key: cord-301067-wk3cf0b7
authors: Corpus-Mendoza, Asiel N.; Ruiz-Segoviano, Hector S.; Rodríguez-Contreras, Sergio F.; Yañez-Dávila, David; Hernández-Granados, Araceli
title: Decrease of mobility, electricity demand, and NO2 emissions on COVID-19 times and their feedback on prevention measures
date: 2020-11-01
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2020.143382
sha: 
doc_id: 301067
cord_uid: wk3cf0b7

The spread of coronavirus disease 2019 (COVID-19) on 2020 has affected human activities in a way never documented in modern history. As a consequence of the prevention measures implemented to contain the virus, cities around the world are experiencing a decrease in urban mobility and electricity demand that have positively affected the air quality. The most extreme cases for cities around the world show a decrease of 90, 40, and 70 % in mobility, electricity demand, and NO2 emissions respectively. At the same time, the inspection of these changes along the evaluation of COVID-19 incidence curves allow to obtain feedback about the timely execution of prevention measures for this and future global events. In this case, we identify and discuss the early effort of Latin-American countries to successfully delay the spread of the virus by implementing prevention measures before the fast growth of COVID-19 cases in comparison to European countries.

as hypertension (Shin et al., 2020) , cardiovascular disease (Mann et al., 2002) , chronic pulmonary disease (Euler et al., 1988) , and a diminished response to viral and bacterial infections (Ciencewicki and Jaspers, 2007) . Moreover, it is also proposed that the same pollutants can participate directly in the transmission of COVID-19 as a coronavirus carrier (Bontempi, 2020; Sasidharan, et al., Wu et al., 2020; Zoran et al., 2020) . However, this last observation is not yet demonstrated, since high levels of air pollutants are usually evident in cities with high human population and hence, high human interaction (Pisoni and Van Dingenen, 2020) .

Therefore, in this article, we conduct a broad evaluation of the impact of the COVID-19 pandemic on the urban mobility, electricity consumption, and NO 2 emissions as a whole for several countries around the world rather than for a single region or sector affected as in previous literature. At the same time, we analyse the evolution of confirmed COVID-19 cases and compare them with the start of prevention measures and changes in sectors affected in different countries to discuss the effectiveness in time in which they are applied. We think that the combination of these two approaches can not only explain how the pandemic affects human activities and the environment, but also how these changes allow us to obtain feedback of the prevention measures applied for this and future events. are obtained from the Global Health Expenditure Database (WHO GHED, 2020) . These datasets are used to find the date of the 100th COVID-19 case (D 100 ) for each country in order to evaluate their daily incidence (I C ) and death incidence (I D ), which show the quantity of confirmed cases and deaths per 100000 habitants respectively. Then, it is possible to obtain the incidence rate (I CR ) from the slope of I C versus time curve during the fastest infection period, as well as the threshold day (T D ) from the x-axis intercept of the slope, which estimates the quantity of days after D 100 in which the infection grows the fastest, as shown in Fig. 1 for Italy. This analysis of the I C curve is inspired by the evaluation of the turn-on voltage and series resistanc of electronic devices such as diodes, and it is a simple approach to assess and compare the evolution of I C between countries. Here, I CR is useful to evaluate the spread speed of the virus, whereas T D identifies the moment in time in which fast growth starts. The combination of these parameters allow to estimate and discuss the effectiveness of the actions implemented to stop the spread of the virus, and to plan for future and similar events. However, the disadvantage of this method is its lagging nature, since the fast growth region of the I C curve is often confirmed at late stages of the pandemic.

Another dataset used is the #COVID19 Government Measures Dataset (ACAPS, 2020), which collects daily country-level data from news, social media, and articles about the prevention measures implemented around the world to fight the pandemic. These measures are classified in 5 categories in the original dataset, however, we reclassify them and discuss them in terms of their effects on health, and economy, but mainly on the environment by analysing changes in mobility, electricity generation, and air quality index (AQI) before and after the pandemic. Here, the mobility around transit stations such as subway, bus, and train stations is selected as the parameter to study rather than mobility around residential areas, grocery shops and pharmacies, or retail and recreation areas since transit stations usually involve a high concentration of people. This information is obtained from the COVID-19 Community Mobility Reports by Google (Google, 2020) and presents the percentage change in the number of people visiting transit stations compared to a baseline level, which is the median value for each day of the week during January 3rd and February 6th, 2020.

Also, hourly and daily electrical power consumption is obtained for 26 countries from their respective Transmission System Operator (TSO) in order to evaluate their daily percentage change in electrical energy consumption between March 1st and June 30th for 2019 and 2020.

Here, the daily data is adjusted to compare days of the week rather than dates. This adjustment is applied because power consumption during the weekends is usually different than during the weekdays. Data for most European countries are available at the European Network of Transmission System Operators for Electricity (ENTSOE, 2020) , whereas other sources are J o u r n a l P r e -p r o o f Journal Pre-proof 8 used for Italy (Terna, 2020) , Spain (Red Eléctrica de España, 2020), Russia (SOUES, 2020), UK (Elexon, 2020), India (Andrew, 2020 and POSOCO, 2020) , Japan (TEPCO, 2020), Singapore (EMA, 2020), Turkey (Exist, 2020), Bolivia (CNDC, 2020), Brazil (ONS, 2020), Chile (CEN, 2020), Colombia (XM, 2020), Mexico (CENACE, 2020), Peru (COES, 2020), Uruguay (ADME, 2020), and USA (EIA, 2020).

Finally, daily AQI index for NO 2 measured by monitoring stations is analysed for 36 capital cities around the world to compare the percentage change between the first half of 2019 and 2020. Here, we select capital cities assuming that they represent a significant amount of population and human activities affected by the pandemic. Also, NO 2 is chosen as the air pollutant to study instead of other pollutants such as CO, CO 2 , SO 2 , PM 2.5 , or PM 10 , since most of the NO 2 in cities is produced by combustion vehicles while driving, a common activity worldwide. These and other environmental data are available at the World Air Quality Index Project (WAQIP, 2020).

J o u r n a l P r e -p r o o f Another point to consider besides the development of the pandemic around the world is the impact that it has in modern life, since the execution of prevention measures implies an adjustment on the usual human activities, and therefore, the environment. Some of the measures applied until now are classified as shown in Fig. 4 , with many of them affecting more than one category. Particularly, the mobility of people around transit stations is clearly lower in terms of percentage for all countries compared to their baseline levels at the beginning of the year, as shown in Fig. 5 . Also, the average mobility curves by continent reveal that the drop in mobility starts in the middle of March for Europe, Asia, and the Americas, reaching levels of approximately -60% compared to the baseline, whereas the change in Africa is lesser and later.

However, there is a significant difference in the average D 100 by continents, since the average mobility curve in Europe is still close to the baseline before its average D 100 . This means that the pandemic in that region had already started by the time mobility measures were applied, whereas some Asian countries and most of the Americas and Africa had already restricted their mobility Similarly to mobility, the electrical energy consumption around the world is also affected by the pandemic, as shown in Fig. 6 , which reveals a decrease of the average electricity consumption curve by continents since the middle of March, 2020 compared to the values of 2019 despite people spending more time at their homes. Therefore, we attribute this change to a decrease of industrial activity, closure or partial operation of transit stations and retail sector, as well as flexible times to work from home. Fig. 6 also shows the dates in which some countries recommended or enforced their citizens to stay at home. These dates do not differ significantly between the nations analysed, which once again demonstrates an early action by most of Latin-America. Finally, the percentage change and absolute change in terms of GWh is shown in Fig.   6d ), where it is observed that electricity consumption decreased in most countries analysed J o u r n a l P r e -p r o o f activities which are probably not heavily affected by the pandemic, which could explain the difference in electrical consumption compared to the other countries. Supplementary Material (Fig. S2 ).

Finally, Fig. 7 shows the percentage decrease in the AQI for NO 2 , which is a measure of the air pollution by NO 2 , where higher values represent a higher risk to health. Particularly, the AQI for NO 2 in cities depend mainly on the combustion of fossil fuels and therefore, driving. Also, the AQI for this and other air pollutants is affected by the weather seasons, with winter slowing the dilution and dispersion of pollutants . This explains the decrease in the It is now observed that the prevention measures applied limited human activities and caused the decrease of urban mobility as well as electricity consumption, which led to a decrease of NO 2 emissions. Therefore, the appearance of the virus paradoxically had a positive effect on the air quality to the point that many authors consider the decrease of NO 2 has saved more human lives than COVID-19 has claimed (Dutheil, 2020) . Some studies now indicate that 24000 to 36000 premature deaths per month have been avoided in China due to an improved air quality , whereas the total COVID-19 deaths in the same country are less than 5000. However, the reopening of human activities after the lockdown demonstrate that the improvement in air J o u r n a l P r e -p r o o f quality is unsustainable (Zambrano-Monserrate), since pollution levels are back to the normal trend compared to previous years (Liu et al., 2021) . These observations should serve as the basis to design and implement actions oriented towards the improvement of human health and air quality, for example, traffic control, investment in public transportation, replacement of face-toface work with online work, renewable energy projects, electric vehicles infrastructure, and more. Detailed data for Rome, Italy in the inset. Philippines had an approximate 70% decrease of mean NO 2 quality index for the period evaluated. Details by country in Supplementary Material (Fig. S3a and Fig. S3b ).

In summary, the adoption of prevention measures to mitigate the impact of COVID-19 on human health has caused a decrease of mobility in transit stations as well as a decline in electricity demand around the world. As a consequence, the air quality has been positively J o u r n a l P r e -p r o o f affected as observed by the decrease of NO 2 in multiple capital cities. Therefore, these observations can be used to implement traffic control programs, investment in public transportation, replacement of face-to-face work with online work, electric vehicles infrastructure, and other green energy projects oriented towards the improvement of air quality and, therefore, human health. At the same time, the analysis of changes in mobility and electricity demand along the evaluation of T D and I CR from the I C curves allow to discuss the timely execution of the prevention measures, which works as a feedback to consider and plan actions for the current pandemic or future global events. Here, it is observed that European 

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Writing -Original draft, Writing -review & editing. Hector S. Ruiz-Segoviano: Data curation, Formal analysis, Investigation, Visualization, Writing -review & editing. Sergio F. Rodríguez-Contreras: Data curation, Formal analysis, Investigation, Visualization, Writing -review & editing. David Yañez-Dávila: Data curation, Formal analysis, Investigation, Writing -review & editing. Araceli Hernández-Granados: Data curation, Formal analysis, Investigation, Writing -review & editing

ANCM thanks the support of Cátedras by Consejo Nacional de Ciencia y Tecnología 
