It has been proposed that ~3.4 billion years ago an ocean fed by enormous catastrophic floods covered most of the Martian northern lowlands. However, a persistent problem with this hypothesis is the lack of definitive paleoshoreline features. Here, based on geomorphic and thermal image mapping in the circum-Chryse and northwestern Arabia Terra regions of the northern plains, in combination with numerical analyses, we show evidence for two enormous tsunami events possibly triggered by bolide impacts, resulting in craters ~30 km in diameter and occurring perhaps a few million years apart. The tsunamis produced widespread littoral landforms, including run-up water-ice-rich and bouldery lobes, which extended tens to hundreds of kilometers over gently sloping plains and boundary cratered highlands, as well as backwash channels where wave retreat occurred on highland-boundary surfaces. The ice-rich lobes formed in association with the younger tsunami, showing that their emplacement took place following a transition into a colder global climatic regime that occurred after the older tsunami event. We conclude that, on early Mars, tsunamis played a major role in generating and resurfacing coastal terrains.

The COVID-19 outbreak has become a global pandemic. The spatial variation in the environmental, health, socioeconomic, and demographic risk factors of COVID-19 death rate is not well understood. Global models and local linear models were used to estimate the impact of risk factors of the COVID-19, but these do not account for the nonlinear relationships between the risk factors and the COVID-19 death rate at various geographical locations. We proposed a local nonlinear nonparametric regression model named geographically weighted random forest (GW-RF) to estimate the nonlinear relationship between COVID-19 death rate and 47 risk factors derived from the US Environmental Protection Agency, National Center for Environmental Information, Centers for Disease Control and the US census. The COVID-19 data were employed to a global regression model random forest (RF) and a local model GW-RF. The adjusted R 2 of the RF is 0.69. The adjusted R 2 of the proposed GW-RF is 0.78. The result of GW-RF showed that the risk factors (i.e. going to work by walking, airborne benzene concentration, householder with a mortgage, unemployment, airborne PM 2.5 concentration and per cent of the black or African American) have a high correlation with the spatial distribution of the COVID-19 death rate, and these key factors driven from the GW-RF were mapped, which could provide useful implications for controlling the spread of the COVID-19 pandemic.

No abstract