Kieu Lan Phuong Nguyen

Ninh Thuan is a coastal province in the central region of Vietnam and is characterized by a climate that is the hottest and driest in the country. Vietnam is also one of the top five countries most vulnerable to the effects of climate change worldwide. The objective of this study was a thorough evaluation of the quality of water supply sources and the impacts of water effluents from shrimp farms in Ninh Thuan province. The comprehensive evaluation was based on an understanding of the water–wastewater cycle employed in coastal shrimp cultivation. We combined qualitative and quantitative analyses in undertaking this study. Secondary data of groundwater and coastal water from the local periodic water quality monitoring program and national technical regulations were collected in the qualitative approach. We also integrated participatory rural appraisal techniques and field observations to understanding shrimp cultivation and the environmental and social impacts of shrimp farm effluents. The quantitative assessment consisted of measuring groundwater and wastewater contamination from shrimp ponds. As a result, four main reasons for water pollution issues were determined including extreme weather events, shrimp cultivation practices, degraded infrastructure, and mismanagement by local governance. Shrimp cultivation practices (feeding, using chemicals) have resulted in elevated levels of suspended solid (TSS, total Coliform), organic and carbon matter (BOD₅, COD), and excessive nutrients (total Nitrogen, NO₂-N, NO₃-N, PO₄-P). According to a local monitoring program, the coastal water and groundwater have experienced nutrient pollution. Groundwater sampling near the shrimp farms identified salinization elevated levels of Coliform from local domestic sewage sources. This study resulted in an integrated approach that evaluated the combined effects of extreme weather events and shrimp farming practices on the quality of coastal water. Also, the finding can be useful in recommending remedial water treatment technologies as a follow-on phase.

Municipal solid waste (MSW) management is a significant problem for developing countries due to lack of sufficient infrastructure, poor management capacity, and low level of waste treatment technology. This study proposes three main groups of criteria, i.e., social, economic, and environmental, that can be used as an effective tool to assess the sustainability of MSW treatment technologies, considering Ho Chi Minh City, Vietnam as a case study. The sustainability assessment criteria consist of a list of indicators which consider potential waste treatment plants. The indicators and technologies then undertake a selection process from identifying assessment goals and key aspects to data collection and consultation of experts. The findings from the previous phase will be used to select the most preferred waste technology through AHP and normalization approaches. As a result, 12 selected indicators are as follows: investment cost, treatment cost, operation and maintenance costs, revenue/benefits, job creation, community consensus, support policy, community health, air pollution, water pollution, greenhouse gas emissions, and land quota. Among three MSW facilities selected, i.e., landfill, compost, and waste-to-energy incineration, waste-to-energy is determined as the best alternative solution for Ho Chi Minh City in a given context of approximate 70% of landfilling being applied. The selection process and indicators found can guide decision-makers and policy on selecting MSW treatment technologies in developing countries. Additionally, Ho Chi Minh City’s governors benefit from finding the most appropriate waste technology. A technology adoption roadmap and its implementation plan should be thought thoroughly to address challenges in MSW management in the city.

Given the limitation of conventional soil pollution monitoring through mapping which is a costly, time-consuming work, the study aims to establish an image recognition model to identify the source of pollution automatically. The study choses a contaminated land and then use a non-destructive instrument that can quickly and effectively measure the content of heavy metals. A two concentration prediction models of Ni, Cu, Zn, Cr, Pb, As, Cd, and Hg using hyperspectral imaging were developed, Decision Tree and Back Propagation Neural Network, in combination of particle swarm optimization employed for optimization algorithm. As a result, random forest is more accurate than the forecast result of back propagation neural network. This study has established an excellent Cu and Cr model, which can accurately capture the pollution source. In addition, through aerial photographs, we also found that there were also high pollution reactions on the banks of the river. The developed model is beneficial for high pollution areas which can be quickly found, thereby following investigation and remediation work can be carried out with less time and cost consuming comparing with the conventional soil monitoring.

Marine litter is a significant threat to the marine environment, human health, and the economy. In this study, beach litter surveys along Vietnamese coasts were conducted in a local context to quantify and characterize marine litter using the modified GESAMP marine litter monitoring guideline. A total of 21,754 items weighing 136,820.2 g was recorded across 14 surveys from September 2020 to January 2021. Plastic was the most abundant type of litter by both quantity (20,744 items) and weight (100,371.2 g). Fishing gear 1 (fishing plastic rope, net pieces, fishing lures and lines, hard plastic floats) and soft plastic fragments were the most frequently observed items (17.65% and 17.24%, respectively). This study not only demonstrates the abundance and composition of marine litter in Vietnam, it also provides valuable information for the implementation of appropriate preventive measures, such as the redesign of collection, reuse, and recycling programs, and informs policy and priorities, with a focus on action and investment in Vietnam. Moreover, insights from this study indicate that citizen science is a useful approach for collecting data on marine litter in Vietnam.

This paper proposed the forecasting model of Influenza-like Illness (ILI) and respiratory disease. The dataset was extracted from the Taiwan Environmental Protection Administration (EPA) for air pollutants data and the Centers for Disease Control (CDC) for disease cases from 2009 to 2018. First, this paper applied the ARIMA method, which trained based on the weekly number of disease cases in time series. Second, we implemented the Long short-term memory (LSTM) method, which trained based on the correlation between the weekly number of diseases and air pollutants. The models were also trained and evaluated based on five and ten years of historical data. Autoregressive integrated moving average (ARIMA) has an excellent model in the five-year dataset of ILI at 2564.9 compared to ten years at 8173.6 of RMSE value. This accuracy is similar to the Respiratory dataset, which gets 15,656.7 in the five-year dataset and 22,680.4 of RMSE value in the ten-year dataset. On the contrary, LSTM has better accuracy in the ten-year dataset than the five-year dataset. For example, on average of RMSE in the ILI dataset, LSTM has 720.2 RMSE value in five years and 517.0 in ten years dataset. Also, in the Respiratory disease dataset, LSTM gets 4768.6 of five years of data and 3254.3 of the ten-year dataset. These experiments revealed that the LSTM model generally outperforms ARIMA by three to seven times higher model performance.

This paper proposed a PM 2.5 forecasting model using Long Short-Term Model (LSTM) sequence to sequence combined with the statistical method. Correlation Analysis, XGBoost, and Chemical processed are used as the methods to select the essential features. The air pollution data is extracted from Taiwan Environmental Protection Agency (EPA) for the Taichung City dataset in 2014-2018. The study points out that chemical processed model of particulate matter 10 micrometers or less in diameter (PM10), Sulfur Dioxide (SO2), and Nitrogen Dioxide (NO2) have the highest accuracy or lowest Root Mean Square Error (RMSE) and more short training and testing time among the other models. The chemical processed model of PM10, SO2, and NO2 (model B) has the highest accuracy (lowest RMSE), approximately 1 point lower RMSE values, and the shortest training and testing period among the other models. Furthermore, RMSE calculations based on the stations reveal that training with the entire station dataset has a 3 point higher RMSE value than training with each station dataset.