Department of Environmental Engineering

Unit of Ecologistics and Environmental Risk Management

SUMMARY OF PROFESSIONAL ACCOMPLISHMENTS

Determining parameters influencing odour emission from passive

sources located on wastewater treatment plants

Author: mgr inż. Piotr Sobczyński

Supervisor: dr hab. inż. Izabela Sówka, prof. P.Wr.

Support Supervisor: dr inż. Michał Mańczak

Reviewers

prof. nzw. dr hab. inż. Andrzej Kulig

dr hab. inż. Jacek Piekarski, prof. PK

Wrocław, 2016

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1. INTRODUCTION

Wastewater treatment plants (WWTPs) are facilities which are often indicated as serious

odour nuisance sources. (Belgiorno et al, 2013). At the same time most important

organization responsible for human health - WHO (World Health Organization) and other

researchers indicate that odours originating from odour emitting facilities, including

wastewater treatment plants, affect human health. In Poland there are 3288 municipal

wastewater treatment plants treating about 18221,234 x 106 a year. Depending on

technological system and management methods, every object of WWTP can be odour

emission source. Due to the rapid growth of city agglomerations, people living In suburbs

are highly exposed to negative odour impact of WWTPs, which are often located In the

suburbs (Sówka et al, 2014). Secondly due to the favorable real estate prices, these areas

have become more attractive in terms of urbanization, thus residential areas borders

development move towards disruptive odor objects. Number of complaints related to odor

nuisance of municipal facilities in Poland continues to grow (Kulig, Szyłak-Szydłowski, 2013).

Progressive urbanization and demographic processes as well as growing awareness of local

communities result in conflicts with WWTP owners. Due to the lack of proper regulations in

Polish law that provide methodology for determining odour impact of planned and existing

facilities, conflicts between WWTP owner and the local communities are bothersome to

resolve.

Object’s odor nuisance evaluation can be performed by measuring odour

concentration using dynamic olfactometry (Sówka et al, 2014; Paoletti et al. 2011) or

concentration of each odorant using analytical methods (Eui-Chan Jeon et al. , 2009, Kim et

al., 2005) to obtain data required to calculate odour emissions. In odour impact assessment

field olfactometry is frequently used (Cesca and Cunnington, 2008; Pan et al., 2007) as well

as electronic nose which artificially simulates human sense of smell (Wilson, Baietto, 2009

Giungato and others, 2016). In odour impact assessment of existing facilities field studies are

also used. Odour intensity measurement results are processed using statistical and

geostatistical tools to to evaluate odor nuisance of installations (Sówka, 2013, Szymanski et

al, 2015). Each of these methods provide reliable information on analyzed object odour

impact range but economically using odour dispersion models seems most advantageous.

Despite high accuracy of analytical methods, they are relatively complicated and not always

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sufficiently describing odor nuisance, because odour composition is a mixture that may

consist of several dozens of different odorants. In complex mixtures of odorants,

determination of relationship between different odorants concentrations and odor

sensation is extremely difficult (Stuetz et al, 1999, Kośmider, 2002). On the other hand, field

studies, require a huge amount of time and human resources, which generates high costs

and long time of analysis. Therefore, the combination of olfactometry analysis and

dispersion modeling is considered to be one of the most versatile solutions to determine

installations’ odour impact range (Capelli et al., 2013; Ranzato et al., 2012). Most

importantly, using dispersion modeling tools allows to assess odour impact range of existing

and planned facilities. Conducting accurate model calculations are not possible without

accurate odour emission determination. Due to high odour emission variability from WWTP,

it is important to examine and determine parameters and conditions affecting the quantity

of odours emitted and to study their impact on odour emission rates in order to accurately

estimate the odour impact range of selected facility.

2. AIM, THESIS AND SCOPE OF THE STUDY

The aim of the study is to determine WWTP’s primary clarifiers odour emission rates

variability and influence of basic wastewater influent and operating parameters on odour

emission rates, as well as determination of the effect of primary clarifiers odour emission

rates annual variability on odour impact range of wastewater treatment plant. Research is

aimed to identify primary clarifier’s odour emission variability range and determine the

significance of its impact on odour dispersion modeling results in terms of WWTP odour

impact estimation.

Research carried out as part of the doctoral thesis allowed to prove the assumed thesis:

"Odour emission originating from primary clarifiers is highly variable depending on the

influent wastewater parameters"

and

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"Primary clarifiers’ odour emission variability have a significant impact wastewater

treatment plant odour impact range"

Scope of work includes theoretical part, review of literature concerning WWTP odour

nuisance, main methods of assessing odour impact, factors affecting odour emission from

passive area sources and review of odour dispersion models used to odour impact

assessment. In the theoretical part methodology of primary clarifiers odour emissions

variation determination as well as applied odour dispersion model selected for odour impact

range estimation was characterized.

Second, research part of the paper presents conducted study results: odor emissions

from WWTP primary clarifiers, statistical analysis of selected factors impact (wastewater

temperature, chemical oxygen demand, primary clarifiers retention time) on odour emission

rates using multiple regression and artificial neural networks, as well as the odour impact

range analysis using AERMOD model for specific emission variants identified on the basis of

conducted studies and taking into account odour emission variability on a monthly basis

determined during measurements and using artificial neural networks.

3. METHODOLOGY

Scope of the research conducted as part of the doctoral thesis included passive area

source selection, which can be considered one of the main WWTP odour source. For this

purpose, odour emission research concerning sources selected on the basis of site visits and

wastewater treatment and sludge treatment technology analysis was conducted. Selected

sources included the following installations / facilities: primary clarifiers, anaerobic and

denitrification chambers, gravity sludge thickeners, digested sludge tank. Conducted

personal odour emission and literature studies from above. buildings and carried out studies

resulted in designation of further research main subject - primary clarifiers, due to their

significant share in the overall odor emission from WWTP.

After confirming the initial assumptions about primary clarifiers emission variability,

main emission studies on selected objects were carried out on an annual basis. Odour

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emission measurements were carried out with the highest possible frequency for a year -

fall, winter, spring and summer. Research carried out as part of the doctoral thesis included:

1. Sampling and analysis of odour samples in order to determine specific odour

emission rates from primary clarifiers

2. Sampling and analysis of wastewater samples to determine wastewater COD

3. Measurement of physical parameters of the wastewater - temperature and pH

4. Estimation of hydraulic retention time of selected primary clarifiers selected during

the initial research

The annual primary clarifier’s odour emission rates measurements enabled to collect data

for selected statistical tools analysis - multiple regression and artificial neural networks.

Determined relationship between selected factors and odour emission rates enaled

selection of the best statistical model which predicts odour emission rates from primary

clarifiers based on readily available parameters – temperature, wastewater COD and

hydraulic retention time.

The last stage of the research was primary clarifiers odour impact range analysis for

the constant odour emission from clarifiers (maximum and minimum) and averaged for

each month of the year (determined on the basis of measurements and determined on the

basis of artificial neural networks analysis) using AERMOD dispersion model.

3.1. ODOUR EMISSION VARIABLITY STUDY

Primary clarifiers’ odour emission variability studies were conducted from October

2014 to November 2015. The studies lasted 12 months and covered all seasons. Sample

collection was performed 33 times, for a total of 66 odour emission measurement results

for a year - 14 for the winter season, for 16 for spring, 18 for summer and 18 for the

autumn.. Besides odour concentration measurements, wastewater influent parameters were

determined: COD, temperature, pH and measurements of environmental conditions were

carried out: air temperature, humidity and atmospheric pressure. Each time six odour

samples were taken in order to obtain two odour concentration results.

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a) Odour concentration measurements

Each measurement day six odour samples were taken in order to obtain two odour

concentration results. Sample collection from primary clarifiers was made taking into

account recommendations of German VDI 3880 standard "Olfactometry; Static Sampling ".

Set used for odor sampling was made of components non interacting with odor-active

compounds, whch dont absorb or release odors. Sampling set consisted of the following

elements: sampling hood for passive area sources made of stainless steel, odour samples

collection probe with programmed avareged sampling time 5, 15 or 30 min, odour sampling

bags made of PTFE (polytetrafluoroethylene).

b) Wastewater COD measurements

Wastewater samples for chemical oxygen demand (COD) determination were taken from the

distribution chamber of primary clarifiers preliminary and were determined according to the

colorimetric method D 5220 (WEF and APHA, 2005). Wastewater samples were collected

from distribution chamber with bucket, then homogenized. The samples before

determination in the spectrophotometer were heated to 148 OC for 120 minutes in the

thermostat NANOCOLOR VARIO-2. After cooling, the absorbance of samples was measured

at wavelength of 600 nm on a spectrophotometer WTW Photometer 3000 MPM.

c) Wastewater temperature

The temperature of the wastewater was measured by an electronic thermometer 38 HGL

with a resolution of 0.1 ° C.

d) Wastewater retention time

Wastewater retention time in primary clarifiers was calculated using data wastewater

flow data provided by MPWiK Wroclaw SA from SCAD system that archives measurements

from gauges located in the WWTP site. Wastewater primary clarifiers inflow data was

derived from the pumping stations flow measurements located by aerated gritters. Data

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collected from the system have been downloaded with 5 - minute interval. The retention

time was calculated for the respective periods when odour samples were taken.

3.2. ODOUR IMPACT RANGE ESTIMATION USING AERMOD DISPERSION

MODEL

AERMOD is new generation stationary Gaussian dispersion model, which in 2006 was

officially stated as ISC3 successor model, which used in the United States for regulatory

purposes. It is currently recommended US Environmental Protection Agency (EPA) dispersion

model for calculations up to 50 km from the source. AERMOD modeling system consists of

meteorological preprocessor AERMET, terrain preprocessor AERMAP and main dispersion

model AERMOD (Fig. 2). Optionally, land cover preprocessor AERSURFACE can be used.

Figure 1. Scheme of AERMOD dispersion model

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4. RESULTS

4.1. ODOUR EMISSION VARIABILITY

The study showed a significant spread in measured primary clarifiers odour emission

values during the period of the study. The highest odour emission from primary clarifiers

were measured in summer months - August and September. In spring the highest odour

emissions dewere measured in May, however, in May (20/05/2015) lowest determined

emission value was only 4.07 ouE/m2/s. The lowest odour emission was measured on

02.04.2015 - 2.57 ouE/m2/s. The difference between the highest emission determined on

07.23.2015 (111.23 ouE/m2/s) and the lowest emission measured on 02.04.2015 (2.57

ouE/m2/s) deviates by more than 43 - times (Fig. 3). The annual average specific emission of

odors determined from all the measurements is 9.30 ouE/m2/s. The ratio of minimum single

measured emission (2.57 Oue/m2/ s) to the annual average (18.38 ouE/m2/s) is 0.14, while

the maximum emission (111,23 ouE/m2/s) to the average (18.38 ouE/m2/s) is 6.05.

Figure 2. Measured odour emission values obtained during 11.2014 - 11.2015

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Determined specific odour emission rates values averaged for each month of the

studies indicate that the largest emission of odors takes place during summer and spring,

while the lowest in the winter. The highest specific odour emission rate values avareaged for

a month period was determined for the month of July - 49.89 ouE/m2/s, August - 43.61

ouE/m2/s, September - 37.39 ouE/m2/s and May - 36.79 ouE/m2/s. The lowest specific odour

emission rate values avareaged for a month period was determined for the month of March

- 3.96 ouE/m2/s, February - 4.86 ouE/m2/s and January - 6.12 ouE/m2/s.

The ratio of minimum specific odour emission rate value averaged for a month period

(3.96 ouE/m2/s) to the annual average (18.38 ouE/m2/s) is 0.22, while the maximum (111.23

ouE/m2/s) to the average (ouE/m2/s) is 2.71.

Figure 3. Specific odour emission rates averaged to months

For 63 for selected measurement values preliminary correlation analysis was carried

out to determine Pearson correlation coefficients. Correlations between measured

parameters and odour emission and the other analyzed factors:

• wastewater temperature - correlation coefficient: 0.76

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• retention time - correlation coefficient - 0.57

• air temperature - correlation coefficient: 0.65

• pH - correlation coefficient - 0.11

• COD - correlation coefficient - 0.10

There is no strong linear correlation between odour emission values and measured

parameters except for the wastewater temperature. Due to the fact that odour emission

from primary clarifiers is caused by more than one parameter, data collected was analyzed

using multiple regression and artificial neural networks (ANN).

Multiple regression

For statistical analysis using multiple regression parameters as wastewater

temperature and retention time were selected. The estimated regression function is:

hscj t,T,,E 1935862353 , where:

Ej – specific odour emission rate, ouE/m2/s

Tść – wastewater temperature, oC

th – primary clarifier retention time, h

The biggest impact on odour emission rates from primary clarifiers has wastewater

temperature - standardized β coefficient for the variable is 0.64. The β coefficient for

retention time variable is 0.21. Standard error of the estimation is 10.20. Correlation

coefficient is R = 0.77 and determination coefficient R2 is 0.60, which means that about 60%

of the variation emission of odor is explained by the multiple regression statistical model.

Analysis of wastewater temperature and retention time effect on odour emission

originating from primary clarifiers showed that the statistical model obtained using multiple

regression does not sufficiently describe the odour emission variablility from the analyzed

object. Analysis results indicate that odour emission have nonlinear character or amount of

variables taken into account is not sufficient.

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Artificial neural network (ANN)

In the first step all main parameters: COD, pH, wastewater temperature and

retention time were analyzed. Preliminary analysis showed that neural networks have the

best prediction ability with three input variables: COD w, wastewater temperature and

retention time. For the above input variables calculations were performed to find best

network enabling primary clarifier odour emission variability prediction. MLP (Multi-Layered

Perceptron ) architecture networks were analyzed with a structure consisting of one input

layer, one hidden layer and one output layer (Fig. 4).

Figure 4. Architecture of analyzed artificial neural networks

In STATISTICA program preliminary analysis of 250 MLP architecture neural network

was carried out. For further analysis, 10 best neural network were selected. All networks

were trained using a learning BFGS algorithm (backpropagation). Analysis of neural networks

selected parameters has shown that best predicted values comparing to the measured

values were obtained using MLP 3-6-1 network(Fig. 44) with the hyperbolic tangent

activation function in hidden layer and linear in output layer.

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Figure 5. Scheme of ANN MLP 3-6-1

The selected neural network model in a much more perfect way describes primary

clarifiers odour emission variability phenomenon compared to multiple regression analysis.

Despite large relative errors for a few measurement days, selected ANN has high

determination coefficients for both neural network learning and validation - respectively

0.90 and 0.85.

Results of conducted neural networks studies show that ANN have greater potential

to describe the phenomenon of odour emission variability than standard statistical methods.

Due to the complexity of description of that phenomenon with mathematical model, use of

neural networks appear to be the best method for obtaining satisfying results. Although the

selected neural network allowed to obtain relatively precise results for longer averaging

times, increase of measurements and the application of additional parameters, will improve

the quality of calculated values for the shorter averaging times.

5. CONCLUSIONS

On the basis of literature studies and own research the following conclusions have

been formulated:

1) Passive area sources located on municipal wastewater treatment plants, including

the primary clarifiers contribute greatly in the total odour emissions from the

wastewater treatment plant.

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2) Odour emission from primary clarifiers is very variable in annual cycle: the ratio

between the maximum and minimum emission values determined during study is

over 43. The highest emission values were observed in the summer months, the

lowest in the winter months.

3) Multiple regression analysis showed no significant association between measured

parameters and odour emission rates. It can be acknowledged that odour emission is

not linearly dependent from any of parameters of measured parameters –

wastewater temperature and wastewater retention time.

4) It is possible to describe with sufficient accuracy odour emissions variability from the

primary clarifiers on a monthly basis, taking into account the impact of wastewater

temperature, wastewater retention time and chemical oxygen demand using artificial

neural networks.

5) Not ta king into account the odour emissions variability from the primary clarifiers

when assessing odour impact range of WWTP facilities using dispersion modeling can

lead to significant overestimation or underestimation of odour impact range up to

300%.

6) Application of odour emissions averaged monthly, calculated by selected neural

network, in the calculation of odour dispersion using AERMOD model can

significantly improve the modeled results comparing to use of emission data

obtained from single on site measurement.

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