Substantial decreases in NO 2 concentrations measured by ground-based monitors in US cities during COVID-19 shutdowns from reduced transportation volumes

The air pollutant NO 2 is derived largely from transportation sources and is known to cause respiratory disease. A substantial reduction in transport and industrial processes around the globe stemming from the novel SARS-CoV-2 coronavirus and subsequent pandemic resulted in sharp declines in emissions, including for NO 2 . Additionally, the COVID-19 disease that results from the coronavirus may present in its most severe form in those who have been exposed to high levels of air pollution and thus have various co-morbidities (Fattorini & Regoli, 2020) . To explore these links, we compared averaged ground-based NO 2 sensor data from 11 US cities from a two-month window (March-April) over the previous five years versus the same window during shutdown in 2020. Levels of NO 2 declined roughly 12-41% in the 11 cities. This decreased coincided with a sharp drop in vehicular traffic from shutdown-related travel restrictions. To explore this link more closely, we gathered more detailed traffic count data in one city, Indianapolis, Indiana, and found a strong correlation between traffic counts/classification and vehicle miles travelled, and a moderate correlation between NO 2 and traffic related data. This five Texas cities analyzed decreasing the least, potentially due to less restrictive lock-down procedures in that state and or the presence of other industry. Vehicle Miles Travelled (VMT) in the 11 cities decreased from 11%-51% in March (compared to January) and 62%-89% in April. Using vehicle count and classification sensors in Indianapolis, we find that there was a good match between NO 2 sensor values and the actual number of vehicles on the road, indicating that in some cases this might be a better metric than VMT at estimated emission concentrations. A more robust network of ground-based sensors that are matched to population density and the potential regions of highest emissions are needed to bridge the gap between regulatory compliance and protecting human health.

impact of stay-at-home orders in March through April 2020 versus a five-year average of calibrated high-quality data from March-April from 2015-2019. We utilize 2020 daily raw data for NO2, from EPA grade sensors in 11 large cities around the US. NO2 concentrations are assessed and compared to traffic volumes by utilizing county vehicle miles travelled (VMT) data as a proxy, after adjusting for population density of the study regions. Additionally, we examined how Indianapolis's major traffic density metric, VMT compares to actual vehicles on the road and NO2 concentrations, which may be a better metric of vehicular emissions in cities.

NO2 and Vehicle Miles Travelled (VMT) data
To examine the impact of stay-at-home orders, NO2 daily averaged data from continuous ground level sensors from 11 major cities in the U.S. was downloaded from the respective state agencies for our study period. These cities were chosen for their population size and the availability of comparable data for air quality. We were unable to gather information on each individual sensor, however, based on Federal Audits required by the Environmental Protection Agency (EPA), generally a difference of < 15 percent difference is an acceptable federal standard that the states have to adhere to (Air Sensor Guidebook, 2014).
Data for NO2 over the months of March and April 2020, were used as lock-down reference months, acknowledging that some states were phasing in lockdowns during March, and that states and cities often had different shut-down policies. This was compared to January 2020 data from those same sensors to determine in-year changes. The 2020 data is also compared to the mean 5-year sensor data (2015)(2016)(2017)(2018)(2019) for March and April, in order to take the meteorological conditions into account. We identified two fixed monitors within each region (Cakmak et al., 2016), however, due to excessive number of missing days of data for San Antonio and Austin we utilized data from one sensor each in those locations. Additionally, for Queens we were able to identify only one fixed continuous monitor maintained by the state. For the remaining 8 cities we averaged NO2 data over two fixed sensors each for 2020 and 2015-2019.
To obtain a uniform scale of vehicle usage, aggregate VMT data, generated at the county level, was accessed from StreetLight Data to examine changes in traffic patterns and emissions (Jia et al., 2020). Streetlight runs over 100 billion location data points gathered from smart phones and navigational devices connected to vehicles (cars and trucks), into an algorithm to aggregate and normalize travel patterns by region. Their metrics are validated not only against public sources or external sources, but also using private data in all states except Hawaii and Alaska (StreetLight Volume Methodology & Validation White Paper, 2019). Population percentage of the study area in the county it resided in was used to normalize the VMT data for this analysis, and from this point forward normalized VMT is presented and referenced in this document.

Indianapolis Traffic Sensor Data
Traffic counts are used in numerous studies to connect urban pollution like NO2 to examine regions, their health impacts and the socio-economic disparities that occur as a result of it (Cakmak et al., 2016;Madariaga et al., 2003;Nicolai et al., 2003). For this study, we downloaded daily traffic volume and classification data of vehicles from 5 continuous sensors placed on major roadways in Indianapolis, identification numbers 990362, 950109, 990309, 990311, and 991392, reported by the Indianapolis Department of Transportation (INDOT). This data is publicly available via INDOT's online Traffic Count Database System (TCDS). March and April 2020 daily counts were examined against the count and classification data from January 2020 for the referenced continuous sensors. INDOT has 15 vehicle classifications, however, we focused on total vehicular traffic, total cars, and classification of motorcycle, car, pickup, and bus as a sub-category (1-4). Classification 5 and above were primarily trucks with varying axles (Traffic Count Database System (TCDS), 2020)

NO2
A reduction between 12%-41% in the 2020 NO2 March-April averages is observed in all locations when compared to the 5-year averages from that respective time frame (Table 1, Table   S1). The percentage drop in NO2 values when 2020 values are compared to the 5-year averages between January and March range from 11%-56% and 4%-43% respectively ( Table 1, Table 1S).
While as January and April reflect a NO2 drop ranging from 14%-65% in 2020 and a drop of 13%-51% in the 5-year averages ( Table 1, Table S1). Between January and March, San Antonio was the only location where the 2020 percent change was lower than the 5-year average percent change ( Figure 1). From January to April (Figure 1), the percent changes in 2020 and the 5-year averages of San Antonio and Austin were almost the same while the other 9 locations showed a sharp reduction in NO2 values in 2020, compared to the same 2-month window from 2015-2019 ( Figure 1). Excluding the cities of Austin and San Antonio from January to April in 2020, Indianapolis had the smallest reduction of NO2 at 33% and San Francisco had the largest reduction of NO2 values at 65% (Table 1).
Seasonal changes in NO2 naturally occur and must be considered. In summer, NOx and other volatile organic compounds from traffic and other sources result in photochemical smog, with December through February having seasonal maximum in the U.S. (A et al., 2008). Oxidation by photochemically produced OH in the summer reduces NOx, while lower concentrations of OH in the winter months results in an increased lifetime of NOx (Shah et al., 2020). Extrapolating further from Table 1, we see this in our multi-city data, with decreases in 2020 NO2 values in March and April ranging from -37.66% to -48.13% compared to their respective average January values. In April 2020, Austin had the smallest reduction of -13.51% with San Francisco having the largest reduction of -64.93% (Table 1). These decreases constitute seasonal changes plus any change related to COVID lock-down policies in the various cities. To determine the typical seasonal decrease in NO2 values and thus remove this from the seasonal plus COVID-related signals, we calculated the five-year averages for each city, with the assumption that this window would normalize for particular weather-related variations year-on-

Location
year. We found that the typical seasonal decreases were significantly less than the COVID-  (Table 1, Table S1), indicating the significant impact of lock-downs and agreeing with the more regional results obtained by satellite analysis (e.g., Goldberg et al., 2020). We can visualize such impacts from the free use of tropospheric NO2 monthly mean averages, from GOME-2 sensor from www.temis.nl, over the U.S. from April 2019 when compared to April 2020 ( Figure   S1) (Boersma et al., 2004).

VMT and NO2
Similar to the NO2 trends between January, March and April in 2020 ( Figure S2), VMT in all the locations significantly dropped with the implementation of stay-at-home orders (Fig 3). March showed a significant reduction in VMT between 11%-51% with NO2 reduction being between 11%-56% (Table 3). April in comparison to January showed a much higher reduction of VMT between 62%-89% (Table 2) with NO2 reduction being between 14%-65% (Table 3, Figure S3).    Figure S4). April, a month into the shutdown period in most states, NO2 changes are consistently higher than the VMT percent changes in that time ( Figure S5).
Spearman rank correlation statistics between NO2 and VMT are presented in Table 4 (Table 7). VMT percentage reduction in April versus January is almost two times that of the average total vehicles in Indianapolis and of the NO2 percentage reduction in that time period (Table 8), indicating that a percentage reduction in the average total vehicles results in almost an equivalent percentage reduction in NO2 in the city in that month. Extrapolating from Table 8 we can make the following 2 points regarding the change from January to April: 1. If a 50,000-unit reduction in average total vehicles, is equivalent to a 32% or a (Matthes et al., 2007)

Discussion
High vehicular emissions can result in corridors of heavy air pollution (Redling et al., 2013) in rural and urban regions. NO2 pollution, a tracer for vehicular emissions, has been linked to adverse health effects for instance increased asthma events in predominantly urban areas (Achakulwisut et al., 2019). A 20 ppb increase in NO2 has been found to increase chronic obstructive pulmonary disease (COPD) hospital visits, cardiovascular disease, lung cancer in adults, and respiratory mortality (Cesaroni Giulia et al., 2013;Peel et al., 2005). The onset of COVID-19, and the stay at home orders in March and April, presented an opportunity to examine the changes in NO2 concentrations and their relationship to VMT in 11 cities in the U.S.
with implications for local health outcomes.
Satellite data has been shown to be under reported in urban regions versus remote regions with daily NO2 retrievals varying up to 40% (Lamsal et al., 2014). Our analysis of the impacts of stay at home orders utilized ground-based sensor data from 11 U.S. cities. We found an average reduction of NO2 of 31% measured in March and April 2020 when compared with their 5-year averages (2015-2019) ( Table 1, Table S1)). January to April 2020 resulted in a drop between 14%-65% versus its respective 5-year average drop between 13%-51%. Four Texas cities had a poor correlation between VMT and NO2 (Ft. Worth, San Antonio, Austin, and Dallas). This consistent offset is likely due to differences in the air being sampled with each approach (i.e., ground-level versus troposphere scale).
The VMT reduction in April 2020 ranged between 62% and 89% (Table 3), when compared to January 2020 Average ratio of NO2/VMT for the 11 locations indicates that for every 1,000,000 less VMT, NO2 decreases by 0.24 ppb (Table 5). A 1,000,000 average VMT drop in San Francisco resulted in the most significant decrease in NO2 (0.65 ppb) and Houston resulted in the least significant decrease (0.07 ppb). The petrochemical industry in Texas and particularly in the greater Houston area, probably plays a significant role in NO2 production (Jobson et al., 2004), and thus the VMT-NO2 relationship is not likely the only factor influencing the scale of observed decreases in NO2.
The lack of observed significant correlations between NO2 and VMT for the four Texas cities remains unresolved. We suggest two options: (1) the locations of the fixed AQ sensors locations in relation to emission sources as related to traffic and non-traffic need to be identified and incorporated with meteorology as their absence may not be ideal for capturing the more regional emission sources that are better characterized by satellite observations (e.g., Goldberg et al., 2020) that might be an issue for more sprawling cities, and/or (2) VMT along with specific traffic volume and classification analysis from platforms like StreetLight, may be a more robust metric for extrapolating the local impacts of NO2 emissions from vehicle sources. A much denser array of high quality ground-based sensors would likely have to be in place to address option (1) above, but with option (2), we can, at least for one of the cities (Indianapolis), compare NO2 to actual vehicle count data for several locations to address the issue.
We can use traffic counts in addition to VMT to create localized indices that can assist local governments to plan and/or to adjust traffic flows to address the impacts of high NO2 values. In future studies, placement of NO2 sensors in relation to the NO2 sources, which would also impact the sensors readings, should be considered. This NO2/VMT ratio (Table 5)  In addition to sensor placement, meteorological conditions like temperature, wind speed, relative humidity, and precipitation also play a role in transport of atmospheric gases (Tobías et al., 2020), which were also not considered in this analysis. Such conditions are not uniform spatially and have shown to cause column NO2 readings to differ by about 15% over monthly timescales (Goldberg et al., 2020) Houston where there is a presence of other significant industry, their emission impacts should also be incorporated for a more comprehensive understanding.
In qualitative terms, the observed substantial reductions in NO2 would, all other things being equal, provide some benefits to human health. With the return to "business-as-usual" practices, these health benefits will be transitory. Satellite measurements of NO2 are outstanding for capturing regional trends, but the heterogeneity of NO2 at the ground level in a given city (e.g., Coppalle et al., 2001) is not well-captured, and thus pinpointing emission sources that are proximal to population centers at the fine scale should be a high priority for city planners and transportation design. This latter point is critical in that the highest concentrations of NO2 and many other criteria air pollutants are disproportionately located in lower income communities (Miranda et al., 2011;Cakmak et al., 2016). The overlapping issues of poor air quality and particular susceptibility of these same communities to severe COVID disease speaks to the need to better constrain ground-level air pollution levels with an eye toward applying health equity solutions in cities.

Conclusions
The pandemic-driven shutdown policies instituted in cities across the U.S. substantially decreased many harmful air pollutants, including NO2 (e.g., Berman and Ebisu, 2020;Goldberg et al., 2020). We find this stable reduction within cities using ground-based monitors, and it is largely tied to reduced traffic volume, with other factors, such as industrial emissions, playing a variable role. Although ground-based monitoring ties the concentration data much more closely to communities and local health impacts than does more regionally comprehensive satellite data, the paucity of monitors and likely disconnects between metrics that are meant to capture traffic volume reduces their effectiveness from a public health standpoint.
This observed reduction in urban NO2 concentrations, a rare silver lining of the devastating pandemic, is likely temporary, but it does point to the tight connection between traffic-related pollution sources and local impacts. This connection prompts the two-fold issue that local air pollution hotspots exacerbate diseases like COVID and are currently under-studied. Two actions that city planners can take to promote health equity in their communities are to implement environmental monitoring programs that link data points (i.e., monitors) more strategically to population density, and to implement local transportation and zoning policies that protect community health and build health equity into the system.

Acknowledgements
This work was partially supported by the Environmental Resilience Institute, funded by Indiana University's Prepared for Environmental Change Grand Challenge Initiative, and by National Science Foundation award ICER-1701132 to Filippelli. The source data for NO2 concentrations was accessed from the public databases available at the Department of Environmental Management or equivalent data hubs for each state. The traffic volume data, an unfunded