目的: 探索网格化模式下诱蚊诱卵器法监测媒介伊蚊的影响因素，为媒介伊蚊及其传播疾病的监测和防制提供科学依据。方法: 2021年7－9月在上海市静安区辖区内选择相邻的3个面积、建筑年代、绿化比例接近的居民区作为监测点，每个居民区以九宫格形式划分为二级监测块（约90 m×60 m），以监测块为单位对媒介伊蚊采用诱蚊诱卵器法和人诱停落法进行现场监测，每月3次。通过比较不同居民区、不同监测块、不同环境特点对媒介伊蚊的监测结果来探索诱蚊诱卵器法监测效果的影响因素。采用Excel 2016和SPSS 16.0软件处理数据，采用Kruskal-Wallis秩和检验、两因素方差分析、Pearson相关分析等进行统计学分析。结果: 共划定二级监测块30个，完成监测8次，设置诱蚊诱卵器131只/次，居民区1、2、3的诱蚊诱卵指数（MOI）分别为8.71、12.38和11.97，差异无统计学意义（χ²=2.750，P=0.253），居民区1、2内各板块间MOI差异有统计学意义（F=2.135，P=0.047；F=2.168，P=0.044）；居民区内居住区域MOI为12.24，社区学校区域为5.76，二者间差异有统计学意义（χ²=6.657，P=0.010）；诱蚊诱卵器放置不同位置的MOI，屋侧绿化区域为14.10，集中绿化区域为8.87，道路侧绿化为7.98，3类环境间差异有统计学意义（χ²=8.372，P=0.015）；诱蚊诱卵器法监测期间，降雨时间 < 2 d时的MOI为13.28，≥2 d时为8.79，差异有统计学意义（χ²=4.218，P=0.047）；居民区1、2、3 MOI均值分别为8.69、12.45和11.88，停落指数均值分别为3.33、8.58和6.50只/（人·h）；MOI与停落指数之比为1∶1~3∶1；经Pearson相关分析，各板块MOI与人诱停落指数高度相关（r=0.549，P=0.005）。结论: 大型居民区中的不同区域媒介伊蚊密度因绿化类型、功能分区等差异都可能有较大的差异。相较其他监测方法，诱蚊诱卵器法操作简便、特异性强等，其监测结果与人诱停落法监测结果高度一致，实际监测中可采用诱蚊诱卵器法进行网格化的布点监测，能有效避免因布点造成的偏差，全面反映媒介伊蚊密度。

Objective: To explore the factors affecting Aedes surveillance by the mosquito ovitrap method in a grid mode, so as to provide a scientific basis for the surveillance and control of Aedes mosquitoes and related mosquito-borne infectious diseases. Methods: Three adjacent residential areas with similar areas, building ages, and greenery ratios were selected as surveillance points in Jing'an District, Shanghai, China. Each residential area was divided into secondary surveillance blocks (about 90 m × 60 m) in a 3×3-grid mode. On-site monitoring was conducted in each surveillance block using the mosquito ovitrap method and the human landing catch method, three times one month from July to September 2021. By comparing the surveillance results of different residential areas, different surveillance blocks, and different environmental characteristics, the factors influencing the positive rate of the mosquito ovitrap method were determined. Excel 2016 and SPSS 16.0 were used to process the data. The Kruskal-Wallis rank sum test, tow-way analysis of variance, and Spearman correlation analysis were performed. Results: A total of 30 secondary surveillance blocks were designated. Eight times of surveillance were completed, and 131 mosquito ovitraps were set each time. The mosquito ovitrap index (MOI) in residential areas 1, 2, and 3 were 8.71, 12.38, and 11.97, respectively, with no significant difference (χ²=2.750, P=0.253). There were significant differences in the MOI among different blocks of residential areas 1 and 2 (F=2.135, P=0.047; F=2.168, P=0.044). In residential areas, the positive rate was 12.24 in living areas and 5.76 in community school areas, with a significant difference (χ²=6.657, P=0.010). The MOI was 14.10 for green areas on the house side, 8.87 for concentrated green areas, and 7.98 for green areas on the road side, with a significant difference (χ²=8.372, P=0.015). During the surveillance period, the MOI was 13.28 when the days of rainfall was < 2 d, and 8.79 when the days of rainfall was ≥2 d, with a significant difference (χ²=4.218, P=0.047). In residential area 1, the average MOI was 8.69, and the average landing index was 3.33 mosquitoes/person·h. In residential area 2, the average MOI was 12.45, and the average landing index was 8.58 mosquitoes/person·h. In residential area 3, the average MOI was 11.88, and the average landing index was 6.50 mosquitoes/person·h. The ratio of the MOI to the landing index was distributed between 1∶1 and 3∶1. Pearson correlation analysis showed that the MOI was highly correlated with the human landing index in each block (r=0.549, P=0.005). Conclusions: The density of Aedes mosquitoes may differ greatly in different areas of large residential areas due to differences in greening types, functional zoning, and other factors. The mosquito ovitrap method has the advantages of simple operation and high specificity compared with other surveillance methods for Aedes mosquitoes, and it is highly consistent with the human landing catch method. The mosquito ovitrap method with grid-based surveillance point distribution can be used in actual practice, which can effectively avoid deviations caused by point selection and fully reflect the density of Aedes mosquitoes.