ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-5343-2018Understanding meteorological influences on PM2.5 concentrations across
China: a temporal and spatial perspectiveUnderstanding meteorological influences on PM2.5 concentrationsChenZiyueXieXiaomingCaiJunhttps://orcid.org/0000-0001-9495-1226ChenDanluGaoBingboHeBinChengNianliangXuBingbingxu@tsinghua.edu.cnState Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, 19 Xinjiekouwai Street, Haidian, Beijing 100875, ChinaJoint Center for Global Change Studies, Beijing 100875,
ChinaMinistry of Education Key Laboratory for Earth System
Modeling, Department of Earth System Science, Tsinghua
University, Beijing 100084, ChinaNational Engineering Research
Center for Information Technology in Agriculture, 11 Shuguang
Huayuan Middle Road, Beijing 100097, ChinaBeijing Municipal
Environmental Monitoring Center, Beijing 100048, ChinaBing Xu (bingxu@tsinghua.edu.cn)19April20181885343535824April201724July201714March20181April2018This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/18/5343/2018/acp-18-5343-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/5343/2018/acp-18-5343-2018.pdf
With frequent air pollution episodes in China, growing research emphasis has
been put on quantifying meteorological influences on PM2.5
concentrations. However, these studies mainly focus on isolated cities,
whilst meteorological influences on PM2.5 concentrations at the national
scale have not yet been
examined comprehensively. This research employs the CCM (convergent cross-mapping)
method to understand the influence of individual meteorological factors on
local PM2.5 concentrations in 188 monitoring cities across China.
Results indicate that meteorological influences on PM2.5 concentrations
have notable seasonal and regional variations. For the heavily polluted North
China region, when PM2.5 concentrations are high, meteorological
influences on PM2.5 concentrations are strong. The dominant
meteorological influence for PM2.5 concentrations varies across
locations and demonstrates regional similarities. For the most polluted
winter, the dominant meteorological driver for local PM2.5
concentrations is mainly the wind within the North China region, whilst
precipitation is the dominant meteorological influence for most coastal
regions. At the national scale, the influence of temperature, humidity and
wind on PM2.5 concentrations is much larger than that of other
meteorological factors. Amongst eight factors, temperature exerts the
strongest and most stable influence on national PM2.5 concentrations in
all seasons. Due to notable temporal and spatial differences in
meteorological influences on local PM2.5 concentrations, this research
suggests pertinent environmental projects for air quality improvement should
be designed accordingly for specific regions.
Introduction
With rapid social and economic growth in China, both the government and
residents are placing more and more emphasis on the sustainability of the
ambient environment, and air quality has become one of the most concerning
social and ecological issues. Since 2013, the frequency of air pollution
episodes with high PM2.5 concentrations and the number of cities
influenced by PM2.5 pollution have increased notably in China.
Statistical records from the national air quality publishing platform
(http://113.108.142.147:20035/emcpublish/, last access:
18 October 2017) revealed that
PM2.5-induced pollution episodes occurred in 25 provinces and more than
100 medium–large cities, whilst there were on average 30 days with hazardous
PM2.5 concentrations for each monitoring city in 2014.
High PM2.5 concentrations not only influence people's daily life (e.g.,
high PM2.5 concentrations caused severe traffic jam), but also severely
threaten the health of residents, who suffer from polluted air quality.
Recent studies have suggested that airborne pollutants, PM2.5 in
particular, are closely related to cardiovascular disease-related mortality
(Garrett and Casimiro, 2011; Li et al., 2015a; Lanzinger et al., 2015),
emergency room visits (Qiao et al., 2014) and all-year non-accidental
mortality (Pasca et al., 2014). Due to its strong negative influences on
public health, scholars have been working towards a better understanding of
sources (Guo et al., 2012; Zhang et al., 2013, 2016; Gu et al., 2014; Liu et
al., 2014; Cao et al., 2014), characteristics (Wei et al., 2012; Zhang et
al., 2013; Hu et al., 2015; F. Zhang et al., 2015; Zhen et al., 2016) and
seasonal variations (Cao et al., 2012; Shen et al., 2014; Yang and
Christakos, 2015; Wang et al., 2015; Chen et al., 2015; Y. Chen et al., 2016;
Z. Chen et al., 2016) of PM2.5. Meanwhile, large-scale research on the
variation and distribution of PM2.5 concentrations has been conducted
using a variety of remote sensing sources and spatial data analysis methods
(Ma et al., 2014; Kong et al., 2016).
One key issue for air quality research is to find the source and influencing
factors for airborne pollutants. Although quantitative contributions of
different sources (e.g., coal burning and automobile exhaust) to airborne
pollutants remain controversial, meteorological influences on airborne
pollutants have been examined in depth by more and more scholars. Recent
studies conducted in different countries indicated that PM2.5
concentrations were closely related to temperature (Pearce et al., 2011;
Yadav et al., 2014; Grundstrom et al., 2015), wind speed (Galindo et al.,
2011; El-Metwally and Alfaro, 2013; Yadav et al., 2014) and precipitation
(Yadav et al., 2014). Meanwhile, meteorological influences on PM2.5
concentrations across China have also become a hot research topic. Yao (2017)
revealed a generally negative correlation between evaporation and PM2.5
concentrations in a series of cities within the North China Plain. Huang et
al. (2015) and Yin et al. (2016) found a negative influence of sunshine
duration and a positive influence of relative humidity on PM2.5
concentrations in Beijing. Li et al. (2015b)
suggested that air pressure and temperature were positively correlated with
PM2.5 concentrations in Chengdu. For Nanjing (T. Chen et al., 2016) and
Hong Kong (Fung et al., 2014), precipitation exerted a strong influence on
PM2.5 concentrations in winter, when the influence of wind speed on
PM2.5 concentrations was weak. Meanwhile, wind speed exerted a major
influence on PM2.5 concentrations in Beijing in winter. Through
experiments, Guo et al. (2016) found that the influence of precipitation on
PM2.5 concentrations in Xi'an was weaker than that in Guangzhou.
H. Zhang et al. (2015) quantified the correlations between meteorological
factors and the main airborne pollutants in three megacities, Beijing,
Shanghai and Guangzhou, and pointed out that the influences of meteorological
factors on the formation and concentrations of PM2.5 varied
significantly across seasons and geographical locations. Chen et al. (2017)
quantified the meteorological influences on local PM2.5 concentrations
in the Beijing–Tianjin–Hebei region and revealed that wind, humidity and
solar radiation were major meteorological factors that significantly
influenced local PM2.5 concentrations in winter. These studies revealed
the correlations between PM2.5 concentrations and a diversity of
meteorological factors in some specific cities. However, findings from these
studies conducted at a local scale cannot reveal regional and national
patterns of meteorological influences on PM2.5 concentrations in China.
In addition, these studies mainly employed short-term observation data (e.g.,
one season or one year) and thus revealed that characteristics of
meteorological influences on PM2.5 concentrations may be biased by
inter-annual variations.
Due to the diversity of meteorological factors and complicated interactions
between them, Pearce et al. (2011) suggested that multiple models and methods
should be comprehensively employed to quantify the influence of
meteorological factors on local airborne pollutants. For complicated
interactions between different factors, Sugihara et al. (2012) suggested that
correlation analysis between two variables in a complicated ecosystem might
lead to mirage correlations and the extracted correlation coefficient between
two variables could be influenced significantly by other variables in the
ecosystem. Therefore, Sugihara et al. (2012) proposed a CCM (convergent
cross-mapping) method
to qualify the bi-direction coupling between two variables without the
influence from other variables. The CCM method can effectively remove mirage
correlations and extract reliable causality between two variables. Our
previous research (Chen et al.,
2017) found that the CCM method performed better in quantifying the influence
of individual meteorological factors on PM2.5 concentrations than
traditional correlation analysis through comprehensive comparison. However,
this study mainly focused on the meteorological influences on PM2.5
concentrations in a specific region. As pointed out by some scholars (He et
al., 2017), interactions between meteorological factors and airborne
pollutants have great variations across different regions. China is a large
country, including many regions with completely different air pollution
levels, geographical conditions and meteorological types. To better
understand the variations of meteorological influences on PM2.5
concentrations, a comparative study at the national scale is required.
According to these challenges, this research aims to analyze and compare the
influence of individual meteorological factors on PM2.5 concentrations
across China. Based on the CCM causality analysis, we quantified the
influence of eight meteorological factors on PM2.5 concentrations in 188
monitoring cities across China using the observation data from March 2014 to
February 2017. To comprehensively understand the spatio-temporal patterns of
meteorological influences on PM2.5 concentrations across China, we
(a) investigated comprehensive meteorological influences on PM2.5
concentrations in 37 regional representative cities, (b) extracted the
seasonal dominant meteorological factor for each monitoring city, and
(c) conducted comparative statistics of the influence of different
meteorological factors on PM2.5 concentrations at the national scale.
MaterialsData sourcesPM2.5 data
PM2.5 data are acquired from the website www.PM25.in (last access:
18 October 2017). This website collects
official data of PM2.5 concentrations provided by the China National
Environmental Monitoring Center (CNEMC) and publishes hourly air quality
information for all monitoring cities. Before 1 January 2015, PM25.in
published data of 190 monitoring cities. Since 1 January 2015, the number of
monitoring cities has increased to 367. By calling a specific API
(Application Programming Interface) provided by PM25.in, we collect hourly
PM2.5 data for target cities. The daily PM2.5 concentration for
each city is calculated using the averaged value of hourly PM2.5
concentrations measured at all available local observation stations. For a
consecutive division of different seasons and multiple-year analysis, we
collected PM2.5 data from 1 March 2014 to 28 February 2017 for the
following analysis.
Meteorological data
The meteorological data for these monitoring cities are obtained from the
“China Meteorological Data Sharing Service System”, part of the National
Science and Technology Infrastructure. The meteorological data are collected
through thousands of observation stations across China. Previous studies
(H. Zhang et al., 2015; Pearce et al., 2011; Yadav et al., 2014) revealed
that such meteorological factors as relative humidity, temperature, wind
speed, wind direction, solar radiation, evaporation, precipitation, and air
pressure might be related to PM2.5 concentrations. Therefore, to
comprehensively understand meteorological driving forces for PM2.5
concentrations in China, all these potential meteorological factors were
selected as candidate factors. To better quantify the role of individual
meteorological factors in affecting local PM2.5 concentrations, these
factors are further categorized into some sub-factors: evaporation (small
evaporation and large evaporation), temperature (daily max temperature, mean
temperature, minimum temperature, and the largest temperature difference for
the day), precipitation (total precipitation from 08:00 to 20:00, total
precipitation from 20:00 to 08:00 and total precipitation for the day), air
pressure (daily max pressure, mean pressure and minimum pressure), humidity
(daily mean and minimum relative humidity), radiation (sunshine duration for
the day, short for SSD), wind speed (mean wind speed, max wind speed and
extreme wind speed), and wind direction (max wind direction for the day).
Some meteorological factors are briefly explained here. Evaporation indicates
the amount of evaporation-induced water loss during a certain period and is
usually calculated using the depth of evaporated water in a container. For
this research, small (large) evaporation indicates the amount of evaporated
water measured using a container with a diameter of 10 cm (30 cm) during
24 h (unit: mm). Generally, the measured values using the two types of
equipment have slight differences. SSD represents the hours of sunshine
measured during a day for a specific location on earth. The max wind speed
indicates the max mean wind speed during any 10 min within a day's time. The
extreme wind speed indicates the max instant (for 1 s) wind speed within a
day's time. The max wind direction indicates the dominant wind direction for
the period with the max wind speed. As there are one or more observation
stations for each city, the daily value for each meteorological factor for
each city was calculated using the mean value of all available observation
stations within the target city. To conduct time-series comparison, we also
collected meteorological data from 1 March 2014 to 28 February 2017.
Study sites
For a comprehensive understanding of meteorological influences on local
PM2.5 concentrations across China, all monitoring cities (except for
Liaocheng and Zhuji, where continuous valid meteorological data were not
available) during the study period were selected for this research. The
188 cities included most major cities (Beijing, Shanghai, Guangzhou, etc.) in
China. For regions (e.g., the Beijing–Tianjin–Hebei region) with heavy air
pollution, the density of monitored cities was much higher than that in
regions with good air quality.
Methods
Due to complicated interactions in the atmospheric environment, it is highly
difficult to quantify the causality of individual meteorological factors on
PM2.5 concentrations through correlation analysis. Instead, a robust
causality analysis method is required.
To extract the coupling between individual variables in complex systems,
Sugihara et al. (2012) proposed the CCM method. Different from Granger
causality (GC) analysis (Granger, 1980), the CCM method is sensitive to weak
to moderate coupling in ecological time series. By analyzing the temporal
variations of two time-series variables, their bidirectional coupling can be
featured with a convergent map. If the influence of one variable on the other
variable is presented as a convergent curve with increasing time-series
length, then the causality is detected; if the curve demonstrates no
convergent trend, then no causality exists. The predictive skill (defined as
the ρ value), which ranges from 0 to 1, suggests the quantitative
causality of one variable on the other.
The principle of CCM algorithms is briefly explained as follows (Luo et al.,
2014). Two time series {X}=[X(1),…,X(L)] and {Y}=[Y(1),…,Y(L)] are defined as the temporal variations of two variables X
and Y. For r=S to L (S < L), two partial time series
[X(1), …, X(LP)] and [Y(1), …, Y(LP)]
are extracted from the original time series (r is the current position,
whilst S is the start position in the time series). Following this, the
shadow manifold MX is generated from {X}, which is a set of
lagged-coordinate vectors x(t)= < X(t), X(t-τ), …,
X(t-(E-1)τ) > for t=1+(E-1)τ to t=r. To
generate a cross-mapped estimate of Y(t)(Y^(t)|MX), the
contemporaneous lagged-coordinate vector on MX, x(t) is located, and then its E+1 nearest
neighbors are extracted, where E+1 is the minimum number of points required
for a bounding simplex in an E-dimensional space (Sugihara and May, 1990).
Next, the time index of the E+1 nearest neighbors of x(t) is denoted as
t1, …, tE+1. These time indexes are used to identify neighbor
points in Y and then estimate Y(t) according to a locally weighted mean
of E+1Y(ti) values (Eq. 1).
Y^tMX=∑i=1E+1wiYti,
where wi is a weight calculated according to the distance between X(t)
and its ith nearest neighbor on MX. Y(ti) are contemporaneous
values of Y. The weight wi is determined according to Eq. (2).
wi=ui/∑j=1E+1uj,
where ui=e-dx¯t,x¯ti/dx¯t,x¯t1, whilst d[x(t)x(ti)] represents the Euclidean distance between two vectors.
In our previous research, interactions between the air quality in
neighboring cities (Z. Chen et al., 2016), and bidirectional coupling
between individual meteorological factors and PM2.5 concentrations
(Chen et al., 2017) were quantified effectively using the CCM method. By
comparing the performance of correlation analysis and CCM method, Z. Chen
et al. (2017) suggested that correlation analysis might lead to a diversity
of biases due to complicated interactions between individual meteorological
factors. Firstly, some mirage correlations (two variables with a moderate
correlation coefficient) extracted using the correlation analysis were
revealed effectively using the CCM method (the ρ value between two variables was 0). Secondly, some weak coupling, which was
hardly detected using the correlation analysis (the correlation between the
two variables were not significant), was extracted using the CCM method (a
small ρ value). Meanwhile, as Sugihara et al. (2012) suggested, the correlation
between two variables could be influenced significantly by other agent
variables and thus the value of correlation coefficient between two
variables could not reflect the actual causality between them. Chen et al. (2017) further revealed that the correlation coefficient between individual
meteorological factors and PM2.5 concentrations was usually much larger
than the ρ value. This indicated that the causality of individual meteorological
factors on PM2.5 concentrations was generally overestimated using the
correlation analysis, due to the influences from other meteorological
factors. In this case, the CCM method is an appropriate tool for quantifying
bidirectional interactions between PM2.5 concentrations and
individual meteorological factors in complicated atmospheric environment.
Illustrative CCM results to demonstrate the bidirectional
coupling between meteorological factors and PM2.5
concentrations in Beijing (2014, winter).
ρ: predictive skills.
L: the length of time series. A xmap B stands for convergent cross
mapping B from A, in other words, the causality of variable B on A. For
instance, PM2.5 xmap mean humidity stands for the
causality of mean humidity on PM2.5 concentrations.
Mean humidity xmap PM2.5 stands for the feedback
effect of PM2.5 concentrations on mean humidity.
ρ indicates the predictive skills of using mean humidity to retrieve
PM2.5 concentrations.
Results
Seasonal variations of PM2.5 concentrations have been revealed in
Beijing (Chen et al., 2015; Y. Chen et al., 2016; Z. Chen et al., 2016),
Nanjing (Shen et al., 2014), Shandong Province (Yang and Christakos, 2015)
and the Beijing–Tianjin–Hebei region (Wang et al., 2015; Z. Chen et al.,
2017). In addition to these local and regional studies, Cao et al. (2012)
further compared seasonal variations of PM2.5 concentrations in seven
southern cities (Chongqing, Guangzhou, Hong Kong, Hangzhou, Shanghai, Wuhan,
and Xiamen) and seven northern cities (Beijing, Changchun, Jinchang, Qingdao,
Tianjin, Xi'an, and Yulin) across China. Hence, the research period was
divided into four seasons. According to traditional season division for
China, spring was set as the period between 1 March and 31 May 2014; summer
was set as the period between 1 June and 31 August 2014; autumn was set as
the period between 1 September and 30 November 2014; and winter was set as
the period between 1 December 2014 and 28 February 2015. For each city, the
bidirectional coupling between individual meteorological factors and
PM2.5 concentrations in different seasons was analyzed, respectively,
using the CCM method. The CCM method is highly automatic and only a few
parameters need to be set for running this algorithm: E (number of
dimensions for the attractor reconstruction), ρ (time lag) and b
(number of nearest neighbors to use for prediction). The value of E can be
2 or 3. A larger value of E produces more accurate convergent maps. The
variable b is decided by E (b=E+1). A small value of ρ leads to
a fine-resolution convergent map, yet requires much more processing time.
Through experiments, we found that the final results were not sensitive to
the selection of parameters and different parameters mainly exerted
influences on the presentation effects of CCM. In this research, to acquire
optimal interpretation effects of convergent cross maps, the value of ρ
was set as 2 days and the value of E was set to 3. For each meteorological
factor, its causality coupling with PM2.5 concentrations can be
represented using a convergent map. Since it is not feasible to present all
these convergent maps here, we simply display some exemplary maps to
demonstrate how CCM works (Fig. 1). As a heavily polluted city, we presented
the interactions between PM2.5 concentrations and meteorological factors
in Beijing in winter, when the local PM2.5 concentration was highest, as
an example. Four major meteorological factors, wind, humidity, radiation and
temperature, which exerted much stronger influences on PM2.5
concentrations than other factors, were employed. Due to the strong
bidirectional coupling between PM2.5 concentrations and these
meteorological factors, Fig. 1 not only demonstrates how CCM output could be
interpreted, but also provides readers with a general comparison of the
magnitude of simultaneous influences of different meteorological factors on
the local PM2.5 concentration and its feedback effects.
According to Fig. 1, one can see that the quantitative influence of
individual meteorological factors on PM2.5 was well extracted using the
CCM method, whilst the feedback effect of PM2.5 on specific
meteorological factors was revealed as well. For Beijing, mean humidity and
maximum wind speed exerted a strong influence on local PM2.5
concentrations in winter (ρ > 0.4), whilst SSD and minimum
temperature also had a weaker influence on local PM2.5 concentrations
(ρ close to 0.2). On the other hand, high PM2.5 concentrations had
an even stronger feedback influence on mean humidity, maximum wind speed and
SSD (ρ close to 0.6), whilst PM2.5 had little influence on minimum
temperature (ρ close to 0). The bidirectional coupling between
PM2.5 concentrations and individual meteorological factors provides a
useful reference for a better understanding of the form and development of
PM2.5-induced air pollution episodes. For Beijing, low wind speed (high
humidity and low SSD) in winter results in high PM2.5 concentrations,
which in turn causes lower wind speed (higher humidity and lower SSD). In
consequence, PM2.5 concentrations are increased further by the changing
wind (humidity and SSD) situation. This mechanism causes a quickly rising
PM2.5 concentration, which brings the atmospheric environment to a
comparatively stable status. In this case, persistent high-concentration
PM2.5 is unlikely to disperse and usually lasts for a long period in
this region. Similarly, bidirectional interactions between PM2.5
concentrations and other meteorological factors can also be quantified using
the CCM method. Since the main aim of this research is to understand the
influence of individual meteorological factors on PM2.5 concentrations
across China, the feedback effect of PM2.5 concentrations on specific
meteorological factors is not explained in detail herein.
The ρ value is a direct indicator of quantitative causality. For this
research, the maximum ρ value of all sub-factors in the same category
was used as the causality of this specific meteorological factor on
PM2.5 concentrations. For example, for a specific city, the maximum
ρ value of max temperature, mean temperature, minimum temperature, and
the largest temperature difference for the day is used as the influence of
temperature on local PM2.5 concentrations. For this research, we
collected meteorological and PM2.5 data for 3 consecutive years. To
avoid the analysis of inconsecutive time series, which may influence the CCM
result, we did not calculate the general influence of individual
meteorological factors on PM2.5 concentrations during 2014–2016 by
analyzing three isolated periods (e.g., April–June 2014, April–June 2015,
and April–June 2016) as a complete data set. Instead, for each city, we
quantified the influence of individual meteorological factors on PM2.5
concentrations for each season in 2014, 2015 and 2016, respectively, and
calculated the mean ρ value during 2014–2016 for each city.
Comprehensive meteorological influences on PM2.5 concentrations
in some regional representative cities
When the ρ value for each meteorological factor was calculated, a wind rose, which
presents the quantitative influences of all individual meteorological
factors on PM2.5 concentrations, can be produced for each city. It is
not feasible to present all 188 wind roses simultaneously, due to severe
overlapping effects. Thus, considering the social-economic factors, 37
regional representative cities (including all 31 provincial capital cities
in mainland China), which are the largest and most important cities for
specific regions, were selected to produce a wind rose map of meteorological
influences on PM2.5 concentrations across China (Fig. 2).
Wind rose map of influences
of eight individual
meteorological factors on PM2.5 concentrations
across mainland China (37 representative cities) during 2014–2016.
Major meteorological influencing factors for PM2.5
concentrations in four mega-cities within different regions.
According to Fig. 2, some spatial and temporal patterns of meteorological
influences on PM2.5 concentrations at the national scale can be found as
follows.
Like seasonal variations of PM2.5 concentrations, the influences
of individual meteorological factors on local PM2.5 concentrations vary
significantly. For a specific city, the dominant meteorological driver for
PM2.5 concentrations in one season may become insignificant in another
season. For example, in winter, one major meteorological influencing factor
for Beijing is wind (the mean ρ value during 2014–2016 was 0.57), which
exerts little influence on PM2.5 concentrations in summer (the mean
ρ value during 2014–2016 was 0.10). Furthermore, it is noted that
seasonal variations of meteorological influences on PM2.5 concentrations
apply to all these representative cities, as the shape and size of the wind
rose for each city change significantly across different seasons. Take
several mega-cities in different regions for instance. During 2014–2016, the
three major meteorological influencing factors for PM2.5 concentrations
in Beijing (North China Plain), Shanghai (Yangtze River Basin), Wuhan
(Central China region) and Guangzhou (South China region) were listed as
Table 1. According to Table 1, notable seasonal variations of meteorological
influences on PM2.5 concentrations were found in these mega-cities
across China.
In spite of notable differences in the shape and size of wind roses,
meteorological influences on PM2.5 concentrations have some regional patterns. PM2.5 concentrations in
cities within the North China region are influenced by similar dominant
meteorological factors, especially in winter, when PM2.5 concentrations
in these cities are high. Take four major cities, Beijing, Tianjin, Taiyuan
and Shijiangzhuang, in the North China Plain for example. For winter, SSD,
evaporation, humidity and wind were the major meteorological factors for
PM2.5 concentrations in the four cities and the ρ value of these
four factors was 0.50, 0.52, 0.76 and 0.57 for Beijing, 0.41, 0.44, 0.56 and
0.50 for Tianjin, 0.44, 0.36, 0.61 and 0.41 for Taiyuan, and 0.62, 0.58, 0.56
and 0.60 for Shijiazhuang, respectively, presenting a similar regional
pattern. Meanwhile, meteorological influences on PM2.5 concentrations in
cities within the Yangtze River Basin, especially the dominant factors, also
had some regional similarities. Take four major cities in the Yangtze River
Basin, Shanghai, Nanjing, Hangzhou and Nanchang, for example. For summer,
precipitation, humidity, temperature and wind were the major meteorological
factors for PM2.5 concentrations in these four cities and the ρ
value of these factors was 0.21, 0.27, 0.40 and 0.38 for Shanghai, 0.29,
0.41, 0.34 and 0.33 for Nanjing, 0.28, 0.27, 0.23 and 0.27 for Hangzhou, and
0.24, 0.33, 0.21 and 0.29 for Nanchang. Despite some differences in the
ρ values, similar dominant meteorological factors and the similar
magnitude of meteorological influences demonstrated regional similarities of
meteorological influences on PM2.5 concentrations in the Yangtze River
Basin. As we can see, meteorological influences on PM2.5 concentrations
in China are mainly controlled by geographical conditions (e.g., terrain and
landscape patterns).
For the heavily polluted North China region, the higher the local
PM2.5 concentrations, the larger the influence meteorological factors
exert on PM2.5 concentrations. PM2.5 concentrations are usually the
highest in winter, causing serious air pollution episodes across China, the
North China region in particular. Meanwhile, PM2.5 concentrations in
spring and summer are comparatively low. Accordingly, there are more
influencing meteorological factors on PM2.5 concentrations for cities
within this region and the ρ value of these meteorological factors is
notably larger in winter. Take the summer and winter major influencing
meteorological factors for PM2.5 concentrations in four major cities in
the North China region for instance (as shown in Table 2). As explained,
bidirectional interactions between meteorological factors and PM2.5
concentrations may lead to complicated mechanisms that further enhance local
PM2.5 concentrations significantly. Therefore, strong meteorological
influences on PM2.5 concentrations in winter are a major cause of the
form and persistence of high PM2.5 concentrations within the North China
region.
Summer and winter major influencing meteorological factors
for PM2.5 concentrations in four major cities in the
North China region.
The dominant meteorological factor for local
PM2.5 concentrations in 188 monitoring cities across
mainland China
The size of symbols indicates the
ρ
value of the meteorological factor on local PM2.5 concentrations.
Spatial and temporal variations of the dominant meteorological influence
on local PM2.5 concentrations across China
Through statistical analysis, we selected the factor with the largest ρ
value as the dominant meteorological factor for local PM2.5
concentrations. The spatial and temporal variations of the dominant
meteorological influence on local PM2.5 concentrations across China are
demonstrated as Fig. 3. According to Fig. 3, some spatio-temporal
characteristics of meteorological influences on PM2.5 concentrations can
be further concluded.
The dominant meteorological factor for PM2.5 concentrations is
closely related to geographical conditions. For instance, the factor of
precipitation may exert a key influence on local PM2.5 concentrations in
some coastal cities and cities within the Yangtze River Basin, whilst this
meteorological factor exerts limited influence on PM2.5 concentrations
within some inland regions. Here we analyzed the ρ value of
precipitation in cities within the Yangtze River Basin and cities within the
Beijing–Tianjin–Hebei region, respectively. For winter, precipitation was
the dominant factor for PM2.5 concentrations in Shanghai, Hangzhou and
Nanchang within the Yangtze River Basin and the ρ value of precipitation
was 0.36, 0.29 and 0.31, respectively. Meanwhile, the ρ value of
precipitation in Beijing, Tianjin and Shijiazhuang within the
Beijing–Tianjin–Hebei region was 0.08, 0.01 and 0.06, respectively.
Some meteorological factors can be the dominant factor for cities within
different regions, whilst some (e.g., evaporation and SSD) are mainly the
dominant meteorological factor for PM2.5 concentrations in cities within
some specific regions. In other words, some factors can be regarded as
regional and national meteorological influencing factors for PM2.5
concentrations, yet some meteorological factors are context-related,
influencing factors for local PM2.5 concentrations. Specifically, such
factors as temperature, wind and humidity serve as the dominant
meteorological factors in many regions, including Northeast, Northwest,
coastal areas and inland areas; meanwhile, such factors as SSD and wind
direction serve as the dominant meteorological factors mainly in some inland
regions. The prevalence of different meteorological factors across China can
also be reflected according to the number of cities where this specific
factor is the dominant factor for local PM2.5 concentrations. For
winter, the number of cities with temperature, wind or humidity as the
dominant factor was 56, 48 and 44, respectively. Meanwhile, the number of
cities with SSD or wind direction as the dominant factor was 3 and 1,
respectively.
Similar to patterns revealed in Fig. 2, the
ρ value for the dominant meteorological factors is much larger in winter
than that in summer. Furthermore, it is noted that the dominant
meteorological factors demonstrate more regional similarity in winter.
Specifically, the dominant meteorological factors for PM2.5
concentrations in the heavily polluted North China region are more
concentrated and homogeneously distributed in winter (mainly the wind and
humidity factor), whilst a diversity of dominant meteorological factors (that
includes humidity, temperature, SSD and air pressure) for PM2.5
concentrations is irregularly distributed within this region in summer. Take
some major cities in the North China region for instance. For winter, the
dominant meteorological factors for Beijing, Tianjin, Taiyuan, Zhangjiakou,
Handan and Jining were humidity (0.76), humidity (0.56), humidity (0.61),
wind (0.62), humidity (0.43) and humidity (0.52), respectively. Meanwhile,
for summer, the dominant meteorological factors for Beijing, Tianjin,
Taiyuan, Zhangjiakou, Baoding, Handan and Jining were humidity (0.39),
precipitation (0.28), temperature (0.23), temperature (0.47), air pressure
(0.21) and SSD (0.18). According to this pattern, when a regional
PM2.5-induced air pollution episode occurs in winter, the regional air
quality is more likely to be simultaneously improved by the same
meteorological factor. This is consistent with the common scene in winter
that regional air pollution episodes in the Beijing–Tianjin–Hebei region
can be considerably mitigated by strong northwesterly synoptic winds, which
are produced by the presence of high air pressure in northwestern Beijing
(NW-High; Tie et al., 2015; Miao et al., 2015). On the other hand, regional
air pollution in summer can hardly be solved simultaneously through one
specific meteorological factor.
Comparative statistics of the influence of individual meteorological
factors on local PM2.5 concentrations across China
In addition to meteorological influences on PM2.5 concentrations for
individual cities, we examined and compared the comprehensive influence of
individual meteorological factors on PM2.5 concentrations at a
national scale. The results are presented as Table 3 and Fig. 4.
The comparison of the influence of individual
meteorological factors on PM2.5 concentrations in 188
cities across China (2014–2016).
SeasonFactorTEMSSDPREEVPPRSRHUWINDir_WINSpringNo. of cities*7611331317641Mean ρ value0.2540.1020.1430.1080.1770.1610.2220.094SD of ρ value0.1060.0710.0880.0810.1230.1050.1020.077Max ρ value0.5720.3660.3850.3970.6530.4750.5950.429SummerNo. of cities7852212032273Mean ρ value0.2720.1360.1830.1370.1630.2190.1910.087SD of ρ value0.0980.0860.0990.0880.1090.1180.0950.062Max ρ value0.6040.4330.5360.3990.5180.5620.4530.311AutumnNo. of cities70113151327481Mean ρ value0.3160.1640.1910.1810.1990.2470.2650.104SD of ρ value0.1090.0980.0930.1170.0910.1250.0890.074Max ρ value0.7020.4790.4300.5140.5240.6620.4880.331WinterNo. of cities563275448441Mean ρvalue0.3060.1830.1660.1900.1800.3040.2990.119SD of ρ value0.0940.1290.1150.1300.0860.1610.1360.092Max ρ value0.5270.6150.4730.5950.4270.7550.6230.560
* No. of cities: the number of cities with this factor as the dominant
meteorological factor (its ρ value is the largest amongst eight factors) on local PM2.5
concentrations.
Violin plots of the influence of eight different
meteorological factors on local PM2.5 concentrations
in 188 cities across China.
No. of cities: the number of cities with this factor as the dominant
meteorological factor (its ρ
value is the largest amongst eight factors) on local
PM2.5 concentrations. The shape of the violin bars
indicated the frequency distribution of
ρ value for 188 cities.
We compared the influence of individual meteorological factors on PM2.5
concentrations from different perspectives.
From a national perspective, temperature, humidity, and wind exert
stronger influences on local PM2.5 concentrations than other factors.
The annual mean ρ value for temperature, wind and humidity was 0.287,
0.244 and 0.233, compared with wind direction (0.101), SSD (0.146),
evaporation (0.155), precipitation (0.171) and air pressure (0.180). Amongst
the eight factors, temperature was found to be the most influential
meteorological factor for general PM2.5 concentrations in China. In
addition to the largest mean ρ value, temperature was the dominant
meteorological factors for most cities in all seasons. Furthermore, the
Coefficient of Variation (SD/mean ⊆ 100%) for temperature was
much smaller than other factors, indicating the consistent influence of
temperature on local PM2.5 concentrations across China.
Although some meteorological factors exert a limited influence on
PM2.5 concentrations at a national scale, these factors may be a key
meteorological factor for local PM2.5 concentrations. As shown in Table 1, the max
ρ
value for each meteorological factor was large than 0.35 for all seasons
(except for the wind direction factor in summer and autumn), indicating a
very strong influence on local PM2.5 concentrations in some specific
regions. As a result, when analyzing meteorological influences on local
PM2.5 concentrations for a specific city, meteorological factors that
have little influence on PM2.5 concentrations at a large scale should
also be comprehensively considered.
Some factors (e.g., precipitation in summer and winter) may be the
dominant meteorological factors for a large number of cities, though the mean
ρ value remained small. This may be attributed to the fact that these
meteorological factors mainly exert influence on local PM2.5
concentrations in those cities (seasons) where (when) the general PM2.5
concentrations are not high. Taking the precipitation as an example, Luo et
al. (2017) pointed out that there may be thresholds for the negative
influences of precipitations on PM2.5 concentrations and Guo et
al. (2016) found that the same amount of precipitation led to a weaker
washing-off effect in areas with higher PM2.5 concentrations. Hence,
precipitation mainly exerts a dominant influence on local PM2.5
concentrations in winter for the Yangtze River Basin or coastal cities, where
the amount of precipitation is large and the PM2.5 concentration is low,
whilst precipitation exerts a limited role in northern China, where the
amount of precipitation is small and the PM2.5 concentration is high.
Therefore, as explained above, comprehensive meteorological influences on
PM2.5 concentrations are limited considerably.
Discussion
Correlations between individual meteorological factors and PM2.5
concentrations have been analyzed in such mega-cities as Nanjing (T. Chen et
al., 2016; Shen and Li, 2016), Beijing (Huang et al., 2015; Yin et al.,
2016), Wuhan (Zhang et al., 2017), Hangzhou (Jian et al., 2012), Chengdu
(Zeng and Zhang et al., 2017) and Hong Kong (Fung et al., 2014). These
studies suggested that meteorological influences on PM2.5 concentrations
varied significantly across regions. The dominant meteorological factors for
P2.5 concentrations demonstrated notable regional differences. For
Nanjing (T. Chen et al., 2016), a mega-city in the Yangtze River, and Hong
Kong (Fung et al., 2014), a mega coastal city, precipitation exerted the
strongest influence, whilst wind speed exerted a weak influence on PM2.5
concentrations in winter. On the other hand, for winter, wind speed was the
dominant meteorological factor for PM2.5 concentrations in Beijing
(Huang et al., 2015), a mega-city in North China, and precipitation played a
weak role in affecting local PM2.5 concentrations. Compared with studies
at a local or regional scale, this research conducted at the national scale
provided a better understanding of spatial and temporal patterns of
meteorological influences on PM2.5 concentrations across China, for the
following reasons. (a) A national perspective. Previous studies conducted at
a local scale mainly focused on a specific city (e.g., Beijing), and can
hardly reveal spatio-temporal patterns of meteorological influences on
PM2.5 concentrations at a large scale (e.g., the North China Plain).
This research, on the other hand, quantified the influence of meteorological
factors on PM2.5 concentrations for 188 cities across China, and thus
revealed some regional patterns of meteorological influences on PM2.5
concentrations in some typical regions (e.g., the North China region or the
Yangtze River Basin). (b) A unified research period and set of meteorological
factors. Previous studies employed short-term observation data (e.g., one
season or one year) in specific cities. Due to the discrepancy in research
periods and sets of meteorological factors, the findings from different
local-scale studies cannot be compared and comprehensively understood. This
research employed daily PM2.5 and meteorological data of three
consecutive years and a unified set of eight meteorological factors for all
188 monitoring cities, and thus meteorological influences on PM2.5
concentrations across China can be effectively compared without significant
influences from inter-annual variations. (c) A robust causality analysis
method. Correlation analysis, as introduced above, may lead to a large bias
in quantifying the meteorological influences on PM2.5 concentrations.
Similarly, the correlation coefficient cannot be used as a reliable indicator
to compare quantitative influences of individual meteorological factors on
PM2.5 concentrations across different cities. This research employed a
robust CCM method, which removes the influence of other factors, and
effectively quantified the coupling between PM2.5 concentrations and a
set of meteorological factors. The ρ value of each meteorological factor
on PM2.5 concentration can be compared between different cities. Based
on national statistics across China, this research concluded that the
influence of temperature, humidity and wind, especially temperature, on
PM2.5 concentrations was much larger than that of other meteorological
factors, which could not be revealed by previous local- and regional-scale
studies.
The findings from this research were consistent with a major extension of
those from previous studies by quantifying the influence of individual
meteorological factors in a large number of cities across China using a more
robust causality analysis method. Similar to previous studies, this study
also revealed notable differences in meteorological influences on PM2.5
concentrations at the national scale, which was mainly attributed to
different meteorological conditions and complicated mechanisms of
PM2.5–meteorology interactions. Firstly, notable differences existed in
meteorological conditions across China. For instance, in winter, the
frequency and intensity of precipitation are much higher and stronger in
coastal areas than those in the North China region, where the frequency of
strong winds is high in winter. Therefore, precipitation exerts a large
influence on PM2.5 concentrations in coastal regions, whilst wind is the
key influencing factor for PM2.5 concentrations in the North China
region in winter. Secondly, in addition to the large variations in the values
of correlation coefficients, the interaction mechanisms between individual
meteorological factors and PM2.5 concentrations may also vary
significantly across regions. For such meteorological influences as wind
speed, its negative effect on PM2.5 concentrations was consistent in
China (He et al., 2017). On the other hand, He et al. (2017) suggested that
temperature and humidity were either positively or negatively correlated with
PM2.5 concentrations in different regions of China. In terms of
humidity, when the humidity is low, PM2.5 concentration increases with
the increase in humidity due to hygroscopic increase in and accumulation of
PM2.5 (Fu et al., 2016). When the humidity continues to grow, the
particles grow too heavy to stay in the air, leading to dry (particles drop
to the ground; Wang and Ogawa, 2015)
and wet deposition (precipitation; Li et al., 2015b), and the reduction of
PM2.5 concentrations. Similarly, there may be thresholds for the
negative influences of precipitations on PM2.5 concentrations (Luo et
al., 2017). Heavy precipitation can have a strong washing-off effect on
PM2.5 concentrations and notably reduce PM2.5 concentrations.
Meanwhile, slight precipitation may not effectively remove the
high-concentration PM2.5. Instead, the slight precipitation may induce
enhanced relative humidity and thus lead to the increase in PM2.5
concentrations. Meanwhile, the washing-off effect from the same amount of
precipitation on PM2.5 concentrations in Xi'an, a city with higher
PM2.5 concentrations, was lower than that in Guangzhou (Guo et al.,
2016), indicating local PM2.5 concentrations also exerted a key role in
the negative effects of precipitation. Meanwhile, temperature can either be
negatively correlated with PM2.5 concentrations by accelerating the flow
circulation and promoting the dispersion of PM2.5 (Li et al., 2015b), or
positively correlated with PM2.5 concentrations through inversion events
(Jian et al., 2012). Given the complexity of interactions between
meteorological factors and PM2.5, characteristics and variations of
meteorological influences on PM2.5 concentrations should be further
investigated for specific regions across China, respectively, based on
long-term observation data.
Due to a highly complicated atmospheric environment and the difficulty in
acquiring true data of exhaust emission, commonly used models for air quality
prediction (e.g., CAMx, CMAQ and WRFCHEM) may lead to large biases and
uncertainty when applied to China. On the other hand, statistical models can
achieve satisfactory forecasting results based on massive historical data
(Cheng et al., 2015). Compared with the static models, dynamic statistical
models additionally consider the meteorological influences on PM2.5
concentrations, and some meteorological factors that have stable,
representative and strong correlations with PM2.5 concentrations are
selected for forecasting PM2.5 concentrations. Meanwhile, many recent
studies (Cheng et al., 2017; Guo et al., 2017; Lu et al., 2017; Ni et al.
2017) have recognized the meteorological influences on the evolution of
PM2.5 concentrations and included some key meteorological factors for
PM2.5 estimation. However, most PM2.5 estimation and forecasting
models mainly employed correlation analysis, and the correlation coefficient
between meteorological factors and PM2.5 concentrations is usually much
larger than the ρ value and overestimates the influence of individual
meteorological factors on PM2.5 concentrations. In this case, this
research provides a useful reference for improving existing statistical
models. By incorporating the ρ value, instead of the correlation
coefficient, of different factors into corresponding GAMs (generalized
additive models) and adjusting parameters accordingly, we may significantly
improve the reliability of future estimation and forecasting of PM2.5
concentrations.
Quantified causality of individual meteorological factors on PM2.5
concentrations provides useful decision support for evaluating relevant
environmental projects. Specifically, a forthcoming Beijing wind-corridor
project
(http://www.bj.xinhuanet.com/bjyw/yqphb/2016-05/16/c_1118870801.htm,
last access: 18 November 2017) has become
a hot social and scientific issue. Herein, our research suggests that wind is
a dominant meteorological factor for winter PM2.5 concentrations in
Beijing and can significantly influence PM2.5 concentrations through
direct and indirect mechanisms (Chen et al., 2017). In consequence, the
wind-corridor project may directly allow in more strong wind, which thus
leads to a larger value of SSD and evaporation and a smaller value of
humidity. The change in SSD, humidity and evaporation values can further
induce the reduction of PM2.5 concentrations. From this perspective, the
Beijing wind-corridor project has good potential to improve local and
regional air quality. In addition, some scholars and decision-makers have
proposed other meteorological means for reducing PM2.5 concentrations.
For instance, Yu (2014) suggested that water spraying from high buildings and
water towers in urban areas was an efficient way to reduce PM2.5
concentrations rapidly by simulating precipitation. However, some
limitations, such as the humidity control and potential icing risk, remained.
In the near future, with growing attention to the improvement of air quality,
more environmental projects should be properly designed and implemented.
According to this research given the diversity of dominant meteorological
factors on local PM2.5 concentrations in different regions and seasons,
it is more efficient to design meteorological means accordingly. For the
heavily polluted North China region, especially the Beijing–Tianjin–Hebei
region, the northwesterly synoptic wind (Tie et al., 2015; Miao et al., 2015)
is much stronger in winter than winds in summer and exerts a dominant
influence on PM2.5 concentrations (Chen et al., 2017). Furthermore, in
North China, the PM2.5 concentration is much more sensitive to the
change in wind speed than that of other meteorological factors (Gao et al.,
2016). Meanwhile, wind-speed-induced climate change led to a change in
PM2.5 concentrations by as much as 12.0 µg m-3, compared
with the change in PM2.5 concentrations by up to
4.0 µg m-3 in southeastern, northwestern and southwestern
China (Tai et al., 2010). Therefore, meteorological means for encouraging
strong winds are more likely to reduce PM2.5 concentrations considerably
in North China. Similarly, Luo et al. (2017) suggested that only
precipitation with a certain magnitude can lead to the washing-off effect of
PM2.5 concentrations, whilst Guo et al. (2016) revealed that the
variation of PM2.5 concentrations was more sensitive to the same amount
of precipitation in areas with lower PM2.5 concentrations. Therefore,
meteorological means for inducing precipitation are more likely to improve
air quality in coastal cities and cities within the Yangtze River Basin,
where there is a large amount of precipitation and relatively low PM2.5
concentrations.
Conclusions
Previous studies examined the correlation between individual meteorological
influences and PM2.5 concentrations in some specific cities and the
comparison between these studies indicated that meteorological influences on
PM2.5 concentrations varied significantly across cities and seasons.
However, these scattered studies conducted at the local scale cannot reveal
regional patterns of meteorological influences on PM2.5 concentrations.
Furthermore, previous studies generally selected different research periods
and meteorological factors, making the comparison of findings from different
studies less robust. Thirdly, these studies employed the correlation
analysis, which may be biased significantly due to the complicated
interactions between individual meteorological factors. This research is a
major extension of previous studies. Based on a robust causality analysis
method CCM, we quantified and compared the influence of eight meteorological
factors on local PM2.5 concentrations for 188 monitoring cities across
China using PM2.5 and meteorological observation data from March 2014
to February 2017. Similar to previous studies conducted at the local scale,
this research further indicated that meteorological influences on PM2.5 concentrations were of notable seasonal and spatial variations at the
national scale. Furthermore, this research revealed some regional patterns
and comprehensive statistics of the influence of individual meteorological
factors on PM2.5 concentrations, which cannot be understood through
small-scale case studies. For the heavily polluted North China region, the
higher PM2.5 concentrations, the stronger influence meteorological
factors exert on local PM2.5 concentrations. The dominant
meteorological factor for PM2.5 concentrations is closely related to
geographical conditions. For heavily polluted winter, precipitation exerts a
key influence on local PM2.5 concentrations in most coastal areas and
the Yangtze River basin, whilst the dominant meteorological driver for
PM2.5 concentrations is wind in the North China regions. At the
national scale, the influence of temperature, humidity and wind on local
PM2.5 concentrations is much larger than that of other factors, and
temperature exerts the strongest and most stable influences on national
PM2.5 concentrations in all seasons. The influence of individual
meteorological factors on PM2.5 concentrations extracted in this
research provides more reliable reference for better modelling and
forecasting local and regional PM2.5 concentrations. Given the
significant variations of meteorological influences on PM2.5
concentrations across China, environmental projects aiming for improving
local air quality should be designed and implemented accordingly.
The PM2.5 data used for this research are available at the website
(http://pm25.in/; China National Environmental Monitoring Center, 2017.), whilst meteorological data are available at the website
(http://www.cma.gov.cn/2011qxfw/2011qsjgx/; China Meteorological Data Sharing Service System, 2017.). Real-time hourly PM2.5 data can be collected, whilst historical PM2.5 data can be obtained upon request.
The authors declare that they have no conflict of
interest.
Acknowledgements
This research is supported by the National Natural Science Foundation of
China (grant nos. 210100066), the National Key Research and Development
Program of China (no. 2016YFA0600104), the Open Project of the State Key
Laboratory of Earth Surface Processes and Resource Ecology (2017-KF-22), the
Fundamental Research Funds for the Central Universities, the Ministry of
Environmental Protection (201409005) and the Beijing Training Support Project
for excellent scholars (2015000020124G059).
Edited by: Sally E. Pusede Reviewed by: three anonymous
referees
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