During 2020, the COVID-19 pandemic resulted in a widespread lockdown in many cities in China. In this study, we assessed the impact of changes in human activities on air quality during the COVID-19 pandemic by determining the relationships between air quality, traffic volume, and meteorological conditions. The megacities of Wuhan, Beijing, Shanghai, and Guangzhou were selected as the study area, and the variation trends of air pollutants for the period January–May between 2016 and 2020 were analyzed. The passenger volume of public transportation (PVPT) and the passenger volume of taxis (PVT) along with data on precipitation, temperature, relative humidity, wind speed, and boundary layer height were used to identify and quantify the driving force of the air pollution variation. The results showed that the change rates of fine particulate matter (PM 2.5 ), NO 2 , and SO 2 before and during the lockdown in the four megacities ranged from -49.9% to 78.2% (average: -9.4% ± 59.3%), -55.4% to -32.3% (average: -43.0% ± 9.7%), and -21.1% to 11.9% (average: -10.9% ± 15.4%), respectively. The response of NO 2 to the lockdown was the most sensitive, while the response of PM 2.5 was smaller and more delayed. During the lockdown period, haze from uninterrupted industrial emissions and fireworks under the effect of air mass transport from surrounding areas and adverse climate conditions was probably the cause of abnormally high PM 2.5 concentrations in Beijing. In addition, the PVT was the most significant factor for NO 2 , and meteorology had a greater impact on PM 2.5 than NO 2 and SO 2 . There is a need for more national-level policies for limiting firework displays and traffic emissions, as well as further studies on the formation and transmission of secondary air pollutants.

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Nighttime light (NTL) intensity is highly associated with the unique footprint of human activities, reflecting the development of socioeconomic and urbanization. Therefore, better understanding of the relationship between NTL intensity and human activities can help extend the applications of NTL remote sensing data. Different from the global effect of human activities on NTL intensity discussed in previous studies, we focused more attention to the local effect caused by the spatial heterogeneity of human activities with the support of the multiscale geographically weighted regression (MGWR) model in this study. In particular, the Suomi National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) NTL data within Chongqing, China were taken as example, and the point of interest (POI) data and road network data were adopted to characterize the intensity of human activity type. Our results show that there is significant spatial variation in the effect of human activities to the NTL intensity, since the accuracy of fitted MGWR (adj.R2: 0.86 and 0.87 in 2018 and 2020, respectively; AICc: 4844.63 and 4623.27 in 2018 and 2020, respectively) is better than that of both the traditional ordinary least squares (OLS) model and the geographically weighted regression (GWR) model. Moreover, we found that almost all human activity features show strong spatial heterogeneity and their contribution to NTL intensity varies widely across different regions. For instance, the contribution of road network density is more homogeneous, while residential areas have an obviously heterogeneous distribution which is associated with house vacancy. In addition, the contributions of the commercial event and business also have a significant spatial heterogeneity distribution, but show a distinct decrement when facing the COVID-19 pandemic. Our study successfully explores the relationship between NTL intensity and human activity features considering the spatial heterogeneity, which aims to provide further insights into the future applications of NTL data.

Layered Molybdenum trioxide MOO3, with a two-dimensional (2D) structure was successfully delaminated into colloidal nanosheets in n-butanol via a soft-chemical process involving intercalation of dodecylamine. X-ray diffration (XRD) showd that: after intercalation spacing of the layered material expend from 1.38nm to 2.69nm. Furthermore, stable nanosheet sol was obtained after exfoliation under ultrasonic condition, Characterizations by transmission electron microscopy (TEM) and scan electron microscopy (SEM) confirmed the formation of unilamellar 2D nanosheet crystallites with an average lateral size of 400 nm, those also suggests that the samples we got is Lamellar structured. Selected Area Electron Diffraction (SEDA) indicates that the obtained nanosheets were crystalline. And the obtained nanosheets exhibited photo-catalytic decolorization properties. Which was evaluated by monitoring the degradation of Methylene Blue, after 40 minutes 90% methylene blue was degradated.