Supporting Information for

Dual-modelling-based source apportionment of NOx in five Chinese

megacities: providing the isotopic footprint from 2013 to 2014

Zheng Zong, Yang Tan, Xiao Wang, Chongguo Tian, Jun Li, Yunting Fang, Yingjun

Chen, Song Cui, Gan Zhang

Contents:

In total 23 pages including:

Text: 8

Figure: 19

Table: 6

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Text S1

PM2.5 can be distinguished in primary, directly emitted from sources, and

secondary, subsequently formed in the atmosphere from chemical processes. The

latter involves a series of precursor gases including NOx, SO2, ammonia (NH3) and a

large number of volatile organic compounds (VOCs), which may react with ozone

(O3), hydroxyl radical (•OH) and other reactive molecules forming secondary

inorganic aerosol and secondary organic aerosol (Squizzato et al., 2013). This was

confirmed as the main reason for the high PM2.5 matter in China (Huang et al., 2014).

NOx could reduce the environmental capacity for SO2, leading to rapid conversion of

SO2 to sulfate (SO42-) (He et al., 2014). Similar phenomenon is also observed relying

on the theory of aqueous oxidation of SO2 by NOx (Wang et al., 2016). With the

supply of oxidants initially produced by NOx, VOCs can be quickly converted into

secondary organic carbon (SOC), concomitantly generating new oxidants (Shen et al.,

2013). Moreover, the declined PH induced by SO42- and NO3

− could accelerate the

transformation of NH3 to ammonium (NH4+) (Guo et al., 2014).

Text S2

Once emitted into the atmosphere, NOx is oxidized to HNO3 or NO3− via the

following chemical pathways (R1-R8) (Fang et al., 2011). In summary, NOx oxygen

atoms are rapidly exchanged with O3 in the NO/NO2 cycle (R1-R3); •OH radicals

result in the oxidation of NO2 to HNO3 (R4; the •OH pathway); NO2 is oxidized by O3

to produce NO3 (R5), which subsequently combines with NO2 to form N2O5 (R6), and

then undergoes hydrolysis to form HNO3 (R7), referred to as the O3 pathway; and the

generated HNO3 combines with alkali to form NO3− (R8). Overall, the •OH and O3

pathways are the two fundamental oxidation pathways for NOx, generally exhibiting

noticeable diurnal and seasonal variation (Elliott et al., 2007). Previous research has

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found that the •OH pathway is more prevalent during the daytime and in summer,

when the relative concentration of •OH is higher. Conversely, the O3 pathway is more

dominant overnight and in winter, because N2O5 is thermally unstable (Hastings et al.,

2003;Xiao et al., 2015). However, quantifying the respective ratios of the two

pathways remains a challenge.

NO + O 3 → NO2 + O2

(R1)

NO 2 + hv → NO + O

(R2)

O + O 2 → O3

(R3)

NO2 + •OH → HNO3 (R4)

NO2 + O3 → NO3+O2 (R5)

NO2 + NO3 → N2O5 (R6)

N2O5 + H2O → 2HNO3

(R7)

HNO 3 + Alkali → NO3−

(R8)

Text S3

The method of emission inventory is an important tool for source apportionment

of atmospheric pollutants. However, the compilation of emission inventory in China is

basically in the initial stage, and there is still great uncertainty in the understanding of

emission change. For the same city, different quantitative methods would lead to

different results (Shimadera et al., 2015). Thus, we consulted the literatures before

starting the study of this paper, and determined a relatively reliable emission

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inventory (Ding et al., 2017), which averaged the results from different methods, such

as EDGAR v4.3.1, REAS v2.1, REAS v2.2, MIX, DECSO-OMI, DECSO-GOME2a,

EnKF-MIROC, EnKF-CHASER. Based on the inventory, we selected five Chinese

megacities, including Beijing, Shanghai, Wuhan, Chengdu, and Guangzhou from

Beijing-Tianjin-Hebei (BTH) region, Yangtze River Delta (YRD), middle reaches of

the Yangtze River (MYR), Sichuan-Chongqing (SC), and Pearl River Delta (PRD)

city clusters, respectively. The selected areas are the most polluted areas in terms of

NOx and have typical representation of Chinese city agglomeration.

Text S4

In this study, organic carbon (OC) and elemental carbon (EC) was analyzed by a

Desert Research Institute (DRI) Model 2001 Carbon analyzer (Atmoslytic Inc.,

Calabasas, CA) following the Interagency Monitoring of Protected Visual

Environment (IMPROVE_A) thermal/optical reflectance (TOR) protocol. Its detailed

information could be found in our pervious study (Zong et al., 2015). For major ionic

species (SO42−, NH4

+, K+ Ca2+, Cl−, Na+, Mg2+, NO3−), briefly, one punch with 47 mm

diameter was cut off from quartz fiber filters, and then was subjected to Milli-Q

water. Samples were ultrasonically extracted for 20 min, and the extracts were

filtered. Anions and cations were measured by the ion chromatograph (Dionex

ICS3000, Dionex Ltd., USA) based on the analysis method (Zong et al., 2016). The

detection limit was 10 ng ml−1 with an error < 5.5%. And all ions concentrations were

blank-corrected by subtracting the average field blank values.

Text S5

Based on the previous NOx emission inventory, power plant, industry, residential

use and transportation were the traditional NOx emission sources in China (Liu et al.,

2017). They could be roughly divided into coal combustion (power plant + industry +

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residential use) and mobile sources (transportation including vehicle exhaust and ship

emission) according to the type of fuel combustion. In addition, more and more recent

studies on emission factors show that biomass combustion is one of the important

emission sources to NOx on a local or global scale (Mehmood et al., 2017).

Especially, the prevalence of large-scale biomass burning and household emission

without any pollution control devices frequently occurs in China, the source could not

be ignored any more. For instance, the emission of NOx from biomass burning has

increased more than 6-fold from 1990 to 2013 in China (Li et al., 2016). Microbial

process emission is another important source for NOx, therein microbial bacteria

widely distributed in soils and oceans consumes accumulated nitrogen (ammonium by

nitrifying microbes, nitrate by denitrifying microbes) and as a by-product, to the

release of large pulses of NO (Jaeglé et al., 2004). Thus, extensive cultivated land

where nitrogen fertilizer abused and ocean surrounding (especially in marine

sediments and estuaries) are also important sources for NOx in China, which are

named microbial processes in this study. Lighting emission is usually significant for

NOx in the low latitude region, such as equator around, where is basically unaffected

by anthropogenic sources (Hastings et al., 2003). Considering the location of the

sampling points, the source was neglected in this paper.

From the aspect of chemical composition, NO3− were strongly correlated with

some typical source signals (e.g., SO42−, K+, NH4

+, Figure S4), confirming the above

view. The use of K+ as a tracer of biomass burning has been broadly established,

while SO42− results from the transformation of SO2, which is mostly produced from

industrial or domestic coal combustion in China (Zong et al., 2016). “Fuel NH4+”

(e.g., vehicle exhaust and power plants) displays a significant relationship with NO3−,

also indicating that they have a common source (Pan et al., 2016). For the source of

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NH3 (the precursor of NH4+), debate continues over whether agricultural emission is

the major source for NH3 in urban and rural areas, although previous global emission

inventories have suggested that agricultural emissions were responsible for more than

80% of the total NH3 emissions (Chang et al., 2016). In fact, the vehicle equipped

with three-way catalytic converters could be a more and more important source for

NH3 with the rapid increase of the vehicle number in nowadays. Previous study

indicated traffic source has become comparable to that of agriculture emission for

NH3 in 2013 (Huang et al., 2018). Besides, coal-fired power utilities equipped with

selective catalytic reduction (SCR) and selective noncatalytic reduction (SNCR)

technologies are also significant sources for “fuel NH3”. For example, based on

nitrogen (N) isotopes analysis, it has been shown that fossil fuel emission contributed

90% of the total NH3 during the haze days in Beijing (Pan et al., 2016).

Overall, coal combustion, mobile sources, biomass burning, and microbial

processes were considered to be the dominant contributors of NOx at the five cities.

The mean and standard deviations (SD) of δ15N-NOx for the four type sources were

collected from literature as listed in Table S4. The mobile source consisted of vehicle

exhaust and ship emission, which was discussed subsequently in the main text.

Unfortunately, the δ15N-NOx range of ship emissions has not been determined, as far

as we know. The δ15N-NOx range of vehicle exhaust was regarded as the proximity of

mobile source, because the previous study has shown δ15N of ship emission could be

comparable to that of vehicle exhaust (Beyn et al., 2015). Similarly, δ15N-NOx from

the oceanic microbial process has not been tested, which was treated close to the

values from the microbial process in soil based on the similar working mechanism in

the microbial activities.

Text S6

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Backward trajectories, generated by the Hybrid Single-Particle

Lagrangian Integrated Trajectory (HYSPLIT) Model, were adopted to

assess the passing areas of air masses encountered during the

sampling time (Bressi et al., 2014). This model was obtained from

the National Oceanic and Atmospheric Administration Air Resource

Laboratory website (www.arl.noaa.gov/ready/hysplit4.html). The 72-

h trajectories with 6 h interval were calculated for air masses starting from the

sampling site at 500 m above ground level. Finally, trajectories were generated and

bunched into clusters in this study.

Text S7

The estimate of SOC was determined using the EC-tracer method:

(9)

where (OC/EC)prim was the ratio of OC to EC for primary emissions. Here, the

minimum OC/EC was adopted as the (OC/EC)prim and this usage has been cited in

previous study (Zong et al., 2016).

Text S8

Generally, season and latitude are important factors influencing OH conversion

ratio of NOx (Alexander et al., 2009;Hastings et al., 2003). In the operation of linear

mixed effect model, we set “latitude” as fixed effect, “season” as random effect, and

“OH conversion ratio” as dependent variable. Using the package of “lmerTest”, the

processes is operated as follows:

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From the analysis results, we can see that latitude does have a significant correlation

(p < 0.01) with OH conversion ratio all year round.

Figure S1. Concentration variation of pollutants in nationwide and typical megacities from

2014 to 2017 in China obtained from Bulletin on China's Environmental Situation 2014-2017.

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Figure S2. Chemical processes between NOx, SO2, NH3 and VOCs in the atmosphere.

Figure S3. The sampling sites accompanied with the average satellite-derived NOx emission

inventory (Ding et al., 2017).

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Figure S4. Correlation coefficients between NO3− and SO4

2−, NH4+ and K+ at the five sites.

Figure S5. The improved PSCF simulation program in this study.

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Figure S6. Contribution of secondary components (SO42−, NO3

−, NH4+ and SOC) to PM2.5 at

the five cities, dashed line represents the concentration of PM2.5.

Figure S7. Time series of PM2.5 and NO3− concentrations at Beijing, Shanghai, Wuhan,

Chengdu and Guangzhou from 2013 to 2014.

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Figure S8. Characteristics of temperature, RH, wind speed during sampling in the five cities.

Figure S9. Time series of δ15N-NO3− at Beijing, Shanghai, Wuhan, Chengdu and Guangzhou,

respectively, during the study period.

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Figure S10. The backward trajectory cluster feature (spring, summer, autumn and winter) in

the five megacities during the observation.

Figure S11. Time series of δ18O-NO3− at Beijing, Shanghai, Wuhan, Chengdu and Guangzhou,

respectively, during the study period.

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Figure S12. Global distribution of the UV-R variable (Andersen et al., 2015).

Figure 13. The characteristics of annual energy consumption from 2013 to 2017 in China

(China Statistical Yearbook).

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Figure S14. National land use map of China (Ning et al., 2018).

Figure S15. Thermal power generation on province level in 2014 (TWh) (Ming et al., 2016).

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Figure S16. NOx emissions for each grid cell over the ocean (Ding et al., 2018).

Figure S17. S/PM2.5 ratio (including Cl−, NO3−, SO4

2−, Na+, NH4+, K+, Mg2+, Ca2+) in HP stage

and NHP stage at the five megacities, respectively.

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Figure S18. δ15N-NO3− feature in HP stage and NHP stage at the five megacities, respectively.

Figure S19. PSCF maps of HP stage (the highest 25% of PM2.5 concentration) combining the

five cities together.

Table S1. Detailed information of sampling sites at Beijing, Shanghai, Wuhan, Chengdu and

Guangzhou.

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Sampling sites Longitude Latitude Sampling

number Sites description

Beijing 116.34°E 39.93°N 104

Located at the top of the 5th floor of the National Geological Experiment and Test Center of the Chinese Academy of

Geosciences of Beijing.

Shanghai 121.50°E 31.29°N 108

Located on the roof of a five-story teaching building in Handan

Campus of Shanghai Fudan University.

Wuhan 104.36°E 30.52°N 91

Located on the top of the 5th floor of the School of

Resources and Environment of Wuhan

University.

Chengdu 104.38°E 30.64°N 115

Located on the top floor of Chengdu Mountain

Disaster and Environment Research Institute.

Guangzhou 113.36°E 23.15°N 94

Located on the 5th floor of the library of Guangzhou Institute of Geochemistry,

Chinese Academy of Sciences.

Comments: Autumn: 15 Oct. 2013 to 19 Nov. 2013; Winter: 21 Dec. 2013 to 23 Jan. 2014; Spring: 20 Mar. to 12 May 2014; Summer: 20 Jun. to 28 Aug. 2014

Table S2. The significant test characteristics of the concentration of NOx in the sampling

period and its season in the five Chinese megacities from 2013 to 2014

(https://www.aqistudy.cn/historydata/).

　 Beijing Shanghai WuhanSeason F pF T pT F pF T pT F pF T pT

Autumn 0.130.72 1.52 0.14 0.63 0.43 -1.49 0.14 1.38 0.25 0.60 0.14

Winter 0.690.41 0.25 0.80 1.30 0.26 1.30 0.20 0.79 0.19 0.47 0.80

Spring 0.800.37 -0.82 0.42 0.70 0.41 0.32 0.75 0.13 0.72 0.16 0.42

Summer 0.001.00 0.39 0.70 0.66 0.42 0.31 0.76 1.26 0.26 0.26 0.70　 Chendgu Guangzhou

Season F pF T pT F pF T pT

Autumn 0.530.47 -0.56 0.58 0.00 1.00 -0.90 0.38

Winter 1.880.29 1.11 0.37 0.00 0.99 1.87 0.07

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Spring 0.940.34 -0.05 0.96 1.35 0.25 1.82 0.06

Summer 0.000.99 -0.49 0.62 0.65 0.42 -1.40 0.16

Table S3. The test constant of A, B, C, D over the settled temperature range (150-450K).

mαX/Y A B C D15NO2/NO 3.8834 -7.7299 6.0101 -0.17928

15N2O5/NO2 0.69398 -1.9859 2.3876 0.1630818NO/NO2 -0.04129 1.1605 -1.8829 0.7472318H2O/OH 2.1137 -3.8026 2.5653 0.59410

Table S4. Statistic information of δ15N-NOx from coal combustion, mobile source, biomass

burning and microbial process based on literature values.

Source Types MeanStandard Statistic

Referencedeviation number

Coal combustion +13.72‰ 4.57‰ 47 (Felix et al., 2012;Heaton and E,

1990)

Mobile sources −7.24‰ 7.77‰ 155 (Miller et al., 2017;Walters et al., 2015a;Walters et al., 2015b)

Biomass burning +1.04‰ 4.13‰ 24 (Fibiger and Hastings, 2016)

Microbial processes −35.4‰ 10.7‰ 26

(Felix and Elliott, 2014;Li and Wang, 2008;Miller et al., 2018;Yu

and Elliott, 2017)

Table S5. Correlation coefficients between meteorological factors and PM2.5, NO3− in the five

cities; absolute values above 0.3 are marked in bold.

　 Beijing Shanghai Wuhan

Parameter PM2.5 NO3− PM2.5 NO3

− PM2.5 NO3−

Temperature -0.41 -0.39 -0.58 -0.44 -0.65 -0.66Relative humidity 0.22 0.31 0.19 0.13 0.15 0.28

Wind Speed -0.45 -0.33 -0.47 -0.37 -0.52 -0.38　 Chendgu Guangzhou

Parameter PM2.5 NO3− PM2.5 NO3

−

Temperature -0.60 -0.61 -0.51 -0.55Relative humidity 0.08 0.10 0.14 0.16

Wind Speed -0.43 -0.55 -0.48 -0.36

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Table S6. The linear regression characteristics of PM2.5 concentration and NO3− concentration,

mean test feature of NO3−/PM2.5 ratio between HP stage and NHP stage in the targeted cities

(For some special parameters, three decimal places are kept).

City

PM2.5 concentration & NO3− concentration NO3

−/PM2.5 ratio

NHP stage HP stageNHP stage HP stage pr p r p

Beijing 0.16 0.436 0.55 0.028 0.069 0.193 0.000Shanghai 0.26 0.068 0.61 0.001 0.104 0.234 0.000Wuhan 0.04 0.846 0.73 0.003 0.082 0.142 0.018

Chengdu 0.04 0.822 0.58 0.001 0.083 0.114 0.049Guangzhou 0.11 0.641 0.54 0.032 0.049 0.183 0.000

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