Observed air quality changes
In the rest of this paper, we will compare the monthly average of the pollutant of interest—NO2 or PM2.5—during the month of April 2020 to the average of the five monthly averages during April of the years 2015 through 2019. There are two reasons for this choice. First, using five years to establish a reference is more meaningful than, say, using just the year 2019, because year-to-year variability can occur regardless of the pandemic. In fact we found that, in general, the year 2019 was relatively clean when compared to the previous five, thus a comparison between April 2020 and April 2019 may underestimate the true impact of COVID-19. Second, although the monthly average is not the design value for either NO2 or PM2.5, it is a value that is representative of the overall air quality during the entire month of April. Alternative metrics, such as the monthly maximum, are more representative of extreme circumstances, like wildfires, that are not necessarily associated with COVID-19.
Starting with NO2, the April 2020 averages were generally below the April 2015–2019 average at the ground monitoring sites, as most sites lay below the 1:1 line in Fig. 2a. In addition, 65% of the sites were characterized by NO2 concentrations in 2020 that were lower than those in all of the previous five years (for the month of April). Only a few sites (5 in total, < 2%) experienced NO2 concentrations in 2020 that were higher than those in all of the previous five years (for the month of April). The average drop in NO2 concentrations in April 2020 with respect to the average of the previous five years was − 2.02 ppb (Tables 1 and 3). To verify that the observed reduction was not simply due to the multi-annual decreasing trend in emissions (Jiang et al. 2018), a trend analysis was performed. The average slope of the linear fit was negative (− 0.24 ppb/year) and, after de-trending, the net contribution of COVID-19 was approximately − 1.3 ppb on average.
The same pattern is confirmed in the satellite-derived data. Out of the 227 pixels with ground monitoring sites, a total of 127 (56%) exhibited lower NO2 in 2020 than in the previous five years and only 5% higher (Fig. 3b). Once all 14,706 pixels with valid satellite retrievals over the continental US are considered, a similar pattern of lower NO2 column totals in 2020 than in the five previous years emerges from these data too (Fig. 3a), but with 28% of the pixels lower in 2020 than in the previous five years and 5% higher (for the month of April, Table 3).
Further insight was gained from the satellite-derived NO2 column totals by differentiating the pixels into two categories: urban versus rural (Socioeconomic Data and Applications Center (SEDAC) 2020). The urban pixels are shown in Fig. 4. For urban pixels, the findings closely resemble those from the AQS ground monitoring sites, with less than 2% of the sites (11 out of 786) experiencing higher NO2 in April 2020 than in the previous five years and over 40% of the sites (325 out of 786) experiencing the opposite (Fig. 3c). This is not surprising, as ground monitoring sites are more likely to be placed in urban locations, where most people live. Basically all the pixels with higher NO2 in April 2020 than in the previous five years from Fig. 3a were rural pixels (Fig. 3d).
In terms of spatial variability, Fig. 5 shows that, although NO2 reductions were recorded all over the country, the highest decreases were observed in California and the Northeast, where the shelter-in-place measures started earlier (March 11 for California, the earliest in the country, and March 22 for New York, third earliest (Mervosh et al. 2020)) and lasted longer (both states still have major restrictions in place as of June 10, 2020 (The Washington Post 2020)). Noticeable exceptions were North Dakota and Wyoming, where either no significant decreases or actual small increases in NO2 concentrations were observed. North Dakota enforced no shelter-in-place measures and in Wyoming only the city of Jackson implemented a stay-at-home order as of April 20, 2020 (Mervosh et al. 2020). However, as discussed in Section 2.3, people’s actual mobility, as opposed to state ordinances, is a more appropriate metric to capture the effect of COVID-19 on air quality because there was no complete compliance with state or city restrictions.
Figure 5 was useful because it included actual NO2 concentrations measured near the ground. However, the spatial coverage was sparse and urban areas were over-sampled compared with rural areas. This weakness is addressed via the NASA OMI satellite data, which are shown in Fig. 7 as the difference between the monthly average of NO2 column total in 2020 and that in 2015–2019 for the month of April. The regions with low coverage of ground concentration of NO2 and mobility in the Midwest are generally characterized by near-normal NO2 column totals. The Northeast hotspot of low mobility is also a hotspot of low NO2, consistent with Bauwens et al. (2020), although it is surrounded by patches of above-normal values that were not detectable from the ground monitoring stations. The Los Angeles area is another hotspot of NO2 decreases, as well as low mobility.
For PM2.5, the ground monitoring stations depict a completely different response to COVID-19 (Fig. 6). Whereas most NO2 sites were laying below the 1:1 line (Fig. 2a), the majority of PM2.5 sites laid above it (Fig. 2b), indicating an overall increase in monthly average PM2.5 in the country in April 2020 with respect to the previous five years. Only 18% of the sites reported concentrations of PM2.5 that were lower in 2020 than in the previous five years (in the month of April), while 24% of the stations reported the highest levels in 2020 compared to the previous five years (for the month of April). The average increase in PM2.5 concentrations with respect to the mean of the previous five years was small, + 0.05 μg/m3 (Tables 2 and 3). Results from the trend analysis indicated that, in the absence of COVID-19, the average concentration of PM2.5 in April 2020 would slightly decrease (slope − 0.12 μg/m3/year). Thus, the net contribution of COVID-19 on PM2.5 concentrations was a slight increase of about 0.28 μg/m3 on average.
In summary, we report a large decrease (− 2.02 ppb, or 27%) in monthly average NO2 concentrations across the US ground monitoring stations, confirmed by the satellite-derived NO2 column total decrease of 7.1 × 1014 molecules/cm2 (or 24%) at the pixels of the ground monitoring stations, during April of 2020 when compared with April of the previous five years. When all the pixels with valid data were included, a drop of 2.4× 1014 molecules/cm2 (or 13%) during April of 2020 was observed when compared to April of the previous five years (Table 3). After de-trending, the net effect of COVID-19 on NO2 concentrations at the AQS ground monitoring stations was − 1.3 ppb. The monthly average of PM2.5, however, increased slightly on average (+ 0.05 μg/m3 when compared with the previous 5-year average) during the same period (Table 3). After de-trending, the effect of COVID-19 was a net increase of PM2.5 concentrations at the AQS ground monitoring stations by + 0.28 μg/m3. In the next Section 3.3, we try to explain the reasons for these differences.
Observed mobility changes
Time series of mobility data at the counties with NO2 ground monitoring sites are shown in Fig. 8a and at the counties with PM2.5 ground monitoring sites in Fig. 8b. Only a few counties had both types of monitoring sites, thus the counties included in the two figures are generally different. Yet, the patterns are very similar. First of all, mobility on average dramatically dropped starting in the second half of March, reaching values around 20% by April, and then started to recover in May, as some states reopened for business or relaxed the shelter-in-place measures (The Washington Post 2020). Second, a distinct minimum in mobility during the month of April is clearly visible, which confirms that this month was the most relevant for air quality impacts from COVID-19. There is some variability around this general behavior, but nonetheless only a few counties barely reached normal mobility in April. Lastly, the typical traffic reduction during the weekends is confirmed in the mobility data, regardless of the pandemic. This adds confidence to the use of mobility data as a proxy for people’s actual behaviors.
In terms of spatial variability, changes in mobility during COVID-19 in the USA were not uniform, although in general mobility was reduced in most states (Fig. 9a). Note the lack of data in many counties in the Midwest (in grey in Fig. 9b), possibly due to low population density, limited smartphone usage and cellular coverage. However, the ground monitoring stations of both NO2 and PM2.5 are generally located in counties with mobility data availability. In general, the strongest decreases in mobility are found around large urban areas throughout the country, e.g., the Northeast corridor from Washington D.C. to Boston; the San Francisco and Los Angeles areas in California; Seattle in the Northwest; and Chicago. A few isolated counties experienced increases in mobility (in red in Fig. 9a). Wyoming stands out as one of the few states with no significant decreases in mobility, consistent with the lack of shelter-in-place measures (Mervosh et al. 2020).
Relationships between air quality and mobility changes
To better interpret the relationship between mobility and the air pollutant of interest, either NO2 or PM2.5, the mobility data were divided into bins, based on the monthly average (in April 2020) of the mobility in the county where each ground monitoring site was located. For most cases, there was only one ground monitoring site per county. But in some cases, such as Los Angeles county in California for NO2 or Maricopa county in Arizona for PM2.5, multiple monitoring sites were located in the same county and therefore they were all paired to the same mobility value. The change in monthly average concentration of the pollutant between April 2020 and the five previous Aprils was then calculated for each mobility bin.
Starting with NO2, there is a clear relationship with mobility (Fig. 10a). Large and negative changes in NO2 concentrations, on the order of − 4 ppb, were found at locations where mobility was basically halted, i.e., where it was less than 1% of normal in April 2020, as in full lockdown. As mobility increased, the NO2 benefits decreased, although not linearly. For example, decreases by 2–3 ppb in NO2 concentrations occurred where mobility was restricted but not to a full lockdown (i.e., between 1 and 20% of normal). Past 20%, the changes in NO2 concentrations were still negative and significant, but not large, less than 1 ppb on average. This suggests that NO2 responds modestly to changes in mobility that are not large, but then, if mobility is reduced dramatically (i.e., by at least 80%, thus it is down to 20% of normal), large decreases in NO2 can occur.
With respect to PM2.5, there is no obvious relationship between the reductions in mobility and the observed concentrations (Fig. 10b). Only for the most extreme mobility reductions, i.e., the bin with < 1% mobility, which indicates that the entire population was sheltering at home for the entire month of April, PM2.5 concentrations decreased by about 1 μg/m3. After the first bin, as mobility increased, both increasing and decreasing concentrations of PM2.5 were found, with large standard deviations and no discernible pattern. We conclude that the changes in PM2.5 were not directly caused by changes in people’s mobility.
How can we reconcile the clear relationship of NO2 with mobility with the lack thereof for PM2.5? The hypothesis we put forward is that the shelter-in-place measures mostly affected people’s driving patterns, thus passenger vehicle—mostly fueled by gasoline—emissions were reduced and so were the resulting concentrations of NO2. Commercial vehicles (generally diesel) and electricity demand for all purposes (often provided by coal-burning power plants), however, remained relatively unchanged; thus, PM2.5 concentrations did not drop significantly and did not correlate with the mobility index.
To test this hypothesis, in a subsequent study, we will use a photochemical model, coupled with a numerical weather prediction model, which we will run with and without emissions from diesel vehicles, while keeping everything else the same. The difference between the concentrations of the pollutants in the two cases will be attributable to diesel traffic alone. Similarly, we will be able to reduce emissions from other sectors, to reflect the effect of COVID-19 on other aspects of life, such as air traffic, business closures, or residential heating. We will explore the relationship between reductions of the mobility index and residential heating increases, as more people staying at home during lockdowns likely cause higher residential heating emissions, including from biomass burning.