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This article in JEQ

  1. Vol. 38 No. 3, p. 1126-1136
     
    Received: May 14, 2008


    * Corresponding author(s): dick.brus@wur.nl
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doi:10.2134/jeq2008.0228

Predictions of Spatially Averaged Cadmium Contents in Rice Grains in the Fuyang Valley, P.R. China

  1. Dick J. Brus *a,
  2. Zhibo Libc,
  3. Jing Songbc,
  4. Gerwin F. Koopmansd,
  5. Erwin J. M. Temminghoffd,
  6. Xuebin Yinb,
  7. Chunxia Yaob,
  8. Haibo Zhangb,
  9. Yongming Luobc and
  10. Jan Japengaa
  1. a Alterra, Wageningen Univ. and Research Center (WUR), Wageningen, the Netherlands
    b Soil and Environment Bioremediation Research Center, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, P.R. China
    c Graduate School of the Chinese Academy of Sciences, Beijing, 100049, P.R. China
    d Dep. of Soil Quality, Wageningen Univ., WUR, Wageningen, the Netherlands

Abstract

Soils in the Fuyang valley (Zhejiang province, southeast China) have been contaminated by heavy metals. Since rice (Oryza sativa L.) is the dominant crop in the valley and because of its tendency to accumulate Cd in its grains, assessment of the human health risk resulting from consumption of locally produced rice is needed. In this study, we used a regression model to predict the average Cd content in rice grains for paddy fields. The multiple linear model for log(Cd) content in rice grains with log(HNO3–Cd), pH, log(clay), and log(soil organic matter, SOM) as predictors performed much better (R 2 adj = 66.1%) than the model with log(CaCl2–Cd) as a single predictor (R 2 adj = 28.1%). This can be explained by the sensitivity of CaCl2–extracted Cd for changes in redox potential and as a result of the drying of the soil samples in the laboratory. Consequently, the multiple linear model was used to predict the average Cd contents in rice grains for paddy fields, and to estimate the probability that the FAO/WHO standard of 0.2 mg kg−1 will be exceeded. Eleven blocks had a probability smaller than 10% of exceeding this standard (safe blocks). If a lognormal distribution is assumed, 35 blocks had a probability larger than 90% (blocks at risk). Hence, risk reduction measures should be undertaken for the blocks at risk. For 27 blocks the probability was between 10 and 90%. For these blocks the uncertainty should be reduced via improvement of the regression model and/or increasing the number of sample locations within blocks.

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