Folate receptor-mediated delivery system based on chitosan coated polymeric nanoparticles for combination therapy of breast cancer.

Combination therapy using two or more drugs with different mechanisms of action is an effective strategy for treating cancer. This is because of the synergistic effect of complementary drugs that enhances their effectiveness. However, this approach has some limitations, such as non-specific distribution of the drugs in the tumor and the occurrence of dose-dependent toxicity to healthy tissues. To overcome these issues, we have developed a folate receptor-mediated co-delivery system that improves the access of chemotherapy drugs to the tumor site. We prepared a nanoplatform by encapsulating paclitaxel (PTX) and curcumin (CUR) in poly(caprolactone)-poly(ethylene glycol)-poly(caprolactone) (PCL-PEG-PCL) co-polymer using a double emulsion method and coating nanoparticles with pH-responsive chitosan-folic acid (CS-FA) conjugate. The nanocarrier's physicochemical properties were studied, confirming successful preparation with appropriate size and morphology. PTX and CUR could be released synchronously in a controlled and acid-facilitated manner. The dual drug-loaded nanocarrier exhibited excellent anti-tumor efficiency in MDA-MB-231 cells in vitro. The active targeting effect of FA concluded from the high inhibitory effect of dual drug-loaded nanocarrier on MDA-MB-231 cells, which have overexpressed folate receptors on their surface, compared to Human umbilical vein endothelial cells (HUVEC). Overall, the nanoengineered folate receptor-mediated co-delivery system provides great potential for safe and effective cancer therapy.

Water Sensitive Urban Design (WSUD) Spatial Prioritisation through Global Sensitivity Analysis for Effective Urban Pluvial Flood Mitigation.

Water Sensitive Urban Design (WSUD) has attracted growing attention as a sustainable approach for mitigating pluvial flooding (also known as flash flooding), which is expected to increase in frequency and intensity under the impacts of climate change and urbanisation. However, spatial planning of WSUD is not an easy task, not only due to the complex urban environment, but also the fact that not all locations in the catchment are equally effective for flood mitigation. In this study, we developed a new WSUD spatial prioritisation framework that applies global sensitivity analysis (GSA) to identify priority subcatchments where WSUD implementation will be most effective for flood mitigation. For the first time, the complex impact of WSUD locations on catchment flood volume can be assessed, and the GSA in hydrological modelling is adopted for applications in WSUD spatial planning. The framework uses a spatial WSUD planning model, the Urban Biophysical Environments and Technologies Simulator (UrbanBEATS), to generate a grid-based spatial representation of catchment, and an urban drainage model, the U.S. EPA Storm Water Management Model (SWMM), to simulate catchment flooding. The effective imperviousness of all subcatchments was varied simultaneously in the GSA to mimic the effect of WSUD implementation and future developments. Priority subcatchments were identified based on their influence on catchment flooding computed through the GSA. The method was tested for an urbanised catchment in Sydney, Australia. We found that high priority subcatchments were clustering in the upstream and midstream of the main drainage network, with a few distributed close to the catchment outlets. Rainfall frequency, subcatchment characteristics, and pipe network configuration were found to be important factors determining the influence of changes in different subcatchments on catchment flooding. The effectiveness of the framework in identifying influential subcatchments was validated by comparing the effect of removing 6% of the Sydney catchment's effective impervious area under four WSUD spatial distribution scenarios. Our results showed that WSUD implementation in high priority subcatchments consistently achieved the largest flood volume reduction (3.5-31.3% for 1% AEP to 50% AEP storms), followed by medium priority subcatchments (3.1-21.3%) and catchment-wide implementation (2.9-22.1%) under most design storms. Overall, we have demonstrated that the proposed method can be useful for maximising WSUD flood mitigation potential through identifying and targeting the most effective locations.

Machine learning approaches for predicting the performance of stormwater biofilters in heavy metal removal and risk mitigation.

The increasing amount of data on biofilter treatment performance over the past decade has made it possible to use data-driven approaches to explore the relationships between biofilter performance and a range of input variables. The knowledge gap lies in lack of models to predict the biofilter performance considering both design and operational variables, especially for heavy metals. In this study, we tested three machine learning (ML) approaches, namely multilinear regression (MLR), artificial neural network (NN), and random forest (RF), to predict biofilter outflow concentrations of heavy metals (Cd, Cr, Cu, Fe, Ni, Pb and Zn) using a range of design and operational factors as input variables. The results show that RF performed relatively better than other two models, with median Nash-Sutcliffe Efficiency (NSE) values of 0.995, 0.317, 0.762, 0.636, 0.726, 0.896 and 0.656 for Cd, Cr, Cu, Fe, Ni, Pb and Zn, respectively during model training. However, all the models were less accurate during model validation, with the better performance found for Cd (average NSE=0.964), Zn (0.530) and Ni (0.393) and poorer performance observed for Cu (0.219), Pb (0.058), Fe (-0.054) and Cr (-0.062). Infiltration rate (IR) and inflow concentration (Cin) were sensitive to all pollutants' removal in biofilters. The ratio of system size to catchment size was also found to be important for Zn, Ni and Cd, while ponding depth was an important variable for Cd. Based on thousands of hypothetical design and operational scenarios (generated using raw data), the best ML models were used to predict the biofilter outflow concentrations and estimate the risk quotient (RQ) values with regards to reuse of treated stormwater for various purposes. Results suggest that biofilters were able to reduce health risks associated with heavy metals in stormwater and therefore produce reliable water fit for reuses such as irrigation, swimming, and toilet flushing. Modelling results showed that biofiltration did not meet the requirements for drinking when Cd contamination exists. Explorative analysis also demonstrated how the key operational and design variables can be optimised to further reduce the health risks that can be fit for drinking purposes (i.e., RQ value <1).

Stormwater management impacts of small urbanising towns: The necessity of investigating the 'devil in the detail'.

In many parts of the world, small towns are experiencing high levels of population growth and development. However, there is little understanding of how urban growth in these regional towns will impact urban runoff. We used the case study of Wangaratta, located in South-East Australia, between 2006 and 2016, to investigate land cover changes and their impacts on urban runoff discharge. Detailed spatio-temporal analysis (including neighbourhood composition analysis and supervised classification of aerial imagery) identified that population, land use and land cover changes in Wangaratta, although subtle, were mostly driven by residential growth in the outskirts of the town, where there were large increases in impervious surface area. Overall, the urban growth was minimal. However, in spite of these small changes, a sub-catchment only SWMM model showed that the increase in impervious surface area nevertheless resulted in a statistically significant increase in total runoff across the town. Particularly, this increase was most pronounced for frequent and shorter storms. The analysis of urban development pattern changes coupled with urban hydrological modelling indicated that land cover changes in regional towns, especially when analysed in detail, may result in hydrological changes in the urban region (likely to be exacerbated in coming years by changing climate) and that adaptation efforts will need to adopt a variety of approaches in both existing and growth zones. Our findings highlight the necessity of detailed fine-scale analyses in small towns as even subtle changes will have substantial future implications and robust planning and adaptation decisions are even more important when compared to larger cities due to the greater economic constraints that small towns face and their important relationship with the surrounding hinterlands.

The impact of stormwater biofilter design and operational variables on nutrient removal - a statistical modelling approach.

Biofiltration systems can help mitigate the impact of urban runoff as they can treat, retain and attenuate stormwater. It is important to select the optimal design characteristics of biofilters (e.g., vegetation, filter media depth) to ensure high treatment performance. Operational conditions (e.g., infiltration rate) can also lead to significant changes in biofilter treatment performance over time. The impact of specific operational conditions on water quality treatment performance of stormwater biofilters is still not well understood. Furthermore, despite the importance of design characteristics and operational conditions on biofilter treatment performance, there is a lack of models that can be used to determine the optimal design and operation. In this paper, we developed a series of statistical models to predict the Total Phosphorus (TP) and Total Nitrogen (TN) removal performance of stormwater biofilters using various numbers of design characteristics and operational conditions. These statistical models were tested using data collected from four extensive laboratory-scale biofilter column studies. It was found that all models performed relatively well with a Nash-Sutcliffe Efficiency (NSE) of 0.42 - 0.61 for TP and 0.37 - 0.63 for TN. The most important design characteristics were filter media type and depth for TP treatment, and vegetation type and submerged zone depth for TN treatment. In addition, infiltration rate and inflow concentrations were the operational conditions that greatly influence outflow TP and TN concentrations from stormwater biofilters. As such, these variables need to be carefully considered when designing and operating stormwater biofilters. Sensitivity analysis results indicate that the model was quite sensitive to all regression coefficients and intercepts. Additional modelling exercises show that the model could be further simplified by reducing the number of cross-correlated parameters. These models can be used by practitioners for not just optimising the design, but also operating biofilters using real-time monitoring and control to achieve optimum performance.

Rainwater harvesting for urban flood management - An integrated modelling framework.

It is well known that rainwater harvesting (RWH) can augment water supply and reduce stormwater pollutant discharges. Due to the lack of continuous 2D modelling of urban flood coverage and its associated damage, the ability of RWH to reduce urban flood risks has not been fully evaluated. Literature suggests that small distributed storage spaces using RWH tanks will reduce flood damage only during small to medium flooding events and therefore cumulative assessment of their benefits is needed. In this study we developed a new integrated modelling framework that implements a semi-continuous simulation approach to investigate flood prevention and water supply benefits of RWH tanks. The framework includes a continuous mass balance simulation model that considers antecedent rainfall conditions and water demand/usage of tanks and predicts the available storage prior to each storm event. To do so, this model couples a rainfall-runoff tank storage model with a detailed stochastic end-use water demand model. The available storage capacity of tanks is then used as a boundary condition for the novel rapid flood simulation model. This flood model was developed by coupling the U.S. EPA Storm Water Management Model (SWMM) to the Cellular-Automata Fast Flood Evaluation (CA-ffé) model to predict the inundation depth caused by surcharges over the capacity of the drainage network. The stage-depth damage curves method was used to calculate time series of flood damage, which are then directly used for flood risk and cost-benefit analysis. The model was tested through a case study in Melbourne, using a recorded rainfall time series of 85 years (after validating the flood model against 1D-2D MIKE-FLOOD). Results showed that extensive implementation of RWH tanks in the study area is economically feasible and can reduce expected annual damage in the catchment by up to approximately 30 percent. Availability of storage space and temporal distribution of rainfall within an event were important factors affecting tank performance for flood reduction.