Coastal zones are critically important ecosystems that are closely tied to human activities, such as tourism, urbanization, transport, and aquaculture. However, managing and monitoring sea water in the coastal areas is often challenging due to the diversity of the pollution sources. Traditional approaches of onsite measurement and surveys have limitations in terms of cost, efficiency and productivity compared with modern remote sensing methods, particularly for larger and longer observations. Optical remote sensing imagery has been proven to be a good data source for water quality assessment in general and for seawater studies in particular with the use of advanced techniques of data processing such as machine learning (ML) algorithms. However, optical remote sensing data also have their own disadvantages as they are much affected by climatic conditions, atmospheric gas and particles as a source of noise in the data. This noise could be reduced, but it is still unavoidable. This study aims to model seawater quality parameters (total suspended solids (TSS), chlorophyll-a (chla), chemical oxygen demand (COD), and dissolved oxygen (DO)) along a 134 km sea coastal area of the Binh Dinh province by applying the current robust machine learning models of decision tree (DT), random forest (RF), gradient boosting regression (GBR), and Ada boost regression (ABR) using Sentinel-2 imagery. To reduce the atmospheric effects, we conducted onsite measurements of sea surface reflectance (SSR) using the German RAMSES-TriOS instrument for calibration of the Sentinel-2 level 2A data before inputting them to the ML models. Our modeling results showed an improvement of the model accuracy using calibrated SSR compared with the original Sentinel-2 level 2A SSR data. The RF predicted the most accurate seawater quality parameters compared with in situ field-measured data (mean R2 = 0.59 using original Sentinel-2 level 2A SSR and R2 = 0.70 using calibrated SSR). The chla was the most precise estimate (R2 = 0.74 when modelled by the RF model) flowing by DO, COD and TSS. In terms of seawater quality estimation, this accuracy is at a good level. The results of the seawater quality distributions were strongly correlated with coastal features where higher values of TSS, chla, COD, and DO are near the river mouths and urban and tourist areas. These spatial water quality data could be extremely helpful for local governments to make decisions when the modelling is continuously conducted (using big data processing), and it is highly recommended for more applications.

Nature-based wastewater treatment employing microalgae and bacteria has gained serious attention due to its combination with valuable biomass production. In wastewater, microalgae serves as the primary source of dissolved oxygen (DO) production for bacterial organic matter degradation. In addition, microalgae can effectively remove nutrients, pathogens as well as various heavy metals. Bacteria, on the other hand, has been widely applied in biological wastewater treatment for stabilization of organic matter, nitrification, denitrification, and under some specific conditions, enhanced biological phosphorus removal. When cultured together, microalgae and bacteria can cooperate effectively for wastewater treatment as well as form big flocs which can be harvested easily via sedimentation. However, some natural antagonistic interactions between them should be expected. Various environmental and operational factors showed significant influences on microalgae and bacteria in wastewater. They can impact system performance individually or in combination with others. Therefore, those factors should be carefully monitored for improving performance of the system.