Assessing Indoor Air Quality Using
Chemometric Models

Azid, Azman; Amran, Mohammad Azizi; Samsudin, Mohd Saiful; Rani, Nurul Latiffah Abd; Khalit, Saiful Iskandar; Gasim, Muhammad Barzani; Yunus, Kamaruzzaman; Saudi, Ahmad Shakir Mohd; Amin, Siti Noor Syuhada Muhammad; Yusof, Ku Mohd Kalkausar Ku

doi:10.15244/pjoes/78154

Cited by 6 publications

(6 citation statements)

References 27 publications

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“…The prediction is a method of predicting the concentration of pollutants in the future through computer high-speed numerical calculation. Numerical prediction is a quantitative and objective method, which is different from previous predictions of polluted weather conditions based on experience and meteorology [7], [8]. Although the accuracy of numerical prediction is high, there are still some shortcomings.…”

Section: Introductionmentioning

confidence: 98%

Research on Data Correction Method of Micro Air Quality Detector Based on Combination of Partial Least Squares and Random Forest Regression

Liu

Wang

et al. 2021

IEEE Access

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The issue of air quality has attracted more and more attention. The main methods for monitoring the concentration of pollutants in the air include national monitoring station monitoring and micro air quality detector testing. Since the electrochemical sensor of the micro air quality detector is susceptible to interference, the monitored data has a certain deviation. In this paper, the combined model of partial least square regression and random forest regression (PLS-RFR) is used to correct the detection data of the micro air quality detector. First, correlation analysis is used to find out the factors that affect the concentration of pollutants. Second, partial least squares regression is used to give the quantitative relationship of the influence of each influencing factor on the concentration of pollutants. Finally, the predicted value of partial least squares regression and various influencing factors are used as independent variables, and the pollutant concentration monitored by the national monitoring station is used as the dependent variable, and the PLS-RFR model is obtained with the help of random forest software package. Relative mean absolute percent error, mean absolute error, goodness of fit, and root mean square error are used as evaluation indicators to compare PLS-RFR model, support vector machine model and multilayer perceptron neural network. The results show that no matter which evaluation index, the overall prediction effect of the PLS-RFR model is the best, and the model has a good prediction effect in the training set or the test set, indicating that the model has good generalization ability. This model can play an active role in the promotion and deployment of micro air quality detectors.INDEX TERMS Micro air quality detector, random forest regression, partial least squares regression, data correction model.

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Section: Introductionmentioning

confidence: 98%

Research on Data Correction Method of Micro Air Quality Detector Based on Combination of Partial Least Squares and Random Forest Regression

Liu

Wang

et al. 2021

IEEE Access

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“…The mechanism model is based on the scientic understanding of atmospheric physical and chemical processes, and uses meteorological principles to simulate the physical and chemical processes of pollutants, and uses the data generated by the simulation to predict the concentration of pollutants. [9][10][11] Since the physical and chemical processes of the formation and propagation of pollutants are very complex, the computational complexity of the mechanism model is relatively high, and the accuracy of the model needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

A method for calibrating measurement data of a micro air quality monitor based on MLR-BRT-ARIMA combined model

Liu,

Jiang

2023

RSC Adv.

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“…Common air quality forecasting models are mainly divided into mechanism models and statistical models. The mechanism model is based on the scienti c understanding of atmospheric physical and chemical processes, and uses meteorological principles to simulate the physical and chemical processes of pollutants, and uses the data generated by the simulation to predict the concentration of pollutants 9,10 . Since the physical and chemical processes of the formation and propagation of pollutants are very complex, the computational complexity of the mechanism model is relatively high, and the accuracy of the model needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

A method for calibrating measurement data of micro air quality monitor based on MLR-BRT-ARIMA combined model

Liu

Jiang

2022

Preprint

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In recent years, air pollution has become an increasingly important issue in the sustainable development of cities. Monitoring air pollutants is of great significance for government departments to effectively control air pollution. The development of micro air quality monitors provides the possibility for grid monitoring and real-time monitoring of air pollutants. However, affected by many factors, the measurement accuracy of the micro air quality monitors need to be improved. In this paper, a combined prediction model of Multiple Linear Regression, Boosted Regression Tree and AutoRegressive Integrated Moving Average model (MLR-BRT-ARIMA) is proposed to calibrate the measurement data of the micro air quality monitor. First, the multiple linear regression model is used to find the linear relationship between the concentration of various pollutants and the measurement data of the micro air quality monitor and obtain the predicted value of the concentration of various pollutants. Second, take the measurement data of the micro air quality monitor and the prediction value of the multiple regression model as the input, and use the boosted regression tree to find the nonlinear relationship between the concentrations of various pollutants and the input variables. Finally, the autoregressive integrated moving average model is used to extract the information hidden in the residual sequence, and finally the establishment of the MLR-BRT-ARIMA model is completed. This combined model combines the advantages of multiple linear regression and boosted regression trees. It can not only give the quantitative relationship between the explained variables and their influencing factors, but also the prediction accuracy is higher than the multiple linear regression and boosted regression tree models alone. Using the ARIMA model to correct the residuals can further improve the prediction accuracy of the model. Root mean square error, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of MLR-BRT-ARIMA model and other commonly used models such as multilayer perceptron neural network, support vector regression machine and nonlinear autoregressive models with exogenous input. The results show that no matter what kind of pollutant, the MLR-BRT-ARIMA combined model proposed in this paper has the best performance in the three indicators. Using this model to calibrate the measurement value of the micro air quality monitor can improve the accuracy by 82.4%~95.4%.

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Assessing Indoor Air Quality Using Chemometric Models

Cited by 6 publications

References 27 publications

Research on Data Correction Method of Micro Air Quality Detector Based on Combination of Partial Least Squares and Random Forest Regression

Research on Data Correction Method of Micro Air Quality Detector Based on Combination of Partial Least Squares and Random Forest Regression

A method for calibrating measurement data of a micro air quality monitor based on MLR-BRT-ARIMA combined model

A method for calibrating measurement data of micro air quality monitor based on MLR-BRT-ARIMA combined model

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