Given the increasing complexity of the chemical composition of PM_2.5, identifying and quantitatively assessing the contributions of pollution sources has played an important role in formulating policies to control particle pollution. This study provides a comprehensive assessment between PM_2.5 chemical characteristics, sources, and health risks based on sampling data conducted over 1 year (March 2018 to February 2019) in Nanjing. Results show that PM_2.5 exhibits a distinct variation across different seasons, which is primarily driven by emissions, meteorological conditions, and the chemical conversion of gaseous pollutants. First, the chemical mass reconstruction shows that secondary inorganic aerosols (62.5 %) and carbonaceous aerosols (21.3 %) contributed most to the PM_2.5 mass. The increasing oxidation rates of SO₂ and NO₂ from summer to winter indicate that the secondary transformation of gaseous pollutants is strongly positively correlated with relative humidity. Second, the positive matrix factorization (PMF) method shows that identified PM_2.5 sources include secondary inorganic aerosol source (SIS, 42.5 %), coal combustion (CC, 22.4 %), industry source (IS, 17.3 %), vehicle emission (VE, 10.7 %), fugitive dust (FD, 5.8 %), and other sources (1.3 %). The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the concentration-weighted trajectory (CWT) analysis are used to further explore different spatial distributions and regional transport of sources. The concentrations (10–11 µg m⁻³) of SIS and CC distribute in Nanjing and central China in winter. The concentrations (8–10 µg m⁻³) of IS and VE are potentially located north of Jiangsu, Anhui, and Jiangxi. Finally, the health risk assessment indicates that the carcinogenic and non-carcinogenic risks of toxic elements (Cr, As, Ni, Mn, V, and Pb) mainly come from IS, VE, and CC, which are within the tolerance or acceptable level. Although the main source of pollution in Nanjing is SIS at present, we should pay more attention to the health burden of vehicle emissions, coal combustion, and industrial processes.

Marsh wetlands are significant ecosystems located between land and water bodies which can both protect species diversity and provide habitats. Changes in the hydrological situation of marsh wetlands as a result of climate change and human activities have led to the degradation of wetland landscapes. Taking the Honghe National Nature Reserve (HNNR) in Heilongjiang Province, China, as an example, this paper gathered information on the reserve’s wetland landscape before and after dam construction. The information was obtained using field survey data and a random forest classification method based on Landsat data powered by the Google Earth Engine (GEE) cloud platform. Then, on the basis of the water level data and the digital elevation model, the wetland landscape dynamics of HNNR under three water level conditions were simulated. The findings were as follows: (1) From 1998 to 2008, the area of marsh and meadow had a downward trend, while the area of forest, farmland and water showed a gradual and upward trend; a marked rise in the area of marsh and a continued rise in the area of forest, farmland and water, and a sharp decline in the area of meadow during 2008 to 2018 was observed. (2) There was a significant increase in the area of marsh under the 20 and 40 cm water level simulation scenarios, with a decrease in the number of patches, and an increase in the aggregation index with rising water levels; in contrast, when the water level rose to 60 cm, the area of marsh and the number of patches decreased, but the aggregation index continued to increase. (3) The correlation between wetland landscape and the water level was a nonlinear one. The area of marsh increased and then decreased with increasing water level, reaching a maximum at the 40 cm water level; therefore, 40 cm was the optimal water level regulation scenario. Hydrological processes are the most fundamental ecological processes in marsh wetlands. Understanding the scientific pattern of the spatial pattern characteristics of species as a function of water level environment is important for scientifically guiding the restoration of marsh vegetation.

Observations of the vertical wind profile in Chongqing, a typical mountainous city in China, are important, but they are sparse and have low resolution. To obtain more wind profile data, this study matched the Aeolus track with ground-based wind observation sites in Chongqing in 2021. Based on the obtained results, verification and quality control studies were conducted on the wind observations of a wind profile radar (WPR) with radiosonde (RS) data, and a comparison of the Aeolus Mie-cloudy and Rayleigh-clear wind products (Aeolus winds measured in cloudy and aerosol-rich atmospheric conditions from Mie-channel-collected data and winds measured in clear-air conditions from Rayleigh-collected data) with WPR data was then performed. The conclusions can be summarized as follows: (1) a clear correlation between the wind observations of WPR and RS was found, with a correlation coefficient (R) of 0.71. Their root mean square deviation increased with height but decreased at heights between 3 and 4 km. (2) After quality control using Gaussian filtering (GF) and empirical orthogonal function construction (EOFc; G=87.23 %) of the WPR data, the R between the WPR and RS reached 0.83 and 0.95, respectively. The vertical distribution showed that GF could better retain the characteristics of WPR wind observations but with limited improvement in decreasing deviations, whereas EOFc performed better in decreasing deviations but considerably modified the original characteristics of the wind field, especially regarding intensive vertical wind shear in strong convective weather processes. (3) In terms of the differences between the Aeolus and WPR data, 56.0 % and 67.8 % deviations were observed within ±5 m s⁻¹ for Rayleigh-clear and Mie-cloudy winds (Aeolus winds measured in cloudy and aerosol-rich atmospheric conditions from Mie-channel-collected data and winds measured in clear-air conditions from Rayleigh-collected data) vs WPR winds, respectively. Vertically, large mean differences of both Rayleigh-clean and Mie-cloudy winds versus WPR winds appeared below 1.5 km, which is attributed to the prevailing quiet and small winds within the boundary layer in Chongqing; in this case the movement of molecules and aerosols is mostly affected by irregular turbulence. Additionally, large mean differences at a height range between 4 and 8 km for Mie-cloudy versus WPR winds may be related to the high content of cloud liquid water in the middle troposphere of Chongqing. (4) The differences in both Rayleigh-clear and Mie-cloudy versus WPR winds had changed. Deviations of 58.9 % and 59.6 % were concentrated within ±5 m s⁻¹ for Rayleigh-clear versus WPR winds with GF and EOFc quality control, respectively. In contrast, 69.1 % and 70.2 % of deviations appeared within ±5 m s⁻¹ for Rayleigh-clear versus WPR and EOFc WPR winds, respectively. These results shed light on the comprehensive applications of multi-source wind profile data in mountainous cities or areas with sparse ground-based wind observations.

Terrestrial ecosystem carbon absorption capacity has become an important component of China’s strategy to enhance nature-based climate mitigation. However, existing studies on China’s terrestrial ecosystem carbon dynamics are still insufficient to support source-oriented control and unified supervision by national-level government departments, particularly in terms of temporal consistency, spatial completeness, and cross-ecosystem comparability. Here, we present a systematic assessment framework for NPP-based ecosystem carbon absorption capacity (NPP-based CAC) designed to support unified ecological and environmental supervision. In this study, NPP-based CAC is defined as the annual vegetation production-based carbon fixation capacity derived from net primary productivity. It represents carbon retained by vegetation after plant autotrophic respiration has been accounted for in the NPP estimation, and it should not be interpreted as gross ecosystem carbon uptake, NEP, NBP, net land–atmosphere carbon exchange, or a carbon sink for carbon neutrality accounting. The framework adopts year as the temporal unit and a 500 m × 500 m grid as the spatial unit, and integrates four core components: temporal series analysis, spatial distribution mapping, protection effectiveness evaluation, and enhancement potential estimation. Applying this framework to China from 2000 to 2022, we found that national NPP-based CAC increased from 0.673 Pg C yr⁻¹ in 2000 to 0.868 Pg C yr⁻¹ in 2022, with carbon absorption intensity rising from 98.47 to 121.37 t C km⁻² yr⁻¹. High-value areas covered 3,009,571 km², accounting for 40.90% of the national area with NPP-based CAC, whereas areas showing degradation signals covered 2,075,286 km², accounting for 29.01%. The national historical reference gap, termed enhancement potential in this framework, was estimated at 152.63 Tg C yr⁻¹, equivalent to 17.6% of the 2022 national total; this value represents the difference between current NPP-based CAC and grid-level historical maxima, rather than a directly attainable restoration target. Because no formal attribution analysis was conducted, the observed temporal and spatial patterns are interpreted as diagnostic changes in NPP-based CAC rather than as direct evidence of policy effects or ecosystem degradation. This framework enables integrated assessment across temporal, spatial, and ecosystem-type dimensions and provides an operational diagnostic basis for national-level ecological supervision, spatial prioritization, and follow-up attribution or field verification.

Observations of vertical wind in Chongqing, a typical mountainous city in China, are important, but sparse and have low resolution. To obtain more wind profile data, this study matched the Aeolus track with ground-based wind observation sites in Chongqing in 2021. Based on the obtained results, verification and quality control studies were conducted on the wind observations of a wind profile radar (WPR) with radiosonde (RS) data, and a comparison of the Aeolus Mie-cloudy and Rayleigh-clear wind products with WPR data was then performed. The conclusions can be summarized as follows: (1) A clear correlation between the wind observations of WPR and RS was found, with a correlation coefficient (R) of 69.92 %. Their root-mean-square deviation increased with height but decreased by 3–4 km. (2) After quality control of Gaussian filtering (GF) and empirical orthogonal function construction (EOFc, G = 87.23 %) of the WPR data, the R between the WPR and RS reached 76.00 % and 95.44 %, respectively. The vertical distribution showed that GF could better retain the characteristics of WPR wind observations, but with limited improvement in decreasing deviations, whereas EOFc performed better in decreasing deviations, but considerably modified the original characteristics of the wind field, especially regarding intensive vertical wind shear in strong convective weather processes. (3) In terms of the differences between the Aeolus and WPR data, 56.0 % and 67.8 % deviations were observed between ±5 m/s for Rayleigh-clear and Mie-cloudy winds vs. WPR winds, respectively. Vertically, the mean differences of both Rayleigh-clean and Mie-cloudy winds versus WPR winds appeared below 1.5 km, which is attributed to the prevailing quiet and small winds within the boundary layer in Chongqing, such that the movement of molecules and aerosols is mostly affected by irregular turbulence. Additionally, large mean differences of 4–8 km for Mie-cloudy versus WPR winds may be related to the high content of cloud liquid water in the middle troposphere, influenced by the topography of the Tibetan Plateau. (4) The differences in both Rayleigh-clear and Mie-cloudy versus WPR winds had changed. Deviations of 58.9 % and 59.6 % were concentrated between ±5m/s for Rayleigh-clear versus WPR winds with GF and EOFc quality control, respectively. In contrast, 69.1 % and 70.2 % of deviations appeared between ±5 m/s for Rayleigh-clear versus WPR and EOFc WPR winds, respectively. These results shed light on the comprehensive applications of multi-source wind profile data in mountainous cities or areas with sparse ground-based wind observations.