‘Erratum to “Application of WRF/Chem-MADRID for real-time air quality forecasting over the Southeastern United States” [Atmos. Environ. 45 (2011) 6241–6250]’

Atmospheric Environment ◽

10.1016/j.atmosenv.2012.07.019 ◽

2012 ◽

Vol 60 ◽

pp. 677-678 ◽

Author(s):

Ming-Tung Chuang ◽

Yang Zhang ◽

Daiwen Kang

Keyword(s):

United States ◽

Air Quality ◽

Real Time ◽

Southeastern United States ◽

Atmos Environ ◽

Air Quality Forecasting

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Real-time air quality forecasting over the southeastern United States using WRF/Chem-MADRID: Multiple-year assessment and sensitivity studies

Atmospheric Environment ◽

10.1016/j.atmosenv.2014.04.024 ◽

2014 ◽

Vol 92 ◽

pp. 318-338 ◽

Author(s):

Khairunnisa Yahya ◽

Yang Zhang ◽

Jeffrey M. Vukovich

Keyword(s):

United States ◽

Air Quality ◽

Real Time ◽

Southeastern United States ◽

Sensitivity Studies ◽

Air Quality Forecasting

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Application of WRF/Chem-MADRID Over Southeastern United States: Real-Time Air Quality Forecasting and Sensitivity Simulations

DEStech Transactions on Environment Energy and Earth Science ◽

10.12783/dteees/edep2017/15548 ◽

2017 ◽

Author(s):

YANG ZHANG ◽

CHINMAY JENA ◽

KAI WANG ◽

CHEN YUAN

Keyword(s):

United States ◽

Air Quality ◽

Real Time ◽

Southeastern United States ◽

Air Quality Forecasting

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Application of WRF/Chem-MADRID for real-time air quality forecasting over the Southeastern United States

Atmospheric Environment ◽

10.1016/j.atmosenv.2011.06.071 ◽

2011 ◽

Vol 45 (34) ◽

pp. 6241-6250 ◽

Author(s):

Ming-Tung Chuang ◽

Yang Zhang ◽

Daiwen Kang

Keyword(s):

United States ◽

Air Quality ◽

Real Time ◽

Southeastern United States ◽

Air Quality Forecasting

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Comprehensive evaluation of multi-year real-time air quality forecasting using an online-coupled meteorology-chemistry model over southeastern United States

Atmospheric Environment ◽

10.1016/j.atmosenv.2016.05.006 ◽

2016 ◽

Vol 138 ◽

pp. 162-182 ◽

Author(s):

Yang Zhang ◽

Chaopeng Hong ◽

Khairunnisa Yahya ◽

Qi Li ◽

Qiang Zhang ◽

...

Keyword(s):

United States ◽

Air Quality ◽

Real Time ◽

Comprehensive Evaluation ◽

Southeastern United States ◽

Air Quality Forecasting

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Corrigendum to “Development of ANFIS model for air quality forecasting and input optimization for reducing the computational cost and time” [Atmos. Environ. 128 (2016) 246–262]

Atmospheric Environment ◽

10.1016/j.atmosenv.2016.08.012 ◽

2016 ◽

Vol 142 ◽

pp. 370 ◽

Author(s):

Kanchan Prasad ◽

Amit Kumar Gorai ◽

Pramila Goyal

Keyword(s):

Air Quality ◽

Computational Cost ◽

Atmos Environ ◽

Anfis Model ◽

Input Optimization ◽

Air Quality Forecasting

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Long-term measurements of the transport of African mineral dust to the southeastern United States: Implications for regional air quality

Journal of Geophysical Research Atmospheres ◽

10.1029/1999jd900072 ◽

1999 ◽

Vol 104 (D13) ◽

pp. 15917-15927 ◽

Author(s):

Joseph M. Prospero

Keyword(s):

United States ◽

Air Quality ◽

Mineral Dust ◽

Southeastern United States ◽

Regional Air Quality

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Sensing air quality: Spatiotemporal interpolation and visualization of real-time air pollution data for the contiguous United States

Spatiotemporal Analysis of Air Pollution and Its Application in Public Health ◽

10.1016/b978-0-12-815822-7.00008-x ◽

2020 ◽

pp. 169-196 ◽

Author(s):

Marc Kalo ◽

Xiaolu Zhou ◽

Lixin Li ◽

Weitian Tong ◽

Reinhard Piltner

Keyword(s):

United States ◽

Air Pollution ◽

Air Quality ◽

Real Time ◽

Spatiotemporal Interpolation

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A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks

Neural Computing and Applications ◽

10.1007/s00521-019-04287-6 ◽

2019 ◽

Vol 32 (11) ◽

pp. 7563-7579 ◽

Author(s):

Ebrahim Eslami ◽

Ahmed Khan Salman ◽

Yunsoo Choi ◽

Alqamah Sayeed ◽

Yannic Lops

Keyword(s):

Neural Networks ◽

Air Quality ◽

Real Time ◽

Deep Neural Networks ◽

Extremely Randomized Trees ◽

Ensemble Approach ◽

Air Quality Forecasting

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Air quality forecasting using artificial neural networks with real time dynamic error correction in highly polluted regions

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.139454 ◽

2020 ◽

Vol 735 ◽

pp. 139454 ◽

Author(s):

Shivang Agarwal ◽

Sumit Sharma ◽

Suresh R. ◽

Md H. Rahman ◽

Stijn Vranckx ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Air Quality ◽

Error Correction ◽

Real Time ◽

Dynamic Error ◽

Time Dynamic ◽

Artificial Neural ◽

Air Quality Forecasting

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Corrigendum to “Air quality impacts from the electrification of light-duty passenger vehicles in the United States” [Atmos. Environ. 208 (2019) 95–102]

Atmospheric Environment ◽

10.1016/j.atmosenv.2020.117487 ◽

2020 ◽

Vol 229 ◽

pp. 117487

Author(s):

Jordan L. Schnell ◽

Vaishali Naik ◽

Larry W. Horowitz ◽

Fabien Paulot ◽

Paul Ginoux ◽

...

Keyword(s):

United States ◽

Air Quality ◽

The United States ◽

Atmos Environ ◽

Passenger Vehicles ◽

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