Design and Implementation of a Software System for Surveillance of

Antibiotics Concentrations in Wastewater

Yousuf Al-Hakim

, Kosmas Dragos

, Kay Smarsly

, Silvio Beier

and Claudia Klümper

Institute of Digital and Autonomous Construction, Hamburg University of Technology, Germany

Chair of Urban Bioengineering for Resource Recovery, Bauhaus University Weimar, Germany

Laboratory of Environmental Analysis, Department HAM 2, Hamm-Lippstadt University of Applied Sciences, Germany

Keywords: Antibiotics, Antibiotic Resistance, Monitoring, Software Design, Wastewater Surveillance.

Abstract: Antibiotics are important drugs for treating infectious diseases. The extensive use of antibiotics for human,

veterinary, and agricultural purposes has led to the permanent release of antibiotics into the environment,

particularly into municipal wastewater. In turn, the widespread release of antibiotics into the environment has

led to the emergence of antibiotic-resistant bacteria and antibiotic-resistant genes (collectively referred to as

“antibiotic resistance”), which reduce the effectivity of antibiotic treatment. To counteract antibiotic

resistance, surveillance of the release of antibiotics into the environment is necessary. Municipal wastewater

surveillance may provide insights into the release of antibiotics into the environment. Current municipal

wastewater surveillance systems, dedicated to antibiotics concentrations, rely on the ad-hoc use of third-party

software, which may compromise the efficiency and user-friendliness of municipal wastewater surveillance

systems. Designing software systems dedicated to the surveillance of antibiotics concentrations in municipal

wastewater, based on well-established software design concepts, has received scarce research attention. In

this study, a software system is proposed, which serves as a technological basis for the surveillance of the

concentration of antibiotics in municipal wastewater in an efficient and user-friendly manner. The software

system implements well-established software design concepts and is capable of conducting on-demand data

analysis, as well as providing various user interfaces. The software system is validated using both data derived

from simulations and real-world wastewater data recorded from a wastewater treatment plant. The results

showcase the efficiency and user-friendliness of the proposed software system for the surveillance of

antibiotics concentrations in municipal wastewater.

1 INTRODUCTION

Antibiotics are used for treating infectious diseases in

human and veterinary medicine as well as for

agricultural purposes (Davies, 2010). Since the

introduction of the antimicrobial agent sulfonamide

in the 1930s, usage of antibiotics has increased

(Adler, 2018), leading to permanent release of

antibiotics into the environment (Rizzo, 2013).

Through feces of animals that have received

antibiotics treatment, as well as through wastewater

treatment plants, antibiotics are released into fields,

soils, and local waters. The release of antibiotics into

the environment poses risks to human and

environmental health (Paulus, 2019). One of the most

https://orcid.org/0000-0002-4204-6547

https://orcid.org/0000-0001-7228-3503

significant health risks is the emergence of antibiotic-

resistant bacteria (ARB) and antibiotic-resistant

genes (ARG), collectively referred to as “antibiotic

resistance” (Nguyen, 2021). Antibiotic resistance

(AR) limits the effectiveness of antibiotics for

treating infectious diseases (CDC, 2021). National

and international institutions have realized the risk of

the emergence of AR and have introduced measures

for reducing the impact of AR on the health of

humans, animals, and the environment (Aminov,

2010; Manzetti, 2014). However, since neither the

EU nor other international and national institutions

have set standards for the maximum allowable

concentration of antibiotics in municipal wastewater

(WHO, 2020), surveillance of antibiotics

Al-Hakim, Y., Dragos, K., Smarsly, K., Beier, S. and Klümper, C.

Design and Implementation of a Software System for Surveillance of Antibiotics Concentrations in Wastewater.

DOI: 10.5220/0012304500003657

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - Volume 2, pages 285-292

ISBN: 978-989-758-688-0; ISSN: 2184-4305

285

concentrations in the environment is rarely

conducted. Gaining insights into the release of

antibiotics into the environment could stand to benefit

from existing approaches on the surveillance of

municipal wastewater at wastewater treatment plants,

the most important literature on which is reviewed in

the following paragraph, focusing on the underlying

software systems.

Seminal works discussing the software of

surveillance systems in general include, for example,

surveillance approaches for geospatial data (Mutuku,

2022) and approaches for livestock surveillance

(Mena, 2019). Generally, the literature shows the

case-specific manner in which software for

surveillance systems is designed, which is underlined

by software development without clear software

design concepts. Regarding the surveillance of

municipal wastewater, a surveillance system

proposed by Selisteanu et al. (2020) has been

designed using three third-party software

environments. Martinez et al. (2020) have used third-

party middleware for designing surveillance systems.

Finally, studies that have focused on the surveillance

of antibiotics concentrations in municipal wastewater

have been conducted by Mtetwa et al. (2021) with

wastewater samples from wastewater treatment

plants in South Africa, by Majlander et al. (2021) with

wastewater samples from two hospitals in Finland,

and by Huijbers et al. (2020), with wastewater

samples from municipal wastewater treatment plants

of several European countries. In summary, the

studies show that municipal wastewater surveillance

systems typically lack underlying software that is

designed specifically for the surveillance of

municipal wastewater. To improve efficiency and

user-friendliness, dedicated software systems based

on well-established design concepts are necessary.

This study proposes a software system for

efficient and user-friendly surveillance of antibiotics

concentrations in municipal wastewater. The

software system supports executing multiple tasks

simultaneously, such as data storage and

management, data analysis, and data visualization. In

data storage and management, the software system

stores wastewater data in a database, specifically

designed for the surveillance of antibiotics

concentrations. In data analysis, algorithms are

implemented to analyze wastewater data and identify

parameters that are critical to the surveillance of

antibiotics concentrations in municipal wastewater.

In data visualization, the parameters are visualized in

multiple views. The software system is validated with

data derived from simulations as well as from a real-

world municipal wastewater treatment plant

(WWTP). The software system increases the

efficiency and user-friendliness of the surveillance of

antibiotics concentrations in municipal wastewater by

organizing and bundling tasks into one software

system. With the software system, surveillance of

municipal wastewater may be used as a reliable tool

to limit the release of antibiotics concentration. The

remainder of this paper is structured as follows: The

design of the software system is discussed in the next

section, followed by the implementation. Then, the

software system is validated and, finally, conclusions

are drawn, and an outlook on future research is given.

2 DESIGN OF THE SOFTWARE

SYSTEM

In this section, the design of the software system is

discussed. Upon defining the requirements, the

software design is presented, showing how the

requirements are met. For designing the software

system for the surveillance of antibiotics

concentrations in municipal wastewater, functional

and non-functional software requirements are

defined.

Functional requirements are directed to project-

related properties and ensure high quality and

feasibility of the software system (Glinz, 2007). The

main functional requirements for the software system

are:

 The software system must allow accessibility

through web browsers and smartphone

applications;

 The visualization of the wastewater data must

be adaptable to the requirements of every user;

 The software system must consider data

analysis functionalities with the option to

switch between algorithms;

 The concepts of the software system must be

valid generally, meaning that the software

system can be used in different environments.

The main non-functional software requirements

for the software system are correctness, robustness,

extensibility, and reusability. For further information

on non-functional software requirements, interested

readers are referred to the IEEE Standard Glossary of

Software Engineering Terminology (IEEE, 1990).

The software system for the surveillance of

antibiotics concentrations in municipal wastewater is

presented in Figure 1. The rectangles represent main

components of the software system. The squares

resemble ports (P1-P3), to which interfaces are

connected. The ports allow extensions to the software

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286

system, which enable users to access the software

system through web browsers and smartphone

applications. The full circles resemble interfaces

provided by a component, and the half circles

resemble interfaces required for a component. For the

software design, the model-view-controller (MVC)

software design concept is pursued because MVC

separates the core of the software system into three

components (Smarsly et al., 2023): The model

component, the view component, and the controller

component. Separating the software system reduces

the complexity, since different functionalities are

separated accordingly. The aspect of separation is an

advantage of MVC over other software design

concepts, such as the microkernel software design

concept or the event-driven software design concept.

In the <<Core>> component, which resembles the

software architecture of the software system, the port

P1 enables the view component to visualize

wastewater data with dashboards, charts, or

spreadsheets, according to the requirements of

respective users. The port P2 enables switching

between algorithms, with which wastewater data is

processed and analyzed. The port P3 offers interfaces

to connect databases to the model component of the

software system, allowing analysis and visualization

of wastewater data from multiple sources. To ensure

general validity of the database of the software

system, switching between different databases as a

whole is enabled by P3, as well.

Figure 1: Software system for the surveillance of antibiotics

concentrations in municipal wastewater.

3 IMPLEMENTATION OF THE

SOFTWARE SYSTEM

This section describes the implementation of the

software system. The components of the software

system are listed, and the functionalities are explained

in detail.

3.1 Model Component

The database system of the software system resides in

a cloud, on which wastewater data can be stored and

shared with project partners. The cloud of the

software system is based on the client-server software

Nextcloud, with which clouds can individually be

scaled and used on various systems such as small

microcontrollers or large data centers. Additionally,

Nextcloud offers a web-based application and a

smartphone application. Nextcloud enables the

assignment of different rights to different users. By

sharing individual access codes, or by including users

to access lists, the users may access the wastewater

data to the degree designated. The cloud is managed

by a data manager, which is responsible for the

following tasks:

 Assigning rights to users;

 Ensuring the wastewater data is always up to

date, old versions are deleted or backed up, and

only the newest version is passed to the model

component;

 Unifying data file types, for example .csv or

.xls, and assuring that the providers of

wastewater data conform to the formats of the

predefined data file types.

 Performing any non-automated task necessary

for the operation of the database system, such

as maintaining the database system structure,

identifying and correcting errors, or performing

updates of hardware and software.

3.2 Controller Component

Once the wastewater data has been collected, the

wastewater data is analyzed. The controller

component performs data analysis tasks, including

correcting the wastewater data and preparing the

wastewater data for forecasting. The controller

component is built using the Python programming

language. The Python programming language is used,

because the native libraries of the Python

programming language include a variety of different

algorithms (Bogdanchikov, 2013), which reduce the

need to install external libraries when analyzing the

wastewater data. Wastewater data is analyzed in the

controller component in four steps, described in the

following sub sections. A flowchart, showcasing the

workflow of the data analysis, in the controller

component is shown in Figure 2.

Design and Implementation of a Software System for Surveillance of Antibiotics Concentrations in Wastewater

287

3.2.1 Data Correction

Raw wastewater data is often not ready for data

analysis immediately after the data is created. Several

factors can lead to gaps in the wastewater data, such

as:

 Disturbances during municipal wastewater

sampling, or even loss of municipal wastewater

samples;

 The human factor, such as illness of the worker

on the day in which collection of municipal

wastewater samples is scheduled;

 Errors in devices for extracting the data

samples.

First, the controller component locates gaps in the

wastewater data. For analyzing wastewater data,

particularly when advanced data analysis methods are

applied to the wastewater data, gaps are detrimental.

The controller component applies linear interpolation

to fill the gaps.

3.2.2 Calculation of Moving Statistics

Once the wastewater data is corrected, moving

statistics are calculated, since moving statistics are

used for modelling, forecasting, and gaining insights

into the wastewater data. First, the controller

component calculates the moving average (MA) over

a range of antibiotics concentrations. Upon

computing the moving average, the controller

component calculates the moving standard deviation

(MSD)

()

SD y MA

=−



(1)

where y

is the wastewater data point. In addition

to the MA and the MSD, the controller calculates the

trend and the seasonality of the wastewater data.

The seasonality is determined by calculating the

seasonal variation (SV) of the wastewater data

SV y MA=−

(2)

Plotted over time, the seasonal variation shows

the recurring, short-term cycle of the wastewater data.

3.2.3 Data Normalization and Stationarity

Calculating the statistics MA and MSD, as well as the

trend and SV, provides insight into the wastewater

data. With the MA, the wastewater data can be

examined for stationarity. Stationarity is defined as

follows (NIST, 2023):

A stationary process has the property that the

mean, variance and autocorrelation structure do not

change over time.

Stationarity is particularly important for modeling

and for forecasting the wastewater data. If the

wastewater data is non-stationary, the wastewater

data is normalized to achieve a constant MA. The

controller component checks the following

normalizations for stationarity:

The logarithmic normalization:

() ()

log ;

xx=

(3)

The square-root normalization:

()

;

xx=

(4)

The cube-root normalization:

()

xx=

(5)

The Augmented Dickey-Fuller Test (ADF)

(Paparoditis, 2013) is performed to select the

appropriate normalization with respect to stationarity.

The normalized dataset is used for modeling and

forecasting of the wastewater data. To increase

efficiency of the software system, the controller

component automatically finds the appropriate

normalization with respect to stationarity.

3.2.4 Forecasting

To estimate the future behavior of the wastewater

data, the controller component performs forecasting.

A model of the wastewater data is built in the

controller component using linear regression, which

is an efficient way to model time series data (Myers,

1990). The Python library XGBoost (XGB) is used for

forecasting. XGB is a decision-tree machine learning

library, used for regression, classification, and

ranking problems (He, 2016). To train the regression

model, the wastewater data is split into three parts.

The first two parts are used by the controller

component for training the regression model. The last

part is used to test the regression model. The metric,

with which the controller component trains the

regression model is the root mean squared error

(RMSE):

()

RMSE y y

=−





(6)

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where y

are the actual measurements and ŷ

are

the corresponding predictions of the wastewater data.

The regression model runs multiple training iterations

to reduce the RMSE as much as possible. For

efficiency, training of the model stops automatically

as soon as the RMSE reaches the lowest value, with a

minimum of 10 training iterations, as an empirical

value.

Figure 2: Flowchart of the data analysis conducted by the

controller component.

3.3 View Component

Once the data analysis has been completed, the

controller component passes the wastewater data to

the view component of the software system, where

the wastewater data is visualized. To visualize the

wastewater data, the software system uses the Python

library Matplotlib. Matplotlib is a library for creating

static, animated, and interactive visualizations with

Python, without the need to install third-party

software. The wastewater data is visualized using

interactive figures that are updated automatically and

that can be formatted individually. In addition to the

visualization techniques provided by Matplotlib, the

Python programming language provides interfaces

that allow several third-party software packages to be

used for visualizing the wastewater data, such as the

Python extension Streamlit, which allows visualizing

wastewater data with dashboards containing

interactive figures and graphics.

3.4 Validation Tests

This section discusses the tests, which have been

devised to validate the software system. The

validation tests consist of a test using data derived

from simulations, as will be explained in the next

paragraph, and a test using real-world municipal

wastewater data.

For validating the software system with data

derived from simulations, a wastewater data set is

simulated using the Monte Carlo Simulation (MCS).

The MCS is used to simulate different outcomes of a

process that cannot be predicted due to the

intervention of random values. The MCS is

conducted in four steps, on which details may be

found in Harrison (2009). The real-world wastewater

data has been measured at the Neugut WWTP in

Dübendorf, Switzerland. The Neugut WWTP treats

wastewater from 150,000 people, with an industrial

contribution of approximately 50%. The flow of the

Neugut WWTP averages 19,000 m³/day at dry

weather conditions (Enz, 1992).

The database system is located on the Nextcloud

server of Hamburg University of Technology. To

validate the functionality of the database system,

some users are assigned the right to download and

reshare the folder, in which the wastewater data is

stored, while other users are assigned the right to edit

the wastewater data and create new files of the

wastewater data. Users of the database have received

a personalized invitation link, with which access to

the database is granted. After reading the wastewater

data, the model component passes the wastewater

data to the controller component, where the

wastewater data is corrected. No gaps are present in

the data derived from simulations, which is why the

controller component skips the data correction step in

the first validation test.

Next, the controller component performs data

analysis on the wastewater data. The MA and the

MSD are calculated, as well as the trend m and the

seasonal variation SV. To calculate the trend and

seasonal variation, the controller component uses the

Python library Statsmodels, which contains statistical

functions for data analysis. The original wastewater

data and the trend are depicted in Figure 3. For the

visualization of the trend, visualization techniques

provided by the internal Python library Matplotlib are

used.

Figure 3: Line graphs for the original wastewater data,

derived from simulations, and the trend.

Since the MCS is used, random values are present

in the wastewater data. The random values of the data

Design and Implementation of a Software System for Surveillance of Antibiotics Concentrations in Wastewater

289

derived from simulations are apparent in the trend, as

the trend line is highly periodic. To achieve

stationarity, the controller component identifies the

appropriate normalization by applying the ADF. The

result of the ADF shows that the cube root

normalization is appropriate with respect to

stationarity.

Next, the controller component uses the data

derived from simulations for training and testing the

regression model. The wastewater data is split into

three parts, the first two being used for training. The

controller component stops the training phase after 18

training iterations, since the RMSE reaches the lowest

value. Finally, the controller component checks the

forecasting capabilities of the regression model, using

the third part of the data derived from simulations for

testing the model.

The software system then performs the data

analysis of the real-world wastewater data similarly

to the data derived from simulations. The controller

component normalizes the real-world wastewater

data with the logarithmic normalization. Next, the

controller component splits the normalized real-

world wastewater data into three parts. When training

the regression model with the real-world wastewater

data, the controller component stops after 39 training

iterations. The visualization and the analysis results

of the real-world wastewater data are conducted using

a dashboard, provided by the view component which

uses the Streamlit extension of Python. The

dashboard is shown in Figure 4.

The dashboard of the software system bundles

multiple visualization techniques in one interactive

screen. The wastewater data is visualized, alongside

the forecast conducted by the regression model. The

data analysis results, such as MA and MSD of

antibiotics concentrations, are depicted in the form of

line charts. The visualization techniques in the

dashboard can be changed interactively, according to

user preferences.

As a result, the validation tests with data derived

from simulations show the efficient und user-friendly

manner with which the database system is designed.

The cloud allows assigning different rights to

different users. Users are able to perform data

management (depending on the rights assigned) from

web-browsers or the Nextcloud smartphone

application. A particularly efficient feature of the

controller component is identifying the normalization

for modelling. The visualization of the real-world

wastewater data increases the user-friendliness of the

software system. In conclusion, the software system

significantly improves the efficiency and user-

friendliness of the surveillance of antibiotics in

municipal wastewater. The database allows user-

friendly data management through the folder

structure and rights assignment. The controller

component analyses data efficiently, without the need

of intervention between calculation steps. The view

component enables user-friendly visualization of

antibiotics concentrations in wastewater data and the

analysis results through a variety of visualization

techniques, including dashboards.

4 SUMMARY AND CONCLUSION

Antibiotic resistance poses serious health risks to

humans, animals, and the environment. High

concentrations of AR, particularly in municipal

wastewater, threaten the potential effectivity of

antibiotics in treating infectious diseases.

Surveillance of municipal wastewater is, therefore, a

vital step to reduce the release of antibiotics in the

environment. This study has proposed a software

system for efficient and user-friendly surveillance of

antibiotics concentrations in municipal wastewater.

The software system is based on the model-view-

controller software design concept. The MVC

concept separates the core of the software system into

three components. In the model component,

wastewater data is stored and managed, the controller

component is responsible for data analysis, and the

view component is designed for visualization. The

software system has been validated using data derived

from simulations as well as wastewater data obtained

from a real-world wastewater treatment plant.

The validation tests have demonstrated the high

efficiency and user-friendliness of the surveillance of

municipal wastewater using the proposed software

system. In particular, the MVC software design

concept has exhibited its positive attributes within

every component of the software system: (i) the

model component has allowed user-friendly data

management with an efficient folder structure, (ii) the

controller component has conducted the data analysis

in an efficient manner, including normalizations to

achieve stationary wastewater data, and (iii) the view

component has allowed a variety of data

visualizations, while enabling multiple project

partners at different locations to collaborate. The

study aspires to provide a tool that helps adhere to

standards and regulations regarding antibiotics

concentrations in municipal wastewater. Future work

may include coupling the software system with digital

models of wastewater treatment plants, building upon

previous studies reported in Söbke et al. (2018; 2021).

The software system focusses on connecting to

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290

Figure 4: Dashboard created by the view component of the software system.

dashboards and similar visualization techniques.

Future work may focus on integrating the monitoring

data into 3D models, such as building information

models. Also, future work may focus on optimizing

the algorithms used for analyzing the wastewater

data, enabling the software system to predict

antibiotics concentrations in municipal wastewater

with high accuracy.

ACKNOWLEDGEMENTS

This collaborative research is partially funded by

Thüringer Aufbaubank (grant number 2021 FE

9143/44) through Bauhaus University Weimar,

Germany (grant number 2360200276-2/21), by the

Deutsche Bundesstiftung Umwelt (DBU) under grant

number 38351/01-25, by the German Research

Foundation (DFG) under grant SM 281/20-1, and by

the German Federal Ministry for Digital and

Transport (BMDV) within the mFUND program

under grant 19FS2059C. The authors would like to

sincerely thank the Swiss Federal Institute of Aquatic

Science and Technology (EAWAG) for openly

sharing data and accelerating scientific progress

through transparency. Any opinions, findings,

conclusions, or recommendations expressed in this

paper are those of the authors and do not necessarily

reflect the views of EAWAG or the sponsors.

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