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.
Environmental Science and Pollution Research - The rapid economic development in China places a large demand for energy, and as a result, thermal power plants in China are producing an enormous... 相似文献
Nanoplastics are widely distributed in freshwater environments, but few studies have addressed their effects on freshwater algae, especially on harmful algae. In this study, the effects of polystyrene (PS) nanoplastics on Microcystis aeruginosa (M. aeruginosa) growth, as well as microcystin (MC) production and release, were investigated over the whole growth period. The results show that PS nanoplastics caused a dose-dependent inhibitory effect on M. aeruginosa growth and a dose-dependent increase in the aggregation rate peaking at 60.16% and 46.34%, respectively, when the PS nanoplastic concentration was 100 mg/L. This caused significant growth of M. aeruginosa with a specific growth rate up to 0.41 d?1 (50 mg/L PS nanoplastics). After a brief period of rapid growth, the tested algal cells steadily grew. In addition, the increase in PS nanoplastics concentration promoted the production and release of MC. When the PS nanoplastic concentration was 100 mg/L, the content of the intracellular (intra-) and extracellular (extra-) MC increased to 199.1 and 166.5 μg/L, respectively, on day 26, which was 31.4% and 31.1% higher, respectively, than the control. Our results provide insights into the action mechanism of nanoplastics on harmful algae and the potential risks to freshwater environments.
