initial conditions, hourly assimilation significantly enhanced low-level temperature and humidity fields, reducing the root-mean-square error (RMSE) for temperature by 6.32 % below 1 km and for water vapor mixing ratio by 1.98 % below 5 km. These improvements extended to forecasts, where 2 m temperature and humidity showed sustained benefits for over 12 h, and precipitation forecasts exhibited improvements to a certain extent. The time-averaged Fractions Skill Score (FSS) for 3 h accumulated precipitation within the 24 h forecasts increased by 0.02-0.04 (3.9 %-10.2 %) for thresholds of 3-6 mm.

The extent to which organic matters (OM) in PM 2.5 affect virus infections and the key organic molecules involved in this process remain unclear. Herein, this study utilized ultra-high resolution mass spectrometry coupled with in vitro experiments to identify the organic molecules associated with respiratory virus infection for the first time. Water-soluble organic matters (WSOM) and water-insoluble organic matters (WIOM) were separated from PM 2.5 samples collected at the urban area of Guangzhou, China. Their molecular compositions were analyzed using Fourier transform ion cyclotron resonance mass spectrometry. Subsequently, in vitro experiments were conducted to explore the impact of WSOM and WIOM exposure on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pseudo-virus infection in A549 cells. Results revealed that WSOM and WIOM respectively promoted 1.7 to 2.1-fold and 1.9 to 3.5-fold upregulation of SARS-CoV-2 pseudo-virus infection in a concentration- dependent manner (at 25 to 100 mu g mL-1) compared to the virus-only control group. Partial least squares model analysis indicated that the increased virus infection was likely related to phthalate ester and nitro-aromatic molecules in WSOM, as well as LipidC molecules with aliphatic and olefinic structures in WIOM. Interestingly, the molecules responsible for upregulating SARS-CoV-2 receptor angiotensin-converting enzyme 2 ( ACE2 ) expression and virus infection differed. Thus, it was concluded that ACE2 upregulation alone may not fully elucidate the mechanisms underlying increased susceptibility to virus infection. The findings highlight the critical importance of aromatic and lipid molecules found in OM in relation to respiratory virus infection.

Epidemiological evidence linked PM2.5 exposure to increased virus transmission, yet the impact of chemical composition of PM2.5 on viral susceptibility remains unclear. We characterized the constituents of water-soluble matters (WSM) in PM2.5 from urban Guangzhou and their association with pseudotyped SARS-CoV-2 infectivity, inflammatory and antiviral responses in A549 cells. Chemical analysis revealed considerable variability in chemical composition across all samples, although all samples were dominated by inorganic ions (e.g., NO3-, SO42-, NH4+) and WSOM (e.g., lipids, oxy-aromatics), with metals constituted similar to 1.2 %, primarily Cu, Fe, and Al. WSM exposure elevated viral infectivity (1.3-5.6-fold), IL-8 and TNF-alpha generation (1.2-5.2-fold), and suppressed IFN-beta by 24 %-74 %, with minimal effects on IFN-alpha and surfactant proteins. An explainable machine learning analysis indicated that transition metals (e.g., Ni, V, Mn, Fe), inorganic ions (e.g., SO42-, Na+, K+), highly unsaturated oxy-aromatics (Xc >= 2.5, DBE >= 6) and protein1 with 0.2 < O/C <= 0.6 and 0.9 < H/C < 2.5 were positively linked to IL-8 and TNF-alpha induction, whereas trace metals (e.g., Cr, Al) and protein2 with 0.6 < O/C <= 1 and 1.2 < H/C < 2.5 were both associated with enhanced infectivity and IFN-beta inhibition. Notably, viral infectivity remained uncorrelated with inflammatory cytokines but showed an inverse correlation with IFN-beta expression consistently observed during WSM-only and WSM + virus co-exposure, suggesting that PM2.5 may impair interferon-mediated defenses to exacerbate viral susceptibility. Our study uncovers an overlooked health risk of PM2.5 thereby filling a critical gap in understanding the multifaceted health effect of PM2.5 exposure.

The capability to assimilate Aerosol Optical Depth (AOD) is developed within the WRFDA system in this study using the three-dimensional variational (3DVar) algorithm, based on the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol scheme of the Weather Research and Forecasting model coupled with online Chemistry (WRF-Chem). Experiments assimilating Himawari-8 satellite AOD retrievals along with surface observations (PM2.5, PM10, SO2, NO2, O3, and CO) are conducted over China for the period from 25 December 2016 to 9 January 2017. The performances of data assimilation for both analyses and forecasts are evaluated against various datasets, including the surface PM2.5 and PM10 measurements, the Himawari-8 AOD and aerosol extinction coefficient (AEC) profile data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The DA experiments exhibit positive effects on the analyses and forecasts of surface PM2.5 and PM10, AOD, and aerosol vertical extinction coefficient to different degrees. Compared to the assimilation of ground-based observations, which is highly effective in improving surface aerosol forecasts, the Himawari-8 AOD assimilation exhibits a greater improvement on the AOD and AEC profile. The experiment assimilating the Himawari-8 AOD and surface observations simultaneously performs the best, in terms of both the horizontal and vertical distributions of aerosols. Results reveal the potential of the combined assimilation of satellite retrievals and surface observations, especially in generating a better aerosol structure for both analyses and forecasts.