Chemical and Light Extinction Characteristics of Atmospheric Aerosols in Suburban Nanjing, China

Abstract

This work reports the chemical and light extinction characteristics of the atmospheric particles collected from January to November 2014 in suburban Nanjing. Size-segregated measurement results showed that more than 80% of the major aerosol components were concentrated in PM_2.5. The concentration of PM_2.5 was highest in winter and lowest in autumn. Specifically, K⁺ concentration peaked in late spring indicating heavy influences from straw burning, while sulfate concentration was highest in summer and its daytime concentration was also higher than its nighttime concentration, both reflecting a significant role of photochemical production. Nevertheless, except for sulfate, all other components had higher concentrations during nighttime, signifying the role of unfavorable meteorological conditions in exacerbating the air pollution. The IMPROVE formula was employed, which can reconstruct the PM_2.5 mass and light extinction well. The light extinction was mainly contributed by (NH₄)₂SO₄ and NH₄NO₃ (together 58.3%). Mass concentrations of all PM_2.5 components increased significantly with the increase of pollution levels, but nitrate increased most rapidly; correspondingly, the contribution of nitrate to light extinction also increased quickly when pollution became heavy. Such results were different from those observed in Beijing-Tianjin-Hebei where sulfate increased most quickly. Our results thus highlight that reduction of vehicular NO₂ is likely a priority for air quality improvement in Nanjing. Back trajectory analysis showed the dominance of the local air mass and the one from Huanghai, yet the air mass originated from Bohai, and passed though Shandong and north of Jiangsu province could deliver highly-polluted air to Nanjing, as well.

1. Introduction

With the rapid economic growth and increasingly-emitted pollutants in China, aerosol pollution has become a serious problem recently [1]. Particulate matter emitted from human and natural sources causes atmospheric haze which results in light extinction and visibility degradation. In recent years, numerous large-scale haze events have occurred over broad areas of China, arousing worldwide attention [2]. Moreover, aerosol particles can jeopardize human health by entering the human body through the respiratory tract [3], influence the climate by absorbing and scattering the atmospheric radiation, and affect precipitation by acting as cloud condensation nuclei (CCN), etc. [4].

A vast number of studies have been conducted to investigate the aerosol properties in China and other areas, which show that the physicochemical characteristics of ambient aerosols could vary spatially and temporally, heavily dependent upon the emission sources, weather conditions, and atmospheric environments. Among various aerosol components, water-soluble ions and carbonaceous species are the main constituents, which altogether can take up ~80% of the total aerosol mass [5,6,7,8]. These species are also major contributors to the atmospheric light extinction. The light extinction characteristics of aerosol are closely related to its chemical composition, size distribution, mixing state, and hygroscopicity, etc. Currently, there are three main methods to quantify the aerosol light extinction: Mie theory, linear regression, and IMPPROVE (Interagency Monitoring of Protected Visual Environments) formula. The Mie calculation requires both particle number concentrations and size distributions of different species, which is often impractical. The linear regression needs to fit into a large amount of data, including both light extinction coefficients and mass concentrations of various components, in order to obtain the unit mass extinction coefficient for each individual component, and then the total light extinction. The IMPROVE formula is relatively simple, and is widely used around the world. Basically, it is an empirical equation derived based on the observational data of visibility and aerosol compositions of over 180 sites across the United States [9,10]. Although the parameterization was established based on US data, some previous investigations have showed its suitability in China [11,12,13,14].

In China, secondary ions (mainly sulfate, nitrate, and ammonium (SNA)) were found to contribute the most to atmospheric light extinction (40–60%), followed by organic aerosols (OA) (20–30%). Elemental carbon (EC) is a main substance for light absorption, but contributes <10% light extinction. However, the light extinction coefficients and contributions of different species can differ greatly [13,15,16,17,18]. For instance, Tao et al. [19] found the extinction coefficients from spring to winter were 243 Mm⁻¹, 95 Mm⁻¹, 473 Mm⁻¹, and 469 Mm⁻¹ in Guangzhou, China and contributions of various components changed seasonally: (NH₄)₂SO₄ dominated in autumn (64%), NH₄NO₃ contributed most in winter (27%), while OM was the major contributor in summer (23%). Li et al. [11] showed that during the 2008 Beijing Olympic Games, SNA, OM, and EC contributed 81.7%, 8.4%, and 4.3% of the light extinction during low-visibility days (bottom 20%); while the SNA contribution was only 33.8% for the high-visibility days (top 20%), but OM and EC contributions increased to 30.7% and 15.7%, respectively. Deng et al. [14] found that the extinction coefficient of regional transported aerosols was 349 Mm⁻¹, with (NH₄)₂SO₄, NH₄NO₃, OM and EC contributing 33.3%, 21.8%, 31.9%, and 9.0% respectively, while the value of long-range transported aerosols was 286 Mm⁻¹, with (NH₄)₂SO₄, NH₄NO₃, OM, and EC contributing 40.0%, 18.2%, 29.2%, and 9.0%, respectively.

The Yangtze River Delta (YRD) region covers only 2.2% of the area of China, but contributes ~20% of the gross domestic product (GDP). Correspondingly, it is also an area with serious air pollution due to large energy consumption and industrial activity. Nanjing is an important megacity in the YRD region and the host city for the 2014 Youth Olympic Games. However, Nanjing is facing serious air pollution and the hazy days can reach over 200 days in a year [20]. Some earlier studies have investigated the aerosol properties in Nanjing, including size distribution [21], optical properties [22], water soluble ions [23], carbonaceous species [24], heavy metals [25], as well as the interplay between atmospheric particles with haze formation, etc. [26,27]. Nevertheless, aerosol chemistry studies are still lacking compared with other megacities, like Beijing, Shanghai, and Guangzhou. Relevant studies regarding the light extinction characteristics are particularly scarce [28,29,30]. This work characterized aerosol samples collected in the suburbs of Nanjing during 2014 with a focus on the light extinction properties, including: (1) the size distribution, seasonal, and day-night difference of the aerosol particles; (2) the light extinction and contributions of different aerosol components using the IMPROVE method; and (3) the characteristics of air masses under different pollution levels and with different origins, as well as their contributions to atmospheric light extinction.

2. Methodology

2.1. Aerosol Sampling

The sampling site was located in the Atmospheric Integrated Observation and Training Base of the China Meteorological Administration at the Nanjing University of Information Science and Technology, Pukou district, northern suburb of Nanjing City (altitude 22 m, 32°12′ N, 118°42′ W) (Figure 1). It is ~15 km away from the city center, with a highway ~1.5 km to the east, Nanjing Iron and Steel Plant ~3.5 km to the east, Nanjing Chemical Industry Zone ~10 km to the northeast, residence areas, villages, and farmland to the west/north and the low-altitude Longwang mountain to the south. The site is influenced by a mix of industry, traffic, residential and agricultural emissions simultaneously, representing the typical atmospheric environment of the suburb area of a megacity. Moreover, the sampling site was set on the side of a flat area surrounded by low-grassland with no tall buildings blocking the air masses.

Aerosol samples were collected using a mid-volume sampler (Laoshan Mountain Electronic Instrument Factory Co., Ltd., KC-120H, Qingdao, China). The instrument is designed based on inertial impaction. Under a certain flow, particles with a greater inertia hit the impactor, while ones with less inertia flow to the next impactor. As the orifice diameters decrease gradually, the flow rate increases accordingly and, finally, particles with different sizes are captured by different impactors. The sampler can classify the particles into four size ranges: <2.5 μm, 2.5–5 μm, 5–10 μm, and >10 μm. We can then obtain the mass concentrations of the total suspended particles (TSP), PM₁₀, and PM_2.5. The sampling flow rate is 100 L/min and can be self-adjusted automatically. The quartz filters (Pall Corp., Port Washington, NY, USA) were used for sampling and were baked at 500 °C for more than 5 h in a muffle furnace before use. After stabilization for 48 hours in a desiccator, the filters were weighed by an analytical balance (Ohaus Corp., DV215CD, Newark, NJ, USA, precision: 0.01 mg). Filters after sampling were weighed in the same way. Each value of mass was determined as an average of three measurements with a deviation <0.02 mg. The mass of collected particles was calculated as the mass of filter loaded with sampled particles subtracting the mass of blank filter. The filters were stored in the refrigerator maintained at −18 °C for further chemical analysis after weighing.

Aerosol samples were collected twice a week during January to November of 2014, one on weekdays (normally Wednesday) and another on weekends (normally Saturday). We collected both daytime and nighttime samples for each sampling day. The daytime sample was from 8:00 am until 8:00 pm, and the nighttime sample was collected from 8:00 pm to 8:00 am of the next day. The sampling schedule was irregularly interrupted at rainy/foggy days, or due to unexpected power failure and instrument malfunction. Finally, we obtained in total 66 sets of daytime samples and 46 sets of nighttime samples.

2.2. Chemical Analysis

The water-soluble ions were analyzed by ion chromatography (Dionex Corp., Sunnyvale, CA, USA). For each sample, a half filter was extracted in ionized water with 30 min of ultra-sonication and stabilized for 12 h, and then the solution was filtrated using a 0.22 µm filter (3M Deutschland GmbH, PES, Wuppertal, Nordrhein-Westfalen, Germany). The cations were analyzed by using an ICS3000 system with a Ion PacCS16 (5 × 250 mm) separation column, an Ion Pac CG16 (5 × 50 mm) guard column, a CSRS-4 mm suppressor, and a 33 mmol·L⁻¹ methane sulfonic acid solution as eluent. The anions were measured by an ICS2000 system equipped with an Ion Pac (4 × 250 mm) separation column, an Ion Pac AG11-HC (4 × 50 mm) guard column, an ASRS-4mm suppressor, and a 10 mmol·L⁻¹ KOH solution as eluent. The minimum detection limit of each ion was less than 0.01 μg·m⁻³.

A thermo/optical carbon analyzer (Atmoslytic Inc., DRI2001A, Calabasas, CA, USA) was used for measuring the organic carbon (OC) and EC concentrations based on the IMPROVE protocol. The filter was heated stepwise to 120 °C, 250 °C, 450 °C, and 550 °C in a pure He atmosphere to determine OC1, OC2, OC3, and OC4, respectively. Consequently, the filter was heated to 550 °C, 700 °C, and 800 °C in a 2% O₂/98% He atmosphere to measure EC1, EC2, and EC3, respectively. The organics were converted to CO₂ through catalytic oxidation, then reduced to methane (CH₄), and were quantified by the flame ionization detector (FID). The coking effect during heating could convert a portion of OC into pyrolysis carbon (OCP) which was determined by 633 nm He-Ne laser. The OC is defined as OC = OC1 + OC2 + OC3 + OC4 + OCP, while the EC is equivalent to EC1 + EC2 + EC3-OCP. The detection limits for OC and EC are 0.45 μg·cm⁻² and 0.06 μg·cm⁻², respectively.

Strict quality controls have been performed during the IC and OC/EC measurements. For each batch of samples, 2–3 blank filters were treated in the same manner as for samples to determine the background values, and results for samples were all blank corrected in this work. Before sampling, two identical samplers were operated in parallel to confine the measurement uncertainty within 10% for each component. Standard solutions (Merck KGaA, Darmstadt, Hesse-Darmstadt, Germany) were used to calibrate the IC systems for each batch of measurements. The IC systems were also checked before and after the measurement on each day to maintain the analytical uncertainty at <5%, otherwise the calibration was re-done. The He/CH₄ standard gas was used to calibrate the OC/EC analyzer to assure the variation of the standard’s peak area to be <5%, and the corresponding FID signal drift within ±3. Additionally, for every ten samples, one sample was randomly selected for a duplicate analysis to guarantee the analytical uncertainty remained at <5%, otherwise the analysis was re-done.

2.3. Data Analysis

2.3.1. IMPROVE Formula

The IMPROVE formula was first proposed in 1990, and was modified in 2007 by assuming the spherical form of aerosols and external mixing of different components [9,10]. The modified IMPROVE formula was used here, as follows:

b_ext = b_ap + b_sp + b_ag + b_sg,

(1)

where b_ap and b_sp represent the absorption and scattering coefficients of aerosol particles, while b_ag and b_sg correspond to gaseous species. b_ap and b_sp can be further calculated below:

b_ap = 10 × [EC],

(2)

b_sp = 2.2 × f_S(RH) × [Small (NH₄)₂SO₄] + 4.8 × f_L(RH) × [Large(NH₄)₂SO₄] + 2.4 × f_S[RH] × [Small NH₄NO₃] + 5.1 × f_L(RH) × [Large NH₄NO₃] + 2.8 × [Small OM] + 6.1 × [Large OM] + 1.7 × f_ss(RH) × [SS] + 1 × [FS] + 0.6[CM].

(3)

In the formula, (NH₄)₂SO₄ = 1.37[SO₄²⁻], NH₄NO₃ = 1.29[NO₃⁻], [OM](organic matter) = 1.8 × [OC]. The method splits (NH₄)₂SO₄, NH₄NO₃ and OM into “large-mode” and “small-mode” particles as particles with different sizes have different mass scattering efficiencies. The separation is based on the log-normal mass size distributions with 0.2 µm (geometric standard deviation, σ_g of 2.2) for small-mode and 0.5 µm (σ_g of 1.5) for large-mode particles. The mass scattering coefficients were then calculated using Mie theory for large and small-mode particles, respectively. The fraction of large particles was calculated using a threshold concentration of 20 μg·m⁻³, as shown below:

[X]_Large = [X]²_Total/(20μg·m⁻³), [X]_Total < 20 μg·m⁻³

[X]_Large = [X]_Total, [X]_Total ≥ 20 μg·m⁻³

[X]_Small = [X]_Total − [X]_Large

(4)

Here X refers to (NH₄)₂SO₄, NH₄NO₃, and OM. In Equation (3), [SS](sea salt) = 1.8 × [Cl⁻], FS(fine soil) = 2.2[Al] + 2.49[Si] + 1.63[Ca] + 2.42[Fe] + 1.94[Ti]. Since the metal elements were not detected, FS was set to be 20 times the mass concentration of Ca²⁺ based on known soil composition [17]. CM(coarse mass) = PM₁₀ − PM_2.5. RH refers to relative humidity of atmosphere. f_S(RH), f_L(RH) and fss(RH) are hygroscopic growth factors [10]. Atmospheric light absorption by gases is caused mainly by NO₂ gas, and b_ag = 161[NO₂] [10]. b_sg refers to the Rayleigh scattering of gas and is set to 10 here. The units of scattering and absorption coefficients are both Mm^–1. The mass concentrations of (NH₄)₂SO₄, NH₄NO₃, OC, EC, PM₁₀, and PM_2.5 are in μg·m⁻³. The unit of NO₂ concentration is mg·m⁻³, and NO₂ data was acquired from the Pukou air quality monitoring site of the Nanjing Environmental Protection Bureau.

2.3.2. Koschmieder’s Formula

The extinction coefficients can be measured directly using nephelometer, aethalometer, and photoacoustic spectrometer, etc. [31]. Since no direct measurement was conducted in this work, the extinction coefficient was estimated from visibility using the Koschmieder’s formula [32]:

$$b_{\mathrm{ext}-\mathrm{V}}=\frac{-\ln (\epsilon )}{V}\times 1000$$

(5)

where V refers to the horizontal visibility (km), b_ext-V refers to the extinction coefficient (Mm⁻¹), ε refers to the contrast threshold of human vision (0.05 in this work). The atmospheric visibilities were observed by the forward scattering visibility meter (CAMA Environment Monitoring Co., Ltd., CJY-1A, Luoyang, China). The instrument assumes the atmosphere is homogenous and the extinction coefficient is solely governed by the scattering of atmospheric particles. In principle, the scattering intensity is proportional to the light extinction and independent of aerosol size distribution and concentrations when the forward scattering angle is in the range of 20°–50°. For this instrument, the emitter launches a beam of infrared light, and the receiver converges the forward atmospheric scattering light to the surface of a photoelectric sensor receiving at the angle of 35° relative to the emitted light. The atmospheric extinction coefficient can be obtained from the electrical signals which can be converted to the visibility value according to Koschmieder’s formula. When the visibility is 0.01–1.5 km, the measurement uncertainty is ±10%, and it is ±20% when the visibility is 1.5–50 km. In terms of the extinction coefficient, the error is about ±20% when it is less than 2000 Mm⁻¹ and is about ±10% when it is more than 2000 Mm⁻¹. In this work, the extinction coefficients were calculated by using this method and compared with those estimated from the IMPROVE method.

2.3.3. Backward Trajectory and Clustering Analysis

The backward trajectory and clustering analysis was used to investigate the air mass pathways and the influences of long-range and regional transportation on atmospheric pollution. The model HYSPLIT4 developed by NOAA (National Oceanic and Atmospheric Administration, Washington, DC, USA) was adopted to calculate the 72 h backward trajectory from 2:00 pm (6:00 am, UT) of each sampling day. Considering the wind field at 500 m altitude can better represent the mean flow characteristics of the boundary layer, this altitude was chosen in the simulation using the Global Data Assimilation System (GDAS) meteorological data with 1° resolution [33].

3. Results and Discussion

3.1. Chemical Characteristics of Atmospheric Aerosol

3.1.1. Size-Segregated Mass Concentrations

The mass concentrations of total aerosol particles and the major chemical components (SO₄²⁻, NO₃⁻, NH₄⁺, OC, and EC) at different size ranges are shown in Figure 2. PM_2.5 could occupy 71.2% of the PM₁₀ mass. This ratio is consistent with the results in Nanjing during 2013 [20], lower than the ratios of Guangzhou in summer and YRD during heavy haze periods [34,35], but higher than the values measured in Xia’men and Beijing [36,37]. Correspondingly, SO₄²⁻, NO₃⁻, NH₄⁺, OC, and EC in PM_2.5 were 88.9%, 80.2%, 91.0%, 86.1%, and 86.4% of those in PM₁₀, respectively, similar to the results observed in Beijing [38], Shanghai [39], Hong Kong [40], and Nanjing [41]. Since the scattering effect of particles with similar sizes as the wavelengths of visible light is strong, the fine particles mainly contributed to aerosol extinction. Our results highlight the importance to reduce the fine aerosol pollution in suburban Nanjing.

3.1.2. Seasonal Differences

Since PM_2.5 dominated the aerosol population, we focus on PM_2.5 and its chemical components here.

Table 1. Seasonal differences of mass concentrations of PM_2.5 and its main chemical components (µg/m³).

Components	Mean ± SD
	Winter (Number = 79)	Spring (Number = 108)	Summer (Number = 123)	Autumn (Number = 132)
PM2.5	166.0 ± 96.3	97.4 ± 35.9	100.4 ± 40.8	85.3 ± 44.4
SO42−	23.3 ± 19.6	15.7 ± 8.32	24.3 ± 9.51	14.5 ± 7.89
NO3−	29.0 ± 22.8	10.9 ± 7.85	12.2 ± 11.3	13.3 ± 12.0
NH4+	16.7 ± 8.37	9.18 ± 4.12	13.1 ± 5.36	12.5 ± 7.45
Na+	0.73 ± 0.30	0.55 ± 0.26	0.59 ± 0.78	0.36 ± 0.11
K+	1.99 ± 1.02	2.03 ± 2.17	1.60 ± 1.40	1.01 ± 0.43
Mg2+	0.09 ± 0.04	0.10 ± 0.07	0.06 ± 0.05	0.08 ± 0.04
Ca2+	0.75 ± 0.60	1.06 ± 0.55	0.50 ± 0.28	1.43 ± 0.75
F-	0.11 ± 0.08	0.08 ± 0.09	0.07 ± 0.06	0.06 ± 0.03
Cl−	3.55 ± 2.15	1.36 ± 1.41	1.15 ± 1.59	1.39 ± 1.44
OC	21.5 ± 12.9	15.0 ± 7.54	13.6 ± 8.24	12.7 ± 6.87
EC	5.10 ± 3.30	3.95 ± 2.72	2.88 ± 1.50	2.06 ± 1.00
SOC	6.66 ± 6.61	4.23 ± 4.38	5.06 ± 4.82	4.44 ± 4.16
SOC/OC	28.9 ± 16.1%	27.4 ± 19.2%	33.7 ± 18.4%	32.8 ± 16.0%

lists the average mass concentrations of PM_2.5 and various species in four seasons (winter: January–February; spring: March–May; summer: June–August; autumn September–November), and

Table 2. Mean daytime and nighttime meteorological parameters in four seasons.

Season	Time	Temperature/°C	Relative Humidity (%)	Wind Velocity (m·s−1)	Mixed Layer Height (m) 1	Rainfall (mm) 2
Winter	Day	6.3	49.0	1.8	962.3	116.8
	Night	2.4	66.8	1.0	153.3
Spring	Day	22.2	36.3	2.0	1384.7	136.4
	Night	17.3	51.4	1.7	135.2
Summer	Day	27.8	59.8	1.4	1361.2	405.5
	Night	24.0	78.9	1.1	147.3
Autumn	Day	20.1	56.2	1.7	1194.1	170.6
	Night	15.9	78.5	1.2	155.3

¹ Data is from the NOAA HYSPLIT model, daytime data is from 14:00 while nighttime data is from 02:00. ² Rainfall data is the accumulated precipitation during the sampling period in that season.

shows the mean daytime and nighttime meteorological parameters. The mass concentration of PM_2.5 was highest in winter, following by summer and spring, and was lowest in autumn. Concentrations of most ionic species, OC, and EC reached their maximum in winter as well, in agreement with the seasonal changes previously observed in Nanjing [23], Peking [42], Hangzhou [43], Xi’an [44], and some other cities. This is due to winter typically having low temperatures, stagnant air, low boundary layer height, and less precipitation (Table 2), which are unfavorable for the dispersion and diffusion of air pollutants.

Typically, high temperature, intense atmospheric convection, and frequent precipitation in summer can significantly reduce the aerosol pollution and many cities indeed have the least aerosol pollution in summer [23,42,43,44]. Nanjing implemented a series of air quality control measures around the 2014 Youth Olympics Games period (16–28 August). Nevertheless, the mass concentration of PM_2.5 in summer in this work appeared to be high. This is likely due to the summer samples not being specifically collected during the Youth Olympics Games period, and we also chose the sunny days with no precipitation for sampling. In such cases, high temperature and strong solar radiation may be in favor of secondary aerosol formation. Indeed, the concentration of SO₄^2– reached its maximum in summer, even higher than that in winter.

We also estimated the concentration of secondary organic carbon (SOC) using the EC-tracer method which has been widely used in other studies [45]:

SOC = OC-EC × (OC/EC)_min.

(6)

where (OC/EC)_min was the minimum measured OC/EC ratio. The mass ratio SOC to OC was also the highest in summer [45], further strongly indicating the significant role of secondary formation.

The PM_2.5 concentration in spring was slightly lower than that in summer while the concentration of K⁺ was highest in spring. As shown in Figure S1, the concentration of K⁺ in late May and early June was remarkably higher than it was during other periods, in accordance with the high concentration of OC. Combining with satellite images of fire points, this was related to straw burning. In the YRD region, this period is the farming season for rapeseed and wheat, and straw burning is frequent. Meanwhile, mass loadings of soil-derived Mg²⁺ and Ca²⁺ in spring were also high, likely due to that dry weather, low RH and high wind speed enhanced the suspension of soil dust. However, the concentration of Ca²⁺ was higher in autumn than that in spring, indicating that the dust due to construction activities can be an important source of Ca²⁺ as well [13]. During the Youth Olympics Games period, building construction was banned by the Nanjing government. Therefore, the restart of construction activities near the site could be the main cause of the burst of Ca²⁺ concentration in autumn. Nevertheless, generally speaking, the aerosol pollution in autumn is lightest and most species, such as SO₄²⁻, OC, and EC reached their minimums in response to the good atmospheric conditions for diffusion and dispersion.

3.1.3. Day–Night Differences

We chose the days with both daytime and nighttime samples and investigated the day-night variations of PM_2.5 and its main components, as shown in Figure 3. Except for SO₄^2–, mass concentrations of PM_2.5 and other components all showed on average higher values during nighttime than those during daytime. This is consistent with results during spring of 2002 in Nanjing by Niu et al. [46]. There are many factors influencing the day-night variations, including emission sources and deposition, formation of secondary species, variability of meteorological conditions, and so on. Although the anthropogenic emissions may be significantly reduced during nighttime, the boundary layer height at night is typically low and the wind speed is low (Table 2), which limits the diffusion of air pollutants and leads to a rise in the concentrations. Moreover, the stagnant meteorological conditions during nighttime are unfavorable to the dry deposition of fine particles, especially those in accumulation mode [47]. As a result, the concentration of primary EC during nighttime was 1.5 times what it was during daytime.

The enhancements of secondary components were relatively small: concentrations of NH₄⁺ and OC at night were 20% higher than those during daytime, and the nighttime concentration of SO₄²⁻ was even less than that during daytime, especially in summer (Figure S2). This result reflects a role of secondary production of sulfate due to photochemical reactions during daytime. However, another secondary component NO₃⁻ showed significant enhancement during nighttime, which was more likely influenced by its volatility. Wang et al. [48] found a similar trend of NO₃⁻ in the YRD region previously. Daytime high temperature can result in significant evaporation/dissociation of NH₄NO₃ while, in turn, nighttime low temperature and high RH can promote the gas-to-particle partitioning of nitrate and/or the heterogeneous formation of NH₄NO₃ via N₂O₅ hydrolysis [49].

3.2. Light Extinction Estimated by the IMPROVE Formula

3.2.1. Reconstruction of PM_2.5 Mass

The mass concentration of PM_2.5 is reconstructed based on the IMPROVE formula. Calculation of the equivalent molar ratios of SO₄²⁻, NO₃⁻, and NH₄⁺ shows that the NH₄⁺ is in excess and can adequately neutralize SO₄²⁻ and NO₃⁻ most of the time (Figure S3). In the IMPROVE formula, the calculation of mass concentrations of (NH₄)₂SO₄ and NH₄NO₃ is, thus, reliable. The OM/OC ratio is largely influenced by emission sources, etc., and the available values range from 1.4–2.2 [11,50]. In this work, we chose 1.8 as the conversion factor by considering previous values, especially those from Nanjing [15,18,51]. Apart from the ocean source, coal combustion may emit a certain amount of Cl⁻ as well, and the IMPROVE formula may overestimate the contribution of sea salt. However, since Nanjing is far away from the ocean and the Cl⁻ concentration was rather low, it is expected to insignificantly affect the mass reconstruction.

On average, the sum of mass concentrations of different components can reconstruct 93.9% of the total PM_2.5 mass, illustrating that the IMPROVE formula can account for the majority of PM_2.5 species in Nanjing. The mass contributions of different components are shown in Figure 4. The main species, OM, (NH₄)₂SO₄, and NH₄NO₃, together occupied 75.6% of the total PM_2.5 mass. In addition, contribution from FS (20 times of Ca²⁺) can reach as much as 17.9%. Yang et al. [8] pointed out that in the continental cities, the soil dust contribution was about 10%. Li et al. [20] used Fe as a soil indicator and estimated the soil dust contribution was about 17% in Nanjing aerosols. Our result also demonstrates the significance of soil-derived aerosols, but more direct and accurate measurements on the elements are necessary. Contributions of SS and EC were rather low, and both occupied ~3% of the total PM_2.5 mass.

3.2.2. Estimation of Extinction Coefficients

The IMPROVE formula was used for further calculation of the light extinction coefficients and compared with the extinction coefficients of b_ext-V calculated from the visibility. As the b_ext-V obtained by the forward scattering visibility meter actually only considered the scattering of aerosol particles, the particle scattering coefficient b_sp from the IMPROVE formula was compared with b_ext-V, and the results are shown in Figure 5. The average values of b_sp and b_ext-V were 642.4 Mm⁻¹ and 731.5 Mm⁻¹, respectively, thus, the IMPROVE formula underestimated the aerosol extinction by about 12.2%, but the correlation between them was very tight (R = 0.89), indicating that the IMPROVE formula could reflect the aerosol scattering reasonably well. Nevertheless, the deviations may be significantly affected by the measurement error of the extinction coefficient calculated from the forward scattering visibility meter. The other possible influencing factors are: (1) the atmospheric pollution in China is much worse than that in the US and the extinction coefficients of different components are likely low; (2) the water uptake by the organic matter is not explicitly included; and (3) influences of coarse particles were not considered as well [52]. Further studies should be conducted in order to obtain the extinction coefficients and hygroscopic growth factors that are especially suitable for Nanjing in order to better describe the light extinction characteristics of aerosol particles.

Based on the IMPROVE estimations, contributions of different components to the total light extinction is shown in Figure 6. It can be seen that the light extinction in Nanjing was mainly contributed by aerosol particles while the gases showed very minor contribution (2.7%). The main aerosol species for light extinction were (NH₄)₂SO₄, NH₄NO₃, and OM, contributing 33.2%, 25.1%, and 24.9%, respectively. Compared with their corresponding mass contributions in PM_2.5, the contribution of SNA to light extinction increased significantly, which was related to the significant water uptake by the SNA while OM’s contribution decreased since IMPROVE formula does not account for its hygroscopic growth. Among other components, EC contributed 5.1% while the rest of the species contributed a rather small amount. The total light extinction contribution of SNA was 58.3%. As in this suburban area, NO₂ from vehicles and SO₂ from coal burning are likely major precursors to the formation of SNA, and the results underscore the importance to reduce such emissions.

3.3. Variations of Chemical and Light Extinction Characteristics under Different Circumstances

3.3.1. Influences of Different Pollution Levels

Haze is the direct consequence caused by light extinction. According to The Meteorological Industry Standard of the People’s Republic of China—Observation and Forecasting levels of Haze (QX/T 113-2010) issued on 2010, when the visibility is less than 10 km and the RH is below 80%, it is defined as haze; when the RH is between 80% and 95% and PM_2.5 is above 75 μg·m⁻³, it is also defined as haze, otherwise it is fog. In terms of the visibility, haze can be divided into four levels, namely, slightly, mild, moderate, and severe. To avoid the influence of radiation fog and the temperature inversion near the ground, we chose the visibility and RH values at 2:00 pm, and obtained the daily concentration by averaging the daytime and nighttime samples of the same day. If the samples were collected only in the daytime or nighttime, that data was regarded as the daily average. In total, we analyzed samples for 68 days. Huang et al. [53,54] classified the haze in the YRD region into three categories: secondary pollution, dust influence and straw combustion. According to the Yearbook of Chinese Meteorological Disaster (2014), we did not collect samples during dust-influenced days. Based on the concentrations of the biomass burning tracer K⁺ and the satellite image of fire points (Figure S4), 28 May, 30 May, and 7 June are typical straw combustion days and were thus removed in the following analysis to focus on the aerosol variations due to secondary pollution. Finally, we identified 47 hazy days and 18 non-haze days, and the occurrence rate of haze was 72%. The classification of hazy days, mass concentrations of various species and the meteorological conditions are shown in

Table 3. Mass concentrations of PM_2.5 and its components, and the meteorological parameters under different haze pollution levels.

Haze Grades	Distinguishing Criteria	Day (Proportion, %)	PM2.5 (μg·m−3)	NH4+ (μg·m−3)	NO3− (μg·m−3)	SO42− (μg·m−3)	OC (μg·m−3)	EC (μg·m−3)	RH 1 (%)	WS (m·s−1)	MLH (m)
Non_haze	V > 10.0 2	18 (27.7)	69.9	7.25	6.58	9.66	9.27	2.06	56.3	2.29	1082.2
Slight	5.0 ≤ V < 10.0	32 (49.2)	98.0	11.9	12.4	17.9	14.5	3.16	62.3	1.35	1279.9
Mild	3.0 ≤ V < 5.0	11 (16.9)	132.8	19.2	22.3	25.5	16.6	3.31	69.7	1.37	1041.0
Moderate	2.0 ≤ V < 3.0	3 (4.6)	165.4	23.2	34.6	30.9	20.4	4.25	66.6	1.68	841.7
Severe	V < 2.0	1 (1.5)	383.7	23.8	80.3	72.7	45.5	9.44	75.4	0.38	772.0

¹ RH refers to relative humidity, WS refers to wind speed, MLH refers to boundary-layer height (at 2:00 pm of each day); ² V refers to visibility, the unit is km. When RH < 80%, non-haze days is defined as V > 10 km; when RH > 80%, the weather could be foggy, and no foggy days are included in the sampling days in this analysis.

.

Obviously, the aerosol pollution during non-haze days are not serious, with an average concentration of PM_2.5 of 69.8 μg·m⁻³, meeting the second-grade of Chinese National air quality standard (75 μg·m⁻³). The mass concentrations of OC and SO₄²⁻ were close to each other and together they contributed significantly to the total PM_2.5 mass. With the increase of haze pollution levels, the mass concentrations of total aerosols and individual components all increased, and the PM_2.5 mass loading reached a maximum of 383.6 μg·m⁻³ in a very hazy day. Among different species, NO₃⁻ concentration increased most significantly, which was 12 times that of non-haze days. SO₄²⁻ ranked second with an increase of 7.5 times. OC and EC increased relatively slowly, with five times and 4.5 times increases, respectively. The results show that the secondary inorganic species increased more rapidly during haze. Previous study shows that the carbonaceous aerosols may dominate in the initial stage of haze, while secondary species can form quickly under high RH [55]. Severe haze events are often accompanied by slow wind, shallow boundary layer height, and high RH. The stagnant atmospheric conditions are beneficial to the accumulation of pollutants while the high RH facilitates the formation of SNA and subsequent hygroscopic growth. Nevertheless, in this work, the wind speed, boundary layer height, and RH did not vary linearly with the haze pollution levels. This is likely due to the number of days with severe haze pollution used for analysis being small, and the influences of weather conditions and regional/long-range transported aerosols. It should be noted that our observation showed that NO₃⁻ increased more rapidly during haze episodes than that of SO₄²⁻. This result is in agreement with those conducted in Nanjing by Zhang et al. [56], and in Lin’an, a background city of the YRD region by Shen et al. [57], but is different from the sharp increase of SO₄²⁻ contribution to PM_2.5 mass during haze pollution in the Beijing-Tianjin-Hebei region. Our findings clearly point out the significantly-different haze formation mechanisms and governing factors in different regions.

Furthermore, we used the IMPROVE formula to calculate the total light extinction and contributions of individual components under different haze pollution levels, as shown in Figure 7. From the figure, during non-haze days, OM contributed the most (29.0%) to the light extinction due to its relatively high mass contribution, (NH₄)₂SO₄ ranked second (27.1%), following by NH₄NO₃ (17.1%). With the increase of haze pollution levels, the contribution of OM gradually decreased, while that of SNA, especially NH₄NO₃ increased quickly and amounted for 38.9% in the severe hazy days, dominating over that of (NH₄)₂SO₄. In line with the rapid increase of NO₃⁻ mass concentration (Table 3), our results suggest that Nanjing may need to carry out strict control measures on vehicle emissions, such as a limit on the use of heavy-duty tracks, etc., as vehicle-emitted NO₂ is the most important precursor for nitrate formation.

3.3.2. Influences of Different Air Masses

The aerosol pollution is not only influenced by the local emissions but also the long-range or regionally transported pollutants. The air parcels arrived at the sampling site can be very different due to their different origins and the pollutants they intercept along with their trajectories. As such, it is valuable to understand the potential source areas and transport pathways by investigating the interplay between different air mass trajectories and the aerosol characteristics. Based on the clustering analysis of HYSPLIT4, the air masses reached the site can be classified into four clusters, and their corresponding aerosol pollution levels and light extinction characteristics are shown in

Table 4. Variations of aerosol concentration and meteorological elements from different air masses.

Mass Source	PM2.5 (μg·m−3)	NH4+ (μg·m−3)	NO3− (μg·m−3)	SO42− (μg·m−3)	OC (μg·m−3)	EC (μg·m−3)	RH (%)	WS (m·s−1)	MLH (m)
1	94.3	12.4	12.7	19.1	12.1	2.52	68.0	1.48	1227.1
2	119.7	14.3	17.9	19.4	18.8	4.06	56.3	1.39	1079.4
3	120.1	13.4	17.9	21.5	15.6	3.37	65.1	1.39	1147.5
4	86.2	7.90	8.41	10.2	13.2	3.30	48.3	2.48	1391.4

and Figure 8, respectively. Among the four clusters, air mass originated from Eastern Huanghai (Type 1) occupied the largest fraction of all trajectories (41%) and were typically with high RH. This is consistent with the main wind direction in the northern suburb of Nanjing: east wind (Figure S5). Type 2 (31%) refers to the air mass close to the sampling location with different directions and short trajectories, which represents the local/regional emissions. Type 3 (17%) and type 4 (11%) refer to the air masses originating from Bohai and northwest inland China.

All four of these air masses can cause haze pollution, but the degrees and frequencies differed. According to Table 4, type 2 and type 3 both include heavy aerosol pollutions. The occurrence rate of haze in type 2 was as much as 84.2% and the corresponding boundary layer height was also the lowest. Type 3 included a severe haze event. On average, the PM_2.5 concentrations of both type 2 and type 3 were close to 120 μg·m⁻³. The SO₄²⁻ concentration was a bit higher in type 3 and concentrations of OC and EC were higher in type 2. Air masses in type 3 travelled through Shandong Peninsula and Northern Jiangsu Province, which may intercept heavy pollution. Type 2 air mass was mostly local, indicating severe local emissions, such as various combustion sources, like straw burning in and close to Nanjing, as it had relatively high OC and EC contents. The pollution in air mass type 1 was relatively light, likely due to it originating from clean marine air, while the concentration of SO₄²⁻ in type 1 was on par with that in type 2, probably due to it passing through some developed cities where SO₂ was abundant. Air mass type 4 started from the relatively clean northwest region, and the fraction of haze days was only 37.5% and they were all slightly hazy days. Its trajectory was the longest, and the mass concentration of PM_2.5 was the lowest with especially much lower SNA concentrations. Only the concentrations of OC and EC in type 4 were slightly larger than those of type 1, likely because of the influence of straw combustion, etc.

Based on the IMPROVE formula, the extinction coefficient of air mass type 1 was 593.8 Mm⁻¹, among which (NH₄)₂SO₄ contributed the most (38.1%) and OM contributed 21.2%. The extinction coefficients in type 2 and type 3 were rather high, with OM and SNA as the most significant contributors, respectively. Type 4 has the least light extinction coefficient of 381.5 Mm⁻¹, with OM contributing 33.7% and a relatively higher contribution from EC (8.4%) than it in other air masses. In summary, the light extinction by OM in the local air mass was significant, while the light extinctions of air masses from Huanghai and Bohai were mainly influenced by SNA; the northwest inland air mass was the clearest with obvious decrease of SNA contribution, yet the contributions of carbonaceous aerosols increased.

4. Conclusions

• PM_2.5 dominated the aerosol pollution, with its highest mass concentration in winter, followed by summer, spring, and autumn. Concentrations of most chemical components were highest in winter too, except that the highest SO₄²⁻ concentration occurred in summer, K⁺ and Mg²⁺ peaked in spring while Ca²⁺ reached its maximum in autumn. Mass concentrations of PM_2.5 and most major species were higher during nighttime than daytime due to unfavorable conditions for pollutant diffusion. For NO₃⁻, its loading was much lower during daytime, owing to its semi-volatile behavior and/or possible nocturnal heterogeneous production. However, SO₄²⁻ concentration was higher during daytime, indicating the significant photochemical production.
• The measured species can reconstruct the total PM_2.5 mass well, and OM, (NH₄)₂SO₄, and NH₄NO₃ comprised the majority of PM_2.5 (75.6%) with another significant component of FS contributing ~18%, and two minor components of SS and EC both occupying ~3%. The IMPROVE formula could estimate the aerosol light extinction reasonably well, with (NH₄)₂SO₄, NH₄NO₃, and OM contributing 33.2%, 25.1%, and 24.9%, respectively. The light extinction was dominated by the SNA, indicating a significant role of secondary ions in visibility degradation.
• Mass concentrations of PM_2.5 and all species increased gradually with the increase of haze pollution levels. The increase of SNA was particularly remarkable, and NO₃⁻ appeared to arise most rapidly among all species. Regarding the light extinction, the contribution from OM continuously decreased, while SNA, in particular nitrate contribution, increased with the increase of pollution levels, indicating a significant role of secondary species in haze formation and also underscoring the priority of reduction of vehicular NO₂ emissions in Nanjing.
• Four clusters of air masses were identified, with differing chemical and light extinction characteristics. The local air mass and air mass originated from Bohai, but travelled through Shandong Province and north of Jiangsu Province appeared to be heavily polluted. The local air mass had high mass loadings of OC and EC and, correspondingly with OM, contributing most to the light extinction. The air mass from Bohai was contributed mainly by the SNA in terms of both mass concentration and light extinction. The air parcel from Huanghai was relatively clean, but with significant light extinction contributed by SO₄²⁻. The air mass starting from the northwest was the cleanest and clearest, and carbonaceous aerosols seemed to contribute most to its light extinction.