Airborne, vehicle-derived Fe-bearing nanoparticles in the urban environment: a review

T Gonet, BA Maher - Environmental Science & Technology, 2019 - ACS Publications
Airborne particulate matter poses a serious threat to human health. Exposure to nanosized
(< 0.1 μm), vehicle-derived particulates may be hazardous due to their bioreactivity, their …

Ambient ultrafine particle (PM0. 1): Sources, characteristics, measurements and exposure implications on human health

SFI Abdillah, YF Wang - Environmental Research, 2023 - Elsevier
The problem of ultrafine particles (UFPs; PM 0.1) has been prevalent since the past
decades. In addition to become easily inhaled by human respiratory system due to their …

[HTML][HTML] A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen …

J Chen, K de Hoogh, J Gulliver, B Hoffmann… - Environment …, 2019 - Elsevier
Empirical spatial air pollution models have been applied extensively to assess exposure in
epidemiological studies with increasingly sophisticated and complex statistical algorithms …

[HTML][HTML] Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations …

X Ren, Z Mi, PG Georgopoulos - Environment international, 2020 - Elsevier
Abstract Background Spatial linear Land-Use Regression (LUR) is commonly used for long-
term modeling of air pollution in support of exposure and epidemiological assessments …

Using a land use regression model with machine learning to estimate ground level PM2. 5

PY Wong, HY Lee, YC Chen, YT Zeng, YR Chern… - Environmental …, 2021 - Elsevier
Ambient fine particulate matter (PM 2.5) has been ranked as the sixth leading risk factor
globally for death and disability. Modelling methods based on having access to a limited …

[HTML][HTML] Map** urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea

CC Lim, H Kim, MJR Vilcassim, GD Thurston… - Environment …, 2019 - Elsevier
Recent studies have demonstrated that mobile sampling can improve the spatial granularity
of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (< …

Machine learning algorithms for smart data analysis in internet of things environment: taxonomies and research trends

MH Alsharif, AH Kelechi, K Yahya, SA Chaudhry - Symmetry, 2020 - mdpi.com
Machine learning techniques will contribution towards making Internet of Things (IoT)
symmetric applications among the most significant sources of new data in the future. In this …

Potential of machine learning for prediction of traffic related air pollution

A Wang, J Xu, R Tu, M Saleh, M Hatzopoulou - … Research Part D: Transport …, 2020 - Elsevier
Land use regression (LUR) has been extensively used to capture the spatial distribution of
air pollution. However, regional background and non-linear relationships can be …

Characterization of annual average traffic-related air pollution concentrations in the Greater Seattle Area from a year-long mobile monitoring campaign

MN Blanco, A Gassett, T Gould… - … science & technology, 2022 - ACS Publications
Growing evidence links traffic-related air pollution (TRAP) to adverse health effects. We
designed an innovative and extensive mobile monitoring campaign to characterize TRAP …

Methods for assessing long-term exposures to outdoor air pollutants

G Hoek - Current environmental health reports, 2017 - Springer
Abstract Purpose of Review Epidemiological studies of health effects of long-term exposure
to outdoor air pollution rely on different exposure assessment methods. This review …