Effects of meteorological factors and atmospheric pollutants on the prevalence of respiratory adenovirus in children in Lanzhou, Northwest China

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Human adenovirus (HADV) is a common pathogen that causes acute respiratory infections in children. This study aimed to investigate the relationship between HAdV infection and meteorological factors and atmospheric pollutants in children in Lanzhou, Northwest China, and to gain insights into the influence of environmental factors on virus transmission. Methods Clinical specimens of acute respiratory tract infections in children from a sentinel hospital in Lanzhou City between January 2023 and February 2025 were collected for respiratory adenoviral nucleic acid testing. The positive detection rates of different ages, sexes, and seasons were analyzed, and stepwise linear regression combined with generalized additive modeling (GAM) was used to explore the correlation between HAdV infection and meteorological factors and air pollutants. Results From January 2023 to February 2025, a total of 1,339 throat swab samples were collected from children with acute respiratory infections (ARI), with a male-to-female ratio of 1.48:1. The HADV positivity rate in 2024 was 11.03% (91/825), higher than the 5.45% (28/514) in 2023. The positivity rates among male and female children were 8.65% (69/798) and 9.24% (50/541), respectively. Among these, the positive detection rates in preschool-aged and school-aged children were higher than those in infant and toddler groups, at 10.43% (44/422), 10.34% (45/435), 5.15% (10/194), and 6.94% (20/288), respectively. HADV was detected in all four seasons, with the highest detection rates in autumn and winter, at 10.85% (28/258) and 12.78% (62/485), respectively. The positive detection rate of HADV showed a significant negative correlation with meteorological factors (temperature, sunshine duration, wind speed), with correlation coefficients of: r = -0.640 ( P < 0.05); r = -0.638 ( P < 0.05); r = -0.621 ( P < 0.05); It showed a significant positive correlation with atmospheric pollutants (CO, NO₂, and SO₂), with correlation coefficients of r = 0.761 ( P < 0.05); r = 0.685 ( P < 0.05); r = 0.716 ( P < 0.05); and a significant negative correlation with atmospheric pollutant O₃, with a correlation coefficient of r = -0.694 ( P < 0.05). GAM analysis showed that meteorological factors (temperature, sunshine duration, and wind speed) were all significantly non-linearly associated with the number of adenovirus infections. Conclusions HADV was detected at a higher rate in children in the preschool and school-age groups, and most cases were detected in the fall and winter seasons. The positive detection rate of HADV was negatively correlated with meteorological factors (temperature, hours of sunshine, and wind speed) and the atmospheric pollutant O₃, and positively correlated with atmospheric pollutants (CO, NO₂, and SO₂). The influence of these pollutants on the prevalence of HAdV infection should not be ignored. Earth and environmental sciences/Climate sciences Health sciences/Diseases Earth and environmental sciences/Environmental sciences Health sciences/Risk factors Acute respiratory infection Respiratory adenovirus Meteorological factors Atmospheric pollutants Correlation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Acute respiratory infections (ARI) are a leading cause of illness and death among children and infants worldwide. Approximately 80% of acute respiratory infections are caused by viruses [ 1 ], with human adenovirus (HADV) accounting for 2–15% of respiratory infections in children [ 2 ][ 3 ]. HADV belongs to the genus Human Adenovirus within the family Adenoviridae. It is a non-enveloped, linear double-stranded DNA virus with an icosahedral symmetry structure [ 4 ]. It can infect the upper and lower respiratory tracts of children, causing diseases such as bronchitis, laryngitis, tonsillitis, and pneumonia [ 5 ][ 6 ]. Climate change and air pollution play important roles in disease occurrence and pathogen transmission. Multiple studies have shown that air pollutants have significant adverse effects on respiratory diseases [ 7 ][ 8 ][ 9 ][ 10 ]. In recent years, researchers have also found that meteorological factors can influence diseases related to respiratory infections [ 11 ][ 12 ][ 13 ]. Lanzhou is an inland city in China with a dry climate, scarce precipitation, and poor natural conditions. This study selected Lanzhou, an inland town in northwestern China, as a representative case. By systematically collecting clinical samples from pediatric acute respiratory infection cases at a sentinel hospital in the Lanzhou region and combining them with concurrent meteorological factors and air pollutant data, this study aims to thoroughly analyze the potential impact of meteorological factors and air pollutants on the epidemiological trends of HADV infections. Materials and methods Overview of the study area Lanzhou City is located in northwestern China, in the central part of Gansu Province, with its city center at 36°03′ north latitude and 103°40′ east longitude. It is a typical mountainous city in China, with the entire urban area situated in a dumbbell-shaped river valley, surrounded by mountains on all sides. The basin has low wind speeds, with a calm wind rate of 62%. The depth-to-width ratio of the Lanzhou Valley is approximately 0.07, indicating an extremely enclosed mountainous basin terrain. Due to the absence of warm, humid maritime air currents, Lanzhou experiences sparse rainfall throughout the year and has a dry climate. The region receives ample sunlight and has high evaporation rates, characteristic of a typical temperate continental climate. Specimen Source and Nucleic Acid Detection This study selected pediatric cases of acute respiratory infections from January 2023 to February 2025 at a sentinel hospital in Lanzhou City, Gansu Province, as the study subjects. Inclusion criteria: ① Age ≤ 16 years; ② Symptoms consistent with acute infection (meeting at least one of the following criteria): fever, chills, abnormal white blood cell distribution count (decreased or increased to 5.0 × 10^9/L to 12.0 × 10^9/L); ③ Clinical symptoms (meeting at least one of the following): rhinorrhea, cough with sputum, wheezing, pharyngeal or laryngeal edema or pain, chest tightness or pain, fatigue, abdominal pain, or diarrhea. Qualified medical staff at the sentinel hospital strictly followed the monitoring protocol to collect nasopharyngeal swab samples and gather case data. Specimens must be stored at 4°C within 24 hours of collection and transported to the laboratory. Specimens submitted for testing after 24 hours must be stored at -70°C. This study used the Xi'an Tianlong Nucleic Acid Rapid Extraction Kit (magnetic bead method) to extract total viral nucleic acid from the samples. The respiratory pathogen nucleic acid detection kit developed by Beijing Zhuocheng Huisheng Biotechnology Co., Ltd. was used in conjunction with the ABI Q5 real-time fluorescent quantitative PCR instrument from the United States for HADV detection. All experimental steps were strictly performed according to the kit instructions. Meteorological and Air Quality Data Collection Meteorological data were obtained from the National Meteorological Science Data Center (https://data.cma.cn/), and air quality data were downloaded from the China Air Quality Online Analysis Platform (https://www.aqistudy.cn/). The data include the average monthly temperature (°C), average monthly relative humidity, average monthly wind speed (m/s), average monthly sunshine hours (h), and average monthly rainfall (mm) for the whole year from 2023 to 2024; air pollution indicators include PM2.5, PM10, SO 2 , NO 2 , CO, and O 3 . Statistical Analysis Data were processed and analyzed using IBM SPSS software (version 26.0). Continuous variables with a normal distribution were compared using t-tests, and non-normally distributed variables were analyzed using the Mann-Whitney U test. Categorical data were analyzed using the chi-square or Fisher's exact tests. Normally distributed data are expressed as mean ± standard deviation, whereas non-normally distributed data are expressed as median and percentiles (P25; P75). Comparisons of means between groups were performed using Duncan's t-test. Pearson’s correlation analysis was used if the data met the assumption of normality; otherwise, Spearman's nonparametric test was used. The interpretation of the correlation coefficient r is as follows: 0 < lrl < 0.2 indicates a weak correlation, 0.2 < lrl < 0.5 indicates a low correlation, 0.5 < lrl < 0.8 indicates a significant correlation, and 0.8 < lrl < 1 indicates a high correlation. Statistical significance was set at P < 0.05. GraphPad Prism 8.0 was used for data visualization. In addition, this study analyzed the nonlinear relationships among meteorological factors, air pollutants, and children's acute respiratory infection adenovirus (HAdV) based on the generalized additive model (GAM) with Poisson distribution, which can flexibly capture the complex nonlinear response between predictor and response variables by introducing a smoothing function (spline), effectively overcoming the limitations of the traditional linear model. Model fitting uses penalized likelihood estimation, and smoothness is automatically optimized by multiple generalized cross-validation (MGCV). The Poisson distribution was used as the error distribution, and multiple monthly data were used for modeling. In the model, the number of HAdV-positive cases was used as the response variable, monthly mean meteorological factors and air pollutants were used as the main predictors, and a smoothing term was introduced for the analysis while adjusting for temporal trends and seasonal factors (monthly series simulated using cyclic B-spline) to control for potential temporal and seasonal confounding effects. The GAM model is as follows: log(E(Y t ))=β 0 +s(temp t ,k=5)+s(t,k=4)+s(month t ,bs="cc",k=6) In this equation, Y t represents the number of positive cases in the tth month, s() denotes the smoothing function, k is the dimension parameter of the basis function, and bs ="cc" indicates the use of cyclic splines to capture the seasonal effects. Results Distributional Characteristics of Children with ARI From January 2023 to February 2025, a total of 1339 acute respiratory cases in children were collected. There were 798 (798/1339, 59.60%) male and 541 (541/1339, 40.40%) female children. The male-to-female ratio was 1.48:1. In the age group, there were 194 cases in the infant group (≤ 1 year), 288 cases in the toddler group (1–3 years), 422 cases in the preschool group (3–6 years), and 435 cases in the school-age group (> 6 years). In the seasonal group, there were 295 cases in spring (March-May), 228 cases in summer (June-August), 258 cases in fall (September-November), and 485 cases in winter (December-February). Table 1 Respiratory adenovirus detection Characteristics 2023(514) 2024(641)/2025(184) Total Age positive,(n) ≤ 1year 55(1) 139(9) 194(10) 1-3years 141(8) 147(12) 288(20) 3-6years 169(14) 253(30) 422(44) > 6years 149(5) 286(40) 435(45) Genderpositive,(n) Male (cases) 299(16) 499(53) 798(69) Female (cases) 215(12) 326(38) 541(50) Season positive,(n) Spring 115(1) 180(14) 295(15) Summer 200(12) 28(0) 228(12) Autumn 85(9) 173(19) 258(28) Winter 157(29) 328(33) 485(62) Epidemiological characteristics of respiratory HADV in children The positive detection rate of HADV was 5.45% (28/514) and 11.03% (91/825) in 2023 and 2024, respectively, and the total positive detection rate was 8.89% (119/1339). The positive detection rate was 8.65% (69/798) in male children and 9.24% (50/541) in female children. The positive detection rate of HADV increased with age, with the highest rate in the preschool group and the second highest in the school-age group. It was 5.15% (10/194) in the infant group, 6.94% (20/288) in the toddler group, 10.43% (44/422) in the preschool group, and 10.34% (45/435) in the school-age group. In the > 3 years age group versus ≤ 3 years, the positive detection rate was statistically significant ( P < 0.05), as shown in Fig. 1 A. Winter is the high season for HADV, and the positive detection rates of HADV in the four seasons were spring: 5.08% (15/295), summer: 5.26% (12/228), autumn: 10.85% (28/258), and winter: 12.78% (62/485). There was a statistically significant (P < 0.01) positive detection rate in fall and winter versus spring and summer, as shown in Fig. 2 B. Relationship between meteorological factors and HADV in Lanzhou City Overview of meteorological elements in Lanzhou City From 2023 to 2024, the average monthly temperature in Lanzhou City was 11.74 ± 10.36°C, the average monthly precipitation was 25.70 ± 3.08 mm, the average monthly relative humidity was 49.81 ± 11.38%, the average monthly wind speed was 1.09 ± 0.19 m/s, and the average monthly sunshine hours were 178.07 ± 44.72, as shown in Table 2 . The temperature in Lanzhou City showed an increasing trend from January to July, with the highest temperature in July, followed by a decline. Relative humidity is low throughout the year, with the highest relative humidity in October, and precipitation peaks in September. The average monthly sunshine hours from May to August are longer than those in other months, and the average monthly wind speed does not change much. The average temperature in Lanzhou varies with the seasons, with the highest in summer and the lowest in winter, reflecting the temperate continental climate of Lanzhou, with cold winters and hot summers, significant temperature differences, low precipitation, and uneven seasonal distribution. The average monthly sunshine hours are significantly longer in spring and summer than in fall and winter, and the average wind speed is higher in spring and summer than in fall and winter. Figure 2 . Table 2 Meteorological Factors, Descriptive Statistical Results of Atmospheric Pollutants Variables Mean Median SD Minimum P25 P75 Maximum Meteorological factor Average temperature(°C) 11.74 11.55 10.37 -4.90 1.60 21.58 25.30 Average humidity(%) 49.81 48.10 11.38 32.00 40.63 57.50 72.60 Average rainfall(mm) 25.70 17.40 28.30 0.00 8.00 35.53 122.70 Average sunshine duration(h) 178.07 171.00 44.72 108.00 147.08 214.58 249.80 Average wind speed(m/s) 1.09 1.10 0.19 0.80 0.90 1.20 1.50 Air Pollutant PM2.5(ug/m 3 ) 40.67 39.50 14.42 21.00 27.00 55.00 66.00 PM10(ug/m 3 ) 80.71 82.00 41.85 39.00 53.50 120.25 174.00 SO 2( mg/m 3 ) 13.50 11.50 5.33 7.00 10.00 16.75 28.00 NO 2 (ug/m 3 ) 36.46 31.50 13.71 20.00 26.00 51.75 61.00 CO(mg/m 3 ) 0.77 0.62 0.34 0.38 0.52 1.03 1.49 O 3 (ug/m 3 ) 98.88 99.50 35.76 23.00 70.50 138.25 149.00 Correlation analysis between HADV and meteorological factors The relationship between respiratory adenovirus (HADV) infection and environmental factors was investigated and validated using correlation heatmap analysis (Fig. 3 a) and regression analysis. Correlation heatmap analysis revealed that HADV infection was negatively correlated with the monthly average wind speed, monthly average temperature, monthly average sunshine duration, and monthly average precipitation, and weakly positively correlated with the monthly average relative humidity. Furthermore, the monthly average wind speed was significantly positively correlated with the monthly average temperature and sunshine duration. Additionally, the monthly average temperature showed a significant positive correlation with the monthly average precipitation, and the monthly average relative humidity also positively correlated with the monthly average precipitation, suggesting complex interactions among these environmental variables. Regression analysis further substantiated these findings, demonstrating that the HADV positivity rate was significantly negatively correlated with the monthly average temperature, monthly average sunshine duration, and monthly average wind speed, with correlation coefficients of r = -0.640 ( P < 0.05), r = -0.638 ( P < 0.05), and r = -0.621 ( P < 0.05), respectively (Fig. 3 b), which closely aligned with the results of the correlation heatmap analysis. Relationship between atmospheric pollutants and HADV in Lanzhou City Overview of atmospheric pollutants in Lanzhou City The air pollutants in Lanzhou City are PM2.5, PM10, CO, NO 2 , SO 2 , and O 3 . The average concentrations for the two-year period from 2023 to 2024 are as follows: 40.67 ± 14.34 µg/m³; 89.71 ± 39.10 µg/m³; 0.77 ± 0.33 mg/m³; 36.46 ± 12.95 µg/m³; 13.50 ± 5.30 mg/m³; 98.88 ± 36.01 µg/m³, as shown in Table 2 . The Air Quality Index (AQI) is an indicator used to measure environmental air quality. The AQI indicates the quantitative severity of primary pollutants, derived from the air quality sub-indices of various air pollutants calculated simultaneously, with the AQI representing the value of primary pollutants. In China, the AQI is divided into six levels: Level 1 is Excellent (0 ~ 50), Level 2 is Good (51 ~ 100), Level 3 is Moderate Pollution (101 ~ 150), Level 4 is Moderate Pollution (151 ~ 200), Level 5 is Severe Pollution (201 ~ 300), and Level 6 is Very Severe Pollution (over 300). The average AQI over the 2-year period was 85.83 ± 15.62. The average concentrations of PM2.5, PM10, CO, NO 2 , and SO 2 were higher at the beginning and end of the year and lower in the middle of the year. O 3 showed the opposite trend. PM2.5 had higher average concentrations in January and December; PM10 had higher concentrations from January to April and in December, while other months had relatively lower concentrations; CO had higher average concentrations in January–February and November–December compared to other months; NO₂had higher monthly average concentrations in January–March and October–December, with lower concentrations in July; SO₂ had higher average concentrations in January–March and November–December compared to other months. Unlike other air pollutants, O₃ gradually reached its peak during the middle of the year. The AQI is higher from January to April, with relatively stable values in other months, as shown in Fig. 4 . Correlation analysis between HADV and air pollutants Correlation heatmap analysis ( Fig. 5a) revealed a complex relationship between human adenovirus (HADV) infections and air pollutants. Specifically, HADV infection showed positive correlations with concentrations of PM2.5, CO, NO₂, and SO₂, while exhibiting a negative correlation with ozone (O₃) levels. Additionally, significant associations among the pollutants were observed: most pollutants were positively correlated with each other, except for O₃, which was negatively correlated with PM2.5, PM10, CO, NO₂, and SO₂, suggesting potential synergistic effects among certain pollutants. Further validation through regression analysis ( Fig. 5b) indicated that the positivity rate of HADV was significantly positively associated with the concentrations of CO, NO₂, and SO₂, with correlation coefficients of r = 0.761 (P < 0.05), r = 0.685 (P < 0.05), and r = 0.716 (P < 0.05), respectively. Conversely, O₃ levels were negatively correlated with HADV positivity (r = -0.694, P < 0.05). These findings suggest that elevated levels of specific air pollutants are associated with increased HADV infection rates, whereas higher ozone concentrations may have a protective or inverse relationship with HADV infection rates. GAM Analysis Results Based on the collected surveillance data, the nonlinear relationship between meteorological factors (monthly mean temperature, monthly mean sunshine hours, and monthly mean wind speed) and the number of respiratory adenovirus infections was systematically analyzed using a GAM. The results showed a significant nonlinear response between mean monthly temperature and the number of positive adenovirus detections, and the model explained 84.80% of the deviation, indicating that temperature has a strong predictive ability for the risk of adenovirus transmission. The peak risk for temperature was − 4.9°C and the relative risk at this temperature was elevated 5.5 times compared to the lowest risk temperature of 7.9°C ( Fig. 6 a). There was also a significant nonlinear relationship between monthly mean sunshine hours and the number of adenovirus infections, with a model explaining up to 97.80% of this deviation. The number of sunshine hours at 108h corresponded to the highest risk of infection, which was significantly higher than the risk at the lowest sunshine hours of 249.8h, with the peak risk elevated by 7.4×10^5 times compared to the lowest value ( Fig. 6 b). A significant nonlinear association was also demonstrated between mean monthly wind speed and the number of adenovirus infections, with a model explaining a deviation of 96.10%. The risk was highest at a wind speed of 0.8 m/s, which elevated the peak risk by a factor of 6.6×10^7 compared with the lowest risk wind speed of 1.5 m/s ( Fig. 6 c). Taken together, all of the above meteorological factors have an important impact on adenovirus transmission, and their nonlinear effects suggest that a multifactorial evaluation should be combined to optimize disease warning, prevention, and control strategies. Discussion This study aimed to investigate and analyze the impact of meteorological factors and atmospheric pollutants on the prevalence of respiratory adenovirus infections in children in the Lanzhou region of Northwest China. Data show that the positive detection rates for respiratory adenovirus in children in 2023 and 2024 were 5.45% (28/514) and 11.03% (91/825), respectively, which were higher than the 2.05% and 9.28% reported in Hangzhou, China, but both showed higher rates in 2024 than in 2023 [ 14 ]. This is because the period from 2023 to 2024 was the post-COVID-19 era, with control measures having been lifted in 2023, coinciding with the widespread circulation of SARS-CoV-2 and influenza viruses [ 15 ]. Public preventive awareness remained strong, with continued adherence to non-pharmaceutical interventions (NPIs) such as mask-wearing and frequent handwashing when outdoors, thereby reducing HADV transmission. However, the positive detection rate for HADV increased in 2024, which may be related to the weakening of NPIs among the public, potentially increasing the risk of HADV infection. In this study, there was no statistically significant difference in the HADV-positive detection rates between the sexes ( P > 0.05). In terms of age groups, the positive detection rates in the infant and toddler groups were significantly lower than those in the preschool and school-age groups ( P < 0.05), a trend consistent with global reports [ 16 ][ 17 ]. This may be related to the gradual relaxation of NPIs and increased contact among school-age children after returning to school, which facilitated HAdV transmission. In seasonal groupings, the positive detection rate was significantly higher in autumn and winter than in spring and summer ( P < 0.05), consistent with the findings of a study by Zhao et al. in Guangdong Province [ 18 ]. Lanzhou, the capital of Gansu Province, is located in the semi-arid region of northwestern China (longitude 102°35′55″ to 104°34′29″E, latitude 35°34′20″ to 37°07′N) in a typical river valley basin, with an arid climate with little rainfall, strong evaporation, low wind speeds, a high frequency of static winds, and a deep inversion layer, which is characterized by a temperate semi-arid climate [ 19 ]. As an important petrochemical, metallurgical, and mechanical industrial base, Lanzhou has long suffered from severe air pollution. Although effective control measures have been implemented since 2013, the PM2.5 concentration is still approximately 1.14 times the national annual standard limit (35㎍/m³). Various theories on the association between meteorological factors and respiratory viruses have been proposed [ 20 ][ 21 ]. The mechanisms by which meteorological factors affect respiratory viruses are characterized by four main aspects: virus survival, infection, transmission, and the human immune response. First, meteorological factors can alter the stability, viability, activity, pathogenicity, and virulence of viruses, thereby prolonging or shortening their survival time. Relevant laboratory studies have shown that most viruses survive longer at lower temperatures and relative humidities [ 22 ]. Some aerosol viability experiments have shown that adenoviruses are more stable at high relative humidity levels [ 23 ]. In this study, a combination of stepwise linear regression analysis and GAM modeling was used to reveal the correlation between meteorological factors, changes in air pollutant indicators, and the positive detection rate of HADV. HADV showed a significant negative correlation with the average monthly temperature, with a higher positive detection rate at colder temperatures, and the risk of HADV infection reached a peak when the average temperature for a given month was − 4.9°C, suggesting strengthened precautions. Therefore, children were more likely to be infected with HADV during the lower temperatures of winter in Lanzhou, consistent with the findings of a previous study. [ 24 ]. HADV showed a significant negative correlation with the average monthly sunshine hours, and the lower the positive detection rate of HADV in the case of longer sunshine (249.8 h), the lower the risk of contracting HADV. This could be attributed to high evaporation due to sufficient sunshine, resulting in a dry climate in the area, which affects the survival of HADV. The risk of HADV peaked under the specific meteorological condition of a monthly average of approximately 108 sunshine hours, suggesting that surveillance and preventive measures should be strengthened under this meteorological condition. HADV showed a significant negative correlation with the monthly average wind speed, which may be due to the fact that the higher the wind speed is, the more quickly HADV spreads and the more difficult it is to attach, and thus the more difficult it is to infect the population. The relative risk of respiratory adenovirus infection in children peaks when the average wind speed in the environment reaches 0.8 m/s in a given month, indicating an increased risk of transmission that warrants special attention and preventive and control measures. Ambient air pollution is considered an important risk factor for viral respiratory infections, and its mechanism of action is complex and may involve multiple dimensions, such as inducing inflammatory responses, promoting cell death, triggering oxidative stress, and regulating viral receptor expression [ 25 ]. The results of this study revealed that elevated levels of air pollutants were closely associated with the detection rate of HADV infection. Air pollutants are mainly categorized into gaseous pollutants and total suspended particulate matter (TSPs). The gaseous pollutants are CO, SO 2 , NO 2 , and O 3 , whereas TSPs are mainly composed of PM2.5 and PM10 particles. Studies have shown that exposure to air pollutants triggers the generation of reactive oxygen species (ROS), leading to oxidative stress and increased mucus secretion and cytokine production, which can adversely affect lung health [ 26 ][ 27 ]. In addition, air pollution affects host defense mechanisms. Exposure to air pollutants may impair phagocytosis by macrophages and disrupt immune function [ 28 ]. Together, these factors increase the susceptibility of the population to respiratory pathogens. Previous studies have shown that SO₂is a known inducer of respiratory inflammation, leading to direct airway damage and disruption of respiratory barrier function [ 29 ][ 30 ]. Our results showed that CO, NO 2 , and SO 2 were all positively correlated with the positive detection rate of HADV but significantly negatively correlated with O 3 , indicating that high concentrations of CO, NO 2 , and SO 2 increased HADV infections, while high concentrations of O 3 decreased HADV infections. The positive correlation between SO₂ and the positive detection rate of HADV is consistent with the results of a study in Wuhan, China [ 31 ]. Therefore, the influence of meteorological factors and air pollutants on the positive detection rate of HADV in children should not be overlooked. More active and appropriate measures should be taken to protect children from respiratory infections during extreme weather events or periods of high air pollution. This study has some limitations. First, the study period was short and focused on only one pathogen, HAdV, and the relationship between meteorological factors and atmospheric pollutants and their positive detection rates remains preliminary. Second, potential confounding factors were not fully controlled, which may affect the comprehensiveness of the association analyses. In addition, the study was limited to Lanzhou, which limits its external applicability. Future studies should expand the time span, cover more respiratory pathogens, and expand the geographic scope to obtain more generalized and in-depth conclusions and further reveal the role of environmental factors in respiratory viral infections. Conclusion In summary, this study thoroughly investigated the potential impact of meteorological factors (temperature, sunshine duration, and wind speed) and atmospheric pollutants (CO, O 3 , NO 2 , and SO 2 ) on the prevalence of human adenovirus (HAdV) infection among children in the Lanzhou region. The results showed that the detection rate of HAdV was relatively high among preschool and school-age children, with a significant concentration during autumn and winter, revealing a close association between environmental factors and HAdV detection rates. This study particularly emphasized the need to strengthen targeted public health intervention measures during periods of low temperatures and high pollution to effectively protect children's health. This study not only fills the gap in research on HAdV epidemiology and its environmental risk factors in the Lanzhou region but also provides valuable data to support the development of more precise and effective public health strategies under different climate and pollution conditions. By elucidating how meteorological factors and changes in air pollutants may influence the occurrence of HAdV infections in children, our research provides important scientific evidence and practical guidance for optimizing disease prevention and control measures and formulating relevant health policies. Declarations Acknowledgements We thank all the participants of this study. Availability of data and materials All data geneerated or analyzed during this study are included in this published article. Fundings This work was supported by the Gansu Provincial Key Research and Development Program-Social Development Field Program Project (Grant NO. 23YFFA0051) . Ethics approval and consent to participate This study was approved by the Ethical Committee of Gansu Provincial Center for Disease Control and Prevention, and carried out strictly in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants and from their legal guardians who were aged ≤16 years. Data were stored and analyzed anonymously. Inclusion criteria: ①age≤16 years old; ②symptoms consistent with acute infection (at least one of the following): fever, chills, abnormal white blood cell distribution count (decreased or increased);③clinical symptoms (at least one of the following): runny nose, coughing and sputum, wheezing, pharyngeal and laryngeal edema or soreness, chest tightness and chest pain, fatigue, abdominal pain and diarrhea. Nasopharyngeal swab specimens were collected by qualified medical staff of the sentinel hospitals in strict accordance with the monitoring program, and case information was collected. Specimens were stored at 4 ℃ for 24 h after collection and transported to the laboratory, and specimens sent for examination for more than 24 h were stored at -70℃. Constent to participate Informed consent was obtained from the parents or guardians of all participants. The parents or guardians were informed of the laboratory results of pathogen detection. Constent for publication Not applicable Competing interests The authos declare no competing interests. Clinical trial number Not applicable. Authors ' contributions B. W. method design, experimental manipulation, first draft writing, software processing, review and editorial writing; H. Z. experimental manipulation, software processing, data management, first draft writing; X. Z. program design, obtaining grants; M. D. program design, project management; S. L. program design, project management; H. W. experimental manipulation; M. W. experimental manipulation.All authors read and approved the final manuscript. 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Society for Experimental Biology and Medicine (New York, N.Y.), 84(3):570-3.https://doi.org/10.3181/00379727-84-20714(1953). Louie Janice K, Kajo Adriana E, Holodniy Mark, et al. Severe pneumonia due to adenovirus serotype 14: a new respiratory threat?. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 46(3):421-5.https://doi.org/10.1086/525261(2008). Otto William R, Lamson Daryl M, Gonzalez Gabriel, et al. Fatal Neonatal Sepsis Associated with Human Adenovirus Type 56 Infection: Genomic Analysis of Three Recent Cases Detected in the United States. Viruses,13(6).https://doi.org/10.3390/v13061105(2021). Tran HM, Tsai FJ, Lee YL, et al. The impact of air pollution on respiratory diseases in an era of climate change: A review of the current evidence. Sci Total Environ. 898:166340.https://doi.org/10.1016/j.scitotenv.2023.166340(2023). Zheng J, Yang X, Hu S, Wang Y, Liu J. Association between short-term exposure to air pollution and respiratory diseases among children in China: a systematic review and meta-analysis. Int J Environ Health Res.32(11):2512-2532.https://doi.org/10.1080/09603123.2021.1974822(2022). Lei J, Chen R, Liu C, et al. Fine and coarse particulate air pollution and hospital admissions for a wide range of respiratory diseases: a nationwide case-crossover study. Int J Epidemiol. 52(3):715-726.https://doi.org/10.1093/ije/dyad056(2023). Jiang S, Tang L, Lou Z, et al. The changing health effects of air pollution exposure for respiratory diseases: a multicity study during 2017-2022. Environ Health. 23(1):36.https://doi.org/10.1186/s12940-024-01083-1(2024). Nie Y, Lu Y, Wang C, et al. Effects and Interaction of Meteorological Factors on Pulmonary Tuberculosis in Urumqi, China, 2013-2019. Front Public Health. 10:951578. https://doi.org/10.3389/fpubh.2022.951578(2022). Liu Y, Liu J, Chen F, et al. Impact of meteorological factors on lower respiratory tract infections in children. J Int Med Res.44(1):30-41.https://doi.org/10.1177/0300060515586007(2016). Lee MH, Mailepessov D, Yahya K, Loo LH, Maiwald M, Aik J. Air quality, meteorological variability and pediatric respiratory syncytial virus infections in Singapore. Sci Rep.13(1):1001.https://doi.org/10.1038/s41598-022-26184-0(2023). Zhou H, Chen D, Ru X, et al. Epidemiological and clinical characteristics of adenovirus-associated respiratory tract infection in children in Hangzhou, China, 2019-2024. J Med Virol. 96(10):e29957.https://doi.org/10.1002/jmv.29957(2024). Zhang G, Wang K, Ba L, Dong S, Gao J. Dynamic changes in the circulation of respiratory pathogens in children during and after the containment of the 2019 coronavirus disease pandemic in Kunming, China. Eur J Clin Microbiol Infect Dis.43(12):2259-2268.https://doi.org/10.1007/s10096-024-04945-1(2024). Fukuda Y, Togashi A, Hirakawa S, et al. A significant outbreak of respiratory human adenovirus infections among children aged 3-6 years in Hokkaido, Japan, in 2023. J Med Virol. 96(7):e29780.https://doi.org/10.1002/jmv.29780(2024). Liu P, Xu M, Lu L, et al. The changing pattern of common respiratory and enteric viruses among outpatient children in Shanghai, China: Two years of the COVID-19 pandemic. J Med Virol. 94(10):4696-4703.https://doi.org/10.1002/jmv.27896(2022). Zhao Q, Ke P, Hu L, et al. Epidemiological Characteristics of Upper Respiratory Tract Pathogens in Children in Guangdong, China. Clin Respir J.18(10):e70011.https://doi.org/10.1111/crj.70011(2024). Chai G, He H, Sha Y, Zhai G, Zong S. Effect of PM2.5 on daily outpatient visits for respiratory diseases in Lanzhou, China. Sci Total Environ. 649:1563-1572.https://doi.org/10.1016/j.scitotenv.2018.08.384(2019). Lowen AC, Steel J. Roles of humidity and temperature in shaping influenza seasonality. J Virol. 88(14):7692-7695.https://doi.org/10.1128/JVI.03544-13(2014). Kutter JS, Spronken MI, Fraaij PL, Fouchier RA, Herfst S. Transmission routes of respiratory viruses among humans. Curr Opin Virol.28:142-151.https://doi.org/10.1016/j.coviro.2018.01.001(2018). HEMMES JH, WINKLER KC, KOOL SM. Virus survival as a seasonal factor in influenza and polimyelitis. Nature.188:430-431.https://doi.org/10.1038/188430a0(1960). Pica N, Bouvier NM. Environmental factors affecting the transmission of respiratory viruses. Curr Opin Virol. 2(1):90-95.https://doi.org/10.1016/j.coviro.2011.12.003(2012). Tang Z, Fan H, Tian Y, Lv Q. Epidemiological characteristics of six common respiratory pathogen infections in children. Microbiol Spectr. Published online May 22, 2025.https://doi.org/10.1128/spectrum.00079-25(2025). GBD 2016 Lower Respiratory Infections Collaborators. Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Infect Dis. 18(11):1191-1210.https://doi.org/10.1016/S1473-3099(18)30310-4(2018). Woodby B, Arnold MM, Valacchi G. SARS-CoV-2 infection, COVID-19 pathogenesis, and exposure to air pollution: What is the connection?. Ann N Y Acad Sci. 1486(1):15-38.https://doi.org/10.1111/nyas.14512(2021). Barraza-Villarreal A, Sunyer J, Hernandez-Cadena L, et al. Air pollution, airway inflammation, and lung function in a cohort study of Mexico City schoolchildren. Environ Health Perspect. 116(6):832-838.https://doi.org/10.1289/ehp.10926(2008). Lee A, Kinney P, Chillrud S, Jack D. A Systematic Review of Innate Immunomodulatory Effects of Household Air Pollution Secondary to the Burning of Biomass Fuels. Ann Glob Health. 81(3):368-374.https://doi.org/10.1016/j.aogh.2015.08.006(2015). Zhu F, Ding R, Lei R, et al. The short-term effects of air pollution on respiratory diseases and lung cancer mortality in Hefei: A time-series analysis. Respir Med. 146:57-65.https://doi.org/10.1016/j.rmed.2018.11.019(2019). Minichilli F, Gorini F, Bustaffa E, Cori L, Bianchi F. Mortality and hospitalization associated to emissions of a coal power plant: A population-based cohort study. Sci Total Environ. 694:133757.https://doi.org/10.1016/j.scitotenv.2019.133757(2019). Zhu Y, Hou Y, Xiang T, et al. Correlation analysis between the prevalence of common respiratory pathogens and exposure to ambient air pollutants in Central China, 2014-2022. Front Public Health. 13:1532507. Published 2025 Mar 6.https://doi.org/10.3389/fpubh.2025.1532507(2025). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7150138","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":490560932,"identity":"55a78ea8-3518-4d84-96c4-c85437693700","order_by":0,"name":"Biao Wang","email":"","orcid":"","institution":"Gansu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Biao","middleName":"","lastName":"Wang","suffix":""},{"id":490560933,"identity":"b34ca1cd-b9a6-48a7-b1af-8946c22524bb","order_by":1,"name":"Hui Zhang","email":"","orcid":"","institution":"Gansu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhang","suffix":""},{"id":490560934,"identity":"737698cb-ba6b-456d-9555-59941097399c","order_by":2,"name":"Maoxing Dong","email":"","orcid":"","institution":"Gansu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Maoxing","middleName":"","lastName":"Dong","suffix":""},{"id":490560935,"identity":"fe27445f-a6e3-4f41-b4da-9cd5cb3a4fc7","order_by":3,"name":"Huan Wei","email":"","orcid":"","institution":"Gansu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Wei","suffix":""},{"id":490560936,"identity":"772026d3-bc47-464c-ab51-6c0e9c5a04b6","order_by":4,"name":"Miao Wang","email":"","orcid":"","institution":"Gansu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Wang","suffix":""},{"id":490560937,"identity":"aca1aee9-ddd0-4158-ba4b-92b9b1419454","order_by":5,"name":"Xiaoshu Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIie3PPwrCMBTH8YTAc4nOz8V4hEhXD5NSaJd27ygUMhXngqK3cE4ROtUTuAheoODSxX+rg/S5OeQDv+19h8eY5/0jfK/LlzMYFY6e8KqNg4lsDD0RY3sMd5jOaYXalEEnQSSWpYz1+WE44dsmRpSQWXZyvGzPw4nAqGEaZWb52ghuCQlgWHRGYwJCaloiMXLojDYA1AQxjqcrZxZWgqlJv6gqDW73x1Op/bW+9Dkh+eB+vPc8z/O+eQEDmDcYuROmjgAAAABJRU5ErkJggg==","orcid":"","institution":"Gansu Provincial Center for Disease Control and Prevention","correspondingAuthor":true,"prefix":"","firstName":"Xiaoshu","middleName":"","lastName":"Zhang","suffix":""},{"id":490560938,"identity":"9773a654-0a5e-40ea-a715-f8d65f7f12f6","order_by":6,"name":"Shu Liang","email":"","orcid":"","institution":"Gansu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Shu","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2025-07-17 14:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7150138/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7150138/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-24515-5","type":"published","date":"2025-11-20T15:58:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87732090,"identity":"d43ea29e-c085-46fc-8e24-49e96b1e592d","added_by":"auto","created_at":"2025-07-28 11:47:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41985,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of HADV infection by population and time. (A)Bar chart showing the gender distribution of HADV infections.(B) Bar chart showing seasonal distribution of HADV infection:“* indicates \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05,*** indicates \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01”\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7150138/v1/4999005c46f19bf3e0389a94.jpg"},{"id":87732091,"identity":"1f8964e6-d3ad-4b42-8170-f58fce70d662","added_by":"auto","created_at":"2025-07-28 11:47:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56028,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly distribution of meteorological factors in the Lanzhou region\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7150138/v1/3588b443bde39b3d690f4005.jpg"},{"id":87733196,"identity":"0480942a-8754-4776-8738-ef7da5ec1e93","added_by":"auto","created_at":"2025-07-28 11:55:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60337,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between meteorological factors and HADV infection (a) Correlation matrix, (b) Regression analysis\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7150138/v1/c90b5a6d7eb51dc74778ddef.jpg"},{"id":87732094,"identity":"dd9156c1-baa3-41ee-895a-311876b513df","added_by":"auto","created_at":"2025-07-28 11:47:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":83616,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly average concentrations of air pollutants and AQI index in Lanzhou\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7150138/v1/18aa66110d6c05c1ac90a18d.jpg"},{"id":87732096,"identity":"1f66d7fe-7119-4d0b-a73b-5cb8e6efc6a5","added_by":"auto","created_at":"2025-07-28 11:47:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":108874,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between atmospheric pollutants and infection with HADV (a) Correlation matrix, (b) Regression analysis\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7150138/v1/bde19944f773079caaa76b7b.jpg"},{"id":87733198,"identity":"b269d6b3-c6ba-43ba-a672-43751611d23d","added_by":"auto","created_at":"2025-07-28 11:55:55","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":136036,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate analysis of meteorological factors (monthly average temperature, monthly average sunshine duration, and monthly average wind speed) and the number of respiratory adenovirus infections in 2023–2024, including three subplots: (time series analysis, temperature-case number seasonal distribution, and monthly case distribution).\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7150138/v1/1bdcb535c6b6f3b7490cb9ec.jpg"},{"id":96650364,"identity":"791b7469-8521-4328-a7ef-a5cda777b0c9","added_by":"auto","created_at":"2025-11-24 16:11:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1412691,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7150138/v1/466b1770-3abf-4a1f-895a-7b0dbafcd34f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of meteorological factors and atmospheric pollutants on the prevalence of respiratory adenovirus in children in Lanzhou, Northwest China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute respiratory infections (ARI) are a leading cause of illness and death among children and infants worldwide. Approximately 80% of acute respiratory infections are caused by viruses [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], with human adenovirus (HADV) accounting for 2\u0026ndash;15% of respiratory infections in children [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e][\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. HADV belongs to the genus Human Adenovirus within the family Adenoviridae. It is a non-enveloped, linear double-stranded DNA virus with an icosahedral symmetry structure [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It can infect the upper and lower respiratory tracts of children, causing diseases such as bronchitis, laryngitis, tonsillitis, and pneumonia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e][\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Climate change and air pollution play important roles in disease occurrence and pathogen transmission. Multiple studies have shown that air pollutants have significant adverse effects on respiratory diseases [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e][\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In recent years, researchers have also found that meteorological factors can influence diseases related to respiratory infections [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e][\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Lanzhou is an inland city in China with a dry climate, scarce precipitation, and poor natural conditions. This study selected Lanzhou, an inland town in northwestern China, as a representative case. By systematically collecting clinical samples from pediatric acute respiratory infection cases at a sentinel hospital in the Lanzhou region and combining them with concurrent meteorological factors and air pollutant data, this study aims to thoroughly analyze the potential impact of meteorological factors and air pollutants on the epidemiological trends of HADV infections.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eOverview of the study area\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLanzhou City is located in northwestern China, in the central part of Gansu Province, with its city center at 36°03′ north latitude and 103°40′ east longitude. It is a typical mountainous city in China, with the entire urban area situated in a dumbbell-shaped river valley, surrounded by mountains on all sides. The basin has low wind speeds, with a calm wind rate of 62%. The depth-to-width ratio of the Lanzhou Valley is approximately 0.07, indicating an extremely enclosed mountainous basin terrain. Due to the absence of warm, humid maritime air currents, Lanzhou experiences sparse rainfall throughout the year and has a dry climate. The region receives ample sunlight and has high evaporation rates, characteristic of a typical temperate continental climate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecimen Source and Nucleic Acid Detection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study selected pediatric cases of acute respiratory infections from January 2023 to February 2025 at a sentinel hospital in Lanzhou City, Gansu Province, as the study subjects. Inclusion criteria:\u0026nbsp;①\u0026nbsp;Age\u0026nbsp;≤\u0026nbsp;16 years;\u0026nbsp;②\u0026nbsp;Symptoms consistent with acute infection (meeting at least one of the following criteria): fever, chills, abnormal white blood cell distribution count (decreased or increased to 5.0\u0026nbsp;×\u0026nbsp;10^9/L to 12.0\u0026nbsp;×\u0026nbsp;10^9/L);\u0026nbsp;③\u0026nbsp;Clinical symptoms (meeting at least one of the following): rhinorrhea, cough with sputum, wheezing, pharyngeal or laryngeal edema or pain, chest tightness or pain, fatigue, abdominal pain, or diarrhea. Qualified medical staff at the sentinel hospital strictly followed the monitoring protocol to collect nasopharyngeal swab samples and gather case data. Specimens must be stored at 4°C within 24 hours of collection and transported to the laboratory. Specimens submitted for testing after 24 hours must be stored at -70°C. This study used the Xi'an Tianlong Nucleic Acid Rapid Extraction Kit (magnetic bead method) to extract total viral nucleic acid from the samples. The respiratory pathogen nucleic acid detection kit developed by Beijing Zhuocheng Huisheng Biotechnology Co., Ltd. was used in conjunction with the ABI Q5 real-time fluorescent quantitative PCR instrument from the United States for HADV detection. All experimental steps were strictly performed according to the kit instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeteorological and Air Quality Data Collection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeteorological data were obtained from the National Meteorological Science Data Center (https://data.cma.cn/), and air quality data were downloaded from the China Air Quality Online Analysis Platform (https://www.aqistudy.cn/). The data include the average monthly temperature (°C), average monthly relative humidity, average monthly wind speed (m/s), average monthly sunshine hours (h), and average monthly rainfall (mm) for the whole year from 2023 to 2024; air pollution indicators include PM2.5, PM10, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, CO, and O\u003csub\u003e3\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were processed and analyzed using IBM SPSS software (version 26.0). Continuous variables with a normal distribution were compared using t-tests, and non-normally distributed variables were analyzed using the Mann-Whitney U test. Categorical data were analyzed using the chi-square or Fisher's exact tests. Normally distributed data are expressed as mean\u0026nbsp;±\u0026nbsp;standard deviation, whereas non-normally distributed data are expressed as median and percentiles (P25; P75). Comparisons of means between groups were performed using Duncan's t-test. Pearson’s correlation analysis was used if the data met the assumption of normality; otherwise, Spearman's nonparametric test was used. The interpretation of the correlation coefficient r is as follows: 0 \u0026lt;\u0026nbsp;lrl\u0026nbsp;\u0026lt; 0.2 indicates a weak correlation, 0.2 \u0026lt;\u0026nbsp;lrl\u0026nbsp;\u0026lt; 0.5 indicates a low correlation, 0.5 \u0026lt;\u0026nbsp;lrl\u0026nbsp;\u0026lt; 0.8 indicates a significant correlation, and 0.8 \u0026lt;\u0026nbsp;lrl\u0026nbsp;\u0026lt; 1 indicates a high correlation. Statistical significance was set at P \u0026lt; 0.05. GraphPad Prism 8.0 was used for data visualization.\u003c/p\u003e\n\u003cp\u003eIn addition, this study analyzed the nonlinear relationships among meteorological factors, air pollutants, and children's acute respiratory infection adenovirus (HAdV) based on the generalized additive model (GAM) with Poisson distribution, which can flexibly capture the complex nonlinear response between predictor and response variables by introducing a smoothing function (spline), effectively overcoming the limitations of the traditional linear model. Model fitting uses penalized likelihood estimation, and smoothness is automatically optimized by multiple generalized cross-validation (MGCV). The Poisson distribution was used as the error distribution, and multiple monthly data were used for modeling. In the model, the number of HAdV-positive cases was used as the response variable, monthly mean meteorological factors and air pollutants were used as the main predictors, and a smoothing term was introduced for the analysis while adjusting for temporal trends and seasonal factors (monthly series simulated using cyclic B-spline) to control for potential temporal and seasonal confounding effects. The GAM model is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003elog(E(Y\u003csub\u003et\u003c/sub\u003e))=β\u003csub\u003e0\u003c/sub\u003e+s(temp\u003csub\u003et\u003c/sub\u003e,k=5)+s(t,k=4)+s(month\u003csub\u003et\u003c/sub\u003e,bs=\"cc\",k=6)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this equation, \u003cem\u003eY\u003csub\u003et\u003c/sub\u003e\u003c/em\u003e represents the number of positive cases in the tth month, \u003cem\u003es()\u003c/em\u003e denotes the smoothing function, \u003cem\u003ek\u003c/em\u003e is the dimension parameter of the basis function, and \u003cem\u003ebs\u003c/em\u003e=\"cc\" indicates the use of cyclic splines to capture the seasonal effects.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDistributional Characteristics of Children with ARI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom January 2023 to February 2025, a total of 1339 acute respiratory cases in children were collected. There were 798 (798/1339, 59.60%) male and 541 (541/1339, 40.40%) female children. The male-to-female ratio was 1.48:1. In the age group, there were 194 cases in the infant group (\u0026le;\u0026thinsp;1 year), 288 cases in the toddler group (1\u0026ndash;3 years), 422 cases in the preschool group (3\u0026ndash;6 years), and 435 cases in the school-age group (\u0026gt;\u0026thinsp;6 years). In the seasonal group, there were 295 cases in spring (March-May), 228 cases in summer (June-August), 258 cases in fall (September-November), and 485 cases in winter (December-February).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRespiratory adenovirus detection\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2023(514)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2024(641)/2025(184)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge positive,(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;1year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-3years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e288(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3-6years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e253(30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422(44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;6years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e435(45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenderpositive,(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale (cases)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e499(53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e798(69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale (cases)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e215(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e326(38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e541(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeason positive,(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e295(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSummer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutumn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173(19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258(28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157(29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e328(33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e485(62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eEpidemiological characteristics of respiratory HADV in children\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe positive detection rate of HADV was 5.45% (28/514) and 11.03% (91/825) in 2023 and 2024, respectively, and the total positive detection rate was 8.89% (119/1339). The positive detection rate was 8.65% (69/798) in male children and 9.24% (50/541) in female children. The positive detection rate of HADV increased with age, with the highest rate in the preschool group and the second highest in the school-age group. It was 5.15% (10/194) in the infant group, 6.94% (20/288) in the toddler group, 10.43% (44/422) in the preschool group, and 10.34% (45/435) in the school-age group. In the \u0026gt;\u0026thinsp;3 years age group versus \u0026le;\u0026thinsp;3 years, the positive detection rate was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA. Winter is the high season for HADV, and the positive detection rates of HADV in the four seasons were spring: 5.08% (15/295), summer: 5.26% (12/228), autumn: 10.85% (28/258), and winter: 12.78% (62/485). There was a statistically significant \u003cem\u003e(P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) positive detection rate in fall and winter versus spring and summer, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between meteorological factors and HADV in Lanzhou City\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverview of meteorological elements in Lanzhou City\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 2023 to 2024, the average monthly temperature in Lanzhou City was 11.74\u0026thinsp;\u0026plusmn;\u0026thinsp;10.36\u0026deg;C, the average monthly precipitation was 25.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08 mm, the average monthly relative humidity was 49.81\u0026thinsp;\u0026plusmn;\u0026thinsp;11.38%, the average monthly wind speed was 1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 m/s, and the average monthly sunshine hours were 178.07\u0026thinsp;\u0026plusmn;\u0026thinsp;44.72, as shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The temperature in Lanzhou City showed an increasing trend from January to July, with the highest temperature in July, followed by a decline. Relative humidity is low throughout the year, with the highest relative humidity in October, and precipitation peaks in September. The average monthly sunshine hours from May to August are longer than those in other months, and the average monthly wind speed does not change much. The average temperature in Lanzhou varies with the seasons, with the highest in summer and the lowest in winter, reflecting the temperate continental climate of Lanzhou, with cold winters and hot summers, significant temperature differences, low precipitation, and uneven seasonal distribution. The average monthly sunshine hours are significantly longer in spring and summer than in fall and winter, and the average wind speed is higher in spring and summer than in fall and winter. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMeteorological Factors, Descriptive Statistical Results of Atmospheric Pollutants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP25\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP75\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeteorological factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage temperature(\u0026deg;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage humidity(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage rainfall(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage sunshine duration(h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e178.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e171.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e214.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e249.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage wind speed(m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAir Pollutant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePM2.5(ug/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePM10(ug/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e174.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e2(\u003c/sub\u003emg/m\u003csup\u003e3\u003c/sup\u003e\u003csub\u003e)\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e(ug/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCO(mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e(ug/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis between HADV and meteorological factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between respiratory adenovirus (HADV) infection and environmental factors was investigated and validated using correlation heatmap analysis (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea) and regression analysis. Correlation heatmap analysis revealed that HADV infection was negatively correlated with the monthly average wind speed, monthly average temperature, monthly average sunshine duration, and monthly average precipitation, and weakly positively correlated with the monthly average relative humidity. Furthermore, the monthly average wind speed was significantly positively correlated with the monthly average temperature and sunshine duration. Additionally, the monthly average temperature showed a significant positive correlation with the monthly average precipitation, and the monthly average relative humidity also positively correlated with the monthly average precipitation, suggesting complex interactions among these environmental variables. Regression analysis further substantiated these findings, demonstrating that the HADV positivity rate was significantly negatively correlated with the monthly average temperature, monthly average sunshine duration, and monthly average wind speed, with correlation coefficients of r = -0.640 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), r = -0.638 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and r = -0.621 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), respectively (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb), which closely aligned with the results of the correlation heatmap analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between atmospheric pollutants and HADV in Lanzhou City\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverview of atmospheric pollutants in Lanzhou City\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe air pollutants in Lanzhou City are PM2.5, PM10, CO, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e. The average concentrations for the two-year period from 2023 to 2024 are as follows: 40.67\u0026thinsp;\u0026plusmn;\u0026thinsp;14.34 \u0026micro;g/m\u0026sup3;; 89.71\u0026thinsp;\u0026plusmn;\u0026thinsp;39.10 \u0026micro;g/m\u0026sup3;; 0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 mg/m\u0026sup3;; 36.46\u0026thinsp;\u0026plusmn;\u0026thinsp;12.95 \u0026micro;g/m\u0026sup3;; 13.50\u0026thinsp;\u0026plusmn;\u0026thinsp;5.30 mg/m\u0026sup3;; 98.88\u0026thinsp;\u0026plusmn;\u0026thinsp;36.01 \u0026micro;g/m\u0026sup3;, as shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The Air Quality Index (AQI) is an indicator used to measure environmental air quality. The AQI indicates the quantitative severity of primary pollutants, derived from the air quality sub-indices of various air pollutants calculated simultaneously, with the AQI representing the value of primary pollutants. In China, the AQI is divided into six levels: Level 1 is Excellent (0\u0026thinsp;~\u0026thinsp;50), Level 2 is Good (51\u0026thinsp;~\u0026thinsp;100), Level 3 is Moderate Pollution (101\u0026thinsp;~\u0026thinsp;150), Level 4 is Moderate Pollution (151\u0026thinsp;~\u0026thinsp;200), Level 5 is Severe Pollution (201\u0026thinsp;~\u0026thinsp;300), and Level 6 is Very Severe Pollution (over 300). The average AQI over the 2-year period was 85.83\u0026thinsp;\u0026plusmn;\u0026thinsp;15.62. The average concentrations of PM2.5, PM10, CO, NO\u003csub\u003e2\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e were higher at the beginning and end of the year and lower in the middle of the year. O\u003csub\u003e3\u003c/sub\u003e showed the opposite trend. PM2.5 had higher average concentrations in January and December; PM10 had higher concentrations from January to April and in December, while other months had relatively lower concentrations; CO had higher average concentrations in January\u0026ndash;February and November\u0026ndash;December compared to other months; NO₂had higher monthly average concentrations in January\u0026ndash;March and October\u0026ndash;December, with lower concentrations in July; SO₂ had higher average concentrations in January\u0026ndash;March and November\u0026ndash;December compared to other months. Unlike other air pollutants, O₃ gradually reached its peak during the middle of the year. The AQI is higher from January to April, with relatively stable values in other months, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis between HADV and air pollutants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation heatmap analysis ( Fig. 5a) revealed a complex relationship between human adenovirus (HADV) infections and air pollutants. Specifically, HADV infection showed positive correlations with concentrations of PM2.5, CO, NO₂, and SO₂, while exhibiting a negative correlation with ozone (O₃) levels. Additionally, significant associations among the pollutants were observed: most pollutants were positively correlated with each other, except for O₃, which was negatively correlated with PM2.5, PM10, CO, NO₂, and SO₂, suggesting potential synergistic effects among certain pollutants. Further validation through regression analysis ( Fig. 5b) indicated that the positivity rate of HADV was significantly positively associated with the concentrations of CO, NO₂, and SO₂, with correlation coefficients of r\u0026thinsp;=\u0026thinsp;0.761 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), r\u0026thinsp;=\u0026thinsp;0.685 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and r\u0026thinsp;=\u0026thinsp;0.716 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), respectively. Conversely, O₃ levels were negatively correlated with HADV positivity (r = -0.694, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings suggest that elevated levels of specific air pollutants are associated with increased HADV infection rates, whereas higher ozone concentrations may have a protective or inverse relationship with HADV infection rates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGAM Analysis Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the collected surveillance data, the nonlinear relationship between meteorological factors (monthly mean temperature, monthly mean sunshine hours, and monthly mean wind speed) and the number of respiratory adenovirus infections was systematically analyzed using a GAM. The results showed a significant nonlinear response between mean monthly temperature and the number of positive adenovirus detections, and the model explained 84.80% of the deviation, indicating that temperature has a strong predictive ability for the risk of adenovirus transmission. The peak risk for temperature was \u0026minus;\u0026thinsp;4.9\u0026deg;C and the relative risk at this temperature was elevated 5.5 times compared to the lowest risk temperature of 7.9\u0026deg;C ( Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). There was also a significant nonlinear relationship between monthly mean sunshine hours and the number of adenovirus infections, with a model explaining up to 97.80% of this deviation. The number of sunshine hours at 108h corresponded to the highest risk of infection, which was significantly higher than the risk at the lowest sunshine hours of 249.8h, with the peak risk elevated by 7.4\u0026times;10^5 times compared to the lowest value ( Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). A significant nonlinear association was also demonstrated between mean monthly wind speed and the number of adenovirus infections, with a model explaining a deviation of 96.10%. The risk was highest at a wind speed of 0.8 m/s, which elevated the peak risk by a factor of 6.6\u0026times;10^7 compared with the lowest risk wind speed of 1.5 m/s ( Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec). Taken together, all of the above meteorological factors have an important impact on adenovirus transmission, and their nonlinear effects suggest that a multifactorial evaluation should be combined to optimize disease warning, prevention, and control strategies.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to investigate and analyze the impact of meteorological factors and atmospheric pollutants on the prevalence of respiratory adenovirus infections in children in the Lanzhou region of Northwest China. Data show that the positive detection rates for respiratory adenovirus in children in 2023 and 2024 were 5.45% (28/514) and 11.03% (91/825), respectively, which were higher than the 2.05% and 9.28% reported in Hangzhou, China, but both showed higher rates in 2024 than in 2023 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This is because the period from 2023 to 2024 was the post-COVID-19 era, with control measures having been lifted in 2023, coinciding with the widespread circulation of SARS-CoV-2 and influenza viruses [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Public preventive awareness remained strong, with continued adherence to non-pharmaceutical interventions (NPIs) such as mask-wearing and frequent handwashing when outdoors, thereby reducing HADV transmission. However, the positive detection rate for HADV increased in 2024, which may be related to the weakening of NPIs among the public, potentially increasing the risk of HADV infection. In this study, there was no statistically significant difference in the HADV-positive detection rates between the sexes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In terms of age groups, the positive detection rates in the infant and toddler groups were significantly lower than those in the preschool and school-age groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), a trend consistent with global reports [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e][\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This may be related to the gradual relaxation of NPIs and increased contact among school-age children after returning to school, which facilitated HAdV transmission. In seasonal groupings, the positive detection rate was significantly higher in autumn and winter than in spring and summer (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), consistent with the findings of a study by Zhao et al. in Guangdong Province [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLanzhou, the capital of Gansu Province, is located in the semi-arid region of northwestern China (longitude 102\u0026deg;35\u0026prime;55\u0026Prime; to 104\u0026deg;34\u0026prime;29\u0026Prime;E, latitude 35\u0026deg;34\u0026prime;20\u0026Prime; to 37\u0026deg;07\u0026prime;N) in a typical river valley basin, with an arid climate with little rainfall, strong evaporation, low wind speeds, a high frequency of static winds, and a deep inversion layer, which is characterized by a temperate semi-arid climate [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. As an important petrochemical, metallurgical, and mechanical industrial base, Lanzhou has long suffered from severe air pollution. Although effective control measures have been implemented since 2013, the PM2.5 concentration is still approximately 1.14 times the national annual standard limit (35㎍/m\u0026sup3;).\u003c/p\u003e\u003cp\u003eVarious theories on the association between meteorological factors and respiratory viruses have been proposed [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e][\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The mechanisms by which meteorological factors affect respiratory viruses are characterized by four main aspects: virus survival, infection, transmission, and the human immune response. First, meteorological factors can alter the stability, viability, activity, pathogenicity, and virulence of viruses, thereby prolonging or shortening their survival time. Relevant laboratory studies have shown that most viruses survive longer at lower temperatures and relative humidities [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Some aerosol viability experiments have shown that adenoviruses are more stable at high relative humidity levels [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study, a combination of stepwise linear regression analysis and GAM modeling was used to reveal the correlation between meteorological factors, changes in air pollutant indicators, and the positive detection rate of HADV. HADV showed a significant negative correlation with the average monthly temperature, with a higher positive detection rate at colder temperatures, and the risk of HADV infection reached a peak when the average temperature for a given month was \u0026minus;\u0026thinsp;4.9\u0026deg;C, suggesting strengthened precautions. Therefore, children were more likely to be infected with HADV during the lower temperatures of winter in Lanzhou, consistent with the findings of a previous study. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. HADV showed a significant negative correlation with the average monthly sunshine hours, and the lower the positive detection rate of HADV in the case of longer sunshine (249.8 h), the lower the risk of contracting HADV. This could be attributed to high evaporation due to sufficient sunshine, resulting in a dry climate in the area, which affects the survival of HADV. The risk of HADV peaked under the specific meteorological condition of a monthly average of approximately 108 sunshine hours, suggesting that surveillance and preventive measures should be strengthened under this meteorological condition. HADV showed a significant negative correlation with the monthly average wind speed, which may be due to the fact that the higher the wind speed is, the more quickly HADV spreads and the more difficult it is to attach, and thus the more difficult it is to infect the population. The relative risk of respiratory adenovirus infection in children peaks when the average wind speed in the environment reaches 0.8 m/s in a given month, indicating an increased risk of transmission that warrants special attention and preventive and control measures.\u003c/p\u003e\u003cp\u003eAmbient air pollution is considered an important risk factor for viral respiratory infections, and its mechanism of action is complex and may involve multiple dimensions, such as inducing inflammatory responses, promoting cell death, triggering oxidative stress, and regulating viral receptor expression [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The results of this study revealed that elevated levels of air pollutants were closely associated with the detection rate of HADV infection. Air pollutants are mainly categorized into gaseous pollutants and total suspended particulate matter (TSPs). The gaseous pollutants are CO, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e, whereas TSPs are mainly composed of PM2.5 and PM10 particles. Studies have shown that exposure to air pollutants triggers the generation of reactive oxygen species (ROS), leading to oxidative stress and increased mucus secretion and cytokine production, which can adversely affect lung health [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e][\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition, air pollution affects host defense mechanisms. Exposure to air pollutants may impair phagocytosis by macrophages and disrupt immune function [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Together, these factors increase the susceptibility of the population to respiratory pathogens. Previous studies have shown that SO₂is a known inducer of respiratory inflammation, leading to direct airway damage and disruption of respiratory barrier function [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e][\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our results showed that CO, NO\u003csub\u003e2\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e were all positively correlated with the positive detection rate of HADV but significantly negatively correlated with O\u003csub\u003e3\u003c/sub\u003e, indicating that high concentrations of CO, NO\u003csub\u003e2\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e increased HADV infections, while high concentrations of O\u003csub\u003e3\u003c/sub\u003e decreased HADV infections. The positive correlation between SO₂ and the positive detection rate of HADV is consistent with the results of a study in Wuhan, China [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, the influence of meteorological factors and air pollutants on the positive detection rate of HADV in children should not be overlooked. More active and appropriate measures should be taken to protect children from respiratory infections during extreme weather events or periods of high air pollution.\u003c/p\u003e\u003cp\u003eThis study has some limitations. First, the study period was short and focused on only one pathogen, HAdV, and the relationship between meteorological factors and atmospheric pollutants and their positive detection rates remains preliminary. Second, potential confounding factors were not fully controlled, which may affect the comprehensiveness of the association analyses. In addition, the study was limited to Lanzhou, which limits its external applicability. Future studies should expand the time span, cover more respiratory pathogens, and expand the geographic scope to obtain more generalized and in-depth conclusions and further reveal the role of environmental factors in respiratory viral infections.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study thoroughly investigated the potential impact of meteorological factors (temperature, sunshine duration, and wind speed) and atmospheric pollutants (CO, O\u003csub\u003e3\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e) on the prevalence of human adenovirus (HAdV) infection among children in the Lanzhou region. The results showed that the detection rate of HAdV was relatively high among preschool and school-age children, with a significant concentration during autumn and winter, revealing a close association between environmental factors and HAdV detection rates. This study particularly emphasized the need to strengthen targeted public health intervention measures during periods of low temperatures and high pollution to effectively protect children's health. This study not only fills the gap in research on HAdV epidemiology and its environmental risk factors in the Lanzhou region but also provides valuable data to support the development of more precise and effective public health strategies under different climate and pollution conditions. By elucidating how meteorological factors and changes in air pollutants may influence the occurrence of HAdV infections in children, our research provides important scientific evidence and practical guidance for optimizing disease prevention and control measures and formulating relevant health policies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the participants of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data geneerated or analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Gansu Provincial Key Research and Development Program-Social Development Field Program Project (Grant NO. 23YFFA0051) .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethical Committee of Gansu Provincial Center for Disease Control and Prevention, and carried out strictly in accordance with the Declaration of Helsinki. \u0026nbsp; \u0026nbsp;Informed consent was obtained from all participants and from their legal guardians who were aged \u0026le;16 years. Data were stored and analyzed anonymously. Inclusion criteria: ①age\u0026le;16 years old; \u0026nbsp; \u0026nbsp; ②symptoms consistent with acute infection (at least one of the following): fever, chills, abnormal white blood cell distribution count (decreased or increased);③clinical symptoms (at least one of the following): runny nose, coughing and sputum, wheezing, pharyngeal and laryngeal edema or soreness, chest tightness and chest pain, fatigue, abdominal pain and diarrhea. \u0026nbsp; \u0026nbsp;Nasopharyngeal swab specimens were collected by qualified medical staff of the sentinel hospitals in strict accordance with the monitoring program, and case information was collected. \u0026nbsp; \u0026nbsp;Specimens were stored at 4 ℃ for 24 h after collection and transported to the laboratory, and specimens sent for examination for more than 24 h were stored at -70℃.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from the parents or guardians of all participants. The parents or guardians were informed of the laboratory results of pathogen detection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authos declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB. W. method design, experimental manipulation, first draft writing, software processing, review and editorial writing; H. Z. experimental manipulation, software processing, data management, first draft writing; X. Z. program design, obtaining grants; M. D. program design, project management; S. L. program design, project management; H. W. experimental manipulation; M. W. experimental manipulation.All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChoi Eunjin, Ha Kee-Soo, Song Dae Jin, et al. Clinical and laboratory profiles of hospitalized children with acute respiratory virus infection. Korean journal of pediatrics, 61(6):180-186.https://doi.org/10.3345/kjp.2018.61.6.180(2018). \u003c/li\u003e\n\u003cli\u003eFinianos Mayda, Issa Randi., Curran Martin D, et al. Etiology, seasonality, and clinical characterization of viral respiratory infections among hospitalized children in Beirut, Lebanon. Journal of medical virology, 88(11):1874-81.https://doi.org/10.1002/jmv.24544(2016).\u003c/li\u003e\n\u003cli\u003eJain Seema, Williams Derek J, Arnold Sandra R, et al. Community-acquired pneumonia requiring hospitalization among U.S. children. 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Published 2025 Mar 6.https://doi.org/10.3389/fpubh.2025.1532507(2025).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute respiratory infection, Respiratory adenovirus, Meteorological factors, Atmospheric pollutants, Correlation","lastPublishedDoi":"10.21203/rs.3.rs-7150138/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7150138/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eClimate change and atmospheric pollution significantly affect disease prevalence and spread. Human adenovirus (HADV) is a common pathogen that causes acute respiratory infections in children. This study aimed to investigate the relationship between HAdV infection and meteorological factors and atmospheric pollutants in children in Lanzhou, Northwest China, and to gain insights into the influence of environmental factors on virus transmission.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eClinical specimens of acute respiratory tract infections in children from a sentinel hospital in Lanzhou City between January 2023 and February 2025 were collected for respiratory adenoviral nucleic acid testing. The positive detection rates of different ages, sexes, and seasons were analyzed, and stepwise linear regression combined with generalized additive modeling (GAM) was used to explore the correlation between HAdV infection and meteorological factors and air pollutants.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFrom January 2023 to February 2025, a total of 1,339 throat swab samples were collected from children with acute respiratory infections (ARI), with a male-to-female ratio of 1.48:1. The HADV positivity rate in 2024 was 11.03% (91/825), higher than the 5.45% (28/514) in 2023. The positivity rates among male and female children were 8.65% (69/798) and 9.24% (50/541), respectively. Among these, the positive detection rates in preschool-aged and school-aged children were higher than those in infant and toddler groups, at 10.43% (44/422), 10.34% (45/435), 5.15% (10/194), and 6.94% (20/288), respectively. HADV was detected in all four seasons, with the highest detection rates in autumn and winter, at 10.85% (28/258) and 12.78% (62/485), respectively. The positive detection rate of HADV showed a significant negative correlation with meteorological factors (temperature, sunshine duration, wind speed), with correlation coefficients of: r = -0.640 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); r = -0.638 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); r = -0.621 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); It showed a significant positive correlation with atmospheric pollutants (CO, NO₂, and SO₂), with correlation coefficients of r\u0026thinsp;=\u0026thinsp;0.761 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); r\u0026thinsp;=\u0026thinsp;0.685 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); r\u0026thinsp;=\u0026thinsp;0.716 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); and a significant negative correlation with atmospheric pollutant O₃, with a correlation coefficient of r = -0.694 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). GAM analysis showed that meteorological factors (temperature, sunshine duration, and wind speed) were all significantly non-linearly associated with the number of adenovirus infections.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eHADV was detected at a higher rate in children in the preschool and school-age groups, and most cases were detected in the fall and winter seasons. The positive detection rate of HADV was negatively correlated with meteorological factors (temperature, hours of sunshine, and wind speed) and the atmospheric pollutant O₃, and positively correlated with atmospheric pollutants (CO, NO₂, and SO₂). The influence of these pollutants on the prevalence of HAdV infection should not be ignored.\u003c/p\u003e","manuscriptTitle":"Effects of meteorological factors and atmospheric pollutants on the prevalence of respiratory adenovirus in children in Lanzhou, Northwest China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 11:47:50","doi":"10.21203/rs.3.rs-7150138/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-14T19:38:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-11T15:15:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-06T03:28:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T08:21:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326433714981129657381956356860751658874","date":"2025-07-25T03:23:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-24T17:38:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215074606613537946621870417339671124350","date":"2025-07-24T15:51:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130166748973886212759058047918155083786","date":"2025-07-24T15:24:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274561728634584889280103902548704658372","date":"2025-07-24T11:23:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100744376315126016702090552211073708106","date":"2025-07-24T10:23:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128736667482880674169927076824862631584","date":"2025-07-24T09:06:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-24T00:13:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-23T15:44:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-22T07:12:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-21T07:02:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-17T14:22:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b68b13e6-7115-4ad7-8e99-26a6a433a9d5","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52087934,"name":"Earth and environmental sciences/Climate sciences"},{"id":52087935,"name":"Health sciences/Diseases"},{"id":52087936,"name":"Earth and environmental sciences/Environmental sciences"},{"id":52087937,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-24T16:07:08+00:00","versionOfRecord":{"articleIdentity":"rs-7150138","link":"https://doi.org/10.1038/s41598-025-24515-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-20 15:58:47","publishedOnDateReadable":"November 20th, 2025"},"versionCreatedAt":"2025-07-28 11:47:50","video":"","vorDoi":"10.1038/s41598-025-24515-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-24515-5","workflowStages":[]},"version":"v1","identity":"rs-7150138","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7150138","identity":"rs-7150138","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}