| 冼炜轩,黎大维,曾淇靖,杨行健.区域氮流失负荷与减排情景分析：以韶关市为例[J].农业环境科学学报,2026,45(5):1237-1247. |
| 区域氮流失负荷与减排情景分析：以韶关市为例 |
| Nitrogen loss loads assessment and mitigation scenarios in Shaoguan City: a regional case study |
| 投稿时间：2025-05-21 |
| DOI：10.11654/jaes.2025-0472 |
| 中文关键词: InVEST模型 氮流失负荷 Mantel分析 情景模拟 源头减排 |
| 英文关键词: InVEST model nitrogen loss load Mantel analysis scenario simulation reduction |
| 基金项目:广东省重点研发计划项目（2023B0202030001）；国家重点研发计划项目（2023YFD1901300） |
| 作者 | 单位 | E-mail | | 冼炜轩 | 华南农业大学资源环境学院, 广州 510642
农业农村部华南耕地保育重点实验室, 华南农业大学资源环境学院, 广州 510642 | | | 黎大维 | 华南农业大学资源环境学院, 广州 510642
中国农业大学资源与环境学院, 北京 100193 | | | 曾淇靖 | 华南农业大学资源环境学院, 广州 510642
农业农村部华南耕地保育重点实验室, 华南农业大学资源环境学院, 广州 510642 | | | 杨行健 | 华南农业大学资源环境学院, 广州 510642
农业农村部华南耕地保育重点实验室, 华南农业大学资源环境学院, 广州 510642 | xjyang@scau.edu.cn |
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| 中文摘要: |
| 为精准量化农业面源污染负荷，支持高质量的农业发展，本研究以广东省韶关市为典型区域，利用InVEST模型、Mantel相关性分析和空间自相关分析等手段，测算了2022年韶关市各区县的总氮排放负荷及其时空分布特征，并明确了关键影响因素，预测了源头减排、过程阻控和末端拦截对氮负荷的减排效果。结果表明：韶关市氮素总负荷为3 419 t，时间上，呈现1—4月负荷较低、6—8月升高、9—12月再次回落的趋势，该变化与区域降水规律密切相关；空间上，总氮负荷在韶关市西北部与东南部较高，城市中部区域较低，表明其与地形及河网汇集有关。Mantel分析表明，韶关市氮负荷空间分布与大豆、水稻和氮施肥量等因素显著相关。InVEST模型预测结果显示，源头减排、过程阻断和末端拦截措施对全市氮负荷减排率分别为50.0%、24.9%和20.0%，说明源头减排措施对研究区内氮减排效果最佳。 |
| 英文摘要: |
| To accurately quantify the agricultural non-point source pollution loads and support high-quality agrarian development, this study investigated the total nitrogen emissions and spatiotemporal distribution characteristics in Shaoguan, 2022, based on the InVEST model, Mantel correlation analysis, and spatial autocorrelation analysis, and identified the key factors influencing total nitrogen emissions. The results showed that the total nitrogen loads in Shaoguan reached 3 419 t, with a temporal distribution trend of lower loads from January to April, higher loads from June to August, and a subsequent decline from September to December. This trend was related to regional precipitation. Spatially, higher nitrogen loads were observed in the northwest and southeast corners of the region, while lower loads were concentrated in the central urban area, possibly influenced by topography and river networks. Mantel analysis indicated that the spatial distribution of nitrogen loads was significantly associated with soybeans and rice planting areas, as well as nitrogen fertilizer application. According to the InVEST model simulations, the predicted reduction rates of total nitrogen loads under city-wide implementation of reduction, retention, and restoration measures were 50.0.%, 24.9%, and 20.0%, respectively, indicating that the reduction measure was the most effective strategy. |
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