This study aims to examine the ability of free floating aquatic plants to remove phosphorus and to predict the reduction of phosphorus from rice mill wastewater using soft computing techniques. A mesocosm study was conducted at the mill premises under normal conditions, and reliable results were obtained. Four aquatic plants, namely water hyacinth, water lettuce, salvinia, and duckweed were used for this study. The growth of all the plants was inhibited in rice mill wastewater due to low pH, high chemical oxygen demand, high conductivity, and high phosphorus concentration. Subsequently, a 1:1 ratio of mill water to tap water was used. A control was maintained to assess the aquatic plant technology. In this study, the aquatic plants reduced the total phosphorus content up to 80 % within 15 days. A comparison between three modeling techniques e.g. Artificial neural network (ANN), Random forest (RF) and M5P has been done considering the reduction rate of total phosphorus as predicted variable. In this paper, the data set has been divided in two parts, 70 % is used to train the model and residual 30 % is used for testing of the model. Artificial neural network shows promising results as compared to random forest and M5P tree modelling. The root mean square error (RMSE) for all the three models is observed as 0.0162, 0.0204 and 0.0492 for ANN, RF and M5P tree, respectively.

Defined as any substance in the air that may harm humans, animals, vegetation, and materials, air pollution poses a great danger to human health. It has turned into a worldwide problem as well as a huge environmental risk. Recent years have witnessed the increase of air pollution in many cities around the world. Similarly, it has become a big problem in Iran. Although ground-level monitoring can provide accurate PM2.5 measurements, it has limited spatial coverage and resolution. As a result, Satellite Remote Sensing (RS) has emerged as an approach to estimate ground-level ambient air pollution, making it possible to monitor atmospheric particulate matters continuously and have a spatial coverage of them. Recent studies show a high correlation between ground level PM2.5, estimated by RS on the one hand, and measurements, collected at regulatory monitoring sites on the other. As such, the present study addresses the relation between air pollution and satellite images. For so doing, it derives RS estimates, using satellite measurements from Landsat satellite images. Monitoring data is the daily concentration of PM2.5 contaminants, obtained from air pollution stations. The relation between the concentration of pollutants and the values of various bands of Landsat satellite images is examined through 19 regression models. Among them, the Ensembles Bagged Trees has the lowest Root-Mean-Square Error (RMSE), equal to 21.88. Results show that this model can be used to estimate PM2.5 contaminants, based on Landsat satellite images.

Cryptosporidium and Giardia are important parasites due to their zoonotic potential and impact on human health, often causing waterborne outbreaks of disease. Detection of (oo)cysts in water matrices is challenging and few countries have legislated water monitoring for their presence. The aim of this study was to investigate the presence and origin of these parasites in different water sources in Northern Greece and identify interactions between biotic/abiotic factors in order to develop risk-assessment models. During a 2-year period, using a longitudinal, repeated sampling approach, 12 locations in 4 rivers, irrigation canals, and a water production company, were monitored for Cryptosporidium and Giardia, using standard methods. Furthermore, 254 faecal samples from animals were collected from 15 cattle and 12 sheep farms located near the water sampling points and screened for both parasites, in order to estimate their potential contribution to water contamination. River water samples were frequently contaminated with Cryptosporidium (47.1%) and Giardia (66.2%), with higher contamination rates during winter and spring. During a 5-month period, (oo)cysts were detected in drinking-water (<1/litre). Animals on all farms were infected by both parasites, with 16.7% of calves and 17.2% of lambs excreting Cryptosporidium oocysts and 41.3% of calves and 43.1% of lambs excreting Giardia cysts. The most prevalent species identified in both water and animal samples were C. parvum and G. duodenalis assemblage AII. The presence of G. duodenalis assemblage AII in drinking water and C. parvum IIaA15G2R1 in surface water highlights the potential risk of waterborne infection. No correlation was found between (oo)cyst counts and faecal-indicator bacteria. Machine-learning models that can predict contamination intensity with Cryptosporidium (75% accuracy) and Giardia (69% accuracy), combining biological, physicochemical and meteorological factors, were developed. Although these prediction accuracies may be insufficient for public health purposes, they could be useful for augmenting and informing risk-based sampling plans.

Pesticides are widely and intensively used in the world for crops protection. High pesticide loadings can potentially pollute the water resource. However, little is known about the usage, environmental emission and fate of pesticides in river basins. Here, we firstly established a pesticide emission estimation method, and investigated the environmental fate of three commonly used pesticides (chlorpyrifos, triazophos, and isoprothiolane) in Dongjiang River basin, southern China using mathematical modelling approach in combination with field monitoring. The distributed hydrological model SWAT (Soil and Water Assessment Tool) was applied to model the emission of the target pesticides from farmland to stream water, and their fate in the basin. A satisfactory model calibration for flow and suspended sediment was obtained based on eight-year observation data of four hydrological monitoring stations in Dongjiang River basin. The differences between the simulation and observation of pesticides were almost within an order of magnitude, including more than 53% differences within 0.5 order of magnitude. In the river basin, 78860 kg of chlorpyrifos, 54990 kg of triazophos and 35320 kg of isoprothiolane were sprayed onto the crops, the estimated annual emissions of the basin come up to 1801 kg, 3779 kg, and 2330 kg under the conditions of rainfall, surface runoff and percolation. After a series of environmental processes including settlement and degradation within the channels, the predicted export masses for chlorpyrifos, triazophos and isoprothiolane were reduced to 266 kg, 1858 kg, 1350 kg, respectively. Successful prediction suggests that the reliable estimation method combing the SWAT modelling can help us understand the source, concentration levels and fate of pesticides in river basin in different scales. Combing the method of emission and fate modelling method we proposed, countries and regions lacking pesticide-application database can facilitate better management of pesticides.

Shortly after an atmospheric release, the interception of radionuclides by crop canopies represents the main uptake pathway leading to food chain contamination. The food chain models currently used in European emergency decision support systems require a large number of input parameters, which inevitably leads to high model complexity. In this study, we have established a new relationship for wet deposited radionuclides to simplify the current modelling approaches. This relationship is based on the hypothesis that the stage of plant development is the key factor governing the interception of radionuclides by crops having horizontally oriented leaves (planophile crops). The interception fraction (f) and the leaf area index normalized (fLAI) and mass normalized (fB) interception fractions were assessed for spinach (Spinacia oleracea) and radish (Raphanus sativus) at different stages of plant development and for different contamination treatments and plant densities. A database of 191 f values for Cs-137 and Th-229 was built and complemented with existing literature covering various radionuclides and crops with similar canopy structure. The overall f increased with the plant growth, while the reverse was observed for fB. The fLAI significantly decreased by doubling the contaminated rainfall deposited. Fitting a multiple linear regression to predict the f value as a function of the standing biomass (B), and the radionuclide form (anion and cation) led to a better estimation of the interception (R² = 81%) than the ECOSYS-87 model (R² = 35%). Hence, the simplified modelling approach here proposed seems to be a suitable risk assessment tool as fewer parameters will minimize the model complexity and facilitate the decision-making procedures in case of emergencies, when countermeasures need to be identified and implemented promptly.

Great Barrier Reef (GBR) catchment management has been constrained by knowledge gaps regarding streambank erosion processes in grazing lands. To help reduce these uncertainties a remote sensing study using high-resolution imagery estimated sediment contributions from cattle traffic on streambanks of a GBR river basin. Results suggest cattle ramps and ramp trails may contribute up to 50% of the modelled streambank sediment supply. Once a suitable delivery ratio is applied, this estimated supply may contribute up to 30% of the modelled fine sediment exported from the Fitzroy River Basin. These findings may also offer a plausible explanation for the first-flush of high sediment concentration observed early in flood hydrographs. Overall, the results could help identify what proportion of currently modelled subsoil erosion is generated by riparian cattle traffic. Future studies applying similar methods could provide useful initial estimates of streambank ramp erosion from grazing land use in other GBR river basins.

A simplified particle-tracking model with an idealised coastline was used to investigate how the interaction between variable winds and water level (VaWWL) operates spatially along a coast. The model included a constant along-coast current, horizontal diffusion, onshore/offshore wind drift, beach/cliff combinations and point/distributed litter sources. The default model reproduced basic properties of observed beach litter loadings (zero net accumulation, negatively skewed loading distributions) and the observed spatial pattern along the Scottish east coast, with average loadings increasing in the coastal current direction. The VaWWL effect moved the along-coast flux of floating litter offshore as debeaching events occur during offshore winds. Varying diffusion, coastal current speed, windage, beach/cliff combinations and different foreshore boundary conditions were investigated. Reconciling model predictions with previous estimates of plastic inflow suggested sinking rates of up to 90% soon after first entry into the sea. The VaWWL effect offers a realistic boundary condition for particle-tracking models.

This study estimates air pollution at urban background level for Quito, Ecuador, using the Urban Background Model (UBM) developed at Aarhus University, Denmark. Hourly concentrations of CO, NO₂, NOₓ, O₃, PM₂.₅ and SO₂ were calculated for the year 2009. UBM performance is evaluated at six monitoring locations. The air pollution emission inventory was scaled, using calibration factors, until modeled concentrations were in line with observations. Predicted values were graphically and statistically evaluated by comparison to measurements. The statistical assessment is conducted for: Fraction of predictions within a factor of two of the observations (FAC2), Fractional mean bias (FB), Normalized mean-square error (NMSE) and Normalized absolute difference (NAD). Results show that the UBM model successfully predicts concentrations of CO, NO₂, NOₓ, O₃ and PM₂.₅ while the predicted SO₂ concentrations are unsatisfactory. PM₂.₅ modeling meets the criteria of acceptance, but their results depend largely on the regional levels, so the quality of this information is extremely relevant. The UBM model was applied for the years 2008 and 2010 using meteorological data retrieved from the modeling sites with emissions and calibration factors derived for the year 2009, showing a performance similar to that of 2009. The findings confirm the applicability of UBM to predict air pollution at the urban background level in Quito. Satisfactory results are obtained by applying meteorological data derived from any of the available monitoring stations. The unsatisfactory results for SO₂ suggest that emission data should be reviewed and that this cannot be obtained simply by scaling.