=Paper=
{{Paper
|id=Vol-3896/short15
|storemode=property
|title=Application of machine learning methods to the prediction of NO2 concentration in the air environment
|pdfUrl=https://ceur-ws.org/Vol-3896/short15.pdf
|volume=Vol-3896
|authors=Iryna Didych,Andrii Mykytyshyn,Andrii Stanko,Mykola Mytnyk
|dblpUrl=https://dblp.org/rec/conf/ittap/DidychMSM24
}}
==Application of machine learning methods to the prediction of NO2 concentration in the air environment==
https://ceur-ws.org/Vol-3896/short15.pdf
Application of machine learning methods to
the prediction of NO2 concentration in the air
environment
Iryna Didych1,∗,† , Andrii Mykytyshyn1,∗,† , Andrii Stanko1,†, Mykola Mytnyk 1,†
1
Ternopil Ivan Puluj National Technical University, Ruska 56, 46001 Ternopil, Ukraine
Abstract
Air quality significantly impacts public health, with nitrogen dioxide (NO 2) being a key pollutant
linked to respiratory and cardiovascular diseases. In this study, we developed a machine learning
model to accurately predict hourly NO2 concentrations in Ternopil, Ukraine, using readily available
meteorological and temporal data. The model was trained on a large dataset and tested using data
from the Ecocity monitoring station, known for recording NO 2 levels exceeding legal limits. By
employing neural networks, the model demonstrated high accuracy in predicting NO2 concentrations,
with the error of 3.9% and 1.4%, respectively, in the test samples. Our findings underscore the potential
of machine learning techniques to enhance air quality monitoring and forecasting, particularly in
urban areas with limited resources. This approach offers a valuable tool for real-time pollution
management and public health protection.
Keywords ⋆1
Air quality, nitrogen dioxide, prediction, machine learning
1. Introduction
Air quality is a complex, multifactorial set of chemical, physical, and biological
characteristics of air, and at the same time a very relevant topic because of its connection to
human health. Numerous studies have demonstrated the link between cardiovascular and lung
diseases and long-term exposure to pollutants, in particular nitrogen dioxide (NO 2) and
particulate matter (PM2.5 and PM10). According to the European Environment Agency [1], in
2018, about 55,000 premature deaths in the EU could be attributed to exposure to NO 2. The
results of several clinical and epidemiological studies show that there is at least moderate
evidence that adverse health effects occur even with short-term exposure to pollutants, such as
exposure below established limits [2].
Increasing concentrations of pollutants in the atmosphere have changed its properties,
making it a harmful environment for humans and other living organisms [3]. Pollutants include
⋆
ITTAP’2024: 4th International Workshop on Information Technologies: Theoretical and Applied Problems, October 23-
25, 2024, Ternopil, Ukraine, Opole, Poland
1∗
Corresponding author.
†
These authors contributed equally.
iryna.didych@tntu.edu.ua (I. Didych); mikitishin@gmail.com (A. Mykytyshyn); stanko.andrjj@gmail.com (A.
Stanko) ; mytnyk@networkacad.net (M. Mytnyk).
0000-0003-2846-6040 (I. Didych); 0000-0002-2999-3232 (A. Mykytyshyn); 0000-0002-5526-2599 (A. Stanko); 0000-
0003-3743-6310 (M. Mytnyk)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
a variety of gases, droplets, and particles that degrade air quality; therefore, their exposure to
humans is believed to lead to serious health problems, especially in urban areas where pollution
levels are high [4]. Air pollutants are chemical, physical (e.g., particulate matter), or biological
agents that alter the natural characteristics of the atmosphere. Particulate matter, cited as an
example of air pollutants, is the main factor that negatively affects human health due to its high
toxicity. Air pollution results in the presence of certain gases in the atmosphere in
concentrations that exceed the standard and can be seriously harmful to human health.
Examples of such gases are nitrogen oxides, sulfur oxides, carbon monoxide, photochemical
oxidants (e.g. ozone), lead, as well as various heavy metals and volatile organic compounds that
are released into the atmosphere as a result of industrialization and transport emissions, thereby
degrading air quality. Air quality is defined as the state of the atmosphere in the environment
that can be affected by pollution from these sources.
Air quality is considered good when it meets a certain level of purity and transparency, and
there are no gaseous pollutants such as smoke, dust, smog, and other impurities in the
atmosphere. The Air Quality Index (AQI) is a numerical indicator used by government agencies
to inform the public about the current state of the air or projected levels of air pollution. When
the AQI rises due to an increase in air pollutants (e.g., during peak traffic hours or when there
are downwind wildfires), a growing proportion of the population can quickly experience serious
negative health effects [3]. The US Environmental Protection Agency has classified these air
pollutants into six main categories [5].
The issue of constant monitoring of air quality in real time is also relevant, as the authors
says in paper [6] and [7]. Several studies have been conducted to monitor air quality in the
environment. Air quality assessments were carried out in the city of Ternopil, Ukraine, at the
Ivan Puluj National Technical University of Ternopil.
The purpose of this study is to assess and analyse the impact of atmospheric pollution on air
quality, as well as to establish its dependence on atmospheric factors.
Many studies have established a link between cardiovascular and pulmonary diseases and
long-term exposure to pollutants, including nitrogen dioxide (NO₂) and particulate matter
(PM2.5 and PM10). According to the European Environment Agency [1], in 2018, approximately
55,000 premature deaths in the EU were attributed to NO₂ exposure. Data from several clinical
and epidemiological studies show at least moderate evidence that adverse health effects can
occur even with short-term exposure to pollutants, including exposures below established limit
values [2].
Natural NO₂ emissions have very low background concentrations. Emissions associated with
anthropogenic activities are the most significant factor affecting human health. The main source
of NO₂ emissions is human-caused combustion processes, such as heat, electricity, or internal
combustion engines. In particular, motor vehicles are the main sources of nitrogen oxides [8].
However, it is important to investigate the dependence of NO₂ concentration on changes in
temperature and humidity in the environment.
Such an analysis requires models that can accurately reflect air quality in order to identify
periods of increased pollution and respond quickly to short-term fluctuations, especially in
urban areas. Prediction of pollutants by deterministic methods is accompanied by significant
uncertainties due to the complexity of the physical and chemical processes that determine the
formation and transport of pollutants in the urban atmosphere [9-11].
In this regard, sophisticated machine learning methods are becoming increasingly common
in air quality modelling, outperforming traditional statistical approaches. In the review by
Cabañeros et al. [12], which analysed the number of studies using artificial neural networks
(ANNs) to model pollutants since 2001, found 139 papers. Of these, 51 studies applied this
method to predict nitrogen oxides, while others focused on modelling various pollutants such as
particulate matter (PM), carbon dioxide (CO₂), and ozone (O₃).
ANNs are capable of detecting complex, nonlinear relationships between meteorological
variables and pollutant concentrations, and generalizing information from training datasets to
form functional relationships between variables, even if the nature of these relationships is
unknown. Unlike regression analysis, ANNs work effectively in the presence of significant noise
in the data [13].
The first successful applications of ANNs for modelling NO₂ concentrations in urban areas
were presented in the works of Gardner and Dorling [14], Kolemainen et al. [15]. The authors in
[16], which demonstrated the advantages of the proposed approaches over regression models.
Since then, many modern studies have also obtained significant results in the use of neural
networks for modelling nitrogen oxides, simulating both national emissions over long periods
[17] and local emissions on an hourly basis [11]. Some studies have included several air
pollutants in the models, such as Jiang et al. [18], where a combination of a neural network and a
heuristic algorithm was used to develop an early warning system for five different pollutants.
A key aspect in developing machine learning-based air quality models is the selection of
appropriate input parameters. The concentration of NO₂ in urban air is influenced by many
variables that reflect meteorological conditions and pollution sources. Several studies have
identified meteorological variables such as temperature, humidity, and wind speed as important
predictors, as well as concentrations of other pollutants [19].
An alternative approach is to use previously measured concentrations of the target pollutant
as predictors, relying on temporal autocorrelation between successive values of the same
variable. This is especially effective for forecasting several hours in advance [20]. In some
studies, this method was combined with long short-term memory (LSTM) recurrent networks to
successfully predict NO₂ up to eight hours in advance [21]. Dai et al. [22] combined LSTMs with
convolutional neural networks (CNNs) to create a model suitable for predicting six different
pollutants. While these models often perform well, they are more computationally intensive
than simple feedforward networks.
Other studies have used traffic data obtained by vehicle counts or other models [23]. Since
traffic is one of the main contributors to elevated NO₂ concentrations, traffic statistics have a
high predictive value.
2. Materials and Methods
2.1. Study Area
In our study, we set out to develop a model that would accurately estimate hourly NO₂
concentrations in Ternopil, Ukraine, using only available standard meteorological and temporal
data as input variables.
As in many cities, air quality in Ternopil is monitored by several separate measurement
stations. In previous years, several of them have recorded high levels of NO₂ that exceeded the
regulatory limits. For this study, the Ecocity station in the central part of Ternopil, known as a
pollution hotspot, was chosen, where NO₂ concentrations often exceed the legal thresholds. This
location is particularly important due to the high density of residential development in the
immediate vicinity of the station.
Ternopil is located in western Ukraine, near the Seret River, on the Ternopil Plateau of the
Podillia Upland of the Eastern European Plain. The city is located in the temperate climate zone
of the broadleaf forest zone. Ternopil has a moderately continental climate with warm and
humid summers and mild winters [24].
The street where the air quality monitoring station used in this study is located is in the
central part of the city. It is a heavily trafficked main transportation artery connecting the
highway with the centre of Ternopil. At the same time, there is a very high density of residential
buildings along this street. For several months, both the concentrations of pollutants measured
at this station and the number of days with peak pollution levels exceeded the established
permissible limits.
2.2. AirFresh air quality monitoring station
The correct choice of input parameters, is crucial for predicting pollutant concentrations.
Ivan Puluj National Technical University in Ternopil (Ukraine), in cooperation with the
program “Clean Air for Ukraine” of the NGO Arnika (Prague, Czech Republic), NGO Free
Arduino (Ivano-Frankivsk, Ukraine) and the public monitoring network EcoCity, installed an
AirFresh air quality measurement station at the university to expand the network and conduct
research. The AirFresh air quality monitoring station is a device that enables real-time
monitoring and recording of ambient air conditions, namely temperature, humidity, and dust
concentrations of PM2.5 and PM10. AirFresh measures the concentrations of dust microparticles
(PM2.5 and PM10), carbon monoxide (CO), ammonia (NH 3), ground-level ozone (O3), and
nitrogen dioxide (NO₂) as shown in Figure 1. Each station may be outfitted with radiation
background sensors or additional sensors for 16 pollutants tracked by EcoCity.[25-27].
Figure 1: Daily measurements of nitrogen dioxide (NO₂) levels by the AirFresh station in
Ternopil
Meteorological parameters, such as the height of the measurement site, have a significant
impact on the concentration of NO₂. At the monitoring station, data on pollutant
concentrations, as well as temperature and relative humidity, are measured at a height of 3
meters.
Experimental data on NO₂ concentrations, temperature and humidity were collected at the
station during the period from August 6 to 13, 2024.
To ensure the ability to recognize temporal patterns in NO₂ fluctuations, the model was given
the ability to detect typical hourly and daily variations of this pollutant. To this end, the analysis
included time variables that reflect different frequency components in the observed data.
3. Results and discussion
The development of a machine learning model consists of several stages, each of which plays
an important role in creating an efficient and accurate model. The main stages of developing a
machine learning model are: data collection; data preprocessing and analysis; selection of a
model and machine learning algorithm; splitting the data into training and test samples; model
training; and evaluation and validation. Tracking NO2 emissions, which is the most active
pollutant gas, and predicting its concentration are important steps towards pollution control.
Therefore, nitrogen dioxide (NO₂) was predicted using experimental data obtained from the
Meteorological Station AirFresh. During learning, the dataset was divided into two unequal
parts training and test samples.
The study was divided into two stages. At the first stage, the training set contained
experimental dependencies of NO₂ concentration on temperature, humidity, and measurement
time for six days, and a sample of a one-day dataset unknown to the system was chosen to test
the quality of forecasting. And at the second stage, the test sample was randomly selected by the
computer from all the experimental data for different days (Figure 2).
Figure 2: Comparison of the two predicting architectures for training and testing
The dataset contained 541 elements. In particular, the training set contained 504 elements
characterising temperature, humidity and measurement time over N days (six days in our
study). The NO₂ concentration was predicted by neural networks and selected as the output
parameter. Based on the experimental results of the NO₂ concentration for one day, a test set of
37 elements was formed to evaluate the quality of prediction. It was found that the built models
can make predictions based on data that were not used in the training sample. Therefore, such
results are informative for studying their quality.
0,06 0,06
NO2_pred, ppm/mg/m3
NO2_pred, ppm/mg/m3
0,05
0,05
0,04
0,04
0,03
0,03 0,04 0,05 0,06
0,03
0,03 0,035 0,04 0,045 0,05 0,055 0,06
NO2_true, ppm/mg/m3
NO2_true, ppm/mg/m3
a) b)
Figure 3: The predicted (NO2pred) and experimental (NO2true) concentrations during August 2024, in
particular, a) August 13 and b) August 6-13 in test sample by method of neural networks
0,07 0,06
0,06
0,05
0,05
NO2, ppm/mg/m3
NO2, ppm/mg/m3
0,04
0,04
0,03
0,02 0,03
Exp Exp
0,01
Pred Pred
0 0,02
0 5 10 15 20 25 30 35 40 0.000010.000120.000250.000380.000530.00063
August 13, 2024 August 6-13, 2024
a) b)
Figure 4: The predicted and experimental dependences of NO 2 concentrations during August 2024, in
particular, a) August 13 and b) August 6-13 in test sample.
The neural networks were used to build (Figure 3 and 4) the dependence of experimental
concentrations (NO2true) on the predicted ones (NO2pred), as well as NO₂-August 13, 2024 for one
day. The NN method gives an error of 3.9%.
It is important that in Figure 3, the points are located quite close to the bisector of the first
coordinate angle, which indicates the consistency of the predicted and experimental data.
The NO₂ concentration was predicted by temperature, humidity and time of measurement
over seven days. The sample contained 541 elements, of which 80% were randomly selected for
the training set and 20% were left to evaluate the quality of the prediction. The parameters of the
neural network are shown in Table 1.
Table 1
The parameters of neural network
Dependencies Algorith Function Function of
Name of Error
m of of hidden output
network function
learning activation activation
NO₂ -August 13,
MLP 3-23-1 BFGS SOS Tangential Exponential
2024
NO₂ -August 6-13,
MLP 3-29-1 BFGS SOS Tangential Exponential
2024
The prediction error was calculated using the Mean Absolute Percent Error (MAPE) formula:
1 | y true − y prediction|
n
MAPE=100 % ⋅ ∑ , (1)
n i=1 | y true|
It was found that the predicting results are in good agreement with the experimental ones.
The error of the NN method is 1.4%.
Conclusions
In this study, a machine learning model was developed to predict NO₂ concentrations based
on meteorological and temporal data in Ternopil, Ukraine.
The concentration of nitrogen dioxide (NO₂) was predicted using experimental data obtained
from the Weather Station during 6-13 August 2024. It was found that regardless of the type of
study (self-selected test sample or randomly selected by a computer), the forecasting results are
in good agreement with the experimental data. The error of the NM method is 3.9% and 1.4%,
respectively, in the test samples.
The proposed model allows for real-time forecasting of pollutant emissions, which is an
important tool for monitoring air quality in urban areas with limited resources.
The use of such models can be an important step toward creating early warning systems for
elevated levels of pollution and rapid response to short-term fluctuations in the concentration of
harmful substances. This could have a positive impact on public health, especially in areas with
heavy traffic and high building density. Future research could focus on integrating additional
factors, such as traffic and other pollutants, to enable even more accurate predictions of air
quality changes in modern urban ecosystems.
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