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Data on assessment of corrosion-scaling potential and chemical parameters of groundwater quality for industrial and agricultural sectors in the Piranshahr Watershed in the West Azerbaijan province, Iran
Data in brief 27 (2019) 104627
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Data Article
Data on assessment of corrosion-scaling potential and chemical parameters of groundwater quality for industrial and agricultural sectors in the Piranshahr Watershed in the West Azerbaijan province, Iran Omid Asadi Nalivan a, *, Eisa Mollaefar b, Elinaz Soltani c, Mahdiyeh Karvarinasab d a
Department of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources (GUASNR), Golestan Province, Gorgan, Iran Department of Natural Resources and Watershed Management of Golestan Province, Gorgan, Iran c Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Fars Province, Shiraz, Iran d Islamic Azad University, Faculty of Natural Resources and Agriculture, Science and Research Branch, Tehran, Iran b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 23 May 2019 Received in revised form 19 September 2019 Accepted 27 September 2019 Available online 9 October 2019
The present work sets out to 1) evaluate the corrosion-scaling potential of groundwater resources for the industrial sector as well as to 2) examine groundwater chemical parameters for the agricultural sector in the Piranshahr Watershed in the West Azerbaijan province, Iran, using geostatistical analyses and the Wilcox diagram in a GIS environment. A total of 145 spring locations as representatives of groundwater potentiality were recorded by a handheld GPS device and the corrosion and scaling potential states were further scrutinized. The latter was carried out on the basis of examining the chemical parameters at each sample location including alkalinity, pH, temperature, Naþ, Caþþ, Mgþþ, TH, 2 2 HCO 3 , Co3 , Cl , SO4 , Electrical conductivity, and Total dissolved solids. The corrosion and scaling potential of groundwater was then evaluated by using Langelier saturation index (LSI), Larson eSkold index (LS), Ryznar stability index (RSI), Aggressive index (AI), and Puckorius scaling index (PSI). Also, the groundwater
Keywords: Corrosion-scaling potential Groundwater quality Wilcox diagram Hydro-geochemical Geostatistical analysis GIS
* Corresponding author. E-mail address: [email protected] (O.A. Nalivan). https://doi.org/10.1016/j.dib.2019.104627 2352-3409/© 2019 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
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O.A. Nalivan et al. / Data in brief 27 (2019) 104627
quality state for agriculture was assessed by the Wilcox diagram on the basis of Electrical conductivity and Sodium adsorption ratio parameters. The provided data are beneficial for researchers, policymakers, and authorities for taking pragmatic actions. Also, the compiled data can be used in the context of corrosion/scaling and groundwater quality assessment can be generalized around the world. © 2019 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
Specifications Table Subject area Specific subject area Type of data How data were acquired
Data format Parameters for data collection
Description of data collection
Data source location Data accessibility
Environmental Science Water quality, water science, groundwater management Table and figure Groundwater samples were collected in two-liter plastic bottles during extensive field surveys. Further analyses (i.e., measuring parameters by standard methods [1]) were carried out in the laboratory of Natural Resources College in the University of Tehran. Laboratory facilities were used to measure the parameters and the commercial software packages i.e. ArcGIS and Microsoft Excel were used for analyzing the derived data. Raw, analyzed Sampling was conducted after the harvesting season. Groundwater samples were collected in 2 L bottles and transported to the laboratory of Natural Resources College in the University of Tehran on the next day and kept at a suitable temperature (4 C). All samples were analyzed according to the standard methods. Firstly, the scope was identified and then, with the help of the local guide and field surveys, water springs were identified and their geographic coordinates were recorded using a handheld global positioning system (GPS) device. Groundwater samples were collected from a total of 145 springs across the Piranshahr Watershed. It was ensured that the collected samples were well distributed across the area. Piranshahr Watershed, West Azerbaijan Province, northwest of Iran. The latitude and longitude coordinates of the springs are presented in Table 1. Repository name: Mendeley Data Data identification number: DOI: 10.17632/zg27cgxk4p.2 Direct URL to data: https://data.mendeley.com/datasets/zg27cgxk4p/2
Value of the data The chemistry of groundwater is a determining factor for its uses in different water sectors: water supply, agriculture, and industry. Due to the scarce information available regarding groundwater data in the area, the provided data can enable the authorities to pinpoint the main water quality issues as well as to undertake precautionary measures. The target communities of this data are the organizations that are continually making decisions on water quality issues, including the Water Resources Management Company, Water and Wastewater Organization, Ministry of Agriculture Jihad and the Ministry of Energy. Also, the compiled data can be used in the context of corrosion/scaling and groundwater quality assessment can be generalized around the world. The dataset can be used for monitoring groundwater quality in Piranshahr Watershed. Calculated scaling and corrosion indices are pivotal for monitoring water supply distribution networks and industrial utilities. From both economic and health security standpoints, assessing groundwater quality metrics, especially scaling and corrosion, are of great importance, which, in turn, will address the degree to which the suitable management plans are required for preventing economic and health consequences in the future. Corrosion imposes many financial and health problems respectively on systems and consumers. Scaling, on the other hand, can cause blocking and head loss in the network. Altogether, corrosion/scaling controls are substantial aspects of safe drinking water supplies and other uses in miscellaneous sectors. The dataset provided in the present work can be rendered into spatial distribution maps of the measured parameters' concentration across the area and also be used for the spatial distribution of groundwater quality across the world.
O.A. Nalivan et al. / Data in brief 27 (2019) 104627
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1. Data This dataset contains 5 Tables and 3 Figures which present in detail the corrosion-scaling potential and chemical parameters of groundwater quality for the industrial and agricultural sectors in the Piranshahr Watershed. Fig. 1 shows the geographical location of the springs (i.e., sampling points) across the Piranshahr Watershed in the West Azerbaijan Province, Iran. Table 1 presents the water 2 quality chemical parameters including alkalinity, pH, temperature, Naþ, Caþþ, Mgþþ, TH, HCO 3 , Co3 , Cl , SO2 , EC, and TDS which have been measured during the field surveys and analyzed in the lab4 oratory. Table 2 shows the descriptive statistics such as maximum, minimum, mean, and standard deviation of hydro-geochemical parameters. Table 3 presents some indices, equations, definitions, and criteria which were further used for classifying water corrosion and scaling [2,3,4,5,6and7]. Table 4 shows the results obtained from the RSI, LSI, AI, PSI and LS analyses as well as other detailed information on pHs and pHeq. Table 5 shows the descriptive statistics such as maximum, minimum, mean, and standard deviation of corrosion and scaling indices. Mapping the corrosion and scaling indices was carried out by geostatistical analyses in ArcGIS 10.3.1 [8] which are shown in Fig. 2. Fig. 3 depicts the groundwater quality classes for the agricultural sector based on the Wilcox diagram in ArcGIS.
2. Experimental design, materials, and methods 2.1. Study area description The Piranshahr Watershed extends for 426.15 km2 and is located within the geographical coordinates of 44 49״40 ׳and 45 11״40 ׳longitude and 36 39״00 ׳and 36 56״00 ׳latitude, in the West Azerbaijan province, northwest of Iran (Fig. 1). The area receives annual mean precipitation of 650 mm,
Fig. 1. The geographical location of the Piranshahr Watershed and the recorded springs in the West Azerbaijan province in Iran.
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Table 1 Hydro-geochemical characterization of 145 groundwater samples in the Piranshahr Watershed. Sample numbers
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 S30 S31 S32 S33 S34 S35 S36 S37 S38 S39 S40 S41 S42 S43 S44 S45 S46 S47 S48 S49 S50 S51 S52 S53 S54 S55 S56 S57 S58
UTM Zone 38 N
(ms/ cm)
UTMX
UTMY
EC
489895 489463 507028 500854 491043 502107 508513 512472 500905 489336 503018 489492 506224 503318 499015 506527 499789 500450 507982 488803 511006 513290 505696 510999 488956 503395 505571 500830 505270 500527 507190 499798 506864 502271 489824 502815 492271 499719 512230 512406 499983 512366 501924 508606 499236 489693 499246 506703 502352 488969 514296 510991 500341 503063 503734 502892 488879 502847
4073692 4078050 4062034 4075624 4077392 4075725 4065028 4070778 4075583 4072764 4076689 4072776 4067765 4076826 4074453 4077079 4076186 4075866 4062881 4073280 4063326 4071876 4075016 4063342 4072973 4076532 4077779 4075753 4076461 4074864 4062507 4076382 4061918 4076724 4073902 4076271 4081165 4074885 4074644 4072929 4076733 4070895 4077966 4064686 4075648 4073875 4075713 4063133 4075839 4072954 4073673 4063427 4075302 4080485 4076407 4080580 4073071 4076196
292 238 375 349 266 212 347 540 294 131 303 266 296 378 431 251 259 337 289 137 339 371 472 345 106 373 289 403 448 418 282 385 265 310 162 304 240 316 359 375 384 412 382 307 316 176 315 277 442 222 336 333 464 161 370 236 138 309
PH
8.26 7.19 7.57 7.36 7.42 7.29 7.72 7.2 7.43 7.08 7.52 7.91 7.63 7.39 7.4 7.53 7.88 7.94 7.78 7.69 7.69 7.89 7.52 7.57 7 7.52 7.67 7.73 7.44 7.4 7.6 8.12 7.68 7.53 7.41 7.76 7.53 7.75 7.5 7.44 8 7.36 7.22 7.59 7.97 7.45 7.78 7.45 7.34 7.76 7.63 7.56 7.63 6.91 7.7 7.7 7.57 7.82
( C)
(mg/l) HCO TDS CO2 3 3 Cl
TH SO2 4
Alkalinity Caþþ Mgþþ T
Naþ
146 119 187 275 133 106 174 270 147 64 151 133 148 189 216 125 128 168 144 68 169 185 235 172 53 186 145 202 224 210 141 187 132 150 81 152 120 158 180 188 142 206 191 153 158 88 157 138 221 110 168 167 232 80 185 118 69 155
110 120 200 160 130 110 190 170 150 140 140 120 120 170 170 150 130 220 160 140 180 200 240 210 140 170 210 230 240 250 140 200 150 130 130 150 150 180 200 160 150 210 220 170 160 100 140 170 240 150 190 250 250 200 180 120 140 160
86.81 161.93 212.98 105.14 155.76 109.54 82.64 159.37 90.29 205.07 112.88 178.31 96.12 173.95 237.34 199.08 180.76 151.01 94.74 160.28 130.53 166.06 104.65 73.01 111.74 203.99 252.75 115.22 163.18 129.35 106.02 124.08 51.08 112.77 69.72 92.22 70.87 145.76 182.37 153.89 117.44 140.11 145.66 116.62 123.45 134.39 177.74 77.39 168.16 85.65 106.94 125.25 163.01 88.14 63.21 102.90 49.37 123.45
153.85 124.32 184.67 138.58 91.08 66.98 55.75 110.61 78.75 55.75 78.75 66.98 61.30 91.08 84.85 78.75 55.75 78.75 72.80 39.95 61.30 72.80 97.45 66.98 34.96 66.98 72.80 72.80 84.85 84.85 45.08 66.98 45.08 34.96 39.95 45.08 39.95 55.75 61.30 50.35 45.08 61.30 61.30 45.08 45.08 20.82 39.95 39.95 72.80 25.39 50.35 61.30 66.98 45.08 55.75 30.11 25.39 39.95
52.03 35.62 36.02 38.02 52.03 18.01 46.02 80.04 38.42 34.21 37.22 28.81 42.42 22.01 38.42 52.03 16.01 32.02 52.03 12.01 52.43 44.02 68.03 62.03 8.00 13.61 41.62 56.03 46.02 50.03 58.43 36.82 48.02 31.62 52.03 32.42 44.02 42.02 42.02 54.43 32.02 76.04 48.02 32.42 38.02 16.01 26.41 56.03 40.02 35.62 52.03 52.03 44.02 20.41 40.02 26.01 28.01 38.02
34.78 126.31 176.96 67.12 103.73 91.53 36.61 79.33 51.87 170.86 75.66 149.50 53.70 151.94 198.93 147.06 164.75 118.99 42.71 148.28 78.11 122.04 36.61 10.98 103.73 190.38 211.13 59.19 117.16 79.33 47.60 87.26 3.05 81.16 17.70 59.80 26.85 103.73 140.35 99.46 85.43 64.07 97.63 84.21 85.43 118.38 151.33 21.36 128.14 50.04 54.92 73.22 118.99 67.73 23.19 76.89 21.36 85.43
14.18 17.73 14.18 21.28 14.18 14.18 10.64 17.73 17.73 14.18 17.73 17.73 10.64 24.82 17.73 14.18 10.64 14.18 10.64 14.18 10.64 14.18 14.18 10.64 10.64 14.18 14.18 14.18 17.73 14.18 10.64 14.18 10.64 10.64 14.18 14.18 14.18 14.18 14.18 14.18 14.18 14.18 14.18 10.64 14.18 14.18 14.18 14.18 14.18 10.64 14.18 14.18 17.73 10.64 17.73 14.18 14.18 14.18
316.08 137.70 304.56 238.76 41.40 103.94 150.62 16.09 119.59 111.43 95.87 23.92 48.37 133.24 51.92 0.48 53.79 162.15 85.35 52.98 52.78 58.64 190.34 111.72 97.60 99.61 32.71 119.64 140.01 170.07 17.96 107.78 63.02 20.27 2.07 69.45 42.89 52.16 53.36 20.41 50.72 17.72 85.35 75.70 38.71 16.28 1.30 27.81 99.28 31.60 52.40 137.70 99.13 160.71 74.25 20.41 64.84 28.00
60.08 32.06 48.10 60.12 36.07 28.06 20.04 60.12 36.07 12.02 48.10 44.09 36.07 64.13 68.14 56.11 40.08 28.06 60.12 16.03 40.08 48.10 76.15 36.07 12.02 60.12 48.10 40.08 60.12 56.11 40.08 52.10 36.07 28.06 40.08 40.08 28.06 48.10 52.10 52.10 48.10 52.10 48.10 40.08 44.09 20.04 44.09 36.07 68.14 8.02 44.09 36.07 52.10 28.06 68.14 40.08 12.02 44.09
2.43 9.73 19.46 2.43 9.73 9.73 34.05 4.86 14.59 26.75 4.86 2.43 7.30 2.43 0.00 2.43 7.30 36.48 2.43 24.32 19.46 19.46 12.16 29.18 26.75 4.86 21.89 31.62 21.89 26.75 9.73 17.02 14.59 14.59 7.30 12.16 19.46 14.59 17.02 7.30 7.30 19.46 24.32 17.02 12.16 12.16 7.30 19.46 17.02 31.62 19.46 38.91 29.18 31.62 2.43 4.86 26.75 12.16
11.5 10 9 10.5 10 11 9 8.4 5 14 11 7 13.3 9 10 11 9 7.5 5 6 10 6 12.2 12 11 10 12 9 7.6 9 11 14 7.5 11 10 11 13 6 12 13 10 9 12 6.7 6 13 10 6 10 9 12 5 7 12 9 8 12 10
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Table 1 (continued ) Sample numbers
S59 S60 S61 S62 S63 S64 S65 S66 S67 S68 S69 S70 S71 S72 S73 S74 S75 S76 S77 S78 S79 S80 S81 S82 S83 S84 S85 S86 S87 S88 S89 S90 S91 S92 S93 S94 S95 S96 S97 S98 S99 S100 S101 S102 S103 S104 S105 S106 S107 S108 S109 S110 S111 S112 S113 S114 S115 S116
UTM Zone 38 N
(ms/ cm)
UTMX
UTMY
EC
489762 501235 509088 510997 499623 506162 502959 506569 506865 514334 506229 500568 501303 514465 511101 506852 506042 502367 506415 500923 501791 503543 500256 504359 504110 511744 508134 499030 507919 490180 501034 508102 500423 505910 507399 502213 502204 507787 501903 490129 501712 508922 501705 502142 501562 500240 493412 501803 508150 503573 501735 499209 508748 500943 502200 508676 500759 498985
4073992 4076169 4076270 4063143 4079089 4067723 4073178 4062916 4061919 4073728 4067785 4077247 4077125 4073946 4074923 4063762 4067874 4076892 4062900 4072627 4080487 4077895 4077743 4075552 4077328 4074496 4062063 4072670 4058846 4074445 4078308 4068804 4078928 4067811 4059229 4076503 4077810 4067834 4078291 4074513 4077530 4064022 4076849 4077381 4077774 4077700 4079059 4077951 4063779 4077824 4071713 4072297 4063981 4072450 4077812 4076141 4078091 4072329
370 309 256 334 370 301 288 283 339 382 277 314 432 434 390 321 275 221 247 287 288 408 306 453 425 448 213 332 334 382 289 363 309 290 280 251 380 217 423 375 331 427 380 372 339 370 312 352 455 377 229 324 282 233 374 259 300 288
PH
7.7 7.39 7.89 7.65 7.48 7.82 7.63 7.65 7.61 7.54 7.66 7.6 7.61 7.3 7.51 7.62 7.7 7.3 7.64 8.05 7.32 7.58 7.95 7.51 7.69 7.38 8.02 7.67 7.6 7.5 7.9 7.67 7.59 7.65 7.42 7.58 7.66 8.35 7.47 7.83 7.52 7.3 7.63 7.63 7.72 7.43 7.45 7.66 7.41 7.65 7.63 7.34 7.44 7.46 8.04 7.88 7.85 7.57
( C)
(mg/l) HCO TDS CO2 3 3 Cl
TH SO2 4
Alkalinity Caþþ Mgþþ T
Naþ
182 155 128 167 185 150 144 141 170 191 138 157 217 218 195 160 135 111 123 143 144 203 153 227 212 225 106 166 167 191 144 181 155 145 140 126 190 108 211 187 165 213 190 185 169 183 156 176 228 188 114 162 141 169 187 129 150 144
140 210 80 220 180 260 170 180 150 220 230 180 230 230 210 230 220 180 130 170 150 210 210 280 220 230 130 200 180 190 120 280 200 230 190 190 210 140 260 180 170 250 200 200 220 210 180 200 290 200 140 180 260 190 220 130 220 150
123.35 178.79 131.35 113.24 113.15 126.28 177.18 188.67 68.33 104.50 115.04 127.13 158.60 152.60 118.20 190.27 121.55 105.28 71.36 130.08 136.82 154.66 152.49 248.17 176.07 183.67 53.29 158.06 109.34 161.27 107.79 104.84 199.79 123.55 131.55 109.30 87.42 82.69 203.07 119.36 166.98 130.20 95.79 130.67 111.83 114.37 248.34 181.45 82.54 131.35 118.19 228.30 88.32 124.97 147.86 129.91 97.99 160.62
34.96 50.35 16.38 50.35 45.08 55.75 34.96 34.96 34.96 50.35 50.35 39.95 50.35 55.75 50.35 45.08 45.08 34.96 25.39 30.11 25.39 45.08 39.95 61.30 45.08 50.35 20.82 39.95 34.96 39.95 20.82 50.35 34.96 39.95 34.96 30.11 34.96 16.38 45.08 34.96 30.11 45.08 30.11 34.96 30.11 34.96 30.11 30.11 50.35 34.96 20.82 30.11 39.95 20.82 30.11 16.38 34.96 20.82
44.02 24.41 52.03 52.83 46.02 26.81 48.42 30.02 50.03 64.83 29.61 36.82 52.43 46.42 72.44 56.03 30.02 29.61 51.23 42.82 28.81 44.82 28.01 48.02 54.03 61.63 49.62 36.02 67.23 88.04 37.62 56.03 21.61 32.02 40.02 32.42 52.03 42.42 57.23 51.63 37.62 60.03 50.03 44.02 52.03 33.22 12.81 48.42 52.03 52.03 23.61 19.61 34.02 44.42 38.02 40.82 40.02 28.81
79.33 154.38 79.33 60.41 67.12 99.46 128.75 158.65 18.31 39.66 85.43 90.31 106.17 106.17 45.77 134.24 91.53 75.66 20.14 87.26 108.01 109.84 124.48 200.15 122.04 122.04 3.66 122.04 42.10 73.22 70.17 48.82 178.18 91.53 91.53 76.89 35.39 40.27 145.84 67.73 129.36 70.17 45.77 86.65 59.80 81.16 235.54 133.02 30.51 79.33 94.58 208.69 54.31 80.55 109.84 89.09 57.97 131.80
10.64 10.64 10.64 14.18 10.64 14.18 10.64 10.64 14.18 14.18 10.64 14.18 10.64 10.64 14.18 10.64 10.64 14.18 10.64 10.64 10.64 14.18 10.64 14.18 14.18 14.18 14.18 10.64 10.64 14.18 10.64 10.64 14.18 14.18 14.18 14.18 14.18 14.18 14.18 14.18 10.64 14.18 14.18 14.18 10.64 14.18 17.73 14.18 10.64 14.18 14.18 14.18 10.64 14.18 14.18 10.64 10.64 10.64
13.35 97.55 118.73 64.60 67.82 186.64 57.44 14.22 17.87 47.12 152.78 47.60 46.16 86.45 5.04 108.16 125.45 75.02 22.29 5.43 9.61 43.03 86.12 61.53 10.66 3.12 17.92 49.52 29.49 80.07 32.13 133.67 42.36 112.05 34.68 63.93 54.85 20.75 18.35 1.73 11.29 58.21 34.63 5.00 45.68 86.55 5.52 54.99 149.61 13.64 13.50 4.08 132.51 9.17 10.13 58.21 94.72 20.22
44.09 44.09 32.06 40.08 52.10 36.07 36.07 28.06 44.09 48.10 48.10 48.10 52.10 68.14 64.13 48.10 44.09 40.08 40.08 36.07 32.06 56.11 40.08 68.14 56.11 68.14 32.06 56.11 52.10 64.13 40.08 48.10 44.09 44.09 52.10 36.07 40.08 16.03 52.10 64.13 52.10 60.12 36.07 52.10 40.08 48.10 48.10 36.07 64.13 56.11 40.08 52.10 44.09 48.10 32.06 32.06 56.11 40.08
7.30 24.32 0.00 29.18 12.16 41.34 19.46 26.75 9.73 24.32 26.75 14.59 24.32 14.59 12.16 26.75 26.75 19.46 7.30 19.46 17.02 17.02 26.75 26.75 19.46 14.59 12.16 14.59 12.16 7.30 4.86 38.91 21.89 29.18 14.59 24.32 26.75 24.32 31.62 4.86 9.73 24.32 26.75 17.02 29.18 21.89 14.59 26.75 31.62 14.59 9.73 12.16 36.48 17.02 34.05 12.16 19.46 12.16
8 5 13 9 5 7 7 11 13 13 9 11 5 7 13 10 14 9 9 14 8 13 14 10 12 14 10 10 8 7 5 6 11 7 5 10 13 11 7 13 9 5 7 8 11 9 14 10 11 10 13 13 7 10 6 12 13 14
(continued on next page)
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Table 1 (continued ) Sample numbers
S117 S118 S119 S120 S121 S122 S123 S124 S125 S126 S127 S128 S129 S130 S131 S132 S133 S134 S135 S136 S137 S138 S139 S140 S141 S142 S143 S144 S145
UTM Zone 38 N
(ms/ cm)
UTMX
UTMY
EC
503166 499096 501069 500920 507437 507064 506138 501394 509406 501789 499715 503638 500018 499995 508088 506369 500214 502162 500838 501393 512772 502736 501868 495825 508347 513091 499205 499917 507988
4078473 4071930 4070642 4072713 4069650 4062505 4064156 4078648 4076588 4073710 4077021 4078371 4080350 4078255 4068205 4064339 4072445 4077854 4072298 4078648 4074497 4078062 4071744 4078269 4064749 4071434 4071866 4078691 4067798
407 284 279 295 514 384 273 379 423 315 300 383 346 256 393 354 328 365 306 378 417 355 216 362 460 393 309 327 233
PH
7.58 7.4 7.57 7.59 7.28 7.17 7.82 8.51 7.48 7.62 7.8 7.54 7.72 7.66 7.19 7.37 7.47 7.81 7.52 8.44 7.6 7.67 7.73 7.53 7.57 7.57 7.46 8.05 8.03
( C)
(mg/l) HCO TDS CO2 3 3 Cl
TH SO2 4
Alkalinity Caþþ Mgþþ T
Naþ
204 142 140 148 257 192 137 189 211 158 150 191 173 128 196 177 164 183 153 189 208 177 108 181 230 196 154 164 117
230 150 240 200 300 240 170 220 200 180 200 300 190 130 270 220 170 260 150 300 300 270 260 200 260 220 170 170 230
255.49 114.63 101.63 142.29 134.10 169.71 88.64 155.83 139.75 181.65 114.39 242.15 180.27 134.56 157.14 119.35 184.89 142.51 108.58 216.66 173.87 246.93 81.69 227.12 75.48 84.17 148.79 161.21 135.51
34.96 20.82 30.11 25.39 45.08 34.96 20.82 25.39 30.11 25.39 30.11 39.95 25.39 16.38 34.96 25.39 20.82 30.11 16.38 30.11 34.96 30.11 25.39 20.82 25.39 20.82 16.38 16.38 12.08
48.02 42.02 41.22 30.02 45.62 69.63 52.03 35.62 36.02 36.42 32.02 43.22 46.02 18.01 42.42 40.02 37.22 54.03 51.23 34.82 64.03 49.22 29.21 17.21 48.02 64.03 43.22 30.02 22.01
207.47 72.61 60.41 112.28 88.48 100.07 36.61 120.21 103.73 145.23 82.38 198.93 134.24 116.55 114.72 79.33 147.67 88.48 57.36 181.84 109.84 197.70 52.48 209.91 27.46 20.14 105.56 131.19 113.50
14.18 10.64 14.18 14.18 17.73 7.09 10.64 14.18 14.18 14.18 14.18 14.18 10.64 10.64 14.18 10.64 10.64 21.28 14.18 10.64 17.73 17.73 10.64 10.64 10.64 10.64 14.18 10.64 10.64
47.36 21.18 94.76 43.71 138.61 19.88 15.23 2.69 40.11 24.50 70.65 54.42 36.02 2.31 84.34 30.26 43.66 36.17 56.00 82.66 34.68 20.41 156.87 8.21 93.95 25.41 46.30 14.12 77.28
52.10 44.09 36.07 36.07 60.12 60.12 40.08 28.06 68.14 56.11 68.14 48.10 56.11 52.10 56.11 48.10 52.10 52.10 48.10 24.05 52.10 48.10 36.07 56.11 48.10 48.10 56.11 60.12 20.04
24.32 9.73 36.48 26.75 36.48 21.89 17.02 36.48 7.30 9.73 7.30 43.78 12.16 0.00 31.62 24.32 9.73 31.62 7.30 58.37 41.34 36.48 41.34 14.59 34.05 24.32 7.30 4.86 43.78
10 7 6 6 12 8 5 12 5 14 11 9 11 14 12 6 12 6 12 9 8 6 9 8 13 8 13 14 9
Table 2 Descriptive statistics of hydro-geochemical parameters. Parameters
EC
PH
TDS
CO2 3
HCO 3
Cl
TH
SO2 4
Alkalinity
Caþþ
Mgþþ
Naþ
T
Max Min Mean St.dev.
540 106 326.67 77.21
8.51 6.91 7.61 0.25
275 53 163.83 39.53
88.04 8.00 41.92 14.04
235.54 3.05 95.66 49.50
24.82 7.09 13.35 2.60
300 80 190.97 46.63
316.08 0.48 64.46 55.94
255.49 49.37 137.58 45.42
76.15 8.02 45.41 13.21
58.37 0 18.97 11.24
184.67 12.08 46.29 26.25
14 5 9.68 2.68
most falls in the form of snow in winter. The area is characterized by cold semi-arid climate according to Emberger classification. Elevation ranges from 1400 to 3254 m [9]. 2.2. Sample collection and analytical procedures In the present work, 145 spring water samples were collected in the Piranshahr Watershed in West Azerbaijan province and further analyzed to measure water quality chemical parameters. For that purpose, 2 L sampling bottles were used for groundwater collection and temperature of the samples was in-situ measured. The EC was measured using an EC meter (CTS-406 Portable Conductivity Meter, EZDO, Taiwan) and then used for measuring the TDS. The acquired samples were transported into the Natural Resources College Laboratory at the University of Tehran for further analysis based on standard methods. The value of (pH) was measured with digital pH meter and chloride was determined by potentiometric titration with a standard silver nitrate solution [1]. Sodium was measured using Flame photometer (XP BWBTech, England). The concentration sulfate was determined using
O.A. Nalivan et al. / Data in brief 27 (2019) 104627
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Table 3 Corrosion and scaling indices, equations, and their associated status [2,3,4,5,6, and 7]. Index
Equation
Index value
Status
AI ¼ pH þ log10(Alkalinity) AI<10 *(H) 10 < AI<12 AI>12 Langelier Saturation LSI ¼ pH- pHs LSI>0 Index (LSI) LSI ¼ 0 LSI<0 Puckorius Scaling PSI ¼ 2pHs- pHeq PSI>7 Index (PSI) PSI<6 Ryznar Stability RSI ¼ 2pHs- pH RSI<6 Index (RSI) 6 < RSI<7
Very aggressive Moderately aggressive Nonaggressive Scale can form, CaCO3 precipitation may occur Borderline scale potential No potential to scale, water will dissolve CaCO3 Likely to dissolve scale Scaling is unlikely to occur Scale tendency increases as the index decreases Calcium carbonate formation probably does not lead to a protective corrosion inhibitor film RSI>7 Mild steel corrosion becomes an increasing problem. LS < 0.8 Chlorides and sulfate unlikely to interfere with natural film formation 0.8 < LS < 1.2 Chlorides and sulfates may interfere with natural film formation. LS > 1.2 High local corrosion tendency expected as the index increases
Aggressive index (AI)
Cl þ SO2 4 Larson-skold index LS ¼ 2 HCO 3 þ CO3 (LS)
Where: pHs¼ (9.3 þ A þ B)-(C þ D); A¼ (Log10 (TDS)-1)/10; B ¼ 13.12 * Log10 (oCþ273) þ 34.55; C ¼ Log10 (Ca2þ as CaCO3) - 0.4; D ¼ Log10 (alkalinity as CaCO3); pHeq ¼ 1.465 þ Log10 (alkalinity) þ 4.54; H in AI index has Calcium hardness (mg/l).
Table 4 Summary of water stability indices (corrosion and scaling) in the area. Sample numbers
pHs
pHeq
RSI
LSI
AI
PSI
LS
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 S30 S31 S32
8.19 7.90 7.60 7.99 7.89 8.08 8.03 7.83 8.17 7.70 7.98 7.93 8.07 7.76 7.61 7.70 7.84 7.73 8.12 7.90 7.83 7.77 7.78 7.98 7.94 7.67 7.43 7.81 7.67 7.73 8.01 7.74
7.94 8.21 8.33 8.03 8.20 8.04 7.92 8.21 7.96 8.24 8.06 8.26 7.99 8.25 8.38 8.30 8.26 8.18 7.98 8.21 8.12 8.23 8.02 7.87 8.05 8.31 8.41 8.07 8.22 8.12 8.03 8.10
8.12 8.61 7.63 8.62 8.36 8.88 8.34 8.45 8.91 8.33 8.44 7.94 8.51 8.13 7.82 7.86 7.80 7.53 8.46 8.10 7.98 7.65 8.03 8.39 8.88 7.82 7.20 7.89 7.91 8.05 8.41 7.35
0.07 0.71 0.03 0.63 0.47 0.79 0.31 0.63 0.74 0.62 0.46 0.02 0.44 0.37 0.21 0.17 0.04 0.21 0.34 0.21 0.14 0.12 0.26 0.41 0.94 0.15 0.24 0.08 0.23 0.33 0.41 0.38
12.24 11.48 12.20 11.59 11.73 11.37 11.92 11.63 11.56 11.46 11.72 12.24 11.69 11.86 12.01 12.01 12.25 12.46 11.96 12.04 12.06 12.41 11.92 11.76 11.19 12.06 12.39 12.15 12.03 11.91 11.77 12.51
8.43 7.59 6.87 7.95 7.58 8.12 8.14 7.44 8.38 7.17 7.91 7.59 8.16 7.27 6.84 7.09 7.42 7.28 8.26 7.58 7.55 7.31 7.53 8.09 7.83 7.02 6.46 7.55 7.13 7.33 7.98 7.37
3.8 0.96 1.5 2.47 0.36 1.08 1.95 0.21 1.52 0.74 1.01 0.23 0.61 0.91 0.29 0.07 0.36 1.17 1.01 0.42 0.49 0.44 1.95 1.68 0.97 0.56 0.19 1.16 0.97 1.42 0.27 0.98
(continued on next page)
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Table 4 (continued ) Sample numbers
pHs
pHeq
RSI
LSI
AI
PSI
LS
S33 S34 S35 S36 S37 S38 S39 S40 S41 S42 S43 S44 S45 S46 S47 S48 S49 S50 S51 S52 S53 S54 S55 S56 S57 S58 S59 S60 S61 S62 S63 S64 S65 S66 S67 S68 S69 S70 S71 S72 S73 S74 S75 S76 S77 S78 S79 S80 S81 S82 S83 S84 S85 S86 S87 S88 S89 S90 S91 S92 S93 S94
8.36 8.01 8.22 8.04 8.10 7.86 7.61 7.76 7.95 7.77 7.66 7.97 7.99 7.99 7.81 8.16 7.61 8.10 7.86 7.81 7.67 7.89 8.17 8.14 8.29 7.91 8.01 7.73 8.11 7.83 8.00 7.74 7.78 7.65 8.13 7.79 7.79 7.82 7.76 7.73 7.76 7.56 7.69 7.93 8.24 7.77 7.93 7.64 7.62 7.38 7.59 7.51 8.34 7.70 7.95 7.79 8.19 7.82 7.58 7.81 7.90 7.88
7.71 8.06 7.85 7.97 7.86 8.17 8.27 8.19 8.07 8.15 8.17 8.07 8.10 8.13 8.25 7.89 8.23 7.94 8.03 8.10 8.22 7.95 7.81 8.02 7.70 8.10 8.10 8.26 8.12 8.06 8.06 8.11 8.25 8.28 7.84 8.02 8.07 8.11 8.21 8.19 8.08 8.28 8.09 8.03 7.86 8.12 8.14 8.19 8.19 8.40 8.25 8.27 7.73 8.20 8.04 8.21 8.04 8.03 8.31 8.10 8.12 8.04
9.04 8.50 9.02 8.32 8.68 7.98 7.71 8.08 7.90 8.17 8.11 8.35 8.01 8.53 7.83 8.87 7.88 8.44 8.08 8.06 7.71 8.86 8.65 8.58 9.00 7.99 8.32 8.07 8.33 8.01 8.52 7.67 7.93 7.64 8.66 8.04 7.93 8.05 7.90 8.16 8.00 7.50 7.68 8.56 8.85 7.50 8.53 7.70 7.28 7.24 7.48 7.64 8.67 7.74 8.30 8.07 8.48 7.97 7.57 7.96 8.38 8.17
0.68 0.48 0.81 0.28 0.57 0.11 0.11 0.32 0.05 0.41 0.44 0.38 0.02 0.54 0.03 0.71 0.27 0.34 0.23 0.25 0.04 0.98 0.47 0.44 0.72 0.09 0.31 0.34 0.22 0.18 0.52 0.08 0.15 0.00 0.52 0.25 0.13 0.22 0.15 0.43 0.25 0.06 0.01 0.63 0.60 0.28 0.61 0.06 0.33 0.13 0.10 0.13 0.32 0.03 0.35 0.29 0.29 0.15 0.01 0.16 0.48 0.30
11.56 11.70 11.37 11.90 11.56 12.17 12.06 11.83 12.25 11.83 11.73 11.89 12.27 11.58 12.18 11.57 11.95 11.87 11.94 12.06 12.24 11.16 11.76 11.79 11.41 12.12 11.94 11.96 11.91 12.05 11.79 12.34 12.11 12.18 11.62 11.90 12.08 11.96 12.17 11.85 11.90 12.26 12.13 11.58 11.61 12.39 11.63 12.09 12.46 12.35 12.28 12.01 11.86 12.17 11.89 11.99 12.01 12.14 12.19 12.10 11.82 11.90
9.01 7.97 8.58 8.11 8.35 7.56 6.95 7.32 7.83 7.38 7.16 7.87 7.88 7.84 7.36 8.42 6.99 8.26 7.68 7.52 7.12 7.82 8.54 8.26 8.87 7.72 7.93 7.20 8.10 7.60 7.95 7.38 7.31 7.01 8.43 7.55 7.52 7.54 7.31 7.28 7.44 6.84 7.29 7.83 8.63 7.43 7.71 7.09 7.04 6.35 6.92 6.76 8.96 7.21 7.86 7.36 8.34 7.62 6.85 7.51 7.68 7.71
1.44 0.27 0.23 0.91 0.81 0.46 0.37 0.22 0.55 0.23 0.68 0.74 0.43 0.23 0.09 0.54 0.67 0.49 0.62 1.21 0.72 1.94 1.46 0.34 1.6 0.34 0.19 0.61 0.98 0.7 0.69 1.59 0.38 0.13 0.47 0.59 1.42 0.49 0.36 0.64 0.16 0.62 1.12 0.85 0.46 0.12 0.15 0.37 0.63 0.31 0.14 0.09 0.6 0.38 0.37 0.58 0.4 1.38 0.28 1.02 0.37 0.71
O.A. Nalivan et al. / Data in brief 27 (2019) 104627
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Table 4 (continued ) Sample numbers
pHs
pHeq
RSI
LSI
AI
PSI
LS
S95 S96 S97 S98 S99 S100 S101 S102 S103 S104 S105 S106 S107 S108 S109 S110 S111 S112 S113 S114 S115 S116 S117 S118 S119 S120 S121 S122 S123 S124 S125 S126 S127 S128 S129 S130 S131 S132 S133 S134 S135 S136 S137 S138 S139 S140 S141 S142 S143 S144 S145
7.89 8.10 7.55 7.82 7.77 7.80 7.99 7.83 7.79 7.85 7.47 7.65 7.82 7.79 7.91 7.53 7.90 7.83 7.78 7.93 7.81 7.74 7.44 8.02 7.89 7.83 7.58 7.64 8.12 7.64 7.87 7.61 7.82 7.37 7.65 7.87 7.54 7.87 7.67 7.72 7.95 7.42 7.54 7.46 7.88 7.59 7.87 7.98 7.74 7.69 7.72
7.95 7.92 8.31 8.08 8.23 8.12 7.99 8.12 8.05 8.06 8.40 8.26 7.92 8.12 8.08 8.36 7.95 8.10 8.17 8.12 8.00 8.21 8.41 8.06 8.01 8.16 8.13 8.23 7.95 8.20 8.15 8.26 8.06 8.39 8.26 8.13 8.20 8.08 8.27 8.16 8.04 8.34 8.25 8.40 7.92 8.36 7.88 7.93 8.18 8.21 8.14
8.11 7.85 7.64 7.81 8.02 8.31 8.35 8.03 7.87 8.27 7.49 7.63 8.23 7.93 8.19 7.72 8.35 8.20 7.52 7.97 7.76 7.90 7.31 8.65 8.21 8.06 7.88 8.11 8.42 6.76 8.26 7.60 7.84 7.20 7.58 8.08 7.90 8.37 7.86 7.63 8.38 6.39 7.47 7.26 8.03 7.65 8.16 8.40 8.02 7.32 7.40
0.23 0.25 0.08 0.01 0.25 0.50 0.36 0.20 0.07 0.42 0.02 0.01 0.41 0.14 0.28 0.19 0.46 0.37 0.26 0.05 0.04 0.17 0.14 0.62 0.32 0.24 0.30 0.47 0.30 0.87 0.39 0.01 0.02 0.17 0.07 0.21 0.35 0.50 0.20 0.09 0.43 1.02 0.06 0.21 0.15 0.06 0.30 0.41 0.28 0.36 0.31
11.92 12.41 12.19 12.16 11.97 11.81 11.91 12.05 12.11 11.81 12.10 12.22 11.79 12.07 11.85 11.95 11.80 11.84 12.55 12.11 12.18 11.95 12.35 11.64 11.96 12.04 11.88 11.78 12.00 13.05 11.93 12.13 12.16 12.40 12.25 11.90 11.82 11.79 11.97 12.38 11.73 13.25 12.32 12.49 12.06 12.19 11.86 11.84 11.86 12.49 12.52
7.83 8.28 6.79 7.55 7.32 7.49 7.99 7.54 7.54 7.63 6.54 7.03 7.72 7.46 7.74 6.70 7.84 7.56 7.38 7.73 7.62 7.26 6.48 7.98 7.77 7.49 7.03 7.05 8.29 7.07 7.59 6.95 7.58 6.35 7.04 7.61 6.89 7.66 7.06 7.28 7.86 6.49 6.83 6.53 7.84 6.82 7.85 8.04 7.30 7.16 7.29
0.79 0.42 0.16 0.13 0.13 0.56 0.51 0.15 0.5 0.88 0.09 0.38 1.94 0.21 0.23 0.08 1.62 0.19 0.16 0.53 1.08 0.19 0.24 0.28 1.07 0.41 1.17 0.16 0.29 0.11 0.39 0.21 0.74 0.28 0.26 0.1 0.63 0.34 0.29 0.4 0.65 0.43 0.3 0.15 2.05 0.08 1.39 0.43 0.41 0.15 0.65
Spectrophotometer (UV-1800, Shimadzu, Japan). The calcium, magnesium and total hardness were measured by the EDTA titration method [1]. The alkalinity is expressed in (mg/L) of calcium carbonate and the basis of the endpoint of titration with sulfuric acid. Based on the Aggressive index, Langelier saturation index, LarsoneSkold index, Ryznar saturation index and Puckorius scaling index were calculated as detailed in Table 3. Lastly, the intensity of scaling and corrosion were determined per spring and the derived values were interpolated over the entire area using geostatistical analysis (Inverse Distance Weighting) in ArcGIS. In addition, the prepared maps were reclassified based on the
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Table 5 Descriptive statistic of corrosion and scaling indices as well as pHs and pHeq. Indices
pHs
pHeq
RSI
LSI
AI
PSI
LS
Max Min Mean St.dev.
8.36 7.37 7.83 0.20
8.41 7.70 8.12 0.15
9.04 6.39 8.05 0.45
1.02 0.98 0.22 0.30
13.25 11.16 11.99 0.31
9.01 6.35 7.54 0.53
3.80 0.07 0.65 0.56
Fig. 2. Spatial distribution of LS, AI, LSI, RSI, and PSI respectively from top to down in the area interpolated by geostatistical analyses.
O.A. Nalivan et al. / Data in brief 27 (2019) 104627
11
Fig. 3. Groundwater quality classes for the agricultural sector based on the Wilcox diagram (C1S1: excellent; C1S2eC2S1eC2S2: very good).
classification method proposed by Abbasnia et al. [3]. To assess the groundwater quality for the agricultural sector, the EC and SAR parameters were examined for each sample and interpolated over the area. The quality status was then determined based on the Wilcox diagram. Examination of SAR follows the equation:
Naþ SAR ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi þþ 0:5 Ca þ Mgþþ
Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.dib.2019.104627. References [1] APHA, Standard Methods for the Examination of Water and Wastewater, APHA, USA, 1999. [2] M. Dehghani, L. Kashtgar, S. Davoodi, N. Shamsedini, F. Zaravar, Data on the trend of corrosivity and scale formation potential of Shiraz groundwater drinking water resources during 2001-2007, Data Brief 23 (2019) 103736, https://doi.org/10. 1016/j.dib.2019.103736. [3] A. Abbasnia, M. Alimohammadi, A. Hossein Mahvi, R. Nabizadeh, M. Yousefi, A.A. Mohammadi, H. Pasalari, M. Mirzabeigi, Assessment of groundwater quality and evaluation of scaling and corrosiveness potential of drinking water samples in
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[4]
[5]
[6]
[7] [8] [9]
O.A. Nalivan et al. / Data in brief 27 (2019) 104627 villages of Chabahr Watershed, Sistan and Baluchistan province in Iran, Data Brief 16 (2018) 182e192, https://doi.org/10. 1016/j.dib.2017.11.003. S. Acharya, S.K. Sharma, V. Khandegar, Assessment and hydro-geochemical characterization for evaluation of corrosion and scaling potential of groundwater in South West Delhi, India, Data Brief 18 (2018) 928e938, https://doi.org/10.1016/j.dib. 2018.03.120. R. Rezaei Kalantari, A.R. Yari, E. Ahmadi, A. Azari, M. Tahmasbi Zade, F. Gharagazloe, Survey of corrosion and scaling potential in drinking water resources of the villages in Qom province by use of four stability indexes (With Quantitative and qualitative analysis), Arch. Hyg. Sci. 4 (2013) 127e134. M. Yousefi, H.N. Saleh, A.H. Mahvi, M. Alimohammadi, R. Nabizadeh, A.A. Mohammadi, Data on corrosion and scaling potential of drinking water resources using stability indices in Jolfa, East Azerbaijan, Iran, Data Brief 16 (2018) 724e731, https://doi.org/10.1016/j.dib.2017.11.099. M. Shams, A.A. Mohammadi, S.A. Sajadi, Evaluation of corrosion and scaling potential of water in rural water supply distribution networks of Tabas, Iran, World Appl. Sci. J. 17 (2012) 1484e1489. Environmental Systems Research Institute (ESRI), ArcGIS 10.3.1. Redlands, CA, 2015. O. Asadi Nalivan, S.S. Ghiasi, S. Feiznia, N. Saghazade, Spatial and temporal variations of nitrate concentration in groundwater, J. Range & Watershed Manag. 71 (2) (2018) 307e319, https://doi.org/10.22059/JRWM.2018.23042 (In Persian).
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Data Article
Data on assessment of corrosion-scaling potential and chemical parameters of groundwater quality for industrial and agricultural sectors in the Piranshahr Watershed in the West Azerbaijan province, Iran Omid Asadi Nalivan a, *, Eisa Mollaefar b, Elinaz Soltani c, Mahdiyeh Karvarinasab d a
Department of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources (GUASNR), Golestan Province, Gorgan, Iran Department of Natural Resources and Watershed Management of Golestan Province, Gorgan, Iran c Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Fars Province, Shiraz, Iran d Islamic Azad University, Faculty of Natural Resources and Agriculture, Science and Research Branch, Tehran, Iran b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 23 May 2019 Received in revised form 19 September 2019 Accepted 27 September 2019 Available online 9 October 2019
The present work sets out to 1) evaluate the corrosion-scaling potential of groundwater resources for the industrial sector as well as to 2) examine groundwater chemical parameters for the agricultural sector in the Piranshahr Watershed in the West Azerbaijan province, Iran, using geostatistical analyses and the Wilcox diagram in a GIS environment. A total of 145 spring locations as representatives of groundwater potentiality were recorded by a handheld GPS device and the corrosion and scaling potential states were further scrutinized. The latter was carried out on the basis of examining the chemical parameters at each sample location including alkalinity, pH, temperature, Naþ, Caþþ, Mgþþ, TH, 2 2 HCO 3 , Co3 , Cl , SO4 , Electrical conductivity, and Total dissolved solids. The corrosion and scaling potential of groundwater was then evaluated by using Langelier saturation index (LSI), Larson eSkold index (LS), Ryznar stability index (RSI), Aggressive index (AI), and Puckorius scaling index (PSI). Also, the groundwater
Keywords: Corrosion-scaling potential Groundwater quality Wilcox diagram Hydro-geochemical Geostatistical analysis GIS
* Corresponding author. E-mail address: [email protected] (O.A. Nalivan). https://doi.org/10.1016/j.dib.2019.104627 2352-3409/© 2019 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
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quality state for agriculture was assessed by the Wilcox diagram on the basis of Electrical conductivity and Sodium adsorption ratio parameters. The provided data are beneficial for researchers, policymakers, and authorities for taking pragmatic actions. Also, the compiled data can be used in the context of corrosion/scaling and groundwater quality assessment can be generalized around the world. © 2019 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
Specifications Table Subject area Specific subject area Type of data How data were acquired
Data format Parameters for data collection
Description of data collection
Data source location Data accessibility
Environmental Science Water quality, water science, groundwater management Table and figure Groundwater samples were collected in two-liter plastic bottles during extensive field surveys. Further analyses (i.e., measuring parameters by standard methods [1]) were carried out in the laboratory of Natural Resources College in the University of Tehran. Laboratory facilities were used to measure the parameters and the commercial software packages i.e. ArcGIS and Microsoft Excel were used for analyzing the derived data. Raw, analyzed Sampling was conducted after the harvesting season. Groundwater samples were collected in 2 L bottles and transported to the laboratory of Natural Resources College in the University of Tehran on the next day and kept at a suitable temperature (4 C). All samples were analyzed according to the standard methods. Firstly, the scope was identified and then, with the help of the local guide and field surveys, water springs were identified and their geographic coordinates were recorded using a handheld global positioning system (GPS) device. Groundwater samples were collected from a total of 145 springs across the Piranshahr Watershed. It was ensured that the collected samples were well distributed across the area. Piranshahr Watershed, West Azerbaijan Province, northwest of Iran. The latitude and longitude coordinates of the springs are presented in Table 1. Repository name: Mendeley Data Data identification number: DOI: 10.17632/zg27cgxk4p.2 Direct URL to data: https://data.mendeley.com/datasets/zg27cgxk4p/2
Value of the data The chemistry of groundwater is a determining factor for its uses in different water sectors: water supply, agriculture, and industry. Due to the scarce information available regarding groundwater data in the area, the provided data can enable the authorities to pinpoint the main water quality issues as well as to undertake precautionary measures. The target communities of this data are the organizations that are continually making decisions on water quality issues, including the Water Resources Management Company, Water and Wastewater Organization, Ministry of Agriculture Jihad and the Ministry of Energy. Also, the compiled data can be used in the context of corrosion/scaling and groundwater quality assessment can be generalized around the world. The dataset can be used for monitoring groundwater quality in Piranshahr Watershed. Calculated scaling and corrosion indices are pivotal for monitoring water supply distribution networks and industrial utilities. From both economic and health security standpoints, assessing groundwater quality metrics, especially scaling and corrosion, are of great importance, which, in turn, will address the degree to which the suitable management plans are required for preventing economic and health consequences in the future. Corrosion imposes many financial and health problems respectively on systems and consumers. Scaling, on the other hand, can cause blocking and head loss in the network. Altogether, corrosion/scaling controls are substantial aspects of safe drinking water supplies and other uses in miscellaneous sectors. The dataset provided in the present work can be rendered into spatial distribution maps of the measured parameters' concentration across the area and also be used for the spatial distribution of groundwater quality across the world.
O.A. Nalivan et al. / Data in brief 27 (2019) 104627
3
1. Data This dataset contains 5 Tables and 3 Figures which present in detail the corrosion-scaling potential and chemical parameters of groundwater quality for the industrial and agricultural sectors in the Piranshahr Watershed. Fig. 1 shows the geographical location of the springs (i.e., sampling points) across the Piranshahr Watershed in the West Azerbaijan Province, Iran. Table 1 presents the water 2 quality chemical parameters including alkalinity, pH, temperature, Naþ, Caþþ, Mgþþ, TH, HCO 3 , Co3 , Cl , SO2 , EC, and TDS which have been measured during the field surveys and analyzed in the lab4 oratory. Table 2 shows the descriptive statistics such as maximum, minimum, mean, and standard deviation of hydro-geochemical parameters. Table 3 presents some indices, equations, definitions, and criteria which were further used for classifying water corrosion and scaling [2,3,4,5,6and7]. Table 4 shows the results obtained from the RSI, LSI, AI, PSI and LS analyses as well as other detailed information on pHs and pHeq. Table 5 shows the descriptive statistics such as maximum, minimum, mean, and standard deviation of corrosion and scaling indices. Mapping the corrosion and scaling indices was carried out by geostatistical analyses in ArcGIS 10.3.1 [8] which are shown in Fig. 2. Fig. 3 depicts the groundwater quality classes for the agricultural sector based on the Wilcox diagram in ArcGIS.
2. Experimental design, materials, and methods 2.1. Study area description The Piranshahr Watershed extends for 426.15 km2 and is located within the geographical coordinates of 44 49״40 ׳and 45 11״40 ׳longitude and 36 39״00 ׳and 36 56״00 ׳latitude, in the West Azerbaijan province, northwest of Iran (Fig. 1). The area receives annual mean precipitation of 650 mm,
Fig. 1. The geographical location of the Piranshahr Watershed and the recorded springs in the West Azerbaijan province in Iran.
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Table 1 Hydro-geochemical characterization of 145 groundwater samples in the Piranshahr Watershed. Sample numbers
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 S30 S31 S32 S33 S34 S35 S36 S37 S38 S39 S40 S41 S42 S43 S44 S45 S46 S47 S48 S49 S50 S51 S52 S53 S54 S55 S56 S57 S58
UTM Zone 38 N
(ms/ cm)
UTMX
UTMY
EC
489895 489463 507028 500854 491043 502107 508513 512472 500905 489336 503018 489492 506224 503318 499015 506527 499789 500450 507982 488803 511006 513290 505696 510999 488956 503395 505571 500830 505270 500527 507190 499798 506864 502271 489824 502815 492271 499719 512230 512406 499983 512366 501924 508606 499236 489693 499246 506703 502352 488969 514296 510991 500341 503063 503734 502892 488879 502847
4073692 4078050 4062034 4075624 4077392 4075725 4065028 4070778 4075583 4072764 4076689 4072776 4067765 4076826 4074453 4077079 4076186 4075866 4062881 4073280 4063326 4071876 4075016 4063342 4072973 4076532 4077779 4075753 4076461 4074864 4062507 4076382 4061918 4076724 4073902 4076271 4081165 4074885 4074644 4072929 4076733 4070895 4077966 4064686 4075648 4073875 4075713 4063133 4075839 4072954 4073673 4063427 4075302 4080485 4076407 4080580 4073071 4076196
292 238 375 349 266 212 347 540 294 131 303 266 296 378 431 251 259 337 289 137 339 371 472 345 106 373 289 403 448 418 282 385 265 310 162 304 240 316 359 375 384 412 382 307 316 176 315 277 442 222 336 333 464 161 370 236 138 309
PH
8.26 7.19 7.57 7.36 7.42 7.29 7.72 7.2 7.43 7.08 7.52 7.91 7.63 7.39 7.4 7.53 7.88 7.94 7.78 7.69 7.69 7.89 7.52 7.57 7 7.52 7.67 7.73 7.44 7.4 7.6 8.12 7.68 7.53 7.41 7.76 7.53 7.75 7.5 7.44 8 7.36 7.22 7.59 7.97 7.45 7.78 7.45 7.34 7.76 7.63 7.56 7.63 6.91 7.7 7.7 7.57 7.82
( C)
(mg/l) HCO TDS CO2 3 3 Cl
TH SO2 4
Alkalinity Caþþ Mgþþ T
Naþ
146 119 187 275 133 106 174 270 147 64 151 133 148 189 216 125 128 168 144 68 169 185 235 172 53 186 145 202 224 210 141 187 132 150 81 152 120 158 180 188 142 206 191 153 158 88 157 138 221 110 168 167 232 80 185 118 69 155
110 120 200 160 130 110 190 170 150 140 140 120 120 170 170 150 130 220 160 140 180 200 240 210 140 170 210 230 240 250 140 200 150 130 130 150 150 180 200 160 150 210 220 170 160 100 140 170 240 150 190 250 250 200 180 120 140 160
86.81 161.93 212.98 105.14 155.76 109.54 82.64 159.37 90.29 205.07 112.88 178.31 96.12 173.95 237.34 199.08 180.76 151.01 94.74 160.28 130.53 166.06 104.65 73.01 111.74 203.99 252.75 115.22 163.18 129.35 106.02 124.08 51.08 112.77 69.72 92.22 70.87 145.76 182.37 153.89 117.44 140.11 145.66 116.62 123.45 134.39 177.74 77.39 168.16 85.65 106.94 125.25 163.01 88.14 63.21 102.90 49.37 123.45
153.85 124.32 184.67 138.58 91.08 66.98 55.75 110.61 78.75 55.75 78.75 66.98 61.30 91.08 84.85 78.75 55.75 78.75 72.80 39.95 61.30 72.80 97.45 66.98 34.96 66.98 72.80 72.80 84.85 84.85 45.08 66.98 45.08 34.96 39.95 45.08 39.95 55.75 61.30 50.35 45.08 61.30 61.30 45.08 45.08 20.82 39.95 39.95 72.80 25.39 50.35 61.30 66.98 45.08 55.75 30.11 25.39 39.95
52.03 35.62 36.02 38.02 52.03 18.01 46.02 80.04 38.42 34.21 37.22 28.81 42.42 22.01 38.42 52.03 16.01 32.02 52.03 12.01 52.43 44.02 68.03 62.03 8.00 13.61 41.62 56.03 46.02 50.03 58.43 36.82 48.02 31.62 52.03 32.42 44.02 42.02 42.02 54.43 32.02 76.04 48.02 32.42 38.02 16.01 26.41 56.03 40.02 35.62 52.03 52.03 44.02 20.41 40.02 26.01 28.01 38.02
34.78 126.31 176.96 67.12 103.73 91.53 36.61 79.33 51.87 170.86 75.66 149.50 53.70 151.94 198.93 147.06 164.75 118.99 42.71 148.28 78.11 122.04 36.61 10.98 103.73 190.38 211.13 59.19 117.16 79.33 47.60 87.26 3.05 81.16 17.70 59.80 26.85 103.73 140.35 99.46 85.43 64.07 97.63 84.21 85.43 118.38 151.33 21.36 128.14 50.04 54.92 73.22 118.99 67.73 23.19 76.89 21.36 85.43
14.18 17.73 14.18 21.28 14.18 14.18 10.64 17.73 17.73 14.18 17.73 17.73 10.64 24.82 17.73 14.18 10.64 14.18 10.64 14.18 10.64 14.18 14.18 10.64 10.64 14.18 14.18 14.18 17.73 14.18 10.64 14.18 10.64 10.64 14.18 14.18 14.18 14.18 14.18 14.18 14.18 14.18 14.18 10.64 14.18 14.18 14.18 14.18 14.18 10.64 14.18 14.18 17.73 10.64 17.73 14.18 14.18 14.18
316.08 137.70 304.56 238.76 41.40 103.94 150.62 16.09 119.59 111.43 95.87 23.92 48.37 133.24 51.92 0.48 53.79 162.15 85.35 52.98 52.78 58.64 190.34 111.72 97.60 99.61 32.71 119.64 140.01 170.07 17.96 107.78 63.02 20.27 2.07 69.45 42.89 52.16 53.36 20.41 50.72 17.72 85.35 75.70 38.71 16.28 1.30 27.81 99.28 31.60 52.40 137.70 99.13 160.71 74.25 20.41 64.84 28.00
60.08 32.06 48.10 60.12 36.07 28.06 20.04 60.12 36.07 12.02 48.10 44.09 36.07 64.13 68.14 56.11 40.08 28.06 60.12 16.03 40.08 48.10 76.15 36.07 12.02 60.12 48.10 40.08 60.12 56.11 40.08 52.10 36.07 28.06 40.08 40.08 28.06 48.10 52.10 52.10 48.10 52.10 48.10 40.08 44.09 20.04 44.09 36.07 68.14 8.02 44.09 36.07 52.10 28.06 68.14 40.08 12.02 44.09
2.43 9.73 19.46 2.43 9.73 9.73 34.05 4.86 14.59 26.75 4.86 2.43 7.30 2.43 0.00 2.43 7.30 36.48 2.43 24.32 19.46 19.46 12.16 29.18 26.75 4.86 21.89 31.62 21.89 26.75 9.73 17.02 14.59 14.59 7.30 12.16 19.46 14.59 17.02 7.30 7.30 19.46 24.32 17.02 12.16 12.16 7.30 19.46 17.02 31.62 19.46 38.91 29.18 31.62 2.43 4.86 26.75 12.16
11.5 10 9 10.5 10 11 9 8.4 5 14 11 7 13.3 9 10 11 9 7.5 5 6 10 6 12.2 12 11 10 12 9 7.6 9 11 14 7.5 11 10 11 13 6 12 13 10 9 12 6.7 6 13 10 6 10 9 12 5 7 12 9 8 12 10
O.A. Nalivan et al. / Data in brief 27 (2019) 104627
5
Table 1 (continued ) Sample numbers
S59 S60 S61 S62 S63 S64 S65 S66 S67 S68 S69 S70 S71 S72 S73 S74 S75 S76 S77 S78 S79 S80 S81 S82 S83 S84 S85 S86 S87 S88 S89 S90 S91 S92 S93 S94 S95 S96 S97 S98 S99 S100 S101 S102 S103 S104 S105 S106 S107 S108 S109 S110 S111 S112 S113 S114 S115 S116
UTM Zone 38 N
(ms/ cm)
UTMX
UTMY
EC
489762 501235 509088 510997 499623 506162 502959 506569 506865 514334 506229 500568 501303 514465 511101 506852 506042 502367 506415 500923 501791 503543 500256 504359 504110 511744 508134 499030 507919 490180 501034 508102 500423 505910 507399 502213 502204 507787 501903 490129 501712 508922 501705 502142 501562 500240 493412 501803 508150 503573 501735 499209 508748 500943 502200 508676 500759 498985
4073992 4076169 4076270 4063143 4079089 4067723 4073178 4062916 4061919 4073728 4067785 4077247 4077125 4073946 4074923 4063762 4067874 4076892 4062900 4072627 4080487 4077895 4077743 4075552 4077328 4074496 4062063 4072670 4058846 4074445 4078308 4068804 4078928 4067811 4059229 4076503 4077810 4067834 4078291 4074513 4077530 4064022 4076849 4077381 4077774 4077700 4079059 4077951 4063779 4077824 4071713 4072297 4063981 4072450 4077812 4076141 4078091 4072329
370 309 256 334 370 301 288 283 339 382 277 314 432 434 390 321 275 221 247 287 288 408 306 453 425 448 213 332 334 382 289 363 309 290 280 251 380 217 423 375 331 427 380 372 339 370 312 352 455 377 229 324 282 233 374 259 300 288
PH
7.7 7.39 7.89 7.65 7.48 7.82 7.63 7.65 7.61 7.54 7.66 7.6 7.61 7.3 7.51 7.62 7.7 7.3 7.64 8.05 7.32 7.58 7.95 7.51 7.69 7.38 8.02 7.67 7.6 7.5 7.9 7.67 7.59 7.65 7.42 7.58 7.66 8.35 7.47 7.83 7.52 7.3 7.63 7.63 7.72 7.43 7.45 7.66 7.41 7.65 7.63 7.34 7.44 7.46 8.04 7.88 7.85 7.57
( C)
(mg/l) HCO TDS CO2 3 3 Cl
TH SO2 4
Alkalinity Caþþ Mgþþ T
Naþ
182 155 128 167 185 150 144 141 170 191 138 157 217 218 195 160 135 111 123 143 144 203 153 227 212 225 106 166 167 191 144 181 155 145 140 126 190 108 211 187 165 213 190 185 169 183 156 176 228 188 114 162 141 169 187 129 150 144
140 210 80 220 180 260 170 180 150 220 230 180 230 230 210 230 220 180 130 170 150 210 210 280 220 230 130 200 180 190 120 280 200 230 190 190 210 140 260 180 170 250 200 200 220 210 180 200 290 200 140 180 260 190 220 130 220 150
123.35 178.79 131.35 113.24 113.15 126.28 177.18 188.67 68.33 104.50 115.04 127.13 158.60 152.60 118.20 190.27 121.55 105.28 71.36 130.08 136.82 154.66 152.49 248.17 176.07 183.67 53.29 158.06 109.34 161.27 107.79 104.84 199.79 123.55 131.55 109.30 87.42 82.69 203.07 119.36 166.98 130.20 95.79 130.67 111.83 114.37 248.34 181.45 82.54 131.35 118.19 228.30 88.32 124.97 147.86 129.91 97.99 160.62
34.96 50.35 16.38 50.35 45.08 55.75 34.96 34.96 34.96 50.35 50.35 39.95 50.35 55.75 50.35 45.08 45.08 34.96 25.39 30.11 25.39 45.08 39.95 61.30 45.08 50.35 20.82 39.95 34.96 39.95 20.82 50.35 34.96 39.95 34.96 30.11 34.96 16.38 45.08 34.96 30.11 45.08 30.11 34.96 30.11 34.96 30.11 30.11 50.35 34.96 20.82 30.11 39.95 20.82 30.11 16.38 34.96 20.82
44.02 24.41 52.03 52.83 46.02 26.81 48.42 30.02 50.03 64.83 29.61 36.82 52.43 46.42 72.44 56.03 30.02 29.61 51.23 42.82 28.81 44.82 28.01 48.02 54.03 61.63 49.62 36.02 67.23 88.04 37.62 56.03 21.61 32.02 40.02 32.42 52.03 42.42 57.23 51.63 37.62 60.03 50.03 44.02 52.03 33.22 12.81 48.42 52.03 52.03 23.61 19.61 34.02 44.42 38.02 40.82 40.02 28.81
79.33 154.38 79.33 60.41 67.12 99.46 128.75 158.65 18.31 39.66 85.43 90.31 106.17 106.17 45.77 134.24 91.53 75.66 20.14 87.26 108.01 109.84 124.48 200.15 122.04 122.04 3.66 122.04 42.10 73.22 70.17 48.82 178.18 91.53 91.53 76.89 35.39 40.27 145.84 67.73 129.36 70.17 45.77 86.65 59.80 81.16 235.54 133.02 30.51 79.33 94.58 208.69 54.31 80.55 109.84 89.09 57.97 131.80
10.64 10.64 10.64 14.18 10.64 14.18 10.64 10.64 14.18 14.18 10.64 14.18 10.64 10.64 14.18 10.64 10.64 14.18 10.64 10.64 10.64 14.18 10.64 14.18 14.18 14.18 14.18 10.64 10.64 14.18 10.64 10.64 14.18 14.18 14.18 14.18 14.18 14.18 14.18 14.18 10.64 14.18 14.18 14.18 10.64 14.18 17.73 14.18 10.64 14.18 14.18 14.18 10.64 14.18 14.18 10.64 10.64 10.64
13.35 97.55 118.73 64.60 67.82 186.64 57.44 14.22 17.87 47.12 152.78 47.60 46.16 86.45 5.04 108.16 125.45 75.02 22.29 5.43 9.61 43.03 86.12 61.53 10.66 3.12 17.92 49.52 29.49 80.07 32.13 133.67 42.36 112.05 34.68 63.93 54.85 20.75 18.35 1.73 11.29 58.21 34.63 5.00 45.68 86.55 5.52 54.99 149.61 13.64 13.50 4.08 132.51 9.17 10.13 58.21 94.72 20.22
44.09 44.09 32.06 40.08 52.10 36.07 36.07 28.06 44.09 48.10 48.10 48.10 52.10 68.14 64.13 48.10 44.09 40.08 40.08 36.07 32.06 56.11 40.08 68.14 56.11 68.14 32.06 56.11 52.10 64.13 40.08 48.10 44.09 44.09 52.10 36.07 40.08 16.03 52.10 64.13 52.10 60.12 36.07 52.10 40.08 48.10 48.10 36.07 64.13 56.11 40.08 52.10 44.09 48.10 32.06 32.06 56.11 40.08
7.30 24.32 0.00 29.18 12.16 41.34 19.46 26.75 9.73 24.32 26.75 14.59 24.32 14.59 12.16 26.75 26.75 19.46 7.30 19.46 17.02 17.02 26.75 26.75 19.46 14.59 12.16 14.59 12.16 7.30 4.86 38.91 21.89 29.18 14.59 24.32 26.75 24.32 31.62 4.86 9.73 24.32 26.75 17.02 29.18 21.89 14.59 26.75 31.62 14.59 9.73 12.16 36.48 17.02 34.05 12.16 19.46 12.16
8 5 13 9 5 7 7 11 13 13 9 11 5 7 13 10 14 9 9 14 8 13 14 10 12 14 10 10 8 7 5 6 11 7 5 10 13 11 7 13 9 5 7 8 11 9 14 10 11 10 13 13 7 10 6 12 13 14
(continued on next page)
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Table 1 (continued ) Sample numbers
S117 S118 S119 S120 S121 S122 S123 S124 S125 S126 S127 S128 S129 S130 S131 S132 S133 S134 S135 S136 S137 S138 S139 S140 S141 S142 S143 S144 S145
UTM Zone 38 N
(ms/ cm)
UTMX
UTMY
EC
503166 499096 501069 500920 507437 507064 506138 501394 509406 501789 499715 503638 500018 499995 508088 506369 500214 502162 500838 501393 512772 502736 501868 495825 508347 513091 499205 499917 507988
4078473 4071930 4070642 4072713 4069650 4062505 4064156 4078648 4076588 4073710 4077021 4078371 4080350 4078255 4068205 4064339 4072445 4077854 4072298 4078648 4074497 4078062 4071744 4078269 4064749 4071434 4071866 4078691 4067798
407 284 279 295 514 384 273 379 423 315 300 383 346 256 393 354 328 365 306 378 417 355 216 362 460 393 309 327 233
PH
7.58 7.4 7.57 7.59 7.28 7.17 7.82 8.51 7.48 7.62 7.8 7.54 7.72 7.66 7.19 7.37 7.47 7.81 7.52 8.44 7.6 7.67 7.73 7.53 7.57 7.57 7.46 8.05 8.03
( C)
(mg/l) HCO TDS CO2 3 3 Cl
TH SO2 4
Alkalinity Caþþ Mgþþ T
Naþ
204 142 140 148 257 192 137 189 211 158 150 191 173 128 196 177 164 183 153 189 208 177 108 181 230 196 154 164 117
230 150 240 200 300 240 170 220 200 180 200 300 190 130 270 220 170 260 150 300 300 270 260 200 260 220 170 170 230
255.49 114.63 101.63 142.29 134.10 169.71 88.64 155.83 139.75 181.65 114.39 242.15 180.27 134.56 157.14 119.35 184.89 142.51 108.58 216.66 173.87 246.93 81.69 227.12 75.48 84.17 148.79 161.21 135.51
34.96 20.82 30.11 25.39 45.08 34.96 20.82 25.39 30.11 25.39 30.11 39.95 25.39 16.38 34.96 25.39 20.82 30.11 16.38 30.11 34.96 30.11 25.39 20.82 25.39 20.82 16.38 16.38 12.08
48.02 42.02 41.22 30.02 45.62 69.63 52.03 35.62 36.02 36.42 32.02 43.22 46.02 18.01 42.42 40.02 37.22 54.03 51.23 34.82 64.03 49.22 29.21 17.21 48.02 64.03 43.22 30.02 22.01
207.47 72.61 60.41 112.28 88.48 100.07 36.61 120.21 103.73 145.23 82.38 198.93 134.24 116.55 114.72 79.33 147.67 88.48 57.36 181.84 109.84 197.70 52.48 209.91 27.46 20.14 105.56 131.19 113.50
14.18 10.64 14.18 14.18 17.73 7.09 10.64 14.18 14.18 14.18 14.18 14.18 10.64 10.64 14.18 10.64 10.64 21.28 14.18 10.64 17.73 17.73 10.64 10.64 10.64 10.64 14.18 10.64 10.64
47.36 21.18 94.76 43.71 138.61 19.88 15.23 2.69 40.11 24.50 70.65 54.42 36.02 2.31 84.34 30.26 43.66 36.17 56.00 82.66 34.68 20.41 156.87 8.21 93.95 25.41 46.30 14.12 77.28
52.10 44.09 36.07 36.07 60.12 60.12 40.08 28.06 68.14 56.11 68.14 48.10 56.11 52.10 56.11 48.10 52.10 52.10 48.10 24.05 52.10 48.10 36.07 56.11 48.10 48.10 56.11 60.12 20.04
24.32 9.73 36.48 26.75 36.48 21.89 17.02 36.48 7.30 9.73 7.30 43.78 12.16 0.00 31.62 24.32 9.73 31.62 7.30 58.37 41.34 36.48 41.34 14.59 34.05 24.32 7.30 4.86 43.78
10 7 6 6 12 8 5 12 5 14 11 9 11 14 12 6 12 6 12 9 8 6 9 8 13 8 13 14 9
Table 2 Descriptive statistics of hydro-geochemical parameters. Parameters
EC
PH
TDS
CO2 3
HCO 3
Cl
TH
SO2 4
Alkalinity
Caþþ
Mgþþ
Naþ
T
Max Min Mean St.dev.
540 106 326.67 77.21
8.51 6.91 7.61 0.25
275 53 163.83 39.53
88.04 8.00 41.92 14.04
235.54 3.05 95.66 49.50
24.82 7.09 13.35 2.60
300 80 190.97 46.63
316.08 0.48 64.46 55.94
255.49 49.37 137.58 45.42
76.15 8.02 45.41 13.21
58.37 0 18.97 11.24
184.67 12.08 46.29 26.25
14 5 9.68 2.68
most falls in the form of snow in winter. The area is characterized by cold semi-arid climate according to Emberger classification. Elevation ranges from 1400 to 3254 m [9]. 2.2. Sample collection and analytical procedures In the present work, 145 spring water samples were collected in the Piranshahr Watershed in West Azerbaijan province and further analyzed to measure water quality chemical parameters. For that purpose, 2 L sampling bottles were used for groundwater collection and temperature of the samples was in-situ measured. The EC was measured using an EC meter (CTS-406 Portable Conductivity Meter, EZDO, Taiwan) and then used for measuring the TDS. The acquired samples were transported into the Natural Resources College Laboratory at the University of Tehran for further analysis based on standard methods. The value of (pH) was measured with digital pH meter and chloride was determined by potentiometric titration with a standard silver nitrate solution [1]. Sodium was measured using Flame photometer (XP BWBTech, England). The concentration sulfate was determined using
O.A. Nalivan et al. / Data in brief 27 (2019) 104627
7
Table 3 Corrosion and scaling indices, equations, and their associated status [2,3,4,5,6, and 7]. Index
Equation
Index value
Status
AI ¼ pH þ log10(Alkalinity) AI<10 *(H) 10 < AI<12 AI>12 Langelier Saturation LSI ¼ pH- pHs LSI>0 Index (LSI) LSI ¼ 0 LSI<0 Puckorius Scaling PSI ¼ 2pHs- pHeq PSI>7 Index (PSI) PSI<6 Ryznar Stability RSI ¼ 2pHs- pH RSI<6 Index (RSI) 6 < RSI<7
Very aggressive Moderately aggressive Nonaggressive Scale can form, CaCO3 precipitation may occur Borderline scale potential No potential to scale, water will dissolve CaCO3 Likely to dissolve scale Scaling is unlikely to occur Scale tendency increases as the index decreases Calcium carbonate formation probably does not lead to a protective corrosion inhibitor film RSI>7 Mild steel corrosion becomes an increasing problem. LS < 0.8 Chlorides and sulfate unlikely to interfere with natural film formation 0.8 < LS < 1.2 Chlorides and sulfates may interfere with natural film formation. LS > 1.2 High local corrosion tendency expected as the index increases
Aggressive index (AI)
Cl þ SO2 4 Larson-skold index LS ¼ 2 HCO 3 þ CO3 (LS)
Where: pHs¼ (9.3 þ A þ B)-(C þ D); A¼ (Log10 (TDS)-1)/10; B ¼ 13.12 * Log10 (oCþ273) þ 34.55; C ¼ Log10 (Ca2þ as CaCO3) - 0.4; D ¼ Log10 (alkalinity as CaCO3); pHeq ¼ 1.465 þ Log10 (alkalinity) þ 4.54; H in AI index has Calcium hardness (mg/l).
Table 4 Summary of water stability indices (corrosion and scaling) in the area. Sample numbers
pHs
pHeq
RSI
LSI
AI
PSI
LS
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 S30 S31 S32
8.19 7.90 7.60 7.99 7.89 8.08 8.03 7.83 8.17 7.70 7.98 7.93 8.07 7.76 7.61 7.70 7.84 7.73 8.12 7.90 7.83 7.77 7.78 7.98 7.94 7.67 7.43 7.81 7.67 7.73 8.01 7.74
7.94 8.21 8.33 8.03 8.20 8.04 7.92 8.21 7.96 8.24 8.06 8.26 7.99 8.25 8.38 8.30 8.26 8.18 7.98 8.21 8.12 8.23 8.02 7.87 8.05 8.31 8.41 8.07 8.22 8.12 8.03 8.10
8.12 8.61 7.63 8.62 8.36 8.88 8.34 8.45 8.91 8.33 8.44 7.94 8.51 8.13 7.82 7.86 7.80 7.53 8.46 8.10 7.98 7.65 8.03 8.39 8.88 7.82 7.20 7.89 7.91 8.05 8.41 7.35
0.07 0.71 0.03 0.63 0.47 0.79 0.31 0.63 0.74 0.62 0.46 0.02 0.44 0.37 0.21 0.17 0.04 0.21 0.34 0.21 0.14 0.12 0.26 0.41 0.94 0.15 0.24 0.08 0.23 0.33 0.41 0.38
12.24 11.48 12.20 11.59 11.73 11.37 11.92 11.63 11.56 11.46 11.72 12.24 11.69 11.86 12.01 12.01 12.25 12.46 11.96 12.04 12.06 12.41 11.92 11.76 11.19 12.06 12.39 12.15 12.03 11.91 11.77 12.51
8.43 7.59 6.87 7.95 7.58 8.12 8.14 7.44 8.38 7.17 7.91 7.59 8.16 7.27 6.84 7.09 7.42 7.28 8.26 7.58 7.55 7.31 7.53 8.09 7.83 7.02 6.46 7.55 7.13 7.33 7.98 7.37
3.8 0.96 1.5 2.47 0.36 1.08 1.95 0.21 1.52 0.74 1.01 0.23 0.61 0.91 0.29 0.07 0.36 1.17 1.01 0.42 0.49 0.44 1.95 1.68 0.97 0.56 0.19 1.16 0.97 1.42 0.27 0.98
(continued on next page)
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O.A. Nalivan et al. / Data in brief 27 (2019) 104627
Table 4 (continued ) Sample numbers
pHs
pHeq
RSI
LSI
AI
PSI
LS
S33 S34 S35 S36 S37 S38 S39 S40 S41 S42 S43 S44 S45 S46 S47 S48 S49 S50 S51 S52 S53 S54 S55 S56 S57 S58 S59 S60 S61 S62 S63 S64 S65 S66 S67 S68 S69 S70 S71 S72 S73 S74 S75 S76 S77 S78 S79 S80 S81 S82 S83 S84 S85 S86 S87 S88 S89 S90 S91 S92 S93 S94
8.36 8.01 8.22 8.04 8.10 7.86 7.61 7.76 7.95 7.77 7.66 7.97 7.99 7.99 7.81 8.16 7.61 8.10 7.86 7.81 7.67 7.89 8.17 8.14 8.29 7.91 8.01 7.73 8.11 7.83 8.00 7.74 7.78 7.65 8.13 7.79 7.79 7.82 7.76 7.73 7.76 7.56 7.69 7.93 8.24 7.77 7.93 7.64 7.62 7.38 7.59 7.51 8.34 7.70 7.95 7.79 8.19 7.82 7.58 7.81 7.90 7.88
7.71 8.06 7.85 7.97 7.86 8.17 8.27 8.19 8.07 8.15 8.17 8.07 8.10 8.13 8.25 7.89 8.23 7.94 8.03 8.10 8.22 7.95 7.81 8.02 7.70 8.10 8.10 8.26 8.12 8.06 8.06 8.11 8.25 8.28 7.84 8.02 8.07 8.11 8.21 8.19 8.08 8.28 8.09 8.03 7.86 8.12 8.14 8.19 8.19 8.40 8.25 8.27 7.73 8.20 8.04 8.21 8.04 8.03 8.31 8.10 8.12 8.04
9.04 8.50 9.02 8.32 8.68 7.98 7.71 8.08 7.90 8.17 8.11 8.35 8.01 8.53 7.83 8.87 7.88 8.44 8.08 8.06 7.71 8.86 8.65 8.58 9.00 7.99 8.32 8.07 8.33 8.01 8.52 7.67 7.93 7.64 8.66 8.04 7.93 8.05 7.90 8.16 8.00 7.50 7.68 8.56 8.85 7.50 8.53 7.70 7.28 7.24 7.48 7.64 8.67 7.74 8.30 8.07 8.48 7.97 7.57 7.96 8.38 8.17
0.68 0.48 0.81 0.28 0.57 0.11 0.11 0.32 0.05 0.41 0.44 0.38 0.02 0.54 0.03 0.71 0.27 0.34 0.23 0.25 0.04 0.98 0.47 0.44 0.72 0.09 0.31 0.34 0.22 0.18 0.52 0.08 0.15 0.00 0.52 0.25 0.13 0.22 0.15 0.43 0.25 0.06 0.01 0.63 0.60 0.28 0.61 0.06 0.33 0.13 0.10 0.13 0.32 0.03 0.35 0.29 0.29 0.15 0.01 0.16 0.48 0.30
11.56 11.70 11.37 11.90 11.56 12.17 12.06 11.83 12.25 11.83 11.73 11.89 12.27 11.58 12.18 11.57 11.95 11.87 11.94 12.06 12.24 11.16 11.76 11.79 11.41 12.12 11.94 11.96 11.91 12.05 11.79 12.34 12.11 12.18 11.62 11.90 12.08 11.96 12.17 11.85 11.90 12.26 12.13 11.58 11.61 12.39 11.63 12.09 12.46 12.35 12.28 12.01 11.86 12.17 11.89 11.99 12.01 12.14 12.19 12.10 11.82 11.90
9.01 7.97 8.58 8.11 8.35 7.56 6.95 7.32 7.83 7.38 7.16 7.87 7.88 7.84 7.36 8.42 6.99 8.26 7.68 7.52 7.12 7.82 8.54 8.26 8.87 7.72 7.93 7.20 8.10 7.60 7.95 7.38 7.31 7.01 8.43 7.55 7.52 7.54 7.31 7.28 7.44 6.84 7.29 7.83 8.63 7.43 7.71 7.09 7.04 6.35 6.92 6.76 8.96 7.21 7.86 7.36 8.34 7.62 6.85 7.51 7.68 7.71
1.44 0.27 0.23 0.91 0.81 0.46 0.37 0.22 0.55 0.23 0.68 0.74 0.43 0.23 0.09 0.54 0.67 0.49 0.62 1.21 0.72 1.94 1.46 0.34 1.6 0.34 0.19 0.61 0.98 0.7 0.69 1.59 0.38 0.13 0.47 0.59 1.42 0.49 0.36 0.64 0.16 0.62 1.12 0.85 0.46 0.12 0.15 0.37 0.63 0.31 0.14 0.09 0.6 0.38 0.37 0.58 0.4 1.38 0.28 1.02 0.37 0.71
O.A. Nalivan et al. / Data in brief 27 (2019) 104627
9
Table 4 (continued ) Sample numbers
pHs
pHeq
RSI
LSI
AI
PSI
LS
S95 S96 S97 S98 S99 S100 S101 S102 S103 S104 S105 S106 S107 S108 S109 S110 S111 S112 S113 S114 S115 S116 S117 S118 S119 S120 S121 S122 S123 S124 S125 S126 S127 S128 S129 S130 S131 S132 S133 S134 S135 S136 S137 S138 S139 S140 S141 S142 S143 S144 S145
7.89 8.10 7.55 7.82 7.77 7.80 7.99 7.83 7.79 7.85 7.47 7.65 7.82 7.79 7.91 7.53 7.90 7.83 7.78 7.93 7.81 7.74 7.44 8.02 7.89 7.83 7.58 7.64 8.12 7.64 7.87 7.61 7.82 7.37 7.65 7.87 7.54 7.87 7.67 7.72 7.95 7.42 7.54 7.46 7.88 7.59 7.87 7.98 7.74 7.69 7.72
7.95 7.92 8.31 8.08 8.23 8.12 7.99 8.12 8.05 8.06 8.40 8.26 7.92 8.12 8.08 8.36 7.95 8.10 8.17 8.12 8.00 8.21 8.41 8.06 8.01 8.16 8.13 8.23 7.95 8.20 8.15 8.26 8.06 8.39 8.26 8.13 8.20 8.08 8.27 8.16 8.04 8.34 8.25 8.40 7.92 8.36 7.88 7.93 8.18 8.21 8.14
8.11 7.85 7.64 7.81 8.02 8.31 8.35 8.03 7.87 8.27 7.49 7.63 8.23 7.93 8.19 7.72 8.35 8.20 7.52 7.97 7.76 7.90 7.31 8.65 8.21 8.06 7.88 8.11 8.42 6.76 8.26 7.60 7.84 7.20 7.58 8.08 7.90 8.37 7.86 7.63 8.38 6.39 7.47 7.26 8.03 7.65 8.16 8.40 8.02 7.32 7.40
0.23 0.25 0.08 0.01 0.25 0.50 0.36 0.20 0.07 0.42 0.02 0.01 0.41 0.14 0.28 0.19 0.46 0.37 0.26 0.05 0.04 0.17 0.14 0.62 0.32 0.24 0.30 0.47 0.30 0.87 0.39 0.01 0.02 0.17 0.07 0.21 0.35 0.50 0.20 0.09 0.43 1.02 0.06 0.21 0.15 0.06 0.30 0.41 0.28 0.36 0.31
11.92 12.41 12.19 12.16 11.97 11.81 11.91 12.05 12.11 11.81 12.10 12.22 11.79 12.07 11.85 11.95 11.80 11.84 12.55 12.11 12.18 11.95 12.35 11.64 11.96 12.04 11.88 11.78 12.00 13.05 11.93 12.13 12.16 12.40 12.25 11.90 11.82 11.79 11.97 12.38 11.73 13.25 12.32 12.49 12.06 12.19 11.86 11.84 11.86 12.49 12.52
7.83 8.28 6.79 7.55 7.32 7.49 7.99 7.54 7.54 7.63 6.54 7.03 7.72 7.46 7.74 6.70 7.84 7.56 7.38 7.73 7.62 7.26 6.48 7.98 7.77 7.49 7.03 7.05 8.29 7.07 7.59 6.95 7.58 6.35 7.04 7.61 6.89 7.66 7.06 7.28 7.86 6.49 6.83 6.53 7.84 6.82 7.85 8.04 7.30 7.16 7.29
0.79 0.42 0.16 0.13 0.13 0.56 0.51 0.15 0.5 0.88 0.09 0.38 1.94 0.21 0.23 0.08 1.62 0.19 0.16 0.53 1.08 0.19 0.24 0.28 1.07 0.41 1.17 0.16 0.29 0.11 0.39 0.21 0.74 0.28 0.26 0.1 0.63 0.34 0.29 0.4 0.65 0.43 0.3 0.15 2.05 0.08 1.39 0.43 0.41 0.15 0.65
Spectrophotometer (UV-1800, Shimadzu, Japan). The calcium, magnesium and total hardness were measured by the EDTA titration method [1]. The alkalinity is expressed in (mg/L) of calcium carbonate and the basis of the endpoint of titration with sulfuric acid. Based on the Aggressive index, Langelier saturation index, LarsoneSkold index, Ryznar saturation index and Puckorius scaling index were calculated as detailed in Table 3. Lastly, the intensity of scaling and corrosion were determined per spring and the derived values were interpolated over the entire area using geostatistical analysis (Inverse Distance Weighting) in ArcGIS. In addition, the prepared maps were reclassified based on the
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O.A. Nalivan et al. / Data in brief 27 (2019) 104627
Table 5 Descriptive statistic of corrosion and scaling indices as well as pHs and pHeq. Indices
pHs
pHeq
RSI
LSI
AI
PSI
LS
Max Min Mean St.dev.
8.36 7.37 7.83 0.20
8.41 7.70 8.12 0.15
9.04 6.39 8.05 0.45
1.02 0.98 0.22 0.30
13.25 11.16 11.99 0.31
9.01 6.35 7.54 0.53
3.80 0.07 0.65 0.56
Fig. 2. Spatial distribution of LS, AI, LSI, RSI, and PSI respectively from top to down in the area interpolated by geostatistical analyses.
O.A. Nalivan et al. / Data in brief 27 (2019) 104627
11
Fig. 3. Groundwater quality classes for the agricultural sector based on the Wilcox diagram (C1S1: excellent; C1S2eC2S1eC2S2: very good).
classification method proposed by Abbasnia et al. [3]. To assess the groundwater quality for the agricultural sector, the EC and SAR parameters were examined for each sample and interpolated over the area. The quality status was then determined based on the Wilcox diagram. Examination of SAR follows the equation:
Naþ SAR ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi þþ 0:5 Ca þ Mgþþ
Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.dib.2019.104627. References [1] APHA, Standard Methods for the Examination of Water and Wastewater, APHA, USA, 1999. [2] M. Dehghani, L. Kashtgar, S. Davoodi, N. Shamsedini, F. Zaravar, Data on the trend of corrosivity and scale formation potential of Shiraz groundwater drinking water resources during 2001-2007, Data Brief 23 (2019) 103736, https://doi.org/10. 1016/j.dib.2019.103736. [3] A. Abbasnia, M. Alimohammadi, A. Hossein Mahvi, R. Nabizadeh, M. Yousefi, A.A. Mohammadi, H. Pasalari, M. Mirzabeigi, Assessment of groundwater quality and evaluation of scaling and corrosiveness potential of drinking water samples in
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[4]
[5]
[6]
[7] [8] [9]
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