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Air pollution remains a critical global health concern, with 91% of the world's population exposed to air quality exceeding World Health Organization (WHO) standards and indoor pollution causing approximately 3.8 million deaths annually due to incomplete fuel combustion. Subways, as major public transportation modes in densely populated cities, can exhibit fine particulate matter (PM) levels that surpass safety limits, even in developed countries. Contributing factors include station location, ambient air quality, train frequency, ventilation efficiency, braking systems, tunnel structure, and electrical components. While elevated PM levels in underground platforms are recognized, the vertical and horizontal variations within stations are not well understood. This study examines the vertical and horizontal distribution of PM2.5 and PM10 levels at Shibuya Station, a structurally complex hub in the Tokyo Subway System. Portable sensors were employed to measure PM concentrations across different platform levels—both above and underground—and at various locations along the platforms. The results indicate that above-ground platforms have significantly lower PM2.5 and PM10 levels compared to underground platforms (17.09 μg/m3 vs. 22.73 μg/m3 for PM2.5; 39.54 μg/m3 vs. 56.98 μg/m3 for PM10). Notably, the highest pollution levels were found not at the deepest platform but at the one with the least effective ventilation. On the same platform, PM levels varied by up to 63.72% for PM2.5 and 120.23% for PM10, with elevated concentrations near the platform extremities compared to central areas. These findings suggest that ventilation efficiency plays a more significant role than elevation in vertical PM variation, while horizontal differences are likely influenced by piston effects from moving trains. This study underscores the risk of exposure to unsafe PM2.5 levels in underground platforms, particularly at platform extremities, highlighting the need for improved ventilation strategies to enhance air quality in subway environments. [ABSTRACT FROM AUTHOR]

The difficulty in identifying collapsed houses and damaged structures in synthetic aperture radar (SAR) images after natural disasters represents a significant challenge in the monitoring of urban structural deformation using SAR. SAR image simulation was conducted on a three-dimensional model of a typical wooden building in Japan to analyze the scattering mechanism of the structure in collapsed and uncollapsed states. Based on the physical properties of the buildings, a correlation was established between the simulated SAR image feature signals and the geometric structures of the buildings. The findings indicate that SAR scattering is more uniform for uncollapsed structures, which is predominantly influenced by their geometry. At low incidence angles, single reflections were the predominant phenomenon, whereas at high incidence angles, multiple reflections became more prevalent. The uncollapsed building's facade formed a dihedral angle, exhibiting bright lines in the SAR image. Multiple reflections occurred at the edges of the building and floor junctions. These findings follow the theoretical predictions. In the case of the collapsed buildings, multiple reflections occurred with greater frequency, and irregular scattering was observed. Notwithstanding the augmented scattering pathways, some walls nevertheless manifested single reflections. The collapsed structures demonstrated a reduced sensitivity to alterations in the angle of incidence. [ABSTRACT FROM AUTHOR]

Palm oil is a widely used agricultural crop in the world, and most of them are produced in Malaysia and Indonesia covering millions of hectares. However, climate change which induces water stress and extreme weather such as El Niño poses a risk to oil palm production. Therefore, it is crucial to identify areas vulnerable to climate change to prioritize adaptation actions for sustainable palm oil production. Several studies have implemented vulnerability assessments for climate change using a limited number of climate variables, despite the complex relationships among vegetation and various water components. Given these situations, this study aims to identify areas vulnerable to climate change for oil palms by analyzing the relationship between vegetation productivity and climate variables, and their changes from 2002 to 2012 and from 2013 to 2022. This analysis utilized satellite-based multiple water components and applied principal component analysis. The identified vulnerable areas were then compared with the biophysical suitability map from another study. Severe El Niño impacts from 2015 to 2016 were also assessed as an indicator of the actual decline in productivity. The results showed that more than half of the study area had experienced water stress recently, particularly in Java, southern Borneo, and the southern New Guinea islands. There was a discrepancy between the biophysical suitability map and the identified vulnerable areas in this study, especially in the southern part of Borneo. These findings highlighted the importance of considering multiple water components in tropical regions and future projections for appropriate palm oil cultivation areas. [ABSTRACT FROM AUTHOR]