Science of the Total Environment 412-413 (2011) 324–335

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Science of the Total Environment

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

Zia U. Ahmed a,⁎, Golam M. Panaullah b,1, Stephen D. DeGloria c,2, John M. Duxbury d,3

a Department of Crop and Soil Sciences, 1002 Bradfield Hall, Cornell University, Ithaca, NY 14853, USAb Cornell University USDA Food For Progress Program (Bangladesh), House 6, Road 6, Sector 3, Uttara, Dhaka-1230, Bangladeshc Department of Crop and Soil Sciences, 1014 Bradfield Hall, Cornell University, Ithaca, NY 14853, USAd Department of Crop and Soil Sciences, 904 Bradfield Hall, Cornell University, Ithaca, NY 14853, USA

⁎ Corresponding author. Tel.: +1 607 255 9387; fax:E-mail addresses: zua3@cornell.edu (Z.U. Ahmed), g

(G.M. Panaullah), sdd4@cornell.edu (S.D. DeGloria), jmd(J.M. Duxbury).

1 Tel.: +880 1713000150.2 Tel.: +1 607 255 6520.3 Tel.: +1 607 255 1732; fax: +1 607 255 2644.

0048-9697/$ – see front matter © 2011 Elsevier B.V. Alldoi:10.1016/j.scitotenv.2011.10.008

a b s t r a c t

a r t i c l e i n f o

Article history:Received 21 June 2011Received in revised form 4 October 2011Accepted 5 October 2011Available online 4 November 2011

Keywords:Groundwater AsPaddy soil AsBase-line concentrationFlooding land typesKrigingCritical probability

Knowledge of the spatial correlation of soil arsenic (As) concentrations with environmental variables is neededto assess the nature and extent of the risk of As contamination from irrigation water in Bangladesh.We analyzed263 paired groundwater and paddy soil samples covering highland (HL) and medium highland-1 (MHL-1) landtypes for geostatistical mapping of soil As and delineation of As contaminated areas in Tala Upazilla, Satkhiradistrict. 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-1 (11.9)>non-rice-HL (7.2). Multiple regression analysesshowed that irrigation water As, Fe, land elevation and years of tubewell operation are the important factorsaffecting the concentrations of As inHL paddy soils. Only years of tubewell operation affected As concentration intheMHL-1 paddy soils. Quantitatively similar increases in soil As above the estimated baseline-As concentrationwere observed for rice soils onHL andMHL-1 after 6–8 years of groundwater irrigation, implying strong retentionof As added in irrigation water in both land types. Application of single geostatistical methods with secondaryvariables such as regression kriging (RK) and ordinary co-kriging (OCK) gave little improvement in predictionof soil As over ordinary kriging (OK). Comparing single prediction methods, kriging within strata (KWS), thecombination of RK for HL and OCK for MHL-1, gave more accurate soil As predictions and showed the lowestmisclassification of declaring a location “contaminated” with respect to 14.8 mg As/kg, the highest valueobtained for the baseline soil As concentration. Prediction of soil As buildup over time indicated that 75% orthe soils cropped to rice would contain at least 30 mg/L As by the year 2020.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

Groundwater from shallow aquifers contaminated by naturallyoccurring arsenic (As) is widely used as source of drinking waterin Bangladesh (BGS/DPHE, 2001). These same aquifers are also usedfor irrigation of boro rice (dry season rice). Use of this As contaminatedwater has the potential to increase soil As over time (Panaullah et al.,2009; Dittmar et al., 2010). For example, application of 1 m depth ofwater containing 0.1 mg As/L to a single rice crop would increasesoil As in the top 15 cm of soil by about 0.5 mg/kg assuming uniform

+1 607 255 2644.mpanaullah@gmail.com17@cornell.edu

rights reserved.

distribution of arsenic across soils in a tubewell command areaand retention of added As in soil. In fact, deposition of As from irri-gationwater has been found to be non-uniform due to its adsorption onfreshly precipitating iron oxides as reduced irrigation water oxidizesduring its flow in irrigation channels and across fields (Hossain et al.,2008; Panaullah et al., 2009). Consequently, buildup of As in soil withina command area is heterogeneous and, in parts, can reach levels that aretoxic to rice (Panaullah et al., 2009). However, remobilization andremoval of As deposited during irrigation of boro (winter) seasonrice have been shown to occur where soil is deeply flooded in the mon-soon season and floodwater flows into rivers (Dittmar et al., 2007;Roberts et al., 2009; Saha and Ali, 2007). Thus, land type (Brammer,1999) appears to be an important control on As retention in soil,with areas that are not deeply flooded or connected to rivers inthe monsoon season likely to have the highest As retention. Amass balance calculation at such a command area in Faridpur dis-trict indicated that more than 90% of As added through irrigationwater over a 16 year period was retained in the soil (Panaullahet al., 2009). In contrast, 13–63% of As added each year in irrigationwater during the dry season was estimated to be exported via flow

325Z.U. Ahmed et al. / Science of the Total Environment 412-413 (2011) 324–335

to rivers during deep flooding in the summer monsoon (Robertset al., 2009).

Managing As contamination in Bangladesh agriculture is a veryimportant issue for food safety (Duxbury et al., 2003; Meharg andRahman, 2003) and food security (Panaullah et al., 2009) for millionsof people in Bangladesh. Identifying geographic areas of soils that arecurrently contaminated with As, or are vulnerable to contamination,is an important step for risk assessment and targeting of managementinterventions. As an integral component of risk assessment, spatialanalysis using geostatistical methods has been widely used to mapenvironmental variables (Goovaerts et al., 2008; Odeh et al., 1995;Triantafilis et al., 2001). Various geostatistical methods utilizingsecondary information such as co-kriging (CK) and simple krigingwith varying local means, or regression kriging (RK), have been usedto map environmental contaminants (Goovaerts et al., 2005; Juangand Lee, 1998; Lado et al., 2008a; Lado et al., 2008b; Liu et al.,2006). Mapping of contaminated areas always has some uncertaintybecause the prediction of an unsampled location is always associatedwith error, no matter which prediction technique is used.

Different geostatistical interpolations or kriging have been appliedfor risk mapping of As concentration in groundwater in Bangladesh(Gaus et al., 2003; Serre et al., 2003; Yu et al., 2003). However, mappingof paddy soil As with or without incorporation of environmentalcorrelations has not yet been done. In this study, our objectives wereto (1) investigate how irrigationwater andpaddy soil properties affectedsoil As concentration in highland (HL) and medium highland type 1(MHL-1) soils in a rice–rice annual cropping pattern in Tala Upazilla,Satkhira district, and (2) explore the use of different kriging methodsto determinewhichmethod gave the best prediction of As contaminatedareas.

Fig. 1. (a) Landtype mapof Bangladesh showing the study area (Tala Upazilla, Satkhira Dist

2. Materials and methods

2.1. Study area

Tala Upazilla, (the smallest administrative unit of Bangladesh) inSatkhira district of south-western Bangladesh (Fig. 1a) was selectedas the study area on the basis of previously observed high levels ofAs in groundwater water (BGS/DPHE, 2001) and the prevalence ofshallow tube wells (STW) for irrigation of rice (BADC, 2004). Thearea of this Upazilla is approximately 33,713 ha within 22°32′ to22°50′ north latitude and 89°05′ to 89°20′ east longitude (Fig. 1b).Sampling sites were restricted to the two major land types, highland(HL) and medium highland type 1 (MHL-1) (Fig. 1b) in order to focuson the rice–rice cropping pattern. The HL is mostly in the High GangesRiver Flood Plain agro-ecological zone (AEZ) and covers 62% of thetotal land area. Most HL is above the normal flood level (Brammer,1999). The MHL-1 is mostly in the Ganges Tidal Flood Plain AEZ andcovers 38% of the total land area. Most of the MHL-1 area is floodedup to 90 cm during the monsoon season. Out of the 32,584 ha of netcropped area, more than 29% is irrigated in the dry season. Approxi-mately 5000 shallow tubewells (STWs) pump groundwater fromb100 m depth, irrigating 9600 ha, while a further 1000 ha is irrigatedby deep tube wells >100 m deep and traditional irrigation systems(NMIC, 2001). About 90% of the available irrigated land in the dryseason is used for boro rice cultivation and the rest is used for otherwinter crops like wheat, maize, vegetables, etc. The HL and MHL-1areas are generally cropped to rice during the summer monsoon(aman) season without supplemental irrigation. Site elevation datawere generated from the publicly available Shuttle Radar TopographyMission (SRTM) 90 m resolution Digital Elevation Model (DEM) of the

rict) and (b) study area with sampling locations and measured soil As concentrations.

326 Z.U. Ahmed et al. / Science of the Total Environment 412-413 (2011) 324–335

country (CGIAR-CSI, 2008) and re-processed to a 100 m×100 m grid(Fig. 2c).

2.2. Sampling and chemical analysis

Two hundred sixty-three STW command areas, 122 from HL and141 fromMHL-1(Fig. 1b), were selected following an irregular patternwith ~3 samples per km2 of STW irrigated boro rice area at the onset of2003–2004 boro rice growing season. Samplingwas clustered in easilyaccessible areas where the intensity of STWs was high. However, aminimumdistance of ~500 m to the nearest neighborwasmaintained.On the other hand, sampling was sparse in the southern part of thestudy area because a significant portion of the land is used for shrimpcultivation.

Water samples were collected from each STWs after at least10–15 min of operation of the pumps. The water samples were imme-diately filtered (0.45 μm) and 90 mL aliquots dispensed into plasticvials pre-filled with 10 mL 2 M HCl and stored for analysis. Compositesoil samples (0–15 cm soil depth) were collected from 1 m2 areas(five points-one from each end of the two diagonals and one from theintersection point of the diagonals) in the middle of the field nearestthe STW during boro rice harvest. All soil samples were air-dried,ground and passed through a 2 mm sieve prior to chemical analysis.

Information on the year of STW installation, distance between thesoil sample collection point and the STW, crop management historyand geographic coordinates was recorded. Data on the year of STWinstallation were verified through the local office of the Departmentof Agricultural Extension. An additional 74 soil samples were similarlycollected from non-paddy fields of HL and MHL-1 areas (Fig. 1b) foruse in estimating baseline soil As concentrations. All soil sampleswere air-dried, ground and passed through a 2 mm sieve prior tochemical analysis.

Arsenic in samples of water and HNO3–HClO4 acid digests of soil(Norvell et al., 2000) was pre-reduced with potassium iodide andascorbic acid (Haring et al., 1982) prior to determination by atomic-absorption spectrophotometry using a Perkin-Elmer AAnalyst 100fitted with a flow injection hydride generation system (FI-HG-AAS).For quality control, a sub-set of water (n=150) and soil samples(n=100) were re-analyzed by ICP-OES (Spectro-CCD, USA) andICP-MS (Agilent Technologies model 7500, USA), respectively (pleasesee supplementary data). The strong one-to-one positive relationshipindicates no significant difference between FI-HG-AAS and ICP orICP-MS determinations (supplementary data, Fig. S1a–b).

Poorly crystalline Fe-oxide (AOFe) in paddy soils was measured byextraction with acidified ammonium-oxalate in the dark (Scheinostand Schwertmann, 1999). The concentrations of Fe and P in watersamples, and Fe in soil extracts were measured by ICP-OES (Spectro-CCD, USA). Soil pH was determined by a glass electrode using a soilto water ratio of 1:2.5. A subset (n=104) of paddy soil samples wasanalyzed for available P (Olsen and Sommers, 1982), organic carbon

Fig. 2. Maps of ancillary variables used for regression kriging: (a)

(Walkley and Black, 1934) and texture (sand, silt and clay) by thehydrometer method.

2.3. Statistical analysis

Statistical analyses includingbivariate correlation, stepwisemultiplelinear regression and analysis of variance (ANOVA) were performedusing R statistical software. Stepwise (backward) multiple regressionanalysis was used to model soil As data using elevation, year of irriga-tion, distance from STW, irrigationwater and soil properties. Geometricmeans (GM) and standard deviations (GSD) were used to estimate thebaseline range of soil As concentration, defined as 95% of the expectedrange of background concentration and calculated as GM/GSD2 andGM×GSD2 (Gough, 1993).

2.4. Geostatistical analysis

The gstat package (Pebesma, 2004; Pebesma and Wesseling, 1998)in theR statistical computing environmentwas used for all geostatisticalanalysis and visualization of map outputs. The paddy soil data set(n=263) was randomly split into 210 calibration data, which wereused for geostatistical analysis and 53 validation data (Fig. 1b) whichwere used for evaluation of different kriging methods. The summarystatistics of soil As concentration of calibration and validation data setsare reasonably close to the entire data set (supplementary data, TableS2). The values at the validation data points were predicted by thefour interpolation methods from the 210 calibration data. Diagnosticmeasures of prediction quality were the mean error (ME), meanabsolute error (MAE) and root mean square error (RMSE):

ME ¼ 1n

Xnα¼1

z uαð Þ−z uαð Þ�� � ð1Þ

MAE ¼ 1n

Xnα¼1

z uαð Þ−z uαð Þ��� �� ð2Þ

RMSE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1n

Xnα¼1

z uαð Þ−z uαð Þ�½ �2vuut ð3Þ

where n is the number of observation points, z(u) and z(u)* arethe observed and predicted values at location α. The calibrationand validation data sets were further separated according to landtype and different prediction methods were evaluated for eachland type.

2.4.1. Kriging predictionBefore kriging, the sample semivariogram or cross-variogram was

used to explain the degree of spatial continuity of soil As data andresiduals of multiple linear regressions. We used the robust variogram

groundwater As, (b) groundwater Fe and (c) land elevation.

327Z.U. Ahmed et al. / Science of the Total Environment 412-413 (2011) 324–335

Eq. (4) (Cressie and Hawkins, 1980) to reduce the effect of outliers onvariogram properties since the distribution of soil As levels waspositively skewed (skewness>1). The semivariogram λ(h) is computedas:

2γ hð Þ ¼

1N hð Þ

XN hð Þ

α¼1

( �����z uαð Þ−z uα þ hð Þ�����12)4

0:457þ 0:494N hð Þ þ 0:045

N2 hð Þð4Þ

whereN(h) is the number of data pairs separated by a distance h, and uαis the vector of spatial coordinates of the αth individuals. Anisotropicsemivariograms with directional rotation were also tested (data notpresented) but did not improve semivariogram structure. The best-fitsemivariogram and cross-variogram model was chosen using leastsquares then used in kriging analysis. Ordinary kriging (OK), regressionkriging (RK), kriging within strata (KWS) and ordinary co-kriging(OCK) were used to predict soil As concentration with a 100×100grid dimension for HL and MHL-1of the study area with 50 searchneighbors.

2.4.1.1. Ordinary kriging (OK). The basic equation used in ordinarykriging (OK) is as follows (Goovaerts, 1997):

ZOK� uð Þ ¼

Xn uð Þ

α¼1

λOKα uð ÞZ uαð Þ with

Xn uð Þ

α¼1

λOKα ¼ 1 ð5Þ

where λαOK(u) is weight assigned to the known soil As concentration

at location (uα), and n(u) is the number of measured values used inestimation of the neighborhood of u. The kriging weights sum to 1so that the predictor provides an unbiased estimation. The modelvariogram is used to compute the weights.

2.4.1.2. Regression kriging (RK). Regression kriging combines amultipleregressionmodelwith simple kriging of the regression residuals (Hengl,2007; Hengl et al., 2004; McBratney et al., 2000). The experimentalvariogram of residuals is first computed and modeled, and thensimple kriging (SK) is applied to the residuals to give the spatialprediction of the residuals. The regression result and the kriged residualsare added to estimate the target variable Z*:

Z�RK uð Þ ¼ m�

RK uð Þ þXn uð Þ

α¼1

λRKα uð ÞR uαð Þ ð6Þ

wherem*RK(uα) is the regression estimate for locationu, andR(uα) are theresiduals [R(uα)−m(uα)] of the observed locations, n(u). We modeledthe residuals from multiple linear regression analysis of the 210calibration data set with X, Y-coordinates, water As, Fe and elevation.Due to the presence of a statistically significant trend in a N–S direction(supplementary data, Fig. S3), we included X and Y coordinates aspredictors in the MLR model (supplementary data, Table S3). Yearof well establishment was not included in spatial modeling becauseinformation related to the year of irrigation at each prediction locationwas not known, which is the basic requirement of regression kriging(RK). Spatial structure ofwater As and Feweremodeled (supplementarydata, Fig. S5) and OK estimated values at each prediction grid of thestudy area (Fig. 2a and b) were used to predict soil As by RK.

2.4.1.3. Ordinary co-kriging (OCK). In order to perform OCK, cross-variograms of soil As and loading factors (water As×year of irrigation)were computed and modeled in the form of a linear model ofco-regionalization (Goulard and Voltz, 1992), and then used to predictsoil As by OK. Unlike RK, OCK does not require that the secondaryinformation is available at all prediction locations. We used theisotopic case of OCK (Goovaerts, 1997), i.e., the soil As and loading

factor at all sampled locations were used to predict soil As atunsampled locations. The loading factor was rescaled, so that itsmean equals that of the soil As. The co-kriging estimator is thenwritten as:

Z�OCK uð Þ ¼

Xn1 uð Þ

α1¼1

λOCKα1

uð ÞZ1 uα1

� �−

Xn2 uð Þ

α2¼1

λOCKα2

uð Þ Z2 uα2

� �−m2 þm1

h ið7Þ

where m1 and m2 are the means and λα1and λα2

are the weights ofthe primary and secondary variables, respectively.

2.4.1.4. Kriging within strata (KWS). For KWS, the study area was firststratified according to the boundaries of the land type map (Fig. 1b).We appliedOK, RK, andOCK forHL, andOKandOCK forMHL-1 samples.A map of soil As for the whole study area was obtained by merging theinterpolations from the best performing method for each land type.

2.4.2. Probability map and delineation of As contaminated areasThe upper-base line soil As concentration was used to delineate As

“contaminated” or “non-contaminated” areas. The probabilities ofexceeding the upper baseline As concentration were computedfrom predicted values and prediction errors for each kriging method.Uncertainty associated with kriging predictions may lead to a falsepositive error (risk-α), the probability of wrongly declaring a location“contaminated”, or false negative error (risk-β), the probability ofwrongly declaring a location “safe” (Goovaerts, 1997). These twotypes of risks can be computed at location u from the conditional cdfmodel, F(u; z|(n):

risk⋅α uð Þ ¼ Pr ob Z uð Þ ≤ zcf jzL uð Þ > zc; nð Þg ¼ F u; zcð j nð ÞÞ ð8Þ

risk⋅β uð Þ ¼ Pr ob Z uð Þ > zcf jzL uð Þ ≤ zc; nð Þg ¼ 1−F u; zcð j nð ÞÞ ð9Þ

where zc is critical concentration threshold (14.8 mg As/soil) and thesymbol (n) means: conditional to the n sample data.

To determine the optimal probability threshold for declaring soil at alocation “contaminated” or “non-contaminated,” the percentage ofvalidation locations thatwerewrongly classified as cleanor contaminatedwith respect to a 14.8 mg As/kg threshold was computed. A series ofprobability thresholds from 0 to 1 was defined with increments of 0.05based on the prediction results of each kriging method at the 53 testlocations. Misclassification rates were plotted against the probabilitythresholds for each of the prediction methods to select the probability(critical probability threshold, Pc) corresponding to the lowest percentageof misclassification. A site was classified as “contaminated” if the proba-bility was greater than the critical probability threshold, Pc (Cattle et al.,2002; Goovaerts, 1997).

3. Results

3.1. Groundwater and soil properties

The concentrations of As in groundwater ranged from below ourequipment detection limit (b0.002 mg/L in the FI-HG-AAS) to0.458 mg/L, with a mean of 0.13 mg/L (Table 1). The Fe–As ratio,which is related to adsorption and co-precipitation of As with precip-itating Fe oxides after the water leaves the pump (Martin et al., 2009;Panaullah et al., 2009) ranged widely from 11 to 1075 (Fig. 3g). Themean As, Fe and P concentrations in ground water of the MHL-1area were significantly higher than the corresponding means for theHL area (Table 1 and supplementary data, Table S1). Soil pH rangedfrom 6.5 to 8.7. The silt and clay contents of 104 samples rangedfrom 16 to 79% and 16 to 75% (Fig. 3n–o), respectively, so soils of thestudy area were clay to silty clay loam in texture. Soil organic carbonranged from 0.54 to 2.64%with a mean value of 1.3% (Fig. 3l). The con-centrations of poorly crystalline Fe oxide varied by a factor of 5, from

Table 1Summary statistics of arsenic concentrations in rice and non-rice soils.

Soilsa Nb CVc

(%)Soil As (mg/kg) Log10[soil As]

Skewness Range Mean±SDd Median Skewness GM±GSD e Range

All 337 49 1.13 3.2–51.8 15.8±7.8 15.0 −0.32 1.12±1.09 0.94–1.35Rice soil 263 41 1.22 4.7–51.8 17.2±7.4 17.2 −0.35 1.20±1.07 1.04–1.37HL 122 39 0.60 4.7–28.2 14.1±5.3 13.6 −0.12 1.10±1.07 0.96–1.27MHL-1 141 34 1.51 5.7–51.8 21.2±7.4 20.0 −0.13 1.29±1.05 1.17–1.43Non-rice soil 74 37 0.76 3.2–17.8 8.4±3.1 7.5 0.02 0.88±1.08 0.75–1.03HL 55 29 0.63 3.2–12.5 7.2±2.1 7.2 −0.09 0.82±1.07 0.72–0.95MHL-1 19 23 −0.14 6.5–17.8 11.9±2.8 12.3 −0.63 1.05±1.05 0.96–1.16

a HL = highland; MHL-1 = medium highland-1.b N = number of samples.c CV = coefficient of variation (%).d Mean±SD = Mean±standard deviation.e GM±GSD = geometric mean±geometric standard deviation.

328 Z.U. Ahmed et al. / Science of the Total Environment 412-413 (2011) 324–335

0.10 to 0.49%. Much (mean of 54%) of the As in paddy soil in this studyarea was found to be associated with non-crystalline Fe oxides.

3.2. Soil arsenic concentration

Arsenic concentrations in 337 soil samples (263 rice and 74 non-ricesoils) showedhigh variability (CV=49%), ranging from3.2 to 51.8 mg/kgwith a mean value of 15.8 mg/kg (Table 1). Both land type and land usesignificantly affected soil As concentration. The mean As concentrationof MHL-1 soils (20.9±7.6 mg/kg, n=160) was significantly higher(F=129, pb0.0001) than that of HL soils (11.9±5.6 mg/kg, n=177).Themean As concentration in paddy soils (17.2 mg/kg) was significantlyhigher (F=184.7, pb0.001) than that of non-paddy soils (7.5 mg/kg).The quartile maps of As concentrations in rice and non-rice soils showedclear spatial distribution patterns in the study area (Fig. 1b). In general,soil As in both paddy and non-paddy soils was higher in MHL-1 of thesouthern–western part of the study area.

In Q–Q plots (Fig. 4a–d), both raw and log-transformed data ofirrigated rice soils from HL and MHL-1 areas showed several outliersat the high end of the plots, suggesting anthropogenic influence onpaddy soil As concentration. In contrast, As concentration in thenon-rice soils sampled from HL and MHL-1 areas followed a nearlystraight line indicating that the data set came from a single statisticalpopulation where anthropogenic influence was limited (Fig. 4e–f).The lower arithmetic mean, geometric mean and median values forAs concentration in non-rice soils compared to rice soils (Table 1)also suggested that As levels in most of the non-rice soils were notsubstantially affected by anthropogenic influence or, if they were,that it was the result of a nonpoint source rather than a consequenceof As input from irrigation water. Thus, the data from non-rice soilswere used to estimate baseline As concentrations in the surface soilsfor this study area. However, because of significant difference in Asconcentration (F=59.7, pb0.001) between non-rice soil from HLand MHL-1 areas, it is appropriate to define different baseline soil Asconcentrations for HL and MHL-1 areas. The range of baseline soil Asconcentrations in HL non-rice soils was 5.2–8.9 mg/kg, while that inMHL-1 non-rice soils was 9.1–14.8 mg/kg. These non-rice soil Asranges are considered to be a good depiction of background As con-centrations in HL and MHL-1 areas of this region.

3.3. Factors affecting arsenic concentration in paddy soil

Although the topography of the study area only varies from 0 to16 m above sea level (Fig. 2c), soil As concentration in rice and non-ricesoils combined was negatively correlated with elevation (r=−0.531,pb0.001, n=337). For all rice soils (n=263), soil As was positivelycorrelated with years of tubewell operation (r=0.53, pb0.001) andirrigation water P (r=0.152, pb0.05), Fe (r=0.315, pb0.001) and As(r=0.24, pb0.001) concentrations. Arsenic loading factor, the product

of water As concentrations and years of irrigation with a given STW,also correlated positively with paddy soil As concentration (r=0.47,pb0.01). Negative correlations were observed between soil As andelevation (r=−0.341, pb0.001), distance from the STW (r=−0.201,pb0.01) and water Fe:As ratio (r=−0.21, pb0.01). However, relation-ships differed with land type. In the MHL-1 area soil As concentrationwas significantly correlated only with years of tubewell operation(r=0.689, pb0.001) and As loading factor (r=0.405, pb0.01). Incontrast, soil As in theHL areawas significantly correlatedwith elevation(r=−0.233, pb0.01), distance from the STW (r=−0.186, pb0.05),irrigation water As (r=0.405, pb0.001), P (r=0.333, pb0.001), Fe(r=0.320, pb0.001) concentrations and water Fe:As ratio (r=−0.285, pb0.001). For the sub-set of 104 paddy soil samples, soil Aswas also correlated with soil organic carbon (r=0.412, pb0.01), sand(r=−0.290, pb0.01), silt (r=−0.317, pb0.001) and clay (r=0.380,pb0.001) contents.

From stepwise multiple linear regression analysis, irrigation waterAs and Fe concentrations, land elevation and years of tubewell operationwere significant (pb0.001) variables in the MLR model [Eq. (10)] thatexplained 43% of the variability in paddy soil As concentration:

Soil As ¼ 10:44þ 14:6 WAsð Þ þ 0:74 WFeð Þ−0:99 Elevationð Þþ0:84 STW Yearsð Þ:

R2 ¼ 0:43 pb0:001ð Þ:ð10Þ

Adding land type as a factor into the MLR model [Eq. (10)]significantly increased R2 from 43 to 47%. These results suggestedthat there was a real difference in As deposition from irrigationwater between HL and MHL-1 soils, and that a land type specificmodel should be used to explain variability in soil As concentration. Inthe HL area, irrigation water As and Fe concentrations, land elevationand years of tubewell operation were significant variables affectingpaddy soil As concentration [Eq. (11)] and the regression modelexplained 34% of the variability in soil As concentration. On the otherhand, years of irrigation was the only significant term in the MLRmodel for MHL-1 [Eq. (12)] which explained 37% of the variability inAs concentration:

Soil As ¼ 10:5þ 6:70 WAsð Þ þ 0:34 WFeð Þ−0:07 Elevationð Þþ1:01 STW yearsð Þ

R2 ¼ 0:34 pb0:001ð Þð11Þ

Soil As ¼ 13:29þ 1:00 STW yearsð ÞR2 ¼ 0:37 pb0:001ð Þ: ð12Þ

When data from the subset of 104 samples were used in stepwiseregression analysis, irrigation water As concentration, land elevation,years of tubewell operation, and soil content of non-crystalline Fe,

Fig. 3. Box and whisker plots showing variations of selected site, irrigation water and soil properties for all samples (All) and samples disaggregated into highland (HL) and mediumhighland type-1 (MHL-1). AOFe = soil amorphous Fe-oxide; As-Loading Factor = water As concentration (WAs)×years of tubewell irrigation (YR). Figures in parentheses are thenumber of samples.

329Z.U. Ahmed et al. / Science of the Total Environment 412-413 (2011) 324–335

organic C, sand and clay were significant (pb0.05) variables and themodel [Eq. (13)] explained 53% of the variability in paddy soil Asconcentrations:

Soil As ¼ 8:7þ 16:36 WAsð Þ � 0:61 Elevationð Þ þ 0:56 STW yearsð Þ

–3:15 SOCð Þ–0:28 Sandð Þ þ 0:09 Clayð Þ–14:21 AOFeð Þ

R2 ¼ 0:53 pb0:001ð Þ

ð13Þ

3.4. Geostatistical analysis

3.4.1. VariogramFig. 5a shows the experimental variogram and fitted model for soil

As concentrations. The variance of soil As increases steadily withincreasing lag distance and approaches its sill asymptotically withina range of 720 m and autocorrelation may be extended to the effec-tive range of 2161 m. Analysis of spatial correlation of residuals ofmultivariate regression with coordinates water As, Fe and elevationshowed a 43% shorter range of spatial dependence and 17% smaller

Fig. 4. Normal quantile (Q–Q) plots with theoretical line of total and log10 transformedarsenic concentrations in rice andnon-rice soils fromhighland (HL) andmediumhighland(MHL-1) areas of Tala Upazilla.

Fig. 5. Variograms and fitted models for (a) rice soil As, (b) OLS residuals of multiplelinear regression with the coordinates groundwater As, Fe, and land elevation, (c) loadingfactors and (d) cross-variogram with soil As and loading factors. RSP = ratio of sill topartial sill.

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sill (Fig. 5b). The secondary variables explained fluctuations of soilAs among the STW command areas beyond a distance of 1000 m.We fitted the linear model of coregionalization to soil As and loadingfactors with exponential models (Fig. 5d). The cross-variogram showsa moderate spatial relationship between soil As and loading factors.

3.4.2. Validation of prediction methodsTo evaluate the performance of different prediction methods,

predicted and true values of soil As at 53 validation locations werecompared (Table 2). None of the kriging methods was successfulin predicting the highest concentrations of soil As, which is expectedas kriging is a smoothing estimator (Isaaks and Srivastava, 1989).However, biases of all prediction methods were rather small and meanabsolute error (MAE) varied from 3.59 to 4.16 mg As/kg, which is

lower than the minimum value of observed soil As concentration(4.7 mg/kg). Compared to OK, RK and OCK predicted a wider datarange and lower MAE and root mean square errors (RMSE). However,the relative improvement of RK and OCK over OK was only 6 and1%, respectively (Table 2).

As discussed earlier, mean soil As concentration varied betweenHL and MHL-1 of the study area (Table 1). Application of RK withsecondary (environmental) variables resulted in the lowest RMSEfor the HL area and showed 9% improvement over OK (Table 2). Incontrast, RK gave no improvement for the MHL-1 area due to the poorcorrelations between soil As and environmental variables. Hence onlyOK and OCK with loading factor were applied for the MHL-1 area.Comparing the true values of the 53 validationdatawith their estimatedvalues using KWS (RK for HL and OCK for MHL-1 samples) gave anRMSE of 4.63 mg As/kg for the whole study area (Table 2), which is

Table 2Summary statistics of observed and predicted soil arsenic concentrations at 53 test locations and performance of different kriging methods for the whole study area (All) andhighland (HL) and medium highland-1 (MHL-1) areas.

Kriging method a Observed soil As levels Predicted soil As levels Validation statistics b

(mg As/kg)

N Mean±SD c Min. Max. Mean±SD c Min. Max. ME MAE RMSE RI (%)

HL OK 25 13.2±5.9 4.7 27.7 14.5±1.9 11.6 18.9 −1.3 3.8 4.9RK 14.3±3.3 7.9 20 −1.1 3.5 4.5 9.3OCK 14.4±2.2 10.2 19.2 −1.2 3.8 4.9 1.1

MHL-1 OK 28 21.3±4.6 12.4 33.9 21.4±2.3 18.3 29.1 −0.2 3.8 4.8OCK 21.4±2.1 18.5 28.3 −0.1 3.7 4.8 0.9

All OK 53 17.5±6.6 4.7 33.9 18.0±3.1 12.6 26.1 −0.5 4.2 5.4RK 18.3±4.1 8.9 24.4 −0.9 4.1 5.0 6.1OCK 18.3±4.1 10.8 26.4 −0.9 4.1 5.3 1.0KWS 18.1±4.8 8.0 28.4 −0.9 3.6 4.6 13.6

a OK = ordinary kriging; RK = regression kriging; OCK = ordinary co-kriging; and KSW = kriging within a strata, RK in HL and OCK in MHL-1 area.b ME=mean error; MAE=mean absolute error; RMSE= rootmean square error, and RI= relative improvement in rootmean square error over OK [(RMSEok−RMSEp)∗100/RMSEok].c Mean±Standard deviation; Min. = minimum and Max. = maximum.

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lower than the RMSE value of 5.03 for RK, the best single predictionmethod. The KWS improved prediction of soil As was by 13% over OK.

3.4.3. Kriging prediction mapsFig. 6a–d shows the interpolated maps of soil As concentration

obtained by the different kriging methods. A KWS map of soil As(Fig. 6d) for the whole study area was obtained by merging the inter-polations of both land types using the best performing method foreach land type: OCK in the MHL-1 (supplementary data, Fig. S7a)and RK in the HL (supplementary data, Fig. S7b). All four maps showa similar global pattern and hot spot locations of soil As concentrationin MHL-1 area. The OK and OCK prediction maps (Fig. 6a and 6c) aremuch smoother than the RK and KWS maps, which provide moredetail of local variability in soil As concentration (Fig. 6b and 6d) dueto the influence of the secondary variables. As expected, the KWSmap shows sudden changes in soil As concentration at the land typeboundaries.

3.4.4. Probability maps and delineation of As contaminated areasMaps of the probability of exceeding the upper baseline soil As

concentration (14.8 mg/kg) are shown in the left panel of Fig. 7. Allprobability maps show similar global patterns and trends in localdetail as observed in the corresponding prediction maps (Fig. 6a–d).The probability of soil As concentrations exceeding 14.8 mg/kg ismuchhigher in theMHL-1 area than in theHL area due to higher baselinesoil As concentrations. In order to find the optimal probability todifferentiate between contaminated and non-contaminated sites, themisclassification rate was computed for different probability thresholdsfor the validation data set. The percentage ofmisclassification is large athigh probability thresholds and decreases at intermediate probabilitythresholds (Fig. 8). Compared to the methods that used secondaryvariables, OK exhibited a higher misclassification rate over most of the

Fig. 6. Interpolated maps of rice soil As concentration in Tala Upazilla by ordinary kriging (Ovariables; ordinary co-kriging (OCK) with loading factors; and kriging within strata (KWS) oland types.

range of probability thresholds. Misclassification was the lowest at aprobability threshold of 0.60 for OK, RK and CK and 0.55 for KWS andthese values were chosen for delineation of land into “contaminated”and “non-contaminated” areas (Fig. 9). Depending on the predictionmethods, between 58 and 73% of the total land area was estimated tobe “contaminated” with respect to the threshold value of 14.8 mgAs/kg. Using KWS, which is considered the best method, 99% and 34%of the MHL-1 and HL areas, respectively were characterized as“contaminated” (Table 3).

4. Discussion

Soil As concentrations in Tala Upazilla were found to be higher inrice soils than in non-rice soils and in MHL-1 than in HL (Table 1). Theland type differences in soil As concentrations for non-rice soils(mean values of 7.2 and 11.9 mg/kg) are considered to reflect differ-ences in background As concentrations in the sediments that formedthe current land surface. Increases in soil As of 6.9 and 9.3 mg/kg forHL and MHL-1, respectively, for soils cropped to rice compared tonon-rice cropping areas result from much greater additions of As fromgroundwater irrigation to rice than to upland crops in the dry season.For rice soils, the significant positive correlation between soil Asconcentration and both years of irrigation from STWs and As loadingfactors in HL and MHL-1 soils indicates that soil As is slowly increasingover time in both land types. An estimate of the expectedmean increasein soil As concentration for soils cropped to rice in the different landtypes can bemade usingmean values for irrigationwater As concentra-tion and years of operation of the tubewell, and assuming the additionof 1 m depth of irrigation water per rice crop, a soil bulk density of1.2 g/cm3 and uniform deposition of As in irrigation water. On thisbasis, mean soil As concentration would have increased by 3.8 and6.4 mg/kg for HL and MHL-1 areas, respectively. The higher observed

K); regression kriging (RK) with coordinates, water As, Fe and elevation as secondaryr landtype: RK in highland and OCK in medium high land type 1. White areas are other

Fig. 7. Probabilty maps of exceeding the upper baseline soil As concentration (14.8 mg/kg) for each of the kriging methods. Right two maps show the risk of wrongly declaring alocation as contaminated (risk-α) or non-contaminated (risk-β).

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mean increases in soil As were undoubtedly due to sampling the fieldclosest to a STW where deposition of As is likely to be the highest(Dittmar et al., 2007; Panaullah et al., 2009; Stroud et al., 2011).The quantitatively similar increases in soil As concentration for HL(56%) andMHL-1 (68%) sites over the6–8 years of groundwater irrigationwith STWs suggest that much of the As added to soils via irrigationwateris retained during summer monsoon flooding in both land types,even though floodwater depth typically exceeds the height ofbunds surrounding the rice fields in the MHL-1 (but not the HL)area and there is a connection to a river. This result contrasts withthe substantial losses of soil As to rivers during summer floodingobserved at a site in Munshiganj district (Roberts et al., 2009).However, there is a large difference in landtype and the nature andduration of flooding between Satkhira and Munshiganj districts.Munshiganj is mostly lowland (Fig. 1a) which is typically floodedfrom 180 to 360 cm deep by the monsoon flood from the Padma(main flow of Ganges) and Meghna rivers from late March to early

October (Brammer, 1999). On the other hand, flooding of MHL-1 inTala Upazilla is more localized, much shallower and of shorter durationthan that in Munshiganj as it is associated with high rainfall events inthe monsoon season when the rainwater volume exceeds the capacityof the local drainage system. Remobilization of As due to the reductionof As-bearing Fe-oxyhydroxides (Dittmar et al., 2007; Saha and Ali,2007) is likely more complete during the prolonged flooding period inMunshiganj compared to Tala Upazilla and As movement to rivers isfacilitated by large volume of water flow in the former situation. Incontrast, the poor correlation between As in irrigation water and soilin the MHL-1 area of Tala Upazilla suggests that some As in soil maybe remobilized during monsoon season and distributed to surroundingrice fields but not lost from the system. These disparate results of Asretention in paddy fields indicate the need for longer-term (Dittmaret al., 2010) and more comprehensive assessment of this topic, includ-ing potential effects of soil and irrigation water chemistry on the extentof remobilization of As during flooding.

Fig. 8. The percentage of the validation data set misclassified as either contaminated(>14.8 mg As/kg soil) or non-contaminated by each of the kriging methods.

333Z.U. Ahmed et al. / Science of the Total Environment 412-413 (2011) 324–335

Prediction of soil As by geostatistical mapping was improvedconsiderably by using a combination of two kriging methods, RKin HL area and OCK MHL-1 area compared to using a single methodover the whole area. This result was found because of differenttypes of environmental correlation with soil As in HL and MHL-1areas. In addition to providing the ‘best’ estimation of soil As atunsampled locations, the probability of exceeding a backgroundsoil As concentration or a regulatory standard, and the uncertaintyassociated with identification of contaminated locations is important forpolicy decisions. Due to lack of a regulatory standard for As concentrationin agricultural soil of Bangladesh, we used 14.8 mg As/kg, the estimatedupper-baseline As concentration for this study area, as the thresholdvalue for a “contaminated” site. Regulatory standards for soil As in othercountries vary considerably and many are not recent. Canada instituteda human health standard of 12 mg As/kg and an environmental healthstandard of 17 mg/kg for agricultural land (Canadian Council of Ministersof the Environment, 2001). The UK has soil As guideline values of 32and 43 mg/kg for residential land and vegetable gardens (allotments),respectively (Environment Agency, 2009). China is the only countrythat specifically addresses soils used for paddy rice and has limits of 25and 30 mg As/kg for paddy and upland crop soils, respectively (StateEnvironmental Protection Administration of China, 2008).

In preparingmaps of As contamination, it is important to minimizemisclassification of land as either contaminated or non-contaminated.There is less uncertainty associated with classifying a site at a highprobability level than at a low probability level. Regardless of theprediction methods, misclassification was minimized at a probabilitylevel between 0.55 and 0.60 which is close to the marginal probability(0.63), i.e. the proportion of observed data actually exceeding

Fig. 9. Delineation of the As contaminated land area (>14.8 mg As/kg) using the critical

14.8 mg/kg (Fig. 8). The probability threshold atwhichmisclassificationis minimal is usually close to the marginal probability of contamination(Goovaerts, 1997). Again, KWS, the combination of RK in HL and OCK inMHL-1, exhibited the smallestmisclassification rate up to the probabilitythreshold of 0.8 (Fig. 8), which indicates that a smaller number oflocations were incorrectly classified as contaminated at lowerprobability levels. The lowest average probability of wrongly declaringvalidation locations or the prediction grid as “contaminated” (risk-α)or “non-contaminated” (risk-β) was also found with KWS (Table 3).Regardless of the prediction method, and as expected, the averagevalue of risk-α was lower than the average value of risk-β. The riskof wrongly classifying a location as non-contaminated (risk-β) wasgreater for the HL area than the MHL-1 area. The spatial distributionof the probabilities of risk-α and risk-β can be used to rank locations foradditional sampling in order to improve the quality of contaminationmaps.

The upazilla scale soil As map presented in this paper does notconsider the high variability in soil As that can be created within aSTWcommandarea by useof As contaminated irrigationwater (Hossainet al., 2008; Panaullah et al., 2009). Thus, the upazilla scale maps shouldnot be interpreted tomean that every field within “contaminated” areaswill exceed the 14.8 mg/kg threshold level. Rather, the map and associ-ated probabilities indicate the broad areas where at least some of thefields within STW command areas will exceed the chosen As thresholdvalue. In contrast, the delineation of “non-contaminated” areas is morelikely to apply to all fields in a STW command area.

In order to estimate the extent of long-term As accumulation inthe top 15 cm of soil, we predicted paddy soil As concentration forHL and MHL-1 using Eqs. (11) and (12), respectively for the year2020, i.e. after an additional 16 years of groundwater irrigation. Totalarsenic within the top 15 cm of soil is predicted to further increaseby factors of 1.3 to 4.5 for HL sites and 1.3 to 3.8 for MHL-1 sites, andwould exceed 30 mg/kg at 75% of the sites (Fig. 10). This level of soilAs contamination is likely to be toxic to a number of the rice varietiesgrown in Bangladesh (Panaullah et al., 2009; Duxbury et al., 2009) andhence would threaten food security within Tala Upazilla.

5. Conclusions

The results of our study in Tala Upazilla showed that:

1. Background soil As concentrations varied with flooding land type,with highland (HL) soils having lower soil As concentrations thanmedium highland type-1 soils. A broader geographic analysis isneeded to determine whether this result applies generally acrossBangladesh.

2. Soil As increased greatly when both HL andMHL-1 land types werecropped to rice using groundwater irrigation and soil As concentrationwas positively correlated with As loading factor (years of irrigation×water As concentration) suggesting that soil As levels will continueto increase over time.

probability (Pc) level for minimal misclassification by each of the kriging methods.

Fig. 10. Estimated changes in rice soil As concentration from 2004 (a and c) to 2020 (b and d) after 16 additional years of groundwater irrigation.

Table 3Probability of misclassification and critical probability of 53 test locations and probability of misclassification and percent contaminated area for the prediction grid.

Krigingmethoda

Test location Prediction grid

Probability ofmisclassificationb

Critical probability(Pc)c

Probability ofmisclassificationb

% Contaminated aread

α-risk β-risk α-risk β-risk Whole study area MHL-1 HL

OK 0.25 0.53 0.60 0.28 0.41 73 96 55RK 0.20 0.47 0.60 0.27 0.36 58 84 39CK 0.22 0.47 0.60 0.25 0.38 58 87 36KWS 0.20 0.37 0.55 0.24 0.32 62 99 34

a OK = ordinary kriging; RK = regression kriging; OCK = ordinary co-kriging; and KSW = kriging within a strata or landtype, RK in highland area (HL) and OCK in mediumhighland– 1 (MHL-1) area.

b α-risk= false-positive, the probability of wrongly declaring a test location or prediction grid as “contaminated”, and β-risk= false-negative, the probability of wrongly declaringa test location or prediction grid as “non-contaminated” on the basis of a 14 mg As/kg threshold.

c Pc = the critical probability where misclassification rates are the lowest.d Calculated on the basis of critical probability (Pc) of each of prediction methods.

334 Z.U. Ahmed et al. / Science of the Total Environment 412-413 (2011) 324–335

3. There was no evidence for lower retention of As added to soils inMHL-1 compared to HL, indicating that summer flooding with con-nection to a river in MHL-1 did not preferentially reduce soil As inthis upazilla.

4. Correlation of soil As with environmental factors varied by floodingland type, and krigingwithin strata (KWS) provided a bettermap ofsoil As concentrations than either ordinary kriging (OK), ordinaryco-kriging (OCK) or regression kriging (RK).

5. Environmental correlation was incorporated into geostatisticalmapping of paddy soil As concentrations to generate probabilitymaps of soil As exceeding the upper baseline concentration of14.8 mgAs/kg. To our knowledge, this is thefirst attempt to delineatesoil As contamination at the upazilla scale in a rice growing area ofBangladesh or elsewhere. Policy makers could combine expertknowledge with uncertainty risk maps of this type for decisionmaking, such as delineation of contaminated areaswhere site specificmanagement measures should be taken.

6. Continued use of current irrigation practices is predicted toincrease soil As levels in soils cropped to rice in Tala Upazilla tolevels that would be toxic to many of the current rice varietiesgrown in Bangladesh in the next 2 decades.

Acknowledgments

Funding from US-AID Bangladesh and support from Department ofAgriculture Extension (DAE), Bangladesh, including all Block Supervisorsof Tala Upazilla for helpwith sample collection are sincerely appreciated.Dr. Pierre Goovaerts, Biomedware, Ann Arbor, MI 48103, USA is

acknowledged for his help with calculation of probability ofmisclassification of different geostatistical analyses.

Appendix A. Supplementary data

Supplementary data to this article can be found online at doi:10.1016/j.scitotenv.2011.10.008.

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