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Zhongyuan Mi

Showing results (1-10 of 13) with videos related to

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Environment International|June 29, 2020
Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United StatesXiang Ren, Zhongyuan Mi, Panos G Georgopoulos
Journal of Exposure Science & Environmental Epidemiology|February 1, 2023
Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approachesXiang Ren, Zhongyuan Mi, Panos G Georgopoulos
Atmospheric Environment (Oxford, England : 1994)|January 27, 2015
Predicting Onset and Duration of Airborne Allergenic Pollen Season in the United StatesYong Zhang, Leonard Bielory, Ting Cai, et al.
Environmental Science & Technology|March 21, 2022
Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone ConcentrationsXiang Ren, Zhongyuan Mi, Ting Cai, et al.
Global Change Biology|October 1, 2014
Allergenic pollen season variations in the past two decades under changing climate in the United StatesYong Zhang, Leonard Bielory, Zhongyuan Mi, et al.
European Journal of Cancer Prevention : the Official Journal of the European Cancer Prevention Organisation (ECP)|July 13, 2018
Enhanced exposure assessment and genome-wide DNA methylation in World Trade Center disaster respondersPei-Fen Kuan, Zhongyuan Mi, Panos Georgopoulos, et al.
Frontiers in Allergy|November 17, 2022
Modeling past and future spatiotemporal distributions of airborne allergenic pollen across the contiguous United StatesXiang Ren, Ting Cai, Zhongyuan Mi, et al.
The Science of the Total Environment|February 15, 2019
Development of a semi-mechanistic allergenic pollen emission modelTing Cai, Yong Zhang, Xiang Ren, et al.
Environmental Health Perspectives|January 6, 2010
Influence of cobalamin on arsenic metabolism in BangladeshMegan N Hall, Xinhua Liu, Vesna Slavkovich, et al.
The Science of the Total Environment|November 9, 2020
A hybrid approach to predict daily NO<sub>2</sub> concentrations at city block scaleXueying Zhang, Allan C Just, Hsiao-Hsien Leon Hsu, et al.
Pageof 2

Showing results (1-10 of 13) with videos related to

Sort By:
Pageof 2
Environment International|June 29, 2020
Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United StatesXiang Ren, Zhongyuan Mi, Panos G Georgopoulos
Journal of Exposure Science & Environmental Epidemiology|February 1, 2023
Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approachesXiang Ren, Zhongyuan Mi, Panos G Georgopoulos
Atmospheric Environment (Oxford, England : 1994)|January 27, 2015
Predicting Onset and Duration of Airborne Allergenic Pollen Season in the United StatesYong Zhang, Leonard Bielory, Ting Cai, et al.
Environmental Science & Technology|March 21, 2022
Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone ConcentrationsXiang Ren, Zhongyuan Mi, Ting Cai, et al.
Global Change Biology|October 1, 2014
Allergenic pollen season variations in the past two decades under changing climate in the United StatesYong Zhang, Leonard Bielory, Zhongyuan Mi, et al.
European Journal of Cancer Prevention : the Official Journal of the European Cancer Prevention Organisation (ECP)|July 13, 2018
Enhanced exposure assessment and genome-wide DNA methylation in World Trade Center disaster respondersPei-Fen Kuan, Zhongyuan Mi, Panos Georgopoulos, et al.
Frontiers in Allergy|November 17, 2022
Modeling past and future spatiotemporal distributions of airborne allergenic pollen across the contiguous United StatesXiang Ren, Ting Cai, Zhongyuan Mi, et al.
The Science of the Total Environment|February 15, 2019
Development of a semi-mechanistic allergenic pollen emission modelTing Cai, Yong Zhang, Xiang Ren, et al.
Environmental Health Perspectives|January 6, 2010
Influence of cobalamin on arsenic metabolism in BangladeshMegan N Hall, Xinhua Liu, Vesna Slavkovich, et al.
The Science of the Total Environment|November 9, 2020
A hybrid approach to predict daily NO<sub>2</sub> concentrations at city block scaleXueying Zhang, Allan C Just, Hsiao-Hsien Leon Hsu, et al.
Pageof 2