The Characteristics and Impact Factors of Sulfate and Nitrate in Urban PM_2.5 over Typical Cities of Hangzhou Bay Area, China

Abstract

PM_2.5 pollution over Hangzhou Bay area, China has received continuous attention. In this study, PM_2.5 samples were collected simultaneously in six typical cities in Zhejiang Province from 15 October 2019 to 15 January 2020 (autumn and winter) and from 1 June to 31 August 2020 (summer), and major water-soluble ions were analyzed. Average concentrations of NO₃⁻ and SO₄²⁻ in the six cities were 3.93–15.64 μg/m³ and 4.61–7.58 μg/m³ in autumn and winter, with mass fractions of NO₃⁻ and SO₄²⁻ in PM_2.5 up to 19.6–34.2% and 13.6–26.3%, respectively, while in summer, they were 1.23–2.64 μg/m³ and 2.22–4.14 μg/m³, with mass fractions of 7.0–15.0% and 14.7~25.1%. Both NO₃⁻ and SO₄²⁻ were mostly from gas-to-particle transformation of precursors. High relative humidity in the six cities was suggested to significantly promote the formation of NO₃⁻ and SO₄²⁻, particularly in autumn and winter, while enhanced atmospheric oxidation favored the formation of SO₄²⁻ in summer. However, the formation of NO₃⁻ was inhibited under a high temperature of >15 °C. The concentrations of SO₄²⁻ and NO₃ were mostly correlated with each other among the six cities. Potential source contribution function analysis indicated that both SO₄²⁻ and NO₃⁻ were mostly from local pollution of Hangzhou Bay area in Zhejiang Province and also transported from Shanghai and the southern region of Jiangsu Province. This study contributed to the understanding of regional characteristics of SO₄²⁻ and NO₃⁻ in Hangzhou Bay area and suggested that joint prevention and control efforts should be strengthened to reduce regional PM_2.5 pollution.

1. Introduction

Atmospheric particles with equivalent diameter less than or equal to 2.5 µm (PM_2.5) can profoundly impact human health [1,2] and climate change [3]. Sulfate (SO₄²⁻) and nitrate (NO₃⁻), with mass fractions of more than 20% and 30% in PM_2.5, respectively [4,5,6,7], are two major chemical components of PM_2.5, and also two critical components that influence the effect of PM_2.5 on climate change [3,8,9].

Since 2013, a variety of pollution prevention and control measures for air quality improvement have been conducted in China. Particularly, the Action Plan on Prevention and Control of Air Pollution (from 2013 to 2017) and the three-year action plan to fight air pollution (from 2018 to 2020) have been effectively conducted, and anthropogenic emissions have decreased substantially throughout China, with remarkable reductions in concentrations of atmospheric PM_2.5, SO₂, and NO₂, as well as concentrations of SO₄²⁻ and primary particulate matter (PPM) in PM_2.5 [4,10,11]. However, steadily enhanced contributions of SO₄²⁻ and NO₃⁻ to PM_2.5 were observed [7,12], which would become more and more important in determining a further decrease in PM_2.5 concentration. Thus, it is critical to clarify the characteristics and key factors that influence the formation of SO₄²⁻ and NO₃⁻ in PM_2.5.

Studies were conducted in the Beijing-Tianjin-Hebei region [4,6,13], Sichuan-Chongqing region [13], Yangtze River Delta region (YRD) [7,14], and Pearl River Delta region [10,15] throughout China to reveal the characteristics and impact factors of SO₄²⁻ and NO₃⁻ in PM_2.5, based on field observations conducted since 2017, as concentrations of their precursors, i.e., SO₂ and NO₂, had substantially decreased [16,17]. It was indicated that the decreases in concentrations of SO₄²⁻ and NO₃⁻ were much lower, particularly in autumn and winter in all of the study regions, compared with the decreases in concentrations of SO₂ and NO₂. Wang et al. [13] revealed that the response of chemical components related to primary emissions and secondary inorganic components varied significantly in different regions, and suggested that the control of PM_2.5 precursors becomes more and more important. The high contribution of SO₄²⁻ to PM_2.5 could be attributed to rapid oxidation of SO₂ by atmospheric oxidants such as NO₂ and H₂O₂ on the surface of hygroscopic particles [18,19,20], and SO₂ could be catalyzed by particulate manganese ions to produce SO₄²⁻ under meteorological conditions of low temperature but high relative humidity [21], as SO₂ concentration dropped to a low level, while enhanced contribution of NO₃⁻ to PM_2.5, especially during wintertime haze days, was influenced by the decrease in SO₂ concentration, enhancement of atmospheric oxidation capacity (AOC), and relatively stable emission of ammonia (NH₃) [4,22]. At the same time, enhanced formation potential of NO₃⁻ at night and vertical input from the residual layer were also important factors that hurt the local effort to reduce NO₃⁻ concentration [14,23]. However, the effects of influence factors such as meteorological conditions and regional transport on sources and formation processes of SO₄²⁻ and NO₃⁻ in PM_2.5 were different in different seasons and different locations [4,17,24].

Hangzhou Bay area is one of the most concerning areas for its high emission of anthropogenic pollutants and high concentrations of atmospheric PM_2.5, NO₂, and O₃ over the Yangtze River Delta region, China. Benefitting from the effective implementation of regional pollution prevention and control measures from 2015 to 2020, the air quality of Hangzhou Bay area has significantly improved. However, the PM_2.5 pollution is still severe, particularly in autumn and winter. At present, studies on the chemical components of PM_2.5 over Hangzhou Bay area were mostly conducted before 2015, which could not effectively reflect the recent regional chemical characteristics of PM_2.5. In this study, PM_2.5 samples were collected simultaneously in six cities of Hangzhou Bay area to study the characteristics of SO₄²⁻ and NO₃⁻ under the scenario of strict control measures for anthropogenic emission, and the dependence of precursors, meteorological factors, and AOC on the formation of SO₄²⁻ and NO₃⁻. Finally, the impact of regional transport on SO₄²⁻ and NO₃⁻ was studied. This study would contribute to the understanding of regional characteristics of SO₄²⁻ and NO₃⁻ in Hangzhou Bay area and provide a scientific basis for further improvement of regional PM_2.5 pollution.

2. Materials and Methods

2.1. Study Sites and Field Sampling

PM_2.5 samples were collected simultaneously in six cities of Hangzhou Bay area, including Hangzhou (HZ), Huzhou (HuZ), Jiaxing (JX), Shaoxing (SX), Ningbo (NB), and Zhoushan (ZS). As the northernmost city in Zhejiang Province, HuZ is an inland lakeside city and lies on the southwest bank of Taihu Lake. HZ is on the south of HuZ and at the west end of Hangzhou Bay, and urban HZ is over 200 km away from the easternmost edge of Hangzhou Bay. SX is on the south of HZ and lies on the southwest bank of Hangzhou Bay. JX is a coastal city lying on the northwest bank of Hangzhou Bay. Both NB and ZS are located at the southeast end of Hangzhou Bay; they are a coastal city and an island city, respectively.

One sampling site of PM_2.5 was set up in an urban area of each city. All the sampling sites were selected according to the Chinese standard method [25]. The sites were as close as possible to existing state-controlled stations in the center of the six cities and mainly surrounded by commercial, cultural, and residential activities. The geographical locations and detailed information of the six sampling sites are shown in Figure 1 and

Table 1. Detailed information of the six PM_2.5 sampling sites.

Sites	Longitude	Latitude	Observation Stations and Surrounding Environment	Terrain Features and Climate Characteristics
HuZ	120°05′17.40″	30°53′24.86″	On the roof of the Science and Technology Museum of Huzhou, ~16 m above ground level (AGL) and surrounded by commercial, traffic, and residential activities.	Inland city, affected by the East Asian monsoon, with northwesterly winds prevailing in winter and southeasterly winds prevailing in summer
HZ	120°11′54.91″	30°11′15.05″	On the roof of a building at Huapu Park, ~15 m AGL and surrounded by commercial, traffic, and residential activities.	Inland city, with urban area surrounded by hills except on the northeast side and affected by the East Asian monsoon, with northwesterly winds prevailing in winter and southeasterly winds prevailing in summer
SX	120°33′17.18″	29°59′40.12″	On the roof of an office building in central urban area, ~20 m AGL and surrounded by commercial, traffic, and residential activities.	Inland city, with urban area surrounded by hills except on the northeast side and affected by the East Asian monsoon, with northwesterly winds prevailing in winter and southeasterly winds prevailing in summer
JX	120°47′21.91″	30°45′04.87″	On the roof of an office building in central urban area, ~14 m AGL and surrounded by commercial, traffic, and residential activities.	Coastal city, affected by the East Asian monsoon, with northerly winds prevailing in winter and southeasterly winds prevailing in summer
NB	121°31′42.52″	29°51′53.94″	On the roof of an office building in central urban area, ~16 m AGL and surrounded by commercial, traffic, and residential activities.	Coastal city, affected by the East Asian monsoon with northerly winds prevailing in winter and southeasterly winds prevailing in summer
ZS	122°12′33.64″	29°58′36.45″	On the roof of an office building in central urban area, ~20 m AGL and surrounded by commercial, traffic, and residential activities.	Island city, affected by the East Asian monsoon, with northerly winds prevailing in winter and southerly winds prevailing in summer

, respectively.

PM_2.5 samples were collected simultaneously in the six cities from 2019 to 2020, the last two years of the three-year action plan to fight air pollution in China. Since seasonal variation in PM_2.5 pollution was distinct over Hangzhou Bay area, highest in autumn and winter and lowest in summer [26], PM_2.5 samples were collected in autumn and winter from 15 October 2019 to 15 January 2020 (autumn and winter) and in summer from 1 June to 31 August 2020 (summer) to reveal the characteristics of particulate SO₄²⁻ and NO₃⁻ under different levels of PM_2.5 pollution. One sample was collected every 3 days in each city for 23 h (from 9:00 a.m. to 8:00 a.m. the next day), as suggested by the China National Environmental Monitoring Center [27] for further comparison between different regions and comparison of annual changes in future. All samples were collected on a Teflon filter (47 mm, Whatman PTEE, Maidstone, KEN, UK) using a four-channel sampler with flow rate of 16.7 L/min for each channel and stored under −20 °C in a refrigerator immediately after sampling. All the procedures were strictly quality-controlled to avoid any possible contamination of the PM_2.5 samples.

2.2. Ion Analysis

One-fourth of each sample filter and blank filters were extracted ultrasonically by 20 mL deionized water. Anions of SO₄²⁻, NO₃⁻, and Cl⁻ were analyzed by Ion Chromatography (IC; Dionex ICS 5000, Sunnyvale, CA, USA), and cations of NH₄⁺, Na⁺, K⁺, Ca²⁺, and Mg²⁺ were analyzed by Ion Chromatography (IC; Dionex ICS 2000, Sunnyvale, CA, USA). All the procedures were strictly quality-controlled according to the Chinese standard method [28,29].

2.3. Concentrations of SO₂, NO₂, O₃, and PM_2.5 and Meteorological Data

Mass concentrations of PM_2.5, SO₂, NO₂, and ozone (O₃) were synchronously measured at a state-controlled station near each PM_2.5 sampling site and obtained from the Ministry of Ecology and Environment (MEE, http://www.mee.gov.cn/hjzl/, accessed from 15 October 2019). The six state-controlled stations were Wolong Bridge in HZ, Renhuangshan New District in HuZ, Qinghe Primary School in JX, Paojiang Station in SX, Environmental Monitoring Center in NB, and Lincheng New District in ZS. All observation data of SO₂, NO₂, O₃, and PM_2.5 were further processed and analyzed according to the PM_2.5 sampling time from 9:00 a.m. to 8:00 a.m. the next day.

Meteorological data were from the National Meteorological Information Center (CMA, http://data.cma.cn, accessed from 15 October 2019), including hourly wind speed (WS, m/s), wind direction (WD), temperature (T, °C), and relative humidity (RH, %).

2.4. Calculation of NOR and SOR

Sulfur oxidation rate (SOR) and nitrogen oxidation rate (NOR) were calculated by Equations (1) and (2) to evaluate the transformation ratios of SO₂ and NO₂ to SO₄²⁻ and NO₃⁻, respectively [30,31].

SOR = [SO₄²⁻]/([SO₄²⁻] + [SO₂]),

(1)

NOR = [NO₃⁻]/([NO₃⁻] + [NO₂]),

(2)

where [SO₄²⁻], [NO₃⁻], [SO₂], and [NO₂] are concentrations of SO₄²⁻, NO₃⁻, SO₂, and NO₂ with all units in equivalent concentration (μeq/m³).

2.5. Statistical Analysis

Statistical analyses were performed using IBM SPSS Statistics 22.0 (IBM, Armonk, NY, USA). The normality of all data was tested using the Shapiro–Wilk test. The correlation between two variables was tested with Spearman correlation coefficient (r). All tests were two sided, and p < 0.05 was considered statistically significant.

2.6. Backward Trajectory and Potential Source Regions of SO₄²⁻ and NO₃⁻

Individual 48 h backward trajectories that arrived at the center of the six sampling cities at 250 m were calculated by the Hybrid Single-Particle Lagrangian Integrated Trajectory model [32], using meteorological data at a resolution of 1° × 1° from the US National Centers for Environmental Prediction’s Global Data Assimilation System (https://www.ncei.noaa.gov/, accessed on 20 February 2023). Potential source contribution function (PSCF) was performed to explore the potential source regions with pollutant concentrations. PSCF algorithms were computed utilizing the TrajStat Plugin from MeteoInfo (http://meteothink.org/, accessed on 20 February 2023), as shown in Wang et al. [33].

The PSCF normalized value for the ijth grid was defined by Equation (3):

$${\mathrm{PSCF}}_{\mathrm{ij}}=\frac{{\mathrm{m}}_{\mathrm{ij}}}{{\mathrm{n}}_{\mathrm{ij}}},$$

(3)

where m_ij denotes the number of “polluted” trajectory endpoints in the ijth cell, and n_ij denotes the total number of endpoints in the ijth cell. The “polluted” criterion in this study was set at average concentrations. For grids with tiny nij values, the weighting function wij was utilized, as shown in Equation (4):

$${\mathrm{w}}_{\mathrm{ij}}=\left\{\begin{array}{c}1.00, 80<{\mathrm{n}}_{\mathrm{ij}}\\ 0.70, 20<{ \mathrm{n}}_{\mathrm{ij}}\le 80\\ 0.42, 10\le { \mathrm{n}}_{\mathrm{ij}}\le 20\\ 0.17, {\mathrm{n}}_{\mathrm{ij}}<10\end{array}\right\}$$

(4)

In this study, the domain was in the range of 18–41° N, 95–132° E with a resolution of 0.5° × 0.5°, and the potential source regions with high-weighted PSCF (WPSCF) values were determined and used in the Section 3.

3. Results and Discussion

3.1. Overview of Meteorological Conditions during the Sampling Periods

As shown in

Table 2. Average concentrations of PM_2.5, SO₂, NO₂, O₃, and SO₄²⁻, NO₃⁻, NH₄⁺ in PM_2.5, average ratios of NO₃⁻ to SO₄²⁻, and average values of meteorological factors including WS, RH, and T in the six cities during the sampling period from 15 October 2019 to 15 January 2020 (autumn and winter) and from 1 June to 31 August 2020 (summer).

Season	Sites	PM2.5 (μg/m3)	SO2 (μg/m3)	NO2 (μg/m3)	O3 (μg/m3)	NO3− (μg/m3)	SO42− (μg/m3)	NH4+ (μg/m3)	NO3−/SO42−	WS (m/s)	RH (%)	T (°C)
autumn and winter	HuZ	37	7	50	42	13.94	7.58	6.38	1.85	2.0	77	11
	HZ	47	6	42	27	14.36	6.65	6.10	2.17	2.1	74	12
	SX	41	8	51	33	14.06	6.95	6.33	2.04	1.3	76	12
	JX	40	9	51	43	15.64	6.84	6.54	2.27	2.1	79	12
	NB	30	10	49	40	9.97	6.44	5.10	1.54	2.4	79	12
	ZS	18	7	22	68	3.93	4.86	2.95	0.81	3.7	76	14
summer	HuZ	17	3	22	67	2.64	4.14	2.29	0.64	2.0	81	28
	HZ	16	4	16	46	1.23	3.06	1.81	0.40	2.0	78	28
	SX	20	4	24	65	1.41	2.97	1.83	0.47	1.6	77	29
	JX	20	6	25	68	2.45	3.24	2.15	0.76	2.1	84	28
	NB	15	6	23	58	1.70	3.36	1.75	0.51	2.0	85	28
	ZS	12	3	16	64	1.48	2.22	1.41	0.66	3.0	88	26

, high RH was observed in all six cities in autumn and winter, with average values ranging from 74% to 79%. In summer, RH was even higher, with average values ranging from 77% to 88%, especially in those coastal cities of ZS, NB, and JX. The average T significantly varied with geographical environments. As the northernmost city among the six cities, average T in HuZ (11 °C) was lowest in autumn and winter, while average T in ZS, an island city, was highest (14 °C). The other four cities had equal average T of 12 °C. In comparison, average T values in summer were SX > HZ, HuZ, JX, NB > ZS.

Average WS in the six cities was ZS > NB > HuZ, HZ, JX > SX, both in autumn and winter and in summer. Obviously, WS was highest in ZS but lowest in SX, at only 1.3 and 1.6 m/s in autumn and winter and in summer, respectively, implying that atmospheric diffusion capacity (ADC) was highest in ZS but lowest in SX. The wind rose in Figure 2 shows that dominant WD in the six cities was apparently different. In autumn and winter, the dominant WD in HuZ was from the west, followed by southwest and south, of which WS was mostly lower than 2 m/s. The dominant WD in HZ and JX was from the north, and in SX, it was from the west and northeast, while the dominant WD in NB and ZS was from the northwest and north, and WS in ZS was substantially higher than the other five cities.

In summer, the dominant WDs in HZ, HuZ, JX, and SX were significantly different from each other, coming from southwest and south, from southeast and east, from southwest to east, and from southwest and northeast, respectively. The dominant WDs in NB and ZS were both from south to southeast, similar to each other. However, hourly WS in NB was mostly lower than 4 m/s and significantly lower than that of ZS.

3.2. Temporal and Spatial Variations in NO₃⁻ and SO₄²⁻ in PM_2.5

3.2.1. PM_2.5 Concentration

As shown in Table 2, PM_2.5 concentrations in HZ, HuZ, SX, and JX were higher than those in NB and ZS. In autumn and winter, average concentrations of PM_2.5 in HZ, HuZ, SX, and JX ranged from 37 μg/m³ to 47 μg/m³, higher than the grade II limit of the National Ambient Air Quality Standards (NAAQS) of China (35 μg/m³), while in NB and ZS, they were much lower, at 30 μg/m³ and 18 μg/m³, respectively. In summer, average PM_2.5 concentrations in the six cities decreased significantly to 12–20 μg/m³, with those in NB and ZS dropping below the grade I limit of the NAAQS of China (15 μg/m³).

High concentrations of PM_2.5 in HZ and SX could be attributed to high anthropogenic emissions of PM_2.5 and its precursors of SO₂, NOx, VOCs [34,35], as well as low ADC for atmospheric pollutants, particularly in autumn and winter, as both urban HZ and SX are surrounded on three sides by hills. HuZ and JX are located in the hinterland of YRD, where anthropogenic emissions from transportation and industry are concentrated. Therefore, high concentrations of PM_2.5 in HuZ and JX might be from both local emission and regional transport. As a coastal city, the lower concentration of PM_2.5 in NB could probably benefit from high ADC, since anthropogenic emissions in NB were highest among the six cities. In comparison, the lowest concentration of PM_2.5 in ZS was suggested to benefit from both low anthropogenic emissions and high ADC.

3.2.2. Concentrations of SO₄²⁻ and NO₃⁻ in PM_2.5

In autumn and winter, the spatial distribution of NO₃⁻ concentration was consistent with that of PM_2.5 (Table 2), with average concentrations in NB (10.0 μg/m³) and ZS (3.9 μg/m³) lower than those in the other four cities (13.9~15.6 μg/m³), while in summer, the average concentrations of NO₃⁻ ranked as HuZ, JX > NB > ZS, SX, HZ. The spatial distribution of SO₄²⁻ in autumn and winter was similar to that in summer, with average concentrations highest in HZ but lowest in ZS.

Both concentrations of NO₃⁻ and SO₄²⁻ in autumn and winter were significantly higher than those in summer in the six cities, consistent with seasonal variations in NO₃⁻ and SO₄²⁻ in Beijing [24], Shanghai [14], Shenzhen [15], and Dongting Lake [5]. Average concentrations of NO₃⁻ in autumn and winter were 2.7–11.7 times those in summer, substantially higher than those of PM_2.5 concentrations (1.5–2.9), resulting in remarkable decreases in mass fractions of NO₃⁻ in PM_2.5 from 19.6–34.2% in autumn and winter to 7.0–15.0% in summer. Average concentrations of SO₄²⁻ in autumn and winter were 1.8–2.3 times those in summer, comparable to those of PM_2.5 concentrations. Therefore, mass fractions of SO₄²⁻ in PM_2.5 had little seasonal variation, at 13.6~26.3% in autumn and winter and 14.7~25.1% in summer, respectively. Apparently, both NO₃⁻ and SO₄²⁻ contributed substantially to the PM_2.5 in autumn and winter in all six cities, and sum fractions of NO₃⁻ and SO₄²⁻ were up to 31.6~35.9%, with higher fractions of NO₃⁻ than SO₄²⁻, while in summer, sum fractions of NO₃⁻ and SO₄²⁻ in PM_2.5 dropped to 15.6~27.3%, and fractions of SO₄²⁻ were substantially higher than those of NO₃⁻.

NO₃⁻ and SO₄²⁻ in atmospheric particles mainly come from gas-to-particle transformation of SO₂ and NOx, respectively, while sea salt also contributes to particulate SO₄²⁻ over the coastal region. In this study, concentrations of SO₄²⁻ from sea salt (ss-SO₄²⁻) were estimated for the six cities according to the mass ratio of SO₄²⁻ to Na⁺ in seawater (0.25) [36], assuming that all Na⁺ measured in PM_2.5 samples came from sea salt. The results showed that concentrations of ss-SO₄²⁻ were lower than 0.1 μg/m³ both in autumn and winter and in summer, even in the coastal cities of ZS, NB, and JX, indicating that SO₄²⁻ in the six cities was all mostly from the gas-to-particle transformation of SO₂.

3.3. Ratio of NO₃⁻ to SO₄²⁻ in PM_2.5

The concentration ratio of NO₃⁻ to SO₄²⁻ can be used to indicate the relative contribution of mobile and stationary sources to PM_2.5 [37,38]. Since particulate NO₃⁻, which mostly exists in the form of NH₄NO₃, is volatile, the ratio of NO₃⁻ to SO₄²⁻ is also related to formation processes and meteorological factors, such as T and RH in different seasons [24].

As shown in Table 2, ratios of NO₃⁻ to SO₄²⁻ in HuZ, HZ, JX, and SX ranged from 1.81 to 2.27 in autumn and winter, significantly higher than those in NB (1.50) and ZS (0.81), suggesting higher contributions from stationary coal burning emission to PM_2.5 in NB and ZS, which was consistent with the relative emissions of NOx and SO₂ from mobile and stationary sources in different cities. At the end of 2019, industrial coal consumption in NB was 34.5 million tons [39], over two times that in JX (17.57 million tons) [40], SX (10.55 million tons) [41], HZ (9.90 million tons) [42], HuZ (9.05 million tons) [43], and ZS (7.28 million tons) [44], while the number of motor vehicles ranked as HZ (2.98 million) [45], NB (2.91 million) [46] > SX (1.67 million) [47], JX (1.27 million) [48] > HuZ (0.89 million) [49] > ZS (0.22 million) [50]. Thus, emissions from coal burning sources were suggested to be largest in NB, while those from mobile sources were largest in HZ and NB. The vehicle number and industrial coal consumption of ZS was 7.6–24.7% and 21.1–80.4% of those in the other five cities, respectively, suggesting a relatively high contribution from stationary sources to PM_2.5 over mobile sources in ZS. In summer, all the ratios of NO₃⁻ to SO₄²⁻ dropped to 0.40–0.76 due to the high T, which did not favor the formation of NO₃⁻ [24], and indicated a relatively lower contribution of mobile sources to PM_2.5 in the six cities.

3.4. Correlation between SO₄²⁻, NO₃⁻, and NH₄⁺ in PM_2.5

Ammonia (NH₃) usually acts as the major alkaline species for the neutralization in the formation process of NO₃⁻ and SO₄²⁻ in PM_2.5 [51,52,53], which mostly exist in the forms of (NH₄)₂SO₄, NH₄HSO₄, and NH₄NO₃ [54,55]. In this study, the correlations between the equivalent concentrations of the sum of NO₃⁻ and SO₄²⁻ ([SO₄²⁻ + NO₃⁻]) and NH₄⁺ ([NH₄⁺]) were analyzed. None of the data fit a normal distribution; thus, Spearman’s rho correlation coefficients are shown in Figure 3. The results showed that [SO₄²⁻ + NO₃⁻] were highly correlated with [NH₄⁺] in the six cities both in autumn and winter and in summer, with all the correlation coefficients higher than 0.71, suggesting that NH₃ was the major alkaline species for the neutralization of NO₃⁻ and SO₄²⁻.

NO₃⁻ and SO₄²⁻ in autumn and winter could not be completely neutralized by NH₃ in the six cities other than ZS, as the linear regression slopes of [NH₄⁺] and [SO₄²⁻ + NO₃⁻] were slightly lower than unity, ranging from 0.90 to 0.94 (Figure 3a–e), and therefore, NO₃⁻ and SO₄²⁻ were suggested to exist in the forms of (NH₄)₂SO₄, NH₄HSO₄, and NH₄NO₃. In ZS, the linear regression slope of [NH₄⁺] and [SO₄²⁻ + NO₃⁻] was nearly unity (Figure 3f), indicating that NO₃⁻ and SO₄²⁻ in PM_2.5 could be completely neutralized by NH₃ and existed mainly in (NH₄)₂SO₄ and NH₄NO₃. However, if the neutralization of Cl⁻ was considered [56], NH₃ deficiency was suggested in all six cities, as the linear regression slopes of [NH₄⁺] and [SO₄²⁻ + NO₃⁻ + Cl⁻] decreased, ranging from 0.84 to 0.93, and thus, all species of (NH₄)₂SO₄, NH₄HSO₄, NH₄NO₃, and NH₄Cl might exist in PM_2.5 in autumn and winter, which was not completely consistent with previous study of YRD [56], implicating that the effect of acid NOx and SO₂ on PM_2.5 pollution was still critical.

In summer, linear regression slopes of [NH₄⁺] and [SO₄²⁻ + NO₃⁻], as well as [NH₄⁺] and [SO₄²⁻ + NO₃⁻ + Cl⁻], were higher than unity in those four cities of HZ, JX, SX, and ZS, indicating that NO₃⁻ and SO₄²⁻ in PM_2.5 could be completely neutralized by NH₃ and existed mainly in (NH₄)₂SO₄ and NH₄NO₃, while in NB and HuZ, linear regression slopes of [NH₄⁺] and [SO₄²⁻ + NO₃⁻] were 0.95 and 0.94 (Figure 3e,f), respectively, suggesting that NH₃ was deficient and all of (NH₄)₂SO₄, NH₄HSO₄, NH₄NO₃, and NH₄Cl were present in NB and HuZ in summer.

3.5. Influence Factors on the Formation of SO₄²⁻ and NO₃⁻

3.5.1. Impact of Precursors on the Formation of SO₄²⁻ and NO₃⁻

As shown in Table 2, concentrations of SO₄²⁻ increased along with the increase in SO₂ concentrations in the six cities, implying that the formation of particulate SO₄²⁻ was significantly influenced by the emission of SO₂. Average concentrations of SO₂ in autumn and winter were 1.4–2.4 times those in summer in all cities, exhibiting slight seasonal variations as similar as those of SO₄²⁻ concentrations. As we all know, benefitting from the effective implementation of ultra-low emission control for power plants as well as other emission control measures for coal burning, SO₂ concentration has decreased significantly throughout China [57]. However, enhanced concentrations of SO₂ in autumn and winter were observed in all six cities. On the one hand, the height of the planetary boundary layer (PBLH) was significantly lower compared with that in summer, which could decrease the vertical diffusion of local emitted SO₂. On the other hand, coal-fired heating in North China caused a significant increase in SO₂ emissions during later autumn and winter [58], which could be transported to Hangzhou Bay area by the prevailing northwest and north wind and contribute significantly to the SO₂ concentration in autumn and winter.

Concentrations of NO₂ were substantially higher than those of SO₂ in the six cities. As NOx emissions from power plants were substantially reduced by ultra-low emission control, NOx emissions from motor vehicles and industrial processes, including the production of steel and building materials, metallurgy industries, and so on, which had not significantly decrease in 2020 [59], became major sources of atmospheric NO₂. Average NO₂ concentrations in autumn and winter were 1.4–2.7 times those in summer, similar to those of SO₂ concentrations, and also could be attributed to the effect of decreased PBLH and transported pollution from North China in autumn and winter. However, autumn and winter/summer ratios for NO₂ concentration were lower than those for NO₃⁻ concentration in the six cities, implying that the gas-to-particle transformation of NO₂ to NO₃⁻ depends on other important factors than NO₂ concentration.

Averaged NOR and SOR in the six cities are shown in Figure 4. In autumn and winter, NOR ranged from 0.11 to 0.19 in the six cities. NOR in NB and ZS was lower than in the other cities, indicating a relatively lower transformation ratio of NO₂ to NO₃⁻. In summer, secondary transformation of NO₂ to NO₃⁻ was negligible in all cities, as NOR dropped to only 0.04–0.09. In comparison, SOR was much higher than NOR in all cities and ranged from 0.28 to 0.40 in autumn and winter and from 0.25 to 0.33 in summer, respectively. SOR was ranked as HuZ > HZ, JX, SX, ZZ > NB in both autumn and winter and in summer, indicating that the transformation ratio of SO₂ to SO₄²⁻ was highest in HuZ but lowest in NB.

3.5.2. Impact of Meteorological Factors on the Formation of SO₄²⁻ and NO₃⁻

Figure 5 shows the relationships between NOR and SOR, T, and RH in the six cities. In autumn and winter, NOR depended significantly on T in all cities, while SOR was not significantly correlated with T. Under the condition of T > 15 °C, NOR decreased significantly, with NOR values lower than 0.15 in HuZ, HZ, SX, and JX and lower than 0.10 in NB and ZS. In comparison, both NOR and SOR were closely related to RH in HZ, JX, HuZ, and SX. Under the condition of RH < 90%, both NOR and SOR increased along with the increase in RH. When RH was higher than 90%, both NOR and SOR decreased, which was probably due to enhanced wet deposition of particulate SO₄²⁻ and NO₃⁻, while in NB and ZS, neither NOR nor SOR was correlated to RH, which might be attributed to the effect of air mass transported by the prevailing northerly wind.

In summer, NOR was mostly lower than 0.15 in the six cities due to the high T condition of more than 20 °C. NOR dropped to lower than 0.06 in ZS under the condition of T > 27 °C, and lower than 0.04 in JX, SX, NB, HuZ, and HZ under the condition of T > 30 °C. NOR increased slightly as RH increased, indicating that RH also had a certain effect on the formation of NO₃⁻ in summer. As for SOR, it increased as T increased, particularly in HZ, but was not significantly correlated with RH. However, under the condition of T > 30 °C, SOR did not depend on T in all cities other than ZS, and high SOR might be attributed to enhanced atmospheric oxidation, which favors the transformation of SO₂ to SO₄²⁻, as concentrations of O₃ were significantly enhanced under the condition of T > 30 °C.

As shown in Figure 6, neither NOR nor SOR was significantly correlated with WS. In autumn and winter (Figure 6a,c), both NOR and SOR were enhanced when WS was higher than 2 m/s in HZ, JX, HuZ, and SX, and when WS was higher than 3 m/s and 5 m/s in NB and ZS, respectively, which might be due to the higher fraction of aged particles in transported air mass that probably have higher fractions of SO₄²⁻ and NO₃⁻. Meanwhile, in summer (Figure 6b,d), neither NOR nor SOR significantly varied with WS in most of the cities.

3.5.3. Impact of Atmospheric Oxidation on the Formation of SO₄²⁻ and NO₃⁻

As shown in Table 2, the concentrations of O₃ varied greatly among the six cities both in autumn and winter and in summer. In summer, O₃ concentrations were substantially enhanced, particularly in HuZ and JX, where the concentrations of SO₄²⁻ and NO₃⁻ were higher than in the other cities. In order to study the effect of atmospheric oxidation on the transformation of SO₄²⁻ and NO₃⁻ from SO₂ and NO₂, the relationships between NOR, SOR, and O₃ concentration were analyzed; only those results in autumn and winter are shown in Figure 7, as NOR in summer was mostly lower than 0.1 in the six cities and not significantly correlated with O₃ concentration. Apparently, neither NOR nor SOR was significantly correlated with O₃ concentration in autumn and winter, indicating that the formation of SO₄²⁻ and NO₃⁻ did not depend significantly on atmospheric oxidation, and other impact factors such as RH and T should have a major effect on the formation of SO₄²⁻ and NO₃⁻, as discussed in Section 3.5.2.

In summer, O₃ had an important effect on the formation of SO₄²⁻ in all cities other than ZS, as SOR significantly depended on O₃ concentration. In ZS, SOR did not increase along with the increase in O₃ concentration, when O₃ concentration was lower than 100 μg/m³. Since the local emission of SO₂ in ZS was lowest among the six cities, SO₄²⁻ might be greatly affected by regional transport. However, SOR in ZS rose significantly to 0.45 when O₃ concentration was higher than 100 μg/m³.

3.6. Impact of Regional Transport

Table 3. Spearman correlation coefficients between NO₃⁻ concentrations in the six cities as well as SO₄²⁻ concentrations in autumn and winter and in summer, respectively.

Species	Sites	HuZ	HZ	SX	JX	NB
NO3− in autumn and winter	HZ	0.87 **
	SX	0.62 **	0.63 **
	JX	0.66 **	0.54 **	0.2
	NB	0.69 **	0.84 **	0.59 **	0.52 *
	ZS	0.62 **	0.51*	0.39	0.64 **	0.54 *
SO42− in autumn and winter	HZ	0.82 **
	SX	0.74 **	0.86 **
	JX	0.50 *	0.52 **	0.50 **
	NB	0.67 **	0.59 **	0.62 **	0.52 **
	ZS	0.58 **	0.63 **	0.45 **	0.47 *	0.74 **
NO3− in summer	HZ	0.44 *
	SX	0.58 **	0.52*
	JX	0.49 **	0.39	0.48 *
	NB	0.4 *	0.27	0.57 **	0.51 **
	ZS	0.21	−0.06	0.53 **	0.29	0.52 **
SO42− in summer	HZ	0.46 *
	SX	0.49 **	0.45 *
	JX	0.45 *	0.44 *	0.56 **
	NB	0.53 **	0.64 **	0.72 **	0.5 **
	ZS	0.56 **	0.29	0.34	0.26	0.46 *

** p < 0.01, * p < 0.05.

shows correlation coefficients between NO₃⁻ concentrations among the six cities as well as SO₄²⁻ concentrations in the two study periods, respectively. In autumn and winter, NO₃⁻ concentration was correlated with itself among the six cities (r = 0.51–0.87), except that NO₃⁻ concentration in SX was not correlated with those in JX and ZS. In comparison, SO₄²⁻ concentration was correlated with itself in all six cities (r = 0.45–0.86). Thus, significant regional pollution of SO₄²⁻ and NO₃⁻ was suggested, which was further indicated by the spatial distributions of WPSCF for SO₄²⁻ and NO₃⁻. As shown in Figure 8a,b, potential source regions of SO₄²⁻ and NO₃⁻ were similar to each other in all six cities. SO₄²⁻ and NO₃⁻ in HuZ and HZ were significantly affected by air masses from Hangzhou Bay area in Zhejiang Province, where anthropogenic emissions of NOx and SO₂ were concentrated [31]. WPSCF of SO₄²⁻ and NO₃⁻ in HuZ were also distributed in Wuxi and Changzhou in the south of Jiangsu Province, while in HZ, they were also distributed in Suzhou in the south of Jiangsu Province and Shanghai. WPSCF of SO₄²⁻ and NO₃⁻ in SX was distributed mostly in Hangzhou Bay area in Zhejiang Province. Potential source regions of SO₄²⁻ and NO₃⁻ were different in those coastal cities of JX, ZS, and NB. In JX, WPSCF of SO₄²⁻ and NO₃⁻ was mostly distributed in HuZ of Zhejiang Province and Suzhou, Wuxi, Changzhou, Zhenjiang, and Nanjing of Jiangsu Province. In ZS and NB, WPSCF of SO₄²⁻ and NO₃⁻ was mostly distributed in Hangzhou Bay area, while in NB, it was also distributed in Suzhou and Nantong in the south of Jiangsu Province, consistent with the prevailing north and northwest wind, as shown in Figure 2.

In summer, the regional pollution of NO₃⁻ was not as significant as that in autumn and winter, as all correlation coefficients between NO₃⁻ concentrations among the six cities were lower than 0.6. The spatial distribution of WPSCF for NO₃⁻ in HuZ, HZ, and JX was mostly concentrated in surrounding cities, including those adjacent cities of Zhejiang Province around Hangzhou Bay as well as Changzhou and Wuxi in Jiangsu Province. WPSCF of NO₃⁻ in SX was mostly distributed in Jinqu basin of Zhejiang Province and the northwest region of Fujian Province. In comparison, NO₃⁻ in NB was mostly influenced by air masses from the coastal cities in the southeast of Zhejiang Province and in the northeast of Fujian Province, and in ZS, it was mostly influenced by the Taiwan Strait.

SO₄²⁻ concentration in summer was correlated with itself among the cities other than ZS (r = 0.44–0.72), while SO₄²⁻ concentration in ZS was correlated with those in NB, JX, and HZ. Thus, significantly regional pollution of SO₄²⁻ was also suggested in summer. As shown in Figure 8d, WPSCF of SO₄²⁻ in the six cities was distributed mostly in those cities of HuZ, HZ, JX, and SX of Zhejiang Province, as well as Changzhou and Wuxi of Jiangsu Province. Furthermore, SO₄²⁻ in HuZ, HZ, and SX was also influenced by air masses from Wenzhou and Taizhou in the southeast of Zhejiang Province, and in SX, it was also substantially impacted by air masses from Jinqu basin of Zhejiang Province and the northwest area of Fujian Province. In comparison, WPSCF of SO₄²⁻ in NB was significantly distributed in coastal cities in the northeast of Fujian Province.

4. Conclusions

PM_2.5 samples were collected simultaneously in six typical cities of Hangzhou Bay area to study the characteristics of SO₄²⁻ and NO₃⁻, the dependence of precursors, meteorological factors, and AOC on the formation of SO₄²⁻ and NO₃⁻, as well as the impact of regional transport. Concentrations of NO₃⁻ and SO₄²⁻ were significantly higher in autumn and winter in the six cities, with average concentrations of 3.93–15.64 μg/m³ and 4.61–7.58 μg/m³, respectively, 2.7–11.7 and 1.8–2.3 times those in summer (1.23–2.64 μg/m³ and 2.22–4.14 μg/m³). In autumn and winter, mass fractions of NO₃⁻ and SO₄²⁻ in PM_2.5 were up to 19.6–34.2% and 13.6~26.3%, respectively, while in summer, mass fractions of SO₄²⁻ were 14.7~25.1%, similar to those in autumn and winter, but mass fractions of NO₃⁻ decreased to 7.0–15.0%.

Spatial variations in NO₃⁻ and SO₄²⁻ were distinct both in autumn and winter and in summer, with SO₄²⁻ concentrations highest in HZ but lowest in ZS and NO₃⁻ concentrations lower in NB and ZS than in the other four cities in autumn and winter. Ratios of NO₃⁻ to SO₄²⁻ were also higher in NB (1.50) and ZS (0.81) than in HuZ, HZ, JX, and SX (1.81 to 2.27); thus, more contribution from stationary coal burning to PM_2.5 in NB and ZS was suggested.

NO₃⁻ and SO₄²⁻ were suggested to mostly come from gas-to-particle transformation of precursors in the six cities. Relationships between SOR, NOR, and meteorological factors indicated that high RH in the six cities could promote the formation of SO₄²⁻ and NO₃⁻, particularly in autumn and winter. Furthermore, enhanced O₃ concentration in summer favored the formation of SO₄²⁻. However, the formation of NO₃⁻ was inhibited at high T conditions, which were higher than 15 °C. Regional pollution of SO₄²⁻ and NO₃⁻ was significant in the six cities, as the concentrations of SO₄²⁻ and NO₃⁻ were highly correlated with each other in most cities. Potential source regions of SO₄²⁻ and NO₃⁻ were mostly from Hangzhou Bay and the surrounding area of Zhejiang Province and also transported from Shanghai and the southern region of Jiangsu Province, including Wuxing, Changzhou, and Suzhou, both in autumn and winter and in summer, implying that joint prevention and control efforts should be further strengthened to reduce regional PM_2.5 pollution.

There may be some possible limitations in this study. Firstly, only one PM_2.5 sampling site was set up in each city, which made it difficult to reveal the average chemical characteristics of urban PM_2.5 over the whole city. Secondly, the collection of each PM_2.5 sample lasted for 23 h, and the chemical composition in samples may change during sampling. In this study, all sampling sites were selected carefully to minimize the impact of surrounding pollution emission, with similar emission sources of commercial, traffic, and residential activities. In addition, the sampling period covered a long time, from 15 October 2019 to 15 January 2020 and from 1 June to 31 August 2020, which could also reduce the impact of surrounding pollution emission. Thus, the results and conclusions of this study are still credible.