Measuring Ammonia Concentration Distributions with Passive Samplers to Evaluate the Impact of Vehicle Exhaust on a Roadside Environment in Tokyo, Japan

Abstract

Evaluating the impact on roadside environments of NH₃ from vehicle emissions is important for protecting the ecosystem from air pollution by fine particulate matter and nitrogen deposition. This study used passive samplers to measure NH₃ and NO_X at multiple points near a major road to observe the distribution of these gases in the area. The impact of NH₃ emitted from vehicles on a major road on the environmental concentration of NH₃ at different distances from the roadside was found to be similar to that of NO_X and NO₂. The concentration of NH₃ rapidly decreased due to dilution and diffusion within approximately 50 m of the road, and after 100 m the concentration remained almost the same or decreased slowly. Furthermore, CO₂ observations taken in the same period along the roadside and in the background yielded a vehicular emission factor of 4–50 mg/km for NH₃, which is comparable with previous research. This emission factor level contributes 4–11 ppb to the NH₃ concentrations in roadside air through the dilution and diffusion process. A correlation was found between the emission factors of NH₃ and NO_X that was different from the trade-off relationship seen when single-vehicle exhaust is measured.

1. Introduction

Ammonia (NH₃) gas is reduced nitrogen, and it is emitted into the atmosphere from various sources, with agriculture accounting for more than 81% of the global total emissions [1]. Satellite observation data has revealed not only the variety of emissions sources but also the spatial distribution of NH₃ emissions [2]. Indeed, it has been shown that the NH₃ concentration in cities around the world increased by 1.2%/yr between 2008 and 2019 [3]. The potential direct impact of NH₃ on the health of the general public in urban areas is not fully explained in the scientific literature, but systematic literature reviews have shown that NH₃ exposure may directly result in decreased lung function, irritation of the throat and eyes, increased coughing and sputum discharge, and early onset of asthma in infants [4].

NH₃ in the atmosphere reacts with acidic gases to form ammonium salts, which can increase the concentration of fine particles in the atmosphere [1,2,3,4,5,6,7,8]. In addition, particulate ammonium formed from NH₃ can be transported far from the source [7,9], and semi-volatile ammonium salts (e.g., Equation (1)) can return to NH₃ under specific meteorological and chemical conditions, such as concentration, temperature, and humidity [10,11,12,13].

$${\mathrm{NH}}_3+{\mathrm{HNO}}_3 \leftrightarrows {\mathrm{NH}}_4{\mathrm{NO}}_3$$

(1)

NH₃ is highly reactive and is quickly removed, so its concentration decreases rapidly as it moves away from the source [14]. However, even at low concentration levels, NH₃ gas has been found to have a direct negative impact on plant communities, and a critical level of 1 µg/m³ (1.4 ppb at 20 °C and 1 atm standard conditions) has been proposed to protect vegetation [15].

It has long been known that NH₃ in the atmosphere is emitted from biological processes in soil, biomass combustion, ammonia-based chemical fertilizers, sewage treatment plants, and the decomposition process of animal waste [7,16]. Since the introduction of three-way catalysts (TWCs) in the 1980s, attention has focused on the contribution of NH₃ emissions from gasoline-powered vehicles to atmospheric NH₃ concentrations [17,18,19,20,21,22]. TWCs are honeycomb-shaped ceramics coated with rare earth metals such as palladium (Pd), platinum (Pt), and rhodium (Rh). They are designed to control the emission of carbon monoxide (CO), unburned hydrocarbons (HC), and nitrogen oxides (NOx) from gasoline vehicles through a series of chemical oxidation–reduction reactions that occur on the surface of the catalyst. Thus, a TWC oxidizes CO and unburned HC compounds into carbon dioxide (CO₂) and water (H₂O) and reduces NO_X such as nitric oxide (NO) to nitrogen gas (N₂) [23,24,25,26,27]. When NO compounds are excessively reduced beyond the capacity to form N₂ gas on the surface of the TWC, NH₃ is formed, which is mainly related to the reaction between NO and H₂ [17,18,19,20,21,28]. This process usually begins with the water–gas shift reaction between CO and H₂O, which produces CO₂ and H₂ (Equation (2)), and NH₃ is formed as a result of the reaction between the produced H₂ and NO (Equations (3) and (4)).

CO + H₂O → CO₂ + H₂

(2)

2 NO + 2 CO + 3 H₂ → 2 NH₃ + 2 CO₂

(3)

2 NO + 5 H₂ → 2 NH₃ + 2 H₂O

(4)

Factors that may affect NH₃ formation in TWCs include aggressive driving [29,30,31], failure to maintain a stoichiometric air–fuel ratio [18,19,30,31,32], ambient temperature and engine temperature [33], and the age and model year of the TWC [34,35,36,37,38]. The efficiency of the TWC depends on the air–fuel ratio, and there is a narrow operating range around the stoichiometric ratio of air and fuel (λ = 1) where the TWC operates most efficiently. For this reason, fuel combustion in gasoline vehicles is generally controlled by a lambda sensor to achieve λ = 1, but aggressive driving can change λ. During lean combustion at λ > 1, NOx emissions usually increase, while during fuel-rich conditions of λ < 1, NH₃ emissions increase, so in general, there is a trade-off relationship between NOx and NH₃ emissions [38]. Another known cause of NH₃ emission is NH₃ slip (emission of unreacted NH₃ into the atmosphere due to excessive supply) on urea selective catalytic reduction (SCR) catalysts, which are used to reduce NOx emissions from diesel vehicles [39,40]. Therefore, to understand the complex dynamics of NH₃ concentrations in urban air and provide evidence of the effectiveness of NH₃ mitigation strategies that have been reported in previous studies e.g., [8], it is necessary to consider the impact of vehicle traffic on amounts of NH₃ in the surrounding environment.

Although many papers have been published on monitoring NH₃ in the atmosphere, there are still many issues to be addressed. Because the concentration of NH₃ varies greatly on spatial and temporal scales due to the rapid deposition of this molecule and its reactivity in the atmosphere, high time-resolution measurements using continuous analyzers have been attempted but suffer from measurement uncertainty depending on which analysis technology is used [41]. In addition, there are no standardized quality control/quality assurance procedures for measuring NH₃ and currently no established traceability or international guidelines to support measurements [42]. Passive samplers, which are a classic method for measuring NH₃ [43,44,45,46,47,48,49], rely on the diffusion of the target gas onto a surface where it is chemically trapped by an adsorbent. Measurement uncertainty arises both from the preparation of the sampler and in the laboratory analysis, as well as the environmental exposure factors [43,44,45,46,47,48,49]. Most studies follow European standard diffusion sampler methodology EN 17346:2020 Ambient air—Standard methods for the determination of NH₃ concentration using diffusion samplers [50]. Examples of commercially available passive samplers include the Stream Sampler (Kogawa Seisakusho, Hyogo, Japan) and the Adaptive Low-cost Passive High Absorber (ALPHA) Sampler (Centre for Ecology and Hydrology, Edinburgh, UK). Because passive samplers take samples at a fairly low frequency (from one week to one month), the results obtained are low time-resolution measurements—average values observed over a long period of time—as opposed to those obtained by numerous types of continuous analyzer that respond to rapid concentration changes due to deposition and reactions in the atmosphere. Passive samplers are inexpensive and do not require a power supply, making them suitable for use in both urban and remote areas, where they can be used to measure at fine spatial resolutions and understand spatial variability. Consequently, previous studies using passive samplers have provided a wide range of knowledge about spatial distributions of air pollutant concentrations [51,52,53]. However, the components that can be measured using passive sampling are limited to those with high reactivity (such as NH₃, NO₂, and O₃), making it difficult to measure CO₂, an inert gas that needs to be measured in parallel to evaluate NH₃ emissions from vehicles in real atmospheric environments. Research that uses expensive continuous analyzers to provide CO₂ measurements is limited.

To investigate the effects of vehicle exhaust gases, this study used passive samplers that do not require a battery to determine the spatial distribution of NH₃ concentrations in a roadside environment. Furthermore, we determined the emission levels from vehicle exhaust gases by simultaneously measuring roadside and background CO₂ using a portable, battery-powered, low-cost sensor. The findings of this research are discussed in detail, comparing them with past research. The NH₃ emission factor of several tens of milligrams per kilometer observed by this study was found to contribute to the NH₃ concentration in roadside environments at a level of several parts per billion.

2. Materials and Methods

2.1. Locations

This study was conducted along a major road in the Tokyo metropolitan area, Japan. Tokyo, the capital of Japan, is one of the major urban areas in the world [54]. According to the latest statistics on vehicle fuel consumption in 2023 [55], Tokyo is the prefecture in Japan with the highest consumption of vehicle fuel, accounting for 5.4% (2,298,961/42,970,264 kL/yr) of gasoline, 4.7% (1,159,338/24,853,290 kL/yr) of diesel, and 1.7% (74,734/4,376,770 kL/yr) of liquefied petroleum gas consumed nationwide. Although there are no specific environmental policies related to NH₃ emissions in effect in Tokyo, it has been shown that the decrease in NH₃ concentrations during the 2020 Tokyo COVID-19 lockdown was mainly due to reductions in emissions from vehicles (20%) and humans (80%) [56]. Ring Road No. 8 (National road No. 311) is one of the major traffic arteries in the Tokyo metropolitan area, and cumulus clouds are frequently observed directly above it as it runs north–south on the west side of a densely populated urban area [54], making it an appropriate observation point for investigating both the urban climate and air pollution in a busy urban area.

To measure the planar concentration distribution of NH₃ and NO_X, passive samplers were installed at 20 sites (Figure 1 and

Table 1. Locations of passive samplers used in this study.

Site	Location	Distance from Road	Latitude	Longitude
0	Noge Station	3.23 m	N 35°36′22.60″	E 139°38′33.71″
1	Streetlight	0.55 m	N 35°36′23.10″	E 139°38′32.88″
2	Streetlight	0.55 m	N 35°36′22.40″	E 139°38′34.80″
3	Streetlight	0.55 m	N 35°36′21.85″	E 139°38′36.87″
4	Streetlight	0.65 m	N 35°36′22.69″	E 139°38′35.95″
5	Streetlight	0.65 m	N 35°36′23.26″	E 139°38′34.06″
6	Pedestrian Bridge	1.20 m	N 35°36′23.93″	E 139°38′32.68″
7	Pedestrian Bridge	1.90 m	N 35°36′24.58″	E 139°38′30.81″
8	Lamppost	16.58 m	N 35°36′22.69″	E 139°38′31.80″
9	Lamppost	60 m	N 35°36′21.35″	E 139°38′31.25″
10	Lamppost	122 m	N 35°36′19.63″	E 139°38′29.77″
11	Lamppost	9.50 m	N 35°36′21.86″	E 139°38′35.35″
12	Lamppost	78 m	N 35°36′19.72″	E 139°38′34.73″
13	Lamppost	98 m	N 35°36′19.09″	E 139°38′34.72″
14	Telegraph Pole	9.15 m	N 35°36′23.70″	E 139°38′33.48″
15	Telegraph Pole	67 m	N 35°36′25.41″	E 139°38′34.28″
16	Telegraph Pole	98 m	N 35°36′26.55″	E 139°38′34.70″
17	Telegraph Pole	17.2 m	N 35°36′22.78″	E 139°38′38.15″
18	Telegraph Pole	57 m	N 35°36′23.99″	E 139°38′38.55″
19	Telegraph Pole	104 m	N 35°36′25.60″	E 139°38′38.76″
20	Setagaya Station	Building Rooftop	N 35°38′48.12″	E 139°39′11.42″

) in the area surrounding a major road in Tokyo. According to Japanese traffic census data [57,58,59], the traffic volume on Ring Road No. 8 near Nogemachi Park is approximately 52,000 vehicles per day, with a large-vehicle ratio of 17.2% (more details can be found in previous studies [60,61,62]).

Passive samplers were placed at varying distances (0–50 m from the road boundary line) around a central observation point (N 35°36′22.60″, E 139°38′33.71″) referred to as the Noge air quality monitoring station [61], which is located beside Ring Road No. 8. The locations were selected from among at least 10 points (i.e., at least 50% of the measurement points), and were selected to ensure that the samplers could be installed at 2.5 m above the ground to be out of reach of pedestrians. Samplers were attached to streetlights along the main road, on lampposts in the park area, and on telegraph poles in the residential area, and observations were carried out. This study determined the emission factors (emission levels) from vehicle exhaust gases in a twin-site study by simultaneously measuring roadside and background CO₂ and NH₃. To additionally measure the background urban atmosphere not directly affected by roadside air, measurements of air pollutants taken at a general monitoring station located approximately 6 km from the roadside of this study were accessed online (measurement station code: 13112010, Setagaya air quality monitoring station, N 35°38′48.12″, E 139°39′11.42″) [63,64].

2.2. Sampling

NH₃ and NO_X were measured using passive samplers (Figure 2) with reference to previous studies that showed the method was consistent with conventional continuous measurement and the Denuder method [43,44,45,46,47,48,49,53]. However, there are methodological limitations and uncertainties inherent in passive sampling, which can be attributed to fluctuations in the blank (i.e., unused collection material); to minimize the potential for these fluctuations, the collection period in this study was set at two weeks.

NH₃ concentration was measured using a short-term passive sampler (Figure 2a) (OG-KN-S, Ogawa & Co., Ltd., Hyogo, Japan) [65] inserted into a rain shelter (OG-SN-S, Ogawa & Co., Ltd., Hyogo, Japan) 2.5 m above the ground. The sampling period was one week. The filter paper (OG-SN-17, Ogawa & Co., Ltd., Hyogo, Japan) was removed from the sampler after collection, extracted with pure water, and the ammonium ion (NH₄⁺) concentration was measured using ion chromatography (Integrion RFIC system, Thermo Fisher Scientific Inc., Waltham, MA, USA). The NH₃ concentration was calculated from this value using a calibration curve prepared separately and then corrected using temperature and humidity data [43,44,45,46,47,48,49,53] from the Japan Meteorological Agency’s Tokyo Regional Meteorological Office [66] for the study periods. The method used to calculate NH₃ atmospheric concentration using passive sampling is described in Appendix A [65].

NO_X was measured using a long-term passive sampler (OG-KN-S, Ogawa & Co., Ltd., Hyogo, Japan) for measuring NO₂ and NO_X (Figure 2b) and filter papers impregnated with reagents, triethanolamine (OG-SN-10) for NO₂ and 2-phenyl-4,4,5,5-tetramethylimidazoline-3-oxide-1-oxyl (OG-SN-11) for NO_X. The sampling period was one week. The extract with pure water was then colored using Salzmann’s reagent, and the absorbance at a wavelength of 545 nm was measured. The NO₂ and NO_X concentrations were calculated from these absorbance values using a calibration curve prepared separately and then corrected in the same way as the NH₃ concentrations [43,44,45,46,47,48,49,53]. The method used to calculate NO₂ and NO_X atmospheric concentrations using passive sampling is described in Appendix B [65].

Sampling using the passive samplers was carried out twice on consecutive weeks in each of the four seasons: Autumn-1 (18 to 25 October, 2017), Autumn-2 (25 October to 1 November 2017), Winter-1 (17 to 24 January 2018), Winter-2 (24 to 31 January 2018), Spring-1 (9 to 16 May 2018), Spring-2 (16 to 23 May 2018), Summer-1 (18 to 25 July 2018), and Summer-2 (25 July to 1 August 2018) to correspond with the monitoring period for PM_2.5 component analysis designated by the Japanese Ministry of the Environment [67].

Low-cost CO₂ sensors (TR-76Ui, T&D Corporation, Matsumoto, Nagano, Japan) were used to roughly measure the effects of vehicle emissions at only two locations: near the roadside where a power supply could be used (Noge Station) and in the background at the general monitoring station in Setagaya. The CO₂ was measured continuously for two weeks with a one-minute resolution during the collection periods by passive sampler.

Microsoft M365 Excel was used to conduct the statistical analysis and construct scatter plots. Pearson product-moment correlation coefficients, calculated by the same software, were used to examine correlations.

3. Results and Discussion

3.1. Spatial Patterns in Concentrations of NH₃ and NO_X

The relationship between distance from the road boundary and concentration is shown in Figure 3, combining all measurements taken at Sites 0–19 in Table 1. The results for all seasons investigated in this study show that the concentrations of NH₃ and NO_X decreased rapidly up to about 50 m from the road, and beyond 100 m they either remained more or less the same or decreased gradually. The ratio of the highest concentration along the road to the background concentration (Site 20, Setagaya station) shows that the concentrations decreased by 90% ± 2% for NH_3, by 61% ± 9% for NO₂, and by 80% ± 6% for NO_X throughout all seasons.

Several previous studies have shown that the concentrations of NH₃ and NO₂ decrease rapidly as you move away from the roadside. In one study that measured NH₃ in suburban roadside environments with different traffic volumes, a 99% decrease in concentration was observed at a distance of 20 m from the roadside [68]. Another study in suburban roadside environments found a 90% decrease in NH₃ concentration from background levels at a distance of 10 m from the roadside and a 90% decrease in NO₂ concentration at a distance of 15 m from the roadside [69]. When NH₃ and NO_X were measured in three roadside environments in urban areas, a reduction in concentration of approximately 80% from background levels several hundred meters away from the road was observed at a distance of 50 m from the roadside [70]. Despite the variation in the results found in these studies [68,69,70], NH₃ concentrations behaved in a similar way to NO_X and NO₂ concentrations across all the areas under observation, decreasing rapidly at around 50 m from a road, and then either remaining more or less the same or decreasing gradually after 100 m. The reason for the variation in the results of these studies is thought to be the differences in conditions, such as the types and numbers of vehicles driving on the roads under observation, the season, and the presence of obstructions [70]. For example, it was found that the NH₃ concentration was 140% higher on average at a distance of 12 km downwind from a major urban area than in the hinterland (upwind of the roadside) of roadside observations in the suburbs [71]. In addition, in urban areas, NH₃ concentrations at roadside locations were observed to be approximately 80% lower than at background locations [72]. Therefore, the observed reduction rate is likely to be affected by the difference between local and background concentrations of pollutants.

This study also found a high correlation between the increase in NH₃ concentration and those of NO_X (Figure 4a–d) and NO₂ (Figure 4e–h) (NO_X: r = 0.953 ± 0.039, NO₂: r = 0.909 ± 0.046). Unexpectedly, the correlations between NO_X or NO₂ and NH₃ were found to be weaker in the summer, and further investigation is needed to elucidate the reason for this finding. Relative to the regression lines for NH₃ concentration over the entire survey period, the slope of the regression line for NO_X concentration was 0.153 ± 0.024, with a relative standard deviation of 16%, and the slope of the regression line for NO₂ concentration was 0.479 ± 0.024, with a relative standard deviation of 21%. As shown in Equation (1), the generation of NH₃ due to the dissociation of NH₄NO₃ at high temperatures, such as through the pre-deposition of ammonium ion (NH₄⁺) species together with water (dew, mist, etc.) on grassland or building walls under high relative humidity at night, is an important process that promotes evaporation in the morning [73,74]. However, in the summer, when temperatures are high, the atmosphere becomes more mixed in the vertical direction (i.e., the NH₃ concentration is diluted), such that dilution in the atmosphere is thought to be greater than the formation of NH₃ through the decomposition of NH₄NO₃ (Equation (1)), causing the NH₃ concentration to decrease. In a previous study [55], the concentration of NH₃ tended to be slightly higher in the winter than in the summer in an urban area of Tokyo. The scatter plots of NH₃, NO_X, and NO₂ (Figure 4) obtained in this study show a high correlation and a slope that is generally consistent regardless of the season, indicating that the exchange of gases and particles around the roadside and the effects of evaporation from deposited ion species are within the range of variation to be expected in observations taken using passive samplers with low time-resolution measurements.

In line with the findings of previous studies [70], the current study found that the impact of NH₃ emitted from vehicles driving on major roads on environmental concentrations at different distances from the roadside was in the same range as that of NO_X and NO₂, and that NH₃ concentrations decreased rapidly due to dilution and diffusion within about 50 m of the road and remained almost unchanged or decreased slowly after 100 m.

Nitrogen monoxide (NO) contained in NO_X (i.e., NOx − NO) is attracting attention as an indicator of primary emissions [75,76]. According to the results of simultaneous measurements taken every second at four of the locations used in the present study, 40% of atmospheric NO at the study location is rapidly oxidized to NO₂ by O₃ (Equation (5)) [76] during advection from the road boundary (0 m) to a point 20 m away [75].

NO + O₂ → NO₂ + O₂

(5)

In the present study, a two-week time resolution was used. However, primary NO emissions are rapidly oxidized. To address this, NO concentrations were included in NO_X concentrations. At the roadside (Site 0), overall NO₂/NO_X ratios of 33–63% were observed, which when stratified by season provided the following ratios: winter (33–46%), autumn (33–43%), summer (44–63%), and spring (49–55%). In the urban background (Site 20), the ratios were higher than along the roadside for each season: winter (61–62%), autumn (79–81%), summer (56–75%), and spring (78–82%). These results are consistent with previous studies [76] indicating that the NO₂/NO_X ratio is higher in spring and summer when reactions with O₃ are progressing [76].

3.2. Comparison with NH₃ Concentrations in Other Regions and Periods

Table 2 and Figure A1 in Appendix C show the NH₃ concentration ranges found using passive samplers in this and previous studies conducted in other years and various regions. In roadside observations of vehicle exhaust emissions, NH₃ concentrations in other studies were observed to be in the range of 1–31 ppb [68,69,70], so the NH₃ concentrations of 4–11 ppb observed at the roadside (Site 0 in Table 1) in 2017–2018 in this study were within the concentration range of previous studies. The variation in NH₃ concentrations at the roadside in other studies depended on the region and length of observation rather than the year of observation. In particular, the highest NH₃ concentrations (32–129 ppb) were reported for measurements taken in road tunnels, where there are no sources other than vehicles and NH₃ concentration is not diluted. It is known that NH₃ is emitted from gasoline, diesel, and liquefied petroleum gas vehicles, and some of the nitrogen oxides emitted from vehicles are reduced to NH₃ by the TWC installed in the vehicle and released into the atmosphere [17,18,19,20,21,22]. Vehicle exhaust is recognized as one of the main sources of NH₃ in urban areas [77,78], and reports have shown that NH₃ emissions are increasing not only in areas with high vehicle density but also in agricultural areas [79,80]. Regarding background levels, Table 2 and Figure A1 show that the NH₃ concentration along the roadside tended to be high as that in one of the industrial city areas (6–11 ppb). The background NH₃ concentration in urban areas is reported to be 1–85 ppb, and in suburban and rural areas 1–78 ppb. The NH₃ concentration of 1–5 ppb observed in the urban background in this study (Site 20 in Table 1) was therefore within the concentration range of previous studies. The comparison of data in Table 2 and Figure A1 also show that the variation in NH₃ concentration in urban areas (1–85 ppb) was wider than the variation in NH₃ concentration in roadside environments (1–31 ppb).

In the locations shown in Table 2 and Figure A1, livestock areas and sewage treatment plants were the sources of the highest concentrations of NH₃ (266–8455 ppb), and a wide range of concentrations were observed in these environments. NH₃ emissions have also been reported from landfill sites and incineration facilities that process general waste generated in residential areas [81], from electronic cigarettes [82], and from plant combustion in urban areas [83]. To understand the various characteristics of NH₃ emissions, detailed data on concentrations and their sources in each case is important. A potential source of NH₃ that has attracted attention in recent years is a technique for co-firing NH₃ to reduce CO₂ emissions from internal combustion engines in vehicles and coal-fired power plants [52]. Additionally, NH₃ emissions due to NH₃ slip are an example of NH₃ combustion [84,85]. Therefore, although it should be emphasized that the increase in NH₃ concentrations found in the present study between the background (i.e., concentrations more than 100 m distance from the road in Figure 3 and at Site 20) and the roadside (concentrations at 0 m distance from the road in Figure 3) is due to vehicle exhausts, it should also be noted that high NH₃ concentrations of non–motor vehicle origin may also be important.

Table 2. NH₃ concentration ranges measured in studies using passive samplers at various locations and in different periods.

Location	Region	Period	NH3 [ppb] *1	Reference
Roadside	Tokyo, Japan	2017–2018	4–11	This Study
Roadside	Saitama, Japan	2005–2007	6–31	[70]
Roadside	London, UK	2006–2019	4–7	[86]
Roadside	Barcelona, Spain	2010–2011	1–25	[81]
Roadside	Gyeonggi, Korea	2020–2021	12–20	[87]
Roadside	2 sites, Korea	2022	8–26	[53]
Road tunnel	Beijing, China	2014–2015	32–129	[88]
Urban	Tokyo, Japan	2017–2018	1–5	This Study
Urban	2 sites, North America	2003–2014	1–4	[89]
Urban	Xi’an, China	2006–2007	1–52	[90]
Urban	Beijing, China	2008–2010	1–85	[91]
Urban	Barcelona, Spain	2010–2011	6–55	[81]
Urban	13 sites, China	2015–2016	4–41	[52]
Urban	New York, USA	2016–2017	0.1–5	[92]
Urban	Beijing, China	2019	13–32	[79]
Urban	Gyeonggi, Korea	2020–2021	4–23	[87]
Urban	3 sites, Korea	2022	2–24	[53]
Urban background	Gyeonggi, Korea	2020–2021	2–5	[87]
Urban and rural	Asia, Africa, and South America	1999–2001	1–20	[51]
Suburban	Edinburgh, UK	2006–2019	1–3	[86]
Suburban	Xi’an, China	2006–2007	1–78	[90]
Suburban	Colorado, USA	2010–2015	3–15	[93]
Rural	11 sites, North America	2003–2015	0.2–6	[52]
Rural	Beijing, China	2007–2010	1–43	[91]
Rural	Colorado, USA	2010–2015	1–8	[93]
Rural, remote	40 sites, China	2015–2016	0.4–25	[52]
Rural	New York, USA	2016–2017	0.2–5	[92]
Rural	Jeongeup, Korea	2019–2020	11–38	[94]
Rural	5 sites, Korea	2022	0.8–6	[53]
Industrial	Gyeonggi, Korea	2020–2021	6–11	[87]
Industrial	10 sites, Korea	2022	4–87	[53]
Agricultural	North Carolina, USA	2003–2004	5–21	[95]
Agricultural	Colorado, USA	2010–2015	5–104	[93]
Agricultural	Navarre, Spain	2013–2015	7–79	[96]
Agricultural	Nanjing, China	2015–2016	7–57	[52]
Agricultural	8 sites, Korea	2022	6–35	[53]
Livestock	Beijing, China	2014–2015	670–2129	[88]
Livestock	Gyeonggi, Korea	2020–2021	32–96	[87]
Livestock	7 sites, Korea	2022	15–266	[53]
Waste plants	Beijing, China	2014–2015	186–8455	[88]

*¹: Reported concentrations of mass per volume (μg/m³) were converted to ppb under standard conditions of 20 °C and 1 atm.

Table 2. NH₃ concentration ranges measured in studies using passive samplers at various locations and in different periods.

Location	Region	Period	NH3 [ppb] *1	Reference
Roadside	Tokyo, Japan	2017–2018	4–11	This Study
Roadside	Saitama, Japan	2005–2007	6–31	[70]
Roadside	London, UK	2006–2019	4–7	[86]
Roadside	Barcelona, Spain	2010–2011	1–25	[81]
Roadside	Gyeonggi, Korea	2020–2021	12–20	[87]
Roadside	2 sites, Korea	2022	8–26	[53]
Road tunnel	Beijing, China	2014–2015	32–129	[88]
Urban	Tokyo, Japan	2017–2018	1–5	This Study
Urban	2 sites, North America	2003–2014	1–4	[89]
Urban	Xi’an, China	2006–2007	1–52	[90]
Urban	Beijing, China	2008–2010	1–85	[91]
Urban	Barcelona, Spain	2010–2011	6–55	[81]
Urban	13 sites, China	2015–2016	4–41	[52]
Urban	New York, USA	2016–2017	0.1–5	[92]
Urban	Beijing, China	2019	13–32	[79]
Urban	Gyeonggi, Korea	2020–2021	4–23	[87]
Urban	3 sites, Korea	2022	2–24	[53]
Urban background	Gyeonggi, Korea	2020–2021	2–5	[87]
Urban and rural	Asia, Africa, and South America	1999–2001	1–20	[51]
Suburban	Edinburgh, UK	2006–2019	1–3	[86]
Suburban	Xi’an, China	2006–2007	1–78	[90]
Suburban	Colorado, USA	2010–2015	3–15	[93]
Rural	11 sites, North America	2003–2015	0.2–6	[52]
Rural	Beijing, China	2007–2010	1–43	[91]
Rural	Colorado, USA	2010–2015	1–8	[93]
Rural, remote	40 sites, China	2015–2016	0.4–25	[52]
Rural	New York, USA	2016–2017	0.2–5	[92]
Rural	Jeongeup, Korea	2019–2020	11–38	[94]
Rural	5 sites, Korea	2022	0.8–6	[53]
Industrial	Gyeonggi, Korea	2020–2021	6–11	[87]
Industrial	10 sites, Korea	2022	4–87	[53]
Agricultural	North Carolina, USA	2003–2004	5–21	[95]
Agricultural	Colorado, USA	2010–2015	5–104	[93]
Agricultural	Navarre, Spain	2013–2015	7–79	[96]
Agricultural	Nanjing, China	2015–2016	7–57	[52]
Agricultural	8 sites, Korea	2022	6–35	[53]
Livestock	Beijing, China	2014–2015	670–2129	[88]
Livestock	Gyeonggi, Korea	2020–2021	32–96	[87]
Livestock	7 sites, Korea	2022	15–266	[53]
Waste plants	Beijing, China	2014–2015	186–8455	[88]

*¹: Reported concentrations of mass per volume (μg/m³) were converted to ppb under standard conditions of 20 °C and 1 atm.

3.3. NH₃ Emission Factors

The NH₃ emission factors obtained in this study were calculated based on passive sampling of NH₃ and NO_X, and of CO₂ using low-cost sensors (see “Section 2.2. Sampling”). A comparison with NH₃ emission factors based on different approaches that require expensive and diverse equipment that can focus on different targets helps to clarify the bulk emission factors from vehicles in a given area. Therefore,

Table 3. NH₃ emission factors for vehicles in other regions and periods.

Location or Target	Measurement Device	Year	NH3 Emissions [mg/km/Vehicle]	Reference
Roadside, Tokyo, Japan	Passive sampler	2017–2018	4–50	This Study
Urban, Tokyo, Japan	Semi-continuous analyzer	2017	3.7–32	[97]
Van Nuys Tunnel, California, USA	Filter pack	1993	61	[32]
Caldecott Tunnel, California, USA	Denuder	1999	46–52	[98]
Gurbrist Tunnel, Switzerland	Continuous analyzer	2002	26–35	[99]
Jânio Quadros Tunnel, São Paulo, Brazil	Impinger	2011	20–64	[100]
Tunnel, Guangzhou, China	Semi-continuous analyzer	2013	216–119	[101]
Handan Tunnel, Shanghai, China	Passive	2014	23–52	[22]
On-road, California, USA	Remote sensing	1996	86–102	[102]
Gasoline vehicle	Chassis dynamometer	2002	2–110	[103]
Gasoline vehicle	Chassis dynamometer	2004	1–31	[104]
Gasoline vehicle	Chassis dynamometer	2006	3–256	[105]
Gasoline vehicle	Chassis dynamometer	2008	3–28	[31]
Gasoline vehicle	Chassis dynamometer	2014	4–70	[106]
Gasoline vehicle	Chassis dynamometer	2017	2–132	[107]
Gasoline vehicle	Chassis dynamometer	2018	5–53	[36]
Gasoline vehicle	Chassis dynamometer	2022	1–53	[108]
Gasoline vehicle	On road	2020	1–53	[109]
Diesel vehicle	On road	2020	1–32	[109]
Compressed natural gas vehicle	On road	2020	38–90	[109]
Gasoline vehicle	Emission inventory	2018	29–104	[80]
Diesel vehicle	Emission inventory	2018	0.6–1.7	[80]
Gasoline vehicle, Japan	Emission inventory	2020	0.1–95	[110]

compares the NH₃ emission factors based on distance and fuel measured in this study, as calculated from the NH₃ to CO₂ conversion ratio, with the results of previous studies obtained from dynamometers, in tunnels, by remote sensing, and in on-road experiments.

Emission factors are also expressed as mg/kg-fuel normalized per CO₂ emission and mg/km normalized per driving distance. Emission factors used to create emission inventories, which are usually used as data to analyze the effectiveness of the vehicle emissions type certification process and emission regulations, express emissions in distance-based factors, such as grams per kilometer or per mile [111].

Methods for estimating emission factors from atmospheric observations have been summarized in a systematic literature review [112]. In this study, emission factors were estimated by Equation (6), which is based on that used for twin-site studies (i.e., simultaneous measurement of the roadside and background) [112]:

$$\mathrm{E}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{F}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}={\displaystyle \frac{\left({\left[{\mathrm{N}\mathrm{H}}_3\right]}_{\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}}-{\left[{\mathrm{N}\mathrm{H}}_3\right]}_{\mathrm{b}\mathrm{a}\mathrm{c}\mathrm{k}\mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}}\right)}{\left({\left[{\mathrm{C}\mathrm{O}}_2\right]}_{\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}}-{\left[{\mathrm{C}\mathrm{O}}_2\right]}_{\mathrm{b}\mathrm{a}\mathrm{c}\mathrm{k}\mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}}\right)}}\times {\mathrm{W}}_{{\mathrm{C}\mathrm{O}}_2}\times \mathrm{F}\mathrm{C}$$

(6)

where [NH₃]_road and [NH₃]_background are the NH₃ concentrations at road (Site 0) and background (Site 20) (mg/m³); [CO₂]_road and [CO₂]_background are the CO₂ concentrations at road (Site 0) and background (Site 20) (kg-CO₂/m³); W_CO2 is the content of carbon in fuel (kg-CO₂/L), taking into account the proportion of gasoline and diesel vehicles; and FC is the distance-based fuel consumption volume (L/km), taking into account the proportion of gasoline and diesel vehicles.

W_CO2 is estimated by Equation (7):

$${\mathrm{W}}_{{\mathrm{C}\mathrm{O}}_2}={\mathrm{W}}_{{\mathrm{C}\mathrm{O}}_2}\left(\mathrm{G}\mathrm{a}\mathrm{s}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{n}\mathrm{e}\right)\times {\displaystyle \frac{100-{\mathrm{P}}_{\mathrm{d}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}}}{100}}+{\mathrm{W}}_{{\mathrm{C}\mathrm{O}}_2}\left(\mathrm{D}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}\right)\times {\displaystyle \frac{{\mathrm{P}}_{\mathrm{d}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}}}{100}}$$

(7)

where W_CO2 (Gasoline) is the content of carbon in gasoline fuel (2.32 kg-CO₂/L) and W_CO2 (Diesel) is the content of carbon in diesel fuel (2.49 kg-CO₂/L) [113], and P_diesel is the proportion of diesel vehicles (heavy-duty vehicles) (17.2%) [57].

FC is estimated by Equation (8):

$$\mathrm{F}\mathrm{C}=\mathrm{F}\mathrm{C}\left(\mathrm{G}\mathrm{a}\mathrm{s}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{n}\mathrm{e}\right)\times {\displaystyle \frac{100-{\mathrm{P}}_{\mathrm{d}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}}}{100}}+\mathrm{F}\mathrm{C}\left(\mathrm{D}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}\right)\times {\displaystyle \frac{{\mathrm{P}}_{\mathrm{d}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}}}{100}}$$

(8)

where FC (Gasoline) is the statistical estimate value for a gasoline vehicle (0.085 L/km) [114] and FC (Diesel) is the statistical estimate value for a diesel vehicle (0.220 L/km) [114].

Distance-based NH₃ emission factors for individual vehicles obtained in the past—from dynamometer experiments, in vehicle tracking studies on roads, and in emission inventories—vary widely from 0.1 to 256 mg/km (Table 3). In general, vehicles driven at aggressive driving cycles usually lead to an increase in the NH₃ emission factor [29,105,106], and the aging of the TWC and of the vehicle [35,36,37] also contribute to the large variation. The measurements taken in previous studies, both in road tunnels and by remote sensing, also showed a wide variation, ranging from 3.7 to 119 mg/km. The results of this study fell within the range of the wide variation in the results of previous experiments and in measurements taken under actual atmospheric conditions. In particular, the results of this study were generally consistent with the results of measurements taken at different locations and using different methods in urban atmospheres.

Measurements of NH₃ taken in road tunnels, on roads, and in urban air cover all types of vehicles, both gasoline and diesel vehicles. However, I view the impact of diesel vehicles on NH₃ emissions to be limited in this study. This is because heavy vehicles account for approximately 17.2% of the vehicles passing along the roadside in this study. In Japan, the introduction of SCR systems using urea for heavy trucks and other vehicles began in the mid-2000s [115]. SCR systems produce more NH₃ [39,40], and there is a possibility that they increase the emission factor of NH₃ from diesel vehicles. At the time this research was conducted in 2017, the number of diesel vehicles (heavy vehicles) sold in Japan with SCR systems installed was 634,000 vehicles [115], which is about 4% of the total number of vehicles owned in Japan (14,652,000 vehicles) [116]. Vehicles without SCR systems generally have low NH₃ emissions [36] and have little effect on the results of emissions, so in this study, the impact of diesel vehicles on NH₃ emissions would be limited.

It is reported that NH₃ emissions from urban soil in green spaces are highly likely to have contributed spikes in atmospheric NH₃ that occur after the morning rush hour [117]. The timing of the morning peak after the morning rush hour, as measured by a device with a one-hour time resolution, is consistent with the results of a previous study [117]. NH₃ emissions from soil are also partially dependent on the ambient temperature. However, a study that estimated the NH₃ emission coefficient by observing the NH₃ concentration in the general atmosphere of Tokyo suggested that the impact of emissions from urban soil in green spaces is small, as although emissions from green space soil may be a source of NH₃ in urban areas, they are insufficient to cause an NH₃ peak equivalent to or greater than that of annual vehicle emissions [97]. Therefore, although the NH₃ emission factor estimated in this study includes emissions from urban soil in green spaces, it is within the range of variation in observations taken using passive samplers at low time-resolution measurements.

This study has found some interesting results, including a correlation between the emission factors for NH₃ and NO_X calculated from measurements taken in the atmosphere (Figure 5). In this study, the use of passive samplers with low time-resolution measurements gave a bulk measurement (averaged result) in which the numbers of gasoline and diesel vehicles were mixed in a specific ratio, whereas previous studies have observed clear differences (both trade-offs and correlations) between the NH₃ and NO_X emission factors measured from gasoline and diesel vehicles individually [100]. Therefore, some explanation is needed of the factors involved in differences between the measured values observed in the atmosphere in this study and the measured values for individual vehicles. First, diesel vehicles emit much less NH₃ than gasoline vehicles but emit more NO_X. In the case of gasoline vehicles, the NO_X emitted from the engine is minimized by the reduction reaction in the TWC. Therefore, there is a trade-off relationship between NO_X and NH₃ emissions, meaning that it is difficult to completely suppress both NO_X and NH₃ emissions from certain vehicle types [38,100]. Second, it has also been reported that as the driving distance of gasoline vehicles increases, both NH₃ and NO_X emissions increase [38,100]. Under certain conditions in TWCs, excessive reduction of NO_X occurs, so the target product of N₂ is not obtained, and NH₃ is produced as an unintended byproduct (Equations (3) and (4)). The efficiency of a TWC depends on the air–fuel ratio, and combustion is controlled to ensure that the TWC operates within a narrow operating range where it is most efficient (air–fuel stoichiometric ratio of λ = 1). When components such as the catalyst surface and lambda sensor deteriorate over time, the air–fuel ratio may become difficult to control. In addition, when the catalyst deteriorates over time, the catalytic activity and oxygen storage capacity decrease, so the reduction efficiency decreases because there is insufficient O₂ for the redox reaction [38]. In Japan, the average age of passenger cars in 2017 was 12.91 years [118], and further research into emissions from vehicles in use will help to clarify the impact of reductions in TWC efficiency. Therefore, we consider that a combination of these effects is present in the correlation between the emission coefficients of NH₃ and NO_X found in the atmospheric observations at low time-resolution measurements taken in this study.

In Figure 5, when comparing winter and summer, the emission factors for both NO_X and NH₃ were increased by more than four times in winter. This is roughly consistent with previous dynamometer measurements conducted at −7 °C and 23 °C that also showed an increase in NH₃ emission factor with decreasing temperature and was higher than the NH₃ emission ratio of 1.4–2.1 [107,108], which was attributed mainly to rich combustion during cold starts. The difference in the amplitude of the increase and decrease is thought to be due to differences in measurement accuracy, as the NH₃ concentration in this study was measured within a range of two orders of magnitude (4–11 ppb) at Roadside (Site 0).

The above comparison of results from previous studies and those found in this study emphasizes the need for further research on different parameters affecting NH₃ concentration to help estimate NH₃ emission factors for large volumes of vehicle activity. Further measurements in roadside environments along multiple roads with different average vehicle speeds, vehicle types, and traffic volumes will clarify the bias in the correlation between NH₃ and NO_X emission factors. In particular, Japan does not have any policies regarding ammonia measurement (such as the EU’s Air Quality Directive [119]). This study highlights the importance of policy decisions based on the correlation between emission factor levels and the NH₃ concentration in roadside air, which was found here to contribute 4–11 ppb to the NH₃ concentration in roadside air through dilution and diffusion processes (i.e., the effect of halving the emission factor would be to halve the concentration of NH₃ in the air at multiple locations).

4. Conclusions

This study used passive samplers to observe NH₃ and NO_X and clarify the planar distribution around the road; that is, the attenuation of concentration with distance from the road. The research design highlights the potential for low-cost, multi-point observation as an evaluation tool that can contribute to improving the air quality of roadside environments. In addition, by adding CO₂ measurements using a low-cost sensor, it was shown that the values were within the range of reasonable vehicle emission factors, compared to the considerably large and varied measurement values of previous studies. A correlation found between the NH₃ and NO_X emission factors obtained in this study is contrary to the trade-off relationship between NH₃ and NO_X measured for individual vehicles in previous studies. The low time-resolution measurement method used in this study means that the average values represent the combined effects of gasoline and diesel vehicles, which both affect the emission factors in different ways. Even if regulations to mitigate the effects of NH₃ concentration are enforced on new and used vehicles, it will take a considerable amount of time for the effects of the regulations to be seen. For this reason, it will also be important to take temporary measures, such as updating road structures (for example, by installing soundproof walls and greening), urban structures (for example, by modifying urban structures to separate traffic networks for people and vehicles to reduce congestion), and traffic regulations. The research design used in this study demonstrates the potential for low-cost, multi-point observation as an evaluation tool that can contribute to improving the air quality of roadside environments.