In situ continuous observations of the chemical and optical properties of atmospheric aerosols and associated precursor gases were made on the rooftop (18 m above ground level) of the Pudong Environmental Monitoring Center (PEMC; 121.5446∘ E, 31.2331∘ N), 5 km east of the Shanghai urban center (the People's Square) (Fig. 1). The site is located in a mixed-use urban area (office, commercial, residential, and traffic) east of downtown Shanghai, with no obvious NH3 point source within 5 km (Zou et al., 2015). As one of the state-controlled sites, Pudong (PD) supersite was designed by the Ministry of Environmental Protection of China and operated by the Shanghai Environmental Monitoring Center, being responsible for the release of hourly air-quality data for PM10, PM2.5, and other criteria pollutants (CO, SO2, NOx, and O3).

From 3 April 2014 to 2 April 2015, using a MARGA instrument (measurement of aerosols and reactive gases analyzer, Metrohm Applikon B.V., NL), water-soluble gases (NH3, HNO3, HONO, HCl, and SO2) and PM2.5 components (NO3-, Cl-, SO42-, Na+, NH4+, K+, Mg2+, and Ca2+) were measured with hourly temporal resolution. The MARGA removes soluble gases in a rotating, wet-walled denuder, while a steam-jet aerosol collector is used for fine particle collection. Meanwhile, aerosol light absorption coefficients (babs) were retrieved every 5 min from an AE31 aethalometer using seven wavelengths (370, 470, 520, 590, 660, 880, and 950 nm) with a PM2.5 cut-off inlet. Black carbon (BC) concentrations for the whole data set were calculated from the absorption coefficient at 880 nm. The measurement process was subjected to rigorous quality assurance and quality control procedures according to the Technical Guideline of Automatic Stations of Ambient Air Quality in Shanghai based on the national specification HJ/T193-2005. Meteorological parameters including temperature, relative humidity, and rainfall were monitored by an automatic meteorological station (Met One Instruments, US), which was co-located at the rooftop of the PD supersite.

To explore the comparability between on-line and off-line methods for NH3 measurement, an Ogawa passive sampling device (PSD) was co-located with MARGA at PD to passively measure weekly ambient NH3 concentration from May 2014 to June 2015. The Ogawa PSD is a double-sided passive sampler equipped with two 14 mm quartz filters (serving as duplicates) impregnated with phosphoric acid provided by the manufacturer. Following the manufacturer's protocols (http://www.ogawausa.com), exposed filter samples were soaked with 8 mL ultra-pure water (18.2 MΩ cm) and analyzed by an ion chromatography system (883 Basic IC plus, Metrohm Co., Switzerland). The detection limit for NH4+ in the passive sampler extracts was 2.8 µg L-1; this corresponds to an ambient NH3 concentration detection limit of approximately 0.1 ppb for a 7-day sample. The NH3 concentrations measured by the MARGA (ppb) were averaged over the same time period as the Ogawa PSDs (ppb). Figure S1 in the Supplement shows a good correlation (y=0.82×+0.56, n=53, R2=0.84, p < 0.001) between the two NH3 measurement methods, validating the reliability of NH3 data from the MARGA platform.

To complement the information obtained from the main monitoring campaign described above, additional measurements of NH3 concentration were performed at eight sites inside and outside of the Handan tunnel from 9 April to 21 May 2014. The Handan tunnel is a 720 m long urban freeway in the northeast of Shanghai, separating the campus of Fudan University into two parts (Fig. 9a). It contains an array of ventilation orifices in the middle section of the tunnel, 200 m in total. The tunnel has two traffic bores; each bore has a cross section of 70 m2 and four lanes with typically 120 000 vehicles (of which 85 % of are light-duty vehicles) passing per day (Li, 2007). Driven by a group of high power fans, the average wind speed measured at the exit of the tunnel was approximately 5 ± 1 m s-1. The maximum vehicle speed limit in the tunnel is 80 km h-1, with typical driving speeds of 50–60 km h-1. Inside the northern bore of the tunnel, four sampling points were located at both ends of the tunnel (10 m from the exit and entrance of the tunnel, or T-d and T-a, for short) and the two ends of an array of ventilation orifices located in the middle section of the tunnel (Fig. 9a; the site near the entrance and the exit, named T-b and T-c for short, respectively). Outside the tunnel, a roadside transect involving four sites perpendicular to the tunnel was established, spanning the distance from 0 m (O0m, for short), 20 m (O20m), 150 m (O150m), to 310 m (O310m). Figure 9a shows the layout of the tunnel and the sampling points.

Using US EPA Method 207.1 (Determination of Ammonia Emissions from Stationary Sources), the NH3 concentration at each site was measured. Briefly, for each sample, ambient air was pumped through two fritted glass bubblers (containing 10 mL 0.005 mol L-1 H2SO4 absorbing solution in each bubbler) for 2 h at a flow rate of 1 L min-1. These two bubblers were connected in series, and the NH3 collection efficiency of the sampling trains was 95 % or better (checked by using four bubblers in series in our pilot study, the collection efficiency = 100 × ([the sum of the values of the first two bubblers])/[the sum of the values of the four bubblers]). Measurements were made during the morning (between 08:00 and 11:00 local time) and afternoon (between 14:00 and 19:00). Due to the proximity of the monitoring sites to the laboratory, all samples could be collected and analyzed by IC swiftly to avoid potential contamination, and field blanks were below the detection limit. Due to the dangers to personnel of sampling at the T-a, T-b, and T-c sites, six samples were collected synchronously at these three sites. Nineteen paired samples were successfully collected and determined at the site of T-d, O0m, O20m, O150m, and O310m.

The Weather Research and Forecasting (WRF) model v3.5.1 (Skamarock et al., 2008) is used for simulating the height of planetary boundary layer. The WRF simulation was performed from a mother domain with a 45 × 45 km horizontal resolution over Asia, and nested down to a second domain of 15 × 15 km covering eastern China, the Korean Peninsula, and Japan, and further nested down to a third domain of 5 × 5 km covering the Yangtze River Delta region. Lambert conformal conic projection was used with true latitude limits of 4 and 44∘ and standing longitude of 115∘. The coverage of three domains is shown in Fig. S4. We chose the RRTM longwave radiation scheme and the Dudhia shortwave radiation scheme. The Yonsei University scheme was used for the planetary boundary layer option. The WRF model configurations can be found elsewhere (Huang et al., 2013a). The National Center for Environmental Prediction (NCEP) Final (FNL) Operational Global Analysis data set (http://rda.ucar.edu/datasets/ds083.2/) with a horizontal resolution of a resolution of 1.0 × 1.0∘ are incorporated as initial and boundary conditions for the model. A one-way nested approach with four-dimensional data assimilation (FDDA) in WRF is applied. We have performed model evaluations of major meteorological parameters against the NCDC surface meteorological network (National Climate Data Center, http://www7.ncdc.noaa.gov/CDO/cdo) within the YRD region (red dots marked in Fig. S4). The evaluation results of surface wind speed, temperature, and humidity are shown in Table S2 in the Supplement. It could be seen that these meteorological parameters are within the benchmarks during most of the months, suggesting our WRF modeling results are reliable. A Meteorology–Chemistry Interface Processor (MCIP) (Otte and Pleim, 2010) v4.1 is used to postprocess the WRF results by outputting the atmospheric height of the planetary boundary layer field, one of the standard MCIP outputs. The simulation period is consistent with the observation, i.e., from April 2014 to April 2015. In this study, planetary boundary layer height (PBLH) derived from the third domain is used. Additionally, the planetary boundary layer depths at 3 h resolution were obtained from the US National Oceanic and Atmospheric Administration (NOAA) Real-time Environmental Applications and Display System (READY) archived Global Data Assimilation System (GDAS) meteorological data (1∘ × 1∘) based on Coordinated Universal Time (UTC). All UTC values are converted to local time (UTC + 8).

Back trajectories of 24 h, arriving at the PD supersite at a height of 500 m, were calculated at 1 h time intervals for each of the four seasons using NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model with GDAS one-degree archive meteorological data (Draxler and Rolph, 2015). An in-depth back trajectory analysis, the potential source contribution function (PSCF), is useful for identifying the possible geographic origin of emission sources; this method calculates the ratio of the number of points with concentration higher than a threshold value (mij) to the total number of points (nij) in the ijth grid cell. Higher PSCF values indicate higher potential source contributions to the receptor site. In this study, the domain for the PSCF was set within the range of (26–42∘ N, 112.5–125.5∘ E) in 0.1∘ × 0.1∘ grid cells. The 75th percentile for CO and NH3 during the four seasons was used as the threshold value mij. To reduce the uncertainties of mij/nij for those grid cells with a limited number of points, a weighting function recommended by Polissar et al. (2001) was applied to the PSCF in each season. Visualizations of the PSCF were mapped using ArcMap 10.2.

The temporal patterns of hourly gaseous NH3 concentrations determined by the MARGA at the Pudong supersite are reported in Fig. 2. Summary statistics for NH3 concentrations (µg m-3) during 3 April 2014–2 April 2015 are shown in Table 1. Using a variety of chemical, physical, and optical techniques, numerous studies have examined ambient NH3 concentrations over the last 3 decades; however, few of them were conducted in urban areas. As a comparison, we compiled previous work related to the measurement of urban NH3 concentrations in Table 2.

The 1-year data set (n=8441; data availability 96.4 %) in the current study represents one of the longest on-line continuous measurement series of atmospheric NH3 in China. During the study period, the NH3 concentrations varied between 0.1 and 39.2 µg m-3, with an average (±1 σ) of 5.5 ± 3.9 µg m-3. Domestically, the annual average NH3 concentrations in Beijing and Xi'an were much higher than in Shanghai (see Table 2). This might be expected since Beijing and Xi'an are located in the North China Plain (NCP) and the Guanzhong Plain (GZP), respectively. The NCP and GZP are two of the most intensive agricultural production regions in China. Moreover, the NH3 loss from soil increases with an increase in soil pH value (Ju et al., 2009). Shanghai and its surrounding regions are dominated by the acid soils of paddy fields (Fig. 1) (Zhao et al., 2009), while Beijing and Xi'an are dominated by the alkaline soils of dry land (Wei et al., 2013). Internationally, the NH3 concentration level in Shanghai was similar to observations from cities in developed and middle-income countries, but much lower than those cities in emerging countries. This is particularly true when comparing with cities in South Asia (e.g., Delhi in India and Lahore in Pakistan), where there is a lack of basic sanitation facilities (e.g., public flush toilets), and significant animal populations (such as cows) coexist with people in urban areas. The higher NH3 concentrations measured at surface sites in South Asia are consistent with spatial patterns from recent satellite remote sensing observations (Clarisse et al., 2009; Van Damme et al., 2014).

The variations of NH3 in spring and summer were generally consistent with fluctuations of temperature (Fig. 2a). In winter, their correlations turned out to be much weaker (Fig. 4a). While in autumn, no significant correlation between temperature and NH3 was observed (Fig. 4a). Monthly, from March to September, the NH3 concentration first increased steadily, with the highest value in July, then decreased gradually, along with falling temperature (Fig. 2c). In summer (June–August), high temperatures favor NH3 volatilization from urea and other N fertilizers applied to croplands (Fu et al., 2013; Huang et al., 2011; Ianniello et al., 2010; Meng et al., 2011). High temperatures in summer also favor NH3 emission from other sources, such as animal housing, landfills, laystalls and pit latrines, animal manure, natural and fertilized soils, vegetation, and municipal solid waste (Fu et al., 2013; Huang et al., 2011). Moreover, given that the equilibrium between ammonium nitrate particles and gaseous ammonia and nitric acid favors the gas-phase compounds at higher temperature, warmer summer conditions promote dissociation of ammonium nitrate particles, shifting the ammonium/ammonia partitioning toward the gas phase (Behera et al., 2013). In this study, the average NH3 concentration in summer (7.3 ± 4.9 µg m-3; n=2181) was 2.4 µg m-3 or 41 % higher than the average of other seasons. The gap between summer and winter in Shanghai was similar to New York, but generally much lower than many other cities. Taking Beijing for example, according to Ianniello et al. (2010), the NH3 concentration in summer was 460 % higher than in winter; this figure was 320 % in Xi'an between 2006 and 2007 (Cao et al., 2009). Smaller seasonal temperature differences and less agricultural activity in Shanghai could be the contributing factors.

Based on the “bottom-up” methodologies, previous emission inventories indicate that livestock feeding and N-fertilizer application contribute around 50 % (48–54.9 %) and 35 % (33.4–40 %) of the total NH3 emissions in the Yangtze River delta region (YRD for short, including Shanghai as well as 24 cities in the provinces of Jiangsu and Zhejiang), respectively (Fu et al., 2013; Huang et al., 2011, 2012). Agricultural production is also the dominant source of NH3 emissions in most other regions worldwide (Bouwman et al., 1997; Olivier et al., 1998; Reis et al., 2009). However, performed at an urban level, many studies in Table 2 concluded that the concentrations and evolution of ambient NH3 in urban areas were influenced by traffic emissions. As one of the world's largest megacities, Shanghai might expect contributions of vehicle-emitted NH3 as well. NH3 concentrations in the atmosphere, however, are also sensitive to other important factors such as changes in temperature, wind speed or direction, and boundary layer depth; other influential factors might include local or regional NH3 emissions, dry and wet deposition, and gas-to-particle partitioning. The relative importance of such factors in controlling ambient NH3 concentration may vary seasonally. For example, the highest and lowest daily NH3 concentrations in Shanghai were observed on 10–11 July 2014 (23.4 ± 6.7 µg m-3) and 10–11 March 2015 (0.5 ± 0.4 µg m-3), respectively. For the two periods, there was no significant difference between them in terms of wind speed and planetary boundary layer height (the relative humidity data for the March period were missed). Although 19.6 mm of rainfall in the July period would be expected to lower NH3 levels, the temperature on this high concentration date (28.4 ∘C) was much higher than on the low concentration March date (4.7 ∘C). Over a longer time frame, even though rainfall in summer was around twice the amount of rainfall in other seasons, other factors such as greater NH3 emissions at higher temperature outweigh the wet scavenging effects of rainfall yielding higher summertime NH3 concentrations. High NH3 concentration episodes during burning of agricultural wheat residues, indicated by a strong and synchronous rise of trace aerosols from biomass burning (e.g., K+ and BC), were also evident in late spring (Fig. 2b). The evolution of this pollution episode induced by biomass burning and its influence on the air quality of Shanghai has been examined in our recent paper (Zou et al., 2015).

In the following, we will examine the (synergistic) effects of various meteorological parameters on measured NH3 concentrations in Shanghai, because these factors may mask the effect of vehicular emissions on the measured NH3 concentrations. Summary statistics for meteorological parameters during 3 April 2014–2 April 2015 are shown in Table 3.

Planetary boundary layer (PBL) height plays a vital role in determining the vertical dispersion of air pollutants that are emitted from the Earth's surface. Decreasing height of PBL can normally hold the pollutants within the shallow surface layer so as to suppress the vertical atmospheric dilution. In many previous studies, as described above, the NH3 concentrations in winter were much lower than those in summer. However, the NH3 concentrations observed here in Shanghai during winter are relatively high. One may argue that weaker vertical mixing and shallow PBL layers in winter could trap NH3 and contribute to elevated concentrations. In Fig. 3a, although the simulated average PBL height in winter is the lowest during our study period, there is no significant difference among different seasons. In Fig. 3b, the average PBL height in winter is even higher than that in spring and summer. Therefore, a relatively high NH3 concentration in winter at PD cannot be fully explained by the strength of vertical mixing or PBL height in this study.

Figure 4a suggests that temperature (T) is an important driver of the increase of NH3 concentration in spring. No clear relationship is seen for other seasons. As the transitional period between winter and summer, springtime in Shanghai has the highest standard deviation of temperature during our study period (Table 3). Additionally, spring is known as the sowing season in southern China, with the greatest application of N-containing fertilizers (mainly in the form of urea) of the year. Warming temperature tends to increase the rate of urea hydrolysis and ammonium conversion to NH3, and therefore volatilization. For example, an increase in temperature from 7.2 to 15.6 ∘C can double volatilization loss when moisture content is kept the same (Ernst and Massey, 1960). For relative humidity (RH), there is no clear evidence to suggest RH as an important factor controlling the dynamics of NH3 concentrations in any of the seasons (Fig. 4b). Figure S2 shows the RH and T dependent distributions of NH3 concentration for each season. Given the generally poor relationship between the NH3 concentration and T and RH as discussed above, NH3 concentrations have no clear dependence on T and RH seasonally.

In Fig. 5a and b the distribution of hourly average wind speeds was calculated for values between 0 and 4.0 m s-1 (99.5 % of occurrence). Figure 5a shows that there is a highly significant relationship between WS and NH3 concentration (R2=0.91, p < 0.001). The highest average NH3 concentrations were measured under the lowest wind speeds and the lowest concentrations were measured at the highest wind speeds. There is no clear relationship between wind frequency (the number of wind occurrence) and average NH3 concentration or WS during the study period (Fig. 5c). Figure S3 shows WS/WD dependence of NH3 concentrations in different seasons. The distribution of NH3 concentration showed an obvious concentration gradient as a function of WS. Seasonally, there are different preferential wind directions for the highest NH3 concentration values. Generally, an overwhelming, higher T in summer tends to greatly enhance the contribution of temperature-dependent emissions to the urban NH3 budget from agricultural areas. And the nearby rural areas around Pudong supersite are in the direction of southeast (Nanhui) and northeast (Chongming) (Fig. 6d). However, it is unexpected that in Shanghai, almost all high NH3 concentration values in summer are concentrated in the wind direction range of south–southwest–west (Fig. S3b), which strongly indicates that the urban area is one of the most important NH3 emission regions in Shanghai.

Figure 6a and b show the RH and T dependent distributions of NH3 and WS for the entire study period, respectively. Although the distribution of higher NH3 favors the condition of high T(>25 ∘C), low RH (< 60 %), and low WS (< 1.2 m s-1), Fig. 6a shows that there is no obvious concentration gradient as a function of RH and T. (Note that in Fig. S2d, higher NH3 concentrations in winter tend to occur at higher T for a given RH range of 60–80 %.) This can be explained by the dominance of low WS (often lower than 1.2 m s-1) during east–southeast and west–northwest wind directions associated with intense local sources for NH3 within the city (Fig. 6c). In brief, our results suggest that there are some temperature-independent and important NH3 sources in the urban area of Shanghai.

Hourly observations over long-term periods offer a unique opportunity to provide robust diurnal profiles for each season. Figure 7 shows the average diurnal profiles of NH3 and CO concentrations across seasons. Historically, CO emissions in Shanghai and its surrounding YRD region mainly came from iron and steel manufacturing and on-road vehicles, which contributed 34 and 30 % of the total, respectively, in 2007 (Huang et al., 2011). Due to changing economic activity, emission sources of air pollutants in China are changing rapidly. For example, over the past several years, China has implemented a portfolio of plans to phase out its old-fashioned and small steel mills, and raise standards for industrial pollutant emissions (Chang et al., 2012). In contrast, China continuously experienced double digit growth in terms of auto sales during the same period, and became the world's largest automobile market since 2009 (Chang, 2014). Consequently, on-road traffic has overtaken industrial sources as the dominant source of CO emissions in eastern China (Zhao et al., 2012). In Fig. 7, CO shows a well-marked bimodal diurnal profile, with maxima in the morning (starting at 05:00 local time) and the evening (starting at 16:00), consistent with the variation of traffic flow in Shanghai (Liu et al., 2012). Therefore, CO variation can be utilized as a robust indicator of vehicle emissions. NH3 also displays a clear bimodal profile during all four seasons, similar to the CO diurnal profile, suggesting a significant influence of on-road traffic (with daily commuting) on ambient NH3 concentrations in the urban environment of Shanghai. We also notice that pools of surface water (i.e., dew or fog), which form on nights that have a high RH, can act as NHx reservoirs that release NH3 upon evaporation in the midmorning, particularly in spring seasons.

Interestingly however, NH3 shows different degrees of a positive relationship with CO as a function of season (Fig. 8b). Specifically, during summertime, NH3 displays a significant relationship with CO (R2=0.48, p < 0.001), while this positive relationship is not observed during the winter season, when heavy traffic volume also occurs. As discussed above, the seasonal variation of NH3 concentration in Shanghai during our study period was quite flat. The seasonal average CO concentrations at PD were 0.71, 0.61, 0.58, and 1.1 ppmv in spring, summer, autumn, and winter, respectively. And the CO level in wintertime was higher than other seasons. Moreover, Fig. 8c suggests that for all seasons, the source region of NH3 in Shanghai is locally dominated. However, the atmospheric lifetime of CO is much longer than that of NH3 (typically several hours depending on meteorology) (Asman et al., 1998). PSCF analysis for CO in winter suggests important contributions from north of Shanghai (Fig. 8a), a region that does not appear as important as a NH3 source (Fig. 8c). Consequently, elevated regional background levels of CO from long-range transport appear to yield a poorer relationship between NH3 and CO in wintertime (Fig. 8b).

Table S1 summarizes statistics of the NH3 concentrations (µg m-3) measured at each sampling point in and out of the Handan tunnel, which have been also been visualized in Fig. 9b. As expected, the highest average NH3 concentration occurred at the exit of the tunnel (T-d). Although NH3 concentration varied temporally, throughout the 2 months of observations, a large spatial gradient in NH3 concentration at near-road sites was present in every sampling event, suggesting that an intensive NH3 source from on-road traffic (not meteorological parameters) is the leading factor in governing the variation of ambient NH3 concentration in a road-side environment. The NH3 concentrations in the tunnel were increased with distance from the entrance of the tunnel (T-a). The NH3 concentration at T-d (64.9 ± 11.5 µg m-3) was over 5 times that at T-a (12.6 ± 3.3 µg m-3). Moreover, the lowest NH3 concentration value obtained at T-d (47.0 µg m-3) was still nearly 10 µg m-3 or 20 % higher than the highest value of other sites (Table S1). These observations provide compelling evidence that on-road traffic is an important emission source of NH3 in the urban atmosphere. Given that there is a significant loss of vehicle exhaust from the tunnel through an array of ventilation orifices in the middle section of the tunnel, the NH3 concentration at T-b (29.2 ± 6.6 µg m-3) was close to that at T-c (31.5 ± 5.9 µg m-3). If we take into account the physical distance (PD; 300 m) and the NH3 concentration gap (CG; 33.4 ± 11.5 µg m-3) between T-c and T-d, the cross section of tunnel bore (CS; 70 m3), the average wind speed (WS; 5 m s-1), and traffic flow (TF; 120 000 vehicles day-1), we can obtain an approximate mileage-based NH3 emission factor (EF) of 28 ± 5 mg km-1 for a single vehicle using the following equation: EF=CG×CS×WS×86 400TF×PD, where 86 400 is the number of seconds in a day. This NH3 emission factor was similar to that observed for the Gurbrist tunnel in Switzerland (31 ± 4 mg km-1) (Emmenegger et al., 2004) and the Caldecott tunnel in California (49 ± 3 mg km-1) (Kean et al., 2000), while much lower than that recently measured in Guangzhou, China (230 ± 14 mg km-1) (Liu et al., 2014). Based on the emission factor we developed, a population of 3.04 million vehicles (average mileage of 15 000 km yr-1) in Shanghai would produce around 1300 t NH3 in 2014. This is very close to the “bottom-up” emission inventory in Shanghai for 2010 (1581.1 t) (Chang, 2014). Previous emission inventories in Shanghai (e.g., Huang et al., 2011 and Fu et al., 2012) made a significant underestimation of the NH3 emissions from city areas. When compared with the NH3 emissions from city areas, the contribution of on-road traffic can reach 12 % of the total NH3 emissions in Shanghai city areas (10 742 t) (Chang, 2014). Moreover, model results have shown that over half of agricultural NH3 emissions would be deposited downwind of their source within 10 km, depending on local meteorological conditions (Asman et al., 1998). Therefore, the relative contribution of NH3 emissions from on-road traffic to urban PM pollution could be higher than the share of its mass contribution. Given that precisely estimating the EF of NH3 from on-road traffic is beyond the scope of this paper, more research is needed to pinpoint this parameter in order to accurately quantify the amount of NH3 emissions from vehicles.

From the tunnel exit to the open environment, the average NH3 concentration at T-d (64.9 ± 11.5 µg m-3) was 11 times more than that at O310m (5.6 ± 2.5 µg m-3), and a general negative relationship was found between distance and ambient NH3 concentration. Over the total measured distance, the maximum percent decrease was observed between the sites of T-d and O0m (50 m apart), indicating a rapid dispersion of vehicle-emitted NH3 from the road tunnel. Still, Figure 9c clearly shows that 64 % (48 %) of the NH3 concentration we observed at the site of O0m (O20m) can be explained by the simultaneous measurements of NH3 concentration at T-d. No significant decrease in the gradients of NH3 concentration was observed between the sites of O150m (5.9 ± 2.5 µg m-3; n=6) and O310m (5.6 ± 2.5 µg m-3; n=6), suggesting that the strongest impact of NH3 emission and transport from local traffic flow on ambient NH3 concentrations in the Shanghai urban area lies within 150 m distance.