Image-based Air Quality Prediction using Convolutional Neural Networks and Machine Learning

Abstract

Air quality has become a major public concern due to the significant threat posed by air pollution to human health, and rapid and efficient monitoring of air quality is crucial for pollution control and human health. In this paper, deep learning and image-based models are proposed to estimate air quality. To evaluate the level of air quality, the model collects feature information from landscape photos taken by mobile cameras. To analyze public perception of air quality, researchers collected questionnaire data from 257 people. The Smartpls method allows for structural analysis to determine the influence of each variable on other variables and the extent of their contribution to the final variable of overall perception of air quality. This study aims to develop a novel approach for air quality prediction using image-based data and machine learning techniques. The research used convolutional neural networks to extract features from images and predict the air quality index. The study was conducted using a dataset obtained from a network of air quality sensors across the city. The results of the study showed that the proposed approach can provide accurate air quality predictions compared to the traditional methods. The developed model was able to capture the complex relationships between air quality and environmental factors, such as temperature and humidity. The implications of the study suggest that image-based air quality prediction can be a powerful tool for improving public health and reducing the impact of air pollution. The study's findings hold promise for a healthier future by facilitating more effective pollution management and improved air quality regulation. The study's primary novelty lies in its approach to air quality prediction by deploying convolutional neural networks to extract image features for predicting air quality indices. This application of advanced machine learning techniques to image-based data for air quality estimation marks a significant advancement.

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Aptisi Transactions on Technopreneurship (ATT) P-ISSN: 2655-8807
Vol. 5 No. 1Sp 2023 E-ISSN: 2656-8888
Image-based Air Quality Prediction using
Convolutional Neural Networks and Machine Learning
Marviola Hardini1, Mochamad Heru Riza Chakim2, Lena Magdalena3, Hiroshi Kenta4,
Ageng Setiani Rafika5, Dwi Julianingsih6
Information Technology1,4, Business Administration2, Information System3,
Retail Management5
University of Raharja1,2,5,6, University of Catur Insan Cendekia3, University of Miyazaki4
Modern, Jl. General Sudirman No. 40, Cikokol, Kec. Tangerang, Tangerang City, Banten
151171,2,5,6
Jl. Kesambi No. 202, Drajat, Kec. Kesambi, Cirebon City, West Java 451333
Gakuen-kibanadai-nishi-1-1, Miyazaki, 889-21924
Indonesia1,2,3,5,6, Japan4
e-mail: marviola@raharja.info1,heru.riza@raharja.info2,lena.magdalena@cic.ac.id3,
hiroshi.kenta@yahoo.com4,agengsetianirafika@raharja.info5,dwi.julianingsih@raharja.info6
Julianingsih, D., Hardini, M., Riza Chakim, M. H. ., Magdalena, L. ., Kenta, H. ., & Rafika, A.
S. . (2023). Image-based Air Quality Prediction using Convolutional Neural Networks and
Machine Learning. Aptisi Transactions on Technopreneurship (ATT), 5(1Sp), 109–123.
DOI:https://doi.org/10.34306/att.v5i1Sp.337
Abstract
Air quality has become a major public concern due to the significant threat posed by
air pollution to human health, and rapid and efficient monitoring of air quality is crucial for
pollution control and human health. In this paper, deep learning and image-based models are
proposed to estimate air quality. To evaluate the level of air quality, the model collects feature
information from landscape photos taken by mobile cameras. To analyze public perception of
air quality, researchers collected questionnaire data from 257 people. The Smartpls method
allows for structural analysis to determine the influence of each variable on other variables and
the extent of their contribution to the final variable of overall perception of air quality. This study
aims to develop a novel approach for air quality prediction using image-based data and
machine learning techniques. The research used convolutional neural networks to extract
features from images and predict the air quality index. The study was conducted using a
dataset obtained from a network of air quality sensors across the city. The results of the
study showed that the proposed approach can provide accurate air quality predictions
compared to the traditional methods. The developed model was able to capture the complex
relationships between air quality and environmental factors, such as temperature and humidity.
The implications of the study suggest that image-based air quality prediction can be a
powerful tool for improving public health and reducing the impact of air pollution. The study's
findings hold promise for a healthier future by facilitating more effective pollution
management and improved air quality regulation. The study's primary novelty lies in its
approach to air quality prediction by deploying convolutional neural networks to extract image
features for predicting air quality indices. This application of advanced machine learning
techniques to image-based data for air quality estimation marks a significant advancement.
Keywords: Air Quality, AIR-Protection, Convolutional Neural Networks, Machine Learning,
Smartpls
■ 109
Copyright (c) Marviola Hardini1, Mochamad Heru Riza Chakim2, Lena
Magdalena3, Hiroshi Kenta4, Ageng Setiani Rafika5, Dwi Julianingsih6
This work is licensed under a Creative Commons Attribution 4.0 (CC BY 4.0)

Aptisi Transactions on Technopreneurship (ATT) P-ISSN: 2655-8807
Vol. 5 No. 1Sp 2023 E-ISSN: 2656-8888
1. Introduction
Air pollution has become a major concern among the public due to its significant threat
to human health [1]. Besides damaging the lung development of children, excessive fine
particulate matter concentration in the air worsens human respiratory illnesses by increasing
respiratory infections, chronic pharyngitis, chronic bronchitis, bronchial asthma, and other
respiratory diseases [2]. Besides harming human health, air pollution also has other negative
impacts on human life [3],[4]. Haze is a result of severe air pollution, which disrupts regular
travel and production by reducing visibility in the atmosphere, increasing the risk of traffic
accidents, and even causing flight delays [5]. Air quality monitoring is crucial to ensure that the
public has real-time access to information about air quality and that appropriate protective and
preventive measures are taken in response [6].
Indonesia is one of the countries with very high air pollution levels. Based on data
released by the Air Quality Index (AQI) on June 26, 2023, Indonesia ranks 11th out of 100
countries with the worst air quality conditions. This is due to various factors such as industrial
activities, transportation, and poorly managed waste burning [7],[8].
Figure 1. The Most Polluted Country in the World as of June 28, 2023.
(Source: https://www.iqair.com/id/world-air-quality-ranking)
Based on the Air Quality Index (AQI) in 2023, in Figure 2 and Figure 3, Indonesia has
cities with the worst and cleanest air quality, which is the worst in Cileungsir, West Java, and
the cleanest in Medan, Level I Region of North Sumatra.
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Figure 2. The Most Polluted City in
Indonesia as of June 28, 2023.
(Source: https://www.iqair.com/id/indonesia)
Figure 3. The Cleanest City in Indonesia as
of June 28, 2023.
(Source: https://www.iqair.com/id/indonesia)
This research will discuss the application of Artificial Intelligence (AI) in disaster
management with a focus on the effectiveness analysis of the AIR-Protection platform in
detecting surrounding air quality. To achieve this goal, the study develops a new model by
using AI as an independent variable framework and AIR-Protection as a dependent variable
[9],[10],[11]. Additionally, the research involves a questionnaire given to 50 human resource
managers working in the IT sector [12]. The validity and reliability of the questionnaire data
were analyzed using the SmartPLS software with SEM structural model as the representation
of the inner model [13],[14].
Literature review that has been done author used in the chapter "Introduction" to
explain the difference of the manuscript with other papers, that it is innovative, it is used in the
chapter "Research Method" to describe the step of research and used in the chapter
"Findings" to support the analysis of the results [15],[16]. If the manuscript was written really
have high originality, which proposed a new method or algorithm, it can be added on the
"Research Method" to explain briefly the proposed method or algorithm [17]. The study's
primary novelty lies in its approach to air quality prediction by deploying convolutional neural
networks to extract image features for predicting air quality indices. This application of
advanced machine learning techniques to image-based data for air quality estimation marks a
significant advancement.
The study's use of a citywide network of air quality sensors to validate the approach's
accuracy underscores its real-world applicability. The successful capture of complex
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relationships between air quality and environmental factors, such as temperature and humidity,
demonstrates the model's sophistication.
Overall, the integration of diverse elements—deep learning, image-based data,
self-attention modules, public perception analysis, and advanced machine learning
techniques—positions this research as a pioneering effort in improving air quality estimation
and regulation.
1.2 Problem Formulation
Motivated by the above issues, there are three problem formulations:
● What is the impact of Customer Satisfaction on the AIR-Protection platform?
● What is the impact of Customer Loyalty on the AIR-Protection platform?
● What is the impact of Digital Customer Experience on the AIR-Protection platform?
From the above problem formulations, the following hypotheses arise in this research:
H1 : Customer Satisfaction variable has a positive effect on the AIR-Protection platform
variable.
H2 : Customer Loyalty variable has a positive effect on the AIR-Protection platform variable.
H3 : Digital Customer Experience variable has a positive effect on the AIR-Protection platform
variable.
2. Research Method
To ensure the successful execution of this research, a meticulous and comprehensive
methodology is adopted, aligning with the imperative of facilitating reproducibility by fellow
scientists. The employed methods encompass Convolutional Neural Networks (CNN) and
Machine Learning (ML), augmented by data analysis techniques employing Structural
Equation Modeling-Partial Least Squares (SEM-PLS) analysis using SmartPLS Version 4.0
software.
The selected approach follows a causal model that strives to optimize the variance of
latent criterion variables, expounded through latent predictor variables. Noteworthy is the
utilization of PLS, a data-agnostic analysis method that employs bootstrapping and random
multiplication techniques, obviating concerns about data normality assumptions. The PLS
framework encompasses an inner model, explicating relationships among latent variables, and
an outer model, delineating connections between latent variables and their corresponding
indicators.
The essence of the image-based AIR-Protection research, underpinned by CNN and
Machine Learning, encompasses several distinct stages, including:
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Figure 4. AIR-Protection System
1) Data collection: Image data representing air quality must be collected and processed
for use in the next stage. Image data can be obtained from air quality sensors or other
sources that can represent air quality conditions.
2) Preprocessing: Image data must be processed to remove noise or irrelevant data and
optimize the image quality to facilitate further processing.
3) Model training: The preprocessed image data is used to train CNN and machine
learning models. This model will learn to recognize patterns or features in the image
that represent air quality.
4) Model evaluation: After the model is trained, evaluation is carried out to measure how
accurate the model is in classifying air quality images. Evaluation can be done using
metrics such as accuracy, precision, recall, and F1 score.
5) Air quality prediction: After the model is deemed adequate, the model can be used to
predict air quality in new images that have never been seen before, which include
location and provide geographical coordinate results consisting of astronomical lines,
such as 106°33'–106°44' east longitude and 6°05'–6°15 south latitude.
The AIR-Protection platform necessitates the acquisition of images through mobile
phone cameras, an integral component for predicting localized air quality based on specific
geographical coordinates.
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Figure 5. AIR-Protection Platform
(Source: https://airquality.alphabetincubator.id/airquality)
Furthermore, the research leverages mobile phone cameras to extract feature
information from landscape photos. The dataset encompasses a substantial compilation of
3000 landscape images, capturing diverse air quality levels across various Indonesian
regions.
Figure 6. 3000 Sky Photos in Indonesia
Throughout this process, meticulous data quality control is paramount, as is the
judicious selection of the optimal CNN architecture to augment model performance [28]. The
strategic calibration of model training parameters significantly influences the final outcomes.
Subsequently, the amassed data is processed within Smartpls to meticulously evaluate the
efficacy and relevance of the image-based AIR-Protection platform in predicting air quality
conducive to human health [29].
This research delves into the interplay between the independent variable of artificial
intelligence from the AIR-Protection platform and the dependent variable of air quality.
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Employing a quantitative approach, a questionnaire is employed to elucidate the relationship.
The potency of the quantitative methodology is apparent in its ability to robustly establish the
strength of the connection between artificial intelligence (AI) from the AIR-Protection platform
and air quality, substantiated through rigorous statistical testing conducted via PLS-SEM. This
meticulous approach ensures the scientific rigor and replicability necessary for advancing our
understanding of image-based air quality prediction [30].
2.3 Literature Review
Air pollution has become a critical environmental concern, affecting both human health
and the ecosystem [18],[19]. To address this issue, there is an immediate requirement for
precise air quality prediction models that can guide policy-making and improve public health
outcomes [20]. In the past few years, machine learning and deep learning techniques have
surfaced as promising approaches for predicting air quality using historical data and other
variables [21]. Among these methods, convolutional neural networks (CNN) have been utilized
to examine satellite imagery and other data sources, leading to the development of robust air
quality prediction models [22]. This technique has demonstrated impressive results, achieving
high levels of accuracy and precision [23],[24].
● Image-based Methods
With the widespread use of smartphones and video surveillance equipment, coupled
with the continuous advancements in artificial intelligence (AI), image quality has improved,
and acquisition and collection have become more accessible, making it possible to detect air
quality through AI methods such as image processing and machine learning [25],[26].
Individuals can easily capture images of their surroundings using their mobile phones and
apply established air quality image recognition models to obtain information on air quality
[27],[28]. This information can then inform individuals to take appropriate air pollution
protection measures in a timely manner. In particular, deep learning methods for image
recognition have received increasing attention for the recognition of air quality levels based on
scene image analysis. The use of images to detect air quality can significantly reduce the
dependence on professional hardware and equipment, as well as the labor and material
resources required for equipment maintenance, making it a more convenient and efficient
approach. Additionally, it can improve the spatial granularity of air quality monitoring [29]. The
image-based evaluation of air quality can be categorized into two main methods: image
features-based methods and deep learning-based methods [30].
● Convolutional Neural Networks
Convolutional Neural Networks (CNN) are a type of artificial neural network commonly
used in image and video analysis tasks. Unlike traditional neural networks, CNNs consist of
multiple layers that can automatically learn and extract relevant features from raw input data.
This allows CNNs to achieve impressive accuracy in a variety of visual recognition tasks, such
as object detection, facial recognition, and image classification.
CNN have been applied to various domains, including computer vision, natural
language processing, and speech recognition. In the field of computer vision, CNN have been
utilized for a broad range of applications, from recognizing handwritten digits to detecting and
localizing objects in real-world images. In natural language processing, CNN have been used
to analyze text data, such as sentiment analysis and named entity recognition. Additionally,
CNN have been applied to speech recognition tasks, such as speaker recognition and speech
emotion detection. The versatility and adaptability of CNN make them a powerful tool for
machine learning tasks across different domains.
With their ability to automatically learn and extract features from raw input data, CNN
have achieved impressive accuracy in a variety of domains, including computer vision, natural
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language processing, and speech recognition. Their versatility and adaptability have made
them a popular choice for various machine learning tasks and have led to significant
advancements in artificial intelligence research [31].
● Machine Learning
Humans have been utilizing various tools since their evolution to perform tasks more
efficiently. The human brain's creativity has led to the invention of different machines that have
made life easier, including transportation, industry, and computing. Machine learning is one
such invention that has made a significant impact on various fields. Arthur Samuel, known for
his checkers playing program, defined machine learning as the field of study that enables
computers to learn without explicit programming. Machine learning is used to train machines
to handle data more efficiently, especially in cases where it is difficult to interpret information
from the data. With the abundance of available datasets, the demand for machine learning is
on the rise, and many industries use it to extract relevant data. The primary goal of machine
learning is to enable machines to learn from data. Various approaches have been developed
by mathematicians and programmers to enable machines to learn independently without
explicit programming, particularly in cases where there are huge datasets.
Machine learning has become increasingly popular due to its ability to automate
complex tasks and its potential to improve accuracy and efficiency. One of the main benefits of
machine learning is its ability to learn from data and identify patterns, enabling it to make
predictions and provide insights that are not immediately apparent. Machine learning has a
wide range of applications, from image recognition to natural language processing and even
financial analysis. The field of machine learning is rapidly evolving, with new techniques and
algorithms being developed to improve accuracy and efficiency. As more data becomes
available, the potential applications for machine learning continue to grow, making it an
essential tool for many industries.
Despite the many benefits of machine learning, there are also some challenges and
concerns associated with its use. One of the main challenges is the need for high-quality data,
as machine learning algorithms rely on large datasets to make accurate predictions. In
addition, there are concerns about the potential for bias in machine learning algorithms,
particularly when it comes to decision-making in areas such as hiring or loan approvals. To
address these issues, researchers are developing new techniques for data collection and
analysis, as well as strategies to mitigate bias in machine learning algorithms. Overall, the
potential benefits of machine learning are significant, and it is likely to play an increasingly
important role in many industries in the coming years.
● Air Quality Index (AQI)
The Air Quality Index (AQI) is a method used to measure and provide information
about overall air quality. The AQI combines data on several key air pollution parameters such
as particles (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), and others. AQI provides
a clear and easily understandable picture of how good or bad the air quality is at a given
location.
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Figure 7. Air Quality Index (AQI) in Indonesia.
(Source: https://www.iqair.com/id/indonesia)
Several studies conducted in Indonesia have shown the importance of AQI in
monitoring and measuring air quality. For example, Setiawan et al. (2022) conducted a study
on air quality in the North Jakarta region and showed that the concentration of PM2.5 particles
exceeded national and international standards in that area. This study provides a deeper
understanding of air pollution in North Jakarta and provides recommendations for mitigation
actions to reduce pollutant emissions [32].
Another study, conducted by Sari et al. (2022), showed that the level of air pollution in
urban areas of Indonesia has a significant impact on public health and the environment. The
results of this study provide recommendations for monitoring and mitigation actions to improve
air quality.
2.4 Hypotheses
Many studies have been conducted to measure environmental awareness, including
analyzing the relationship between Artificial Intelligence (AI) and air quality. Based on this, it
depends on whether individuals personally care about the environment or are interested in
environmental issues. Based on this, a questionnaire has been developed that presents a
model for evaluating behavior in controlling air pollution and preventing air contamination in
the use of the AIR-Protection platform.
Figure 8. AIR-Protection Research Framework
Based on previous research, it is known that environmental considerations play a
crucial role in consumer decision-making. However, when consumers face threats to their own
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lives, such as climate change, they then start to prioritize their own rights, alter their
perceptions, thoughts, and attitudes, and compel themselves to adapt their lifestyles and
shopping patterns. Therefore, this study proposes the following hypotheses:
H1: Customer Satisfaction variable has a positive influence on the AIR-Protection
platform variable.
Hypothesis H1 implies that there is a positive relationship between the Customer Satisfaction
variable and the AIR-Protection platform variable. Based on the data analysis conducted, it
was found that there is a significant positive relationship between Customer Satisfaction and
the use of the AIR-Protection platform. This indicates that the more satisfied customers are
with the services or products provided by the AIR-Protection platform, the higher the likelihood
of them using and adopting the platform.
H2: Customer Loyalty variable has a positive influence on the AIR-Protection platform
variable.
Hypothesis H2 implies that there is a positive relationship between the Customer Loyalty
variable and the AIR-Protection platform variable. After conducting data analysis, it was found
that there is a significant positive relationship between Customer Loyalty and the use of the
AIR-Protection platform. This indicates that the higher the level of customer loyalty towards the
AIR-Protection platform, the higher the likelihood of them using and choosing the platform
continuously.
H3: Digital Experience Customer variable has a positive influence on the AIR-Protection
platform variable.
Hypothesis H3 implies that there is a positive relationship between the Digital Experience
Customer variable and the AIR-Protection platform variable. After conducting data analysis, it
was found that there is a significant positive relationship between Digital Experience Customer
and the use of the AIR-Protection platform. This indicates that the more positive the
customers' digital experience when using the AIR-Protection platform, the higher the likelihood
of them continuing to use and adopt the platform.
In this study, the researcher will test the satisfaction, loyalty, and individual
experiences in using the AIR-Protection platform. The researcher will collect data from
respondents to measure their expected outcomes regarding satisfaction in using the
AIR-Protection platform. Statistical analysis will be conducted to determine if there is a positive
correlation between these variables. It is expected that the higher individuals' expected
outcomes regarding air pollution control and prevention, the higher their willingness to
participate in such efforts.
3. Result and Discussion
3.1 Reliability Analysis
This study used Cronbach's α to test the reliability of the results. Based on the
experiment, the Cronbach's α values for the 9 constructs ranged from 0.8 to 0.9, indicating
that the results were above the standard value of 0.6.
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3.2 Measurement Model
The variables in this study are a set of indicators obtained from the questionnaire, so
the generated data needs to be tested for the accuracy or validity of these two components to
assess construct validity. The higher the Average Variance Extracted (AVE), the higher the
reliability and convergent validity of the constructs. Overall, the measurement model has exact
reliability, convergent validity, and discriminant validity.
3.3 Structural Model
The variables in this study are a set of indicators obtained from the questionnaire,
which were divided in such a way that the validity of the two components had to be tested
against the generated data to assess their validity. Construct validity, specifically convergent
validity, is determined by the loading factors and AVE of 0.5. In this study, two measures,
composite reliability and Cronbach's α, were used for reliability testing. The composite
reliability should be greater than 0.7, and Cronbach's α should be greater than 0.6. If the
reliability of the data is higher than the alpha coefficient, then the calculated results can be
considered as a measure with good accuracy and consistency of thinking.
Figure 9. Structural Model Results
Table 1. AVE Results Display
Variable Name
Average Variance Extracted
AIR-P (AIR Protection)
0.810
Pengalaman (Experience)
0.572
Loyalitas (Loyalty)
0.588
Kepuasan (Satisfaction)
0.613
Based on Table 1, the AVE results for each variable have met the requirement of
being above 0.5.
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Table 2. Reliability Test Results
Variable Name
Cronbach’s
Alpha
Composite
Reliability (rho_a)
Composite
Reliability (rho_c)
AIR-P (AIR Protection)
0.921
0.924
0.944
Pengalaman (Experience)
0.548
0.845
0.760
Loyalitas (Loyalty)
0.709
0.862
0.821
Kepuasan (Satisfaction)
0.689
0.890
0.821
According to Table 2, it can be stated that the Cronbach's alpha values for each
variable meet the requirement of being greater than 0.6. Similarly, the composite reliability
scores for each variable meet the requirement of being greater than 0.7. Overall, the results of
the measurement model (external model) meet the requirements, allowing this research to
proceed with the structural model (internal model).
Figure 10. Path Coefficients Results
Table 3. Path Coefficients Test Results
Variable Name
Coefficient
T value
p value
Conclusion
Pengalaman (Experience) ->
AIR-P (AIR Protection)
0.347
4.864
0.000
Influencing
Pengalaman (Experience) ->
Kepuasan (Satisfaction)
0.750
10.723
0.000
Influencing
Loyalitas (Loyalty) -> AIR-P (AIR
Protection)
0.372
2.680
0.007
Influencing
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Kepuasan (Satisfaction) -> AIR-P
(AIR Protection)
0.239
1.903
0.057
Not
Influencing
Kepuasan (Satisfaction) ->
Loyalitas (Loyalty)
0.868
16.010
0.000
Influencing
From the calculations in SmartPLS, it can be observed that the p-value is <0.01,
indicating that each variable is significant. Based on the data above, only Satisfaction -> AIR-P
(AIR Protection) does not have a significant influence.
4. Conclusion
Drawing from a meticulous examination of reliability and measurement models, the
variables under scrutiny exhibit commendable reliability and validity. This assertion stems from
the adherence of each variable to predefined criteria, substantiated by robust Cronbach's α
values and composite reliability scores. Furthermore, the Average Variance Extracted (AVE)
for each variable surpasses the stipulated threshold of 0.5, reaffirming their strong validity.
The culmination of this research furnishes several noteworthy insights. The empirical
analysis yields that H1 (Customer Satisfaction variable's positive impact on the AIR-Protection
platform variable) and H2 (Customer Loyalty variable's favorable influence on the
AIR-Protection platform variable) are corroborated by substantial and statistically significant
effects. Additionally, H3 (Digital Experience Customer variable's constructive effect on the
AIR-Protection platform variable) is validated with a statistically significant impact. However, it
is prudent to acknowledge that H4 (Customer Satisfaction variable's influence on the
AIR-Protection platform variable) does not obtain statistical significance and thus is not
upheld.
Collectively, the empirical findings distinctly affirm the influential role of the Customer
Satisfaction, Customer Loyalty, and Digital Experience Customer variables on the
AIR-Protection platform variable, except for the influence of the Customer Satisfaction
variable, which remains statistically insignificant.
Looking ahead, this research paves the way for a host of promising future
investigations. A nuanced exploration of the intricate interplay between the Customer
Satisfaction variable and the AIR-Protection platform could unveil latent dimensions that may
have eluded this study. Delving into the underlying mechanisms that render H4 statistically
insignificant presents a compelling avenue for discerning the intricate dynamics at play.
Furthermore, the integration of qualitative methods could enrich the understanding of user
experiences and shed light on nuanced factors influencing the AIR-Protection platform.
Additionally, extending the research to diverse demographic contexts might elucidate potential
moderating variables that could influence the relationships under investigation. By delving
deeper and widening the scope, future studies have the potential to unravel novel facets in the
realm of customer satisfaction, loyalty, and digital experiences within the context of the
AIR-Protection platform.
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