Interactive comment on “ The role of traffic emissions in particulate organics and nitrate at a downwind site in the periphery of Guangzhou , China

Particulate matter (PM) pollution on the peripheries of rapidly expanding megacities in China can be as serious as in the cities due to direct emission and transport of primary PM from cities and effective formation of secondary PM. To investigate the emission and formation of PM on the periphery of Guangzhou (a megacity in southern China), a suite of real-time instruments were deployed at Panyu, downwind of Guangzhou, for PM measurements from November to December 2014. Dominated by organics, PM1 (particles with diameter less than 1 μm) concentrations in Panyu were higher (average ~ 55.4 μg/m 3 ) than those in nearby cities such as Hong Kong and Shenzhen. Five sources for organic aerosols (OA) were resolved by positive matrix factorization (PMF) analysis with the multilinear engine (ME-2). These sources are hydrocarbon-like organic aerosol (HOA), cooking organic aerosol (COA), biomass burning related organic aerosol (BBOA), as well as semi-volatile oxygenated organic aerosol (SVOOA) and low-volatile oxygenated organic aerosol (LVOOA). The use of the COA mass spectrum obtained in our earlier study at a urban site in Hong Kong as a constraining factor in ME-2 produced the most interpretable results for the Panyu dataset. Freshly emitted HOA contributed 40 % to the high concentrations of organics at night. The mass concentration of SOA (SVOOA + LVOOA) continuously increased as odd oxygen (O x  = O 3  + NO 2 ) increased during daytime, attributable to the secondary production of PM facilitated by photochemistry. The SOA-to-O x ratio was higher than those reported in previous studies in North America (covering the period from spring to summer), indicating efficient photochemical production of SOA even in late autumn and early winter at this subtropical downwind site. The efficient SOA formation during daytime was likely fueled by the sufficient SOA precursors in the atmosphere. The large input of NO x , which tracked well with HOA from automobile emissions, resulted in the significant formation of nitrate in both daytime and nighttime. Strong correlations between particulate nitrate and excess ammonium ([NH 4 + ]/[SO 4 2− ] − 1.5) × [SO 4 2− ]) were observed. Higher partitioning of nitrate into the gas phase was found in November than in December, likely due to the lower temperatures in December. Results from this study suggest that there is much room to mitigate the PM pollution in urbanized areas such as Guangzhou, as well as their peripheries, by reductions in traffic-related pollutants.


Introduction:
The Pearl River Delta (PRD) economic zone is one of the most urbanized and industrialized regions in China, producing about 19% of China's gross domestic product each year (Zhong et al., 2013).The fast economic development in the PRD has led to rapid deterioration in air quality (Chan and Yao, 2008;Ho et al., 2003).The PRD is situated in a subtropical region where photochemistry may differ from those in other urbanized city clusters in China.For instance, the relatively high photochemical activity, high temperature, and high relative humidity (RH) in this subtropical region facilitate effective secondary formation of particulate matter (PM) in PRD (Lee et al., 2013;Li et al., 2013;Qin et al., 2016a).With densely populated cities including two megacities Guangzhou and Shenzhen and other smaller but also highly urbanized ones, the PRD region is emerging into a giant city cluster.There are, nevertheless, some less populated areas between those cities and presumably can serve as a "buffer" zone in regional air quality.Given the complex and non-linear processes in secondary production of PM, the impacts of the adjacent cities to the air quality of these "buffer" areas can be crucial but they have not been thoroughly investigated.
Over the last decade, many studies in PRD have revealed that organic aerosols (OA) and sulfate are the most abundant components of fine particles (e.g., Hagler et al., 2006;Huang et al., 2006;Louie et al., 2005).The main precursor of sulfate, SO 2 , began to decrease since 2006, because of the widespread application of flue-gas desulfurization devices in power plants in response to a new policy implemented by the Chinese government (Lu et al., 2010;Zhang et al., 2012b).Over the same period, however, emissions from vehicles, e.g.NO x , volatile organic compounds (VOCs), black carbon (BC) and traffic-related organics, however, have increased (Wang et al., 2013;Zhang et al., 2012a).A recent study showed that 4.16 million tons (Mt) of hydrocarbon, 7.72 Mt of NO x , and 0.37 Mt of PM 2.5 (particles with diameter less than 2.5 micron) were produced by vehicular emissions in China in 2013 (Wu et al., 2016).Although the high-polluting vehicles (such as heavyduty diesel trucks) are not allowed to enter the inner areas of many megacities during the day in China, they are nevertheless active at night until early morning, especially on the peripheries of these cities.This regulatory policy results in nighttime peaks in vehicular pollutants commonly observed in many cities in China (Zhang and Cao, 2015).The oxidation of traffic-related gaseous pollutants (e.g., NO x and VOCs) leads to generally less volatile products, which have the tendency to either nucleate or condense onto pre-existing particles (Hallquist et al., 2009), result in the Atmos.Chem. Phys. Discuss., doi:10.5194/acp-2017-116, 2017 Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 15 March 2017 c Author(s) 2017.CC-BY 3.0 License.
formation of secondary PM.Particles from vehicle exhaust, such as hydrocarbon-like organic aerosol (HOA), can also undergo chemical transformation in the atmosphere (Zhang et al., 2015) and lead to photochemical smog on the following day.
To better understand the impact of traffic emissions in Guangzhou to its peripheral areas, we deployed an Aerodyne HR-ToF-AMS at a site downwind Guangzhou in late autumn to early winter 2014, during a period of prevailing northerly winds, to measure non-refractory PM 1 (NR-PM 1 ).Factor analysis of OA for source apportionment was performed with a new positive matrix factorization (PMF) procedure using the multilinear engine (ME-2) with the source finder (SoFi) (Canonaco et al., 2013).The procedure allows an effective exploration of the solution space, a more objective selection of the best solution, and an estimation of the rotational uncertainties (Canonaco et al., 2013;Crippa et al., 2014;Elser et al., 2016;Fröhlich et al., 2015;Paatero and Hopke, 2009).The source and formation of nitrate and SOA at this downwind site of Guangzhou will be discussed in detail.

Sampling site description
The sampling site is located at Panyu District, a downwind site from downtown Guangzhou.This suburban site is about 15 km south of the city center (Tan et al., 2013;Zou et al., 2015;Cheung et al., 2016).It is located at the summit of Dazhengang (23 • 00 N, 113 • 21 E) at an altitude of about 150 m (Fig. S1 in the Supplement) and surrounded by residential neighborhoods with no significant industrial sources nearby.Ambient sampling was conducted from November 07 th , 2014 to January 3 rd , 2015.

Measurements
In the HR-ToF-AMS measurements (DeCarlo et al., 2006), ambient air was sampled through a PM 2.5 cyclone on the rooftop, with a flow rate of approximately 0.084 L/min drawn by the AMS and the remainder drawn by an auxiliary pump.A diffusion drier was used to dry the sampled air stream, which reduced the RH of air to below 30 % before going into the HR-ToF-AMS.Other data presented in this work were obtained from collocated instruments, which included a Grimm 180 for PM 2.5 , a thermo-optical ECOC analyzer (Sunset Laboratory Inc.), a Magee AE33, a dual spot filter based instrument for black carbon (Drinovec et al., 2015), a gas analyzer system (Teledyne Instruments), and a monitor for aerosols and gases in ambient air (MARGA).The PM 2.5 Atmos.Chem. Phys. Discuss., doi:10.5194/acp-2017-116, 2017 Manuscript under review for journal Atmos.Chem.Phys.was estimated by Aerosol Inorganic Model (E-AIM II) (Clegg et al., 1998).The AMS collected 5-minute average particle mass spectra spanning from m/z 12 to 300 for the V + particle time-offlight (pToF) mode (high sensitivity) and the W mode (high resolution).And the dataset were only analyzed with m/z below 200 for the high resolution analysis.AMS calibrations, including ionization efficiency (IE) calibration, flow rate calibration, and size calibration, as well as data quality assurance protocols were used (DeCarlo et al., 2006;Lee et al., 2013;Schurman et al., 2015).IE calibrations with DMA size-selected (mobility diameter, D m = 400 nm) pure ammonium nitrate particles were carried out weekly.Filtered ambient air with a HEPA filter was sampled on a daily basis for 30 minutes to obtain background signals.Flow rate calibrations with a Gilian Gilibrator and PToF size calibrations were made with Nanosphere TM PSL particles (Duke Scientific, Palo Alto, CA, USA) and ammonium nitrate particles with selected size by scanning mobility particle sizer (SMPS) in the range of 178 to 800 nm were performed both before and after the sampling campaign.Gaseous CO 2 contributions to m/z 44 were quantified using a CO 2 monitor (PICARRO 2301).

Data analysis
AMS data analysis was performed using the SQUIRREL (v1.56D) and PIKA (v1 However, organic compounds, less bouncy than inorganics, dominated at the measurement site. They may also hinder the complete efflorescence of particles in the drier and further reduce the particle bounce effect and increase the particle collection efficiency.The mass concentrations of PM 1 (sum of NR-PM 1 and BC) were comparable to those of PM 2.5 with a slope of 1.1 and Pearson correlation coefficient (R p ) of 0.95 (Fig. S2a) when using a CE of 0.7.Furthermore, total AMS organics were also correlated with organic matter (OM) concentrations derived from the OC measurements (Fig. S2b) with a slope of 1.1 and R p of 0.82.The OC to OM conversion was calculated by organic matter to organic carbon ratio (OM:OC) data from the HR-ToF-AMS elemental analysis.AMS-measured sulfate, nitrate, and ammonium were comparable to those measured by MARGA with slopes of 1.0, 0.9, and 0.7, respectively.These comparisons supported a choice of CE = 0.7 is appropriate for the OA-dominating NR-PM 1 in this study.
Source apportionment for OA was performed by the newly developed ME-2 and controlled via the interface SoFi coded in Igor Pro (Canonaco et al., 2013).Fully unconstrained runs (PMF) were first explored.However, the three-factor solution suffers from mixing of factors of the HOA factor with the cooking organic aerosol (COA) factor (Fig. S3), as well as SVOOA with biomass burning related organic aerosol (BBOA) factor.The four-factor solution splits highly oxidized low-volatile OOA (LVOOA) into two sub-factors (Fig. S4).The inclusion of additional factors still cannot resolve pure primary OA factors (HOA, COA and BBOA), as they may have similar time series or profiles.Zhang et al. (2013) also reported that the PCA-resolved HOA might be affected by cooking emission with a distinct noontime peak in Beijing in spring and summer.The ME-2, however, by introducing a priori information of source profiles for HOA, COA and BBOA, provides additional control over the rotational ambiguity (Canonaco et al., 2013;Paatero and Hopke, 2009).The a value, ranging from zero to unity, stands for the percentage by which each m/z signal of the final solution spectra may differ from the anchor.A value of 0 means no deviation is allowed, while a value of 1 means 100% deviation is allowed.Several source profiles from previously reported HR-ToF-AMS data with different a values were explored.However, some ions were missing from the reference source profile when compared with our dataset.For these ions, the signal intensities were estimated based on the unit-mass-resolution (UMR) source profile from the average of multiple ambient data sets (Ng et al., 2011) as follows: derived from the ratio of total signal intensities in the UMR and HR profiles, which accounts for the difference in total signal intensity between the profiles; I UMR(m/z) is the total signal intensity at UMR level for the missing ions in the UMR profile; and I HRrest(m/z) is the sum of the signal intensities of the rest of the ions from HR reference profile that shares the same integer m/z as the missing ions.For these ions, whose intensities are derived from the above equation, an a value of 1 (100% deviation) was used.
To tackle the problem of mixing of traffic and cooking source in our PMF runs, we used the ME-2 solver with reference HOA and COA mass spectra adopted from the Paris campaign (Crippa et al., 2013).The a values, ranging from 0 to 0.5 in increments of 0.1, were tested.However, the resolved-HOA factor contributed no more than 5% of total OA, and only exhibited a small morning rush peak in the diurnal pattern (Fig. S5).Furthermore, another factor was observed to share similar features with the HOA mass spectrum (Fig. S5).This factor exhibited clear rush-hour peaks during the morning and late afternoon, as with traffic-related pollutants (e.g.NO x and BC).Also, the mass fraction of this factor is 3-4 times higher than the resolved-HOA factor (Fig. S5).The factor was observed, even with additional input for BBOA source profile and solutions with additional factors (Fig. S5) and continued to persist even after several HOA source profiles were tested.Alternatively, we directly extracted a local HOA source profile (HOA loc ) from the data set using a separate PMF run in selected time series with peaks in organic mass concentration.Similar approach has also been reported in by Fröhlich et al. (2015).We performed PMF on a short-term peak in the organic time series (Fig. S6) as well as on all of the short peaks with organic concentration above 30 µg/m 3 (Fig. S6).These two methods resolved similar mass spectra for local primary OA factors with R uc =0.99 (Fig. S7a, S7b).The results remain the same with even more factors (Fig. S7c).We then used the HOA source profile obtained from all of the short peaks with organic concentration above 30 µg/m 3 (HOA local_2 ) as the input HOA source profile.However, constrained HOA alone cannot resolved an environmentally reasonable solution either (Fig. S8).We then tried to added a COA constrain.For COA source profile, we compared the COA reference from the Paris campaign (Crippa et al., 2013) and the Mong Kok campaign in Hong Kong (Lee et al., 2015).When the two different COA source profiles were used in the ME-2 runs, similar COA (both in mass spectra and time series) were obtained for the current dataset (Fig. S9).The time series of the mass concentration of the COA factors tracked well with that of the COA tracer ion (C 3 H 3 O + ) and had clear mealtime peaks (Fig. S9).The ME-2 runs using these two COA source profiles differed slightly in terms of the proportion of COA and LVOOA, with results using the COA profile from the Paris campaign yielding a lower concentration of COA and a higher concentration of LVOOA.
The discrepancy was within 2 µg/m 3 (Fig. S9).As cooking styles and ingredients in Guangzhou are more similar to those in Hong Kong than in Paris, we chose the COA source profile from the Hong Kong campaign (HK) to constrain our ME-2 runs.For the BBOA factor, we used the reference BBOA profile from MILAGRO (Aiken et al., 2009).The resolved BBOA factor tracked well with its tracer ion (C 2 H 4 O 2 + ) and potassium (K + ) in time series.
A four-factor (HOA, COA, BBOA and a free factor) solution had a higher Q/Q exp , while a sixfactor (HOA, COA, BBOA and three free factors) solution seemed to split OOA factors without obvious physical meaning.We first tried to explore the a value with the range of a values from 0 to 0.5.An five-factor solution with a values of 0.1, 0.2, 0.3 for HOA local , COA HK , and BBOA MILAGRO , respectively, was finally adopted.The a values for these POA factors were also in line with previous ME-2 studies (Canonaco et al., 2013;Crippa et al., 2014;Fröhlich et al., 2015).
We further run the ME-2 with the optimal conditions with 10 runs to explore the stability of solution.The time series and mass spectra for each runs were quite steady (Fig. S10).The final solution came from results obtained with averaging these ten runs.Two oxygenated organic aerosol factors, SVOOA and LVOOA, were assigned based on their degree of oxygenation.The mass spectra of all OA factors and their mass concentrations will be discussed in Section 2.2.The correlations of OA factors with external tracers are shown in Table S1.

Results and Discussion:
3.1.Overall composition Fig. 1 shows the time series of NR-PM 1 species (sulfate, nitrate, ammonium, chloride, and organics), BC, and meteorological factors (precipitation, RH, temperature, wind direction, and wind speed) for the whole campaign.Northerly winds (hourly integrated) prevailed through the whole campaign, bringing pollutants from downtown area of Guangzhou.NO x ranged from 9 to 333 ppb, averaging at 40.8 ppb.The high NO x levels reflect heavy influences from traffic emissions.Ozone ranged from 0.2 to 119.9 ppb with an average of 22.7 ppb.An episodic high O 3 period occurred from the November 14 th to 27 th , 2014, and peaked in every afternoon.The average temperature was 5 °C lower in December than in November.A detailed discussion of the diurnal patterns of meteorological conditions together with gaseous and particulate pollutants is presented in the supplementary information (Section 1).Mass concentrations of PM 1 ranged from 1.7 to 208.4 µg/m 3 , with an average of 55.4 µg/m 3 .The concentration was significantly higher than those in Hong Kong (Lee et al., 2013(Lee et al., , 2015;;Li et al., 2015;Qin et al., 2016a) and Shenzhen (He et al., 2011), similar with those in Lanzhou and Sichuan basin (Hu et al., 2016;Xu et al., 2016), and lower than those in Beijing (Hu et al., 2016) and Sichuan Basin.In our study, organics accounted for 24.5 µg/m 3 (or 46.3 %) of the PM 1 mass on average.Sulfate, nitrate, ammonium, BC, and chloride accounted for 23.1%, 11.2%, 9.1%, 8.3%, and 1.9% of the PM 1 mass, respectively (Fig. 2 a).Fig. 2b shows the variations in species mass fractions in PM 1 species as a function of total PM 1 (with BC inclusive) mass loading, as well as the probability density of PM 1 mass loading.
Organic mass fraction was the highest across different PM 1 levels with little variation.On the contrary, the mass fraction of sulfate dropped from 0.25 to 0.15 as PM 1 concentration increased from 10 to 160 µg/m 3 .The decrease in sulfate mass fraction was compensated by the increased mass fraction of nitrate, and to a lesser extent, chloride.The increase in the relative contribution of nitrate on highly polluted days was also observed in AMS studies in other locations in China, such as Shenzhen (He et al., 2011), Beijing (Huang et al., 2010), and Changdao (Hu et al., 2013).

Organics
Since OA accounted for over 45% of PM 1 in this study, it is worth investigating the detailed characteristics of this portion of PM.Elemental analysis of OA (ratios of H:C, O:C and OM:OC) provides useful information for assessing OA characteristics and their evolution.Ions in the high resolution mass spectra were used to calculate the elemental ratios using the Improved-Ambient method (Canagaratna et al., 2015).Results obtained from the Aiken-Ambient (Aiken et al., 2007) protocol were also listed in Table 1 for comparison with elemental ratios reported in the literature.
We further used empirical constants (11% for H:C, 27% for O:C, and 9% for OM:OC) from Canagaratna et al. (2015) to estimate the ratios accounting for possible underestimation of the O:C ratio in earlier studies.As there are significant differences in O x , RH and temperature between November and December, we conducted the following analysis in November and December separately.However, the average O:C, H:C, and OM:OC ratios showed little variation between the two months, with average values of 0.53 for O:C, 1.63 for H:C, 1.87 for OM:OC in November, and 0.53 for O:C, 1.65 for H:C and 1.87 for OM:OC in December.The observed elemental ratios generally agreed with other AMS-based reported values in PRD (Table 1).The H:C ratio was similar to those at rural sites in Kaiping (1.64) and Heshan (1.65) and slightly higher than that at suburban HKUST (1.54 and 1.55) but lower than those at urban sites in Shenzhen (1.81) and Mong Kok (1.84).O:C and OM:OC ratios, on the contrary, are higher than those at the urban sites and lower than that in Kaiping, but similar to those in Heshan and at HKUST.Overall, the relatively low H:C ratio and high O:C ratio suggest that OA at this site has degree of oxygenation higher than those at urban sites (e.g.Shenzhen), but lower than those at rural sites (e.g.Kaiping).
The degree of oxygenation reflects the characteristics of OA at the peripheries of urbanized areas as a mixture of background air with urban recently formed secondary OA.
As shown in Fig. 3, the diurnal variations in H:C, O:C, OM:OC and carbon oxidation state (OS c ≈ 2×O:C-H:C) showed similar patterns during both November and December.The average H:C ratio ranged from 1.6 to 1.7, with a pronounced increase in the late afternoon from 16:00 to 20:00, and remaining at a maximum value until mid-night.This observation suggests important fresh organic source at evening and nighttime.The O:C, OM:OC ratios and OS c increased during daytime with afternoon peaks at around 15:00, likely due to high photochemical activity and production of SOA during daylight hours.
The mass spectra of all OA factors and their mass concentrations from PMF analysis with ME-2, together with the time series of external tracers, are shown in Fig. 4. HOA correlated well with NO x since both are traffic-related species.While COA shares spectral similarities with HOA, it is distinguished from HOA by a higher contribution of C 3 H 3 O + at m/z 55, and a much lower contribution of ions at m/z 57 compared to HOA (He et al., 2010;Mohr et al., 2012).The time series of the COA was highly correlated with one of its tracer ions, C 3 H 3 O + .The other primary factor, BBOA, is characterized by the presence of signals at m/z 60 (C 2 H 4 O 2 + ) and m/z 73 , which are typically associated with levoglucosan (Alfarra et al., 2007;Schneider et al., 2006).The time series of BBOA also tracked well with the one of its marker ions.temperatures were also observed during this period (Fig. 1).LVOOA also correlated well with sulfate in time series, as both species are regional pollutants.On average, HOA, SVOOA and LVOOA were the main sources of OA, made up 26%, 31% and 30% of the total OA respectively (Fig. 5).
The diurnal patterns of mass concentrations and fractions for the five OA factors are depicted in Fig. 6.HOA exhibited two typical peaks during both November and December, first during the morning rush hours at 09:00, before starting to increase again at 16:00 prior to peaking between 20:00 and mid-night.The high HOA level in the evening and at night was likely due to the heavily polluting trucks passing by en route to the downtown area at night (22:00 to 07:00).The diurnal variations in HOA corresponded to those in H:C ratio and NO x , and BC (Fig. S11), suggesting that vehicle-related pollutants are the main contributor to this OA factor.On the other hand, COA had clear peaks at lunch and dinner times.Even though COA only contributed to 8% of total OA on average, its contribution could be as high as 15% of OA during mealtime in both months.
Contribution of COA to OA at this peripheral site of the megacity Guangzhou, however, is still far lower than those directly measured at urban areas, which often had COA contribution of 15 -20% of OA (Crippa et al., 2013;Lee et al., 2015;Sun et al., 2016Sun et al., , 2013)).BBOA did not exhibit a clear diurnal variation in November, but it had a daytime valley in December, possibly due to intensive biomass burning activities at night.SVOOA had a clear noon-to-afternoon peak in November, consistent with peaks in ozone (Fig. S11), but this peak was less pronounced in December.The noon-to-afternoon peak for SVOOA has been commonly observed worldwide under the photochemically oxidation process (Hayes et al., 2013;Qin et al., 2016).Zotter et al. (2014) further found that the noon-to-afternoon SVOOA peak can be caused by the large increase in fossil carbon.
LVOOA, in our study, showed a relatively flat diurnal pattern as in Shenzhen (He et al., 2011), but significant noon-to-afternoon peaks for LVOOA were observed at rural sites in Kaiping (Huang et al., 2011) and Hesan (Gong et al., 2012) .

Formation of SOA
The evolution of AMS OA factors has been used to infer SOA formation via photochemical oxidation in the PRD (Lee et al., 2013;Li et al., 2013;Qin et al., 2016a).Odd-oxygen (O x ) concentrations are closely linked to the extent of photochemical oxidation in an air mass because Fig. 7a shows that SOA (SVOOA+LVOOA) continuously increased as O x increased during daytime (7:00 to 18:00) in both November and December.The regression slope for SOA versus O x are 0.23 ±0.014 (R pr =0.85) and 0.25 ±0.025 (R pr =0.55) µg /(sm 3 ppb) in November and December, respectively.The volume unit "sm" stands for volume under standard-temperaturepressure conditions.The slopes are higher than those reported in earlier studies during spring to summer in North America (Table 2), indicating efficient SOA photochemical production even in late autumn and early winter at this subtropical site.Such a significant production of SOA might have been fueled by photo-oxidation of large amounts of accumulated VOCs between the city and this peripheral site of Guangzhou.Meteorological parameters can also influence the formation of SOA in several ways.Such influences can be effective by changing SOA partitioning at high temperature conditions, or promoting aqueous-phase chemistry at high liquid water content conditions.The temperature effect on gas-particle partitioning did not appear to be important in this study.Higher temperatures should favor the partitioning of SOA to the vapor phase, but higher SOA mass loading was associated with higher temperature in our case (Fig. 7b).In November, LWC stayed in a relatively narrow range (Fig. 7c) when SOA experienced the drastic changes.In December, LWC was lower than that in November, yet, SOA increased as LWC increased.The enhanced LWC may facilitate the SOA formation in December yet influence was not clear in November.Furthermore, there is no correlation between SOA/OA and LWC in either month.

Formation of nitrate
With the growth of NO x emissions in recent years, the concentration and proportions of nitrate in PM have increased significantly in most Chinese megacities (Pan et al., 2016;Wen et al., 2015;Xue et al., 2014).As shown in Fig. 2, an obvious increased proportion of nitrate was also observed in high-PM episodes in our study.The increased contribution of nitrate to PM calls for a more detailed investigation of this species.have successfully used NO + to NO 2 + ratio to estimate organic nitrates from HR-ToF-AMS measurement.The rationale behind such an estimation was that the response of NO + /NO 2 + of inorganic nitrate differed greatly those from organic nitrates.Xu et al.(2015) used the NO + /NO 2 + values of 5 and 10, which likely correspond to the upper and lower bounds of the ratios from organic nitrates.This method using NO + /NO 2 + (Method 1) was adopted in this study to estimate the contributions of inorganic and organic nitrates.However, it should be noted that the vast array of possible organic nitrate parent compounds in ambient particles and the variations of the NO + /NO 2 + ratios between instruments may led to some bias in the calculation.A lower bound of organic nitrate concentration can also be estimated using the organic concentration and elemental ratios (OM:OC and N:C) from HR-ToF-AMS measurement (Method 2) (Schurman et al., 2015b).
A detailed calculation of the two methods is presented in the supplemental information (Text S2).
Using these two methods, the estimated inorganic nitrate concentrations were derived by subtracting the contributions of organic nitrates from the total nitrate as measured by the HR-ToF-AMS.Overall, the contribution from organic nitrate ranged from around 10% to 25%, while inorganic nitrate contributed to the majority signals of AMS-measured nitrate.The scatter plot of estimated inorganic nitrate versa nitrate from HR-ToF-AMS measurement is shown in Fig. 8.The estimated inorganic nitrate tracked well (R p 2 ≥ 0.95) with the total HR-ToF-AMS nitrate concentration and followed with the 1 to 1 line.Even though organic nitrates also contributed to the total nitrate we measured, both the variation and the concentration of the nitrate did not change significantly after subtracting the organic nitrates.Furthermore, as shown in Fig. S2, concentrations of nitrate from AMS were comparable to those from MAGRA, which uses chromatographic speciation, with a correlation slope of 0.9 and a R p of 0.95.The influence from organic nitrates in our calculation of total nitrate should be minor.Given the uncertainties associated with each estimation, we prefer to use the raw HR-ToF-AMS nitrate concentration in the following discussion.In Fig. 9, the total nitrate is closely correlated with NO x during both daytime and nighttime.At the same NO x , total nitrate increased as O x increased during daytime, suggesting that NO x was photochemically oxidized to nitrate species.At night (Fig. 9b), O x concentration was relatively low when NO x concentrations were high.The slope of total nitrate against NO x during daytime is steeper than that at nighttime.Nighttime formation of nitric acid involves the consumption of NO x and O 3 (Seinfeld and Pandis, 2006).While NO x can be replenished by primary emissions, O 3 is mainly produced during daytime and is consumed at night.
Thus, it is reasonable to observe that high total nitrate concentration was correlated with high NO x and low O 3 at night.
The formation of particulate nitrate is limited by the availability of ammonia.Gas-to-particle partitioning of nitrate species to form particulate nitrate can be affected by the concentrations of ammonium and sulfate.An increase in ammonium or a decrease in sulfate could enhance the formation of particulate nitrate (Seinfeld and Pandis, 2006).Many studies indicated that the molar ratio of ammonium to sulfate of 1.5 demarcates the observation of particulate nitrate (Pathak et al., 2004, Griffith et al., 2015;Liu et al., 2015).Under ammonium rich (AR, conditions, additional ammonia is available to transfer HNO 3 to the particle phase.In contrast, under (AP, [NH 4 + ]/[SO 4 2-] < 1.5) conditions, all of the ammonia is used to neutralize H 2 SO 4 until (NH 4 ) 3 H(SO 4 ) 2 (letovicite) is formed.AR conditions prevailed during the whole campaign.Excess ammonium, defined as ( ), tracked well with particulate nitrate concentration with a slope of 0.93 (Fig. 10).In Fig. 11, we also examined the relationship between nitrate in PM 2.5 and excess ammonium, and alkali cations (Na + and Ca 2+ , mainly from soil dust and sea salt particles, respectively).Results showed that nitrate tracked better with excess ammonium than the alkali cations did.The slope of nitrate to excess ammonium was smaller than 1, indicating that ammonium was sufficient to neutralize the particulate nitrate.The molar concentration of alkali cations was around 7-10 times lower than that of nitrate and their role in stabilizing nitrate was negligible in our study.
The partitioning of nitrate between the gas and particle phases shows significant differences between November and December (data points in Fig. 12).The average ratio of nitrate to HNO 3 (the slope of scatter plot) was 3.2 in November, compared to 7.8 in December.The NH 3 concentration in the gas phase was not a limiting factor as the concentration of NH 3 was even higher in November (Fig. 11a).Lower temperatures in December shifted the equilibrium toward the particle phase, increasing the nitrate concentration in the particle phase (Fig. 12b).Higher RH or particle liquid water content favored the thermodynamic equilibrium to form particulate nitrate.
However, this effect fails to explain the gap between the different months (Fig. 12 c,d) as the higher nitrate to HNO 3 ratio (December data) was associated with the lower RH and LWC.Overall, the differences in the ambient temperature may be the key factor causing the large difference in and daytime, again demonstrating a close tie to traffic emissions at this site peripheral to the megacity.However, the slope of total nitrate against NO x at daytime is steeper than that at Discussion started: 15 March 2017 c Author(s) 2017.CC-BY 3.0 License.mass concentration by Grimm 180 was corrected by the daily PM 2.5 mass concentration with quartz filter measurement.Meteorological data (e.g.wind, temperature and RH) were obtained from a weather station located near the sampling site.Solar irradiance data were measured from a station in Nansha District, around 27.5 km from the sampling site.Particle liquid water content (LWC) Atmos.Chem.Phys.Discuss., doi:10.5194/acp-2017-116,2017   Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 15 March 2017 c Author(s) 2017.CC-BY 3.0 License.
Atmos.Chem.Phys.Discuss., doi:10.5194/acp-2017-116,2017   Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 15 March 2017 c Author(s) 2017.CC-BY 3.0 License.O 3 production results from OH reactions with VOCs and CO.The ratio of SOA to O x , therefore, provides a useful metric for quantifying the dependence of SOA concentration on photochemical oxidation.Following the work of Hayes et al. (2013), we examine the correlations of SOA with O x , instead of O 3 , to account for the titration of O 3 by freshly emitted NO, which produces NO 2 .
Atmos.Chem.Phys.Discuss., doi:10.5194/acp-2017-116,2017   Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 15 March 2017 c Author(s) 2017.CC-BY 3.0 License.gas-particlepartitioning of nitrate in different time of a year.The implication for this finding is that meteorological parameters might couple with chemistry in the formation of PM species such as the semi-volatile nitrate.As such, the complex effects in PM formation deserve further investigations, especially in city peripheries where precursors are abundant and photochemistry is active.4.ConclusionsThis study presents the emission sources and formation routes of PM at Panyu District in peripheral Guangzhou, from November to December 2014.The measured PM 1 (sum of NR-PM 1 and BC) mass concentration ranged from 1.7 to 208.4 µg/m 3 , with an average of 55.4 µg/m 3 .The PM 1 concentration is high compared to other cities in PRD, but slightly lower than those in the northern and north-western parts of China.OA was the overwhelmingly dominate species (> 45%) at all PM 1 levels.The mass fraction of sulfate decreased from 0.25 to 0.15 as PM 1 concentration increased from 10 to 160 µg/m 3 .The decrease in sulfate mass fraction was compensated by the increased mass fraction of nitrate.For organics, the average H:C ratio showed a pronounced increase starting in late afternoon and lasting until mid-night, suggesting the presence of an important fresh OA source at nighttime in peripheral Guangzhou, most likely heavy-duty diesel trucks.The O:C and OM:OC ratios, as well as OS c increased during daytime with clear afternoon peaks, likely due to efficient photochemical production of SOA during daytime.OA components were resolved by PMF analysis with ME-2.Fully unconstrained runs (PMF) suffers from the mixing of factors of the HOA factor with the COA factor, as well as the SVOOA) with the BBOA factor.A systematic approach to minimize the mixing of factors is explored.The final resolved components are HOA, COA, BBOA, SVOOA, and LVOOA.Results show that HOA was an important contributor (40%) to the high organic concentrations at night.SOA (SVOOA+LVOOA) plays a more important role during daytime.SOA concentration changed with the photochemical oxidation marker (O x ).More interestingly, the ratios of SOA to O x during the late autumn and early wintertime were higher than those reported for several urban sites in North American during the summer (e.g.Pasadena and Mexico City), indicating the efficient SOA production.This efficient SOA formation was probably a result of active photochemistry at this subtropical site and was fueled by the large source of SOA precursors (e.g.HOA and VOCs) in the periphery of Guangzhou.The formation of total nitrate was closely correlated with NO x during both nighttime Atmos.Chem.Phys.Discuss., doi:10.5194/acp-2017-116,2017 Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 15 March 2017 c Author(s) 2017.CC-BY 3.0 License.

Figure 2a )
Figure 2a) Average mass fraction of PM 1 (with BC inclusive) during the whole campaign; b) Mass fraction variation of PM 1 species as well as the probability density of PM 1 as a function of total PM 1 mass loading.The probability density distribution describes the relative likelihood for the PM 1 mass loading in a certain range of concentrations.

Figure 3
Figure 3 Diurnal variation of H:C and O:C OM:OC ratio and carbon oxidation state (OS c ≈ 2×O:C-H:C) during Nov. and Dec.(25th and 75th percentile boxes, 5th and 95th percentile whiskers, median as line in box, and mean as solid colored line).

Figure. 4 .
Figure. 4. The mass spectra of all OA factors and the time series of their mass concentrations together with external tracers.

Figure 5 .
Figure 5. Campaign average mass fraction of each factor.

Figure 7
Figure 7 (a)SOA against Ox; (b)SOA against temperature; (c) SOA against LWC during daytime..The concentration of SOA has been converted to standard-temperature-pressure conditions in order to compare the slope of SOA/Ox with literature values.

Figure 8
Figure 8 Scatter plot of estimated inorganic nitrate versa nitrate from HR-ToF-AMS measurement.

Figure 9
Figure 9 Correlations between total nitrate HNO 3 (g)+ NO3(p)) and NO x (NO+NO 2 ).Triangles and circles represent data forNovember and December respectively.Data are color-coded by O x during daytime and O 3 during nighttime .

Figure 10
Figure 10 Time series of nitrate, excess NH4 and NH4-to-SO4 molar ratio.

Figure 11
Figure 11 Scatter plots of NO 3 molar concentration from MARGA (PM 2.5 ) against those of Excess NH 4 + , Na + and Ca 2+ .

Figure 12 .
Figure 12.Distribution of nitrate species between HNO 3 (g) and NO 3 -(p).Colored by (a)NH 3 concentration (b) temperature, (c) R nighttime.During daytime, at the same NO x level, total nitrate increased as O x increased, suggesting that NO x photochemically oxidizes to nitrate.At night, high total nitrate concentration was associated with high NO x but low O x .Ammonium-rich conditions prevailed throughout the whole campaign and particulate nitrate was highly associated with the excess ammonium.The fraction of nitrate in the particulate phase out of total nitrate is higher in December, primarily due to difference in temperature.Nevertheless, in addition to its close tie to traffic emissions (NO x as the precursors), formation of particulate nitrate may be affected by meteorological parameters as well.This study underlines the effects of traffic emissions to peripheral Guangzhou.The contributions to PM can be direct contribution of primary OA (HOA), secondary formation of semi-volatile OA (SVOOA), as well as secondary formation and subsequent partitioning of nitrate during late autumn and early winter.This work was supported by the National Key Project of the Ministry of Science and Technology of the People's Republic of China (2016YFC0201901) and the Natural Science Foundation of China (41375156).We also thank Dr. Mai Boru for providing CO 2 data.Yi Ming Qin gratefully acknowledges support from the HKUST Asian Future Leaders Scholarship. .Chem.Phys.Discuss., doi:10.5194/acp-2017-116,2017 Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 15 March 2017 c Author(s) 2017.CC-BY 3.0 License.
Canagaratna et al. 2015ental ratios were estimated from the A-A method with the empirical constants from M. R.Canagaratna et al. 2015   Atmos.Chem.Phys.Discuss., doi:10.5194/acp-2017-116,2017Manuscriptunder review for journal Atmos.Chem.Phys.Discussion started: 15 March 2017 c Author(s) 2017.CC-BY 3.0 License. Figure 1 Time series of NR-PM 1 species (sulfate, nitrate, ammonium, chloride, and organics), BC, and meteorological factors (precipitation, relative humidity, temperature, wind direction and wind speed) for the campaign.Hourly averages are shown.