These authors contributed equally to this work.
The implementation of strict emission control during the 11th National Minority Games (NMG) in September 2019 provided a valuable opportunity to
assess the impact of such emission controls on the characteristics of VOCs and other air pollutants. Here, we investigated the characteristics of
VOCs and the
Volatile organic compounds (VOCs), important precursors for the generation of near-surface ozone (
Air quality assurance refers to the systematic emission reduction and control measures of pollution sources to ensure air quality during special
activities. Temporarily enhanced control measures could provide a scenario to analyze the response relationship between emission sources of pollutants
and ambient air quality. Many scholars have carried out research on pollutant characteristics and their source apportionment under different control
measures for a variety of special activities. Those studies included the 2008 Beijing Olympic Games (Schleicher et al., 2012; Wang et al., 2009), the
2010 World Expo in Shanghai (Chan et al., 2015; Wang et al., 2014), the 2014 Asia-Pacific Economic Cooperation Summit in Beijing (Li et al., 2015,
2017), the 70th China Victory Day Parade anniversary (Huang et al., 2018; Ren et al., 2019), and the G20 summit in Hangzhou (H. Li et al.,
2019; Zhang et al., 2020). These studies all suggested that enhanced emission-reduction strategies
had significant effects on improving air quality.
From 8–16 September 2019, the 11th National Minority Games (NMG) was held in Zhengzhou, China. As the host city, Zhengzhou took emergency pollution
control measures in the city and neighboring regions from 26 August to 18 September for enhancing air quality during the NMG period. Considering the
ozone pollution is the main type of pollution in the region in September (Yu et al., 2020), the Zhengzhou municipal government focused on the emission
reduction of VOCs and
This study measured 106 VOC species using an online gas chromatograph–mass spectrometer with a flame ionization detector (GC-MS/FID). Meanwhile, the Weather
Research and Forecasting Community Multiscale Air Quality (WRF-CMAQ) model was used to investigate the nonlinearity of
The sampling site is located on the rooftop of a four-story building at the municipal environmental monitoring station (MEM; 113.61
VOC samples were collected from 6 August to 30 September 2019 and were divided into three periods, including the pre-NMG period (6–25 August), the NMG period (26 August to 18 September), and the post-NMG period (19–30 September). By comparing the characteristics of VOC pollution during the three periods, the effects of control policies by government can clearly be identified and assessed.
It should be pointed out that the MEM station is located in the air monitoring network operated by Zhengzhou environmental monitoring center. The
meteorological parameters (temperature, relative humidity, atmospheric pressure, wind direction, and wind speed) and trace gases (
Ambient VOCs were collected and analyzed continuously using an online GC-MS/FID, and the time resolution is 1
To ensure the validity and reliability of observation data, these chemical analyses were subjected to quality assurance and quality control
procedures. We used external and internal standard methods to quantify the
The WRF-CMAQ modeling system was applied to simulate ozone concentration and investigate
In this paper, the simulation period was from 00:00 LST on 5 August 2019 to 23:00 LST on 30 September 2019, which corresponded to the NMG sampling
periods. To eliminate the impact of the initial conditions, a 5
The CMAQ developed by the US EPA was used to simulate the ozone pollution processes in Zhengzhou in August and September 2019. The sensitivity of emission sources to ozone pollution in Zhengzhou was analyzed using the DDM-3D source sensitivity identification tool (Hakami et al., 2007).
Positive matrix factorization (PMF) analysis of VOCs was performed with the US EPA PMF 5.0 program; this receptor model is widely used for source analysis. Detailed information about this method is described in the user manual and related literature (Norris et al., 2014; Xiong and Du, 2020; Yenisoy-Karakas et al., 2020).
It must be said that not all of the VOC species were used in the PMF analysis. According to previous studies, the principles for VOC species choice
are listed as follows (Hui et al., 2019): (1) species with more than 25 % data missing or that fell below the MDLs were rejected; (2) species
with a signal-to-noise ratio lower than 1.5 were excluded; and (3) species with representative source tracers of emission sources were
retained. Eventually, a total of 42 VOC species were selected for the source apportionment analysis. In this study, a seven-factor solution was chosen
in the PMF analysis based on two parameters (Ulbrich et al., 2009): (1)
The potential source contribution function (PSCF) is a function with conditional probability for calculating backward trajectories and identifying
potential source regions. The detailed descriptions of this method were described by Bressi et al. (2014) and Waked et al. (2014). Briefly, PSCF
analysis is normally used to identify possible source areas of pollutants, such as ozone,
In this study, the PSCFs were calculated by applying the TrajStat plugins on MeteInfoMap software version 1.4.4. The 48 h backward trajectories
arriving at Zhengzhou with a trajectory height of 200
To understand the impact of the VOC species on ozone formation, ozone formation potential (OFP) was used, employing the following equation (Carter,
2010a):
The risk assessment derived from the guidelines proposed by the US EPA (2009) was used to evaluate the adverse health effects of each identified VOC in
ambient air to human health and evaluate the impact of emission reduction on health risks. In this paper, the carcinogenic and non-carcinogenic risks
were calculated to assess the impacts of VOCs on human health, using Eqs. (4)–(7).
Out of all measured species in this paper, only 46 VOC species with known toxicity values were considered, including 44 noncarcinogenic species and 21 carcinogenic species. Target VOCs and associated toxicity values of health risk assessment are presented in Table S4 in the Supplement.
Time series of VOCs and trace gases during the sampling period in Zhengzhou.
Figure 1 shows the temporal trends of the
During the control period, the
On the whole, the concentration of ozone precursors decreased during the control period (as shown in Fig.S4), and the ozone pollution was severe
during the entire observation period. It should be noted that the maximum value of max 8 h
Meteorological conditions can significantly influence pollutant concentrations, which makes it difficult to evaluate the emission reduction brought by
emission control. In this paper, the meteorological data throughout the three periods in Zhengzhou were compared, including temperature (
The average weighted PSCF maps for
Meteorological conditions can influence the transmission and circulation of regional air pollutants (Ren et al., 2019). In this paper, the air
clusters were analyzed using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) to distinguish the differences of potential source
contributions in the three periods. In previous studies, it is shown that regional transport has an important influence on Zhengzhou's air quality, especially the air
mass from the BTH region (Jiang et al., 2019; P. Wang et al., 2019). Figure S4 shows the 48 h
backward trajectory results during the sampling period. The dominant trajectory was from the east or southeast of Zhengzhou in the three
periods. For the pre- and post-NMG periods, Zhengzhou is greatly affected by the air mass from the BTH region, where there are high concentrations of air
pollutants from anthropogenic emissions. Based on the PSCF results in Fig. 2, the high PSCF values for pollutants (including
As illustrated in Fig. 1, the mixing ratios of hourly total VOCs (TVOCs) show an average value of 150
The percentage distributions of VOC groups were similar in the three sampling periods (Fig. S6 in the Supplement). Alkanes were the dominant group, accounting for 37 %, 35 %, and 33 % of the total VOC concentration for the three periods, respectively, followed by halocarbons. Notably, OVOCs slightly decreased in the entire sampling period, comprising 17 %, 16 %, and 15 %, respectively. However, the active components of aromatics increased over time. In addition to the impact of emission sources, meteorological conditions and transport might be key factors that can influence VOC compositions (Su et al., 2021).
Concentrations of the 20 most abundant species in Zhengzhou (unit:
The top 20 VOC species are summarized in Table 1. The top 20 substances were similar in the three stages, but the concentration levels were quite different. Tracers of solvent sources including hexane and dichloromethane (Huang and Hsieh, 2019; Wei et al., 2019) decreased in the control period, reducing by 42 % and 47 %, respectively. The reduction of vinyl acetate and tetrachloroethylene is relatively large, which may be attributed to industrial emission reduction (Hsu et al., 2018; Zhang et al., 2015). In addition, the concentration of acetylene is reduced by 55 % compared with the pre-NMG period, as a potential result of the control of combustion sources (Liu et al., 2020; F. Wu et al., 2016).
The mean diurnal variations of TVOCs and their compounds before, during, and after the control period are shown in Fig. S7 in the Supplement. Clearly,
the diurnal variations of TVOCs during the three periods are similar, showing higher values from evening till morning rush hours, while they are lowest in the
afternoon. The composition of alkanes, alkenes, alkynes, and aromatics shows similar daily variations. Previous studies have suggested that VOCs can be
oxidized by
Diurnal variations in concentrations of some reactive VOC species in Zhengzhou during the three periods.
As each source type has its own fingerprint, the mean diurnal variations of tracers during the three periods are presented in Fig. 3. Isopentane and
During the sampling period, the great changes in the mixing ratios of VOCs may be caused by the altered contribution of emission sources. Ratios of specific VOCs have commonly been used to identify emission sources.
Because
The toluene / benzene (
Source profiles calculated using the PMF model.
The 42 most abundant species, accounting for almost 90 % total VOC concentrations, were selected to be applied in the PMF receptor model to analyze the relative contribution of each potential source. The factor profiles of seven emission sources, namely, liquefied petroleum gas (LPG) evaporation, industrial processes, vehicle exhausts, biomass burning, biogenic source, solvent usage, and coal combustion, are identified in Fig. 4.
Source 1 was characterized by both high proportions and high abundances of ethane (38 %), ethene (53 %), propane (43 %), and other
Source 2 accounts for larger percentages of carbon disulfide and halohydrocarbon, such as
Source 3 was characterized by a high percentage of some
Source 4 has high concentrations of chloromethane, which is a typical tracer of biomass burning (Ling et al., 2011; Zhang et al., 2019). The
percentages of benzene and toluene were lower, but they could still not be neglected, and the
Source 5 accounts for larger percentages of isoprene, accounting for 86 % of the TVOCs in the source. Isoprene is an indicator of biogenic emissions and is emitted from many plants (Guenther et al., 1995 and 1997). This factor also included a considerable proportion of intermediate products (Liu et al., 2019), such as acetone, 2-hexanone, and 2-butanone. Therefore, this source is considered to be biogenic emissions.
Source 6 was differentiated by
Source 7 was dominated by acetylene, which accounted for 75 % of the TVOCs in the source. Acetylene is a typical tracer of combustion emission (Hui et al., 2019; R. Wu et al., 2016). Some of the VOC species, such as alkanes and benzene, are the main components in emissions from coal burning (Liu et al., 2019; M. Song et al., 2019; Yang et al., 2018). Thus, factor 7 was assigned to combustion emission.
Time series of each identified source contributions and accumulated relative VOC contributions.
The concentrations of hourly mixing ratio and the relative contributions of each VOC sources are illustrated in Figs. 5 and S10 in the Supplement. Compared with the non-control periods, the contributions of coal combustion, vehicle exhausts, and solvent utilization are significantly reduced during the control period.
Conversely, the mixing ratios of LPG showed higher values during the control period. Peak values of biomass combustion were frequently present during the second period, and biomass combustion accounts for a relatively high proportion in this stage. The highest concentration was observed in the afternoon of the 18 September. Zhengzhou and its surrounding areas are in the harvest period of crops in September, so the emissions of biomass combustion need to be considered. Figure S11 in the Supplement shows the hotspots diagram of Zhengzhou and its surrounding areas during the observation period, and the number of fire spots in September was significantly higher than that in August.
Source contributions to VOC concentration in the PMF model during the three periods.
Time series of each identified source contributions are shown in Fig. 6. During the first period, solvent utilization (33
During the control period, solvent utilization made the largest contribution (23 %) to atmospheric VOCs, with the concentration of
23
For the third period, the largest contributor was fuel combustion related to vehicle exhausts, with 30
In summary, the concentrations of solvent utilization were reduced to the greatest extent during the control period, with the value of
10
In this section, the atmospheric environmental implications of VOCs are discussed by calculating the values of risk assessment and ozone formation potential (OFP).
Non-carcinogenic risks of HQ and carcinogenic risks for individual VOC species.
In addition to the impacts on ambient air quality, some VOC species are also toxic with various health impacts. In this paper, the non-carcinogenic
risk (expressed by HQ) and carcinogenic risk (expressed by lifetime cancer risk, LCR) of hazardous VOC species were investigated, and the acceptable
safety thresholds were 1 and 1
During the entire observation period, a total of seven VOCs may pose potential risks to human health. Health risk assessment in Zhengzhou was
compared with other cities, as shown in Table S3. Overall, the values of risk assessment in this study are evidently lower than those reported in
Beijing (Gu et al., 2019) and Langfang (Yang et al., 2019a), whereas they are higher than the summer of 2018 in Zhengzhou (Li et al., 2020). Evaluated health
risk assessment before, during, and after the control period shows cumulative LCR was 5.8
The OFP and their compositions during the three periods are shown in Fig. S13 and Table S6 in the Supplement. The total OFP during the control period
was 183
OFP contributions (
Daily variations in the
The source contributions to OFP were calculated using the PMF model (Table 2). The most important source to ozone formation was traffic emissions. Industrial
emissions and solvent usage were the second and third sources of photochemical ozone formation. Among them, solvent use has the greatest contribution
to the OFP reduction with the emission reduction during the control period, explaining a 48 % reduction in OFP. Although combustion contributes
only 10 % of the total OFP, this source played an important role in the reduction in OFP, explaining 33 % of the OFP reduction. At the
same time, control of traffic and industry also reduced the OFP during the games. Thus, solvent utilization and combustion controls were the most
important measures taken to reduce OFP during the National Minority Games 2019 in Zhengzhou. However, the current knowledge about formation mechanisms of
The impact of ozone precursors on ozone formation can be described as either a
The diurnal variations of the
At the peak time of
It should be noted that ozone sensitivity can only be initially determined by the
Spatial comparison of the
As shown in Fig. 9, the values of sensitivity_
An
The Empirical Kinetic Modeling Approach (EKMA) of
During the NMG period, the government carried out stringent emission controls. The concentrations of ozone precursors showed a decreasing trend,
but the ozone pollution was still serious. Unreasonable emission reduction may be an important factor leading to ozone pollution. Combined with the
results of this study, it is suggested that reduction ratios of the precursors (VOCs :
A number of strict emission control measures were implemented in Zhengzhou and its surrounding area to ensure good air quality during the NMG period. The
concentrations of VOCs and
The mixing ratios of TVOCs during the control period were 121
The
The data set is available to the public and can be accessed upon request from Ruiqin Zhang (rqzhang@zzu.edu.cn).
The supplement related to this article is available online at:
SYu and RZ planned and organized the study and were deeply involved in the writing of the manuscript. FS, SYi, and SW performed the atmospheric measurements and data analysis and wrote the manuscript. BH, XF, and MY assisted heavily with the atmospheric measurements and data analysis. FS and RX conducted the model development and data analysis. Other coauthors provided useful insights on data analysis and contributed to the writing of the manuscript. SYu and FS contributed equally to this work.
The authors declare that they have no conflict of interest.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was supported by the Study of Collaborative Control of PM
This research has been supported by the Study of Collaborative Control of PM
This paper was edited by Christopher Cantrell and reviewed by two anonymous referees.