三带喙库蚊发生量与气象因子相关性初步分析

A preliminary study on the relationship between occurrence of Culex tritaeniorhynchus and meteorological factors

目的了解三带喙库蚊发生量与气象因子的相关性。方法采用光催化灯诱集蚊虫;以南京市郊区牲畜棚2006年8月至2007年12月调查的三带喙库蚊密度(Y1)和雌性三带喙库蚊密度(Y2)为因变量,以同期的气象因子,平均气温(X1)、最高气温(X2)、最低气温(X3)、降雨量(X4)、平均气压(X5)、平均相对湿度(X6)、平均风速(X7)和日照时数(X8)为自变量,应用SPSS16.0软件进行多元逐步回归分析。结果南京市郊区牲畜棚蚊虫调查中三带喙库蚊占90%以上,雌蚊占95%以上,高密度期(>60只/h)在6-10月。X3与三带喙库蚊密度变化存在显著的线性相关,但回归不显著;多数气象因子与三带喙库蚊密度存在显著的非线性相关;获得较优的回归方程:Y1=10∧[18.645Lg(X1+1)-21.403Lg(X2+1)-0.930X7+0.104X8+2.470]-1,Y2=10∧[19.051Lg(X1+1)-21.994Lg(X2+1)-0.945X7+0.106X8+8.675]-1,R2>0.8。结论通过气象资料,可初步预测同期三带喙库蚊的发生量;该方法可扩展应用于其他病媒生物及其相关性疾病的预测。

Objective To determine the correlation between the occurrence of Culex tritaeniorhynchus and meteorological factors. Methods Photocatalytic light traps were used to collect mosquitoes in Nanjing suburbs from August in 2006 to December in 2007. Multivariant stepwise regression was conducted using SPSS 16.0 with mosquito density （Y1）, female mosquito density （Y2） as the dependent variables and synchronous meteorological factors, including mean temperature （X1）, maximum temperature （X2）, minimum temperature （X3）, precipitation （X4）, mean air pressure （X5）, mean relative humidity （X6）, mean wind velocity （X7） and sunshine hours （X8）, as the independent variables. Results Cx. tritaeniorhynchus accounted for over 90% in Nanjing suburbs, and female mosquitoes 95%. High density （〉60 individuals per hour） was observed in the period from June to October. Signifieant linear correlation was present between X3 and the changing trend in mosquito densities, despite insignificant regression link. Most meterological factors were significantly nonlinear con＇elated with the density of Cx. tritaeniorhynchus. Favorable results were obtained via the following regression equations：Y1=10∧[18.645Lg（X1＋1）-21.403Lg（X2＋1）-0.930X7＋0.104X8＋2.470]-1,Y2=10∧[19.051Lg（X1＋1）-21.994Lg（X2＋1）-0.945X7＋0.106X8＋8.675]-1,R^2〉0.8. Conclusion The occurrence of Cx. tritaeniorhynchus could be forecasted based on the concurrent meteorological factors. This prediction method could be applied to other vectors and relevant diseases.