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. 2014 May 19;11(5):5431-44.
doi: 10.3390/ijerph110505431.

Geographical distribution patterns of iodine in drinking-water and its associations with geological factors in Shandong Province, China

Affiliations

Geographical distribution patterns of iodine in drinking-water and its associations with geological factors in Shandong Province, China

Jie Gao et al. Int J Environ Res Public Health. .

Abstract

County-based spatial distribution characteristics and the related geological factors for iodine in drinking-water were studied in Shandong Province (China). Spatial autocorrelation analysis and spatial scan statistic were applied to analyze the spatial characteristics. Generalized linear models (GLMs) and geographically weighted regression (GWR) studies were conducted to explore the relationship between water iodine level and its related geological factors. The spatial distribution of iodine in drinking-water was significantly heterogeneous in Shandong Province (Moran's I = 0.52, Z = 7.4, p < 0.001). Two clusters for high iodine in drinking-water were identified in the south-western and north-western parts of Shandong Province by the purely spatial scan statistic approach. Both GLMs and GWR indicated a significantly global association between iodine in drinking-water and geological factors. Furthermore, GWR showed obviously spatial variability across the study region. Soil type and distance to Yellow River were statistically significant at most areas of Shandong Province, confirming the hypothesis that the Yellow River causes iodine deposits in Shandong Province. Our results suggested that the more effective regional monitoring plan and water improvement strategies should be strengthened targeting at the cluster areas based on the characteristics of geological factors and the spatial variability of local relationships between iodine in drinking-water and geological factors.

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Figures

Figure 1
Figure 1
The location of study area—Shandong Province in China.
Figure 2
Figure 2
Distribution of iodine in drinking-water in Shandong province, China.
Figure 3
Figure 3
Spatial clusters detected by Local Moran’s I in Shandong Province, China.
Figure 4
Figure 4
Spatial distributions of the detected clusters of high iodine areas in Shandong Province, China.
Figure 5
Figure 5
The coefficients of risk factors in Geographically Weighted Regression.
Figure 6
Figure 6
The p values of risk factors in geographically weighted regression.

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