Zhaosheng Wang

Quantifying the large-scale impact of combined ozone (O₃) and nitrogen dioxide (NO₂) pollution on terrestrial carbon sinks remains a major challenge. Here, we develop a parsimonious yet robust empirical framework that leverages high-resolution remote sensing datasets (CHAP O₃/NO₂ and MODIS NPP, 2008–2021) to characterize nonlinear threshold responses of terrestrial net primary productivity (NPP) across China’s diverse ecosystems. Our observational analysis identifies only associative temporal relationships between annual NPP variability and pollutant concentrations, with NPP positively correlated with O₃ (Pearson’s r = 0.714, p < 0.01) and negatively correlated with NO₂ (r = −0.599, p < 0.05). Notably, the ecosystem-specific threshold values (O₃: 28,324–34,391 μg m⁻³ yr⁻¹; NO₂: 3646–4968 μg m⁻³ yr⁻¹) are statistically derived from spatially aggregated pixel-level records across the full 14-year period, independent of the national annual time-series correlation analyses. Distinct from previous single-pollutant national evaluations, our study advances a novel analytical framework focusing on the interactive and combined impacts of O₃ and NO₂ co-exposure. The results demonstrate that NPP displays an increasing trend under low-level pollutant exposure but declines substantially once pollutant loads exceed the identified threshold ranges. Based on K-means clustering and segmented regression analyses, we estimate a national average NPP reduction of 17.4% per year (−0.68 Pg C yr⁻¹), resulting in a cumulative carbon loss of −9.48 Pg C over the 14-year study period—equivalent to 2.45 years of China’s total terrestrial carbon uptake. Among all ecosystem types, forestlands experience the largest cumulative carbon loss (−4.22 Pg C), with prominent loss hotspots concentrated on the Tibetan Plateau and Northwest China. This refined national-scale assessment of dual-pollutant impacts provides observation-based evidence of substantial terrestrial carbon sink degradation, underscoring the necessity of combined air pollution mitigation strategies to sustain ecosystem stability and climate mitigation targets.

Coastal wetlands are critical blue carbon sinks, yet their spatiotemporal dynamics under environmental change remain insufficiently quantified. Taking the Yellow River Delta (YRD) as a natural laboratory, this study synthesizes multi‐source remote sensing data, including optical and radar imagery spanning from 1984 to 2023, to explore the evolution of blue carbon stocks following the 1976 river diversion. Two distinct zones were identified: Zone A, characterized by an abandoned channel and sediment starvation, and Zone B, representing an active estuary with hydrological stability. Results show that: (1) Divergent vegetation trajectories: From 1984 to 2023, distinct vegetation dynamics were observed between the two zones. (2) Divergent carbon trajectories: Between 1984 and 2023, Zone A experienced fluctuations in carbon stocks, ranging from 9.90 × 10 ⁴ t to 407.35 × 10 ⁴ t, primarily due to land reclamation and altered hydrological conditions. In contrast, Zone B maintained a dynamic vegetation equilibrium under stable hydrological inputs, with carbon stocks rising from 33.80 × 10 ⁴ t to 547.01 × 10 ⁴ t. (3) Differential invasion impacts: The invasion of Spartina alterniflora after 2010 significantly increased carbon storage in Zone B by 46.60 × 10 ⁴ t. Conversely, the primary driver in Zone A was the hydrologic alteration following the river diversion, leading to heightened salinization. This condition limited the invasion of Spartina alterniflora . (4) Spatial reorganization: High‐carbon zones transitioned from being concentrated in Phragmites australis ‐dominated riverbanks during 1984–2010 to encompassing Spartina alterniflora ‐invaded areas within Zone B during 2010–2020. In salt marsh wetlands, hydrological conditions act as a critical regulator of blue carbon dynamics, primarily by governing vegetation cover and succession trajectories.

The Yellow River Basin (YRB), a core ecological–economic corridor in China, plays a critical role in meeting climate, land, and water-related Sustainable Development Goals (SDGs). To capture the dynamic evolution of Eco-environmental Quality (EEQ), this study developed a process-oriented space-time cube framework based on the Remote Sensing Ecological Index (RSEI) using MODIS data from 2000 to 2021 on the Google Earth Engine (GEE) platform. By integrating Emerging Hotspot Analysis (EHSA) with the RSEI cube, we identified and tracked 17 EEQ hotspot–coldspot evolution types and revealed their spatial clustering, temporal persistence, and cumulative transition effects. The driving mechanisms were further investigated through a comprehensive analytical framework combining Geodetector (GD), Ordinary Least Squares (OLS), and Multiscale Geographically Weighted Regression (MGWR). Results show that: (1) the RSEI-based space–time cube effectively detects co-evolving hotspot–coldspot trajectories and uncovers fine-grained spatiotemporal instability across the basin; (2) dynamic hotspot types, including diminishing, oscillating, and sporadic patterns, occupy a larger proportion of the basin than persistent hotspots, indicating widespread ecological fluctuation; (3) hotspot transition analysis reveals that only 4.94% of the 17.04% emerging hotspots became stable from 2000 to 2010, to 2011 to 2021, while the majority remained transitional or reverted to coldspots, reflecting fragile early-stage ecological recovery; and (4) Fractional vegetation cover (FVC) exhibited the highest explanatory power for EEQ hotspot–coldspot transition dynamics, followed by topography and temperature, whereas anthropogenic factors exerted their influence primarily through interactions with natural factors. These findings provide a robust scientific basis for precision ecological restoration, risk identification in fragile ecological zones, and adaptive sustainable management of large river basins under complex environmental pressures.

Climate change‐driven conservation strategies commonly project habitat availability but may not account for local adaptation among populations of the same species, which can influence prediction accuracy. Using the giant panda ( Ailuropoda melanoleuca ) as a case study, we developed a regional‐scale species distribution model (SDM) and 33 population‐specific local models to assess niche divergence and climate‐induced habitat shifts (current vs. 2080–2100, SSP2‐4.5). Comparisons between the two model scales, validated against observed habitat distributions, revealed clear differences in predicted habitat range, area, quality, and fragmentation among local populations. Specifically, regional‐scale models predicted lower climate threats for 15 local populations, higher threats for 10, and did not identify suitable habitats for 8 populations, particularly those that were smaller and more isolated. These findings highlight the importance of incorporating population‐specific climatic niche differentiation into conservation planning to improve the reliability of climate impact assessments and to guide population‐level strategies for biodiversity conservation under future climate change.

With the acceleration of urbanization in China, air pollution is becoming increasingly serious, especially PM_2.5 pollution, which poses a significant threat to public health. The study employed different deep learning models, including recurrent neural network (RNN), artificial neural network (ANN), convolutional Neural Network (CNN), bidirectional Long Short-Term Memory (BiLSTM), Transformer, and novel hybrid interpretable CNN–BiLSTM–Transformer architectures for forecasting daily PM_2.5 concentrations on the integrated dataset. The dataset of meteorological factors and atmospheric pollutants in Qingdao City was used as input features for the model. Among the models tested, the hybrid CNN–BiLSTM–Transformer model achieved the highest prediction accuracy by extracting local features, capturing temporal dependencies in both directions, and enhancing global pattern and key information, with low root Mean Square Error (RMSE) (5.4236 μg/m³), low mean absolute error (MAE) (4.0220 μg/m³), low mean absolute percentage error (MAPE) (22.7791%) and high correlation coefficient (R) (0.9743) values. Shapley additive explanations (SHAP) analysis further revealed that PM₁₀, CO, mean atmospheric temperature, O_3, and SO₂ are the key influencing factors of PM_2.5. This study provides a more comprehensive and multidimensional approach for predicting air pollution, and valuable insights for people’s health and policy makers.

Desertification threatens 24% of the world’s land area across 126 countries and affects 35% of the global population. However, mainstream global efforts to combat desertification prioritize short-term vegetation greening over addressing resource constraints and local livelihoods, posing hidden challenges to long-term Sustainable Development Goals (SDGs) attainment. Here, we evaluated the socioeconomic trade-offs of China’s long-standing desertification reversal strategies and explored the potential benefits of innovative agricultural practices using high-resolution suitability models for major crops and cultivated pasture. Compared with a no-intervention baseline, continuing the Grain-for-Green Program and grazing exclusion under projected 2023–2050 warming and wetting trends would lead to reductions of up to 54 to 55% in grain output, 81% in livestock production, and 61% in agricultural income. Conversely, strategies emphasizing pasture cultivation and crop switching could substantially lower national expenditures on combating desertification while restoring ecosystems, enhancing agricultural productivity and incomes, and conserving water resources. These benefits would contribute 0.7%, 9.8%, and 3.8% toward global progress on SDG 1 (no poverty), SDG 2 (zero hunger), and SDG 6 (clean water and sanitation), respectively, while exhibiting trade-offs across multiple SDGs. This study provides a reimagined strategy framework for combating global desertification and promoting sustainable development in vulnerable arid regions worldwide.

China’s rapid economic growth since its reform and opening-up has come at the cost of worsening atmospheric pollution. This study investigates the spatiotemporal evolution and driving mechanisms of PM_2.5 concentrations in Shandong province, a key industrial region, during 2014–2023, using comprehensive air quality monitoring, meteorological observations, and socioeconomic datasets. Through spatial analysis and geodetector methods, we identify that (1) The annual PM_2.5 concentration decreases significantly by 50.9%; spatially, heterogeneity is observed with the western urban agglomeration experiencing more severe pollution, while the eastern coastal urban agglomeration exhibits better air quality. (2) Gravity model analysis shows that the centroids of PM_2.5 pollution undergo distinct migration phases. (3) PM_2.5 levels show a distinct seasonal pattern, peaking in winter at a level 143.7% higher than the summer average. (4) The meteorological driving factors are primarily air temperature (r = 0.511) and wind speed (r = −0.487), while the socioeconomic factors are tertiary industry production (r = −0.971), particulate matter emissions (r = 0.956), and sulfur dioxide emissions (r = 0.938). Concurrently, the combined effect of tertiary industry production and PM emissions account for 99.5% of PM_2.5 variability. Notably, we validate an Environmental Kuznets Curve relationship (R² = 0.805) between economic development and air quality improvement, demonstrating that clean production policy integration can reconcile environmental and economic objectives. These findings provide empirical evidence supporting circular economy strategies for air pollution mitigation in industrializing regions.