Environmental Modeling and Traffic Simulation: A multivariate approach to monitor urban air pollutant agents. Article Swipe
Israel L. C. Vasconcelos
,
André L. L. Aquino
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5753/jisa.2023.2378
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5753/jisa.2023.2378
This work presents an interdisciplinary assessment examining air quality tracking in urban environments. This application is well suited to be approached with wireless sensor networks' paradigm in their overall variations. The proposed modeling application takes advantage of Vehicle Sensor Networks (VSN) by embedding sensor nodes in public transportation, addressing this study case with bus lines so that the mobiles spread the sampling activity through many different places visited during the route. Simultaneously, it alleviates power management restrictions, packaging dimensions (size and weight), and general maintenance issues. We perform environmental modeling based on real data considering temporal and spatial multivariate behavior on observed phenomena. We consider the city of São Paulo in our case study and parse the asserted data to create a multivariate map of samples, showing the behavior of five different air pollutants from fossil-fueled vehicles (CO, O3, PM10, NO2 and SO2) simultaneously while it also varies in time. Furthermore, the experiment considers a detailed description of roads, bus lines, vehicle itineraries, and general traffic information. The input data that has unformatted or missing information due to being sourced from real sensors is handled to create the map mentioned above. Our methodology addresses the following: 1) the mentioned environmental simulation, 2) the deployment of mobile sensor nodes and performing sensing process, 3) the implementation of network activity and delivery of collected data, 4) visualization of monitored environment based on gathered data using Voronoi Diagrams to fill blank data at non-reached areas. Finally, our VSN-based approach improved 126 times lower error and 11 times higher coverage compared to conventional monitoring with air quality stations.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5753/jisa.2023.2378
- https://sol.sbc.org.br/journals/index.php/jisa/article/download/2378/2240
- OA Status
- diamond
- Cited By
- 3
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384705221
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4384705221Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5753/jisa.2023.2378Digital Object Identifier
- Title
-
Environmental Modeling and Traffic Simulation: A multivariate approach to monitor urban air pollutant agents.Work title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-02Full publication date if available
- Authors
-
Israel L. C. Vasconcelos, André L. L. AquinoList of authors in order
- Landing page
-
https://doi.org/10.5753/jisa.2023.2378Publisher landing page
- PDF URL
-
https://sol.sbc.org.br/journals/index.php/jisa/article/download/2378/2240Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://sol.sbc.org.br/journals/index.php/jisa/article/download/2378/2240Direct OA link when available
- Concepts
-
Computer science, Wireless sensor network, Real-time computing, Visualization, Air quality index, Multivariate statistics, Environmental data, Voronoi diagram, Process (computing), Data mining, Computer network, Geography, Machine learning, Political science, Operating system, Mathematics, Geometry, Law, MeteorologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.our | 111, 243 |
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| abstract_inverted_index.case | 51, 112 |
| abstract_inverted_index.city | 106 |
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| abstract_inverted_index.that | 56, 170 |
| abstract_inverted_index.this | 49 |
| abstract_inverted_index.well | 16 |
| abstract_inverted_index.with | 21, 52, 260 |
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| abstract_inverted_index.Paulo | 109 |
| abstract_inverted_index.based | 90, 228 |
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| abstract_inverted_index.lower | 249 |
| abstract_inverted_index.nodes | 44, 207 |
| abstract_inverted_index.parse | 115 |
| abstract_inverted_index.power | 74 |
| abstract_inverted_index.study | 50, 113 |
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| abstract_inverted_index.their | 27 |
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| abstract_inverted_index.times | 248, 253 |
| abstract_inverted_index.urban | 11 |
| abstract_inverted_index.using | 232 |
| abstract_inverted_index.while | 144 |
| abstract_inverted_index.Sensor | 38 |
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| abstract_inverted_index.areas. | 241 |
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| abstract_inverted_index.higher | 254 |
| abstract_inverted_index.lines, | 160 |
| abstract_inverted_index.mobile | 205 |
| abstract_inverted_index.places | 66 |
| abstract_inverted_index.public | 46 |
| abstract_inverted_index.roads, | 158 |
| abstract_inverted_index.route. | 70 |
| abstract_inverted_index.sensor | 23, 43, 206 |
| abstract_inverted_index.spread | 59 |
| abstract_inverted_index.suited | 17 |
| abstract_inverted_index.varies | 147 |
| abstract_inverted_index.Vehicle | 37 |
| abstract_inverted_index.Voronoi | 233 |
| abstract_inverted_index.general | 83, 164 |
| abstract_inverted_index.handled | 184 |
| abstract_inverted_index.issues. | 85 |
| abstract_inverted_index.missing | 174 |
| abstract_inverted_index.mobiles | 58 |
| abstract_inverted_index.network | 216 |
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| abstract_inverted_index.perform | 87 |
| abstract_inverted_index.quality | 8, 262 |
| abstract_inverted_index.sensing | 210 |
| abstract_inverted_index.sensors | 182 |
| abstract_inverted_index.showing | 126 |
| abstract_inverted_index.sourced | 179 |
| abstract_inverted_index.spatial | 97 |
| abstract_inverted_index.through | 63 |
| abstract_inverted_index.traffic | 165 |
| abstract_inverted_index.vehicle | 161 |
| abstract_inverted_index.visited | 67 |
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| abstract_inverted_index.approach | 245 |
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| abstract_inverted_index.consider | 104 |
| abstract_inverted_index.coverage | 255 |
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| abstract_inverted_index.gathered | 230 |
| abstract_inverted_index.improved | 246 |
| abstract_inverted_index.modeling | 32, 89 |
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| abstract_inverted_index.sampling | 61 |
| abstract_inverted_index.temporal | 95 |
| abstract_inverted_index.tracking | 9 |
| abstract_inverted_index.vehicles | 136 |
| abstract_inverted_index.weight), | 81 |
| abstract_inverted_index.wireless | 22 |
| abstract_inverted_index.VSN-based | 244 |
| abstract_inverted_index.addresses | 193 |
| abstract_inverted_index.advantage | 35 |
| abstract_inverted_index.collected | 221 |
| abstract_inverted_index.considers | 153 |
| abstract_inverted_index.different | 65, 131 |
| abstract_inverted_index.embedding | 42 |
| abstract_inverted_index.examining | 6 |
| abstract_inverted_index.mentioned | 189, 198 |
| abstract_inverted_index.monitored | 226 |
| abstract_inverted_index.networks' | 24 |
| abstract_inverted_index.packaging | 77 |
| abstract_inverted_index.stations. | 263 |
| abstract_inverted_index.addressing | 48 |
| abstract_inverted_index.alleviates | 73 |
| abstract_inverted_index.approached | 20 |
| abstract_inverted_index.assessment | 5 |
| abstract_inverted_index.deployment | 203 |
| abstract_inverted_index.dimensions | 78 |
| abstract_inverted_index.experiment | 152 |
| abstract_inverted_index.following: | 195 |
| abstract_inverted_index.management | 75 |
| abstract_inverted_index.monitoring | 259 |
| abstract_inverted_index.performing | 209 |
| abstract_inverted_index.phenomena. | 102 |
| abstract_inverted_index.pollutants | 133 |
| abstract_inverted_index.application | 14, 33 |
| abstract_inverted_index.considering | 94 |
| abstract_inverted_index.description | 156 |
| abstract_inverted_index.environment | 227 |
| abstract_inverted_index.information | 175 |
| abstract_inverted_index.maintenance | 84 |
| abstract_inverted_index.methodology | 192 |
| abstract_inverted_index.non-reached | 240 |
| abstract_inverted_index.simulation, | 200 |
| abstract_inverted_index.unformatted | 172 |
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| abstract_inverted_index.Furthermore, | 150 |
| abstract_inverted_index.conventional | 258 |
| abstract_inverted_index.information. | 166 |
| abstract_inverted_index.itineraries, | 162 |
| abstract_inverted_index.multivariate | 98, 122 |
| abstract_inverted_index.environmental | 88, 199 |
| abstract_inverted_index.environments. | 12 |
| abstract_inverted_index.fossil-fueled | 135 |
| abstract_inverted_index.restrictions, | 76 |
| abstract_inverted_index.visualization | 224 |
| abstract_inverted_index.implementation | 214 |
| abstract_inverted_index.simultaneously | 143 |
| abstract_inverted_index.Simultaneously, | 71 |
| abstract_inverted_index.transportation, | 47 |
| abstract_inverted_index.interdisciplinary | 4 |
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| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.59048261 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |