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Article

The Variation in Chemical Composition and Source Apportionment of PM2.5 before, during, and after COVID-19 Restrictions in Zhengzhou, China

by
Jinting Huang
1,2,
Aomeng Cai
2,3,
Weisi Wang
4,
Kuan He
1,*,
Shuangshuang Zou
2 and
Qingxia Ma
2,3,*
1
College of Surveying and Mapping Engineering, Yellow River Conservancy Technical Institute, Kaifeng 475004, China
2
Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
3
Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security, Kaifeng 475004, China
4
Henan Ecological and Environmental Monitoring Center, Zhengzhou 450007, China
*
Authors to whom correspondence should be addressed.
Toxics 2024, 12(1), 81; https://0-doi-org.brum.beds.ac.uk/10.3390/toxics12010081
Submission received: 6 December 2023 / Revised: 13 January 2024 / Accepted: 15 January 2024 / Published: 17 January 2024
(This article belongs to the Special Issue Atmospheric Aerosols: Source Apportionment, Characterizations, and Detection)
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Abstract

:
Despite significant improvements in air quality during and after COVID-19 restrictions, haze continued to occur in Zhengzhou afterwards. This paper compares ionic compositions and sources of PM2.5 before (2019), during (2020), and after (2021) the restrictions to explore the reasons for the haze. The average concentration of PM2.5 decreased by 28.5% in 2020 and 27.9% in 2021, respectively, from 102.49 μg m−3 in 2019. The concentration of secondary inorganic aerosols (SIAs) was 51.87 μg m−3 in 2019, which decreased by 3.1% in 2020 and 12.8% in 2021. In contrast, the contributions of SIAs to PM2.5 increased from 50.61% (2019) to 68.6% (2020) and 61.2% (2021). SIAs contributed significantly to PM2.5 levels in 2020–2021. Despite a 22~62% decline in NOx levels in 2020–2021, the increased O3 caused a similar NO3− concentration (20.69~23.00 μg m−3) in 2020–2021 to that (22.93 μg m−3) in 2019, hindering PM2.5 reduction in Zhengzhou. Six PM2.5 sources, including secondary inorganic aerosols, industrial emissions, coal combustion, biomass burning, soil dust, and traffic emissions, were identified by the positive matrix factorization model in 2019–2021. Compared to 2019, the reduction in PM2.5 from the secondary aerosol source in 2020 and 2021 was small, and the contribution of secondary aerosol to PM2.5 increased by 13.32% in 2020 and 12.94% in 2021. In comparison, the primary emissions, including biomass burning, traffic, and dust, were reduced by 29.71% in 2020 and 27.7% in 2021. The results indicated that the secondary production did not significantly contribute to the PM2.5 decrease during and after the COVID-19 restrictions. Therefore, it is essential to understand the formation of secondary aerosols under high O3 and low precursor gases to mitigate air pollution in the future.

1. Introduction

Despite significant improvements in air quality since strict restrictions on residential and industrial activities were implemented to prevent the spread of COVID-19 [1,2,3,4,5,6], haze formed in the North China Plain (NCP). Many studies have reported that the efficient formation of secondary aerosol species, including nitrate and sulfate, induced occurrences of haze during the restrictions [7,8,9]. The formation of secondary aerosol species changed significantly before, during, and after the COVID-19 restrictions, but the factors of this variation remain unclear.
Nitrate (NO3−) and sulfate (SO42−) are two main species of secondary inorganic aerosols that contributed to the haze pollution [10,11]. Their precursor gases (NOx and SO2) are primarily produced by anthropogenic primary emissions. Due to COVID-19 restrictions, NOx and SO2 concentrations have been reduced in the NCP. However, secondary pollution was enhanced in these anomalies, and they dominated the PM2.5 level [12,13,14].
SO42− is formed from SO2 through gas, aqueous, and heterogeneous reactions like those in the aqueous phase [11,12,15,16,17]. Studies found that the RH of ambient air was a major factor in SO42− conversion via heterogeneous reactions, and a high RH could enhance the heterogeneous formation of SO42− [18,19,20]. NO and NO2 are oxidized by a hydroxyl radical (OH) and O3 to produce nitric gas (HNO3), and then HNO3 is aerosolized with inorganic cations [21,22,23,24,25,26,27]. Wang et al. (2016) found that when NO2 levels are high, aerosol heterogeneous reactions from SO2 and NO2 produce SO42− and NO3−. In addition, the formation reactions of SO42− and NO3− are influenced by NH3 and O3 concentrations, relative humidity (RH), and temperature [13,24,27,28].
COVID-19 restrictions, such as reduced transportation on roads, industry, construction sites, and restaurants, influenced anthropogenic pollutant emissions [29,30,31,32,33]. However, the large reduction (30~70%) in NOx and decrease (15~30%) in PM2.5 did not eliminate the occurrence of haze [8,13,30,31,34]. Instead, O3 levels increased by 100~200% [32,35]. Studies have attempted to elucidate why haze events occurred during COVID-19 restrictions in China. Some papers confirmed that the contribution of SIAs to PM2.5 increased significantly in Beijing–Tianjin–Hebei and cities in northern China during restrictions, which prevented the continued decline in PM2.5, resulting in PM2.5 levels being higher than China’s National Ambient Air Quality Standards (NAAQS, PM2.5 of 75 μg m−3) [8,14,36,37,38,39]. It was reported that the enhanced contribution of sulfate and oxygenated organic aerosol inhibited further PM2.5 reduction in Shanghai, Lanzhou, and Xi’an [7,37,38]. Recent papers revealed that efficient secondary aerosol formation might have offset the reduction in primary emissions or decreased PM2.5 in Beijing and Hangzhou [8,9,38,40].
COVID-19 restrictions led to substantially reduced emissions of anthropogenic pollutants in big cities in the NCP. They provided an opportunity to investigate the response of air quality to reductions in air pollutants caused by human activities and the processes that caused the haze. Zhengzhou is one of the most populous cities in the southern NCP. Despite significant improvements in air quality in recent years, haze still occurs in Zhengzhou [41,42]. Restrictions were implemented in Zhengzhou (24 January to 18 February, 2020) to strictly regulate human, industrial, and construction activities to stop the spread of COVID-19. As the COVID-19 pandemic is still ongoing in Zhengzhou, changes in the chemical composition and source apportionment of PM2.5 and the reasons for these variations must be investigated in detail. This study compared the ionic composition and sources of PM2.5 in the city during restrictions with those before and after restrictions (24 January to 18 February, 2019 and 2021). This study analyzed the changes in NO3− and SO42− formation before, during, and after restrictions and explored the causes of haze.

2. Methods

2.1. Observation Site and Instruments

The observation site was at the environment monitoring supersite of Henan province in Zhengzhou (34.76° N, 113.70° E, Figure 1). This study utilized observational data from 24 January to 18 February in 2019, 2020, and 2021. The three observation periods were studied and compared: 24 January to 18 February 2020 (the restriction period in Zhengzhou); 24 January to 18 February 2019 (the comparison period before the restriction period); and 24 January to 18 February 2021 (the comparison period after the restriction).
The meteorological conditions (pressure, temperature, relative humidity, wind speed, and wind direction) were measured by a weather station (WS601-UMB, Lufft Instruments, Inc., Stuttgart, Germany). The concentrations of PM2.5, PM10, O3, SO2, NOx, and CO were routinely measured through a series of pollutant detectors (5030, 5014i, 49I, 42I, 43I, 48I; Thermo Fisher Scientific, Waltham, MA, USA) at a time resolution of 5 min. A one-hour average of air pollutants and weather conditions was applied.
An online analyzer for monitoring aerosols and gases (MARGA, model ADI 2080 Applikon Analytical B. V. Corp., Petten, The Netherlands) measured the hourly mass concentrations of NH4+, Na+, K+, Ca2+, Mg2+, SO42−, NO3−, and Cl− in PM2.5 at a flow rate of 16.7 L min−1 [43,44]. To ensure data accuracy, the seven-point calibration curve of each species and an internal standard solution were conducted before each campaign. In addition, sampling flow calibration and cyclone cleaning were conducted to eliminate instrumental drifts. The concentrations of organic carbon (OC) and elemental carbon (EC) in PM2.5 were measured using a thermo-optical OC/EC analyzer (Model RT-4, Sunset Lab, Tigard, OR, USA). Elements in PM2.5, including K, Ca, Zn, Cu Ni, Fe, Pb, Se, Cr, Mn, As, Co, Ti, Mo, Sc, V, Ba, Br, and Si, were quantified using an online analyzer for monitoring (AMMS-100, Focused Photonics Inc., Hangzhou, China). The formulas of major components in PM2.5 and the conversion rates of SO2 to sulfate (SOR) and NOx to nitrate (NOR) are listed in Table 1.

2.2. PMF Model

The origins of PM2.5 were analyzed using the USEPA version 5.0 positive matrix factorization (PMF) model. This model is a widely used receptor model, has high efficiency and convenience without using pollution discharge conditions, and allows additional constraints to be added into the factor profiles or contributions to reduce results’ uncertainties. Input factors of the model include eight water-soluble ions (e.g., Na+, NH4+, K+, Mg2+, Ca2+, NO3−, SO42−, and Cl−) and nine target metal elements (e.g., Si, Fe, Ti, Zn, Sb, Mn, Cu, Pb, and Cr), as well as the PM2.5 mass concentration. In this study, five source types were tested in the analysis. More details about PMF running and result diagnosis are in the Supplementary Materials.

3. Results

3.1. Weather Conditions and Air Pollutants

Figure 2 illustrates the variations in wind direction (Wd), wind speed (Ws), relative humidity (RH), temperature (T), and air pollutant concentrations (PM10, PM2.5, CO, O3, NO2, NO, and SO2). The average wind speed (Wd) was 1.67 ± 1.01 m s−1 during the 2020 COVID-19 restrictions, 1.79 ± 1.06 m s−1 in 2019, and 1.67 ± 1.00 m s−1 in 2021. The results indicate that the wind was weak in all periods. The average temperature (T) was 3.20 ± 4.17 °C in 2019 and increased to 6.72 ± 4.41 °C in 2020 and 9.81 ± 3.99 °C in 2021. The average RH was 60–61% during the 2020 COVID-19 restrictions and in 2019, but 46% in 2021.
The average concentrations of PM10 and PM2.5 were 148.49 and 102.49 μg m−3 in 2019, and they decreased by 38% and 29% in 2020 and 11% and 28% in 2021 (Figure 3), respectively, resulting in a significant decline in particles in 2020 and 2021. However, the episodic haze formed with a PM2.5 value of more than 75 μg m−3 in 2020 and 2021. At the same time, the average concentrations of NO2, NO, SO2, and CO in 2019 were 44.45 ± 25.74 μg m−3, 24.89 ± 49.49 μg m−3, 14.90 ± 6.11 μg m−3, and 1.25 ± 0.49 mg m−3, respectively. NO2, NO, SO2, and CO concentrations decreased by approximately 51%, 90%, 28%, and 6% in 2020, respectively. In addition, NO2, NO, SO2, and CO concentrations in 2021 were lower by 18%, 58%, 7%, and 11% than in 2019. NO2, NO, SO2, and CO are mainly emitted by primary emissions. The reductions in NO2, NO, SO2, and CO levels indicated that primary emissions may have decreased in 2020 and 2021. In contrast, the concentration of O3 increased from 46.70 μg m−3 in 2019 to 58.24 μg m−3 in 2020 and 56.75 μg m−3 in 2021. The increased O3 concentration was found in many cities (e.g., Xi’an, Shanghai, Beijing, and Hangzhou) [7,9,39].

3.2. Chemical Compositions of PM2.5

The concentration of secondary inorganic aerosols (SIAs) was 51.87 μg m−3 in 2019, 50.25 μg m−3 during the 2020 COVID-19 restrictions, and 45.21 μg m−3 in 2021. Compared with that in 2019, the SIA concentration decreased by 3.1% in 2020 and 12.8% in 2021. In contrast, the contributions of SIAs to PM2.5 increased from 50.61% in 2019 to 68.6% in 2020 and 61.2% in 2021. Between 2019 and 2021, SIAs were the major components of PM2.5, with a greater impact in 2020 and 2021. Compared with that in 2019, the concentration of PM2.5 decreased by 29% during the 2020 COVID-19 restrictions and 28% in 2021, but the SIA concentration decreased by 3% and 13%, respectively. The different declines between PM2.5 and their SIAs may be attributed to NO3− and SO42− formation.
The average concentrations of NO3−, NH4+, and SO42− were 22.93 μg m−3, 13.69 μg m−3, and 15.25 μg m−3 in 2019 (Figure 4a). The average NO3− and NH4+ concentrations were reduced by 9.8% and 5%, respectively, during the 2020 restriction period. In contrast, SO42− increased by 8.6% in 2020. These results indicate that the reduction in precursor gases (NOx and SO2) did not result in proportionate reductions in NO3− and SO42− during the 2020 restriction period compared to 2019. Mismatched decreases in NO3− levels with marked NOx reductions and the puzzling SO42− increase during the 2020 restriction period offset the decrease in PM2.5 levels in Zhengzhou. The average concentrations of NO3− in 2021 were similar to that in 2019. However, the average NH4+ and SO42− concentrations decreased by 16.9% and 28.9%, respectively, in 2021. The large reductions (18% and 58%) in NO2 and NO also could not help alleviate NO3− formation in 2021, which was related to the high conversion rate of NOx to nitrate (NOR). The restriction resulted in a decrease in PM2.5 levels; however, air quality remained episodically bad.
The average concentrations of OC and EC were 12.71 and 3.64 μg m−3 in 2019, accounting for 12% and 4% of PM2.5 mass (Figure 4b). The average concentrations of OC and EC were 8.97 and 2.76 μg m−3 in 2020, 29% and 24% lower than those in 2019, respectively. In 2021, the average concentrations of OC and EC decreased by 38% and 15%, respectively. Although the average concentrations of OC and EC showed significant reductions in 2020 and 2021, the contributions to PM2.5 were similar. The value of OC/EC was 4.31 (1.83~11.93) in 2019, 3.64 (1.99~9.71) in 2020, and 2.77 (1.16~8.74) in 2021. OC and EC are majorly emitted from fossil fuel combustion and biomass burning. Carbonaceous aerosols from biomass-burning sources have richer OC than EC, implying high OC/EC values. When OC/EC ratios were lower than 2.0, carbonaceous aerosols were from vehicular and industrial emissions. In addition, high OC/EC ratios (>2.0) indicate the presence of secondary organic aerosols (SOAs) [7,37,43]. OC/EC ratios mostly exceeded 2 in 2019 and 2021 (Figure S1), suggesting carbonaceous aerosols from biomass-burning sources. The mass concentration of SOA decreased from 10.31 μg m−3 in 2019 to 5.83 μg m−3 in 2020 and 6.88 μg m−3 in 2021 (Figure 4 and Table 1). The reductions in SOA were 43.49% in 2020 and 33.21% in 2021. The changes in secondary inorganic species were much smaller than those in secondary organic aerosols.
The average concentrations of Cl− and K+ decreased from 2.03 and 3.84 μg m−3 in 2019 to 1.25 and 2.45 μg m−3 in 2020 and to 1.47 and 2.79 μg m−3 in 2021, implying a reduction in emissions from coal combustion and biomass burning in 2020 and 2021. The average concentrations of Ca2+, Mg2+, and Na+ in 2019 were 0.46, 0.19, and 0.29 μg m−3, respectively, which were 40%, 31%, and 26% higher than those in 2020 and 93%, −2%, and 7% lower than those in 2021.

3.3. Nitrate and Sulfate Formations over the Three Years

It is a puzzling phenomenon that SIA contributions to PM2.5 were higher in 2020 (~68%) and in 2021 (~60%) than in 2019 (51%), when PM2.5 decreased from 102.49 μg m−3 (2019) to 73.92 μg m−3 (2020) and 76.78 μg m−3 (2021). SO2 and NOx are NO3− and SO42−’s key precursor gases. To elucidate the puzzling phenomenon, the formation of NO3− and SO42− was compared with their precursor gases, PM2.5, and conversion ratios under different periods.
NO3− positively increased with NOx between 2019 and 2021 (Figure 5 and Table S2, p < 0.01), indicating that high concentrations of the precursor contributed to high NO3− levels and then led to the remarkable increases in PM2.5. The result was supported by the positive relationship between NO3− and PM2.5 (Table S2, p < 0.01). At a given precursor concentration, higher conversion ratios led to higher NO3− and SO42− concentrations, implying that secondary conversion ratios were important for the formation of NO3− and SO42−. It was found that NO3− and SO42− levels were low in the region with high precursor gases region and low conversion ratios. The results suggested that the absolute concentrations of NO3− and SO42− were more closely related to the secondary conversion rates than the precursor concentrations. A similar phenomenon was found in the summer of 2019 and 2020 [3].
There were some exceptions in the same NOx region when NO3− concentrations were higher in 2020 and 2021 than in 2019. In 2020 and 2021, the higher NO3− levels in the same NOx region were closely related to the higher NOR. Meanwhile, in 2020, higher SO42− concentrations in the same SO2 region significantly correlated with higher SOR levels. It is equally inconceivable that higher conversion ratios occurred in 2020 and 2021 to cause high secondary formation, especially NO3−.
RH and O3 are key factors regulating the oxidation pathways for nitrate and sulfate formation [14,15,37,45]. Both NOR and SOR were positively correlated with RH, as shown in Figure 6 and Table S4 (p < 0.01), suggesting that high RH favors the conversion of gaseous precursors (e.g., NOx and SO2) to NO3− and SO42−. The NOR concentration was high in a high-O3 region at a given RH, while the SOR was not (Figure 6). NOR was positively correlated with O3 and O3/Ox in 2019–2021 (p < 0.01,Table S4), but SOR was not. The results indicated that NOx was efficiently oxidized by O3. O3 increased in 2020 and 2021; higher NOR values were accompanied by higher O3 and O3/Ox values in a given RH, compared with 2019 (Figure 6), whereas the conversion rate of SO2 to sulfate was not. O3 was likely the major oxidant for the high level of NO3− formation.

3.4. Sources during the Three Periods

The profiles of six factors were identified using the EPA PMF v5.0 in Figure 7. Factor 1 was characterized by the highly explained variations in Ni, Zn, Se, Mg2+, Fe, and Mn in the three periods, identified as traffic emissions. Zn is widely used as an additive for lubricants in engines, vehicular tailpipe exhausts [45], debris from brake wear, and worn tires [46,47]. Factor 2 was dominated by high K+, Ba, and Mg2+ concentrations, generally indicators of biomass burning [48,49]. Factor 3 was characterized by highly explained variations in Ni, As, Cu, Ni, Mo, OC, and EC in 2019 and 2020, whereas Ba, As, OC, and EC in 2021 were identified as industrial emissions. This result was consistent with different industrial processes in the three areas. Studies have shown that steel sintering can emit large amounts of As, Zn, Ni, and Cu [21,50]. Factor 4 was dominated by high Cl−, OC, and EC concentrations, generally indicators of coal combustion [21,40,51]. Factor 5 included large amounts of Mg2+, Ca2+, and Si [52,53,54]. Previous studies have shown that soil is also represented by a high loading of Ca, Mg, Si, and Al [47,49,54]; therefore, this factor was attributed to dust. Factor 6 was characterized by high NO3−, SO42−, NH4+, and OC content, as well as relatively high concentrations of OC. These species are mainly associated with secondary processes [46,50]. Thus, this factor was classified as a secondary aerosol.
Secondary processes (43.09 μg m−3), coal combustion (29.18 μg m−3), biomass burning (7.25 μg m−3), traffic emissions (6.59 μg m−3), industrial emissions (3.48 μg m−3), and dust (1.87 μg m−3) were the six main sources of PM2.5 in Zhengzhou in 2019, contributing 42.04%, 28.47%, 7.08%, 6.43%, 3.39%, and 1.82% of the PM2.5, respectively (Table S5). Compared with 2019, the PM2.5 mass concentration from the secondary aerosols in 2020 was similar to that in 2019, but the contribution of this source to PM2.5 increased by 13.32%, which was consistent with the secondary inorganic components. In 2020, the PM2.5 mass concentrations emitted by coal combustion, biomass burning, traffic emissions, industrial emissions, and dust showed obvious decreases. However, the contribution of coal combustion to PM2.5 mass concentrations slightly increased by 2.87%, and the contributions of other sources to PM2.5 mass concentrations slightly decreased by 0.41~3.27%. These results indicate that the changes in primary emissions from coal combustion, biomass burning, traffic emissions, industrial emissions, and dust were similar to those in the PM2.5 level in 2020. The PM2.5 mass concentrations from secondary aerosols, coal combustion, and traffic emissions in 2021 were lower than those in 2019, but the PM2.5 mass concentrations emitted by industrial emissions and dust were higher. In 2021, the contribution of secondary aerosols to PM2.5 levels was higher by 12.92% than that in 2019. However, coal combustion’s contribution declined by 12.69%, suggesting that primary emissions from coal combustion were significantly reduced in 2021, while the influence of the secondary process on the PM2.5 decreases was small. Secondary aerosols were the main source of PM2.5 between 2019 and 2021, playing the more dominant role in PM2.5 levels in 2020 and 2021 despite substantial reductions in gas emissions in 2020 and 2021. The primary emissions (biomass burning, traffic, and dust) were reduced by 29.71% in 2020 and 27.7% in 2021 compared to 2019. However, the PM2.5 concentrations from the secondary processes in 2020 and 2021 were slightly decreased, and the contribution of the secondary processes to PM2.5 increased by 13.32% in 2020 and 12.94% in 2021. The reductions in PM2.5 levels in 2020 and 2021 were mainly due to a decline in primary emissions rather than the secondary process. A similar scenario, with higher contributions from secondary pollution to unexpected PM2.5 levels, was found in many cities with COVID-19 restrictions [28,38,51,52,53,54].

4. Discussion

Significant improvements in air quality occurred during and after the COVID-19 outbreak in China, but haze still formed [1,2,13,34,41,55]. Many studies have found that enhancing secondary pollution inhibits these anomalies’ continuous decline in PM2.5 levels [9,32,38,56]. Changes in secondary aerosol formation under low precursor gas (NOx and SO2) levels with high O3 levels throughout this pandemic remain unclear. Due to restrictions on residential and industrial activities implemented to prevent the spread of COVID-19 in Zhengzhou, PM2.5 concentrations showed a remarkable decline of 29% during the 2020 COVID-19 restrictions compared with 2019. In addition, after the COVID-19 restrictions were lifted, PM2.5 concentrations in 2021 were 28% lower than in 2019 (Figure 3). Moreover, the average molar concentration of total nitrogen compounds (NOx + NO3−) in the air decreased from 1.60 µmol m−3 in 2019 to 0.89 µmol m−3 in 2020 and 1.44 µmol m−3 in 2021. Meanwhile, that of sulfur compounds (SO2 + SO42−) also slightly decreased from 0.39 µmol m−3 (2019) to 0.34 µmol m−3 (2020) and 0.33 µmol m−3 (2021), indicating that the reduction in nitrogen compounds during the 2020 COVID-19 restrictions was larger than that in 2021, while the decline in sulfur compounds was small. Ma et al. (2022) found that the average molar concentration of total nitrogen compounds before COVID-19 restrictions (January 1 to 23, 2020) was 2.74 µmol m−3, obviously higher than that during the COVID-19 restrictions; meanwhile, that of sulfur compounds (0.36 µmol m−3) was slightly higher than that during the COVID-19 restrictions.
Despite significant decreases in total nitrogen in 2020 and 2021, NO3− levels were similar to that in 2019, while NO and NO2 levels were significantly lower than that in 2019. This result indicates that nitrogen levels significantly changed when partitioning into NO3−, NO2, and NO in 2020 and 2021. It is a puzzle why a higher distribution of NO3− occurred with lower NO and NO2 in 2020 and 2021 than in 2019. It was found that the O3 concentration was 1.21 μmol m−3 in 2020 and 1.18 μmol m−3 in 2021, higher than 0.97 μmol m−3 in 2019. O3 molar concentrations in 2020 and 2021 were far greater than NO2 molar concentrations (0.47 μmol m−3 and 0.80 μmol m−3), respectively, implying that high O3 promoted the conversion of NO to NO2, then to NO3, and finally to NO3−. Thus, NO levels in 2020 and 2021 were obviously lower than in 2019 (Table S1). The high production of NO3− from NOx could have offset the effect of reduced total nitrogen on PM2.5 levels [1,2].
NOR positively correlated with RH in 2019–2021 (Figure 6a,b and Table S4, p < 0.01). NOR was much higher in 2020 and 2021 than in 2019, when RH was higher than 40%. This result is consistent with high RH favoring NO3− formation via NOx in 2020 and 2021 (Figure 6, Table S4). The increase in NOR with RH in the daytime was significantly higher than at nighttime (Figure 8). Moreover, the variations in NOR, O3/Ox, and O3 concentration were similar in the daytime (high) and nighttime (low) in 2019–2021 (Figure S4). Additionally, high O3/Ox and O3 concentrations in the daytime in 2020–2021 were accompanied by high NOR. In 2020 and 2021, high NOR meant high NO3− formation from NOx. As shown in Figure 6, the high NOR was positively related to high O3 and O3/Ox (Table S4). The formation was likely because high O3 promoted the conversion of NO to NO2, resulting in more NO2 to form NO3−. If O3 concentrations in 2020 and 2021 were similar to those in 2019, the NOR might have been similar to that (0.25) in 2019. NO3− concentrations in 2020 decreased from 20.36 μg m−3 to 11.44 μg m−3 in 2020, while NH4+ levels fell by 3.32 μg m−3. Thus, PM2.5 levels decreased from 73.24 to 61.00 μg m−3 during the COVID-19 restriction period. Similarly, NO3− and NH4+ declined by 5.16 μg m−3 and 1.49 μg m−3 in 2021, resulting in a decrease (~7 μg m−3) in PM2.5 levels. Enhanced NOR hindered a ~17% and ~10% decline in PM2.5 levels during and after the COVID-19 restrictions in Zhengzhou. As a result of the increased O3, NO3− formation was enhanced by oxidizing NO to NO2, offsetting the reduction in PM2.5 levels.
SOR increased with RH between 2019 and 2021 (Figure 6c,d). This result indicates that moist air favors the conversion of SO2 to SO42−. Moreover, the variation in SOR with RH was similar in the daytime and nighttime. O3 concentration was high in the daytime and low at nighttime (Figures S4 and S5). On the contrary, SOR and RH levels were low in the daytime and high at nighttime. Figure 6a–c show that high SOR did not follow high O3 levels under certain RH values between 2019 and 2021. Figure S3 and Table S4 depict the poor correlation between SOR and O3. The results suggest that photochemical reactions were not the major pathway of the SO42− formation. Thus, the heterogeneous reactions on the particle surface were the major route for SO42− formation, rather than photochemical reactions.
As shown in Figure S2, the slopes of NH4+ and NO3− + 2 SO42− were 1.11 in 2019, 1.02 in 2020, and 1.02 in 2021, implying that NH4+ was rich in 2019. The enhanced formation of nitrate and sulfate in 2020 and 2021 may not have been caused by rich NH4+. The wind speed was generally less than 2 m s−1 in 2019–2021 (Figure S6). Temperature has negative correlations with NO3−, SO42−, and conversion ratios (NOR and SOR) (Tables S2–S4). The NO3− had similar levels in 2019–2021, despite lower total nitrogen compounds in 2020 and 2021 than in 2019. Thus, wind speed and temperature were not major factors for the enhanced NO3−. The normalized concentrations of NO3− by EC were to counteract the effect of the atmospheric physical processes and represent the contribution from chemical reactions [56] because EC was produced only from primary emissions and was a very inertial species to the chemical reactions. Its variability was mainly controlled by emission intensity and atmospheric physical processes. The NO3−/EC ratio increased from 6.30 in 2019 to 7.50 in 2020 and 7.44 in 2021, reflecting an elevated production of NO3−. The results were consistent with the variations in NO3− contributions to PM2.5. An earlier study also observed a similar phenomenon [38,52,53,54]. Another earlier study reported that NOR increased from 0.14 to 0.21 in 2020 when Shanghai implemented restrictions [1]. In Xi’an, NOR increased from 0.33 in non-restriction periods to 0.57 in restriction periods, when O3 concentration increased from 35.3 to 53.6 μg m−3 [38]. An investigation of the published data on haze occurrence during and after COVID-19 restrictions in many cities in China revealed a similar scenario, with photochemically enhanced NOR causing major contributions of NO3− to PM2.5 and inhibiting the continuous decline in PM2.5 levels [1,9,25,38].

5. Conclusions

Despite significant improvements in air quality during and after COVID-19 restrictions, due to strict regulations on residential and industrial activities to curb the spread of COVID-19, haze still occurred in Zhengzhou. The average concentration of PM2.5 decreased from 102.49 μg m−3 before the restrictions (2019) to 73.24 μg m−3 during the restrictions (2020) and 73.90 μg m−3 after the restrictions (2021). In 2019, SIA concentrations declined by 3.1% in 2020 and 12.8% in 2021. At the same time, the contributions of SIAs to PM2.5 increased to 68.6% (2020) and 61.2% (2021), higher than 50.61% in 2019. SIAs caused major contributions to PM2.5 levels in 2020 and 2021. NOx levels in 2020 and 2021 were 22~62% lower than that in 2019; however, NO3− concentrations had similar levels (20.69~23.00 μg m−3) in 2019, 2020, and 2021, which was attributed to the enhanced conversion rate of NOx to nitrate by the increased O3 levels in 2020 and 2021. The inconsistent reduction between nitrate and NOx inhibited further PM2.5 reduction and contributed to haze formation in Zhengzhou.
Six of the PM2.5 sources were secondary inorganic aerosols, industrial emissions, coal combustion, biomass burning, soil dust, and traffic emissions in 2019, 2020, and 2021. Compared with 2019, the primary emissions (biomass burning, traffic, and dust) were reduced by 29.71% in 2020 and 27.7% in 2021. However, the PM2.5 from the secondary process in 2020 and 2021 decreased slightly, and the contribution of the secondary process to PM2.5 increased by 12.94%~13.32% in 2020 and 2021. The reductions in PM2.5 levels in 2020 and 2021 were mainly due to a decline in primary emissions rather than the secondary process. Thus, a better understanding of the formation of secondary aerosols under high O3 and low precursor gases would be needed to help mitigate air pollution in the future.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/toxics12010081/s1. Figure S1: The OC/EC ratios in 2019 (a), 2020 (b) and 2021 (c); Figure S2: NOR and SOR with O3 in 2019, 2020 and 2021; Figure S3: NOR and SOR with O3 in 2019, 2020 and 2021; Figure S4: The diurnal variations of NOR and SOR in 2019 (a), 2020 (b) and 2021 (c); Figure S5: The diurnal variations of RH, O3 and O3/Ox in 2019 (a), 2020 (b) and 2021 (c); Figure S6: Statistical analysis of wind speed in 2019–2021; Table S1: Average mass concentrations (µg m−3) of air pollutants and species in PM2.5 in 2019, 2020 and 2021; Table S2: Correlation analysis between NO3−, PM2.5, T, RH and NOR in 2019–2021 (Pearson correlation coefficients); Table S3: Correlation analysis between SO42−, PM2.5, T, RH and SOR in 2019-2021 (Pearson correlation coefficients); Table S4: Correlation analysis between conversion ratios (NOR and SOR), RH, O3 and O3/Ox in 2019–2021 (Pearson correlation coefficients); Table S5: Average contributions (%) of six sources to PM2.5 in 2019, 2020 and 2021.

Author Contributions

Conceptualization, Q.M. and K.H.; methodology, W.W.; software, A.C. and K.H.; validation, S.Z. and J.H.; formal analysis, A.C. and Q.M.; data curation, Q.M.; writing—original draft preparation, S.Z. and Q.M.; writing—review and editing, J.H.; K.H.; and Q.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Natural Science Foundation of China (42105071) and the project of science and technology of the Henan province for tackling key problems (212102110390).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Chang, Y.; Huang, R.-J.; Ge, X.; Huang, X.; Hu, J.; Duan, Y.; Zou, Z.; Liu, X.; Lehmann, M. Puzzling haze events in China during the coronavirus (COVID-19) shutdown. Geophys. Res. Lett. 2020, 47, e2020GL088533. [Google Scholar] [CrossRef]
  2. Le, T.H.; Wang, Y.; Liu, L.; Yang, J.N.; Yung, Y.L.; Li, G.H.; Seinfeld, J.H. Unexpected air pollution with marked emission reductions during the COVID–19 outbreak in China. Science 2020, 369, 702. [Google Scholar] [CrossRef] [PubMed]
  3. Li, R.; Zhao, Y.L.; Fu, H.B.; Chen, J.M.; Peng, M.; Wang, C.Y. Substantial changes in gaseous pollutants and chemical compositions in fine particles in the North China Plain during the COVID-19 lockdown period: Anthropogenic vs. meteorological influences. Atmos. Chem. Phys. 2021, 21, 8677–8692. [Google Scholar] [CrossRef]
  4. Pei, Z.; Han, G.; Ma, X.; Su, H.; Gong, W. Response of major air pollutants to COVID–19 lockdowns in China. Sci. Total Environ. 2020, 743, 140879. [Google Scholar] [CrossRef]
  5. Shi, X.Q.; Brasseur, G.P. The response in air quality to the reduction of Chinese economic activities during the COVID-19 outbreak. Geophys. Res. Lett. 2020, 47, e2020GL088070. [Google Scholar] [CrossRef] [PubMed]
  6. Wang, Y.C.; Wu, R.; Liu, L.; Yuan, Y.; Liu, C.G.; Ho, S.S.H.; Ren, H.H.; Wang, Q.Y.; Lv, Y.; Yan, M.Y.; et al. Differential health and economic impacts from the COVID-19 lockdown between the developed and developing countries: Perspective on air pollution. Environ. Pollut. 2022, 293, 118544. [Google Scholar] [CrossRef]
  7. Chen, H.; Huo, J.; Fu, Q.; Duan, Y.; Xiao, H.; Chen, J. Impact of quarantine measures on chemical compositions of PM2.5 during the COVID–19 epidemic in Shanghai, China. Sci. Total Environ. 2020, 743, 140758. [Google Scholar] [CrossRef]
  8. Huang, G.Y.; Sun, K. Non-negligible impacts of clean air regulations on the reduction of tropospheric NO2 over East China during the COVID-19 pandemic observed by OMI and TROPOMI. Sci. Total Environ. 2020, 745, 141023. [Google Scholar] [CrossRef]
  9. Sun, Y.; Lei, L.; Zhou, W.; Chen, C.; He, Y.; Sun, J.; Li, Z.; Xu, W.; Wang, Q.; Ji, D.; et al. A Chemical Cocktail during the COVID–19 Outbreak in Beijing, China: Insights from Six–Year Aerosol Particle Composition Measurements during the Chinese New Year Holiday. Sci. Total Environ. 2020, 742, 140739. [Google Scholar] [CrossRef]
  10. Xie, X.D.; Hu, J.L.; Qin, M.M.; Guo, S.; Hu, M.; Ji, D.S.; Wang, H.L.; Lou, S.R.; Huang, C.; Liu, C.; et al. Particle Phase State and Aerosol Liquid Water Greatly Impact Secondary Aerosol Formation: Insights into Phase Transition and Role in Haze Events. Atmos. Chem. Phys. 2023, 23, 10563–10578. [Google Scholar] [CrossRef]
  11. Gen, M.S.; Zhang, R.F.; Huang, D.D.; Li, Y.J.; Chan, C.K. Heterogeneous Oxidation of SO2 in Sulfate Production during Nitrate Photolysis at 300 nm: Effect of pH, Relative Humidity, Irradiation Intensity, and the Presence of Organic Compounds. Environ. Sci. Technol. 2019, 53, 8757–8766. [Google Scholar] [CrossRef] [PubMed]
  12. Li, X.; Zhao, B.; Zhou, W.; Shi, H.; Yin, R.; Cai, R.; Yang, D.; Dallenbach, K.; Deng, C.; Fu, Y.; et al. Responses of gaseous sulfuric acid and particulate sulfate to reduced SO2 concentration: A perspective from long–term measurements in Beijing. Sci. Total Environ. 2020, 721, 137700. [Google Scholar] [CrossRef] [PubMed]
  13. Ma, Q.X.; Wang, W.S.; Wu, Y.F.; Wang, F.; Jin, L.Y.; Song, X.Y.; Han, Y.; Zhang, R.J.; Zhang, D.Z. Haze caused by NOx oxidation under restricted residential and industrial activities in a mega city in the south of North China Plain. Chemosphere 2022, 305, 135489. [Google Scholar] [CrossRef] [PubMed]
  14. Peng, J.F.; Hu, M.; Shang, D.J. Explosive Secondary Aerosol Formation during Severe Haze in the North China Plain. Environ. Sci. Technol. 2021, 55, 2189–2207. [Google Scholar] [CrossRef] [PubMed]
  15. Fang, Y.; Ye, C.; Wang, J.; Wu, Y.; Hu, M.; Lin, W.; Xu, F.; Zhu, T. Relative humidity and O3 concentration as two prerequisites for sulfate formation. Atmos. Chem. Phys. 2019, 19, 12295–12307. [Google Scholar] [CrossRef]
  16. Liu, T.; Clegg, S.L.; Abbatt, J.P.D. Fast oxidation of sulfur dioxide by hydrogen peroxide in deliquesced aerosol particles. Proc. Natl. Acad. Sci. USA 2020, 117, 1354–1359. [Google Scholar] [CrossRef]
  17. Pandis, S.N.; Seinfeld, J.H.; Pilinis, C. Heterogeneous sulfate production in an urban fog. Atmos. Environ. Part A-Gen. Top. 1992, 26, 2509–2522. [Google Scholar] [CrossRef]
  18. Ding, J.; Dai, Q.L.; Zhang, Y.F.; Xu, J.; Huangfu, Y.Q.; Feng, Y.C. Air humidity affects secondary aerosol formation in different pathways. Sci. Total Environ. 2021, 759, 143540. [Google Scholar] [CrossRef]
  19. Li, G.; Bei, N.; Cao, J.; Huang, R.; Wu, J.; Feng, T.; Wang, Y.; Liu, S.; Zhang, Q.; Tie, X.; et al. A possible pathway for rapid growth of sulfate during haze days in China. Atmos. Chem. Phys. 2017, 17, 3301–3316. [Google Scholar] [CrossRef]
  20. Ma, Q.X.; Wu, Y.F.; Zhang, D.Z.; Wang, X.J.; Xia, Y.J.; Liu, X.Y.; Tian, P.; Han, Z.W.; Xia, X.A.; Wang, Y.; et al. Roles of regional transport and heterogeneous reactions in the PM2.5 increase during winter haze episodes in Beijing. Sci. Total Environ. 2017, 599–600, 246–253. [Google Scholar] [CrossRef]
  21. Cheng, Y.F.; Zheng, G.J.; Wei, C.; Mu, Q.; Zheng, B.; Wang, Z.B.; Gao, M.; Zhang, Q.; He, K.B.; Carmichael, G.; et al. Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China. Sci. Adv. 2016, 2, 11. [Google Scholar] [CrossRef]
  22. George, C.; Ammann, M.; D’Anna, B.; Donaldson, D.J.; Nizkorodov, S.A. Heterogeneous photochemistry in the atmosphere. Chem. Rev. 2015, 115, 4218–4258. [Google Scholar] [CrossRef] [PubMed]
  23. Fu, X.; Wang, T.; Gao, J.; Wang, P.; Liu, Y.; Wang, S.; Zhao, B.; Xue, L. Persistent heavy winter nitrate pollution driven by increased photochemical oxidants in northern China. Environ. Sci. Technol. 2020, 54, 3881–3889. [Google Scholar] [CrossRef] [PubMed]
  24. Li, H.; Ma, Y.L.; Duan, F.K.; Zhu, L.D.; Ma, T.; Yang, S.; Xu, Y.Z.; Li, F.; Huang, T.; Kimoto, T.; et al. Stronger secondary pollution processes despite decrease in gaseous precursors: A comparative analysis of summer 2020 and 2019 in Beijing. Environ. Pollut 2021, 279, 116923. [Google Scholar] [CrossRef] [PubMed]
  25. Liu, P.F.; Ye, C.; Xue, C.Y.; Zhang, C.L.; Mu, Y.J.; Sun, X. Formation mechanisms of atmospheric nitrate and sulfate during the winter haze pollution periods in Beijing: Gas–phase, heterogeneous and aqueous–phase chemistry. Atmos. Chem. Phys. 2020, 20, 4153–4165. [Google Scholar] [CrossRef]
  26. Khoder, M.I. Atmospheric conversion of sulfur dioxide to particulate sulfate and nitrogen dioxide to particulate nitrate and gaseous nitric acid in an urban area. Chemosphere 2002, 49, 675–684. [Google Scholar] [CrossRef]
  27. Wang, G.H.; Zhang, R.Y.; Gomez, M.E. Persistent sulfate formation from London fog to Chinese haze. Proc. Natl. Acad. Sci. USA 2016, 113, 13630–13635. [Google Scholar] [CrossRef]
  28. Lin, Y.C.; Zhang, Y.L.; Fan, M.Y.; Bao, M.Y. Heterogeneous formation of particulate nitrate under ammonium-rich regimes during the high-PM2.5 events in Nanjing, China. Atmos. Chem. Phys. 2020, 20, 3999–4011. [Google Scholar] [CrossRef]
  29. Fan, C.; Li, Y.; Guang, J.; Li, Z.; Elnashar, A.; Allam, M.; de Leeuw, G. The impact of the control measures during the COVID-19 outbreak on air pollution in China. Remote Sens. 2020, 12, 1613. [Google Scholar] [CrossRef]
  30. Feng, S.Z.; Jiang, F.; Wang, H.M.; Wang, H.K.; Ju, W.M.; Shen, Y.; Zheng, Y.H.; Wu, Z.; Ding, A.J. NOx emission changes over China during the COVID-19 epidemic inferred from surface NO2 observations. Geophys. Res. Lett. 2020, 47, e2020GL090080. [Google Scholar] [CrossRef]
  31. Liang, Y.X.; Gui, K.; Che, H.Z.; Li, L.; Zheng, Y.; Zhang, X.T.; Zhang, X.D.; Zhang, P.; Zhang, X.Y. Changes in aerosol loading before, during and after the COVID-19 pandemic outbreak in China: Effects of anthropogenic and natural aerosol. Sci. Total Environ. 2023, 857, 159435. [Google Scholar] [CrossRef]
  32. Tian, J.; Wang, Q.Y.; Zhang, Y.; Yan, M.Y.; Liu, H.K.; Zhang, N.N.; Ran, W.K.; Cao, J.J. Impacts of primary emissions and secondary aerosol formation on air pollution in an urban area of China during the COVID-19 lockdown. Environ. Inter. 2021, 150, 106426. [Google Scholar] [CrossRef]
  33. Zhang, Q.Q.; Pan, Y.P.; He, Y.X.; Walters, W.W.; Ni, Q.Y.; Liu, X.Y.; Xu, G.Y.; Shao, J.L.; Jiang, C.L. Substantial nitrogen oxides emission reduction from China due to COVID-19 and its impact on surface ozone and aerosol pollution. Sci. Total Environ. 2021, 753, 142238. [Google Scholar] [CrossRef]
  34. Lian, X.B.; Huang, J.P.; Huang, R.J.; Liu, C.W.; Wang, L.N.; Zhang, T.H. Impact of city lockdown on the air quality of COVID-19-hit of Wuhan city. Sci. Total Environ. 2020, 742, 140556. [Google Scholar] [CrossRef] [PubMed]
  35. Liu, L.; Zhang, J.; Du, R.G.; Teng, X.M.; Hu, R.; Yuan, Q.; Tang, S.S.; Ren, C.H.; Huang, X.; Xu, L.; et al. Chemistry of Atmospheric Fine Particles during the COVID–19 Pandemic in a Megacity of Eastern China. Geophys. Res. Lett. 2021, 48, e2020GL091611. [Google Scholar] [CrossRef] [PubMed]
  36. Wang, J.F.; Li, J.Y.; Ye, J.H.; Zhao, Y.; Wu, J.Z.; Hu, J.L.; Liu, D.T.; Nie, D.Y.; Shen, F.Z.; Huang, X.P.; et al. Fast sulfate formation from oxidation of SO2 by NO2 and HONO observed in Beijing haze. Nat. Commun. 2020, 11, 2844. [Google Scholar] [CrossRef] [PubMed]
  37. Chang, X.; Zheng, H.T.; Zhao, B.; Yan, C.; Jiang, Y.Q.; Hu, R.L.; Song, A.J.; Dong, Z.X.; Li, S.Y.; Li, Z.Q.; et al. Drivers of High Concentrations of Secondary Organic Aerosols in Northern China during the COVID-19 Lockdowns. Environ. Sci. Technol. 2023, 57, 5521–5531. [Google Scholar] [CrossRef]
  38. Duan, J.; Huang, R.J.; Chang, Y.H. Measurement report of the change of PM2.5 composition during the COVID-19 lockdown in urban Xi’an: Enhanced secondary formation and oxidation. Sci. Total Environ. 2021, 791, 148126. [Google Scholar] [CrossRef]
  39. Li, Y.; Han, Z.W.; Song, Y.; Li, J.W.; Sun, Y.L.; Wang, T.T. Impacts of the COVID-19 lockdown on atmospheric oxidizing capacity and secondary aerosol formation over the Beijing-Tianjin-Hebei region in Winter-Spring 2020. Atmos. Environ 2023, 295, 119540. [Google Scholar] [CrossRef]
  40. Du, H.Y.; Li, J.; Wang, Z.F.; Yang, W.Y.; Chen, X.S.; Wei, Y. Sources of PM2.5 and its responses to emission reduction strategies in the Central Plains Economic Region in China: Implications for the impacts of COVID-19. Environ. Pollut. 2021, 288, 117783. [Google Scholar] [CrossRef]
  41. Yang, J.R.; Wang, S.B.; Zhang, R.Q.; Yin, S.S. Elevated particle acidity enhanced the sulfate formation during the COVID-19 pandemic in Zhengzhou, China. Environ. Pollut. 2022, 296, 118716. [Google Scholar] [CrossRef] [PubMed]
  42. Chen, X.; Walker, J.T.; Geron, C. Chromatography related performance of the Monitor for AeRosols and GAses in ambient air (MARGA): Laboratory and field-based evaluation. Atmos. Meas. Tech. 2017, 10, 3893–3908. [Google Scholar] [CrossRef]
  43. Stieger, B.; Spindler, G.; Fahlbusch, B.; Müller, K.; Grüner, A.; Poulain, L.; Thöni, L.; Seitler, E.; Wallasch, M.; Herrmann, H. Measurements of PM10 ions and trace gases with the online system MARGA at the research station Melpitz in Germany—A five-year study. J. Atmos. Chem. 2018, 75, 33–70. [Google Scholar] [CrossRef]
  44. Wu, C.; Wu, D.; Yu, J.Z. Estimation and Uncertainty Analysis of Secondary Organic Carbon Using 1 Year of Hourly Organic and Elemental Carbon Data. J. Geophys. Res. Atmos. 2019, 124, 2774–2795. [Google Scholar] [CrossRef]
  45. Li, Z.Y.; Liu, J.J.; Zhai, Z.; Liu, C.; Ren, Z.Z.; Yue, Z.Y.; Yang, D.Y.; Hu, Y.; Zheng, H.; Kong, S.F. Heterogeneous changes of chemical compositions, sources and health risks of PM2.5 with the “Clean Heating” policy at urban/suburban/industrial sites. Sci. Total Environ. 2023, 854, 158871. [Google Scholar] [CrossRef]
  46. Zhou, H.J.; Liu, T.; Sun, B.; Tian, Y.L.; Zhou Xj Hao, F.; Chun, X.; Wan, Z.Q.; Liu, P.; Wang, J.W.; Du, D.L. Chemical characteristics and sources of PM2.5 in Hohhot, a semi-arid city in northern China: Insight from the COVID-19 lockdown. Atmos. Chem. Phys. 2022, 22, 12153–12166. [Google Scholar] [CrossRef]
  47. Hao, Q.; Jiang, N.; Zhang, R.Q.; Yang, L.M.; Li, S.L. Characteristics, sources, and reactions of nitrous acid during winter at an urban site in the Central Plains Economic Region in China. Atmos. Chem. Phys. 2020, 20, 7087–7102. [Google Scholar] [CrossRef]
  48. Zhang, R.J.; Jing, J.; Tao, J.; Hsu, S.-C.; Wang, G.; Cao, J.; Lee, C.S.L.; Zhu, L.; Chen, Z.; Zhao, Y.; et al. Chemical characterization and source apportionment of PM2.5 in Beijing: Seasonal perspective. Atmos. Chem. Phys. 2013, 13, 7053–7074. [Google Scholar] [CrossRef]
  49. Cui, Y.; Ji, D.S.; Maenhaut, W.; Gao, W.K.; Zhang, R.J.; Wang, Y.S. Levels and sources of hourly PM2.5-related elements during the control period of the COVID-19 pandemic at a rural site between Beijing and Tianjin. Sci. Total Environ. 2020, 744, 140840. [Google Scholar] [CrossRef]
  50. Zheng, H.; Kong, S.; Chen, N.; Yan, Y.; Liu, D.; Zhu, B.; Xu, K.; Cao, W.; Ding, Q.; Lan, B.; et al. Significant changes in the chemical compositions and sources of PM2.5 in Wuhan since the city lockdown as COVID-19. Sci. Total Environ. 2020, 739, 140000. [Google Scholar] [CrossRef]
  51. Liu, H.; Tian, H.; Zhang, K.; Liu, S.; Cheng, K.; Yin, S.; Liu, Y.; Liu, X.; Wu, Y.; Liu, W.; et al. Seasonal variation, formation mechanisms and potential sources of PM2.5 in two typical cities in the Central Plains Urban Agglomeration, China. Sci. Total Environ. 2019, 657, 657–670. [Google Scholar] [CrossRef] [PubMed]
  52. Kong, S.F.; Li, L.; Li, X.X.; Yin, Y.; Chen, K.; Liu, D.T.; Yuan, L.; Zhang, Y.J.; Shan, Y.P.; Ji, Y.Q. The impacts of firework burning at the Chinese Spring Festival on air quality: Insights of tracers, source evolution and aging processes. Atmos. Chem. Phys. 2015, 15, 2167–2184. [Google Scholar] [CrossRef]
  53. Tian, Y.; Zhang, Y.; Liang, Y.; Niu, Z.; Xue, Q.; Feng, Y. PM2.5 source apportionment during severe haze episodes in a Chinese megacity based on a 5-month period by using hourly species measurements: Explore how to better conduct PMF during haze episodes. Atmos. Environ. 2020, 224, 117364. [Google Scholar] [CrossRef]
  54. Hong, Y.; Xu, X.; Liao, D.; Zheng, R.; Ji, X.; Chen, Y.; Xu, L.; Li, M.; Wang, H.; Xiao, H.; et al. Source apportionment of PM2.5 and sulfate formation during the COVID-19 lockdown in a coastal city of southeast China. Environ. Pollut. 2021, 286, 117577. [Google Scholar] [CrossRef] [PubMed]
  55. Huang, X.; Ding, A.; Gao, J.; Zheng, B.; Zhou, D.; Qi, X.; Tang, R.; Wang, J.; Ren, C.; Nie, W.; et al. Enhanced secondary pollution offset reduction of primary emissions during COVID–19 lockdown in China. Natl. Sci. Rev. 2020, 8, nwaa137. [Google Scholar] [CrossRef]
  56. Ma, Q.X.; Wu, Y.F.; Fu, S.L.; Zhang, D.Z.; Han, Z.W.; Zhang, R.J. Pollution severity-dependent aerosol light scattering enhanced by inorganic species formation in Beijing haze. Sci. Total Environ. 2020, 719, 137545. [Google Scholar] [CrossRef]
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Figure 1. Location of the site (Zhengzhou) and topography in China.
Figure 1. Location of the site (Zhengzhou) and topography in China.
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Figure 2. Evolution of meteorological conditions (a,b), particulate matter (PM10 and PM2.5) (c), and gaseous pollutants (NO, NO2, SO2, CO, and O3) (d,e) in 2019, 2020, and 2021.
Figure 2. Evolution of meteorological conditions (a,b), particulate matter (PM10 and PM2.5) (c), and gaseous pollutants (NO, NO2, SO2, CO, and O3) (d,e) in 2019, 2020, and 2021.
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Figure 3. Change ratios of pollutant concentrations in 2020 and 2021 compared to 2019.
Figure 3. Change ratios of pollutant concentrations in 2020 and 2021 compared to 2019.
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Figure 4. Average mass concentrations (a) and mass fractions (b) of PM2.5 constituents (SO42−, NO3−, NH4+, POA, SOA, EC, Cl−, K+, Na+, Ma2+, and Ca2+), NOR, and SOR in 2019, 2020, and 2021.
Figure 4. Average mass concentrations (a) and mass fractions (b) of PM2.5 constituents (SO42−, NO3−, NH4+, POA, SOA, EC, Cl−, K+, Na+, Ma2+, and Ca2+), NOR, and SOR in 2019, 2020, and 2021.
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Figure 5. Correlations between NO3−, SO42−, and precursor gases in 2019 (a,d), 2020 (b,e), and 2021 (c,f). Symbols in (a–f) are scaled by conversion ratios (NOR and SOR) and colored by PM2.5 concentrations.
Figure 5. Correlations between NO3−, SO42−, and precursor gases in 2019 (a,d), 2020 (b,e), and 2021 (c,f). Symbols in (a–f) are scaled by conversion ratios (NOR and SOR) and colored by PM2.5 concentrations.
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Figure 6. Correlations between conversion ratios (NOR and SOR) and RH in 2019 (a,d), 2020 (b,e), and 2021 (c,f). Symbols in (a–f) are scaled by O3 concentration and colored by O3/Ox. The oxidants (Ox) are the sum of NO2 and O3, a proxy for atmospheric oxidation caused by photochemical reactions.
Figure 6. Correlations between conversion ratios (NOR and SOR) and RH in 2019 (a,d), 2020 (b,e), and 2021 (c,f). Symbols in (a–f) are scaled by O3 concentration and colored by O3/Ox. The oxidants (Ox) are the sum of NO2 and O3, a proxy for atmospheric oxidation caused by photochemical reactions.
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Figure 7. Six sources’ profiles (bars) (in units of μg μg−1) and contribution percentages (black dots) from each source factor resolved from the PMF model in 2019 (a), 2020 (b), and 2021 (c).
Figure 7. Six sources’ profiles (bars) (in units of μg μg−1) and contribution percentages (black dots) from each source factor resolved from the PMF model in 2019 (a), 2020 (b), and 2021 (c).
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Figure 8. NOR and SOR with RH in the daytime (08:00–19:00) (a,c) and nighttime (20:00–07:00) (b,d) in 2019 2020 and 2021.
Figure 8. NOR and SOR with RH in the daytime (08:00–19:00) (a,c) and nighttime (20:00–07:00) (b,d) in 2019 2020 and 2021.
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Table 1. Formulas and references of main components in PM2.5.
Table 1. Formulas and references of main components in PM2.5.
Main ComponentsFormulasReferences
Secondary inorganic aerosols (SIAs)SO42− + NO3− + NH4+[27]
Primary organic aerosol (POA)ρ[POA] = 1.6 × ρ[EC] × (ρOC/ρEC)min[36]
Secondary organic aerosol (SOA)ρ[SOA] = 1.6×(ρ[OC] − ρ[POC])[43]
The conversion ratios of SO2 to sulfate
(SOR)		[SO42−]/([SO42−] + [SO2])[37]
The conversion ratios of NOx to nitrate
(NOR)		[NO3−]/([NO3−] + [NO] + [NO2])[13]
Where ρ[x] is the mass concentration of x species (μg m−3). [x] is the molar concentration of x species (μmol m−3).
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Huang, J.; Cai, A.; Wang, W.; He, K.; Zou, S.; Ma, Q. The Variation in Chemical Composition and Source Apportionment of PM2.5 before, during, and after COVID-19 Restrictions in Zhengzhou, China. Toxics 2024, 12, 81. https://0-doi-org.brum.beds.ac.uk/10.3390/toxics12010081

AMA Style

Huang J, Cai A, Wang W, He K, Zou S, Ma Q. The Variation in Chemical Composition and Source Apportionment of PM2.5 before, during, and after COVID-19 Restrictions in Zhengzhou, China. Toxics. 2024; 12(1):81. https://0-doi-org.brum.beds.ac.uk/10.3390/toxics12010081

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Huang, Jinting, Aomeng Cai, Weisi Wang, Kuan He, Shuangshuang Zou, and Qingxia Ma. 2024. "The Variation in Chemical Composition and Source Apportionment of PM2.5 before, during, and after COVID-19 Restrictions in Zhengzhou, China" Toxics 12, no. 1: 81. https://0-doi-org.brum.beds.ac.uk/10.3390/toxics12010081

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