{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T04:52:10Z","timestamp":1762145530019,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,16]],"date-time":"2020-02-16T00:00:00Z","timestamp":1581811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>The lower cost of sensors is making possible the acquisition of big data sets in several applications and research areas. Indoor air quality and commuter exposure to pollutants are some of these areas, which can have impacts on our livelihood. The main objective of this exploratory research was to assemble portable equipment along with a prototype, one low-cost and easy to replicate in any location worldwide. We answer how CO2, noise and energy expenditure compare in different transportation modes with indoor environments (metro, bus and car). It was intended to be carried by a subject on all commutes. The low-cost equipment assembled has the ability to measure ambient CO2, noise levels, heart rate and geographic coordinates. The field campaign was conducted on an urban commuting route, in Lisbon city, between Rossio (downtown of Lisbon city) and Campo Grande (near FCUL campus). It took place during 3 weeks in school break and 3 weeks in the school period to grasp some differences between these periods of the year. The heart rate data was used to calculate the subject energy expenditure and the geographic coordinate data allowed for time and spatial analysis using a geospatial software package. Our measurements totaled 70 one-way trips and 358,140 data points. Temporal and spatial analysis yielded the following results: The metro presents the lowest median CO2 concentrations of 693 ppm and the bus the highest with 1085 ppm. The bus had an equivalent continuous sound average (Leq) of 75 dBA, while the metro had 85.2 dBA. Based on the metabolic equivalent of task (MET) calculations, the metro displays the least sedentary behavior, while the bus presents the most sedentary behavior with up to 96.5% of its commute spent in this classification. The metro was the fastest mode of transportation based on the consistency of its travel times compared to the bus, which despite also being consistent, was slower by 1.8 times. The car measurement values reside in the middle of the metro and bus results. Despite this, it is considered the worst mode of transportation, as it goes against the idea of a less congested and clean city. It also has a highly variable commuting time, which sometimes makes it slower than the metro, especially during the school period. According to our results, we concluded that the metro had efficient indoor ventilation while the bus did not. There were several instances of inefficient ventilation with concentrations exceeding 1000 ppm, particularly between Restauradores and Saldanha due to overcrowding. Referring to the health impacts of noise, the metro dBA levels are not sustained for enough time to have any measurable negative impact. Sensor performance was considered acceptable for the CO2 sensor. The dBA and heart rate (HR) sensors were considered acceptable to sometimes irregular in nature, which was expected and taken into consideration.<\/jats:p>","DOI":"10.3390\/en13040861","type":"journal-article","created":{"date-parts":[[2020,2,19]],"date-time":"2020-02-19T04:06:01Z","timestamp":1582085161000},"page":"861","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Exploratory Research of CO2, Noise and Metabolic Energy Expenditure in Lisbon Commuting"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8693-1214","authenticated-orcid":false,"given":"Angelo","family":"Soares","sequence":"first","affiliation":[{"name":"Instituto Dom Luiz (IDL), Faculdade de Ci\u00eancias da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal"}]},{"given":"Cristina","family":"Catita","sequence":"additional","affiliation":[{"name":"Instituto Dom Luiz (IDL), Faculdade de Ci\u00eancias da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1439-9214","authenticated-orcid":false,"given":"Carla","family":"Silva","sequence":"additional","affiliation":[{"name":"Instituto Dom Luiz (IDL), Faculdade de Ci\u00eancias da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.envint.2014.11.019","article-title":"The rise of low-cost sensing for managing air pollution in cities","volume":"75","author":"Kumar","year":"2015","journal-title":"Environ. 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