1

Extended Abstract

ASSESSMENT OF STORMWATER RUNOFF QUALITY USING PRINCIPAL COMPONENT ANALYSIS

Clara PIRES

Keywords: Stormwater runoff; urban drainage; Principal Components Analysis (PCA); microbial source tracking; faecal contamination; polymerase chain reaction (PCR).

1 INTRODUCTION

Urban development can dramatically alter the hydrologic response of the watershed, by increasing impervious surfaces. Water which was previously pounded on the forest floor, infiltrated into the soil and converted to groundwater is now converted directly into surface runoff. In addition to increases in runoff volume, land development often results in the accumulation of pollutants on the land surface that runoff can mobilize and transport to receiving water bodies.

Stormwater runoff carries several pollutants accumulated on the surface of the

drainage basins, especially during dry weather, such as: heavy metals (such as zinc

and lead), significant amounts of organic matter derived from plant residues and

bacteria of fecal origin from different animal waste. Several studies have been

conducted in several countries (Gromaire-Mertz et al, 1999;. Choe et al., 2002; Field et

al, 2003;. Taebi and Droste, 2004; Gnecco et al, 2005;. Cited by Ferreira, 2006 ), which

showed that the pollutant load conveyed by stormwater runoff, which can be significant,

depends on the land use, land cover, geomorphological and climatic characteristics of

the basin. Additionally, studies conducted in Italy and France (Gnecco et al, 2005;..

Gromaire-Mertz et al, 1999) show that the quality of stormwater runoff depends

strongly on antecedent dry weather period and the characteristics of the rainstorm

itself, such as the maximum intensity.

The main focus of this communication is to characterize stormwater runoff in

several drainage basins in Lisbon. This study also broadens and deepens several

studies already made in those basins (Ferreira, 2006; gondim, 2008; Queiroz, 2012),

particularly in the parishes of Alcantara, Madalena and e S. Jorge de Arroios. The

study area was strategically divided into several characteristics of occupation: zones of

reduced impervious area from houses with gardens or heavily urbanized areas, with

intense traffic and commercial activity.

Thus, the goal of this study is to evaluate, through robust statistical tools, what

factors influence the results obtained, in particular the occupation of the basin, the

antecedent dry weather period and the characteristics of the rainstorm itself (e.g,

maximum intensity). The report is based on the compilation of results from previously

performed experimental campaigns (in which samples from stormwater runoff were

collected in order to be analyzed from many physical, chemical and microbiological

parameters), the origin of fecal contamination was also traced based on analysis of

mitochondrial markers. The treatment of these data was performed using Principal

Component Analysis software (PCA), a tool with high potential in urban drainage,

which analyzes an extensive array of data, reduces the information to a limited number

of independent factors, studies the most striking features of the data and highlights

what relates (or differentiates) the various quantities under consideration.

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2 STUDY AREAS AND BASIC INFORMATION

2.1 Study basins located in Lisbon city

The experimental basins in Lisbon are in Alcantara (A), Bairro das Ilhas (I) and Madalena Street (M), and they are quite dissimilar in their topographic, morphologic and land use characteristics. Rain data was supplied by Instituto Geofísico Dom Luiz (IGIDL). Figure 1 shows the location of the studied basins and the location of the rain gauge.

Figure 1- Location of the experimental catchments in Lisbon, and the rain gauge.

2.2 Alcantara Basin

Alcantara is the most plural basin of the three. It has residential areas and areas

more dedicated to commerce. There are streets with intense traffic, and both steep and

flat streets. Most streets have trees in the sidewalk, intensifying the presence of vegetal

debris. Figure 2 shows the location of the collection points.

This basin had two previous studies assessing stormwater pollution, also

evaluating COD levels (Ferreira, 2006; Gondim, 2008). Table 1 shows the main

characteristics of the six sampled points, and the authors that have previously studied

it. Also, in Figure 2 and Table 1 presents, for each sink, direct contributory sub-basin

(limited upstream by other interception devices) and the respective potential sub-basin,

which corresponds to the entire area that can contribute to inflows in the reference

section, since the limit bedside.

Figure 2- Location of the collection points in Alcantara.

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Table 1-Descriotion of the collection points in Alcantara basin.

# Location Description Área [ha] Previous

Studies contrib. potential

A1 Next to south-east gate of ISA.

Little urban occupation and impreviousness; area with vegetation coverage and trees; medium slope.

0.84 2.22 Ferreira (2006) Gondim (2008) Queiroz (2012)

A2 In Joao de Barros St, next to Pedro Calmon St..

Residential area, without commercial activity; streets with trees; smooth slope.

0.05 0.16 Ferreira (2006) Queiroz (2012).

A3 Taxi stop in Luis de Camoes St..

Residential area with buildings, intense traffic, bus lines; Near café; steep slope.

0.01 10.70 Ferreira (2006) Gondim (2008) Queiroz (2012).

A4 Bus stop in Calvario Sq.

Intense commercial activity and traffic; bus and tram lines; smooth slope.

0.21 4.09 Ferreira (2006) Gondim (2008) Queiroz (2012).

A5 North side of Fontainhas Sq..

Intense commercial activity and traffic; bus and tram lines; smooth slope.

0.05 4.09 Ferreira (2006) Gondim (2008) Queiroz (2012).

A6 Square at the end of Cozinha Economica St.

Parking area, with high traffic; frequent flooding of stormdrain; flat slope.

0.05 5.27 Ferreira (2006) Queiroz (2012).

A7 Bus stop in José Dias Coelho St.

Residential and commercial area, moderate traffic near the bus stop; gentle slope.

0.04 2.14 Gondim (2008)

2.3 Madalena basin

The third set of points is entirely in Madalena Street, right in the middle of the 18th century historical centre of Lisbon. The street has an intense commercial activity and traffic, with bus lines and at the end of it, next to Martim Moniz Plaza trams as well. It has a steep slope, with its highest point around the middle length of the street, in Adelino Amaro da Costa Square.

Table 2 shows the three sample points that were selected: one going up the street, at the top of it, in Adelino Amaro da Costa Square, and the last near the bottom of the street, were the trams join in. Figure 4 shows the location of these points in Madalena Street.

Table 2- Description of the collection points in Madalena basin.

# Location Description Area [ha] Previous

Studies contrib. potencial

M1

Madalena St, upward, in the gutter next to number 127.

Intense traffic and commercial activity; impervious surfaces; steep slope.

0.08 3.71 Queiroz (2012)

M2 Madalena St, next to Adelino Amaro da Costa Sq.

Intense traffic and commercial activity; impervious surfaces; next

to restaurants; steep slope. 0.04 3.16

Queiroz (2012)

M3

Madalena St, downward, corner with Condes de Monsanto St..

Intense traffic and commercial activity; cobblestone pavement;

tram lines; steep slope. 0.08 2.12

Queiroz (2012)

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Figure 3- Location of the sampling points in Madalena catchment.

2.4 Bairro das Ilhas basin

The basin in Bairro das Ilhas is significantly smaller than the previous one, and is mainly residential with little commercial activity, tight one-way streets and the average slope is generally smooth. The traffic is of low intensity with areas of exclusive pedestrian access, and the only relevant green space is the Cesário Verde garden (4000 m2). It is also possible to note that some of the buildings' rooftops drain directly to the pavement. The location of the collection points can be seen in Figure 3. The total study area is of 6.25 ha, and six sampling stormdrains were selected, although one of them is most likely redundant due to its proximity to another point in the same conditions. The characteristics of the sampled points in this basin can be seen in Table 3.

Table 3- Description of the collection points in Ilhas basin.

# Location Description Area [ha] Previous

Studies contrib. potential

I1 South-east corner of Cesário Verde garden..

Vegetation present; previous areas; cobblestone surfaces; little traffic, mostly pedestrian; medium slope.

0.02 0.60 Queiroz (2012)

I2 Cidade da Horta St, next to Ilha do Pico St (stairs).

Wide stairs; roof runoff drains directly to pavement; medium slope

0.13 1.52 Queiroz (2012)

I3 Arroios St, corner with Ponta Delgada St..

Somewhat higher traffic than the rest; step slope.

0.08 0.72 Queiroz (2012)

I4 Square at the end of Açores St.

Completely impervious area; roof runoff drains into pavement; flat surface.

0.16 0.58 Queiroz (2012)

I5

Stairs connecting Ilha Terceira St. and Cesário Verde garden.

Narrow pedestrian area, very little pedestrian traffic; medium slope.

0.03 0.09 Queiroz (2012)

I6 Same stairs as I5, but farther down.

Same as I5. 0.01 0.14 Queiroz (2012)

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Figure 4- Location of the collection point in Ilhas experimental catchment.

3 METHODOLOGY

3.1 Sample collection

The collection campaigns took place from October 2012 to September 2013 with the objective of collecting stormwater samples in urban contextes. Stormwater was captured in plastic boxes and taken to the laboratory. Figure 5 shows the boxes used to sample stormwater.

Figure 5- Sampling during the experimental campaign. September 27, 2013.

This study refers to data collected from four experimental periods. The first trial

was held from 11 November 2005 to 23 March 2006 (Ferreira, 2006), the second from

February 17 to April 18, 2008 (gondim, 2008), the third from October 25 2011 to May 7,

2012 (Queiroz, 2012), and specifically for this work, an experimental period was

performed from 20 October 2012 to 27 September 2013.

3.2 The Laboratory

With the aim of evaluating the quality of stormwater the following parameters were

determined: Biochemical Oxygen Demand after 5 days at a temperature of 20 ° C

(BOD5), Chemical Oxygen Demand (COD) and Total Suspended Solids (TSS), Total

Coliform (TC), Fecal Coliform (FC), Escherichia coli (E.coli) and intestinal enterococci

(E.int). Table 4 refers to the analytical methods used for determining the parameters of

interest, and the reference of earlier studies that have determined these same

parameters.

Also DNA was extracted from each sample, and specific sequences of mitochondrial DNA were reproduced using PCR techniques, in order to match with the species-specific control markers for Humans, Cats and Dogs. The samples were centrifuged and the mtDNA was extracted using the QIAamp DNA Mini Kit (Qiagen).

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From the extracted DNA, PCR assays were conducted – in a first step a convencional PCR, then a nested-PCR – and compared with the mtDNA markers from the target species: Humans, Cats and Dogs.

Table 4- Analytical methods in physical, chemical and microbiological tests.

Parameter Analytical method Norm Previous Studies

Physico-chemical parameters

COD Volumetry SMEWW 2540C

(Ferreira,2006) (Gondim, 2008) (Queiroz, 2012)

BOD5 Volumetry SMEWW 5220B

(Ferreira,2006) (Gondim, 2008)

TSS Gravimetry SMEWW 5210B

(Ferreira,2006) (Gondim, 2008)

Microbiological parameters

TC Multiple tube (Most Probable Number) MM 9.2 (Ferreira,2006) (Gondim, 2008)

FC Multiple tube (Most Probable Number) MM 9.2 (Gondim, 2008)

E. Coli Multiple tube (Most Probable Number) MM 9.2 (Gondim, 2008) (Queiroz, 2012)

E. Int Multiple tube (Most Probable Number) MM 9.2 (Gondim, 2008) (Queiroz, 2012)

3.3 Characteristics of rainfall events

The number of precipitation records was processed in order to separate registers

in precipitation events, assuming the simultaneous application of the following criteria

(Ferreira, 2006):

a) ; intensity of less than 0.25 mm / h, which is considered corresponding to a residual

rainfall precipitation, equivalent to dry weather;

b) the occurrence of a dry weather period between each rainstorm with 120 minutes.

This criteria is justified given the fact that sub-basins under study present a small

time of concentration (lag time), well below this value, thus ensuring that the

response of the drainage system, for each storm event, is analyzed as a whole.

3.4 PCA models

The study of water quality in urban drainage often relies on the monitoring of

different magnitudes, so it involves multivariate analysis. Through the statistical method

of Principal Components Analysis it is possible to detect the most striking features of

the analyzed data and realize what relates (or differentiates) the various elements of

the sample under analysis (Brito, 2012).

Simplifying, but without significant loss of information contained in the samples, the

model seeks the main trend of the data, identifying new variables (Principal

Components, PC), in a fewer number than the original set, which are able to express

the most relevant variations common to the different elements of the sample (Reis,

1990). Thus, the PCA model reflects the most striking features of the data through

linear combination of the PC series (Jolliffe, 2003; Davies, 2005; Shlens, 2009). This

tool was used to understand the relation of stormwater runoff quality with the the basins

characteristics and the rainfall events characteristics, namely the antecedent dry

weather period, the medium and maximum intensity. Using Matlab 6.0 software with

statistical toolbox PLS 3.0, the PLS methodology was applied to the compilation of all

data related to the four experimental periods.

For the early processing of data, the normalization method was used (autoscaling).

This method consists in subtracting the mean value and dividing by the standard

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deviation value, centering the data and reducing its variability to a certain range. Once

the variables to be considered are of different nature and are expressed in units of

measurement that are not comparable, this method reduced the variables, making

them dimensionless in order to obtain average equal to zero and variance equal to one.

The compilation of all campaigns occurred in the four experimental periods have

several samples with parameters that were not evaluated, this occurred for a number of

reasons: due to some unexpected events occurred in the collection of the campaigns;

the inclusion of new parameters as the study progressed over time; and abandonment

of certain parameters in more recent campaigns. Given this discrepancy in the

evaluation of parameters/variables several PCA models were made. In a first approach

matrices with fewer parameters but with larger number of samples were analyzed. With

the evolution of the study there were analyzed matrices with larger number of

parameters but with fewer samples.

4 RESULTS

4.1 Physical-chemical and microbiological parameters

In tab 5 are gathered the summary of the main statistical parameters described on

the quality of stormwater runoff in the first and second experimental period. The same

is shown in Table 6 for the third and fourth experimental periods.

Analyzing the results obtained in Alcantara basin over the four experimental

periods, it is found that the average concentrations, with more relevance for COD, have

increased over time.

Comparing the three experimental basins, and as would be expected given the

uses and characteristics of the three sub-basins, stormwater runoff from the bairro das

Ilhas, globally, have the lowest levels of pollution. Stormwater runoffs from the

Madalena assume intermediate values, although similar to those recorded in Alcantara.

The average concentration values for COD, E. coli and enterococci – shown in

Table 5 and 6 – are extremely relevant to explain the impact that untreated stormwater

discharges have in receiving waters. The concentrations clearly exceed the legislated

limit for COD discharge of 150mg/l, defined by DL-236/98 (Portugal). Also for E. coli

and enterococci, the sampled stormwaters systematically exceeded the values

legislated by Directive 2006/7/EC, which stipulates limits of 200 MPN/100mL and 500

MPN/100mL, for E. coli and enterococci respectively.

Table 5- Summary of the main statistical parameters described on the quality of stormwater runoff in the first and second experimental period (adapted from Ferreira, 2006 and gondim, 2008).

Statistical parameters

CQO [mg/l]

CBO5 [mg/l]

SST [mg/l]

CT [NMP/ml]

CF [NMP/ml]

E.coli [NMP/ml]

EI [NMP/ml]

First experimental period

Alc

ân

tara

Average 203 32 390 6.70E+06 - - -

Standart deviation

239 46 477 1.80E+07 - - -

Minimum 2 2 8 2.60E+04 - - -

Maximum 1100 241 2300 7.90E+07 - - -

Samples 59 59 59 32 - - -

Second experimental period

Alc

ân

tara

Average 189 54 199 2.2 E+07 3.1 E+05 3.1 E+05 2.8 E+05

Standart deviation

233 68 197 1.1 E+08 9.4 E+05 9.4 E+05 9.0 E+05

Minimum 21.00 3 10 2.0 E+04 2.2 E+03 2.2 E+03 1.3 E+03

Maximum 1100 250 770 6.6 E+08 5.0 E+06 5.0 E+06 5.2 E+06

Samples 34 34 34 34 34 34 34

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Table 6- Summary of the main descriptive statistical parameters , concerning the quality of stormwater runoff in the third and fourth trial (adapted from Queiroz, 2012).

Statistical

parameters

Alcantara Ilhas Madalena

CQO

[mg/l]

E.coli

[NMP/ml]

E. Int

[NMP/ml]

CQO

[mg/l]

E.coli

[NMP/ml]

E. Int

[NMP/ml]

CQO

[mg/l]

E.coli

[NMP/ml]

E. Int

[NMP/ml]

Third experimental period

Average 439 5.1 E+04 6.2 E+04 102 6.0 E+03 1.3 E+04 341 2.6 E+04 4.7 E+04

Standart deviation

348 6.5 E+04 1.1 E+05 76 1.4 E+04 1.7 E+04 291 6.9 E+04 5.7 E+04

Minimum 33 1.0 E+02 8.0 E+01 45 1.3 E+02 9.3 E+02 76 1.7 E+02 1.5 E+03

Maximum 1091 2.4 E+05 4.8 E+05 346 4.8 E+04 4.8 E+04 937 2.4 E+05 1.6 E+05

Samples 19 18 18 17 12 12 14 12 12

Fourth período experimental

Average 573 5.0 E+04 4.2 E+04 214 6.6 E+03 1.5 E+04 204 2.0 E+04 2.7 E+04

Standart deviation

405 1.2 E+05 1.4 E+05 234 1.6 E+04 2.6 E+04 290 3.3 E+04 4.5 E+04

Minimum 131 2.6 E+02 2.4 E+02 56 2.1 E+02 2.3 E+01 66 1.3 E+02 2.6 E+02

Maximum 1100 6.4 E+05 7.5 E+05 850 8.8 E+04 9.9 E+04 1100 1.2 E+05 1.8 E+05

Samples 12 30 30 11 40 40 11 23 23

4.2 Results of mtDNA testing

In tab are gathered the results of the positive/negative detection of faecal contamination from the targeted species.

Table 7- Positive/negative counts for Human, Dog and cat mtDNA detections.

Basins Results Human Dog Cat Gull

Alcantara Positive 32 55% 26 34% 23 30% 19 61%

Negative 25 44% 51 66% 54 70% 12 39%

Total 57 77 77 31

Ilhas Positive 25 47% 34 49% 25 36% 19 45

Negative 29 53% 35 51% 44 64% 23 55

Total 53 69 69 42

Madalena Positive 16 50% 19 40% 17 36% 10 43%

Negative 16 50% 28 60% 30 64% 13 57%

Total 32 47 47 23

Total Positive 73 51% 79 41% 65 34% 48 50%

Negative 69 49% 114 59% 128 66% 48 50%

Total samples 142 193 193 96

According to these results there is a strong faecal presence of human origin, especially in the Alcantara basin, possibly due to intense pedestrian traffic and commercial activities such as restaurants and cafes, but also to the relevant night life with discos. In Bairro das Ilhas there is the highest Cat and Dog rate of positives, which might reflect the quiet, residential occupation of the area. Madalena Street shows signs of lower animal faecal contamination than Ilhas, though the human ratio remains the same.

4.3 Parameters associated with stormwater runoff

The method for separation of rainfall events was applied to the four experimental

periods. After that, for each experimental campaign, the following parameters were

determined: antecedent dry weather period (number of hours elapsed since the

previous rainfall event and the rainfall event in analysis); maximum and medium rainfall

intensity, recorded since the cleaning of sinks until the collection of samples. Table 8

summarizes this information for each experimental campaign.

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Table 8- Antecedent dry weather periods and intensities of rainfall per campaign in each experimental period.

Campaign [-]

ADWP [h]

Imax [mm/h]

Imed [mm/h]

Campaign [-]

ADWP [h]

Imax [mm/h]

Imed [mm/h]

First experimental period Third experimental period

C1 18.0 5.4 2.9 C1 3.0 3.6 1.3

C2 9.0 5.4 2.0 C2 1077.0 8.4 3.7

C3 12.0 3.5 1.6 C3 334.0 12.6 5.8

C4 83.0 1.3 0.6 C4 1.0 6.6 1.5

C5 96.0 4.9 1.8 C5 59.0 1.2 1.0

C6 32.0 1.0 0.4 C6 149.0 1.2 0.8

C7 359.0 5.7 0.8 C7 129.0 6.6 3.1

C8 179.5 1.9 0.7 C8 0.0 6.0 2.9

C9 44.0 8.4 1.9 C9 21.0 3.6 1.1

C10 142.0 1.4 0.9 C10 74.0 3.6 1.1

C11 71.0 17.0 3.0 C11 21.0 1.2 0.8

C12 143.0 2.8 1.5 C12 34.0 4.8 1.6

C13 8.0 4.8 1.4 C13 46.0 10.8 2.7

C14 54.0 1.6 0.7 Fourth experimental period

C15 19.0 6.5 1.4 C1 100.0 4.2 1.92

Second experimental period C2 19.0 1.8 1.1

C1 329 4.2 2.4 C3 79. 4.8 1.7

C2 3 44.4 6.5 C4 35.0 6.0 1.8

C3 28 7.7 7.7 C5 14.0 6.0 1.5

C4 10 4.9 2.5 C6 7.0 7.2 2.1

C5 381 5.1 2.5 C7 6.0 11.4 2.8

C6 1 3.4 0.4 C8 27.0 3.3 1.1

C7 3 0.6 0.5

C8 38 4.1 3.7

C9 18 6.7 1.9

4.4 PCA models

The results for quality of stormwater runoff, with regard to physical, chemical and

microbiological parameters of the four experimental periods, were subjected to

statistical analysis in order to analyze its variability.

In order to understand the relationship between the quality of stormwater runoff

with the basin and precipitation characteristics, various PCA models were built.

Thus, matrices were constructed with the physical, chemical and microbiological

parameters (COD, BOD5, TSS, TC, FC, E. coli and EI.), with parameters that

characterize stormwater runoff (ADWP, Imax and Imed) and the respective basins

associated with each sink (contributory area, CA; potential area, PA). And also with the

results of PCR markers on the presence of fecal contamination from different origins:

human (DNAH), canine (DNAC) and feline (DNAF) and the quantitative contributions

were also taken into account: canine (qDNAC), feline (qDNAF), and human (qDNAH).

Table 9 presents the various parameters analyzed in PCA models that were built.

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Table 9- Identification of parameters analyzed in the PCA models.

Model Basins Period Samples Parameters 1 (A); (M); (I) 1º, 2º, 3º, 4º 183 COD; ADWP; Imax; Imed; CA; PA 2 (A); (M); (I) 2º, 3º, 4º 201 COD; E coli; EI; ADWP; Imax; Imed; CA; PA 3 (A); (M); (I) 2º, 3º, 4º 173 E coli; EI; ADWP; Imax; Imed; CA; PA

4 (A); (M); (I) 3º, 4º 193 DNAH; DNAD; DNAC; DNAP 5 (A); (M); (I) 4º 96 qDNAH; qDNAD; qDNAC

6 (A) 1º, 2º, 3º, 4º 128 COD; ADWPt; Imax; Imed; CA; PA 7 (A) 1º, 2º 97 COD; BOD5; TC; TSS; ADWP; Imax; Imed; CA; PA

8 (A) 2º 34 COD; BOD5; TC; TSS; FC; E coli; EI; ADWP; Imax; Imed; CA; PA

9 (A) 2º, 3º, 4º 90 COD; E coli; EI; ADWP; Imax; Imed; CA; PA 10 (A) 3º, 4º 66 DNAH; DNAD; DNAC; DNAP 11 (A) 4º 77 qDNAH; qDNAD; qDNAC

12 (M) 1º, 2º,3º, 4º 27 COD;ADWP; Imax; Imed; CA; PA 13 (M) 2º, 3º, 4º 35 COD; E coli; EI; ADWP; Imax; Imed; CA; PA

14 (M) 3º, 4º 47 DNAH; DNAD; DNAC; DNAP 15 (M) 4º 23 qDNAH; qDNAD; qDNAC

16 (I) 1º, 2º, 3º, 4º 28 COD; ADWP; Imax; Imed; CA; PA A17 (I) 2º, 3º, 4º 63 COD; E coli; EI; ADWP; Imax; Imed; CA; PA

18 (I) 3º, 4º 70 DNAH; DNAD; DNAC; DNAP 19 (I) 4º 42 qDNAH; qDNAD; qDNAC

(A)-Alcantara; (I)-Ilhas; (M)-Madalena

In all models analyzed so far it was found systematically, in the proximity of graphs

loadings, the proximity between COD and ADWP parameters. As an example the

features and results of model 1 are presented in detail in table 10, illustrating this issue.

For the optimization of the model, the samples were pre-processed in order to identify

outliers. In most cases, as it was intended to build models that represent the general

trend of the behavior of the variables, it was decided to remove samples that presented

values out of context, whenever they do not fit in a confidence interval of 95% in the

scores plot. This representation is shown in Figure 6a), for model 1, and reveals that

the samples of the various basins do not differ, lying generally dispersed in the scores

map.

Figure 6- Results of model 1 in PCA. a) scores map of samples with the identification of the

confidence interval f 95%; b) map loadings of the variables.

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Table 10- Characteristics of model 1

Observations:

Samples 184 Samples pertaining to all campaigns that occurred in the four experimental periods and the three basins studied. Matrix [184x6] Parameters

CQO;ADWP; Imax; Imed; CA, PA;

Nº of outliers

29 Usually excluded due to excessively high concentrations of COD, on all the samples.

Nº PC 4

% Var cap 81.76% Good percentage of variance captured by the model. With four PC the model is explained almost entirely.

As shown in Table 9, after the joint analysis of the three experimental basins, PCA

models for each basin separately have been implemented; also in these models the

COD – ADWP association was enhanced. Moreover, to this association the BOD5 and

TSS parameters has to be added in Alcantra basin, since only in this basin this

parameter was assessed.

Once the human pathogens contribute noticeably to the degradation of the quality of

stormwater runoff, several PCA models were built, in order to trace their origin. And again the

results showed that the source of fecal contamination depends on the characteristics and uses

of the basins. By way of example, are shown in detail the features and results of the model 5,

which illustrate this point, in Table 11 and in Figure 7 is verified that the samples collected in

Alcântara and Madalena basins contain larger amounts of human feces compared to those of

dogs and cats. In the Bairro das Ilhas basin are these animals (dog and cat) the ones who

contribute more to this fecal contamination. This fact has already occurred in the analysis of

PCR markers and is justified by the occupation of the areas. The Alcântara and Madalena

basins have greater commercial and nocturnal activity, while the Ilhas basin is fundamentally a

residential area with possibly more pets.

Figure 7- Results of the PCA model 5. Biplot map of scores and variables.

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Table 11- Characteristics of model 5

Observations:

Samples 96 Samples pertaining to all campaigns that occurred in the fourth experimental period and the three basins studied.Matrix [96x3] Parameters

qDNAH; qDNAC; qDNAC

Nº of outliers

29 Usually excluded due to excessively high concentrations, on all the samples.

Nº PC 4

% Var cap 81.76% Good percentage of variance captured by the model. With two PC the model is almost fully explained.

5 CONCLUSIONS

The present study discloses the experimental work that was done in three basins located in the city of Lisbon, in order to characterize stormwater runoff quality in urban areas. With regard to physical, chemical and microbiological parameters, the results of campaigns conducted in the four experimental periods were subjected to a statistical treatment, in order to characterize urban stormwater runoff quality. This procedure allowed the identification of several troubling aspects, mainly the identification of high mean values, especially in COD, which exceed the emission limit values established by Decree-Law 236/98. This situation deserves attention since it clearly demonstrates the significant impact that stormwater runoff has in receiving water bodies, thus giving rise to the interest in understanding the phenomenon of contamination of stormwater runoff in urban areas, in order to develop watershed management practices to eliminate or reduce the pollution risk.

Through Principal Component Analysis several models were built in order to

understand the relationship between the physical, chemical and microbiological

parameters, the characteristics of the rainfall events and the contributory and potential

areas. In all models the relationship COD-ADWP was found, and in a more detailed

analysis, intended only to Alcântara basin, BOD5-COD-TSS-ADWP relationship was

also evident. As in studies conducted in Italy and in Paris (Gnecco et al, 2005;..

Gromaire-Mertz et al, 1999), the results obtained so far show that the quality of

stormwater runoff depends strongly on antecedent dry weather period, rather than the

characteristics of the rainstorm itself (namely medium and maximum intensity) or the

tributary volume (represented in this study by contributory/potential basin areas). This

relationship is justified, since the higher the antecedent dry weather period, the greater

the concentrations of pollutants accumulated on the basin surfaces will be. The

magnitude of precipitation events is not so important because even events with lower

intensity contribute to the entrainment of pollutants loads from the basin surface.

Climate changes, that are expected in short term, the increased periods without

precipitation and the occurrence of rainfall events with greater intensity do anticipate

the worsening of this problem, with the significantly increase of contaminated

stormwater runoff.

It was confirmed the presence of fecal contamination of human, canine and /or

feline origin at each sampling point of Alcântara basin, Bairro das Ilhas and Rua da

Madalena (Queiroz, 2012). This conclusion highlights the need for implementing

intervention measures in order to mitigate this problem. For example the population

can be reeducated for the collection of waste from pets, in the case of fecal

contamination of canine and feline origin; or public toilets must be provided in order to

reduce fecal contamination of human origin. Through PCA models, again was found

that the origin of fecal contamination depends on the properties and uses of the basins.

The models presented in this study can be improved with the realization of more

experimental campaigns for the characterization of stormwater runoff, which will

13

improve the quality and quantity of available data. It is also important to extend the

research of contamination for other townsmen animals (seagulls and rats) with their

respective contribution.

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