A Robust IoT-based Air Quality Monitoring Node for Multi-Location Deployment

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A Robust IoT-based Air Quality Monitoring Node for Multi-Location Deployment

Reginald Ekene Ogu

Dept of Software Engineering Federal University of Technology, Owerri, Nigeria

Nkwachukwu Chukwuchekwa

Dept. of Electrical and Electronic Engineering Federal University of Technology,

Owerri, Nigeria

Gloria A. Chukwudebe

Dept. of Electrical and Electronic Engineering Federal University of Technology,

Owerri, Nigeria

Isaac A. Ezenugu

Dept of Electrical/Electronic Engineering Imo State University,

Owerri, Nigeria

Ifeyinwa E. Achumba

Dept. of Electrical and Electronic Engineering Federal University of Technology,

Owerri, Nigeria

AbstractThe target of monitoring the global air quality is facing challenges as existing IoT devices are handicapped, lacking the ability to be deployed in heterogenous environments. Existing IoT devices can connect to the Internet through a designated wireless network. Thus, becoming unsuitable to monitor environments without the communication network they are designed to connect to. This paper presents the development of an Adaptive IoT Node for Air Quality Monitoring. The IoT node developed herein has the capability to connect to the Internet through WiFi, Bluetooth and mobile networks. This capability makes the node versatile and deployable in heterogenous scenarios. The Arduino MKR 1010 ensures that the system can connect to WiFI and Bluetooth networks while the MKR 1400 enables the IoT node to connect to mobile network. Other support modules and components were connected to the controllers to form a functional system. A suitable dashboard was developed to display the air quality in a user-friendly manner using a combination of JavaScript, CSS and HTML. The developed IoT node was able to upload air quality data to Google Firebase cloud database and the ThingSpeak IoT platform on two separate testing instances.

KeywordsEnvironmental management, Hazard Identification, Internet of Things, Pollution control, Sustainable Development

  1. INTRODUCTION

    Air pollution (AP) is a highly rated hazard. The Air Quality (AQ) is directly associated with the human health. Good AQ promotes healthy living and promotes human existence while poor AQ threatens the existence of humans [1][5].

    Despite the efforts channelled towards improving the air quality, AP is on the increase [6][9]. AP increases with increase in productivity level of a society [10][12]. Specifically, industrialization and urbanization are the main causes of increased air pollution [2], [8], [13][19]. Due to industrialization and urbanization, liquid droplets, solid particles and gas molecules are emitted to the atmosphere to cause pollution [11].

    Also, the existence of inadequate scheme and infrastructure for effective waste management in developing countries is

    contributing to air pollution. This is a serious point because air quality is directly linked to waste management. When a waste substance is improperly managed, it pollutes the air [20], [21]. Today, curbing AP is a serious agenda in the world.

    Consequently, AQ monitoring has become the subject of many discussions and researches [22][26]. The goal is to reduce AP because by improving the AQ, cities can reduce the risks of diseases like stroke, heart disease, lung cancer and other respiratory diseases.This is because poor AQ is attributed to cardiovascular diseases, which accounts for 31.85% of death annually [27]. According to the World Health Organization, 91% of the world population were living in places with poor outdoor AQ as of 2016. AP was estimated to cause more than

    3.5 million premature deaths worldwide. AP reduces lifespan by three years [28]. AP has an impact on overall health as well as on the reproductive function [29]. Exposure to air pollution increases the chances of infertility by 20% [30][32]. Air pollution affects not only the environment but the body organs and respiratory system as well [26], [33][39]. Air pollution is one of the biggest environmental risk to health [40].

    91% of the premature deaths occurred in low- and middle- income countries [41]. Particle pollution is known to have many environmental effects from poor visibility to more serious consequences such as acid rain, which pollutes soil and water [42].

    AP control cannot be achieved only through social legislations and policies. Over the last few years, the IoT paradigm has evolved as one of the biggest technological advancements [43]. AQ monitoring is an area in which IoT is currently being applied [7]. IoT has become useful through the utilization different kinds of sensors and devices to monitor AQ [44]. It is envisioned that rapid deployment of IoT nodes for AQ will foster the attainment of the United Nations Sustainable Goals (SDGs). Hence the necessity for this research.

    The long-term impact of the SDGs is to create a smart world. Target 3.9 of the global goals is predicated on reducing the number of deaths and illnesses that are caused by AP and other hazards. Also, indicator 11.6 is concerned with paying

    attention to air quality and other things, as actions to be taken in the development of sustainable smart cities. Hence, the mitigation of air pollution necessitates accurate air quality monitoring and forecast [19], [46]. Smart Environment Monitoring play an important role in achieving the United Nations Sustainable Development Goals (SDGs) [47]. This research is geared towards the attainment of the Sustainable Development Goals 3 and 11.

    The primary objective of this work is to develop an Adaptive IoT Node that can be deployed in Heterogenous Environments for Air Quality Monitoring to foster the attainment of the sustainable development goals. The IoT node developed herein has the capability to connect to the Internet through Wi-Fi, Bluetooth and mobile networks. This work is a giant in ensuring the rapid development of IoT nodes for AQ monitoring because existing IoT solutions are not suitable to be readily deployed in resource constrained environments where there exists no Wi-Fi network like in the rural places.

    The works of [16], [25], [26], [44], [48][58] made remarkable efforts towards air quality monitoring. However, the authors could not develop an IoT node with the capability of being deployed in heterogenous scenarios. In plain terms, each of the systems designed a wireless network to connect to the Internet. This fact has made the existing AQ IoT nodes to become handicapped in monitoring the air quality in locations where there is no such network available.

    Kumar [59] had identified scalability, modularity, interoperability and openness as key design issues for an efficient IoT architecture in a heterogeneous environment. This implies that IoT architectures must be designed with an objective to fulfil the requirements of cross-domain interactions, multi-system integration with the potentials for simple and scalable management functionalities, big data analytics and storage, and user-friendly applications. Thus, it has become a compelling necessity to develop a versatile AQ IoT node since the existing nodes were proposed without considering the peculiarities of developing countries like Nigeria. Among the peculiarities are unreliable electricity supply and low quality of Internet service. To this end, the IoT node proposed herein considers the two most available network around the globe.

    This is because, an AQ IoT node with the capability to connect to more than one form of network will boost the chances of monitoring the air quality around the globe. This will go a long way in the attanment of the sustainable development goals since air quality monitoring is a key indicator of two goals.

  2. METHODOLGY

    1. System Requirements

      The features of air quality monitoring devices stated by [7] were adopted as the requirements for the sensory node developed in this work: low-cost, capability of detecting sufficiently low concentration levels of pollutants, low energy demanding capability, easy to be deployed and silent operation. This IoT node should be capable to transmit the air quality data to a cloud database using any desired protocol and through

      any desired Internet connectivity technique.

    2. Hardware System Design

      With reference to the cost element of the scope triangle, it is practically unfeasible to measure all air pollutants in this research. Hence, the following pollutants are measured in this work: Particulate Matter 2.5, Particulate Matter 10, Carbon II Oxide and Nitrogen IV Oxide. In view of the system requirements above, the block diagram of the IoT node developed in this work is presented in Fig 1.

      Fig. 1. Block Diagram of the air quality data capturing node.

      A 1800mAh Li-Po battery was selected for the system. TP4056 monolithic Integrated Circuit is selected as the charging element for the Li-Po battery. The TP4056 is desired to charge the battery slowly to ensure that the battery life span is not destroyed. Hence, the charge controlled will be programmed to charge the battery with 0.4A. The reason for this choice is because it is not advisable to charge a battery with huge amount of current.

      The TPS63805 buck-boost converter integrated circuit is employed to regulate the 3.7V battery voltage to 5V required by the components of the circuit. The choice of the values of the discrete components connected to the above ICs was based on the recommendations in the datasheets.

      The DSM501 sensor is employed to measure particulate matter (PM 2.5 and PM 10) while the MICS-6814 module is employed to measure Carbon Monoxide (CO) and Nitrogen Dioxide (NO2).

      The popular Arduino embedded systems development platform was adopted. By this, the Arduino MKR 1010 and Arduino MKR 1400 boards form the hearts of the system. Depending on the environment that the node is to be installed a simple switch is activated to select the available network: Wi- Fi/Bluetooth and mobile network. The circuit diagram of the system is shown in Fig 2.

      While the sensors operate on a 5V level, the MKR controllers operates on a 3.3V voltage level and it was recommended that a voltage that is more than 3.3V should not be connected to the pins of the controller. Hence, the addition of voltage divider networks between the sensor outputs and the MKR 1010. By this, resistors R10, R11, R12 and R13 dorm the upper sides of the networks while R14, R15, R16 and R17 are the lower side of the voltage divider network.

      PV1

      1. VCC

      2. GND

        C3

        L1

        10 L1

        VIN 1

        EN 2

        MODE 3

        GND 4

        U2

        9 L2

        8 VOUT

        7 PG

        6 FB

        5 AGND

        R7

        R8 C4

        R9

        U3

        MiCS-6814

        C5

        CO 1

        NH3 2

        NO2 3

        GND 4

        VCC 5

        The Arduino Integrated Development Environment (IDE) was used in the development of the software for the embedded system. The source code/sketch was written in a mixture of C++ and Arduino Processing Language.

        R4

        Battery NTC pin R1

        U1

        TPS63805

        LED 1 LED 2

        RED GREEN

        R5 R6

        Control 1

        Vout2 2

        VCC 3

        Vout1 4

        GND 5

        U4

        Input Power

        D. System Testing

        The components of the system were connected as shown in Fig 4, tested and confirmed to be working.

        C1

        TEMP 1

        PROG 2

        GND 3

        R2 R3 VCC 4

        8 CE

        7 CHRG

        6 STDBY

        5 BAT

        3.7V Output

        R12

        R13

        R10

        R11

        TP4056 C2

        AREF 1 28 5V

        A0 2 27 VIN

        A1 3 26 3V3

        A2 4 25 GND

        A3 5 24 RESET

        A4 6 23 D14/TX

        A5 7 22 D13/RX

        A6 8 21 D12/SCL

        D0 9 20 D11/SDA

        D1 10 19 D10/CIPO

        D2 11 18 D9/SCK

        D3 12 17 D8/COPI

        D4 13 16 D7

        D5 14 15 D6

        SW1

        BAT1

        R16

        R17

        R14 R15

        MKR 1010

        AREF 1 28 5V

        A0 2 27 VIN

        A1 3 26 3V3

        A2 4 25 GND

        A3 5 24 RESET

        A4 6 23 D14/TX

        A5 7 22 D13/RX

        A6 8 21 D12/SCL

        D0 9 20 D11/SDA

        D1 10 19 D10/CIPO

        D2 11 18 D9/SCK

        D3 12 17 D8/COPI

        D4 13 16 D7

        D5 14 15 D6

        MKR 1400

        SW-SPDT

        Fig. 2. Circuit Diagram of the air quality data capturing node.

    3. Software System Design

    An embedded system is incomplete without the software component. This is because it is the software the governs the operation of the system. Hence, the system is programmed according to

    the flow chart of the Fig 3 is the algorithm of the system under development.

    Fig. 3. Algorithm of the system.

    Fig. 4. Experimental set-up of the key components of the node.

  3. RESULTS AND DISCUSSIONS

    During testing, the AQ node successfully connected to available WiFi, bluetooth and mobile networks as designed. Subsequently, AQ data from the IoT node was successfully transmitted two different cloud databases. Specifically, data was logged to the Google Firebase Cloud database as shown in Fig 5 using WebSocket.

    In another test instant, AQ data from the IoT node was successfully logged to the ThingSpeak platform. The dashboard of the ThingSpeak channel (https://thingspeak.com/channels/146226) for the IoT node is shown in Fig 6 while Fig 7 shows a user-friendly dashboard developed in this work for easy visualization of AQ.

    Fig. 5. Snapshot of AQ data readings on the Firebase Database.

    Fig. 6. Dashboard of the ThingSpeak IoT platform.

    Fig. 7. A User-friendly dashboard for AQ.

    The ability of the IoT AQ node to transmit data securely to two different cloud-based databases shows interoperability. Furthermore, the successful tunneling and visualization of the AQ data on a different web application is a further prove of the interoperability inherent among the microservices.

    The results clearly proves that IoT nodes connected to any form of Internet ready wireless communication network (WLAN, Bluetooth, LoRa, ZigBee or 2G/3G/4G/5G) can be part of the IoT architecture. As a result of success of using two different communication protocols: REST over HTTP and WebSocket, it means any other protocol can still be used for data transmission to the cloud.

    The adoption of REST over HTTP of the ThingSpeak platform is good choice. In as much as data tunnelling through REST over HTTP is characterized by higher latency than Message Queue Telemetry Transport (MQTT), tunnelling AQ data using RESTFUL APIs require less bandwidth as per the findings of [4]. Hence, there is no shortfall to employing REST over HTTP because air quality monitoring is not considered mission critical. Thus, little latency is allowable.

    The format of the data stored on the ThingSpeak database is similar to that of [56]. This is because the data transmission protocol employed in the two studies are the same: HTTP (POST) request using necessary APIs. Here, interoperability was achieved through the tunnelling of AQ data as JavaScript Object Notation (JSON) objects to cloud centric databases. According to [60], cloud-based IoT applications help smart cities in gathering data. Thus, this work was geared towards making the world smart by employing microservices based cloud-based databases.

    For IoT development, cloud computing provides platforms for storing and processing device data [60]. The application of cloud computing is to make the IoT systems robust and cost effective. The adoption of cloud services ensure that IoT node data and services are available in real-time. Through cloud centric IoT applications, accurate data can be accessed and analyzed to assist experts, businesses, and people in making smarter policies to enhance the standard of peoples life.

    The realization of smart city is promoted through the integration and interoperation of IoT and cloud computing [4]. Through this, there is interactions among different components of the IoT ecosystem assist in improving performance, efficiency, QoS, and intelligent decision-making [61].

    Air quality monitoring is an essential feature of smart cities [4]. This approach of this work is in line with state-of-the art practice in IoT development. Remarkably, the ThingSpeak IoT

    platform employed in this work is among the notable Cloud- Based IoT Solutions mentioned by [60].

    The addition of artificial intelligence to the architecture proposed in this work covers the phase two (data tracking and analytics) and three (analysis of data using machine learning) stated among the six-phases of the start-up model for the development of a smart city by [60].

    The approach towards the implementation of the components of the IoT architecture in this work was simple as recommended by [61]. The reason for being simple was to create a foundation through which future development can be made and scalability of the entire IoT ecosystem being easy to do.

  4. CONCLUSION

This work has been able to achieve its primary objective of developing an adaptive IoT Node in Heterogenous Environments for Air Quality Monitoring as an enablement for the attainment of the SDGs. Specifically, an AQ node capable for connecting to the Internet using Wi-Fi, Bluetooth and mobile network (2G/3G) was proposed and prototyped. In addition, a web dashboard was developed based to serve as the user interface.

To this end, this research has made a remarkable effort towards the attainment of the United Nation Sustainable Development Goals (UN SDGs) by developing a system that can be easily deployed in many locations to measure air quality which is a key indicator to SDGs 3 and 11.

REFERENCES

[1] H. Zhao, Y. Zheng, and T. Li, Air quality and control measures evaluation during the 2014 Youth Olympic Games in Nanjing and its surrounding cities, Atmosphere (Basel)., vol. 8, no. 6, 2017, doi: 10.3390/atmos8060100.

[2] X. Li and L. Peng, Deep learning architecture for air quality predictions, Environ. Sci. Pollut. Res., 2016, doi: 10.1007/s11356-016- 7812-9.

[3] K. B. K. Sai, S. Mukherjee, and H. Parveen Sultana, Low Cost IoT Based Air Quality Monitoring Setup Using Arduino and MQ Series Sensors with Dataset Analysis, Procedia Comput. Sci., vol. 165, no. 2019, pp. 322327, 2019, doi: 10.1016/j.procs.2020.01.043.

[4] D. Múnera, V. Diana, J. Aguirre, and N. G. Gómez, IoT-based air quality monitoring systems for smart cities: A systematic mapping study, Int. J. Electr. Comput. Eng., vol. 11, no. 4, pp. 34703482, 2021, doi: 10.11591/ijece.v11i4.pp3470-3482.

[5] Y. Manzhu and L. Qian, Deep learning-based downscaling of tropospheric nitrogen dioxide using ground-level and satellite observations, Sci. Total Environ., vol. 773, no. 145145, 2021, doi: 10.1016/j.scitotenv.2021.145145.

[6] Yamunathangam, K. Pritheka, and P. Varuna, IoT enabled air pollution monitoring and awareness creation system, Int. J. Recent Technol. Eng., vol. 7, no. 4, pp. 398400, 2019.

[7] K. K. Khedo and V. Chikhooreeah, Low-Cost Energy-Efficient Air Quality Monitoring System Using Wireless Sensor Network, in Wireless Sensor Networks – Insights and Innovations, vol. i, no. tourism, 2016, p. 13.

[8] S. Neogi, Y. Galphat, P. Narkar, H. Punjabi, and M. Jethani, IoT Based Air Quality Monitoring Systems – A Survey, vol. 31. Springer International Publishing, 2020.

[9] H. A. Attia and H. Y. Ali, Electronic design of liquefied petroleum gas leakage monitoring, alarm, and protection system based on discrete components, Int. J. Appl. Eng. Res., vol. 11, no. 19, pp. 97219726, 2016.

[10] Y. Liang, Y. Maimury, and A. H. Chen, Machine Learning-Based Prediction of Air Quality, Appl. Sci., no. 2, pp. 117, 2020.

[11] Doreswamy, K. S. Harishkumar, K. M. Yogesh, and I. Gad, Forecasting Air Pollution Pollution Particulate Matter ( PM2. 5 ) Using Machine Learning Regression Models, Procedia Comput. Sci., vol. 171, no. 2019, pp. 20572066, 2020, doi: 10.1016/j.procs.2020.04.221.

[12] A. Suleiman, M. Tight, and A. Quinn, Applying machine learning methods in managing urban concentrations of traffic-related particulate, Atmos. Pollut. Res., 2021, doi: 10.1016/j.apr.2018.07.001.

[13] L. M. B. Ventura, F. de Oliveira Pinto, L. M. Soares, A. S. Luna, and A. Gioda, Evaluation of air quality in a megacity using statistics tools, Meteorol. Atmos. Phys., vol. 130, no. 3, pp. 361370, 2018, doi: 10.1007/s00703-017-0517-x.

[14] H. E. Obanya, N. H. Amaeze, O. Togunde, and A. A. Otitoloju, Air pollution monitoring around residential and transportation sector locations in Lagos Mainland, J. Heal. Pollut., vol. 8, no. 19, pp. 110, 2018, doi: 10.5696/2156-9614-8.19.180903.

[15] P. Gandotra and B. Lall, Evolving Air Pollution Monitoring Systems for Green 5G: From Cloud to Edge, in IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2020, pp. 12311235, doi: 10.1109/ICRITO48877.2020.9197950.

[16] H. Srivastava, S. Mishra, S. K. Das, and S. Sarkar, An IoT-Based Pollution Monitoring System Using Data Analytics Approach, in Electronic Systems and Intelligent Computing, Lecture Notes in Electrical Engineering, vol. 686, P. K. Mallick, P. Meher, A. Majumder, and S. K. Das, Eds. Springer, 2020.

[17] H. Mokrani, R. Lounas, M. T. Bennai, D. E. Salhi, and R. Djerbi, Air quality monitoring using IoT: A survey, in 2019 IEEE International Conference on Smart Internet of Things, SmartIoT 2019, 2019, pp. 127 134, doi: 10.1109/SmartIoT.2019.00028.

[18] P. Das, S. Ghosh, S. Chatterjee, and S. De, Energy Harvesting-enabled 5G Advanced Air Pollution Monitoring Device, 2020 IEEE 3rd 5G World Forum, 5GWF 2020 – Conf. Proc., pp. 218223, 2020, doi: 10.1109/5GWF49715.2020.9221330.

[19] Q. Liao, M. Zhu, L. Wu, X. Pan, X. Tang, and Z. Wang, Deep Learning for Air Quality Forecasts: a Review, Curr. Pollut. Reports, vol. 6, no. 2, pp. 399409, 2020, doi: 10.1007/s40726-020-00159-z AIR.

[20] WHO, Ambient (outdoor) air pollution, 2018. https://www.who.int/en/news-room/fact-sheets/detail/ambient- (outdoor)-air-quality-and-health (accessed Mar. 06, 2020).

[21] J. Saffell, Air quality in cities and the vital role of monitoring – Air Quality News, 2020. https://airqualitynews.com/2020/02/28/air- quality-in-cities-and-the-vital-role-of-monitoring/ (accessed Mar. 07, 2020).

[22] S. Kaivonen and E. C. H. Ngai, Real-time air pollution monitoring with sensors on city bus, Digit. Commun. Networks, vol. 6, no. 1, pp. 23 30, 2020, doi: 10.1016/j.dcan.2019.03.003.

[23] M. Lee, L. Lin, C. Chen, Y. Tsao, T. Yao, and M. Fei, Forecasting Air Quality in Taiwan by Using Machine Learning, Sci. Rep., pp. 113, 2020, doi: 10.1038/s41598-020-61151-7.

[24] C. Toma, A. Alexandru, M. Popa, and A. Zamfiroiu, IoT solution for smart cities pollution monitoring and the security challenges, Sensors (Switzerland), vol. 19, no. 15, 2019, doi: 10.3390/s19153401.

[25] S. Dhingra, R. B. Madda, A. H. Gandomi, R. Patan, and M. Daneshmand, Internet of things mobile-air pollution monitoring system (IoT-Mobair), IEEE Internet Things J., vol. 6, no. 3, pp. 55775584, 2019, doi: 10.1109/JIOT.2019.2903821.

[26] J. Saini, M. Dutta, and G. Marques, Indoor air quality monitoring systems based on internet of things: A systematic review, Int. J. Environ. Res. Public Health, vol. 17, no. 14, pp. 122, 2020, doi: 10.3390/ijerpp7144942.

[27] WEF, The Global Risks Report 2020 (15th Edition), 2020. Accessed: Mar. 07, 2020. [Online]. Available: https://apps.who.int/iris/bitstream/handle/10665/250141/978924151135 3-eng.pdf?sequence=1.

[28] N. Davis, Outdoor ai pollution cuts three years from human lifespan study | Environment | The Guardian, 2020. https://www.theguardian.com/environment/2020/mar/03/outdoor-air- pollution-cuts-three-years-from-human-lifespan-study (accessed Mar. 07, 2020).

[29] J. Carré, N. Gatimel, J. Moreau, J. Parinaud, and R. Léandri, Does air pollution play a role in infertility?: A systematic review, Environ. Heal. A Glob. Access Sci. Source, vol. 16, no. 82, Jul. 2017, doi: 10.1186/s12940-017-0291-8.

[30] Q. Li et al., Association between exposure to airborne particulate matter less than 2.5 m and human fecundity in China, Environ. Int., vol. 146, Jan. 2021, doi: 10.1016/j.envint.2020.106231.

[31] C. Burke, Infertility risk linked to air pollution – BioNews, 2021. https://www.bionews.org.uk/page_154886 (accessed Jun. 13, 2021).

[32] D. Carrington, Air pollution significantly raises risk of infertility, study finds | Air pollution | The Guardian, Environment, Feb. 17, 2021.

[33] G. Marques, C. R. Ferreira, and R. Pitarma, Indoor Air Quality Assessment Using a CO2 Monitoring System Based on Internet of Things, J. Med. Syst., vol. 43, no. 3, 2019, doi: 10.1007/s10916-019- 1184-x.

[34] G. Marques and R. Pitarma, An indoor monitoring system for ambient assisted living based on internet of things architecture, Int. J. Environ. Res. Public Health, vol. 13, no. 11, 2016, doi: 10.3390/ijerpp3111152.

[35] G. Marques, J. Saini, M. Dutta, P. K. Singh, and W. C. Hong, Indoor air quality monitoring systems for enhanced living environments: A review toward sustainable smart cities, Sustain., vol. 12, no. 10, 2020, doi: 10.3390/SU12104024.

[36] D. Saravanan, K. S. Kumar, R. Sathya, and U. Palani, An Iot Based Air Quality Monitoring And Air Pollutant Level Prediction System Using Machine Learning Approach Dlmnn, vol. 13, no. 4, pp. 925945, 2020.

[37] G. K. Kang, J. Z. Gao, S. Chiao, S. Lu, and G. Xie, Air Quality Prediction: Big Data and Machine Learning Approaches, Int. J. Environ. Sci. Dev., vol. 9, no. 1, pp. 816, 2018, doi: 10.18178/ijesd.2018.9.1.1066.

[38] Z. Guan and R. O. Sinnott, Prediction of Air Pollution through Machine Learning Approaches on the Cloud, in IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT), 2018, pp. 5160, doi: 10.1109/BDCAT.2018.00015.

[39] J. Xing et al., Deep learning for prediction of the air quality response to emission changes, Environ. Sci. Technol., 2020, doi: 10.1021/acs.est.0c02923.

[40] N. Koyana, E. D. Markus, and A. M. Abu-Mahfouz, A Survey of Network and Intelligent air pollution monitoring in South Africa, 2019, doi: 10.1109/IMITEC45504.2019.9015916.

[41] E. Coker and S. Kizito, A Narrative Review on the Human Health Effects of Ambient Air Pollution in Sub-Saharan Africa: An Urgent Need for Health Effects Studies, Int J Env. Res Public Heal. 2, vol. 15, no. 3, p. 427, 2018, doi: 10.3390/ijerpp5030427.

[42] J. Whalley and S. Zandi, Particulate Matter Sampling Techniques and Data Modelling Methods, in Air Quality – Measurement and Modeling, 2016, pp. 2954.

[43] T. M. Behera, S. K. Mohapatra, U. C. Samal, M. S. Khan, M. Daneshmand, and A. H. Gandomi, I-SEP: An Improved Routing Protocol for Heterogeneous WSN for IoT-Based Environmental Monitoring, IEEE Internet Things J., vol. 7, no. 1, pp. 710717, 2020, doi: 10.1109/JIOT.2019.2940988.

[44] D. Ibaseta, J. Molleda, F. Díez, and J. C. Granda, An IoT platform for indoor air quality monitoring using the web of things, WIT Trans. Ecol. Environ., vol. 236, no. 2, pp. 4556, 2019, doi: 10.2495/AIR190051.

[45] L. Morawska et al., Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?, Environ. Int., vol. 116, no. April, pp. 286299, 2018, doi: 10.1016/j.envint.2018.04.018.

[46] B. Lyu, Y. Zhang, and Y. Hu, Improving PM2.5 air quality model forecasts in China using a bias-correction framework, Atmosphere (Basel)., vol. 8, no. 8, pp. 115, 2017, doi: 10.3390/atmos8080147.

[47] S. L. Ullo and G. R. Sinha, Advances in smart environment monitoring systems using IoT and sensors, Sensors (Switzerland), vol. 20, no. 11, 2020, doi: 10.3390/s20113113.

[48] A. Tiele, S. Esfahani, and J. Covington, Design and Development of a Low-Cost, Portable Monitoring Device for Indoor Environment Quality, 2018, doi: 10.1155/2018/5353816.

[49] P. Rana and N. Sikarwar, Air Pollution Monitoring using Mobile Phone in Real Time, Int. J. Innov. Res. Sci. Technol., vol. 5, no. 2, pp. 4954, 2018.

[50] T. W. Ayele and R. Mehta, Air pollution monitoring and prediction using IoT, in International Conference on Inventive Communication and Computational Technologies, ICICCT 2018, 2018, no. Icicct, pp. 17411745, doi: 10.1109/ICICCT.2018.8473272.

[51] Y. C. Wu et al., Air quality monitoring using mobile microscopy and machine learning, Light Sci. Appl., vol. 6, no. 9, 2017, doi: 10.1038/lsa.2017.46.

[52] Y. Cheng et al., AirCloud: A Cloud-based Air-Quality Monitoring System for Everyone, SenSys14, pp. 251265, 2014.

[53] R. Mumtaz et al., Internet of things (IoT) Based Indoor air quality sensing and predictive analytica covid-19 perspective, Electronics, vol. 10, no. 2, pp. 126, 2021, doi: 10.3390/electronics10020184.

[54] B. S. H. Mathur, G. K. Sudheer, M. Sanchana, C. Boddu, and J. Aravinth, High Resolution Air Pollution Mapping using Wireless Sensor Nodes, in 2020 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020, 2020, pp. 430435, doi: 10.1109/ICACCS48705.2020.9074396.

[55] P. Kalia and M. A. Ansari, IOT based air quality and particulate matter concentration monitoring system, Mater. Today Proc., vol. 32, no. xxxx, pp. 468475, 2020, doi: 10.1016/j.matpr.2020.02.179.

[56] C. H. Kok, M. A. M. Azlan, and S. E. Ong, IoT based Low Cost Distributed Air Quality Monitoring System for Big Data Collection, in 10th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2020, 2020, no. August, pp. 710, doi: 10.1109/ICCSCE50387.2020.9204954.

[57] J. Jo, B. Jo, J. Kim, S. Kim, and W. Han, Development of an IoT-Based Indoor Air Quality Monitoring Platform, J. Sensors, vol. 2020, p. 8749764, 2020, doi: 10.1155/2020/8749764.

[58] H. Gupta, D. Bhardwaj, H. Agrawal, V. A. Tikkiwal, and A. Kumar, An IoT Based Air Pollution Monitoring System for Smart Cities, 1st IEEE

Int. Conf. Sustain. Energy Technol. Syst. ICSETS 2019, no. February, pp. 173177, 2019, doi: 10.1109/ICSETS.2019.8744949.

[59] S. Kumar, P. Tiwari, and M. Zymbler, Internet of Things is a revolutionary approach for future technology enhancement: a review, J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0268-2.

[60] T. Alam, Cloud-Based IoT Applications and Their Roles in Smart Cities, Smart Cities, vol. 4, no. 3, pp. 11961219, 2021, doi: 10.3390/smartcities4030064.

[61] B. N. Silva, M. Khan, and K. Han, Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities, Sustain. Cities Soc., vol. 38, no. August 2017, pp. 697 713, 2018, doi: 10.1016/j.scs.2018.01.053.

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