Research Article Integrated Evaluation of Urban Water

Research ArticleIntegrated Evaluation of Urban Water Bodies for PollutionAbatement Based on Fuzzy Multicriteria Decision Approach

Sarfraz Hashim1 Xie Yuebo1 Muhammad Saifullah1

Ramila Nabi Jan2 and Adila Muhetaer1

1Department of Hydrology and Water Resources Hohai University Nanjing 210098 China2Department of Geotechnical Civil Engineering Hohai University Nanjing 210098 China

Correspondence should be addressed to Sarfraz Hashim engr sarfrazhashimhhueducn

Received 16 March 2015 Accepted 1 April 2015

Academic Editor Eldon R Rene

Copyright copy 2015 Sarfraz Hashim et alThis is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Todayrsquos ecology is erected with miscellaneous framework However numerous sources deteriorate it such as urban rivers thatdirectly cause the environmental pollution For chemical pollution abatement from urban water bodies many techniques wereintroduced to rehabilitate the water quality of these water bodies In this research Bacterial Technology (BT) was applied to urbanrivers escalating the necessity to control the water pollution in different places (Xuxi River (XXU) Gankeng River (GKS) XiaZhang River (XZY) Fenghu and Song Yang Rivers (FSR) Jiu Haogang River (JHH)) in China For data analysis the physiochemicalparameters such as temperature chemical oxygen demand (COD) dissolved oxygen (DO) total phosphorus (TP) and ammonianitrogen (NH

3N) were determined before and after the treatment Multicriteria Decision Making (MCDM) method was used for

relative significance of different water quality on each station based on fuzzy analytical hierarchy process (FAHP) The overallresults revealed that the pollution is exceeding at ldquoJHHrdquo due to the limit of ldquoCODrdquo as critical water quality parameter and aftertreatment an abrupt recovery of the rivers compared with the average improved efficiency of nutrients was 79 74 68 and70 of CODDO TP andNH

3N respectivelyThe color of the riverrsquos water changed to its original form and aquatic living organism

appeared with clear effluents from them

1 Introduction

Massive ecosystem wide effects have been associated withtheir broad proliferation and toxin production [1]The urbanrivers or streams have always been the recipient of sewagewater from various sources that have different kinds of thedomestic agricultural and industrial foreign particles [2]The odors released from these water bodies are offensive forenvironmental pollution the release of odors has been oftenunavoidable due to its natural phenomena [3]Themunicipalsewage is the mixture of various organic matters and thedecomposition of these matters produces harmful gases thatin fact deteriorate the environment and logically createdthe infection diseases [4] The inherent expectation is thatvillagers under this sewer situation have been leaving theirhouses because they face polluted potable water and skin andwaterborne infectious diseases and therefore urban environ-ment situation has become more alarming [5 6] Municipal

wastewater is the main cause of environmental impact if it isdirectly discharged into urban water bodies or rivers withoutany sanitation preliminary treatment Moreover due to anincrease in the shortage of clean water there is a need forconvenient management of accessible water resources

To control environmental pollution would be a hugechallenge for the planner and policy maker due to treatmentcost as well as the unbridled population acceleration Here weneed solution that efficiently resolves these critical pollutionproblems and could be used to rehabilitate the existingsystems In the last decades some conventional technologiesand methods have been developed and applied To begin theapplication of reaeration (traditional technology) as adoptinga series of weirs [2 7] moving the wastage discharges placeand local oxygenator were used for pumping air into thewater body for wastewater treatment [8 9]The application ofMultifunctional Constructed Wetland has been used for thetreatment of wastewater and the purification of river water

Hindawi Publishing CorporationBioMed Research InternationalVolume 2015 Article ID 327280 10 pageshttpdxdoiorg1011552015327280

2 BioMed Research International

[10ndash12] Nitrogen and some other nutrient wastes were thevital source of pollution problems in China and unable torestore system through these conventional techniques Sothe anaerobic ammonia oxidation researches tend to moveinto full-scale treatment plant Till now at least 5 genera and13 species have been identified using culture-independent-molecular techniques [13] Within the last decades somespecialized reactor systems such as sequencing batch reactorrotating biological contactor trickling filter UBF reactorgranular sludge bed reactor and membrane bioreactor havebeen introduced in both laboratory and full scale to obtainhigh removal rate and finally we noticed that these reactorsplayed an important role in securing high rate performancefor the product of nitrogen removal Nitrogen pollutioncauses serious environmental problems and it not only threat-ens the sustainable development of fisheries agriculturetourism and so forth but also is harmful to the livingenvironment of human beings The amounts of phosphorusand ammonia nitrogen in domestic wastewater have beennoticed from 10 to 17 and 30 to 50mgL respectively [14]

Ecofriendly and sustainable environmental demand isthe hot and impressive topic due to public economic andlegislation pressureThe best selection in complex frameworkis based on the sustainability of nutrient removal biodegra-dation of suspended particles and removal efficiency of thesystem In this situation the major preference is to adoptany system that is more reliable for energy consumptionsconversion of chemicals into biomass complex infrastruc-ture and the repairing or maintenance cost of the systemBacterial Technology (BT) provides a plenty of opportunitiesfor effectively treating these issues [2] BT is an applicationof bioremediation that uses microorganism metabolism toremove nutrients from the water bodies and regenerate upto the original condition [2 6 9] and its operational cost isrelatively low [15] which generally have a high public interestDue to its smart application it is popular in the research areaof environmental sciences and engineering Temperature isthe major concern that is directly effective in the process ofthe degradation of the substances [9]

Due to these considerations we can adopt the newtechnique as BT with complete confidenceTheir bacteria areusually hired to vitiate pollutants or nutrients into simpleor nontoxic entity and produce suitable effluents [16] Thistechnology has reassuring advantages compared to othertraditional techniques as already discussed BT has beensuccessfully implemented to recover the polluted lakes [17]restore polluted rivers and assimilate effluent of wastewatertreatment plant [9] To control the urban river pollution BThas been employed in different places in China that is fortreating the polluted urban water bodies [2 15] It was deter-mined to be successful with reliable results in boosting up therecovery processes of all water bodies compared to the othertraditional technologies It extends for the rehabilitation ofpolluted lakes rivers and streams and is also reliable forthe requirement of the wastewater effluent standards withoutconstructing massive structures as compared to the otherconventional methods [15 16]

In the past the water quality index approach was con-sidered as the best tool to determine the water quality of

the water bodies [17 18] Numerous researches represent theintegrated uncertainty in evaluating the water quality Someof these are under the base on fuzzy impartial optimization[19 20] andMCDM problems using AHP [21] Further a fewyears ago AHP and FAHP were acquiring popularity in thehydraulics and environmental engineering fields [22] Srdje-vic and Medeiros [23] have used FAHP for the managementplans and Singh et al [19] have used FAHP to determinethe water quality of the Yamuna River that is tributary ofGanga In this research the urban rivers pollution abatementis being extensively determined at five various places suchas Xuxi River Wuxi City Gankeng River Shenzhen CityXia Zhang River Yixing City Fenghu and Song Yang RiversRuian City Jiu Haogang River Hangzhou City A location-wise variation of the water quality parameters (temperatureDO COD TP and NH

3N) was determined before and after

the treatment of the BT For the relative significance of waterquality parameters AHP has been applied in the selectedsites In addition FAHP is developed for the present researchto determine the original status of water quality on eachurban river based on MCDM framework For current studythe data matrix is very complicated because the spatial andtemporal parameters vary from site to site It is not possiblewith simple AHP to evaluate the pollution status on eachstation of each site Therefore FAHP is the best techniquewhich can help to determine the water quality parametersvalues as compared to the other techniques In this paperwe briefly discuss practical implementation of BT on urbanpolluted rivers and argue with MCDM based on FAHP thatBT is simple affordable and sustainable for restoring pollutedwater bodies

2 Materials and Methods

21 Study Area and Samples Collection Xuxi River (XXR)is situated in Wuxi City Chang Nan District of Chinageographically as (31∘56291015840N and 120∘28141015840E) Its upperstream starts from the Jing-Hang main canal and travelstowards the ancient small canal The selected river lengthfor the experiment is 1360m with 45m of upstream surfaceaverage width and about the average depth of 14m River isunder north subtropical humid zone and ismarked bymuddysediments This zone is facing four distinct seasons with thephenomenon of climatic influence circulation Fenghu (FH)and Song Yong (SY) Rivers were selected FH River is placedon the tail of the SY River and both of them are situatedin Wenzhou (Ruirsquoan) City (120∘39131015840E and 27∘46491015840N)Zhejiang Province China Most of the Wenzhou area isplaced under the typhoon zone and the FH River is takingwater fromWenruitang and Liangmian Rivers The SY Riveris starting from cave bridge and falls directly into the FHRiver The SY River length is about 280m with the averagebreadth 5ndash18m and 1ndash3m water depth and FH river lengthis about 740m with the average breadth 6ndash15m and 1mwater depth For monitoring and sample collection of theexperiment the selected reaches of both rivers were dividedinto six points The appearance of the river water color wasblackish or greenish and bubbles were blowing on the surfaceof water These rivers are situated under the commercial and

BioMed Research International 3

Table 1 Water quality parameters before BT and Chinese National Standard

Sampling time Monitoring projectWater

temperature ∘C pH DOmgL

CODmgL

TPmgL

TNmgL

NH3NmgL

National standard GB3838-2002 Class V index 6ndash9 200 1500 00 200 200

A 1440 161 75 25 1090 096 1480 1060River water quality class mdash V V Inferior V Inferior V Inferior V

B 1600 272 877 281 1210 082 1490 1120River water quality class mdash V V Inferior V Inferior V Inferior V

lowastA B represent two criteria based on pH and temperature value

highly polluted area and almost 2000m3 sewage water entersinto them [2] Therefore the average depth of sediments is01m and the riverrsquos water quality was unsuitable for anypurpose

The remaining sites of Gankeng River (22∘33241015840N and114∘34631015840E) Xia Zhang River (31∘26491015840N and 119∘49131015840E)and Jiu Haogang River (30∘18591015840N and 120∘09071015840E) areplaced in Shenzhen City Yixing City and Hangzhou City ofChinaThe averaged physiographic conditions of these riversare the same as the above rivers On the basis of Chinesesurface water quality standard the rank or class of waterquality in the source section was determined to be gradeV (Table 1) Class V shows the worst (poor) by the ChineseNational Standard board and least water quality standardFor any purposes this water quality of the river is extremelyunsuitable Hence these sites selected for small urban riversbelong to the worst Class V category by the Chinese NationalStandard (CNS) board

22 Bacterial Implementation Bacterial Technology (BT) isapplied in a simple way and its procedure is held underthree kinds of material as Bacterial Clusterization (BC)Nature Liquid and Biological Filter Media (4 3 3) BC is animportant material that has a mixture of three types of ingre-dients as beneficial bacteria (bacilli Bacteroides brown-rotspindle and Lactobacillales denitrifying with 6 4 3 4 3)mix medium (catalyst process as glucose sucrose cellu-lose liquid yeast cream liquorice root magnesium sulfatepotassium hydride mannitol tartaric acid (Na K) folicacid and ammonium nitrate) and water [24] The mixingratio represents that it is harmless and has no any adverseeffects Nature Liquid (NL) is the mixture of trace elementmultiple enzymes humic acid amino acid and vitaminsand composition of each adequate substance on judgmentBiological Filter Media are used on a domestic level as thegap string filter media

By implementation of BT on site the bacterial amountas BC is added to the selected points of each river as shownin Figure 1 (example on XXU site) As the bacterial agent isused to effectively work under relatively constant and slowflow of velocity an artificial weir is installed at the end of theriver reach which is the small wood bridge to stop effluentIt was technically built at about 50 cm high above the watersurface level in order to extend the hydraulic retention timeThis experiment was conducted from May 31 to July 31

To employ BT operation the implementation procedurecould vary based on the physical condition of the siteHowever the method of adding beneficial bacteria directlyto the polluted water body has proven to achieve desirableresults for restoration programs The addition of beneficialbacteria to polluted river is usually termed the BacterialTechnology

23 Samples Collection Procedure The sampling networkwasmanaged to cover the complete range along the inlet andoutlet points of the rivers and determined the dominantpoint sources that have an impact on the water quality Bothof the sites are located under the area of population andindustrialization so the samples were collected from variousdepths (05 ft and gt15 ft) at each monitoring station Thesamples were collected from 830 AM to 430 PM duringthe period of experiment and 5 to 8 times in each monthTo evaluate the water quality the samples were kept inpolyethylene bottles and stored in insulated ice cooler thatwere delivered to the laboratory on the same day All thesamples were saved at 4∘C until the analysis and processing

3 Numerical Calculations for Data Treatment

All mathematical and statistical calculation was analyzedby using Excel 2007 and MATLAB Fuzzy Logic FunctionThere have been various methods onMultiattribute DecisionMaking (MADM) and the most useful is AHP which espe-cially is based on pairwise comparisons on a ratio scale [25]According to someAHP limitations the fuzzymodification ofAHP (FAHP) was then posed that is the subject of this study

31 Analytical Hierarchy Process (AHP) AHP is an MCDMmethod that provides the hierarchical framework to illustratethe concern objective and developed the scale of prioritybased on the application judgment [25] The AHP operationbelongs to six essential steps [26] as shown in Figure 2

311 Define the Unstructured Problem We define the con-cern objectives and consequence of the unstructured problemand the recognition of the specific characteristics

312 Developing the AHP Hierarchy The AHP is based onthe decision disintegration of the hierarchy unstructured


Grand Canal

1 2 3 4 5

Gu Canal

70m 200m 300m 200m 220m 200m 170m

June 4

June 13 June 24

July 31

May 31 35 tons May 31 35 tons May 31 35 tons

08 tons

03 tons

03 tons

22 tons

Figure 1 Schematic diagram of the Xuxi River and sampling points during BT

Compute weight for each water quality parameter

AHP steps (i) Define the unstructured problem

(ii) Developing the AHP hierarchy(iii) Pairwise comparison(iv) Estimate the relative weights(v) Check the consistency

Water quality assessment of urban rivers

Select sites for bacterial implementation

Identify the critical water quality parameters

Data collection from each site

Pairwise comparison of water quality parameter

Location-wise comparison of each parameter

Determine overall ranking for locations

Figure 2 AHP for judgment

Attribute site

Criterion map

Objective Objective

Attribute n

n1 n2Site_A Site_B

Figure 3 Hierarchical structure of decision problem

problem that resides in the decision problem of the mostimportant element [27]The complicated task is decomposedinto a hierarchical structure (Figure 3) with the elements ofdecision

313 Pairwise Comparison For pairwise comparison matri-ces of each element of the hierarchy structure are comparedas follows

119860 =

[[[[[[[[[[

[

1 11990811199082

sdot sdot sdot

1199081119908119899

11990821199081

1 sdot sdot sdot

1199082119908119899

119908119899

1199081

119908119899

1199082sdot sdot sdot 1

]]]]]]]]]]

]

(1)

Table 2 Scales for pairwise comparison [25]

1 Equal importance3 Moderate importance5 Strong importance7 Very strong importance9 Extreme importance2 4 6 8 Intermediate values between adjacent scale values

where 119860 is matrix of pairwise comparison 1199081is element

weight 1 1199082is element weight 2 and 119908

119899is element weight

119899For the decision of the relative significance between

hierarchy elements in matrix119860 a linguistic scale is employedfor the values to be rated from 1 to 9 (Table 2)

314 Estimate the Relative Weights The relative weights ofelements in each pairwise comparison matrix are deter-mined by somemethods like eigenvalue methodThe relativeweights (119882) of matrix 119860 are determined as

(119860minus 120582max 119868) times120596 = 0 (2)

where 120582max is matrix 119860 as biggest eigenvalue and 119868 is unitmatrix

315 Check the Consistency The matrices consistency prop-erty is determined to ensure that the judgments of deci-sion makers either are consistent or need more iterations


Table 3 Random inconsistency indices [25]

Number ofcriteria 1 2 3 4 5 6 7 8 9 10

RI 0 0 058 09 112 124 132 141 145 149

Consistency Index (CI) can be measured from the followingequation

CI =

120582max minus 119899

119899 minus 1 (3)

The reciprocal matrix is generated from the randomConsistency Index that would be known as the random index(RI) A sample size of 100 was used to generate the averageRI for the matrices of order of 1ndash15 [28] The Saaty matricesrepresent the RI (Table 3) that can be seen in the order of 1ndash10 [25] At last if CR lt 01 the judgments from the aboveprocedure are consistent and the derived elementrsquos weightscan be considered for the further analysis The formulationof CR is

CR =

CIRI

(4)

316 Obtain the Overall Rating At the end the relativedecisions of element weights are compiled to gain the wholealternatives rating as follows

119908119894

119904=

119895=119898

sum

119895=1119908119894119895

119904119908119895

119886119894 = 1 119899 (5)

where119908119894

119904 is total weight of ldquo119894rdquo site119908119894119895

119904 is weight of alternative119894 associated with attribute 119895 119908

119895

119886 is weight of attribute 119895 119899 isnumber of sites and 119898 is number of attributes

32 Fuzzy Analytical Hierarchy Process (FAHP) Despite therecognition of AHP often this method is censured to suffi-ciently handle its failure for the imprecision and latent uncer-tainty associated with the grading of the decision makerrsquosperception of exact values [29] Fuzzy AHP as an extensionof AHP investigate to be more efficient tool in the watermanagement decision problems [30 31] Since vagueness andfuzziness are ordinary characteristics in a number of deci-sions a FAHPmethod should be able to indulge ambiguity orvagueness [32] In FAHP the eigenvectormethod is applied tosimulate the reciprocalmatrix and to evaluate the importanceand alternative performance across the criteria The additiveweighting method is applied for the determination of the useof alternative across criteriaWhen complexmultifeatures areconsidered for decision making problems FAHP has skillof capturing an uncertainty of human assessment [33] Thisprocedure is applied to determine the crisp judgments intofuzzy judgments [34] This classic fuzzy set theory allowed[0 1] range of real numbers to operate the participationfunctions The major fuzziness function is the individualsgrouping elements into classes without clearly defining theboundaries [35] The uncertainty judgment of comparison

1

0 l m kx

120583(120572)

Figure 4 Fuzzy triangular Number

can be indicated by the fuzzy number A fuzzy number ofthe triangle is defined by three real numbers (Figure 4) whichbelong to special class expressed as (119909 119887 119896) The fuzzynumbers of the triangle are determined as follows

120583 (120572) =

(119909 minus 119897) (119898 minus 119897) 119897 le 119909 le 119898

(119896 minus 119909) (119896 minus 119898) 119898 le 119909 le 119896

0 otherwise

(6)

In order to compose pairwise alternatives comparisonunder each criterion or benchmark a triangular fuzzy com-parison matrix is indicated as follows

[[[[[[

[

(1 1 1) (11989712 11989812 11989612) sdot sdot sdot (1198971119899 1198981119899 1198961119899)

(11989721 11989821 11989621) (1 1 1) sdot sdot sdot (1198972119899 1198982119899 1198962119899)

(1198971198991 1198981198991 1198961198991) (119897

1198992 1198981198992 1198961198992) sdot sdot sdot (1 1 1)

]]]]]]

]

(7)

where 119894119895

= (119897119894119895 119898119894119895 119896119894119895) 119894119895

minus1= (119897119895119894 119898119895119894 119896119895119894) for 119894 119895 =

1 119899 and 119894 = 119895Total alternatives preferences andweights can be acquired

from different method In this study these two approaches ortechniques will be posed in renewal

321 Fuzzy Logic Process for Experiment A fuzzy analyticalhierarchy process (FAHP) has been developed to evaluatethe status of water quality at the selected stations along eachriver under a Multicriteria Decision Making framework Adecision support mechanism has been introduced to selectand prioritize stations with specific reference to the universalprinciple as written below

(a) Moving water tends to contain more DO than stag-nant water

(b) The DO concentration is inversely proportional totemperature

(c) Health of water quality is based on the requirement oforganism that lives in it

(d) pH scale verifies the acidity and alkalinity of wastew-ater

(e) The overenrichment of a body of water by nutrientslike nitrates and phosphates is cause of eutrophica-tion


Table 4 Water quality data of experiment

Parameters GB2828-2002 (CNS) XXU GKS XZY FSR JHH

Tem (∘C) 15ndash30Mean

Std deviationMinimumMaximum

247029197278

255046246258

165032162171

162034156168

287035279294

DO (mgL) 2Mean


1691080433

179090427

183190548

114070818

341380745

COD (mgL) 15Mean


1456854177

16424541181

59717529672

43523524786

590135224139

NH3N (mgL) 2Mean


1521977942273

16871023801269

2249999120429

135975087157

21441044876267

TP (mgL) 0Mean


091057014186

1205018157

08304900712

130470517

190420728

The various water quality parameters have been consid-ered as criteria to evaluate water quality status at a givenstation of each project site Pairwise comparisons of thecriteria and the stations have been performed to assess waterquality using linguistic variables

4 Result and Discussion

The selected urban rivers are situated under the appallingenvironment and the riverrsquos conditions were awful before theoperation of BT The huge amount of sewage was loadeddirectly and entered into these rivers In addition it isobserved that there was not any preliminary facility to controlor dump the domestic sewage Therefore the sewage ispartially or directly a part of the urban river without anypretreatment Under this alarming situation the riverrsquos colorwas changed into greenish representing the thick oil floatsand debris Therefore in this sewer condition any livingorganism in the river water could not exist

BT has been applied and water samples from the selectedpoints were collected before and after the treatment of theexperiment The physiochemical parameters were collectedon the specific monitoring points on every site The rangemean and standard values of each parameter are in Table 4after determining the values we compared the improvedefficiency of nutrients before and after bacterial action whichwas 79 74 68 and 70 of DO COD TP and NH

3N

respectively From the results the DO was the most criticalparameter for aquatic life of rivers that have maximumefficiency To protect the environmental pollution we deter-mined that TP andCODefficiency also have favorable resultsFormore consideration the color and algal fromevery site arealso recovered as shown in Figures 5(a)ndash5(e)

Table 5 Pairwise comparison matrix of the various water qualityparameters

Parameters DO COD TP NH3N TemperatureDO 1 13 1 115 15COD 3 1 2 115 1TP 1 12 1 12 1NH3N 15 12 2 1 115Temperature 5 1 15 15 1

41 Numerical Evaluation of the Experiment The fundamen-tal statistics of these restoration experiments are based on2760 total water samples (23 sampling stations times 4 samplingfrequencies times 5 replications times 6months) and are summarizedin Table 4 which represents the range mean and standarddeviation of the results for each parameter The data werecollected during 6 months and each station of the site wasmonitored with spatial as well as temporal variation

FAHP developed a selection support tool that describesthe pairwise priority of the station with the particular bene-ficial reference such as domestic aquatic status irrigationand recreational and industrial enterprises The pairwisecomparison matrix was formulated due to the variationsand the complicity of the water quality parameters on eachsite and the comparisons were accomplished based on theconvincing of engineering results and each water qualityparameter of all sites was formulated in Table 5

For the evaluation of the relative weights the compar-isons of all five locations with each parameter (Tables 6ndash10)were measured with the ambition to determine the actualstatus of the water quality improvement from before and afterthe BT operation


(a)

(b)

(c)

(d)

Figure 5 Continued


(e)

Figure 5 (a) Contrast diagram of Fenghu and Song Yang Rivers (b) Contrast diagram of Xia Zhang River (c) Contrast diagram of XuxiRiver (d) Contrast diagram of Jin Haogang River (e) Contrast diagram of Gankeng River

Table 6 Location-wise comparison matrix for temperature

Site XXU GKS XZY FSR JHHXXU 100 033 067 200 029GKS 300 100 149 400 067XZY 150 067 100 200 100FSR 050 025 050 100 033JHH 350 150 300 300 100

Table 7 Location-wise comparison matrix for DO


Table 8 Location-wise comparison matrix for COD


The main concern in this present contribution is toexplain the actual BT function to mitigate the pollution fromurban water bodies AHP based on FAHP results are appliedto the ranking of the water quality parameters (Table 11) aswell as the location ranking with an overall inconsistencyof 0076 (Table 12) The location-wise variations score isillustrated in Figure 6 which represent the high ranking ofthe JHH site as compared to the others

After the evaluation of the current status from the resultsthe actual pollution status of each site after the BT operationis revealed Figure 6 displays the summary of the results with

Table 9 Location-wise comparison matrix for NH3N


Table 10 Location-wise comparison matrix for TP


Table 11 Criteria ranking of water quality parameters

Parameters Scores RankingTemperature (∘C) 0305 1COD (mgL) 0277 2DO (mgL) 0204 3TP (mgL) 0116 4NH3N (mgL) 0097 5

the overall inconsistency of 0076 less than 10 Its describethe COD value is extremely high (up to 139 mgL) at JHH sitewhich display the major cause of pollution Besides the riveris placed in the industrial area Similarly the value of TP is alsohigh because in the middle a cement factory is working andthe wastewater directly enters into the river Instead of all forthe evaluation of BT in the beginning of operation there wasan appalling condition as blackish water and odors that madepart of the pollution When we applied BT operation thepolluted river was changed up to reliable condition without


Table 12 Criteria ranking of sites (overall inconsistency = 0076)

Site Scores RankingJHH 0310 1FSR 0241 2XXU 0191 3XZY 0175 4GKS 0083 5

0

01

02

03

XXU GKS XZY FSR JHH

Scor

e

Location

TemDoCOD

TPNH3N

Figure 6 Ranking criteria of each water quality parameter in eachlocation

any odors The river water color was also changed fromblackish to its original form BT restored the JHHRiver and itcame back to its habitat environment However we can adoptthese advanced technologies to rehabilitate our environmentbut there is a need to manage or fix horrific point sources ofpollution

42 Cost Benefit Ratio Based on Conventional TechnologiesTemperature is the major concern under the metabolismprocess of the bacteria with higher temperature during thesummer (20ndash30∘C) and lower values in thewinter season (10ndash18∘C)The plus advantage of BT does not require destructionof an already built system BT has no effect on the naturalenvironment because it does not involve the use of chemicalsTherefore it is helpful for friendly ecology It is free from allother issues as high construction and maintenance costs canbe a huge burden to organization and policy makers

In view of this with revolutionary calculations theadoption of BT has been concluded to be themost convenientapproach for developing countries [15] The cost to treat thetons of wastewater is about 241asymp321$ where WWTPs arebeing built under construction or already built and theoperation cost is between 012 and 022$ per ton According tothis statement for the municipal sewerage operation systemneeds to spend above 16 times 10

7$ on single attempt If the pipenetwork of municipal administration is built the amount oftotal cost will exceed 32 times 10

7$ and the operational cost israised up to 4 times 10

7$ annually Therefore with the additionof bacteria to treat the sewerage wastewater the one-off

investment cost is only 65$ and this method is simple easy tooperate and affordable For the long term BT maintenanceand artificial dregs cannot be needed for 10 years in thefuture [36] In addition the existing sanitation systems aredeteriorating due to many-imperfection care So BT hasability to restore these systems due to its self-purificationproperty Similarly the maintenance cost of the seweragesystem is unfavorable due to economic collapse So we canprefer this technology due to its simplicity and low cost

5 Conclusions

In order to rehabilitate the urbanized water bodies as lakesrivers and streams BT is sustainable and reliable for publichealth with no maintenance and further general costs tominimize the traditional system In this study we demon-strate the interpretation of the water pollution problemsof a complex dataset through MCDM techniques becausechemometric research enables us to discuss the similaritiesand dissimilarities along the observing stations among thevariables that could not be clearly visible for assessmentof the analytical data in a table This research emphasizedthat the BT offers an ingenious and innovative solution forrehabilitation of the urban water bodies up to the suitablewater quality BT is efficient due to its simplicity beingeconomically affordable and reproducible on any scale ofthe operation hence it can provide tenable and long-termsolution to the various water related pollution problems allover the world

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research is held under the project of ldquoEnvironmentProtection and Microbial Water Purification DemonstrationProjectrdquoThe authors thank ldquoShenzhenBersonBiotechnologyCo Ltdrdquo for supporting and helping them to complete theexperiment The authors are also grateful to all anonymousreviewers and proofreader that have helped to improve thequality of this paper

References

[1] A Akkoyunlu and M E Akiner ldquoPollution evaluation instreams using water quality indices a case study from TurkeyrsquosSapanca Lake Basinrdquo Ecological Indicators vol 18 pp 501ndash5112012

[2] D Yudianto and X Yuebo ldquoThe feasibility of bacteria applica-tion for treating the polluted urban stream from the perspectiveof numerical modellingrdquo Polish Journal of Environmental Stud-ies vol 19 no 2 pp 419ndash427 2010

[3] G Onkal-Engin I Demir and S N Engin ldquoDeterminationof the relationship between sewage odour and BOD by neuralnetworksrdquo Environmental Modelling and Software vol 20 no 7pp 843ndash850 2005


[4] J Chen ldquoRapid urbanization in China a real challenge to soilprotection and food securityrdquo Catena vol 69 no 1 pp 1ndash152007

[5] X Dong W Zhou and S He ldquoRemoval of anaerobic solublemicrobial products in a biological activated carbon reactorrdquoJournal of Environmental Sciences vol 25 no 9 pp 1745ndash17532013

[6] X Yuebo T A Kabo-Bah and S Yajing ldquoBacterial technologyas a sustainable solution to polluted urbanized rivers andwastewater treatment systems in Chinardquo Journal of AppliedTechnology in Environmental Sanitation vol 2 no 2 pp 87ndash932012

[7] M Campolo P Andreussi and A Soldati ldquoWater qualitycontrol in the river Arnordquo Water Research vol 36 no 10 pp2673ndash2680 2002

[8] P R Kannel S Lee Y-S Lee S R Kanel and G J PelletierldquoApplication of automated QUAL2Kw for water quality model-ing and management in the Bagmati River Nepalrdquo EcologicalModelling vol 202 no 3-4 pp 503ndash517 2007

[9] T A Kabo-Bah X Y Bo and Y Song ldquoRegression modelsfor determining the fate of BOD5 under biological treatmentmethod in polluted riversrdquo Journal of Hydrology CurrentResearch vol 3 no 3 2012

[10] J B Zhou M M Jiang B Chen and G Q Chen ldquoEmergyevaluations for constructed wetland and conventional wastew-ater treatmentsrdquo Communications in Nonlinear Science andNumerical Simulation vol 14 no 4 pp 1781ndash1789 2009

[11] KGunes B Tuncsiper S Ayaz andADrizo ldquoThe ability of freewater surface constructed wetland system to treat high strengthdomestic wastewater a case study for the MediterraneanrdquoEcological Engineering vol 44 no 2 pp 278ndash284 2012

[12] J A Alvarez I Ruız and M Soto ldquoAnaerobic digesters as apretreatment for constructed wetlandsrdquo Ecological Engineeringvol 33 no 1 pp 54ndash67 2008

[13] M Ali L-Y Chai C-J Tang et al ldquoThe increasing interest ofANAMMOX research in China bacteria process developmentand applicationrdquo BioMed Research International vol 2013Article ID 134914 21 pages 2013

[14] E Foresti M Zaiat andM Vallero ldquoAnaerobic processes as thecore technology for sustainable domestic wastewater treatmentconsolidated applications new trends perspectives and chal-lengesrdquo Reviews in Environmental Science and Biotechnologyvol 5 no 1 pp 3ndash19 2006

[15] S Hashim Y B Xie I Hashim and I Ahmad ldquoUrbanriver pollution control based on bacterial technologyrdquo AppliedMechanics and Materials vol 692 pp 127ndash132 2014

[16] D Yudianto and Y Xie ldquoInfluences of limited ammoniumnitrogen and water temperature on the urban stream restora-tion using bacterial technologymdashview from the perspective ofnumerical modellingrdquo Journal of Water Resource amp Protectionvol 2 no 3 pp 227ndash234 2010

[17] S-M Liou S-L Lo and S-HWang ldquoA generalized water qual-ity index for Taiwanrdquo Environmental Monitoring and Assess-ment vol 96 no 1ndash3 pp 35ndash52 2004

[18] C Simsek and O Gunduz ldquoIWQ Index a GIS-integratedtechnique to assess irrigation water qualityrdquo EnvironmentalMonitoring and Assessment vol 128 no 1ndash3 pp 277ndash300 2007

[19] A P Singh S K Ghosh and P Sharma ldquoWater qualitymanagement of a stretch of river Yamuna an interactive fuzzymulti-objective approachrdquo Water Resources Management vol21 no 2 pp 515ndash532 2007

[20] A P Singh ldquoAn integrated fuzzy approach to assess waterresourcesrsquo potential in a watershedrdquo ACFAI Journal of Compu-tational Fluid Mathematics vol 1 no 1 pp 7ndash23 2008

[21] D-Y Chang ldquoApplications of the extent analysis method onfuzzy AHPrdquo European Journal of Operational Research vol 95no 3 pp 649ndash655 1996

[22] A P Singh and S K Dubey ldquoOptimal selection of a landfilldisposal site using a modified fuzzy utility approachrdquo FuzzyInformation and Engineering vol 4 no 3 pp 313ndash338 2012

[23] B Srdjevic and Y D P Medeiros ldquoFuzzy AHP assessment ofwater management plansrdquo Water Resources Management vol22 no 7 pp 877ndash894 2008

[24] S Hashim X Yuebo and A T Kabo-Bah ldquoBeneficial bacteriahelpful to restore water bodiesrdquo Oriental Journal of Chemistryvol 30 no 3 pp 1397ndash1399 2014

[25] T L Saaty ldquoA scaling method for priorities in hierarchicalstructuresrdquo Journal of Mathematical Psychology vol 15 no 3pp 234ndash281 1977

[26] A H I Lee W-C Chen and C-J Chang ldquoA fuzzy AHP andBSC approach for evaluating performance of IT departmentin the manufacturing industry in Taiwanrdquo Expert Systems withApplications vol 34 no 1 pp 96ndash107 2008

[27] S Boroushaki and J Malczewski ldquoImplementing an extensionof the analytical hierarchy process using ordered weighted aver-aging operators with fuzzy quantifiers in ArcGISrdquo Computers ampGeosciences vol 34 no 4 pp 399ndash410 2008

[28] F F Nobre L T F Trotta and L F A M Gomes ldquoMulti-criteria decision making an approach to setting priorities inhealth carerdquo Statistics inMedicine vol 18 no 23 pp 3345ndash33541999

[29] H Deng ldquoMulticriteria analysis with fuzzy pairwise compar-isonrdquo International Journal of Approximate Reasoning vol 21no 3 pp 215ndash231 1999

[30] C G E Boender J G de Graan and F A Lootsma ldquoMulti-criteria decision analysis with fuzzy pairwise comparisonsrdquoFuzzy Sets and Systems vol 29 no 2 pp 133ndash143 1989

[31] P J van Laarhoven andW Pedrycz ldquoA fuzzy extension of Saatyrsquospriority theoryrdquo Fuzzy Sets and Systems vol 11 no 3 pp 229ndash241 1983

[32] L Mikhailov and P Tsvetinov ldquoEvaluation of services usinga fuzzy analytic hierarchy processrdquo Applied Soft ComputingJournal vol 5 no 1 pp 23ndash33 2004

[33] Y C Erensal T Oncan and M L Demircan ldquoDeterminingkey capabilities in technologymanagement using fuzzy analytichierarchy process a case study of Turkeyrdquo Information Sciencesvol 176 no 18 pp 2755ndash2770 2006

[34] L A Zadeh ldquoFuzzy setsrdquo Information and Computation vol 8pp 338ndash353 1965

[35] H S Hansen ldquoGIS-based multi-criteria analysis of wind farmdevelopmentrdquo in Proceedings of the 10th Scandinavian ResearchConference on Geographical Information Science pp 75ndash87Royal Institute of Technology Stockholm Sweden 2005

[36] Y Song Y Xie and P Fu ldquoEco-friendly domestic sewagetreatment with bacterial technologyrdquo in Proceedings of theInternational Symposium onWater Resource and EnvironmentalProtection (ISWREP rsquo11) pp 1294ndash1297 Xirsquoan City China May2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


[10ndash12] Nitrogen and some other nutrient wastes were thevital source of pollution problems in China and unable torestore system through these conventional techniques Sothe anaerobic ammonia oxidation researches tend to moveinto full-scale treatment plant Till now at least 5 genera and13 species have been identified using culture-independent-molecular techniques [13] Within the last decades somespecialized reactor systems such as sequencing batch reactorrotating biological contactor trickling filter UBF reactorgranular sludge bed reactor and membrane bioreactor havebeen introduced in both laboratory and full scale to obtainhigh removal rate and finally we noticed that these reactorsplayed an important role in securing high rate performancefor the product of nitrogen removal Nitrogen pollutioncauses serious environmental problems and it not only threat-ens the sustainable development of fisheries agriculturetourism and so forth but also is harmful to the livingenvironment of human beings The amounts of phosphorusand ammonia nitrogen in domestic wastewater have beennoticed from 10 to 17 and 30 to 50mgL respectively [14]

Ecofriendly and sustainable environmental demand isthe hot and impressive topic due to public economic andlegislation pressureThe best selection in complex frameworkis based on the sustainability of nutrient removal biodegra-dation of suspended particles and removal efficiency of thesystem In this situation the major preference is to adoptany system that is more reliable for energy consumptionsconversion of chemicals into biomass complex infrastruc-ture and the repairing or maintenance cost of the systemBacterial Technology (BT) provides a plenty of opportunitiesfor effectively treating these issues [2] BT is an applicationof bioremediation that uses microorganism metabolism toremove nutrients from the water bodies and regenerate upto the original condition [2 6 9] and its operational cost isrelatively low [15] which generally have a high public interestDue to its smart application it is popular in the research areaof environmental sciences and engineering Temperature isthe major concern that is directly effective in the process ofthe degradation of the substances [9]

Due to these considerations we can adopt the newtechnique as BT with complete confidenceTheir bacteria areusually hired to vitiate pollutants or nutrients into simpleor nontoxic entity and produce suitable effluents [16] Thistechnology has reassuring advantages compared to othertraditional techniques as already discussed BT has beensuccessfully implemented to recover the polluted lakes [17]restore polluted rivers and assimilate effluent of wastewatertreatment plant [9] To control the urban river pollution BThas been employed in different places in China that is fortreating the polluted urban water bodies [2 15] It was deter-mined to be successful with reliable results in boosting up therecovery processes of all water bodies compared to the othertraditional technologies It extends for the rehabilitation ofpolluted lakes rivers and streams and is also reliable forthe requirement of the wastewater effluent standards withoutconstructing massive structures as compared to the otherconventional methods [15 16]

In the past the water quality index approach was con-sidered as the best tool to determine the water quality of

the water bodies [17 18] Numerous researches represent theintegrated uncertainty in evaluating the water quality Someof these are under the base on fuzzy impartial optimization[19 20] andMCDM problems using AHP [21] Further a fewyears ago AHP and FAHP were acquiring popularity in thehydraulics and environmental engineering fields [22] Srdje-vic and Medeiros [23] have used FAHP for the managementplans and Singh et al [19] have used FAHP to determinethe water quality of the Yamuna River that is tributary ofGanga In this research the urban rivers pollution abatementis being extensively determined at five various places suchas Xuxi River Wuxi City Gankeng River Shenzhen CityXia Zhang River Yixing City Fenghu and Song Yang RiversRuian City Jiu Haogang River Hangzhou City A location-wise variation of the water quality parameters (temperatureDO COD TP and NH

3N) was determined before and after

the treatment of the BT For the relative significance of waterquality parameters AHP has been applied in the selectedsites In addition FAHP is developed for the present researchto determine the original status of water quality on eachurban river based on MCDM framework For current studythe data matrix is very complicated because the spatial andtemporal parameters vary from site to site It is not possiblewith simple AHP to evaluate the pollution status on eachstation of each site Therefore FAHP is the best techniquewhich can help to determine the water quality parametersvalues as compared to the other techniques In this paperwe briefly discuss practical implementation of BT on urbanpolluted rivers and argue with MCDM based on FAHP thatBT is simple affordable and sustainable for restoring pollutedwater bodies

2 Materials and Methods

21 Study Area and Samples Collection Xuxi River (XXR)is situated in Wuxi City Chang Nan District of Chinageographically as (31∘56291015840N and 120∘28141015840E) Its upperstream starts from the Jing-Hang main canal and travelstowards the ancient small canal The selected river lengthfor the experiment is 1360m with 45m of upstream surfaceaverage width and about the average depth of 14m River isunder north subtropical humid zone and ismarked bymuddysediments This zone is facing four distinct seasons with thephenomenon of climatic influence circulation Fenghu (FH)and Song Yong (SY) Rivers were selected FH River is placedon the tail of the SY River and both of them are situatedin Wenzhou (Ruirsquoan) City (120∘39131015840E and 27∘46491015840N)Zhejiang Province China Most of the Wenzhou area isplaced under the typhoon zone and the FH River is takingwater fromWenruitang and Liangmian Rivers The SY Riveris starting from cave bridge and falls directly into the FHRiver The SY River length is about 280m with the averagebreadth 5ndash18m and 1ndash3m water depth and FH river lengthis about 740m with the average breadth 6ndash15m and 1mwater depth For monitoring and sample collection of theexperiment the selected reaches of both rivers were dividedinto six points The appearance of the river water color wasblackish or greenish and bubbles were blowing on the surfaceof water These rivers are situated under the commercial and





CODmgL

TPmgL

TNmgL

NH3NmgL

















Grand Canal

1 2 3 4 5

Gu Canal

70m 200m 300m 200m 220m 200m 170m

June 4

June 13 June 24

July 31


08 tons

03 tons

03 tons

22 tons













Attribute site

Criterion map

Objective Objective

Attribute n

n1 n2Site_A Site_B




119860 =

[[[[[[[[[[

[

1 11990811199082

sdot sdot sdot

1199081119908119899

11990821199081

1 sdot sdot sdot

1199082119908119899

119908119899

1199081

119908119899


]]]]]]]]]]

]

(1)









(119860minus 120582max 119868) times120596 = 0 (2)






RI 0 0 058 09 112 124 132 141 145 149


CI =

120582max minus 119899

119899 minus 1 (3)


CR =

CIRI

(4)


119908119894

119904=

119895=119898

sum

119895=1119908119894119895

119904119908119895

119886119894 = 1 119899 (5)

where119908119894



119895



1

0 l m kx

120583(120572)



120583 (120572) =

(119909 minus 119897) (119898 minus 119897) 119897 le 119909 le 119898

(119896 minus 119909) (119896 minus 119898) 119898 le 119909 le 119896

0 otherwise

(6)


[[[[[[

[

(1 1 1) (11989712 11989812 11989612) sdot sdot sdot (1198971119899 1198981119899 1198961119899)

(11989721 11989821 11989621) (1 1 1) sdot sdot sdot (1198972119899 1198982119899 1198962119899)

(1198971198991 1198981198991 1198961198991) (119897

1198992 1198981198992 1198961198992) sdot sdot sdot (1 1 1)

]]]]]]

]

(7)

where 119894119895

= (119897119894119895 119898119894119895 119896119894119895) 119894119895

minus1= (119897119895119894 119898119895119894 119896119895119894) for 119894 119895 =














247029197278

255046246258

165032162171

162034156168

287035279294

DO (mgL) 2Mean


1691080433

179090427

183190548

114070818

341380745

COD (mgL) 15Mean


1456854177

16424541181

59717529672

43523524786

590135224139

NH3N (mgL) 2Mean


1521977942273

16871023801269

2249999120429

135975087157

21441044876267

TP (mgL) 0Mean


091057014186

1205018157

08304900712

130470517

190420728





3N








(a)

(b)

(c)

(d)

Figure 5 Continued


(e)




















0

01

02

03

XXU GKS XZY FSR JHH

Scor

e

Location

TemDoCOD

TPNH3N









5 Conclusions




Acknowledgments


References













































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





CODmgL

TPmgL

TNmgL

NH3NmgL

















Grand Canal

1 2 3 4 5

Gu Canal

70m 200m 300m 200m 220m 200m 170m

June 4

June 13 June 24

July 31


08 tons

03 tons

03 tons

22 tons













Attribute site

Criterion map

Objective Objective

Attribute n

n1 n2Site_A Site_B




119860 =

[[[[[[[[[[

[

1 11990811199082

sdot sdot sdot

1199081119908119899

11990821199081

1 sdot sdot sdot

1199082119908119899

119908119899

1199081

119908119899


]]]]]]]]]]

]

(1)









(119860minus 120582max 119868) times120596 = 0 (2)






RI 0 0 058 09 112 124 132 141 145 149


CI =

120582max minus 119899

119899 minus 1 (3)


CR =

CIRI

(4)


119908119894

119904=

119895=119898

sum

119895=1119908119894119895

119904119908119895

119886119894 = 1 119899 (5)

where119908119894



119895



1

0 l m kx

120583(120572)



120583 (120572) =

(119909 minus 119897) (119898 minus 119897) 119897 le 119909 le 119898

(119896 minus 119909) (119896 minus 119898) 119898 le 119909 le 119896

0 otherwise

(6)


[[[[[[

[

(1 1 1) (11989712 11989812 11989612) sdot sdot sdot (1198971119899 1198981119899 1198961119899)

(11989721 11989821 11989621) (1 1 1) sdot sdot sdot (1198972119899 1198982119899 1198962119899)

(1198971198991 1198981198991 1198961198991) (119897

1198992 1198981198992 1198961198992) sdot sdot sdot (1 1 1)

]]]]]]

]

(7)

where 119894119895

= (119897119894119895 119898119894119895 119896119894119895) 119894119895

minus1= (119897119895119894 119898119895119894 119896119895119894) for 119894 119895 =














247029197278

255046246258

165032162171

162034156168

287035279294

DO (mgL) 2Mean


1691080433

179090427

183190548

114070818

341380745

COD (mgL) 15Mean


1456854177

16424541181

59717529672

43523524786

590135224139

NH3N (mgL) 2Mean


1521977942273

16871023801269

2249999120429

135975087157

21441044876267

TP (mgL) 0Mean


091057014186

1205018157

08304900712

130470517

190420728





3N








(a)

(b)

(c)

(d)

Figure 5 Continued


(e)




















0

01

02

03

XXU GKS XZY FSR JHH

Scor

e

Location

TemDoCOD

TPNH3N









5 Conclusions




Acknowledgments


References













































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Grand Canal

1 2 3 4 5

Gu Canal

70m 200m 300m 200m 220m 200m 170m

June 4

June 13 June 24

July 31


08 tons

03 tons

03 tons

22 tons













Attribute site

Criterion map

Objective Objective

Attribute n

n1 n2Site_A Site_B




119860 =

[[[[[[[[[[

[

1 11990811199082

sdot sdot sdot

1199081119908119899

11990821199081

1 sdot sdot sdot

1199082119908119899

119908119899

1199081

119908119899


]]]]]]]]]]

]

(1)









(119860minus 120582max 119868) times120596 = 0 (2)






RI 0 0 058 09 112 124 132 141 145 149


CI =

120582max minus 119899

119899 minus 1 (3)


CR =

CIRI

(4)


119908119894

119904=

119895=119898

sum

119895=1119908119894119895

119904119908119895

119886119894 = 1 119899 (5)

where119908119894



119895



1

0 l m kx

120583(120572)



120583 (120572) =

(119909 minus 119897) (119898 minus 119897) 119897 le 119909 le 119898

(119896 minus 119909) (119896 minus 119898) 119898 le 119909 le 119896

0 otherwise

(6)


[[[[[[

[

(1 1 1) (11989712 11989812 11989612) sdot sdot sdot (1198971119899 1198981119899 1198961119899)

(11989721 11989821 11989621) (1 1 1) sdot sdot sdot (1198972119899 1198982119899 1198962119899)

(1198971198991 1198981198991 1198961198991) (119897

1198992 1198981198992 1198961198992) sdot sdot sdot (1 1 1)

]]]]]]

]

(7)

where 119894119895

= (119897119894119895 119898119894119895 119896119894119895) 119894119895

minus1= (119897119895119894 119898119895119894 119896119895119894) for 119894 119895 =














247029197278

255046246258

165032162171

162034156168

287035279294

DO (mgL) 2Mean


1691080433

179090427

183190548

114070818

341380745

COD (mgL) 15Mean


1456854177

16424541181

59717529672

43523524786

590135224139

NH3N (mgL) 2Mean


1521977942273

16871023801269

2249999120429

135975087157

21441044876267

TP (mgL) 0Mean


091057014186

1205018157

08304900712

130470517

190420728





3N








(a)

(b)

(c)

(d)

Figure 5 Continued


(e)




















0

01

02

03

XXU GKS XZY FSR JHH

Scor

e

Location

TemDoCOD

TPNH3N









5 Conclusions




Acknowledgments


References













































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




RI 0 0 058 09 112 124 132 141 145 149


CI =

120582max minus 119899

119899 minus 1 (3)


CR =

CIRI

(4)


119908119894

119904=

119895=119898

sum

119895=1119908119894119895

119904119908119895

119886119894 = 1 119899 (5)

where119908119894



119895



1

0 l m kx

120583(120572)



120583 (120572) =

(119909 minus 119897) (119898 minus 119897) 119897 le 119909 le 119898

(119896 minus 119909) (119896 minus 119898) 119898 le 119909 le 119896

0 otherwise

(6)


[[[[[[

[

(1 1 1) (11989712 11989812 11989612) sdot sdot sdot (1198971119899 1198981119899 1198961119899)

(11989721 11989821 11989621) (1 1 1) sdot sdot sdot (1198972119899 1198982119899 1198962119899)

(1198971198991 1198981198991 1198961198991) (119897

1198992 1198981198992 1198961198992) sdot sdot sdot (1 1 1)

]]]]]]

]

(7)

where 119894119895

= (119897119894119895 119898119894119895 119896119894119895) 119894119895

minus1= (119897119895119894 119898119895119894 119896119895119894) for 119894 119895 =














247029197278

255046246258

165032162171

162034156168

287035279294

DO (mgL) 2Mean


1691080433

179090427

183190548

114070818

341380745

COD (mgL) 15Mean


1456854177

16424541181

59717529672

43523524786

590135224139

NH3N (mgL) 2Mean


1521977942273

16871023801269

2249999120429

135975087157

21441044876267

TP (mgL) 0Mean


091057014186

1205018157

08304900712

130470517

190420728





3N








(a)

(b)

(c)

(d)

Figure 5 Continued


(e)




















0

01

02

03

XXU GKS XZY FSR JHH

Scor

e

Location

TemDoCOD

TPNH3N









5 Conclusions




Acknowledgments


References













































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






247029197278

255046246258

165032162171

162034156168

287035279294

DO (mgL) 2Mean


1691080433

179090427

183190548

114070818

341380745

COD (mgL) 15Mean


1456854177

16424541181

59717529672

43523524786

590135224139

NH3N (mgL) 2Mean


1521977942273

16871023801269

2249999120429

135975087157

21441044876267

TP (mgL) 0Mean


091057014186

1205018157

08304900712

130470517

190420728





3N








(a)

(b)

(c)

(d)

Figure 5 Continued


(e)




















0

01

02

03

XXU GKS XZY FSR JHH

Scor

e

Location

TemDoCOD

TPNH3N









5 Conclusions




Acknowledgments


References













































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


(a)

(b)

(c)

(d)

Figure 5 Continued


(e)




















0

01

02

03

XXU GKS XZY FSR JHH

Scor

e

Location

TemDoCOD

TPNH3N









5 Conclusions




Acknowledgments


References













































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


(e)




















0

01

02

03

XXU GKS XZY FSR JHH

Scor

e

Location

TemDoCOD

TPNH3N









5 Conclusions




Acknowledgments


References













































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




0

01

02

03

XXU GKS XZY FSR JHH

Scor

e

Location

TemDoCOD

TPNH3N









5 Conclusions




Acknowledgments


References













































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology










































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology








Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

Documents

Research Article Integrated Evaluation of Urban Water