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Soil is a key component of Earth's critical zone. It provides essential services for agricultural production, plant growth, animal habitation, biodiversity, carbon sequestration and environmental quality, which are crucial for achieving the United Nations' Sustainable Development Goals (SDGs). However, soil degradation has occurred in many places throughout the world due to factors such as soil pollution, erosion, salinization, and acidification. In order to achieve the SDGs by the target date of 2030, soils may need to be used and managed in a manner that is more sustainable than is currently practiced. Here we show that research in the field of sustainable soil use and management should prioritize the multifunctional value of soil health and address interdisciplinary linkages with major issues such as biodiversity and climate change. As soil is the largest terrestrial carbon pool, as well as a significant contributor of greenhouse gases, much progress can be made toward curtailing the climate crisis by sustieving the SDGs. this website In this study, we aimed at analyzing the associations between transmission of and deaths caused by SARS-CoV-2 and meteorological variables, such as average temperature, minimum temperature, maximum temperature, and precipitation. Two outcome measures were considered, with the first aiming to study SARS-CoV-2 infections and the second aiming to study COVID-19 mortality. Daily data as well as data on SARS-CoV-2 infections and COVID-19 mortality obtained between December 1, 2019 and March 28, 2020 were collected from weather stations around the world. The country's population density and time of exposure to the disease were used as control variables. Finally, a month dummy variable was added. Daily data by country were analyzed using the panel data model. An increase in the average daily temperature by one degree Fahrenheit reduced the number of cases by approximately 6.4 cases/day. There was a negative correlation between the average temperature per country and the number of cases of SARS-CoV-2 infections. This association remained strong even with the incorporation of additional variables and controls (maximum temperature, average temperature, minimum temperature, and precipitation) and fixed country effects. There was a positive correlation between precipitation and SARS-CoV-2 transmission. Countries with higher rainfall measurements showed an increase in disease transmission. For each average inch/day, there was an increase of 56.01 cases/day. COVID-19 mortality showed no significant association with temperature. Recently, the coronavirus disease 2019 (COVID-19) has become a worldwide public health threat. Early and quick identification of the potential risk zones of COVID-19 infection is increasingly vital for the megacities implementing targeted infection prevention and control measures. In this study, the communities with confirmed cases during January 21-February 27 were collected and considered as the specific epidemic data for Beijing, Guangzhou, and Shenzhen. We evaluated the spatiotemporal variations of the epidemics before utilizing the ecological niche models (ENM) to assemble the epidemic data and nine socioeconomic variables for identifying the potential risk zones of this infection in these megacities. Three megacities were differentiated by the spatial patterns and quantities of infected communities, average cases per community, the percentages of imported cases, as well as the potential risks, although their COVID-19 infection situations have been preliminarily contained to date. With higher risks that were predominated by various influencing factors in each megacity, the potential risk zones coverd about 75% to 100% of currently infected communities. Our results demonstrate that the ENM method was capable of being employed as an early forecasting tool for identifying the potential COVID-19 infection risk zones on a fine scale. We suggest that local hygienic authorities should keep their eyes on the epidemic in each megacity for sufficiently implementing and adjusting their interventions in the zones with more residents or probably crowded places. This study would provide useful clues for relevant hygienic departments making quick responses to increasingly severe epidemics in similar megacities in the world. An outbreak of respiratory illness which is proven to be infected by a 2019 novel coronavirus (2019-nCoV) officially named as Coronavirus Disease 2019 (COVID-19) was first detected in Wuhan, China and has spread rapidly in other parts of China as well as other countries around the world, including Malaysia. The first case in Malaysia was identified on 25 January 2020 and the number of cases continue to rise since March 2020. Therefore, 2020 Malaysia Movement Control Order (MCO) was implemented with the aim to isolate the source of the COVID-19 outbreak. As a result, there were fewer number of motor vehicles on the road and the operation of industries was suspended, ergo reducing emissions of hazardous air pollutants in the atmosphere. We had acquired the Air Pollutant Index (API) data from the Department of Environment Malaysia on hourly basis before and during the MCO with the aim to track the changes of fine particulate matter (PM2.5) at 68 air quality monitoring stations. It was found that the PM2.5 concentrations showed a high reduction of up to 58.4% during the MCO. Several red zone areas (>41 confirmed COVID-19 cases) had also reduced of up to 28.3% in the PM2.5 concentrations variation. The reduction did not solely depend on MCO, thus the researchers suggest a further study considering the influencing factors that need to be adhered to in the future. Gas/particle (G/P) partitioning of semi-volatile organic compounds (SVOCs) such as polybrominated diphenyl ethers (PBDEs), is an important atmospheric process due to its significance in governing atmospheric fate, wet/dry deposition, and long-range atmospheric transport. In this article, eight models published to predict the G/P partitioning of PBDEs are reviewed. These eight models are used to calculate the G/P partitioning quotient and particulate phase fraction of selected PBDE congeners. A comparison of the predicted results from the eight models with monitoring data published by several research groups worldwide leads to the following conclusions 1) when the values of the logarithm of the octanol-air partition coefficient (logKOA) fall below 11.4 (the first threshold value, logKOA1), all 8 models perform well in predicting the G/P partitioning of PBDEs in the atmosphere, and 2) when logKOA is >11.4, and especially above 12.5 (the second threshold value, logKOA2), the Li-Ma-Yang model, a steady-state model developed based on wet and dry deposition of the particles (Li et al.