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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Public Health</journal-id>
<journal-title>Frontiers in Public Health</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Public Health</abbrev-journal-title>
<issn pub-type="epub">2296-2565</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fpubh.2022.864537</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Public Health</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>A Time-Series Study for Effects of Ozone on Respiratory Mortality and Cardiovascular Mortality in Nanchang, Jiangxi Province, China</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Wu</surname> <given-names>Hao</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1646125/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Lu</surname> <given-names>Keke</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Fu</surname> <given-names>Junjie</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x0002A;</sup></xref>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Jiangxi Provincial Center for Disease Control and Prevention</institution>, <addr-line>Jiangxi</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>School of Public Health, Nanchang University</institution>, <addr-line>Jiangxi</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Shengzhi Sun, Boston University, United States</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Jinhui Li, Boston University, United States; Jing Han, Jinan Municipal Center for Disease Control and Prevention, China</p></fn>
<corresp id="c001">&#x0002A;Correspondence: Junjie Fu <email>1326621040&#x00040;qq.com</email></corresp>
<fn fn-type="other" id="fn001"><p>This article was submitted to Environmental health and Exposome, a section of the journal Frontiers in Public Health</p></fn></author-notes>
<pub-date pub-type="epub">
<day>26</day>
<month>04</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>10</volume>
<elocation-id>864537</elocation-id>
<history>
<date date-type="received">
<day>28</day>
<month>01</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>31</day>
<month>03</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2022 Wu, Lu and Fu.</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>Wu, Lu and Fu</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<sec>
<title>Objective</title>
<p>Most evidence comes from studies show that ambient ozone(O<sub>3</sub>) pollution has become a big issue in China. Few studies have investigated the impact of ozone spatiotemporal patterns on respiratory mortality and cardiovascular mortality in Nanchang city. Thus, this study aimed to explore the health effect of ozone exposure on respiratory mortality and cardiovascular mortality in Nanchang, Jiangxi Province.</p>
</sec>
<sec>
<title>Methods</title>
<p>Using the daily mortality data, atmospheric routine monitoring data and meteorological data in Nanchang from 2014 to 2020, we performed a generalized additive model (GAM) based on the poisson distribution in which time-series analysis to calculate the risk correlation between respiratory mortality and cardiovascular mortality and ozone exposure level (8h average ozone concentration, O<sub>3</sub>-8h). Besides, analyses were also stratified by season, age and sex.</p>
</sec>
<sec>
<title>Results</title>
<p>In the single-pollutant model, for every 10 &#x003BC;g/m<sup>3</sup> increase in ozone, respiratory mortality increased 1.04% with 95% confidence interval (CI) between 0.04 and 1.68%, and cardiovascular mortality increased 1.26% (95%CI: 0.68 &#x0007E; 1.83%). In the multi-day moving average lag model, the mortality of respiratory diseases and cardiovascular diseases reached a relative risk peak on the cumulative lag5 (1.77%,95%CI: 0.99 &#x0007E; 2.57%) and the cumulative lag3 (1.68%,95%CI: 0.93 &#x0007E; 2.45%), respectively. The differences were statistically significant (<italic>P</italic> &#x0003C; 0.05). Results of the stratified analyses showed the effect value of respiratory mortality in people aged &#x02265;65 years was higher than aged &#x0003C;65 years, whereas the greatest effect of cardiovascular mortality in people aged &#x0003C;65 years than aged &#x02265;65 years. Ozone had a more profound impact on females than males in respiratory diseases and cardiovascular diseases. In winter and spring, ozone had a obvious impact on respiratory mortality, and effects of ozone pollution on cardiovascular mortality were stronger in summer and winter. There was a statistically significant difference of respiratory mortality in winter and spring and of cardiovascular mortality in summer and winter (<italic>P</italic> &#x0003C; 0.05).</p>
</sec>
<sec>
<title>Conclusions</title>
<p>In the long run, the more extreme the pollution of ozone exposure, the higher the health risk of residents&#x00027; respiratory mortality and cardiovascular mortality. Therefore, the government should play an important role in the prevention and control ways of decreasing and eliminating the ozone pollution to protect the resident&#x00027;s health. The findings provide valuable data for further scientific research and improving environmental policies in Nanchang city.</p>
</sec></abstract>
<kwd-group>
<kwd>ozone pollution</kwd>
<kwd>cardiovascular mortality</kwd>
<kwd>respiratory mortality</kwd>
<kwd>time-series analysis</kwd>
<kwd>stratified analysis</kwd>
</kwd-group>
<counts>
<fig-count count="8"/>
<table-count count="4"/>
<equation-count count="2"/>
<ref-count count="63"/>
<page-count count="16"/>
<word-count count="8917"/>
</counts>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<title>Introduction</title>
<p>With the rapid development of modern industrialization, the air quality around us is increasingly affected (<xref ref-type="bibr" rid="B1">1</xref>). The World Health Organization (WHO) estimated that air pollution causes the deaths of 4.2 million people each year, which accounts for 6% of total deaths worldwide (<xref ref-type="bibr" rid="B2">2</xref>). Based on the analysis of atmospheric environment in 1,600 cities and regions around the world, it was found that only 12% of the study areas met the safety standards stipulated by WHO (<xref ref-type="bibr" rid="B3">3</xref>), indicating that the status and development of global air pollution cannot be ignored. Since the Chinese government implemented China&#x00027;s Action Plan of Prevention and Control of Air Pollution in 2013, atmospheric particulate matter has dropped significantly (<xref ref-type="bibr" rid="B4">4</xref>). Nevertheless, along with the rapid development of urban basic infrastructure and growth in motor vehicle number, ozone concentration has dramatically increased recently (<xref ref-type="bibr" rid="B5">5</xref>). Ozone is a secondary pollutant, its formation is due to the interaction between the hydrocarbons and oxides of nitrogen released from car exhaust and sunlight (UV), leading to photochemical smog. It can affect air quality at local, regional and even global scales, beside, it has an important impact on animal health, plant growth, climate change and global ecological balance. At the same time, the adverse effects on human life and ecological environment are gradually increasing (<xref ref-type="bibr" rid="B6">6</xref>). The more serious the air pollution is, the more the residents&#x00027; medical expenses will increase (<xref ref-type="bibr" rid="B7">7</xref>).</p>
<p>According to the 2020 China Ecological Environment Bulletin, ozone has been become an important pollutant besides particulate matter (PM<sub>2.5</sub>, PM<sub>10</sub>) at the national level (<xref ref-type="bibr" rid="B8">8</xref>). Compared with Japan, South Korea, Europe and the United States, the magnitude and frequency of high-ozone events in China are much more significant (<xref ref-type="bibr" rid="B9">9</xref>). China experiences high ozone concentrations with highest annual 8-h maximum concentration in eastern China of 78 &#x003BC;g/m<sup>3</sup> and was followed by southern (73&#x003BC;g/m<sup>3</sup>), north-western (69&#x003BC;g/m<sup>3</sup>), northern (68&#x003BC;g/m<sup>3</sup>), central (67&#x003BC;g/m<sup>3</sup>), north eastern (65&#x003BC;g/m<sup>3</sup>) and south-western China (59&#x003BC;g/m<sup>3</sup>) (<xref ref-type="bibr" rid="B10">10</xref>). The majority of the Chinese population lives in the eastern part of China, especially in the three most developed regions, Jing-Jin-Ji (Beijing-Tianjin-Hebei), the Yangtze River Delta (including Shanghai-Jiangsu -Zhejiang-Anhui), and the Pearl River Delta (including Guangzhou, Shenzhen, and Hong Kong). These regions consistently have the highest emissions of anthropogenic precursors, which have led to severe region-wide air pollution (<xref ref-type="bibr" rid="B11">11</xref>). Studies showed that according to monitoring results from 74 Chinese cities, the mean daily 8-h maximum concentrations of ozone increased from approximately 69.5 ppbv in 2013 to 75.0 ppbv in 2015, while the percentage of non-compliant cities increased from 23 to 38% (<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B10">10</xref>). Jiangxi Province lies on the southern bank of the Yangtze River&#x00027;s lower and middle sections, it has a sub-tropical climate, warm and humid. Kan (<xref ref-type="bibr" rid="B12">12</xref>) using the data of environmental monitoring stations data in Jiangxi Province from January 2017 to June 2020, showed that from 2017 to 2019, the time and mass concentration of ozone exceeding the limit showed an increasing trend year by year. April to June and August to October were the periods of high incidence of ozone pollution. Nanchang City is the Central City of Jiangxi Province, and it is one of the first cities to implement new ambient air quality standards. The daily 8-hour maximum concentration of ozone in Nanchang from 2013 to 2015 was analyzed, showing that the seasonal variation of ozone was high in spring and summer and low in autumn and winter (<xref ref-type="bibr" rid="B13">13</xref>).</p>
<p>Many epidemiological studies had been conducted to assess the characteristics of ozone pollution exposure in China, related research showed that the concentration of ozone in Yangtze River Delta, Beijing-Tianjin-Hebei, Pearl River Delta and Chengdu-Chongqing cities was increasing year by year (<xref ref-type="bibr" rid="B14">14</xref>&#x02013;<xref ref-type="bibr" rid="B17">17</xref>). There is a growing interest in studying the potential effect of ozone levels, and its subsequent effects on public health. Epidemiological and toxicological studies had shown that ozone exposure is not only associated with adverse health outcomes (<xref ref-type="bibr" rid="B18">18</xref>&#x02013;<xref ref-type="bibr" rid="B22">22</xref>), but also leads to significant economic burdens (<xref ref-type="bibr" rid="B23">23</xref>). A series of epidemiological studies reported that short-term ozone exposure is strongly associated with the death risk of cardiovascular diseases (<xref ref-type="bibr" rid="B24">24</xref>, <xref ref-type="bibr" rid="B25">25</xref>) and respiratory diseases (<xref ref-type="bibr" rid="B26">26</xref>). China recorded 93,351 (95%CI: 11,001&#x02013;169,898) ozone related premature mortality in 2015 with 42,673 (95%CI: 11,001-69,586) respiratory mortality and 50,678 (95%CI: 0&#x02013;100,312) cardiovascular mortality. Northern and eastern China recorded high ozone related mortality with 18,230 (95%CI: 4,700&#x02013;29,727), 12,261 (95%CI: 3,161&#x02013;19,993) respiratory, 21,662 (95%CI: 0&#x02013;42 877) and 14,528 (95%CI:0&#x02013;28 757) cardiovascular deaths respectively (<xref ref-type="bibr" rid="B11">11</xref>). Kamal and Anil (<xref ref-type="bibr" rid="B27">27</xref>) showed that the proportion of all-cause, cardiovascular and respiratory premature deaths attributed to short-term environmental ozone exposure in China in 2019 increased by 19.6, 19.8, and 21.2% in comparison with those in 2015. Ozone is one of the most powerful oxidizing molecule to which living beings can be exposed. Accordingly, ozone inhalation may cause oxidative damages and inflammation, which could expand from the respiratory system to the periphery and to the brain, for which the nose and olfactory pathway is another portal of entry (<xref ref-type="bibr" rid="B28">28</xref>). Animal and human exposure studies had been proved that ozone has a stimulating effect on human mucosa through the eyes, nose, and mouth into the lungs, which will also cause damage to the lung tissue. Therefore, excessive ozone concentration will increase the probability of human suffering from respiratory diseases, and also aggravate the condition of patients with respiratory diseases such as asthma and chronic lung diseases (<xref ref-type="bibr" rid="B29">29</xref>). Some scholars pointed out that Short-term ozone exposure at levels was associated with platelet activation and blood pressure increases, suggesting a possible mechanism by which ozone may affect cardiovascular health (<xref ref-type="bibr" rid="B30">30</xref>). The proposed mechanisms include systemic inflammation and oxidative stress, autonomic nervous system imbalance, and abnormal epigenetic changes (<xref ref-type="bibr" rid="B31">31</xref>).</p>
<p>In areas with good air quality, ozone has gradually become the main pollution factor affecting the air quality compliance rate, which is closely related to climate change, and its composition is complex and difficult to control. In the preparation of the &#x0201C;14th Five-Year Plan&#x0201D; ecological and environmental protection plan, it is necessary to pay attention to the coordinated treatment of PM<sub>2.5</sub> and ozone in view of the prominent problems of ozone pollution (<xref ref-type="bibr" rid="B32">32</xref>). Among China&#x00027;s 2030 climate target, as the largest developing country in the world, China has overcome its own economic and social difficulties, implemented a series of strategies, measures and actions to cope with climate change, participated in global climate governance, and achieved positive results in addressing climate change. China has always attached great importance to non-carbon dioxide greenhouse gas emissions and China has accepted the Kigali Amendment to the Montreal Protocol on Substances that Deplete the Ozone Layer, which has entered a new stage in protecting the ozone layer and responding to climate change (<xref ref-type="bibr" rid="B33">33</xref>). Nitrogen oxides (NOx) and volatile organic compounds (VOCs) are important precursors to ozone formation. In order to control ozone pollution during the &#x0201C;14th Five-Year Plan&#x0201D; period, Jiangxi will promote the energy structure, adjustment and optimization of industrial structure and traffic structure to reduce NOx and VOCs emissions from the source (<xref ref-type="bibr" rid="B34">34</xref>). Nanchang is an important central city in the middle and lower reaches of the Yangtze River in China, due to the city is a lack of research on the effects of ozone pollution, and the association of human health with ambient ozone have not been fully understood. Therefore, the main objective of this study is to evaluate the association between ozone exposure and respiratory mortality and cardiovascular mortality, at the same time, we aimed to evaluate individual characteristics (sex and age) and season as potential effect modifiers of the ozone-mortality association, which providing a basis for further research and formulation of local environmental prevention and control policies in Nanchang City.</p>
</sec>
<sec sec-type="materials and methods" id="s2">
<title>Materials and Methods</title>
<sec>
<title>Study Area</title>
<p>Nanchang is located in the north-central part of Jiangxi Province, at the end of the Ganjiang River and the Fuhe River. It is the capital of Jiangxi Province and is considered to be a provincial political, economic, cultural, scientific, technological and information center. Importantly, it is the core city of the Poyang Lake Ecological Economic Zone, which promotes the economic development and urbanization of the province (<xref ref-type="bibr" rid="B35">35</xref>). The study area is a typical subtropical monsoon climate with mild climate, sufficient sunshine, abundant rainfall and four distinct seasons with a long frost-free period, spring and autumn are short, while winter and summer are long. In recent years, with the optimization of economic structure and industrial overall layout of Nanchang, people&#x00027;s living standards have improved significantly, but also brought a series of air pollution problems.</p>
</sec>
<sec>
<title>Data Collection</title>
<p>The daily mortality data of the respiratory diseases and cardiovascular diseases in Nanchang from 2014 to 2020 were selected, included sex, age, death time and underlying cause of death code (ICD-10), which were cardiovascular diseases death (I05&#x02013;I52) and respiratory diseases death (J00&#x02013;J99), respectively. The data were derived from the death cause registration system of Chinese Center for Disease Control and Prevention. Atmospheric pollutant data and meteorological data of Nanchang City are from the Nanchang Environmental Monitoring Center and Meteorological Bureau of Nanchang Municipality during the same period, respectively. Atmospheric pollutants included O<sub>3</sub>-8h(&#x003BC;g/m<sup>3</sup>), SO<sub>2</sub>(&#x003BC;g/m<sup>3</sup>), NO<sub>2</sub>(&#x003BC;g/m<sup>3</sup>), PM<sub>2.5</sub>(&#x003BC;g/m<sup>3</sup>), PM<sub>10</sub>(&#x003BC;g/m<sup>3</sup>), CO(mg/m<sup>3</sup>), which is the arithmetic mean of the data from eight nationally controlled monitoring sites in Nanchang city; meteorological data included daily average temperature (&#x000B0;C), daily average relative humidity (%), daily average air pressure (kPa) and daily average wind speed (m/s). The World Health Organization proposed that daily 8-h maximum ozone concentration as an appropriate indicator to study the relationship between environmental ozone exposure and health in 2020 (<xref ref-type="bibr" rid="B36">36</xref>). It is usually divided into four seasons according to the position of the earth around the solar orbit. In China, March to May is generally counted as spring (MMA), June to August as summer (JJA), September to November as autumn (SON), and December to February as winter (DJF).</p>
</sec>
<sec>
<title>Time-Series Analysis</title>
<p>Generalized additive model based on Quasi-Poisson regression was used to estimate the correlation between O<sub>3</sub>-8h exposure and respiratory mortality and cardiovascular mortality in Nanchang city. The model was specified as follows:</p>
<disp-formula id="E1"><label>(1)</label><mml:math id="M1"><mml:mtable class="eqnarray" columnalign="left"><mml:mtr><mml:mtd><mml:mtext>Log</mml:mtext><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtext>E</mml:mtext><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mtext>y</mml:mtext></mml:mrow><mml:mrow><mml:mtext>i</mml:mtext></mml:mrow></mml:msub></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mi>&#x003B1;</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mtext>&#x003B2;</mml:mtext></mml:mrow><mml:mrow><mml:mtext>i</mml:mtext></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mtext>X</mml:mtext></mml:mrow><mml:mrow><mml:mtext>i</mml:mtext></mml:mrow></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:mtext>ns</mml:mtext><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mtext>time</mml:mtext><mml:mo>,</mml:mo><mml:mtext>df</mml:mtext></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mo>&#x0002B;</mml:mo><mml:mtext>ns</mml:mtext><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mtext>Z</mml:mtext></mml:mrow><mml:mrow><mml:mtext>i</mml:mtext></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mtext>df</mml:mtext></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mtext>&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;</mml:mtext><mml:mo>&#x0002B;</mml:mo><mml:mtext class="textrm" mathvariant="normal">DOW</mml:mtext><mml:mo>&#x0002B;</mml:mo><mml:mtext class="textrm" mathvariant="normal">PH</mml:mtext></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
<p>In this equation, i refers to the day of the observation; E (y<sub>i</sub>) is the expected number of non-accidental mortality of residents on day i; &#x003B1; is the intercept; x<sub>i</sub> refers to the concentration level of O<sub>3</sub>-8h (&#x003BC;g/m<sup>3</sup>) on day i; &#x003B2;i represents the regression coefficient of the corresponding air pollutants; ns is the natural smoothing spline function, and df represents the degree of freedom. Previous studies have usually set the degrees of freedom of time to 5 to 7 and meteorological factors to 6 (<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B37">37</xref>); DOW as the variables of weeks; PH is the holiday effect; Zi as the meteorological factor of day i, including daily average temperature and daily average relative humidity; time is the date variable, the appropriate degree of freedom is selected by using the minimum sum of the absolute values of the partial autocorrelation function (PACF) of the basic model residual to effectively control the long-term and seasonal fluctuation trend of the pollution-death series data. The excess risk of non-accidental death (ER) caused by 10 &#x003BC;g/m<sup>3</sup> increase of O<sub>3</sub>-8h concentration was calculated by formula (2).</p>
<disp-formula id="E3"><label>(2)</label><mml:math id="M3"><mml:mtable class="eqnarray" columnalign="left"><mml:mtr><mml:mtd><mml:mtext>ER</mml:mtext><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtext>exp</mml:mtext><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mtext>&#x003B2;</mml:mtext></mml:mrow><mml:mrow><mml:mtext>i</mml:mtext></mml:mrow></mml:msub><mml:mo>&#x000D7;</mml:mo><mml:mn>10</mml:mn></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>&#x000D7;</mml:mo><mml:mn>100</mml:mn><mml:mtext>%</mml:mtext></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
<p>The O<sub>3</sub>-8h pollutant model was introduced to analyze the concentration of O<sub>3</sub>-8h on the same day (Lag0) and the number of non-accidental deaths of residents. Meanwhile, the lag effect and cumulative lag effect were analyzed. According to the significance of the model analysis results, Lag0-Lag7 was selected for analysis (<xref ref-type="bibr" rid="B38">38</xref>, <xref ref-type="bibr" rid="B39">39</xref>). Lag0 represents the average concentration of O<sub>3</sub>-8h on the day and Lag1 represents the average concentration of O<sub>3</sub>-8h on the lag one day, and so on. Previous studies have shown that the cumulative effect of multi-day lag is greater than that of single-day lag of air pollutants (<xref ref-type="bibr" rid="B40">40</xref>, <xref ref-type="bibr" rid="B41">41</xref>). Therefore, we further used the moving average of air pollutant concentrations from 2nd day to 8th day (lag01 to lag07) in the analysis, where Lag01 represents the moving average concentration of O<sub>3</sub>-8h on the current day and the previous day, Lag02 represents the moving average concentration of O<sub>3</sub>-8h on the current day and the previous 2 days, and so on.</p>
</sec>
<sec>
<title>Statistical Analysis</title>
<p>The data did not conform to the normal distribution, so the median (M), quartile spacing (P<sub>25</sub>, P<sub>75</sub>), minimum and maximum values were used to describe the air pollutant level, meteorological factors and respiratory mortality and cardiovascular mortality in the study period. In addition, we conducted stratified analyses by age, sex and season. Spearman rank correlations were performed to evaluate the relationship between O<sub>3</sub>-8h and other atmospheric pollutants and meteorological conditions. Temporal changes of air pollutant were summarized by Origin 8.0 software; SPSS 23.0 was used to describe the air pollutants, meteorological factors and the number of daily mortality in respiratory and cardiovascular disease. Non-normal distributions are performed using independent sample nonparametric tests (Kruskal-WallisTest). The time-series statistical analysis was conducted using R software, version 4.1.2. The statistical significance of all analyses was set as <italic>P</italic> &#x0003C; 0.05.</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>Results</title>
<sec>
<title>Overview of Air Pollution in Study Area</title>
<p>From 2014 to 2020, we monitored eight ozone monitoring sites and obtained ozone concentration and meteorological data in Nanchang City (<xref ref-type="fig" rid="F1">Figure 1</xref>). During the study period, the average annual concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, and SO<sub>2</sub> all showed a steady downward trend, from 49.63 &#x003BC;g/m<sup>3</sup>, 82.02 &#x003BC;g/m<sup>3</sup>, 24.32 &#x003BC;g/m<sup>3</sup> in 2014 to 31.75 &#x003BC;g/m<sup>3</sup>, 55.07 &#x003BC;g/m<sup>3</sup>, 8.49 &#x003BC;g/m<sup>3</sup> in 2020 in Nanchang City. The average annual concentration of NO<sub>2</sub> increased firstly, reached the peak in 2017 (40.44 &#x003BC;g/m<sup>3</sup>) and then began to decline, and fell to 26.98 &#x003BC;g/m<sup>3</sup> in 2020. The annual concentration of CO increased firstly, then decreased rapidly to 0.6 mg/m<sup>3</sup> in 2020. Among the six air pollutants, the average annual concentration of O<sub>3</sub>-8h is increasing year by year, reaching 90.98 &#x003BC;g/m<sup>3</sup> in 2020 (<xref ref-type="fig" rid="F2">Figure 2</xref>).</p>
<fig id="F1" position="float">
<label>Figure 1</label>
<caption><p>Ozone monitoring sites in Nanchang city, Jiangxi province, 2014&#x02013;2020.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpubh-10-864537-g0001.tif"/>
</fig>
<fig id="F2" position="float">
<label>Figure 2</label>
<caption><p>The annual change trend of air pollutants in Nanchang city, 2014&#x02013;2020.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpubh-10-864537-g0002.tif"/>
</fig>
</sec>
<sec>
<title>The Basic Situation of Air Pollution, Meteorological Factors and Daily Mortality</title>
<p>The number of mortality from respiratory diseases was 28,938, with 19,015 males and 9,920 females, accounting for 65.7 and 34.3%, respectively. The number of respiratory mortality in people aged &#x02265;65 years was 23,943 and aged &#x0003C;65 years was 4,993, accounting for 82.7 and 17.3%, respectively. Besides, There were 29,481 deaths of cardiovascular diseases, 15,457 deaths of males and 14,002 deaths of females, accounting for 52.4 and 47.6%, respectively. The number of cardiovascular mortality in people aged &#x02265;65 years was 25,757 and aged &#x0003C;65 years was 3,703, accounting for 87.4 and 12.6%, respectively. The median of daily respiratory mortality was 11 (8&#x02013;14) and cardiovascular mortality was 11 (8&#x02013;15). The daily median concentrations of SO<sub>2</sub>, NO<sub>2</sub>, O<sub>3</sub>-8h, CO, PM<sub>2.5</sub> and PM<sub>10</sub> were 11.50 (7.27&#x02013;18.67)&#x003BC;g/m<sup>3</sup>, 28.15 (21.20&#x02013;39.08)&#x003BC;g/m<sup>3</sup>, 85.00 (55.00&#x02013;115.23)&#x003BC;g/m<sup>3</sup>, 0.89 (0.70&#x02013;1.08)mg/m<sup>3</sup>, 33.00 (21.00&#x02013;50.67)&#x003BC;g/m<sup>3</sup>, 61.14 (39.66&#x02013;91.77)&#x003BC;g/m<sup>3</sup>, respectively. During the research period in Nanchang city, the median of daily average air pressure, daily average temperature, daily average relative humidity and daily average wind speed were 1,009.80 (1,002.20&#x02013;1,016.85) hpa, 20.20 (11.40&#x02013;26.30)&#x000B0;C, 75.00 (64.80&#x02013;85.00)% and 1.70 (1.30&#x02013;2.40)m/s, respectively. As important meteorological factors affecting atmospheric pollutants, daily average temperature and daily average relative humidity are significantly different in four seasons. The maximum of daily average relative humidity in spring is 77.65 (66.00&#x02013;86.45)%, whereas the minimum of daily average relative humidity is 73.00 (62.55&#x02013;83.00)% in autumn. Besides, the median of average temperature in summer is the highest, that is 29.00 (26.50&#x02013;31.18)&#x000B0;C, and the median of average temperature in winter is the lowest, the value is 8.10 (5.90&#x02013;10.30)&#x000B0;C. The distribution characteristics of atmospheric pollutants, meteorological indicators and daily mortality of respiratory and cardiovascular disease in Nanchang was shown in <xref ref-type="table" rid="T1">Table 1</xref>.</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p>Distribution characteristics of air pollutants, meteorological indicators and non-accidental deaths in Nanchang City, 2014&#x02013;2020.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>Variables</bold></th>
<th valign="top" align="center"><bold>Min</bold></th>
<th valign="top" align="center"><bold>P<sub><bold>25</bold></sub></bold></th>
<th valign="top" align="center"><bold>M</bold></th>
<th valign="top" align="center"><bold>P<sub><bold>75</bold></sub></bold></th>
<th valign="top" align="center"><bold>Max</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left" colspan="6"><bold>Air pollutants</bold></td>
</tr>
<tr>
<td valign="top" align="left">SO<sub>2</sub> (&#x003BC;g/m<sup>3</sup>)</td>
<td valign="top" align="center">2.00</td>
<td valign="top" align="center">7.27</td>
<td valign="top" align="center">11.50</td>
<td valign="top" align="center">18.67</td>
<td valign="top" align="center">78.50</td>
</tr>
<tr>
<td valign="top" align="left">NO<sub>2</sub> (&#x003BC;g/m<sup>3</sup>)</td>
<td valign="top" align="center">6.10</td>
<td valign="top" align="center">21.20</td>
<td valign="top" align="center">28.15</td>
<td valign="top" align="center">39.08</td>
<td valign="top" align="center">123.00</td>
</tr>
<tr>
<td valign="top" align="left">CO (mg/m<sup>3</sup>)</td>
<td valign="top" align="center">0.20</td>
<td valign="top" align="center">0.70</td>
<td valign="top" align="center">0.89</td>
<td valign="top" align="center">1.08</td>
<td valign="top" align="center">2.70</td>
</tr>
<tr>
<td valign="top" align="left">O<sub>3</sub>-8h (&#x003BC;g/m<sup>3</sup>)</td>
<td valign="top" align="center">5.70</td>
<td valign="top" align="center">55.00</td>
<td valign="top" align="center">85.00</td>
<td valign="top" align="center">115.23</td>
<td valign="top" align="center">217.40</td>
</tr>
<tr>
<td valign="top" align="left">PM<sub>10</sub> (&#x003BC;g/m<sup>3</sup>)</td>
<td valign="top" align="center">5.20</td>
<td valign="top" align="center">39.66</td>
<td valign="top" align="center">61.14</td>
<td valign="top" align="center">91.77</td>
<td valign="top" align="center">362.20</td>
</tr>
<tr>
<td valign="top" align="left">PM<sub>2.5</sub> (&#x003BC;g/m<sup>3</sup>)</td>
<td valign="top" align="center">4.00</td>
<td valign="top" align="center">21.00</td>
<td valign="top" align="center">33.00</td>
<td valign="top" align="center">50.67</td>
<td valign="top" align="center">304.00</td>
</tr>
<tr>
<td valign="top" align="left" colspan="6"><bold>Meteorological indicators</bold></td>
</tr>
<tr>
<td valign="top" align="left">Daily mean air pressure (hpa)</td>
<td valign="top" align="center">989.00</td>
<td valign="top" align="center">1,002.20</td>
<td valign="top" align="center">1,009.80</td>
<td valign="top" align="center">1,016.85</td>
<td valign="top" align="center">1,038.20</td>
</tr>
<tr>
<td valign="top" align="left">Daily mean wind speed (m/s)</td>
<td valign="top" align="center">0.40</td>
<td valign="top" align="center">1.30</td>
<td valign="top" align="center">1.70</td>
<td valign="top" align="center">2.40</td>
<td valign="top" align="center">10.00</td>
</tr>
<tr>
<td valign="top" align="left">Daily mean relative humidity (%)</td>
<td valign="top" align="center">31.00</td>
<td valign="top" align="center">64.80</td>
<td valign="top" align="center">75.00</td>
<td valign="top" align="center">85.00</td>
<td valign="top" align="center">100.00</td>
</tr>
<tr>
<td valign="top" align="left">Spring (MAM)</td>
<td valign="top" align="center">34.00</td>
<td valign="top" align="center">66.00</td>
<td valign="top" align="center">77.65</td>
<td valign="top" align="center">86.45</td>
<td valign="top" align="center">98.00</td>
</tr>
<tr>
<td valign="top" align="left">Summer (JJA)</td>
<td valign="top" align="center">44.00</td>
<td valign="top" align="center">67.00</td>
<td valign="top" align="center">76.00</td>
<td valign="top" align="center">85.00</td>
<td valign="top" align="center">99.00</td>
</tr>
<tr>
<td valign="top" align="left">Autumn (SON)</td>
<td valign="top" align="center">33.50</td>
<td valign="top" align="center">62.55</td>
<td valign="top" align="center">73.00</td>
<td valign="top" align="center">83.00</td>
<td valign="top" align="center">100.00</td>
</tr>
<tr>
<td valign="top" align="left">Winter (DJF)</td>
<td valign="top" align="center">31.00</td>
<td valign="top" align="center">60.00</td>
<td valign="top" align="center">74.00</td>
<td valign="top" align="center">85.00</td>
<td valign="top" align="center">97.00</td>
</tr>
<tr>
<td valign="top" align="left">P</td>
<td valign="top" align="center"><italic>P</italic> &#x0003C; 0.05</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left"><bold>Daily mean temperature (&#x000B0;C)</bold></td>
<td valign="top" align="center">&#x02212;1.30</td>
<td valign="top" align="center">11.40</td>
<td valign="top" align="center">20.20</td>
<td valign="top" align="center">26.30</td>
<td valign="top" align="center">34.90</td>
</tr>
<tr>
<td valign="top" align="left">Spring (MAM)</td>
<td valign="top" align="center">3.70</td>
<td valign="top" align="center">14.40</td>
<td valign="top" align="center">19.50</td>
<td valign="top" align="center">23.10</td>
<td valign="top" align="center">31.60</td>
</tr>
<tr>
<td valign="top" align="left">Summer (JJA)</td>
<td valign="top" align="center">19.50</td>
<td valign="top" align="center">26.50</td>
<td valign="top" align="center">29.00</td>
<td valign="top" align="center">31.18</td>
<td valign="top" align="center">34.90</td>
</tr>
<tr>
<td valign="top" align="left">Autumn (SON)</td>
<td valign="top" align="center">4.00</td>
<td valign="top" align="center">16.50</td>
<td valign="top" align="center">20.50</td>
<td valign="top" align="center">24.90</td>
<td valign="top" align="center">31.80</td>
</tr>
<tr>
<td valign="top" align="left">Winter (DJF)</td>
<td valign="top" align="center">&#x02212;1.30</td>
<td valign="top" align="center">5.90</td>
<td valign="top" align="center">8.10</td>
<td valign="top" align="center">10.30</td>
<td valign="top" align="center">22.30</td>
</tr>
<tr>
<td valign="top" align="left">P</td>
<td valign="top" align="center"><italic>P</italic> &#x0003C; 0.05</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left" colspan="6"><bold>Daily mortality counts of respiratory disease</bold></td>
</tr>
<tr>
<td valign="top" align="left">All</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">14</td>
<td valign="top" align="center">38</td>
</tr>
<tr>
<td valign="top" align="left">Male</td>
<td valign="top" align="center">0</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">24</td>
</tr>
<tr>
<td valign="top" align="left">Female</td>
<td valign="top" align="center">0</td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">21</td>
</tr>
<tr>
<td valign="top" align="left">&#x02265;65</td>
<td valign="top" align="center">0</td>
<td valign="top" align="center">6</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">12</td>
<td valign="top" align="center">32</td>
</tr>
<tr>
<td valign="top" align="left">&#x0003C;65</td>
<td valign="top" align="center">0</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">11</td>
</tr>
<tr>
<td valign="top" align="left" colspan="6"><bold>Daily mortality counts of cardiovascular disease</bold></td>
</tr>
<tr>
<td valign="top" align="left">All</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">15</td>
<td valign="top" align="center">42</td>
</tr>
<tr>
<td valign="top" align="left">Male</td>
<td valign="top" align="center">0</td>
<td valign="top" align="center">4</td>
<td valign="top" align="center">6</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">24</td>
</tr>
<tr>
<td valign="top" align="left">Female</td>
<td valign="top" align="center">0</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">21</td>
</tr>
<tr>
<td valign="top" align="left">&#x02265;65</td>
<td valign="top" align="center">0</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">13</td>
<td valign="top" align="center">40</td>
</tr>
<tr>
<td valign="top" align="left">&#x0003C;65</td>
<td valign="top" align="center">0</td>
<td valign="top" align="center">0</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">12</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Moreover, the Spearman correlation analysis in <xref ref-type="table" rid="T2">Table 2</xref> showed that O<sub>3</sub>-8h exposure is positively correlated with NO<sub>2</sub> and CO (<italic>P</italic> &#x0003C; 0.05), but not related to PM<sub>2.5</sub> (<italic>P</italic> &#x0003E; 0.05). It is negatively correlated with daily average air pressure, daily average relative humidity, and precipitation, whereas positively correlated with daily average temperature, but has nothing to do with daily average wind speed. It was remarkably observed that meteorological indicators are one of the significant factors affecting ozone pollution.</p>
<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p>Correlation analysis of air pollutants and meteorological indicators in Nanchang city.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>O<sub><bold>3</bold></sub>-8h (&#x003BC;g/m<sup><bold>3</bold></sup>)</bold></th>
<th valign="top" align="center" colspan="4" style="border-bottom: thin solid #000000;"><bold>Air pollutants</bold></th>
<th valign="top" align="center" colspan="4" style="border-bottom: thin solid #000000;"><bold>Meteorological conditions</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center"><bold>NO<sub><bold>2</bold></sub> (&#x003BC;g/m<sup><bold>3</bold></sup>)</bold></th>
<th valign="top" align="center"><bold>CO (mg/m<sup><bold>3</bold></sup>)</bold></th>
<th valign="top" align="center"><bold>PM<sub><bold>10</bold></sub> (&#x003BC;g/m<sup><bold>3</bold></sup>)</bold></th>
<th valign="top" align="center"><bold>PM<sub><bold>2.5</bold></sub> (&#x003BC;g/m<sup><bold>3</bold></sup>)</bold></th>
<th valign="top" align="center"><bold>Daily mean air pressure (hpa)</bold></th>
<th valign="top" align="center"><bold>Daily mean temperature (&#x000B0;C)</bold></th>
<th valign="top" align="center"><bold>Daily mean relative humidity (%)</bold></th>
<th valign="top" align="center"><bold>Daily mean wind speed (m/s)</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">r</td>
<td valign="top" align="center">&#x02212;0.096</td>
<td valign="top" align="center">&#x02212;0.14</td>
<td valign="top" align="center">0.282</td>
<td valign="top" align="center">0.03</td>
<td valign="top" align="center">&#x02212;0.398</td>
<td valign="top" align="center">0.585</td>
<td valign="top" align="center">&#x02212;0.573</td>
<td valign="top" align="center">0.053</td>
</tr>
<tr>
<td valign="top" align="left">P</td>
<td valign="top" align="center">&#x0003C;0.05</td>
<td valign="top" align="center">&#x0003C;0.05</td>
<td valign="top" align="center">&#x0003C;0.05</td>
<td valign="top" align="center">&#x0003E;0.05</td>
<td valign="top" align="center">&#x0003C;0.05</td>
<td valign="top" align="center">&#x0003C;0.05</td>
<td valign="top" align="center">&#x0003C;0.05</td>
<td valign="top" align="center">&#x0003E;0.05</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec>
<title>The Health Effect of O<sub>3</sub> Exposure</title>
<p>This study emphatically analyzed the effect of ozone exposure on respiratory mortality and cardiovascular mortality in different sex, age and season. By drawing the exposure-response relationship between ozone and the risk of death of residents in <xref ref-type="fig" rid="F3">Figure 3</xref>, it can be seen that the increase of ozone concentration is positively correlated with the excessive risk of death on respiratory diseases and cardiovascular diseases. The influence curve of ozone on the death of residents is approximately linearly increasing, and there is no discernible threshold. At low concentrations, it also has a certain impact on the death of residents. <xref ref-type="fig" rid="F4">Figure 4</xref> indicates that the estimated lag structure of the effects of a 10 &#x003BC;g/m<sup>3</sup> increase in O<sub>3</sub>-8h concentration on respiratory diseases and cardiovascular diseases for the whole group. After adjusting for time, meteorological factors, day of the week effects, holiday effects and other confounding factors, it is focused on that under different hysteresis models with the average concentration of O<sub>3</sub>-8h increase of 10&#x003BC;g/m<sup>3</sup> leads to respiratory mortality and cardiovascular mortality (ER,95%CI). For the single lag model, the strongest effect of O<sub>3</sub>-8h on respiratory mortality and cardiovascular mortality on the current day (lag0) and 1th day (lag1), respectively, that is, for every 10&#x003BC;g/m<sup>3</sup> increase in O<sub>3</sub>-8h, the risk of death of respiratory diseases and cardiovascular diseases increased by 1.04% (95%CI: 0.40&#x02013;1.68%), 1.26%(95%CI: 0.68&#x02013;1.8%), respectively. The multi-day moving average lag model showed that respiratory mortality and cardiovascular mortality were the highest on the 15 day (1.77%, 95%CI:0.99&#x02013;2.57%) and the third day (1.68%,95%CI: 0.93&#x02013;2.45%) of the cumulative lag, respectively. The differences were statistically significant (<italic>P</italic> &#x0003C; 0.05). To avoid multiple colinearities, only the two-pollutant model was used to detect the robustness of the model (<xref ref-type="bibr" rid="B42">42</xref>). <xref ref-type="table" rid="T3">Table 3</xref> shows that in the two-pollutant models, the effect of O<sub>3</sub>-8h were still significantly after adding SO<sub>2</sub>, NO<sub>2</sub>, CO, PM<sub>2.5</sub> and PM<sub>10</sub> on respiratory mortality and cardiovascular mortality. Besides, the effect of respiratory mortality had no significant change, while the risk of death from cardiovascular diseases caused by O<sub>3</sub>-8h is reduced by 0.23, 0.23, 0.35, 0.38, and 0.27%, respectively.</p>
<fig id="F3" position="float">
<label>Figure 3</label>
<caption><p>Exposure-response curve of O<sub>3</sub>-8h to major death causes of residents; <bold>(A)</bold> O<sub>3</sub>-8h lead to respiratory mortality; <bold>(B)</bold> O<sub>3</sub>-8h lead to cardiovascular mortality.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpubh-10-864537-g0003.tif"/>
</fig>
<fig id="F4" position="float">
<label>Figure 4</label>
<caption><p>The ER (95%CI) of mortality associated with 10 &#x003BC;g/m<sup>3</sup> increase of O<sub>3</sub>-8h concentration; <bold>(A)</bold> O<sub>3</sub>-8h lead to respiratory mortality; <bold>(B)</bold> O<sub>3</sub>-8h lead to cardiovascular mortality.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpubh-10-864537-g0004.tif"/>
</fig>
<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p>The excess risk (95%CI) of air pollutant associated with 10&#x003BC;g/m<sup>3</sup> increase of O<sub>3</sub>-8h concentration in the double-pollutant models.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>Air pollutant models</bold></th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;"><bold>ER (95%CI)</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center"><bold>Respiratory mortality</bold></th>
<th valign="top" align="center"><bold>Cardiovascular mortality</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><bold>Single-pollutant model</bold></td>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">O<sub>3</sub>-8h</td>
<td valign="top" align="center">1.04 (0.40,1.68)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="center">1.26 (0.68,1.83)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td valign="top" align="left"><bold>Two-pollutant models</bold></td>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">O<sub>3</sub>-8h&#x0002B;SO<sub>2</sub></td>
<td valign="top" align="center">1.05 (0.41,1.70)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="center">1.03 (0.38,1.68)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td valign="top" align="left">O<sub>3</sub>-8h&#x0002B;NO<sub>2</sub></td>
<td valign="top" align="center">1.03 (0.39,1.68)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="center">1.03 (0.38,1.68)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td valign="top" align="left">O<sub>3</sub>-8h&#x0002B;PM<sub>2.5</sub></td>
<td valign="top" align="center">0.99 (0.35,1.64)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="center">0.91 (0.26,1.56)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td valign="top" align="left">O<sub>3</sub>-8h&#x0002B;PM<sub>10</sub></td>
<td valign="top" align="center">0.96 (0.32,1.61)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="center">0.88 (0.23,1.54)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td valign="top" align="left">O<sub>3</sub>-8h&#x0002B;CO</td>
<td valign="top" align="center">1.03 (0.39,1.68)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="center">0.99 (0.35,1.64)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="TN1">
<label>&#x0002A;</label>
<p><italic>P &#x0003C; 0.05</italic>.</p></fn>
</table-wrap-foot>
</table-wrap>
<p><xref ref-type="table" rid="T4">Table 4</xref> shows the excessive risk and 95% confidence intervals of mortality for respiratory diseases and cardiovascular diseases in the optimal lag days in the stratified analysis by age, sex, and season. Results of single-pollutant and multi-pollutant models from respiratory diseases for different age and sex are presented in <xref ref-type="fig" rid="F5">Figure 5</xref>. In the cumulative lag model, for every 10 &#x003BC;g/m<sup>3</sup> increase of O<sub>3</sub>-8h, the greatest excessive risk of respiratory mortality among people aged &#x0003C;65 years, aged &#x02265;65 years, females and males at lag05, lag05, lag05, lag04 increased by 1.04% (95%CI: &#x02212;0.45 &#x0007E; 2.56%), 1.93%(95%CI: 1.09 &#x0007E; 2.78%), 2.17% (95%CI: 0.99 &#x0007E; 3.37%), 1.61% (95%CI: 0.76 &#x0007E; 2.47%), respectively. When we analyzed the effect of a 10 &#x003BC;g/m<sup>3</sup> increase in O<sub>3</sub>-8h on cardiovascular diseases, as presented in <xref ref-type="fig" rid="F6">Figure 6</xref>, we saw a similar pattern to respiratory diseases. In this case, the greatest excessive risk of cardiovascular mortality in the cumulative lag model among people aged &#x0003C;65 years, aged &#x02265;65 years, males, and females at lag06, lag03, lag01, lag03 increased by 2.11% (95%CI: 0.31 &#x0007E; 3.96%), 1.67%(95%CI: 0.86 &#x0007E; 2.48%), 1.44% (95%CI: 0.57 &#x0007E; 2.31%), 2.03 (95%C I:1.06 &#x0007E; 3.01%), respectively. Except that not statistically significant for age &#x0003C;65 years from respiratory mortality (<italic>P</italic> &#x0003E; 0.05), statistically significant associations were observed in different age and sex groups (<italic>P</italic> &#x0003C; 0.05). In summary, the female group had the highest association with ozone exposure compared to the male group, and the death effect value of respiratory diseases is higher for people age &#x02265;65 years than age &#x0003C;65 years. Conversely, for cardiovascular diseases, the death effect value of people age &#x0003C;65 years is higher than age &#x02265;65 years. Season-specific associations for respiratory and cardiovascular disease in single day lag and cumulative day lag models of O<sub>3</sub>-8h are presented in <xref ref-type="fig" rid="F7">Figures 7</xref>, <xref ref-type="fig" rid="F8">8</xref>. The strongest effects of O<sub>3</sub>-8h exposure of respiratory mortality in spring, summer, autumn, and winter at lag05, lag06, lag01, and lag03 increased by 0.88% (95%CI: &#x02212;0.71 &#x0007E; 2.49%), &#x02212;0.61% (95%CI: &#x02212;2.12 &#x0007E; 0.92%), 0.61% (95%CI: &#x02212;0.86 &#x0007E; 2.11%), 5.87% (95%CI: 3.61 &#x0007E; 8.18%), respectively. And the effects of O<sub>3</sub>-8h concentration of cardiovascular mortality in spring, summer, autumn, and winter reached the maximum at lag02, lag06, lag06, lag03 increased by 0.45%(95%CI: &#x02212;1.88 &#x0007E; 2.10%), 1.51% (95%CI: &#x02212;0.09 &#x0007E; 3.12%), 0.41% (95%CI: &#x02212;0.96 &#x0007E; 1.79%), 4.14% (95%CI: 1.92 &#x0007E; 6.40%), respectively. We did see statistically significant contributions from O<sub>3</sub>-8h on respiratory mortality and cardiovascular mortality in winter (<italic>P</italic> &#x0003C; 0.05). Additionally, the season fluctuation of air pollution demonstrated that O<sub>3</sub>-8h concentration had a stronger association with respiratory mortality in winter and spring and with cardiovascular mortality in summer and winter.</p>
<table-wrap position="float" id="T4">
<label>Table 4</label>
<caption><p>The ER (95%CI) of respiratory mortality and cardiovascular mortality in the optimal lag days for different age, sex and season.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>Variables</bold></th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;"><bold>Respiratory mortality</bold></th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;"><bold>Cardiovascular mortality</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="left"><bold>Lag(d)</bold></th>
<th valign="top" align="center"><bold>O3-8h (&#x003BC;g/m3)</bold></th>
<th valign="top" align="left"><bold>Lag(d)</bold></th>
<th valign="top" align="center"><bold>O3-8h (&#x003BC;g/m3)</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><bold>All</bold></td>
<td valign="top" align="left">lag0</td>
<td valign="top" align="center">1.04 (0.40,1.68)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="left">lag1</td>
<td valign="top" align="center">1.26 (0.68,1.83)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td valign="top" align="left">lag05</td>
<td valign="top" align="center">1.77 (0.99,2.57)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="left">lag03</td>
<td valign="top" align="center">1.68 (0.93,2.45)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td valign="top" align="left"><bold>Sex</bold></td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">Male</td>
<td valign="top" align="left">lag0</td>
<td valign="top" align="center">0.95 (0.24,1.67)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="left">lag1</td>
<td valign="top" align="center">1.16 (0.48,1.84)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td valign="top" align="left">lag04</td>
<td valign="top" align="center">1.61 (0.76,2.47)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="left">lag01</td>
<td valign="top" align="center">1.44 (0.57,2.31)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td valign="top" align="left">Female</td>
<td valign="top" align="left">lag0</td>
<td valign="top" align="center">1.20 (0.24,2.16)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="left">lag1</td>
<td valign="top" align="center">1.46 (0.72,2.20)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td valign="top" align="left">lag05</td>
<td valign="top" align="center">2.17 (0.99,3.37)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="left">lag03</td>
<td valign="top" align="center">2.03 (1.06,3.01)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td valign="top" align="left"><bold>Age(years)</bold></td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">&#x02265;65</td>
<td valign="top" align="left">lag1</td>
<td valign="top" align="center">1.11 (0.51,1.71)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="left">lag1</td>
<td valign="top" align="center">1.29 (0.68,1.90)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td valign="top" align="left">lag05</td>
<td valign="top" align="center">1.93 (1.09,2.78)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="left">lag03</td>
<td valign="top" align="center">1.67 (0.86,2.48)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td valign="top" align="left">&#x0003C;65</td>
<td valign="top" align="left">lag0</td>
<td valign="top" align="center">1.12 (&#x02212;0.10,2.35)</td>
<td valign="top" align="left">lag1</td>
<td valign="top" align="center">1.39 (0.12,2.67)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td valign="top" align="left">lag05</td>
<td valign="top" align="center">1.04 (&#x02212;0.45,2.56)</td>
<td valign="top" align="left">lag06</td>
<td valign="top" align="center">2.11 (0.31,3.96)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td valign="top" align="left"><bold>Season</bold></td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">Spring</td>
<td valign="top" align="left">lag5</td>
<td valign="top" align="center">0.91 (0.09,1.73)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="left">lag1</td>
<td valign="top" align="center">0.24 (&#x02212;0.75,1.24)</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">lag05</td>
<td valign="top" align="center">0.88 (&#x02212;0.71,2.49)</td>
<td valign="top" align="left">lag02</td>
<td valign="top" align="center">0.45 (&#x02212;1.88,2.10)</td>
</tr>
<tr>
<td valign="top" align="left">Summer</td>
<td valign="top" align="left">lag6</td>
<td valign="top" align="center">0.41 (&#x02212;0.44,1.27)</td>
<td valign="top" align="left">lag6</td>
<td valign="top" align="center">1.12 (0.23,2.02)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td valign="top" align="left">lag06</td>
<td valign="top" align="center">&#x02212;0.61 (&#x02212;2.12,0.92)</td>
<td valign="top" align="left">lag06</td>
<td valign="top" align="center">1.51 (&#x02212;0.09,3.12)</td>
</tr>
<tr>
<td valign="top" align="left">Autumn</td>
<td valign="top" align="left">lag1</td>
<td valign="top" align="center">0.52 (&#x02212;0.54,1.60)</td>
<td valign="top" align="left">lag5</td>
<td valign="top" align="center">0.51 (&#x02212;0.31,1.33)</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">lag01</td>
<td valign="top" align="center">0.61 (&#x02212;0.86,2.11)</td>
<td valign="top" align="left">lag06</td>
<td valign="top" align="center">0.41 (&#x02212;0.96,1.79)</td>
</tr>
<tr>
<td valign="top" align="left">Winter</td>
<td valign="top" align="left">lag1</td>
<td valign="top" align="center">3.71 (2.06,5.39)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="left">lag2</td>
<td valign="top" align="center">3.07 (1.58,4.58)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td valign="top" align="left">lag03</td>
<td valign="top" align="center">5.87 (3.61,8.18)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="left">lag03</td>
<td valign="top" align="center">4.14 (1.92,6.40)<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="TN2">
<label>&#x0002A;</label>
<p><italic>P &#x0003C; 0.05</italic>.</p></fn>
</table-wrap-foot>
</table-wrap>
<fig id="F5" position="float">
<label>Figure 5</label>
<caption><p>The ER (95%CI) of respiratory mortality in age and sex lag-response relationship associated with 10 &#x003BC;g/m<sup>3</sup> increase of O<sub>3</sub>-8h concentration; <bold>(A)</bold> The risks of respiratory mortality associated with 10 &#x003BC;g/m<sup>3</sup> increase of O<sub>3</sub>-8h; <bold>(B)</bold> The cumulative risks of respiratory mortality associated with 10 &#x003BC;g/m<sup>3</sup> increase of O<sub>3</sub>-8h.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpubh-10-864537-g0005.tif"/>
</fig>
<fig id="F6" position="float">
<label>Figure 6</label>
<caption><p>The ER (95%CI) of cardiovascular mortality in age and sex lag-response relationship associated with 10 &#x003BC;g/m<sup>3</sup> increase of O<sub>3</sub>-8h concentration; <bold>(A)</bold> The risk of cardiovascular mortality associated with 10 &#x003BC;g/m<sup>3</sup> increase of O<sub>3</sub>-8h; <bold>(B)</bold> The cumulative risks of cardiovascular mortality associated with 10 &#x003BC;g/m<sup>3</sup> increase of O<sub>3</sub>-8h.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpubh-10-864537-g0006.tif"/>
</fig>
<fig id="F7" position="float">
<label>Figure 7</label>
<caption><p>The ER (95%CI) of mortality in season lag-response relationship associated with 10 &#x003BC;g/m<sup>3</sup> increase of O<sub>3</sub>-8h concentration; <bold>(A)</bold> O<sub>3</sub>-8h lead to respiratory mortality; <bold>(B)</bold> O<sub>3</sub>-8h lead to cardiovascular mortality.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpubh-10-864537-g0007.tif"/>
</fig>
<fig id="F8" position="float">
<label>Figure 8</label>
<caption><p>The cumulative excess risk (95%CI) of mortality in season lag-response relationship associated with 10 &#x003BC;g/m<sup>3</sup> increase of O<sub>3</sub>-8h concentration; <bold>(A)</bold> O<sub>3</sub>-8h lead to respiratory mortality; <bold>(B)</bold> O<sub>3</sub>-8h lead to cardiovascular mortality.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpubh-10-864537-g0008.tif"/>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<p>In this study, we found that air pollution has an important correlation with residents&#x00027; health. This paper describes a approach to evaluate the health effect of ozone exposure on respiratory mortality and cardiovascular mortality in Nanchang City from 2014 to 2020. The exposure-response relationship reflects that ozone has an approximately linear positive correlation with the respiratory mortality and cardiovascular mortality. Studies in many cities in the United States, which showed that daily changes in ambient ozone exposure are linked to premature mortality, even at very low pollution level. And they also found robust evidence of the exposure-response relationship between ozone exposure and mortality showed an approximate linear curve (<xref ref-type="bibr" rid="B43">43</xref>). Moreover, the single- and two-pollutant models in our studies were constructed to further analyze the lag-response effect of ozone on the risk of death of residents, it was found a significant association for both O<sub>3</sub>-8h and respiratory mortality and cardiovascular mortality with higher effects at the cumulative exposure level.</p>
<p>The results of survey showed that a correlation between O<sub>3</sub>-8h and respiratory mortality and cardiovascular mortality, with percent changes in excessive risk of 1.04% (95%CI: 0.40 &#x0007E; 1.68%) and 1.26% (95%CI: 0.68 &#x0007E; 1.83%) for a 10 &#x003BC;g/m<sup>3</sup> increase in O<sub>3</sub>-8h at lag0 and at lag1, respectively. The multi-day moving average lag effect showed that the greatest excessive risk of respiratory mortality and cardiovascular mortality at lag05 and lag03 increased by 1.77% (95%CI: 0.99 &#x0007E; 2.57%), 1.68% (95%CI: 0.93 &#x0007E; 2.45%), respectively, which is similar to the effect value of related research at home and abroad (<xref ref-type="bibr" rid="B42">42</xref>, <xref ref-type="bibr" rid="B44">44</xref>, <xref ref-type="bibr" rid="B45">45</xref>). In our studies, we found that ozone exposure had immediate effect on respiratory mortality and cardiovascular mortality, and the risk of death of cardiovascular diseases was especially susceptible to ozone pollution. But with the cumulative lag effect, it is worth noting that ozone has a greater impact on respiratory mortality. The association between O<sub>3</sub>-8h exposure and respiratory mortality and cardiovascular mortality has been well documented in the epidemiological literature. Chao (<xref ref-type="bibr" rid="B41">41</xref>) also found that for every 10 &#x003BC;g/m<sup>3</sup> increase in O<sub>3</sub>-8h concentration, a single-day lag of 1st-2nd day and multi-day cumulative lag of 3rd day had the greatest effect on cardiovascular disease mortality in Jiangsu Province. Qi&#x00027;s (<xref ref-type="bibr" rid="B21">21</xref>) research in Nanjing city discovered that the risk of cardiovascular mortality increased by 1.25% (95%CI: 0.78 &#x0007E; 1.72%) for a 10 &#x003BC;g/m<sup>3</sup> increase in O<sub>3</sub>-8h, while the effect of O<sub>3</sub>-8h on the risk of respiratory mortality was not statistically significant. A study in Sichuan Province (<xref ref-type="bibr" rid="B46">46</xref>) consider that for every 10 &#x003BC;g/m<sup>3</sup> increase in ozone, respiratory mortality would increase by 0.78%(95%CI: 0.12 &#x0007E; 1.44%). The same environmental and health cohort study followed up in Canada for 16 years showed that (<xref ref-type="bibr" rid="B47">47</xref>), for every 10 &#x003BC;g/m<sup>3</sup> increase in ozone on respiratory mortality and cardiovascular mortality will increase by 0.97%(95%CI: 0.95 &#x0007E; 0.99%) and 1.03% (95%CI: 1.03 &#x0007E; 1.05%), respectively. The inconsistency of research results in different regions may be related to temperature (<xref ref-type="bibr" rid="B5">5</xref>), population composition, geographical structure and urbanization scale (<xref ref-type="bibr" rid="B16">16</xref>, <xref ref-type="bibr" rid="B39">39</xref>, <xref ref-type="bibr" rid="B44">44</xref>). In our studies, in the dual-pollution model, after adjusting for SO<sub>2</sub>, NO<sub>2</sub>, CO, PM<sub>2.5</sub>, and PM<sub>10</sub>, the death risk of respiratory diseases had no significant change, while the death risk of cardiovascular diseases caused by O<sub>3</sub>-8h decreased by 0.23, 0.23, 0.35, 0.38, and 0.27%, respectively. Qi (<xref ref-type="bibr" rid="B21">21</xref>) and Yebin (<xref ref-type="bibr" rid="B25">25</xref>) discovered that O<sub>3</sub> had a certain degree of reduction in the risk of cardiovascular mortality after adjusting for PM<sub>2.5</sub>,PM<sub>10</sub>, NO<sub>2</sub>, SO<sub>2</sub> and CO; Lihong (<xref ref-type="bibr" rid="B35">35</xref>) found that after adding PM<sub>2.5</sub> and NO<sub>2</sub>, the risk of respiratory diseases was not significantly different from the effect value of O<sub>3</sub> single pollutant, which is consistent with our findings. Furthermore, there was a research showed (<xref ref-type="bibr" rid="B48">48</xref>) that after incorporating other pollutants into the single pollutant model, there was no difference in the impact on the health of residents before and after. The reason for the difference may be related to the constructed model, or the single air pollutant may be only considered in the single pollution model effect, and the effect of air pollutant may be affected by the co-linearity or spatial nature of other air pollutants (<xref ref-type="bibr" rid="B47">47</xref>).</p>
<p>Regarding gender differences, the effect of ozone exposure on excessive risk of respiratory mortality and cardiovascular mortality in female was greater than in male. It has been proved that females were more likely to suffer from the mortality of respiratory diseases and cardiovascular diseases than males, and females are more susceptible to O<sub>3</sub>-8h than males, which is similar to the results of previous studies at home and abroad (<xref ref-type="bibr" rid="B48">48</xref>, <xref ref-type="bibr" rid="B49">49</xref>). The reason for this result may be females have higher gas sensitivity, shorter respiratory tract and more susceptible to air pollutants compared with males (<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B37">37</xref>, <xref ref-type="bibr" rid="B44">44</xref>). However, there is also evidence that males are more vulnerable to the effects of ozone on respiratory mortality than females. A study conducted in Li et al. (<xref ref-type="bibr" rid="B50">50</xref>) explained that pneumonia and bronchitis are more common in males with a history of smoking and exposure to different occupations, which may exacerbate the impact of O<sub>3</sub>-8h on respiratory mortality in males. Mechanistically, as a result of reduced uptake of ozone into conducting airways, cigarette smoking may shift the longitudinal distribution of ozone uptake distally toward the respiratory airways, thereby leading to respiratory diseases in the population (<xref ref-type="bibr" rid="B51">51</xref>). However, there is no detailed study on whether the smoking rate of adults will affect the association between ozone exposure and cardiovascular diseases. Our studies have also exhibited that the elevated concentration of O<sub>3</sub>-8h significantly increased the risk of respiratory mortality among residents aged &#x02265;65 years (compared to aged &#x0003C;65 years), which is consistent with the results of previous studies (<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B19">19</xref>&#x02013;<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B44">44</xref>). The structure of the respiratory system changes as we age, with decreased chest wall compliance, respiratory muscle strength, and vital capacity, which could lead to the risk of death of respiratory diseases in older people (<xref ref-type="bibr" rid="B52">52</xref>). As for the risk of cardiovascular mortality, the effect value of residents aged &#x0003C;65 years is higher than that of residents aged &#x02265;65 years, which is inconsistent with previous researches found that people aged &#x02265;65 years were more closely related to cardiovascular mortality risks (<xref ref-type="bibr" rid="B37">37</xref>, <xref ref-type="bibr" rid="B48">48</xref>, <xref ref-type="bibr" rid="B50">50</xref>). On the one hand, the younger with these cardiometabolic conditions had a higher prevalence of cardiovascular diseases in association with higher air pollution exposures than individuals without these cardiometabolic conditions (<xref ref-type="bibr" rid="B53">53</xref>). On the another hand, some scholars detected stronger associations between air pollutants and cardiometabolic risk factors in younger participants and for those with a family history of cardiovascular diseases (<xref ref-type="bibr" rid="B54">54</xref>). In summary, ozone exposure is closely related to the risk of respiratory mortality and cardiovascular mortality in people of different ages and sexes. Among them, females and people aged &#x02265;65 years are sensitive groups. Nevertheless, ozone as an important air pollutant, we also should also be paid attention to the health risk of people aged &#x0003C;65 years because they are more closely linked to cardiovascular mortality for a 10 &#x003BC;g/m<sup>3</sup> increase in O<sub>3</sub>-8h.</p>
<p>Climate change has a negative impact on human health due to increased exposure to adverse climate-related stresses (<xref ref-type="bibr" rid="B55">55</xref>). According to the climatic characteristics and the analysis of daily average temperature and daily average relative humidity of Nanchang City during the study period, it is known that the higher the temperature in the warm season (spring, summer), the greater the humidity. When considering differential effects by season, it had been reported that seasonal changes will affect the impact of air pollutants on human health (<xref ref-type="bibr" rid="B56">56</xref>). We conducted that season-specific associations for the mortality risk of respiratory diseases showed stronger associations in winter and spring for a 10 &#x003BC;g/m<sup>3</sup> increase in O<sub>3</sub>-8h, which is in agreement with the finding of the study by Yuqi (<xref ref-type="bibr" rid="B42">42</xref>) in Lishui district. And the risk of cardiovascular mortality was stronger associated with O<sub>3</sub>-8h in summer and winter. In summer, ozone precursors in the air produce ozone more quickly with the increase of temperature (<xref ref-type="bibr" rid="B57">57</xref>). The reduced levels of nitrogen dioxide and carbon monoxide in summer months can be attributed to the contribution of these compounds to photochemical reactions occurring under the influence of solar radiation which result in the formation of ozone (<xref ref-type="bibr" rid="B58">58</xref>). Most importantly, we indicated the excessive risk of ozone pollution on respiratory mortality and cardiovascular mortality occurred earlier and had a greatest effect in winter than in the warm season. Higher levels of ozone pollution in winter months may also be associated with increased low emissions from local home furnaces, as well as more frequent in these periods, inversion of temperature resulting in smog events (<xref ref-type="bibr" rid="B59">59</xref>).</p>
<p>This study has several limitations that should be addressed. First, the air pollution exposure data of the study is the monitoring point data, which may be affected by distance, climate and other reasons, and cannot fully reflect the individual exposure (<xref ref-type="bibr" rid="B39">39</xref>, <xref ref-type="bibr" rid="B40">40</xref>, <xref ref-type="bibr" rid="B48">48</xref>), may overestimate the impact of air pollutants on the death of residents. Second, there exists another source of misclassification of ozone concentrations as this study did not use personal ozone exposure, because some personal behavior factors were not taken into consideration, such as air conditioner use, and time spent outdoors (<xref ref-type="bibr" rid="B60">60</xref>). Third, this study was conducted in a single city (Nanchang), thus, our findings cannot be generalized to other cities with different environmental and economic characteristics. Finally, the research only examined the correlation of respiratory mortality and cardiovascular mortality and ozone pollution, we were not able to involve more diseases, which is also a limitation of this study. As an important pollutant, ozone has independent health hazards. According to the source and changes of ozone in Nanchang city, government departments should strengthen the monitoring and control of ozone pollutant emissions, actively formulate energy-saving and emission reduction measures for ozone to reduce the heavy burden. In this vein, government departments also should promote actions and measures to enhance numerous aspects around the subject. Boosting education, training and public participation are some of the relevant actions for maximizing the opportunities to achieve the targets and goals on the crucial matter of ozone pollution. Without any doubt, technological improvements makes our world easier and it seems difficult to reduce the harmful impact caused by gas emissions, we could limit its use by seeking reliable approaches (<xref ref-type="bibr" rid="B61">61</xref>). When carrying out the prevention and control of ozone pollution, the relationship between PM<sub>2.5</sub> and ozone is often discussed. In winter, PM<sub>2.5</sub> treatment should be carried out, and in summer, the synergy between ozone and PM<sub>2.5</sub> should be explored. As a secondary pollutant, ozone has a complex reaction mechanism, and it is necessary to implement multi-target and multi-pollutant coordinated control. In the entire prevention and control work, more emphasis should be placed on the importance of atmospheric oxidation (<xref ref-type="bibr" rid="B62">62</xref>). Last but not least, taking appropriate protective measures for the entire population, sensitive groups, and high-risk groups to improve residents&#x00027; awareness of self-protection. At the same time, identifying vulnerable subpopulations and the impact of ozone on these subpopulations will help in establishing air quality standards that will better protect these groups (<xref ref-type="bibr" rid="B53">53</xref>). A study (<xref ref-type="bibr" rid="B63">63</xref>) have shown that masks containing activated carbon interlayer have good protection against ozone at different pollution levels.</p>
</sec>
<sec sec-type="conclusions" id="s5">
<title>Conclusion</title>
<p>This study provides evidence of evaluating the effects of O<sub>3</sub>-8h exposure on respiratory mortality and cardiovascular mortality in Nanchang city from 2014 to 2020. To our knowledge, results confirm that ozone pollution would increase the risk of respiratory mortality and cardiovascular mortality. Our findings complement previous studies that are lacking by revealing that ozone pollutants have a lag effect on the health of the population in Nanchang city, China. With the rapid of economic growth and the development of processing industries such as electric power, gas and non-ferrous metal smelting in Nanchang city, the government should introduce corresponding control policies, take actions to reduce air pollution and make interventions for sensitive individuals to improve our health.</p>
</sec>
<sec sec-type="data-availability" id="s6">
<title>Data Availability Statement</title>
<p>The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s7">
<title>Author Contributions</title>
<p>HW and JF conceived and designed the work. HW led the study, carried out the time-series studies, analyzed the data, and approved the version to be published. KL involved in the study design and the interpretation of the results. JF helped to conceptualize the study, provided intellectual advice, and revise various drafts of the manuscript. All authors read and approved the final manuscript.</p>
</sec>
<sec sec-type="funding-information" id="s8">
<title>Funding</title>
<p>This work was funded by Science and Technology Plan of Jiangxi Health Commission (20204847).</p>
</sec>
<sec sec-type="COI-statement" id="conf1">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s9">
<title>Publisher&#x00027;s Note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
</body>
<back>
<ack><p>We wish to thank for the strong support at the Jiangxi Provincial Center for Disease Control and Prevention. We thank the Nanchang Environmental Monitoring Center and Meteorological Bureau of Nanchang Municipality for providing data.</p>
</ack>
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