David Broday

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Research activity per year

Research interests

Main research interests:

Air pollution – dispersion of airborne pollutants, source apportionment, spatiotemporal variability of pollutant concentrations
Atmospheric physics – satellite-borne remote sensing/ Earth observation, aerosols, climate change
Emerging environmental technologies – wireless distributed air quality sensor networks, atmospheric moisture harvesting, environmental data science
Estimation of exposure to air pollution

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Dive into the research topics where David Broday is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

1 Similar Researchers

113 Article
3 Chapter
3 Conference contribution
2 Review article
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Revealing causality in the associations between meteorological variables and air pollutant concentrations
Yuval, Levi, Y. & Broday, D. M., 15 Mar 2024, In: Environmental Pollution. 345, 123526.
Research output: Contribution to journal › Article › peer-review
Accounting for the aerosol type and additional satellite-borne aerosol products improves the prediction of PM_2.5 concentrations
Falah, S., Kizel, F., Banerjee, T. & Broday, D. M., 1 Mar 2023, In: Environmental Pollution. 320, 121119.
Research output: Contribution to journal › Article › peer-review
6 Scopus citations
Prenatal and postnatal exposure to NO₂ and rapid infant weight gain – A population-based cohort study
Alterman, N., Youssim, I., Nevo, D., Calderon-Margalit, R., Yuval, Broday, D., Hauzer, M. & Raz, R., Nov 2023, In: Paediatric and Perinatal Epidemiology. 37, 8, p. 669-678 10 p.
Research output: Contribution to journal › Article › peer-review

Open Access
1 Scopus citations
Exposure errors due to inaccurate residential addresses and their impact on epidemiological associations: evidence from a national neonate dataset
Harari-Kremer, R., Calderon-Margalit, R., Zudman, Y., Broday, D., Kloog, I. & Raz, R., 2022, In: International Journal of Hygiene and Environmental Health. 246, 114032
Research output: Contribution to journal › Article › peer-review

1 Scopus citations
Imputation of Missing PM_2.5 Observations in a Network of Air Quality Monitoring Stations by a New kNN Method
Belachsen, I. & Broday, D. M., Nov 2022, In: ATMOSPHERE. 13, 11, 1934.
Research output: Contribution to journal › Article › peer-review

Open Access
3 Scopus citations

David Broday

Personal profile

Research interests

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Collaborations and top research areas from the last five years

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Revealing causality in the associations between meteorological variables and air pollutant concentrations

Accounting for the aerosol type and additional satellite-borne aerosol products improves the prediction of PM_2.5 concentrations

Prenatal and postnatal exposure to NO₂ and rapid infant weight gain – A population-based cohort study

Exposure errors due to inaccurate residential addresses and their impact on epidemiological associations: evidence from a national neonate dataset

Imputation of Missing PM_2.5 Observations in a Network of Air Quality Monitoring Stations by a New kNN Method

David Broday

Personal profile

Research interests

Fingerprint

Collaborations and top research areas from the last five years

Research output

Revealing causality in the associations between meteorological variables and air pollutant concentrations

Accounting for the aerosol type and additional satellite-borne aerosol products improves the prediction of PM2.5 concentrations

Prenatal and postnatal exposure to NO2 and rapid infant weight gain – A population-based cohort study

Exposure errors due to inaccurate residential addresses and their impact on epidemiological associations: evidence from a national neonate dataset

Imputation of Missing PM2.5 Observations in a Network of Air Quality Monitoring Stations by a New kNN Method

Accounting for the aerosol type and additional satellite-borne aerosol products improves the prediction of PM_2.5 concentrations

Prenatal and postnatal exposure to NO₂ and rapid infant weight gain – A population-based cohort study

Imputation of Missing PM_2.5 Observations in a Network of Air Quality Monitoring Stations by a New kNN Method