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Estimating changes in air pollutant levels due to COVID-19 lockdown measures based on a business-as-usual prediction scenario using data mining models: A case-study for urban traffic sites in Spain.
González-Pardo, Jaime; Ceballos-Santos, Sandra; Manzanas, Rodrigo; Santibáñez, Miguel; Fernández-Olmo, Ignacio.
  • González-Pardo J; Department of Chemical and Biomolecular Engineering, Universidad de Cantabria, Av. de Los Castros s/n, 39005 Santander, Cantabria, Spain. Electronic address: jaime.diez@unican.es.
  • Ceballos-Santos S; Department of Chemical and Biomolecular Engineering, Universidad de Cantabria, Av. de Los Castros s/n, 39005 Santander, Cantabria, Spain. Electronic address: sandra.ceballos@unican.es.
  • Manzanas R; Santander Meteorology Group, Dpto. De Matemática Aplicada y Ciencias de la Computación, Universidad de Cantabria, Santander 39005, Spain. Electronic address: rodrigo.manzanas@unican.es.
  • Santibáñez M; Department of Nursing, Global Health Research Group, Universidad de Cantabria, Avda. Valdecilla s/n, 39008 Santander, Cantabria, Spain; Research Nursing Group, IDIVAL, Calle Cardenal Herrera Oria s/n, 39011 Santander, Cantabria, Spain. Electronic address: miguel.santibanez@unican.es.
  • Fernández-Olmo I; Department of Chemical and Biomolecular Engineering, Universidad de Cantabria, Av. de Los Castros s/n, 39005 Santander, Cantabria, Spain. Electronic address: ignacio.fernandez@unican.es.
Sci Total Environ ; 823: 153786, 2022 Jun 01.
Article in English | MEDLINE | ID: covidwho-1676913
ABSTRACT
In response to the COVID-19 pandemic, governments declared severe restrictions throughout 2020, presenting an unprecedented scenario of reduced anthropogenic emissions of air pollutants derived mainly from traffic sources. To analyze the effect of these restrictions derived from COVID-19 pandemic on air quality levels, relative changes in NO, NO2, O3, PM10 and PM2.5 concentrations were calculated at urban traffic sites in the most populated Spanish cities over different periods with distinct restrictions in 2020. In addition to the changes calculated with respect to the observed air pollutant levels of previous years (2013-2019), relative changes were also calculated using predicted pollutant levels for the different periods over 2020 on a business-as-usual scenario using Multiple Linear Regression (MLR) models with meteorological and seasonal predictors. MLR models were selected among different data mining techniques (MLR, Random Forest (RF), K-Nearest Neighbors (KNN)), based on their higher performance and accuracy obtained from a leave-one-year-out cross-validation scheme using 2013-2019 data. A q-q mapping post-correction was also applied in all cases in order to improve the reliability of the predictions to reproduce the observed distributions and extreme events. This approach allows us to estimate the relative changes in the studied air pollutants only due to COVID-19 restrictions. The results obtained from this approach show a decreasing pattern for NOx, with the largest reduction in the lockdown period above -50%, whereas the increase observed for O3 contrasts with the NOx patterns with a maximum increase of 23.9%. The slight reduction in PM10 (-4.1%) and PM2.5 levels (-2.3%) during lockdown indicates a lower relationship with traffic sources. The developed methodology represents a simple but robust framework for exploratory analysis and intervention detection in air quality studies.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Air Pollutants / Air Pollution / COVID-19 Type of study: Case report / Observational study / Prognostic study / Randomized controlled trials / Reviews Limits: Humans Country/Region as subject: Europa Language: English Journal: Sci Total Environ Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Air Pollutants / Air Pollution / COVID-19 Type of study: Case report / Observational study / Prognostic study / Randomized controlled trials / Reviews Limits: Humans Country/Region as subject: Europa Language: English Journal: Sci Total Environ Year: 2022 Document Type: Article