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Posts tagged “modeling”

Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya

Johnson, M., Piedrahita, R., Pillarisetti, A., Shupler, M., Menya, D., Rossanese, M., DelapeƱa, S., Penumetcha, N., Chartier, R., Puzzolo, E. and Pope, D. (2021), Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya. Indoor Air. https://doi.org/10.1111/ina.12790

Boiled or Bottled: Regional and Seasonal Exposures to Drinking Water Contamination and Household Air Pollution in Rural China

Cohen A, Pillarisetti A, Luo Q, Zhang Q, Li H, Zhong G, Zhu G, Colford JM, Smith KR, Ray I, Tao Y. Boiled or Bottled: Regional and Seasonal Exposures to Drinking Water Contamination and Household Air Pollution in Rural China. Env. Health. Perspect. 2020; 128(12). doi.org/10.1289/EHP7124

Impacts of household sources on air pollution at village and regional scales in India

Rooney B, Zhao R, Wang Y, Bates KH, Pillarisetti A, Sharma S, Kundu S, Bond TC, Lam NL, Ozaltun B, Xu L, Goel V, Fleming LT, Weltman R, Meinardi S, Blake DR, Nizkorodov SA, Edwards RD, Yadav A, Arora NK, Smith KR, Seinfeld JH (2019) Impacts of household sources on air pollution at village and regional scales in India. Atmos Chem Phys 19:7719-7742.

Machine-learned modeling of PM2.5 exposures in rural Lao PDR

Hill LD, Pillarisetti A, Delapena S, Garland C, Pennise D, Pelletreau A, Koetting P, Motmans T, Vongnakhone K, Khammavong C, Boatman MR, Balmes K, Hubbard A, Smith Kr. 2019. Machine-learned modeling of PM2.5 exposures in rural Lao PDR. Science of The Total Environment, https://doi.org/10.1016/j.scitotenv.2019.04.258.

Tracking ambient PM2.5 build-up in Delhi national capital region during the dry season over 15 years using a high-resolution (1 km) satellite aerosol dataset

Chowdhury S, Dey S, Di Girolamo L, Smith KR, Pillarisetti A, Lyapustin A. Tracking ambient PM2.5 build-up in Delhi national capital region during the dry season over 15 years using a high-resolution (1 km) satellite aerosol dataset. Atmospheric Environment 204: 142-150. doi.org/10.1016/j.atmosenv.2019.02.029