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Verified CAS / Academic Author2 Decoded Studies

Prof. SUN Chuqi

State Key Laboratory of Regional Environment and Sustainability, School of Environment, Beijing Normal University

Research Publications & English Decoded Briefs

Showing 2 publications
Journal of Environmental Engineering Technology2026DOI: 10.13205/j.hjgc.202605010

Colonization Requirements of Submerged Macrophytes Based on Underwater Light Environment

The underwater light environment is a critical limiting factor for the colonization of submerged macrophytes and the ecological restoration of shallow lakes. Previous studies rarely quantified the contribution of aquatic environmental factors to the water quality–underwater light–macrophyte relationship, nor did they comprehensively consider factor correlations or establish thresholds for macrophyte colonization. This study, conducted in a typical national wetland nature reserve (Hongze Lake), measured photosynthetically active radiation, light attenuation coefficient (Kd), euphotic depth (Zeu), water transparency (SD), total suspended solids (TSS), chlorophyll-a (Chl-a), total nitrogen (TN), and total phosphorus (TP). A simulation model for Kd was developed, spatial distributions of environmental factors were analyzed, and contribution rates to light attenuation were quantified. Results showed that the mean Kd was 10.31±3.76 m⁻¹, and the mean Zeu (0.53±0.24 m) was lower than the mean water depth (0.94±0.29 m), with a spatial pattern of shallower Zeu in the west and deeper in the east. TSS and Chl-a were the primary direct influencing factors, while TN acted mainly indirectly. To achieve effective macrophyte colonization under average water depth conditions, thresholds were determined: Zeu ≥ 0.94 m, SD ≥ 0.41 m, Kd ≤ 4.95 m⁻¹, and Chl-a ≤ 3.8 μg/L. These findings provide quantitative guidance for lake restoration and water quality management.

Journal of Environmental Engineering Technology2026DOI: 10.13205/j.hjgc.202605009

Carbon Dioxide Emission Forecasting for Medium and Large Reservoirs in China

Reservoir CO2 emissions are a critical component of the global carbon cycle, yet existing prediction models often neglect reservoir size stratification, mixing data from large, medium, and small reservoirs. This study addresses this gap by constructing a training dataset exclusively for large and medium reservoirs in China and developing a neural network-based CO2 flux prediction model. The model revealed significant latitudinal zonation, with CO2 fluxes in southern low-latitude basins (e.g., Yangtze, Pearl) substantially higher than in northern high-latitude basins (e.g., Yellow, Songliao). Statistical analysis identified total phosphorus (TP) content as the strongest driver, exhibiting a positive exponential correlation with CO2 flux (p < 0.01). Reservoir age and latitude showed significant negative correlations, while physical parameters such as reservoir area, pre-impoundment submersion ratio, and average water depth showed no significant national-scale correlation. The study underscores the heterogeneity of dominant drivers across basins and proposes differentiated carbon mitigation strategies: for southern high-emission basins, controlling phosphorus inputs through wastewater treatment and restricting aquaculture; for northern basins, enhancing soil conservation to reduce carbon-rich sediment influx; and for new reservoirs, implementing vegetation clearance before impoundment and optimizing flood discharge schedules. These findings provide a theoretical basis for carbon emission characterization and support low-carbon management of large and medium reservoirs in China.