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Ecological Indicators 29 (2013) 6–17

Contents lists available at SciVerse ScienceDirect

Ecological Indicators

jo ur n al homep ag e: www.elsev ier .com/ locate /eco l ind

evelopment of pollution indices for the middle section of the Lower Seyhanasin (Turkey)

ehmet Golge, Firdes Yenilmez, Aysegul Aksoy ∗

epartment of Environmental Engineering, Middle East Technical University (METU), 06800 Ankara, Turkey

r t i c l e i n f o

rticle history:eceived 16 March 2012eceived in revised form 29 August 2012ccepted 20 November 2012

eywords:ir quality indexater quality index

ollution index

a b s t r a c t

Pollution indices aggregate concentrations of several water or air quality parameters into a single quan-tity to indicate the general status of pollution in a region. Several pollution index models are presentin literature. However, their application for different cases may require modifications based on imple-mentation goals and available data. In this study, modified pollution indices were used to evaluate thepollution status in the middle section of the Lower Seyhan River Basin by employing a geographicalinformation system (GIS) software (ArcGIS 9.3) for data processing, estimations and evaluations. Airquality index (AQI) and water quality index (WQI) were utilized to evaluate air and water pollution lev-els, respectively. Moreover, a composite air–water quality index (AWQI) was developed to perform a

eyhan BasinrcGISriging

general assessment about the overall pollution status. The WQI and AQI were calculated for 2004–2010and 2007–2010, respectively. The AWQI was developed for the period of 2007–2010. Results indicatedthat for the available data and time frame considered in the study, air and water qualities were in goodconditions (low pollution), in general. Yet, precautions could still be taken for improvement. Results alsoindicated the need for improvement of monitoring network for better assessment of the environmental

n. In g

quality in the whole basi

. Introduction

Despite the fact that pollution indices are simplifications, theyan be useful in assessing environmental quality (Sanchez et al.,007). Instead of evaluating a long list of numerical quantitiesi.e., concentrations) belonging to various water and/or air qualityarameters, one can easily obtain information about the generaltatus of environmental quality with a single quantity defined byhe index (Debels et al., 2005). Pollution indices can be used toet priorities and allocate funds for environmental management byecision makers. People can be informed about the environmentaltatus. Moreover, environmental conditions in different areas cane compared and temporal change in the environmental quality of

given location can be followed (Pykh et al., 2000).In literature, several studies that focus on the development

nd application of water quality and air quality indices (WQI andQI, respectively) can be found (Bishoi et al., 2009; Bordalo et al.,001, 2006; Boyacioglu, 2007; Cairncross et al., 2007; Cheng et al.,007; Jonnalagadda and Mhere, 2001; Kassomenos et al., 2012;

han et al., 2003; Kyrkilis et al., 2007; Lermontov et al., 2009;urena, 2004; Pesce and Wunderlin, 2000; Sanchez et al., 2007;

imoes et al., 2008; Srebotnjak et al., 2012; Stambuk-Giljanovic,

∗ Corresponding author. Tel.: +90 312 2105874; fax: +90 312 2102646.E-mail address: aaksoy@metu.edu.tr (A. Aksoy).

470-160X/$ – see front matter © 2012 Elsevier Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.ecolind.2012.11.021

eneral, GIS tools were very helpful in the development of the indices.© 2012 Elsevier Ltd. All rights reserved.

1999). These indices vary by the formulations employed, the setsof parameters considered, and application goals. Yet, in all of them,parameters of concern are aggregated to obtain an index valuewithin a specific overall range (i.e., 0–100). Then, the index valueis compared to sub-ranges which indicate the level of pollution(i.e., a sub-index range of 0–20 may indicate a very bad quality,where as 80–100 may stand for pristine conditions). Although dif-ferent parameters and aggregation functions can be used (Changet al., 2001; Kumar and Alappat, 2004; Lermontov et al., 2009; Singhet al., 2008), pollution levels are ultimately represented in an outputthat conveys a common message to all parties that have differentexpertise in evaluating environmental quality data. Beyond scien-tific studies, WQI and, especially, AQI have found way in real lifeapplications. For example, US EPA reports AQI (calculated based onO3, particulate matter, CO, SO2 and NO2 concentrations in ambi-ent air with respect to national ambient air quality standards) daily(EPA, 2010; Cairncross et al., 2007; Kyrkilis et al., 2007).

Although AQI and WQI have been implemented for variouspurposes, an overall environmental pollution index that takes dif-ferent polluted media (i.e., water, air, and soil) into considerationis relatively less common. Zaharia and Surpateanu (2006) andZaharia and Murarasu (2009) introduced global pollution indices

to identify the environmental impacts of a heat and power co-generation plant and a chemical industrial unit, respectively. Themagnitude of potential pollution generated by these facilities wereestablished based on physico-chemical analysis of specific air

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M. Golge et al. / Ecologic

ollutants (e.g., SO2, NOx, CO, and solid particles), water pollutantse.g., suspended solids, extractible substances, organic matter asOD5, chloride, and sulfate), and likelihood of soil pollution (e.g.,H, extractible substances, and total organic carbon). Ludwig andulbere (1996) presented an AWQI in which AQI and WQI wereggregated using linguistic interpretation and fuzzy logic. Pykht al. (2000) tested structural-regression models, thermodynamic-ype models, diagram models, and complex system simulation

odels in developing a composite pollution index. Butter andyden (1998) constructed a composite overall index for the evalua-ion of environmental policy in the Netherlands by aggregating thennual time series data collected on seven theme indicators. Kangt al. (2002) emphasized the need to construct a composite environ-ental index for Korea. This index was based on greenhouse effect,

zone layer depletion, acidification, eutrophication, ecotoxication,atural resource depletion, photo-oxidation, loss of biodiversity,nd noise-vibration-odor. Sub-indices were calculated and thenntegrated with certain weights set by environmental experts.

Even though several pollution index models are present in lit-rature, their application in different countries for different casesay be limited due to the availability of required environmental

uality data. Therefore, modifications may be required in the exist-ng models to accommodate available data and varying goals. Inhis study, the typical pollution indices reported in literature are

odified and applied to assess the pollution status in the middleection of the Lower Seyhan Basin. Use of pollution indices for eval-ation of pollution status is not common in Turkey. The Seyhanasin is important due to its vulnerable areas including wetlandsnd national parks. Some of the vulnerable regions are located inhe Lower Seyhan Basin and open to the impact of pollution orig-nating in upstream. Deteriorating water quality and agriculturalctivities are listed among the factors that impact the diversity inhese areas (WWF-Turkey, 2008; Ayaz et al., 2010).

In order to evaluate the pollution status in the study area, AQInd WQI are calculated individually. Based on the locations ofational monitoring stations and availability of data, the indicesre calculated to reflect the situation in the middle section of theower Seyhan Basin only. The changes in water and air quality inecent years are evaluated based on the indices. In addition, anWQI is developed to assess the overall environmental pollutiontatus, which is obtained by aggregating AQI and WQI. For this pur-ose, 2-dimensional kriging was employed in order to determinehe air quality levels and AQI values at the locations where wateruality monitoring stations are located. Data processing and eval-ations are employed using ArcGIS 9.3, a geographical informationystem software.

. Methodology

.1. Study area

The Seyhan Basin is located in the Eastern Mediterranean Regionf Turkey as depicted in Fig. 1. It is located between latitudes 36◦30′

nd 39◦15′ North, and longitudes 34◦45′ and 37◦00′ East. It coversn area of 22,139 km2. Majority of the basin area is located withinhe province borders of Adana and Kayseri. Minor areas are withinhe borders of Sivas, Kahramanmaras and Nigde provinces. Yet,dana is the only province that has its city center located in theasin. Adana is one of the most populated cities in Turkey with aopulation of about 1.8 million (TUIK, 2010).

The Seyhan Basin is bordered with mountains that have a height

p to 3524 m at north, east, and west. Mediterranean Sea estab-

ishes the south border. The Zamanti River (306 km of length) andhe Goksu River (199 km of length) merge to form the Seyhan River.he Seyhan River flows for about 191 km before it discharges to the

icators 29 (2013) 6–17 7

Mediterranean Sea. The northern part of the basin (Upper SeyhanBasin) is characterized by mountainous, harsh topography. In south(Lower Seyhan Basin), lowlands prevail as depicted in Fig. 1 (MEGARInc., 2009).

The climate in the basin is strongly influenced by topography.The northern part of the basin exhibits the characteristics of cen-tral Anatolian climate. Annual precipitation is around 350–500 mm.The highest precipitation is observed at highlands, particularlyaround the Aladag region with an annual quantity of 1500 mm.The region between the coastal zone and Taurus Mountains hasa semi-arid meso-thermal Mediterranean climate with dry and hotsummers, and rainy and warm winters. The annual precipitation inthis region is in the range of 600–800 mm (MEGAR Inc., 2009).

The population density in the Upper Seyhan Basin (northern partof the basin) is low (45 capita/km2) compared to the Lower SeyhanBasin (753 capita/km2) (Ayaz et al., 2010). According to 2010 cen-sus, 98,264 people (6% of the total population) live in the northernpart in small settlements at the border of Kayseri province (Ayazet al., 2010). This region has rich mining areas (chromium, iron,and marble). Although water reuse or closed systems are appliedin several mining areas, heavy metal pollution has been detectedin water samples taken in the northern part of the basin. Yet, theSeyhan Dam at the inlet of the Lower Seyhan Basin acts as a natu-ral treatment system in removal of the heavy metals (MoEF, 2008;Ayaz et al., 2010). Settlements in the Lower Seyhan Basin are muchlarger and populated. In rural areas, economy is driven by agricul-tural sector such that at least 80% of the population is involved inagriculture. In populated areas, service sector has the highest sharein economy. In overall, the economy in the whole basin is driven byagriculture, industry and service by 27%, 19%, and 54%, respectively(MEGAR Inc., 2009).

The Seyhan Basin is rich in biodiversity (MoeF, 2007; WWF-Turkey, 2008; Ayaz et al., 2010; Ramsar, 2012). Various vulnerableareas are present which are depicted in Fig. 2. There are 8 wildlifereserve sites, 1 national park and 3 wetlands. One of the wet-lands (Lake Akyatan) has been declared as a Ramsar site (a wetlandof international importance according to the Ramsar conventionsigned in 1971 by member countries) (Ramsar, 2012). As a result,environmental protection is important not just for human health,but also for the sustainability of the vulnerable areas which hostspecies such as green sea turtle (chelonia mydas), 250 different birdspecies (159 of them are under protection based on Appendix III ofthe Convention on the Conservation of European Wildlife and Natu-ral Habitats), aleppo pinen (pinus halepensis), endemic species, etc.(WWF-Turkey, 2008). More information about biological diversitycan be found in Zeydanli and Ulgen (2009). Deteriorating waterquality and agricultural activities have been declared among themajor stresses affecting the biodiversity (WWF-Turkey, 2008; Ayazet al., 2010).

2.2. Approach in index development

Pollution indices (AQI, WQI and AWQI) were developed andapplied for the middle section of the Lower Seyhan Basin only(depicted by the rectangular area in Fig. 3) due to limited waterand air quality monitoring stations and unavailability of continu-ous data for the whole basin. First, the watershed was delineatedby ArcGIS 9.3. Watershed delineation was performed using thedigital elevation model (DEM) produced from 1/250,000 scaledtopographic maps of the basin. Stream networks (the main riverstems are shown in Fig. 3) were derived from the DEM by exploitingthe functions under the Spatial Analyst tools of ArcGIS 9.3. Then, a

database was constructed including the locations of water and airquality monitoring stations, water and air quality data, land coverclassification, and others. Land cover classification was establishedaccording to CORINE land use/cover classification system (Bossard

8 M. Golge et al. / Ecological Indicators 29 (2013) 6–17

of the

eooaHqJ(AsA

Fig. 1. Location

t al., 2000). The land cover data was obtained from the Ministryf Environment and Forestry (MoEF) and used as support materialnly in the evaluation of AQI, WQI, and AWQI values. The water andir quality data were obtained from the General Directorate of Stateydraulic Works (DSI) and MoEF, respectively. In the region, wateruality samples are taken and analyzed by relevant DSI units in

anuary, May, August and November, using the standard methods

APHA, 2012). These months cover wet and dry weather conditions.ir quality is measured daily. Air and water quality monitoringtations are located as depicted in Fig. 3. As will be discussed inWQI development methodology, the air quality stations outside

Seyhan Basin.

the basin were used to derive the relevant air pollutant concentra-tions at the water quality monitoring stations using kriging. Basedon the locations of water quality monitoring stations and availabil-ity of water quality data, pollution indices were developed only forthe middle section of the Lower Seyhan Basin (basin area shownby the rectangle in Fig. 3). It must be noted that the indices andevaluations are valid for the available data and quality parameters

considered.

The general procedures followed in the development of AQI andWQI were similar. First, water or air quality parameters of concernwere selected. Then concentrations were converted to sub-indices

M. Golge et al. / Ecological Indicators 29 (2013) 6–17 9

reas,

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tot

Fig. 2. Locations of wild life reserves, national parks, agricultural a

o transform the units and dimensions of the quality parametersnto a common scale. Afterwards, these were weighted accordingo their relative importance. Finally, the sub-indices were aggre-ated to obtain a single quality score; AQI and WQI. Water qualityarameter selection and sub-index development for WQI wereased on NSF indexing method (Ott, 1978). This method has a widecceptance in literature. For AQI, sub-indices and weights used inggregation of air quality parameters were realized according tohe modified AQI of US EPA (Murena, 2004) in which EU air qualityriteria were utilized.

Following the quantification of AQI and WQI for the middle sec-ion of the Lower Seyhan Basin, AWQI development was carriedut in order to represent an overall environmental pollution sta-us based on air and water quality. The AWQI was developed by

forests and semi-natural areas, and artificial surfaces in the basin.

two different methods; one based on graphical interpretation andthe other on an aggregation function. In aggregation, the AQI andWQI are summed up using the weighted arithmetic mean function.Since the locations of the water and air quality monitoring stationswere different, air quality data was interpolated. Two-dimensionalkriging was used for that purpose. The AQI, WQI, and AWQI weredeveloped based on annually averaged data for consistency, sincethe monitoring frequencies for air and water qualities were dissim-ilar. The WQI was developed for 2004–2010 and AQI for 2007–2010,because of the limitations in data availability. The air quality mon-

itoring stations of MoEF in the basin have been in operation since2007. The AWQI was developed for 2007–2010, since only for theseyears both AQI and WQI could be determined. Index developmentdetails are provided in the following subsections.

10 M. Golge et al. / Ecological Indicators 29 (2013) 6–17

ea (th

2

h(owm

Fig. 3. Locations of water and air quality monitoring stations and the study ar

.2.1. WQI developmentThe WQI was developed for the middle section of the Lower Sey-

an Basin using the data gathered from four monitoring stations

1801-Egner, 1804-Taskopru, 1805-Hacili, and 1816-Nergizlik)perated by DSI (Fig. 3). The weighted arithmetic mean functionas used as the aggregation function for WQI. This is a commonethod employed in WQI development (Singh et al., 2008). This

e area given in the rectangle is the middle section of the Lower Seyhan Basin).

function is also used in the traditional NSF method (Ott, 1978) andcan be represented as

∑nwiqi

WQI = i=1∑n

i=1wi

(1)

where wi is the weighting factor for variable i, qi is the sub-indexvalue for the water quality parameter i, and n is the number of

M. Golge et al. / Ecological Indicators 29 (2013) 6–17 11

Table 1Water quality parameter weights used in WQI development.

Variablesindicatingpollution level

Weights used inNSF method (Ott,1978)

Modified weightsused in this study

Dissolved oxygen 0.17 0.225Fecal coliform 0.15 –pH 0.12 0.175BOD5 0.10 0.155Nitrates 0.10 0.155Phosphates 0.10 –Temperature 0.10 0.155Turbidity 0.08 –Total solids 0.08 0.135

wwontaafiipsbTNteg

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atppf

Table 2Sub-index values for the dissolved oxygen expressed in concentration units in WQIcalculations (Pesce and Wunderlin, 2000).

DO range (mg/L) Sub-index, q

DO ≤ 1.0 02.0 ≥ DO > 1.0 103.0 ≥ DO > 2.0 203.5 ≥ DO > 3.0 304.0 ≥ DO > 3.5 405.0 ≥ DO > 4.0 506.0 ≥ DO > 5.0 606.5 ≥ DO > 6.0 707.0 ≥ DO > 6.5 807.5 ≥ DO > 7.0 90DO ≥ 7.5 100

Table 3NSF water quality index classification for WQI (Ott, 1978; Terrado et al., 2009).

Range of WQI values Class name

100–91 Excellent90–71 Good70–51 Moderate

TN

Total 1.00 1.000

ater quality parameters used in the development of WQI. Theater quality parameters used for WQI development are based

n the NSF method. In this method, DO, fecal coliform, pH, BOD5,itrates, phosphates, temperature, turbidity, and total solids arehe parameters of concern (Ott, 1978). However, not all of thesere measured continuously at the sampling stations. Fecal coliformnd turbidity measurements are not conducted continuously. Inact, turbidity is not among the regulated water quality parametersn the Water Pollution Control Regulation of Turkey. Therefore, its not reported at all at the given stations. Moreover, data on totalhosphates (PO4) was missing in 2005, 2006, 2008 and 2009 at thetations considered. Therefore, parameters that were not applica-le (fecal coliforms, turbidity, and PO4) were omitted from the WQI.he weights used in aggregation (wi) were selected according to theSF approach. These are listed in Table 1. However, since some of

he parameters were omitted, weights belonging to these param-ters in the NSF method were distributed evenly among others asiven in Table 1.

In the NSF WQI, sub-index values are extracted from the qual-ty graphics of parameters. These sub-index graphics can be foundn Ott (1978) and Wilkes University CEQ (2010). The sub-indexalculation graphic for DO given by Ott (1978) is in terms of sat-ration percent. However, the DO concentrations measured by DSIre in concentration units and data is not complete for conversiono saturation quantities. Therefore, in order to identify the sub-ndex values for DO, the evaluation approach given by Pesce and

underlin (2000) was used (Table 2). Following the derivation ofhe sub-indices, WQI was calculated using Eq. (1) and the weightsiven in Table 1. Water quality classification was employed as givenn Table 3 (Ott, 1978; Terrado et al., 2009).

The water quality at the monitoring stations was also evalu-ted based on the national inland water quality criteria given inhe Water Pollution Control Regulation of Turkey in order to com-

are the results obtained through WQI and traditional water qualityarameter based evaluations. Summary of the water quality criteriaor the relevant parameters are provided in Table 4.

able 4ational inland water quality criteria for DO, NO3, BOD5, pH, total solids, total P and wate

Water quality class Class Ia high-quality water Class IIb slightly pollute

DO (mg/L) 8 6

NO3 (mg/L) 5 10

pH 6.5–8.5 6.5–8.5

Temperature (◦C) 25 25

BOD5 (mg/L) 4 8

Total solids (mg/L) 500 1500

Total P (mg/L) 0.02 0.16

a Water that can be used for drinking purposes following simple physical treatment (i.b Water that can be used as a potable water resource following appropriate treatment.c Water that can be used by industries that do not require high quality water.

50–26 Bad25–0 Very bad

2.2.2. AQI developmentIn literature, a variety of air quality parameters are used in

the development of AQI. Sulfur dioxide (SO2), particulate matter(PM10), carbon monoxide (CO), and ozone (O3) are the commonones (EPA, 1999). In this study, parameters were selected basedon the availability and continuity of data at the air quality mon-itoring stations. According to this constraint, SO2 and PM10 werethe parameters of concern. The weights used in aggregation of thesub-indices belonging to SO2 and PM10 were assigned according toSharma et al. (2008). Sharma et al. (2008) assigned 0.105/1.00 and0.165/1.00 as the weights for SO2 and PM10, respectively. Keepingthe same ratio between the contributions of SO2 and PM10 to theAQI, the weights were adjusted to sum up to 1.0. As a result, theweights for the sub-indices of SO2 and PM10 were determined as0.389 and 0.611, respectively.

The sub-indices for AQI were calculated as proposed by Murena(2004). Murena (2004) modified EPA’s AQI by taking the limit andtarget values established by the European Community Directivesas the reference scale. Since the Turkish regulation on the assess-ment and management of air pollution was harmonized with EU’sCouncil Directive on Ambient Air Quality Assessment and Manage-ment, the same breakpoints were considered. The reference scale ofthe air pollution sub-indices for SO2 and PM10 and classification ofAQI are given in Table 5. Air pollution category titles were replacedwith the corresponding water quality category titles (Table 3) for

consistency. Since air quality data at the air quality monitoring sta-tions (Fig. 3) were reported on a daily basis, annual averages werecalculated for SO2 and PM10. AQI values were calculated based

r temperature in the Water Pollution Control Regulation of Turkey.

d water Class IIIc polluted water Class IV highly polluted water

3 <320 >20

6.0–9.0 <6.0 or >9.030 >3020 >20

5000 >50000.65 >0.65

e., filtration) and disinfection.

12 M. Golge et al. / Ecological Indicators 29 (2013) 6–17

ributio

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2

o2awcttidkHavsq2

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analysis is provided for SO2 in Fig. 5. In this figure, each vertical barrepresents the location and value of each data point. The pointsare projected onto perpendicular planes in an east-west and a

Fig. 4. An example Q–Q plot obtained for the dist

n the annual mean concentrations in 2007–2010. Data belong-ng to a total of 16 air quality monitoring stations were utilizedn determination of the AQI values in and around the study area.ince air pollution can be transported through advection acrosshe basin borders, stations outside the basin were considered asell in evaluations. Four of the stations (Adana-Meteoroloji, Adana-alilik, Adana-Catalan, and Adana-Dogankent) were in the studyrea (the middle section of the Lower Seyhan Basin). Moreover,ll stations were used in prediction of the SO2 and PM10 values athe water quality monitoring stations as will be discussed in AWQIevelopment. All stations are close to urban and/or industrial sites.herefore, air quality data can be deemed as conservative, espe-ially for air quality predictions at the water quality monitoringtations.

.2.3. AWQI developmentThe AWQI was developed by the aggregation of AQI and WQI. In

rder to estimate the air quality at the water monitoring stations,-dimensional kriging was employed. Kriging can be used to assignn estimated value to a particular location. A kriged estimate is aeighted combination of the sample values around the point of

oncern (Mohamed and Antia, 1998; Kanevski, 2008). In this study,he geostatistical analyst tool of ArcGIS 9.3 was used to estimatehe air pollutant concentrations at the locations where water qual-ty stations are situated. Then, AWQI was calculated. In order toetermine the air quality at water quality monitoring stations byriging, all air quality monitoring stations were taken into account.owever, contributions of these in predicting the concentrationst a given water quality monitoring station was based on a semi-

ariogram model. The model was generated based on the spatialtructure (autocorrelation) of data and the distance between airuality and water quality monitoring stations (Webster and Oliver,007). Therefore, contributions of air quality monitoring stations in

able 5ir quality index classification and category names used in this study for AQI (pol-

ution category names in parentheses are the original titles for AQI (Murena, 2004)).

Pollution category AQI SO2 (�g/m3) PM10 (�g/m3)

Very bad (unhealthy) 100–86 1000–501 500–239Bad (unhealthy for sensitive groups) 85–71 500–251 238–145Moderate (moderate pollution) 70–51 250–126 144–51Good (low pollution) 50–26 125–21 50–41Excellent (good quality) 25–0 20–0 40–0

n of SO2 at station in 2008 (check for normality).

determination of the air quality at a given water quality monitoringstation were not equal.

Kriging can be employed on normally distributed data or ontransformed data which conform to a normal distribution (Johnstonet al., 2001). Therefore, firstly Q–Q plots were derived for all airquality stations and for all years using the EasyFit software. Con-formity to normal distribution was checked based on Q–Q plotsand Kolmogorov–Smirnov statistics. In Kolmogorov–Smirnov test,the hypothesis regarding the distributional form is rejected at thechosen significance level (˛) if the test statistic is greater than thecritical value. In this study, was chosen as 0.05 to evaluate thenull hypothesis (H0). The results of the Kolmogorov–Smirnov testobtained via EasyFit suggested that conformity to normal distribu-tion cannot be rejected. An example Q–Q plots for SO2 in 2008 isdepicted in Fig. 4.

Following the check for conformity to normal distribution, trendremoval was employed. Existence of a trend in data may result innot adequately portraying the surface. The trend may indicate thenonrandom (deterministic) component of data. Therefore, it is bet-ter to remove it before any further analysis and model the residualsonly (Johnston et al., 2001). An example for trend determination

Fig. 5. Trends in the air quality data (SO2 in 2008).

M. Golge et al. / Ecological Indicators 29 (2013) 6–17 13

in 20

ntfliFntpo

Wiihfcs

Fig. 6. Annual average SO2 distribution

orth-south plane. A best-fit line (a polynomial) is drawn throughhe projected points to reveal the trends in given directions. While aat line indicates the inexistence of a trend, a U-shaped or a linearly

ncreasing/decreasing line shows a trend (Johnston et al., 2001). Inig. 5, strong trends can be observed both in the east-west andorth-south directions. The trend removal functions embedded inhe geostatistical analyst tool of ArcGIS were used in trend removalrior to variogram modeling and kriging. An example distributionbtained via 2-dimensional kriging is provided in Fig. 6.

In the development of the AWQI, the contribution of AQI andQI were considered as equal (i.e., wi = 0.5). The critical point here

s that WQI has a decreasing scale index (Table 3). Thereby, a lowndex value indicates poor water quality. The AQI, on the other

and, has an increasing scale. As a result, a low index value stands

or good quality (Table 5). Hence, one of the indices needs to beonverted to an increasing or decreasing scale. However, even thecales are converted, a problem still exists since the ranges of index

08 obtained via 2-dimensional kriging.

values that map into similar quality classes in AQI and WQI may notbe exactly the same. Therefore, in order to overcome these difficul-ties, two different methodologies were proposed in aggregating theAQI and WQI to determine the AWQI. These were based on graphicsand aggregation by weighted arithmetic mean function.

Using the original classifications for AQI (Table 5) and WQI(Table 3), the graph depicted in Fig. 7 was developed. In Fig. 7,the intersection areas of both indices that define the similar qual-ity classes are shown by grids with dark borders. For example, theWQI is in the excellent category when it is in the range of 90–100.The AQI is in the excellent category when it is in the range of 0–25.Therefore, the common area shown by the grids with dark bordersat the intersection of the areas indicating the excellent categories

both for AQI and WQI also shows the excellent category for AWQI.This area is referred to as the perfect intersection area (PIA). In a PIA,WQI and AQI, and therefore AWQI, are all in the same quality class.However, AWQI class areas should be extended beyond PIAs to

14 M. Golge et al. / Ecological Indicators 29 (2013) 6–17

o colo

hFsF(obehtbcd

t(tpigg

E

wWwi

the AWQI classes given in Fig. 4, while being more conservative forthe excellent quality class.

Table 6Assigned sub-index scores for WQI and AQI in AWQI calculation.

WQI AQI si

91–100 0–25 571–90 26–50 451–70 51–70 326–50 71–85 2

0–25 86–100 1

Table 7Range of AWQI values and category names for AWQI calculated by the weightedarithmetic mean function.

AWQI Classification

4.8 ≤ AWQI ≤ 5 Excellent

Fig. 7. AWQI category classes. (For interpretation of the references t

andle the areas that map into different AQI and WQI index classes.or extension, the upper left-hand side and the lower right-handide corners of a given PIA were determined (circular points inig. 7). Then, for each of the five quality classes, two other pointsthe triangular points in Fig. 7) were acquired. These points werebtained by drawing rectangular areas containing the PIAs of neigh-oring quality classes (i.e., an area that would include the PIAs of thexcellent and good quality classes) and then marking the upper left-and side and the lower right-hand side corners. Curves passinghrough the corner points of the PIAs and two other points definedy the extended areas were used to set the areas of AWQI qualitylasses. In Fig. 7, different AWQI classes are shown by the areas ofifferent colors (shades).

Although the graphical method is straightforward in implemen-ation, it is not open to enhancement if another pollution mediumi.e., soil and groundwater) is to be considered in composite pollu-ion index development. To leave room for the inclusion of otherotentially polluted media into an overall environmental quality

ndex (EQI), a method based on weighted arithmetic mean aggre-ation function is suggested. In this method, EQI is calculated asiven is below equation

QI =n∑

i=1

wisi (2)

here si is the index score for a given medium i (i.e., AQI for air,QI for water, etc.), n is the number of polluted media (i.e., air,ater, and soil) and wi is the weighting factor for polluted medium

. In this study, since only air and water pollution could be assessed,

r in the text, the reader is referred to the web version of the article.)

two media (air and water) are considered. Therefore, EQI score isequivalent to AWQI. Corresponding scores used in EQI determina-tion for different AQI and WQI classes are given in Table 6. Theweights were taken as 0.5 both for water and air media. Followingthe calculation of EQI, classification was made according to Table 7.The ranges for EQI class scores in Table 6 were selected to match

3.6 ≤ AWQI < 4.8 Good2.4 ≤ AWQI < 3.6 Moderate1.2 ≤ AWQI < 2.4 Bad1.0 ≤ AWQI < 1.2 Very bad

M. Golge et al. / Ecological Indicators 29 (2013) 6–17 15

3

3

votrdpoS2id(0sCTpobbet(

dcw6wccceiwmwedi

mapped to moderate pollution category at Kahramanmaras and

Fig. 8. WQI values at different stations in 2004–2010.

. Results and discussion

.1. WQI

The minimum and maximum annual average concentrations oralues for DO, pH, BOD5, NO3, water temperature, and total solidsbserved in the period of 2004–2010 are given in Table 8. Whenhese values were compared to the national water quality crite-ia (Table 4), it was seen that all parameters considered in WQIevelopment other than DO were in Class I at all stations in theeriod 2004–2010. DO was in Class II in 2004 at all stations. In thether years, DO was in Class I, except at Taskopru and Nergizliktations. At Taskopru Station, DO was in Class II in 2007, 2009, and010. At Nergizlik Station, the Class II conditions were observed

n DO in 2005. As mentioned earlier, PO4 was omitted from WQIue to the discontinuity in data. In the years PO4 was measured2004, 2007 and 2010), observed concentrations ranged between.02 mg/L and 0.24 mg/L. Compared to the national water qualitytandards (Table 4), the water quality in terms of PO4 may be inlass II at best, given that the criteria is given in terms of total P.here was a significant increase in PO4 concentrations in the Tasko-ru Station which was the most downstream station among thethers. In 2007 and 2010, PO4 concentrations were in Class III atest. PO4 concentrations up to 0.24 mg/L were observed. This maye due to agricultural residues and industrial activities. It can bexpected that water quality may deteriorate at locations closer tohe outlet of the basin where agricultural activities are predominantFig. 2).

Changes in the WQI values at different stations in 2004–2010 areepicted in Fig. 8. At all stations and in all years, WQI values indi-ated good conditions except at Taskopru Station in 2010 for theater quality parameters considered. WQI values ranged between

9 and 79. At all stations, WQI deteriorated in 2010. This yearas significantly dry compared to other years. Therefore, low flow

onditions showed its impact on WQI values. As discussed before,omparison of water quality parameter values against water qualityriteria (Table 4) indicated high quality for all water quality param-ters considered in this study other than DO. This situation was notgnored in WQI. WQI values stated that in overall the water quality

as not in excellent condition and there was need for improve-ent. Due to the limited number of WQI values, trend analysisas not performed. In general, the Nergizlik and Taskopru Stations

xhibited the lowest WQI values. There is no water quality stationownstream of Taskopru Station (the station numbered as 1804

n Fig. 3) where agricultural pollution can be pronounced due to

Fig. 9. AQI at different air quality monitoring stations (solid markers show thestations in the study area).

agricultural lands (Fig. 2). Therefore, although WQI indicates goodconditions in general for the available data, results obtained in thisstudy are limited to the study area only (the rectangular area inFig. 3). Moreover, higher WQI values might have been obtained,if PO4 could have been included as a parameter. As a result, it isrequired to increase the coverage area of the monitoring system tofully evaluate the environmental quality at the south of the basin.Land cover information (Fig. 2) can be used as a baseline for thatpurpose. Moreover, monitored water quality parameters should berevisited if WQI is to be considered as a means to assess the overallwater quality status.

3.2. AQI

When the annual average SO2 concentrations were compared tothe limit concentration set in the Air Pollution Control Regulationof Turkey (20 �g/m3), it was observed that limits were exceededat Aksaray, Kahramanmaras , Kahramanmaras-Elbistan, Kayseri-Hurriyet, Kayseri-Melikgazi, Nevsehir, Nigde, and Sivas air qualitymonitoring stations. In fact, a significant increase was observed inSO2 concentrations in winter time which might be due to heatingby coal burning.

PM10 concentrations at almost all air quality monitoring sta-tions were higher than the annual allowable limit set in theAir Pollution Control Regulation of Turkey (40 �g/m3). Only inAdana-Catalalan and Adana-Dogankent, PM10 concentrations werebelow the air quality limit. Compared to other air monitoring sta-tions, Adana-Catalalan and Adana-Dogankent Stations are situatedin areas of lesser population and urbanization. High particulatematter concentrations could have been originated from severalsources including motor vehicles, wood burning stoves, windblowndust from open lands, waste combustion, agricultural activitiesand industrial sources. It was observed that PM10 concentrationsincreased significantly in winter especially at locations where win-ter season was associated with relatively cold temperatures suchas Kahramanmaras and Sivas. Heating systems used may causethat since wood and/or coal burning are the predominant heatingsystems in the area.

As seen in Fig. 9, the majority of the AQIs at the givenstations remained in the range of 26–50, which indicated lowpollution levels. While the air pollution levels at Catalalan andDogankent stations were in the good quality category, levels

Kahramanmaras-Elbistan air monitoring stations in 2007. This maybe due to the coal fired thermal power plant operating close to thesestations. However, improvement was observed and low pollution

16 M. Golge et al. / Ecological Indicators 29 (2013) 6–17

Table 8The range of annual average water quality parameter concentrations (or values) observed at the water quality monitoring stations in the period of 2004–2010.

Water quality monitoring station 1801-Egner 1804-Taskopru 1805-Hacili 1816-Nergizlik

DO (mg/L) 7.8–9.7 6.9–9.0 8.0–9.3 7.8–9.4NO3 (mg/L) 0.6–1.4 0.3–0.8 0.9–1.7 0.3–0.8pH 7.7–8.3 7.6–8.4 7.8–8.3 7.9–8.4

◦ 1

2

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3

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eatbpl

Temperature ( C) 14.5–17.5

BOD5 (mg/L) 0.6–1.4

Total solids (mg/L) 319–461

tatus was achieved in the following years due to the control of airollutant emissions from the plant.

AQIs improved from 2007 to 2010 for most of the stations excepthe Kayseri-Hurriyet, Kayseri-Melikgazi, Nigde, and Osmaniye Sta-ions. In general, air quality improved with time compared to 2007.his was the case for the stations in the study area as well (solidarkers in Fig. 9). The AQI in the Adana city center (Adana-Valilik)

ndicated low pollution in all years. The indices calculated fordana-Catalalan and Adana-Dogankent Stations remained in theange of 0–25, showing excellent air quality conditions in all years.elatively good air quality in these stations can be attributed to

ower population density and human activity around these stations.ilution due to air streams at the given locations may be another

actor impacting the level of pollution. In general, the highest AQIalues belonged to Kayseri-Hurriyet, Aksaray, and Kahramanmarastations. This is probably due to the presence of a coal fired ther-al power plant in Kahramanmaras, industries, and coal burning

or heating. The same situation was valid for the Sivas monitoringtation as well.

In general, AQI values were in line with expectations. Yet, airuality in the basin should be watched in coming years to evalu-te whether the decrease in AQIs with time exhibits a stationarymprovement or not. Precautions should be taken to improve their quality. However, this may require a greater effort beyond theorders of the basin due to the scale of air pollution transport.

.3. AWQI

The annual average SO2 and PM10 concentrations at the wateruality monitoring stations were obtained through 2-dimensionalriging. An example distribution obtained through kriging is pro-ided in Fig. 6. It must be noted that, since air quality monitoringtations are located closer to urban and/or industrial regions, krigedir pollutant concentrations at the water quality monitoring sta-ions may be conservative. Kriged SO2 and PM20 concentrations athe water quality stations mapped to AQI values ranging between8 and 38 for the years considered. This range identifies the goodategory (low pollution) (Table 5). As given before, the WQI valuesanged between 69 and 79 at the water quality monitoring sta-ions in 2007–2010. The intersection points for relevant AQI and

QI values were in the AWQI area defined for the good compositeir–water quality status (Fig. 7). EQI values that were determinedsing Eq. (2) and the classification given in Table 6 indicated thathe composite air–water quality was in a good state at all stationsEQI = 4) in the study area as well. These results indicated that forhe parameters considered and available data, air and water in thetudy area were not significantly polluted. Yet, improvements cantill be required to reach the excellent state in environmental qual-ty; both for air and water qualities.

Although traditional individual air and/or water quality param-ter based evaluations may provide a superior assessment of causend effect relationships, or better means of identifying the poten-

ial sources of pollution, a single composite index (AWQI) maye useful in screening or taking the attention of public and otherarties. This may especially be helpful if indices are applied over

arge areas (i.e., whole country) to compare the pollution status

7.0–20.0 14.0–16.3 17.3–21.01.0–2.3 0.5–1.9 0.8–3.559–337 303–372 200–369

over a larger scale. Pollution indices can be considered as an initialassessment tool of an environmental action plan. If an index indi-cates deteriorating air–water quality, further detailed data analysesthat focus on individual environmental quality parameters can beundertaken. The importance of composite pollution indices can bemore pronounced, if the indices are tailored toward assessmentof specific environmental pollution cases that require both air andwater quality control. Examples can be eutrophication and green-house effect control. Nutrients are transported not only by flowingwaters but also by air masses. Greenhouse gases are not emittedonly from stacks but also from water bodies that have anoxic con-ditions. For such cases, use of composite pollution indices may bemore effective. Yet, AWQI or EQI values can still be used to followthe general trends in overall environmental quality. However, thiswould require a good monitoring network design.

As was the case for WQI, if monitoring stations can be installed atlocations that may better represent the pollution stress due to agri-cultural and industrial activities, the pollution status in the wholeLower Seyhan Basin could have been assessed. Land use/coverinformation provided in Fig. 2 indicates that the present locationsof the water quality monitoring stations are not adequate to reflectthe impact of pollution loads from the Adana province and agricul-tural areas closer to the outlet of the basin. In addition to the needfor new monitoring stations, the number of air and water qualityparameters that are monitored should be increased as well. By thisway, a better classification can be achieved for different goals. Forexample, instead of expressing the general pollution status, pol-lution levels can be assessed in terms of eutrophication potential,etc. Nevertheless, pollution indices can be useful in environmentalpollution management, especially in dissemination activities thatinvolve parties of different backgrounds.

4. Conclusions

Although pollution indices are simplifications, they can still behelpful in expressing the general status of environmental pollution.Results obtained in this study state that for the available data andthe time frame considered, air quality and water quality are in goodconditions (low pollution), in overall, in the middle section of theLower Seyhan River Basin. Yet, in order to reach excellent water andair quality. Land cover/use information showed that the monitoringnetwork was not sufficient to examine the pollution status in thewhole basin. Therefore, enhancement of the network is requiredfor better assessment of the environmental quality. Moreover, dataunavailability was among the main constraints in the developmentof the pollution indices. It was not possible to include some of thequality parameters (i.e., PO4, ozone, and CO) used in traditional airand water quality indices. Therefore, results obtained in this studyare valid for the parameters considered in the development of theindices.

This study showed the effectiveness of ArcGIS tools in pollutionindex development. GIS tools can be effectively used to estimate

the required information for index calculations. Moreover, addi-tional information obtained through GIS tools and applications,such as the land cover/use information, can help in assessment ofthe index values.

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