An Enhanced Approach for Air Quality Forecasting Using Machine Learning

2021 ◽  
Vol 8 (5) ◽  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Air Quality Forecasting

Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods

2016 ◽  
Vol 10 (2) ◽  
pp. 195-211 ◽  
Author(s):  
Huiping Peng ◽  
Aranildo R. Lima ◽  
Andrew Teakles ◽  
Jian Jin ◽  
Alex J. Cannon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Air Quality Forecasting

scholarly journals Hybridization of Air Quality Forecasting Models Using Machine Learning and Clustering: An Original Approach to Detect Pollutant Peaks

2016 ◽  
Vol 16 (2) ◽  
pp. 405-416 ◽  
Author(s):  
Wani Tamas ◽  
Gilles Notton ◽  
Christophe Paoli ◽  
Marie-Laure Marie-Laure Nivet ◽  
Cyril Voyant
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Forecasting Models ◽  
Original Approach ◽  
Air Quality Forecasting

Air quality forecasting system based on chemical transport models

2019 ◽  
Vol 4 ◽  
pp. 203-218
Author(s):  
I.N. Kusnetsova ◽  
◽  
I.U. Shalygina ◽  
M.I. Nahaev ◽  
U.V. Tkacheva ◽  
...  
Keyword(s):  
Air Quality ◽  
Chemical Transport ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Forecasting System ◽  
Air Quality Forecasting

scholarly journals Machine Learning-Based Prediction of Air Quality

2020 ◽  
Vol 10 (24) ◽  
pp. 9151
Author(s):  
Yun-Chia Liang ◽  
Yona Maimury ◽  
Angela Hsiang-Ling Chen ◽  
Josue Rodolfo Cuevas Juarez
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Random Forest ◽  
Prediction Models ◽  
Superior Performance ◽  
Support Vector ◽  
Economic Activities ◽  
Adaptive Boosting ◽  
Series Of Experiments ◽  
Artificial Neural Network Ann

Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.

scholarly journals Description and Evaluation of the Fine Particulate Matter Forecasts in the NCAR Regional Air Quality Forecasting System

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 302
Author(s):  
Rajesh Kumar ◽  
Piyush Bhardwaj ◽  
Gabriele Pfister ◽  
Carl Drews ◽  
Shawn Honomichl ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Information Dissemination ◽  
Fine Particulate Matter ◽  
Model Errors ◽  
Air Quality Forecast ◽  
Fine Particulate ◽  
Regional Air Quality ◽  
Forecasting System ◽  
Air Quality Forecasting

This paper describes a quasi-operational regional air quality forecasting system for the contiguous United States (CONUS) developed at the National Center for Atmospheric Research (NCAR) to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the atmospheric science community in their research. This system aims to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA), not to replace them. A publicly available information dissemination system has been established that displays various air quality products, including a near-real-time evaluation of the model forecasts. Here, we report the performance of our air quality forecasting system in simulating meteorology and fine particulate matter (PM2.5) for the first year after our system started, i.e., 1 June 2019 to 31 May 2020. Our system shows excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but shows relatively larger errors in wind speed. The model also captures the seasonal cycle of surface PM2.5 very well in different regions and for different types of sites (urban, suburban, and rural) in the CONUS with a mean bias smaller than 1 µg m−3. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at a NCAR webpage. We invite the community to use our forecasting products for their research, as input for urban scale (<4 km), air quality forecasts, or the co-development of customized products, just to name a few applications.

Sign in / Sign up

Export Citation Format

Share Document