Abstract: Objective To analyze the lagged effects of meteorological factors on mosquito density in the urban areas of Ji’nan, China, so as to provide a basis for further scientific prevention and control of mosquitoes and mosquito-borne infectious diseases.Methods We collected the mosquito data monitored with BG-traps and the concurrent meteorological data in the urban areas of Ji’nan in 2021 to construct a distributed lag nonlinear model to study the lagged effects of air temperature, humidity, air pressure, and precipitation on the mosquito density.Results A total of 31 018 mosquitoes were captured, including Culex pipiens pallens (22 436/31 018, 72.33%), Aedes albopictus (8 570/31 018, 27.63%), and Armigeres subalbatus (12/31 018, 0.04%). When the air temperature was 23.64 ℃, the effect on mosquito density was maximum at a lag of 2 d (relative risk [RR]=1.021, 95% confidence interval [CI]:1.002-1.058). When the relative humidity was 98.00%, mosquito density was highest at a lag of 11 d (RR=1.232, 95%CI: 1.002-1.514). When the air pressure was 984.99 hPa, the mosquito density was highest at a lag of around 17 d (RR=1.376, 95%CI: 1.205-1.559). When the daily precipitation was 21.25 mm, the effect on mosquito density peaked at a lag of around 8 d (RR=1.352, 95%CI: 1.133-1.590).Conclusions Air temperature, humidity, precipitation, and air pressure have non-linear and lagged effects on mosquito density. The peak of mosquito density occurs in hot and rainy summer since high temperature and humidity can increase mosquito density. Relevant departments should take early intervention measures according to meteorological cond itions to effectively reduce the risk of mosquito-borne diseases.

Key words: Disease vector, Mosquito monitoring, Meteorological factor, Distributed lag non-linear model

摘要： 目的 分析气象因素对济南市城区蚊密度的影响与滞后效应,为进一步科学防制蚊虫及蚊媒传染病提供依据。方法 采用BG-trap捕蚊器法收集2021年济南市城区蚊虫监测资料和同期相应的气象资料,构建分布滞后非线性模型,研究气温、湿度、气压、降水对蚊密度的滞后效应。结果 共捕获31 018只蚊虫,其中库蚊属淡色库蚊22 436只,占72.33%,伊蚊属白纹伊蚊8 570只,占27.63%；阿蚊属骚扰阿蚊12只,占0.04%。在23.64 ℃时,滞后2 d时蚊密度效应最大,相对危险度（RR）=1.021[95%置信区间（CI）：1.002～1.058]。在相对湿度为98.00%,滞后11 d时,蚊密度最高,RR=1.232（95%CI：1.002～1.514）。气压在984.99 hPa处,滞后17 d左右,蚊密度最高,RR=1.376（95%CI：1.205～1.559）。日降水量在21.25 mm,滞后8 d左右后,蚊密度效应达最高,RR=1.352（95%CI：1.133～1.590）。结论 气温、湿度、降水和气压对蚊密度具有非线性影响,存在滞后效应,蚊密度高峰期出现在雨热同期的夏季,较高温度和湿度会使蚊密度升高,有关部门应根据气象条件,及早采取干预措施,切实降低蚊传疾病的风险。

关键词: 媒介生物, 蚊虫监测, 气象因素, 分布滞后非线性模型