Investigation of Meteorological Drought Indices for Environmental Assessment of Yesilirmak Region

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 374-381

J. Environ. Treat. Tech.

ISSN: 2309-1185

Journal web link: http://www.jett.dormaj.com

Investigation of Meteorological Drought Indices for

Environmental Assessment of Yesilirmak Region

Alyar Boustani *, Asli Ulke

Ph.D. Graduated Student, Civil Engineering Dept., Ondokuz Mayis Univ., Kurupelit Campus, Atakum, Samsun, Turkey

Asst. Prof. Dr., Civil Engineering Dept., Ondokuz Mayis Univ., Kurupelit Campus, Atakum, Samsun, Turkey

Received: 08/09/2019

Accepted: 19/12/2019

Published: 20/02/2020

Abstract

Generally, as drought, inadequate rainfall means; the concept of drought is not just about reducing rainfall. When years of humidity in

an area are lower than average, this is caused by the disruption of our balance between rainfall and evapotranspiration. The reason for droughts

is not always the same. It is also difficult to estimate the start and end of the drought. Drought, a mysterious pest, has emerged mysteriously,

showing its effects slowly and it goes on for a long time. In this study, drought analysis of the Yesilirmak River basin area in the Black Sea

region between 1970 and 2014 was performed. Initially to conduct research, meteorological stations in the basin area that had been collecting

data for a long time were investigated, required hydro meteorological data have been obtained from the Meteorological Office which is

calculated accurately based on meteorological drought values. Rainfall and drought performance have been investigated with different

indices. In this study, monthly precipitation data for inland basin stations, with 7 different meteorological drought indices (PN, DI, RAI, ZSI,

CZI, MCZI and SPI) on 7 time scales 1,3,6,12,24,36 and 48 are used and so there is a comparison between drought indices and time periods

and all droughts were also recorded during the study period. Finally, the 12-month SPI index was selected as the best index and time scale

for the basin and the drought maps for the area were extracted using the SPI index results.

Keywords: Yesilirmak River Basin, Drought Indices, Meteorological Drought, GIS drought map

Thus, at the most sensitive time when the plant needs water,

Introduction¹

agricultural drought occurs when the soil does not have enough

moisture. Agricultural drought, even if the soil depth is rich in

water, reduces crop yields by a significant percentage. This

reduction can also reduce the amount of crop and cause a serious

deterioration by preventing the animals from being properly fed.

Agricultural drought is a special situation between meteorological

and hydrological droughts [3,4].

Although drought is one of the most dangerous natural pests,

there is still no precise definition of it in world literature. At the

same time, the effects of drought are becoming more and more

evident all over the world. Humans generally become aware of

droughts as water shortages increase [1].

.1 Types of drought

.1.1 Meteorological drought

.1.3 Hydrological drought

The decrease in precipitation over certain periods of time (at

Hydraulic drought, which means a decrease in surface and

least 30 years) relative to its normal values is called

meteorological drought. The first sign of drought is a decrease in

rainfall. For this reason, meteorological drought is the first stage

of drought. Continued meteorological droughts may increase

rapidly or end abruptly [2]. The drought started with

meteorological drought and continued with agricultural and

hydrological droughts, respectively, and ended with famine

droughts. Figure 1 illustrates this process.

groundwater, is due to a prolonged decrease in rainfall. In other

words, with the one-year surface flow lower than its average over

long years, it can be said that hydrological drought has begun [5].

Hydrological drought is usually caused by a combination of

meteorological drought and agricultural drought, which results in

socio-economic drought [6]. Hydrologic calculations cannot be

one of the early signs of hydrological drought, when there is a

decrease in rainfall, a delay in running water, and a decrease in

water storage reserves. Even after long periods of meteorological

drought, hydrological droughts can continue [7,8].

.1.2 Agricultural drought

Agricultural drought, which means the lack of water needed

by the plant in the soil, is caused by a decrease in water resources.

Plants need different amounts of water during their growth stages.

Agricultural drought can also mean the lack of water in the root

part of the plant, depending on its growth and development needs.

Model Scope

The Yesilirmak basin, in northern Anatolia, covers the area

between the waters of the Yesilirmak River and the Black Sea.

Corresponding author: Alyar Boustani, Ph.D. Graduated Student, Civil Engineering Dept., Ondokuz Mayis Univ., Kurupelit Campus,

Atakum, Samsun, Turkey. E-mail: alyar_boostani@yahoo.com.

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 374-381

Figure 1: Types of droughts and their effects

Eastbound with Canik, Giresun, Gumushane, Pulur, Cimen,

Kizildag, Kose, Tekeli, Yildiz, Camlibel, Akdaglar, Karababa,

Inegol and a divisive blue line crossing the hot Kunduz Dagi and

the Black Sea is surrounded. The basin area is approximately

873280 hectares. The basin area is about 5% of Turkey's total

area and 3% of the total population of Turkey. The basin area of

Yesilirmak basin area is 39129 sq. Km. The average annual

rainfall is 646 mm. The average annual water flow is 5.80 cubic

kilometers, and the basin average efficiency is 5.1 liters/s/km

[9,11]. Within the Yesilirmak River basin are the provinces of

Tokat, Samsun, Amasya, Corum, Sivas, Yozgat. The settlements

in the basin area are shown in Figures 2. As can be seen from the

map, the Yesilirmak River basin area is in neighborhood of the

Kizilirmak, Euphrates-Tigris,and East and West of Black Sea

basins.

Figure 2: Yesilirmak basin area

Research Methodology

.1 Drought indices

With such a drought index at different time intervals and long

periods, regional and temporal analyzes can be performed.

Typically, the time frame used is one month or one year for

drought analysis. It can be said that monthly time series are more

suitable for agricultural problem analysis and water supply [10].

We know that many drought indicators have been created to date.

There have been many written studies and classification studies

on these indices, although the indices have been compared to each

other in order to have a single definition of drought, and there is

also no single drought index that covers all purposes. The World

There are indicators that in drought expression accept a

certain amount of rainfall as a base [1]. An index created for one

region cannot be used for another, or the research that is being

done cannot be comparable. For this reason, spatial and temporal

analyzes cannot be performed with such indices. Each of the

drought indices is generally evaluated as a review of climate

change.

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 374-381

Meteorological Organization has suggested that rainfall

parameters alone or in combination with other meteorological



−ꢁ

(3)

parameters

obtained

drought

indices.



This section explains the indicators selected in the research that

were the result of previous research.

In this equation: R

: precipitation data converted to normal,

.2 Percent of normal (PN)

Percentage of normal precipitation is one of the simplest

R: observed precipitation data, Indicates the parameter of the

equation (obtained by trial-error method). The classification of DI

given in Table 2.

criteria for precipitation in an area. When it is used for an area or

season, it is very effective. The percentage of normal precipitation

as seen in Equation 1-3 is obtained by dividing the actual

precipitation by the minimum 30-year average of the precipitation

value and multiplied by 100. The percentage of normal

precipitation can also be calculated for time scales. Usually these

time scales can be based on a month that represents a particular

season rather than a series of months, years, or water years [12].

Table 2: Classification of DI index

DI Classification

Value of DI Index

Decile number

Mild drought

30 - 40

Fourth

Semi-severe

drought

20 - 30

10 - 20

Third

Second

First

Severe drought

PN= _X100

(1)

Very severe

drought

And less than 10

In this equation: PN: Normal Precipitation Size, Xi: In a given

series (month, season, year) of each precipitation value,

represents the mean X of long years (at least 30 years). In (12,

3.4 Rainfall Anomaly Index (RAI)

3), the classification of normal precipitation index is proposed in

Created by Rainfall Anomaly Index [13], it is based on

calculating precipitation deviation from its original value. For this

purpose, by calculating the mean of long-run series (p), select the

10 values that have the most value between time points of the

Table 1.

Table 1: Classification of PN Index

PN Classification

Value of PN Index

70 - 80

work environment and calculate their mean (M) and so on the 10

values that have the least value. Selection and mean are computed

Mild drought

Semi-severe drought

Severe drought

(

ꢃ), after obtaining the data Equations 4 and 5 are used and the

55 - 70

index value is obtained. If p> p, the anomaly is positive and the

index value is calculated by:

40 - 55

Very severe drought

40 and less than

P−P

RAI=+3[

]

(4)

ꢄ−P

.3 The Decile Index

The Precipitation Decile Index has been developed by [12] to

If P <p, the anomaly is negative and the index value will be

calculated by:

avoid the disadvantages of the normal precipitation index.

Precipitation decimals are calculated using real precipitation

series. Initially, precipitation values (in months or groups of

months) range from small to large and the cumulative frequency

distribution is created. This distribution is then divided into decks

P−P

X−P

RAI=-3[

]

(5)

The classification of the Rainfall Anomaly Index given in

Table 3.

(one-tenths of a dozen), each of which is 10% dry to wet. The first

sequence has the lowest rainfall group, indicating the driest

months and the last the wettest months. It is easier to calculate the

decay method than other indicators, which is why it has been

chosen as the drought detection method in the Australian drought

monitoring system [10]. One of the disadvantages of integer

computation is the need to use long-term recorded meteorological

data. To calculate the precipitation deck, the following steps are

performed:

Table 3: Classification of RAI Index

RAI Classification

Value of RAI Index

(0) ~ (-1.2)

(-1.2) ~ (-2.1)

(-2.1) ~ (-3)

Mild drought

Semi-severe drought

Severe drought

Very severe drought

And less (-3)

The data is sorted from small to large,

.5 Z-Score Index (ZSI)

The Z-Score Index calculation method, which is a

The degree of decay is determined by Equation 2,

ꢀ

∗(n+ꢁ)

(2)

ꢁꢂ

dimensionless index, uses the original precipitation data. As can

be seen in Equation 6, the specified time period before the

precipitation becomes normal distribution is obtained by

subtracting it from the mean and dividing by the standard

deviation. The Z-Score Index has standard deviation and standard

deviation, which means that the standard deviation of the Z-Score

Index (0) and their standard deviation are equal to (1), high values

are positive and low values are negative.

In equation: D

the number of precipitation data.

: denotes decile to i, i: denotes tenth, n: denotes

On each deck, the rainfall limit is specified,

Each period is categorized by decks.

However, given that the precipitation in their general state is

not proportional to the normal distribution, the precipitation data

should be converted to the normal distribution. For this purpose

and for the sake of simplicity, the "Box-Cox" conversion created

by Box and Cox in 1964 is used.

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 374-381

Xi ꢅ X

(weekly and monthly) are important in terms of agricultural water

demand and water potential, long-term time series, such as year

ZSI=

(6)

(

12, 24, 36 months) in terms of network water supply, water

^H^e^r^e^:^ꢃꢀ Precipitation values over time (month, season, or

resources management and groundwater activities are important.

The Standardized Precipitation Index (SPI) is computable by

normal scatter, normal logarithm, and gamma. However, print

sources have shown that, among the scattering, gamma scattering

shows the best of the precipitation series. Gamma scattering is

similar to Equations 12 and 13.

year), ꢃ: Mean of all precipitation data over time, σ: Standard

deviation of all precipitation data over time. The Z-Score Index

classification is given in Table 4.

Table 4: Classification OF ZSI Index

ZSI Classification

Value of ZSI Index

ꢁ

x^ꢏ⁻^ꢁꢐ⁻^ꢑ^⁄^β

(12)

g(x) ꢎ

β Г(ꢏ)

Mild drought

(0) ~ (-0.99)

Here: α> 0 and α: Formal parameter β> 0 and β: Critical

parameter x> 0 and x: Precipitation, respectively.

Semi-severe drought

Severe drought

(-1) ~ (-1.49)

(-1.5) ~ (-1.99)

∞

Г(ꢒ) ꢎ ∫_ꢂy^ꢏ⁻^ꢁꢐ⁻^ꢓdy

(13)

Very severe drought

And less than that (-2)

Γ (α): is a gamma function. For a station, the Standardized

Precipitation Index (SPI) needs to provide a probabilistic gamma

density function for the scattering frequency of the given

precipitation. The alpha and beta parameters of the gamma

probability density function are estimated for each station and for

each time criterion separately. For maximum likelihood solutions,

the average estimates of α and β are used. α and β are calculated

as equations 14-16.

.6 China-Z index (CZI)

The Z-index (CZI) is an index that accepts precipitation data

matching with the Pearson Type 3 scattering. It has been used

throughout the country since 1995 to monitor drought conditions

by the National Climatic Center of China and is calculated as

shown in Equations 7 and 8.

ꢉ

ꢁ

1 ꢇ _ꢕꢖ

CZİ = ꢆ ₂ZSİ ꢇ 1ꢈ - + ₆

ꢒ ꢎ ₄_Aꢔ1 ꢇ

√

(14)

(7)

ꢑ̅

ꢗ ꢎ

ꢏ

ꢌ

(15)

∑

(X −X)

j=ꢉ

ꢊ =

ꢋ

(8)

_n_∗_σ3

In this equation: Xj: The amount of precipitation that has

become normal dispersion over time, n: Sum of time zones, ZSI:

∑ ꢛn(ꢑ)

ꢘ ꢎ lꢙ(ꢃ) ꢚ

(16)

Z-Score Index results, ꢊ : Time zones show the skewness

coefficient of precipitation data.

ꢋ

Here n represents the number of observations, the parameters

obtained in Equation 17 being used to create the probability

function.

.7 China-Z index (CZI) modified

The modified China-Z index (MCZI) calculations are similar

ꢑ

ꢁ

ꢑ

∫ x^ꢏ⁻^ꢁꢐ⁻^ꢑ^⁄^βdx

(17)

to the China-Z index (CZI) calculations, with only the mean

values used in Equations 7 and 8 instead of the mean. The method

of obtaining the index is described in Equations 9-11.

G(x) ꢎ ∫ g(x)dx ꢎ

ꢂ

βαГ(ꢏ)

ꢂ

When we consider t = x/β, then the gamma function is

transformed into Equation 18;

ꢉ

퐂퐬

MCZİ = ^ꢆ2 φ ꢇ 1ꢈ - ⁺ퟔ

(9)

ꢍ

ꢁ

ꢑ

ꢏ

−ꢜ

G(x) ꢎ

∫ t ꢐ dt

(18)

Г(ꢏ)

ꢂ

ꢌ

(X −ꢄe)³

j=ꢉ

∑

ꢊ =

(10)

(11)

ꢋ

n∗σ³

Gamma scattering for zero x values is meaningless, however,

because the precipitation series can have zero values, for zero and

different from zero scattering, the cumulative probability is

transformed into Equation 19.

Xj ꢅ ꢄe

φ =

ꢍ

In Equations φ : Standard Variable Me: represents the

median value of all precipitation over time.

ꢍ

H(x) ꢎ q ꢇ (1 ꢚ q)G(x)

(19)

Here the "q" is a zero probability. If "m" in the precipitation

series represents zero, it is estimated as q = m/n. The probability

function H(x) is converted to the Standardized Precipitation Index

.8 Standardized Precipitation Index (SPI)

In [14], they created the Standardized Precipitation Index

(SPI) for the purpose of introducing and tracking regional

(SPI) by a random normal standard with a mean of zero and one

droughts. The Standardized Precipitation Index (SPI), in

principle, provides standardized probability of precipitation under

observation and can be calculated in the required time ranges of

variance. The value of Standardized Precipitation Index (SPI) is

calculated according to H (x) according to Equations 3-20 and 21.

, 3, 6, 9, 12, 24 and 48 months. While short-term timeframes

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 374-381

cꢞ+cꢉꢜ+cꢟꢜ^ꢟ

values are created, the values of ZSI, CZI, MCZI, and SPI, as soon

as they fall below negative one (-1), we realize that the drought

has begun. The drought continues until the value of the index

becomes zero and when it reaches a positive value, the drought is

over, this begins in the RAI Abnormal Rainfall Index when the

value drops below-1.2. The time elapsed from the beginning to

the end of the drought is called the "duration". The magnitude of

the drought event is called the "width" and "degree" is the

cumulative name of the index values obtained during the drought

and calculated according to Equation 24.

SꢝI ꢎ ꢚ ꢆ t ꢚ _ꢁ

SꢝI ꢎ ꢇ ꢆt ꢚ _ꢁ

₃ꢈ

0 < H(x) ≤ 0.5

(20)

(21)

ꢟ

+ꢠꢉꢜ+ꢠꢟꢜ +ꢠ3ꢜ

cꢞ+cꢉꢜ+cꢟꢜ^ꢟ

₃ꢈ

0.5 < H(x) < 1.0

ꢟ

+ꢠꢉꢜ+ꢠꢟꢜ +ꢠ3ꢜ

The t in these equations is obtained from Equations 22 and

ꢁ

t ꢎ √lꢙ ꢆ₍

_ꢟꢈ

0 < H(x) ≤ 0.5

(22)

(23)

ꢡ(ꢑ))

푫풓풐풖품풉풕 풅풆품풓풆풆

푫

ꢁ

t ꢎ ꢢlꢙ ꢆ_ꢁ

ꢟ^ꢈ

0.5 < H(x) < 1.0

ꢎ ꢚ ꢣ 푰풏풅풆풙 풗풂풍풖풆 (풊)

(ꢥꢦ)

−(ꢡ(ꢑ))

풊ꢤퟏ

0 1 2 1 2 3

On the other hand, the values of C , C , C , d , d and d are

constant throughout the equation and their sizes are as follows;

The value of the drought power is proportional to the length

of time the value of the elapsed time is calculated as follows:

= 2.515517; C

= 0.802853; C

= 0.010328; d

= 1.432788;

푀

(

^퐼⁾^ꢎ퐷

(25)

= 0.189269; d = 0.001308

The return period (L) is the time between two droughts.

As a result of standardizing the Standardized Precipitation

Index (SPI) values, within the selected time period, dry periods

and wet periods are simulated. When a drought is assessed

according to the Standardized Precipitation Index (SPI), it is

interpreted as a "dry period" in the period when the index is

consistently negative. In fish where the index drops below a

negative one, the beginning of the drought is considered, and in

the fish that the index increases positively, the end of the drought

is considered. Table 5 defines the drought classification according

to the Standardized Precipitation Index (SPI) values.

4 Results

Changes in monthly precipitation directly affect annual

averages. On the other hand, there are large differences in mean

values between years. In Figure 3-5 for the Samsun, Gumushane

and Sivas Graphic stations, rainfall variations are plotted over

long years. The graphs show that for 1981, Samsun recorded the

lowest and highest rainfall with an average of 497 mm and 999

mm of precipitation, respectively. For Gomushane also showed

the lowest and highest rainfall in 1988 with an average of 651 mm

and 1994 with 311 mm, while May was the month with the

highest rainfall of the year with 142 mm of precipitation falls

within the driest month. The highest and lowest average annual

precipitation, respectively, was found in the Sivas data of 2012

with 587 and 1973 with 285 mm of precipitation. The least

amount of rainfall occurs in August in all regions, with 6.1 mm of

rainfall at Tokat Station in August, with the highest amount of

rainfall with 170.2 mm at Giresun Station and October. When the

stations are checked, October and April are the most productive

months, respectively, and July and August are the least humid

months. Giresun, on the other hand, is designated as the most

abundant station with an annual rainfall of 1267.3 mm.

Table 5: Classification of SPI Index

SPI Classification

Mild drought

Value of SPI Index

(0) ~ (-0.99)

Semi-severe drought

Severe drought

(-1) ~ (-1.49)

(-1.5) ~ (-1.99)

Very severe drought

And less than that (-2)

.9 Drought quantities

To quantify, track and interpret the drought, some quantities

need to be analyzed. Among these quantities can be severity,

duration (D), degree (M: magnitude), power (I: intensity) and

return duration (L: length). In a series of times when drought

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 374-381

Figure 3: Total changes in annual rainfall in Samsun

Figure 4: Total changes in annual rainfall in Gumushane

Figure 5: Total changes in annual rainfall in Sivas

.1 Results of Precipitation Indicators

In 19 stations studied within this study, using 7 rainfall and

Table 7: Correlation Coefficient for Gumushane Station (1970-2014)

heat data over a 45 years period, 7 indices at 7 different time

points were investigated. As a result of this analysis all the dry

periods and duration and severity of these droughts have been

determined. In this study, not only the Yesilirmak basin stations

were used, but also some of the Black Sea coastal strip stations

such as Sinap, Unye, Ordu, Giresun and Trabzon. The reason is

when we look at the drought in the Yesilirmak basin by looking

around it, we see the whole together. Based on the findings, it has

been determined that droughts with similar intensity have

occurred throughout the basin area at similar intervals. Tables 6-

ZSI

CZI

0.97

0.99

0.95

0.98

0.96

MCZI

0.97

0.99

0.94

0.98

0.95

SPI

0.94

0.95

0.94

RAI

0.92

0.98

0.96

0.89

0.96

0.95

0.93

0.96

ZSI

CZI

MCZI

SPI

RAI

0.97

0.94

0.92

0.89

0.96

0.93

0.96

Table 8: Correlation Coefficient for Sivas Station (1970-2014)

ZSI

CZI

0.99

0.98

0.93

MCZI

0.99

0.98

0.93

SPI

0.99

0.98

RAI

0.99

0.98

0.99

0.93

0.91

ZSI

CZI

MCZI

SPI

RAI

, show the correlations matrices prepared for the Samsun,

0.99

0.93

Gumushane and Sivas stations, respectively.

Table 6: Correlation Coefficient for Samsun Station (1970-2014)

ZSI

CZI

0.97

0.99

0.95

0.98

0.96

MCZI

0.97

0.99

0.94

0.98

0.95

SPI

0.94

0.95

0.94

RAI

0.92

0.98

0.96

0.89

0.96

0.95

0.93

0.96

0.99

0.91

ZSI

CZI

MCZI

SPI

RAI

0.91

0.97

0.94

0.92

0.89

In the tables for each indicator at seven time points, the

droughts are reviewed and the longest and longest are identified,

with the drought start and end values given. While the degree of

drought is obtained by adding up the index values during this

period, the power is also obtained by dividing the degree of

0.96

0.93

0.96

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 374-381

drought by the duration of the drought. It is thus seen that both

the 3-station indices selected as models and the indices that have

been in the basin area are or are in complete harmony. All results

are increasing with increasing intervals, duration and degree of

drought.

identified. As the maps show, the drainage over the entire basin

area during 1974, 2001 and 2014 was severe and in 2010 was the

45th most water year in the study area. A closer look at the maps

reveals that the Yesilirmak River basin has had more and more

droughts than the Black Sea coastline. Figure 6 demonstrates the

drought maps for mentioned years. By aggregating 45-year maps,

the droughts in the Yesilirmak basin area and the Black Sea

coastal area were mostly dry and moderate water and it also

indicates that the region has had a moderate climate except for

certain years.

.2 Drought maps

With the maps prepared, the dry and blue zones are visible in

a given year, on the other hand, with the accumulation of all maps

in the 45 years studied, the dry years of the basin area are

A) Years of severe drought

B) Watery years

Figure 6: Comparison of drought maps for drought and watery years

years of 1974, 2001 and 2014, with severe droughts, 2010 was the

5th most probable year in the basin area. It is recommended that

Conclusion

In this study, the drought model trend in the Yesilirmak area

further research be carried out on agricultural and hydrological

drought. Indicators can be found from different value sources and

they can be used for drought analysis. Different modeling

methods such as ANFIS or neural network can also be assisted

and comparisons can be made between the models while

improving the models developed in this study. Drought analysis

and drought maps prepared for each individual basin area are

important for the preparation of practical basin maps by the

Department of Water and the Ministry of Environment. In terms

of integrated management of basin continuity, drought analysis

due to climate change in the basin area will be possible by further

determining existing water potentials.

was calculated by examining 7 different meteorological drought

indices that require precipitation data (PN, DI, RAI, ZSI, CZI,

MCZI, and SPI) and drought quantities (severity, duration, degree

and power) have been investigated. By comparing the droughts

within the Yesilirmak River Basin and the Black Sea coastal area,

it was found that severe droughts have occurred over the years in

both parts. In that sense there is a kind of identity between the

basin and the beach but between the droughts in the interior of the

basin area, while the worst of them were -1.88 and -1.89

respectively, on the Black Sea coast these values were -0.26 and

1.9, respectively. In summary, the droughts in the interior areas

have been more severe and more severe. By evaluating all

workplace drought maps, droughts have been identified for 45

years. As shown in the maps throughout the basin area during the

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 374-381

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