Prototype of Air Quality Sensor for Gas Pollutants Monitoring System in Industrial and Residential Estates Article Swipe
Enndi Chiu
,
Joni Welman Simatupang
,
Rijal Hakiki
,
Filson Maratur Sidjabat
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.25105/jetri.v19i1.9812
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.25105/jetri.v19i1.9812
Globalization causes the industrial and information era to develop in massive ways to produce and distribute goods for human needs using big trucks. Due to massive transportation passing by, it is also required to monitor the the level of gases pollutants that can endanger the human respiratory system. This work presents the integrated sensor consists of Arduino Uno and Raspberry Pi 1 Model B+ that can monitor 3 different parameters such as CO, CO2, and NH3 to monitor the industrial area in Lippo Cikarang for the measurement. For monitoring system, the author uses Node-RED for implementing the internet of things system. For gas sensor, author uses MQ-135 air quality sensor to detect 3 gases parameters which have been mentioned with graphs. The main goal of this work is to contribute data for monitoring system of gas pollutants in industrial and residential areas. The experiment has been successfully done where table and graph are shown to be analyzed and summarized. Measurement results are then compared with the environmental air quality standard. As conclusion, the average level of ppm in industrial area for CO2 is 30 ppm, for NH3 is 28.9 ppm, and for CO is 27.08 ppm. While, the average level of ppm in residential area for CO2 is 21.32 ppm, for NH3 is 19.3 ppm, and for CO is 20.93 ppm. Globalisasi menyebabkan era industri dan informasi berkembang pesat dalam memproduksi dan mendistribusi barang – barang untuk kebutuhan manusia menggunakan truk. Karena banyak transportasi yang lalu lalang, diperlukan divais untuk memonitor kadar polutan gas yang dapat membahayakan sistem pernapasan manusia. Penelitian ini menyajikan sensor terintegrasi yang terdiri dari Arduino Uno dan Raspberry Pi 1 Model B+ yang dapat memonitor 3 jenis gas yang berbeda seperti CO, CO2, and NH3 di area industri. Untuk sistem monitor, penulis menggunakan Node-RED untuk implementasi sistem Internet of Things (IoT). Untuk sensor gas, penulis menggunakan sensor kualitas udara MQ-135 untuk mendeteksi 3 jenis gas yang sudah disebutkan dengan kalibrasi dan grafik agar dapat memonitor tingkatan polutan gas. Tujuan utama dari penelitian ini adalah untuk memberikan kontribusi hasil data untuk sistem monitoring gas polutan di kawasan industri dan perumahan. Eksperimen sudah dilakukan dengan baik dimana tabel dan grafik ditampilkan untuk dianalisis dan dirangkum. Hasil dari pengukuran dibandingkan dengan standar baku mutu lingkungan. Rata – rata level dari ppm di area industri untuk CO2 adalah 30 ppm, untuk NH3 adalah 28.9 ppm, dan untuk CO adalah 27.08 ppm. Dan juga rata – rata level dari ppm di tempat warga tinggal (perumahan) untuk CO2 adalah 21.32 ppm, untuk NH3 adalah 19.3 ppm, dan untuk CO adalah 20.93 ppm.
Related Topics
Concepts
Air quality index
Air pollutants
Truck
Pollutant
Environmental science
Work (physics)
Raspberry pi
Computer science
Table (database)
Automotive engineering
Transport engineering
Air pollution
Internet of Things
Database
Meteorology
Engineering
Embedded system
Mechanical engineering
Geography
Chemistry
Organic chemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.25105/jetri.v19i1.9812
- OA Status
- diamond
- Cited By
- 2
- References
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3201441478
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3201441478Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.25105/jetri.v19i1.9812Digital Object Identifier
- Title
-
Prototype of Air Quality Sensor for Gas Pollutants Monitoring System in Industrial and Residential EstatesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-09-03Full publication date if available
- Authors
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Enndi Chiu, Joni Welman Simatupang, Rijal Hakiki, Filson Maratur SidjabatList of authors in order
- Landing page
-
https://doi.org/10.25105/jetri.v19i1.9812Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.25105/jetri.v19i1.9812Direct OA link when available
- Concepts
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Air quality index, Air pollutants, Truck, Pollutant, Environmental science, Work (physics), Raspberry pi, Computer science, Table (database), Automotive engineering, Transport engineering, Air pollution, Internet of Things, Database, Meteorology, Engineering, Embedded system, Mechanical engineering, Geography, Chemistry, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
4Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.CO | 192, 217, 397, 426 |
| abstract_inverted_index.Pi | 60, 272 |
| abstract_inverted_index.as | 71 |
| abstract_inverted_index.be | 155 |
| abstract_inverted_index.di | 289, 348, 382, 409 |
| abstract_inverted_index.in | 9, 81, 137, 177, 202 |
| abstract_inverted_index.is | 30, 127, 182, 187, 193, 207, 212, 218 |
| abstract_inverted_index.it | 29 |
| abstract_inverted_index.of | 38, 55, 98, 124, 134, 175, 200, 302 |
| abstract_inverted_index.to | 7, 12, 24, 33, 76, 110, 128, 154 |
| abstract_inverted_index.CO, | 72, 285 |
| abstract_inverted_index.CO2 | 181, 206, 386, 415 |
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| abstract_inverted_index.Due | 23 |
| abstract_inverted_index.For | 87, 101 |
| abstract_inverted_index.NH3 | 75, 186, 211, 288, 391, 420 |
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| abstract_inverted_index.Uno | 57, 269 |
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| abstract_inverted_index.and | 4, 14, 58, 74, 139, 150, 157, 190, 215, 287 |
| abstract_inverted_index.are | 152, 161 |
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| abstract_inverted_index.era | 6, 223 |
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| abstract_inverted_index.gas | 102, 135, 253, 281, 318, 346 |
| abstract_inverted_index.has | 144 |
| abstract_inverted_index.ini | 261, 336 |
| abstract_inverted_index.ppm | 176, 201, 381, 408 |
| abstract_inverted_index.the | 2, 35, 36, 44, 51, 78, 85, 90, 96, 165, 172, 197 |
| abstract_inverted_index.– | 234, 377, 404 |
| abstract_inverted_index.19.3 | 213, 422 |
| abstract_inverted_index.28.9 | 188, 393 |
| abstract_inverted_index.CO2, | 73, 286 |
| abstract_inverted_index.Rata | 376 |
| abstract_inverted_index.This | 48 |
| abstract_inverted_index.agar | 326 |
| abstract_inverted_index.also | 31 |
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| abstract_inverted_index.baku | 373 |
| abstract_inverted_index.been | 117, 145 |
| abstract_inverted_index.dari | 267, 334, 368, 380, 407 |
| abstract_inverted_index.data | 130, 342 |
| abstract_inverted_index.done | 147 |
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| abstract_inverted_index.gas. | 331 |
| abstract_inverted_index.goal | 123 |
| abstract_inverted_index.have | 116 |
| abstract_inverted_index.juga | 402 |
| abstract_inverted_index.lalu | 245 |
| abstract_inverted_index.main | 122 |
| abstract_inverted_index.mutu | 374 |
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| abstract_inverted_index.ppm. | 195, 220, 400, 429 |
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| abstract_inverted_index.that | 41, 64 |
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| abstract_inverted_index.with | 119, 164 |
| abstract_inverted_index.work | 49, 126 |
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| abstract_inverted_index.20.93 | 219, 428 |
| abstract_inverted_index.21.32 | 208, 417 |
| abstract_inverted_index.27.08 | 194, 399 |
| abstract_inverted_index.Hasil | 367 |
| abstract_inverted_index.Lippo | 82 |
| abstract_inverted_index.Model | 62, 274 |
| abstract_inverted_index.Untuk | 292, 305 |
| abstract_inverted_index.dalam | 229 |
| abstract_inverted_index.dapat | 255, 277, 327 |
| abstract_inverted_index.gases | 39, 113 |
| abstract_inverted_index.goods | 16 |
| abstract_inverted_index.graph | 151 |
| abstract_inverted_index.hasil | 341 |
| abstract_inverted_index.human | 18, 45 |
| abstract_inverted_index.jenis | 280, 317 |
| abstract_inverted_index.kadar | 251 |
| abstract_inverted_index.level | 37, 174, 199, 379, 406 |
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| abstract_inverted_index.pesat | 228 |
| abstract_inverted_index.shown | 153 |
| abstract_inverted_index.sudah | 320, 354 |
| abstract_inverted_index.tabel | 359 |
| abstract_inverted_index.table | 149 |
| abstract_inverted_index.truk. | 240 |
| abstract_inverted_index.udara | 312 |
| abstract_inverted_index.untuk | 236, 249, 298, 314, 338, 343, 363, 385, 390, 396, 414, 419, 425 |
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| abstract_inverted_index.utama | 333 |
| abstract_inverted_index.warga | 411 |
| abstract_inverted_index.where | 148 |
| abstract_inverted_index.which | 115 |
| abstract_inverted_index.(IoT). | 304 |
| abstract_inverted_index.Karena | 241 |
| abstract_inverted_index.MQ-135 | 106, 313 |
| abstract_inverted_index.Things | 303 |
| abstract_inverted_index.Tujuan | 332 |
| abstract_inverted_index.While, | 196 |
| abstract_inverted_index.adalah | 337, 387, 392, 398, 416, 421, 427 |
| abstract_inverted_index.areas. | 141 |
| abstract_inverted_index.author | 91, 104 |
| abstract_inverted_index.banyak | 242 |
| abstract_inverted_index.barang | 233, 235 |
| abstract_inverted_index.causes | 1 |
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| abstract_inverted_index.detect | 111 |
| abstract_inverted_index.dimana | 358 |
| abstract_inverted_index.divais | 248 |
| abstract_inverted_index.grafik | 325, 361 |
| abstract_inverted_index.sensor | 53, 109, 263, 306, 310 |
| abstract_inverted_index.sistem | 257, 293, 300, 344 |
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| abstract_inverted_index.tempat | 410 |
| abstract_inverted_index.things | 99 |
| abstract_inverted_index.Arduino | 56, 268 |
| abstract_inverted_index.average | 173, 198 |
| abstract_inverted_index.berbeda | 283 |
| abstract_inverted_index.develop | 8 |
| abstract_inverted_index.graphs. | 120 |
| abstract_inverted_index.kawasan | 349 |
| abstract_inverted_index.lalang, | 246 |
| abstract_inverted_index.manusia | 238 |
| abstract_inverted_index.massive | 10, 25 |
| abstract_inverted_index.monitor | 34, 66, 77 |
| abstract_inverted_index.passing | 27 |
| abstract_inverted_index.penulis | 295, 308 |
| abstract_inverted_index.polutan | 252, 330, 347 |
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| abstract_inverted_index.quality | 108, 168 |
| abstract_inverted_index.results | 160 |
| abstract_inverted_index.sensor, | 103 |
| abstract_inverted_index.seperti | 284 |
| abstract_inverted_index.standar | 372 |
| abstract_inverted_index.system, | 89 |
| abstract_inverted_index.system. | 47, 100 |
| abstract_inverted_index.terdiri | 266 |
| abstract_inverted_index.tinggal | 412 |
| abstract_inverted_index.trucks. | 22 |
| abstract_inverted_index.Cikarang | 83 |
| abstract_inverted_index.Internet | 301 |
| abstract_inverted_index.Node-RED | 93, 297 |
| abstract_inverted_index.analyzed | 156 |
| abstract_inverted_index.compared | 163 |
| abstract_inverted_index.consists | 54 |
| abstract_inverted_index.endanger | 43 |
| abstract_inverted_index.industri | 224, 350, 384 |
| abstract_inverted_index.internet | 97 |
| abstract_inverted_index.kualitas | 311 |
| abstract_inverted_index.manusia. | 259 |
| abstract_inverted_index.monitor, | 294 |
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| abstract_inverted_index.required | 32 |
| abstract_inverted_index.Raspberry | 59, 271 |
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| abstract_inverted_index.dilakukan | 355 |
| abstract_inverted_index.industri. | 291 |
| abstract_inverted_index.informasi | 226 |
| abstract_inverted_index.kalibrasi | 323 |
| abstract_inverted_index.kebutuhan | 237 |
| abstract_inverted_index.memonitor | 250, 278, 328 |
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| abstract_inverted_index.standard. | 169 |
| abstract_inverted_index.tingkatan | 329 |
| abstract_inverted_index.Eksperimen | 353 |
| abstract_inverted_index.Penelitian | 260 |
| abstract_inverted_index.berkembang | 227 |
| abstract_inverted_index.contribute | 129 |
| abstract_inverted_index.dianalisis | 364 |
| abstract_inverted_index.diperlukan | 247 |
| abstract_inverted_index.dirangkum. | 366 |
| abstract_inverted_index.disebutkan | 321 |
| abstract_inverted_index.distribute | 15 |
| abstract_inverted_index.experiment | 143 |
| abstract_inverted_index.industrial | 3, 79, 138, 178 |
| abstract_inverted_index.integrated | 52 |
| abstract_inverted_index.kontribusi | 340 |
| abstract_inverted_index.memberikan | 339 |
| abstract_inverted_index.mendeteksi | 315 |
| abstract_inverted_index.menyajikan | 262 |
| abstract_inverted_index.monitoring | 88, 132, 345 |
| abstract_inverted_index.parameters | 69, 114 |
| abstract_inverted_index.penelitian | 335 |
| abstract_inverted_index.pengukuran | 369 |
| abstract_inverted_index.pernapasan | 258 |
| abstract_inverted_index.perumahan. | 352 |
| abstract_inverted_index.pollutants | 40, 136 |
| abstract_inverted_index.(perumahan) | 413 |
| abstract_inverted_index.Globalisasi | 221 |
| abstract_inverted_index.Measurement | 159 |
| abstract_inverted_index.conclusion, | 171 |
| abstract_inverted_index.ditampilkan | 362 |
| abstract_inverted_index.information | 5 |
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| abstract_inverted_index.menyebabkan | 222 |
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| abstract_inverted_index.respiratory | 46 |
| abstract_inverted_index.summarized. | 158 |
| abstract_inverted_index.dibandingkan | 370 |
| abstract_inverted_index.implementasi | 299 |
| abstract_inverted_index.implementing | 95 |
| abstract_inverted_index.measurement. | 86 |
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| abstract_inverted_index.successfully | 146 |
| abstract_inverted_index.terintegrasi | 264 |
| abstract_inverted_index.transportasi | 243 |
| abstract_inverted_index.Globalization | 0 |
| abstract_inverted_index.environmental | 166 |
| abstract_inverted_index.mendistribusi | 232 |
| abstract_inverted_index.transportation | 26 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
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| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.73325469 |
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| citation_normalized_percentile.is_in_top_10_percent | False |