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Andrade P, Silva I, Silva M, Flores T, Cassiano J, Costa DG. A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions. SENSORS 2022; 22:s22103838. [PMID: 35632247 PMCID: PMC9143421 DOI: 10.3390/s22103838] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/08/2022] [Accepted: 05/14/2022] [Indexed: 01/18/2023]
Abstract
Vehicles are the major source of air pollution in modern cities, emitting excessive levels of CO2 and other noxious gases. Exploiting the OBD-II interface available on most vehicles, the continuous emission of such pollutants can be indirectly measured over time, although accuracy has been an important design issue when performing this task due the nature of the retrieved data. In this scenario, soft-sensor approaches can be adopted to process engine combustion data such as fuel injection and mass air flow, processing them to estimate pollution and transmitting the results for further analyses. Therefore, this article proposes a soft-sensor solution based on an embedded system designed to retrieve data from vehicles through their OBD-II interface, processing different inputs to provide estimated values of CO2 emissions over time. According to the type of data provided by the vehicle, two different algorithms are defined, and each follows a comprehensive mathematical formulation. Moreover, an unsupervised TinyML approach is also derived to remove outliers data when processing the computed data stream, improving the accuracy of the soft sensor as a whole while not requiring any interaction with cloud-based servers to operate. Initial results for an embedded implementation on the Freematics ONE+ board have shown the proposal’s feasibility with an acquisition frequency equal to 1Hz and emission granularity measure of gCO2/km.
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Affiliation(s)
- Pedro Andrade
- Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil; (M.S.); (T.F.); (J.C.)
- Correspondence: (P.A.); (I.S.)
| | - Ivanovitch Silva
- Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil; (M.S.); (T.F.); (J.C.)
- Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil
- Correspondence: (P.A.); (I.S.)
| | - Marianne Silva
- Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil; (M.S.); (T.F.); (J.C.)
| | - Thommas Flores
- Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil; (M.S.); (T.F.); (J.C.)
| | - Jordão Cassiano
- Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil; (M.S.); (T.F.); (J.C.)
| | - Daniel G. Costa
- INEGI, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal;
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Olson DA, Riedel TP, Offenberg JH, Lewandowski M, Long R, Kleindienst TE. Quantifying wintertime O 3 and NO x formation with relevance vector machines. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2021; 259:1-118538. [PMID: 34385886 PMCID: PMC8353961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
This paper uses a machine learning model called a relevance vector machine (RVM) to quantify ozone (O3) and nitrogen oxides (NOx) formation under wintertime conditions. Field study measurements were based on previous work described by Olson et al. (2019), where continuous measurements were reported from a wintertime field study in Utah. RVMs were formulated using either O3 or nitrogen dioxide (NO2) as the output variable. Values of the correlation coefficient (r2) between predicted and measured concentrations were 0.944 for O3 and 0.931 for NO2. RVMs are constructed from the observed measurements and result in sparse model formulations, meaning that only a subset of the data is used to approximate the entire dataset. For this study, the RVM with O3 as the output variable used only 20% of the measurement data while the RVM with NO2 used 16%. RVMs were then used as a predictive model to assess the importance of individual precursors. Using O3 as the output variable, increases in three species resulted in increased O3 concentrations: hydrogen peroxide (H2O2), dinitrogen pentoxide (N2O5), and molecular chlorine (Cl2). For the two termination products measured during the study, nitric acid (HNO3) and formic acid (CH2O2), no change in O3 concentration was observed. Using NO2 as the output variable, only increases in N2O5 resulted in increased NO2 concentrations.
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De Vito S, Esposito E, Massera E, Formisano F, Fattoruso G, Ferlito S, Del Giudice A, D’Elia G, Salvato M, Polichetti T, D’Auria P, Ionescu AM, Di Francia G. Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation. SENSORS 2021; 21:s21155219. [PMID: 34372456 PMCID: PMC8348778 DOI: 10.3390/s21155219] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 07/20/2021] [Indexed: 11/16/2022]
Abstract
A pervasive assessment of air quality in an urban or mobile scenario is paramount for personal or city-wide exposure reduction action design and implementation. The capability to deploy a high-resolution hybrid network of regulatory grade and low-cost fixed and mobile devices is a primary enabler for the development of such knowledge, both as a primary source of information and for validating high-resolution air quality predictive models. The capability of real-time and cumulative personal exposure monitoring is also considered a primary driver for exposome monitoring and future predictive medicine approaches. Leveraging on chemical sensing, machine learning, and Internet of Things (IoT) expertise, we developed an integrated architecture capable of meeting the demanding requirements of this challenging problem. A detailed account of the design, development, and validation procedures is reported here, along with the results of a two-year field validation effort.
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Affiliation(s)
- Saverio De Vito
- ENEA CR-Portici, TERIN-FSD Division, P. le E. Fermi 1, 80055 Portici, Italy; (E.M.); (F.F.); (G.F.); (S.F.); (A.D.G.); (G.D.); (M.S.); (T.P.); (G.D.F.)
- Correspondence: (S.D.V.); (E.E.)
| | - Elena Esposito
- ENEA CR-Portici, TERIN-FSD Division, P. le E. Fermi 1, 80055 Portici, Italy; (E.M.); (F.F.); (G.F.); (S.F.); (A.D.G.); (G.D.); (M.S.); (T.P.); (G.D.F.)
- Correspondence: (S.D.V.); (E.E.)
| | - Ettore Massera
- ENEA CR-Portici, TERIN-FSD Division, P. le E. Fermi 1, 80055 Portici, Italy; (E.M.); (F.F.); (G.F.); (S.F.); (A.D.G.); (G.D.); (M.S.); (T.P.); (G.D.F.)
| | - Fabrizio Formisano
- ENEA CR-Portici, TERIN-FSD Division, P. le E. Fermi 1, 80055 Portici, Italy; (E.M.); (F.F.); (G.F.); (S.F.); (A.D.G.); (G.D.); (M.S.); (T.P.); (G.D.F.)
| | - Grazia Fattoruso
- ENEA CR-Portici, TERIN-FSD Division, P. le E. Fermi 1, 80055 Portici, Italy; (E.M.); (F.F.); (G.F.); (S.F.); (A.D.G.); (G.D.); (M.S.); (T.P.); (G.D.F.)
| | - Sergio Ferlito
- ENEA CR-Portici, TERIN-FSD Division, P. le E. Fermi 1, 80055 Portici, Italy; (E.M.); (F.F.); (G.F.); (S.F.); (A.D.G.); (G.D.); (M.S.); (T.P.); (G.D.F.)
| | - Antonio Del Giudice
- ENEA CR-Portici, TERIN-FSD Division, P. le E. Fermi 1, 80055 Portici, Italy; (E.M.); (F.F.); (G.F.); (S.F.); (A.D.G.); (G.D.); (M.S.); (T.P.); (G.D.F.)
| | - Gerardo D’Elia
- ENEA CR-Portici, TERIN-FSD Division, P. le E. Fermi 1, 80055 Portici, Italy; (E.M.); (F.F.); (G.F.); (S.F.); (A.D.G.); (G.D.); (M.S.); (T.P.); (G.D.F.)
| | - Maria Salvato
- ENEA CR-Portici, TERIN-FSD Division, P. le E. Fermi 1, 80055 Portici, Italy; (E.M.); (F.F.); (G.F.); (S.F.); (A.D.G.); (G.D.); (M.S.); (T.P.); (G.D.F.)
| | - Tiziana Polichetti
- ENEA CR-Portici, TERIN-FSD Division, P. le E. Fermi 1, 80055 Portici, Italy; (E.M.); (F.F.); (G.F.); (S.F.); (A.D.G.); (G.D.); (M.S.); (T.P.); (G.D.F.)
| | - Paolo D’Auria
- ARPA Campania, Via Vicinale Santa Maria del Pianto Centro Polifunzionale, Torre 1, 80143 Napoli, Italy;
| | - Adrian M. Ionescu
- NanoLab, EPFL-Ecole Politechnique Federal de Lausanne, 1015 Lausanne, Switzerland;
| | - Girolamo Di Francia
- ENEA CR-Portici, TERIN-FSD Division, P. le E. Fermi 1, 80055 Portici, Italy; (E.M.); (F.F.); (G.F.); (S.F.); (A.D.G.); (G.D.); (M.S.); (T.P.); (G.D.F.)
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