Ma Hongran, Qu Furong, Dong Jiyuan, Wang Jiancheng
School of Public Health, Lanzhou University, Lanzhou730000, People's Republic of China.
Gansu Health Vocational College, Lanzhou 730050, People's Republic of China.
Environ Sci Process Impacts. 2025 Jul 21. doi: 10.1039/d4em00748d.
The effects of meteorological factors and air pollutants on upper respiratory tract infection (URTI) varied across different regions depending on climate zones. Previous studies have identified potential interactions between air pollutants and meteorological factors (temperature and relative humidity, , RH) on URTI morbidity. However, research in the inland provinces of Northwest China remains limited. Variations in air pollution levels, pollutant composition, climatic conditions, and population susceptibility across regions contribute to substantial heterogeneity in findings, rendering existing evidence inapplicable to Northwest inland provinces. Therefore, it is necessary to conduct region-specific investigations in representative cities within this area. In this study, we selected cities from different climatic zones in Gansu Province for analysis (temperate continental climate: Jiuquan; temperate semi-arid continental climate: Dingxi; temperate subhumid climate: Tianshui). This study explored several major meteorological factors, including air pollution, temperature and RH, to identify potential modifiable risk factors and their interactive effects on URTI in the three cities in different climate zones. Data from 2017 to 2019 on URTI outpatient visits, air pollutants, and weather in three cities with varying climates were analyzed using generalized additive models and distribution lag nonlinear model (DLNM) to assess the delayed impact of meteorological factors on URTI. Further, bivariate and stratified models explored the interaction between pollutants and meteorological factors on URTI outpatient visits. Our results indicated that PM, PM, NO, and CO were significantly associated with increased hospital outpatient visits for URTI, with lagged effects observed. The maximum relative risks (RRs) of PM were 1.134 (95% CI: 1.057, 1.218) in Jiuquan (lag014), 1.118 (95% CI: 1.069, 1.168) in Dingxi (lag014), and 1.035 (95% CI: 1.013, 1.057) in Tianshui (lag03). For PM, the maximum RRs were 1.045 (95% CI: 1.026, 1.064) in Jiuquan (lag014) and 1.020 (95% CI: 1.005, 1.035) in Tianshui (lag010), while PM has no significant association in Dingxi. For NO, the maximum RRs were 1.118 (95% CI: 1.022, 1.224) in Jiuquan (lag011) and 1.158 (95% CI: 1.104, 1.215) in Tianshui (lag011), while NO has no significant association in Dingxi. For CO, the maximum RRs were 5.433 (95% CI: 2.818, 10.475) in Jiuquan (lag014), 2.289 (95% CI: 1.659, 3.156) in Dingxi (lag014), and 1.835 (95% CI: 1.509, 2.231) in Tianshui (lag012). Stratified analyses indicated that the associations were stronger in males and children (0-14 years). Furthermore, the associations were stronger in cold season than in warm season. Our results also revealed that both low and high temperatures could elevate the risk of outpatient visits for URTI. Compared with the median temperature of each city, the maximum RRs of low temperatures were 1.455 (95% CI: 1.365, 1.550) at lag08, 1.073 (95% CI: 1.027, 1.121) at lag014, and 1.127 (95% CI: 1.067, 1.190) at lag014 for Jiuquan, Dingxi, and Tianshui, respectively. For the high temperature exposure, we only observed significant associations in Jiuquan and Tianshui [RR = 1.143 (95% CI: 1.090, 1.200) at lag05 in Jiuquan, RR = 1.023 (95% CI: 1.008, 1.038) at lag14 in Tianshui], while no significant associations with high temperatures were detected in Dingxi. Stratified analyses by gender and age revealed that extremely low temperatures had a more pronounced effect on males and children aged 0-14 years across the three cities, whereas extremely high temperatures exhibited adverse effects only among males and individuals aged 15-64 years in Jiuquan. Similarly, both low and high RH were associated with increased risk of URTI outpatient visits in the three cities, though the impact of extreme RH varied among them. The effect of extremely low RH on URTI outpatient visits was strongest at lag07 for Jiuquan (RR = 1.296, 95% CI: 1.264, 1.329), lag06 for Dingxi (RR = 1.091, 95% CI: 1.031, 1.155), and lag07 for Tianshui (RR = 1.279, 95% CI: 1.176, 1.390). Adverse effects of extremely high RH were observed exclusively in Dingxi and Tianshui, with the strongest associations at lag7 and lag07, respectively. The relative risk (RR) for Dingxi was 1.043 (95% CI: 1.019, 1.069) and for Tianshui it was 1.069 (95% CI: 1.002, 1.140). Stratified analyses by gender and age indicated that extremely low RH had a more pronounced impact on males and children aged 0-14 years across all three cities, while extremely high RH exerted a greater effect on males and children aged 0-14 years in Dingxi and Tianshui. Meteorological factors and air pollutants have an interactive effect on URTI. The response surface analysis indicated that the adverse effects of the four air pollutants on URTI incidence were most pronounced under low temperature and high concentration conditions across the three cities. Stratified analysis demonstrated that, under low temperature, each 10 μg m increase in pollutant concentration (CO: 1 mg m) was associated with elevated outpatient risk of URTI in Jiuquan, with RRs as follows: PM (RR = 1.112, 95% CI: 1.023, 1.203), PM (RR = 1.041, 95% CI: 1.021, 1.065), NO (RR = 1.341, 95% CI: 1.230, 1.462), and CO (RR = 2.603, 95% CI: 1.433, 4.728). In Dingxi, the corresponding RRs were: PM (RR = 1.148, 95% CI: 1.062, 1.241), PM (RR = 1.052, 95% CI: 1.018, 1.087), NO (RR = 1.128, 95% CI: 1.055, 1.206), and CO (RR = 2.294, 95% CI: 1.842, 2.857). In Tianshui, the RRs were: PM (RR = 1.150, 95% CI: 1.095, 1.208), PM (RR = 1.038, 95% CI: 1.022, 1.054), NO (RR = 1.305, 95% CI: 1.162, 1.466), and CO (RR = 1.682, 95% CI: 1.462, 1.935). Similarly, the response surface plots indicate that the adverse effects of the four air pollutants on URTI incidence in the three cities are most pronounced under low RH and high concentration conditions. Stratified analyses reveal that, under low RH, each 10 μg m increase in pollutant concentration (CO: 1 mg m) is associated with the following RRs for URTI outpatient visits in Jiuquan: PM (RR = 1.101, 95% CI: 1.032, 1.176), PM (RR = 1.042, 95% CI: 1.015, 1.069), NO (RR = 1.236, 95% CI: 1.056, 1.446), and CO (RR = 2.569, 95% CI: 1.625, 4.060). In Dingxi, the corresponding RRs are: PM (RR = 1.171, 95% CI: 1.129, 1.214), PM (RR = 1.063, 95% CI: 1.037, 1.090), NO (RR = 1.141, 95% CI: 1.042, 1.249), and CO (RR = 2.071, 95% CI: 1.645, 2.607). In Tianshui, the RRs are: PM (RR = 1.090, 95% CI: 1.058, 1.124), PM (RR = 1.043, 95% CI: 1.024, 1.062), NO (RR = 1.180, 95% CI: 1.115, 1.248), and CO (RR = 1.894, 95% CI: 1.631, 2.210). In conclusion, both air pollutants and meteorological factors had an influence on URTI outpatient visits, and the influence on URTI outpatient visits may have an interaction.
气象因素和空气污染物对不同地区上呼吸道感染(URTI)的影响因气候带而异。以往研究已确定空气污染物与气象因素(温度和相对湿度,RH)对URTI发病率存在潜在相互作用。然而,中国西北内陆省份的相关研究仍然有限。不同地区空气污染水平、污染物成分、气候条件和人群易感性的差异导致研究结果存在显著异质性,使得现有证据不适用于西北内陆省份。因此,有必要在该地区具有代表性的城市开展针对性调查。本研究选取甘肃省不同气候带的城市进行分析(温带大陆性气候:酒泉;温带半干旱大陆性气候:定西;温带半湿润气候:天水)。本研究探讨了包括空气污染、温度和RH在内的几个主要气象因素,以确定潜在的可改变风险因素及其对不同气候带三个城市URTI的交互作用。利用广义相加模型和分布滞后非线性模型(DLNM)分析了2017年至2019年三个气候不同城市的URTI门诊就诊数据、空气污染物和天气情况,以评估气象因素对URTI的延迟影响。此外,双变量和分层模型探讨了污染物与气象因素对URTI门诊就诊的相互作用。我们的结果表明,PM、PM、NO和CO与URTI医院门诊就诊增加显著相关,并观察到滞后效应。酒泉(滞后014)的PM最大相对风险(RRs)为1.134(95%CI:1.057,1.218),定西(滞后014)为1.118(95%CI:1.069,1.168),天水(滞后03)为1.035(95%CI:1.013,1.057)。对于PM,酒泉(滞后014)的最大RRs为1.045(95%CI:1.026,1.064),天水(滞后010)为1.020(95%CI:1.005,1.035),而定西的PM无显著关联。对于NO,酒泉(滞后011)的最大RRs为1.118(95%CI:1.022,1.224),天水(滞后011)为1.158(95%CI:1.104,1.215),而定西的NO无显著关联。对于CO,酒泉(滞后014)的最大RRs为5.433(95%CI:2.818,10.475),定西(滞后014)为2.289(95%CI:1.659,3.156),天水(滞后012)为1.835(95%CI:1.509,2.231)。分层分析表明,男性和儿童(0 - 14岁)的关联更强。此外,寒冷季节的关联比温暖季节更强。我们的结果还表明,低温和高温都会增加URTI门诊就诊风险。与每个城市的中位温度相比,酒泉、定西和天水低温的最大RRs分别在滞后08时为1.455(95%CI:1.365,1.550)、滞后014时为1.073(95%CI:1.027,1.121)和滞后014时为1.127(95%CI:1.067,1.190)。对于高温暴露,我们仅在酒泉和天水观察到显著关联[酒泉滞后05时RR = 1.143(95%CI:1.090,1.200);天水滞后14时RR = 1.023(95%CI:1.008,1.038)],而定西未检测到与高温的显著关联。按性别和年龄分层分析表明,极低温度对三个城市的男性和0 - 14岁儿童影响更明显,而极高温度仅对酒泉的男性和15 - 64岁个体有不良影响。同样,低RH和高RH均与三个城市URTI门诊就诊风险增加相关,尽管极端RH的影响在不同城市有所不同。极低RH对URTI门诊就诊的影响在酒泉滞后07时最强(RR = 1.296,95%CI:1.264,1.329),定西滞后06时(RR = 1.091,95%CI:1.