Managing air quality: Predicting exceedances of legal limits for PM10 and O3 concentration using machine learning methods

2021 ◽  
Author(s):  
Maryna Krylova ◽  
Yarema Okhrin
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Legal Limits

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 AQ-Bench: a benchmark dataset for machine learning on global air quality metrics

2021 ◽  
Vol 13 (6) ◽  
pp. 3013-3033
Author(s):  
Clara Betancourt ◽  
Timo Stomberg ◽  
Ribana Roscher ◽  
Martin G. Schultz ◽  
Scarlet Stadtler
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Tropospheric Ozone ◽  
Environmental Science ◽  
Quality Metrics ◽  
Quality Data ◽  
Easy Access ◽  
Linear Regression Method ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract. With the AQ-Bench dataset, we contribute to the recent developments towards shared data usage and machine learning methods in the field of environmental science. The dataset presented here enables researchers to relate global air quality metrics to easy-access metadata and to explore different machine learning methods for obtaining estimates of air quality based on this metadata. AQ-Bench contains a unique collection of aggregated air quality data from the years 2010–2014 and metadata at more than 5500 air quality monitoring stations all over the world, provided by the first Tropospheric Ozone Assessment Report (TOAR). It focuses in particular on metrics of tropospheric ozone, which has a detrimental effect on climate, human morbidity and mortality, as well as crop yields. The purpose of this dataset is to produce estimates of various long-term ozone metrics based on time-independent local site conditions. We combine this task with a suitable evaluation metric. Baseline scores obtained from a linear regression method, a fully connected neural network and random forest are provided for reference and validation. AQ-Bench offers a low-threshold entrance for all machine learners with an interest in environmental science and for atmospheric scientists who are interested in applying machine learning techniques. It enables them to start with a real-world problem relevant to humans and nature. The dataset and introductory machine learning code are available at https://doi.org/10.23728/b2share.30d42b5a87344e82855a486bf2123e9f (Betancourt et al., 2020) and https://gitlab.version.fz-juelich.de/esde/machine-learning/aq-bench (Betancourt et al., 2021). AQ-Bench thus provides a blueprint for environmental benchmark datasets as well as an example for data re-use according to the FAIR principles.

scholarly journals AQ-Bench: A Benchmark Dataset for Machine Learning on Global Air Quality Metrics

2021 ◽  
Author(s):  
Clara Betancourt ◽  
Timo Stomberg ◽  
Scarlet Stadtler ◽  
Ribana Roscher ◽  
Martin G. Schultz
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Tropospheric Ozone ◽  
Environmental Science ◽  
Quality Metrics ◽  
Quality Data ◽  
Easy Access ◽  
Linear Regression Method ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract. With the AQ-Bench dataset, we contribute to the recent developments towards shared data usage and machine learning methods in the field of environmental science. The dataset presented here enables researchers to relate global air quality metrics to easy-access metadata and to explore different machine learning methods for obtaining estimates of air quality based on this metadata. AQ-Bench contains a unique collection of aggregated air quality data from the years 2010–2014 and metadata at more than 5500 air quality monitoring stations all over the world, provided by the first Tropospheric Ozone Assessment Report (TOAR). It focuses in particular on metrics of tropospheric ozone, which has a detrimental effect on climate, human morbidity and mortality, as well as crop yields. We validate these data as a machine learning benchmark by providing a well-defined task together with a suitable evaluation metric. Baseline scores obtained from a linear regression method, a fully connected neural network and random forest are provided for reference. AQ-Bench offers a low-threshold entrance for all machine learners with an interest in environmental science and for atmospheric scientists who are interested in applying machine learning techniques. It enables them to start with a real-world problem relevant to humans and nature. The dataset and introductory machine learning code are available at https://doi.org/10.23728/b2share.30d42b5a87344e82855a486bf2123e9f (Betancourt et al., 2020) and https://gitlab.version.fz-juelich.de/toar/ozone-mapping . AQ-Bench thus provides a blueprint for environmental benchmark datasets as well as an example for data re-use according to the FAIR principles.

Comparison of Different Machine Learning Methods to Forecast Air Quality Index

2019 ◽  
pp. 235-245
Author(s):  
Bo Liu ◽  
Chao Shi ◽  
Jianqiang Li ◽  
Yong Li ◽  
Jianlei Lang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Identify the contribution of elevated industrial plume to ground air quality by optical and machine learning methods

2020 ◽  
Vol 2 (2) ◽  
pp. 021005
Author(s):  
Limin Feng ◽  
Ting Yang ◽  
Dawei Wang ◽  
Zifa Wang ◽  
Yuepeng Pan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals A concept of the air quality monitoring system in the city of Lublin with machine learning methods to detect data outliers

2019 ◽  
Vol 252 ◽  
pp. 03009 ◽  
Author(s):  
Tomasz Cieplak ◽  
Tomasz Rymarczyk ◽  
Robert Tomaszewski
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Monitoring System ◽  
Low Cost ◽  
Quality Monitoring ◽  
Spatiotemporal Variability ◽  
Air Quality Monitoring ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The City

This paper presents a concept of the air quality monitoring system design and describes a selection of data quality analysis methods. A high level of industrialisation affects the risk of natural disasters related to environmental pollution such ase.g.air pollution by gases and clouds of dust (carbon monoxide, sulphur oxides, nitrogen oxides). That is why researches related to the monitoring this type of phenomena are extremely important. Low-cost air quality sensors are more commonly used to monitor air parameters in urban areas. These types of sensors are used to obtain an image of the spatiotemporal variability in the concentration of air pollutants. Aside from their low price , which is important from a point of view of the economic accessibility of society, low-cost sensors are prone to produce erroneous results compared to professional air quality monitors. The described study focuses on the analysis of outliers as particularly interesting for further analysis, as well as modelling with machine learning methods for air quality assessment in the city of Lublin.

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