TINJAUAN LITERATUR: PEMANFAATAN KECERDASAN BUATAN DALAM PEMANTAUAN KUALITAS UDARA MELALUI INOVASI GOOGLE PROJECT AIR VIEW

Authors

  • Eko Prasetio Widhi Universitas Darussalam Gontor
  • Firlana Umi Azzakiy Universitas Darussalam Gontor
  • Mar’ah Rofidah Abidah Khosyatullah Universitas Darussalam Gontor

DOI:

https://doi.org/10.37859/seis.v5i1.8347
Keywords: Kecerdasan Buatan, Pemantauan Kualitas Udara, Google Project Air View, IoT, Data Besar, Perencanaan Kota Berkelanjutan

Abstract

Polusi udara merupakan salah satu masalah lingkungan yang berdampak signifikan terhadap kesehatan masyarakat dan ekosistem. Seiring perkembangan teknologi, kecerdasan buatan (AI) telah menjadi alat penting dalam meningkatkan efektivitas dan efisiensi pemantauan kualitas udara. Artikel ini menyajikan tinjauan literatur tentang pemanfaatan AI dalam pemantauan kualitas udara, dengan fokus pada inovasi Google Project Air View. Teknologi ini menggunakan kendaraan Google Street View yang dilengkapi sensor canggih untuk menghasilkan data kualitas udara secara real-time dengan resolusi tinggi. Melalui analisis data besar dan algoritma pembelajaran mesin, sistem ini mampu memetakan konsentrasi polutan seperti karbon dioksida (CO₂), nitrogen dioksida (NO₂), dan partikel halus (PM2.5) secara lebih akurat dibandingkan metode tradisional berbasis sensor statis. Artikel ini juga membahas keunggulan teknologi AI, termasuk integrasi dengan IoT, penerapan UAV, edge computing, dan model prediktif berbasis data besar, serta dampaknya dalam mendukung kebijakan publik dan perencanaan kota berkelanjutan. Meskipun terdapat tantangan dalam implementasi teknologi ini, seperti kebutuhan akan infrastruktur yang kompleks dan validasi data, potensi AI untuk mengatasi tantangan polusi udara tetap besar. Artikel ini menyimpulkan bahwa pengembangan lebih lanjut pada sistem berbasis AI dapat memberikan manfaat signifikan bagi pengelolaan kualitas udara global dan mendukung pembangunan yang lebih ramah lingkungan.

Downloads

Download data is not yet available.

References

A. Octaviano, S. Sofiana, D. O. Agustino, and P. Rosyani, “Pemantauan Kualitas Udara Berbasis Internet O Things,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 3, no. 2, pp. 147–156, 2022, [Online]. Available: https://djournals.com/klik

F. Rahim and Y. R. Camin, “Kondisi Kualitas Udara (So2, No2, Pm10 Dan Pm2,5) Di Dalam Rumah Di Sekitar Cilegon Dan Gangguan Pernapasan Yang Diakibatkannya,” Al-Kauniyah J. Biol., vol. 11, no. 2, pp. 82–90, 2018, doi: 10.15408/kauniyah.v11i2.5710.

I. Manisalidis, E. Stavropoulou, A. Stavropoulos, and E. Bezirtzoglou, “Environmental and Health Impacts of Air Pollution: A Review,” Front. Public Heal., vol. 8, Feb. 2020, doi: 10.3389/fpubh.2020.00014.

M. Kampa and E. Castanas, “Human health effects of air pollution,” Environ. Pollut., vol. 151, no. 2, pp. 362–367, Jan. 2008, doi: 10.1016/j.envpol.2007.06.012.

C. Hong et al., “Impacts of climate change on future air quality and human health in China,” Proc. Natl. Acad. Sci. U. S. A., vol. 116, no. 35, pp. 17193–17200, Aug. 2019, doi: 10.1073/pnas.1812881116.

B. Haryanto, “Climate Change and Urban Air Pollution Health Impacts in Indonesia,” in Springer Climate, 2018, pp. 215–239. doi: 10.1007/978-3-319-61346-8_14.

R. Kaur and P. Pandey, “Air Pollution, Climate Change, and Human Health in Indian Cities: A Brief Review,” Front. Sustain. Cities, vol. 3, Aug. 2021, doi: 10.3389/frsc.2021.705131.

S. Dhingra, R. B. Madda, A. H. Gandomi, R. Patan, and M. Daneshmand, “Internet of things mobile-air pollution monitoring system (IoT-Mobair),” IEEE Internet Things J., vol. 6, no. 3, pp. 5577–5584, Jun. 2019, doi: 10.1109/JIOT.2019.2903821.

D. Zhang and S. S. Woo, “Real Time Localized Air Quality Monitoring and Prediction through Mobile and Fixed IoT Sensing Network,” IEEE Access, vol. 8, pp. 89584–89594, 2020, doi: 10.1109/ACCESS.2020.2993547.

G. Mani, J. K. Viswanadhapalli, and P. Sriramalakshmi, “AI powered IoT based Real-Time Air Pollution Monitoring and Forecasting,” J. Phys. Conf. Ser., vol. 2115, no. 1, p. 012016, Nov. 2021, doi: 10.1088/1742-6596/2115/1/012016.

V. H. K. M. Gowda, “A PEER-TO-PEER AIR QUALITY MONITORING SYSTEM USING IOT SENSORS AND CLOUD PLATFORMS,” TMP Univers. J. Res. Rev. Arch., vol. 1, no. 2, Dec. 2022, doi: 10.69557/ujrra.v1i2.110.

S. Pandiarajan, S. Premkumar, B. Ezhavarasan, and S. G. Raja Shree, “IoT-Based Air Quality Navigation System for Vulnerable Populations,” in Proceedings of 2024 International Conference on Science, Technology, Engineering and Management, ICSTEM 2024, Apr. 2024, pp. 1–6. doi: 10.1109/ICSTEM61137.2024.10560650.

E. Kulikova, V. Sulimin, and V. Shvedov, “Artificial intelligence for ambient air quality control,” E3S Web Conf., vol. 419, p. 03011, Aug. 2023, doi: 10.1051/e3sconf/202341903011.

K. P. Messier et al., “Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression,” Environ. Sci. Technol., vol. 52, no. 21, pp. 12563–12572, Nov. 2018, doi: 10.1021/acs.est.8b03395.

C. H. Huang, W. T. Chen, Y. C. Chang, and K. T. Wu, “An Edge and Trustworthy AI UAV System With Self-Adaptivity and Hyperspectral Imaging for Air Quality Monitoring,” IEEE Internet Things J., vol. 11, no. 20, pp. 32572–32584, Oct. 2024, doi: 10.1109/JIOT.2024.3422470.

X. Su et al., “Intelligent and Scalable Air Quality Monitoring with 5G Edge,” IEEE Internet Comput., vol. 25, no. 2, pp. 35–44, Mar. 2021, doi: 10.1109/MIC.2021.3059189.

D. Manongga, U. Rahardja, I. Sembiring, Q. Aini, and A. Wahab, “Improving the Air Quality Monitoring Framework Using Artificial Intelligence for Environmentally Conscious Development,” HighTech Innov. J., vol. 5, no. 3, pp. 794–813, Sep. 2024, doi: 10.28991/HIJ-2024-05-03-017.

C. T. Yang, H. W. Chen, E. J. Chang, E. Kristiani, K. L. P. Nguyen, and J. S. Chang, “Current advances and future challenges of AIoT applications in particulate matters (PM) monitoring and control,” J. Hazard. Mater., vol. 419, p. 126442, Oct. 2021, doi: 10.1016/j.jhazmat.2021.126442.

N. ANDREI and A. IOANID, “POTENTIAL USE OF ARTIFICIAL INTELLIGENCE AND GEOSPATIAL ANALYSIS IN ENVIRONMENTAL MONITORING: Air quality in a large city,” in Towards Increased Business Resilience:Facing Digital Opportunities and Challenges, 2023, pp. 369–376. doi: 10.56177/11icmie2023.31.

K. P. Messier et al., “Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression,” Environ. Sci. Technol., vol. 52, no. 21, pp. 12563–12572, Nov. 2018, doi: 10.1021/acs.est.8b03395.

J. S. Apte et al., “High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data,” Environ. Sci. Technol., vol. 51, no. 12, pp. 6999–7008, Jun. 2017, doi: 10.1021/acs.est.7b00891.

P. A. Solomon, D. Vallano, M. Lunden, B. Lafranchi, C. L. Blanchard, and S. L. Shaw, “Mobile-platform measurement of air pollutant concentrations in California: Performance assessment, statistical methods for evaluating spatial variations, and spatial representativeness,” Atmospheric Measurement Techniques, vol. 13, no. 6. pp. 3277–3301, Jan. 28, 2020. doi: 10.5194/amt-13-3277-2020.

Y. Guan et al., “Fine-Scale Spatiotemporal Air Pollution Analysis Using Mobile Monitors on Google Street View Vehicles,” J. Am. Stat. Assoc., vol. 115, no. 531, pp. 1111–1124, Jul. 2020, doi: 10.1080/01621459.2019.1665526.

M. E. S. Sabedotti, A. C. O’Regan, and M. M. Nyhan, “Data Insights for Sustainable Cities: Associations between Google Street View-Derived Urban Greenspace and Google Air View-Derived Pollution Levels,” Environ. Sci. Technol., vol. 57, no. 48, pp. 19637–19648, Dec. 2023, doi: 10.1021/acs.est.3c05000.

S. Dalsgaard and R. Tyge Haarløv, “Mobilising Uncertainties in Air Pollution Science in Copenhagen,” STS Encount., vol. 15, no. 2, Sep. 2023, doi: 10.7146/stse.v15i2.139809.

Y. Yang, Z. Zheng, K. Bian, L. Song, and Z. Han, “Real-Time Profiling of Fine-Grained Air Quality Index Distribution Using UAV Sensing,” IEEE Internet Things J., vol. 5, no. 1, pp. 186–198, Feb. 2018, doi: 10.1109/JIOT.2017.2777820.

N. Liu, Z. Wu, G. Li, X. Liu, Y. Wang, and L. Zhang, “MAIC: Metalearning-Based Adaptive In-Field Calibration for IoT Air Quality Monitoring System,” IEEE Internet Things J., vol. 9, no. 17, pp. 15928–15941, Sep. 2022, doi: 10.1109/JIOT.2022.3150849.

Y. Du et al., “A visual analytics approach for station-based air quality data,” Sensors (Switzerland), vol. 17, no. 1, p. 30, Dec. 2017, doi: 10.3390/s17010030.

Downloads

Published

2025-01-31

How to Cite

Widhi, E. P., Firlana Umi Azzakiy, & Mar’ah Rofidah Abidah Khosyatullah. (2025). TINJAUAN LITERATUR: PEMANFAATAN KECERDASAN BUATAN DALAM PEMANTAUAN KUALITAS UDARA MELALUI INOVASI GOOGLE PROJECT AIR VIEW. Journal of Software Engineering and Information System (SEIS), 5(1), 9–14. https://doi.org/10.37859/seis.v5i1.8347

Issue

Section

Articles