In recent 2 years, the incidence of influenza showed a slight upward trend in Guangxi; therefore, some joint actions should be done to help preventing and controlling this disease. The factors analysis of affecting influenza and early prediction of influenza incidence may help policy-making so as to take effective measures to prevent and control influenza. In this study, we used the cross correlation function (CCF) to analyze the effect of climate indicators on influenza incidence, ARIMA and ARIMAX (autoregressive integrated moving average model with exogenous input variables) model methods to do predictive analysis of influenza incidence. The results of CCF analysis showed that climate indicators (PM2.5, PM10, SO2, CO, NO2, O3, average temperature, maximum temperature, minimum temperature, average relative humidity, and sunshine duration) had significant effects on the incidence of influenza. People need to take good precautions in the days of severe air pollution and keep warm in cold weather to prevent influenza. We found that the ARIMAX (1,0,1)(0,0,1)12 with NO2 model has good predictive performance, which can be used to predict the influenza incidence in Guangxi, and the predicted incidence may be useful in developing early warning systems and providing important evidence for influenza control policy-making and public health intervention.
A campaign was conducted to assess and compare the personal exposure in L3 of Tianjin subway, focusing on PM2.5 levels, chemical compositions, morphology analysis, as well as the health risk of heavy metal in PM2.5. The results indicated that the average concentration of the PM2.5 was 151.43 μg/m3 inside the train of the subway during rush hours. PM2.5 concentrations inside car under the ground are higher than those on the ground, and PM2.5 concentrations on the platform are higher than those inside car. Regarding metal concentrations, the highest element in PM2.5 samples was Fe; the level of which is 17.55 μg/m3. OC is a major component of PM2.5 in Tianjin subway. Secondary organic carbon is the formation of gaseous organic pollutants in subway. SEM–EDX and TEM–EDX exhibit the presence of individual particle with a large metal content in the subway samples. For small Fe metal particles, iron oxide can be formed easily. With regard to their sources, Fe-containing particles are generated mainly from mechanical wear and friction processes at the rail–wheel–brake interfaces. The non-carcinogenic risk to metals Cr, Ni, Cu, Zn and Pb, and carcinogenic hazard of Cr and Ni were all below the acceptable level in L3 of Tianjin subway.
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