Predicting indoor concentrations of black carbon in residential environments

Isiugo, Kelechi; Jandarov, Roman; Cox, Jennie; Chillrud, S. N.; Grinshpun, Sergey A.; Hyttinen, Marko; Yermakov, Michael; Wang, Julian; Ross, James; Reponen, Tiina

doi:10.1016/j.atmosenv.2018.12.053

Cited by 18 publications

(23 citation statements)

References 51 publications

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“…The study in [23] aims to demonstrate the use of machine learning methods in predicting ambient CO 2 . Other research also attempt to develop proxies for estimating BC indoors and outdoors using regression analysis [24], [25] and machine learning methods [12], [26], [27].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

et al. 2020

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This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models. While, due to the non-linear relationship to reference instruments, fine particulate matter (PM2.5) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO2) does not present correlation to reference instrument. As a result, the LCS for CO2 is not feasible to be calibrated. Hence, to estimate the CO2 concentration, mathematical models are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities.

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Section: Introductionmentioning

confidence: 99%

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

et al. 2020

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“…Usually about 70% of the data are used for training, while the other 30% are used for model validation (15%) and testing (15%) . The most common validation algorithm is the leave‐one‐out cross‐validation . The model may also be tested using new data sets obtained in other cases than the original data set .…”

Section: Resultsmentioning

confidence: 99%

“…46,47 The most common validation algorithm is the leave-oneout cross-validation. 48 The model may also be tested using new data sets obtained in other cases than the original data set. 49 More detailed information on some statistical models that have been applied to the field of IAQ is presented below.…”

Section: Modelmentioning

confidence: 99%

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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“…Examples include the global transport model [28], regional climate-atmospheric chemistry model [29], land use regression models [30], and the BC mixing state model [31]. Recently, BC has been modelled using statistical distributions [32] and the linear mixed-effect model [33]. However, these models do not act as a proxy where BC concentrations can be estimated using other measured variables.…”

Section: Data-driven Air Pollution Modelsmentioning

confidence: 99%

Bayesian Proxy Modelling for Estimating Black Carbon Concentrations using White-Box and Black-Box Models

et al. 2019

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Black carbon (BC) is an important component of particulate matter (PM) in urban environments. BC is typically emitted from gas and diesel engines, coal-fired power plants, and other sources that burn fossil fuel. In contrast to PM, BC measurements are not always available on a large scale due to the operational cost and complexity of the instrumentation. Therefore, it is advantageous to develop a mathematical model for estimating the quantity of BC in the air, termed a BC proxy, to enable widening of spatial air pollution mapping. This article presents the development of BC proxies based on a Bayesian framework using measurements of PM concentrations and size distributions from 10 to 10,000 nm from a recent mobile air pollution study across several areas of Jordan. Bayesian methods using informative priors can naturally prevent over-fitting in the modelling process and the methods generate a confidence interval around the prediction, thus the estimated BC concentration can be directly quantified and assessed. In particular, two types of models are developed based on their transparency and interpretability, referred to as white-box and black-box models. The proposed methods are tested on extensive data sets obtained from the measurement campaign in Jordan. In this study, black-box models perform slightly better due to their model complexity. Nevertheless, the results demonstrate that the performance of both models does not differ significantly. In practice, white-box models are relatively more convenient to be deployed, the methods are well understood by scientists, and the models can be used to better understand key relationships.

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Predicting indoor concentrations of black carbon in residential environments

Cited by 18 publications

References 51 publications

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

Machine learning and statistical models for predicting indoor air quality

Bayesian Proxy Modelling for Estimating Black Carbon Concentrations using White-Box and Black-Box Models

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