A working person on an average spends 1.5–2 h every day traveling either to their places of work or for other daily activities, using metros, trams, buses, and cars, as common modes of travel. Most of such commuters regularly suffer from health conditions like headache, breathless condition, drowsiness, etc. Numerous accidents have been reported due to drowsiness while driving, which may occur due to the build-up of carbon-dioxide (CO2) build in the vehicle chamber. This paper attempts to monitor, analyze, and predict air quality inside the vehicle. This work proposes a sensing system using an off-the-shelf sensor Sensordrone which is connected to an Android Smartphone using Bluetooth Low Energy. The data obtained from the proposed sensing system are then utilized to perform predictive analysis of CO2 build-up inside the vehicular chamber using Auto Regressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR). Root-Mean-Square Error for SVR and ARIMA models is 47.91 ppm and 55.32 ppm CO2, respectively, indicating that SVR outperformed ARIMA in predicting the CO2 build-up inside the vehicle.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Aaafoundation (2019) American driving survey, 2014–2017. https://aaafoundation.org/american-driving-survey-2014-2017/. Accessed 10 Jan 2021
Arndt M, Sauer M (2005) Infrared carbon dioxide sensor and its applications in automotive air-conditioning systems. In: Valldorf J, Gessner W (eds) Advanced microsystems for automotive applications 2005. Advanced microsystems for automotive applications. Springer, Berlin. https://doi.org/10.1007/3-540-27463-4_24
Atkinson WJ, Hill WR, Mathur GD (2017) The impact of increased air recirculation on interior cabin air quality. SAE Tech Pap Ser. https://doi.org/10.4271/2017-01-0169
Fruin SA, Hudda N, Sioutas C, Delfino RJ (2011) Predictive model for vehicle air exchange rates based on a large representative sample. Environ Sci Technol 45(8):3569–3575. https://doi.org/10.1021/es103897u
Fu X (2019) In-vehicle exposures at transportation and the health concerns. Indoor Environ Qual Health Risk Healthier Environ . https://doi.org/10.1007/978-981-32-9182-9_6
Gładyszewska-Fiedoruk K, Teleszewski TJ (2020) Modeling of humidity in passenger cars equipped with mechanical ventilation. Energies 13(11):2987. https://doi.org/10.3390/en13112987
Grady ML, Jung H, Kim Y, Park JK, Lee BC (2013) Vehicle cabin air quality with fractional air recirculation. SAE Tech Pap Ser. https://doi.org/10.4271/2013-01-1494
Huber J, Weber C, Eberhardt A, Wöllenstein J (2016) Photoacoustic CO2-sensor for automotive applications. Proc Eng 168:3–6. https://doi.org/10.1016/j.proeng.2016.11.111
Hyundai controls CO2 level inside Genesis cabin (2018) https://www.sae.org/news/2014/11/hyundai-controls-co2-level-inside-genesis-cabin. Accessed 10 Jan 2021
Jain S (2017) Exposure to in-vehicle respirable particulate matter in passenger vehicles under different ventilation conditions and seasons. Sustain Environ Res 27(2):87–94. https://doi.org/10.1016/j.serj.2016.08.006
Jung H (2013) Modeling CO2 concentrations in vehicle cabin. SAE Tech Pap Ser. https://doi.org/10.4271/2013-01-1497
Laussmann D, Helm D (2011) air change measurements using tracer gases methods and results. Significance of air change for indoor air quality. Chem Emiss Control Radioact Pollut Indoor Air Qual. https://doi.org/10.5772/18600
Lee ES, Zhu Y (2014) Application of a high-efficiency cabin air filter for simultaneous mitigation of ultrafine particle and carbon dioxide exposures inside passenger vehicles. Environ Sci Technol. https://doi.org/10.1021/es404952q
Lohani D, Acharya D (2016) Real time in-vehicle air quality monitoring using mobile sensing. IEEE Ann India Conf INDICON. https://doi.org/10.1109/indicon.2016.7839099
Lohani D, Barthwal A, Acharya D (2018) Predictive modelling of in-vehicle CO2 concentration using sensor data analytics. IEEE Sens 2018:1–4. https://doi.org/10.1109/icsens.2018.8589883
Lu X, Lu T, Viljane M (2011) Estimation of space air change rates and CO2 generation rates for mechanically-ventilated buildings. Adv Comput Sci Eng. https://doi.org/10.5772/16062
Luo A, Li X, Li Y, Li J (2018) Application of accurate online support vector regression in atmospheric SO2 concentration prediction. Chin Control Decis (CCDC). https://doi.org/10.1109/ccdc.2018.8408231
Micucci D, Corno F (2019) Reliability on pervasive well-being: will it soon become a reality? J Reliab Intell Environ 5(3):129–130. https://doi.org/10.1007/s40860-019-00087-w
Nishi Y (1981) Chapter 2 measurement of thermal balance of man. Stud Environ Sci. https://doi.org/10.1016/s0166-1116(08)71079-3
Ott W, Klepeis N, Switzer P (2007) Air change rates of motor vehicles and in-vehicle pollutant concentrations from secondhand smoke. J Eposure Sci Environ Epidemiol 18(3):312–325. https://doi.org/10.1038/sj.jes.7500601
Palumbo F, La Rosa D, Ferro E, Bacciu D, Gallicchio C, Micheli A, Chessa S, Vozzi F, Parodi O (2017) Reliability and human factors in ambient assisted living environments. J Reliab Intell Environ 3(3):139–157. https://doi.org/10.1007/s40860-017-0042-1
Qi C, Stanley N, Pui DYH, Kuehn TH (2008) Laboratory and on-road evaluations of cabin air filters using number and surface area concentration monitors. Environ Sci Technol 42(11):4128–4132. https://doi.org/10.1021/es703216c
Rastogi K, Lohani D (2020) An internet of things framework to forecast indoor air quality using machine learning. Commun Comput Inf Sci. https://doi.org/10.1007/978-981-15-4301-2_8
Satish U, Mendell MJ, Shekhar K, Hotchi T, Sullivan D, Streufert S, Fisk WJ (2012) Is CO2 an indoor pollutant? Direct effects of low-to-moderate CO2 concentrations on human decision-making performance. Environ Health Perspect 120(12):1671–1677. https://doi.org/10.1289/ehp.1104789
SELTOS | Inspired by the Badass in You (2019) https://www.kia.com/in/our-vehicles/seltos/showroom.html. Accessed 10 Jan 2021
Sensordrone: The 6th Sense of Your Smartphone...& Beyond! (2013) https://www.kickstarter.com/projects/453951341/sensordrone-the-6th-sense-of-your-smartphoneand-be. Accessed 10 Jan 2021
Siris VA, Fotiou N, Mertzianis A, Polyzos GC (2019) Smart application-aware IoT data collection. J Reliab Intell Environ 5(1):17–28. https://doi.org/10.1007/s40860-019-00077-y
Taneja K, Ahmad S, Ahmad K, Attri SD (2016) Time series analysis of aerosol optical depth over New Delhi using Box-Jenkins ARIMA modeling approach. Atmos Pollut Res 7(4):585–596. https://doi.org/10.1016/j.apr.2016.02.004
United States Environmental Protection Agency (US EPA) (1991) Introduction to indoor air quality. A reference manual
Vande JD, Sonderfeld H, Jeanjean APR, Panchal R, Leigh RJ, Allen MA, Monks PS (2018) Experimental and modeling assessment of a novel automotive cabin PM removal system. Aerosol Sci Technol 52(11):1249–1265. https://doi.org/10.1080/02786826.2018.1490694
Wang H, Li C (2018) Distributed quantile regression over sensor networks. IEEE Trans Signal Inf Process Netw 4(2):338–348. https://doi.org/10.1109/tsipn.2017.2699923
World Health Organization (2010) Regional Office for Europe. WHO guidelines for indoor air quality: selected pollutants. World Health Organization. Regional Office for Europe. https://apps.who.int/iris/handle/10665/260127. Accessed 10 Jan 2021
Xu X, Duan L (2017) Predicting crash rate using logistic quantile regression with bounded outcomes. IEEE Access 5:27036–27042. https://doi.org/10.1109/access.2017.2773612
Zhu JY, Sun C, Li VOK (2017) An extended spatio-temporal granger causality model for air quality estimation with heterogeneous urban big data. IEEE Trans Big Data 3(3):307–319. https://doi.org/10.1109/tbdata.2017.2651898
Zhu Y, Eiguren-Fernandez A, Hinds WC, Miguel AH (2007) In-cabin commuter exposure to ultrafine particles on Los Angeles freeways. Environ Sci Technol 41(7):2138–2145. https://doi.org/10.1021/es0618797
Zulauf N, Dröge J, Klingelhöfer D, Braun M, Oremek GM, Groneberg DA (2019) Indoor air pollution in cars: an update on novel insights. Int J Environ Res Public Health 16(13):2441. https://doi.org/10.3390/ijerph1613244
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Lohani, D., Barthwal, A. & Acharya, D. Modeling vehicle indoor air quality using sensor data analytics. J Reliable Intell Environ (2021). https://doi.org/10.1007/s40860-021-00137-2
- Carbon dioxide
- Mobile sensing
- Support vector regression
- Vehicle indoor air quality