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Black carbon (char and soot) has attracted increasing attention due to its important role in the global carbon cycle, adsorption of pollutants (polycyclic aromatic hydrocarbons (PAHs) and heavy metals), climate effects and threats to human health. However, few studies have included source analysis of black carbon (char and soot). In this study, the levels of char, soot and PAHs in sediments of West Taihu Lake were assessed, and an absolute principal component analysis followed by multiple linear regression (APCA-MLR) receptor model was used to successfully analyze the material sources of char and soot, providing a new perspective and method for exploring the sources of char and soot. The contributions of coal combustion sources to char and soot are 62.0% and 43.2%, respectively, which are significantly higher than those of biomass combustion sources (13.7% and 19.8%). The contributions of oil combustion sources to char and soot are 24.3% and 37.0%, respectively. The contributions of coal, oil and biomass combustion to char and soot have similar spatial distributions the coal combustion sources and biomass combustion sources are mainly affected by urban development, which is largely distributed in the northwest of the study area, whereas the oil combustion sources are mainly affected by automobile traffic and lake ports, which are mainly distributed in the west of the study area, and these effects decrease with an increase in offshore distance.Studies on fine particulate matter with an aerodynamic diameter of 2.5 μm or smaller (PM2.5) are closely related to the atmospheric environment and human activities but are often limited by ground-level in situ observations. Satellite remote sensing techniques have been widely used to estimate the PM2.5 concentration over large areas where ground-monitoring sites are unavailable. However, satellite-retrieved aerosol optical depth (AOD) products usually feature a coarse resolution, which is insufficient for the estimation of the urban-scale PM2.5 concentration. We developed a new improved random forest (IRF) model based on machine learning and a newly released AOD product with a high resolution of 1-km, which could more effectively and accurately estimate the PM2.5 concentration over Shenzhen in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China. Daily PM2.5 concentrations from 2016 to 2018 were estimated from ground-level PM2.5 and meteorological variable data. The popular linear regression model, geographically and temporally weighted regression (GTWR) model and random forest (RF) model without spatiotemporal information were employed for comparison and validation purposes through the 10-fold cross-validation (CV) approach. The IRF model attained an overall R2 value of 0.915 and a root mean square error (RMSE) value of 3.66 μg m-3. This suggests that the IRF model can estimate the urban PM2.5 concentration with a high spatial resolution at the daily, seasonal and annual scales, and the improved machine learning method is better than the linear model proposed by previous studies in terms of the estimation accuracy of the PM2.5 concentration. [https://www.selleckchem.com/products/n-acetyl-dl-methionine.html Selleck N-Acetyl-DL-methionine] Generally, the IRF model coupled with AOD data with a 1-km resolution can significantly improve the calculation accuracy of the atmospheric PM2.5 concentration over coastal urban areas in the future.The objective of this work is to present and to apply a method to environmentally evaluate a permeable pavement system used to harvest stormwater for non-potable water uses in a building. Two pavement systems were compared through life cycle assessment (LCA). The first system consists of a permeable pavement; in this case, the stormwater filtered by the pavement is used for non-potable water purposes in a building. The second system consists of a flexible pavement (impermeable), with no stormwater harvesting, and with conventional water supply in the building. The method was applied in a case study in a public building in southern Brazil. Water consumption surveys were made and the potential for potable water and electricity savings in the building were estimated. In the inventory, input and output data related to each stage of the life cycle of the systems were gathered and quantified. In the impact assessment, it was found that, for both pavement systems, the most significant damages were related to the implementation and end-of-life stages. The permeable pavement system presented a lower potential for environmental impacts in most midpoint categories evaluated, and also lower overall potential impact in the endpoint approach. The results also showed that the categories with the greatest environmental impact for both systems were fine particulate matter formation and global warming. The method proposed can be used as a basis for guiding planning and decision-making to improve water infrastructure management through stormwater harvesting in urban centres.Cropland expansion and intensification are the two main strategies for increasing food production. Here, we investigated the spatio-temporal patterns of global cropland expansion and intensification between 2000 and 2010 using the GlobeLand30 dataset. In doing so, we first analyzed the expansion and loss of global cropland at different spatial scales. Second, we quantified cropland intensification from the perspective of output and mapped its global spatial distribution. Third, nine coupled patterns of cropland expansion and intensification were identified, and the contributions of these two strategies to global crop production were finally estimated and compared. The results show that global cropland increased slightly (2.19%) during 2000-2010, with the American continent having the largest net increase (0.21 million km2) and Africa having the highest magnitude of increase (7.42%) as well as the most substantial spatial variation. Among the world's top ten countries with the largest cropland areas, China was-temporal patterns of global cropland expansion and intensification, which can provide helpful insights for the international community and individual countries to better guide land use planning, adjust agricultural structure and coordinate food trade so as to achieve a sustainable development of agriculture.