Air Quality Analysis in Lima, Peru Using the NO2 Levels during the COVID-19 Pandemic Lockdown

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Abstract

The emergence of the new COVID-19 virus in Peru forced the Peruvian government to take swift measures to stop its proliferation. Consequently, a state of emergency was declared, which included mandatory social isolation and quarantine. This action meant that people would transit only in emergency cases. In this context, this study’s objective is to analyze the air quality changes in terms of the capital city’s NO2 levels due to these government decisions using satellite imagery data obtained from the Sentinel-5P satellite. One critical problem is the lack of spatially distributed air quality data. The Peruvian Meteorological Service only monitors air quality in Lima, the capital city. In addition, the air quality ground stations are not always functioning. Thus, there is a need to find new reliable methods to complement the official data obtained. One method of doing so is the use of remote sensing products, although the accuracy and applicability are yet to be determined; therefore, this is the article’s focus. A temporal and spatial analysis was developed quantitatively and qualitatively to measure the levels of NO2 in eighteen regions of Lima to contrast the quarantine’s effect on polluting gas emission levels. The measurements are also compared with the official Peruvian data from ground sensors using Pearson correlation coefficients, thus, showing that Sentinel-5P data can be used for changes in the mean daily concentration of NO2. We also developed the first version of an open platform that converts the satellite data into a friendly format for visualization. The results show NO2 ambient concentration reductions compared to 2019 of between 60% and 40% in the first two weeks and between 50% and 25% in the following two weeks of the COVID-19 lockdown. However, this effect could not be observed two months after the start of the lockdown.

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Correlation Coefficients

Patrick Schober, Christa Boer, Lothar A Schwarte (2018)

Correlation in the broadest sense is a measure of an association between variables. In correlated data, the change in the magnitude of 1 variable is associated with a change in the magnitude of another variable, either in the same (positive correlation) or in the opposite (negative correlation) direction. Most often, the term correlation is used in the context of a linear relationship between 2 continuous variables and expressed as Pearson product-moment correlation. The Pearson correlation coefficient is typically used for jointly normally distributed data (data that follow a bivariate normal distribution). For nonnormally distributed continuous data, for ordinal data, or for data with relevant outliers, a Spearman rank correlation can be used as a measure of a monotonic association. Both correlation coefficients are scaled such that they range from -1 to +1, where 0 indicates that there is no linear or monotonic association, and the relationship gets stronger and ultimately approaches a straight line (Pearson correlation) or a constantly increasing or decreasing curve (Spearman correlation) as the coefficient approaches an absolute value of 1. Hypothesis tests and confidence intervals can be used to address the statistical significance of the results and to estimate the strength of the relationship in the population from which the data were sampled. The aim of this tutorial is to guide researchers and clinicians in the appropriate use and interpretation of correlation coefficients.

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Changes in air quality during the lockdown in Barcelona (Spain) one month into the SARS-CoV-2 epidemic

Aurelio Tobías, Cristina Carnerero, Cristina Reche … (2020)

Lockdown measures came into force in Spain from March 14th, two weeks after the start of the SARS-CoV-2 epidemic, to reduce the epidemic curve. Our study aims to describe changes in air pollution levels during the lockdown measures in the city of Barcelona (NE Spain), by studying the time evolution of atmospheric pollutants recorded at the urban background and traffic air quality monitoring stations. After two weeks of lockdown, urban air pollution markedly decreased but with substantial differences among pollutants. The most significant reduction was estimated for BC and NO2 (−45 to −51%), pollutants mainly related to traffic emissions. A lower reduction was observed for PM10 (−28 to −31.0%). By contrast, O3 levels increased (+33 to +57% of the 8 h daily maxima), probably due to lower titration of O3 by NO and the decrease of NOx in a VOC-limited environment. Relevant differences in the meteorology of these two periods were also evidenced. The low reduction for PM10 is probably related to a significant regional contribution and the prevailing secondary origin of fine aerosols, but an in-depth evaluation has to be carried out to interpret this lower decrease. There is no defined trend for the low SO2 levels, probably due to the preferential reduction in emissions from the least polluting ships. A reduction of most pollutants to minimal concentrations are expected for the forthcoming weeks because of the more restrictive actions implemented for a total lockdown, which entered into force on March 30th. There are still open questions on why PM10 levels were much less reduced than BC and NO2 and on what is the proportion of the abatement of pollution directly related to the lockdown, without meteorological interferences.

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Assessing nitrogen dioxide (NO 2 ) levels as a contributing factor to the coronavirus (COVID-19) fatality rate

Yaron Ogen (2020)

Nitrogen dioxide (NO2) is an ambient trace-gas as a result of both natural and anthropogenic processes. Long-term exposure to NO2 may cause a wide spectrum of severe health problems such as hypertension, diabetes, heart and cardiovascular diseases and even death. The objective of this study is to examine the relationship between long-term exposure to NO2 and fatality caused by the coronavirus. The Sentinel-5P is used for mapping the tropospheric NO2 distribution and the NCEP/NCAR reanalysis for evaluating the atmospheric capability to disperse the pollution. The spatial analysis has been conducted on a regional scale and combined with the number of death cases taken from 66 administrative regions in Italy, Spain, France and Germany. Results show that out of the 4443 fatality cases, 3487 (78%) were in five regions located in north Italy and central Spain. Additionally, the same five regions show the highest NO2 concentrations combined with downwards airflow which prevent an efficient dispersion of air pollution. These results indicate that the long-term exposure to this pollutant may be one of the most important contributors to fatality caused by the COVID-19 in these regions and maybe across the whole world.