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Air pollution is a global challenge to human health and the ecological environment. Identifying the relationship among pollutants, their fundamental sources and detrimental effects on health and mental well-being is critical in order to implement appropriate countermeasures. The way forward to address this issue and assess air quality is through accurate air pollution prediction. Such prediction can subsequently assist governing bodies in making prompt, evidence-based decisions and prevent further harm to our urban environment, public health, and climate, all of which co-benefit our economy. In this study, the main objective is to explore the strength of features and proposed a two stage feature engineering approach, which fuses the advantage of influential factors along with the decomposition approach and generates an optimum feature combination for five major pollutants including Nitrogen Dioxide (NO2), Ozone (O3), Sulphur Dioxide (SO2), and Particulate Matter (PM2.5, and PM10). The experiments are conducted using a dataset from 2015 to 2020 which is publicly available and is collected from Belfast-based air quality monitoring stations in Northern Ireland, UK. In stage-1, using the dataset new features such as trigonometric and statistical features are created to capture their dependency on the target pollutant and generated correlation-inspired best feature combinations to improve forecasting model performance. This is further enhanced in stage-2 by an optimum feature combination which is an integration of stage-1 and Variational Mode Decomposition (VMD) based features. This study employed a simplified Long Short Term Memory (LSTM) neural network and proposed a single-step forecasting model to predict multivariate time series data. Three performance indicators are used to evaluate the effectiveness of forecasting model: 1) root mean square error (RMSE), 2) mean absolute error (MAE), and 3) R-squared (R2). The results demonstrate the effectiveness of proposed approach with 13% improvement in performance (in terms of R2) and the lowest error scores for both RMSE and MAE.

Rapid growth in urbanization and industrialization leads to an increase in air pollution and poor air quality. Because of its adverse effects on the natural environment and human health, it’s been declared a “silent public health emergency”. To deal with this global challenge, accurate prediction of air pollution is important for stakeholders to take required actions. In recent years, deep learning-based forecasting models show promise for more effective and efficient forecasting of air quality than other approaches. In this study, we made a comparative analysis of various deep learning-based single-step forecasting models such as long short term memory (LSTM), gated recurrent unit (GRU), and a statistical model to predict five air pollutants namely Nitrogen Dioxide (NO2), Ozone (O3), Sulphur Dioxide (SO2), and Particulate Matter (PM2.5, and PM10). For empirical evaluation, we used a publicly available dataset collected in Northern Ireland, using an air quality monitoring station situated in Belfast city centre. It measures the concentration of air pollutants. The performance of forecasting models is evaluated based on three performance metrics: (a) root mean square error (RMSE), (b) mean absolute error (MAE) and (c) R-squared ( $\text{R}^{2}$ ). The result shows that deep learning models consistently achieved the least RMSE compared to the statistical models with a value of 0.59. In addition, the deep learning model is also found to have the highest $\text{R}^{2}$ score of 0.856.

Monitoring surface water provides vital information in water management; however, limited data is a fundamental challenge for most developing countries, such as Vietnam. Based on advanced remote sensing technologies, the authors proposed a methodology to process satellite images and use their outcomes to extract surface water in water resource management of Quang Nam province. Results of the proposed study show good agreement with in situ measurement data when the obtained Overall Accuracy and Kappa Coefficient were greater than 90% and 0.99, respectively. Three potential applications based on the surface water results are selected to discuss sustainable water management in Quang Nam province. Firstly, reservoir operating processes can be examined, enhanced, and even developed through long-term extracted water levels, which are the interpolation results between the extracted surface water area and the water level–area–volume curve. Secondly, the long-term morphological change for the Truong Giang river case between 1990 and 2019 can also be detected from the Water Frequency Index performance and provided additional information regarding permanent and seasonal water changes. Lastly, the flood inundation extent was extracted and separated from permanent water to assess the damage of the Mirinae typhoon on 2 November 2009 in terms of population and crop aspects. HIGHLIGHTS Excellent performance of extractions for surface water features, i.e., reservoirs and rivers, from water indices and satellite images.; Reservoir's dynamic assessment for water level monitoring and operations.; Long-term morphological changes in the Truong Giang river and its adjacent areas for sustainable management.; Flood extent detection for flood hazard and risk assessment.;

Coastal erosion poses a significant threat to the infrastructure of the coastal community at the mouth of the Ma River in Thanh Hoa Province, Vietnam. In response, emergency solutions such as hard, protective structures are often implemented. However, this approach exacerbates the problem as the underlying mechanisms of coastal erosion are not adequately investigated and understood. In this study, the long-term configuration of the mouth of the Ma River in Thanh Hoa Province, Central Vietnam, is investigated using Landsat imagery spanning from 1987 to 2023. An analytical solution of a one-line model for shoreline change was also used to examine the sand discharge from the Ma River and the diffusion coefficient for the sand transported along the shore by breaking waves. The results showed an asymmetric configuration of the mouth of the Ma River over the past 37 years. The supply of sand from the Ma River is around 350,000 m3/year. The majority of sand (ranging from 55% to 75%) is mainly transported to the northern beach of the Ma River delta. This uneven distribution of sand from the Ma River has led to the asymmetrical morphology of the delta apex in which the northern part of the Ma River delta is experiencing northward movement while the southern part of the Ma River Delta is moving southward and landward. The asymmetrical morphology of the delta at the mouth of the Ma River has recently been identified as the cause of severe coastal erosion. The diffusion coefficient value determined for the transportation of longshore sand along the deltaic lobes of the Ma River delta corresponds to 90 m2/day. This study offers a practical method for investigating morphological changes in cuspate deltas, especially when measured field data are limited.

Nha Trang Coast is located in the South Central Vietnam and the coastal erosion has occurred rapidly in recent years. Hence it is crucial to accurately monitor the shoreline changes for better coastal management and reduction of risks for communities. In this paper, we explored a statistical forecasting model, Seasonal Auto-regressive Integrated Moving Average (SARIMA), and two Machine Learning (ML) models, Neural Network Auto-Regression (NNAR) and Long Short-Term Memory (LSTM), to predict the shoreline variations from surveillance camera images. Compared to the Empirical Orthogonal Function (EOF), the most common method used for predicting shoreline changes from cameras, we demonstrate that the SARIMA, NNAR and LSTM models outperform the EOF model significantly in terms of prediction accuracy. The forecasting performance of the SARIMA model, NNAR model and LSTM model is comparable in both long and short-term predictions. The results suggest that these models are highly effective in detecting shoreline changes from video cameras under extreme weather conditions.

Coastal erosion and accretion along the Quang Nam coast in Vietnam have been increasing in recent years, causing negative impacts on the inhabitants and local ecology. The Cua Lo estuary in Nui Thanh district has a complex hydrodynamic regime owing to its connection with two estuaries and three different tributaries. Therefore, a detailed study of the mechanisms and processes of these phenomena is crucial to understand the potential impact of a proposed 50,000-ton cargo port. In this study, the Delft3D model is employed to evaluate the morpho-dynamic changes in the area of Cua Lo under monsoon wave climate, storm, and flood conditions both before and after port and navigation channel construction. Results indicate that in the absence of the port, tidal currents and waves during monsoon storms cause significant erosion on the south bank and accretion on the north bank. Furthermore, the GenCade model is utilized to predict the future shoreline changes after the construction of two jetties. The model reveals that after 50 years of operation, the shoreline modifications will extend 449 m towards the sea, in comparison to natural conditions. However, the design of the northern jetty will ensure safe and proper operation without impacting the navigation channel. This study offers valuable insights into the morphological changes in the Cua Lo area and their potential implications, which can aid in the development of sustainable coastal management strategies for the region.

Analytical solutions for a one-line model for shoreline changes are employed to investigate the formation of two wave-dominated river delta coastlines, along with a small-scale laboratory experiment. Since the present analytical solution can be applied only to a river delta with infinite shorelines, a new analytical solution was developed to consider the effects of lateral boundaries to the evolution of delta coastlines. It was determined that two demarcations represented by the dimensionless times t* can be used to judge whether the lateral boundaries have affected the coastline evolution or not. After the successful application of a new analytical solution to the experimental data, the new analytical solution was applied to predict the formation and deformation of the shorelines of the Ombrone River and Funatsu River deltas. Results obtained from the analysis showed that the new analytical solution can be used to describe well the formation and deformation of finite river-delta shorelines. Based on the two demarcations as represented by the dimensionless time t*, the shorelines of the Ombrone River and Funatsu River deltas are classified as finite shorelines.

A Quang Nam coastline, located in the central of Vietnam, has been strongly affected by severe erosion and accretion due to climate change and human activities. Thus, understanding the causes and mechanisms of coastal morphology changes is essential to offer optimal coastal management and protection solutions. In this research, the Digital Shoreline Analysis System (DSAS) technique developed by the United States Geological Survey was adopted to calculate rate-of-change statistics from multiple historical coastline positions of Quang Nam province extracted from satellite images, i.e., Landsat and Sentinel images from 1990 to 2019. The coastal dynamic was digitized, visualized, and compared by two statistical parameters provided in DSAS, namely End Point Rate (EPR) and Linear Regression Rate (LRR). The final results reveal that the Quang Nam coast experienced remarkable phenomena of erosion and accumulation over the past three decades. The total number of erosional and accretional transects obtained from the EPR results are 401 and 414, with annual change rates of −1.7 and 0.77 m/year, respectively. The LRR parameter was also considered and illustrated a significant correlation with the EPR, as the obtained R2 value of 0.96. The lowest value of EPR over the period 1990–2019 is −42.4 m/year, highlighting the most significant erosion at the north of Cua Dai estuary, whereas coastline advance is recorded in the south segment. As a result, this study’s outcomes provide helpful information for better and sustainable coastal management in Quang Nam province of Vietnam.

Tidal inlets along the central coast of Vietnam are located in a microtidal, wave-dominated coastal environment. In addition, the Vietnam coast is highly influenced by the seasonal monsoon regime, which is characterized by large northeast waves from October to March and calm southeast waves from April to September every year. Consequently, the tidal inlet entrance morphologies often suffer from a dynamic seasonal evolution due to distinct differences in the direction of wave-induced longshore sediment transport (LST) between the two monsoon seasons. The migration or closure of tidal inlets causes a lot of problems for socio-economic development in the region since these are the main reasons leading to an increase in the risk of coastal flooding and the obstruction of navigation. This paper presents a comprehensive study of the morphological evolutions of natural tidal inlets on the central coast of Vietnam using long-term remote sensing data sets and by the Delft3D numerical model. Surprisingly, the estimated LST rates from the former method are in an order of magnitude agreement with the results from the latter one for all of the areas in this study. Based on the conservation equation for sand and comprehensive data collection, a new simple empirical formula for predicting the sand spit elongation rate as a function of the sand spit width is developed. Although the breaching of sand spit might happen during an extreme flood event at some tidal inlets, the growth rate of the spit before and after the breaching is almost unchanged. These findings are very useful information for supporting the local coastal authorities to find better management solutions in terms of sustainable development.

A field experiment was conducted at a tropical microtidal intermediate sandy beach with a low tide terrace (Nha Trang, Vietnam) to investigate the short-term swash-zone hydrodynamics and morphodynamics under variable wave conditions. Continuous 2D Lidar scanner observations of wave height at the lower foreshore, subsequent run-up and swash-induced topographic changes were obtained. These data were complemented by detailed real-time kinematic GPS topographic surveys. Variable wave and tide conditions were experienced during the field experiment with relatively large swell waves (offshore significant wave height, Hs = 0.9 m to 1.3 m; peak wave period, Tp = 8 to 12 s) concomitant with spring tides at the beginning of the period, followed by mild wind waves (offshore Hs under 0.5 m and Tp 5 s) and neap tides. This resulted in the following morphological sequence: berm erosion followed by rapid neap berm reformation and beach recovery within a few days. New insights into the link between intra-tidal swash dynamics and daily beach profile evolution were found using the Lidar dataset. While waves directly cause morphology changes on a wave-by-wave basis, tidal levels were found to be a key factor in determining the morphological wave-effect (accretive or erosive) due to modulated interaction between surf and swash hydro-morphodynamics.

Although coastal morphology results essentially from underwater sediment transports, the evolution of underwater beach profiles along the diverse coastlines of the world is still poorly documented. Bathymetry inversion from shore-based video cameras set forth a more systematic evaluation and is becoming more commonly used. However, there are limitations to this profiling method that are insufficiently assessed, undermining confidence in operational applications. In this paper, we investigate the daily evolution of a low tide terrace (LTT) in Nha Trang beach, Vietnam, under strong seasonal forcing: from weak wind waves during summer monsoon to moderate waves during winter monsoon. A new error estimation for depth inversion is presented based on tidal evaluation. The method compares video-based estimate and direct measurement of tidal amplitudes to provide a quality criterion. It reveals three types of errors, the main one being a deep water error associated with physical limits—loss of celerity-bathymetry relationship in deep water. This error is dependent on wave period and thus has a strong seasonal pattern in Vietnam. It is generally detrimental to depth inversion where wind waves are dominant (in summer here). On the contrary, the second error type is larger for larger waves and is located at breakpoint, altering wave detection. The last error type is due to nonlinear effects and wave setup in shallow water. After removing the faulty data, we finally present the first reliable three-year time-series of a beach profile in Nha Trang, Vietnam. A main result is the overall stability demonstrated for the LTT beach, with rapid exchange of sediment between the terrace and the upper beach during typhoons, monsoon events or seasonal cycles. These tropical environments may provide faster beach recovery compared with mid-latitude configurations.

This work analyzes the components of the total water level (TWL) that cause flooding in a tropical coastal area (Nha Trang beach, Southeast of Vietnam), and examines their link with global mean sea level rise (GMSLR). Interactions between the wave induced run-up (R) and astronomical tide (AT) were responsible for 43% of the 35 flooding events identified between 1993 and 2015. Most of these events (97%) took place during the winter monsoon season, when long-lasting extreme R and positive non-tidal residual (NTR) are likely to occur. Removal of the GMSLR trend from the NTR was found to affect the flood occurrence of 17% of these events, while the trend in wave height did not have any detectable impact. Our research highlights the direct connection between global climate changes and coastal flooding events.

Integrated fixed-film activated sludge (IFAS) technology greatly enhances nitrogen removal effectiveness and treatment capacity in municipal wastewater treatment plants, addressing the issue of limited land availability. Hence, this method is appropriate for treating household wastewater from office buildings. The research was conducted at the wastewater treatment plant in an office building in Ho Chi Minh City, Vietnam. Experiments were conducted to ascertain the most favorable working conditions, including hydraulic retention time (HRT), alkalinity dosage, and dissolved oxygen (DO). According to the study, the IFAS system had the highest nitrogen removal effectiveness when operated at a hydraulic retention time (HRT) of 7 h, an alkalinity dose of 7.14 mgCaCO3/mgN-NH4+, and a dissolved oxygen (DO) value of 6 mg/L. The nitrification efficiency ranges from 89.2% to 98.8%. The N-NO3− concentration post-treatment is within the range of 27–45 mgN-NO3−/L, which is lower than the allowable discharge limit of 60 mg/L as per Vietnam's wastewater discharge requirements. The research findings have enhanced the efficiency of the office building management process, thereby promoting the sustainable growth of society. [ABSTRACT FROM AUTHOR]

The application of fixed activated sludge with an attached growth process (FASAG) with optimal operating conditions (hydraulic retention time (HRT) of 7 h, dissolved oxygen (DO) of 6 mg/L, and alkalinity dosage of 7.14 mgCaCO3/mgN-NH4+) treats wastewater generated from office buildings to meet discharge requirements (as per the regulation in the nation where the study was conducted) with typical parameters such as pH of 6.87–7.56, chemical oxygen demand (COD) of 32–64 mg/L, suspended solids (SS) of 8–11 mg/L, N-NH4+ of 1–7 mg/L, and denitrification efficiency reaches 53%. In addition, the FASAG is an outstanding integration that makes both economic and environmental sense when applied in local wastewater treatment systems. In particular, this process combines aerobic and anoxic processes in a creation tank. This explains why this approach can save investment and operating costs, energy, and land funds. In office building regions, where land area is frequently limited, saving land funds presents numerous options to enhance the density of green cover. Furthermore, as a new aspect, investing in reusing wastewater after treatment to irrigate plants or flush toilets in office buildings contributes to a decrease in the quantity of wastewater released into the environment, saving water resources and supporting sustainable development. [ABSTRACT FROM AUTHOR]