Phan thCttif
Danh gia dat - Quan ly dat - Quy hoach sur dung dat
UNG DUNG MANG NdRON VA GIS TRONG DANH GIA DAT DAI HUYEN BAO LAM ^ TINH LAM DONG
Le Canh Dmh * (1). Xay dung eo sd dQ lieu (CSDL) GIS ve tai nguyen da't dai, trong dd bao gdm cac loai ban dd: hien trang sif dung da't, thd nhudng, dp ddc, tang day tang da't mat, kha nang tucJi,...
(2). TU CSDL GIS, ehpn eae ban dd dan tinh (ban dd da't, ban dd tang day, ...), chdng xep (overlay) cac ban dd don tinh de xay dung ban dd don vi dat dai (Land Mapping Unit-LMU).
(3). Trong CSDL GIS, ehpn ban dd hien trang sir dung dat, phan tich de lua ehpn cac loai hinh sir dung dat (LUT) cd trie'n vpng de danh gia thich hgp dat dai.
Tren co sd cac LUT dugc lua chgn, ket hgp vdi kien thirc chuyen gia (ngudi si!r dung da't, nha ndng hpe, nha quan ly, ...) de dua ra cae yeu eau sir dung da't (LUR) eho tirng LUT.
(4). Nhap (input) vao he thdng mang noron dugc thief ke vgi sd noron Idp nhap bang sd tinh chat hoae chat lugng da't dai eua ban dd don vi dat dai.
(5). Ket hgp cac tinh chat da't dai v(i)i yeu eau sir dung da't ciia timg LUT de xay dung bp dU lieu miu ve thich hgp dat dai diing trong viee huan luyen mang noron.
(6). Ldp ra (output layer) eiia mang cd 4 naron tuong ung vgi 4 mifc thich hgp (SI, S2, S3, N), ket qua lien ket vdi GIS de the hien ban dd kha nang thfch hgp dat dai cho ti/ng LUT,
(7). Chdng xep cac ban do thich hgp tirng LUT, xay dung ban do thich hcrp da't dai.
1.DATVANDE
Danh gia kha nang thich hgp dat dai (gpi tat la danh gia da't dai) nham muc tieu cung cap thdng tin ve su thuan Igi va khd khan trong viec sir dung cac vung dat, lam can cir de ra quyet dinh chien luge ve quan ly va sir dung hgp ly tai nguyen dat dai.
Trong nhUng nam gan day, cdng nghe thdng tin dugc irng dung rat hieu qua trong cdng tac danh gia dat dai, dae biet la viee irng dung md hinh " Tfch hap phan mem ALES va GIS trong danh gia dat dai", trong dd:
(1). ifng dung he thdng thdng tin dia ly (GIS) thanh lap ban dd don vi da't dai (LMU); (2). ifng dung ALES (Automated Land Evaluation System) de danh gia kha nang thich hgp da't dai, sau dd xua't du lieu sang GIS de bieu dien ban dd thich hgp da't dai.
Mpt each tie'p can khac cung rat hieu qua la ifng dung mang noron (Neural Network-NN) de thay the vai trd ALES trong md hinh neu tren. Trong bai viet nay, Chiing tdi nghien cifu thie't ke va huan luyen mang na ron de xay dung mdt he thdng mang noron ed the danh gia kha nang thich hgp da't dai va ifng dung md hinh tich hap GIS va NN trong danh gia da't dai huyen Bao Lam - tinh Lam Ddng.
2. PHUUNG PHAP
Md hinh tich hgp mang noron va GIS trong danh gia thich hgp dat dai the hien d hinh 1, tien trinh cu the nhu sau:
CSDL GIS
JIL
(3)
Loai hinh sif dung dat (LUT) •
Yeu cau sir dung dat (LUR)
(2)
(5) Ban do don vi
dat dai (LC/LQ) (4) Mang ncrron (Neural Netwoks)
(6)
Thich hop
timg LUT (7)
4
Ban dd thich hcip dat dai
Hinh 1. Md hinh tfch hc^^ "fiang noron va GIS trong danh gia thich hap dat dai
• Phan Vien Quy hoach va Thief ke Nong nghiep
2.1. Mang ndron (neural networks)
Mang noron (NN) la mdt md hinh tinh toan nham md phong theo kha nang nhan biet ciia con ngudi. NN bao gdm eae niit (naron) lien ket truyen thdng v(5i nhau theo mdt cau triic nhat dinh de giai quyet van de eu the.
NN gidng nhu con ngudi, dugc dao tao (training) va luu tru nhung hieu biet de sir dung trong nhu'ng tinh hudng phii hgp.
Oe xay dung mpt mang can xae dinh thdng tin cau true mang va thdng tin cac (rpng so ciia tirng naron trong mang [1], Mang naron dunp frong danh gia da't dai gdm nhieu Idp lan truyen thang dugc md ta nhu hinh 2, trong dd;
ftj. Cau triic (architecture) mang ndron: gdm 3 l()p, cufhenhusau:
- ig^^ vao (input layer) tuong irng v(fti cac tinh chat hoae chat lugng dat dai (cd bao nhieu LC/LQ thi ed bay nhieu noron dau vao). Trong trudng hgp huyen Bao Lam, ban dd don vi dat dai ed 8 tinh chat, nen lap vao CO 8 noron (Naron 1: la dau vao ciia tinh chat dat (ky hieu So): tcrang tif. Naron 2: tang day (De): Naron 3:
gley (GI): Naron 4: ket von (Cd): Naron 5: dia hinh (To):
Naron 6: do doc (SI): Naron 7: kha nang tt/gi (Ir): Naron 8: thai gian ngap (Tf)).
• Lap ra (output layer) tuang Ung vdi' ke't qua tra ve, trong danh gia dat dai ket qua ed 4 cap thich hop, nen igp nay cd 4 noron {Naron 1: rat thich hgp (SI):
Naron 2: thich hgp trung binh (S2): Naron 3: it thich hgp (S3): Naron 4: khong thich hgp (N)).
• Lap an (hidden layer) ket ndi giUa l(Sp vao va lap ra, mpt mang noron cd the diing nhieu Idp an, tuy nhien LiMin Fu (1994) da ehUng minh dugc rang chi can mpt Idp an ciia mang la dii md hinh hda mpt ham bat ky.
Khdng ed nguyen tac nao huiJng din l(3p an cd bao nhieu naron, viee xae djnh nay dua tren kinh nghiem hoae phep thQ va sai, thdng thuang sd naron ciia Idi an nhieu hon sd noron eiia lap xua't it nha't 1 naron, sc noron d \gp an nhieu thi mang van hanh cham va kha nang suy luan kem.Trong truang hop sd lieu miu ciia huyen Bao Lam tinh Lam Ddng, trong qua trinh huan luyen mang naron, Chung tdi nhan thay Idp an cd 6 noron cho ke't qua tdt han Idp an cd 5 noron. Do vay, trong cau triic eiia mang chung tdi thiet ke Idp an cd 6 noron.
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Hinh 2. Mo hinh mang noron trong danh gia thich help dat dai
(2). Xac dinh trgng so cua tifng ndron: Phuang phap xae dinh truyen thdng nhat la ap dung giai thuat lan truyen ngugc (Back - Propagation). Giai thuat hgc nay dugc xem nhu giai thuat hgc ed thay (supervised learning), nghla la de mang ed the hoc dugc thi phai cd bd du lieu mau; dir lieu dau vao (Xi) la eae tinh chat dat dai, dii' lieu ket qua mong mudn dau ra (Yi) (ggi la target value) la cap thich hgp. Vdi mdi cap du' lieu (Xi,Yi) giai thuat thuc hien ed hai pha:
- Pha 1: Xi dugc nhap vao mang va dugc lan truyen trong mang de thu dugc dau ra Oi.
- Pha 2; Ham sai sd Ei = (Yi - Oi)^ lan truyen ngugc lai dau vao nham cap nhat trpng sd ciia mang.
De thu dugc ket qua tdt, mang phai thue hien hang ngan vdng lap, cudi eiing thu dugc bd trpng sd sao eho sai sd E la nhd nha't.
2.2. Huan luyen mang ndron (Training Neural Network)
Mang noron dugc huan luyen md phdng theo phuang phap danh gia dat dai eiia FAO (hinh 3). Thire chat day la qua trinh dieu chinh gia tri bp trpng sd sao cho gia tri xuat phu hgp vgi gia tri mong mudn. Khi cau true dugc xac djnh, kha nang xif ly ciia mang noron phu thuiDC rat nhieu vao qua trinh huan luyen, dieu nay phu thudc vao bp dif lieu miu (Xi,Yi), bd dii' lieu miu phai dai dien cho tat ca tap hgp tinh chat da't dai.
Tinh chat dat dai (LC)
Phuong phap danh gia dat ciia FAO
Training
Xi ?
^
Mang noron (Neural network)
Ldp thfch hop (S1,S2, S3,N)
4 ^ '
^
Mang da huan luyen (BlackBox)
Hinh 3. Huan luyen mang noron cho danh gia thich hap dat dai Sau khi mang noron da dugc huan luyen xong, ta
dugc mpt BlackBox, cd kha nang nhan biet va danh gia thich hgp da't dai (tuong tu nhu da xay dung eay quye't dinh eho danh gia dat dai trong phan mem ALES).
3. KET QUA NGHIEN CliU
Tren dja ban huyen Bao Lam, ehpn 6 loai hinh sif dung da't (LUT) cd then vpng de danh gia thich hgp dat dai: 2 vu liia mau (LUT1), Chuyen mau (LUT2), Dau tam (LUT3), Ca phe (LUT4), Che (LUT5), Cay an qua (LUT6).
De mang noron ed kha nang danh gia thich hgp cho cae LUT, chiing ta phai xay dung bd du lieu miu (Xi,Yi) va huan luyen mang noron eho tung LUT.
3.1. Xay dimg bo du'lieu miu (training data) DQ lieu miu dau vao (Xi) la cae tinh chat da't dai cua ban dd don vi da't dai. Nd dugc xay dung tren ca sd chdng xep 8 ban dd don tinh: loai da't (ky hien la So, chia thanh 4 nhdm), tang day (De, chia 4 cap), gley (GI, chia 2 cap), ket von (Cd, chia 2 cap), dang dia hinh (To, chia 4 cap), dp ddc (SI, chia 6 cap), dieu kien tudi (Ir, chia 3 cap), thdi gian ngap (Tf, chia 2 cap). Ket qua, toan huyen ed 24 don vi da't dai (bang 1).
DCr lieu mau dau vao (Xi) la ma tran (trong khung in dam - bang 1) gdm 8 hang (tuang ung 8 tinh chat, bang sd naron d lap dau vao), 24 cot (tuong ung vai 24 don vi dat dai, trong dd cac tinh chat dat dai da duac ma hda (vi du: Tang day (De) sd "1" la 30-50cm, sd "2"
Ia50-70cm,...).
f LMU
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• o
-R
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»-
So De ni Cd To 31 Ir Tf
1 1 4 2 1 1 1 3 2
2 1 4 2 1 1 1 2 2
Bang 3 2 3 1 1 2 2 2 1
4 2 1
? 2 2 2 2 1
7. Md ta tinh chat cac 5
2 2 1 3 2 2 2 1
6 2 4 1 1 2 3 2 1
7 2 4 1 1 2 3 2 1
8 2 3 1 3 2 3 2 1
9 2 2 1 3 2 3 1 1
don 10
2 3 1 3 2 4 2 1
VI dat dai - huyen Bao Lam ti'nh Lam Ddng 11 1 12 1 13
2 2 2 4 2 1 1 1 1 3 3 2 2 2 2 4 4 4 1 2 2 1 1 1
14 2 2 1 3 3 5 1 1
15 3 3 1 3 2 3 2 1
16 3 3 1 3 2 4 2 1
17 3 3 1 3 2 4 1 1
18 3 4 1 1 2 4 1 1
19 3 3 1 3 3 5 1 1
20 4 3 1 3 2 3 2 1
21 4 3 1 3 2 4 1 1
22 4 3 1 3 3 5 1 1
23 3 3 1 3 4 6 1 1
24 4 3 1 3 4 6 1 1 Tren co sd yeu cau sir dung dat (LUR) eiia tUng dung dugc bang thich hgp dat dai ciia tirng LUT (bang 2 LUT va tinh chat da't dai ciia tirng don v| dat dai, xay la mpt vi du ve ma hda phan cap thich hgp eay ca phe).
.' Bang 2. Phan cap thich hgp cay ca phe - huyen Bao Lam tinh Lam Odng ' LMU
31 S2 S3 N
1 0 1 0 0
2 0 1 0 0
3 0 1 0 0
4 0 0 0 1
5 0 0 1 0
6 1 0 0 0
7 1 0 0 0
8 0 1 0 0
9 0 0 0 1
10 0 1 0 0
11 0 0 0 1
12 0 0 1 0
13 0 0 0 1
14 0 0 0 1
15 0 0 1 0
16 0 0 1 0
17 0 0 0 1
18 0 0 0 1
19 0 0 0 1
20 0 0 0 1
21 0 0 0 1
22 0 0 0 1
23 0 0 0 1
24
1.
C 0 1 D(? lieu mau (Yi) gia tri mong mudn (target value)
dat dugc trong qua trinh huan luyen, la ma tran (trong khung in dam • bang 2) gdm 4 hang (tuang irng v(i(i 4 cap thich hgp, bang sd noron d l(!fp dau ra), 24 ept (tuang Ung vdi 24 dan vi da't dai) dugc ma hda tuong Ung theo cap thich hop (vi du; xem i^ang 2, eay ca phe thich hgp S1 tren LMU6, S2/LMU2, S3/LMU5, N/LMU,,).
Qua trinh huan luyen he thdng mang noron cho cac LUT CO cung mpt bd du lieu mau dau vao (Xi), mdi LUT se cd neng mpt bp du lieu (Yi). Nhu vay, cd bao nhieu LUT can danh gia thich hgp dat dai thi ed bay nhieu BlackBox_LUTj (ddi vdi huyen Bao Lam: j =1,2,...6).
3.2. Huan luyen mang ndron (training)
Sau day md ta qua trinh huan luyen mang naron Cho danh gia thich hgp eay ea phe (hinh 4). Sau khi da xay dung xong bp du lieu m i u (Xi, Yi), dung chuong tnnh NNTool trong MABLAT 7.0 de huan luyen mang noron (hinh 4). Md ta tdm tat nhu sau;
- Khai bao eae thdng sd ciia he thdng mang naroi (cd ten: NNforLE-cafe): diing thuat toan lan truyer, ngugc, sd Idp bang 2 (Idp vao la layer 0, Idp an la layer 1, ldp ra la layer 2), sd noron trong mdt igp nhu hinh 2,...tao ra hop thoai nhu hinh 4.
- Trong hop thoai inputs: nhap dur lieu dau vao Xi.
- Trong hdp thoai Targets: nhap dur lieu Yi.
- Huan luyen: click vao Tram de huan luyen, ket thiie qua trinh huan luyen thu dugc gia tri dau ra d d output (la gia tri dau ra O i ) Vci gia tri sai sd d d Errors (bd sai sd Ei = (Yi - Oi)^ dat gia tn nhd nhat).
Sau khi mang da duoc huan luyen xong, ta dugc mpt BlackBox cho danh gia thich hop cay ca phe (luu trong MATLAB co ten NNforLE^cafee), ed kha nang nhan biet va danh gia thich hop dat dai cay ea phe.
Tuang tu, tien hanh huan luyen mang noron de danh gia thich hgp dat dai cho cac LUT edn lai.
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(XI)
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Hinh 4. Huan luyen mang noron (trong MATLAB 7.0)
Sur dung mang da huan luyen (trained neural Ke't qua thich hgp lien ket vgi GIS de xay di/ng networks): chay NNTool trong M A I L A B (nhu hinh 4). ban dd kha nang thich hgp da't dai cho tUng LUT, chdng import cae BlackBox_LUTj, nhap du lieu dau vao, chay xep eae ban dd nay thu dugc ban dd kha nang thich Simulate de danh gia thich hgp eho LUTj. hgp da't dai.
Banc, (3. Ket qua danh gie kha Don vi dat dai
Ky hieu 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 . 23
24
Ma Sol
Sol So2 So2 So2 So2 So2 So2 So2 So2 So2 So2 So2 So2 So3 So3 So3 So3 So3 So4 So4 So4 So3 So4
De4 De4 De3 Del De2 De4 De4 De3 De2 De3 De4 De2 Del De2 De3 De3 De3 De4 De3 De3 De3 De3 De3 De3
GI2 G12 Gil G12 Gil GII GII GII GII GII GII GII GII Gil GI1 GII GI1 GII Gil Gil G11 Gil GII Gil
Cd1 Cdl Gdi Cd2 Cd3 Cdl Cd1 Cd3 Cd3 Cd3 Cd3 CdS Cd2 Cd3 Cd3 Cd3 Cd3 Cdl Cd3 Cd3 Cd3 Cd3 CdS CdS
Toi Toi To2 To2 To2 To2 To2 To2 To2 To2 To2 To2 To2 ToS To2 To2 To2 To2 ToS To2 To2 ToS To4 To4
SII
sn
SI2 S12 S12 SIS SIS SIS SIS SI4 S14 S14 S14 SIS SIS S14 S14 S14 S15 SIS S14 SIS S16 S16
lr3 Ir2 Ir2 Ir2 lr2 lr2 lr2 lr2 Ir1 Ir2 Iri Ir2 lr2 Iri Ir2 Ir2 Irl lr1 Irl lr2 Ir1 Irl lr1 Irl
Tf2 Tf2 Tfl Tfl Tfl Tf1 Tf1 Tfl Tfl Tfl Tfl Tfl Tfl Tfl Tfl Tfl Tfl Tf1 Tfl Tfl Tfl Tfl Tfl Tf1
nang thief 2 V u l u a ,
mau S2
N N N N N N N N N N N N N N N N N N N N N N N
hap dat dai - huyen Bao Lam tinh Lam Ddng Chuyen
mau SI SI S2 SS S2 N N N N N N N N N N N N N N N N N N N
Dau t§m S2 S2 SI S3 S2 SS SS S3 S3 N N N N N N N N N N N N N N N
Ca phe
S2 S2 S2 N S3 SI SI S2 N S2 N SS N N S3 SS N N N N N N N N
Che S2 S2 SI S3 S2 SI SI SI S2 S2 S2 S2 SS SS S1 S2 S2 S2 S3 S3 SS SS N N
Cay an qua
S2 S2 S2 N SS S2 S2 S2 N SS N S3 N N S2 S3 N N N SS N N N N Song sudi
Tc ing dien tich t l / n h i e n
Dien tich ha 6.096 1.942 6.S85 4.453 1.780 4.706 499 3.217 15.413
1.518 5.715 3.383 1.910 6.1S2 1.287 6.524 3.065 1.805 3.942 1.540 548 4.733 26.342 31.561 1.653 146.347
%
4.17 1,S3 4,50 3,04 1,22 3,22 0,34 2,20 10,53
1,04 3,91 2,31 1,31 4,19 0,88 4,46 2,09 1,23 2,69 1,05 0,37 3,23 18,00 21,57 1,13 100,00
tJ'ng dung GIS va NN trong danh gia da't dai huyen Bao Lam eho ket qua rat tdt (bang 3). phu hgp vdi ket qua danh gia thich hgp da't dai bang md hinh tich hgp GIS va ALES tren dia ban huyen Bao Lam (Phan vien Quy hoach va Thief ke Ndng nghiep, 2007).
Nhan xet: Trong trudng hgp ed thay ddi, ehinh sira ban dd don tinh, thi ehi chay lai md hinh, nhap lai bg dU lieu dau vao (Xi), BlackBox tinh toan (Simulate) cho ke't qua thich hop da't dai. Vi du: Hien nay (2008), loai hinh ea phe khdng thich hgp (N) do thieu nudc, den nam 2010 do xay dung them hd thuy Igi, van de nude dugc giai quyet thi ea phe thich hgp cao (SI), Khi dd chi can cap nhat ban dd tudi va chay lai md hinh thi se ed ban dd thich hgp cho nam 2010 (van de nay neu lam thii cdng thi mat rat nhieu thdi gian, ddi khi phai lam mdij.
4. KET LUAN
Mang noron ed the dugc huan luyen de md phong theo phuang phap danh gia da't dai eua FAO. GIS la cdng eu rat huu ich trong phan tich du lieu khdng gian, nd eung cap thdng tin true quan, kip fhgi va chinh xac.
Tich hgp mang naron va GIS trong danh gia da't dai se tiet kiem thai gian, nang cao nang sua't lao dpng, ket qua hd trg rat tdt eho cac nha quan ly trong qua trinh ra quyet dinh,
Md hinh tieh hop mang naron va GIS trong danh gia thich hgp da't dai da dugc kiem chirng d dia ban huyen Bao Lam tinh Lam Ddng, ket qua tuang ddi phu hgp vgi tinh hinh thue tidn eua Dia phuang. Trong tuong lai, cd the nhan rdng md hinh nay eho danh gia da't dai d cae dia phuang khac trong toan qud'c.
TAI LIEU THAM KHAO 1 Bui Cong Cuang. Nguyen Doan Phuac (2006), He md - mang no ron va irng dung, NXB Khoa hoc Ky thuat (in lan thii hai).
2. Ben Kroese, Patrick Van der Smagt (1996), An introduction Neural Network, The University of Amsterdam, The Netherlands.
3. FAO (1976), A framework for land evaluation, soils bulletin 32, Rome, Italy
4. Martin T. Hagan, Howard B. Dmuth (1996), Neural
Network Design. PWS Publishing Company, USA.
5. Simon Hayldn (1999), Neural Network: A comprehensive Foundation, Second edition. Prentice Hall Inc. USA
Summary
A P P L I C A T I O N OF NEURAL N E T W O R K A N D CIS POR LAND EVALUATION, CASE S T U D Y OF B A O LAM D I S T R I C T - LAM D O N C PROVINCE
Objective of this study is to apply information technology in land evaluation. We design a neural network system that has 3 layers: input layer has 8 neurons; hidden layer has 6 neurons; output layer has 4 neurons. An en-or back- propagation was used for training a neural network system.
Result of training is a BlackBox. GIS is applied for building a Land Mapping Unit by using overlay function to unite some Land Characteristics. Attribute file of LMU has been imported into BlackBox. BlackBox were stored the knowledge base as "a
LeCanh Dinh*
FAO framework for land evaluation' for each land use type (LUT). BlackBox can calculate land evaluation and transfer the result to GIS to display the suitability map of each LUT. GIS unite these maps for creating a suitable map of all LUTs. The model case study of Bao Lam district - Lam Dong province has given a good result that is appropriate for the real condition of the district (This result is similar to that of ALES).
Keywords: Neural network, GIS, Land evaluation.
• Sub-National Institute for Agricultural Planning and Projection (subNlAPP in the South), 20 Vo Thi Sau St., Dist. 1, Ho Chi Minh City.
NGHIEN CCfU CAC DANG ASEN TRONG DAT 6 NHIEM...
(Tiep trang 91)
TAI LIEU THAM KHAO 1. Le Dire (djch), 1979. Nguyen td vi lugng trong trong
trot (tap 2). Nhd xuat ban Khoa hpc va Ky thuat. Ha Noi.
2. Le Van Khoa. Nguyen Xuan Cir, Tran Khac Hiep, Le Dire va nnk 2000. Phuong phap phan tich dat-nudc-phan bon va cSy trdng. Nha xuat ban Giao due, Hd Ni?i.
S. Tran Kong Tau. 2005. Vat ly thd nhuong moi trudng.
Nha xuat ban Oai hoc Qudc gia Ha Noi
4. R. R. Rodriguez etal 2003. Chemical Extraction Methods to Assess Bioavailable Arsenic in Soil and Solid Media. Published in J. Environ. Qual. 32: 876 - 884.
5. S. A. Wassay etal. 2000. Arsenic Pollution of a Loam Soil-retention form and decontamination. Journal of Soil Contamination, 9 (1): 51 - 64
Summary
ARSENIC COMPOUNDS IN SOIL CONTAMINATED BY TIN MINING ACTIVITIES IN HA THUONG COMMUNE - DAI TU DISTRICT - THAI NGUYEN PROVINCE
Le Due
Nguyen Canh Tien Trinh Pham Viet Dzung Nguyen Thi Thu Nhan was high (about 30% of total content) leading to arsenic contamination in studied areas. The immobile fractions (F4, F5 and F6) of arsenic in soils were also occupied high percentage of sum of fractions and could be tranfered to mobile ones. There was a significant relation beetween mobile fractions (F1 and F2) extracted by Manful method and those extracted by Olsen and Oniani method.
Soil samples were collected in Hathuong commune, Daitu district, Thainguyen province to research arsenic compounds in soils contaminated by tin mining activities. The results show that there was a significant difference in physio - chemical properties (pH, %0M, CEC, SO,^" content and soil texture) between directly affected soils and indirectly ones.
The percentage of mobile fractions (Fl, F2 and FS) of arsenic
