After the first confirmed COVID-19 case, occurred in Peru on March 6, 2020 (see Fig. 2), the government, through the Ministry of Health (Ministerio de la Salud—MINSA), put into operation a National Preparedness and Response Plan for the Risk of Introduction of the Coronavirus to strengthen surveillance, containment and response systems (MINSA 2020). On March 11, a state of health emergency was decreed at the national level (see Fig. 2). From March 12 to 15, the number of positive cases in Peru increased to a total of 71 cases and were concentrated in the LMA (MINSA 2020). On March 15, a state of emergency was decreed (ED 2020), making Peru the first country in South America to take strict measures to prevent an increase in positive cases (see Fig. 2). These measures included mandatory social isolation (via a national lockdown) and a complete lockdown of the border, starting on March 16. On March 18, a national curfew was enacted to tighten the mandatory social distancing measures because people were not conforming to the lockdown restrictions, and the use of private vehicles was prohibited from March 19. Despite all the sanitary measures implemented, the number of confirmed cases in Peru surpassed 100,000 on May 20 (MINSA 2020).
The impact of the measures implemented during the state of emergency was to freeze production activities, as reflected by electricity consumption (COES 2020). Figure 2 shows the mean daily electricity consumption in Peru: the application of isolation and immobilization measures is coincident with a clear decrease of approximately 40% in the energy demand. This decrease in production activity is not counteracted by overconsumption by people forced to stay at home. The decrease in the electricity demand reflects the freezing of production activities and attendant potential sources of air pollutant emissions.
By April 30, 2020, that is, six weeks after the declaration of the national state of emergency on March 16, 2020, LMA contamination, in terms of the PM10, PM2.5 and NO2 concentrations, had decreased (Fig. 3, Table 1). Likewise, compared with the historical period (the three previous years), similar behavior was observed. These observed changes are analyzed in the following sections.
A 40% decrease in the PM10 level between the pre-lockdown and lockdown periods was observed. A similar trend was observed for the PM2.5 level, in terms of the magnitude of the relative change. The ANOVA results revealed a statistically significant decrease in the PM10 and PM2.5 concentrations from the pre-lockdown to the lockdown: the p-value was smaller than the significance level of 0.01 at a 99% confidence level. The highest decrease for both PM10 and PM2.5 levels was observed at the STA station. The smallest relative changes were observed at the CDM stations, which are located in a commercial area with large malls, small businesses, restaurants, etc. and are therefore highly impacted by emissions from vehicle traffic. Sustained commercial activity produced a smaller decrease in PM concentrations because of the lockdown for the CDM area than other zones. These other zones are mainly residential areas with unpaved streets, for which the reduction in vehicular motion reduced vehicle emissions as well as particle resuspension. Thus, the lockdown produced a decrease in the PM concentration of approximately 50%, which is similar in magnitude to the decrease in the NOx levels. This reduction can be explained in terms of the decrease in vehicular emissions by the lockdown measures, considering that the 2.2 million motor vehicles in the LMA make a total of nine million trips a day.
Ozone (O3) is the only pollutant for which an increase in concentration (of 59%) was observed during the lockdown period compared to the non-lockdown period, at the CDM station. This increase was shown to be statistically significant using ANOVA (the p-value was smaller than the significance level of 0.01 at a 99% confidence level). The increase in the O3 concentration can be explained in terms of the complex chemistry of O3 formation from mixtures of volatile organic compounds (VOCs) and NOx (Finlayson-Pitts and Pitts 2000). A reduction in NOx concentrations leaves a higher number of OH radicals available to react with VOCs, promoting ozone formation. O3 is also eliminated via rapid reaction with NO. Thus, a reduction in available NO increases O3 atmospheric levels. This behavior is consistent with a previous report that O3 formation in the LMA is hydrocarbon-limited (Silva et al. 2018).
The variability in the PM10, PM2.5, NO2 and O3 concentrations over the historical period (March 16 to April 30) from 2017 and 2019 was similarly analyzed. The same general trends were observed as compared to the pre-lockdown period of 2020, albeit with more extreme concentration decreases. The highest decreases in the PM10, PM2.5 and NO2 concentrations were 73%, 64% and 48%, respectively. A significant increase in variability was observed at the CDM station for O3 (166%) during the lockdown compared with the concentration for 2017–2019 and 2020. A comparison of the concentration values for the pairs of each pollutant and station for 2017–2019 and during the lockdown period in 2020 using ANOVA showed that the differences between these periods was significant (p-value < 0.01 at a 99% confidence level).
No statistically significant differences were found between the temperatures, relative humidity and surface winds recorded during the pre-lockdown and lockdown periods in the present study. In general, in LMA the highest PM10 concentrations are observed in the summer and early autumn (February-April). On the other hand, regarding the PM2.5 concentrations, the highest concentrations are recorded between late autumn and winter (May and September). This could be explained by the subsidence thermal inversion weakens in the middle of spring and early fall and because the humidity decreases, which is detrimental to the formation of secondary particulate matter and contrary to what occurs during the cold and humid period (May–September) (Silva et al. 2017).
Figure 4 shows the spatial variability in the NO2 column (µmol/cm 2) obtained from remote sensing provided by the Copernicus Sentinel-5 Precursor Tropospheric Monitoring Instrument (S5p/TROPOMI) for the pre-lockdown and lockdown periods, as well as the corresponding relative change (%).
NO2 distribution in LMA is influenced by its proximity to the sea and winds, see Fig. 4a. In general, in the coastal area of the city, there are lower concentrations due to the coastal wind that blows from the ocean to the mountains (Silva et al. 2017). The main sources of outdoor air pollution are automotive fleet for these the reason the area of greatest impact turns out to be the downtown of the city where most vehicle traffic is focused and where important economic activity takes place (Romero et al. 2020; Silva et al. 2017). Likewise, the wind disperses the atmospheric pollution emitted in the downtown area towards the mountain slopes. Restrictive measures to prevent the spread of COVID-19 clearly reduce the sources of NO2 emissions in the city (see Fig. 4b).
The relative changes in the LMA NO2 concentration are on the order of -40% and are homogeneously distributed over the domain, which is consistent with the surface records from the STA station (see Table 1). Significant decreases in NO2 levels in the 40% to 30% range are evident over most of the urban area of the domain. The decreases in NO2 levels are on the same order of magnitude and exhibit the same trend as the surface records.