The hydrological module of SWAT was calibrated and further used to simulate the fate and transport of micropollutants in the soil and water body. Sensitivity analyzes of the PAH soil and PAH water parameters were also able to determine the critical processes in the TR watershed: degradation, deposition, volatilization, and washing mechanism.
INTRODUCTION
- Background of the Study
- Statement of the Problem
- Objectives of the Study
- Limitations of the Study
Monitoring studies have shown that remediation is necessary to reduce the concentrations of pollutants in the environment (Augusto, Máguas, Matos, Pereira, & Branquinho, 2010; Jongeneelen, 1997). To determine fate and transport parameters of the micropollutants in the atmosphere, soil and water within the catchment area.
REVIEW OF RELATED LITERATURE
Micropollutants
- Polycyclic Aromatic Hydrocarbons
- Pesticides
Micropollutant models
- Air
- Soil
- Water and Sediments
- Multimedia Models
However, the results were able to determine the temporal pattern of PAH concentrations from 1978 to 2048; was unable to describe the fate and transport of PAHs in soil. The model was able to calculate storm runoff PAH loads in the receiving channel.
Soil and Water Assessment Tool
- Hydrology Module
- Pesticide module
This occurrence adds to the uncertainty of the model, therefore it is recommended to have a thorough characterization of the degradation rates for future modeling works. 2010) were able to determine the sources of PAHs, pesticides and alkylphenols in sediments of the Ebro River basin in Spain using the Principal Component Analysis (PCA). The curve number varies according to the permeability of the soil, land use and groundwater conditions.
SOIL AND WATER ASSESSMENT TOOL
- Overview
- Study Areas
- Taehwa River Basin
- Pagsanjan-Lumban Watershed
- SWAT streamflow calibration
- Sensitivity Analysis
- Results and Discussion
- TR basin
- PL watershed
- Conclusion
The watershed is located in the southeastern part of the Laguna de Bay basin in the Southern Tagalog Region of the Philippines. Banahaw in the southernmost part of the basin has relatively uniform distribution of rainfall throughout the year (Cruz, Pillas, Castillo, & Hernandez, 2012). The PL watershed, on the other hand, has a limited set of flow rate data which is one of the limitations of the PL watershed model.
The Latin Hypercube (LH) sampling method was used for the sensitivity analysis of PAH parameters. These parameters show that the physical characteristics of the TR watershed, as well as the main channel itself, have a significant impact on the stream's flow. The PL watershed SWAT model was limited to a small data set which was a major disadvantage during flow velocity calibration and validation.
This can also be related to the result of the effective hydraulic conductivity in the alluvium of the tributary channel (CH_K1). The rest of the parameters differ between the channel characteristics of the basin (TR basin) and tributaries (PL basin).
DEVELOPMENT OF MICROPOLLUTANT MODEL
Overview
PAH fate and transport model
- Air-Soil Interaction
- Air-Water Interaction
- Soil-Water Interaction
- PAH in Water
The direction of the flux for each day was determined by the fugacity threshold; if ff (from Eq. 1) is less than 0.5, the direction for the D value from air to soil (deposition) will be different from the direction from soil to air (volatilization). The appropriate daily D value of each compound was determined to calculate for the net flux of the compound. The same fugacity method was applied for the air-water exchange fluxes of the PAH compounds.
Soil PAH compositions that were transported to the channel were assumed to be the concentration of suspended particulate PAHs exported from the saturated soils of the TR watershed to the river. Using SWAT parameters, OM was determined by dividing the mass of soil carbon in soil organic matter by water yield. 8 can also be determined using the bulk of PAH in soil fractions (Bergknut et al., 2010):. and the rate constant of PAH bound to DOC.
PAHs undergo advection, dispersion, photodegradation, and deposition once they reach the channel. The initial condition was the first simulation of the PAH concentration at the upper boundary of the channel.
Pesticide fate and transport model
- Accumulation of pesticide
- Pesticide in Soil
- Pesticide in Water
Pesticides are then exposed to the processes of advection, dispersion, photodegradation and sedimentation upon entering the channel (Ligaray et al., 2016). The photodegradation term in the equation was added to the advection and dispersion equation to fit the pesticide model.
PAH APPLICATION
Introduction
PAH monitoring methods have been implemented for many years to perform risk assessments of possible PAH contamination and identify their dominant sources (Bourgeault & Gourlay-Francé, 2013; del Rosario Sienra, Rosazza, & Préndez, 2005; Oros, Ross, Spies, & Mumley. , 2007). However, chemical analyzes of samples require too much time and effort; it is expensive to carry out intensive monitoring activities for a large number of PAHs (Pampanin & Sydnes, 2013). Water quality models address this problem by using available data to determine the spatial and temporal patterns of PAHs in different media (C. Wang et al., 2012).
The mathematical models help simulate the PAH concentration at a given time frame, taking advantage of the physical and chemical characteristics of each environmental medium. Unfortunately, these existing models are limited to mass transfers and have yet to perform a basin-scale simulation of PAH transport. The factories and factories are mainly involved in steel machinery, petrochemicals and transportation equipment, which are expected to be the main sources of PAHs.
Interactions between the atmosphere, water body, and soil are considered to simulate PAH transport within the watershed, while the SWAT model provides the watershed characteristics of the TR watershed. This study is the first watershed-scale PAH model that was able to investigate the spatial distribution, temporal patterns, and transport of PAH fate in the watershed.
Methodology
- Monitoring data
- Sensitivity Analysis
In order to simulate transport processes associated with multiple media, recent studies have used a box modeling approach (Scheringer & Wania, 2003). Box models help compartmentalize complex environmental systems by using boxes that are related to mass flows. Each box represents an environmental medium and can denote one or more state variables (Rene P. Schwarzenbach, Gschwend and Imboden were able to simulate PAH concentrations and evaluate their spatial variability using a multimedia fate model with a box model approach (Greenfield & Davis, 2005; Hauck et al., 2008).
This section addressed the need to have a watershed-scale PAH model by developing a multimedia model coupled with the Soil and Water Assessment Tool (SWAT) for the Taehwa River (TR) in Ulsan, South Korea. Ulsan City is the industrial capital of South Korea and has the largest GDP per capita in the country. The concentration of PAH in water represents the soluble and particulate compositions of PAH which were taken from February 2011 to November 2012.
All samples were taken on a seasonal basis and seven of these samplings were considered. In this study, the PAH model was applied to 12 PAH compounds: Fluorene (Flu) and phenanthrene (Phe), anthracene (Ant), fluoranthene (Flt), pyrene (Pyr), benzo(a)anthracene (BaA), chrysene ( Chr ), benzo(b)fluoranthene (BbF), benzo(a)pyrene (BaP), benzo(k)fluoranthene (BkF), indeno(1,2,3-c,d)pyrene (IcdP) and benzo( g, h,i)perylene (BghiP). Table 7 summarizes the available sampling events for each PAH while Table 8 shows the physical and chemical properties of each PAH compound.
Results and Discussion
- Sensitivity Analysis of PAH Soil
- Sensitivity Analysis of PAH Water
- Air-Soil Interface Module
- Soil-Water Interface Module
- Water Module
The simulated loading of the 12 PAH compounds was compared with the observed data in Figs. As shown in the figure, most PAH compounds have comparable simulated loading to the observed data. This increased the concentration levels of PAH compounds, especially the 5-ring PAH compounds (BbF, BkF and BaP) and Phe (Lee, Coleman, Jones, Jones, & Lohmann, 2005).
These processes will result in the efflux of PAH compounds from the soil, lowering the predicted load. The simulated results of the figure show that high loads of PAH compounds occurred in the spring and summer season. The PAH compounds that accumulate on the soil during the winter season were then washed away by surface runoff and ended up in the main channel.
The temporal patterns of the PAH compounds with the same number of rings also have similar patterns, which were revealed in the Air-Soil Interface Module. However, in the figure, PAH compounds have their peaks during the summer; the 3-ring PAH group has varying temporal patterns from the rest of the groups.
Conclusion
The model was able to visualize urbanized sub-basins in the watershed by spatially distributing the simulated PAH concentrations of each sub-watershed on the map. These conclusions suggest that the model may be useful in predicting PAH loads at a particular time of year, as well as in areas of the watershed vulnerable to PAH pollution.
PESTICIDE APPLICATION
- Introduction
- Methodology
- Monitoring data
- SWAT pesticide model
- Micropollutant model
- Sensitivity Analysis
- Evaluation Criteria
- Results and Discussion
- SWAT pesticide model
- Micropollutant model
- Comparative Study
- Conclusion
Implementation of a watershed-scale analysis of the fate and transport of malathion in the Laguna de Bay watershed using watershed models will provide insight into the dominant processes affecting pesticide loading to soil and water. The most sensitive parameter was the application efficiency (AP_EF) of malathion with a calibrated value of 0.13. The degradation rate constant of freely dissolved malathion particles (μk, fd) was the second most sensitive.
The (a) logarithmic scale and (b) true scale of the observed and simulated malathion loads at Lucban Station. This shows the comparison of the observed malathion data and the simulated load by the SWAT and micropollution model. However, the malathion simulations peaked during the duration of the pesticide application; the peaks of the micropollution model showed more consistency compared to the increasing peaks of the SWAT model.
The temporal patterns of the target sub-basin simulated by the models showed that the micropollution model exhibits more consistent peaks during the duration of the pesticide application compared to SWAT. However, further model development is needed to integrate pesticide application scenarios and the interaction of soil and water media with the atmosphere.
RECOMMENDATION
The nature and sources of particulate-associated polycyclic aromatic hydrocarbons (PAHs) in the atmospheric environment an. Fluxes of polycyclic aromatic hydrocarbons to marine and lake sediments in the northeastern United States. Indoor levels of polycyclic aromatic hydrocarbons in homes with and without wood burning for heating.
Degradation of polycyclic aromatic hydrocarbons (PAHs) in contaminated soil with Fenton's reagent: A multivariate evaluation of the importance of soil properties and PAH properties. Measurement and source identification of polycyclic aromatic hydrocarbons (PAHs) in the aerosol of Xi'an, China, using automated column chromatography and application of positive matrix factorization (PMF). Atmospheric concentrations and dry deposition rates of polycyclic aromatic hydrocarbons (PAHs) for Tampa Bay, Florida, USA.
Long-range transport of polycyclic aromatic hydrocarbons (PAHs) from the East Asian continent to Kanazawa, Japan by Asian dust. Seasonal changes of polycyclic aromatic hydrocarbons in soil and air of Dalian areas, China: assessment of soil-air exchange.
