Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization
2. Related Work
3. Underlaying Concepts
3.1. Internet of Things (IoT)
3.2. Smart Health
- Kaa IoT is a versatile, multifunctional, open-source platform for the implementation of complete IoT solutions, connected applications, and intelligent products. Kaa has professional-grade IoT functions that can be connected and used to implement a vast majority of IoT use cases .
- Vista data vision is a comprehensive software solution perfect for data management and visualization environmental monitoring projects. Data are sent from the data logger to a server, where they are automatically converted into a system-compatible format and imported into the database. Then, the data can be viewed on the web-based interface .
- InteliS integrates the most critical devices and machines in order to find solutions for remote diagnostics, preventive maintenance, resource optimization, quality improvement, or delivery time reduction .
- Remote supervision. Gadgets can measure the patient’s vital parameters and the corresponding data could be associated with the user’s profile, where nurses and doctors can access them, analyze, and give feedback.
3.3. Air Quality Index
- Portable particulate monitor PM 10/PM 2.5 ;
- Huma-i HI-150 (advanced portable air quality monitor indoor/outdoor that measures CO2, volatile organic compounds (VOC), particle matter, temperature, and humidity);
- “Air quality meter” application that can measure the PM10 in an outdoor environment using a smartphone camera and many others.
4. System Architecture
- A module for the collection of data from the environment equipped with several sensors that allow the monitoring of the following parameters: relative humidity, air temperature, atmospheric pressure, suspended dust concentrations, and concentrations of gaseous pollutants (SO2, NO2, CO, CO2, VOC).
- A multiprotocol gateway module that allows for collection of the data from the sensors and ensures their transmission to the cloud through Ethernet/4G/3G/GPRS communication protocols .
- An intermediate data transmission component that operates as the communication medium between the gateway and the data persistence level possible.
- The decision support component , which is a module for processing the data collected from the sensors. The result of the decision support component analysis is transmitted further to the data presentation and visualization mode.
- A data visualization module: this module is a UT, where the information can be observed . The data displayed include the values of monitored pollutants, contextual messages for the general and sensitive population, and a chart with the values registered in the last hours.
- Sensors and instruments for measuring chemical compounds in the atmosphere and weather sensors . The developed web application queries these sensors through a set of Application Programming Interfaces (APIs) that model a Representational State Transfer (REST) mechanism. Although some models are portable, due to the need to report a measurement in a particular area, the environment sensors are defined as a fixed type. They have an associated area in the system that can be an indoor location or a surface on a map.
- Garmin IoT Wearable bracelets are utilized for measuring the factors related to the physical and health status of the wearer . These sensors are defined as mobile and are represented by the combination of a bracelet and a smartphone. The bracelet monitors the vital parameters of a user and transmits these data via BLE to the Garmin app installed on the smartphone. Through an API, the platform loads the data provided by the Garmin mobile application.
- Smartphones for detecting and reporting the presence of a user in an outdoor area using Global Positioning System (GPS) technology or in an indoor area using BLE beacons [40,41]. These sensors are also defined in the platform as mobile. In this case, the sensor is the smartphone that sends information about the presence, proximity, and location on the platform. The user needs to download the mobile application and activate it. Next, the user can go with it to various locations. The platform uploads the data provided by the mobile app through an API.
- In addition to the values measured by sensors, the developed platform will be also able to use data from an external source (through specialized interfaces) such as files exported from other platforms and applications.
5. Developed Decision Support Component
5.1. Algorithm Description
- The main element that is responsible for the color visualization. It is provided by the pollutant with the highest concentration compared to the limit values;
- The secondary component is responsible for the color intensity. This component is determined by the levels of the other pollutants.
5.2. Decision Support Component Implementation
- Extracting data from GM Meshlium’s MySQL database :
- The query for connection verification with the GW database;
- The query for retrieving values (PM10, PM2.5, and NO2 parameters)  and calculating the moving average.
- Application of the air quality index calculation algorithm.
- Storing the values calculated above.
- Displaying the AQI graph for the last 24 h and the decision support component.
5.3. Experimental Results
Conflicts of Interest
- World Health Organization. Air Pollutions. Available online: https://www.who.int/airpollution/infographics/en/ (accessed on 15 February 2020).
- Suciu George, S.G.; Pasat Adrian, P.A.; Balaceanu, B.C.; Manolescu, M.L.; Preda, P.M. IoT platform for enhancing the quality of life: ESTABLISH. Med. A J. Clin. Med. 2018, 13. [Google Scholar] [CrossRef]
- Olshannikova, E.; Ometov, A.; Koucheryavy, Y. Towards Big Data Visualization for Augmented Reality. In Proceedings of the IEEE 16th Conference on Business Informatics, Washington, DC, USA, 14 July 2014; Volume 2, pp. 33–37. [Google Scholar]
- Xing, Y.-F.; Xu, Y.-H.; Shi, M.-H.; Lian, E.-X. The impact of PM2.5 on the human respiratory system. J. Thorac. Dis. 2016, 8, 69–74. [Google Scholar]
- Zhe Yang, Z.Y.; Qihao Zhou, Q.Z.; Lei, L.; Kan Zheng, K.Z.; Wei Xiang, W.X. An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare. J. Med. Syst. 2016, 40, 286. [Google Scholar] [CrossRef]
- Rodriguez, J.P.C.; De Rezende Segundo, D.B.; Junqueira, H.A.; Sabino, M.H.; Prince, R.M.; Al-Muhtadi, J.; De Albuquerque, C.V.H. Enabling Technologies for the Internet of Health Things. IEEE Access 2018, 6, 13129–13141. [Google Scholar] [CrossRef]
- Godoi, B.; Amorim, G.; Quiroga, D.; Holanda, V.; Júlio, T.; Tournier, M. Parkinson’s Disease and Wearable Devices, New Perspectives for a Public Health Issue: An Integrative Literature Review. Rev. Assoc. Méd. Bras. 2019, 65, 1413–1420. [Google Scholar] [CrossRef][Green Version]
- Mahoney, E.; Mahoney, D. Acceptance of Wearable Technology by People with Alzheimer’s Disease: Issues and Accommodations. Am. J. Alzheimer’s Dis. Other Dementias. 2010, 25, 527–531. [Google Scholar] [CrossRef]
- Shah, S.-A.; Fan, D.; Zhao, N.; Yang, X.; Tanoli, S. Seizure Episodes Detection via Smart Medical Sensing System. J. Ambient Intell. Human. Comput. 2018, 1–13. [Google Scholar] [CrossRef][Green Version]
- Sodhro, A.; Kumar, A.; Id, H.; Lohano, S.; Pirbhulal, S. An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications. Sensors 2018, 18, 923. [Google Scholar] [CrossRef][Green Version]
- Muzammal, M.; Talat, R.; Sodhro, A.H.; Pirbhulal, S. A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks. Inf. Fusion 2020, 53, 155–164. [Google Scholar] [CrossRef]
- Pirbhulal, S.; Pombo, N.; Felizardo, V.; Garcia, N.; Sodhro, A.H.; Mukhopadhyay, S.C. Towards Machine Learning Enabled Security Framework for IoT-based Healthcare. In Proceedings of the 2019 13th International Conference on Sensing Technology (ICST), Sydney, Australia, 2–4 December 2019; pp. 1–6. [Google Scholar]
- Environmental Protection Agency. 40 CFR Part 58, Air Quality Index Reporting; Final Rule. Available online: https://www.airnow.gov/sites/default/files/2018-06/air-quality-index-reporting-final-rule.pdf (accessed on 18 February 2020).
- Taieb, D.; Brahim, A.B. Methodology for Developing an Air Quality Index (AQI) for Tunisia. Int. J. Renew. Energy Technol. 2013, 4, 86. [Google Scholar] [CrossRef]
- World’s Air Pollution: Real-time Air Quality Index. Available online: https://waqi.info/#/c/22.928/-13.175/2.4z (accessed on 18 February 2020).
- Pan, S.; Du, S.; Wang, X.; Zhang, X.; Xia, L.; Jiaping, L.; Pei, F.; Wei, Y. Analysis and Interpretation of the Particulate Matter (PM10 and PM2.5) Concentrations at the Subway Stations in Beijing, China. Sustain. Cities Soc. 2018, 45, 366–377. [Google Scholar] [CrossRef]
- Carteni, A.; Cascetta, F. Particulate Matter Concentrations in a High-Quality Rubber-Tyred Metro System: The Case Study of Turin in Italy. Int. J. Environ. Sci. Technol. 2018, 15, 1921–1930. [Google Scholar] [CrossRef]
- Suciu, G.; Bălănescu, M.; Birdici, A.; Orza, O.; Pasat, A.; Dobrea, M.A.; Bălăceanu, C.M. Assessment of Particulate Matter Concentration in Underground Transport Work EnvironmentIn. In Proceedings of the Air and Water—Components of the Environment Conference, Cluj-Napoca, Romania, 22–24 March 2019; pp. 37–46. [Google Scholar]
- Barmparesos, N.; Assimakopoulos, V.; Assimakopoulos, M.; Tsairidi, E. Particulate Matter Levels and Comfort Conditions in the Trains and Platforms of the Athens Underground Metro. Environ. Sci. 2016, 3, 199–219. [Google Scholar] [CrossRef]
- Nigam, S.; Rao, B.; Kumar, N.; Mhaisalkar, V. Air Quality Index—A Comparative Study for Assessing the Status of Air Quality. Res. J. Eng. Technol. 2015, 6, 267–274. [Google Scholar] [CrossRef]
- Analysis of Air Quality Index. Available online: https://www.researchgate.net/publication/327160642_Analysis_of_Air_Quality_Index (accessed on 25 March 2020).
- Benefits of Internet of Things (IoT) Solutions—IQVIS. Available online: https://iqvisblog.wordpress.com/2017/10/23/4-benefits-of-internet-of-things-iot-solutions/ (accessed on 18 February 2020).
- Suciu George, S.G.; Pasat Adrian, P.A.; Balaceanu, B.M.; Nadrag Carmen, N.C. Multi-source cloud platform for enhancing the quality of life. SGEM 2018, 18, 523–529. [Google Scholar]
- Oleg Chertov, O.C.; Tymofiy Mylovanov, T.M.; Yuriy Kondratenko, Y.K.; Janusz Kacprzyk, J.K.; Vladik Kreinovich, V.K.; Vadim Stefanuk, V.S. Recent Developments in Data Science and Intelligent Analysis of Information. In Proceedings of the XVIII International Conference on Data Science and Intelligent Analysis of Information, Kyiv, Ukraine, 4–7 June; 2018; 391. [Google Scholar]
- Colombo, A.B.C.; Bittencourt, T.N. Development of a Web Interface for Structural Health Monitoring Data Visualization and Structural Performance Assessment. In Maintenance, Monitoring, Safety, Risk and Resilience of Bridges and Bridge Networks; CRC Press: Boca Raton, FL, USA, 2016. [Google Scholar]
- Ometov, A.; Solomitckii, D.; Olsson, T.; Bezzateev, S.; Shchesniak, A.; Andreev, S.; Harju, J.; Koucheryavy, Y. Secure and Connected Wearable Intelligence for Content Delivery at a Mass Event: A Case Study. J. Sens. Actuator Netw. 2017, 6, 5. [Google Scholar] [CrossRef][Green Version]
- Ometov, A.; Kozyrev, D.; Rykov, V.; Andreev, S.; Gaidamaka, Y.; Koucheryavy, Y. Reliability-Centric Analysis of Offloaded Computation in Cooperative Wearable Applications. Wirel. Commun. Mob. Comput. 2017, 2017, 9625687. [Google Scholar] [CrossRef][Green Version]
- El-Gayar, O.F.; Ambati, L.S.; Nawar, N. Wearables, Artificial Intelligence, and the Future of Healthcare. In AI and Big Data’s Potential for Disruptive Innovation; IGI Global: Hershey, PA, USA, 2020; pp. 104–129. [Google Scholar]
- George Suciu, G.S.; Cristina Butca, C.B.; Adelina Ochian, A.O.; Simona Halunga, S.H. Wearable Sensors for Health Monitoring. ATOMN 2015, 39, 141. [Google Scholar]
- Karan Gupta, K.G.; Nitin Rakesh, N.R.; Neetu Faujdar, N.F.; Nidhi Gupta, N.G. IoT Based Solution for Automation of Hospital Activities with High Authentication; Elsevier: London, UK, 2020. [Google Scholar]
- Smart Health and IoT. Available online: https://medium.com/@iotap/smart-health-and-iot-68125f95c405 (accessed on 18 February 2020).
- NowCast Calculator. USEPA. Available online: https://www3.epa.gov/airnow/aqicalctest/nowcast.htm (accessed on 18 February 2020).
- How is the NowCast Algorithm Used to Report Current Air Quality? Available online: https://www.airnow.gov/faqs/how-nowcast-algorithm-used-report/ (accessed on 29 March 2020).
- Samir Lemeš, S.L. Air Quality Index (AQI)—Comparative Study and Assesment of an Appropiate Model for B&H. In Proceedings of the 12th Scientific/Research Symposium with International Participation “METALLIC AND NONMETALLIC MATERIALS” B&H, Vlašić, Bosnia and Herzegovina, 19–20 April 2018. [Google Scholar]
- Services to Develop an EU Air Quality Index. Available online: https://ec.europa.eu/environment/air/pdf/Air%20quality%20index_final%20report.pdf (accessed on 25 February 2020).
- Aileni, R.M.; Suciu, G.; Suciu, V.; Pasca, S.; Strungaru, R. Health Monitoring Using Wearable Technologies and Cognitive Radio for IoT. In Cognitive Radio, Mobile Communications and Wireless Network; Springer: New York, NY, USA, 2018; pp. 143–165. [Google Scholar]
- Yang, D.; Li, X.; J.Jiao, R.; Wang, B. Decision Support to Product Configuration Considering Component Replenishment Uncertainty: A Stochastic Programming Approach; Elsevier: London, UK, 2018. [Google Scholar]
- Riazul Islam, S.M.; Kwak, D.; Kabir, M.H.; Hossain, M.; Kwak, K.-S. The Internet of Things for Health Care: A Comprehensive Survey. IEEE Access 2015, 3, 678–708. [Google Scholar] [CrossRef]
- Szulczyński, B.; Gębicki, J. Currently Commercially Available Chemical Sensors Employed for Detection of Volatile Organic Compounds in Outdoor and Indoor Air. Environments 2017, 4, 21. [Google Scholar] [CrossRef][Green Version]
- Alqhatani, A.A. Understanding and Designing for Privacy in Wearable Fitness Platforms. ICSGW 2020, 39–43. [Google Scholar]
- Ta, V.-C.; Dao, T.K.; Vaufreydaz, D.; Castelli, E. Collaborative Smartphone-Based User Positioning in a Multiple-User Context Using Wireless Technologies. Sensors 2020, 20, 405. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Tapashetti, A.; Vegiraju, D.; Ogunfunmi, A. IoT-Enabled Air Quality Monitoring Device: A Low-Cost Smart Health Solution. In Proceedings of the 2016 IEEE Global Humanitarian Technology Conference (GHTC), Seattle, WA, USA, 13–16 October 2016; pp. 682–685. [Google Scholar]
- Budid, D.A.; Mangrulkar, R.S. Design and Implementation of Smart HealthCare System Using IoT. In Proceedings of the 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, 17–18 March 2017; pp. 1–7. [Google Scholar]
- Harun Al Rasyid, M.U.; Nadhori, I.U.; Alnovinda, Y.T. CO and CO2 Pollution Monitoring Based on Wireless Sensor Network. In Proceedings of the 2015 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES), Bali, Indonesia, 3–5 December 2015; pp. 1–5. [Google Scholar]
- Review of the UK Air Quality Index A report by the Committee on the Medical Effects of Air Pollutants. Available online: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/304633/COMEAP_review_of_the_uk_air_quality_index.pdf (accessed on 25 February 2020).
|35.5||55.4||101||150||Unhealthy for Sensitive Groups|
|Class||Pollutant Concentration (µg/m3)|
|Class||Pollutant Concentration (µg/m3)|
|People in Danger||General Population|
|Enjoy your usual outdoor activities.||Enjoy your usual outdoor activities.|
|Adults and children with lung problems and adults with heart problems, who experience symptoms, should consider reducing strenuous physical activity, particularly outdoors.||Enjoy your usual outdoor activities.|
|Adults and children with lung problems and adults with heart problems should reduce strenuous physical exertion, particularly outdoors, and particularly if they experience symptoms. People with asthma may find they need to use their reliever inhaler more often. Older people should also reduce physical exertion.||Anyone experiencing discomforts such as sore eyes, cough, or sore throat should consider reducing activity, particularly outdoors.|
|Adults and children with lung problems, adults with heart problems, and older people should avoid strenuous physical activity. People with asthma may find they need to use their reliever inhaler more often.||Reduce physical exertion, particularly outdoors, especially if you experience symptoms such as cough or sore throat.|
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Svertoka, E.; Bălănescu, M.; Suciu, G.; Pasat, A.; Drosu, A. Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization. Sensors 2020, 20, 5931. https://doi.org/10.3390/s20205931
Svertoka E, Bălănescu M, Suciu G, Pasat A, Drosu A. Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization. Sensors. 2020; 20(20):5931. https://doi.org/10.3390/s20205931Chicago/Turabian Style
Svertoka, Ekaterina, Mihaela Bălănescu, George Suciu, Adrian Pasat, and Alexandru Drosu. 2020. "Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization" Sensors 20, no. 20: 5931. https://doi.org/10.3390/s20205931