scholarly journals Effects of Optimized Water Management on the Uptake and Translocation of Cadmium and Arsenic in Oryza Sativa L. In Two Contaminated Soils

Author(s):  
Qiongli Bao ◽  
Wankui Bao ◽  
Yongzhen Ding ◽  
Yizong Huang

Abstract Pot experiments were conducted to identify the most efficient water management strategy for reducing Cd and As accumulations and amino acid (AA) synthesis in rice in two soils with different Cd and As contents. A treatment consisting of five days of flooding followed by three days of drainage (F5D3, repeated every eight days) was identified as the most effective treatment for simultaneously decreasing Cd and As in grains, with reductions of grain Cd and As contents of more than 80.0% and 73.1%, respectively, compared with either a drained treatment or a flooded treatment alone; this is probably related to the high efficiency of the F5D3 treatment in reducing dissolved Cd and As according to its minimum “trade-off value”, due to the variations in grain Cd and As contents were significantly correlated with the variations in soil solution Cd (R2 = 0.98) and As (R2 = 0.92, p = 0.0001) concentrations. Additionally, grain Cd content was also significantly related to the organs Cd contents (especially root Cd content, R2 = 0.99) and the root-to-shoot Cd translocation factors (R2 = 0.99), whereas grain As content was significantly related to soil Eh (R2=-0.82, p = 0.003) and pH (R2 = 0.88, p = 0.0008). The AA contents in organs under the F5D3 treatment were lower than those under the Flooded and Drained treatments. These results indicated that the F5D3 treatment was the most effective water management strategy for simultaneously reducing grain Cd and As contents and AA synthesis in rice, which was probably due to there being no need for rice to synthesize abundant AAs to chelate metal ions.

2007 ◽  
Vol 22 (01) ◽  
pp. 59-68 ◽  
Author(s):  
Ahmed S. Abou-Sayed ◽  
Karim S. Zaki ◽  
Gary Wang ◽  
Manoj Dnyandeo Sarfare ◽  
Martin H. Harris

2017 ◽  
Vol 76 ◽  
pp. 319-327
Author(s):  
Wenlong Zhang ◽  
Yi Li ◽  
Chao Wang ◽  
Peifang Wang ◽  
Qing Wang ◽  
...  

2020 ◽  
Author(s):  
Katja Friedrich ◽  
Kyoko Ikeda ◽  
Sarah Tessendorf ◽  
Jeffrey French ◽  
Robert Rauber ◽  
...  

<p>Cloud seeding has been used as one water management strategy to overcome the increasing demand for water despite decades of inconclusive results on the efficacy of cloud seeding. In this study snowfall accumulation from glaciogenic cloud seeding is quantified based on snow gauge and radar observations from three days in January 2017, when orographic clouds in the absent of natural precipitation were seeded with silver iodide (AgI) in the Payette basin of Idaho during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE). On each day, a seeding aircraft equipped with AgI flares flew back and forth on a straight-line flight track producing a zig-zag pattern representing two to eight lines of clouds visible through enhancements in radar reflectivity. As these seeding lines started to form precipitation, they passed over several snow gauges and through the radar observational domain. For the three cases presented here, precipitation gauges measured increases between 0.05-0.3 mm as precipitation generated by cloud seeding pass over the instruments. A variety of relationships between radar reflectivity factor and liquid equivalent snowfall rate were used to quantify snowfall within the radar observation domain. For the three cases, snowfall occurred within the radar observational domain between 25 -160 min producing a total amount of water generated by cloud seeding ranging from 123,220 to 339,540 m3 using the best-match Ze-S relationship. Uncertainties in radar reflectivity estimated snowfall are provided by considering not only the best-match Ze-S relationship but also an ensemble of Ze-S relationships based on the range of coefficients published from previous studies and then examining the percentile of snowfall estimates based on all of the Ze-S relationships within the ensemble. Considering the interquartile range and 5<sup>th</sup>/95<sup>th</sup> percentiles, uncertainties in total amount of water generated by cloud seeding can range between 20-45% compared to the best-math estimates. These results provide new insights towards understanding how cloud seeding impacts precipitation and its distribution across a region.</p>


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