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Article

Characteristics of PM2.5 Chemical Species in 23 Chinese Cities Identified Using a Vehicular Platform

by
Hui Chen
1,*,
Jingjing Liu
1,
Peizhi Wang
1,
Xiao Lin
1,
Jingjin Ma
2,3 and
Chunying Wang
2,3,*
1
Key Laboratory of Organic Compound Pollution Control Engineering, School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
2
Ecological Environment Internet of Things and Big Data Application Technology National Engineering Research Center, Shijiazhuang 050035, China
3
Hebei Advanced Environmental Protection Industry Innovation Center Co., Ltd., Shijiazhuang 050035, China
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(6), 2340; https://doi.org/10.3390/su16062340
Submission received: 15 January 2024 / Revised: 3 March 2024 / Accepted: 8 March 2024 / Published: 12 March 2024
(This article belongs to the Special Issue Aerosols and Air Pollution)
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Abstract

:
PM2.5 pollution remains a significant concern in China due to its adverse environmental and health implications. This study aims to explore in depth the differences in the causes of PM2.5 pollution between some regions in China based on high temporal resolution PM2.5 component information. We used a particulate matter chemical composition vehicle (PMCCV) as a mobile monitoring platform which travelled among 23 cities in China from March 2018 to December 2019 to collect PM2.5 concentrations and chemical composition data. Observations revealed that PM2.5 concentrations were notably higher in northern cities compared than their southern counterparts. Seasonal variation was evident, with peak concentrations during winter and troughs during summer. In regions experiencing severe winter pollution, such as Hebei and Shanxi (HB/SX), organic matter (OM) emerged as the dominant contributor (47.3%), escalating with increasing PM2.5 concentrations. OM significantly impacted PM2.5 levels during autumn in Jiangxi and Anhui (AH/JX) and across the monitoring period in Liuzhou, Guangxi (GX), with the former related to vehicle emissions and the latter related to bagasse reuse and biomass burning emissions. Conversely, nitrate (NO3−) made the highest contribution to PM2.5 during winter in the AH/JX region (34.4%), which was attributed to reduced SO2 levels and favorable low-temperature conditions conducive to nitrate condensation. Notably, nitrate contribution to HB/SX rose notably in heavily polluted winter conditions and during light–moderate pollution episodes in the autumn. Sulfate (SO42−) was dominant among PM2.5 components during summer in the study regions (29.9% in HB/SX, 36.1% in HN/SD, and 49.7% in AH/JX). Additionally, pollution incidents in Chuzhou, Anhui Province, and Baoding, Hebei Province, underscored nitrates and organic matter, respectively, as the primary causes of sharp PM2.5 increases. These incidents highlighted the influence of large emissions of primary aerosols, gaseous precursors, and stagnant meteorological conditions as pivotal factors driving haze pollution in the HB/SX region.

1. Introduction

In recent decades, China has witnessed a concerning decline in air quality, marked by frequent occurrences of severe haze events [1]. Among the pollutants contributing to hazy weather, PM2.5 has attracted much attention [2,3]. To combat this issue, China implemented stringent air pollution prevention and control measures from 2013 onwards, resulting in a significant 32% reduction in the nationwide average annual concentration of PM2.5 within five years [4]. Despite these efforts, ongoing research has continued to report instances of pollution, suggesting that the existing measures have not entirely curbed PM2.5 pollution in China [5,6,7]. Studies, such as the model analysis by Wang et al. [8], have highlighted that substantial reductions in emissions from transportation and industrial production were insufficient to prevent haze pollution, particularly during unfavorable weather conditions in winter. The accumulation of gaseous pollutants such as SO2, NOx, NH3, and volatile organic compounds (VOCs) under adverse meteorological conditions remains a crucial factor leading to the formation of secondary aerosols in PM2.5, contributing significantly to haze pollution [9,10].
Despite progress, a significant disparity still exists between current PM2.5 concentrations and the established target standards, necessitating a deeper understanding of PM2.5 components and an exploration into the causes of haze events. Primary particle emissions, the efficient transformation of gaseous precursors into secondary particles, regional transport, adverse meteorological conditions, and their synergistic effects have been identified as the primary determinants influencing the frequency and severity of haze formation in northern China [11,12]. Research conducted by Fu and Chen [13] emphasized that sulfate, nitrate, and ammonium constituted over 40% of PM2.5 mass concentration during haze formation in China. Notably, Chen et al. [14] observed a significant decrease in sulfate proportions, with nitrate emerging as the dominant PM2.5 component in northern China during specific periods.
Various monitoring methods have been developed to investigate the chemical composition of PM2.5, ranging from offline membrane sampling to online stationary or mobile monitoring. While offline membrane sampling offers broad sampling coverage, its reliance on 12 or 24 h integrated filters leads to lower time resolutions, obscuring the diurnal characteristics of PM2.5 components. Stationary fixed monitoring systems have low uncertainty errors and can monitor changes in air quality over time, but they are very expensive, highly engineered systems and are often limited in the number of installations [15]. Mobile monitoring can efficiently acquire high spatial resolution data under a variety of conditions and can fill the data gaps provided by stationary fixed monitoring systems [16]. For example, monitoring air conditions in places (rural and/or urban) that have not been included in the monitoring grid so far. There are two types of sampling for mobile monitoring: one is sampling on the move, which is often used to study pollution characterization and source tracking at the urban scale. The other is sampling at fixed locations, which is often used to compare pollution levels in different areas.
An approach to using joint mobile and stationary systems is growing in the field of urban air pollution monitoring. There are several ongoing studies on mobile air monitoring conducted worldwide. For instance, Mohr et al. [17] investigated the spatial variation and sources of submicron aerosols in Zurich during winter using a mobile aerosol mass spectrometer. Hsu et al. [18] used commercial instruments with high time resolution deployed in a mobile laboratory platform to analyze the spatial distribution, number, surface area, and mass concentration of fine particles in suburban and rural regions during and after PM increases in Taiwan Province of China. Ye et al. [19] conducted ground-based mobile measurements in the metropolitan area of Pittsburgh, Pennsylvania, USA, utilizing a single-particle mass spectrometer to analyze source-specific particles and their evolution. Zhang et al. [20] conducted ground movement measurements during the winter haze pollution period in Lanzhou City and used the Aerodyne aerosol mass spectrometer (AMS) to study the mass concentration and chemical composition of submicron particles. The above-mentioned research mainly focuses on urban street-scale air pollution monitoring, which requires high-temporal resolution equipment to reveal the spatiotemporal changes in pollutant concentrations. There are fewer studies related to sampling at fixed locations using mobile platforms. Ye et al. [19] studied the chemical composition of particulate at the Carnegie Mellon University (CMU) campus in the U.S. using AMS and SPAMS instruments deployed on mobile platforms.
Here, we introduced a particulate matter chemical composition vehicle (PMCCV), which is equipped with online monitoring equipment for PM2.5, organic carbon, elemental carbon (OC/EC), and ion chromatography, to realize the monitoring on a mobile platform and to conduct fixed-point observation in different cities to obtain high-resolution chemical composition data of PM2.5. In this work, our main goal is to explore the differences in the causes of PM2.5 pollution between parts of China based on high temporal resolution PM2.5 component information. Moreover, it delves into the characteristics and influencing factors of haze pollution in different regions by analyzing typical pollution cases in Anhui and Hebei during autumn and winter. This study provides scientific support for the further development of effective PM2.5 control strategies in various regions of China and provides a practical basis for understanding the characteristics and influencing factors of haze pollution in Anhui and Hebei.

2. Material and Methods

2.1. Observation City Sites

PMCCV was used for the online monitoring of fine particulate matter components in 23 cities across 7 provinces in China from March 2018 to December 2019. The 23 cities were Shijiazhuang, Baoding, Tangshan, Langfang, Xinji, Cangzhou, Hengshui, and Dingzhou in Hebei (HB); Jinzhong in Shanxi (SX); Anyang, Jiyuan, Jiaozuo, Xinxiang, Luoyang, and Zhengzhou in Henan (HN); Heze in Shandong (SD); Ganzhou and Yingtan in Jiangxi (JX); Bozhou, Suzhou, Chuzhou, and Fuyang in Anhui (AH); and Liuzhou in Guangxi (as shown in Figure 1a,b). Table S1 shows the climate and geographic information of the study regions. These cities were classified into four regions that were HB/SX, HN/SD, AH/JX, and GX from the north to the south. The Hebei and Shanxi provinces have developed heavy industries and are the primary industrial areas in China. According to an official release, the total industrial added value in Hebei accounted for 38.0% of the province’s gross domestic product (GDP) in 2018 (http://tjj.hebei.gov.cn/, accessed on 23 January 2024). Henan and Shandong are China’s most populous provinces, which consume large amounts of energy. By 2019, Henan and Shandong had 96.4 million and 10.1 million permanent residents, respectively. Anhui and Jiangxi are typical southern provinces in China. Guangxi has the lowest latitude in China.
The sampling duration in the different regions is shown in Figure 1c. A more detailed sampling duration in the different cities can be found in the Supplementary Materials (Figure S1). The seasonal mean concentrations of PM2.5 in the HB/SX, HN/SD, AH/JX, and GX regions were derived from MEE (Ministry of Ecology and Environment) networks in 2018–2019 and are shown in Figure 1d. Winter was the most polluted season for all the regions, while summer was the least. Seasonal concentrations in the HB/SX and HN/SD regions were close to each other and were higher than those in the AH/JX and GX regions.

2.2. Instruments

2.2.1. PMCCV

Figure S2 shows a schematic diagram of the mobile platform “PMCCV”. The PMCCV was 5.8 m long, 2.1 m wide, and 2.9 m high. The PMCCV was equipped with instruments including an environmental particulate matter monitor (BAM-1020, Met One, Grants Pass, OR, USA), an in situ gas and aerosol compositions monitor (IGAC, Fortelice International Co., Ltd., Taiwan, China), and a semi-continuous OC/EC carbon aerosol analyzer (model 4, Sunset Laboratory Inc., Oregon, USA), as well as additional components including a fire extinguisher, voltage-stabilized source, indoor lighting, and display screen. The sampling port is located above the roof of the vehicle, 3.5 m above the ground. During measurements, the PMCCV was placed in a fixed place, which is an open urban area away from buildings and local emission sources. During the measurement, the power supply of the PMCCV was reliant on an external municipal cable rather than the truck engine or a fuel generator. At the same time, meteorological parameters are monitored on site, including surface temperature (T), relative humidity (RH), surface wind speed (WS), and wind direction (WD).

2.2.2. Water-Soluble Inorganic Ions in the PM2.5

An in situ gas and aerosol composition monitor measured the water-soluble inorganic ions (WSIIs) in the gas and particulate-phase simultaneously with a time resolution of 1-h in this study. A detailed introduction to the IGAC can be found in the paper of Young et al. [21]. Briefly, the IGAC was composed of three major units, including a wet annular denuder (WAD) to collect gases into an aqueous solution, a scrub and impact aerosol collector (SCI) to collect particles into the solution, and a sample analysis unit composed of two ion chromatographs (Dionex ICS-1000) for analyzing anions and cations. Ambient air was drawn through a PM10 inlet followed by a PM2.5 cyclone at a flow rate of 16.7 L/min, and then the gases and particles were sequentially collected using the WAD and SCI. The LiBr solution, with a concentration of 0.14 ppm, was used as the internal standard and was added continuously to the impaction plate in the SCI at a flow rate of 0.1 mL/min. Both gaseous and aerosol samples were injected into 10 mL glass syringes that were then connected to the IC for analysis (30 min for each sample). The concentrations of NH4+, Na+, K+, Ca2+, Mg2+, SO42−, NO3−, and Cl− in the PM2.5 were then measured [22].

2.2.3. Carbonaceous Contents in the PM2.5

Organic carbon (OC) and elemental carbon (EC) were simultaneously monitored using a semi-continuous OC/EC carbon aerosol analyzer with a 1 h time resolution. Volatile organic gases were removed using an inline parallel carbon denuder installed upstream of the analyzer. A round 16 mm quartz filter was used to collect the PM2.5, with a sampling flow rate of 8 L/min. The Sunset OC/EC analyzer uses a modified NIOSH 5040 thermal–optical protocol as its default protocol, which produces a relatively reliable determination of OC and EC [23]. The sampling period was 30 min, and the analysis process lasted for 15 min. An internal standard CH4 mixture (5.0% in ultra-high-purity He) was automatically injected to calibrate the analyzer at the end of the analysis [22]. More detailed information can be found in the paper by Bauer et al. [23]. In this study, most of the research results were used to obtain a multiple close to two to convert the amount of OC to organic matter (OM) [24].

2.3. Quality Assurance and Control

The maintenance of the equipment is handled by dedicated personnel, and all data involved in this article were obtained in accordance with the quality control standards of each equipment. In addition to calibrating the instrument before and after each urban observation, we also performed an inspection of water-soluble inorganic ions’ chromatographs, internal standard, anion and cation balance, the split point of OC and EC, and PM2.5 mass reconstruction. The data of all devices was centralized to the data collection platform for unified processing and management.

3. Results and Discussion

3.1. Seasonal Variation in PM2.5 and Chemical Species

3.1.1. Spatiotemporal Distribution of PM2.5

Overview and spatial distributions of PM2.5 pollution in China: Average PM2.5 concentrations exhibited regional variations, ranked in the order of HB/SX (63.4 μg/m3) > HN/SD (53.5 μg/m3) > AH/JX (42.2 μg/m3) > GX (29.2 μg/m3). Throughout the campaign, the spatial distributions of PM2.5 were consistent with those derived from the MEE network datasets (Figure 1d). Ji et al. [25] also observed higher non-refractory PM concentrations in the BTH (Beijing–Tianjin–Hebei) and GZ (Guangzhong) regions. Quarterly averages for the four regions were above 35 μg/m3 for all seasons, except summer in AH/JX and GX.
Figure 2a illustrates the distribution of PM2.5 concentrations in the four regions during the campaign. The HB/SX region, encompassing Shijiazhuang, Baoding, Tangshan, Langfang, Hengshui, and Jinzhong, exhibited the widest concentration range (~300 μg/m3). This region, known for its high density of coal consumption and heavy industries such as iron, steel, and cement, recorded the highest concentrations [26]. In contrast, the lowest PM2.5 concentrations (~90 μg/m3) were observed in Guangxi Province (specifically, cities in Liuzhou) and the southern regions due to fewer anthropogenic emissions and conducive meteorological conditions favoring atmospheric dispersion and dilution. Despite the GX region’s observation gap during summer, its PM2.5 concentrations remained comparatively lower than those in the heavily industrialized HB/SX and HN/SD regions.
Seasonal variation in PM2.5 pollution in the four regions: Considerable seasonal variability was evident in PM2.5 concentrations across all four regions, peaking during winter (78.7 μg/m3) and reaching its nadir during summer (45.4 μg/m3) (Figure 2a). This finding aligns with the conclusions of Ding and Liu [27] on the long-term evolution of haze in China, emphasizing that haze predominantly occurs during winter, with substantially fewer occurrences in summer. Li et al. [28] also found that PM2.5 concentrations in 336 prefectural-level cities in China were highest in winter and lowest in summer. The heightened winter PM2.5 concentrations were attributed to increased anthropogenic emissions from fossil fuel combustion and biomass burning, compounded by unfavorable meteorological conditions—such as frequent occurrences of stagnant weather and temperature inversions—limiting pollution dispersion [29,30,31].
Seasonal trends varied spatially. For the HB/SX region, the average PM2.5 concentrations ranked highest during winter (90.5 μg/m3), followed by autumn (62.5 μg/m3), spring (54.7 μg/m3), and summer (49.0 μg/m3). In the HN/SD region, the average PM2.5 concentrations in autumn (63.4 μg/m3) and spring (56.9 μg/m3) are higher than those in summer (41.7 μg/m3). As indicated in Figure 1d, winter can be the more polluted season in the HN/SD region. In AH/JX, the trend slightly differed: winter (55.4 μg/m3) > spring (49.1 μg/m3) > autumn (40.9 μg/m3) > summer (21.4 μg/m3). The lower winter concentrations observed in the GX region were likely influenced by sampling duration limitations, biasing the seasonal trend. Notably, during winter, PM2.5 concentrations in the north were notably elevated due to widespread heating, although the increase multiple was lower compared to the south. This indicates that adverse diffusion conditions remain a significant contributor to winter haze pollution in northern China. Additionally, Yi et al. [32] highlighted that energy consumption intensifies during the cold season, and regional transport and stagnant meteorology further augment PM2.5 levels during autumn and winter. Chen and Wang [33] noted that haze events in northern China predominantly occur during winter, accounting for about 32.8% of annual occurrences, while relatively few occur during summer. Cao et al. [34] also reported minimal regional haze-fog occurrences in spring, and less likelihood of haze–fog during summer compared to autumn and winter in the North China Plain (NCP) region. Elevated PM2.5 levels during autumn in the HN/SD regions were attributed to increased open biomass burning during the agricultural harvest season [29]. Conversely, reduced anthropogenic emissions, including fossil fuel and biomass burning for domestic heating, contributed to decreased PM2.5 levels in summer. Moreover, the Asian summer monsoon’s large wet depositions of aerosols and clean air masses from the ocean, coupled with enhanced atmospheric mixing, led to significant aerosol dilution in eastern China during summer.

3.1.2. Characterization of Chemical Components

Table 1 offers a statistical summary of atmospheric concentrations for WSIIs, OM, and EC during the sampling period, with a visual representation of the composition in different regions and seasons in Figure 2b. OM notably contributed the most during the winter season, aligning with the most severe PM2.5 pollution in the HB/SX region, averaging 41.7 μg/m3, comprising approximately 47.3% of the total components. This observation resonates with studies by Huang et al. [11], Xie et al. [35], and Zhao et al. [36], highlighting the substantial presence of organic matter during winter in major Chinese cities like Beijing and Shijiazhuang. This spike in carbon-containing pollutants during winter correlated with increased emissions from coal and biomass burning for heating, complemented by conducive low temperatures promoting aerosol formation from semi-volatile organic compounds [37].
In the AH/JX region, the OM during the spring and autumn were 13.1 μg/m3 and 12.1 μg/m3, accounting for 34% and 43%, respectively, which were the largest components in PM2.5. High levels of OM in PM2.5 has been widely observed in YRD. Li et al. [38] showed that OM was the main contributor to PM2.5 in Ningbo. A study by Yang et al. [39] showed annual average OC concentrations in 2014 in urban Shanghai of 8.4 μg/m3. Duan et al. [40] showed that the annual average OC concentration in Hangzhou urban area during the period from October 2020 to August 2021 was 8.76 μg/m3. Cao et al. [41] also concluded that carbonaceous aerosols constituted 42% of the PM2.5 mass in suburban Shanghai. The primary sources of carbonaceous aerosols include vehicle exhausts, coal combustion, and biomass burning [42]. Zhao and Xu [3] showed that motor vehicles were the most critical drivers of increasing PM2.5 concentration. In the past decade, emissions from coal combustion have been largely controlled, while emission factors from motor vehicles have similarly decreased over the past decade, but a dramatic increase in private vehicle ownership has worsened air pollution in Shanghai [43]. The pollution caused by carbon components in the Yangtze River Delta and its surrounding areas was basically the same as that in Shanghai.
The component of Guangxi showed that the highest concentrations of OM during spring, autumn, and winter were 11.9 μg/m3, 8.1 μg/m3, and 6.5 μg/m3, accounting for 52.1%, 43.9%, and 49.2%, respectively. Liuzhou, Guangxi, was in the peak season of sugarcane sugar pressing during the monitoring period, and the city’s 11 sugar enterprises worked for 157 days in total. According to official statistics, during the 2018–2019 press season, the city’s raw cane import volume was 5.621 million tons, and the sugar production volume was 638,500 tons. Therefore, the higher OM ratio in Guangxi may have been related to the reutilization of bagasse and biomass combustion emissions.
In contrast, the AH/JX region showed higher concentrations of nitrate during winter (16.6 μg/m3), contributing approximately 34.4% to the total components. This rise in nitrate levels stemmed from substantial reductions in SO2 concentrations, favoring nitrate condensation due to low ambient temperatures. Similar trends of elevated nitrate ratios during low-temperature seasons like spring and autumn were observed in HB/SX (24.9–26.7%) and HN/SD (28.2–30.8%), supporting evidence that lower temperatures promote nitrate accumulation.
Conversely, during summer, sulfate concentrations were highest among the PM2.5 components in the HB/SX (11.9 μg/m3), HN/SD (12.8 μg/m3), and AH/JX (6.1 μg/m3) regions, contributing 29.9%, 36.1%, and 49.7%, respectively. This increase in summertime sulfate concentrations correlated with enhanced photochemistry and relatively higher humidity, accelerating the conversion rate of SO2 to particulate forms despite lower precursor SO2 concentrations. This pattern aligns with findings independently discovered by Yao et al. [44] and Yuan et al. [45], indicating maximum sulfate concentrations during summer in cities like Beijing and the Yangtze River Delta. However, the precursor SO2 concentrations in the HB/SX, HN/SD, and AH/JX regions were lower during summer than in other seasons (Figure S3) because of low emissions from fossil fuel combustion. One might further conclude that photochemistry plays a more vital role in sulfate aerosol formation and variability than the change in precursor SO2 emissions. Therefore, the high summertime sulfate concentration was attributed to enhanced photochemistry during summer, while the relatively high humidity accelerates the conversion rate of SO2 to the particulate matter. In addition, the proportion of chloride salt during summer was the lowest among the four seasons, which was likely related to the low coal combustion during summer. Ammonia is an important alkaline gas in the atmosphere and can be neutralized to produce ammonium in aerosols [46]. It is known that among the ammonium-associated compounds, (NH4)2SO4 is preferentially formed and the least volatile. This observation also may be ascribed to the volatility of ammonium nitrate at relatively high temperatures, which makes the nitrate contribution relatively low during summer.
In summary, due to specific seasonal conditions affecting their formation, nitrate predominates during winter and autumn, while sulfate dominates during summer. This aligns with observations by Xie et al. [35] showing SO42− and NO3− as dominant components during respective seasons. Sulfate’s higher productivity during summer, influenced by temperature-sensitive gas-phase reactions of SO2, contributes significantly to PM2.5 during this season. SO42− can be either produced from the gas-phase reaction of SO2 with OH radicals, or from heterogeneous or aqueous reactions (H2O2/O3 oxidation or metal catalyzed oxidation) [47,48]. The gas-phase reaction between SO2 and the OH radical is significantly influenced by temperature, so the productivity of sulfate is higher in summer and has a significant impact on PM2.5 in this season.

3.2. Components Varied with PM2.5 Levels

Figure 3 illustrates the proportional variations of components across different PM2.5 levels. For analysis purposes, due to limited data in Guangxi and the absence of summer data, the focus remained on the HB/SX, HN/SD, and AH/JX regions. In the HB/SX region, OM accounts for the largest proportion of PM2.5 in winter, especially when PM2.5 pollution is severe (>250 μg/m3). Xie et al.’s [35] study in Shijiazhuang similarly found that OM was the dominant species in winter which increased from 17.1 μg/m3 for clean days (28.7% of PM2.5) to 169.1 μg/m3 (38.4% of PM2.5). Organic aerosol (OA) comprised greater than 50% of the total mass of PM2.5 during haze events, and its formation is less understood than that of the inorganic fraction [49]. The observed pollutant concentrations were not only controlled by chemical reactions but can also be subject to the influence of boundary layer developments. Therefore, it is highly uncertain to conclude a stronger/weaker chemical production based purely on concentration data without considering the boundary layer effect [50].
CO-scaled OM could eliminate the influence of different dilution and mixing conditions on the variation of the observed pollutant concentrations. The primary causes of the high concentration of OM during winter in the HB/SX region were identified using the hourly component data after CO calibration. During winter, strong daily variations in the OM/CO ratio were observed in the HB/SX region that were characterized by the minima in the afternoon and the maxima at night (Figure S4a). The peak OM/CO concentration at night was approximately 2.7 times higher than that of the valley during the afternoon. This variation can be explained in two ways. One reason is the higher emission intensity supported by the higher EC/CO ratio at night. Note that a small peak for organics was observed at noontime due to the influences of cooking and vehicle emissions (Figure S4b), and the other peak was due to the relatively lower wind speed and higher humidity at nighttime [51]. In addition, stronger gas-phase photochemical reactions and increased atmospheric oxidative capacity in winter might have contributed to the rise in secondary organic carbon (SOC) levels during haze days [11]. Organic aerosol (OA) is broadly classified as primary OA (POA) that is directly emitted or as a secondary OA (SOA) that forms in the atmosphere. A higher RH and aerosol acidity could accelerate multiphase chemical reactions, resulting in increasing the level of SOC, as well as the slower atmospheric diffusion rate enhancing the contact time for gas–particle reactions during haze days [52]. The calculation performed by Huang et al. [11] of the contributions by fossil SOA to the OA mass was 1.1–2.4 times larger for high pollution events than for low pollution events, highlighting the importance of fossil SOA in particulate pollution. Therefore, in the HB/SX region, the control of the OM source should be paid more attention to heavy pollution weather during winter.
In the AH/JX region, during light pollution periods (76–115 μg/m3) in autumn and winter, nitrate contributes the most to PM2.5 concentration (Figure 3i,k). The prevalence of secondary inorganic ions, especially NO3−, was emphasized in the AH/JX region. The results of this study demonstrated that the role of secondary inorganic ions, especially NO3−, was more crucial in the AH/JX region than in other regions, which was consistent with the findings in Shanghai [53]. They also found that during explosive growth events, the major component of PM2.5 was NO3−. It also should be noted that the HB/SX region was severely polluted during winter (151–250 μg/m3) (Figure 3j) and light and moderately polluted (76–150 μg/m3) during autumn (Figure 3g). The contribution of nitrate to HB/SX increased significantly. However, there were significant differences in the diurnal variation of NO3−/CO in the HB/SX and AH/JX regions. The value of NO3−/CO in the HB/SX region showed a linear increase from 8.0 at 8:00 to 13.8 at 16:00, with a growth rate of ~0.73 /h, while the value in the AH/JX region was primarily concentrated from 0:00 am to 4:00 am (Figure S4c). NO3− formation primarily originated through two homogeneous reaction paths [54]. (1) NO2 reacted with OH radicals and formed HNO3. Then, HNO3 reacted with ammonia (NH3) to form nitrate NO3- aerosols. This pathway was dominant during the daytime. (2) During the nighttime, NO2 first reacted with nitrate radicals (produced by O3) and then reacted with the dinitrogen N2O5 [55]. Finally, the hydrolysis of N2O5 on wet aerosol surfaces released HNO3, which further led to NO3− formation, as in the first path. Obviously, there were differences in the formation mechanisms of nitrate in the HB/SX and AH/JX regions during winter, with HB/SX being driven primarily by gas-phase photochemical reactions and AH/JX by a combination of reaction pathways.
The contribution of the components in each region during summer was completely different from that during winter and autumn. Summer in the HB/SX region showcased sulfate as the primary contributor during cleaner days (PM2.5 < 75 μg/m3), while nitrate exhibited a slight upward trend during haze (PM2.5 > 75 μg/m3) days (Figure 3d). Earlier studies in Beijing performed by Sun et al. [56] also reported the presence of SO42− WSIIs species during non-haze days, while NO3− was the highest WSIIs species during haze days. Similarly, in the AH/JX region, OM showed a slight increase with rising PM2.5 levels. Noteworthy diurnal peaks of OM/CO ratios in AH/JX at 14:00 and 20:00 indicated influences of secondary organic aerosols formed during midday and increased vehicle emissions in the evening. Moreover, PM pollution emitted from diesel truck traffic, which is allowed only during the nighttime, additionally increased the PM burden because the emission factors of heavy-duty vehicles are six times those of light-duty vehicles [57]. It should be noted that such a traffic restriction has been applied in many Chinese megacities, which may affect the diurnal pattern of PM2.5 and its chemical compositions. The concentration range in the HN/SD region was 0–100 μg/m3, and the proportion of the components was stable.
Spring showed relatively stable contributions from components, potentially impacted by an increased concentration and contribution of mineral dust to PM2.5 [35]. In the HN/SD region, during autumn, chloride salt showed a noticeable upward trend during moderate pollution periods (>115 μg/m3), suggesting a significant influence from combustion sources such as coal and biomass burning on autumn haze [58,59]. This means that the autumn haze in the HN/SD region was significantly affected by the combustion source.
In summary, secondary inorganic aerosols played a pivotal role in heavy and severe pollution events during summer and autumn, while organic matter predominated during severe winter pollution. Spring might be more influenced by mineral dust, and specific regional variations in component contributions reflect diverse pollution characteristics across different seasons and regions.

3.3. Special Event Analysis

During the observation period, we selected two pollution events with distinct characteristics for in-depth analysis, aiming at demonstrating the evolution of the chemical components of PM2.5 during extremely polluted weather and exploring the drivers of the pollution process. By analyzing these data, we were able to gain insights into the sources and composition of the pollutants as well as their evolution patterns during extreme pollution events. This approach is not directly comparable to other monitoring methods, but it still provides us with an in-depth understanding of pollution events and can provide a valuable reference for further research and response to extreme pollution events.
The investigation of PM2.5 chemical composition in Chuzhou identified secondary inorganic aerosols (SIA), particularly nitrate, as dominant contributors to haze formation. Between 19–24 October, several episodes of rapid nitrate concentration growth were noted, averaging 39.5 μg/m3 (Figure 4). SIA accounted for an average of 65.8% of the PM2.5, corroborating the significance of SIA in severe haze incidents, a finding echoed by various field studies [11]. Yuan et al. [45] in the same region highlighted SO42−, NO3−, and NH4+ as major WSIIs in PM2.5 during haze events, comprising 57.6% of the PM2.5.
The higher nitrate levels were attributed to low temperatures and high relative humidity (RH), facilitating gas-to-particle partitioning. Notably, on 21 October, both PM2.5 and ozone concentrations peaked simultaneously. Quan et al. [60] suggested that during severe haze periods, increased conversion ratios of N from NOx to nitrate (N ratio) and S from SO2 to sulfate (S ratio) occurred due to low visibility, reduced photochemical activity, and sharply increased RH, promoting accelerated NOx and SO2 conversion via heterogeneous aqueous reactions.
Trajectory analysis indicated an east–northeast origin of air masses reaching Chuzhou (Figure 4), leading to poor horizontal and vertical diffusion, thereby accumulating pollutant concentrations. Similar stable synoptic conditions, reduced wind speeds, and lower planetary boundary layer heights (PBLH) during polluted days in nearby Nanjing were observed by Yin et al. [12], indicating conditions favorable for pollutant accumulation.
Figure 5 illustrates the variation trend in particulate matter components in Baoding City (Qingyuan and Dingzhou) during the pollution episode from 30 November to 10 December 2018. Qingyuan and Dingzhou recorded average PM2.5 concentrations of 160.3 μg/m3 and 97.6 μg/m3, respectively, primarily influenced by OM. OM comprised 51.3% and 75.5% of the total components in Qingyuan and Dingzhou, respectively. Wei et al. [61] observed that most rapid growth events occurred during the nighttime, with a substantial increase in OM and chloride concentrations in the NCP. Meteorological conditions, such as low wind speed, led to local OM accumulation. This event was the result of anthropogenic emissions coupled with unusual atmospheric circulation, suppressing dispersion due to weakened cold air activity. Factors like decreased PBLH during haze events and sluggish horizontal transport due to weak winds often contribute significantly to haze formation. In addition to meteorology factors, secondary particle formation can significantly contribute to heavy haze events as well [11]. Secondary particle formation, alongside large emissions of primary aerosols and gaseous precursors, has also been identified as crucial in the evolution of haze pollution in the NCP [49,51,52].

4. Conclusions

This study scrutinized PM2.5 concentrations and chemical species data collected through a PMCCV across 23 major cities from March 2018 to December 2019. The analysis revealed higher PM2.5 concentrations in northern cities compared to southern regions, ranking them as follows: HB/SX (63.4 μg/m3) > HN/SD (53.5 μg/m3) > AH/JX (42.2 μg/m3) > GX (29.2 μg/m3). This suggests regional differences in pollution levels and the need to tailored regional control strategies based on local/regional emissions and meteorological characteristics. Seasonal variations in PM2.5 concentrations were evident, peaking in winter and troughing in summer across all four regions. Winter peaks were linked to increased anthropogenic emissions from fossil fuel combustion and biomass burning, exacerbated by unfavorable meteorological conditions like stagnant weather and temperature inversions hindering pollution dispersion.
OM contributed the most during winter, when PM2.5 pollution was the most severe PM2.5 pollution in the HB/SX region, averaging 41.7 μg/m3 and accounting for about 47.3% of the total composition. In the AH/JX region, the OM during the spring and autumn were 13.1 μg/m3 and 12.1 μg/m3, respectively, which was the largest contribution to the PM2.5. The primary source of carbonaceous aerosols was vehicle exhaust. Guangxi showed the highest concentration of OM during the spring, autumn, and winter, accounting for 52.1%, 43.9%, and 49.2%, respectively. This may have been related to the reutilization of bagasse and biomass combustion emissions. However, in the AH/JX region, nitrate contributed more to the PM2.5 than OM in winter, contributing about 34.4% to the total composition. This phenomenon can be explained by the significant decrease in SO2 in recent years and by the fact that the lower temperatures during winter facilitate the condensation of nitrate. It also should be noted that the contribution of nitrate in the HB/SX region tended to increase when the PM2.5 was heavily polluted (151–250 μg/m3) during winter and lightly and moderately polluted (76–150 μg/m3) during autumn. During summer, sulfate was predominant among PM2.5 components analyzed in the three regions despite lower precursor SO2 concentrations, emphasizing the role of photochemistry in sulfate aerosol formation rather than SO2 emission changes.
Strong daily variations in the OM/CO ratio were observed in the HB/SX region which was characterized by the minima in the afternoon and the maxima at night. This variation can be explained in two ways. One is the higher emission intensity supported by the higher EC/CO at night, and the other is the relatively lower wind speed and higher humidity at nighttime. There were significant differences in the diurnal variation of NO3−/CO in the HB/SX and AH/JX regions. The value of NO3−/CO in the HB/SX region showed a linear increase from 8.0 at 8:00 to 13.8 at 16:00, while the value in the AH/JX region was primarily concentrated from 0:00 am to 4:00 am. The winter in the HB/SX region is primarily driven by the gas-phase photochemical reaction, while the winter in the AH/JX region is the result of the combined effect of gas-phase photochemistry and a liquid-phase reaction.
The pollution incident in Chuzhou, Anhui Province, proved, once again, that a sharp increase in PM2.5 is primarily caused by nitrates in SIA. It was found that the variation in nitrate and the relative humidity were highly consistent. Low temperatures and high RHs facilitate gas-to-particle partitioning, explaining the high nitrate concentration during haze events during autumn and winter. In addition, the peak ozone concentration on 21 October was also the highest during the monitoring period at 196 μg/m3. These results indicate that the high conversion of S and N during haze events was caused by the combined effect of photochemical and heterogeneous aqueous activity during the autumn. The incident in Baoding, Hebei Province, revealed OM as the primary contributor, exacerbated by stagnant meteorological conditions, emphasizing the dominance of primary and secondary aerosol emissions in haze formation in Hebei Province.
Our study is based on high temporal resolution PM2.5 composition information and aims to explore the differences in causes of PM2.5 pollution in certain regions of China. However, there are still some shortcomings in the current mobile platform. The specific limitations can be summarized as follows: (1) since our mobile measurements were only conducted at certain fixed locations in the city, it was not possible to comprehensively characterize the chemical components of PM2.5 across the whole city, which may lead to an insufficient overall understanding of the pollution sources, and (2) we deployed only one mobile platform and did not take measurements in different areas at the same time, which may affect the completeness of our assessment of regional differences in PM2.5. Therefore, more comprehensive mobile experiments covering multiple locations and extending the measurement period are needed in the future to provide a more comprehensive understanding of the spatial and temporal variation patterns of PM2.5 composition and to provide more reliable data support for the development of more effective pollution control strategies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su16062340/s1, Table S1. Study regions climate and geographic information. Figure S1. The sampling time distribution of the 23 cities in the four regions from 2018 to 2019. Figure S2. Side view (top) and top view (bottom) of the PMCCV used for mobile measurements. Figure S3. Daily variation in the sulfur oxidation rate (SOR), nitrogen oxidation rate (NOR), and other gases for four seasons in the three regions. Figure S4. Diurnal variations of the hourly PM2.5 components concentrations during winter and summer in the different regions. Figure S5. Backward trajectory that occurred on 19–22 October 2019 in Chuzhou.

Author Contributions

Conceptualization, H.C. and C.W.; methodology, H.C., J.L. and X.L.; software, H.C. and J.M.; validation, H.C., J.L. and P.W.; formal analysis, H.C. and J.L.; investigation, H.C. and J.L.; resources, C.W.; data curation, J.M.; writing—original draft preparation, H.C. and J.L.; writing—review and editing, H.C. and J.L.; visualization, P.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Hebei Province Key Research and Development Plan project under grant 19213716D; the S&T Program of Hebei (20313701D); the Hebei Province Youth Top Talent Support Program; and the Science and Technology Plan Program of Shijiazhuang (236240267A).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data is available upon request. To obtain the data, please contact Hui Chen ([email protected]) or Chunying Wang ([email protected]).

Conflicts of Interest

Authors Jingjin Ma and Chunying Wang were employed by Hebei Advanced Environmental Protection Industry Innovation Center Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Sampling locations and durations of the particulate matter chemical composition vehicle (PMCCV). (a,b) show the regional distribution of the sampling locations (green for the HB/SX region, yellow for the HN/SD region, blue for the AH/JX region, and orange for the GX region; a red dot represents the site of the sampling cities). (c) shows the sampling durations for each region. (d) shows the seasonal mean PM2.5 concentration in each region averaged using the MEE networks at the corresponding cities in 2018–2019.
Figure 1. Sampling locations and durations of the particulate matter chemical composition vehicle (PMCCV). (a,b) show the regional distribution of the sampling locations (green for the HB/SX region, yellow for the HN/SD region, blue for the AH/JX region, and orange for the GX region; a red dot represents the site of the sampling cities). (c) shows the sampling durations for each region. (d) shows the seasonal mean PM2.5 concentration in each region averaged using the MEE networks at the corresponding cities in 2018–2019.
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Figure 2. Seasonal variation of PM2.5 concentrations (a) and distribution of PM2.5 components (b) in different regions.
Figure 2. Seasonal variation of PM2.5 concentrations (a) and distribution of PM2.5 components (b) in different regions.
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Figure 3. Variation in the PM2.5 compositions as a function of the PM2.5 mass concentrations during different seasons and in different regions. (a,d,g,j) represent the spring, summer, autumn, and winter seasons in the HB/SX region, respectively. (b,e,h) represent the spring, summer, and autumn seasons in the HN/SD region, respectively. (c,f,i,k) represent the spring, summer, autumn, and winter seasons in the AH/JX region, respectively.
Figure 3. Variation in the PM2.5 compositions as a function of the PM2.5 mass concentrations during different seasons and in different regions. (a,d,g,j) represent the spring, summer, autumn, and winter seasons in the HB/SX region, respectively. (b,e,h) represent the spring, summer, and autumn seasons in the HN/SD region, respectively. (c,f,i,k) represent the spring, summer, autumn, and winter seasons in the AH/JX region, respectively.
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Figure 4. Time series of (a) meteorological parameters; (b) composition of PM2.5 during a pollution episode (21 October 2019) in Chuzhou.
Figure 4. Time series of (a) meteorological parameters; (b) composition of PM2.5 during a pollution episode (21 October 2019) in Chuzhou.
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Figure 5. Time series of (a) meteorological parameters; (b) composition of PM2.5 during a pollution episode (30 November to 2 December 2018) in Baoding.
Figure 5. Time series of (a) meteorological parameters; (b) composition of PM2.5 during a pollution episode (30 November to 2 December 2018) in Baoding.
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Table 1. Statistical summary showing the means of atmospheric concentrations for the selected species (in unit μg m−3) during the four seasons in the four regions.
Table 1. Statistical summary showing the means of atmospheric concentrations for the selected species (in unit μg m−3) during the four seasons in the four regions.
DistrictSeasonOMECNH4+Cl−NO3−SO42−
HB/SXSPR13.72.16.42.112.49.7
SUM9.31.37.21.78.611.9
AUT17.63.37.01.713.410.8
WIN41.75.68.34.115.612.9
HN/SDSPR11.31.66.91.312.510.6
SUM9.61.36.60.44.812.8
AUT14.92.34.60.712.45.3
AH/JXSPR13.12.34.31.19.38.6
SUM4.20.50.80.30.56.1
AUT12.11.62.50.44.27.0
WIN12.62.26.21.116.69.5
GXSPR11.91.62.90.61.64.2
AUT8.11.22.10.61.55.0
WIN6.50.62.00.91.61.6
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Chen, H.; Liu, J.; Wang, P.; Lin, X.; Ma, J.; Wang, C. Characteristics of PM2.5 Chemical Species in 23 Chinese Cities Identified Using a Vehicular Platform. Sustainability 2024, 16, 2340. https://doi.org/10.3390/su16062340

AMA Style

Chen H, Liu J, Wang P, Lin X, Ma J, Wang C. Characteristics of PM2.5 Chemical Species in 23 Chinese Cities Identified Using a Vehicular Platform. Sustainability. 2024; 16(6):2340. https://doi.org/10.3390/su16062340

Chicago/Turabian Style

Chen, Hui, Jingjing Liu, Peizhi Wang, Xiao Lin, Jingjin Ma, and Chunying Wang. 2024. "Characteristics of PM2.5 Chemical Species in 23 Chinese Cities Identified Using a Vehicular Platform" Sustainability 16, no. 6: 2340. https://doi.org/10.3390/su16062340

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