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Article

Spatiotemporal Variation in Ground Level Ozone and Its Driving Factors: A Comparative Study of Coastal and Inland Cities in Eastern China

by
Mengge Zhou
1,2,
Yonghua Li
1,* and
Fengying Zhang
3,*
1
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
China National Environmental Monitoring Centre, Beijing 100012, China
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(15), 9687; https://doi.org/10.3390/ijerph19159687
Submission received: 6 July 2022 / Revised: 29 July 2022 / Accepted: 3 August 2022 / Published: 5 August 2022
(This article belongs to the Section Environmental Earth Science and Medical Geology)
Browse Figures
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Abstract

:
Variations in marine and terrestrial geographical environments can cause considerable differences in meteorological conditions, economic features, and population density (PD) levels between coastal and inland cities, which in turn can affect the urban air quality. In this study, a five-year (2016–2020) dataset encompassing air monitoring (from the China National Environmental Monitoring Centre), socioeconomic statistical (from the Shandong Province Bureau of Statistics) and meteorological data (from the U.S. National Centers for Environmental Information, National Oceanic and Atmospheric Administration) was employed to investigate the spatiotemporal distribution characteristics and underlying drivers of urban ozone (O3) in Shandong Province, a region with both land and sea environments in eastern China. The main research methods included the multiscale geographically weighted regression (MGWR) model and wavelet analysis. From 2016 to 2019, the O3 concentration increased year by year in most cities, but in 2020, the O3 concentration in all cities decreased. O3 concentration exhibited obvious regional differences, with higher levels in inland areas and lower levels in eastern coastal areas. The MGWR analysis results indicated the relationship between PD, urbanization rate (UR), and O3 was greater in coastal cities than that in the inland cities. Furthermore, the wavelet coherence (WTC) analysis results indicated that the daily maximum temperature was the most important factor influencing the O3 concentration. Compared with NO, NO2, and NOx (NOx ≡ NO + NO2), the ratio of NO2/NO was more coherent with O3. In addition, the temperature, the wind speed, nitrogen oxides, and fine particulate matter (PM2.5) exerted a greater impact on O3 in coastal cities than that in inland cities. In summary, the effects of the various abovementioned factors on O3 differed between coastal cities and inland cities. The present study could provide a scientific basis for targeted O3 pollution control in coastal and inland cities.

Graphical Abstract

1. Introduction

Ozone (O3) is a typical secondary pollutant gas produced via photochemical oxidation reactions [1]. Recently, O3 pollution has received profound attention in China. Through various efforts over the past few years, the concentration of fine particulate matter (PM2.5) has been effectively controlled in most parts of China, but O3 pollution is increasing [2,3]. From 2016 to 2020, the proportion of Chinese cities at the prefecture level and above with the daily maximum 8-h average (DMA8) O3 concentration exceeding the national secondary standard (160 μg/m3) was 17.5%, 32.3%, 34.6%, 30.6%, and 16.6% [4,5,6,7,8]. Many previous studies have examined O3 pollution in the North China Plain, Yangtze River Delta, and Pearl River Delta [9,10,11,12,13,14,15,16,17]. When a certain O3 concentration is exceeded, this can exert an adverse impact on the ecological environment, food safety, human health, and climate change [18,19,20,21,22,23,24,25]. Therefore, the Chinese government issued relevant policies in 2020 to promote the prevention and control of O3 pollution [26].
Volatile organic compounds (VOCs) and nitrogen oxides (NOx ≡ NO + NO2) are precursors of O3 [27]. Many studies have explored the relationship, photochemical reaction mechanism, and influence factors between O3 and its precursors [28,29,30,31,32,33]. Meteorological conditions are important factors of O3 generation and transmission. Radiation enhancement, temperature increase, and sunshine time extension facilitate O3 generation, while a high relative humidity reduces O3 [3,34,35,36]. In addition to meteorological conditions, there may also be a relationship between socioeconomic factors and the spatial distribution of air pollution [37]. Many studies have analyzed the correlation between PM2.5 and socioeconomic factors [38,39]. However, there is little research on the socioeconomic factors of O3. Even more notably, few studies have examined the combined impact of the O3 precursors, meteorological conditions, and socioeconomic factors on O3 pollution from a spatiotemporal perspective. These influencing factors may have different effects in different regions, which is worth exploring.
The difference in thermal conditions between land and oceans results in distinct climates between inland and coastal areas. Coupled with differences in the geographical environment, there could occur a comprehensive effect leading to different economic development and population distributions between inland and coastal areas. Within the context of global warming, the contrast between land and ocean warming levels is increasing (land warming enhanced), which intensifies aerosol pollution [40]. Considering that the influence of various factors on the O3 concentration in coastal and inland cities may differ, it is therefore necessary to evaluate these differences.
The multiscale geographically weighted regression (MGWR) model is an extension of the geographically weighted regression (GWR) model, which is commonly used in the analysis of spatial influencing factors [41]. The MGWR model considers that different spatial scales exert different effects of the action mode and intensity on the spatial relationship between the considered factors and dependent variables, so it can explore the spatial heterogeneity of influencing factors [41]. MGWR has been widely used in the environmental field, such as PM2.5, PM10, NO2, and other air pollutants [42,43,44]. Zhan et al. (2022) has examined the spatial heterogeneity effects of both socioeconomic factors and natural factors on continuous air pollution with a MGWR [45]. Thus, we attempted to analyze the underlying drivers affecting the spatial distribution of O3 and its spatial heterogeneity from socioeconomic aspects using MGWR.
Wavelet analysis was developed based on the Fourier transform [46]. The wavelet transform extends time series into the time–frequency space to overcome the limitations of the Fourier transform [47]. Wavelet analysis is a powerful analysis tool highly suitable to study nonstationary processes in the finite space–time domain [46,48]. This method has been widely applied in geophysics, economics, and public health research [49,50,51,52,53,54,55,56]. Therefore, this study will explore the potential drivers affecting the spatial distribution of O3 from the perspective of meteorological conditions and other air pollutants through wavelet analysis (wavelet coherence (WTC)).
Shandong Province is located in eastern China, bordering the Bohai Sea and Yellow Sea. There occur both typical coastal cities and typical inland cities due to the large east–west span. In addition, Shandong Province ranks third in terms of its economy and second in terms of its population in China. The well-developed economy, large population, and high consumption of resources have caused serious air pollution, including O3 pollution, in this area. Based on the above, we reasonably assume that the differences in the socioeconomic and natural environment of Shandong Province lead to significant differences in O3 concentrations between coastal and inland areas, and the influence of various factors in different cities is different. This study will first attempt to combine MGWR and wavelet analysis to explore the influence of various factors on O3 from multiple perspectives, so as to identify the key factors influencing the O3 distribution. This study can provide the Chinese government with a more targeted reference for the treatment of O3 pollution in coastal and inland cities.

2. Data and Methods

2.1. Study Area

Shandong Province is located between 34°22.90′~38°24.01′ N and 114°47.50′~122° 42.30′ E (Figure 1). The region contains a complex, diverse terrain, and a long coastline, with four typical coastal cities: Qingdao, Yantai, Weihai and Rizhao. Shandong Province exhibits four distinct seasons, belonging to the warm temperate monsoon climate. The weather is changeable in spring, with less rain and windy sand-prone conditions. In summer, controlled by the southeast marine monsoon, southerly winds prevail. In autumn, the weather is sunny and moderate. Winter is controlled by the continental monsoon climate, mostly involving northerlies. The mountains in the central part of the territory are raised, which comprise mountains with an altitude higher than a kilometer, such as Mount Tai, Mount Lu, Mount Yi, and Mount Meng. The surrounding areas gradually transition from low mountains and hills into plains.

2.2. O3 and Other Air Pollutants Data

The DMA8 O3 concentration and the 24-h average concentrations of NO, NO2, NOx, PM2.5, and respirable particles (PM10) were considered in this study. Data pertaining to 16 cities in Shandong Province from 1 January 2016, to 31 December 2020, were obtained from the China National Environmental Monitoring Centre (CNEMC, http://www.cnemc.cn/, accessed on 13 September 2021). The annual O3 concentration is defined as the 90th percentile of DMA8 O3 concentration [57]. O3 monitoring and recording were carried out in strict accordance with Chinese national standards [58]. The data statistical validity of each evaluation project was implemented in accordance with the relevant provisions of the Chinese national standard [59].

2.3. Socioeconomic and Meteorological Data

The socioeconomic data were derived from the statistics, including data on the population, gross domestic product (GDP), industrial power consumption, and number of civil vehicles (NCV) for 16 cities from 2016 to 2020 [60,61,62,63,64]. We also compiled the daily meteorological data from National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (https://www.ncei.noaa.gov/maps/global-summaries/, accessed on 5 September 2021). The meteorological monitoring sites are located in Jinan (116.9833° E, 36.6833° N) and Qingdao (120.3744° E, 36.2661° N). The specific monitoring items include average wind speed (WDSP, m/s), maximum continuous wind speed (MXSPD, m/s), maximum temperature (MAX, °C), and average temperature (TEMP, °C).

2.4. Methods and Mechanism

We used ArcGIS to visualize the spatiotemporal variation characteristics of O3 concentration in Shandong Province from 2016 to 2020 and conduct hot spot analysis. OLS analysis was performed in R 4.0.5 invented by Rose Ihaka and Robert Gentleman of New Zealand. The GWR and MGWR models were implemented in MGWR 2.2 provided by the School of Geographical Sciences and Urban Planning at Arizona State University (https://sgsup.asu.edu accessed on 5 September 2021). Wavelet analysis was performed using publicly available MATLAB code.

2.4.1. GWR and MGWR

The GWR model is a new method to incorporate spatial correlation into a regression model, which allows regression coefficients to vary in space [41]. The expression of GWR model is as follows:
y i = β 0 ( u i , v i ) + ∑ j β j ( u i , v i ) x i j + ε i
where (ui, vi) denotes the spatial coordinates of the ith observation point, β (ui, vi) is the regression coefficient of the jth independent variable at the ith observation point, β0 (ui, vi) is the intercept of the model at the ith observation point, and ei is the error term.
The MGWR model is an extension of the GWR model [41]. The largest difference between these models is the bandwidth. All variables in the GWR model exhibit the same bandwidth. However, the MGWR model specifies a dedicated bandwidth for each variable, thus reducing estimation errors and ensuring a more realistic and useful spatial process model. Different bandwidths can reveal the scale effect of different factors on O3 concentration changes. Generally, the larger the bandwidth is, the lower the spatial heterogeneity [44]. The calculation equation is as follows:
y i = β 0 ( u i , v i ) + ∑ j β b w j ( u i , v i ) x i j + ε i
where (ui, vi) denotes the coordinates of position i, bwj denotes the bandwidth considered by the regression coefficient of the jth variable, and βbw j(ui, vi) is the regression coefficient of the jth variable at i.

2.4.2. Wavelet Analysis

Wavelet analysis is based on Fourier analysis. Elucidation of the localization characteristics of the analyzed object in the time and frequency domains constitutes the advantage of wavelet analysis [65]. The continuous wavelet transform (CWT) method entails wavelet superposition of different scales and displacement levels [66]. One frequently applied wavelet function is the Morlet wavelet, which is also adopted in this paper. Based on two CWTs, Grinsted et al. (2004) constructed the cross-wavelet transform (XWT), which exposes the high common power and relative phase in time–frequency space [67]. The XWT of two time series xn and yn is denoted as WXY, and the cross-wavelet power is denoted as |WXY|. The complex argument arg(WXY) can be interpreted as the local relative phase between xn and yn in time–frequency space and is given as follows:
ϕ ( s , t ) = tan − 1 ( I m ( W ↔ y , x ( s , t ) ) / R e W ↔ y , x ( s , t ) )
where W ↔ y , x ( s , t ) is the matrix of the smoothed cross-wavelet power spectra between xn and yn and Im and Re denote the imaginary and real parts, respectively, of W ↔ y , x ( s , t ) [51]. The phase angle can be determined to analyze the variation relationship (lag or consistent change) between two time series [49]. Consequently, left- and right-pointing phase angles indicate antiphase and in-phase relationships, respectively.
WTC analysis can be employed to study the correlation between two data series in time–frequency space. The WTC can significantly enhance the linear relationship and determine the covariance intensity between two time series [49,51]. Grinsted et al. (2004) defined the WTC of two time series as follows:
R   n 2 ( s ) = | S ( s − 1 W n X Y ( s ) ) | 2 S ( s − 1 | W n X ( s ) | 2 ) · S ( s − 1 | W n X ( s ) | 2 )
where S is a smoothing operator and WX and WY are the CWTs of xn and yn, respectively. R2 ranges from 0 to 1.
S ( W ) = S s c a l e ( S t i m e ( W n ( s ) ) )
where Sscale denotes the smoothing level along the wavelet scale axis and Stime denotes the smoothing level in time.

2.5. Choice of Two Typical Cities

Qingdao is the most economically well-developed city in Shandong Province, and Jinan is the capital city of Shandong Province. Furthermore, Qingdao is a coastal city, while Jinan is an inland city (Figure 1). There are great geographical differences between these cities. Thus, this study chose Jinan and Qingdao as typical representative cities to explore the differences of the time–frequency relationship between O3 and meteorological factors and other air pollutants between coastal and inland cities.

3. Results and Discussion

3.1. Spatiotemporal Distribution Characteristics of O3

The annual O3 concentration changes in 16 cities in Shandong Province are shown in Figure 2. Significant spatiotemporal variations in the O3 distribution were observed. Over the past five years, only three cities in Shandong saw a slight decrease in O3 concentration in 2020, while the other 13 cities saw an increase. The growth range of the O3 concentration in the 13 cities was 2.42~49.60%, with an average increase of 15.40%. From 2016 to 2019, the O3 concentration in most cities increased year by year. An important reason for the observed increase could be enhancement in anthropogenic precursors. However, in 2020, the O3 concentration was reduced due to strict control measures implemented by the Chinese government and the COVID-19 pandemic. The same trend is also shown in Figure 3, which indicates the annual changes in the O3 concentration attainment rate in each city. The standard Ⅰ concentration limit of the DMA8 O3 concentration is 100 μg/m3, which is consistent with the air quality guideline (AQG) level of the World Health Organization Global Air Quality Guidelines (WHOGAQG) [68]. The standard Ⅱ concentration limit of the DMA8 O3 concentration is 160 μg/m3, which is consistent with the interim target 1 level of the WHOGAQG [68]. From 2016 to 2020, the proportion of the number of days when the O3 concentration reached standard Ⅰ in Shandong Province was 60.55%, 55.92%, 51.61%, 46.61%, and 49.62%, respectively. The number of days when the O3 concentration reached standard Ⅱ accounted for 90.83%, 87.00%, 84.47%, 79.95%, and 85.11%, respectively. The most serious O3 pollution occurred in 2019, with 53.39% and 21.05% exceeding standard Ⅰ and standard Ⅱ, respectively. Only three coastal cities (Qingdao: 207/365; Rizhao: 188/365; Yantai: 187/365) exhibited more than half of the total number of days with the concentration reaching standard Ⅰ.
Moreover, there were obvious differences in the O3 distribution between inland and coastal areas. The O3 concentration in the inland areas (central and northwestern Shandong) was higher than that on the Jiaodong Peninsula (mainly coastal cities), which is consistent with Yao et al. (2019) [69]. This study further explored the spatial distribution characteristics of O3 through hot spot analysis (Figure 4). O3 cold spot areas were located in Qingdao, Yantai and Weihai. In addition, O3 high risk areas expanded from Jinan in 2016 to Jinan and surrounding areas in 2020, including Dezhou, Binzhou and Zibo.

3.2. Socioeconomic Impacts

In this study, redundant factors were eliminated according to the variance inflation factor (VIF) value (VIF < 7.5) of the ordinary least squares (OLS) model. VIF is a multicollinearity test method. The closer the VIF value is to 1, the lighter the multicollinearity is, and vice versa. Seven factors were selected: time, population density (PD, 10,000 people·hm−2), urbanization rate (UR, %), proportion of the secondary industry (PSI, %), output value of farming, forestry, animal husbandry and fishery (OFFAF, CNY), industrial power consumption (IPC, Billion kW·h), and NCV (unit). We tried to use PD to describe the population distribution, UR to represent the level of regional economic development, PSI to show the industrial structure, OFFAF to represent the development level of the primary industry, IPC to mean the industrial emissions of ozone, and NCV to represent the motor vehicle emissions of ozone. Three models, namely, OLS, GWR, and MGWR models, were employed to explore the socioeconomic relationship between these factors and the O3 concentration.
According to the corrected AICc, R2 and adj-R2 values of these three models (Table 1), the MGWR model achieved the best fitting effect. Compared with the OLS model, the R2 and adj-R2 values of the GWR and MGWR were greatly improved. But MGWR can more truly reflect the relationship between socioeconomic factors and O3 than GWR because the bandwidth of the MGWR model is variable, while the bandwidth of the GWR model is fixed. The MGWR model can reflect the spatial difference of the relationship between the independent variable and the dependent variable.
According to the OLS results (Table 1), there was a significant correlation between Time, PD, UR, PSI, and O3. Combined with Figure 2 and Figure 3, the O3 concentration had a distinct increasing trend from 2016 to 2020. The effects of PD and PSI on the O3 concentration were positive across the whole province. An increase in PD and PSI could lead to an increase in the O3 concentration to varying degrees. Densely populated areas contributed more to O3 due to the consumption of many resources as fuel [70]. Cities with high industrial proportions produced more O3 precursors, resulting in higher O3 concentration. In most areas of Shandong Province, especially in coastal areas, there existed a stronger negative correlation between UR and the O3 concentration. This is because coastal cities had a high degree of urbanization, but their O3 concentration was low because of low emissions and favorable diffusion conditions. OFFAF, IPC, and NCV slightly affected O3 and the relationship between them was not significant. Figure 5 indicates that compared with vehicle exhaust emissions, industrial emissions contributed more in the south of Shandong Province. The socioeconomic environment had a certain and extremely complex impact on the formation of O3. Greater population density and higher industrialization levels may lead to more O3 precursors. In addition, due to the implementation of air pollution prevention and control policies, the areas with higher economic development levels are willing to invest higher more in air pollution control and better treatment effects. Notably, Lee et al. (2021) found that the number of houses, number of parking lots, and area of reconstruction projects were positively correlated with O3 rates [71].
In this study, the effects of the mode and intensity of Time, PD, PSI, and NCV on the change in O3 concentration were roughly similar on a large spatial scale, and the spatial relationship tended to remain stable (bandwidth is 71). The spatial heterogeneity in the impact of UR, OFFAF, and IPC on the O3 concentration was high (bandwidths are 62, 57, and 57 respectively). What is more, Figure 5 shows the relationship between PD, UR, and O3 is greater in coastal cities than that in the inland cities.
As a secondary air pollutant, processes leading to variations in surface O3 are complex because they are impacted by natural and human factors. The impact of socioeconomic conditions on O3 is only a small part, while the meteorological environment has a greater impact on O3. Therefore, it is difficult to explain the O3 variations simply by socioeconomic statistics. To this end, we used meteorological and other air pollution data in the next section to further analyze its impact on O3.

3.3. Impact of Other Air Pollutants and Meteorological Factors

3.3.1. Wavelet Power Spectrum of O3 in Jinan and Qingdao

The area enclosed by the black line in the Figure 6 indicates that the periodicity within the time series is significant. There occurred an obvious annual cycle in Qingdao and Jinan. The O3 concentrations in those two cities frequently fluctuated in summer (June to August) and autumn (September to November). In addition, the Qingdao O3 concentration exhibited a moderate cycle of approximately 150 days from 2016 to 2019. This indicates that double peaks occurred in the O3 concentration change in Qingdao, one peak in May or June and the other peak in September (Supplementary Figure S1). Affected by the summer monsoon climate, Qingdao is rainy in July and August. This led to a significant decline in the O3 concentration in summer due to lack of light [34,72].

3.3.2. Wavelet Coherence Coefficient

In this study, NO, NO2, NOx, NO2/NO, PM10, PM2.5, WDSP, MXSPD, TEMP, and MAX were selected to explore the corresponding time–frequency relationship with O3. The arithmetic mean value of the WTC coefficient (ARsq) is provided in Table 2. In summary, the variation in O3 in Qingdao was more closely related to other air pollutants than that in Jinan. Perhaps it is because Qingdao is greatly affected by the Yellow Sea, unlike Jinan, which is affected by many surrounding cities (Supplementary Figure S2). The time–frequency relationship between O3 and other gas pollutants in Jinan was more complex. Among the meteorological elements, the coherence between the temperature and O3 in Jinan was higher than that in Qingdao, while the coherence between the wind speed and O3 in Qingdao was relatively higher. Previous studies have reported that O3 generation largely depends on high temperatures and strong solar radiation [3,73]. Jinan’s temperature fluctuated more greatly than that in Qingdao because Jinan is located far from the ocean. As a result, O3 in Jinan was more vulnerable to the temperature. The wind speed notably impacted O3 in Qingdao due to the prevalence of sea and land breezes. The daily maximum temperature exerted the strongest impact on O3 in Jinan and Qingdao, followed by NO2/NO. Li et al. (2020) also believes that high temperature is the main meteorological driver in summer. In contrast to Jinan, PM2.5 had a greater impact on O3 in Qingdao [73].

3.3.3. Time Frequency and Phase Relationship between O3 and Other Air Pollutants

(1)
NO, NO2, NOx, and NO2/NO. In the annual cycle, nitrogen oxides (including NO, NO2, and NOx) and O3 attained an inverse phase relationship, in which the value/phase of one parameter increased and that of the other parameter decreased (Figure 7). NO and O3 exhibited an inverse phase relationship with a short period (less than 14 days). This may occur as a result of NO titration reaction (R1). NO2 and O3, NOx and O3 mostly attained a positive phase relationship in summer and an inverse phase relationship in winter in the short period. O3 production in winter was generally in the NOx-saturated regime that high NOx concentration restrains O3 formation. Simultaneously, high NO emissions titration contributed to a reduction in O3 [11]. In summer, O3 was in the NOx-limited regime that the increase of NO2 was conducive to O3 formation (R2). Xia et al. (2021) similarly showed that the daily O3 concentration is higher when the daily NO2 concentration is higher in summer [36]. In addition to NOx, VOCs are also important precursors for O3 formation, which readily affect the variation of O3 concentration in winter [74,75,76]. However, VOCs were not regarded as an influencing factor in this paper. The unexplained part of the figure may be mainly attributed to VOCs.
NO + O3—→NO2 R1
NO2 + O2 + hv—→NO + O3 R2
In order to further clarify the relationship between NOx and O3, wavelet coherence analysis of NO2/NO and O3 was performed (Figure 7). As can be seen from the Table 2 and Figure 7, the coherence between NO2/NO and O3 was stronger than that of NO, NO2, and NOx. There was an obvious annual cycle between the NO2/NO ratio and O3. In the annual cycle, the NO2/NO ratio and O3 concentration in Jinan changed at the same time, while the arrow in Qingdao gradually tilted downward, indicating that the NO2/NO ratio had fluctuated before O3 in Qingdao since 2018. In the short cycle, NO2/NO ratio had strong coherence with O3 from May to July, and both increased simultaneously. This is consistent with the findings of Shao et al. (2009) and Wang et al. (2019) that the O3 concentration increases with the NO2/NO ratio [28,74]. In spring, O3 shifted from the NOx-saturated regime to the NOx-limited regime due to NOx concentration decrease (NO2 gradually decreased, while NO plummeted). The magnitude of each change of NO2 concentration in spring and summer was greater than that of NO (Supplementary Figure S3). Therefore, the increase of NO2 concentration not only promoted the formation of O3, but also led to the increase of NO2/NO ratio. Wang et al. (2018) believe that NO titration is generally more significant on high-O3 days, which further leads to an increase in the NO2/NO ratio [77]. Similarly, the reduction of NO2 concentration inhibits the formation of O3. During the transitional stage between the NOx-saturated regime and the NOx-limited regime (spring and autumn), the changes in NO2/NO ratio and O3 were reversed. This phenomenon was prominent in Jinan during the transition from NOx-saturated to NOx-limited (April and May), while Qingdao was more significant in the transition from NOx-limited to NOx-saturated (October). The concentration of NO2 decreased gradually in spring, resulting in the decrease of the NO2/NO ratio. O3 was weakened by NOx limitation, and its concentration gradually increased under favorable meteorological conditions (temperature rise and light enhancement). Li et al. (2021) also reached the same conclusion that the increase in O3 observed in spring is driven by the reduction of NOx emissions [11]. In October, the ozone concentration in Qingdao was still at a high level. Under the condition of high concentration of NO2, high temperature and strong radiation, the concentration of O3 increases. The consumption of NO2 reduces the NO2/NO ratio, so that NO2/NO showed the opposite relationship with O3.
(2)
PM10and PM2.5. Particulate matter (PM) (including PM10 and PM2.5) fluctuate frequently in winter and spring in Jinan and Qingdao. The phase trend of PM with O3 was inclined to the left during the annual cycle, indicating that PM and O3 exhibited an inverse phase relationship, and the change in O3 preceded that in PM (Figure 7). In the short term (1~30 days), PM2.5 and O3 revealed opposite phases in winter and spring but the same phase in summer. In winter and spring, the PM2.5 concentration was high due to the increase in energy consumption (heating). An ultrahigh PM2.5 concentration could lead to light radiation weakening, which could reduce O3 production. In addition, PM2.5 scavenges HO2 and NOx radicals, resulting in O3 reduction [78]. O3 production is high in summer due to the high temperatures and strong solar irradiation. The PM concentration was low in summer. When the PM2.5 concentration increases to a certain extent, this could enhance the photochemical reactions of O3 [36]. PM10 attained a similar periodic relationship with O3. In this study, the relationship between PM and O3 in summer was closer in coastal areas. This is consistent with the findings of Xia et al. (2021) that the daily maximum PM2.5 concentration has a greater impact on the daily maximum O3 concentration [36]. While Hu et al. (2021) and Wang et al. (2020) both showed that PM10 has a much greater impact on O3 than PM2.5 due to different data accuracy and study area [14,79].

3.3.4. Time Frequency and Phase Relationship between O3 and Meteorological Factors

(1)
Wind speed. Short-term (1~30 days) in-phase variation between WDSP and O3 in Jinan mostly occurred in winter and spring (Figure 8). The low temperature and low light in winter and spring lead to less O3 production. The observed increase in the O3 concentration probably occurred due to external transport. When the wind speed is high, exogenous O3 may be transported. This could cause an O3 increase in Jinan. Affected by sea and land winds, a complex correlation existed between WDSP and O3 in Qingdao. Sea breezes can not only lead to the accumulation of land air pollutants but can also lead to pollutant diffusion and transport [17]. This could result in the absence of obvious short-term coherence between WDSP and O3. In addition to WDSP, an obvious annual cycle was observed between MXSPD and O3. The corresponding phase relationship trended downward, indicating that the change in O3 preceded that in the wind speed. High wind speeds occur in winter and spring in both Qingdao and Jinan. In contrast, the O3 concentration is low in winter and spring.
(2)
Temperature. TEMP and O3 attained a high correlation over the annual cycle. Over the annual cycle, the trend was inclined upward, indicating that O3 change lagged behind that in TEMP. This is because the temperature rises slowly in spring, and the O3 concentration gradually increases thereafter. In the short term (within 14 days), mostly the same phase was observed. This indicates that O3 and TEMP simultaneously increased or decreased. A high temperature and notable radiation results in a high O3 production rate. The time–frequency relationship between O3 and MAX was the same as that between O3 and TEMP. However, the ARsq value between O3 and MAX (JN: 0.6283; QD: 0.5444) was higher than that between O3 and TEMP (JN: 0.5037; QD: 0.5185), which is consistent with Zhao et al. (2020) [3]. This may occur because the O3 concentration was the highest in the afternoon, and the highest temperature throughout the day also occurred in the afternoon [3.36]. Compared with Jinan, Qingdao attained a lower coherence between O3 and temperature.

3.4. Limitations

However, the present study also suffers certain uncertainties and limitations. It is widely acknowledged that VOCs are some of the most important precursors for O3 generation. Unfortunately, due to the lack of available high-resolution VOC data, this study did not comprehensively evaluate the possible impact of VOCs on the spatiotemporal distribution of O3. The analyzed O3 data encompassed the average values of urban internal monitoring data according to Chinese national standard HJ 663-2013, while the obtained meteorological data included single-point data. Therefore, the spatial matching results of the meteorological and O3 data were not completely consistent, which could lead to deviations in the analysis results in regard to the time–frequency relationship between O3 and meteorological parameters.

4. Conclusions

The combination of MGWR and wavelet analysis is excellent in exploring the effects of O3 distribution from multiple perspectives. This is an innovative aspect of this study, which has not been seen in previous studies. There were significant spatiotemporal differences in the O3 distribution in Shandong Province. On the one hand, the O3 concentration increased annually from 2016 to 2019 but declined in 2020 under the influence of China’s strict O3 pollution cleanup policies and the global COVID-19 pandemic. On the other hand, the O3 concentration in inland areas (namely, central, and northwestern Shandong) was higher, whereas that in eastern coastal areas was lower. In general, PD, UR, the wind speed (WDSP and MXSDP), nitrogen oxides, and PM2.5 generated a greater impact on O3 in a coastal city (Qingdao, China) than that in an inland city (Jinan, China). Whether coastal or inland cities, the ratio of NO2/NO was more coherent with O3 than that NO, NO2, and NOx.
In summary, the reasons for the observed increase in the O3 concentration in winter include the following: 1. reduction in nitrogen oxides; 2. reduction in the particle concentration; 3. transport of exogenous pollutants (VOCs and O3) under the action of wind; 4. temperature rise. The observed O3 concentration increase in summer is attributed to the high temperature, PM refraction and increase in nitrogen oxides. And the reasons for the decrease in O3 concentration in summer include rainfall, the NO titration reaction, and reduction of NO2.
Nevertheless, China is located in eastern Asia and the west coast of the Pacific Ocean, with many important coastal and inland cities. Therefore, clarification of the differences in the main driving factors of O3 between coastal and inland cities could enhance our understanding of O3 pollution and improve the performance of O3 prediction models. In addition, it would enable the Chinese government better control O3 pollution. Regional O3 pollution is a comprehensive reflection of the environment, society, economy, and development, and strengthening the leadership role of the government is therefore of crucial importance.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph19159687/s1, Figure S1: Histogram of monthly average O3 concentration changes from 2016 to 2020; Figure S2: ARsq among cities in Shandong province from 2016 to 2020. * is indicates passing the 95% significance level test; Figure S3: Line chart of the daily change of the daily maximum 8-hour average (DMA8) O3 concentration in Jinan and Qingdao.

Author Contributions

All authors contributed to the study conception and design. M.Z.: Investigation, Methodology, Visualization, Writing—original draft, Data curation, Software, Formal analysis. F.Z.: Resources, Supervision. Y.L.: Conceptualization, Supervision, Writing—review and editing, Funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Science (No. XDA26050202).

Institutional Review Board Statement

This article does not contain any studies involving human participants or animals performed by any of the authors.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets were derived from the following public domain resources: http://www.cnemc.cn/ (accessed on 13 September 2021), https://www.ncei.noaa.gov/maps/global-summaries/ (accessed on 5 September 2021), http://tjj.shandong.gov.cn/tjnj/nj2021/zk/indexch.htm (in Chinese) (accessed on 3 March 2022), http://tjj.shandong.gov.cn/tjnj/nj2020/zk/indexch.htm (in Chinese) (accessed on 3 March 2022), http://tjj.shandong.gov.cn/tjnj/nj2019/indexch.htm (in Chinese) (accessed on 3 March 2022), http://tjj.shandong.gov.cn/tjnj/nj2018/indexch.htm (in Chinese) (accessed on 3 March 2022), http://tjj.shandong.gov.cn/tjnj/nj2017/indexch.htm (in Chinese) (accessed on 3 March 2022).

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Kley, D.; Kleinmann, M.; Sanderman, H.; Krupa, S. Photochemical oxidants: State of the science. Environ. Pollut. 1999, 100, 19–42. [Google Scholar] [CrossRef]
  2. Lu, X.; Hong, J.; Zhang, L.; Cooper, O.R.; Schultz, M.G.; Xu, X.; Wang, T.; Gao, M.; Zhao, Y.; Zhang, Y. Severe Surface Ozone Pollution In Chinese: A Global Perspective. Environ. Sci. Technol. Lett. 2018, 5, 487–494. [Google Scholar] [CrossRef]
  3. Zhao, S.; Yin, D.; Yu, Y.; Kang, S.; Qin, D.; Dong, L. PM2.5 and O3 pollution during 2015–2019 over 367 Chinese cities: Spatiotemporal variations, meteorological and topographical impacts. Environ. Pollut. 2020, 264, 114694. [Google Scholar] [CrossRef] [PubMed]
  4. Ministry of Ecology and Environment of the People’s Republic of China (M.E.E.). Bulletin of the Ecological Environment of China in 2016. 2017. Available online: http://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/201706/P020170605833655914077.pdf (accessed on 26 May 2022). (In Chinese)
  5. Ministry of Ecology and Environment of the People’s Republic of China (M.E.E.). Bulletin of the Ecological Environment of China in 2017. 2018. Available online: https://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/201805/P020180531534645032372.pdf (accessed on 26 May 2022). (In Chinese)
  6. Ministry of Ecology and Environment of the People’s Republic of China (M.E.E.). Bulletin of the Ecological Environment of China in 2018. 2019. Available online: https://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/201905/P020190619587632630618.pdf (accessed on 26 May 2022). (In Chinese)
  7. Ministry of Ecology and Environment of the People’s Republic of China (M.E.E.). Bulletin of the Ecological Environment of China in 2019. 2020. Available online: http://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/202006/P020200602509464172096.pdf (accessed on 26 May 2022). (In Chinese)
  8. Ministry of Ecology and Environment of the People’s Republic of China (M.E.E.). Bulletin of the Ecological Environment of China in 2020. 2021. Available online: http://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/202105/P020210526572756184785.pdf (accessed on 26 May 2022). (In Chinese)
  9. Dong, Y.; Li, J.; Guo, J.; Jiang, Z.; Chu, Y.; Chang, L.; Yang, Y.; Liao, H. The impact of synoptic patterns on summertime ozone pollution in the North China Plain. Sci. Total Environ. 2020, 735, 139559. [Google Scholar] [CrossRef]
  10. Gong, C.; Liao, H.; Zhang, L.; Yue, X.; Dong, R.; Yang, Y. Persistent ozone pollution episodes in North China exacerbated by regional transport. Environ. Pollut. 2020, 265, 115056. [Google Scholar] [CrossRef]
  11. Li, K.; Jacob, D.J.; Liao, H.; Qiu, Y.; Shen, L.; Zhai, S.; Kelvin, H.; Bates, K.H.; Melissa, P.; Sulprizio, M.P.; et al. Ozone pollution in the North China Plain spreading into the late-winter haze season. Proc. Natl. Acad. Sci. USA 2021, 118, e2015797118. [Google Scholar] [CrossRef]
  12. Qin, Y.; Li, J.; Gong, K.; Wu, Z.; Chen, M.; Qin, M.; Huang, L.; Hu, J. Double high pollution events in the Yangtze River Delta from 2015 to 2019: Characteristics, trends, and meteorological situations. Sci. Total Environ. 2021, 792, 148349. [Google Scholar] [CrossRef]
  13. Zhang, X.; Feng, T.; Zhao, S.; Yang, G.; Quan Zhang, Q.; Qin, G.; Liu, L.; Long, X.; Sun, W.; Cao, G.; et al. Elucidating the impacts of rapid urban expansion on air quality in the Yangtze River Delta, China. Sci. Total Environ. 2021, 799, 149426. [Google Scholar] [CrossRef]
  14. Hu, M.; Wang, Y.; Wang, S.; Jiao, M.; Huang, G.; Xia, B. Spatial-temporal heterogeneity of air pollution and its relationship with meteorological factors in the Pearl River Delta, China. Atmos. Environ. 2021, 254, 118415. [Google Scholar] [CrossRef]
  15. Fang, T.; Zhu, Y.; Wang, S.; Xing, J.; Zhao, B.; Fan, S.; Li, M.; Yang, W.; Chen, Y.; Huang, R. Source impact and contribution analysis of ambient ozone using multi-modeling approaches over the Pearl River Delta region, China. Environ. Pollut. 2021, 289, 117860. [Google Scholar] [CrossRef]
  16. Wu, J.; Wang, Y.; Liang, J.; Yao, F. Exploring common factors influencing PM2.5 and O3 concentrations in the Pearl River Delta: Tradeoffs and synergies. Environ. Pollut. 2021, 285, 117138. [Google Scholar] [CrossRef]
  17. Zhao, D.; Xin, J.; Wang, W.; Jia, D.; Wang, Z.; Xiao, H.; Liu, C.; Zhou, J.; Tong, L.; Ma, M.; et al. Effects of the sea-land breeze on coastal ozone pollution in the Yangtze River Delta, China. Sci. Total Environ. 2022, 807, 150306. [Google Scholar] [CrossRef]
  18. Li, P.; Feng, Z.; Catalayud, V.; Yuan, X.; Xu, Y.; Paoletti, E. A meta-analysis on growth, physiological, and biochemical responses of woody species to ground-level ozone highlights the role of plant functional types. Plant Cell Environ. 2017, 40, 2369–2380. [Google Scholar] [CrossRef]
  19. Singh, A.A.; Agrawal, S.B. Tropospheric ozone pollution in India: Effects on crop yield and product quality. Environ. Sci. Pollut. Res. 2017, 24, 4367–4382. [Google Scholar] [CrossRef]
  20. Mills, G.; Sharps, K.; Simpson, D.; Pleijel, H.; Broberg, M.; Uddling, J.; Jaramillo, F.; Davies, W.J.; Dentener, F.; Berg, M.V.; et al. Ozone pollution will compromise efforts to increase global wheat production. Glob. Chang. Biol. 2018, 24, 3560–3574. [Google Scholar] [CrossRef]
  21. Yin, P.; Brauer, M.; Aaron, J.; Cohen, A.J.; Wang, H.; Li, J.; Burnett, R.T.; Stanaway, J.D.; Causey, K.; Larson, S.; et al. The effect of air pollution on deaths, disease burden, and life expectancy across China and its provinces, 1990–2017: An analysis for the Global Burden of Disease Study 2017. Lancet Planet. Health 2020, 4, e386–e398. [Google Scholar] [CrossRef]
  22. Lu, X.; Zhang, L.; Wang, X.; Gao, M.; Li, K.; Zhang, Y.; Yue, X.; Zhang, Y. Rapid Increases in Warm-Season Surface Ozone and Resulting Health Impact In Chinese since 2013. Environ. Sci. Technol. Lett. 2020, 7, 240–247. [Google Scholar] [CrossRef]
  23. Guan, Y.; Yang, X.; Wang, Y.; Zhang, N.; Chun, C. Long-term health impacts attributable to PM2.5 and ozone pollution In Chinese’s most polluted region during 2015–2020. J. Clean. Prod. 2021, 321, 128970. [Google Scholar] [CrossRef]
  24. Wu, S.; Mickley, L.J.; Jacob, D.J.; Rind, D.; Streets, D.G. Effects of 2000–2050 changes in climate and emissions on global tropospheric ozone and the policy-relevant background surface ozone in the United States. J. Geophys. Res. 2008, 113, D18312. [Google Scholar] [CrossRef] [Green Version]
  25. Jacob, D.J.; Winner, D.A. Effect of climate change on air quality. Atmos. Environ. 2009, 43, 51–63. [Google Scholar] [CrossRef] [Green Version]
  26. Ministry of Ecology and Environment of the People’s Republic of China (M.E.E.). 2020 Plan for against Volatile Organic Compounds. 2020. Available online: https://www.mee.gov.cn/xxgk2018/xxgk/xxgk03/202006/t20200624_785827.html (accessed on 28 May 2022). (In Chinese)
  27. Sillman, S. The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments. Atmos. Environ. 1994, 33, 1821–1845. [Google Scholar] [CrossRef]
  28. Shao, M.; Zhang, Y.; Zeng, L.; Tang, X.; Zhang, J.; Zhong, L.; Wang, B. Ground-level ozone in the Pearl River Delta and the roles of VOC and NOx in its production. J. Environ. Manag. 2009, 90, 512–518. [Google Scholar] [CrossRef]
  29. Zhang, Q.; Yuan, B.; Shao, M.; Wang, X.; Lu, S.; Lu, K.; Wang, M.; Chen, L.; Chang, C.-C.; Liu, S.C. Variations of ground-level O3 and its precursors in Beijing in summertime between 2005 and 2011. Atmos. Chem. Phys. 2014, 14, 6089–6101. [Google Scholar] [CrossRef] [Green Version]
  30. Li, B.; Ho, S.S.H.; Gong, S.; Ni, J.; Li, H.; Han, L.; Yang, Y.; Qi, Y.; Zhao, D. Characterization of VOCs and their related atmospheric processes in a central Chinese city during severe ozone pollution periods. Atmos. Chem. Phys. 2019, 19, 617–638. [Google Scholar] [CrossRef] [Green Version]
  31. Li, H.; Ma, Y.; Duan, F.; Zhu, L.; Ma, T.; Yang, S.; Xu, Y.; Li, F.; Huang, T.; Kimoto, T.; et al. Stronger secondary pollution processes despite decrease in gaseous precursors: A comparative analysis of summer 2020 and 2019 in Beijing. Environ. Pollut. 2021, 279, 116923. [Google Scholar] [CrossRef] [PubMed]
  32. Xie, Y.; Cheng, C.; Wang, Z.; Wang, K.; Wang, Y.; Zhang, X.; Li, X.; Ren, L.; Liu, M.; Li, M. Exploration of O3-precursor relationship and observation-oriented O3 control strategies in a non-provincial capital city, southwestern China. Sci. Total Environ. 2021, 800, 149422. [Google Scholar] [CrossRef] [PubMed]
  33. Li, R.; Xu, M.; Li, M.; Chen, Z.; Zhao, N.; Gao, B.; Yao, Q. Identifying the spatiotemporal variations in ozone formation regimes across China from 2005 to 2019 based on polynomial simulation and causality analysis. Atmos. Chem. Phys. 2021, 21, 15631–15646. [Google Scholar] [CrossRef]
  34. Liu, P.; Song, H.; Wang, T.; Wang, F.; Li, X.; Miao, C.; Zhao, H. Effects of meteorological conditions and anthropogenic precursors on ground-level ozone concentrations in Chinese cities. Environ. Pollut. 2020, 262, 114366. [Google Scholar] [CrossRef]
  35. Yang, Z.; Yang, J.; Li, M.; Chen, J.J.; Ou, C.Q. Nonlinear and lagged meteorological effects on daily levels of ambient PM2.5 and O3: Evidence from 284 Chinese cities. J. Clean. Prod. 2021, 278, 123931. [Google Scholar] [CrossRef]
  36. Xia, N.; Du, E.; Guo, Z.; Vries, W.D. The diurnal cycle of summer tropospheric ozone concentrations across Chinese cities: Spatial patterns and main drivers. Environ. Pollut. 2021, 286, 117547. [Google Scholar] [CrossRef]
  37. Liang, D.; Wang, Y.Q.; Wang, Y.J.; Ma, C. National air pollution distribution In Chinese and related geographic, gaseous pollutant, and socio-economic factors. Environ. Pollut. 2019, 250, 998–1009. [Google Scholar] [CrossRef]
  38. Zhao, H.; Guo, S.; Zhao, H. Characterizing the Influences of Economic Development, Energy Consumption, Urbanization, Industrialization, and Vehicles Amount on PM2.5 Concentrations of China. Sustainability 2018, 10, 2574. [Google Scholar] [CrossRef]
  39. Jin, J.Q.; Du, Y.; Xu, L.J.; Chen, Z.Y.; Chen, J.J.; Wu, Y.; Ou, C.Q. Using Bayesian spatio-temporal model to determine the socioeconomic and meteorological factors influencing ambient PM2.5 levels in 109 Chinese cities. Environ. Pollut. 2019, 254, 113023. [Google Scholar] [CrossRef]
  40. Allen, R.J.; Hassan, T.; Randles, C.A.; Su, H. Enhanced land–sea warming contrast elevates aerosol pollution in a warmer world. Nat. Clim. Chang. 2019, 9, 300–305. [Google Scholar] [CrossRef]
  41. Fotheringham, A.S.; Yang, W.B.; Kang, W. Multiscale geographically weighted regression (MGWR). Ann. Am. Assoc. Geogr. 2017, 107, 1247–1265. [Google Scholar] [CrossRef]
  42. Guo, B.; Wang, X.; Pei, L.; Su, Y.; Zhang, D.; Wang, Y. Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018. Sci. Total Environ. 2021, 751, 141765. [Google Scholar] [CrossRef]
  43. Zeng, C.; Yang, L.; Zhu, A.X.; Rossiter, D.G.; Liu, J.; Liu, J.; Qin, C.; Wang, D. Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method. Geoderma 2016, 281, 69–82. [Google Scholar] [CrossRef]
  44. Wu, T.; Zhou, L.; Jiang, G.; Meadows, M.E.; Zhang, J.; Pu, L.; Wu, C.; Xie, X. Modelling Spatial Heterogeneity in the Effects of Natural and Socioeconomic Factors, and Their Interactions, on Atmospheric PM2.5 Concentrations In Chinese from 2000–2015. Remote Sens. 2021, 13, 2152. [Google Scholar] [CrossRef]
  45. Zhan, D.; Zhang, Q.; Xu, X.; Zeng, C. Spatiotemporal distribution of continuous air pollution and its relationship with socioeconomic and natural factors In Chinese. Int. J. Environ. Res. Public Health 2022, 19, 6635. [Google Scholar] [CrossRef]
  46. Lau, K.M.; Weng, H. Climate Signal Detection Using Wavelet Transform: How to Make a Time Series Sing. Bull. Am. Meteorol. Soc. 1995, 76, 2391–2402. [Google Scholar] [CrossRef] [Green Version]
  47. Torrence, C.; Compo, C.P. A Practical Guide to Wavelet Analysis. Bull. Am. Meteorol. Soc. 1998, 79, 61–78. [Google Scholar] [CrossRef] [Green Version]
  48. Yi, H.; Shu, H. The improvement of the Morlet wavelet for multi-period analysis of climate data. Comptes Rendus Geosci. 2012, 344, 483–497. [Google Scholar] [CrossRef]
  49. Ng, E.K.W.; Chan, J.C.L. Geophysical Applications of Partial Wavelet Coherence and Multiple Wavelet Coherence. J Atmos. Ocean Technol. 2012, 29, 1845–1853. [Google Scholar] [CrossRef]
  50. Hu, W.; Si, B.C. Technical note: Multiple wavelet coherence for untangling scale-specific and localized multivariate relationships in geosciences. Earth Syst. Sci. 2016, 20, 3183–3191. [Google Scholar] [CrossRef] [Green Version]
  51. Cheng, Q.; Zhong, F.; Wang, P. Baseflow dynamics and multivariate analysis using bivariate and multiple wavelet coherence in an alpine endorheic river basin (Northwest China). Sci. Total Environ. 2021, 772, 145013. [Google Scholar] [CrossRef]
  52. Bilgili, F.; Lorente, D.B.; Kuşkaya, S.; Ünlü, F.; Gençoģlu, P.; Rosha, P. The role of hydropower energy in the level of CO2 emissions: An application of continuous wavelet transform. Renew. Energy 2021, 178, 283–294. [Google Scholar] [CrossRef]
  53. Trott, C.B.; Subrahmanyam, B.; Washburn, C.E. Investigating the Response of Temperature and Salinity in the Agulhas Current Region to ENSO Events. Remote Sens. 2021, 13, 1829. [Google Scholar] [CrossRef]
  54. Mensi, W.; Rehman, M.U.; Maitra, D.; Al-Yahyaee, K.H.; Vo, X.V. Oil, natural gas and BRICS stock markets: Evidence of systemic risks and co-movements in the time-frequency domain. Resour. Policy 2021, 72, 102062. [Google Scholar] [CrossRef]
  55. Tankanag, A.V.; Grinevich, A.A.; Kirilina, T.V.; Krasnikov, G.V.; Piskunova, G.M.; Chemeris, N.K. Wavelet phase coherence analysis of the skin blood flow oscillations in human. Microvasc. Res. 2014, 95, 53–59. [Google Scholar] [CrossRef]
  56. Rahman, M.S.; Azad, M.A.; Hasanuzzaman, M.; Salam, R.; Islam, A.R.; Rahman, M.M.; Hoque, M.M. How air quality and COVID-19 transmission change under different lockdown scenarios? A case from Dhaka city, Bangladesh. Sci. Total Environ. 2021, 762, 143161. [Google Scholar] [CrossRef]
  57. Ministry of Ecology and Environment of the People’s Republic of China (M.E.E.). Technical Regulation for Ambient Air Quality Assessment (on Trial) (HJ 663—2013). 2013. Available online: https://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/jcffbz/201309/W020131105548549111863.pdf (accessed on 27 September 2021). (In Chinese)
  58. Ministry of Ecology and Environment of the People’s Republic of China (M.E.E.). Technical Regulation for Selection of Ambient Air Quality Monitoring Stations (on Trial) (HJ 664—2013). 2013. Available online: https://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/jcffbz/201309/W020131105548727856307.pdf (accessed on 27 September 2021). (In Chinese)
  59. Ministry of Ecology and Environment of the People’s Republic of China (M.E.E.). Ambient Air Quality Standards (GB 3095-2012). 2012. Available online: https://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/dqhjbh/dqhjzlbz/201203/W020120410330232398521.pdf (accessed on 25 September 2021). (In Chinese)
  60. Shandong Province Bureau of Statistics (SPBS). Shandong Statistical Yearbook-2021. 2021. Available online: http://tjj.shandong.gov.cn/tjnj/nj2021/zk/indexch.htm (accessed on 3 March 2022). (In Chinese)
  61. SPBS. Shandong Statistical Yearbook-2020. 2020. Available online: http://tjj.shandong.gov.cn/tjnj/nj2020/zk/indexch.htm (accessed on 3 March 2022). (In Chinese)
  62. SPBS. Shandong Statistical Yearbook-2019. 2019. Available online: http://tjj.shandong.gov.cn/tjnj/nj2019/indexch.htm (accessed on 3 March 2022). (In Chinese)
  63. SPBS. Shandong Statistical Yearbook-2018. 2018. Available online: http://tjj.shandong.gov.cn/tjnj/nj2018/indexch.htm (accessed on 3 March 2022). (In Chinese)
  64. SPBS. Shandong Statistical Yearbook-2017. 2017. Available online: http://tjj.shandong.gov.cn/tjnj/nj2017/indexch.htm (accessed on 3 March 2022). (In Chinese)
  65. Domingues, M.O.; Mendes, O., Jr.; da Costa, A.M. On wavelet techniques in atmospheric sciences. Adv. Space Res. 2005, 35, 831–842. [Google Scholar] [CrossRef] [Green Version]
  66. Hermida, L.; López, L.; Merino, A.; Berthet, C.; García-Ortega, E.; Sánchez, J.L.; Dessens, D. Hailfall in southwest France: Relationship with precipitation, trends and wavelet analysis. Atmos. Res. 2015, 156, 174–188. [Google Scholar] [CrossRef]
  67. Grinsted, A.; Moore, J.C.; Jevrejeva, S. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process Geophys. 2004, 11, 561–566. [Google Scholar] [CrossRef]
  68. World Health Organization. WHO Global Air Quality Guidelines. Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide. CC BY-NC-SA 3.0 IGO. 2021. Available online: https://www.who.int/publications/i/item/9789240034228 (accessed on 3 March 2022).
  69. Yao, Y.; He, C.; Li, S.Y.; Ma, W.; Li, S.; Yu, Q.; Mi, N.; Yu, J.; Wang, W.; Yin, L.; et al. Properties of particulate matter and gaseous pollutants in Shandong, China: Daily fluctuation, influencing factors, and spatiotemporal distribution. Sci. Total Environ. 2019, 660, 384–394. [Google Scholar] [CrossRef]
  70. Li, L.; An, J.Y.; Shi, Y.Y.; Zhou, M.; Yan, R.S.; Huang, C.; Wang, H.L.; Lou, S.R.; Wang, Q.; Lu, Q.; et al. Source apportionment of surface ozone in the Yangtze River Delta, China in the summer of 2013. Atmos. Environ. 2016, 144, 194–207. [Google Scholar] [CrossRef]
  71. Lee, G.; Lee, J.H. Spatial correlation analysis using the indicators of the anthropocene focusing on atmospheric pollution: A case study of Seoul. Ecol. Indic. 2021, 125, 107535. [Google Scholar] [CrossRef]
  72. Yang, L.; Luo, H.; Yuan, Z.; Zheng, J.; Huang, Z.; Li, C.; Lin, X.; Louie, P.K.K.; Chen, D.; Bian, Y. Quantitative impacts of meteorology and precursor emission changes on the long-term trend of ambient ozone over the Pearl River Delta, China, and implications for ozone control strategy. Atmos. Chem. Phys. 2019, 19, 12901–12916. [Google Scholar] [CrossRef] [Green Version]
  73. Li, K.; Jacob, D.J.; Shen, L.; Lu, X.; De Smedt, I.; Liao, H. Increasing in surface ozone pollution In Chinese from 2013 to 2019: Anthropogenic and meteorological influences. Atmos. Chem. Phys. 2020, 20, 11423–11433. [Google Scholar] [CrossRef]
  74. Wang, M.Y.; Yim, S.H.L.; Wong, D.C.; Ho, K.F. Source contributions of surface ozone In Chinese using an adjoint sensitivity analysis. Sci. Total Environ. 2019, 662, 385–392. [Google Scholar] [CrossRef]
  75. Wang, X.; Fu, T.M.; Zhang, L.; Cao, H.; Zhang, Q.; Ma, H.; Shen, L.; Evans, M.J.; Ivatt, P.D.; Lu, X.; et al. Sensitivities of Ozone Air Pollution in the Beijing-Tianjin-Hebei Area to Local and Upwind Precursor Emissions Using Adjoint Modeling. Environ. Sci. Technol. 2021, 55, 5752–5762. [Google Scholar] [CrossRef]
  76. Zhang, Y.; Zhao, Y.; Li, J.; Wu, Q.; Wang, H.; Du, H.; Yang, W.; Wang, Z.; Zhu, L. Modeling Ozone Source Apportionment and Performing Sensitivity Analysis in Summer on the North China Plain. Atmosphere 2020, 11, 992. [Google Scholar] [CrossRef]
  77. Wang, H.; Lyu, X.; Guo, H.; Wang, Y.; Zou, S.; Ling, Z.; Wang, X.; Jiang, F.; Zeren, Y.; Pan, W.; et al. Ozone pollution around a coastal region of South China Sea: Interaction between marine and continental air. Atmos. Chem. Phys. 2018, 18, 4277–4295. [Google Scholar] [CrossRef] [Green Version]
  78. Li, K.; Jacob, D.J.; Liao, H.; Zhu, J.; Shah, V.; Shen, L.; Bates, K.H.; Zhang, Q.; Zhai, S. A two-pollutant strategy for improving ozone and particulate air quality In Chinese. Nat. Geosci. 2019, 12, 906–910. [Google Scholar] [CrossRef]
  79. Wang, Z.B.; Li, J.X.; Liang, L.W. Spatio-temporal evolution of ozone pollution and its influencing factors in the Beijing-Tianjin-Hebei Urban Agglomeration. Environ. Pollut. 2020, 256, 113419. [Google Scholar] [CrossRef]
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Figure 1. Location of the study area.
Figure 1. Location of the study area.
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Figure 2. Distribution of annual O3 concentration in Shandong province from 2016 to 2020.
Figure 2. Distribution of annual O3 concentration in Shandong province from 2016 to 2020.
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Figure 3. Annual change of O3 concentration attainment rate from 2016 to 2020.
Figure 3. Annual change of O3 concentration attainment rate from 2016 to 2020.
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Figure 4. Hot spot analysis results of annual O3 concentrations from 2016 to 2020. Red is the high-value cluster area of O3, and blue is the low-value cluster area of O3.
Figure 4. Hot spot analysis results of annual O3 concentrations from 2016 to 2020. Red is the high-value cluster area of O3, and blue is the low-value cluster area of O3.
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Figure 5. Distribution of MGWR regression coefficient.
Figure 5. Distribution of MGWR regression coefficient.
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Figure 6. Wavelet power spectrum of O3. The left axis is the period (days), the bottom axis is time (days). The thick contour enclosed regions of greater than 95% confidence for a red noise process, the cone of influence where edge effects might distort the picture is shown as a lighter shade. The legend on the right shows the intensity of the wavelet power.
Figure 6. Wavelet power spectrum of O3. The left axis is the period (days), the bottom axis is time (days). The thick contour enclosed regions of greater than 95% confidence for a red noise process, the cone of influence where edge effects might distort the picture is shown as a lighter shade. The legend on the right shows the intensity of the wavelet power.
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Figure 7. Wavelet coherence between O3 and NO (a,b), NO2 (c,d), NOx (e,f), NO2/NO (g,h), PM2.5 (i,j), PM10 (k,l). The unit of the period is days. The thick contour enclosed regions of greater than 95% confidence for a red noise process, the cone of influence where edge effects might distort the picture is shown as a lighter shade. The legend on the right shows the intensity of the wavelet power. The relative phase relationship is shown as arrows (with in-phase pointing right, anti-phase pointing left, and O3 leading factors by 90° pointing straight down) [67].
Figure 7. Wavelet coherence between O3 and NO (a,b), NO2 (c,d), NOx (e,f), NO2/NO (g,h), PM2.5 (i,j), PM10 (k,l). The unit of the period is days. The thick contour enclosed regions of greater than 95% confidence for a red noise process, the cone of influence where edge effects might distort the picture is shown as a lighter shade. The legend on the right shows the intensity of the wavelet power. The relative phase relationship is shown as arrows (with in-phase pointing right, anti-phase pointing left, and O3 leading factors by 90° pointing straight down) [67].
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Ijerph 19 09687 g008 550
Figure 8. Wavelet coherence between O3 and meteorological factors. The unit of the period is days. The thick contour enclosed regions of greater than 95% confidence for a red noise process, the cone of influence where edge effects might distort the picture is shown as a lighter shade. The legend on the right shows the intensity of the wavelet power. The relative phase relationship is shown as arrows (with in-phase pointing right, anti-phase pointing left, and O3 leading factors by 90° pointing straight down) [67].
Figure 8. Wavelet coherence between O3 and meteorological factors. The unit of the period is days. The thick contour enclosed regions of greater than 95% confidence for a red noise process, the cone of influence where edge effects might distort the picture is shown as a lighter shade. The legend on the right shows the intensity of the wavelet power. The relative phase relationship is shown as arrows (with in-phase pointing right, anti-phase pointing left, and O3 leading factors by 90° pointing straight down) [67].
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Table
Table 1. Regression results of the ordinary least squares (OLS) model, the geographically weighted regression (GWR) model, and the multiscale geographically weighted regression (MGWR) model. C is coefficient; Max is maximum value of coefficient; Mean is average value of coefficient; Min is minimum value of coefficient; AICc (Akaike information criterion) is a correction for small sample sizes, the smaller the value, the higher the goodness of fit.
Table 1. Regression results of the ordinary least squares (OLS) model, the geographically weighted regression (GWR) model, and the multiscale geographically weighted regression (MGWR) model. C is coefficient; Max is maximum value of coefficient; Mean is average value of coefficient; Min is minimum value of coefficient; AICc (Akaike information criterion) is a correction for small sample sizes, the smaller the value, the higher the goodness of fit.
FactorsOLS GWRMGWR
CPVIFMaxMeanMinMaxMeanMinBandwidth
Time0.5770.0001.4710.7520.5070.2750.5340.4810.44171
PD0.2940.0321.9630.4950.096−0.3670.1350.0790.05471
UR−0.2490.0321.3940.282−0.097−0.4600.158−0.065−0.36262
PSI0.3360.0292.4580.8210.214−0.1420.1490.1350.12571
OFFAF−0.0920.5442.3610.170−0.079−0.3680.1580.003−0.08157
IPC0.1800.1191.3770.3510.058−0.1810.1550.012−0.06457
NCV−0.0100.9543.0500.305−0.001−0.166−0.026−0.057−0.08771
AICc218.54 180.85 171.102
R20.305 0.683 0.700
Adj. R20.237 0.599 0.630
Bandwidth 62
Table
Table 2. ARsq (wavelet coherence coefficient) of meteorological conditions and other air pollutants with O3.
Table 2. ARsq (wavelet coherence coefficient) of meteorological conditions and other air pollutants with O3.
NONO2NOxNO2/NOPM2.5PM10WDSPMXSPDTEMPMAX
Jinan0.3970.4060.4040.4880.4460.4580.4010.3870.6040.628
Qingdao 0.4350.4120.4190.5270.5090.4540.4180.4030.5190.544
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Zhou, M.; Li, Y.; Zhang, F. Spatiotemporal Variation in Ground Level Ozone and Its Driving Factors: A Comparative Study of Coastal and Inland Cities in Eastern China. Int. J. Environ. Res. Public Health 2022, 19, 9687. https://doi.org/10.3390/ijerph19159687

AMA Style

Zhou M, Li Y, Zhang F. Spatiotemporal Variation in Ground Level Ozone and Its Driving Factors: A Comparative Study of Coastal and Inland Cities in Eastern China. International Journal of Environmental Research and Public Health. 2022; 19(15):9687. https://doi.org/10.3390/ijerph19159687

Chicago/Turabian Style

Zhou, Mengge, Yonghua Li, and Fengying Zhang. 2022. "Spatiotemporal Variation in Ground Level Ozone and Its Driving Factors: A Comparative Study of Coastal and Inland Cities in Eastern China" International Journal of Environmental Research and Public Health 19, no. 15: 9687. https://doi.org/10.3390/ijerph19159687

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