PM2.5 Estimation with the WRF/Chem Model, Produced by Vehicular Flow in the Lima Metropolitan Area

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"PM2.5 Estimation with the WRF/Chem Model, Produced by Vehicular Flow in the Lima Metropolitan Area"
written by Warren Reátegui-Romero, Odón R. Sánchez-Ccoyllo, María de Fatima Andrade, Aldo Moya-Alvarez,
published by Open Journal of Air Pollution, Vol.7 No.3, 2018

has been cited by the following article(s):

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[1]	Deep neural networks for spatiotemporal PM2. 5 forecasts based on atmospheric chemical transport model output and monitoring data
	Environmental Pollution, 2022

[2]	Evaluating the Impact of Vehicular Aerosol Emissions on Particulate Matter (PM2.5) Formation Using Modeling Study
	Ccoyllo, A Llacza, E Ayma-Choque… - Atmosphere, 2022

[3]	Atmospheric particulate pollution in South American megacities
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[4]	Un enfoque de visualización espacio-temporal para la exploración de datos de concentración de PM10 en Lima Metropolitana
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[5]	A Spatio-Temporal Visualization Approach of PM10 Concentration Data in Metropolitan Lima
	2021

[6]	Comparative Study of Spatial Prediction Models for Estimating PM Concentration Level in Urban Areas
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[7]	A New Model Using Multiple Feature Clustering and Neural Networks for Forecasting Hourly PM2. 5 Concentrations, and Its Applications in China
	2020

[8]	Evaluation of anthropogenic air pollutant emission inventories for South America at national and city scale
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[9]	Association of modeled PM2. 5 with aerosol optical depth: model versus satellite.
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[10]	Implementación de un modelo algorítmico para la estimación del nivel de concentración de contaminante PM2, 5 en zonas urbana
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[11]	Association of modeled PM 2.5 with aerosol optical depth: model versus satellite
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[12]	Association of modeled PM2.5 with aerosol optical depth: model versus satellite
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[13]	Analysis of fine particle pollution data measured at 29 US diplomatic posts worldwide
	2019

[14]	NGHIÊN CỨU VÀ ỨNG DỤNG PHƯƠNG PHÁP ƯỚC TÍNH NỒNG ĐỘ BỤI TỪ ẢNH VỆ TINH
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[15]	Simulation and Evaluation of PM10 in the City of Lima Using the Model WRF-Chem
	International Proceedings of Chemical, Biological and Environmental Engineering, 2018

[16]	Model-based real-time monitoring of large-scale urban traffic networks for decision making.

[1]	Handbook of Air Quality and Climate Change 2023 DOI:10.1007/978-981-15-2527-8_5-1

[2]	Handbook of Air Quality and Climate Change 2023 DOI:10.1007/978-981-15-2760-9_5

[3]	An Air Quality Monitoring and Forecasting System for Lima City With Low-Cost Sensors and Artificial Intelligence Models Frontiers in Sustainable Cities, 2022 DOI:10.3389/frsc.2022.849762

[4]	Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data Environmental Pollution, 2022 DOI:10.1016/j.envpol.2022.119348

[5]	Evaluating the Impact of Vehicular Aerosol Emissions on Particulate Matter (PM2.5) Formation Using Modeling Study Atmosphere, 2022 DOI:10.3390/atmos13111816

[6]	Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data Environmental Pollution, 2022 DOI:10.1016/j.envpol.2022.119348

[7]	A Spatio-Temporal Visualization Approach of PM10 Concentration Data in Metropolitan Lima Atmosphere, 2021 DOI:10.3390/atmos12050609

[8]	Information Management and Big Data Communications in Computer and Information Science, 2021 DOI:10.1007/978-3-030-76228-5_12

[9]	Atmospheric particulate pollution in South American megacities Environmental Reviews, 2021 DOI:10.1139/er-2020-0105

[10]	Evaluation of anthropogenic air pollutant emission inventories for South America at national and city scale Atmospheric Environment, 2020 DOI:10.1016/j.atmosenv.2020.117606

[11]	A New Model Using Multiple Feature Clustering and Neural Networks for Forecasting Hourly PM2.5 Concentrations, and Its Applications in China Engineering, 2020 DOI:10.1016/j.eng.2020.05.009

[12]	Association of modeled PM2.5 with aerosol optical depth: model versus satellite Natural Hazards, 2019 DOI:10.1007/s11069-019-03590-8

[13]	Analysis of fine particle pollution data measured at 29 US diplomatic posts worldwide Atmospheric Environment, 2019 DOI:10.1016/j.atmosenv.2019.05.070

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