Analysis and prediction of indoor air pollutants in a subway station using a new key variable selection method

Lim, Jung Jin; Kim, Yong Su; Oh, Tae Suk; Kim, Min Jung; Kang, OnYu; Kim, Jeong Tai; Kim, In Won; Kim, Jo Chun; Jeon, Jae Sik

doi:10.1007/s11814-011-0278-z

Cited by 11 publications

(11 citation statements)

References 18 publications

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“…Crowding, dwelling type, and proximity to major roads are reported as important predictors of PM 2.5 levels, and this also emerged in our study.…”

Section: Discussionsupporting

confidence: 84%

“…In addition, we observed that the gap between the wall and the roof and cross-ventilation also had a positive effect in the model. Crowding, dwelling type, and proximity to major roads are reported as important predictors of PM 2.5 levels, 8,[20][21][22] and this also emerged in our study.…”

Section: Discussionsupporting

confidence: 84%

Section: Resultsmentioning

confidence: 68%

“…This study builds upon other studies predicting the PM 2.5 levels, providing evidence that PM 2.5 levels can be predicted using the household characteristics, occupant activities, and outdoor pollution sources. 1,8,10,[20][21][22] The study provides confidence that the model can be used to predict residential indoor PM 2.5 exposure for participants in the MACE cohort. This model is sufficiently robust to be employed in other similar urban settings that are not using biomass fuel as a primary energy source and experience resource constraints for more ambitious exposure assessment techniques.…”

Section: Discussionmentioning

confidence: 77%

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Developing a predictive model for fine particulate matter concentrations in low socio-economic households in Durban, South Africa

et al. 2017

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In low-resource settings, there is a need to develop models that can address contributions of household and outdoor sources to population exposures. The aim of the study was to model indoor PM using household characteristics, activities, and outdoor sources. Households belonging to participants in the Mother and Child in the Environment (MACE) birth cohort, in Durban, South Africa, were randomly selected. A structured walk-through identified variables likely to generate PM . MiniVol samplers were used to monitor PM for a period of 24 hours, followed by a post-activity questionnaire. Factor analysis was used as a variable reduction tool. Levels of PM in the south were higher than in the north of the city (P < .05); crowding and dwelling type, household emissions (incense, candles, cooking), and household smoking practices were factors associated with an increase in PM levels (P < .05), while room magnitude and natural ventilation factors were associated with a decrease in the PM levels (P < .05). A reasonably robust PM predictive model was obtained with model R of 50%. Recognizing the challenges in characterizing exposure in environmental epidemiological studies, particularly in resource-constrained settings, modeling provides an opportunity to reasonably estimate indoor pollutant levels in unmeasured homes.

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“…Crowding, dwelling type, and proximity to major roads are reported as important predictors of PM 2.5 levels, and this also emerged in our study.…”

Section: Discussionsupporting

confidence: 84%

Section: Discussionsupporting

confidence: 84%

Section: Resultsmentioning

confidence: 68%

Section: Discussionmentioning

confidence: 77%

See 2 more Smart Citations

Developing a predictive model for fine particulate matter concentrations in low socio-economic households in Durban, South Africa

et al. 2017

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“…For the prediction of PM concentrations in a subway station, both MLR and PLS models were used. 66 A comparison of the RMSE between the MLR and PLS models shows that the RMSE values for the PLS model are higher than those for the MLR for training, but lower for testing ( Figure 3). The RMSE values for training and testing are similar, indicating the robustness of the PLS models for the studied cases.…”

Section: Regressionsmentioning

confidence: 95%

Machine learning and statistical models for predicting indoor air quality

et al. 2019

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Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario-based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed.Thirty-seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM 2.5 and PM 10 ) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees. K E Y W O R D Sartificial neural networks, data mining, IAQ, partial least squares, particulate matter, regression | 705 WEI Et al.

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Probabilistic concentration prediction of PM2.5 in subway stations based on multi-resolution elastic-gated attention mechanism and Gaussian mixture model

Fang,

Liu

2023

J. Cent. South Univ.

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Analysis and prediction of indoor air pollutants in a subway station using a new key variable selection method

Cited by 11 publications

References 18 publications

Developing a predictive model for fine particulate matter concentrations in low socio-economic households in Durban, South Africa

Developing a predictive model for fine particulate matter concentrations in low socio-economic households in Durban, South Africa

Machine learning and statistical models for predicting indoor air quality

Probabilistic concentration prediction of PM2.5 in subway stations based on multi-resolution elastic-gated attention mechanism and Gaussian mixture model

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