Assessment of CO2 Reduction Potential of Indoor Plants Using Artificial Neural Network in Classrooms

Sattaya Manokeaw, Thatsaneeya Nim-Anutsonkun, Takdanai Chaiya, Warut Timprae, Damrongsak Rinchumphu


Carbon dioxide gas (CO2) is one of the critical factors used to measure indoor air quality that affects the well-being of school building occupants daily. Therefore, efforts to reduce the indoor-CO2 amounts have been made by adding indoor plants to absorb the CO2. The critical knowledge is to understand the factors affecting the rate of CO2 adsorption. This research aims to study the relationship between indoor CO2 reduction using trees and environments. First, a flowerpot with snake plants is placed in a room of 24.5 m2 for the data collection of the temperature, the relative humidity, light intensity, and the amount of CO2 using sensors. Then, the data were used to create a forecast model using the Artificial Neural Network (ANN) technique, which its accuracy was 99.64%. The results showed that the snake plants could reduce 2.13% of the indoor CO2. The suitable environment for plant photosynthesis is a temperature of 25 to 30°C and relative humidity of 40% at a light intensity of 200 Lux. The results can be used as data in the design of rooms in educational institutions to effectively increase the air quality in response to building occupants' health.


Keywords: indoor plants, indoor air quality, CO2, artificial neural network.

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LAZOVIĆ I., STEVANOVIĆ Ž. M., JOVAŠEVIĆ-STOJANOVIĆ M., ŽIVKOVIĆ M. M., and BANJAC M. J. Impact of CO2 Concentration on Indoor Air Quality and Correlation with Relative Humidity and Indoor Air Temperature in School Buildings in Serbia. Thermal Science, 2016, 20: 297-307.

CANDANEDO L. M., & FELDHEIM V. Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Energy and Buildings, 2016, 112: 28-39.

KARAPETSIS A., & ALEXANDRI E. Indoor environmental quality and its impacts on health – case study: school buildings. Proceedings of the 5th International Conference “ENERGY in BUILDINGS 2016”, Athens, 2016.

SUHAIMI M. M., LEMAN A. M., AFANDI A., HARIRI A., IDRIS A. F. A., DZULKIFLI S. N. M., and GANI P. Effectiveness of indoor plant to reduce CO2 in indoor environment. MATEC Web Conference, 2017, 103: 05004.

CHIRAMONGKOLKAN U. Saint George's sword (5th). Bangkok: Amarin Printing and publishing public company limited, 2008.

INSTITUTE FOR INNOVATIVE LEARNING. Know science 4 plants and organisms with photosynthesis, 2005.

NATIONAL GEOGRAPHIC. Photosynthesis process manufacturer's duties, 2018.

RIHAM JABER A., DEJAN M., and MARCELLA U. The effect of indoor temperature and CO2 levels on cognitive performance of adult females in a University Building in Saudi Arabia. Energy Procedia, 2017, 122: 451-456.

DECHACHAN R. Guide to planting ornamental plants in office building. Bangkok: Thaiqualitybooks, 2011.

RINCHUMPHU D., PHICHETKUNBODEE N., POMSURIN N., SUNDARANAGA C., TEPWEERAKUN S., and CHAICHANA C. Outdoor thermal comfort improvement of campus public space. Advances in Technology Innovation, 2021, 6(2).

GUBB C., BLANUSA T., GRIFFITHS A., and PFRANG C. Can houseplants improve indoor air quality by removing CO2 and increasing relative humidity? Air Quality, Atmosphere & Health, 2018, 11(10): 1191-1201.

BOUSSABAINE A. H. The use of Artificial Neural Networks in construction management: a review. Construction Management and Economics, 1996, 14(5): 427-436.

PANYAFONG A., NEAMSORN N., and CHAICHANA C. Heat load estimation using Artificial Neural Network. Energy Reports, 2020, 6: 742-747.

DECHKAMFOO C., SITTHIKANKUN S., KRIDAKORN NA AYUTTHAYA T., MANOKEAW S., TIMPRAE W., TEPWEERAKUN S., TENGTRAIRAT N., ARYUPONG C., JITSANGIAM P., & RINCHUMPHU D. Impact of rainfall-Induced landslide susceptibility risk on mountain roadside in Northern Thailand. Infrastructures, 2022, 7(2).

POLAT G. ANN approach to determine cost contingency in international construction project. Journal of Applied Management and Investments, 2012, 1(2): 195-201.

RANJAN C. Rules-of-thumb for building a Neural Network, 2019.

GEETHA A., & NASIRA G. M. Artificial Neural Networks’ application in weather forecasting–using RapidMiner. International Journal of Computational Intelligence and Informatics, 2014, 4(3): 177-182.

TIMPRAE W. Factors affecting solar radiation in the wavelengths used by plants for photosynthesis in greenhouse cultivation. Chiang Mai University, Thailand, 2021.

ÇELIK U., & BAŞARIR Ç. The prediction of precious Metal Prices Via Artificial Neural Network by using RapidMiner. Alphanumeric Journal, 2017, 5(1): 45-54.

SITTHIKANKUN S., RINCHUMPHU D., BUACHART C., & PACHARAWONGSAKDA E. Construction cost estimation for government building using Artificial Neural Network technique. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 2021, 12(6): 12A16G.

THUPPHONG B. Documents about photosynthesis, n.d.

WATTANACHAI P., SUNDARANAGA C., AYUTTHAYA T. K. N., PHICHETKUNBODEE N., & RINCHUMPHU D. Study of universal thermal comfort index in hosing estate public space in Bangkok, Thailand. Journal of Design and Built Environment, 2021, 21(2).


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