地理
中国
中国大陆
碘
变化(天文学)
分布(数学)
自然地理学
环境科学
数学
化学
考古
物理
天体物理学
数学分析
有机化学
作者
Xin Hou,Meng Zhao,Jia Li,Jing Wang,Ming Li,Lixiang Liu,Peng Liu,Fangang Meng,Lijun Fan,Hongmei Shen,Dianjun Sun
标识
DOI:10.1016/j.scitotenv.2023.164628
摘要
To identify the current spatial distribution of iodine concentration in drinking water (dWIC) at the township-level across China and its influencing factors through visualization and spatial statistical analysis by the geographic information system. The dWIC for each township was used to describe the distribution by ArcGIS 10.7. The spatial aggregation characteristics were analyzed by spatial auto-correlation analysis. The inverse distance weight method was used to predict the dWIC at nonsampling sites. The correlation between the dWIC and the distance from each township to the Yellow River as well as the depth of tube wells were analyzed by ordinary least squares and geographically weighted regression, respectively. A total of 37,541 townships were included in this study. dWIC ranged from 0 to 1113.7 μg/L, and the median was 3.3 μg/L. There were 35,606 townships < 40 μg/L (94.85 % of surveyed townships), 40 μg/L ≤ 1015 townships ≤100 μg/L (2.70 % of surveyed townships), and 920 townships > 100 μg/L (2.45 % of surveyed townships). The results were statistically significant of global autocorrelation analysis (Moran's I = 0.43, Z = 922.15, P < 0.01). local Moran's I showed that 3128 townships (8.33 % of surveyed townships) belong to H-H cluster areas. The dWIC were partially negatively correlated with the distance from each township to the Yellow River, as well as positively correlated with the depth of tube wells in partial areas. The dWIC varied widely across mainland China (from 0 μg/L to 1113.7 μg/L). 94.85 % of surveyed townships were below 40 μg/L and 2.45 % of surveyed townships were exceeding 100 μg/L. Moreover, the distance from each township to the Yellow River may be one of the geneses of iodine-excess areas. Finally, this study has provided a visible reference of dWIC for the precise control strategy and focused monitoring in China.
科研通智能强力驱动
Strongly Powered by AbleSci AI