Chemical characterization and source apportionment of PM2.5 aerosols in a megacity of Southeast China

Atmospheric Research 181 (2016) 288–299

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Chemical characterization and source apportionment of PM2.5 aerosols in a megacity of Southeast China Huiming Li a, Qin'geng Wang a,b, Meng Yang b,c, Fengying Li b,c, Jinhua Wang a, Yixuan Sun a, Cheng Wang a, Hongfei Wu a, Xin Qian a,b,⁎ a

State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, China c Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China b

a r t i c l e

i n f o

Article history: Received 24 March 2016 Received in revised form 5 July 2016 Accepted 5 July 2016 Available online 06 July 2016 Keywords: PM2.5 Source apportionment Positive matrix factorization Chemical mass closure Haze-fog episode

a b s t r a c t PM2.5 aerosol samples were collected during a haze–fog event in winter, as well as in spring, summer, and fall in 2013 within an urban area (Xianlin) and city center area (Gulou) of Nanjing, a megacity of SE China. The PM2.5 showed typical seasonality of waxing in winter and waning in summer or fall with annual average concentrations − + − + of 145 and 139 μg/m3 in Xianlin and Gulou, respectively. Concentrations of SO2− 4 , NO3 , NH4 , Cl , and K , EC, OC, secondary organic carbon, and most elements were elevated in winter. The sulfur oxidation ratio and concentra− tions of SO2− 4 and Cl were significantly higher in Xianlin than Gulou (p b 0.05), whereas the nitrogen oxidation − ratio and NO3 concentrations were significantly higher in Gulou than Xianlin (p b 0.05). A chemical mass closure construction was used to apportion PM2.5 fractions. Using the positive matrix factorization model, six source factors were identified as having contributed to PM2.5. These were secondary nitrate, road dust, sea salt and ship emissions, coal combustion, secondary sulfate, and the iron and steel industry, which contributed annual averages of 17.8 ± 15.1, 10.6 ± 9.53, 4.50 ± 3.28, 12.4 ± 9.82, 46.3 ± 14.4, and 8.42 ± 5.15%, respectively, to the PM2.5 mass in Xianlin, and 34.5 ± 16.2, 7.82 ± 7.21, 7.27 ± 5.61, 10.5 ± 9.35, 33.0 ± 16.6, and 7.00 ± 6.1%, respectively, in Gulou. Distinct seasonal patterns of the source factors in the two areas associated with the main chemical components were identified, which could be explained by various sources and meteorological conditions. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Atmospheric particulate matter (PM), are classified as group 1 contaminants by the International Agency for Research on Cancer (Sun et al., 2014). It has been reported that exposure to high concentrations of fine particulate matter (PM2.5, particles having an aerodynamic diameter of 2.5 μm or less) in ambient air can cause lung diseases, heart diseases, and premature death in humans (WHO, 2006; Janssen et al., 2013; Ostro et al., 2014). PM2.5 originates from natural and anthropogenic sources, and includes both primary and secondary particle species. Secondary particle formation occurs through gas-to-particle conversion by chemical transformation, nucleation, and condensation of gaseous precursors, like volatile organic compounds (VOCs) and sulfurous gases (Hallquist et al., 2009). The chemical constituents of PM2.5, such as water-soluble inorganic ions and carbonaceous species have ⁎ Corresponding author at: State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, China. E-mail address: [email protected] (X. Qian).

http://dx.doi.org/10.1016/j.atmosres.2016.07.005 0169-8095/© 2016 Elsevier B.V. All rights reserved.

attracted great attention because of their abundance in the atmosphere, their complex formation mechanisms and their multiple environmental effects (Yang et al., 2011; Bressi et al., 2013). Haze is defined as a phenomenon that reduces atmospheric visibility to b 10 km with relative humidity (RH) of b80%, whereas fog comprises fine water droplets and/or ice crystals suspended in the air near the Earth's surface (visibility ≤ 10 km, RH N 95%) (Wu et al., 2009). The phenomenon that reduces visibility to b 10 km when RH is 80–95% is called mist (Wu et al., 2009). Periods of haze, fog, and mist can alternate frequently and these mixed periods are referred to as haze–fog (HF) episodes. Several severe HF periods in China have been studied to establish their chemical characteristics such as the major aerosol components, including sulfate, nitrate, ammonium, and organic aerosols of particulate matter (PM) (Kang et al., 2013; Lin et al., 2014; Gao et al., 2015; Tan et al., 2016). However, information is limited on the seasonal source patterns of PM2.5 particularly during HF episodes in different urban areas of China. Positive matrix factorization (PMF), developed by Paatero and Tapper (1994), is a receptor model that has been applied widely in

H. Li et al. / Atmospheric Research 181 (2016) 288–299

many studies for source apportionment using PM data (Polissar et al., 2001; Bove et al., 2014; Wang et al., 2015). PMF has the advantage of scaling each data point individually using an uncertainty matrix and it imposes non-negative constraints in the factor computational process. Adjustment of the corresponding error estimates allows it to handle both missing data and those data that fall below the detection limit. Moreover, the implementation of non-negative constraints provides factors that are physically more explainable (Paatero and Tapper, 1994). The input species for PMF are typically inorganic ions, carbonaceous compounds, and trace elements, and many tracer compounds specific to particular source types have been identified in many studies (Polissar et al., 2001; Ogulei et al., 2005; Bullock et al., 2008; Bove et al., 2014). In this study, PM2.5 samples were collected from suburban areas and city center areas in Nanjing during a serious HF episode that occurred in the winter of 2013, as well as on non-HF days in spring, summer, and fall. There were three primary objectives in this study: (1) to identify the chemical species in the PM2.5 samples collected in the four seasons in the two areas, including water-soluble inorganic ions, carbonaceous fractions, and trace elements; (2) to quantify the source contributions to PM2.5 using a PMF model coupled with a chemical mass closure construction; and (3) to analyze the regional differences and seasonal variations of the chemical components and source contributions.

289

were marked in Fig. 1 and used for the PM2.5 source analysis in Section. 3.4. 2.2. Sample collection

2. Material and methods

PM2.5 samples were collected onto quartz microfiber filters (Whatman Inc., UK) using high-volume PM samplers manufactured by Tisch Environmental, Inc. (model TE-6070VFC, Cleves, OH, USA), with a flow rate of 1.13 m3 /min. Daytime (8:00 a.m. to 6:00 p.m.) and nighttime (7:00 p.m. to 7:00 a.m.) sampling was conducted from 2 to 12 December 2013, between 24 April to 5 May (spring), 11–28 August (summer), and 10–18 October (fall). To ensure the samples were representative, we avoided sampling during rainy or windy weather in the four seasons. The sampling was conducted simultaneously at the two sites, yielding a total of 140 samples (70 for each site) for analysis. Before sampling, quartz microfiber filters were pre-heated at 500 °C for 6 h and conditioned in a desiccator at 25 °C and 40% relative humidity for 48 h. Filters were then weighed using a microbalance (Mettler Toledo, Switzerland), with a sensitivity of ± 1 μg. After aerosol sampling, all filters were conditioned in a desiccator for 48 h, then reweighed to determine their PM2.5 mass. Each filter was weighed at least four times before and after sampling on consecutive days, with differences among replicate weights mostly b 10 μg for each sample. Hourly meteorological data and gaseous pollutant concentrations were recorded simultaneously at air quality monitoring stations at the sampling sites.

2.1. Sampling site

2.3. Chemical analysis

Nanjing (32°03′N, 118°46′E), the second largest city in the Yangtze River Delta (YRD) region, is the industrial production base and the main transport hub for southeastern China. In 2014, Nanjing had a total area of 6587.02 km2, with a population of N8.2 million. PM2.5 samples were collected from the Xianlin and Gulou campuses of Nanjing University (Fig. 1). Xianlin campus is in the northern suburbs near the industrial zones of Nanjing. Urban area has mountains on three sides and atmospheric pollutants from the northern industrial zones will not diffuse effectively when atmospheric conditions are stable. The air quality in Gulou Campus, which is located in the densely populated city center is thus affected by high levels of traffic emissions and the accumulation from industrial pollutants emitted in suburban areas. Locations of the important enterprises with large air pollutant emissions

2.3.1. Ions A quarter of each filter was cut into pieces and extracted in 10-ml Milli-Q water under ultrasonic agitation for 1 h. The extracted solutions were filtered (0.25 μm, PTFE; Whatman) for analysis. The water-soluble ion concentrations were determined using ion chromatography (IC; Dionex ICS-1100, Dionex Corp., Sunnyvale, CA, USA). Cation (NH+ 4 , Na+, K+, Mg2 +, Ca2 +) concentrations were determined using an IonPac CS12A column (4 × 250 mm), with 20 mM methane sulfonate − as an eluent. The anions (SO24 −, NO− 3 , Cl ) were separated using an IonPac AS22 column (4 × 250 mm), with a solution of 4.5 mM Na2CO3 + 1.4 mM NaHCO3 as an eluent. The detection limits were − − 0.09, 0.08, 0.02 mg/L for SO2− 4 , NO3 and Cl , and 0.05 mg/l for all cat+ + + 2+ 2+ ions (NH4 , Na , K , Mg , Ca ).

Fig. 1. Location of the sampling sites.

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H. Li et al. / Atmospheric Research 181 (2016) 288–299

Fig. 2. PM2.5 concentrations during the sampling periods in Xianlin and Gulou.

2.3.2. Carbon An area of 0.526 cm2, punched from each quartz filter, was analyzed for eight carbon fractions, following the IMPROVE_A thermal/optical reflectance (TOR) protocol on a DRI model 2001 carbon analyzer (Atmoslytic Inc., Calabasas, CA, USA). This protocol has been reported previously (Cao et al., 2003). 2.3.3. Trace elements Trace elements in one-eighth of PM2.5 samples were digested with a mixture of HClO4, HNO3 and HF. Elements in these digestion solutions (Al, As, Ba, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Sr, Ti, V, and Zn) were measured using an inductively coupled plasma atomic emission

spectrometer (Optima 5300, Perkin Elmer, Waltham, MA, USA) and an inductively coupled plasma mass spectrometer (Elan 9000, Perkin Elmer). The detection methods can be found elsewhere (Li et al., 2016). Quality assurance and control were ensured through the analysis of the certified reference material SRM 1649a (urban particulate matter). Recovery was within ± 10% of the certified values for most elements. 2.4. PMF model PMF is a multivariate receptor model that decomposes a matrix of sample data into two matrices: factor contributions and factor profiles.

Fig. 3. The 72-h backward air trajectories arriving at Gulou Campus of Nanjing University (32.05°N, 118.77°E) at a height of 1000 m AGL during the four seasons (2–5 May, 13–16 August, 11–14 October 2013 and 5–8 December of 2013).

The daily PM2.5 concentrations and temporal trends of meteorological factors recorded by the Nanjing Meteorological Bureau are shown in Figs. 2 and S1, respectively. The average PM2.5 concentrations found in Xianlin decreased in the order of 250 μg/m3 (range: 85–518 μg/m3) in winter, 123 μg/m3 (range: 85–193 μg/m3) in spring, 89 μg/m3 in fall (range: 50–142 μg/m3), and 80 μg/m3 in summer (range: 51–93 μg/m3), whereas in Gulou they decreased in the order of 243 μg/m3 (range: 67–431 μg/m3) in winter, 110 μg/m3 (range: 64–157 μg/m3) in spring, 86 μg/m3 (range: 66–111 μg/m3) in summer, and 77 μg/m3 (range: 41–123 μg/m3) in fall. The PM2.5 concentrations found in Xianlin were significantly higher than Gulou during spring and fall (p b 0.05), whereas no significant differences in PM2.5 concentrations were found between the two sites during summer and winter (p N 0.05). About 86% and 81% of the observed daily PM2.5 concentrations at

12.6 ± 8.41 7.34 ± 2.80 3.78 ± 1.92 1.79 ± 0.74 0.78 ± 0.29 0.70 ± 0.37 0.062 ± 0.016 1.04 ± 0.48 14.7 ± 5.13 9.64 ± 2.20 4.68 ± 1.11 2.66 ± 1.39 1.07 ± 0.41 0.89 ± 0.31 0.086 ± 0.024 1.38 ± 0.45 0.22 ± 0.096 0.11 ± 0.031 18.4 ± 5.20 8.11 ± 3.21 5.37 ± 1.20 2.61 ± 0.67 1.46 ± 0.24 0.34 ± 0.097 0.087 ± 0.018 1.02 ± 0.22 21.3 ± 4.67 9.61 ± 4.00 6.27 ± 1.22 3.07 ± 0.76 1.72 ± 0.32 0.40 ± 0.13 0.10 ± 0.026 1.21 ± 0.29 0.29 ± 0.061 0.16 ± 0.084 23.1 ± 5.64 11.7 ± 6.45 6.43 ± 1.31 2.05 ± 1.37 1.08 ± 0.089 0.70 ± 0.33 0.10 ± 0.027 1.70 ± 0.68 21.1 ± 3.12 10.5 ± 4.71 5.93 ± 0.84 1.79 ± 0.89 1.02 ± 0.19 0.62 ± 0.20 0.097 ± 0.026 1.61 ± 0.59 0.26 ± 0.063 0.15 ± 0.11 25.4 ± 19.7 17.2 ± 16.4 6.80 ± 4.43 2.87 ± 1.79 1.28 ± 0.53 0.76 ± 0.58 0.098 ± 0.053 1.46 ± 0.66 18.0 ± 4.99 11.4 ± 4.01 5.14 ± 1.31 2.37 ± 1.13 1.12 ± 0.46 0.59 ± 0.28 0.086 ± 0.033 1.30 ± 0.55 0.27 ± 0.11 0.18 ± 0.10

Fall Summer Spring Winter

52.3 ± 35.7 32.6 ± 28.9 7.24 ± 3.39 7.38 ± 4.43 2.07 ± 1.09 0.93 ± 0.57 0.12 ± 0.088 1.87 ± 0.65 19.6 ± 2.92 10.81 ± 4.54 3.13 ± 0.69 2.89 ± 1.08 0.83 ± 0.24 0.36 ± 0.18 0.065 ± 0.030 1.00 ± 0.58 0.39 ± 0.19 0.21 ± 0.15 Cl (μg/m ) Na+ (μg/m3) K+ (μg/m3) Mg2+ (μg/m3) Ca2+ (μg/m3) SO2− 4 /PM2.5 (%) NO− 3 /PM2.5 (%) NH+ 4 /PM2.5 (%) Cl−/PM2.5 (%) Na+/PM2.5 (%) K+/PM2.5 (%) Mg2+/PM2.5 (%) Ca2+/PM2.5 (%) SOR NOR

3.1. PM2.5 concentrations

Gulou

3. Results and discussion

Annual

The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT-4.8) model developed by the US National Oceanic and Air Administration Air Resources Laboratory was used to calculate 72-hour air-mass back trajectories using 3-hourly archived meteorological data provided by the US National Centers for Environmental Prediction global data assimilation system (Draxler and Rolph, 2010). The back trajectories were performed in Gulou Campus (city center areas) for each sampling period at a height of 1000 m above ground level (AGL).

Fall

2.5. Back trajectory analysis

Summer

where sij is the uncertainty of the j-th species concentration of the i-th sample, n is the number of samples, and m is the number of species. The principles of PMF have been described in detail in the PMF 5.0 User Guide (USEPA, 2014). The default robust mode was run to reduce the effects of extreme values on the PMF solution. Species that had a signal-to-noise ratio of 0.2–2.0 and those that had below the detection limit values of N50% were considered as weak variables for the PMF model. The element Co was considered “weak,” meaning that the PMF tripled the uncertainties. The PMF solutions were explored for multiple values of the Fpeak coefficient (between −1.0 and +1.0) to control rotational ambiguity (Paatero et al., 2002). Different source numbers were tested by applying a trial and error method to determine the optimal solutions.

Spring

ð2Þ

Annual

sij

Xianlin

i¼1 j¼1

Ions

Q¼

Xp " #2 n X m X xij − k¼1 gik f kj

Table 1 Major water-soluble ion concentrations (μg/m3) and their proportions of PM2.5 (%), sulfur oxidation ratio (SOR), and nitrogen oxidation ratio (NOR) in all four seasons in Xianlin and Gulou.

where xij is the species concentration of j-th for the i-th sample, gik is the contribution of the k-th factor to the i-th sample, and fkj is the j-th species fraction from the k-th source, eij is the residual associated with the jth species concentration measured in the i-th sample, and p is the total number of independent sources. PMF can provide a solution to minimize an object function Q based on the uncertainties of each species measured. The values of gik and fkj are adjusted until a minimum value of Q for a given p is found. Q is defined as:

17.3 ± 8.19 5.62 ± 3.23 3.59 ± 1.68 2.24 ± 1.07 0.88 ± 0.17 0.64 ± 0.25 0.069 ± 0.013 1.53 ± 0.65 18.8 ± 4.00 6.14 ± 2.51 3.90 ± 0.94 2.84 ± 1.66 1.03 ± 0.15 0.77 ± 0.36 0.082 ± 0.022 1.68 ± 0.32 0.32 ± 0.081 0.11 ± 0.058

Winter

ð1Þ

17.7 ± 3.88 3.33 ± 1.55 4.46 ± 1.27 2.07 ± 1.16 1.12 ± 0.32 0.36 ± 0.11 0.075 ± 0.015 1.31 ± 0.48 22.1 ± 2.25 4.10 ± 1.58 5.58 ± 1.09 2.83 ± 2.37 1.48 ± 0.55 0.45 ± 0.12 0.097 ± 0.026 1.69 ± 0.59 0.35 ± 0.099 0.10 ± 0.055

g ik  f kj þ eij

k¼1

25.9 ± 9.19 7.26 ± 3.11 6.19 ± 1.86 2.10 ± 0.66 0.99 ± 0.15 0.77 ± 0.42 0.087 ± 0.019 2.18 ± 0.51 20.8 ± 3.89 5.83 ± 1.80 5.02 ± 0.84 1.73 ± 0.45 0.83 ± 0.17 0.61 ± 0.20 0.073 ± 0.014 1.80 ± 0.34 0.27 ± 0.061 0.12 ± 0.041

p X

30.3 ± 25.7 14.0 ± 20.6 5.53 ± 2.72 3.78 ± 3.55 1.33 ± 0.81 0.69 ± 0.45 0.091 ± 0.055 1.73 ± 0.66 20.3 ± 3.46 7.07 ± 3.99 4.29 ± 1.31 2.60 ± 1.56 1.02 ± 0.41 0.53 ± 0.27 0.078 ± 0.054 1.49 ± 0.58 0.34 ± 0.13 0.14 ± 0.11

xij ¼

SO2− (μg/m3) 4 3 NO− 3 (μg/m ) 3 NH+ 4 (μg/m ) − 3

With measured source profile information and emission inventories, the source type can be determined. The EPA PMF 5.0 program was used for the receptor modeling in this work. The bilinear factor analytic model can be expressed as:

291 41.4 ± 27.2 34.9 ± 18.7 10.3 ± 6.16 4.44 ± 2.10 1.66 ± 0.64 1.15 ± 0.79 0.13 ± 0.079 1.92 ± 0.60 15.7 ± 3.37 14.8 ± 2.17 4.07 ± 0.63 2.08 ± 1.05 0.81 ± 0.28 0.50 ± 0.19 0.067 ± 0.039 1.08 ± 0.63 0.31 ± 0.15 0.25 ± 0.11

H. Li et al. / Atmospheric Research 181 (2016) 288–299

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H. Li et al. / Atmospheric Research 181 (2016) 288–299

Xianlin and Gulou, respectively, were beyond the Chinese National Ambient Air Quality Standard (NAAQS) limit (75 μg/m3). The prolonged haze episode in which samples were collected led to atmospheric pollution at the “serious pollution level”, the highest level defined in the relevant air pollution standards, from 4 to 8 December in Nanjing. The visibility on December 7 and 8 (defined as HF days with RH N 80%, according to Wu et al. (2009)) was b 200 m (Fig. S1). Furthermore, the highest PM2.5 concentration recorded on December 7 in Xianlin (518 μg/m3) was nearly seven times higher than the NAAQS limit (75 μg/m3). The surface wind speed (range: 2–12 km/h) and temperature (range: 3–10 °C) were typically low in winter which favors the accumulation of air pollution (Fig. S1). A cold front passed through the YRD region on 9 December, ending the long haze episode of serious air pollution. The 72-hour backward trajectories simulation was shown in Fig. 3. During spring, the air mass didn't go far at 1000 m height. The air masses at the three heights arriving at Nanjing during summertime were from the southeast and traveled over the Pacific Ocean. The air masses during fall came throughout a journey from Monggol, Liaoning province of China, Korea, and Shanghai to Nanjing. By contrast, the air mass didn't travel far and its transportation distances were b500 km during HF days, indicating the atmosphere conditions are very stable.

NO2 in the atmosphere (Ohta and Okita, 1990). In general, SOR and NOR both had higher values in winter and summer. As expected, SORs showed significant higher values in Xianlin than Gulou (p b 0.05), whereas NOR had significant higher values in Gulou than Xianlin (p b 0.05). This is perhaps because the chemical characterization of PM2.5 in Xianlin is affected by atmospheric pollutants from the northern industrial zone whereas in Gulou is mainly affected by the vehicle exhaust. Many studies have reported that the formation of nitrate NO− 3 depends on the temperature, atmospheric levels of NOX, and RH (Seinfeld and Pandis, 1998; Song et al., 2001; Heo et al., 2009). The correlations between SORs, NORs, and meteorological factors were calculated using two-tailed t-tests and both SOR and NOR were found to be correlated significantly with RH (correlation coefficient (r) of 0.516 for SOR (p b 0.01) and r = 0.422 for NOR (p b 0.01)). This indicates that the formation of these secondary pollutants was dominated by heterogeneous processes related to higher RH (McMurry and Wilson, 1983; Xu et al., 2012). Extremely high values in winter were also shown in the temporal trends of SOR and NOR (Fig. S2), particularly on December 7 and 8, 2013, which indicates that higher RH during HF episodes is conducive to the formation of these secondary pollutants. The NOR also had a significant negative correlation (r = − 0.357) with temperature (p b 0.01), consistent with higher NORs in winter.

3.2. Chemical composition of PM2.5 3.2.1. Water-soluble ions The mass concentrations of water-soluble ions, as well as their contributions to the PM2.5 concentrations, sulfur oxidation ratio (SOR), and nitrogen oxidation ratio (NOR) for this study are summarized in Table 1. − + Clearly, SO2− 4 , NO3 , and NH4 dominated the water-soluble inorganic species, accounting for 20.3 ± 3.46%, 7.07 ± 3.99%, and 4.29 ± 1.31%, respectively, of PM2.5 mass in Xianlin, and 18.0 ± 4.99%, 11.4 ± 4.01%, and 5.14 ± 1.31%, respectively, of PM2.5 mass in Gulou. Mg2+ had the lowest − concentration among all the detected ions. Concentrations of SO2− 4 , NO3 , − + , Cl , and K were highest in winter, followed by spring, whereas NH+ 4 Na+ and Mg2+ had higher concentrations in summer, which is related mainly to the transportation of sea salt particles from the Pacific Ocean (Fig. 3). Ca2+ had the highest concentrations in spring. Comparison of and Cl− (typical the two areas revealed that the concentrations of SO2− 4 tracers of coal combustion) and their contributions to PM2.5 mass were significantly higher in Xianlin than Gulou (p b 0.05), whereas the NO− 3 concentration (related to vehicle exhaust) and its contribution to PM2.5 mass was significantly higher in Gulou than Xianlin (p b 0.05). To evaluate the extent of atmospheric conversion of SO2 to SO2− 4 and NO2 to NO− 3 , the SOR and NOR were used (Ohta and Okita, 1990):   2− SOR ¼ nSO2− 4 = nSO4 þ nSO2 ðin molÞ   − NOR ¼ nNO− 3 = nNO3 þ nNO2 ðin molÞ

ð3Þ

3.2.2. Carbonaceous compounds As shown in Table 2, EC and OC all showed high average concentrations in winter compared with other seasons. There were no significant differences for the concentrations of EC and OC between the two areas. It is known that OC is composed of primary organic carbon (POC) and secondary organic carbon (SOC). The OC/EC ratio has long been recognized to be an indicator of relative contribution of primary and secondary organic aerosols (SOA) (Tao et al., 2014b). Though the OC/EC ratio is dependent on the sampling duration and analysis methods, in general, if the ratio of OC/EC exceeds 2.0–2.2, the secondary organic carbon (SOC) may be formed (Turpin and Huntzicker, 1995; Chow et al., 1996; Zhang et al., 2013). As shown in Table 2 and Fig. S3, the OC/EC ratio here suggests the possible presence of secondary organic aerosols in the atmosphere with annual OC/EC ratios of 3.01 in Xianlin and 3.06 in Gulou. In particular, higher OC/EC ratios were recorded during winter and summer compared with spring and fall. The secondary organic carbon (SOC) was estimated to understand its contribution to secondary organic aerosol formation. The minimum ratios of OC/EC were taken as representative of the particulate organic carbon (although they might be overestimates) and the OC above those ratios was taken as the SOC (Yuan et al., 2005; Lai et al., 2016), which was calculated from Eq. (5): ð5Þ

SOC ¼ OC−POC ¼ OC−EC  ðOC=ECÞ min

ð4Þ

SORs and NORs for the four sampling periods at two sites were all N0.10, which indicates there was photochemical oxidation of SO2 or

where SOC (μg/m3) is the concentration of SOC, OC (μg/m3) is the concentration of OC, and (OC/EC)min is the minimum OC/EC ratio during different sampling periods at each site. On average, SOC contributes 7.16 ± 4.69% and 7.78 ± 4.56% to the PM2.5 mass annually in Xianlin

Table 2 Seasonal distribution of carbonaceous species found in all four seasons in Xianlin and Gulou. Carbonaceous

EC (μg/m3) OC (μg/m3) SOC (μg/m3) EC/PM2.5 (%) OC/PM2.5 (%) OC/EC SOC/OC (%) SOC/PM2.5 (%)

Xianlin

Gulou

Annual

Spring

Summer

Fall

Winter

Annual

Spring

Summer

Fall

Winter

9.34 ± 5.44 27.8 ± 17.7 9.69 ± 8.43 7.00 ± 1.96 20.3 ± 3.89 3.07 ± 0.86 33.3 ± 17.1 7.16 ± 4.69

8.34 ± 1.88 23.1 ± 2.76 6.12 ± 3.06 6.87 ± 1.21 19.3 ± 2.83 2.86 ± 0.50 26.6 ± 13.4 5.29 ± 3.13

5.30 ± 1.22 18.9 ± 6.13 7.28 ± 5.55 6.68 ± 0.96 23.3 ± 4.46 3.62 ± 1.13 33.2 ± 20.1 8.53 ± 6.16

8.50 ± 3.92 19.2 ± 4.52 7.70 ± 3.64 9.31 ± 2.18 22.3 ± 2.90 2.55 ± 0.77 41.8 ± 19.3 9.70 ± 5.10

13.6 ± 7.04 43.8 ± 24.1 15.5 ± 12.0 5.66 ± 1.22 17.4 ± 2.11 3.18 ± 0.67 32.2 ± 13.8 5.69 ± 2.84

6.67 ± 1.91 18.0 ± 4.04 8.26 ± 5.04 6.12 ± 1.32 16.7 ± 3.50 2.94 ± 1.19 43.3 ± 19.6 7.78 ± 4.56

6.65 ± 2.46 19.7 ± 2.57 9.20 ± 3.80 7.69 ± 2.46 23.0 ± 1.98 3.23 ± 0.84 46.9 ± 18.4 10.9 ± 4.46

7.14 ± 3.64 15.8 ± 6.35 8.26 ± 4.24 8.96 ± 2.83 20.4 ± 3.37 2.58 ± 1.15 50.7 ± 22.4 10.9 ± 5.66

14.2 ± 8.98 46.0 ± 30.5 13.3 ± 11.5 5.45 ± 0.96 17.2 ± 4.31 3.14 ± 0.49 24.7 ± 11.1 4.62 ± 2.80

9.13 ± 6.43 26.7 ± 21.7 10.0 ± 7.59 6.92 ± 2.38 19.2 ± 4.24 2.99 ± 0.94 40.0 ± 20.4 8.21 ± 5.06

H. Li et al. / Atmospheric Research 181 (2016) 288–299

293

Table 3 Concentrations (ng/m3) of metal elements in PM2.5 over all four seasons in Xianlin and Gulou. Metal

Al As Ba Cd Co Cr Cu Fe Mn Mo Ni Pb Sr Ti V Zn

Xianlin

Gulou

Annual

Spring

Summer

Fall

Winter

Annual

Spring

Summer

Fall

Winter

867 ± 684 13.4 ± 10.3 21.9 ± 14.5 5.16 ± 3.18 0.76 ± 0.55 28.8 ± 10.3 70.2 ± 43.8 943 ± 411 74.0 ± 39.0 4.28 ± 2.34 16.0 ± 8.11 231 ± 130 10.9 ± 6.49 72.6 ± 24.8 5.35 ± 2.81 494 ± 239

808 ± 380 10.9 ± 4.19 14.3 ± 3.88 4.94 ± 1.15 0.52 ± 0.20 26.6 ± 6.71 68.9 ± 24.3 1048 ± 377 76.3 ± 26.3 4.36 ± 1.62 15.6 ± 5.05 216 ± 75.7 8.92 ± 2.46 64.8 ± 20.8 6.22 ± 3.02 510 ± 164

360 ± 195 6.39 ± 3.94 13.9 ± 5.01 2.89 ± 1.07 0.29 ± 0.25 22.3 ± 10.4 31.1 ± 9.61 652 ± 218 32.5 ± 13.0 3.27 ± 1.48 6.29 ± 2.08 104 ± 34.7 6.75 ± 3.18 56.3 ± 20.8 5.00 ± 1.67 225 ± 37.7

332 ± 159 7.44 ± 3.16 11.2 ± 4.41 3.94 ± 2.37 0.60 ± 0.56 28.6 ± 8.98 41.3 ± 15.1 1076 ± 538 63.0 ± 30.1 3.12 ± 2.74 14.4 ± 2.29 150 ± 55.3 9.27 ± 4.76 72.4 ± 15.1 1.88 ± 1.30 374 ± 118

1670 ± 543 24.7 ± 10.6 39.9 ± 8.24 7.86 ± 3.84 1.38 ± 0.22 35.2 ± 10.3 121 ± 34.7 979 ± 354 111 ± 30.4 5.80 ± 2.22 24.6 ± 6.22 393 ± 57.7 16.4 ± 7.76 88.0 ± 12.6 7.51 ± 1.22 764 ± 123

756 ± 694 13.5 ± 11.8 20.9 ± 16.8 4.85 ± 2.97 0.82 ± 0.65 30.1 ± 10.8 72.8 ± 45.9 861 ± 375 72.5 ± 41.8 4.41 ± 4.45 16.1 ± 8.48 237 ± 150 7.36 ± 5.31 71.7 ± 32.2 5.03 ± 2.42 500 ± 276

590 ± 250 10.6 ± 4.46 13.5 ± 4.38 4.35 ± 1.35 0.43 ± 0.15 26.1 ± 6.78 61.8 ± 18.6 873 ± 347 66.4 ± 21.3 3.19 ± 1.49 14.7 ± 4.22 205 ± 75.5 7.31 ± 3.86 55.7 ± 16.4 5.82 ± 2.77 463 ± 125

248 ± 84.8 6.21 ± 4.65 10.4 ± 2.43 2.84 ± 0.99 0.42 ± 0.24 22.4 ± 6.26 36.8 ± 12.1 504 ± 73.8 30.3 ± 7.47 4.08 ± 7.67 6.24 ± 1.01 95.6 ± 20.7 4.64 ± 1.64 44.7 ± 7.00 3.81 ± 1.76 232 ± 78.5

433 ± 182 9.09 ± 4.75 12.1 ± 4.58 3.72 ± 1.84 0.82 ± 0.77 30.0 ± 9.17 45.7 ± 15.5 880 ± 294 62.9 ± 28.8 4.30 ± 2.23 15.3 ± 3.94 149 ± 45.4 5.80 ± 2.14 67.2 ± 14.8 3.67 ± 2.67 361 ± 135

1510 ± 772 24.0 ± 15.4 42.0 ± 11.7 7.49 ± 3.62 1.49 ± 0.44 39.8 ± 10.3 127 ± 41.6 1100 ± 390 115 ± 38.5 5.95 ± 1.53 25.2 ± 7.20 427 ± 92.0 10.5 ± 7.68 109 ± 25.6 6.34 ± 1.32 824 ± 203

and Gulou, respectively. SOC concentrations were also highest in winter, whereas SOC contributed more to OC and PM2.5 mass in fall and summer compared with spring and winter. The SOC/OC and SOC/PM2.5 ratios were both significantly higher in Gulou than Xianlin (p b 0.05), indicating that the unique topography surrounding the city center areas is conducive to air pollutant accumulation and thus, to the formation of SOC.

3.2.3. Metal elements As shown in Table 3, in general, Zn, Pb, Mn, and Cu were the most abundant trace elements in the PM2.5 collected in Nanjing, while Cd, Co, Mo, and V had lower concentrations (Li et al., 2015, 2016). Nearly all elements had the highest mean concentrations in winter (except Fe in Xianlin, which had the highest mean concentration in fall), whereas most metals showed the lowest mean concentrations in summer (except Al, Ba, Mo, and V in Xianlin, which had the lowest mean concentrations in fall). Fe and Sr showed significantly higher concentrations in Xianlin than Gulou (p b 0.05). The average concentrations of As found in the four seasons were all higher than the NAAQS limit of 6 ng/m3. The highest As concentrations, found in winter, were 7.84 and 9.01 times greater than this limit in Xianlin and Gulou, respectively. In contrast, only the average Cd concentrations in the PM2.5 samples collected in winter exceeded the NAAQS and WHO limit of 5 ng/m3 . The average Ni concentration found in Gulou in winter was also slightly above the WHO limit of 25 ng/m3. The concentrations over the four seasons for Pb were all lower than the NAAQS and WHO limit of 500 ng/m3. Similarly, the average concentrations of Mn and V were both lower than the WHO limits (150 ng/m3 for Mn; 1000 ng/m3 for V). Enrichment factors (EF) based on the normalization of a given metal against a conservative reference element has been widely used to

distinguish between anthropogenic influences and natural background contents (Zheng et al., 2004; Betha et al., 2014). In this study, EF values were calculated with respect to Ti, according to     EF ¼ Cn =Cref sample = Bn =Bref crust ;

ð6Þ

where (Cn/Cref)sample and (Bn/Bref)crust were the concentration ratios of the target metal to the reference element Ti in the samples and in the continental crust (Taylor and Mclennan, 1995), respectively. In this study, EF b 10 indicates that the target element is not enriched. An EF N 10 indicates that the element has anthropogenic sources. For 10 b EF b 100, target elements are moderately enriched, while EF N 100 indicates elements are anomalously enriched. Fig. S4 showed that Al, Ba, Ca, Co, Fe, K, Mg, Mn, Na, Sr and V had average EF values below 10, indicating that these metals were not enriched. Cr and Ni were moderately enriched (10 b EF b 100), while As, Cd, Cu, Mo, Pb and Zn (with EF N 100) were anomalously enriched. Generally, Al and Sr showed significant higher EF values in Xianlin than Gulou. Many metals showed higher EF values in winter (i.e., Al, As, Ba, Cd, Co, Cu, Mn, Ni, Pb, Sr and Zn in Xianlin, whereas Al, As, Ba, Co, Cu and Pb in Gulou, respectively) and lower EF values in autumn or summer. 3.3. Chemical mass closure An aerosol chemical mass closure was constructed on a seasonal basis by considering secondary inorganic aerosol (SIA, including the + sum of SO24 −, NO− 3 , NH4 ), organic matter (OM), EC, mineral dust (MD), sea salt (SS), and trace elements (TE). A factor of 1.8 which was usually used to convert OC to POM in Chinese cities (Wang et al.,

Fig. 4. Average compositional fractions (%) of PM2.5 in all four seasons in Xianlin (a) and Gulou (b).

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Fig. 5. Six source profiles (bars) resolved from the PMF model (units: μg μg−1). Also shown are the contribution percentages (black dots) from each source factor.

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2006a; Wang et al., 2006b; Guinot et al., 2007; Tao et al., 2014a) was applied in this study. The mineral dust fraction was calculated as: ½Mineral ¼ Al=0:07;

ð7Þ

where 0.07 is the average Al content (7%) in dust aerosols reported by Zhang et al. (2003). Similar estimates have been used previously (Hsu et al., 2010; Zhang et al., 2013). The marine contribution was calculated, assuming that soluble Na+ in aerosol samples comes solely from sea salt, as the sum of Na+ concentration and fractions of concentrations of Cl−, Mg2+, K+, Ca2+ and SO2− 4 based at a standard sea water composition (Seinfeld and Pandis, 1998). h i  i    h − ½SS ¼ Naþ þ ½ss‐Cl  þ ss‐Mg2þ þ ss‐Kþ þ ss‐Ca2þ h i þ ss‐SO2− 4

ð8Þ

where ss-Cl− is calculated as total [Na+] times 1.8, ss-Mg2 + as total [Na+] times 0.12, ss- K+ as total [Na+] times 0.036, ss-Ca2 + as total as total [Na+] times 0.252. In addition, [Na+] times 0.038, and ss-SO2− 4 2− from total SO2− non-SO4 was calculated by subtracting ss-SO2− 4 4 . Relative compositional contributions to the PM2.5 mass are shown in Fig. 4. The annual averages of SIA, OM, EC, SS, MD, and TE accounted for 26.9%, 37.3%, 7.11%, 3.44%, 7.35%, and 1.59% and 30.1%, 35.6%, 7.15%, 3.66%, 6.88%, and 1.56% of the PM2.5 mass in Xianlin and Gulou, respectively. The unaccounted mass is 16.3% and 15.0% in Xianlin and Gulou, respectively, which could be attributable to water content and inadequate estimation of crustal components. In summer, OM and SS had slightly higher contributions and MD had a lower contribution to the PM2.5 mass than in other seasons. In fall, EC and TE both had higher contributions and SIA had a lower contribution to the PM2.5 mass than in other seasons. Except for SIA, which showed higher contributions in Gulou than Xianlin, other components displayed no significant differences between the two areas. 3.4. Source apportionment using PMF models In the present study, 26 chemical components were used for the − + − + + 2+ , Ca2 +, Al, PMF model: OC, EC, SO2− 4 , NO3 , Cl , NH4 , Na , K , Mg As, Ba, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Sr, Ti, V, and Zn. Twenty runs were performed for each factor and the lowest value of Qrobust was 11,334 with a Qrobust/Qtrue ratio of 0.92. Six appropriate source factors and a value of Fpeak = 0.0 with a clear profile and physical meaning were deduced. Fig. S5 showed the estimated PM2.5 concentrations deduced from the PMF agree well with the observed PM2.5 concentrations (slope: 0.969, R2 = 0.982). The source profiles together with the relative contributions of the individual sources to the sum of the species are shown in Fig. 5. The

295

annual and seasonal averages of the absolute (μg/m3) and fractional (%) contributions from each source are summarized in Table 4, based on their daily simulated values. The modeled time series of the contributions (μg/m3) from each source was compared with the observed time series of certain species, which could represent the respective sources (Fig. 6). As expected, relatively high correlation coefficients were obtained between the source contributions and the concentrations of the corresponding species, except for the obvious seasonal source, i.e., sea salt and ship emissions, which showed R2 values that were relatively lower. This suggests that the PMF results might be reasonable. The first source is secondary nitrate, characterized by high NO− 3 concentrations. Cu, Zn, Pb, Ni and Cd also showed high loadings for this source. Secondary nitrate is mainly formed from the oxidation of precursor nitrogen oxides (NOX), which are mainly emitted from vehicle exhausts, coal combustion, and biomass burning (Tao et al., 2014a; Meng et al., 2016). Secondary nitrate accounted for 17.8 ± 15.1% and 34.5 ± 16.2% of the PM2.5 mass in Xianlin and Gulou, respectively. Contributions from this source were higher in winter (23.2 ± 20.9% and 43.1 ± 16.6% in Xianlin and Gulou, respectively) than in other seasons, consistent with the seasonal patterns of NO− 3 concentrations (Fig. 6). The contributions from secondary nitrate were higher in Gulou, which is the area with the greater quantity of vehicle exhaust emissions. The second source is categorized as road dust resuspension caused by wind-blown soils, urban traffic, and urban construction activities, which is characterized by elevated levels of Al, Ba, Sr, Co, Pb, Cu, V, Zn, and Ti. The EF values of Al, Sr, Co, and V were all b10, indicative of the dominance of a mineral of natural origin. Ba, Cu, and Zn can originate from tire abrasion, brake linings, lubricants, and corrosion of vehicular parts (Fernάndez Espinosa and Ternero Rodríguez, 2004; Duan and Tan, 2013). Pb is derived from the use of leaded gasoline (Li et al., 2013). Road dust accounted for 10.6 ± 9.53% and 7.82 ± 7.21% of the PM2.5 mass in Xianlin and Gulou, respectively. The two areas had small variations in the contributions from this source. Road dust contributes more in winter, which might be attributable to the low surface wind speed in winter, whereas it has the lowest contribution in summer because increased precipitation leads to the removal of dust particulates from the atmosphere. The third source comprises the mixed sea salt and ship emissions, characterized by high Na+, V, and Mg2 + concentrations. This profile contained higher contributions from Na+ and Mg2 +, which were assigned to the sea salt source (Wang et al., 2015). Nanjing belongs to the northern subtropical monsoon climatic zone and its prevailing wind is normally from the southeast in summer and from the northwest in winter. Although Nanjing is not a coastal city, it is possible that the air masses that arrive from the southeast have traveled over the Pacific Ocean, particularly in summer (Fig. 3). In addition, this source also included a contribution from V and Mo, the tracer of heavy oil, which is always used as the energy source in shipping (Chow and Watson, 2002;

Table 4 Contributions from the six identified sources of PM2.5 in Nanjing. Sources

Xianlin

3

μg/m % Road dust μg/m3 % Sea salt and ship emissions μg/m3 % Coal combustion μg/m3 % Secondary sulfate μg/m3 % Iron and steel industry μg/m3 % Secondary nitrate

Gulou

Annual

Spring

Summer

Autumn

Winter

Annual

Spring

Summer

Autumn

Winter

35.7 ± 54.2 17.8 ± 15.1 13.0 ± 10.6 10.6 ± 9.53 6.01 ± 5.37 4.50 ± 3.28 22.9 ± 31.4 12.4 ± 9.82 56.0 ± 19.6 46.3 ± 14.4 10.1 ± 5.82 8.42 ± 5.15

24.6 ± 19.0 18.7 ± 12.0 12.0 ± 4.92 10.2 ± 4.45 3.77 ± 1.34 3.30 ± 1.36 9.21 ± 6.82 7.48 ± 3.68 60.3 ± 10.1 50.9 ± 8.69 11.4 ± 4.63 9.39 ± 2.90

10.3 ± 9.43 12.6 ± 11.6 6.54 ± 3.77 6.29 ± 3.48 7.02 ± 3.06 8.78 ± 3.81 4.04 ± 1.53 5.08 ± 2.29 49.1 ± 12.3 59.4 ± 8.55 5.06 ± 1.88 6.21 ± 2.14

15.7 ± 15.3 14.7 ± 9.24 5.37 ± 3.01 7.84 ± 3.76 2.97 ± 1.15 3.41 ± 1.50 10.6 ± 8.36 13.4 ± 10.6 43.3 ± 15.4 47.4 ± 9.04 13.2 ± 6.42 14.9 ± 5.79

76.8 ± 80.6 23.2 ± 20.9 24.0 ± 11.6 15.9 ± 14.7 9.13 ± 8.01 3.06 ± 2.01 55.6 ± 38.8 20.7 ± 9.49 67.2 ± 24.7 32.5 ± 13.1 10.5 ± 5.99 4.63 ± 1.72

54.5 ± 55.5 34.5 ± 16.2 8.84 ± 7.45 7.82 ± 7.21 7.08 ± 4.32 7.27 ± 5.61 18.3 ± 24.0 10.5 ± 9.35 44.4 ± 33.2 33.0 ± 16.6 7.02 ± 4.92 7.00 ± 6.10

35.8 ± 22.3 30.3 ± 15.1 8.53 ± 5.96 8.11 ± 5.90 6.63 ± 1.64 6.18 ± 2.22 8.79 ± 10.38 6.94 ± 4.89 44.3 ± 10.9 40.8 ± 11.9 8.32 ± 3.31 7.62 ± 3.18

27.8 ± 14.3 31.1 ± 12.8 2.71 ± 2.32 3.20 ± 2.74 11.1 ± 4.01 13.0 ± 5.25 2.40 ± 1.75 2.71 ± 1.94 40.7 ± 11.9 46.6 ± 11.9 2.98 ± 1.75 3.46 ± 2.03

27.8 ± 20.6 29.2 ± 16.6 5.47 ± 3.32 8.54 ± 6.94 4.23 ± 3.48 6.45 ± 6.31 8.13 ± 5.74 12.1 ± 9.28 26.9 ± 21.5 29.2 ± 13.8 10.5 ± 5.53 13.5 ± 8.01

107 ± 71.0 43.1 ± 16.6 16.0 ± 7.39 10.4 ± 9.07 6.59 ± 4.51 4.51 ± 4.17 44.3 ± 27.3 17.5 ± 8.96 60.1 ± 51.1 20.1 ± 14.1 6.50 ± 4.94 4.44 ± 4.22

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Fig. 6. Time series of daily contributions from each identified source (continuous line) and specific species (red dots for Xianlin and blue dots for Gulou), and their relationship during the sampling periods in Xianlin (a, c, e, g, i, and k) and Gulou (b, d, f, h, j, and l).

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Yuan et al., 2006). The freight volume transported through the Yangtze River waterway ranks the highest in China's inland water transport system. Thus, the contribution from ship emissions and sea salt to the PMbound chemicals cannot be ignored. It is reported that the ratio of V/Ni varied from 0.5 to 3.5 for domestic heavy fuel oil in China (Zhao et al., 2013). However, the average ratios of V/Ni were relatively lower with 0.42 ± 0.36 in Xianlin and 0.37 ± 0.24 in Gulou, indicating the presence of Ni-rich atmospheric pollution sources. This factor contributes 4.50 ± 3.28% and 7.27 ± 5.61% to the PM2.5 mass in Xianlin and Gulou, respectively. Wang et al. (2015) also obtained sea salt and ship emission factors using a PMF model, and found they contributed 7% of the total OC of the PM2.5 in a case study in Dongguan, which is located in the Pearl River Delta, China. This source contributes most to the PM2.5 mass in summer, whereas it is the lowest in winter. Gulou showed higher contributions from this source than Xianlin. The fourth source is coal combustion, which is characterized by high concentrations of Cl − and As. The presence of chloride in coals and fine particles from coal combustion has been documented in many papers (Huggins and Huffman, 1995; Yudovich and Ketris, 2006; Wang et al., 2015). This source accounted for 12.4 ± 9.82% and 10.5 ± 9.35% of the PM2.5 mass in Xianlin and Gulou, respectively, and it contributes most in winter and fall. Coal combustion has been identified as one of the most important sources for airborne PM in many cities of China (Song et al., 2006; Geng et al., 2013; Tao et al., 2014a; Liu et al., 2016). According to the Nanjing Statistical Yearbook 2015, the total coal consumption in Nanjing was about 27,489 million tons in 2014, which accounted for 76% of the total energy consumption. There are many various coal-fired plants and large coal-consuming plants such as petrochemical corporations and thermal power plants in the north of the city (Fig. 1). As expected, this source contributed more in Xianlin, which is nearer than Gulou to the northern industrial zones. The fifth source is secondary sulfate, characterized by high SO2− 4 and NH+ 4 concentrations. This source accounted for 46.3 ± 14.4% and 33.0 ± 16.6% of the PM2.5 mass in Xianlin and Gulou, respectively. Secondary sulfate is formed by the oxidation of precursor SO2 derived from the combustion of sulfur-containing fuels. Gaseous NH3 emitted mainly from the agricultural sector (most notably animal manure and fertilizer application) and non-agricultural NH3 sources (e.g., coal combustion and waste incineration, sewage, and landfill) (Chang et al., 2012) and the reaction to form NH+ 4 aerosols, allows its long-range transportation in the particulate phase from rural sources to urban receptors. Although secondary sulfate showed the highest concentrations (μg/m3) in winter, which well resembles the long-term concentrations of SO2− 4 (Fig. 6), its contributions to PM2.5 were highest in summer and lowest in winter. There are other sources with greater contributions in winter, such as secondary nitrate and coal combustion. Its high contribution in summer might reflect the oxidation of gaseous SO2 to SO2− 4 , promoted by high temperatures and strong solar radiation (Quan et al., 2008; Xu et al., 2012). The sixth source is the iron and steel industry, characterized by a host of metals such as Fe, K+, Mn, Cr, Ni, Ti, and Zn. This factor contributed 8.42 ± 5.15% and 7.00 ± 6.10% to the PM2.5 mass in Xianlin and Gulou, respectively (Table 4). Fe, Mn, Cr, and Ni in ambient aerosols can be categorized as steel-related metals (Sweet et al., 1993; Mooibroek et al., 2011; Tao et al., 2014a), but other metals such as Ni, Zn, K, and Ti are also associated with iron and steel processing (Song et al., 2001; Mooibroek et al., 2011). There are two large steel mills (belonging to the Nanjing Iron and Steel Group Co., Ltd. and Shanghai Meishan Iron and Steel Group Co., Ltd.) located to the north of the Yangtze River and in the south of the city (Fig. 1), which had annual crude steel production of 8.17 and 7.02 million tons, respectively, in 2014. These two large steel mills contribute most of this source. The iron and steel industry factor contributes less in winter and summer, whereas its contribution is greatest in fall. As described above, the air

297

mass during fall travels across Liaoning Province (Fig. 3), which is one of the most important iron and steel industry bases in China and this might have some effect on the atmospheric environment of Nanjing. China ranks as the world's largest iron and steel producer with crude steel production accounting for 49.4% of the global production in 2014 (http://www.miit.gov.cn/n1146290/n1146402/n1146435/c3328580/ content.html). However, only a few reports have identified this source in terms of its contribution to airborne PM. For example, vehiclerelated and steel industrial sources were found to have contributed 26.5% to the PM2.5 in Beijing (Gao et al., 2014), and iron and steel manufacturing were found to have contributed 11.0 ± 9.0% (Tao et al., 2014a) and 31.9% (Liu et al., 2015) to the PM2.5 in Chengdu and Hangzou, respectively.

4. Conclusions Chemical composition and source apportionment were performed for suburban (Xianlin) and city center (Gulou) areas of the Chinese megacity Nanjing during spring, summer, fall, and an HF episode in winter. Over 80% of the daily PM2.5 concentrations were found to exceed the NAAQS limit (75 μg/m3). PM2.5 concentrations in winter were extremely elevated, with a mean concentration of 250 μg/m3 (range: 85–518 μg/ m3) in Xianlin and 243 μg/m3 (range: 67–431 μg/m3) in Gulou. The average concentrations of As observed in all seasons were higher than the NAAQS limit of 6 ng/m3. The average Cd and Ni concentrations observed in winter exceeded the NAAQS limit of 5 ng/m3 and the WHO limit of 25 ng/m3, respectively. − + − + Concentrations of SO2− 4 , NO3 , NH4 , Cl , K , EC, OC, SOC, SOR, and NOR, as well as most trace elements, were all highest in winter, whereas Na+ and Mg2+ had higher concentrations in summer. The concentraand Cl−, which are typical tracers of industrial coal comtions of SO2− 4 bustion, and their contributions to the PM2.5 mass were significantly higher in Xianlin than Gulou (p b 0.05), consistent with the higher SORs found in Xianlin. Meanwhile, the concentration of NO− 3 , which is related to vehicle exhausts, and its contribution to the PM2.5 mass was significantly higher in Gulou than Xianlin (p b 0.05), consistent with the higher NORs in Gulou. Both the SOR and NOR were correlated significantly with RH, indicating that secondary inorganic pollutants were dominated by heterogeneous processes. A chemical mass closure construction was used to apportion the PM2.5 sources. Six sources were identified using the PMF model. The contributions from each source varied with season and area. In winter, the source contributions of secondary nitrate, coal combustion, and road dust were all higher compared with the other seasons and the possible explanations were discussed. The source of sea salt and ship emissions, as well as secondary sulfate, contributed more in summer. The source of the iron and steel industry contributed most in fall. The most important sources for PM2.5 were secondary sulfate in Xianlin and secondary nitrate in Gulou. Ongoing economic growth and urbanization in Nanjing have sharply increased the local anthropogenic emissions annually, which have contributed to the increasing levels of PM2.5 and to the regular occurrence of haze episodes. Stricter emission standards are needed to alleviate the elevated PM2.5 loadings in this region. Acknowledgments This work was supported by the National Natural Science Foundation of China (grant nos. 41271511 and 41501549) and Natural Science Foundation of Jiangsu Province, China (grant no. BK20150915).

Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.atmosres.2016.07.005.

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