地球科学进展 ›› 2025, Vol. 40 ›› Issue (6): 635 -646. doi: 10.11867/j.issn.1001-8166.2025.043

研究论文 上一篇    下一篇

顾及环境相似性的中国北方沙尘强度空间模拟与人口暴露评估
马学海1(), 周亮1,2,3(), 车涛4, 黄春林4, 高鸿1,2,3, 孙钦珂1,2,3   
  1. 1. 兰州交通大学 测绘与地理信息学院,甘肃 兰州 730070
    2. 地理国情监测技术应用国家地方联合 工程研究中心,甘肃 兰州 730030
    3. 甘肃省地理国情监测工程实验室,甘肃 兰州 730030
    4. 中国科学院西北生态环境资源研究院,甘肃 兰州 730000
  • 收稿日期:2025-04-23 修回日期:2025-05-30 出版日期:2025-06-10
  • 通讯作者: 周亮

Spatial Simulation of Dust Intensity and Population Exposure in Northern China Based on Environmental Similarity

Xuehai MA1(), Liang ZHOU1,2,3(), Tao CHE4, Chunlin HUANG4, Hong GAO1,2,3, Qinke SUN1,2,3   

  1. 1. School of Surveying, Mapping and Geoinformatics, Lanzhou Jiaotong University, Lanzhou 730070, China
    2. National and Local Joint Engineering Research Center for the Application of Geographic National Conditions Monitoring Technology, Lanzhou 730030, China
    3. Gansu Provincial Geographic National Conditions Monitoring Engineering Laboratory, Lanzhou 730030, China
    4. Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
  • Received:2025-04-23 Revised:2025-05-30 Online:2025-06-10 Published:2025-08-04
  • Contact: Liang ZHOU
  • Supported by:
    the National Key Research & Development Program-Intergovernmental International Science and Technology Innovation Cooperation Project(2024YFE0198602); The Lanzhou Science and Technology Plan Project(2024-3-93)

沙尘天气在干旱及半干旱地区日趋频繁,已对生态环境与居民健康造成严重威胁。沙尘对人口的危害不仅取决于气候变化,还与人口规模和沙尘空间分布密切相关。目前仍缺少对于沙尘人口暴露的长时序和系统性的评估。依据环境相似性理论,整合沙尘站点数据与9种环境因子数据,运用随机森林方法构建沙尘强度模拟模型,结合人口数据建立沙尘人口暴露风险指数系统评估2000—2020年中国北方沙尘强度及人口暴露风险的时空演变规律。结果表明:①沙尘强度模型决定系数均大于0.6(p<0.01),对模型贡献度最高的3个因子分别为干旱指数、风速和归一化植被指数。②中国北方沙尘强度呈现显著空间异质性。极高、高和较高强度的沙尘主要集中于西北地区,而华北和东北地区以极低、低和较低强度为主。研究期内沙尘强度总体降低53%,累计有1.9×106 km2的极高、高和较高强度的沙尘区域转化为极低、低和较低强度的沙尘区域。③中国北方3.5亿人口遭受沙尘威胁,沙尘人口暴露高风险区集中于人口密集的京津冀、陕晋南部以及沙尘强度高的新疆南部地区。研究区沙尘人口暴露风险显著下降,暴露于极高、高和较高强度的沙尘人数减少0.73亿人。上述结论揭示了中国北方地区沙尘人口暴露风险的时空分布特征,为地方政府制定针对性的沙尘防治和应对措施提供了科学依据。

The frequent occurrence of sand and dust causes serious harm to the ecological environment and public health in arid and semiarid regions. The risk of population exposure depends not only on climate change but also on the population size and spatial distribution of sand and dust. However, systematic evaluations of long-term exposure to dust are still insufficient. Based on the theory of environmental similarity, this study integrated data from sand and dust monitoring stations and nine environmental variables. Using the random forest method to construct a simulation model of sand and dust intensity, combined with population data to establish a sand and dust population exposure risk index system, and evaluated the spatiotemporal evolution of sand and dust intensity and population exposure risk in northern China from 2000 to 2020. The results showed that the coefficients of determination of the dust intensity model were all greater than 0.6 (p<0.01). The three most influential factor with the highest contribution to the model were the drought index, wind speed, and NDVI. Sand and dust intensities in the study area showed significant spatial heterogeneity. The extremely high, high, and high intensities of sand and dust were mainly concentrated in Northwest China, while the extremely low, low, and low intensities of sand and dust were mainly concentrated in North and Northeast China. During the study period, the sand and dust intensity decreased by 53%, and the extremely high- , high- , and high-intensity sand and dust areas of 1.9×106 km2 were transformed into very low, low, and low sand and dust areas. In northern China, 350 million people are exposed to dust and sand, and high-risk areas of dust exposure are concentrated in the Beijing-Tianjin-Hebei region, southern Shaanxi and Shanxi provinces with high population density, and southern Xinjiang with high dust intensity. The overall risk of exposure to sand and dust in the study area decreased significantly, with 73 million people exposed to extremely high, high, and high-intensity sand and dust. The findings reveal the spatial and temporal distribution characteristics of sand and dust exposure risk in northern China and provide a scientific basis for local governments to formulate targeted sand and dust prevention and control measures.

中图分类号: 

图1 中国北方概况图
Fig. 1 Overview map of northern China
表1 环境因子数据
Table 1 Environmental factor data
图2 20002020年随机森林模型精度散点图
Fig. 2 Scatter plot of model accuracy from 2000 to 2020
图3 20002020年中国北方沙尘强度分布图
Fig. 3 Dust intensity distribution in northern China from 2000 to 2020
图4 20002020年中国北方各强度沙尘面积变化主要方向及面积
Fig. 4 The main direction and area of dust area of different intensities in northern China from 2000 to 2020
图5 20002020年中国北方沙尘人口暴露风险等级图 (a) 喀什市、阿图什市、疏附县和疏勒县;(b) 和田市、墨玉县、洛浦县和和田县;(c) 泽普县、莎车县和叶城县;(d) 京津冀地区;(e) 陕西和山西南部地区。
Fig. 5 Population exposure risk level of sand and dust in northern China from 2000 to 2020 (a) Kashgar City, Artush City, Shufu County, Shule County; (b) Hotan City, Moyu County, Luopu County, Hotan County; (c) Zepu County, Yache County, Yecheng County; (d) Beijing-Tianjin-Hebei region; (e) Shaanxi and the southern region of Shanxi.
图6 20002020年中国北方沙尘人口暴露风险空间集聚特征
Fig. 6 Spatial agglomeration characteristics of sand and dust exposure risk in northern China from 2000 to 2020
图7 20002020年高风险区年均沙尘人口暴露指数图 地区a:喀什市、阿图什市、疏附县和疏勒县;地区b:和田市、墨玉县、洛浦县和和田县;地区c:泽普县、莎车县和叶城县;地区d:京津冀地区;地区e:陕西和山西南部地区。Region a: Kashgar City, Artush City, Shufu County, Shule County; Region b: Hotan City, Moyu County, Luopu County, Hotan County; Region c: Zepu County, Yache County, Yecheng County; Region d: Beijing-Tianjin-Hebei region; Region e: Shaanxi and the southern region of Shanxi.
Fig. 7 Annual average population exposure index of sand and dust in high-risk areas from 2000 to 2020
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