Boruta-grid-search least square
support vector machine for NO2
pollution prediction using big data
analytics and IoT emission sensors
Habeeb Balogun, Hafiz Alaka and Christian Nnaemeka Egwim
Big Data Technologies and Innovation Laboratory, University of Hertfordshire,
Hatfield, UK
Abstract
Purpose – This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular
standalone algorithms used to build NO2 prediction models. The purpose of this paper is to pre-process a
relatively large data of NO2 from Internet of Thing (IoT) sensors with time-corresponding weather and traffic
data and to use the data to develop NO2 prediction models using BA-GS-LSSVM and popular standalone
algorithms to allow for a fair comparison.
Design/methodology/approach – This research installed and used data from 14 IoT emission sensors to
develop machine learning predictive models for NO2 pollution concentration. The authors used big data
analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months.
Weather data from the UK meteorology department and traffic data from the department for transport were
collected and merged for the corresponding time and location where the pollution sensors exist.
Findings – The results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine
learning predictive Model for NO2 pollution.
Practical implications – This paper’s hybrid model provides a basis for giving an informed decision on the
NO2 pollutant avoidance system.
Originality/value – This research installed and used data from 14 IoT emission sensors to develop machine
learning predictive models for NO2 pollution concentration.
Keywords IoT, Bigdata, Air pollution prediction, Hybrid machine learning
Paper type Research paper
1. Introduction
Air pollution, a release of pollutants into the air, remains one of the significant challenges in
the UK and globally, with over 25,000 associated deaths recorded yearly in the UK [1] and
around 8.8 million deaths recorded globally [2]. Apart from deaths, air pollution exposure can
result in various short and long-term health challenges [3, 4]. Examples of short-term health
challenges include eye pain, throat irritation, headaches, allergic reactions, and upper
respiratory infections. While lung cancer, brain damage, liver damage, kidney damage, heart
disease, respiratory disease, and suchlike are examples of long-term health challenges [5].
Aside from the severe impact of air pollutants on health, air pollution has significant
consequences on the UK and the global economy. It costs the UK government approximately
£40bn yearly [6] and around £3 trillion economic costs globally [7]. Recent studies by the
centre for research on energy and clean air (CREA) links over 1.5 billion days of absence from
NO2 pollution
prediction
©Habeeb Balogun, Hafiz Alaka and Christian Nnaemeka Egwim. Published inApplied Computing and
Informatics. Published by Emerald Publishing Limited. This article is published under the Creative
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The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2210-8327.htm
Received 21 April 2021
Revised 24 June 2021
5 July 2021
Accepted 7 July 2021
Applied Computing and
Informatics
Emerald Publishing Limited
e-ISSN: 2210-8327
p-ISSN: 2634-1964
DOI 10.1108/ACI-04-2021-0092
work, over 3.5million new cases of asthma and approximately 2million preterm births to air
pollutants leading to an increase in health care cost and decrease in economic productivity.
Air pollutants are airborne substances, usually of two categories: particulate matter and
gases. Of the gases, Nitrogen dioxide (NO2) is arguably the most dangerous to human health
[8]. NO2 pollutant emanates from combustion processes such as vehicle emissions, and this
was noted during the Covid-19 pandemic, with a 20% decrease in global NO2 concentration
[9]. However, a recent finding hypothesized that NO2 will still exceed the (Air quality index)
AQI limit by 2025 [3]. Therefore, the NO2 AQI estimate poses a responsibility to stakeholders
and researchers to devise strategic means to curb exposure to this UK’s pollutant.
Arguably, predicting NO2 concentration is among the most efficient and effective ways to
save lives from exposure to this deadly pollutant in different geographical locations.
Furthermore, this prediction can help people avoid such areas when they have high NO2
concentration levels.
1.1 Related Work
Studies on NO2 prediction models have thus justifiably increased since the turn of the
millennium. However, if they are helpful to users vulnerable to pollution, e.g. coronavirus
patients, the effectiveness of such models depends on the model’s performance. Lesser
performance can be misguiding and could expose the user to a pollution hotspot, triggering
life-threatening attacks.
A machine learning-built predictive model’s performance is, among other factors, vastly
dependent on the machine learning algorithm used [10, 11]. Several studies have thus
compared some of the most popular algorithms (e.g. artificial neural network, support vector
machine, and suchlike) in terms of their performance in predicting NO2 [12– 14] with Random
forest, support vector machine usually performing better. However, despite clear proof from
the literature that hybrid algorithms have performed better than standalone, they have not
been vastly employed in the comparison studies [15, 16].
One such hybrid is the optimal-hybrid artificial intelligent algorithm based on the Least
squares support vectormachine optimized by grid search, whose featureswere selected using
the Boruta Algorithm (BA-GS-LSSVM). The Least square support vector machine (LSSVM)
differs from the classical SVM due to improved objective function. LSSVM is widely used for
classification and regression problems due to its high predictive ability compared to classical
SVM. Findings from research like the prediction of gasoline’s price [17], speed of wind’s
forecast [18] indicated that this model presents more operation speed and convergence
accuracy. However, some shortcomings are associated with this algorithm’s performance,
including optimizing parameter and feature selection. The BA-GS-LSSVM solves these
comings.
Thus, this paper seeks to assess the performance levels of BA-GS-LSSVM compared to
popular standalone algorithms used to build NO2 prediction models. The objectives are as
follows:
(1) To pre-process a relatively large data of NO2 from IoT sensors with time-
corresponding weather and traffic data
(2) To use the data to develop NO2 prediction models using BA-GS-LSSVM and popular
standalone algorithms to allow for a fair comparison.
It is imperative to describe the symbols used in this researchwork. Table 1 definesmost of the
symbols and their description.
The rest of this paper organized as follows: section two presents a brief explanation of the
source of data and data volume. Section three presents feature selection techniques used in
selecting the valuable features for developing the hybrid model. Section four presents the
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hybrid model detailing the theoretical/mathematical representation of the model and how it
differs from classical SVM. Lastly, Section five describes the development of the BA-GS-
LSSVM, other popular standalone machine learning algorithms for NO2 prediction and their
performance assessment for comparison. Finally, the conclusion and discussion form part of
the fifth section.
2. Data description and big data analytics
Many UK cities, just like other cities of the world, suffer from air pollution. A significant
contributor to air pollution is increasing traffic emissions [19]. Air pollution caused by traffic
depends on the type of vehicle (diesel, gasoline, petrol, electric), level of congestion, time spent
in the traffic jam, and the atmospheric/geographical features of the environment at a
given time.
To monitor/reduce exposure to air pollution, most cities now deploy monitoring sensors
for measuring traffic intensity, weather characteristics, and air quality of the environment.
The data is collected at specified frequencies (seconds, minutes, hours, days, and suchlike)
depending on the users’ preference. For this project, a total of 14 Internet of Things (IoT)
monitoring sensors for NO2 and other pollutant concentrations represented as blue circles
were deployed across Wolverhampton City in the UK (see Figure 1).
The sensors collected NO2 concentration and other harmful pollutant’s data every 10 s for
five months (i.e. December 2019 and April 2020). Over ten billion (i.e. 10 3 6 3
603 603 243 303 53 14) data points were generated for this period which was massive.
The data through the Middleware gateway deployed on elastic bean of amazon web service
Symbol Description
yI Observed NO2 concentration
y*i Predicted value for NO2 concentration
Log(n) Depth of tree
n Number of rows
d Number of features
t Number of trees
k Number of k neigbour
γ Regularisation parameter
σ2 width of Kernel parameter
Table 1.
Symbol and
description
Figure 1.
Map showing the 14
NO2 IoT monitoring
sensors deployed at
Wolverhampton
City, UK
NO2 pollution
prediction
(AWS) directly dump the data into an AWS Elastic Computing cloud two (EC2) Relational
database.We used the AWSEC2 infrastructure to run the big data analytics required for this
study due to the extensive data.
For the development of the BA-GS-LSSVM, the data from the 14 IoTs monitoring sensors
for NO2 pollutant concentration was the dependent variable. In contrast, weather, other
pollutants, e.g. PMx and Ozone and traffic data, were the independent variables. The traffic
data was sourced from the UK’s Department for Traffic (DfT) and included mainly vehicle
counts, split into various vehicle types (see Table 2). The traffic data, covering the same
period as the data from the sensors, were retrieved. The weather data for a similar period was
recovered from the UK Met Office. It included various weather variables like ambient
pressure and humidity, among others (see Table 2). Traffic and weather data were provided
hourly, and each had over fifty thousand data points. To match the weather and traffic data
with the pollutant data from the sensors, the hourly average of the pollutant concentration
was used to match the corresponding hourly weather and traffic data leading to
(24 hrs 3 30 days 3 5months 3 14 IoTs) data points.
The concentration of NO2 from December 2019 to April 2020 across the 14 installed
sensors indicates some interesting trends, with some outliers around the end of 2019 (see
Figure 2). These outliers at the end of the year are arguable due to the shopping, Christmas
and other season celebration. In addition, the pollution concentration is arguably
S/N Features Unit Data source
1 Ambient humidity RH UKMETOFFICE
2 Ambient pressure Pa
3 Ambient temp 0C
4 Humidity RH
5 Temp 0C
6 Road type – DFT
7 Link length in Km –
8 Link length in miles –
9 Pedal cycles –
10 Two wheeled motor –
11 Cars and taxis –
12 Buses and coaches –
13 Lgvs –
14 Hgvs 2 rigid Axle –
15 Hgvs 3 rigid Axle –
16 Hgvs 4 or more rig –
17 Hgvs 3 or 4 Articulate Axle –
18 Hgvs_5_Articulated_Axle –
19 Hgvs_6_Articulated_Axle –
20 All Hgvs –
21 All motor vehicles –
22 Zid – IoT
23 Date –
24 Holiday –
25 Day of the week –
26 X (3d coordinates) –
27 Y (3d coordinates) –
28 Z (3d coordinates) –
29 Pm1 mg/m3
30 Pm10 mg/m3
31 Pm25 mg/m3
Table 2.
Independent features
after matching the
three data sources
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influenced by the national lockdown imposed across the UK cities during the covid19
pandemic (see Figure 2).
Another exciting exploration is the outliers discovered within some days of the week (see
Figure 3). Looking at the boxplot, it is arguably the days with the highest amount of traffic as
we can hypothetically say these are days many go out to bars, clubs and other gatherings at
the end of the week.
After pre-processing was completed on the AWS Big data infrastructure, the complete data
was split into data (60%) for training and (40%) for model testing at random to avoid biases
and other shortcomings.
3. Feature selection
The predictive capability of various machine learning depends on the features’
dimensionality; LSSVM is not an exception [20]. Not all features impact the prediction,
making feature/variable selection critical in developing/building machine learning predictive
models.
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Figure 2.
The hourly averaged
NO2 concentration
across the 14 IoT
emission sensors
Figure 3.
Day of the week NO2
Concentration collected
across the 14 IoT
sensors
NO2 pollution
prediction
Dimensionality reduction has been proven to help make predictive models perform better
[21]. Of the reduction techniques, feature selection is selecting the most impactful features
from the original set of features as the new input features.
Since Random forest (RF) has consistently proven in past studies, e.g. [21–24], to be very
good at selecting themost impactful features, thewrapper Boruta algorithm(BA) built around
the RF was implemented for feature selection in this study. BA uses the same strategy as the
classical RF classifier model introduced by [25]. The BA is implemented using the
following steps:
(1) Replicate and add a copy of all input features, i.e. weather and traffic features, to form
an information system (IS)
(2) Shuffle the IS and remove correlations among features in the IS
(3) Apply a random forest classifier on the comprehensive IS
(4) compute the Z scores represented as n for all the features and Identify themaximum n
among shadow features (MnSF)
(5) Assign a value to every feature that scores more than MnSF.
(6) Carry out a test of equality and drop features lower than MnSF
(7) Eliminate all the shadow features
(8) Repeat the procedure
After applying the feature selection process, a total of 13 essential features were selected by
the BA, namely, Timestamp, O3, All motor vehicles, Humidity, Ambient pressure,
Temperature, PM10, PM2.5, PM1, Day of the week, and x,y,z which is the 3d-geocentric-
representation of the longitude and latitude. The timestamp arguably suggests some level
of consistency in the pollutant levels at a specific time. For instance, the morning peak
period or close of the day peak periods results in higher traffic. Afterwards, the 13 selected
features will be used in developing an LSSVM predictive model, as discussed in the next
section.
4. Least square support vector machine
LSSVM, improvement to SVMwas proposed [15]. LSSVMprovides a linear equation solution
with an improvement in the objective function of classical SVM.
We use xk as the 13 feature selected with BA and yk is the NO2 concentration
Then the improved SVM model can be mathematically written:
yðxÞ ¼ ωT :∅ðxÞ þ b (1)
where, ∅ðxÞ5 nonlinear mapping function, ω5 weight, and b 5 bias.
The equation can be expressed:
min
ω;b;e
ðω; eÞ ¼ 1
2
ωTω þ 1
2
γ
Xn
k¼1
e2k (2)
Subject to
yk ¼ ωT∅ðxkÞ þ b þ ek; k ¼ 1; 2; . . . n (3)
Where, γ 5 regularisation parameter and ek 5 error term.
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The model can be optimized using the LaGrange function as follows
Lðω; b; e; αÞ ¼ 1
2
ωTω þ 1
2
γ
Xn
k¼1
e2k 
Xn
k¼1

αk

ωT∅ðxkÞ þ b þ ek  yk

(4)
where
αk ∈ R 5 Lagrange multiplier
From Karush-Kuhn-Tucker (KKT) equation given as.
8>>><
>>>:
ω ¼
Xn
k¼1
αkwðxkÞ
Xn
k¼1
αk ¼ 0
αk ¼ ekγ; ωTwðxkÞ þ bþ ek  yk ¼ 0
(5)
The optimization equation can be transformed to the linear equation given as Eqn 6, after
eliminating the variables ω and ek.2
4
0 1 . . . 1
1 Kðx1; x1Þ þ 1=γ . . . Kðx1; xiÞ
1 Kðxj; x1Þ . . . Kðxj; xiÞ þ 1=γ
3
5
2
4
b
α1
αj
3
5 ¼
2
4
0
y1
yj
3
5 (6)
The final equation of the LSSVM model is.
f ðxÞ ¼
Xj
k¼1
∝ kKðx;xiÞ þ b (7)
where
Kðx;xiÞ ¼ wðxÞT * wðxjÞ is the kernel function:
The finite response and the Radial basis function (RBF) kernel function was used in this
research and mathematically expressed as,
Kðx;xiÞ ¼ exp

−γ*jx xij2

(8)
Where, γ ¼ 1
2σ2
4.1 Grid search
After the development of the LSSVM using the features selected with the BA, the
optimization of the LSSVM model parameter: regularisation parameter ( γ ) and kernel-
parameter ðσ2Þ is another challenging area that should not be ignored as it could lead to poor
prediction performance if not carefully chosen.
The choice is where the grid search algorithm comes in. It does this by pairing the all-
possible values of regularisation and kernel parameter ðγ; σ2Þ. It is applied to optimize these
two parameters to have an improved prediction capability. Each pair of regularisation and
kernel parameters is subjected to cross-validation and hence producing MSE value.
For this study, the LSSVM parameters were initialized, taking a search range [0.01, 1000]
and [0.1, 1000] for γ and σ2, respectively. Afterwards, cross-validation, with all possible values
of γ, and σ2, the pair with the minimum MSE is the best, and that was used to create
GS-LSSVM.
NO2 pollution
prediction
5. Model development process and performance measures
Related works on the development of predictive models (e.g. [26– 29], identified Random
forest (RF), Support vector machine (SVM), Decision tree (DT), XGboost (XGB), Adaboost,
Artificial neural network (ANN) and Linear Regression (LR) as powerful machine learning
algorithms for prediction. These popular algorithms were developed and compared with
BA-GS-LSSVM.
Given that feature selection may not be entirely favourable to some algorithms [11, 20], we
developed the predictive models for each algorithm in two ways to allow fairer comparison.
The first was to develop the models using all the available variables before the feature
selection processes. The results from this were recorded and compared (see section 5 on
results). The second was to develop the models using the 13 features selected with the Boruta
algorithm. The results from this were also recorded and compared (see section 5 on results).
Finally, the best results for each algorithm (whether from the first or second development)
were compared to determine the best algorithm overall.
The LSSVM Regression model has no specific python package, so we have implemented
the Scikit learn package in python. Figure 4 presents the flow chart and overall procedures to
build the hybrid GS-LSSVM predictive model to predict the concentration of NO2.
To determine the predictive capability of a regression predictive machine learning model,
various metrics measures loss and score models. Among these metrics, four, including the
mean absolute error (MAE), mean square error (MSE), Explained variance score (EVS) and R
Squared (R2), were used in this paper because of their popularity and are briefly
described below.
MAE is the average absolute variation(error) between each point in a scatter plot between
the actual observation and the corresponding predicted value. It is a risk metric
corresponding to the expected value of the absolute error loss. The best possible score is
0.00; the higher the MAE, the worse the predictive model’s performance. The MAE can be
mathematically written as
MAE ¼ 1=n
Xn
i
yi  y*i
 (9)
Data source
IoT sensor
Traffic data
Meteorology data
Jupyter
API
ETL
RDS
Amazon S3
AWS Platform/Bigdata
Feature Selection
Data pre-process Feature extraction
Optimize LSSVM
Sagemaker Model Building
API Gateway
Amazon
EC2
API: Application programme interface
ETL: Extraction, Transform and Loading
RDS: Relational database
EC2: Amazon Elastic compute cloud
IoT: internet of Things
S3: Simple storage service
AWS: Amazon web service
Jupyter: Local Python IDE environment
Data Matching Using Unique keys
Figure 4.
Flowchart for the
GS-LSSVM
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MSE is another risk metric that corresponds to the average of all the error squares between
the predicted value and the actual value of the target variable. It is also referred to as mean
squared deviation. MSE value is strictly positive, ranging between [0,1], and values closer to
zero signifies a better predictive model. The mathematical definition of MSE is as follows.
MSE ¼ 1=n
Xn
i¼0

yi  y*i
2
(10)
Unlike the risk metric functions (i.e. MAE, MSE), The Explained variance score and R-square
score depicts a better regression model when the score is getting closer to 1.0 and not zero.
The EVS score measures the variation (a measure of dispersion) of the test data set. The best
possible score of EVS is 1.0, and it is mathematically written as.
EVS ¼ 1 yi  y
*
i
yi
(11)
Lastly, R-squared referred to as the coefficient of determination is the proportion of
dispersion in the feature(s) and the target variable. It indicates the goodness of fit and aid the
measurement of how well-unseen data are likely to be predicted by the model. The best
possible value for R-square is 1.0. It is mathematically given as.
R2 ¼ 1
Pn
i¼0

yi  y*i
2
Pn
i¼0ðyi  yiÞ2
(12)
Where yI ¼
Pn
i¼1
yi
n
5.1 Discussion of result
In this study, an optimal-hybrid artificial intelligent algorithm based on the Least squares
support vectormachine was optimized by grid search, whose features were selected using the
Boruta Algorithm (BA-GS-LSSVM) to predict NO2 pollutant concentration were developed.
We identified the most optimal values for the parametric functions of LSSVM to be
γ ¼ 1000 and σ2 ¼ 10 using grid the search.
The model was all developed following the union of the three data sources, including the
weather, traffic, and IoT data on a big data platform considering many data points recorded
(i.e. 24 hrs 3 30 days 3 5 months 3 14 IoTs). The data were merged and matched for the
development of the predictive models. We then compared the performance capability of the
proposed hybrid model and other powerful standalone machine learning in predicting NO2,
however, in two streams to achieve a fair comparison with no bias. The two streams of
comparisons to avoid biases are; (1) All models implemented without feature selection (2) All
Models implemented with feature selection. Table 3 and Figure 5 presents metrics for
developed models with (WF) and without feature (WoF) selection for a fair comparison. The
BA-GS-LSSVM and all other ML models developed were done on a big data platform due to
the algorithms’ time complexity. The time complexity of the models developed are
GS-LSSVM:O(n3), RF:O(d*log(n)), KNN:O(knd), ANN:O(n4), DT:O(n*log(n)*d), SVR:O(n3),
XGB:O(n*d*log(n)), LR:O(nd), LSSVM:O(n3), and ADB:O(nd2).
As shown in Figure 5, the error measures, including the MAE, MSE for all the developed
models, were presented in decreasing order. The order, in this case, shows the Adaboost (AB)
to have the maximum error, followed by the LSSVM and linear Regression (LR) implemented
without feature selection. At the same time, we can see GS-LSSVM with feature selection, i.e.
BA-GS-LSSVM with the most negligible error score value. This explains the higher
performance ability of the hybrid model over other standalone models.
NO2 pollution
prediction
In addition, the doughnut chart shows the R-squared score for all the models developed, and
the maximum score was yielded in the development of BA-GS-LSSVM. i.e. 6.35%(0.82). The
assertion of the bias caused by feature selection was proved in this paper; for instance, the
first approach (i.e. model’s implementation without feature selection) shows poor and woeful
model performance.
In addition, themodels developedwith feature selectionwas subjected to the 10-fold cross-
validation to ensure efficient/unbias evaluation. For this, the research present in Figure 6, a
box and whisker plot showing the spread in different performance metrics across each cross-
validation fold for each algorithm
From these results, BA-GS-LSSVM is identified the best considering its minimal error
metric scores (i.e. lowest MAE andMSE score) compared to other ML developed. At the same
time, BA-GS-LSSVM has scored the highest EVS and R-square score. Thus, our model
Conclusively performs best compared to all other standard and powerful standalone ML
models developed in this paper.
The use of the big-data platform reduced the computational complexity for most of the
models implemented. Also, the lower computational complexity of the LSSVM over the SVM
is another outstanding advantage recognized in this research.
Model/Metrics
MAE MSE R-square EVS
WoF WF WoF WF WoF WF WoF WF
GS-LSSVM 8.32 6.91 114.57 97.08 0.76 0.82 0.76 0.82
KNN 7.6 7.59 152.9 131.93 0.73 0.77 0.73 0.77
RF 5.8 7.66 110.8 132.87 0.79 0.77 0.79 0.77
ANN 8.3 8.26 164 134.3 0.71 0.77 0.71 0.77
LSSVM 11.6 8.32 285.5 114.57 0.51 0.76 0.51 0.76
XGB 9.4 9.01 219 189.1 0.62 0.74 0.62 0.74
SVR 9.5 9.01 219.5 189.1 0.62 0.67 0.62 0.67
LR 9.5 10.3 219.5 199.98 0.62 0.62 0.62 0.62
DT 8.1 10.49 192 267.48 0.67 0.54 0.67 0.54
ADB 18.4 14.5 523 332.67 0.1 0.42 0.1 0.42
Figure 5.
The NO2 concentration
predictive models
developed
Table 3.
Results of the overall
model developed
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Figure 6.
Graphical
illustrationss of the
spread in (a) R-square,
(b) MAE, (c) MSE, and
(d) EVS score for each
algorithm developed
with feature selection
NO2 pollution
prediction
6. Conclusions
High-precision NO2 prediction is critical to people’s well-being, especially those that are
vulnerable to air pollution. However, the BA-GS-LSSVM model in this paper happens to be
appealing and proves to be better than popular algorithms. To demonstrate the advantages
of the BA-GS-LSSVM model, nine different algorithms were compared. At the end of the
study, the following list of the inferences can be reached, including:
(1) Boruta, a dimensionality selection technique, improves the performance of the ML
model
(2) Compared with all other standalone models developed, our model, BA-GS-LSSVM,
exhibits a better predictive ability in NO2 concentration
(3) The BA-GS-LSSVMmodel provides a basis for delivering an informed decision on the
NO2 pollutant avoidance system.
Future studies should explore the use of BA-GS-LSSVM to predict other pollutants in the UK
and other parts of the world experiencing this outburst in air pollution concentration.
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Corresponding author
Hafiz Alaka can be contacted at: hafizalaka@outlook.com
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NO2 pollution
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