Implementation of an Air Quality Monitoring System and Prediction Using LSTM Ensemble Modeling for the Availability of Multi-use Facilities of Health-vulnerable Groups: a Preliminary Study
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This study aims to ascertain the implementation of an air quality monitoring system and artificial intelligence modeling for predicting air quality in a multi-use facility setting. We designed and deployed a system with sensors for temperature, humidity, TVOC, CO, CO2, PM10, and PM2.5 in a wireless real-time environment within a multi-use facility, collecting 4373 sensor measurements during a month-long trial. We conducted LSTM ensemble modeling and predictions for each of the seven sensor measurements and calculated the RMSE, MAE, MAPE, accuracy, and loss metrics to measure the model performance. The monthly average values of TVOC (1853.54±2859.28, Q1: 168, Q3: 2114), PM10 (9.42±6.89), and PM2.5 (8.93±6.01) were analyzed. Monitoring the air quality of a multi-use facility for a month revealed daily and weekly periodicity in TVOC, CO2, PM10, and PM2.5 through seasonal decomposition analysis. Predictions were performed using the LSTM ensemble network, and the MAPE of the temperature and humidity were analyzed as 0.04 and 0.06, respectively. This preliminary study confirmed that the developed system and AI model, which measure and predict air quality, can be deployed in hospitals and other multi-use facilities that cater to vulnerable groups.
Abstract
This study aims to ascertain the implementation of an air quality monitoring system and artificial intelligence modeling for predicting air quality in a multi-use facility setting. We designed and deployed a system with sensors for temperature, humidity, TVOC, CO, CO2, PM10, and PM2.5 in a wireless real-time environment within a multi-use facility, collecting 4373 sensor measurements during a month-long trial. We conducted LSTM ensemble modeling and predictions for each of the seven sensor measurements and calculated the RMSE, MAE, MAPE, accuracy, and loss metrics to measure the model performance. The monthly average values of TVOC (1853.54±2859.28, Q1: 168, Q3: 2114), PM10 (9.42±6.89), and PM2.5 (8.93±6.01) were analyzed. Monitoring the air quality of a multi-use facility for a month revealed daily and weekly periodicity in TVOC, CO2, PM10, and PM2.5 through seasonal decomposition analysis. Predictions were performed using the LSTM ensemble network, and the MAPE of the temperature and humidity were analyzed as 0.04 and 0.06, respectively. This preliminary study confirmed that the developed system and AI model, which measure and predict air quality, can be deployed in hospitals and other multi-use facilities that cater to vulnerable groups.
참고문헌 (35)
- Wilczak JM, "Analysis of regional meteorology and surface ozone during the TexAQS II field program and an evaluation of the NMM-CMAQ and WRF-Chem air quality models", J Geophys Res Atmos, 114(D7) (2009)
- Zhaoqi G, "A review of the CAMx, CMAQ, WRFChem and NAQPMS models: Application, evaluation and uncertainty factors", Environ Pollut, pp.123183~ (2023)
- Kampa M, "Human health effects of air pollution", Environ Pollut, 151, pp.362~367 (2008)
- Shin H, "Indoor air quality and human health risk assessment for un-regulated small-sized sensitive population facilities", J Environ Health Sci, 44, pp.397~407 (2018)
- Kim H, "Human-building interaction for indoor environmental control : Evolution of technology and future prospects", Autom Constr, 152, pp.104938~ (2023)
- Becerik-Gerber B, "Ten questions concerning human-building interaction research for improving the quality of life", Build Environ, 226, pp.109681~ (2022)
- Jones AP, "Indoor air quality and health", Atmos Environ, 33, pp.4535~4564 (1999)
- Wu F, "Improving indoor environmental quality for public health : impediments and policy recommendations", Environ Health Perspect, 115, pp.953~957 (2007)
- Zhang L, "Air quality predictions with a semi-supervised bidirectional LSTM neural network", Atmos Pollut Res, 12, pp.328~339 (2021)
- Liu J-H, "Developed urban air quality monitoring system based on wireless sensor networks", 2011 Fifth International Conference on Sensing Technology (2011), IEEE
- Mansour S, "Wireless sensor network-based air quality monitoring system", 2014 international conference on computing, networking and communications (ICNC) (2014), IEEE
- Freeman BS, "Forecasting air quality time series using deep learning", J Air Waste Manag Assoc, 68, pp.866~886 (2018)
- Zaini Na, "A systematic literature review of deep learning neural network for time series air quality forecasting", Environ Sci Pollut Res, pp.1~33 (2022)
- Mo X, "A novel air quality early-warning system based on artificial intelligence", Int J Environ Res Public Health, 16, pp.3505~ (2019)
- Arora S, "Air Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network", Comput Intell Neurosci (2022)
- Zhang Z, "A hybrid deep learning technology for PM2.5 air quality forecasting", Environ Sci Pollut Res, 28, pp.39409~39422 (2021)
- Tham KW, "Indoor air quality and its effects on humans-A review of challenges and developments in the last 30 years", Energy Build, 130, pp.637~650 (2016)
- Dai X, "Achieving better indoor air quality with IoT systems for future buildings : Opportunities and challenges", Sci Total Environ, pp.164858~ (2023)
- Tran VV, "Indoor air pollution, related human diseases, and recent trends in the control and improvement of indoor air quality", Int J Environ Res Public Health, 17, pp.2927~ (2020)
- Tagliabue LC, "Data driven indoor air quality prediction in educational facilities based on IoT network", Energy Build, 236, pp.110782~ (2021)
- Yu Y, "A review of recurrent neural networks : LSTM cells and network architectures", Neural Comput, 31, pp.1235~1270 (2019)
- Liang X, "Interpretable structure-evolving LSTM", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
- Ying X, "An overview of overfitting and its solutions", J Phys Conf Ser (2019), IOP Publishing
- J abłońska-Trypuć A, "Inanimate surfaces as a source of hospital infections caused by fungi, bacteria and viruses with particular emphasis on SARS-CoV-2", Int J Environ Res Public Health, 19, pp.8121~ (2022)
- Stockwell RE, "Indoor hospital air and the impact of ventilation on bioaerosols : a systematic review", J Hosp Infect, 103, pp.175~184 (2019)
- Bonadonna L, "Microbial air quality in healthcare facilities", Int J Environ Res Public Health, 18, pp.6226~ (2021)
- Tian Y, "A review on factors related to patient comfort experience in hospitals", J Health Popul Nutr, 42, pp.125~ (2023)
- Saini J, "Indoor air quality prediction systems for smart environments : a systematic review", J Ambient Intell Smart Environ, 12, pp.433~453 (2020)
- Salman N, "Real time wireless sensor network(WSN)based indoor air quality monitoring system", IFAC-PapersOnLine, 52, pp.324~327 (2019)
- Ortiz Perez A, "A wireless gas sensor network to monitor indoor environmental quality in schools", Sensors, 18, pp.4345~ (2018)
- Chiesa G, "Multisensor IoT platform for optimising IAQ levels in buildings through a smart ventilation system", Sustainability, 11, pp.5777~ (2019)
- Chen A, "An efficient network behavior anomaly detection using a hybrid DBN-LSTM network", Comput Secur, 114, pp.102600~ (2022)
- Londhe AN, "Semantic segmentation of ECG waves using hybrid channel-mix convolutional and bidirectional LSTM", Biomed Signal Process Control, 63, pp.102162~ (2021)
- Liu F, "An ensemble model based on adaptive noise reducer and over-fitting prevention LSTM for multivariate time series forecasting", IEEE Access, 7, pp.26102~26115 (2019)