| 长三角地区PM2.5浓度对土地利用/覆盖转换的空间异质性响应 |
| 摘要点击 3095 全文点击 971 投稿时间：2021-06-04 修订日期：2021-08-03 |
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| 中文关键词 PM2.5 土地利用/覆盖转换 K-means聚类 随机森林 空间计量 多尺度地理加权回归(MGWR) |
| 英文关键词 PM2.5 land use/cover conversion K-means clustering random forest spatial econometric model multi-scale geographically weighted regression model (MGWR) |
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| 中文摘要 |
| 长三角地区PM2.5污染受区域空间效应的影响，其可持续治理方向仍不清晰.结合随机森林、空间计量模型和多尺度地理加权回归模型（MGWR），探讨PM2.5浓度对土地利用/覆盖转换的多尺度空间响应过程.结果表明：① 2000~2018年长三角地区PM2.5浓度呈现出4类空间连续聚集的时空变化模式，区域性同步变化强烈；②土地转换对PM2.5浓度的相对影响表现复杂，耕地与林地的源-汇效应显著.邻域分析表示周围聚集性土地利用/覆盖转换普遍比单一像元时作用更显著，空间效应明显；③ PM2.5浓度变化与林地、草地转换类型大多呈显著负相关，与耕地、建设用地和水体之间的转换类型呈显著正相关.随机森林模型重要性排序及相关系数强度表明：耕地-耕地（29.65%及0.650）、林地-林地（26.98%及0.726）、建设用地-耕地（22.57%及0.519）、耕地-林地（17.84%及0.602）和耕地-建设用地（16.34%及0.424）之间转换对PM2.5浓度变化贡献度较高.空间杜宾模型显示PM2.5浓度变化存在显著的空间依赖性和空间溢出效应；④ MGWR模型揭示了不同土地利用/覆盖转换方式作用于PM2.5浓度变化的尺度效应及空间关系的非平稳性特征，其空间关系表现出强烈的转移类型差异.而多模型结果显示不同土地转换对PM2.5浓度变化的驱动方式不同，因此需分类别分层制定针对性联合管理策略. |
| 英文摘要 |
| The sustainable management direction of PM2.5 concentrations in the Yangtze River Delta region remains unclear due to regional spatial effects. This study combined the random forest model, spatial econometric model, and multi-scale geographically weighted regression model (MGWR) to explore the multi-scale spatial response of PM2.5 concentration to land use/cover conversion. The results show that:① PM2.5 concentrations in the Yangtze River Delta region from 2000 to 2018 showed four types of spatial-temporal patterns of spatially continuous aggregation, with strong regional synchronous changes. ② The relative influence of land conversion on PM2.5 concentrations showed a complex performance, and the source-sink effect of cultivated land and forest land was obvious. Neighborhood analysis indicated that the effect of surrounding aggregated land use conversion was generally more significant than that of single cells on PM2.5 concentration change, and the spatial effect was obvious. ③ PM2.5 concentration changes were mostly significantly negatively correlated with forest land and grassland conversion types and significantly positively correlated with conversion types between cropland, construction land, and water bodies. The importance ranking of the random forest model and correlation coefficient intensity indicated that the conversion between cropland-cropland (29.65%; 0.650), forest land-forest land (26.98%; 0.726), construction land-cropland (22.57%; 0.519), cropland-forestland (17.84%; 0.602), and cropland-construction land (16.34%; 0.424) contributed more to the variation in PM2.5 concentration. The spatial Durbin model revealed a significant spatial dependence of the change in PM2.5 concentration and a strong spatial spillover effect. ④ The MGWR model revealed the scale effects and non-stationary characteristics of the spatial relationships between different land use conversions acting on PM2.5 concentration change, and its spatial relationship showed strong differences in transfer types. However, the multi-models revealed that different land conversions drove the PM2.5 concentration change in different ways, so it is necessary to formulate targeted joint management strategies in a categorical and hierarchical manner. |
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