Regression Model of PM2.5 Concentration in a Single-Family House

Załuska, Monika; Gładyszewska-Fiedoruk, K.

doi:10.3390/su12155952

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…MLR is applied to test the relative impact of both outdoor pollutants and indoor environmental parameters (i.e. temperature and humidity), as possible covariates, on IAQ (Załuska and Gładyszewska-Fiedoruk, 2020).…”

Section: Background and Framework Definitionmentioning

confidence: 99%

Indoor air quality monitoring and management in hospitality: an overarching framework

Zanni

Mura

Longo

et al. 2022

IJCHM

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Purpose This study aims to provide a comprehensive framework for the study of indoor air quality (IAQ) in hospitality premises. The goal is to identify the drivers of air pollution, both at the exogenous and endogenous level, to generate insights for facility managers. Design/methodology/approach The complexity of hospitality premises requires an integrated approach to properly investigate IAQ. The authors develop an overarching framework encompassing a monitoring method, based on real-time sensors, a technological standard and a set of statistical analyses for the assessment of both IAQ performance and drivers, based on correlation analyses, analysis of variance and multivariate regressions. Findings The findings suggest that the main drivers of IAQ differ depending on the area monitored: areas in contact with the outdoors or with high ventilation rates, such as halls, are affected by outdoor air quality more than guestrooms or fitness areas, where human activities are the main sources of contamination. Research limitations/implications The results suggest that the integration of IAQ indicators into control dashboards would support management decisions, both in defining protocols to support resilience of the sector in a postpandemic world and in directing investments on the premises. This would also address guests’ pressing demands for a broader approach to cleanliness and safety and support their satisfaction and intention to return. Originality/value To the best of the authors’ knowledge, this is the first study developing a comprehensive framework to systematically address IAQ and its drivers, based on a standard and real-time monitoring. The framework has been applied across the longest period of monitoring for a hospitality premise thus far and over an entire hotel facility.

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Section: Background and Framework Definitionmentioning

confidence: 99%

Indoor air quality monitoring and management in hospitality: an overarching framework

Zanni

Mura

Longo

et al. 2022

IJCHM

View full text Add to dashboard Cite

show abstract

“…Therefore, the PM 2.5 measurement results by the SPS 30 sensor are set as the input variable based on previous literature [10]. Environmental variables such as temperature and humidity have an effect on decreasing the accuracy of a low-cost sensor based on the light-scatter method [13]. Therefore, two environmental variables were also set as input conditions to model the complex physical characteristics among PM 2.5 , temperature and humidity.…”

Section: Air Quality Measurement Instrumentsmentioning

confidence: 99%

Assessment and Calibration of a Low-Cost PM2.5 Sensor Using Machine Learning (HybridLSTM Neural Network): Feasibility Study to Build an Air Quality Monitoring System

et al. 2021

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Commercially available low-cost air quality sensors have low accuracy. The improved accuracy of low-cost PM2.5 sensors allows the use of low-cost sensor systems to reasonably investigate PM2.5 emissions from industrial activities or to accurately estimate individual exposure to PM2.5. In this work, we developed a new PM2.5 calibration model (HybridLSTM) by combining a deep neural network (DNN) optimized in calibration problems and a long short-term memory (LSTM) neural network optimized in time-dependent characteristics to improve the performance of conventional calibration algorithms of low-cost PM sensors. The PM2.5 concentrations, temperature and humidity by low-cost sensors and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmarks (multiple linear regression model (MLR), DNN model) and low-cost sensor results. The gravimetric measurements were used as reference data to evaluate sensor accuracy. For root-mean-square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41–60% of error when compared with the raw data of low-cost sensors, reduced 30–51% of error when compared with the MLR model and reduced 8–40% of error when compared with the MLR model. R2 of HybridLSTM, DNN, MLR and raw data were 93, 90, 80 and 59%, respectively. HybridLSTM showed the state-of-the-art calibration performance for a low-cost PM sensor. In other words, the proposed ML model has state-of-the-art calibration performance among the tested calibration algorithms.

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“…Therefore, the PM2.5 measurement results by the SPS 30 sensor is set as the input variable. Environmental variables such as temperature and humidity have an effect on decreasing the accuracy of low-cost sensor based on light-scatter method [12]. Therefore, two environmental variables were also set as input conditions to model the complex physical characteristics among PM2.5, temperature and humidity.…”

Section: Air Quality Measurement Instrumentsmentioning

confidence: 99%

Assessment and Calibration on Low-cost PM2.5 Sensor using Machin Learning (Hybrid-LSTM Neural Network): Feasibility Study to Build Air Quality Monitoring System

Park¹,

Yoo²,

Park³

et al. 2021

Preprint

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Although commercially-available low-cost air quality sensors have low accuracy, the sensor system are being used to collect the data for the regulation of PM2.5 emission caused by industrial activities or to estimate the personal exposure for PM2.5. In this work, to solve the accuracy problem of low-cost PM sensor, we developed a new PM2.5 calibration model by combining the deep neural network (DNN) optimized in calibration problem and a LSTM optimized in time-dependent characteristics. First, two datasets were generated to test the accuracy performance and generalization performance of the PM2.5 calibration machine learning (ML) model. The PM2.5 concentrations, temperature and humidity by low-cost sensor and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmark (multiple linear regression model) and low-cost sensor results. For root mean square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41-60% of error compared to the raw data of low-cost sensor, and reduced 30-51% of error compared to the benchmark model. R2 of ML model, MLR and raw data were 93, 80 and 59 %. Also, the developed model still showed consistent calibration performance when calibrated with new sensors in different locations. Low-cost sensors combined with ML model not only can improve the calibration performance of benchmark, but also can be applied to the sensor monitoring systems for various epidemiologic investigations and regulatory decisions.

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Regression Model of PM2.5 Concentration in a Single-Family House

Cited by 11 publications

References 29 publications

Indoor air quality monitoring and management in hospitality: an overarching framework

Indoor air quality monitoring and management in hospitality: an overarching framework

Assessment and Calibration of a Low-Cost PM2.5 Sensor Using Machine Learning (HybridLSTM Neural Network): Feasibility Study to Build an Air Quality Monitoring System

Assessment and Calibration on Low-cost PM2.5 Sensor using Machin Learning (Hybrid-LSTM Neural Network): Feasibility Study to Build Air Quality Monitoring System

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