Constructing transferable and interpretable machine learning models for black carbon concentrations

Fung, Pak Lun; Savadkoohi, Marjan; Zaidan, Martha Arbayani; Niemi, Jarkko V.; Timonen, Hilkka; Pandolfi, Marco; Alastuey, Andrés; Querol, Xavier; Hussein, Tareq; Petäjä, Tuukka

doi:10.1016/j.envint.2024.108449

Cited by 4 publications

(2 citation statements)

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“…Regardless of the number of data points, they all except the "warm burning" scenario obtained satisfactory to good coefficient of determination (0.63 < R 2 < 0.77) with high Pearson coefficients of correlation (0.80 < r < 0.88). This satisfactory to good accuracy for estimating indoor eBC is comparable to the results conducted for ambient eBC concentrations (Fung et al, 2024). The exception "warm burning" had a relatively low accuracy (R 2 = 0.49) presumably because the particles emitted through burning processes during the warm season were not well captured by the measured aerosol variables.…”

Section: Evaluation Of the Indoor Bc Proxysupporting

confidence: 74%

“…Among all models, white-box models input-adaptive proxy (IAP) and least absolute shrinkage and selection operator (LASSO) have been recommended due to their flexibility and efficiency (Fung et al, 2021). These two models have also demonstrated their high transferability and replicability to upscale BC concentrations from one environment to another (Fung et al, 2024). Since indoor BC has been shown to highly correlate with outdoor BC concentration (Isiugo et al, 2019), these models for outdoor BC would be good alternatives to be deployed for indoor BC.…”

Section: Introductionmentioning

confidence: 99%

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Indoor Black Carbon Concentrations and their Sources in Residential Environments: Validation of an Input-adaptive Proxy Model

Zhao,

Fung,

Zaidan

et al. 2024

Aerosol Air Qual. Res.

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Exposure to black carbon (BC) in the residential environment was found to be positively associated with elevated blood pressure and cardiovascular disease. However, BC has been under-measured and under-studied compared to other common indoor gaseous and particulate pollutants. Representative indoor mass concentrations of equivalent black carbon (eBC) and the sources' contribution from indoors and outdoors in real-life residential environments in 40 German households were evaluated and presented in this work. During the 500 measurement days, the mean indoor eBC mass concentration was 0.6 µg m -3 , which is less than half of the outdoor concentration in the urban background in Germany. However, common indoor sources contributed differently to indoor eBC, which also resulted in higher mass concentrations in the cold season than in the warm season. Indoor pollutant measurements are often performed with only a limited number of instruments and pollutant data. To fill in the missing knowledge of indoor BC, a proxy model was developed. This proxy model can predict indoor eBC concentrations based on existing indoor databases or in cases where direct measurements of indoor eBC concentrations are not available. Due to the complex influence of climate and indoor activities, the model separated six scenarios for weather (including warm and cold seasons) and indoor activities (burning, non-burning, and other activities) for typical urban residential environments in Germany. In this study, indoor eBC mass concentrations were found to be best estimated by indoor and outdoor PM1. For different scenarios, the model achieved a satisfactory to good coefficient of determination (0.49 < R 2 < 0.77). With the aid of this model, a more accurate prediction of indoor eBC mass concentration and the resulting exposure and health risk assessment can be achieved for households under similar climatic conditions and activity habits of the occupants, e.g., in Central Europe.

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Section: Evaluation Of the Indoor Bc Proxysupporting

confidence: 74%