scholarly journals Rigorous Quantification of the statistical significance of COVID-19 lockdown effect on air quality: The case from ground-based measurements in Ontario, Canada

2020 ◽  
Author(s):  
Hind A. Al-Abadleh ◽  
Martin Lysy ◽  
Lucas Neil ◽  
Priyesh Patel ◽  
Wisam Mohammed ◽  
...  
Keyword(s):  
Air Quality ◽  
Statistical Significance ◽  
Statistical Testing ◽  
Quality Data ◽  
Satellite Measurements ◽  
Similar Time ◽  
Southern Ontario ◽  
Testing Framework ◽  
Pollutant Level ◽  
Air Quality Data

Preliminary analysis of satellite measurements from around the world showed drops in nitrogen dioxide (NO<sub>2</sub>) with lockdowns due to the COVID-19 pandemic. A number of studies have found these drops to be correlated with local decreases in transportation and/or industry. None of these studies, however, has rigorously quantified the statistical significance of these drops relative to natural meteorological variability and other factors that influence pollutant levels during similar time periods in previous years. Here, we develop a novel statistical testing framework that accounts for seasonal variability, transboundary influences, and new factors such as COVID-19 restrictions in explaining trends in several pollutant levels at 16 ground-based measurement sites in Southern Ontario, Canada. We find statistically significant and temporary drops in NO<sub>2</sub> (11 out 16 sites) and CO (all 4 sites) in April-June 2020, with pollutant levels 20% lower than in the previous three years. Much fewer sites (2-3 out of 16) experienced statistically significant drops in O<sub>3</sub> and PM2.5.<b> </b>The statistical testing framework developed here is the first of its kind applied to air quality data, and highlights the need for rigorous assessment of statistical significance, should analyses of pollutant level changes post COVID-19 lockdowns be used to inform policy decisions in Ontario, Canada. See Methods section in the manuscript.

scholarly journals Statistical Quantification of COVID-19 Lockdown Effect on Air Quality from Ground-Based Measurements in Ontario, Canada

2020 ◽  
Author(s):  
Hind A. Al-Abadleh ◽  
Marin Lysy ◽  
Lucas Neil ◽  
Priyesh Patel ◽  
Wisam Mohammed ◽  
...  
Keyword(s):  
Air Quality ◽  
Statistical Significance ◽  
Statistical Testing ◽  
Quality Data ◽  
Satellite Measurements ◽  
Similar Time ◽  
Southern Ontario ◽  
Testing Framework ◽  
Pollutant Level ◽  
Air Quality Data

Preliminary analysis of satellite measurements from around the world showed drops in nitrogen dioxide (NO<sub>2</sub>) with lockdowns due to the COVID-19 pandemic. A number of studies have found these drops to be correlated with local decreases in transportation and/or industry. None of these studies, however, has rigorously quantified the statistical significance of these drops relative to natural meteorological variability and other factors that influence pollutant levels during similar time periods in previous years. Here, we develop a novel statistical testing framework that accounts for seasonal variability, transboundary influences, and new factors such as COVID-19 restrictions in explaining trends in several pollutant levels at 16 ground-based measurement sites in Southern Ontario, Canada. We find statistically significant and temporary drops in NO<sub>2</sub> (11 out 16 sites) and CO (all 4 sites) in April-June 2020, with pollutant levels 20% lower than in the previous three years. Much fewer sites (2-3 out of 16) experienced statistically significant drops in O<sub>3</sub> and PM2.5.<b> </b>The statistical testing framework developed here is the first of its kind applied to air quality data, and highlights the need for rigorous assessment of statistical significance, should analyses of pollutant level changes post COVID-19 lockdowns be used to inform policy decisions in Ontario, Canada. See Methods section in the manuscript.

scholarly journals Rigorous Quantification of the statistical significance of COVID-19 lockdown effect on air quality: The case from ground-based measurements in Ontario, Canada

2020 ◽  
Author(s):  
Hind A. Al-Abadleh ◽  
Martin Lysy ◽  
Lucas Neil ◽  
Priyesh Patel ◽  
Wisam Mohammed ◽  
...  
Keyword(s):  
Air Quality ◽  
Statistical Significance ◽  
Statistical Testing ◽  
Quality Data ◽  
Satellite Measurements ◽  
Similar Time ◽  
Southern Ontario ◽  
Testing Framework ◽  
Pollutant Level ◽  
Air Quality Data

Preliminary analysis of satellite measurements from around the world showed drops in nitrogen dioxide (NO<sub>2</sub>) with lockdowns due to the COVID-19 pandemic. A number of studies have found these drops to be correlated with local decreases in transportation and/or industry. None of these studies, however, has rigorously quantified the statistical significance of these drops relative to natural meteorological variability and other factors that influence pollutant levels during similar time periods in previous years. Here, we develop a novel statistical testing framework that accounts for seasonal variability, transboundary influences, and new factors such as COVID-19 restrictions in explaining trends in several pollutant levels at 16 ground-based measurement sites in Southern Ontario, Canada. We find statistically significant and temporary drops in NO<sub>2</sub> (11 out 16 sites) and CO (all 4 sites) in April-June 2020, with pollutant levels 20% lower than in the previous three years. Much fewer sites (2-3 out of 16) experienced statistically significant drops in O<sub>3</sub> and PM2.5.<b> </b>The statistical testing framework developed here is the first of its kind applied to air quality data, and highlights the need for rigorous assessment of statistical significance, should analyses of pollutant level changes post COVID-19 lockdowns be used to inform policy decisions in Ontario, Canada. See Methods section in the manuscript.

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

aiRe - A web-based R application for simple, accessible and repeatable analysis of urban air quality data

2021 ◽  
Vol 138 ◽  
pp. 104976
Author(s):  
Juan José Díaz ◽  
Ivan Mura ◽  
Juan Felipe Franco ◽  
Raha Akhavan-Tabatabaei
Keyword(s):  
Air Quality ◽  
Urban Air Quality ◽  
Quality Data ◽  
Urban Air ◽  
Web Based ◽  
Air Quality Data
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