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Vital Surveillances: The Relationship Between the Atmospheric Environment and Road Traffic Fatalities — Shandong Province, China, 2012–2021

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  • Abstract

    Introduction

    This study aims to analyze the potential impact of the meteorological environment and air pollutants on road traffic fatalities.

    Methods

    Road traffic fatality data in Shandong Province from 2012 to 2021 were obtained from the Population Death Information Registration Management System. Meteorological and air pollutant data for the same period were collected from the U.S. National Oceanic and Atmospheric Administration and the Ecological Environment Monitoring Center of Shandong Province, China. Pearson’s correlation and ridge regression were used to analyze the impact of the meteorological environment and air pollutants on road traffic fatalities.

    Results

    From 2012 to 2021, there were 163,863 road traffic fatality cases. The results of the ridge regression analysis showed that the daily average temperature was negatively correlated with total fatalities and passengers and positively correlated with pedestrians, nonmotorized drivers, and motorized drivers. The daily minimum temperature was negatively correlated with total fatalities and positively correlated with motorized drivers. The daily maximum temperature was positively correlated with both pedestrian and nonmotorized drivers. The daily accumulated precipitation was negatively correlated with pedestrians. Sunshine duration was positively correlated with both nonmotorized and motorized drivers. Inhalable particulate matter (PM10) and nitrogen dioxide (NO2) were positively correlated with total fatalities, pedestrians, and nonmotorized drivers. Sulfur dioxide (SO2) was positively correlated with total fatalities but negatively correlated with nonmotorized drivers, passengers, and motorized drivers.

    Conclusions

    Atmospheric factors associated with the occurrence of road traffic fatalities include air temperature, daily accumulated precipitation, sunshine duration, and air pollutants such as PM10, NO2, and SO2.

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  • [1] Ma XL. Thematic analysis of tort civil liability for road traffic accidents. Beijing: China University of Political Science and Law Press. 2018; p. 3-4. https://book.kongfz.com/181694/6226319591/. (In Chinese). 
    [2] World Health Organization. Global status report on road safety 2018. https://www.who.int/publications/i/item/9789241565684. [2023-10-10].
    [3] Yang TF, Zeng HT, Yang XA, Kong JW, Chen XZ, Zhou N, et al. Characteristics of road traffic accident types and casualties in Guangzhou, China, from 2007 to 2020: a retrospective cohort study based on the general population. Heliyon 2023;9(1):e12822. https://doi.org/10.1016/j.heliyon.2023.e12822CrossRef
    [4] Liang MM, Zhao DD, Wu YL, Ye PP, Wang Y, Yao ZH, et al. Short-term effects of ambient temperature and road traffic accident injuries in Dalian, Northern China: a distributed lag non-linear analysis. Accid Anal Prev 2021;153:106057. https://doi.org/10.1016/j.aap.2021.106057CrossRef
    [5] Fountas G, Sarwar T, Anastasopoulos PC, Blatt A, Majka K. Analysis of stationary and dynamic factors affecting highway accident occurrence: a dynamic correlated grouped random parameters binary logit approach. Accid Anal Prev 2018;113:330 − 40. https://doi.org/10.1016/j.aap.2017.05.018CrossRef
    [6] Gupta SK, Elumalai SP. Exposure to traffic-related particulate matter and deposition dose to auto rickshaw driver in Dhanbad, India. Atmos Pollut Res 2019;10(4):1128 − 39. https://doi.org/10.1016/j.apr.2019.01.018CrossRef
    [7] Gao JH, Chen XJ, Woodward A, Liu XB, Wu HX, Lu YG, et al. The association between meteorological factors and road traffic injuries: a case analysis from Shantou city, China. Sci Rep 2016;6:37300. https://doi.org/10.1038/srep37300CrossRef
    [8] Liang MM, Min M, Guo XW, Song QX, Wang H, Li N, et al. The relationship between ambient temperatures and road traffic injuries: a systematic review and meta-analysis. Environ Sci Pollut Res 2022;29(33):50647 − 60. https://doi.org/10.1007/s11356-022-19437-yCrossRef
    [9] Li DZ, Zhu XW, Huang GY, Feng HB, Zhu SY, Li X. A hybrid method for evaluating the resilience of urban road traffic network under flood disaster: an example of Nanjing, China. Environ Sci Pollut Res 2022;29(30):46306 − 24. https://doi.org/10.1007/s11356-022-19142-wCrossRef
    [10] Bilal M, Hassan M, Tahir DBT, Iqbal MS, Shahid I. Understanding the role of atmospheric circulations and dispersion of air pollution associated with extreme smog events over South Asian megacity. Environ Monit Assess 2022;194(2):82. https://doi.org/10.1007/s10661-021-09674-yCrossRef
    [11] Barthwal V, Jain S, Babuta A, Jamir C, Sharma AK, Mohan A. Health impact assessment of Delhi’s outdoor workers exposed to air pollution and extreme weather events: an integrated epidemiology approach. Environ Sci Pollut Res 2022;29(29):44746 − 58. https://doi.org/10.1007/s11356-022-18886-9CrossRef
    [12] Li JJ, Zhang W. Research progress on the relationship between air pollution and cognitive impairment and its mechanism. Chin J Neuroimmunol Neurol 2020;27(2):151 − 4. https://doi.org/10.3969/j.issn.1006-2963.2020.015CrossRef
    [13] Wang TB, Wang YH, Xu TM, Li LB, Huo ML, Li X, et al. Epidemiological and clinical characteristics of 3327 cases of traffic trauma deaths in Beijing from 2008 to 2017: a retrospective analysis. Medicine 2020;99(1):e18567. https://doi.org/10.1097/MD.0000000000018567CrossRef
  • TABLE 1.  Correlations between meteorological conditions and road traffic fatalities in Shandong Province, China, 2012–2021.

    VariableTotal
    (N=163,863)
    Pedestrian
    (n1=90,726)
    Nonmotorized driver
    (n2=23,730)
    Passenger
    (n3=14,339)
    Motorized driver
    (n4=35,068)
    Maximum wind speed−0.095*−0.093*−0.051*−0.036*−0.022*
    Daily average temperature0.048*0.018*0.038*0.016*0.047*
    Daily minimum temperature0.034*0.0020.032*0.013*0.047*
    Daily maximum temperature0.057*0.026*0.043*0.019*0.047*
    Daily accumulated precipitation−0.019*−0.021*−0.0040.000−0.004
    Sunshine duration0.016*−0.0050.014*0.012*0.031*
    Relative humidity0.0070.012*0.006−0.005−0.004
    * P<0.05.
    Download: CSV

    TABLE 2.  Correlations between air pollutants and road traffic fatalities in Shandong Province, China, 2012–2021.

    Variable Total
    (N=163,863)
    Pedestrian
    (n1=90,726)
    Nonmotorized driver
    (n2=23,730)
    Passenger
    (n3=14,339)
    Motorized driver
    (n4=35,068)
    AQI 0.097* 0.124* 0.016* 0.035* −0.021*
    PM2.5 (μg/m3) 0.077* 0.110* 0.006 0.033* −0.034*
    PM10 (μg/m3) 0.112* 0.135* 0.024* 0.044* −0.010*
    SO2 (μg/m3) 0.063* 0.095* −0.024* 0.046* −0.026*
    NO2 (μg/m3) 0.112* 0.116* 0.036* 0.049* 0.019*
    O3 (μg/m3) 0.008 −0.014* 0.017* −0.009 0.036*
    CO (mg/m3) 0.089* 0.125* 0.013* 0.046* −0.042*
    Abbrevitation: AQI=air quality index; PM2.5=fine particulate matter; PM10=inhalable particulate matter; SO2=sulfur dioxide; NO2=nitrogen dioxide; O3=ozone; CO=carbon monoxide.
    * P<0.05.
    Download: CSV

    TABLE 3.  Ridge regression analysis of the atmospheric environment and road traffic fatalities in Shandong Province, China, 2012–2021.

    Variable Total Pedestrian Nonmotorized driver Passenger Motorized driver
    β t P β t P β t P β t P β t P
    Intercept 84.706 <0.001 52.112 <0.001 92.243 <0.001 198.917 <0.001 143.573 <0.001
    Maximum wind speed −0.030 −7.252 <0.001 −0.054 −12.791 <0.001 −0.035 −8.321 <0.001 0.002 0.419 0.675 −0.006 −1.380 0.168
    Daily average temperature −0.009 −4.408 <0.001 0.014 4.497 <0.001 0.022 −9.549 <0.001 −0.006 −3.104 0.002 0.010 4.608 <0.001
    Daily minimum temperature −0.007 −2.309 0.021 0.006 1.708 0.088 −0.005 −1.787 0.074 0.024 7.725 <0.001
    Daily maximum temperature 0.002 0.678 0.498 0.034 10.876 <0.001 0.035 10.976 <0.001 −0.002 −0.782 0.434 0.005 1.608 0.108
    Daily accumulated precipitation 0.006 1.348 0.178 −0.010 −2.38 0.017
    Sunshine duration −0.005 −1.086 0.278 0.014 3.310 0.001 0 −0.098 0.922 0.013 3.148 0.002
    Relative humidity 0.013 2.942 0.003
    PM2.5 (μg/m3) −0.021 −4.743 <0.001 −0.008 −1.820 0.069 0.008 1.788 0.074 −0.030 −6.732 <0.001
    PM10 (μg/m3) 0.019 4.175 <0.001 0.069 15.700 <0.001 0.013 2.784 0.005 0 −0.079 0.937 0.009 1.915 0.056
    SO2 (μg/m3) 0.009 1.998 0.046 0.001 0.231 0.818 −0.038 −7.979 <0.001 −0.015 −3.256 0.001 −0.019 −4.017 <0.001
    NO2 (μg/m3) 0.010 2.093 0.036 0.021 4.296 <0.001 0.056 11.400 <0.001 −0.011 −2.265 0.024 0.045 9.032 <0.001
    O3 (μg/m3) −0.030 −6.446 <0.001 −0.028 −5.741 <0.001 −0.006 −1.135 0.256
    CO (mg/m3) 0.001 0.267 0.790 0.015 3.499 <0.001 −0.003 −0.737 0.461 −0.006 −1.448 0.147 −0.008 −1.829 0.067
    Abbrevitation: AQI=air quality index; PM2.5=fine particulate matter; PM10=inhalable particulate matter; SO2=sulfur dioxide; NO2=nitrogen dioxide; O3=ozone; CO=carbon monoxide.
    Download: CSV

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The Relationship Between the Atmospheric Environment and Road Traffic Fatalities — Shandong Province, China, 2012–2021

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Abstract

Introduction

This study aims to analyze the potential impact of the meteorological environment and air pollutants on road traffic fatalities.

Methods

Road traffic fatality data in Shandong Province from 2012 to 2021 were obtained from the Population Death Information Registration Management System. Meteorological and air pollutant data for the same period were collected from the U.S. National Oceanic and Atmospheric Administration and the Ecological Environment Monitoring Center of Shandong Province, China. Pearson’s correlation and ridge regression were used to analyze the impact of the meteorological environment and air pollutants on road traffic fatalities.

Results

From 2012 to 2021, there were 163,863 road traffic fatality cases. The results of the ridge regression analysis showed that the daily average temperature was negatively correlated with total fatalities and passengers and positively correlated with pedestrians, nonmotorized drivers, and motorized drivers. The daily minimum temperature was negatively correlated with total fatalities and positively correlated with motorized drivers. The daily maximum temperature was positively correlated with both pedestrian and nonmotorized drivers. The daily accumulated precipitation was negatively correlated with pedestrians. Sunshine duration was positively correlated with both nonmotorized and motorized drivers. Inhalable particulate matter (PM10) and nitrogen dioxide (NO2) were positively correlated with total fatalities, pedestrians, and nonmotorized drivers. Sulfur dioxide (SO2) was positively correlated with total fatalities but negatively correlated with nonmotorized drivers, passengers, and motorized drivers.

Conclusions

Atmospheric factors associated with the occurrence of road traffic fatalities include air temperature, daily accumulated precipitation, sunshine duration, and air pollutants such as PM10, NO2, and SO2.

  • 1. Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
  • 2. Department for Chronic and Non-Communicable Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong Province, China
  • Corresponding authors:

    Xiaolei Guo, guoxiaolei@126.com

    Cunxian Jia, jiacunxian@sdu.edu.cn

    Online Date: March 29 2024
    Issue Date: March 29 2024
    doi: 10.46234/ccdcw2024.052
  • Road traffic accidents are incidents that occur on a road or highway and involve at least one moving vehicle, resulting in injuries, property damage, or loss of life (1). The current global annual death toll from road traffic accidents is approximately 1.3 million people. Moreover, road traffic accidents have emerged as the primary cause of death for children and young adults globally (2). Road traffic accidents in China result in more than 250,000 deaths annually, accounting for approximately 19% of the total deaths worldwide(3). Investigating the potential factors influencing road traffic accidents is essential for reducing and preventing them.

    Road traffic accidents involve three elements: humans, vehicles, and the environment. Meteorological conditions, including sunshine duration, precipitation, and high temperatures, contribute to road traffic accidents (4). Meteorological conditions directly impact the friction between vehicle tires and the road surface while also indirectly influencing drivers’ emotions, perceptions, and behavior (5). Although it may seem that there is no relationship between air pollution and traffic accidents, haze caused by air pollution reduces visibility, which can lead to limited visibility. Moreover, air pollutants can cause both immediate injuries and long-term hazards to human health. Previous research (6) has suggested that air pollutants directly stimulate drivers' eyes, resulting in redness and pain, as well as headaches due to long-term effects. Therefore, exploring the impact of the meteorological environment and air pollutants on road traffic fatalities is critical for reducing road traffic accidents.

    • Data on road traffic fatalities in Shandong Province were obtained from the Population Death Information Registration Management System of the Chinese Centers for Disease Control and Prevention during the period of death from January 2012 to December 2021. According to the cause of death classification and coding range of the International Classification of Diseases 10th Revision (ICD-10) in the Technical Guidance for Reporting Cause of Death, road traffic accident data that were included in the analysis were divided into four subgroups: pedestrian, nonmotorized driver, passenger, and motorized driver.

      The atmospheric environmental data were divided into two parts: meteorological data and air pollution data.

      The meteorological data of Shandong Province were obtained from the National Centers for Environmental Information (NCEI), which is part of the National Oceanic and Atmospheric Administration (NOAA) (http://www.ncei.noaa.gov) in the U.S from January 2012 to December 2021. The meteorological data consist of the following parameters: daily average temperature, daily maximum temperature, daily minimum temperature, maximum wind speed, relative humidity, daily accumulated precipitation, and sunshine duration.

      The air pollution data were obtained from the Ecological Environment Monitoring Center of Shandong Province, China (http://www.sdem.org.cn/), from January 2012 to December 2021. The air pollutant data include the air quality index (AQI), fine particulate matter (PM2.5), inhalable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO).

      R software (version 4.3.1; R Core Team, Vienna, Austria) was used for statistical analysis. We employed Pearson’s correlation analysis to investigate the association between road traffic fatalities and the atmospheric environment. Ridge regression analysis was used to investigate the influence of the atmospheric environment on road traffic fatalities while accounting for multicollinearity. The minimum k value, which occurs when the standardized regression coefficients of each variable tend to stabilize, was chosen as the model’s k value selection principle. The significance level of α was 0.05.

    • From 2012 to 2021, there were 163,863 road traffic fatality cases in Shandong Province. The mean age of individuals involved in road traffic fatalities was 51.97 years [standard deviation (SD): 18.10 years]. The findings indicated a positive correlation between daily average temperature and daily maximum temperature and total fatalities and each subgroup. The daily minimum temperature and sunshine duration were positively correlated with total fatalities and associated subgroups, except for pedestrians. The daily accumulated precipitation exhibited negative correlations with total fatalities and pedestrians. There was a positive correlation between relative humidity and pedestrians, but no association was found with total fatalities or other subgroups (Table 1).

      VariableTotal
      (N=163,863)
      Pedestrian
      (n1=90,726)
      Nonmotorized driver
      (n2=23,730)
      Passenger
      (n3=14,339)
      Motorized driver
      (n4=35,068)
      Maximum wind speed−0.095*−0.093*−0.051*−0.036*−0.022*
      Daily average temperature0.048*0.018*0.038*0.016*0.047*
      Daily minimum temperature0.034*0.0020.032*0.013*0.047*
      Daily maximum temperature0.057*0.026*0.043*0.019*0.047*
      Daily accumulated precipitation−0.019*−0.021*−0.0040.000−0.004
      Sunshine duration0.016*−0.0050.014*0.012*0.031*
      Relative humidity0.0070.012*0.006−0.005−0.004
      * P<0.05.

      Table 1.  Correlations between meteorological conditions and road traffic fatalities in Shandong Province, China, 2012–2021.

      There were associations between the AQI and total fatalities and the fatality subgroups. Specifically, the AQI was negatively associated with motorized drivers and positively associated with both total fatalities and the remaining subgroups. Further analysis revealed that PM10, SO2 and CO were positively correlated with pedestrians, nonmotorized drivers, and passengers and negatively correlated with motorized drivers. NO2 was positively associated with both total fatalities and fatality subgroups. There was a positive correlation between O3 and both nonmotorized and motorized drivers and a negative correlation between O3 and pedestrians (Table 2).

      Variable Total
      (N=163,863)
      Pedestrian
      (n1=90,726)
      Nonmotorized driver
      (n2=23,730)
      Passenger
      (n3=14,339)
      Motorized driver
      (n4=35,068)
      AQI 0.097* 0.124* 0.016* 0.035* −0.021*
      PM2.5 (μg/m3) 0.077* 0.110* 0.006 0.033* −0.034*
      PM10 (μg/m3) 0.112* 0.135* 0.024* 0.044* −0.010*
      SO2 (μg/m3) 0.063* 0.095* −0.024* 0.046* −0.026*
      NO2 (μg/m3) 0.112* 0.116* 0.036* 0.049* 0.019*
      O3 (μg/m3) 0.008 −0.014* 0.017* −0.009 0.036*
      CO (mg/m3) 0.089* 0.125* 0.013* 0.046* −0.042*
      Abbrevitation: AQI=air quality index; PM2.5=fine particulate matter; PM10=inhalable particulate matter; SO2=sulfur dioxide; NO2=nitrogen dioxide; O3=ozone; CO=carbon monoxide.
      * P<0.05.

      Table 2.  Correlations between air pollutants and road traffic fatalities in Shandong Province, China, 2012–2021.

      The ridge regression analysis included variables in the atmosphere that were correlated with total road traffic fatalities and associated variables. For total fatalities, the ridge regression model yielded a k value of 0.198 and an F value of 9.638 (P<0.001), indicating the statistical significance of the model. The results showed that maximum wind speed (β=−0.030, P<0.001), daily average temperature (β=−0.009, P<0.001), daily minimum temperature (β=−0.007, P=0.021), and PM2.5 (β=−0.021, P<0.001) were negatively correlated with total fatalities, whereas PM10 (β=0.019, P<0.001), SO2 (β=0.009, P=0.046), and NO2 (β=0.010, P=0.036) were positively correlated. For pedestrians, the ridge regression model yielded a k value of 0.195 and an F value of 64.389 (P<0.001), indicating the statistical significance of the model.

      The maximum wind speed (β=−0.054, P<0.001), daily accumulated precipitation (β=-0.010, P=0.017), and O3 (β=−0.030, P<0.001) were negatively correlated with pedestrians. Conversely, daily average temperature (β=0.014, P<0.001), daily maximum temperature (β=0.034, P<0.001), relative humidity (β=0.013, P=0.003), PM10 (β=0.069, P<0.001), NO2 (β=0.021, P<0.001), and CO (β=0.015, P<0.001) were positively correlated with pedestrians. For nonmotorized drivers, the ridge regression model yielded a k value of 0.183 and an F value of 38.579 (P<0.001), indicating the statistical significance of the model. There were negative correlations with maximum wind speed (β=−0.035, P<0.001), SO2 (β=−0.038, P<0.001), and O3 (β=−0.028, P<0.001), while positive correlations were observed with daily average temperature (β=0.022, P<0.001), daily maximum temperature (β=0.035, P<0.001), sunshine duration (β=0.014, P=0.001), PM10 (β=0.013, P=0.005), and NO2 (β=0.056, P<0.001). For passengers, the ridge regression model yielded a k value of 0.200 and an F value of 2.919 (P=0.001), indicating the statistical significance of the model. There were negative correlations between the daily average temperature (β=−0.006, P=0.002), SO2 (β=−0.015, P=0.001), and NO2 (β=−0.011, P=0.024) and passengers. The ridge regression model for motorized drivers had a k value of 0.195 and an F value of 15.868 (P<0.001), indicating the statistical significance of the model. There were positive correlations with daily average temperature (β=0.010, P<0.001), daily minimum temperature (β=0.024, P<0.001), sunshine duration (β=0.013, P=0.002), and NO2 (β=0.045, P<0.001), while there was a negative correlation with SO2 (β=−0.019, P<0.001) (Table 3).

      Variable Total Pedestrian Nonmotorized driver Passenger Motorized driver
      β t P β t P β t P β t P β t P
      Intercept 84.706 <0.001 52.112 <0.001 92.243 <0.001 198.917 <0.001 143.573 <0.001
      Maximum wind speed −0.030 −7.252 <0.001 −0.054 −12.791 <0.001 −0.035 −8.321 <0.001 0.002 0.419 0.675 −0.006 −1.380 0.168
      Daily average temperature −0.009 −4.408 <0.001 0.014 4.497 <0.001 0.022 −9.549 <0.001 −0.006 −3.104 0.002 0.010 4.608 <0.001
      Daily minimum temperature −0.007 −2.309 0.021 0.006 1.708 0.088 −0.005 −1.787 0.074 0.024 7.725 <0.001
      Daily maximum temperature 0.002 0.678 0.498 0.034 10.876 <0.001 0.035 10.976 <0.001 −0.002 −0.782 0.434 0.005 1.608 0.108
      Daily accumulated precipitation 0.006 1.348 0.178 −0.010 −2.38 0.017
      Sunshine duration −0.005 −1.086 0.278 0.014 3.310 0.001 0 −0.098 0.922 0.013 3.148 0.002
      Relative humidity 0.013 2.942 0.003
      PM2.5 (μg/m3) −0.021 −4.743 <0.001 −0.008 −1.820 0.069 0.008 1.788 0.074 −0.030 −6.732 <0.001
      PM10 (μg/m3) 0.019 4.175 <0.001 0.069 15.700 <0.001 0.013 2.784 0.005 0 −0.079 0.937 0.009 1.915 0.056
      SO2 (μg/m3) 0.009 1.998 0.046 0.001 0.231 0.818 −0.038 −7.979 <0.001 −0.015 −3.256 0.001 −0.019 −4.017 <0.001
      NO2 (μg/m3) 0.010 2.093 0.036 0.021 4.296 <0.001 0.056 11.400 <0.001 −0.011 −2.265 0.024 0.045 9.032 <0.001
      O3 (μg/m3) −0.030 −6.446 <0.001 −0.028 −5.741 <0.001 −0.006 −1.135 0.256
      CO (mg/m3) 0.001 0.267 0.790 0.015 3.499 <0.001 −0.003 −0.737 0.461 −0.006 −1.448 0.147 −0.008 −1.829 0.067
      Abbrevitation: AQI=air quality index; PM2.5=fine particulate matter; PM10=inhalable particulate matter; SO2=sulfur dioxide; NO2=nitrogen dioxide; O3=ozone; CO=carbon monoxide.

      Table 3.  Ridge regression analysis of the atmospheric environment and road traffic fatalities in Shandong Province, China, 2012–2021.

    • From 2012 to 2021, there were a large number of road traffic fatalities in Shandong Province, China. Atmospheric conditions were correlated with road traffic fatalities, air temperature, daily accumulated precipitation, sunshine duration, PM10, SO2, and NO2 were positively or negatively correlated with total road traffic fatalities and each subgroup.

      Multiple atmospheric factors were found to be correlated with road traffic fatalities. The maximum wind speed was negatively correlated with pedestrian and nonmotorized driver speeds, similar findings to those of Gao et al. (7). Pedestrians and nonmotorized drivers often refrain from traveling or choose alternative modes of transport under high winds due to their light weight and inadequate wind resistance. Temperature was also associated with road traffic fatalities. There was a positive correlation between daily minimum temperature and road traffic fatalities, affecting motorized drivers. Low temperatures may cause icy road surfaces, leading to tire skidding and consequently increasing the risk of traffic accidents. High temperatures can also contribute to an increase in road traffic fatalities. High temperatures can alter the physical properties of vehicle tires (8), resulting in modified vehicle performance and posing risks to safe driving behavior. Li et al. (9) demonstrated that precipitation, particularly extreme precipitation, increases the risk of road traffic accidents. In this study, precipitation was found to be negatively correlated only with road traffic fatalities involving pedestrians. Precipitation can make travel inconvenient, particularly for pedestrians. Therefore, during precipitation events, pedestrians tend to venture outdoors less frequently, consequently reducing the risk of accidents in vulnerable traffic conditions.

      In this study, several air pollutants that were significantly correlated with total road traffic fatalities and specific subgroups were identified. PM10 exhibited positive correlations with road traffic fatalities involving pedestrians and nonmotorized drivers. Particulate matter decreases atmospheric visibility (10), increasing the likelihood of traffic accidents due to limited driver visibility. Particulate matter, which frequently includes heavy metals, can impact a driver’s visual and olfactory senses (11), ultimately affecting the driver's behavior behind the wheel.

      This study revealed that there was a positive correlation between NO2 concentration and road traffic fatalities. Nitrogen oxides (NOx), which originate mainly from motor vehicle exhaust, are the primary pollutants responsible for the formation of photochemical smog. Photochemical smog is a potent irritant that causes acute effects on the human eye and respiratory tract, resulting in allergic reactions, including tearing, sneezing, and coughing. In addition, NOx can have psychological and cognitive impacts on drivers (12). Both the acute and long-term effects of NOx can significantly harm the human body, particularly drivers, and pose substantial risks to traffic safety.

      The atmospheric data used in this study correspond to the day of the fatality rather than the atmospheric conditions during the traffic accident, and the atmospheric conditions may also have a lag effect on road traffic fatalities, which may result in misleading associations. A study demonstrated (13) that approximately 60% of individuals do not survive beyond the first hour of a road traffic accident, indicating a high rate of timely mortality. Additionally, the current study was an ecological study, and individual information on road traffic fatalities, particularly regarding unsafe driving behavior, was not collected. In the future, a survey will be conducted to collect individual information on road traffic fatalities in Shandong Province, aiming to analyze the contributing factors to road traffic fatalities comprehensively. This study is based on an analysis of the cause of death and atmospheric monitoring, which has positive implications for the prevention of road traffic fatalities and the development of related policies.

      The occurrence of road traffic fatalities was found to be linked to meteorological factors, including air temperature; daily accumulated precipitation, and air pollutants such as particulate matter and NO2. The implementation of atmospheric environmental management to reduce air pollution levels, as well as timely and accurate weather warnings and forecasts, has a positive impact on reducing the incidence of road traffic fatalities.

    • No conflicts of interest.

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