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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.
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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).
Variable Total
(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 temperature 0.048* 0.018* 0.038* 0.016* 0.047* Daily minimum temperature 0.034* 0.002 0.032* 0.013* 0.047* Daily maximum temperature 0.057* 0.026* 0.043* 0.019* 0.047* Daily accumulated precipitation −0.019* −0.021* −0.004 0.000 −0.004 Sunshine duration 0.016* −0.005 0.014* 0.012* 0.031* Relative humidity 0.007 0.012* 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.
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