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山东沿海城市灰水足迹变化特征及其影响因素OA

Variation patterns of grey water footprint and its influencing factors in Shandong's coastal cities

中文摘要英文摘要

为探究山东沿海地区 2002−2021年灰水足迹时空变化特征及其影响机制,本文以青岛、潍坊等 7个沿海城市为研究区域,核算并分析其灰水足迹时序变化与空间分异格局;采用随机森林模型,通过相对重要性与偏依赖分析识别关键影响因素;运用结构方程模型,定量解析各因素对灰水足迹的直接效应与间接效应.结果表明:区域灰水足迹以年均 3.4%比例减小,呈"先升后稳再降"三阶段特征,农业灰水足迹平均贡献率最大,为首要来源;在空间上,灰水足迹与农业灰水足迹呈"中部高、四周低"格局,生活与工业灰水足迹分布分别与人口规模、产业结构密切相关;面积耕地化肥施用量、面积水产养殖产量、畜禽当量污染物排放量、工业废水排放强度为区域灰水足迹的核心影响因素.偏依赖关系揭示了各因素对灰水足迹的边际作用规律,结构方程模型进一步印证了农业面源污染是灰水足迹增加的主要影响因素.农业污染管控和工业污染治理是实现山东沿海地区灰水足迹减排的主要途径.

To investigate the spatiotemporal variation and driving mechanism of the grey water footprint(Q)in coastal areas of Shandong province from 2002 to 2021,this paper selects seven coastal cities as the study area to calculate and analyze its temporal trends and spatial patterns.The random forest model is adopted to identify the key influencing factors through relative importance analysis and partial dependence analysis.Furthermore,the structural equation model is used to quantitatively analyze the direct and indirect pathways among factors.The results indicate that the regional grey water footprint decreased at an average annual rate of 3.4%,following a three-stage pattern:an initial increase,subsequent stabilization,and a final decrease.Qagr accounts for the largest proportion on average,making it the primary source of the total Q.In terms of spatial distribution,both the total Q and Qagr present a pattern of higher values in the central region and lower values in the surrounding areas.Meanwhile,the distribution of Qdom and Qind is highly correlated with population size and industrial structure,respectively.Fertilizer application rate per unit cultivated land(Un−irr),aquaculture yield per unit area(Yagr),pollutant emission per standard livestock equivalent(Upol−liv),and industrial wastewater discharge intensity(Uind)are the core factors for the regional Q.The partial dependence relationship reveals the marginal effect of each factor on the Q.The structural equation model further confirms that agricultural non-point source pollution is the main driving factor for the increase in Q.Agricultural pollution control and industrial pollution treatment are the primary approaches for the reduction in Q.

李鹏飞;李玲玲;张启超;王占金;张传兴;李占杰;姚晓磊;李爽

生态环境部陆海统筹生态治理与系统调控重点实验室,山东省生态环境规划研究院,山东济南生态环境部陆海统筹生态治理与系统调控重点实验室,山东省生态环境规划研究院,山东济南生态环境部陆海统筹生态治理与系统调控重点实验室,山东省生态环境规划研究院,山东济南生态环境部陆海统筹生态治理与系统调控重点实验室,山东省生态环境规划研究院,山东济南生态环境部陆海统筹生态治理与系统调控重点实验室,山东省生态环境规划研究院,山东济南北京师范大学水科学研究院,城市水循环与海绵城市技术北京市重点实验室,北京北京师范大学水科学研究院,城市水循环与海绵城市技术北京市重点实验室,北京北京市北运河管理处,北京

资源环境

灰水足迹时空变化随机森林影响因素山东沿海城市

grey water footprintspatiotemporal variationrandom forestinfluencing factorsShandong's coastal cities

《北京师范大学学报(自然科学版)》 2026 (4)

505-514,10

10.12202/j.0476-0301.2025245

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