3.1. Population Density
Before exploring the effects of the lockdowns in our three case study cities, we explored the population density distribution at the census tract level (the administrative boundary that was used to generate mobility data). The main purpose was to identify the high–low density clusters to understand the effects of lockdown on subsequent changes in mobility patterns, nitrogen dioxide concentrations as well as power usage due to the change (i.e., drop) in economic activities. Figure 2
a,b depict the population density distribution for LA County at the census tract level. These data show that the highest density tracts were clustered in the south-central part of LA County (specifically, surrounding LA city, highlighted by the square in Figure 2
a), and this area was surrounded by high to moderate density tracts (hotspots in Figure 2
b). The northern half, southwestern part, and southern-most part of the county were occupied by very low-density census tracts (coldspots in Figure 2
b). Essentially, the southern half of the county is densely populated with high spatial variability, while the northern half of the county is sparsely populated.
In the city of Chicago, moderate to heavily populated tracts are clustered in the northern and northeastern part of the city (Figure 3
a). While the sparsely populated tracts were clusters in the southern and northwestern part of the city; the low population density tracts were spread out across the city. From the hotspot analysis output, it was clear that the heavy density tracts were clustered along the north-eastern part of the city (hotspots with high significance in Figure 3
b), along the lake shore, while the low population density clusters were located in the southern and northwestern parts of the city (coldspots in Figure 3
b). There was a random distribution of moderate to low density tracts across the entire city (Figure 3
The majority of Washington, DC population is concentrated in the central part of the city, with a few clusters of low and very low-density tracts are spread out across the entire city (Figure 4
a,b). It was clear from the hotspot analysis that the densely population tracts were clustered in the center of Washington, DC with few clusters of low-density population. The remainders of the tracts were less densely populated.
3.2. Variations in Mobility Patterns
The immediate impact of lockdown measures in the cities was a reduction in mobility due to the tele-working and shutdown of all businesses except essential businesses and services. Because stay-at-home measures were in place by March 2020 in all three cities, we explored the change in mobility (distance and pattern) at the census tract level. Figure 5
and Figure 6
show the mobility distribution in LA County during February through April. The data indicate that mean travel distance dropped from 268 km in February to 50 km in March and 42.6 km in April. In February, the high mobility areas were concentrated in the downtown area of LA City as well as in the northeast and northwest part of LA County, which has a very sparse population (Figure 5
a, Figure 2
Evidently, the highest distance traveled dropped by more than 75% in the entire county. While more than 50 km distance on average was traveled in the northwestern part of the county, mobility was no more than 20 km in the downtown area of the county (central part of the county closer to LA City (red box in Figure 5
a)). Essentially, the lock-down-induced telecommuting appears to have impacted on the reduction of mobility in LA city by more than 90%. Moderate mobility was still observed in March near Malibu, Long Beach, and Santa Monica (southwestern part of the county represented by black boxes in Figure 5
b). Although there were some pockets of high mobility in the high-density areas of LA County, mobility appeared to have dropped in areas surrounding the downtown LA (central part of the County) but was still higher in sparsely populated counties of LA along the northeast and northwestern part of the county. The maximum distanced traveled in LA County by April was approximately 42 km (Figure 5
c). Nevertheless, the areas experiencing moderate mobility remained the same as they were in March, and these areas included the low-density tracts of the county as well as Santa Monica, Long Beach, and Malibu. Preliminary analysis of the income data from the US Census (2018 American Community Survey) revealed that the mobility reduced significantly in the impoverished part of LA County.
Although mobility was reduced by 50–100% by March in many parts of LA County, including closer to LA City (Figure 6
a), majority of LA experienced a drop in mobility by 50–100% by April (Figure 6
b). During February–April, mobility increased significantly only in few census tracts of LA. A future analysis of the tracts experiencing mobility increase will be conducted to explore the effects of the underlying socio-economic characteristics as well as businesses that might have contributed to the mobility increase.
Before the lockdown, travel in Chicago in February appears to have been concentrated near the downtown area (black box in Figure 7
a), near the Chicago Midway International Airport (northwestern part of the City), Whiting (southern part), and near the Chicago Midway International Airport (red box in Figure 7
a). It also appears that travel in Chicago was not concentrated in the high-density tracts that are located in the northeastern part of the metropolis (Figure 3
Following the lockdown, by March, the maximum distance traveled dropped by 50% (81 km to 44.6 km) (Figure 7
b). However, the moderate to very high mobility clusters were still located near the downtown area of Chicago, near the airports, and Whiting (Figure 7
b, red and black boxes). Mobility dropped by another 50% in April in Chicago (Figure 7
c), but like February and March, high to moderate mobility areas were present in the central Southern and Southwestern part of Chicago (Figure 7
c). Evidently, mobility was still higher near O’Hare International Airport (northwestern part of Chicago. Figure 7
a,b depict the percent change in mobility in Chicago during February through April. Immediately after the lockdown, mobility dropped by more than 50% in many tracts across Chicago, while it also increased by more than 100% in the northeastern part of the City (black box in Figure 8
a) near Uptown—a residential neighborhood. By April, mobility reduced by more than 75% (Figure 8
b) across the entire city. The mobility reduction was evident in the central, northeastern, northwestern (near O’Hare International Airport), and southern (near Whiting) part of the City. Although the mobility reduction near the airports was approximately 25%, the reduction in residential neighborhoods of the city (high density tracts, Figure 3
a) appeared to be due to the fact of tele-commuting.
The densely populated tracts in Washington DC were clustered near the central and southeastern part (Figure 4
a). The central part of DC was also where White House is located. Therefore, it is no surprise that mobility was higher in February in the central part of DC (near the White House and the downtown area) (Figure 9
a). In March, after the beginning of the lockdown, mobility dropped in Washington DC by almost 50% from about 73 km to 38 km (Figure 9
a,b), but the highest mobility was reported to be near the White House, Capital Hills, and the Washington, DC downtown. Residential neighborhoods surrounding the central part of DC exhibited low mobility. In comparison to March, mobility dropped by almost 50% from in April. However, the clusters of high to moderate mobility were still concentrated near White House, downtown DC, and Capital Hills (central part of DC, Figure 9
c) where most of the policy makers were meeting regularly to address the spread of the pandemic. Mobility appeared to have dropped significantly in the residential areas of DC (surrounding areas of White House and downtown), which could be attributed to tele-working.
Between February and March, mobility dropped by more than 50% in few places across DC, but mobility was higher near the White House in March (Figure 10
a). By April, however, mobility dropped by at least 16% percent across DC, and it was higher than 90% in a few locations (Figure 10
b). Even the central part of DC (near White House and downtown areas) experienced a 25–50% reduction in mobility by April. While mobility reduced in high-density tracts (nearer to downtown area) immediately after the lockdown in March, by April, all across DC significant reductions in mobility were observed. However, unlike LA and Chicago, DC did not experience an increase in mobility in March or April. This could be attributed to the fact that LA County has sparsely populated census tracts as opposed to DC and Chicago, which are mainly occupied urban tracts.
3.3. Analysis of NO2
is one of many byproducts of industrial processes that are considered hazardous to the health of humans and the environment (EPA, 2015). From the NO2
trends observed by TROPOMI over Los Angeles (Figure 11
), it can be seen that there was a large reduction in the monthly average total tropospheric column NO2
from February to March, and the reduction continued through April.
More than half of the signal was due to the lockdown, with only the highest concentrations of NO2
(due to industrial activity) remaining near the LA Port region, where most of the refinery and industrial capability in Los Angeles is located, as well as the Inland Empire (San Bernardino Valley), which is a major shipping hub. A recent study [44
] showed that while total NO2
reduction in Los Angeles during 15 March 15 to 30 April 2020 compared to the same time period in 2019 (Business as Usual, BAU) was about 66%, NO2
reduction due to the lockdown measures was 35% and with the remaining 31% being due to the fact of seasonality. Even during the lockdown, it would be expected that there would be some industrial activity to support essential services. The trend in NO2
is correlated to the mobility pattern observed during the lockdown. In Figure 12
, the left image (a) is a histogram of the total column NO2
for February, March, and April 2020, and the right image (b) is the distribution of distance a given cellphone travelled during the daytime period (i.e., when most movement occurs) for each corresponding month. As can be seen from Figure 12
, February and March exhibited a wider range of mobility compared to April. The curve shifted to the left with high concentrations (tails of the curves), nearly 50% lower than the values observed in February and May.
Chicago, Illinois had an earlier lockdown than Los Angeles. As discussed previously, the economy is focused around the downtown region by Lake Michigan in the financial and professional services sectors. However, there is a heavy manufacturing presence in the Chicago metropolitan area, particularly close to the southeastern part of Lake Michigan and into the western part of Indiana.
As one might expect, the areas where people commute to on a regular basis showed a dramatic decrease in NO2
in the downtown region, while the areas of heavy industry, such as powerplants and refineries, remained at elevated (though reduced) NO2
levels (Figure 13
). A recent study [44
] reported that reductions in NO2
as observed by TROPOMI due to the lockdown were ~14% for 15 March to 30 April 2020 compared to the same time in 2019.
While there was a similar trend in decreased mobility with Los Angeles, the spread of the total column of NO2
was much narrower in Chicago (Figure 14
). This is partially due to the fact that the area observed was much smaller than the Los Angeles basin. The other notable difference was that that the average distance for commuting was much shorter for Chicago than Los Angeles.
The Washington, DC metropolitan area is heavily driven by businesses and federal agencies. However, unlike Los Angeles and Chicago, there is no heavy industry. Maryland and the DC area implemented their lockdown on 17 March. The primarily I-95 travel corridor in Maryland, Delaware, and southern New Jersey can easily be seen in Figure 15
, which is the total tropospheric NO2
column density for February.
Again, as seen in the histogram shown below (Figure 16
), which is for Washington, DC, as the amount of mobility (i.e., the amount of movement from a given device) decreases, as does the peak in the NO2
column density. In the mobility data, April 2020 peak distance was the lowest but in the NO2
histogram, March NO2
concentrations were slightly lower than those in April or nearly the same.
shows the comparison between the monthly averaged NO2
concentrations and mobility information distance over the entire region of each study site. As can be seen, the average mobility distance was at its lowest in the April 2020 peak distance, which corresponds with the lowest average NO2
concentrations in all three sites.
There were some variations between each of the sites that can be seen in Table 1
. Los Angeles, for example, had a significant drop in movement between February and March, which was accompanied by a significant drop in NO2
density. The leveling off in mobility did not directly correlate to the steady decrease in the rate of NO2
density. There are a number of factors which could be a reason for this, including the time it takes to turn off various industrial processes or the number of cars on the highway.
In the case of Chicago, their lockdown was not as abrupt (only 17% drop in NO2 for 26% drop in mobility between February and March), but even so, the decrease in movement resulted in a decrease in NO2 density. The Washington DC metropolitan area is notable in that the shutdown did not occur until late March, meaning that the largest drop in mobility would have occurred in late-March. Even then, the NO2 density decreased by 30% between February and March while the drop in NO2 between March and April is quite significant, at 42%. The mandatory telework is continuing in the Washington, DC area and the trend in NO2 for the whole year (2020) will shed light on how policy makers can introduce work schedules to the federal employees in the area to minimize air pollution.
The photochemical smog that leads to poor air quality is a chemical soup of noxious gases (NOx
= NO+ NO2
) among other volatile organic compounds (VOCs) that lead to ozone and PM2.5
formation. Ozone is harmful to humans as well as plants, whereas PM2.5
is harmful to humans. Both are pollutants that were declared as criteria pollutants by the United States Environmental Protection Agency (EPA). While NO2
and VOCs are precursors for ozone and secondary aerosol formation, PM2.5
can also be directly emitted (soot from cars) or photochemically formed from NO2,
, and VOCs which are precursors. Because of the extreme reductions in the SO2 emissions beginning in the 1970s to curb acid rain, SO2
is no longer a main source for secondary aerosol formation. NO2
and VOCs remain the main precursors leading to the formation of secondary nitrate and organic aerosols. Figure 17
concentrations in February, March, and April of 2020 decreased compared to the same months in 2019 with the exception of February 2020 in Los Angeles which was higher than the values observed in February 2019. Note that the lockdowns did not start until March and the differences could be due to the unique seasonal differences between the two years. Of the three cities, Chicago saw the largest reduction in PM2.5
3.4. Distribution of Nighttime Lights (NTL)
The Day/Night Band (DNB) on S-NPP (and NOAA-20) has the ability to detect visible/near-infrared (500–900 nm spectral response) imagery for both day and nighttime conditions. The instrument is sensitive enough to detect not just the light emitted from a single isolated streetlamp but also emitted light from the mesosphere reflected off cloud features as well as density perturbations within the mesosphere itself. While it has a wide range of applications, the DNB was used here as a proxy of power usage (economic activities). Because the DNB is only able to observe electrical light at night, generally around 1–2 a.m. local time, this means that it is only a measurement of nighttime/early morning activity. Despite this limitation, there is significant activity at night like traffic movement that is captured by the DNB imagery.
Immediately after the lockdown measures were in place, businesses were shut down and the majority of the economic activities stopped in LA as is evident from Figure 18
b, except for some activities that were still ongoing near downtown LA and the Long Beach area. The reduction in activities in March aligns with the reduction in mobility seen in LA County, except for the downtown area and near Long Beach where probably the port activities were still underway to some extent (Figure 5
). The limited traffic movement in March (Figure 18
b) could be due to the travel to essential businesses, such as grocery stores and hospitals. Although the lockdown measures were still active in April, economic activities appear to have resumed in LA in April (Figure 18
c). The highest NTL intensity values (nWatts·cm−2
) in February, March and April were 7610, 5730, and 3569 respectively. Evidently, the April NTL intensity was 53% lower than February radiance and ~38% lower than March radiance. However, it is clear that the economic activities and associated mobilities in April were concentrated in the downtown LA area, near the port in Long Beach, and along the LA-San Bernardino and LA–San Fernando corridors as evident from Figure 18
d,e, where the blue indicates a measured increase in light intensity, while red indicates a measured decrease in intensity (radiance). This color scheme has been used in other studies regarding DNB radiance differences [29
Not surprisingly a similar pattern was observed in Chicago in March after the lockdown measures were in place in Illinois, particularly in the Chicago Metropolitan area. Economic activities dropped in Chicago region except for the downtown Chicago area near Millennium Park (Figure 19
). Some activities are also observed near Rosemont area (O’Hare International Airport) and Clearing (Chicago Midway International Airport). Comparison of the light intensity between February and March (892 and 839 nWatts·cm−2
, respectively) indicates that the reduction in economic activities was not that drastic as was the case in LA (Figure 19
d). By April, economic activities and traffic movement started in the Chicago region (Figure 19
c), but the comparison of light intensity between March and April (839 and 1207 nWatts·cm−2
, respectively) (Figure 19
e) indicates that economic activities and mobility increased way more than what was observed in February in certain parts of Chicago rather than the broader region. In fact, the radiance dropped by 6% in March, but increased by ~44% in April. This is corroborated by PM2.5
observations in April, which showed very limited reduction in April 2020 as compared to April 2019, whereas March 2020 showed substantial reductions in PM2.5
compared to March 2019 (Figure 17
). The activities are concentrated in the Chicago metropolis rather than beyond the metropolis in the suburbs as was the case in February.
The pattern of reduced economic activities and mobility in March followed by an increase in activities in April was observed in DC and its surrounding urban areas of Pittsburgh, Maryland, etc. Although the radiance values dropped by 22% in DC (Figure 20
d, from 490 to 383 nWatts·cm−2
), there was an increase in activity (lighting was bright) in the neighboring areas of DC in Arlington and Bethesda. An obvious decline in activity in College Park was also observed due to the closure of the university campus and federal buildings near the campus. The drop in central DC in March was concentrated in the downtown area where businesses closed down immediately following the lockdown orders. By April, though economic activities increased to some extent (radiance increased by 7% than what was observed in March, Figure 20
e), economic activities in the DC area beyond the downtown and White house area reduced in April as most employees started working remotely. This drop in radiance aligns with what was observed with regard to mobility change during March and April in the broader DC region. By contrast to DC metro area, economic activities and traffic movement appear to have resumed by April in the Baltimore, Columbia, MD areas.