One of the best management practices (BMPs) for stormwater quality and quantity control is a bioretention system. The removal efficiency of different pollutants under this system is generally satisfactory, except for nitrogen which is deficient in certain bioretention systems. Nitrogen has a complex biogeochemical cycle, and thus the removal processes of nitrogen are typically slower than other pollutants. This study summarizes recent studies that have focused on nitrogen removal for urban stormwater runoff and discusses the latest advances in bioretention systems. The performance, influencing factors, and design enhancements are comprehensively reviewed in this paper. The review of current literature reveals that a bioretention system shows great promise due to its ability to remove nitrogen from stormwater runoff. Combining nitrification and denitrification zones with the addition of a carbon source and selecting different plant species promote nitrogen removal. Nevertheless, more studies on nitrogen transformations in a bioretention system and the relationships between different design factors need to be undertaken.

The full dependency on chemical fertilizers in oil palm plantation poses an enormous threat to the ecosystem through the degradation of soil and water quality through leaching to the groundwater and contaminating the river. A greenhouse study was conducted to test the effect of combinations of biofertilizers with chemical fertilizer focusing on the soil fertility, nutrient uptake, and the growth performance of oil palms seedlings. Soils used were histosol, spodosol, oxisol, and ultisol. The three treatments were T1: 100% chemical fertilizer (NPK 12:12:17), T2: 70% chemical fertilizer + 30% biofertilizer A (CF + BFA), and T3: 70% + 30% biofertilizer B (CF + BFB). T2 and T3, respectively increased the growth of oil palm seedlings and soil nutrient status but seedlings in oxisol and ultisol under T3 had the highest in almost all parameters due to the abundance of more efficient PGPR. The height of seedlings in ultisol under T3 was 22% and 17% more than T2 and T1 respectively, with enhanced girth size, chlorophyll content, with improved nutrient uptake by the seedlings. Histosol across all treatments has a high macronutrient content suggesting that the rate of chemical fertilizer application should be revised when planting using the particular soil. With the reduction of chemical fertilizer by 25%, the combined treatment with biofertilizers could enhance the growth of the oil palm seedlings and soil nutrient properties regardless of the soil orders.

Flood prediction and control are among the major tools for decision makers and water resources planners to avoid flood disasters. The Muskingum model is one of the most widely used methods for flood routing prediction. The Muskingum model contains four parameters that must be determined for accurate flood routing. In this context, an optimization process that self-searches for the optimal values of these four parameters might improve the traditional Muskingum model. In this study, a hybrid of the bat algorithm (BA) and the particle swarm optimization (PSO) algorithm, i.e., the hybrid bat-swarm algorithm (HBSA), was developed for the optimal determination of these four parameters. Data for the three different case studies from the USA and the UK were utilized to examine the suitability of the proposed HBSA for flood routing. Comparative analyses based on the sum of squared deviations (SSD), sum of absolute deviations (SAD), error of peak discharge, and error of time to peak showed that the proposed HBSA based on the Muskingum model achieved excellent flood routing accuracy compared to that of other methods while requiring less computational time.

Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.

Azo dyes including C. I. Basic Brown 16 (BB16) are one of the coloured organic compounds that have adverse effects on human health and the environment. The current work aims to optimise the adsorption of C.I BB16 in aqueous solution using durian (Durio zibethinus murray) shell as a low-cost green adsorbent. Durian shell was characterised by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The adsorption process was optimised with response surface methodology (RSM) based on pH (4–8), time (30–240 min), durian shell dosage (0.1–1.0 g/L) and initial concentration of C.I BB16 (10–20 ppm). The removal efficiency was determined based on the reduction of chemical oxygen demand (COD) and the decolourisation of C.I BB16. The techno-economic analysis was described in the current work to know the economic feasibility of durian shells as an adsorbent. The SEM images showed that durian shell adsorbent has a smooth surface with no pores. FTIR spectra confirmed the presence of -C-O, =C–H, C=C, -C-O-C and O-H bonds in durian shell. Maximum decolourisation (77.6%) and COD removal (80.6%) for C.I BB16 was achieved with the interaction between pH, time and adsorbent dose and initial concentration of C.I BB16. The optimal operating factors for adsorption of C.I BB16 recorded at pH 8, time (30 min), durian shell dosage (1 g/L) and 15 mg /L of C.I BB16 concentrations were 77.61 vs. 74.26 (%) of C.I BB16 removal and 80.60 vs. 78.72 (%) of COD removal with an R² coefficient of 0.94 at p < 0.05. The specific cost of durian shell coagulant production is USD 172.71 per ton which is lower than the market price of honeydew peels-activated carbon (HDP-AC) (USD 261.81) and the commercial market price of activated carbon which is USD 1000.00/tons. These findings indicated that the durian adsorbent provides alternative methods for treating hair dye wastewater. These findings indicated that durian shells have a high potential for the adsorption of C.I BB16 in aqueous solution.

Increasing awareness on the detrimental effects of inorganic chemical coagulants and sustainable treatment have paved the way to develop environmentally-benign natural coagulants for water and wastewater treatment. In this research, brown, green, and red lentil (Lens culinaris) extracts have been evaluated as a natural coagulant for the turbidity reduction. The physicochemical characteristics properties of lentil extract were evaluated through field emission scanning electron microscopy (FESEM), Fourier-transform infrared spectroscopy (FTIR), zeta potential analysis, and energy-dispersive X-ray (EDX) analysis. Red lentil extract was found to be an anionic polymer with hydroxyl and carboxyl functional groups. The effects of three major operating parameters, pH, the concentration of coagulant, and settling time, were studied and optimized through response surface methodology (RSM) using the Box–Behnken Design (BBD). The red lentil extract as coagulant was outperformed alum in acidic wastewater treatment, which achieved up to 98.87% turbidity removal with an optimum concentration of 26.3mg/L in 3min. Besides, the red lentil extract as coagulant reduced 29.42% of material cost, generated 15.6% lower amount of sludge and produced flocs with better settling characteristic (SVI: 7.39) as compared to alum. Based on the experimental observation and characterization study, absorption and bridging mechanisms were proposed for red lentil extract as the coagulant in turbidity reduction.