Wei Qing, Xue Lianqing, Jia Zichen, Chen Yongqi, Chen Peipei
State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Ministry of Education Key Laboratory of Yangtze River Water Environment, Tongji University, Shanghai 200092, China.
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
Sci Total Environ. 2025 Aug 10;989:179886. doi: 10.1016/j.scitotenv.2025.179886. Epub 2025 Jun 12.
Net primary productivity (NPP) is a critical indicator of ecosystem carbon sequestration and vegetation growth, directly influencing global carbon cycles and climate change mitigation. However, understanding the spatiotemporal patterns of NPP in rapidly developing regions like the Yangtze River Delta (YRD) remains challenging due to complex interactions between climatic factors and human activities. This study addresses this gap by analyzing the spatiotemporal variations of NPP in the YRD from 2001 to 2020 using multi-source data and an advanced convolutional neural network (CNN) optimized by the dung beetle optimization (DBO) algorithm. The dynamic search strategy of the DBO algorithm enables it to to avoid local optima effectively and is particularly suitable for complex nonlinear problems such as NPP modelling. The proposed model demonstrated strong performance, with an R of 0.914 and an RMSE of 21.1 gC/m/yr, indicating reliable NPP estimation. The average NPP was 0.795 KgC/yr, with a significant annual increase of 3.72 gC/m/yr. Spatially, NPP exhibited a northwest-to-southeast gradient, reflecting regional climate and land cover variations. Shapley additive explanations (SHAP) analysis highlighted sunshine hours, elevation, precipitation, and temperature as dominant factors influencing NPP. Partial correlation analysis further confirmed that elevation and precipitation were positively correlated with NPP, while sunshine hours and temperature showed negative correlations. Future predictions based on shared socioeconomic pathways (SSP) scenarios suggest a gradual decline in NPP from 2030 to 2050, with SSP245 maintaining higher NPP values than SSP585. The findings indicate that climate factors are the primary drivers of NPP increases, while human activities are primarily responsible for NPP declines. This study offers new insights into the complex dynamics of NPP in the YRD, providing a more accurate model for predicting future NPP trends and their implications for carbon management strategies.
净初级生产力（NPP）是生态系统碳固存和植被生长的关键指标，直接影响全球碳循环和气候变化缓解。然而，由于气候因素和人类活动之间的复杂相互作用，了解长江三角洲（YRD）等快速发展地区NPP的时空格局仍然具有挑战性。本研究通过使用多源数据和由蜣螂优化（DBO）算法优化的先进卷积神经网络（CNN）分析2001年至2020年YRD地区NPP的时空变化，填补了这一空白。DBO算法的动态搜索策略使其能够有效避免局部最优，特别适用于NPP建模等复杂非线性问题。所提出的模型表现出强大的性能，R值为0.914，RMSE为21.1 gC/m/yr，表明NPP估计可靠。平均NPP为0.795 KgC/yr，每年显著增加3.72 gC/m/yr。在空间上，NPP呈现出从西北向东南的梯度，反映了区域气候和土地覆盖的变化。Shapley加法解释（SHAP）分析突出了日照时数、海拔、降水和温度是影响NPP的主要因素。偏相关分析进一步证实，海拔和降水与NPP呈正相关，而日照时数和温度呈负相关。基于共享社会经济路径（SSP）情景的未来预测表明，从2030年到2050年NPP将逐渐下降，SSP245的NPP值高于SSP585。研究结果表明，气候因素是NPP增加的主要驱动因素，而人类活动是NPP下降的主要原因。本研究为YRD地区NPP的复杂动态提供了新的见解，为预测未来NPP趋势及其对碳管理策略的影响提供了更准确的模型。
