A Multi-model Air Quality System for Health Research: Road model development and evaluation

Seaton, Martin; O'Neill, James J.; Bien, Brian; Hood, Christina; Jackson, Mark; Jackson, Rose M.; Johnson, Kate; Oades, Molly; Stidworthy, Amy; Stocker, Jenny; Carruthers, David

doi:10.1016/j.envsoft.2022.105455

Cited by 12 publications

(2 citation statements)

References 44 publications

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“…For particulate matter PM 10 and PM 2.5 and NO 2 , the model uses annual concentrations modeled with the high-resolution model ADMS-URBAN. 47 , 48 The principle of the AZUR model of AtmoSud is the study of the relationship between annual values and daily values given by the measuring stations in nitrogen dioxide in ambient air. We found that for all pairs of measurement stations and for all ranges of values, the ratio of their daily values is a polynomial function of the ratio of their annual values.…”

Section: Methodsmentioning

confidence: 99%

Air pollution exposure induces a decrease in type II interferon response: A paired cohort study

Allouche

Crémoni²,

Brglez³

et al. 2022

eBioMedicine

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Section: Methodsmentioning

confidence: 99%

Air pollution exposure induces a decrease in type II interferon response: A paired cohort study

Allouche

Crémoni²,

Brglez³

et al. 2022

eBioMedicine

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“…[44] suggested using multi-models FL to deal with the heterogeneous statistic. Currently, some studies on the multi-model in AQI prediction are found in [88][89][90], but these applications are implemented on a single machine. Several authors have studied multi-model FL for other fields, as shown in [91][92][93] and [86,94,95].…”

Section: Multi-models Federated Learningmentioning

confidence: 99%

Insights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting

Khoa

Dao

et al. 2022

Algorithms

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The air quality index (AQI) forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning (ML) for air quality analysis have been published. However, most of those studies focused on traditional centralized processing on a single machine, and there had been few surveys of federated learning (FL) in this field. This overview aims to fill this gap and provide newcomers with a broader perspective to inform future research on this topic, especially for the multi-model approach. In this survey, we went over the works that previous scholars have conducted in AQI forecast both in traditional ML approaches and FL mechanisms. Our objective is to comprehend previous research on AQI prediction including methods, models, data sources, achievements, challenges, and solutions applied in the past. We also convey a new path of using multi-model FL, which has piqued the computer science community’s interest recently.

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Turbulent Kinetic Energy and Turbulence Dispersion Characteristics Over a Mixed Crop Area in Benin

Mamadou,

Hounsinou,

Kounouhéwa

2024

Advances in Science, Technology &Amp; Innovation

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A Multi-model Air Quality System for Health Research: Road model development and evaluation

Cited by 12 publications

References 44 publications

Air pollution exposure induces a decrease in type II interferon response: A paired cohort study

Air pollution exposure induces a decrease in type II interferon response: A paired cohort study

Insights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting

Turbulent Kinetic Energy and Turbulence Dispersion Characteristics Over a Mixed Crop Area in Benin

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