Search

The COVID-19 is still a huge challenge that seriously threatens public health globally. Previous studies focused on the influence of air pollutants and probable meteorological parameters on confirmed COVID-19 infections via epidemiological methods, whereas the findings of relations between possible variables and COVID-19 incidences using geographical perspective were scarce. In the present study, data concerning confirmed COVID-19 cases and possible affecting factors were collected for 325 cities across China up to May 27, 2020. The geographically weighted regression (GWR) model was introduced to explore the impact of probable determinants on confirmed COVID-19 incidences. Some results were obtained. AQI, PM 2.5 , and PM 10 demonstrated significantly positive impacts on COVID-19 during the most study period with the majority lag group (P< 0.05). Nevertheless, the relation of temperature with COVID-19 was significantly negative (P< 0.05). Especially, CO exhibited a negative effect on COVID-19 in most study period with the majority lag group. The impacts of each possible determinant on COVID-19 represented significantly spatial heterogeneity. The obvious influence of the majority of possible factors on COVID-19 was mainly detected during the after lockdown period with the lag 21 group. Although the COVID-19 spreading has been effectively controlled by tough measures taken by the Chinese government, the study findings remind us to address the air pollution issues persistently for protecting human health., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.)

Background: Since December 2019, the coronavirus disease 2019 (COVID-19) has spread quickly among the population and brought a severe global impact. However, considerable geographical disparities in the distribution of COVID-19 incidence existed among different cities. In this study, we aimed to explore the effect of sociodemographic factors on COVID-19 incidence of 342 cities in China from a geographic perspective., Methods: Official surveillance data about the COVID-19 and sociodemographic information in China's 342 cities were collected. Local geographically weighted Poisson regression (GWPR) model and traditional generalized linear models (GLM) Poisson regression model were compared for optimal analysis., Results: Compared to that of the GLM Poisson regression model, a significantly lower corrected Akaike Information Criteria (AICc) was reported in the GWPR model (61953.0 in GLM vs. 43218.9 in GWPR). Spatial auto-correlation of residuals was not found in the GWPR model (global Moran's I = - 0.005, p = 0.468), inferring the capture of the spatial auto-correlation by the GWPR model. Cities with a higher gross domestic product (GDP), limited health resources, and shorter distance to Wuhan, were at a higher risk for COVID-19. Furthermore, with the exception of some southeastern cities, as population density increased, the incidence of COVID-19 decreased., Conclusions: There are potential effects of the sociodemographic factors on the COVID-19 incidence. Moreover, our findings and methodology could guide other countries by helping them understand the local transmission of COVID-19 and developing a tailored country-specific intervention strategy.

Park playgrounds recently are suffering serious heavy metals contamination in China. It is urgent to assess the ecological risk and identify the sources for heavy metals. A total of 111 topsoil samples were collected from four park playgrounds in Xi'an, and the X-ray fluorescence (XRF) instrument was used to measure the concentrations of heavy metals including chromium(Cr), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), lead (Pb), manganese (Mn), and cobalt (Co), respectively. Ecological risk ([Formula: see text]) and potential ecological risk index (RI) were introduced to determine the pollution level and ecological risk, and the absolute principal component score-multiple linear regression (APCS-MLR) model was implemented to identify the sources for heavy metals. The main results were as follows. (1) Except As, the mean concentrations of measured heavy metals of four park playgrounds surpassed the soil background values of Shaanxi Province. (2) In each park playground, the [Formula: see text] was below a "low" risk level ([Formula: see text]=10) for Cr, Ni, Zn, As, and Mn; Cu was between a "moderate" and "considerable" risk level; Pb was between a "low" and "moderate" risk level; and [Formula: see text] was between a "considerable" and "high" risk level for Co. Besides, the RI index was on a "high" risk level (120 < RI < 240) with an obvious spatial distinction. (3) The anthropogenic factors were the main sources for heavy metals, and mixed sources and natural sources were considered as the minor sources for metals. (4) The sources contributions for Co had obvious spatial heterogeneity in each park situated in four different urban planning districts.