| 兰州市耕地表层土壤重金属的积累特征及其影响因素分析 |
| 摘要点击 1467 全文点击 505 投稿时间：2022-04-08 修订日期：2022-06-08 |
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| 中文关键词 耕地表层土壤 重金属 积累特征 空间分异 影响因素 兰州市 |
| 英文关键词 cultivated surface soil heavy metal accumulation characteristic spatial differentiation influencing factor Lanzhou |
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| 中文摘要 |
| 以兰州市耕地表层土壤中的As、Cd、Cr、Hg和Pb为研究对象，运用描述性统计、单因子积累指数、综合积累指数、地统计学方法和地理加权回归模型（GWR）相结合的方法，探究其积累现状、空间分布和其影响因素，为研究区重金属积累风险的精准管控提供科学依据.结果表明：①单因子积累指数显示，研究区Hg和Cd的积累程度最大，其次为Pb和As，Cr的最小；综合积累指数显示，重金属的积累率高达98.11%，其中54.81%为轻度积累，43.30%为中度和以上积累；可见，虽然兰州市耕地表层土壤不存在重金属污染问题，但是普遍存在积累现象，少部分地区有潜在的污染风险.②空间自相关分析显示，Cr、Hg和Pb单因子积累指数的空间自相关性中等，表明其受随机性因素和结构性因素的共同影响；其它2种重金属的空间自相关程度弱，表明其主要受随机性因素的影响，其中人为因素的影响较大.As和Cr单因子积累指数由西向东递减，Cd则由西向东递增，Hg单因子积累指数中间高四周低；综合累积指数从西北向东南增加，从中间向两边降低.③GWR分析表明，海拔高度、坡长、距河流距离、土壤有机质（SOM）含量、降水量、气温和地表温度都表现为正向驱动作用，其余因子都表现为负向驱动，并表现出显著的空间异质性和不稳定性（P<0.05）.因子影响的大小依次为：土壤特征>地形特征>气候特征>位置特征>社会经济特征>植被特征，其中SOM含量的贡献率最大，海拔高度和气温的次之，NDVI值的最小；与其它特征因子相比，位置特征因子影响的空间异质性更强. |
| 英文摘要 |
| In this study, As, Cd, Cr, Hg, and Pb in the surface soil of cropland in Lanzhou were studied, and the combination of descriptive statistics, single-factor accumulation index, comprehensive accumulation index, geostatistical method, and a geographically weighted regression model (GWR) was used to investigate their accumulation status, spatial distribution, and influencing factors, in order to provide scientific basis for the precise control of heavy metal accumulation risk in the study area. The results showed that:①the single-factor accumulation index showed that the accumulation of Hg and Cd in the study area was the largest, followed by that of Pb and As, and that of Cr was the smallest; the comprehensive accumulation index showed that the accumulation rate of heavy metals was as high as 98.11%, of which 54.81% was mild accumulation and 43.30% was moderate and above accumulation. However, there was no heavy metal pollution in the surface soil of cultivated land in Lanzhou. ② Spatial autocorrelation analysis showed that there was no heavy metal pollution in the surface soil of Lanzhou, but there was accumulation in a few areas. The spatial autocorrelation analysis showed that the spatial autocorrelation of the Cr, Hg, and Pb single-factor accumulation indices was moderate, indicating that they were influenced by both random and structural factors; the spatial autocorrelation of the other two heavy metals was weak, indicating that they were mainly influenced by random factors, among which human factors were more influential. The Hg single-factor accumulation index was high in the middle and low in the fourth; the integrated accumulation index increased from northwest to southeast and decreased from the middle to both sides. ③ Geographically weighted regression (GWR) analysis showed that altitude, slope length, distance from rivers, soil organic matter (SOM) content, precipitation, air temperature, and surface temperature all showed positive driving effects, whereas the rest of the factors showed negative driving effects and significant spatial heterogeneity and instability (P<0.05). The magnitudes of the factor effects were in the order of soil characteristics>topographic characteristics>climate characteristics>location characteristics>socioeconomic characteristics>vegetation characteristics, with the largest contribution of SOM content, followed by that of altitude and air temperature, and the smallest of normalized difference vegetation index (NDVI) values. Compared with the other feature factors, the location feature factors had stronger spatial heterogeneity. |
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