Development of a modeling approach to estimate indoor-to-outdoor sulfur ratios and predict indoor PM2.5 and black carbon concentrations for Eastern Massachusetts households

Tang, Chia Hsi; Garshick, Eric; Grady, Sue C.; Coull, Brent A.; Schwartz, Joel; Koutrakis, Petros

doi:10.1038/jes.2017.11

Cited by 33 publications

(24 citation statements)

References 34 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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“…More thoroughly, I/O concentration ratios calculated for sulfate suggest that during all campaigns an effective penetration of fine particles occurs indoors. This species is, in fact, considered as a good proxy for the estimation of infiltration factor in indoor environments, since it has no indoor sources and is thermally stable over time in both outdoor and indoor environments [52][53][54].…”

Section: Discussionmentioning

confidence: 99%

Comparison Study between Indoor and Outdoor Chemical Composition of PM2.5 in Two Italian Areas

2020

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Outdoor air quality guidelines have been constantly implemented during the last decades. Nonetheless, no international regulations have been put into action in terms of indoor air quality standards and standardized procedures for indoor pollution measurements. In this study, we investigated the chemical composition of PM 2.5 collected outdoors and indoors at six dwellings located in two Italian areas. The selected sites concerned inland/central and southern Italy, including urban, peri-urban, rural and coastal settings. The seasonal and site-specific particulate matter (PM) variations were analyzed outdoors and indoors, by estimating the impact of the main macro-sources and the contribution of the macro-and micro-components. Outdoors, organic matter represented the main contribution at inland and coastal sites, respectively during winter and summer. A clear, seasonal variation was also observed for secondary inorganic species. A site-specific dependence was exhibited by traffic-related components. Indoors, organic and soil-related species were influenced by the presence of the inhabitants. Some specific tracers allowed to identify additional local source contributions and indoor activities. Although the sampling season and site location defined the outdoor air quality, the higher PM concentrations and the chemical composition indoors were influenced by the infiltration of outdoor air and by the indoor activities carried out by its inhabitants.Atmosphere 2020, 11, 368 2 of 27 discussion of the scientific evidences about the adverse health effects of air pollution. This technical paper highlights that several components contribute to the health effects of PM, but that there is not enough evidence to associate the health outcomes to specific emission sources. The report underlines that most of the evidences are accumulated on cardiovascular and respiratory outcomes, that are generally related to traffic emissions, coal and oil combustion and biomass combustion, particularly in indoor environments located in low-income countries [5].Although scientific literature demonstrated that people spend more than 90% of their time in indoor environments, notably in dwellings, schools, offices and means of transport, no supranational legislation has been implemented in order to encompass indoor air quality standards [6,7]. In addition, no standard procedures have been introduced yet, in order to monitor indoor contaminants.Indeed, the evaluation of the exposure to indoor pollutants is far from being a simple task. The concentration and chemical composition of indoor air, in fact, depends on the release of contaminants from indoor sources as well as on their penetration from outdoors. As far as PM is concerned, the few studies whose focus was set on the chemical properties of particles suspended in indoor environments have required remarkable efforts. For this reason, they mostly concerned specific group of hazardous components such as elements, carbonaceous compounds or classes of species like polycyclic aromatic hydrocarbons [...

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“…Yuchi and Clark [ 13 , 14 ] used a multiple linear regression (MLR) model to predict indoor home PM 2.5 levels. Tang and his co-workers used the mass balance method to create a prediction model for indoor PM 2.5 levels in homes [ 15 ]. Elbayoumi used an MLR model to estimate indoor PM 2.5 levels in schools [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…Our previous study observed that outdoor air more critically affects indoor air quality in tropical and subtropical regions than in cold or temperate regions [ 21 ], particularly during cold seasons. Outdoor air is a critical source of PM 2.5 in homes [ 13 , 22 ], and indoor sources, such as cigarettes, cooking, or coal smoke from home heating stoves, are major indoor sources of PM 2.5 [ 13 , 14 , 15 ]. In tropical and subtropical regions, buildings are designed to reduce indoor heat and facilitate air conditioning operations for several hours, whereas in temperate regions, buildings are designed to trap heat.…”

Section: Introductionmentioning

confidence: 99%

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Development of Hourly Indoor PM2.5 Concentration Prediction Model: The Role of Outdoor Air, Ventilation, Building Characteristic, and Human Activity

Jung

Lin

Hsu

et al. 2020

IJERPH

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Exposure to indoor particulate matter less than 2.5 µm in diameter (PM2.5) is a critical health risk factor. Therefore, measuring indoor PM2.5 concentrations is important for assessing their health risks and further investigating the sources and influential factors. However, installing monitoring instruments to collect indoor PM2.5 data is difficult and expensive. Therefore, several indoor PM2.5 concentration prediction models have been developed. However, these prediction models only assess the daily average PM2.5 concentrations in cold or temperate regions. The factors that influence PM2.5 concentration differ according to climatic conditions. In this study, we developed a prediction model for hourly indoor PM2.5 concentrations in Taiwan (tropical and subtropical region) by using a multiple linear regression model and investigated the impact factor. The sample comprised 93 study cases (1979 measurements) and 25 potential predictor variables. Cross-validation was performed to assess performance. The prediction model explained 74% of the variation, and outdoor PM2.5 concentrations, the difference between indoor and outdoor CO2 levels, building type, building floor level, bed sheet cleaning, bed sheet replacement, and mosquito coil burning were included in the prediction model. Cross-validation explained 75% of variation on average. The results also confirm that the prediction model can be used to estimate indoor PM2.5 concentrations across seasons and areas. In summary, we developed a prediction model of hourly indoor PM2.5 concentrations and suggested that outdoor PM2.5 concentrations, ventilation, building characteristics, and human activities should be considered. Moreover, it is important to consider outdoor air quality while occupants open or close windows or doors for regulating ventilation rate and human activities changing also can reduce indoor PM2.5 concentrations.

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Development of a modeling approach to estimate indoor-to-outdoor sulfur ratios and predict indoor PM2.5 and black carbon concentrations for Eastern Massachusetts households

Cited by 33 publications

References 34 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

Comparison Study between Indoor and Outdoor Chemical Composition of PM2.5 in Two Italian Areas

Development of Hourly Indoor PM2.5 Concentration Prediction Model: The Role of Outdoor Air, Ventilation, Building Characteristic, and Human Activity

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