The ability of mangroves in taking up and storing heavy metal (HM) helps in reducing HM pollution. However, HMs likewise adversely affect the growth of mangroves. We assess the effects of the long-term soil HMs enrichment on the growth of Rhizophora apiculata forest in the Can Gio Mangrove Forest (Southern Vietnam) in different environmental conditions of soil salinity, ground elevation, and tree density based on a novel set of measured data. These data were analyzed and were used to calibrate and validate for a tree growth model with influencing factors salinity, elevation, tree density, and heavy metals content. Three scenario simulations were performed to predict the mangrove dynamics under different levels of heavy metal pollution in combined environmental conditions of salinity and elevation. Simulation results show the decline of total forest biomass from 1,750,000 tons (baseline scenario with no HM pollution) down to 850,000 tons and 350,000 tons for the current HM pollution and double HM pollution scenarios, respectively. Both data analysis and simulations have shown that although mangroves can assist in reducing HM pollution, the quality and health of this ecosystem will be severely affected if the environment is excessively polluted. In addition, a data-and-model driven management tool is devised for the sustainable management of the mangrove environmental resources.

In this study, chromium (Cr) retention by the mangroves in the Thi Vai catchment located in the south of Vietnam was simulated using a coupled model of the hydrodynamic model Delft3D with Cr transport and a model for the uptake of Cr by mangroves. This coupled model was calibrated and validated using data from four hydrodynamic stations and data from phytoremediation studies. To analyze the effect of mangroves on reducing Cr pollution, three scenarios were run by the model. Scenario 1 (SC1) is based on the actual situation concerning discharges and the distribution of mangroves. Scenario 2 (SC2) simulates the deterioration of the actual situation by deforestation on the west bank and the establishment of more industrial zones on the east bank. Scenario 3 (SC3) simulates an eco-friendly development comprising the channeling of wastewater through constructed wetlands with mangroves prior to the discharge into the river. Simulation results showed that the total Cr uptake by mangroves in SC3 was higher than in the other two scenarios. In total, 33 kg Cr in water were absorbed by the constructed wetlands in SC3 within one month. The simulation results helped in overcoming the difficulties and challenges in assessing the capacity of mangrove forests on the retention of chromium at catchment scale.

Phosphonate-based fungicides are believed to control fungal diseases while also supplying nutrients to plants. However, opinions differ on whether they truly serve as nutrients for plants, and the residues of their transformation products have not yet been thoroughly evaluated or mathematically characterized. To address this gap, this study analyzed data from a two-factorial experiment investigating the effects of Agrifos 400 (potassium phosphonate) application. The experiment involved two soil types: red basalt soil and an organically enriched soil. Three-month-old pepper plants (Piper nigrum L.) were treated with Agrifos at application intervals of 10 and 20 days. The soils were inoculated with pathogenic Pythium spp., known to cause root rot diseases in plants. The soil chemical concentrations were analyzed every ten days, while plant growth parameters (height and leaf numbers) were recorded weekly. A mathematical model describing the fate of Agrifos transformation products was developed and parameterized using this experimental data. The results from the two-month experiment indicated that Agrifos did not enhance plant growth during this period. However, it led to a dramatic increase in soil phosphate (PO₄³⁻) levels, which could pose environmental risks. Despite this, the developed mathematical model demonstrated strong explanatory power, accurately capturing the observed data trends. Consequently, future research should consider integrating this model into broader biogeochemical cycle simulations, particularly those that incorporate chemical transport through soil water. Such integration would support more accurate predictions of the long-term environmental impacts of phosphonate-based products like Agrifos.

Environmental pollution and climate change have been reported to severely affect the growth and productivity of mangroves. However, it is still unclear how the mangroves will fare if stressed by these adverse conditions, and how the mangroves might fare if these conditions improve. In this study, the trends of mangrove forests in the Thi Vai catchment (Vietnam) were assessed using mathematical models, addressing the polluted environment under climate change conditions. This simulated study was conducted based on the analysis of different types of data. Data on 18 elements’ concentrations accumulated in mangrove tissues in this catchment were analyzed in relation to the states of tree growth rates. Data on the economic productivity and water quality of the Thi Vai River in the five years from 2017 to 2021 were analyzed to detect the main sources of pollution that induced damage to mangrove forests. The results achieved from data analysis are the linear and nonlinear interactions between the concentrations of tissue-accumulated substances and the growth rates of trees. Concentrations of P, Mg, and Sr in mangrove leaves have a linear relationship with plant growth while Cr, Cu, and Ni accumulated in roots have a nonlinear relationship. The mining industry and accommodation and food services are the main contributing sources of Cr and Cu, which affect mangrove health. Information supplied from the data analysis helped in designing the scenarios of different combined environmental conditions for model simulations. Our previously developed mangrove dynamics model was applied to predict the trajectory of the mangrove forest in this area under a total of 16 combined environmental condition scenarios.