Study on spatiotemporal distribution of airborne ozone pollution in subtropical region considering socioeconomic driving impacts: A case study in Guangzhou, China

Journal Pre-proof Study on Spatiotemporal Distribution of Airborne Ozone Pollution in Subtropical Region Considering Socioeconomic Driving Impacts: A Case Study in Guangzhou, China Xuan Tang, Xing Gao, Changlong Li, Qiuping Zhou, Chen Ren, Zhuangbo Feng

PII:

S2210-6707(19)33530-9

DOI:

https://doi.org/10.1016/j.scs.2019.101989

Reference:

SCS 101989

To appear in:

Sustainable Cities and Society

Received Date:

27 September 2019

Revised Date:

16 November 2019

Accepted Date:

25 November 2019

Please cite this article as: Tang X, Gao X, Li C, Zhou Q, Ren C, Feng Z, Study on Spatiotemporal Distribution of Airborne Ozone Pollution in Subtropical Region Considering Socioeconomic Driving Impacts: A Case Study in Guangzhou, China, Sustainable Cities and Society (2019), doi: https://doi.org/10.1016/j.scs.2019.101989

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Study on Spatiotemporal Distribution of Airborne Ozone Pollution in Subtropical Region Considering Socioeconomic Driving Impacts: A Case Study in Guangzhou, China

Xuan Tanga*, Xing Gaob, Changlong Lia, Qiuping Zhoua, Chen Renc, Zhuangbo Fengc a

School of Economics and Statistics, Guangzhou University, Guangzhou, China, 510006

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School of Management, Guangzhou University, Guangzhou, China, 510006

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Academy of Building Energy Efficiency of Guangzhou University, School of Civil Engineering, Guangzhou University, Guangzhou, China, 510006 *

Corresponding author: [email protected]

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1 The correlations between O3, environmental/meteorological factors were investigated. 2 Air temperature was the primary factor affecting O3 concentration with the significant positive correlation. 3 The concentrations of CO, relative humidity and NO2 were negatively correlated with O3. 4 Precipitation exhibited positive correlation with ozone. 5 The quantitative-relationship curve between economic growth and ozone shows a U-shaped trend.

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Highlights

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Abstract Ozone has become the primary air pollutant in the subtropical regions (i.e., Guangzhou, China). This significantly hinders the sustainable development of cities. The purpose of this work was to find out the main influencing factors of ozone, then put forward feasible control strategies. We first analyzed the spatiotemporal variation characteristics of ozone in 2016–2018. The effects of environmental factors (e.g., CO) and meteorological factors (e.g., temperature) on ozone were investigated comprehensively. Furthermore, the relationship between economic growth and ozone in Guangzhou was analyzed. There were 48 days with excessive ozone in 2017. Ozone exceeding rate displayed an “M” curve. Moreover, the number of months with excessive ozone increased each year. About the influential factors, temperature was the primary factor affecting ozone with a significant positive correlation. CO, relative humidity, and NO2 were negatively correlated with ozone. Because of the “contemporaneous rain and heat” climate, precipitation exhibited a positive correlation with ozone. The quantitative-relationship curve between economic growth and ozone displayed U-shaped trend. The proposed plans for ozone control could contribute to the sustainable development of Guangzhou 1

through source control, departmental coordination, and government policy. Keywords: Ozone pollution; Spatiotemporal distribution characteristics; Multiple influencing factors; Economic analysis; Control strategies

1. Introduction Ozone is an important trace component in the atmosphere, with 90% distributed in the stratosphere and 10% in the troposphere. Although the ozone in the stratosphere can absorb strong ultraviolet radiation from the sun to protect human health, an ozone pollution problem is also caused

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when the ozone concentration increases beyond a certain threshold. Moreover, ozone is a greenhouse gas and the intermediate product of photochemical smog formation. Most of the ozone originates as secondary pollutants from photochemical reactions of common pollutants in the atmosphere emitted by production activities, such as nitrogen oxide (NOx) and volatile organic chemicals (VOCs) (Brauner et al. 2016, Cao, Cheng and Yu 2018). Because the complex formation mechanism of ozone

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may involve chemical, meteorological, and environmental aspects, ozone treatment could be significantly more challenging than the treatment of other pollutants such as CO and NO2 (Cao et al.

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2017, Zhou et al. 2017). Apart from endangering human health, ozone pollution can significantly impact urban economic development. Even some studies have proved that air pollution will seriously threaten the progress of sustainable development (Addanki and Venkataraman 2017, Velasco and

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Retama 2017, Zhu et al. 2019, Yang et al. 2019a). Therefore, the study of ozone pollution has attracted wide attention from scholars, government, and society. Recently, developed countries (such as European countries and the United States) have sustained

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severe ozone pollution with the rapid increase in its concentration level (Ashmore 2005). However, in China, ozone pollution appears to be more severe and challenging to control owing to the interaction with heavy PM2.5 pollution (Ren et al. 2016, Ren, Cao and Liu 2018). Considering the "Environmental

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Air Quality Standard"(GB3095-2012), ozone is the only one of six environmental indicators (PM2.5, PM10, NO2, SO2, CO, and ozone) that appears to exhibit a rising variation trend of concentration as of

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2018. Specifically, the proportion of excessive ozone pollution for 338 cities of China is up to 8.3%. This occurs mainly in China's developed urban agglomerations such as Beijing–Tianjin–Hebei Region, Yangtze River Delta, and Pearl River Delta. Therefore, it is urgently necessary for the Chinese government to limit ozone pollution. Guangzhou, as the key city of the Pearl River Delta region and Guangdong–Hong Kong–Macao Greater Bay Area (GBA), is one of the first coastal open cities in south China. With the rapid urbanization and industrialization (e.g., thermal power plants), and the use of motor vehicles, the emission and accumulation of ozone precursors such as NOx and VOCs has accelerated. Moreover, 2

Guangzhou has a subtropical monsoon climate, with a high atmospheric temperature lasting for a long time. This climate condition may directly increase the likelihood of ozone generation, which also intensifies the ozone pollution problem in Guangzhou. To summarize, the analysis of the regularity and the factors influencing ozone concentration in Guangzhou is of high significance to the design of an ozone pollution control strategy. It can also support the sustainable development of Guangdong– Hong Kong–Macao Greater Bay Area (GBA) and China. In recent years, many studies on ozone have determined the following as the main factors influencing ozone pollution: (i) concentrations of related precursors (e.g., NOx and VOCs) (An et al. 2016, Zhong, Lee and Haghighat 2017, Tan et al. 2018, Liu et al. 2018, Yang et al. 2019b), (ii) atmospheric environmental pollutants (e.g., CO, NO2, and SO2) (Abdul-Wahab, Bakheit and Al-Alawi

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2005, Al-Husseini 2018, Li et al. 2019b), (iii) and meteorological conditions (e.g., temperature, relative humidity, precipitation, solar radiation, and wind direction) (Camalier, Cox and Dolwick 2007, Shan et al. 2009, Jin, Loisy and Brown 2013, Alghamdi et al. 2014, Jaffe and Zhang 2017, Li et al. 2017, Kalisa et al. 2018, Liu et al. 2019). Most of these studies were based mainly on data monitoring or numerical simulation for obtaining the spatial and temporal distributions of ozone concentration. The

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scholars either explored the effects of ozone precursors, atmospheric environmental pollutants, and meteorological parameters on the ozone concentration level, or analyzed the source of ozone pollution

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(by numerical simulation). In addition, a few of these above-mentioned works were on the ozone pollution in Guangzhou. Li et al. utilized an online testing method to obtain the VOC and ozone data in the ambient air in Guangzhou (November 5–9, 2009). The results revealed that vehicle exhaust and

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gasoline evaporation were the main sources of VOCs, which in turn resulted in ozone formation (Li et al., 2013). Zou et al. discussed the relationships between ozone, non-methane hydro carbons (NMHCs), and NOx in Guangzhou in 2011 and determined that the control of the highly reactive NMHCs and

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NOx can effectively reduce the ozone concentration (Zou et al. 2019). Zou et al. also investigated the effects of CO, NO2, and meteorological factors on ozone in Panyu District of Guangzhou (2010–2016). They concluded that high NO2 concentrations, strong solar radiation, and low wind speeds could

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contribute to ozone accumulation (Zou et al., 2019). According to the aforementioned research, certain reference values or guidelines may be provided

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for understanding the distribution characteristics of ozone pollution, particularly in Guangzhou. However, there are other problems that need to be solved, such as finite information on ozone concentration from limited monitoring stations (i.e., limited spatial representation of ozone data), and short-term scale of ozone concentration. Moreover, comprehensive analysis and studies on the relationships between the spatiotemporal variations in ozone concentrations and various influencing factors are few. In summary, the innovations of this work aimed at overcoming the above limitations can be summarized as follows: (i) extension of the time span, i.e., the dynamic variation in the ozone concentration during 2016–2018 was captured accurately; (ii) expansion of monitoring points (data), 3

i.e., ozone data were obtained from 51 monitoring stations in Guangzhou (including 11 administrative regions) to ensure that the sample data effectively reflected the evolving distribution characteristics of ozone in Guangzhou; and (iii) comprehensive consideration of the influence of environmental factors, meteorological factors, and economic growth. The purpose of this paper is to discover the major factors influencing the ozone variation in Guangzhou, and to propose a set of precise and feasible ozone pollution control strategies aimed at contributing to the sustainable development of Guangzhou, the Pearl River Delta region, and China.

2. Methods This study comprised four parts. The first part involved ozone data collection and analysis. We

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analyzed the temporal and spatial distribution characteristics of ozone concentration in terms of daily, monthly, and annual variations. We also analyzed the scenario wherein the monthly average ozone concentration is above the standard limit prescribed. The analysis was based on the daily average ozone concentration data of 51 meteorological stations in Guangzhou from 2016 to 2018. The second part

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was to explore the main factors influencing the ozone concentration variation. Beginning with the environmental factors (i.e., NO2 and CO) and meteorological factors (i.e., temperature, relative humidity, and precipitation), the correlations between various factors and ozone concentration was

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analyzed statistically. After the comprehensive investigation, the primary and secondary factors influencing the ozone concentration in Guangzhou were discussed. The third part was a further analysis

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of the relationship between the economic growth and ozone pollution in Guangzhou. The fourth part was to formulate a plan for ozone pollution control. Considering the key factors affecting ozone pollution, we discussed and formulated feasible schemes aimed at ozone pollution control and urban

1.1 Research area

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sustainable development in Guangzhou, as shown in Fig. 1.

Guangzhou city was selected as the research area for this study. Guangzhou is located at 112°57'–

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114°3' (east longitude) and 22°26'–23°56' (north latitude). As the key city of Guangdong—Hong Kong—Macao Greater Bay Area and the Pearl River Delta metropolitan areas, Guangzhou belongs to

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the subtropical coastal zone. It has a maritime subtropical monsoon climate with warm and rainy weather, sufficient light and heat, and long summer. The annual average atmospheric temperature in Guangzhou is 20–22 °C, which is one of the smallest annual average temperature differences in China. At present, Guangzhou comprises 11 municipal districts (as shown in Fig. 2). In this study, these 11 administrative regions were represented by the initials of their names. Table 1 lists the initials and regional characteristics of the administrative regions.

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1.2 Ozone data In this study, ozone-8h data ("ozone") from 51 air quality monitoring sites in Guangzhou were obtained (2016–2018). These include 10 national control assessment points (marked with “★”), 1 national control point (marked with “ ◆ ”), 38 urban air quality assessment points, scenic spots, ecological environment, air quality monitoring points, and 2 roadside air pollution monitoring points of traffic trunk lines (marked with “▲”), as shown in Fig. 3. Among these, the ozone concentration data of the whole Guangzhou city (rather than the 11 administrative regions separately) were collected from 10 national control assessment sites. In this work, we set the ozone standard limit as 160 μg∙m-3 (the national air quality secondary standard). We obtained the monthly and annual ozone data from

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Guangzhou Municipal Environment Bureau. Considering that Guangzhou Municipal Environment Bureau did not disclose the daily average ozone concentrations, we collected the corresponding data through another website to satisfy the research requirements (both sites are listed in Table 3).

In the present study, we analyzed the ozone data from three aspects: daily, monthly, and annual average ozone concentrations. According to Technical Regulations of Environmental Air Quality

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Index (AQI) (Trial Implementation) (HJ633 2012), daily average ozone concentration levels could be divided into five grades. The monthly average ozone concentration levels were divided into 11 concentration grades with the standard limit of 160 μg∙m-3 and an equidistant interval of 20 μg∙m-3.

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The annual average ozone concentration levels were bounded by the standard limit of 160 μg∙m-3 and an equidistant interval of 10 μg∙m-3 (eight grades). Table 2 lists the ranges and division grades for daily,

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monthly, and annual average ozone concentration. Then, based on the grades of ozone pollution, a spatial distribution map of the monthly and annual ozone data in Guangzhou could be generated corresponding to each range.

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1.3 Environmental, meteorological, and economic data The environmental indicators selected in this paper included CO and NO2. Data on these were

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obtained mainly from Guangzhou Eco-environmental Bureau. The meteorological indicators included temperature, relative humidity, and precipitation. The temperature and precipitation data were obtained from Guangzhou Meteorological Bureau. The relative humidity data were collected from another

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website because these data are not disclosed publicly. The economic indicator used was the ere expressed by regional gross domestic product (GDP), which was obtained from Guangzhou Statistical Bureau. In this work, the environmental and meteorological data were monthly data, whereas those of the economic indicators were annual data. All the variable parameters are presented in Table 3.

1.4 Research and analysis methods We used correlation analysis to assess the influence of the above factors on ozone concentration. The correlation coefficient is a quantity signifying the degree of linear correlation between variables. 5

In general, when the correlation coefficient is higher (smaller), the correlation degree between the different factors is higher (lower). The correlation coefficient can be indicated by r. The most commonly used correlation coefficient is the Pearson correlation coefficient (Cyrys et al. 2008). It can be calculated as the covariance of two variables divided by their standard deviation, as shown below. r(X,Y)=

Cov(X,Y)

(1)

√Var[X]Var[Y]

Where Cov (X, Y) is the covariance of the variables X and Y, Var [X] is the variance of X, and Var [Y] is the variance of Y. If 0 <∣r∣< 1, there is a linear correlation between the two variables. When ∣r∣ is closer to 1, the relationship between the two variables is closer. For the present study, the correlation analysis was implemented by the Statistical Product and Service Solutions (SPSS) software.

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Furthermore, the correlation analysis method was used mainly to analyze two groups of variables: (i) correlation analysis between monthly average ozone concentration and environmental parameters (i.e., CO and NO2) in Guangzhou from 2016 to 2018; (ii) correlation analysis between monthly average ozone concentration and meteorological parameters (i.e., temperature, relative humidity, and

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precipitation) in Guangzhou from 2016 to 2018.

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3. Results

In this section, we first present an analysis of the temporal and spatial variation characteristics of

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the daily, monthly, and annual average ozone concentration data, and the excessive rate of ozone concentration. Second, we present a discussion on the variations in the meteorological and environmental indicators and an analysis of their influence on the monthly average ozone concentration (for Guangzhou and for each of the 11 administrative districts). Finally, we present our

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observations with regard to the main factors influencing ozone concentration, based on the correlation results. Then, we present a strategy for ozone pollution control.

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2.1 Temporal and spatial variation characteristics of ozone in Guangzhou 1.1.1 Ozone distribution characteristics

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First, the daily, monthly, and annual ozone distribution characteristics were analyzed (for further

details, refer to Fig. A1, Fig. A2, and Fig. A3 in Appendix A). According to the data on daily variation in the ozone concentration, the daily maximum concentrations of ozone for 2016, 2017, and 2018 appeared on July 25, September 27, and May 23, respectively. The corresponding concentrations were 275 μg∙m-3 , 287 μg∙m-3 , and 215 μg∙m-3 , respectively, which exceeded the standard limit by 71.9%, 79.4%, and 34.45%, respectively. The highest monthly average value of ozone pollution in 2016 occurred in September (PY district), which was 211 μg∙m-3 . In 2017, it appeared in May (NS district), attaining 251 μg∙m-3 6

(56.9% higher than the standard limit). The highest ozone concentration in 2018 was in September (ZC district), attaining 206 μg∙m-3 and exceeding the standard limit by 28.8%. The annual ozone concentration ranges from 118–166 μg∙m-3 , 143–177 μg∙m-3 , and 152–177 μg∙m-3 in 2016, 2017, and 2018, respectively. 1.1.2 Exceeding situation for monthly average values of ozone Fig. 4 shows the monthly average values of ozone for Guangzhou, as well as the instances where the concentration exceeded the standard limit, from 2016 to 2018. In 2016, the ozone concentration exceeded the standard limit mainly from July to September. The highest excess ozone occurred in PY district, with an excess of 31.88% over the standard limit, in September. In 2017, the scenario of excess ozone concentration was focused mainly in April–May and July–September. In May, the ozone

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concentrations in NS and PY districts exceeded the standard by 56.8% and 52.5%, respectively. Furthermore, in 2018, the exceeding rates of ozone pollution in Guangzhou were mainly concentrated in March–June and August–October, with the maximum exceeding rate occurring in September (ZC district; 28.8%).

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2.2 Annual variation characteristics of environmental and meteorological factors and their correlations with ozone concentration

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3.2.1 Annual variation characteristics of environmental factors

Fig. 5 presents the annual variation characteristics of the environmental factors (i.e., CO and NO2)

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in the 11 administrative regions of Guangzhou from 2016 to 2018. On the one hand, China began to implement the secondary standard of the national ambient air quality standard in 2013(GB3095-2012). This mainly included six indicators, namely, CO, NO2, SO2, PM10, PM2.5, and ozone, which are related

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to the ambient air quality. On the other hand, photochemical reaction is a main source of ozone in the bottom of the atmosphere, and its concentration is closely related to CO and NO2. With the increase of solar radiation and temperature, the photochemical reaction is strengthened. At this time, CO and

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NO2 may be consumed to form ozone(Wang et al. 2014). And referring to the practice of other scholars, most scholars use CO, NO2 and other indicators when studying the relationship between environmental factors and ozone (Xie et al. 2015, He et al. 2017). Therefore, CO and NO2 were

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selected as the environmental variables. The standard limit of CO concentration is 4 mg∙m-3 according to Environmental Air Quality Standard. The CO concentrations in 2016, 2017, and 2018 were 1.3 mg∙m-3 , 1.2 mg∙m-3 , and 1.2 mg∙m-3 , respectively. The overall CO concentration level was low and continued to decline. As stipulated in Environmental Air Quality Standard, the standard limit of NO2 concentration was set as 40 μg∙m-3 . The NO2 concentrations in 2016, 2017, and 2018 exceeded the standard limit by 15%, 30%, and 20%, respectively. The overall trend for NO2 was fluctuating and declining. In addition, the annual variation trends of CO and NO2 were contrary to that of ozone.

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3.2.2 Correlations between ozone concentration and environmental factors

Table 4 presents the correlations between the monthly average ozone concentration and the environmental factors, for Guangzhou. The monthly mean ozone concentration was negatively correlated with CO and NO2. The correlation coefficient between the monthly average ozone concentration and CO concentration was -0.355 (through a significance test at the 1% level). The correlation coefficient between the monthly average ozone concentration and NO2 concentration was -0.122 (through a significance test at the 5% level).

Table 5 displays the correlations between the monthly average ozone concentration and

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environmental factors (CO and NO2), for the 11 administrative districts in Guangzhou. It is evident that for each of nine administrative districts (excluding NS district and CH district), the average ozone concentration was negatively correlated with the CO concentration. In particular, for HP district, the correlation coefficient was -0.736. BY and HD districts also exhibited strong negative relationships between the ozone and CO concentration, with correlation coefficients of -0.553 and -0.520,

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respectively. However, the monthly average ozone concentration did not exhibit significant correlation with NO2 concentration.

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3.2.3 Annual variation characteristics of meteorological factors

Fig. 6 shows the annual variation characteristics of the meteorological factors (including

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temperature, relative humidity, and precipitation) in Guangzhou from 2016 to 2018. Evidently, the temperature did not fluctuate abnormally between 2016 and 2018. The average annual temperatures in 2016, 2017, and 2018 were 22.4 °C, 22.8 °C, and 22.7 °C, respectively. Unlike the temperature

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variation, the average annual relative humidity decreased each year, from 76% in 2016 to 55% in 2018. In addition, precipitation also decreased each year, from 2638.3 mm in 2016 to 1962.2 mm in 2018. It is noteworthy that the annual variation trends of temperature, relative humidity, and precipitation were

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converse to those of ozone.

3.2.4 Correlations between ozone concentration and meteorological factors

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Table 6 presents the correlations between the monthly average ozone concentration and

meteorological factors (temperature, relative humidity, and precipitation) in Guangzhou. According to Table 6, temperature was positively correlated with the monthly average ozone concentration, and the correlation coefficient was 0.547. Relative humidity was negatively correlated with the monthly average ozone concentration, and the correlation coefficient was -0.119. Furthermore, precipitation was positively correlated with the monthly average ozone concentration, and the correlation coefficient was 0.223.

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Table 7 presents the correlations between the monthly average ozone concentration and meteorological factors (temperature, relative humidity, and precipitation) of the 11 administrative regions in Guangzhou, from 2016 to 2018. It is evident that for each of 10 administrative districts (except for PY district), the monthly average ozone concentration was positively correlated with temperature (through a significance test at the 1% level). The highest correlation coefficient of 0.762 was for HD district. Following this, the correlation coefficients for BY and LW districts were 0.722 and 0.668, respectively. The relative humidity was negatively correlated with the ozone concentration of HP district, whereas the other 10 administrative regions exhibited no significant correlations with ozone concentration. For LW and PY districts, the correlation coefficients between precipitation and the monthly average ozone concentration were 0.387 and 0.349, respectively.

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Because the correlation coefficient between the monthly average ozone concentration and temperature was the highest, further statistical analysis of the meteorological factors were performed (as shown in Table 8). The analyses revealed that the monthly average ozone concentration increased with an increase in temperature, when the temperature was below 25 °C (without exceeding the standard limit overall). As the temperature increased to 25 °C, the average ozone concentration attained

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162.67 μg∙m-3 . When the temperature increased beyond 30 °C, the monthly average ozone concentration attained 171.8 μg∙m-3 , exceeding the standard limit by 7.3%. Unlike for temperature,

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when the humidity was higher than 60%, the ozone monthly average concentration gradually decreased. In particular, when the humidity was above 80%, the monthly average ozone concentration decreased

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sharply. In addition, when the precipitation exceeded 600 mm, the monthly average ozone concentration exceeded the standard limit.

2.3 Economic analysis

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In this work, we verified that temperature is the main factor affecting ozone concentration and that CO is the secondary factor. Moreover, relative humidity is negatively correlated with ozone concentration, and precipitation is positively correlated with ozone concentration. All the above factors

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exert direct influence at the micro level. At the macro level, a country's total economic volume and even economic structure may largely affect the environmental quality. The Chinese government first

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put forward the concept of "green finance" at the G20 (Group of Twenty) Summit. It is aimed at achieving sustainable social, economic, and environmental development through financial means. In the fields of energy, environment, and economy, many scholars have studied the relationship between economic growth and environmental pollution. However, most of the research only revealed the phenomenon wherein economic growth may aggravate environmental pollution or alleviate environmental pollution owing to the interference of the development stage, model selection, and other factors. They did not discuss or formulate efficient policies to address environmental pollution (Jalil and Feridun 2011, Javid and Sharif 2016, Adams and Klobodu 2018, Ouyang and Li 2018). 9

Considering this limitation, this study discusses the relationships between economic growth and ozone pollution in Guangzhou, as illustrated in Fig. 7. To improve the accuracy of the research conclusions, we altered the research time range to 2012–2018. Fig. 7 shows that from 2012 to 2018, the GDP displayed a sustained growth trend from 1369.791 billion RMB (2012) to 2285.93 billion RMB (2018), with a growth rate of up to 66.9%. In addition, the annual ozone concentration displayed an increasing trend after the fluctuation. Table 9 lists the regression results between economic growth and ozone concentration in Guangzhou from 2012 to 2018. The following regression equation is obtained: Y = 579.131 + 1.24e6

X2 - 0.046X. It exhibited a "U" curve with the inflection point A (as presented in Fig. 8). According

to Table 9 and Fig. 8, in the early stage, the positive effect of technological progress generated by

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economic growth was higher than the negative effect of energy consumption. Ozone pollution was apparently reduced because of the positive effect of technological progress. However, when the economic growth exceeded 1854.838.7 billion RMB at the inflection point A, the negative effects of energy consumption surpassed the positive effects of technological progress. This curve also indicated that the present level of ozone pollution in Guangzhou could be problematic and could necessitate

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immediate implementation of effective control strategies by the government.

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2.4 Ozone pollution control scheme

In the previous sections, the effects of different factors on ozone pollution were discussed in detail.

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A correlation analysis was carried out from the perspectives of meteorological and environmental factors, as well as the relationship between economic growth and ozone concentration. It was observed that temperature is the main factor affecting the variation in ozone concentration. After temperature,

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relative humidity, CO concentration, etc. affect the ozone concentration to a certain extent. To achieve the strategic goals of urban sustainable development, this study proposes the following measures for ozone pollution control:

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3.4.1 Implementing high temperature precaution measures Guangzhou city has a typical climate with a long duration of high temperature and rainy days in

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summer. From table 8, it can be seen that when the temperature is more than 25 ℃ and less than 30 ℃, the ozone concentration is 162.67 μg∙m-3 , and when the temperature is more than 30 ℃, the ozone concentration reaches 171.8 μg∙m-3 .The Guangzhou environmental sanitation department should purchase more sprinkler equipment to sprinkle water on the city’s roads (both in the morning and noon) to reduce the temperature near the ground surface. The spray system can be installed in the large public squares and Bus Rapid Transit (BRT) public transport platforms to achieve cooling and dust removal purposes (Feng et al. 2018, Feng and Cao 2019). In the long term, highway construction units and Forestry and Landscape Administration should opt more for pollution-resistant and purification 10

vegetation on both sides of highways, interchanges, and central isolation zones to achieve the effects of shading, cooling, and air purification (Li et al. 2019a). 3.4.2 Controlling traffic pollution sources Considering that automobile exhausts are sources of CO pollution, which is negatively correlated with ozone, Guangzhou Vehicle Administration Bureau should continue to (i) stringently control the number of new motor vehicles to reduce the emission of automobile exhaust, at the same time, promoting the use of qualified and more efficient tail gas treatment device (Xu et al. 2019); (ii) add BRT lines to facilitate public travel and reduce the frequency of taxi usage; (iii) support and encourage new energy vehicles to enter the travel service market, such as ON TIME. In addition, Guangzhou Transportation Administration should improve road traffic planning and road management. Consider

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the example of YX district, which is an old zone with relatively narrow roads. Here, the government should effectively use open space to add more traffic lanes. Meanwhile, the labor allocation for road control during peak period should be increased to significantly shorten the congestion time of motor vehicles (the traffic congestion index of Guangzhou in 2016, 2017, and 2018 were 5.27, 4.50, and 5.13

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respectively; this implies that the traffic operation was ineffective.) (http://www.gzjt.gov.cn).

3.4.3 Issuance and management of pollutant discharge permits for industrial enterprises

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The emission of pollutants from industrial enterprises (e.g., petrochemical industry, print coatings, and furniture manufacturing) will also increase the urban NOx concentration (Law of the People's

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Republic of China on the Prevention and Control of Atmospheric Pollution). In the districts experiencing severe ozone pollution (e.g., HD district, PY district, and NS district), industrial enterprises with substantial energy consumption, heavy pollution level, and low efficiency should be closed and transferred in accordance with environmental protection regulations and administrative and

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economic control measures. For the industrial enterprises that can be permitted to continue operation, it is necessary to accelerate the issuance of emission permits, stipulate annual maximum discharge of pollution, and charge fees according to the grades of pollutant concentration. Based on the practice in

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the areas surrounding Guangzhou, a bonus under "promoting reduction by awards" for sewage treatment should be instituted, and 30000 RMB should be awarded for each ton of pollutant emission

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reduction. It is also necessary to strengthen the performance of the ventilation equipment in the factory to prevent the accumulation of pollutants. (Zhang et al. 2016a, Zhang, Shao and Long 2016b, Zhang et al. 2017, Ren and Liu 2019, Zhang et al. 2019). 3.4.4 Improving residents' awareness of air quality problem In addition to administrative measures, ozone pollution control requires the active cooperation of the city’s residents. The government can disseminate ozone pollution-related knowledge to the citizens in the public areas (e.g., install weather display screen exhibiting ozone related data) to improve the 11

public's awareness of air quality and encourage citizens to proactively adopt measures for environmental pollution control, such as giving priority to the subway and buses while traveling. Furthermore, citizens purchasing cars can actively opt for new energy vehicles. Taxi and freight drivers can perform timely maintenance of their vehicles to ensure that vehicle emissions satisfy the standards.

4. Discussion The previous sections described the correlation analysis between ozone concentration and environmental factors (CO and NO2) and meteorological factors (temperature, relative humidity, and precipitation) carried out for the whole Guangzhou city and for each of the 11 administrative regions of Guangzhou. The following results were discussed in detail:

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(1) CO was negatively correlated with ozone, particularly in HP district. On the one hand, after the merger of the previous HP district and Luogang district into the new HP district in 2015, the economic development potential for HP district has been increased. In recent years, the real estate industry of Guangzhou has exhibited a tendency to shift to HP district. This has further promoted the

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increase in the base number of automobiles and that in exhaust emissions. On the other hand, HP district is the strongest industrial zone in Guangzhou. Iron and steel, petrochemical, furniture manufacturing, paint printing, and other factories are located here. These can produce significant

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amounts of CO during the production process and accelerate ozone pollution. (2) Temperature is the key factor influencing ozone pollution. Consistent with the research

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conclusions of many scholars, temperature could be recognized as the main influencing parameter of ozone concentration. This is notwithstanding that the influence of other meteorological parameters may have varied across studies (David and Nair 2011, Singla et al. 2012, Kuo, Chiu and Yu 2015, Chen

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et al. 2019). Considering that ozone is produced by the photochemical reaction of other pollutants (CO, NO2, and VOCs) under solar radiation, a higher temperature could potentially result in stronger solar radiation and higher ozone concentration. For HD district, the influence of temperature was the most

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prominent. This may be because HD district is located "inland" of the Pearl River Delta, where the diffusion conditions for ozone are relatively worse than those in the other administrative regions, and the forest cover is normal (36.55%). Furthermore, the corresponding adsorption capability of ozone

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pollutant appears to be weak, and it is convenient to form ozone for HD district in the Pearl River Delta region.

(3) Ozone concentration was positively correlated with precipitation, which was contrary to the

conclusions of certain studies (Shan et al. 2009, Kavassalis and Murphy 2017). However, there is a scholar reached a similar conclusion from their analysis of ozone pollution in Guangzhou, and indicated that the downdraft of convective weather could carry air masses to the ground, which may cause an increase in the monitored ozone concentration close to the ground (Jia, Xu and Lin 2015, Huang et al. 2018). As mentioned above, Guangzhou belongs to the subtropical monsoon climate, and 12

the period with abundant precipitation is accompanied by high temperature weather. Although rainy days are frequent in the summer of Guangzhou, most of these are thunderstorms. Therefore, the precipitation cannot dilute the ozone concentration to satisfactory levels owing to the interference of other factors such as temperature, relative humidity, and CO and NO2 concentrations. These are used as control variables to test the relationship between ozone and precipitation, as illustrated in Table 10. The regression results in Table 10 reveals that only temperature and relative humidity displayed significant linear relationships with ozone concentration. Moreover, the coefficient for precipitation was negative (-0.014). It is evident that the influence of precipitation on ozone concentration was mainly affected by a certain factor. Then, temperature and relative humidity were tested as control variables, as displayed in Table 11. It was determined that under controlled temperature, precipitation

ro of

was negatively correlated with the monthly average ozone concentration (the correlation coefficient was -0.037 in a significance test at the 1% level). The results also indicated that the precipitation induced by high temperature had no apparent effect on ozone dilution. This also verified that temperature was the main meteorological factor affecting the ozone concentration.

-p

The spatial and temporal distribution characteristics of ozone in Guangzhou and the effects of different factors (meteorological and environmental factors) on ozone have been studied in detail.

re

Ozone pollution may affect human health. Next, we will examine the relationship between ozone concentration and mortality (http://tjj.gz.gov.cn/) in Guangzhou. First, the time range is 2012–2018; second, the meteorological and environmental variables that have a certain influence on the ozone

lP

formation process are used as control variables. As presented in Table 12, there is a positive correlation between ozone concentration and mortality in Guangzhou (the higher the ozone concentration, the higher the mortality rate). The mortality data used in this study is the citywide mortality rate in

na

Guangzhou. According to other scholars' research on ozone concentration and health loss (Day, Xiang and Mo 2017, Maji et al. 2019), the mortality index should use non-accidental death, cardiovascular death, and respiratory diseases. Therefore, in the future research, we will collect more mortality related

ur

data for more detailed analysis.

In this study, Guangzhou was considered as the sample area to discuss the daily, monthly, and

Jo

annual variation characteristics of ozone pollution and to analyze the major influencing factors, to propose corresponding ozone pollution control strategies. In the future work, we will incorporate indoor ozone pollution into the research and expand the sample areas to the Pearl River Delta and Guangdong–Hong Kong–Macao Greater Bay Area (GBA) to explore the regional differences in ozone pollution, to provide a more effective reference and guideline for urban ozone pollution control.

13

5. Conclusions In order to promote the process of urban sustainable development in Guangzhou, the influencing factors of ozone pollution were explored from different perspectives by analyzing the variation characteristics of the daily, monthly, and annual ozone concentration as well as the scenarios of excess ozone. The following conclusions are drawn: First, during 2016–2018, there were 48 days with excessive ozone pollution in 2017, with high ozone concentration occurring mainly in March, May, and August–September. The average ozone concentration increased each year, and the ozone concentration in the suburban areas was higher than that in the central urban areas, from 2016 to 2017. However, in 2018, the pollution problem in the

ro of

central urban areas became increasingly prominent. With regard to the exceeding rate of ozone, an "M"-shaped relationship curve was obtained, and the monthly exceeding rate increased each year. Second, atmosphere temperature was the main factor affecting ozone pollution. In HD district, particularly when the temperature is above 25 °C, the ozone concentration began to exceed the standard limit (160 μg∙m-3). CO is the secondary influencing factor, particularly for HP district. Moreover, the

-p

ozone concentration decreased significantly when the humidity was above 80%. Ozone was negatively correlated with CO and NO2.

re

Third, notwithstanding the typical subtropical monsoon climate characteristics in Guangzhou, the effect of precipitation on the reduction in ozone concentration was not apparent. Finally, from 2012 to 2018, the relationship between the economic growth and ozone

lP

concentration in Guangzhou exhibited a U-shaped variation curve. This indicated that the present ozone pollution problem in Guangzhou could potentially deteriorate without efficient ozone control

na

strategies.

Acknowledgements

ur

The authors would like to express their gratitude to Yangchen Scholarship (201831837), Science Found of Guangdong (GD18JRZ01), and National Social Science Fund of China (18BJL065).

Jo

Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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18

2.

Geographic

locations

ro of and

administrative

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Fig.

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-p

Fig. 1. Framework of present research.

19

zoning

of

Guangzhou.

ro of -p re

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Fig. 3. Distributions of the 51 air quality-monitoring stations in Guangzhou.

20

ro of -p re lP

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Fig. 4. Exceeding rates of monthly ozone concentration in 11 administrative regions of Guangzhou from 2016 to 2018.

Fig. 5. Annual variation characteristics of environmental parameters (CO and NO2) and ozone 21

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concentrations in Guangzhou from 2016 to 2018.

Fig. 6. Annual variation characteristics of meteorological parameters (temperature, relative humidity, and

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precipitation) in Guangzhou from 2016 to 2018.

Fig. 7. Annual variation characteristics of Guangzhou's economic growth and ozone concentration from 2012

to 2018.

22

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Fig. 8. Regression curve between economic growth and ozone concentration in Guangzhou from 2012 to

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2018.

23

Table 1. Abbreviations and regional characteristics of the 11 administrative regions in Guangzhou

Abbreviation of district name

Baiyun District

BY

Haizhu District

HZ

Panyu District

PY

Huadu District

HD

Huangpu District

HP

LW

ur

Liwan District

Jo

Nansha District

ro of

YX

-p

Yuexiu District

re

TH

The average annual temperature is approximately 22.9 °C, the average annual rainfall is approximately 2000–2540 mm, and the average annual relative humidity is 65%. The forest cover is low (27%). The average annual temperature is 23.3 °C, the average annual rainfall is 2000–2400 mm, and the average annual relative humidity is 65%. The average annual temperature is 23.4 °C, the average annual rainfall is 1800–2400 mm, and the average annual relative humidity is 65%. The forest cover is relatively high (44.49%). The average annual temperature is 23.5 °C, the average annual rainfall is 1800–2200 mm, and the average annual relative humidity is 65%. The average annual temperature is 23.5 °C, the average annual rainfall is 1700–2700 mm, and the average annual relative humidity is 64%. The average annual temperature is 23 °C, the average annual rainfall is 1900–2600 mm, and the average annual relative humidity is 69%. The forest cover is normal (36.55%). The average annual temperature is 22.1 °C, the annu al precipitation is approximately 1800–3000 mm, and the average annual relative humidity is 65%. It is Guangzhou's most industrialized district. The average annual temperature is 24.2 °C, the annual precipitation is approximately 1900–2700 mm, and the average annual relative humidity is 65%. The average annual temperature is 23.4 °C, the annual precipitation is approximately 1500–2900 mm, and the average annual relative humidity is 67%. The average annual temperature is 22.4 °C, the annual precipitation is approximately 1700–2500 mm, and the average annual relative humidity is 68%. The average annual temperature is about 21.7 °C, the annual precipitation is approximately 1600–2700 mm, and the average annual relative humidity is 73%. The forest cover is the highest (67.2%).

na

Tianhe District

Regional characteristics

lP

Full zone name

NS

Zengcheng District

ZC

Conghua District

CH

24

Table 2. Division and grades of daily, monthly, and annual average ozone concentrations. Distribution of daily concentration interval of ozone

Daily average pollution level of ozone

Corresponding grade

Excellent

1

101–160 μg∙m-3

Good

2

161–215 μg∙m-3

Mild pollution

3

216–265 μg∙m-3

Moderately pollution

4

266–800 μg∙m-3

Severe pollution

5

Distribution of monthly concentration interval of ozone Interval of monthly average

of

81–100 μg∙m-3

101–120 μg∙m-3

121–140 μg∙m-3

Interval of monthly average

of

161–180 μg∙m-3

181–200 μg∙m-3

201–220 μg∙m-3 > 241 μg∙m-3

ozone

141–160 μg∙m-3

-p

ozone

excessive

61–80 μg∙m-3

221–240 μg∙m-3

re

eligible

0–60 μg∙m-3

ro of

1–100 μg∙m-3

Distribution of annual concentration interval of ozone

eligible

average

of

131–140 μg∙m-3

121–130 μg∙m-3 141–150 μg∙m-3

151–160 μg∙m-3

ozone

average

of

> 181 μg∙m-3

Jo

ur

ozone

161–170 μg∙m-3

na

Interval of monthly annual

0–120 μg∙m-3

lP

Interval of annual

25

171–180 μg∙m-3

Table 3. Description of variable influencing indicators.

Variable

Indicator

index

description

Data source

Arithmetic average of Ozone daily ozone average

daily

maximum 8h average https://www.aqistudy.cn/historydata/

concentration concentration at each monitoring point Ozone monthly

Moving average of

average

ozone-8h

http://sthjj.gz.gov.cn/

ro of

Ozone

concentration

concentration Environmental

CO

factors

NO2 Temperature

Meteorological

Relative

factors

humidity

of

sliding http://sthjj.gz.gov.cn/

average

http://sthjj.gz.gov.cn/ http://sthjj.gz.gov.cn/

Regional

http://gd.cma.gov.cn/gzsqxj/ https://tianqi.911cha.com/ http://gd.cma.gov.cn/gzsqxj/ http://tjj.gz.gov.cn/

GDP

Jo

ur

Economic factor

ozone-8h

na

Precipitation

percentile

-p

average

90th

re

annual

lP

Ozone

26

Table 4. Relevant analysis results of correlation between ozone concentration and environmental factors (CO and NO2) in Guangzhou.

Ozone

CO -0.355** 0.000 396

Pearson correlation coefficient Significance (double tail) N

NO2 -0.122* 0.015 396

*. At the 0.05 level (double tails), the correlation was significant.

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na

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-p

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**. At the 0.01 level (double tails), the correlation was significant.

27

Table 5. Relevant analysis results of correlation between ozone concentration and environmental factors (CO

and NO2) in the 11 administrative regions of Guangzhou.

CO -0.344* -0.428** -0.553** -0.422* -0.736** -0.485** -0.520** -0.397* 0.071 -0.371* -0.151

TH YX BY HZ HP LW HD PY NS ZC CH

NO2 -0.166 -0.197 -0.116 -0.221 -0.053 -0.408* -0.279 -0.310 -0.070 0.037 0.253

*. At the 0.05 level (double tail), the correlation was significant.

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na

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re

-p

**. At the 0.01 level (double tail), the correlation was significant.

ro of

Ozone

28

Table 6. Relevant analysis results between ozone concentration and meteorological factors (temperature, relative humidity, and precipitation).

Ozone

Pearson correlation coefficient Significance (double tail) N

Temperature 0.574** 0.000 396

Relative humidity Precipitation -0.119* 0.223** 0.017 0.000 396 396

*. At the 0.05 level (double tail), the correlation was significant.

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na

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**. At the 0.01 level (double tail), the correlation was significant.

29

Table 7. Correlation analysis between ozone concentration and meteorological factors (temperature, relative humidity, and precipitation) in the 11 administrative areas of Guangzhou

Temperature 0. 591** 0.563** 0.722** 0.526** 0.507** 0.668** 0.762** 0.284 0.460** 0.646** 0.373*

TH YX BY HZ HP LW HD PY NS ZC CH

Relative humidity -0.159 -0.182 -0.137 -0.160 -0.343* 0.066 -0.028 -0.007 -0.050 -0.066 -0.204

*. At the 0.05 level (double tail), the correlation was significant.

Jo

ur

na

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re

-p

**. At the 0.01 level (double tail), the correlation was significant.

30

Precipitation 0.218 0.250 0.287 0.228 0.132 0.387* 0.291 0.349* 0.066 0.233 0.030

ro of

Ozone

Table 8. Analysis of impacts of different meteorological elements on ozone concentration.

Temperature （°C）

Ozone (μg∙m-3 )

Relative humidity (%)

Ozone concentration (μg∙m-3 )

T ≤ 15

98.24

relative humidity ≤ 40%

108.43

15–20

125.04

40–50%

145.92

precipitation ≤ 200 200–400

20–25

149.46

50–60%

154.28

400–600

160.87

25–30

162.67

60–70%

141.49

precipitation > 600

168.40

T ≥ 30

171.8

70–80%

144.36

relative humidity > 80%

125.32

-p re lP na ur Jo 31

Ozone concentration (μg∙m-3 ) 139.45 150.69

ro of

Precipitation (mm)

Table 9. Regression analysis between economic growth and ozone concentration in Guangzhou from 2012 to 2018.

Coef. -0.046 1.24e-6

St. Err. 0.012 0.000

t-value -4.00 3.96

p-value 0.016 0.017

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Ozone GDP GDP2

32

Table 10. Regression coefficients of CO, NO2, temperature, relative humidity, and precipitation with ozone.

Unstandardized Coefficient Model B St. Err Precipitation -0.014 0.012 CO -0.860 8.243 NO2 1 0.054 0.127 Temperature 4.307 0.370 Relative humidity -0.731 0.146

t

Sig.

-1.128 -0.104 0.420 11.632 -5.009

0.260 0.917 0.674 0.000 0.000

Dependent variable: ozone

Jo

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na

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-p

ro of

a.

Standardized coefficient Beta -0.058 -0.006 0.021 0.657 -0.227

33

Table 11. Regression coefficients of temperature and precipitation with ozone.

Unstandardized coefficient B St. Err

Model

1

Precipitatio -0.037 n

T

Sig.

0.012

-0.151

-3.048

0.002

0.325

0.660

13.316

0.000

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Temperature 4.327 a. Dependent variable: ozone

Standardized coefficient Beta

34

Table 12. Regression coefficients of death rate and ozone

Death rate Ozone Temperature Relative humidity Precipitation

CO

Coef. 0.032 0.981 0.075 -0.002 -1.920

St. Err. 0.007 0.335 0.005 0.000 0.385

t-value 4.40 2.92 14.76 -12.13 -4.98

p-value 0.005 0.026 0.000 0.000 0.002

[95% Conf. 0.014 0.160 0.063 -0.002 -2.863

Interval] 0.049 1.801 0.088 -0.001 -0.977

Jo

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-p

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*** p < 0.01, ** p < 0.05, * p < 0.1

35