This study investigates the influence of the Chinese New Year (CNY)
celebrations on local air quality in Beijing from 2013 through 2019. CNY
celebrations include burning of fireworks and firecrackers, which
consequently has a significant short-term impact on local air quality. In
this study, we bring together comprehensive observations at the
newly constructed Aerosol and Haze Laboratory at Beijing University of
Chemical Technology – West Campus (BUCT-AHL) and hourly measurements from
12 Chinese government air quality measurement stations throughout the
Beijing metropolitan area. These datasets are used together to provide a
detailed analysis of air quality during the CNY over multiple years, during
which the city of Beijing prohibited the use of fireworks and firecrackers
in an effort to reduce air pollution before CNY 2018. Datasets used in this
study include particulate matter mass concentrations (PM
Anthropogenic emissions associated with festivities, notably fireworks and firecrackers (hereafter simply fireworks), are known for their hazardous effects, and even short-term exposure can have significant impacts on human health (Bach et al., 2007; Chen et al., 2011; Jiang et al., 2015; Yang et al., 2014). Firework celebrations are found to increase the concentrations of trace gases and particle concentrations (Kong et al., 2015; Li et al., 2013). Furthermore, some studies have related these festivities to the occurrence of haze episodes in the days following a firework event (Li et al., 2013; Feng et al., 2012).
The Chinese New Year (CNY) is a traditional annual holiday occurring in wintertime – in January or in February as the exact date is based on the lunar cycle. Because of the adverse impacts on health, pollution from fireworks during the CNY has gathered attention worldwide. For instance, studies including Yang et al. (2014) in Jinan, Shi et al. (2014) in Tianjin, and Feng et al. (2012) and Zhang et al. (2010) in Shanghai have shown that there is noticeable degradation in air quality associated with Chinese New Year celebrations in these cities. Wang et al. (2007) has shown that firework celebrations emit significant amounts of sulfur dioxide and black carbon. The effects of fireworks on air pollution are known for various holidays in other countries as well. Studies in India, for example, during the country's annual Diwali festival in the late autumn have also shown results of high pollution from firework use (Ravindra et al., 2003; Mönkkönen et al., 2004; Barman et al., 2007; Singh et al., 2009; Yerramesetti et al., 2013). As another example, a study by Liu et al. (1997) in southern California, USA, showed enhanced concentrations of particulate matter and trace gas pollutants during firework celebrations.
Because of the rising awareness of air quality problems during holiday celebrations, the government of Beijing decided to implement a prohibition on firework burning within the 5th Ring Road of Beijing in an effort to reduce air pollution, which is described in a study by Liu et al. (2019). Their study reported that the prohibition resulted in about a 40 % decrease in the total number of fireworks and firecrackers sold in the city of Beijing during the 2018 CNY holiday compared to 2016. Furthermore, Liu et al. (2019) reported that observed concentrations of air pollutants during the 2018 CNY was significantly less than that in 2016.
Therefore, an aim of this study is to confirm the conclusions of the Liu et al. (2019) study, using not only a 2016 vs. 2018 comparison, but a longer study of each year between 2013 and 2019. Furthermore, this study offers a spatial comparison of the area where fireworks were prohibited (inside the 5th Ring) with a region where there was no prohibition (outside the ring). Currently, there are no previous studies that perform such a side-by-side comparison of areas with different firework burning policies.
This paper provides a detailed view of how CNY celebrations have influenced air quality and atmospheric chemistry in the Beijing metropolitan area. We start with an in-depth analysis of data from 2018 and 2019, and then we expand with the longer 7-year dataset. Combined, these datasets provide perspective into the impacts of the imposed restrictions on firework use in the Beijing area. The specific questions we aim to answer include (1) how the CNY celebrations and associated increase in precursor and aerosol emissions reflect the atmospheric concentrations of trace gases and particulate matter and particle number size distribution; (2) how these changes are connected with meteorological conditions; (3) how the influence of CNY affects regional air quality variation spatially over the Beijing area; and (4) how the influence of CNY on Beijing air quality has changed during the recent years, including the result of the firework prohibition beginning in 2018.
The observations used in this study include measurements collected from the Beijing University of Chemical Technology Aerosol and Haze Laboratory (BUCT-AHL), an academic research station in Beijing, China (Liu et al., 2020), along with 7 years of data from 12 measurement stations throughout the Beijing metropolitan area, operated by the Chinese Ministry of Environmental Protection (MEP). The long-term datasets also provide spatial context on the scale of the greater Beijing area, including a comparison of measurements inside versus outside of the prohibition area. Here we investigated years 2013–2019. Although data from the 2020 CNY are available, we have decided not to include them in this study because of the widespread impacts of the COVID-19 virus that affected China during this time. Due to the unfortunate circumstance, many Chinese citizens refrained from travel, public celebrations, and time spent in public. Consequently, the 2020 CNY is not directly comparable to previous years.
This study is novel and unique in a few ways. First, it is one of only a few studies to not only show measurements for a single CNY (or similar celebratory holidays in other countries), but it studies the holiday over 7 continuous years. This offers the ability to show trends and effects of, for example, policy changes over time. Furthermore, this study uses data from multiple institutions, which demonstrates the value of collaborations between different institutions when it comes to solving major global problems such as air pollution. This study also compares the CNY inside the center of the city to the greater Beijing area, which is unique compared to any previous CNY (or similar holiday) air quality study that uses data at a single location. Our insights offer value to scientists and policymakers around the world who are interested in improving air quality during holidays that involve firework celebrations. Improving air quality, even short-term, can have a significant positive impact on the health and wellbeing of citizens.
This study uses data collected from two sources. First, we used data from
the newly constructed station near the 3rd Ring Road of Beijing
(39
Location of the BUCT-AHL site within the Beijing metropolitan area. © OpenStreetMap contributors 2022. Distributed under the Open Data Commons Open Database License (ODbL) v1.0.
In our analysis, the following datasets from BUCT-AHL during the 2018 and
2019 CNY are used: (1) trace gas concentrations – nitrogen oxides (NO
Additionally, we obtained datasets from several national air quality
monitoring sites within the Beijing metropolitan area (NAQMS; Song et al.,
2017; Tao et al., 2016). These datasets were obtained from the Chinese
Ministry of Environmental Protection (MEP), which contain the following: (1)
fine and coarse particulate matter mass concentrations (PM
Concentrations of carbon monoxide (CO), sulfur dioxide (SO
Meteorological datasets for 2018–2019 at BUCT-AHL were collected with a Vaisala automatic weather station, AWS310, including wind speed and direction, ambient air temperature, and relative humidity. Boundary layer height (BLH) was measured using a Vaisala CL-51 ceilometer. Meteorological and BLH measurements were taken on the rooftop of BUCT-AHL.
Archived meteorological data for Beijing from 2013–2017 were obtained from
the Weather Underground website (
Particle size distribution (PSD) between 3 nm and 1
Aerosol particle sizes have been further divided into four modes, based on particle diameter: cluster mode (sub-3 nm), nucleation mode (3–25 nm), Aitken mode (25–100 nm), and accumulation mode (100–1000 nm). The method is described in Zhou et al. (2020).
Sulfuric acid was measured by a chemical ionization atmospheric-pressure
interface time-of-flight mass spectrometer equipped with a nitrate chemical
ionization source (CI-APi-TOF; Jokinen et al., 2012). The ionization was
done with NO
An aethalometer AE33 (Magee Scientific) monitored the light absorption related to the aerosol. Equivalent black carbon (eBC) was computed based on the change in time of the light attenuation using procedures presented in Virkkula et al. (2015).
Beginning in 2013, the Chinese Ministry of Environmental Protection (MEP)
began installing a China-wide network of air quality monitoring stations to
measure local and regional air quality. Real-time datasets from this sensor
network are published hourly by the China Environmental Monitoring Center
(CEMC), which includes PM
In this study, data from 12 MEP sites throughout Beijing are used (see Table S1 for a list of these sites and their locations). The Guangyuan (GY) site is the closest site to BUCT-AHL, about 5 km east. The data used in this paper have been quality-controlled, described in Wu et al. (2018).
Back trajectories to the BUCT station were calculated using the Hybrid
Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. This model is
developed by the National Oceanic and Atmospheric Administration (NOAA) Air
Resources Laboratory and the Australian Bureau of Meteorology Research
Centre, and it is one of the most widely used models to determine the origin
of an air mass (Stein et al., 2015). In this work, HYSPLIT trajectories were
calculated for the CNY each year from 2013–2019, with the trajectories
arriving between 18:00 and 06:00 (all times refer to local time, UTC
Higher atmospheric concentrations due to elevated pollutant emissions during the Chinese New Year were observed at both BUCT-AHL and the MEP sites during the analysis periods. The observed features include sudden spikes in concentrations of trace gases, aerosol particles, and BC. These observations agree with the previous studies showing a connection between holiday-related firework celebrations and degraded air quality (Jiang et al., 2015; Yang et al., 2014; Shi et al., 2014; Feng et al., 2012; Zhang et al., 2010). In the sections below, we delve into these results, which can broaden scientific understanding of the impacts of firework celebrations on local and regional air quality, especially in the context of a wide metropolitan area over the course of several years.
The CNY was on 16 February 2018 and 5 February 2019. Figure 2 shows a
time series of air pollutant concentrations from 8 d before to 8 d after the 2018 and 2019 CNY at BUCT-AHL (except for PM
Concentrations of main pollutants measured and boundary layer height in Beijing during the 2018 CNY (orange) and 2019 CNY (blue).
In contrast, in 2019, PM
The measurements showed elevated nighttime concentration of H
However, there appeared to be little to no effect of CNY on BC in 2019. The
measurements showed an elevated concentration of NO
Figure 2 also shows that during the CNY celebrations in 2018 concentrations
of the primary pollutants, SO
Interestingly, in addition to the short-term enhancement of pollutant concentrations, Fig. 2 shows degraded air quality between 16–20 February 2018, following the Chinese New Year, which closely resembles the characteristics of a haze event as described in Zhao et al. (2013, 2011) and Zhang et al. (2020). Using the data from BUCT-AHL, this period was quantifiably classified as a haze event using the algorithm in Zhou et al. (2020). These haze events have elevated concentrations of pollution continuously for multiple days, and concentrations gradually increase throughout the episodes. The haze eventually ends with sudden decline, often caused by an arrival of a cold front or change in synoptic weather conditions. Several previous studies, including Jiang et al. (2015) and Li et al. (2013), suggest that fireworks likely contribute to haze formation. It is plausible that the increased level of pollutants observed overnight during the 2018 CNY likely contributed to this subsequent haze period. However, the meteorological conditions and air mass origins are also important for haze formation and are discussed in Sect. 3.2.
Because the meteorological conditions during CNY vary between different years, it is important to address the impact of local- and synoptic-scale meteorological parameters on air pollution when comparing different years to each other. Specifically, wind speed and direction, relative humidity (RH), boundary layer height, and precipitation can affect pollutant concentrations during and after the fireworks.
However, none of the measured local meteorological variables showed drastic
differences between CNY nights of 2018 and 2019. The wind speed during the
night of the 2018 CNY peaked at
Meteorological conditions during CNY night
The lower concentrations observed during the emission spike in 2019 can be either due to lower emission rates in the area with which the measured air mass is in contact or due to a shorter exposure to roughly similar emissions during both years. Figure 4 shows 96 h back trajectories by HYSPLIT, during the night of CNY in 2018 and 2019, showing the sources of the air masses. This provides further insights into the history of the air masses in Beijing, including how clean we can expect the air masses to be before CNY and whether the air masses are stagnant around Beijing or whether clean air is being transported into the city.
These trajectories show the following: in 2018, the air masses from 6 h prior to CNY through CNY are from the southwest, and from 2 through 6 h after CNY, the air mass is from the west. In 2019, air masses from 6 h prior to CNY through 2 h prior to CNY are from the east, and following the CNY the air masses are primarily from the west.
Based on Wang et al. (2019), air masses from the east are expected to be cleaner than from the southwest due to more diffusion and fewer emissions from industry. However, we observed the opposite: from 6 through 2 h prior to midnight (i.e., the background value before the spike in pollution), the background pollutant concentrations are higher in 2019 than in 2018. This gives further indication that the emission sources are likely localized and short-term as opposed to long-range transport.
HYSPLIT 96 h back trajectories for air masses arriving at BUCT-AHL between 18:00 and 06:00 LT, the night of CNY in 2013 through 2019. The markers are every 12 h.
Further exploring the effects of the fireworks on air pollution, Fig. 5
shows PSD at BUCT-AHL from the day before to the day after CNY. Shortly
before midnight on CNY in 2018, an elevated concentration of aerosol
particles with diameters of roughly 100 nm was observed, simultaneously with the
spike in SO
Aerosol particle number size distribution (PSD) from 1 d
before the CNY through 1 d following the CNY in 2018 and 2019, overlain
with aerosol mass concentration PM
In 2019, secondary aerosol mass formation was also observed as the particle
mode grew in diameter steadily between 18:00 and 06:00, and the PM
Figure 6 shows the particle number concentrations in four size modes,
specifically sub-3 nm cluster mode, 3–25 nm nucleation mode, 25–100 nm
Aitken mode, and 100–1000 nm accumulation mode, as a function of PM
Aerosol particle number concentrations in cluster, nucleation,
Aitken, and accumulation modes as a function of PM
In short, the CNY activities seem to not cause any major deviance for the typical aerosol dynamics other than the enhancement of the source of accumulation-mode particles.
Figure 7 depicts the cluster-, nucleation-, Aitken-, and accumulation-mode particle number concentrations as a function of gas-phase sulfuric acid concentration in 2018 and in 2019 inside and outside of the CNY period. Looking at the clusters, the results show a general strong dependency on the sulfuric acid as it is one of the main precursors driving the process of gas-to-particle conversion (e.g, Sipilä et al., 2010; Kulmala et al., 2013; Yao et al., 2018). However, the high nocturnal sulfuric acid concentration during CNY celebrations in 2018 does not lead to high cluster- or nucleation-mode concentration. In fact, the particle number concentrations in these modes deviate from the otherwise clear response to sulfuric acid concentrations. The reason for this is visible in the panel for accumulation-mode concentration vs. sulfuric acid concentration: during the CNY 2018 the high concentrations of accumulation-mode particles correlate with sulfuric acid concentration, thus plausibly neglecting the enhanced particle cluster and particle formation rates by an enhanced coagulation sink as explained earlier.
Aerosol particle number concentrations in cluster, nucleation,
Aitken, and accumulation modes as a function of gas-phase sulfuric acid
concentration in 2018 (purple) and 2019 (green), separated from 21:00 through
05:00 the night of the CNY (filled circles), and those of CNY
Fireworks were formally prohibited within the 5th Ring Road of Beijing beginning in 2018, whereas outside the 5th Ring Road, there were no prohibitions (Liu et al., 2019). Still, there was some evidence of firework burning observed at BUCT-AHL, which is within the prohibition area.
A longer-term multi-year study can be useful in demonstrating whether or not the policy is effective in reducing firework-related pollution and if there is an overall decreasing trend of pollution effects from fireworks over multiple years. To investigate this question, it is useful to compare the 2018 and 2019 CNY with previous years in Beijing. Datasets have been analyzed from 12 MEP stations in the Beijing area from 2013 through 2019.
Figure 8 shows that each year, there was a spike in pollution around
midnight during the CNY. The highest levels were observed in 2016, with the
peak in PM
PM
Data from the CNYs have also been compiled into box plots in Fig. 9,
depicting the distributions of pollutant concentrations from 18:00 on CNY
Eve to 06:00 on the CNY day each year at all 12 MEP stations. The highest
PM concentrations during this time were in 2016, and the 75th and
99th percentile concentrations have decreased after that. On the other
hand, the median concentration remained high during 2017 and 2018 but
decreased in 2019 by roughly a factor of 2. Concentrations of NO
Boxplots of PM
Based on HYSPLIT back trajectories (Fig. 4), we see that in 2013–2015 the air masses spent more time in the BTH area prior to arrival. This differs from the air mass sources in 2016–2017, where the air masses come directly from the northwest. These areas to the northwest of Beijing, including Inner Mongolia and Mongolia, usually contain fewer pollutants due to low anthropogenic emissions, and thus we can expect air masses from this region to be cleaner (Xu et al., 2008). Based on the air mass history, if emissions were the same, then there should be higher concentrations in 2013–2015; however, we see the highest concentrations of pollutants in 2016, followed by a decline after that. In 2018 and 2019, the air masses spent around 2 d in the BTH area leading up to arrival at the station. Based on air mass source alone, we would have expected higher pollutant concentrations in 2018 and 2019, but this is not the case. Thus, we can conclude that emissions must have been highest in 2016, with lower emissions in 2018 and 2019. This agrees with Liu et al. (2019).
Next, we performed a spatial comparison of the MEP measurements across the
Beijing region. This includes comparing the observations inside the 5th
Ring Road, where fireworks were prohibited, to outside the ring. Figure 10
maps the 12 MEP stations in the Beijing region for 2013–2019, showing the
ratio between mean PM
The 12 MEP sites mapped in the Beijing metropolitan area, showing
the ratio of overnight PM
Figure 10 illustrates that in 2013 and 2014, the enhancement in PM
Figure 11 shows differences between the PM
Differences between mean PM
In this study, we looked at comprehensive measurements over CNY 2018 and 2019 at a measurement station in Beijing, along with long-term datasets across the Beijing metropolitan area.
Our study confirms that CNY consistently impacts air quality in Beijing.
Based on our observations at the BUCT-AHL station in Beijing, in 2018, we
detected higher-than-typical nighttime concentrations of particulate mass
(PM
Our results suggest that the regulations from CNY 2018 to limit firework use have improved the air quality within the restriction zone inside the 5th Ring Road in Beijing, and from 2016 to 2019 there has been a decrease in the effects of holiday-related pollution, which offers an optimistic outlook on the air quality impacts caused by CNY and the consequential public health concerns stemming from air pollution.
During the CNY night in 2018, we observed the appearance of particles with diameters of roughly 100 nm that seemed to be linked to enhanced sulfur dioxide, sulfuric acid, and black carbon concentrations, most likely as a result of firework burning. Based on the MEP data, the peaks in concentrations of different pollutants were lower than in the previous years. In 2019, a peak in pollution was observed overnight, but it was significantly lower than in 2018, while meteorological conditions were comparable in both years. The significant year-to-year variability depended presumably on the meteorological conditions. A common phenomenon for both 2018 and 2019 CNY nights was the accumulation of secondary aerosol throughout the night, seen as a diameter growth of the dominant particle mode in particle number size distributions. Measurements at BUCT-AHL showed that in 2018 a moderate haze episode began 1 d following the CNY, potentially related to the firework burning.
Comparing the level of increase in pollutant concentrations during CNY night inside Beijing's 5th Ring Road (firework prohibition area) to outside revealed that in 2019 the increase inside this area was smaller than outside. During most – but not all – of the previous CNYs, the increase in concentration was higher inside than outside. This was also the case in 2018. However, as also in previous years the ratio of inside and outside concentrations during CNY has varied, it is unclear if this is related to the efficacy of the emission prohibition or, for example, to larger-scale air mass movements or simply due to the fact that fireworks are sporadic and localized emission sources. Nonetheless, in terms of absolute concentrations, our results show a decrease in CNY pollution within the prohibition area since 2016 and especially in 2019. This is in agreement with the previous Liu et al. (2019) study, which compared the 2016 and 2018 CNY (before and after the prohibition took effect).
To conclude, this long-term analysis, which combines BUCT data with multiple years of Chinese government data at 12 locations in the Beijing area, demonstrates the importance of analyzing multiple data sources to determine overall trends rather than making conclusions based on a single dataset. This also demonstrates the usefulness of long-term measurements. Using these datasets together, we see excellent potential that can be utilized to investigate the changes in (a) atmospheric chemistry, such as ozone dynamics and sulfuric acid formation; (b) atmospheric gas-to-particle conversion; (c) boundary layer dynamics; and (d) air quality. Using CNY as a case study offers excellent insight into how rapid changes in emissions will affect air quality, health, and quality of life, especially in megacities such as Beijing. To confirm and quantify the influence of banning the firework burning in Beijing and the impact of varying meteorological conditions, similar data from coming CNYs are needed. Therefore, we suggest ongoing measurements at both BUCT-AHL and MEP sites into multiple future years.
Data used for this study can be found at
The supplement related to this article is available online at:
All BUCT-affiliated authors, plus KRD, BC, YW, TC, and PR, contributed to measurement collection at BUCT. LW provided the quality-controlled MEP data. BF, LD, KRD, TP, FB, PP, and MK conceptualized and conducted the data analysis. TVK, MoK, REP, and RB participated in the data analysis. TVK and MoK provided the meteorology data. KRD, TP, FB, PP, and MK supervised the study. BF visualized the data with assistance from SG. BF wrote the original draft and prepared the manuscript. PP, TP, and all other authors reviewed and edited the manuscript.
The contact author has declared that none of the authors has any competing interests.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The authors wish to acknowledge the Finnish Centre for Scientific Computing (CSC) – IT Center for Science, Finland, for computational resources.
The work has been supported by the Academy of Finland via the Center of Excellence in Atmospheric Sciences (project no. 272041) and via ACCC Flagship (project no. 337549) and by the European Research Council via ATM-GTP (grant no. 742206). This research has also received funding from the Academy of Finland (project nos. 316114, 315203, and 307537); Business Finland via the MegaSense project (grant no. 6884/31/2018); the European Commission via ERA-NET Cofund project SMart URBan Solutions for air quality, disasters and city growth (grant no. 689443); Jane and Aatos Erkko Foundation project “Quantifying carbon sink, CarbonSink+ and their interaction with air quality”; the European Union’s Horizon 2020 research and innovation program (RI-URBANS, grant agreement no. 101036245); and the Doctoral Programme in Atmospheric Sciences at the University of Helsinki. Partial support was provided by the National Key R&D Program of China (grant no. 2016YFC0200500) and the National Natural Science Foundation of China (grant nos. 91544231 and 41725020).Open-access funding was provided by the Helsinki University Library.
This paper was edited by Zhanqing Li and reviewed by four anonymous referees.