This study analyzes horizontal crustal movements across the Red River Fault Zone (RRFZ), one of the major fault systems in northern Vietnam, using GNSS data collected over nearly 30 years (1994–2023). In the ITRF2008 reference frame, GNSS stations along the fault show an average East-Southeastward motion at a rate of approximately 32.65±0.4 mm/yr, consistent with the general movement of the South China (SC) block. A slight velocity difference of about 2.5 mm/yr between stations on the SC and Sundaland (SU) blocks indicates relatively stable regional tectonic conditions, although localized deformation persists. Relative velocity analysis between the two fault flanks reveals a right-lateral strike-slip rate of approximately 2±0.5 mm/yr, accompanied by a minor extensional component of about 1±0.3 mm/yr. These findings suggest a generally stable tectonic regime for the SC block, while also implying possible contributions from subsidiary fault structures or local deformation zones.

This case study evaluates an innovative methodological approach that integrates soil chemical analysis (including pH, organic matter, total nitrogen, and grain size composition) with tree-ring δ¹³C measurements to assess long-term forest soil acidification processes. The study was conducted in Nang Pine Forest, located in Phong Nha - Ke Bang National Park, central Vietnam. Soil grain size analysis reveals a predominance of fine particles over coarse particles. There is a difference in the distribution of soil components at depth. The results indicate that the soil tends to be acidic, with a low pH that increases in depth, while it contrasts with the total nitrogen content. This acidity may result from natural processes and human activities, such as the release of acid rain. The findings also highlight the influence of topography and climate on soil properties. Furthermore, soil pH was negatively correlated with δ¹³C in pine tree rings in central Vietnam. This relationship could serve as a valuable tool for assessing past soil degradation processes, reconstructing historical environmental changes, or analyzing the connection between δ¹³C content in pine tree rings and climate change in the region. The results suggest that mitigating soil acidity and supporting healthier tree growth are essential to improve soil pH through the planting of suitable tree species and effective vegetation management in the study area. This study's integrated approach provides both current assessments and historical reconstructions, establishing a replicable and cost-effective methodological framework applicable to other tropical forest ecosystems under acidification stress. This framework provides valuable insights to advance both research and conservation management.

Fecal indicator bacteria (FIB) are used worldwide as indicators of fecal contamination in water, particularly in drinking water. This paper aims to investigate the level of FIB contamination and its associated public health risks in rainwater samples collected at 22 sites in inner-urban and suburban districts of Hanoi City during the period from May 2023 to May 2025. The results showed higher FIB values in inner-urban districts where high population densities were reported. The mean values of TC and EC in raw rainwater at most sites observed were higher than the limits of the Vietnam national technical regulation for domestic water use and also the WHO (2022) guideline for drinking water. A high public health risk was identified, and rainwater was unsuitable for direct potable use without treatment. Thus, our results underscore the importance of preventive and treatment measures to mitigate the risks associated with using harvested rainwater. Both natural and anthropogenic factors are primarily influencing the spread and persistence of FIB in this study. Our results provide a scientific basis for policy-making in air environmental protection for Hanoi City, as well as other large cities in developing countries in Asia.

The occurrence of natural disasters, especially with landslides, threatens mountainous districts and has serious consequences on local tourism development. Future disaster management must develop efficient innovative tools to control the rising frequency and intensity of landslides due to the impacts of economic development and climate change. Minimizing the risk and effects of these occurrences relies on the establishment of an optimal early warning system. This study focuses on the integration of artificial intelligence approaches to identify landslides and evaluate their susceptibility, with an emphasis on early warning systems on tourist routes in Da Bac district. As the first tool in the system, advanced deep learning models using satellite data at high resolution assist in identifying landslides. As a result, a developed DeepLab-v3 model demonstrated high performance by reaching 0.213 dice coefficient and 96.8% accuracy for landslide detection without restrictions from specific input resolution sizes. As the second tool, various machine learning tools, such as Random Forest and Support Vector Machine, utilize the identified landslide locations from the first tool to assess and map their susceptibility based on environmental and human-made factors. Accordingly, the study proposed an early warning system for landslide disaster management using real-time ecological factors and historical data. The proposed integrated system helps tourists and local communities take preventive actions that reduce landslide impacts, thus achieving safety goals in tourism activities, particularly in the Da Bac district of Hoa Binh province, Vietnam. It enhances strategies to minimize risk, increases the ability to predict landslide-prone tourist areas, and aids in implementing sustainable tourism in the future.

This study is the first in Vietnam to assess the impact of urbanization on the local climate in Hanoi and Ho Chi Minh City using the Regional Climate Model version 5 (RegCM5) at convection-permitting (CP) resolution. Simulations were conducted at a horizontal grid spacing of 2 km, driven by the fifth-generation global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5), and incorporating annually updated urban information from 2000 to 2020. Unlike previous studies that relied on coarse-resolution simulations and static urban data, the latest version of RegCM5 enables stable long-term simulations with significantly reduced computational demands. The results yield three key findings. First, the 2 km simulations outperform those at 10 km resolution, highlighting the advantages of CP-scale modeling. In specific cases, the 2 km simulations even surpass the performance of the Vietnam Gridded Climate (VnGC) dataset when validated against station observations. Second, incorporating updated urban information allows the model to more accurately capture the temporal evolution of urbanization impacts, particularly evident from late afternoon (around 16:00 local solar time, LST) to midnight (around 01:00 LST). Third, the study finds that the urban heat island (UHI) effect in both cities is stronger during this late afternoon to midnight period and intensifies with increasing background temperatures. The UHI magnitude in Hanoi is generally greater than in Ho Chi Minh City.

The A Vuong Formation, located within the southern Truong Son Belt in central Vietnam, comprises low- to high-grade metamorphic rocks that record crucial insights into the region’s tectonic evolution. Our field investigations reveal that it mainly consists of the psammitic and pelitic schists, intercalated with some mafic or siliceous layers, and locally cut by leucocratic veins. The indicative mineral assemblages in the pelitic to psammitic schists define two metamorphic zones: a greenschist-facies biotite-chlorite zone and an amphibolite-facies staurolite zone. Based on conventional geothermobarometry, the peak metamorphic condition in the staurolite zone was estimated at ca. 5.5–8.7 kbar and 590–640℃, followed by a retrograde stage at 4.4–7.4 kbar and 530–570℃. Notably, a porphyroblastic andalusite observed in a pelitic schist from this zone is rimmed by kyanite with a preferred orientation. In addition, it includes kyanite, sillimanite, and an intergrowth of staurolite + quartz as inclusions. These features suggest their equilibrium during the prograde stage, reflecting a pressure-temperature transition from andalusite to kyanite stability. The prograde metamorphic condition is thus constrained at the triple point of andalusite-kyanite-sillimanite. The petrological evidence and estimated metamorphic conditions for the current pelitic schists support a unique anti-clockwise pressure-temperature trajectory, rather than a clockwise one. Its thermobaric feature is well aligned with the amphibolite-facies metamorphism of the neighboring Dai Loc Complex, and contrasts with that of the Kham Duc Complex (Kontum Massif).

Pliocene to Pleistocene monogenic basalt volcanism in North Central Vietnam is typical of the East and Southeast Asian' diffuse igneous province'. Exposed at Khe Sanh (4.5 Ma), Gio Linh (1.5–1.3 Ma), and Con Co Island (0.35 Ma) the Miocene activity marks the southeastern tip of the Red River Shear Zone (RRSZ) and appears to relate to the rifting of the Hue Sub-basin, triggered by the Song Ca-Rao Nay Fault system (SCRNFS). Petrological analysis of primitive samples suggests that their formation occurred via decompression melting of spinel peridotite mantle at temperatures of 1350–1400°C and pressures of 15–29 kbar. The basalts display oceanic island basalt (OIB)-type geochemical signatures, characterized by enrichment in radiogenic isotopes of Sr, Nd, Pb, and Hf. They are relatively high in TiO2 and K2O, and incompatible element concentrations. These suggest a fertile, spinel-peridotite source, influenced by in situ metasomatism and/or the presence of recycled subducted oceanic sediment melt. Given the lack of evidence for mantle plume activity beneath the region, we propose that North Central Vietnamese monogenic volcanism reflects decompression melting of a hotter-than-average (Tp ≈ 1400°C) mantle, triggered by lithospheric stretching (greater than 1.5) within the Red River and Hue Sub-basins. In the context of previous regional studies of east/southeast Asian Cenozoic volcanism, Dupal-like mantle affinity is ascribed to eastward asthenospheric flow from the Neo-Tethyan mantle following its closure due to the India-Eurasia collision in the Tertiary.

The Kon Tum Massif (KTM) in central Vietnam represents a key geological component of the Viet-Cam Terrane (VCT), whose early Paleozoic evolution remains poorly constrained, especially along its southwestern margin of the KTM. This study presents new field, petrographic, zircon U-Pb geochronological, and Hf isotopic data, as well as whole-rock geochemical data, from early Paleozoic S-type magmatic rocks in the southwestern KTM, to refine their emplacement age, tectonic setting, and regional geological implications. Zircon U-Pb dating of two samples yields crystallization ages of 445.4±3.5 Ma and 427.5±1.4 Ma, consistent with late Ordovician and early Silurian magmatism. The negative εHf(t) values and rich inherited Paleo- and Neo-Proterozoic zircon indicate a sedimentary-dominated crustal source. At the same time, trace element signatures are characteristic of post-collisional S-type granites. These new findings, in conjunction with a comparable S-type Chu Lai complex in the northern KTM, support a widespread post-collision magmatism during the late Ordovician-Silurian within the KTM. Furthermore, the presence of post-collisional magmatic rocks across the KTM and TSB suggests that the post-collisional tectonic phase had a broad impact on most of the eastern Indochina block during the Late Ordovician-Silurian. This further implies that the collision between the KTM and TSB should have occurred before the late Ordovician.

Debris flow inventory is an essential task for scientists and managers to mitigate danger to humans, especially in mountainous areas. However, rapid land use and cover change, as well as technological limitations, make it a challenging task. Monitoring debris-flow efforts, especially in hilly places with limited transportation and technology, may improve management to minimize damage caused by this hazard. This work assesses U-shaped deep learning architectures, focusing on the roles of image size, optimization procedures, and data quality in debris flow trace identification using U-Net and U2-Net. While new debris flows can be detected through machine learning modeling, the U-Net model, combined with the Adam optimizer and an input size of 64×64, has been proven to be efficient, accurate, and stable. Small debris traces that can be used for planning debris thickness maps were easily identified in Worldview-2 and UAV images but not in the medium-resolution remote sensing data. When applied to Bat Xat district, Vietnam, the models identified that the distribution of debris flows is not uniform and depends on natural factors, such as rainfall and human-interpolated factors, including the construction of structures. The study also establishes the need to continually assess and incorporate big data for enhanced debris flow hazard assessment and mitigation. Further developments should focus on the effective use of multi-spectral and large-scale topographic data to strengthen disaster risk identification and provide recommendations for disaster risk reduction.

Mapping the spatial distribution of exposed coal sites over a large territory and ensuring temporal continuity is essential for environmental assessment. It helps identify relationships between coal mining activities and environmental issues such as water pollution from coal contamination or air pollution from particulate matter in the coastal area of Ha Long - Cam Pha. The carbon index has proven to be an effortless yet effective technique. It is calculated using the spectral ratio between two shortwave infrared bands (Band 11 - SWIR1 and Band 12 - SWIR2) of Sentinel-2 imagery. The selection of an appropriate carbon threshold value - used to distinguish exposed coal from other land cover types, particularly urban areas - was validated through visual interpretation of high-resolution satellite images from the Google Earth dataset. This approach, leveraging Sentinel-2 imagery with higher spatial resolution, differs from previous studies that applied complex algorithms or multiple spectral indicators to lower-resolution images for exposed coal site detection. The results, with an overall accuracy of 87.35% and a Kappa coefficient of 0.73, provide valuable support for monitoring and managing natural resource exploitation. Additionally, this method identifies illegal coal mines and seawater contamination, aiding in coal industry management, etc. These findings lay a crucial foundation for assessing the environmental impact of coal mining activities and proposing solutions for ecological restoration in coastal areas where diverse and complex socio-economic activities occur.