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Environmental Processes

On-road Chemical Transformation as an Important Mechanism of NO2 Formation Bo Yang, K. Max Zhang, Wei Xu, Shaojun Zhang, Stuart A Batterman, Richard Baldauf, Parikshit Deshmukh, Richard Snow, Ye Wu, Qiang Zhang, Zhenhua Li, and Xian Wu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b05648 • Publication Date (Web): 22 Mar 2018 Downloaded from http://pubs.acs.org on March 24, 2018

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Environmental Science & Technology

On-road Chemical Transformation as an Important Mechanism of NO2 Formation

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Abstract

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Nitrogen dioxide (NO2) not only is linked with adverse effects on the respiratory system, but also

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contributes to the formation of ground-level ozone (O3) and fine particulate matter (PM2.5). Our

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curbside monitoring data analysis in Detroit, MI and Atlanta, GA strongly suggests that a large

24

fraction of NO2 is produced during the “tailpipe-to-road” stage. To substantiate this finding, we

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designed and carried out a field campaign to measure the same exhaust plumes at the tailpipe-level

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by a Portable Emissions Measurement System (PEMS) and at the on-road level by an Electric

27

Vehicle-based mobile platform. Furthermore, we employed a turbulent reacting flow model, CTAG,

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to simulate the on-road chemistry behind a single vehicle. We found that a three-reaction (NO-NO2-

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O3) system can largely capture the rapid NO to NO2 conversion (with timescale ~ seconds)

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observed in the field studies. To distinguish the contributions from different mechanisms to near-

31

road NO2, we clearly defined a set of NO2/NOx ratios at different plume evolution stages, namely

32

tailpipe, on-road, curbside, near-road and ambient background. Our findings from curbside

Bo Yang1, K. Max Zhang1, W. David Xu1, Shaojun Zhang1, Stuart Batterman2, Richard W. Baldauf3,4, Parikshit Deshmukh5, Richard Snow3, Ye Wu6,7, Qiang Zhang6, Zhenhua Li6, Xian Wu6 1

Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA 2 School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA 3 U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, Research Triangle Park, NC 27711, USA 4 U.S. EPA, Office of Transportation and Air Quality, National Vehicle and Fuels Emissions Laboratory, Ann Arbor, MI 48105, USA 5 Jacobs Technology, Inc., Durham, NC 27713, USA 6 School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, PR China 7 State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, PR China

1

ACS Paragon Plus Environment


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monitoring, on-road experiments and simulations imply the on-road oxidation of NO by ambient O3

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is a significant, but so far ignored, contributor to curbside and near-road NO2.

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TOC/Abstract Art

Task1: Curbside monitoring data analysis RBG : Background NO2/NOx ratio

RCS : Curbside NO2/NOx ratio ROR : On-Road NO2/NOx ratio

RNR : Near-road NO2/NOx ratio ~ 100 m

Ambient wind Ambient O3 RTP : Tailpipe NO2/NOx ratio

Task2: On-Road Chasing and PEMS data analysis

Task 3: On-Road CFD Simulation

PEMS ROR (%)

37 38 39

Key Finding: The on-road chemistry is a major contributor to the curbside NO2 concentration. Word count: 5155 (text) + 1500 (5 figures x 300 words per figure) + 300 (1 table x 300 words per table) = 6955 words

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Introduction

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The transportation sector contributes about 30% to 40% of the emitted nitrogen oxides (NOx) 1, 2,

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and the health effects of nitrogen dioxide (NO2) are a public health concern worldwide 3-6. As

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part of the revised the National Ambient Air Quality Standards (NAAQS), the U.S.

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Environmental Protection Agency (USEPA) established monitoring requirements in urban areas

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to measure NO2 levels within 50 m of major roads as part of the NO2 NAAQS. In Europe, NO2 is

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one of the major contributors to urban air quality problems, and has been routinely monitored in

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the near-road or street canyon environments3. The European Environment Agency estimated

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71,000 premature deaths due to NO2 exposure in Europe, higher than the estimated mortality

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caused by ground-level ozone 3. From around 1990 onwards, the total emissions of NOx declined

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significantly in Europe by ~56% 1, but roadside concentrations of NO2 declined much less than

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expected 7. ~94% of exceedances of the European NO2 annual limit were observed at traffic

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monitoring sites 3. In China, ambient NO2 concentrations in some megacities (e.g., Beijing,

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Shanghai) have persisted and exceeded the annual standard limit even though the concentrations

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of other gaseous pollutants have decreased significantly during the same period 3, 8, 9. Even

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though China does not have a nationwide near-road air quality monitoring network, recently

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released near-road concentrations well exceed the current standard (e.g., an average exceedance

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of 20 to 30 µg m-3 for five near-road sites in Beijing)10. Furthermore, besides being a criteria

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pollutant itself, NO2 is also widely adopted as a marker for traffic-related air pollution in health

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studies11-13. For example, positive associations of NO2 with mortality suggest that traffic

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pollution relates to premature death 14.

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In the complex roadway environments, direct tailpipe NO2 emissions, secondary NO2 through

62

on-road and near-road NOx titration and background NO2 all lead to near-road NO2. Therefore, a

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better understanding of the contributions from different pathways to near-road NO2 is necessary

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for not only effective air quality management, but also evaluating the role of NO2 as a marker in

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health studies, as near-road NO2 could be dominated by different pathways at different

66

conditions.

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Moreover, the NO2/NOx ratio (typically by volume) is a widely used parameter in air quality

68

management due to its applicability across the scales, from tailpipes to ambient environments.

69

Both highway dispersion modeling (for NO2) and regional air quality modeling (for O3, PM2.5,

70

etc.) require inputs of NO2/NOx ratios, and previous studies have shown that both types of

71

modeling are sensitive to those inputs 15, 16. Thus, another major motivation for our study is to

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elucidate the physical and chemical processes governing near-road NO2 in order to provide

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appropriate NO2/NOx ratios for highway dispersion and regional air quality modeling efforts.

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To facilitate the subsequent discussions, it is necessary to clearly define NO2/NOx ratios at

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different stages of plume evolution, listed in Table 1. The tailpipe NO2/NOx ratio (RTP), also

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called the primary NO2/NOx emission ratio, is defined as the NO2/NOx ratio at the vehicle

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tailpipe exit. RTP for gasoline vehicles is typically around 5% or lower17-19, but can be higher for

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diesel vehicles equipped with diesel particulate filters (DPF)20-22. The curbside NO2/NOx ratio,

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RCS, is defined as the ratio at the edge of roadways. We further define the ratio between the

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tailpipe exit and the curbside as the on-road NO2/NOx ratio, ROR. The ratio in the near-road

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environments (from the curbside to ~ 100 m) is defined as near-road NO2/NOx ratio, RNR. Lastly,

82

the ratio in the ambient environments is defined as background (or ambient) NO2/NOx ratio, RBG.

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It is well recognized that NOx transforms chemically from NO to NO2 through titration of O3 in

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the near-road environment, resulting in RNR differing from RCS. The near-road chemical

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transformation has been observed in several field studies23-26. In our previous study, we

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estimated RCS to be around 20 to 40% based on near-road monitoring data collected at several

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Texas roadways in the summer of 2007 16. The roadways studied had low diesel traffic, and the

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penetration of diesel particulate filters was very low in 2007. Therefore, the relatively high RCS

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must be explained by mechanisms in addition to the presence of diesel traffic.

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Table 1. The defined NO2/NOx ratio at different locations in a traffic environment Location

NO2/NOx ratio

Description

Tailpipe exit

RTP

The NO2/NOx ratio at the tailpipe exit, also called the primary NO2/NOx ratio, which can be measured by emission testing.

On-road

ROR

The NO2/NOx ratios on the roadway, from the tailpipe to the curbside

Curbside

RCS

The NO2/NOx ratio at the curbsides

Near-road

RNR

The NO2/NOx ratio from the curbside to about 100 m away from the roadways

Ambient background

RBG

The NO2/NOx ratio from the curbside to the near-road region (0 ~ 100 m)

91

This study aims to investigate the physiochemical mechanisms that lead to enhanced curbside

92

NO2/NOx ratios (RCS). We posit that on-road (i.e., “tailpipe-to-road” 27) chemical reactions are a

93

major contributor to NO2 production, resulting in an evolution from RTP to ROR and to RCS. To

94

our knowledge, on-road chemical transformation of NOx has not been explicitly studied.

95

However, several recent studies have implicitly indicated the significant role of on-road NO2

96

production. The curbside measurements of NO, NO2 and O3 in street-canyon environments at

97

two German cities showed that secondary NO2 production was responsible for a major fraction

98

of measured NO228. One can deduce from the study that the referred production of NO2 most

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likely took place on the streets. Another study of near-road monitoring data at Las Vegas, NV

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indicated the RCS ranging from 0.25 to 0.35, substantially higher than the anticipated RTP29, which

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we argue can potentially be explained by the on-road production of NO2. Even though the term

102

“on-road” NO2/NOx ratio was used in the Las Vegas study29, a close examination of the reported

103

methodology suggested that the term was referred to RCS according to our definition.

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In this study, we present the supporting evidence from 1) curbside monitoring near two major

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highways in Detroit, MI and Atlanta, GA, respectively, 2) an on-road field experiment in

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Research Triangle Park (RTP), NC, and 3) on-road simulations using a turbulent reacting flow

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model. This work is intended to improve future modeling of near-road NO2 concentrations, air

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quality and transportation management, and health studies on traffic-related pollutants.

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Curbside Monitoring

110 111

Site description and field measurements

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Detroit, MI Field measurements were performed at several monitoring sites in Detroit, MI,

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including the Eliza Howell near-road sites, starting fall 2010 and concluded in late summer 2011.

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For this study, we used data collected between September 2010 and June 2011, when the traffic

115

and meteorological conditions were both well characterized. These sites were originally

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deployed as part of the Federal Highway Administration (FHWA) and USEPA National Near-

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Road Study for a one-year period, then continued by the Michigan Department of Environmental

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Quality (MDEQ) to the present. Data from these sites have being used in the Near-Road

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Exposures and Effects of Urban Air Pollutants Study (NEXUS), which examined the relationship

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between near-roadway exposures to air pollutants and respiratory outcomes in a cohort of

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asthmatic children who live close to major roadways in Detroit, Michigan 30.

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The Eliza Howell 1 monitoring site (EH1; 42°23'09.6"N, 83°15'58.9"W) is placed at the

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curbside of Interstate I-96/Jeffries Freeway (Figure S1a in the Supporting Information (SI)). On

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the opposite side, site EH4 is located ~100 m south of I-96, which served as an upwind site when

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EH1 was downwind of I-96, and allowed us to quantify the contribution of background NO and

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NO2 to those measured at EH1. The immediate area is mostly open parkland with some trees and

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playing fields; surrounding areas are mostly low-rise commercial and residential buildings. 6


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NO/NO2/NOx were measured at a height of 3 m using chemiluminescence (Teledyne ML9841B

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Nitrogen Oxides Analyzer)31, which reports NO2 as the difference between NOx and NO. Wind

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speed and wind direction were measured at a height of 10 m. Both NOx and surface meteorology

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were also measured at EH4, Hourly ozone (O3) measurements from the Allen Park (AP) site

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(AQS Site ID: 261630001; 42°13'43.0"N, 83°12'29.9"W), located ~18.5 km south of EH1 and

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~200 m southeast from the closest major roadway (I-75), were obtained from the USEPA Air

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Quality System (www.epa.gov/ttn/airs/airsaqs/). A closer O3 measurement site, E7Mile (AQS

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Site ID: 261630019; 42°25'50.9"N, 83°00'01.0"W), was not selected due to more than three

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months missing data between September 2010 and June 2011.

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Traffic on I-96 is normally free-flowing with an established speed of 70 mph for cars and 65

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mph for trucks. The annual average daily traffic (AADT) on this I-96 segment are 155,000 31.

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Based on vehicle classification data measured during the study period at the EH1 site (using

140

radar to classify vehicle size), the percentage of heavy-duty diesel trucks (HDDT) for every 5

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minutes averaged ~ 3.0%.

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Atlanta, GA Continuous air quality monitoring data of NO, NO2, and O3 were obtained at two

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sites in Atlanta, GA: a curbside site off Interstate I-85 Freeway near Georgia Institute of

144

Technology (NRGT; AQS Site ID:13-121-0056; 33°46'41.934" N, 84°23'29.1048" W) and a

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background site in South DeKalb (SDK; AQS Site ID: 13-089-0002; 33°41'16.692" N,

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84°17'25.7280" W), about 13.5 km southeast from the NRGT site. Figure S1b in the SI depicts

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the locations of the two sites. The monitoring at NRGT started near the end of May 2014. We

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analyzed data collected between January 2015 and March 2017 since minute-level data of of NO,

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NO2, and O3 from both NRGT and SDK are available during this period.

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150

I-85 is a major Interstate Freeway that runs northeast–southwest in Georgia. The AADT on this

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segment is ~332,000 in 2015, and ~353,700 in 2016, accordingly to the closest traffic monitoring

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station (ID: 1215481; 33°46'23.8800" N, 84°23'24.0000" W)

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(http://trafficserver.transmetric.com/gdot-prod/tcdb.jsp?siteid=1215481). The truck percentage is

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~3.7% based on another nearby station (ID: 1216118; 33°46'32.88" N, 84°23'24.72" W)

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(http://trafficserver.transmetric.com/gdot-prod/tcdb.jsp?siteid=1216118).

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Curbside Data Analysis

157

To investigate the role of on-road chemical transformation of NOx, we screened the Detroit data

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using the following criteria: First, we selected daytime data (6AM-6PM) under south-dominant

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wind conditions (101.25 < θ < 258.75, θ is the wind direction in degrees) so that EH1 was

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downwind of I-96. Next, we subtracted the upwind NO2 and NOx concentrations (i.e., at EH4)

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from the corresponding values at EH1, thus reflecting the net contributions of highway

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emissions. Finally, we set a net NOx concentration threshold to ensure the curbside data reflect

163

the highway influence. In other words, the curbside NOx concentrations are expected to be higher

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than the upwind NOx concentrations. The higher the threshold value, the more confident we are

165

of the highway influence, given the uncertainties and detection limits in the NOx measurements.

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But the same time, a higher threshold value may also exclude some low-traffic conditions. We

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tested the sensitivity of our results to the net NOx concentration threshold. We followed similar

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steps to analyze the Atlanta data. First, we selected daytime data (8AM-5PM) under east-

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dominant wind conditions (11.25 < θ < 168.75) so that NRGT was downwind of I-85. Next, we

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subtracted the ambient NO2 and NOx concentrations (i.e., at SDK) from the corresponding values

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at NRGT to reflect the net contributions of highway emissions. We also tested the sensitivity of

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net NOx concentration thresholds.

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Data Kept R-squared Slope Intercept

20

d) Atlanta, GA (I-85)

80

18

60

16

0.8 0.4

R-squared

0.6

-1 -2

Intercept

40

O3 (ppb)

10

20

30

40

50

20

30

40

0.2

12 10

20 0

10

0.0

0.2 0.0

-4

0.2

20 0

0.0

0.2 0.0

-3

40

0.4

Slope

60

0.6

% of data were kept

0.6

R-squared

0.4

20

Intercept

Data Kept R-squared Slope Intercept

0

14

c) Detroit, MI (I-96)

0

80

1.0

O3 (ppb)

20

1.0

60

0

40

1.0

20

0.8

0.8

1.0

173

0

100

0

0

0.8

20

50

0.6

0

100

Slope

50

150

40

0.4

100

100

150

40

NOx (ppb) 200

80

NOx (ppb) 200

60

60

Curbside NO2 NOx , R CS (%)

60

b) Atlanta, GA (I-85), Daytime East Dominated Wind

40

a) Detroit, MI (I-96), Daytime South Dominated Wind

80

Curbside NO2 NOx , R CS (%)

80

% of data were kept

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174 175 176 177 178 179 180

Figure 1. Curbside NO2/NOx ratio (RCS) vs ambient O3 concentrations (ppb) with net NOx threshold 40 ppb at a) Detroit, MI (I-96) during the daytime and south dominated wind (101.25 < θ < 258.75) and; b) Atlanta, GA (I-85) during the daytime and east dominated wind (11.25 < θ < 168.75); Linear regression parameters (slope, intercept and R2) of the RCS vs O3 relationships and the amount of data included (as percentage of the total data available) as functions of net NOx concentration thresholds for (c) Detroit, MI (I-96) and (d) Atlanta, GA (I-85).

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Figure 1 summarizes the main findings from our curbside analysis of the Detroit and Atlanta

182

datasets. The relationships between net curbside NO2/NOx ratios (RCS) and ambient O3

183

concentrations (with net NOx concentrations represented by color scales) are illustrated in

184

Figures 1a and 1b, for Detroit and Atlanta, respectively. The overall linear relationships between

185

O3 concentrations and net RCS from these two curbside measurements support the significance of

NOx Concentration Threshold (ppb)

NOx Concentration Threshold (ppb)

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186

on-road chemical transformation. In other words, higher ambient O3 concentrations, once mixed

187

with highway emissions, convert NO to NO2 during the tailpipe-to-road process. Conversely, if

188

the role of on-road chemical transformation was insignificant, then net RCS would not show a

189

relationship with O3 concentrations.

190

Figures 1c and 1d depict the sensitivities of linear regression parameters (slope, intercept and R2

191

as shown in Figures 1a and 1b) and the amount of data included (as percentage of the total data

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available) to the different net NOx concentration thresholds, ranging from 10 to 50 ppb. As

193

expected, the percentage of data included decreases with the net NOx threshold for both

194

highways. At the 10-ppb threshold, around 76% data are included in Detroit, MI and 94% data

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were kept in Atlanta, GA. At the 50-ppb threshold, both of the two datasets dropped to around

196

50%. However, the slopes from both datasets appear not sensitive to the net NOx threshold, only

197

changing from 0.71 to 0.81 for Detroit and from 0.66 to 0.73 for Atlanta, respectively. The R2

198

values for Detroit, varying from 0.73 to 0.76, were not sensitive to the NOx threshold, while

199

those for Atlanta, were more sensitive to the threshold, increasing from 0.57 to 0.70 as the NOx

200

threshold increases from 10 to 50 ppb. Figures 1a and 1b were both plotted at 40 ppb net NOx

201

threshold to achieve a balance of high percentage data inclusion and high R2.

202

The intercepts for Detroit and Atlanta both appear sensitive to the NOx threshold, but with

203

opposite directions. Physically, the intercepts represent the tailpipe NO2/NOx ratios, RTP. The

204

intercepts for Detroit are negative (albeit small in magnitude), approaching positive values as the

205

NOx threshold increases. The intercepts for Atlanta are positive, starting with relative large

206

magnitude, e.g., 15% when the NOx threshold is set to 10 ppb and decreasing to ~11% at the 50-

207

ppb net NOx threshold. One contributing factor to the negative intercepts in Detroit was found to

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be associated with the traffic patterns. Figure 1a indicates high NOx conditions predominately 10


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appear when ambient O3 concentrations are low, which represented the early morning and late

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afternoon traffic peaks. Those high-NOx and low-O3 conditions lead to a small fraction of NO

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being converted and thus low NO2/NOx ratios. The clustering of data points of low NO2/NOx

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ratios and low O3 concentrations pushes the linear regression towards the negative intercept side.

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By contrast, high NOx conditions are evenly distributed at the NGRT site across the whole range

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of O3 concentrations, suggesting a weak diurnal traffic pattern. It is worth noting that I-85

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segment near NRGT is one of the most trafficked highways in the U.S.

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Figure 1a raises the question over the contribution of on-road chemistry to curbside NO2

217

concentrations in Detroit since conditions with high net RCS and high ambient O3 appear when

218

net NOx concentrations are relatively low. Figure S2 in the SI illustrates the relationship between

219

net NO2 concentrations (i.e., from both primary NO2 emissions and on-road chemical

220

transformation) and ambient O3 from the Detroit dataset, which presents a positive linear trend

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and implies a significant contribution from on-road chemical transformation to curbside NO2

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concentrations. Furthermore, Figure 1b (based on the Atlanta data) corroborates this conclusion

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as conditions with high net RCS and high ambient O3 appear with high net NOx concentrations

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(thus high net NO2 concentrations). Moreover, Figure S3 in the SI depicted the relationships

225

between RCS and solar radiation at different ambient O3 concentrations. The results suggest that

226

RCS is not very sensitive to the NO2 photolysis rates (with solar radiation as a surrogate), which

227

in turn indicates that the titration of O3 by NO is the dominant reaction. Future studies are needed

228

to conduct detailed analysis of the contributions from different pathways (such as ambient

229

background, tailpipe or primary NO2 emissions, on-road chemistry, near-road chemistry) to

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curbside/near-road NO2 concentrations.

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We acknowledge the uncertainties in the curbside data analysis. For example, the NRGT does

232

not have a near-road upwind site for us to reliably estimate net NOx contributions, which at least

233

partially explain why the regression parameters are much more sensitive to the net NOx threshold

234

in Atlanta than Detroit. In addition, we did not have near-road upwind O3 measurements for

235

either Detroit or Atlanta. Several studies reported spatial variations of O3 concentrations in urban

236

areas 32-34. The O3 concentrations measured at the ambient sites may not accurately represent the

237

near-road upwind conditions. Nevertheless, the curbside data from two different highways

238

showed very consistent trends. Together, the curbside analysis provides strong evidence for the

239

significant role of on-road NOx transformation.

240 241

On-Road Experiment

242 243

Field measurements

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Motivated by the finding from the curbside data analysis described earlier, we designed an on-

245

road field campaign to gather further evidence on the on-road NOx chemical transformation. The

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overall concept of the on-road experiment was to measure the same exhaust plumes both at the

247

tailpipe-level by Portable Emissions Monitoring System (PEMS) and at the on-road level (after

248

initial dilution) by a mobile chasing platform. Then we would expect to observe noticeable

249

differences in the tailpipe-level and on-road level NO2/NOx ratios with significant on-road NOx

250

chemical transformation. However, it is technically challenging to directly measure on-road

251

NO2/NOx ratios (ROR) because reliable, fast-responding instruments for NO (or NOx) are not

252

readily available. Wild and coworkers recently developed a fast-responding instrument via

253

Cavity Ring-Down Spectroscopy 35. But we did not have access to that instrument. As an

254

alternative, our analysis relied on CO2 and NO2 by defining CO2-normalized NO2 concentration

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as △NO2/△CO2, essentially using CO2 as a dilution indictor, for PEMS and mobile platform

256

measurements, respectively. Details for this analysis are provided later.

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In our experiment, we employed an electric vehicle-based mobile platform equipped with air

258

quality analyzers at 1 s sampling intervals to conduct the on-road chasing measurements,

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capturing the on-road concentrations of CO2 (Quantum Cascade Laser, Aerodyne Research, Inc.)

260

and NO2 (Cavity Attenuation Phase Shift, Aerodyne Research, Inc.). The sampling probe was

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positioned at the bottom front of the mobile platform, around the same height as the exhaust

262

tailpipe of the vehicle being followed. We detected a six-second delay in response due to the

263

sampling line, and this delay was accounted for in the subsequent data analysis. The chasing

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routes were recorded using a high-precision GPS unit (Crescent R100, Hemisphere GPS). A

265

webcam was operated in the front seat of the mobile monitoring vehicle and recorded the traffic

266

conditions continuously during the on-road measurements.

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The vehicle being chased (referred to as “target vehicle”) was a 2003 Ford F350 diesel powered

268

truck (engine displacement 6.0 L, 8 cylinders, ~53,000 miles, 7940 lbs actual weight). Tailpipe-

269

level CO2 and NO2 concentrations were measured using a Portable Emissions Monitoring

270

System (PEMS) (SEMTECH® Ecostar; Sensors, Inc.). The PEMS samples directly from the

271

subject vehicle’s raw exhaust and is capable of providing on-board CFR 1065 compliant

272

emissions determinations at a rate of 1 Hz as the subject vehicle is being operated on the

273

highway. The PEMS utilizes a nondispersive infrared (NDIR) sensor for CO2 measurements and

274

a nondispersive ultraviolet (NDUV) sensor for NO2 measurements.

275

The on-road field campaign took place from February 22nd till March 4th, 2016 in Research

276

Triangle Park (RTP), NC. The measurements conducted in the afternoon of February 29

277

(FEB29PM), the morning of March 1 (MAR01AM), and the afternoon of March 1 (MAR01PM)

278

focused on comparing PEMS and on-road chasing results. The corresponding ambient ozone 13



279

concentrations (with standard deviation) during the sampling periods were 39.0 ± 2.6 ppb, 31.5 ±

280

6.5 ppb, and 38.6 ± 2.5 ppb, respectively.

281

On-Road Data Analysis

282

We started the data analysis by screening the videos recorded during the experiment to select the

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time segments without the presence of heavy duty vehicles around the target vehicle. Emissions

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from heavy duty vehicles may distort the on-road chasing results. For the selected time segments,

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we processed the chasing data using 2-second average to remove noise in the raw 1-second data.

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Then a two-step synchronization was adopted to further process the data. The first-step was to

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synchronize the chasing CO2 and NO2 signals, which usually required 1-second or 2-second time

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shift in time series, to identify synchronized CO2 and NO2 spikes. At the end of the first step, we

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obtained a series of synchronized CO2 and NO2 spikes that captured on-road plume signals with

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high confidence. The second step was to synchronize chasing plume signals with PEMS data.

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The distance between the chasing object and the mobile platform was usually within 20 m. Given

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the exit velocity ~20 m s-1, the time delay between the PEMS and chasing signals should be ~ 1

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second. In practice, the acceleration of the truck usually caused a large spike in both PEMS and

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chasing signals, which helped us to determine the time delay. At the end of the second step, we

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acquired a list of paired PEMS and chasing signals, representing the same exhaust plumes

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measured by the two methods.

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The CO2-normalized NO2 concentration (△NO2/△CO2) can be visualized by the slope of NO2

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vs. CO2 scatter plots. Figure 2 illustrates how we calculated CO2-normalized NO2 concentration

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using one pair of synchronized plume signals as an example.

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300 301

Figure 2. One pair of synchronized plume signals of NO2 vs CO2 concentrations based on (a)

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on-road chasing measurement and (b) tailpipe-level PEMS measurement for deriving CO2-

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normalized NO2 concentrations.

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We identified a total of 109 synchronized plume signals, and calculated the ratio of CO2-

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normalized NO2 concentrations from on-road chasing over tailpipe PEMS measurements for

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each plume signal, referred to as RatioChase/PEMS, i.e., a RatioChase/PEMS > 1 implies on-road NO2

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production. Figure 3 shows the distributions of this ratio and coefficients of determination (R2)

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for the linear regressions (as indicated in Figure 2). RatioChase/PEMS > 1 for all 109 plume signals

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with the median value as 1.87, and R2 values for both PEMS and on-road chasing measurements

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are high (with the median values over 0.8), suggesting the results are reasonably robust.

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15



312 313 314 315 316 317 318

Figure 3. The distributions of (a) RatioChase/PEMS (i.e., the ratio of CO2-normalized NO2 concentrations from on-road chasing over tailpipe PEMS measurements) and (b) R2 values for deriving CO2-normalized NO2 concentrations from Chasing and PEMS measurements. In both (a) and (b), the box represents the middle 50% of the data, extending from the 25th to the 75th percentiles; the horizontal line through the center of the box is the median; the whiskers represent 10th and 90th percentiles.

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In summary, the results from the on-road experiment further indicate the significant role of on-

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road NOx chemical transformation.

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Coupled on-road turbulence and chemistry modeling

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Methodology for on-road simulations

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As the third part of our study, we employed the Comprehensive Turbulent Aerosol Dynamics

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and Gas Chemistry (CTAG) model to simulate the coupled on-road turbulent mixing and

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chemistry of individual exhaust plumes to test whether the rapid transformation observed is

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kinetically feasible under typical on-road conditions. CTAG is an appropriate model for this

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purpose as it was designed to simulate transport and transformation of multiple air pollutants,

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e.g., from emission sources to ambient background 36-41.

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Shown in Figure 4, the computational domain was constructed based on a single lane with an

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individual passenger car. The domain dimensions are 30 m (L) x 15 m (W) x 10 m (H). The

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positive Y direction is the downwind crosswind direction. The geometry of the passenger car

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was modeled using a realistic shape rather than block shapes.

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Periodic Conditions

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Figure 4. Computational domain and mesh around the car

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While full-scale simulations with detailed site-specific conditions (e.g., capturing multiple

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vehicles corresponding to traffic mix, highway geometry, etc.) are beyond the scope of this

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paper, we utilized the environmental conditions captured in the Detroit dataset to set up the

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simulations. The velocity inlet boundary was specified on the left and top sides of the domain to

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simulate the ambient south crosswind speed. The periodic boundary conditions with the flow

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rate based on vehicle speed (70 mph or 31.3 m s-1 as in the I-96 segment), specified on both ends

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of the computational domain, was adopted to represent the continuous traffic flow. The

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passenger car’s tailpipe, 60 mm in diameter, is parallel to the road and specified as a mass flow

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inlet with a total mass flow rate of 0.055 kg s-1 and exhaust temperature of 480 K 42. The

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tailpipe-level NOx emission rate was estimated by using EPA's MOtor Vehicle Emission

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Simulator (MOVES). A NO2/NOx volume ratio of 5% was assigned at the tailpipe level (i.e.,

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RTP=5%). The road surface was modeled as a moving wall at the vehicle speed, thus representing

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the relative motion between the vehicle and the road. Ambient background NOx and NO2

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concentrations for each case are specified based on the Detroit data (EH4).

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We modeled the dispersion and chemical reactions using steady-state Reynolds averaged Navier-

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Stokes (RANS) turbulence model to simulate the flow fields and species transport. The Finite 17



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Rate (FR) chemical reaction model43 was coupled with the eddy dissipation method (EDM)44,

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referred to as the EDM/FR model, in which turbulence–chemistry interaction is accounted for

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through the representation of the source term the species transport equations. Both an Arrhenius

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rate based on the global chemistry mechanism, and a turbulent mixing rate based on the

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Magnussen–Hjertager expression are estimated44. The smaller of the two rates is then used as the

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source term. We adopted a simplified chemical mechanism using a four reaction (NO-NO2-O3)

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system shown in Table S1 with reaction constants taken from the CB05 mechanism 45. These

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reactions have been shown to be dominant in near-source environments 16, 46. The photolysis rate

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of NO2 was determined based on our previous work16 (also described in Section S4 of the SI)

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and average solar radiation at the near-road site in Detroit obtained from the U.S. National Solar

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Radiation Database. The governing equations of the Realizable k-e model (one type of RANS

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models) and the species transport equation were presented in Section S2. The mesh resolution

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sensitivity study was illustrated in Figures S4 and S5.

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Results from on-road simulations

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Figure 5a illustrates the contour of ROR on a horizontal plane at the tailpipe height in the simulation

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domain (Figure 4) with the ambient ozone concentration set to be 40 ppb. Some representative ROR

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values are marked in along the tailpipe centerline with the corresponding residence time, estimated

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by dividing the distance to the tailpipe center by the driving velocity. Figure 5b shows ROR values

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along the tailpipe centerline as a function of resident time at levels of ambient O3 concentrations

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(20 ppb, 40 ppb, and 60 ppb).

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a) ROR (%)

24% 0.8 s 24 m

23% 0.6 s 18 m

21% 0.4 s 12 m

17% 0.2 s 6m

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372 373 374 375 376 377 378 379

Figure 5. a) On-road NO2/NOx ratio (ROR) contour plot on a horizontal plane at the tailpipe height in the simulation domain illustrated in Figure 4. The ambient ozone concentration is set to be 40 ppb. The black solid line is the tailpipe centerline. Values along the solid line represent the resident time and corresponding ROR. b) ROR along the tailpipe centerline for different ambient ozone concentrations.

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expected, ROR remain nearly a constant if chemical reactions are turned off. Furthermore, the

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coupled turbulent mixing and simplified NO-NO2-O3 chemistry can achieve the NO2 conversion

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rate similar to the order of magnitude of the observed values, though a direct comparison is not

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feasible. The values of RatioChase/PEMS derived from the on-road experiment are roughly equivalent

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of changes in NO2/NOx ratios. Given the ~1-second transport from the tailpipe of the target vehicle

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to the probe of the mobile platform, a RatioChase/PEMS of 2 suggests doubling of NO2/NOx ratios in

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less than 1 second at ambient O3 concentration ~40 ppb. The modeling results show that even at

It is revealed from Figure 5 that higher the ambient O3 concentration leads to higher ROR. As

19



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relatively low ambient O3 level (e.g., 20 ppb), ROR can double of RTP in less than 1 second. Figure

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S6 in the SI illustrates the ROR values along the tailpipe centerline as a function of resident time at

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different NO2 photolysis rates with ambient O3 concentration at 40 ppb. The results suggest that

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ROR is not sensitive to NO2 photolysis rates, which further supports NO+O3 as the dominant

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reaction during the “tailpipe-to-road” stage. As mentioned earlier, ROR also depends on the relative

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amounts of NOx and O3. More in-depth studies are needed to explore how different traffic and

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meteorology conditions affect on-road NOx transformation.

394 395

Implications

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Our study has provided experimental evidence, through curbside data analysis and on-road field

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measurements, that substantial amounts of freshly emitted NO are oxidized to NO2 during the

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“tailpipe-to-road” stage. The subsequent modeling analysis indicates that chemical conversion is

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rapid, and that NO-NO2-O3 chemistry significantly increases NO2/NOx ratios in the on-road

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environment from the relatively low ratio at the tailpipe. The findings have raised the importance

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of differentiating NO2/NOx ratios at various stages of exhaust plume evolution in order to

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account for the contributions of primary emissions, on-road chemistry, near-road chemistry and

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ambient background to near-road NO2 concentrations. We have shown that on-road chemistry is

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a major contributor to curbside NO2, and likely near-road NO2. In general, RTP < ROR < RCS

On-Road Chemical Transformation as an Important ... - ACS Publications

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