Data on corrosive water in the sources and distribution network of drinking water in north of Iran

Javad Alimoradi; Dariush Naghipour; Hossein Kamani; Ghorban Asgari; Mohammad Naimi-Joubani; Seyed Davoud Ashrafi

doi:10.1016/j.dib.2017.12.057

. 2018 Jan 4;17:105–118. doi: 10.1016/j.dib.2017.12.057

Data on corrosive water in the sources and distribution network of drinking water in north of Iran

Javad Alimoradi ^a, Dariush Naghipour ^a, Hossein Kamani ^b, Ghorban Asgari ^c, Mohammad Naimi-Joubani ^a, Seyed Davoud Ashrafi ^a,^d,^⁎

PMCID: PMC5988215 PMID: 29876379

Abstract

This study aimed to determine the parameters of scaling and corrosion potential of drinking water in sources and distribution networks of water supply in two cities of north of Iran. The results of Amlash water sampels analysis in winter revealed that the average values of Langelier, Ryznar, Aggressive, Pockorius, and Larson- skold indices was −1.31, 9.73, 11.5, 9.74 and 0.24, respectively, but, in summer they were −1.51, 10.71, 11.36, 10.72 and 0.25, respectively. For Rudsar, the results of water sampels analysis in winter illustrated that the average values of Langelier, Ryznar, Aggressive, Pockorius, and Larson was −1.12, 9.69, 11.33, 9.19 and 0.16, respectively, while, in summer they were −1.05, 10.04, 11.92, 10.18 and 0.19, respectively. The beneficial of this data is showing the clear image of drinking water quality and can be useful for preventing the economical and safety problems relating to corrosion and scaling of drinking water.

Keywords: Drinking water, Corrosive water, Scaling potential, Amlash, Rudsar

Specifications Table

Subject area	Environmental Sciences
More specific subject area	Drinking water chemistry
Type of data	Table and figure
How data was acquired	Measurements of all parameters was done according to standard methods based on Standard Methods for the Examination of Water and Wastewater.
	Hardness parameters, alkalinity, calcium, bicarbonate and chloride were measured by titration method.
	Digital pH meter (Metrohm) was applied for pH analyzing.
	Sulfate was measured using Hach DR5000 spectrophotometer.
	Temperature was determined by digital thermometer.
	TDS was measured by scaling method.
Data format	Raw, analyzed
Experimental factors	The data were obtained monthly in both cold and warm season, winter and summer, and the pH and temperature measured in the place other samples after taking as standard method were stored in a dark place at 4 °C temperature and transferred to laboratory under 3 hours.
Experimental features	All the above mentioned parameters were acquired and the levels of all indices were calculated.
Data source location	Guilan Province, North of Iran, Iran (Fig. 1).
Data accessibility	All data are available within this article.

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Value of the data

•
The data shown here can be helpful for water and wastewater companies, water resources and treatment management, and for who related with water quality engineering and management.
•
The materials and ingredient of pipes, fittings and valves in distribution networks solved due to corrosive water and make some health, aesthetic and economic problems. So that, the determination of corrosion and scale potential of drinking water using reliable methods is useful for preventing of these problems.
•
The zoning of the Langelier, Ryznar, Aggressive, Pockorius, and Larson indices was done to make a clear picture of the corrosion and scaling potential in the water resources and distribution network in these study area.

1. Data

The subject of safe drinking water is important topic in the world [1], [2], [3], [4], [5]. The data of this paper present the information about the saturation situation of water supply quality for both season of winter and summer. Five stability indices, Langelier, Ryznar, Aggressive, Pockorius, and Larson were calculated using especial equations which summarized in Table 1. In the winter for Amlash county the mean values of pH, temperature, TDS, ${H C O}_{3}^{-}$ , ALK, ${S O}_{4}^{-}$ , Cl⁻ and Ca²⁺ were 7.56, 11.43 °C, 156.64, 170.91, 138.38, 23.68, 17.46 and 50.69 mg/L, respectively. But, in the summer season the mean values for those parameters were 7.65, 18.18 °C, 209.97, 173.52, 141.91, 28.28, 16.71 and 34.51 mg/L, respectively (Table 2). In the other case, Rudsar county, in the winter the mean values of pH, temperature, TDS, ${H C O}_{3}^{-}$ , ALK, ${S O}_{4}^{-}$ , Cl⁻ and Ca²⁺ were 7.31, 11.04 °C, 248.2, 213.39, 174.34, 21.68, 13.52 and 91.97 mg/L, respectively. But, in the summer season the mean values for those parameters were 7.91, 19.46 °C, 271.04, 197.96, 162.14, 24.35, 15.23 and 68.32 mg/L, respectively (Table 3). The data reveled that in both season of winter and summer all of the water supply of Amlash were low corrosive to extremely corrosive according to Langelier, Ryznar, Pockorius, and Larson indices, but, all of the water supply except one case of sampling point in the winter, were neutral according to Aggressive index (Table 4). In the case of Rudsar, the data reveled that in both season of winter and summer all of the water supply were low corrosive to extremely corrosive according to Langelier, Ryznar, Pockorius, and Larson indices, but, all of the water supply except one case of sampling point in the winter, and six case of sampling point in the summer were neutral according to Aggressive index (Table 5). Zoning map of five calculated indices in Amlash and Rudsar were shown in Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6 and Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11, respectively.

Table 1.

Equations and classifications of Langelier, Ryznar, Aggressive, Pockorius, and Larson-skold indices [8], [9].

Index	Equation	Value	Water situation
Langelier saturation	LSI=pH – pH_S	LSI<0	Corrosive
	pH_S=(9.3+A+B)−(C+D)	LSI=0	Equilibrium
	A=(log[TDS]−1)/10	LSI>0	Scaling

	B=−13.2(log(°C+273))+34.55
	C= log [Ca⁺²+CaCO₃]−0.4
	D= log [Alkalinity as CaCO₃]

Ryznar stability	RSI=2pH_S−pH	RSI<5.5	Heavy scale formation
		5.5< RSI<6.2	Some scale
		6.2< RSI<6.8	Non-scaling or corrosive
		6.8< RSI<8.5	Corrosive
		RSI>8.5	Extremely corrosive

Aggressive	AI=pH+log [(Ca⁺²) (Alk)]	AI<10	Highly corrosive
		10<AI<12	Moderate corrosive
		AI>12	Scaling

Puckorius scaling	PI=2pH_S−pHeq	PSI>7	Corrosive

	pH_eq=1.465 log (Alkalinity)+4.54	PSI<6	Scaling
	Alkalinity=[HCO₃⁻]+2[CO₃^-2]+[OH^-]	PSI<6	Scaling

Larson-skold	LS=[C_(CL^-₎+C_(SO4^-2₎] / [C_(HCO3^-₎+C_(CO3^-2₎]	LI>1.2	Corrosive

	C=Concentration (mg/L)	1.2>LI>0.8	Moderate corrosive
	C=Concentration (mg/L)	LI<0.8	Low corrosive

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Table 2.

Values of analyzed parameters and calculated indices in two seasons in Amlash.

	pH	Temp°C	TDSmg/L	HCO₃⁻mg/L	ALKmg/L CaCO₃	SO₄⁻mg/L	Cl⁻mg/L	Ca²⁺mg/L	LSI	RSI	AI	PSI	LI
Winter
AW1	7.38	9.69	175.4	216.54	176.44	25.47	18.13	61.69	0.14	9.6	11.42	9.15	−1.1
RAW3	7.01	13.53	113.54	109.81	79.49	25.34	15.06	35.92	0.36	10.94	10.52	10.55	−1.96
AW4	7.42	14.74	205.03	231.24	188.99	14.64	43.06	56.8	0.25	9.93	11.45	9.48	−1.25
AW5	7.51	13.67	186.28	121.06	98.48	27.54	15.86	54.51	0.36	10.45	11.23	10.51	−1.46
AW6	7.57	10.29	182.56	165.54	134.22	35.67	18.25	44.32	0.32	10.02	11.34	9.94	−1.22
AW7	8.18	8.31	114.06	141.05	115.36	27.5	15.57	53.21	0.3	8.98	11.97	9.61	−0.39
AW8	7.61	8.9	165.54	212.89	172.44	21.04	13.04	62.86	0.16	9.24	11.65	9.04	−2.44
AN	7.52	14.39	161.1	147.99	123.7	20.77	9.63	43.68	0.2	10.34	11.25	10.26	−1.41
RAN	7.9	9.42	106.31	192.12	156.38	15.15	8.57	43.26	0.12	8.09	12.67	9.18	−0.57
Min	7.01	8.31	106.31	109.81	79.49	14.64	8.57	35.92	0.12	8.09	10.52	9.04	−2.44
Max	8.18	14.74	205.03	231.24	188.99	35.67	43.06	62.86	0.36	10.94	12.67	10.55	−0.39
Mean	7.56	11.43	156.64	170.91	138.38	23.68	17.46	50.69	0.24	9.73	11.5	9.74	−1.31
St.Dev.	0.32	2.58	36.3	44.15	37.61	6.6	10.17	9.29	0.09	0.86	0.58	0.59	0.63
Summer
AW1	7.55	18.38	237.48	180.48	147.69	34.29	15.73	37.08	0.27	10.84	11.28	10.68	−1.64
RAW3	7.32	17.98	168.62	136.75	111.66	28.56	14.88	33.8	0.31	11.07	10.89	10.85	−1.87
AW4	7.56	18.14	256.09	205.87	168.38	29.61	31.46	38.18	0.29	10.43	11.67	10.49	−1.28
AW5	7.72	18.3	260.21	199.8	164	32.38	20.78	37.04	0.28	10.7	11.47	10.66	−1.49
AW6	7.45	18.28	227.27	169.09	137.5	39.03	19.44	37.51	0.34	10.96	11.14	10.74	−1.75
AW7	7.62	18.24	269.73	188.19	154.16	30.85	16.77	40.99	0.25	10.74	11.41	10.62	−1.56
AW8	7.54	18.76	193.27	189.93	154.99	28.82	12.56	47.1	0.21	10.43	11.4	10.23	−1.44
AN	7.95	17.19	150.18	124.95	102.1	17.26	9.43	18.71	0.22	10.94	11.2	11.43	−1.49
RAN	8.15	18.4	126.96	166.7	136.72	13.79	9.36	20.19	0.14	10.31	11.85	10.8	−1.08
Min	7.32	17.19	126.96	124.95	102.1	13.79	9.36	18.71	0.14	10.31	10.89	10.23	−1.87
Max	8.15	18.76	269.73	205.87	168.38	39.03	31.46	47.1	0.34	11.07	11.85	11.43	−1.08
Mean	7.65	18.18	209.97	173.52	141.91	28.28	16.71	34.51	0.25	10.71	11.36	10.72	−1.51
St.Dev.	0.25	0.42	52.12	27.49	22.6	7.97	6.8	9.29	0.06	0.26	0.28	0.32	0.23

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Table 3.

Values of analyzed parameters and calculated indices in two seasons in Rudsar.

	pH	Temp °C	TDS mg/L	HCO₃⁻mg/L	ALK mg/L CaCO₃	SO₄⁻mg/L	Cl⁻mg/L	Ca²⁺mg/L	LSI	RSI	AI	PSI	LI
Winter
RON	7.18	11.53	243.25	186.18	152.39	20.94	10.53	76.73	0.17	10.16	10.16	9.61	−1.48
KN	7.12	12.42	327.87	224.49	184.1	18.84	8.17	77.19	0.12	10.06	11.43	9.32	−1.47
RHN	7.75	11.77	133.5	173.45	141.21	20.38	11.08	46.08	0.18	9.61	11.56	9.67	−0.92
VN	7.25	11.89	335.59	244.95	199.55	20.43	18.47	116.62	0.15	9.8	11.61	9.14	−0.13
CHN	6.89	12.26	323.43	224.55	183.94	25.23	13.65	110.41	0.17	10.28	11.2	9.32	−1.69
TR	7.81	10.17	137.32	183.49	149.83	18.77	5.92	49.82	0.13	9.3	9.39	9.39	−0.74
ROW1	7.53	8.45	195.81	212.12	173.1	28.64	15.17	96.54	0.2	9.05	11.75	8.76	−0.76
ROW2	7.23	7.93	164.35	230.78	188.61	19.76	13.44	102.6	0.14	8.99	11.51	8.35	−0.88
ROW4	7.7	11.06	207.93	215.28	176.27	22.13	9.59	105.19	0.15	9.08	11.96	8.94	−0.69
KW1	6.65	11.17	317.87	223.67	182.88	28/5	11.23	98.19	0.17	10.53	10.9	9.33	−1.93
KW2	7.11	11.03	307.01	241.36	196.88	24.72	12.05	107.57	0.15	9.85	11.44	9.07	−1.36
KW3	6.73	11.19	292.88	258.04	210.83	20.47	14.04	107.13	0.13	10.18	11.08	8.97	−1.72
RHW2	7.48	12.85	170.66	227.89	186.21	17.8	17.76	94.14	0.14	9.3	11.72	8.92	−0.91
RHW3	7.1	13.28	158.42	223.68	182.99	21.19	12.87	99.58	0.15	9.61	11.36	8.87	−1.25
RHW4	7.34	8.23	178.16	220.6	179.88	21.67	12.45	96.06	0.15	9.09	11.57	8.59	−0.87
VW1	7.69	9.81	313.91	204.97	167.49	17.29	21.58	101.37	0.19	9.41	11.91	9.3	−0.86
VW2	7.27	10.53	292.26	210.18	171.5	23.87	15.23	86.86	0.18	9.93	11.42	9.4	−1.33
CHW2	7.52	11.16	301.17	194.24	158.99	22.48	15.42	93.84	0.19	9.78	11.68	9.53	−1.13
CHW3	7.67	13.2	314.47	154.63	125.83	25.77	18.23	81.61	0.28	10.13	11.68	10.19	−1.23
Min	6.65	7.93	133.5	154.63	125.83	17.29	5.92	46.08	0.12	8.99	9.39	8.35	−1.93
Max	7.81	13.28	335.59	258.04	210.83	28.64	21.58	116.62	0.28	10.53	11.96	10.19	−0.13
Mean	7.31	11.04	248.2	213.39	174.34	21.68	13.52	91.97	0.16	9.69	11.33	9.19	−1.12
St.Dev.	0.34	1.57	74.25	25.74	21.1	3.3	3.83	18.86	0.35	0.46	0.62	0.42	0.43
Summer
RON	7.81	20.15	236.07	173.42	142.39	20.15	9.08	51.31	0.16	10.4	11.67	10.52	−1.29
KN	7.92	20.86	338.86	208.23	170.83	24.08	12.58	85.04	0.18	10.04	12.08	10.15	−1.06
RHN	8.03	18.32	139.93	163.45	134.16	16.96	5.67	22.55	0.13	10.43	11.5	10.8	−1.2
VN	7.78	18.41	351.11	202.19	165.77	24.11	18.17	70.94	0.21	10.3	11.84	10.29	−1.25
CHN	7.62	17.99	313.3	201.2	162.66	24.73	17.87	77.05	0.21	10.27	11.71	10.11	−1.32
TR	7.78	17.62	140.96	166.65	136.61	15.11	7.91	23.46	0.14	10.58	11.29	10.7	−1.39
ROW1	7.92	20.02	244.79	200.76	163.33	28.79	22.1	75.14	0.25	9.87	12	10.01	−0.97
ROW2	8.07	19.98	221.34	213.25	173.88	26.15	12.22	80.99	0.19	9.5	12.22	9.75	−0.71
ROW4	8.07	19.89	235.14	207.42	169.44	24.38	12.79	85.1	0.18	9.52	12.23	9.8	−0.72
KW1	7.83	19.3	315.08	202.13	170.27	26.34	12.3	78.54	0.19	10.07	11.95	10.09	−1.12
KW2	8.7	20.09	330.82	192.8	157.61	29.62	16.72	88.33	0.24	9.24	12.84	10.19	−0.26
KW3	7.82	20.05	315.33	214.79	175.77	26.35	18.54	86.2	0.21	10	11.99	9.99	−1.09
RHW2	7.93	19.16	211.86	210.06	171.66	24.44	14.44	80.55	0.18	9.58	12.07	9.7	−0.82
RHW3	7.76	19.25	188.02	206.59	168.99	26.17	21.51	76.71	0.23	9.71	11.87	9.67	−0.97
RHW4	7.95	20.36	198.98	216	176.72	24.12	19.93	73.83	0.2	9.61	12.06	9.73	−0.82
VW1	7.8	19.22	350.7	215.61	175.88	23.84	20.45	71.92	0.2	10.21	11.9	10.19	−1.2
VW2	7.84	20.25	362.7	204.62	167.83	23.69	16.54	47.26	0.19	10.67	11.73	10.72	−1.41
CHW2	7.96	19.43	328.07	195.73	160.83	26.67	14.26	58.79	0.21	10.3	11.93	10.48	−1.16
CHW3	7.78	19.44	326.76	166.41	136.11	27.01	16.34	64.48	0.26	10.52	11.72	10.64	−1.36
Min	7.62	17.62	139.93	163.45	134.16	15.11	5.67	22.55	0.13	9.24	11.29	9.67	−1.41
Max	8.7	20.86	362.7	216	176.72	29.62	22.1	88.33	0.26	10.67	12.84	10.8	−0.26
Mean	7.91	19.46	271.04	197.96	162.14	24.35	15.23	68.32	0.19	10.04	11.92	10.18	−1.05
St.Dev.	0.22	0.86	73.31	17.42	14.19	3.59	4.61	19.55	0.03	0.41	0.32	0.37	0.29

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Table 4.

The condition of drinking water in view of scaling and corrosion indices in Amlash.

Sampling point	LSI	RSI	AI	PSI	LI
Winter
AW1	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
RAW3	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
AW4	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
AW5	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
AW6	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
AW7	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
AW8	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
AN	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
RAN	Corrosive	Extremely corrosive	Scaling	Corrosive	Corrosive
Summer
AW1	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
RAW3	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
AW4	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
AW5	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
AW6	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
AW7	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
AW8	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
AN	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
RAN	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive

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Table 5.

The condition of drinking water in view of scaling and corrosion indices in Rudsar.

Sampling point	LSI	RSI	AI	PSI	LI
Winter
RON	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
N	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
RHN	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
VN	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
CHN	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
TR	Corrosive	Extremely corrosive	Highly corrosive	Corrosive	Corrosive
ROW1	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
ROW2	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
ROW4	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
KW1	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
KW2	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
KW3	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
RHW2	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
RHW3	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
RHW4	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
VW1	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
VW2	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
CHW2	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
CHW3	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
Summer
RON	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
KN	Corrosive	Extremely corrosive	Scaling	Corrosive	Corrosive
RHN	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
VN	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
CHN	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
TR	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
ROW1	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
ROW2	Corrosive	Extremely corrosive	Scaling	Corrosive	Corrosive
ROW4	Corrosive	Extremely corrosive	Scaling	Corrosive	Corrosive
KW1	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
KW2	Corrosive	Extremely corrosive	Scaling	Corrosive	Corrosive
KW3	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
RHW2	Corrosive	Extremely corrosive	Scaling	Corrosive	Corrosive
RHW3	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
RHW4	Corrosive	Extremely corrosive	Scaling	Corrosive	Corrosive
VW1	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
VW2	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
CHW2	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive
CHW3	Corrosive	Extremely corrosive	Moderate corrosive	Corrosive	Corrosive

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Fig. 2 — Zoning map of Langelier index in Amlash.

Fig. 3 — Zoning map of Ryznar index in Amlash.

Fig. 4 — Zoning map of Aggressive index in Amlash.

Fig. 5 — Zoning map of Pockorius index in Amlash.

Fig. 6 — Zoning map of Larson-skold index in Amlash.

Fig. 7 — Zoning map of Langelier index in Rudsar.

Fig. 8 — Zoning map of Ryznar index in Rudsar.

Fig. 9 — Zoning map of Aggressive index in Rudsar.

Fig. 10 — Zoning map of Pockorius index in Rudsar.

Fig. 11 — Zoning map of Larson-skold index in Rudsar.

2. Experimental design, materials and methods

2.1. Study area description

The selected study area were Amlash (Population; 18,580) and Rudsar (Population; 93,970) county, located in Guilan, the major province in north of Iran, which shown in Fig. 1 [6]. In the both county of Amlash and Rudsar, the climate is warm and temperate and in winter, there is much more rainfall than in summer. The average annual rainfall in Amlash and Rudsar is 1162 and 1178 mm, respectively. In addition, the average annual temperature in both county is 15.8 °C. Most of the water distribution network in Amlash and Rudsar are made of metal materials with the length of 97 and 400 km, respectively.

Fig. 1 — Study area; Amlash and Rudsar County, Guilan Province, north of Iran.

2.2. Sample collection and analytical procedures

This research was a cross-sectional study during two season of winter and summer in 2017, and each month one sample were taken from each sample point. Therefore, fifty two samples (27 in winter and 27 in summer) were taken from nine sample point of Amlash, and one hundred and fifteen samples (57 in winter and 57 in summer) were taken from nineteen sample point of Rudsar. All measurements of the above parameters were carried out according to standard methods manual [7]. The samples were obtained monthly in winter and summer, and the pH and temperature were measured in the sampling place, other samples were stored in a dark cold box (4 °C) and transferred to laboratory of school of health under 3 h. Hardness parameters, alkalinity, calcium, bicarbonate and chloride were measured by titration method according to Standard Methods for the Examination of Water and Wastewater. Sulfate was measured using spectrophotometry method and total dissolved solid was measured by scaling method. Statistical analysis of the data was done using Microsoft Excel 2013 and spatial distribution of five calculated indices were done using Arc GIS.

Acknowledgements

The authors gratefully acknowledge staff of the Water and Wastewater Company of Amlash and Rudsar, Guilan, Iran.

Acknowledgments

Funding sources

This article was a part of master science thesis of the first author that has been registered in Ethics Committee under ID no: IR.GUMS.REC.1395.354, and the finance of this work was provided by Guilan University of Medical Sciences, Rasht, Iran. (Grant number; 95112303).

Footnotes

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Transparency data associated with this article can be found in the online version at doi:10.1016/j.dib.2017.12.057.

Transparency document. Supplementary material

Supplementary material

mmc1.docx^{(12.4KB, docx)}

Refrences

1.Kamani H., Bazrafshan E., Ashrafi S.D., Sancholi F. Efficiency of Sono-nano-catalytic process of Tio2 nano-particle in removal of erythromycin and metronidazole from aqueous solution. J-Mazand-Univ.-Med.-Sci. 2017;27:140–154. [Google Scholar]
2.Yousefi N., Fatehizedeh A., Ghadiri K., Mirzaei N., Ashrafi S.D., Mahvi A.H. Application of nanofilter in removal of phosphate, fluoride and nitrite from groundwater. Desalin. Water Treat. 2016;57:11782–11788. [Google Scholar]
3.Ashrafi S.D., Nasseri S., Alimohammadi M., Mahvi A.H., Faramarzi M.A. Optimization of the enzymatic elimination of flumequine by laccase-mediated system using response surface methodology. Desalin. Water Treat. 2016;57:14478–14487. [Google Scholar]
4.Ashrafi S.D., Kamani H., Mahvi A.H. The optimization study of direct red 81 and methylene blue adsorption on NaOH-modified rice husk. Desalin. Water Treat. 2016;57:738–746. [Google Scholar]
5.Ashrafi S.D., Kamani H., Jaafari J., Mahvi A.H. Experimental design and response surface modeling for optimization of fluoroquinolone removal from aqueous solution by NaOH-modified rice husk. Desalin. Water Treat. 2016;57:16456–16465. [Google Scholar]
6.Hosseinipour Dizgah S., Taghavi K., Jaafari J., Roohbakhsh E., Ashrafi S.D. Data on pollutants content in the influent and effluent from wastewater treatment plant of Rasht in Guilan Province, Iran. Data Brief. 2018;16:271–275. doi: 10.1016/j.dib.2017.11.042. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Federation W.E., Association A.P.H. American Public Health Association (APHA); Washington, DC, USA: 2005. Standard Methods for the Examination of Water and Wastewater. [Google Scholar]
8.Asgari G., Ramavandi B., Tarlaniazar M., Fadaie nobandegani A., Berizie Z. Survey of chemical quality and corrosion and scaling potential of drinking water distribution network of Bushehr city. Iran. South Med. J. 2015;18:353–361. [Google Scholar]
9.Abbasnia A., Alimohammadi M., Mahvi A.H., Nabizadeh R., Yousefi M., Mohammadi A.A., Pasalari H., Mirzabeigi M. Assessment of groundwater quality and evaluation of scaling and corrosiveness potential of drinking water samples in villages of Chabahr city, Sistan and Baluchistan province in Iran. Data Brief. 2018;16:182–192. doi: 10.1016/j.dib.2017.11.003. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material

mmc1.docx^{(12.4KB, docx)}

[bib1] 1.Kamani H., Bazrafshan E., Ashrafi S.D., Sancholi F. Efficiency of Sono-nano-catalytic process of Tio2 nano-particle in removal of erythromycin and metronidazole from aqueous solution. J-Mazand-Univ.-Med.-Sci. 2017;27:140–154. [Google Scholar]

[bib2] 2.Yousefi N., Fatehizedeh A., Ghadiri K., Mirzaei N., Ashrafi S.D., Mahvi A.H. Application of nanofilter in removal of phosphate, fluoride and nitrite from groundwater. Desalin. Water Treat. 2016;57:11782–11788. [Google Scholar]

[bib3] 3.Ashrafi S.D., Nasseri S., Alimohammadi M., Mahvi A.H., Faramarzi M.A. Optimization of the enzymatic elimination of flumequine by laccase-mediated system using response surface methodology. Desalin. Water Treat. 2016;57:14478–14487. [Google Scholar]

[bib4] 4.Ashrafi S.D., Kamani H., Mahvi A.H. The optimization study of direct red 81 and methylene blue adsorption on NaOH-modified rice husk. Desalin. Water Treat. 2016;57:738–746. [Google Scholar]

[bib5] 5.Ashrafi S.D., Kamani H., Jaafari J., Mahvi A.H. Experimental design and response surface modeling for optimization of fluoroquinolone removal from aqueous solution by NaOH-modified rice husk. Desalin. Water Treat. 2016;57:16456–16465. [Google Scholar]

[bib6] 6.Hosseinipour Dizgah S., Taghavi K., Jaafari J., Roohbakhsh E., Ashrafi S.D. Data on pollutants content in the influent and effluent from wastewater treatment plant of Rasht in Guilan Province, Iran. Data Brief. 2018;16:271–275. doi: 10.1016/j.dib.2017.11.042. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib7] 7.Federation W.E., Association A.P.H. American Public Health Association (APHA); Washington, DC, USA: 2005. Standard Methods for the Examination of Water and Wastewater. [Google Scholar]

[bib8] 8.Asgari G., Ramavandi B., Tarlaniazar M., Fadaie nobandegani A., Berizie Z. Survey of chemical quality and corrosion and scaling potential of drinking water distribution network of Bushehr city. Iran. South Med. J. 2015;18:353–361. [Google Scholar]

[bib9] 9.Abbasnia A., Alimohammadi M., Mahvi A.H., Nabizadeh R., Yousefi M., Mohammadi A.A., Pasalari H., Mirzabeigi M. Assessment of groundwater quality and evaluation of scaling and corrosiveness potential of drinking water samples in villages of Chabahr city, Sistan and Baluchistan province in Iran. Data Brief. 2018;16:182–192. doi: 10.1016/j.dib.2017.11.003. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Data on corrosive water in the sources and distribution network of drinking water in north of Iran

Javad Alimoradi

Dariush Naghipour

Hossein Kamani

Ghorban Asgari

Mohammad Naimi-Joubani

Seyed Davoud Ashrafi

Abstract

1. Data

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

Fig. 6.

Fig. 7.

Fig. 8.

Fig. 9.

Fig. 10.

Fig. 11.

2. Experimental design, materials and methods

2.1. Study area description

Fig. 1.

2.2. Sample collection and analytical procedures

Acknowledgements

Acknowledgments

Funding sources

Footnotes

Transparency document. Supplementary material

Refrences

Associated Data

Supplementary Materials

ACTIONS

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