Diabetes is a global public health problem, and the impact of air pollutants on type 2 diabetes mellitus (T2DM) has attracted people's attention. This study aimed to assess the association of short-term exposure to six criteria air pollutants with T2DM outpatient visits in Lanzhou, China. We collected data on daily outpatient visits for T2DM, daily meteorological data and hourly concentrations of air pollutants in Lanzhou from 2013 to 2019. An over-dispersed passion generalized addictive model combined with a distributed lag non-linear model was applied to estimate the associations and stratified analyses were performed by gender, age, and season. The models were fitted with different lag structures, including single lag days from the current to the previous seven days (lag0 to lag7) and moving average concentrations over seven lag days (lag01 to lag07). A positive association between multiple air pollutants, especially PM_2.5, NO₂, O₃8h and CO and hospital outpatient visits for T2DM was observed. The largest association between T2DM outpatient visits and PM_2.5 was observed at lag06 (RR 1.013, 95% CI: 1.001, 1.027), NO₂ at lag03 (RR 1.034, 95% CI: 1.018, 1.050), O₃8h at lag05 (RR 1.012, 95% CI: 1.001, 1.023) for an increase of 10 μg m⁻³ and CO at lag03 (RR 1.084, 95% CI: 1.029, 1.142) for an increase of 1 mg m⁻³ in the concentrations. In addition, people aged <65 and males are more susceptible, and air pollutants have a greater impact on the cold season. This study showed that although the air pollution in Lanzhou was improved, there was still a statistical correlation between air pollution exposure and T2DM outpatient visits. Therefore, the local government still needs to strengthen the control of air pollution and enhance the protection awareness of the diabetic population through education and publicity.

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, i.e., 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_2.5, PM₁₀, NO₂, and CO were significantly associated with increased hospital outpatient visits for URTI, with lagged effects observed. The maximum relative risks (RRs) of PM_2.5 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 PM₁₀, 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 PM₁₀ has no significant association in Dingxi. For NO₂, 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 NO₂ 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⁻³ increase in pollutant concentration (CO: 1 mg m⁻³) was associated with elevated outpatient risk of URTI in Jiuquan, with RRs as follows: PM_2.5 (RR = 1.112, 95% CI: 1.023, 1.203), PM₁₀ (RR = 1.041, 95% CI: 1.021, 1.065), NO₂ (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_2.5 (RR = 1.148, 95% CI: 1.062, 1.241), PM₁₀ (RR = 1.052, 95% CI: 1.018, 1.087), NO₂ (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_2.5 (RR = 1.150, 95% CI: 1.095, 1.208), PM₁₀ (RR = 1.038, 95% CI: 1.022, 1.054), NO₂ (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⁻³ increase in pollutant concentration (CO: 1 mg m⁻³) is associated with the following RRs for URTI outpatient visits in Jiuquan: PM_2.5 (RR = 1.101, 95% CI: 1.032, 1.176), PM₁₀ (RR = 1.042, 95% CI: 1.015, 1.069), NO₂ (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_2.5 (RR = 1.171, 95% CI: 1.129, 1.214), PM₁₀ (RR = 1.063, 95% CI: 1.037, 1.090), NO₂ (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_2.5 (RR = 1.090, 95% CI: 1.058, 1.124), PM₁₀ (RR = 1.043, 95% CI: 1.024, 1.062), NO₂ (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.

Emerging evidence indicates an increasing prevalence of allergic rhinitis (AR), potentially linked to air quality. The aim of the present study was to assess the relationship between traffic-related air pollutants (TRAPs) and outpatient visits for AR. Daily outpatient data for AR, air pollutant concentrations, and meteorological data were collected from January 2018 to December 2020 in Dingxi, Longnan, and Tianshui. Utilizing a Quasi-Poisson distribution, a generalized additive model (GAM) was employed in conjunction with distributed lag non-linear models (DLNM) to explore the association and lag effects of TRAPs on AR outpatient visits across the three cities. Stratified analyses based on gender, age, and season were conducted. A total of 11 106 outpatient visits for AR were recorded in the three cities. For an increase of 10 μg m⁻³ in PM_2.5, the effect estimates in Dingxi, Longnan, and Tianshui reached their maximum at lag04, lag06, and lag07, respectively, with relative risk (RR) values of 4.696 (95% CI: 1.890, 11.614), 2.842 (95% CI: 2.102, 4.922), and 1.102 (95% CI: 1.066, 1.140). For NO₂, the highest associations were exhibited in Dingxi (RR = 1.262, 95% CI: 1.081, 1.473) at lag07, in Longnan (RR = 2.554, 95% CI: 2.100, 4.805) at lag06, and in Tianshui (RR = 1.158, 95% CI: 1.106, 1.213) at lag07. Meanwhile, the strongest effects observed for a 1 mg m⁻³ increase in CO were 2.786 (95% CI: 1.467, 5.291) for Dingxi at lag07, 1.502 (95% CI: 1.096, 2.059) for Longnan at lag05, and 1.385 (95% CI: 1.137, 1.686) for Tianshui at lag04. Adults aged 15–64 years appeared to be more susceptible to TRAPs, and the associations were stronger in the cold season. The results of the present study indicate that exposure to TRAPs was positively correlated with outpatient visits for AR.