TY - GEN
T1 - Predicting indoor concentrations of black carbon in residential environments
AU - Isiugo, Kelechi
AU - Jandarov, Roman
AU - Cox, Jennie
AU - Yermakov, Michael
AU - Wang, Julian
AU - Hyttinen, Marko
AU - Reponen, Tiina
N1 - Publisher Copyright:
© 2018 15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Exposure to black carbon (BC) is associated with adverse health effects. Cooking and heating with solid fuels are the major contributors of indoor BC, whereas vehicular emissions are the major outdoor source. Home characteristics such ventilation filters can reduce indoor levels of airborne particles, including BC. In this study, we developed and validated a predictive model for indoor BC concentrations. To achieve this task, concentrations of indoor and outdoor BC were measured in homes in Cincinnati. Home characteristics that could potentially modify indoor levels of BC were documented. A linear mixed effect model was developed to predict indoor BC by using the measured outdoor BC concentrations and documented housing characteristics as predictors of indoor BC. After the model was developed, a cross validation algorithm was used to test the accuracy of the predictive model. Predicted indoor BC concentrations explained 77% of the variability in the measured indoor BC concentrations.
AB - Exposure to black carbon (BC) is associated with adverse health effects. Cooking and heating with solid fuels are the major contributors of indoor BC, whereas vehicular emissions are the major outdoor source. Home characteristics such ventilation filters can reduce indoor levels of airborne particles, including BC. In this study, we developed and validated a predictive model for indoor BC concentrations. To achieve this task, concentrations of indoor and outdoor BC were measured in homes in Cincinnati. Home characteristics that could potentially modify indoor levels of BC were documented. A linear mixed effect model was developed to predict indoor BC by using the measured outdoor BC concentrations and documented housing characteristics as predictors of indoor BC. After the model was developed, a cross validation algorithm was used to test the accuracy of the predictive model. Predicted indoor BC concentrations explained 77% of the variability in the measured indoor BC concentrations.
UR - http://www.scopus.com/inward/record.url?scp=85105624465&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85105624465&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85105624465
T3 - 15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018
BT - 15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018
PB - International Society of Indoor Air Quality and Climate
T2 - 15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018
Y2 - 22 July 2018 through 27 July 2018
ER -