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Factors affecting paddy soil arsenic concentration in Bangladesh: Prediction and uncertainty of geostatistical risk mapping

机译:孟加拉国稻田土壤砷浓度的影响因素:地统计学风险图的预测和不确定性

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Knowledge of the spatial correlation of soil arsenic (As) concentrations with environmental variables is needed to assess the nature and extent of the risk of As contamination from irrigation water in Bangladesh. We analyzed 263 paired groundwater and paddy soil samples covering highland (HL) and medium highland-1 (MHL-1) land types for geostatistical mapping of soil As and delineation of As contaminated areas in Tala Upazilla, Satkhira district. We also collected 74 non-rice soil samples to assess the baseline concentration of soil As for this area. The mean soil As concentrations (mg/kg) for different land types under rice and non-rice crops were: rice-MHL-1 (21.2)>rice-HL (14.1)>non-rice-MHL-l (11.9)>non-rice-HL (7.2). Multiple regression analyses showed that irrigation water As, Fe, land elevation and years of tubewell operation are the important factors affecting the concentrations of As in HL paddy soils. Only years of tubewell operation affected As concentration in the MHL-1 paddy soils. Quantitatively similar increases in soil As above the estimated baseline-As concentration were observed for rice soils on HL and MHL-1 after 6-8 years of groundwater irrigation, implying strong retention of As added in irrigation water in both land types. Application of single geostatistical methods with secondary variables such as regression kriging (RK) and ordinary co-kriging (OCK) gave little improvement in prediction of soil As over ordinary kriging (OK). Comparing single prediction methods, kriging within strata (KWS), the combination of RK for HL and OCK for MHL-1, gave more accurate soil As predictions and showed the lowest misdassification of declaring a location "contaminated" with respect to 14.8 mg As/kg, the highest value obtained for the baseline soil As concentration. Prediction of soil As buildup over time indicated that 75% or the soils cropped to rice would contain at least 30 mg/L As by the year 2020.
机译:需要了解土壤砷(As)浓度与环境变量在空间上的相关性,以评估孟加拉国灌溉水中砷污染风险的性质和程度。我们分析了Satkhira地区Tala Upazilla的263对成对的地下水和稻田土壤样品,覆盖了高地(HL)和中高地1(MHL-1)土地类型,以对土壤As进行地统计学测绘和As污染区的划分。我们还收集了74个非大米土壤样品,以评估该地区土壤的基线浓度。水稻和非水稻作物下不同土地类型的平均土壤As浓度(mg / kg)为:水稻-MHL-1(21.2)>水稻-HL(14.1)>非水稻-MHL-1(11.9)>非大米HL(7.2)。多元回归分析表明,灌溉水As,Fe,土地海拔和管井作业年限是影响HL稻田土壤As含量的重要因素。只有多年的试管操作会影响MHL-1稻田土壤中的As浓度。定量灌溉后,在土地灌溉6-8年后,在HL和MHL-1上的稻田土壤中,As的浓度估计高于基线,这表明这两种土地类型中灌溉水中添加的As都有很强的保留能力。与回归克里格(RK)和普通协同克里格(OCK)等具有次级变量的单一地统计学方法的应用与普通克里格(OK)相比,对土壤As的预测几乎没有改善。比较单一的预测方法,地层内的克里格(KWS),HL的RK以及MHL-1的OCK的组合,给出了更准确的土壤As预测,并且相对于14.8 mg As /显示了最低的宣告“污染”的位置。千克,是基线土壤砷浓度的最高值。土壤的预测随着时间的推移,到2020年,有75%的土壤或种植水稻的土壤将至少含有30 mg / L As。

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