Quantifying the interactive effects of meteorological, socioeconomic, and pollutant factors on summertime ozone pollution in China during the implementation of two important policies

Abstract

In recent years, ground-level ozone (O3) pollution has increasingly impacted both climate and human health in China. This study used the Bayesian space-time hierarchy model to reveal the spatial heterogeneity of the summertime O3 concentrations based on the monitoring data of 331 cities in China from 2015 to 2020. The GeoDetector model was then used to quantify the associations of meteorological, socioeconomic factors, air pollutants, and their interactive effects with summertime O3 concentrations in six representative regions of China during the implementation of two policies: the Air Pollution Prevention and Control Action Plan (2015–2017) and the Blue Sky Defense Action policy (2018–2020). The results showed that O3 concentrations increased by 11.33% from 2015 to 2017 and slightly decreased by 4.2% from 2018 to 2020. Spatially, hot spots (high-risk areas) of O3 pollution are mainly clustered in North China (Beijing-Tianjin-Hebei, Shandong, Henan, and Shanxi) and the Yangtze River Delta (Shanghai, Anhui, and Jiangsu), while cold spots (low-risk areas) are mainly distributed in Northeast China (Heilongjiang, and Jilin) and Southwest China (Tibet, Sichuan, Guangxi, and Yunnan). Furthermore, regional differences exist among the examined meteorological, socioeconomic, and pollutant factors in terms of impacting the spatiotemporal heterogeneity of O3 concentration: exactly, the dominant meteorological factors impacting O3 concentration were average temperature and air pressure; population density and industrial output were the most important socioeconomic factor, which have impact on O3 concentration; and the dominant pollutant factors impacting O3 concentration were PM2.5 and PM10 in six representative regions of China during 2015–2017 and 2018–2020. These findings increase our understanding of the spatiotemporal characteristics of O3 pollution in China and can assist in urban policy-making.

Full text

Atmospheric Pollution Research 12 (2021) 101248
Available online 28 October 2021
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Quantifying the interactive effects of meteorological, socioeconomic, and
pollutant factors on summertime ozone pollution in China during the
implementation of two important policies
Xiangxue Zhang
a
,
b
, Bin Yan
c
, Chaojie Du
b
, Changxiu Cheng
a
,
b
,
d
,
*
, Hui Zhao
e
,
**
a
Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing, 100875, China
b
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China
c
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing, 100101, China
d
National Tibetan Plateau Data Center, Beijing, 100101, China
e
Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China
ARTICLE INFO
Keywords:
Bayesian space-time hierarchy model
GeoDetector
Ozone pollution
Spatiotemporal characteristics
ABSTRACT
In recent years, ground-level ozone (O
3
) pollution has increasingly impacted both climate and human health in
China. This study used the Bayesian space-time hierarchy model to reveal the spatial heterogeneity of the
summertime O
3
concentrations based on the monitoring data of 331 cities in China from 2015 to 2020. The
GeoDetector model was then used to quantify the associations of meteorological, socioeconomic, pollutant
factors, and their interactive effects with summertime O
3
concentrations in six representative regions of China
during the implementation of two policies: the Air Pollution Prevention and Control Action Plan (2015–2017)
and the Blue Sky Defense Action policy (2018–2020). The results showed that O
3
concentrations increased by
11.33% from 2015 to 2017 and slightly decreased by 4.2% from 2018 to 2020. Spatially, hot spots (high-risk
areas) of O
3
pollution are mainly clustered in North China (Beijing-Tianjin-Hebei, Shandong, Henan, and Shanxi)
and the Yangtze River Delta (Shanghai, Anhui, and Jiangsu), while cold spots (low-risk areas) are mainly
distributed in Northeast China (Heilongjiang, and Jilin) and Southwest China (Tibet, Sichuan, Guangxi, and
Yunnan). Furthermore, regional differences exist among the examined meteorological, socioeconomic, and
pollutant factors in terms of impacting the spatiotemporal heterogeneity of O
3
concentration: exactly, the
dominant meteorological factors impacting O
3
concentration were average temperature and air pressure; pop-
ulation density and industrial output were the most important socioeconomic factor, which have impact on O
3
concentration; and the dominant pollutant factors impacting O
3
concentration were PM
2.5
and PM
10
in six
representative regions of China during 2015–2017 and 2018–2020. These ndings increase our understanding of
the spatiotemporal characteristics of O
3
pollution in China and can assist in urban policy-making.
1. Introduction
In recent decades, rapid urbanization and energy-intensive devel-
opment have caused severe problems of air pollution in China (An et al.,
2019; Liu et al., 2016, 2017; Wang et al., 2014). It poses adverse impacts
on climate, ecological environment, and human health (Bai et al., 2018;
Cohen et al., 2018; Lelieveld et al., 2015; Li et al., 2017). The air pol-
lutants common reported were the particulate matter with an
aerodynamic diameter of less than 2.5
μ
m (PM
2.5
) and 10
μ
m (PM
10
),
ozone (O
3
), sulfur dioxide (SO
2
), nitric dioxide (NO
2
), and carbon
monoxide (CO) (Huang et al., 2018; Mannucci and Franchini, 2017;
Shen et al., 2020). There have complex interactions among these pol-
lutants and if the concentration of one pollutant changes, it will inevi-
tably cause the concentration of other pollutants to change accordingly.
Of the various atmospheric pollutants, O
3
is regarded as a secondary
pollutant, and its level has been continuously increasing in recent years
Peer review under responsibility of Turkish National Committee for Air Pollution Research and Control.
* Corresponding author. Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing, 100875, China.
** Corresponding author.
E-mail addresses: chengcx@bnu.edu.cn (C. Cheng), zhaohui_nuist@163.com (H. Zhao).
Contents lists available at ScienceDirect
Atmospheric Pollution Research
journal homepage: www.elsevier.com/locate/apr
https://doi.org/10.1016/j.apr.2021.101248
Received 6 June 2021; Received in revised form 26 October 2021; Accepted 26 October 2021

Atmospheric Pollution Research 12 (2021) 101248
2
(Li et al., 2014b; Zhang et al., 2013; Zhao et al., 2021). It is estimated
that O
3
can cause more than 0.7 million death each year worldwide
(Anenberg et al., 2010). Of greater concern, previous epidemiological
studies have demonstrated that O
3
pollution can increase morbidity and
mortality due to respiratory and cardiovascular diseases, and cancer
(WHO, 2013). O
3
pollution experts a great threat to human health, thus,
more attention was paid to the increase of O
3
concentration (Maji et al.,
2019; Salonen et al., 2018).
In 2013, the Chinese government implemented the Air Pollution
Prevention and Control Action Plan (APPCAP) to reduce PM
2.5
pollution
and related haze days (Maji et al., 2019; Wang et al., 2010b; Guo et al.,
2019). However, in recent years, O
3
pollution is so high, particularly in
summer, because the maximum daily 8 h average O
3
concentration
(MDA8 O
3
) frequently exceeding 200
μ
g/m
3
in many major cities of
China (Lu et al., 2018). Zhao et al. (2021) indicated that the annual
MDA8 O
3
increased by 3.4
μ
g/m
3
, from 2015 to 2018. Also, a previous
study indicated that the O
3
levels in summer from 2013 to 2017 in
Beijing-Tianjin-Hebei, Yangtze River Delta (YRD), and Pearl River Delta,
and Sichuan Basin increased by 3.1, 2.3, 0.56, and 1.6 ppbv per year,
respectively (Li et al., 2019). From 2001 to 2007, the annual O
3
con-
centration increased by 1.7
μ
g/m
3
in southern China; and in the time
period from 2003 to 2015, the annual O
3
concentration increased at the
rate of about 2.2
μ
g/m
3
in the northern region (Osterman et al., 2008;
Ziemke et al., 2011). The results of Gao et al. (2020) showed that the
annual average MDA8 O
3
concentrations increased from 81.3 to 87.4
μ
g/m
3
during 2015–2018 and the nonattainment days kept increasing.
Moreover, strong wind shear enhanced the downward transport of O
3
into the boundary layer, which leads to an increase in O
3
concentration
(Chen et al., 2020). Stratospheric O
3
invasion is an additional source of
near-surface tropospheric O
3
concentration, which aggravates O
3
pollution in China (Ni et al., 2019).
Many previous studies demonstrated that meteorological factors
such as, temperature, relative humidity, and precipitation impacted
greatly on O
3
; socioeconomic conditions brought by rapid urbanization
and industrial development and pollutants level were also related to O
3
pollution. For example, Simon et al. (2015) showed that the level of
VOC/NOx ratio exert impact on the concentration of O
3
. Li et al. (2019)
demonstrated that the increase of O
3
concentrations may be related to
the reduction of PM
2.5
concentrations. Using climate-chemistry model,
Doherty et al. (2013) demonstrated that meteorological factors greatly
impacted on surface O
3
, such as, temperature, relative humidity, and
precipitation. Gao et al. (2020) showed that O
3
pollution episodes in
northeast China were correlated with meteorological conditions of high
temperature, low relative humidity, long sunshine duration. Wu and Xie
(2017) demonstrated that socioeconomic conditions have signicant
effect on the O
3
pollution, such as, for example, the usage of fossil fuel
has been considered as a primary source of O
3
precursors. The study of
Mo et al. (2021) showed that the serious O
3
pollution in North China
(NC) may be attributed to the large amount of precursor emissions in
these high-density areas. However, these previous studies only quanti-
ed the effect of an individual inuencing factor on O
3
concentration,
their interactions have been rarely studied. Moreover, the associations
were quantied based on the global perspective instead of considering
the spatial-temporal stratied heterogeneity, thus, the effects of these
factors so far have not been systematically quantied. Furthermore,
traditional methods have a disadvantage in quantifying the interactions
of factors affecting air pollution. For example, the interactions of two
factors can take many forms of coupling in reality, but in traditional
regression methods, it is usually the product of two factors, and they
have poor ability to explain the phenomenon of spatial stratication
heterogeneity (Wang et al., 2016).
Therefore, the aims of this study are to: (1) explore the long-term,
city-level spatiotemporal heterogeneity of O
3
pollution in 331 cities of
China; (2) reveal the hot spots (high-risk areas) and cold spots (low-risk
areas) of O
3
pollution and their associated mechanisms; and (3) quantify
the determinant powers of meteorological, socioeconomic, and
pollutant factors and their interactive effects during the implementation
of two policies: 2015–2017 and 2018–2020 for identifying regional
dominant factors of O
3
pollution in six representative regions of China.
2. Methods
2.1. Data collection
This study was based on daily concentration for six air pollutant
(MDA8 O
3
, PM
2.5
, PM
10
, SO
2
, NO
2
, and CO), meteorological conditions,
socioeconomic situations in 331 Chinese cities from 2015 to 2020. These
cities covered all the 31 provinces (excluding HongKong, Macau and
Taiwan) across China, and were classied into six representative regions
according to the geographical environment and climatic conditions
(Fig. 1), that is: North China (NC, including Beijing, Tianjin, Hebei,
Henan, Shandong, and Shanxi), Northeast China (NE, including Hei-
longjiang, Jilin, and Liaoning), Northwest China (NW, including Inner
Mongolia, Ningxia, Gansu, Shaanxi, and Xinjiang), Southeast China (SE,
including Jiangsu, Anhui, Zhejiang, Shanghai, Hubei, Hunan, Jiangxi,
Guangdong, Hainan, and Fujian), Southwest China (SW, including
Sichuan, Chongqing, Guizhou, Yunnan, and Guangxi), and Tibetan
Plateau (TP, including Tibet and Qinghai).
Considering the characteristics of O
3
, results from previous studies
and data availability (Doherty et al., 2013; Wu and Xie, 2017), 15
meteorological, socioeconomic, and pollutant factors were selected in
this study. Daily data on air pollutant concentrations from January 1,
2015 to December 31, 2020 were obtained from National Urban Air
Quality Real-time Publishing Platform (http://106.37.208.233:200
35/), including MDA8 O
3
, PM
2.5
, PM
10
, SO
2
, NO
2
, and CO. Monthly
averaged concentrations in each city for each pollutant were calculated
by averaging the daily data of all monitoring sites in this city.
Daily meteorological data included average temperature (AT), air
pressure (AP), precipitation (PP), relative humidity (RH), sun hours
(SH), and wind speed (WS), which were collected from China Meteo-
rological Data Sharing Service System for the same period. Monthly
meteorological data for each city was obtained by averaging the daily
data of all stations in this city. Annual city-level socioeconomic data of
population density (PD), industrial output (IO), per capita gross do-
mestic product (GDP) (hereafter PG), and proportion of secondary in-
dustry (PS) were obtained from the governmental economic statistical
yearbooks of China. And we have carried out quality control of the data
according to Ma et al. (2019).
2.2. Statistical analysis
A Bayesian space-time hierarchy model (BSTHM) was used to
examine the spatial heterogeneity of O
3
pollution, and reveal hot spots
and cold spots of O
3
pollution in China from 2015 to 2020. The Geo-
Detector method was then used to quantify the determinant powers of
meteorological, socioeconomic, and pollutant factors, and their inter-
active effects that affect the spatiotemporal pattern of O
3
concentrations
in six regions of China during the implementation of two policies
including APPACP in 2015–2017 and the Blue Sky Defense Action
(BSDA)in 2018–2020.
2.2.1. Bayesian space-time hierarchy model (BSTHM)
BSTHM can effectively solve the problem of a small sample in a
spatiotemporal perspective and can take advantage of spatiotemporal
correlation by using prior information, in that Bayesian statistics does
not require a large sample, which is better than the traditional method.
Modeling using BSTHM is a statistical approach used to explore the
representative spatiotemporal patterns in data, which can effectively
address the problem of small sample sizes in spatiotemporal phenomena
(Li et al., 2014a). It is more informative than traditional spatiotemporal
analysis techniques, and has been widely used in epidemiology, geog-
raphy, and atmospheric sciences (Barboza, 2019; Zhang et al., 2020a,
X. Zhang et al.

Atmospheric Pollution Research 12 (2021) 101248
3
2020b).
Specically, BSTHM can be used to analyze the temporal trend and
spatial distribution pattern of O
3
pollution, and solve the problem of
non-homogeneous variances that may appear in time and space. To be
more detailed combined with the two policies temporally, in this study,
BSTHM was used to reveal the spatial heterogeneity of O
3
pollution from
2015 to 2020 in China.
Here, the monthly MDA8 O
3
data (2015–2020) for the 331 cities
were used as the spatiotemporal data, and BSTHM with Normal distri-
bution was used to model, as follows:
yit ∼N(uit,
σ
2
y)(1)
log(uit) =
α
+si+ (b0t*+vt) + b1it*+
ε
it (2)
where y
it
is the mean MDA8 O
3
concentration of city i (i =1, 2, …, 331)
in month t (t =1, 2, …, 72), u
it
represents the potential pollution risk,
σ
y2
is the variance of the corresponding normal distribution,
α
indicates the
xed effect of pollution risk in the study area for the period 2015–2020,
s
i
describes the spatial heterogeneity of the pollution risk during the
study period, (b
0
t* +v
t
) denotes the overall temporal variation of
pollution risk, t* expresses the mid-observation period, b
1i
represents the
local temporal trend deviating from b
0
, and
ε
it
~ N (0,
σ
ε
2
) denotes the
Gaussian noise (Gelman, 2006).
Then the hot spots and cold spots (and neither) of O
3
pollution were
identied following this process (Richardson et al., 2004). That is, a city
was dened as a hot spot or cold spot when it’s the posterior probability
that the spatial relative risk of a certain research unit (such as a county)
is greater than 1 (p (exp (s
i
) >1 | data)) was greater than 0.80 or less
than 0.20, respectively. Those cities falling between these values were
neither dened as hot spots nor cold spots according to the above two
stages. In this study, Bayesian estimation is performed using WinBUGS
(Lunn et al., 2000), which is specically for Bayesian statistics based on
the Markov Chain Monte Carlo (MCMC) method, and the posterior
distribution of all parameters in the model is obtained through Markov
Chain Monte Carlo (MCMC) simulation. This study used an MCMC chain
of 400,000 iterations, including 200,000 burn-in periods and 200,000
estimated iterations.
2.2.2. GeoDetector
The GeoDetector tool (www.geodetector.cn) assumed that the factor
X (meteorological, socioeconomic, and pollutant factors) has a signi-
cant impact on the response variable Y (O
3
concentration), the response
variable Y will show a similar spatial distribution to that of X (Wang
et al., 2010a, 2016). It can not only quantify the determinant power of a
single factor, but also estimates the interactive effects of two different
factors, which is more comprehensive and informative than traditional
methods and better reects geographical phenomena.
For example, in this study, GeoDetector is used to quantify the
determinant power of meteorological, socioeconomic, and pollutant
factors individually and interactions on O
3
pollution. The determinant
power of each factor and their interactions can be determined by the q
value in the GeoDetector. The input data includes a dependent variable
and the hierarchical information of each factor, here we classied the
values of each impact variable into 3 levels, the denition of q value is as
follows:
q=1−1
N
σ
2∑
L
h=1
Nh
σ
2
h(3)
Fig. 1. The spatial distribution of 331 cities in China.
X. Zhang et al.

Atmospheric Pollution Research 12 (2021) 101248
4
where q quanties the determinant powers of single or interactive fac-
tors ranging from 0 to 1; h (h =1, 2, …, L) is the spatial stratication of a
single factor X or the crossed strata of multifactor X values (Wang et al.,
2010a; Wang et al., 2016); N and N
h
are the numbers of cities in the
study area and strata h, respectively; and
σ
2
and
σ
h2
indicate variations
in the study area or strata h, respectively.
The interactive effects of different factors (Xs) can also be quantied
using the GeoDetector, which can further reveal whether the interactive
effects of different factors (X1∩X2) weaken or enhance the inuence on
Y. The variables q (X1), q (X2), and q (X1∩X2) represent the q values of
X1 and X2, and the interactive effects of X1 and X2 (X1∩X2), respec-
tively. The F test was used to measure the signicance of the q-statistic
values with a signicance level of 0.05.
3. Results
3.1. Spatiotemporal heterogeneity
The overall temporal variations of summertime MDA8 O
3
concen-
tration in 331 cities of China during 2015–2020 were examined (Fig. 2).
During APPCAP and BSDA, the concentration of O
3
in China showed an
overall increasing and then decreasing trend, respectively. The APPCAP
policy aims to reduce the PM
2.5
concentration before the end of 2017.
Meanwhile, the O
3
concentration showed an overall increasing trend
during the implemented period of this policy. Notably, in addition to
temporal variations, signicant regional differences were also identi-
ed. The O
3
concentrations, in the Beijing-Tianjin-Hebei region,
increased until 2018, peaked at 160.6
μ
g/m
3
, subsequently decreased,
but generally increased. In the YRD, O
3
concentrations notably
increased until 2019 and reached a peak value of 127.0
μ
g/m
3
, and then
decreased.
The spatial relative risk (RR) at the city-level from 2015 to 2020
show signicant spatial heterogeneity (Fig. 3). The spatial RRs in NC,
YRD, and PRD were higher, indicating that these regions have more
severe O
3
pollution risks. Conversely, cities like NE, NW, and TP have
relatively low O
3
pollution risks.
Among the 331 cities, 103 (31.1%) and 95 (28.7%) were identied as
pollution hot spots and cold spots, respectively; the remaining 133
(40.2%) cities did not fall into either classication (Fig. 4). It can be
found that the hot spots are mainly concentrated in NC (Beijing-Tianjin-
Hebei, Henan, and Shandong), the YRD (Anhui, Jiangsu, and Shanghai);
the cold spots are mainly distributed in the southwest (Tibet, Sichuan,
Guangxi, and Yunnan) and northeast (Heilongjiang and Jilin).
3.2. Impact factors analysis
Meteorological, socioeconomic, and pollutant conditions may have
affected the observed spatiotemporal heterogeneity of O
3
pollution to
varying degrees. Based on the GeoDetector analysis, determinant
powers of impact factors in six regions during the two time periods were
analyzed (Tables 1 and 2). We showed the detailed results of the most
interesting regions.
During 2015–2017, in North China, the q values for meteorological
factors all were not signicant; the socioeconomic factors with the
highest determinant powers with respect to O
3
concentrations were in-
dustrial output (q =0.15), while the q value for PD, PG, and PS was not
signicant; the pollutant factors with the highest determinant powers
with respect to O
3
concentrations were PM
10
(q =0.37), followed by
PM
2.5
(q =0.31), while the q value for NO
2
, SO
2
, and CO was not sig-
nicant (Table 1).
Fig. 2. Temporal variations of O
3
concentration during the implemental period of two important policies. Note: APPACP represents the Air Pollution Prevention and
Control Action Plan; TDBS represents the Blue Sky Defense Action policy.
X. Zhang et al.

Atmospheric Pollution Research 12 (2021) 101248
5
In South East region, the meteorological factors with the highest
determinant powers with respect to O
3
concentrations were AP (q =
0.34), followed by AT (q =0.23), PP (q =0.20), SH (q =0.16), and RH
(q =0.15), while the q value for WS was not signicant; the socioeco-
nomic factors with the highest determinant powers with respect to O
3
concentrations were IO (q =0.25), followed by PD (q =0.16), while the
q value for PG and PS was not signicant; the pollutant factors with the
highest determinant powers with respect to O
3
concentrations were
PM
10
(q =0.45), followed by PM
2.5
(q =0.38), NO
2
(q =0.26), SO
2
(q =
0.13), and CO (q =0.12) (Table 1).
In South West region, the meteorological factors with the highest
determinant powers with respect to O
3
concentrations were RH (q =
0.25), followed by SH (q =0.16), AP (q =0.34), PP (q =0.20), WS (q =
0.18), and AT (q =0.12); the socioeconomic factors with the highest
determinant powers with respect to O
3
concentrations were PD (q =
0.40), followed by PS (q =0.33), IO (q =0.26), PG (q =0.18); the
pollutant factors with the highest determinant powers with respect to O
3
concentrations were PM
2.5
(q =0.58), followed by PM
10
(q =0.52), NO
2
(q =0.38), SO
2
(q =0.18), and while the q value for CO was not sig-
nicant (Table 1).
During 2018–2020, in North China, the meteorological factors with
the highest determinant powers with respect to O
3
concentrations were
AP (q =0.20), while the q values for other meteorological factors were
not signicant; the q values for all socioeconomic factors were not sig-
nicant; the pollutant factors with the highest determinant powers with
respect to O
3
concentrations were PM
2.5
(q =0.64), followed by PM
10
(q
=0.54) and SO
2
(q =0.33), while the q value for NO
2
and CO was not
signicant (Table 2).
In South East region, the meteorological factors with the highest
determinant powers with respect to O
3
concentrations were PP (q =
0.47), followed by SH (q =0.41), RH (q =0.32), AP (q =0.25), AT (q =
0.22), while the q value for WS was not signicant; the socioeconomic
factors with the highest determinant powers with respect to O
3
con-
centrations were IO (q =0.16), followed by PS (q =0.12), while the q
values for PD and PG were not signicant; the pollutant factors with the
highest determinant powers with respect to O
3
concentrations were
PM
2.5
(q =0.55), followed by PM
10
(q =0.47), NO
2
(q =0.20), SO
2
(q =
0.15), and CO (q =0.12) (Table 2).
In South West region, the meteorological factors with the highest
determinant powers with respect to O
3
concentrations were SH (q =
0.25), followed by AP (q =0.21), AT (q =0.08), PP (q =0.07), while the
q values for RH and WS were not signicant; the socioeconomic factors
with the highest determinant powers with respect to O
3
concentrations
were PD (q =0.43), followed by PS (q =0.36), IO (q =0.29), and PG (q
=0.23); the pollutant factors with the highest determinant powers with
respect to O
3
concentrations were NO
2
(q =0.44), followed by PM
2.5
(q
=0.40), PM
10
(q =0.36), while the q value for SO
2
and CO was not
signicant (Table 2).
3.3. Interactive impacts between different factors
The interactive determinant powers of any two different factors of
the 15 inuencing factors in six regions of China during 2015–2017 and
2018–2020 were determined using GeoDetector. The O
3
concentration
was not affected by a single factor, but was largely inuenced by the
interactive effects of factors (Figs. 5–7).
During 2015–2017, the maximum interactive effects are relative
humidity and industrial output, average temperature and population
density, sun hour and industrial output, average temperature and in-
dustrial output, relative humidity and population density, sun hour and
Fig. 3. The spatial relative risks of O
3
pollution from 2015 to 2020.
X. Zhang et al.

Atmospheric Pollution Research 12 (2021) 101248
6
Fig. 4. Hot spots and cold spots of O
3
pollution from 2015 to 2020.
Table 1
The q values of each inuence factor from 2015 to 2017.
Inuence factors NC NE NW SE SW TP
Average
temperature
(◦C)
0.09 0.57** 0.03 0.23** 0.12** 0.08
Relative humidity
(%)
0.16 0.37** 0.05 0.15** 0.25** 0.20
Air pressure (hPa) 0.06 0.51** 0.14 0.34** 0.20** 0.42
Sun hours (h) 0.05 0.40** 0.19 0.16** 0.22** 0.17
Wind speed (m/s) 0.03 0.20 0.04 0.08 0.14* 0.15
Precipitation (mm) 0.06 0.17* 0.03 0.20** 0.18** 0.09
Population density
(10
4
person/
km
2
)
0.10 0.61** 0.20 0.16** 0.40** 0.54**
Industrial output
(10
4
CNY)
0.15** 0.46** 0.21 0.25** 0.26** 0.51**
Per capita GDP
(10
4
CNY)
0.08 0.36** 0.13 0.11 0.18** 0.15
Proportion of
secondary
industry (%)
0.08 0.53** 0.14 0.09 0.33** 0.50**
PM
2.5
(
μ
g/m
3
) 0.31** 0.44** 0.31** 0.38** 0.58** 0.37**
PM
10
(
μ
g/m
3
) 0.37** 0.43** 0.26* 0.45** 0.52** 0.37*
NO
2
(
μ
g/m
3
) 0.14 0.42** 0.24** 0.26** 0.38** 0.33*
SO
2
(
μ
g/m
3
) 0.07 0.17 0.22 0.13** 0.18** 0.27*
CO (mg/m
3
) 0.16 0.35** 0.06 0.12* 0.04 0.24
Note: Bold font indicates the maximum q value of meteorological, socioeco-
nomic, and pollutant factors.
Table 2
The q values of each inuence factor from 2018 to 2020.
Inuence factors NC NE NW SE SW TP
Average
temperature
(◦C)
0.08 0.67** 0.08 0.22** 0.08* 0.39
Relative humidity
(%)
0.43 0.63** 0.09 0.32** 0.03 0.20
Air pressure (hPa) 0.20* 0.41* 0.35* 0.25** 0.21** 0.75**
Sun hours (h) 0.03 0.13 0.15 0.41** 0.25** 0.42*
Wind speed (m/s) 0.06 0.19 0.06 0.05 0.05 0.42*
Precipitation (mm) 0.09 0.32** 0.43** 0.47** 0.07* 0.33
Population density
(10
4
person/
km
2
)
0.05 0.66** 0.21 0.09 0.43** 0.27
Industrial output
(10
4
CNY)
0.09 0.45** 0.24** 0.16** 0.29** 0.49
Per capita GDP
(10
4
CNY)
0.12 0.32** 0.19 0.04 0.23** 0.23
Proportion of
secondary
industry (%)
0.08 0.43** 0.20 0.12* 0.36** 0.40
PM
2.5
(
μ
g/m
3
) 0.64** 0.77** 0.39** 0.55** 0.40** 0.48**
PM
10
(
μ
g/m
3
) 0.54** 0.53** 0.45** 0.47** 0.36** 0.30
NO
2
(
μ
g/m
3
) 0.25 0.44** 0.27* 0.20** 0.44** 0.33
SO
2
(
μ
g/m
3
) 0.33** 0.43** 0.23 0.15** 0.05 0.53
CO (mg/m
3
) 0.45 0.63** 0.18 0.12** 0.09 0.32*
Note: Bold font indicates the maximum q value of meteorological, socioeco-
nomic, and pollutant factors.
X. Zhang et al.

Atmospheric Pollution Research 12 (2021) 101248
7
population density in NC, NE, NW, SE, SW, and TP, respectively (Fig. 5);
and during 2018–2020, the maximum interactive effects are relative
humidity and population density, relative humidity and population
density, precipitation and proportion of secondary industry, precipita-
tion and industrial output, sun hour and population density, air pressure
and population density in NC, NE, NW, SE, SW, and TP, respectively
(Fig. 5), indicating that the interactive effects of meteorological and
socioeconomic factors on the summertime O
3
concentration were the
increased, that is, the determinant powers (q values) of interactive ef-
fects between any two meteorological and socioeconomic factors were
Fig. 5. Interactive effects of meteorological and socioeconomic factors during the implemental period of two important policies. Note: average temperature (AT), air
pressure (AP), precipitation (PP), relative humidity (RH), sun hours (SH), and wind speed (WS); population density (PD), industrial output (IO), per capita GDP (PG),
and proportion of secondary industry (PS).
Fig. 6. Interactive effects of meteorological factors and other ve pollutants during the implemental period of two important policies. Note: average temperature
(AT), air pressure (AP), precipitation (PP), relative humidity (RH), sun hours (SH), and wind speed (WS).
Fig. 7. Interactive effects of socioeconomic factors and other ve pollutants during the implemental period of two important policies. Note: population density (PD),
industrial output (IO), per capita GDP (PG), and proportion of secondary industry (PS).
X. Zhang et al.

Atmospheric Pollution Research 12 (2021) 101248
8
greater than the determinant power of an independent meteorological or
socioeconomic factor.
During 2015–2017, the maximum interactive effects are precipita-
tion and PM
10
, air pressure and NO
2
, sun hour and PM
10
, air pressure
and PM
10
, wind speed and PM
2.5
, relative humidity and PM
2.5
in NC, NE,
NW, SE, SW, and TP, respectively (Fig. 6); and during 2018–2020, the
maximum interactive effects are relative humidity and PM
2.5
, average
temperature and CO, air pressure and PM
2.5
, precipitation and PM
2.5
,
sun hour and NO
2
, air pressure and PM
10
in NC, NE, NW, SE, SW, and TP,
respectively (Fig. 6), indicating that the interactive effects of meteoro-
logical factors and pollutant factors on the summertime O
3
concentra-
tion were the increased.
During 2015–2017, the maximum interactive effects are industrial
output and PM
10
, population density and NO
2
, per GDP and PM
2.5
, in-
dustrial output and PM
10
, the proportion of secondary industry and
PM
2.5
, industrial output and PM
2.5
in NC, NE, NW, SE, SW, and TP,
respectively (Fig. 7); and during 2018–2020, the maximum interactive
effects are per GDP and PM
2.5
, industrial output and PM
2.5
, per GDP and
PM
2.5
, industrial output and PM
2.5
, proportion of secondary industry
and NO
2
, proportion of secondary industry and PM
10
in NC, NE, NW, SE,
SW, and TP, respectively (Fig. 7), indicating that the interactive effects
of socioeconomic factors and pollutant factors on the summertime O
3
concentration were the increased. These results suggest that the inter-
active effects of any two factors will strengthen the impact on O
3
pollution.
4. Discussion
Here, we revealed the spatiotemporal heterogeneity of summertime
O
3
pollution in China, and further identied pollution hot spots and cold
spots. Then, we quantied the determinant powers of meteorological,
socioeconomic factors, air pollutants, and their interactive effects on O
3
concentrations in six representative regions during two different stages
of implemented policies. The identied O
3
pollution risks show notable
spatiotemporal heterogeneity, with hot spots distributed in the North
China and South East China area. Moreover, the dominant factor was
different in different regions during 2015–2017 and 2018–2020.
During the implement of APPACP (2015–2017) and BSDA
(2018–2020) policies, as presented, the PM
2.5
concentrations has been
greatly reduced especially in the implement of APPACP policy (Maji
et al., 2020; Zhao et al., 2021). Accordingly, O
3
pollution is increasing
with the reduction of PM
2.5
concentration, which was similarly with
other studies (Li et al., 2018; Lou et al., 2014). For example, Li et al.
(2019) showed that after removing the inuence of meteorological
variations, the most important reason for the increase in O
3
seems to be
the reduction of PM
2.5
, which slows down the sinking of free radicals of
hydroperoxides, thereby accelerating the production of O
3
during
2013–2017. Meantime, socioeconomic conditions are crucially impor-
tant, such as, population density and industrial output, being widely
accepted as impact the PM
2.5
pollution, further indirect impacts on the
O
3
pollution. Wang et al. (2017) and Yang et al. (2018) demonstrated
that population density and industrial output have the strongest corre-
lation with PM
2.5
concentration in China, respectively. Moreover, the
interactive effects of population density and other factors, such as a
meteorological factors and other pollutants, also had a stronger impact
on the O
3
concentrations. This indicates that population conditions will
not only have a great impact on the O
3
concentrations, such as,
continuous increase in anthropogenic emission in densely populated
cities, but greatly increases the impact of other factors on the O
3
con-
centrations. Because in densely populated cities, the road, fuel evapo-
ration, and electronics manufacturing have been demonstrated as the
largest contributor of VOCs emission in China (Wu and Xie, 2017; Zhang
et al., 2013; Guo et al., 2017). That is, population conditions would
increase the concentrations of O
3
. Meantime, industrial development
and urbanization have caused increase in energy consumption and
economic activities in densely populated cities in China, making greatly
contribution to emission of NOx and VOCs, which caused the increase-
ment of O
3
concentrations (Wu and Xie, 2017; Zhang et al., 2013; Guo
et al., 2017).
Moreover, our results demonstrated that meteorological factors have
great impacts on the changes of O
3
pollution, among meteorological
factors, relative humidity, air pressure, average temperature, sun hour,
and precipitation had the strongest association with O
3
pollution risk in
six representative regions of China during 2015–2017 and 2018–2020,
that is, the different region has a different dominant factor, which cor-
roborates previous ndings (Jeong et al., 2020; Yu et al., 2020; Zhao
et al., 2020). In addition to the individual effects of meteorological
factors, the interactive effects of meteorological factors, such as, average
temperature and other factors had a stronger impact on the O
3
con-
centrations, indicating that temperature will not only have a great
impact on the O
3
concentrations, but greatly increases the impact of
other factors on the O
3
concentrations. On the one hand, these associ-
ations may reect that higher temperature accelerate the formation of
secondary aerosols and thus exert an impact on the concentrations of O
3
.
On the other hand, high temperature can facilitate the volatilization of
ammonium nitrate and also affects the pollution emission rates of
household heating and power generation systems, thereby affecting the
concentrations of atmospheric PM
2.5
, further impacting the concentra-
tions of O
3
(Li et al., 2019).
It is reported that other air pollutants inuence the accumulation and
diffusion of O
3
pollution in a region (Zhao et al., 2021). Our results
showed that among the other ve pollutants, PM
2.5
, PM
10
, and NO
2
had
the strongest association with O
3
pollution risk in six representative
regions of China during 2015–2017 and 2018–2020, which corroborates
previous ndings. For example, Kuerban et al. (2020) found that a
signicantly negative relationship between O
3
and the other ve pol-
lutants in China. The interactive effects of the ve pollutants, meteo-
rological and socioeconomic factors had a stronger impact on the O
3
concentrations, indicating that pollutants will not only have a great
impact on the O
3
concentrations, but greatly increase the impact of other
factors on the O
3
concentrations. The potential mechanism may be that
city is the spatial carrier of the above sources and one changes in
response to the changes of others.
The interactive effects identied between different meteorological,
socioeconomic and pollutant factors were all associated with an
enhanced inuence of O
3
pollution relative to their individual effects.
Anthropogenic activities associated with signicant changes in envi-
ronmental, meteorological, and living conditions are strongly related to
O
3
pollution in China during these two periods (Gong et al., 2012).
Therefore, in addition to the ve pollutants, the interactive effects be-
tween socioeconomic, meteorological factors have an impact on the O
3
pollution patterns to some extent during the study period, acting to
reinforce one another with respect to O
3
concentrations. Specically, O
3
pollution is affected by many factors, including meteorological, socio-
economic, and other pollutant factors. Based on the analysis of inter-
active effects of various factors, this study provides a new perspective for
the study of O
3
pollution, and can provide some references and contri-
butions to policy-making. That is, when formulating policies about
controlling O
3
pollution, it is necessary to consider not only the impact
of a single factor, but also the interactions between the factors, which
will help understand more specically about O
3
. Moreover, cities are the
common space carriers of O
3
pollution and its inuencing factors, and no
factor exists alone. Therefore, further research should pay more atten-
tion to the comprehensive interaction of factors on O
3
pollution.
Based on the above results, we make the following recommenda-
tions. First, human and industrial activities are important factors
affecting air pollution in China, such as, cities with active industrial
activities do have high air pollution, such as NC and the YRD. Therefore,
in the process of rapid urbanization, industrial expansion, especially the
expansion of polluting industries, should be appropriately controlled.
Moreover, according to the associations between pollutants, coordi-
nated control of PM
2.5
and O
3
is urgently needed in China after the
X. Zhang et al.

Atmospheric Pollution Research 12 (2021) 101248
9
implementation of these policies. Furthermore, since air pollutants are
diffused due to their mobility, regional cooperation should be
strengthened and ensure sustainable development.
This work had some limitations. First, other risk factors associated
with O
3
pollution likely exist that were not included in our model.
Second, most data on O
3
pollution were only obtained from urban areas,
the air situations of rural were not investigated. With the updating of
data, future studies can further improve on these aspects.
5. Conclusions
This study found the rise of O
3
during APPCAP, which is mainly due
to the combined inuence of meteorological, socioeconomic, and other
pollutant factors. At the same time, the decrease in O
3
during the
implementation of BSDA was also reported in our study. The results of
cold and hot spots indicate that we should strengthen the prevention and
control of O
3
pollution in these areas, and to solve this problem, we
should focus on areas with high temperature and high population den-
sity. Moreover, the interactive effects between meteorological, socio-
economic, and pollutant factors had a stronger impact on the O
3
pollution. These ndings provide deeper insight into the impact mech-
anism of O
3
pollution, which means that different regions need more
specic strategies for prevention, control and governance to reduce the
O
3
concentration, loss of human health, and social economy attributable
to O
3
pollution. Overall, our ndings offer a clearer picture of the factors
responsible for patterns of O
3
pollution across the different regions of
China and different time period, which can serve as a useful guide for the
development of future air-quality policy and remediation measures.
Credit author statement
Xiang-xue Zhang: Conceptualization, Methodology, and Writing-
Original draft preparation. Bin Yan and Chao-jie Du: Visualization,
Investigation. Chang-xiu Cheng and Hui Zhao: Writing-Reviewing and
Editing, Supervision.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Acknowledgment
This study was nancially supported by the following grants: Na-
tional Key Research and Development Plan of China
(2019YFA0606901), China; the Visiting Fellowship from China Schol-
arship Council (No. 202106040072), China; China Postdoctoral Science
Foundation (2020M681157), China.
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