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## Water, heat and contaminant transport in paved urban soils

PostDoc: Dr. Basem Aljoumani

Supervisors: Prof. Dr. Gerd Wessolek and Prof. Dr. Eva Paton

**Background information of the objectives**

An on-going challenge for environmental monitoring is the interpretation of temporal and spatial trends from monitoring data (Richards et al. 2010). Mostly, this data and the relation in between are complex, multivariate and nonlinear. In particular, there are only few studies dealing with the roadside soil topic of soil solution concentrations related to the run-off concentrations of high ways (i.e. Dierkes and Geiger 1999; Kocher et al. 2005; Hjortenkrans et al. 2008; Kluge and Wessolek 2011; Kluge et al. 2014; Kluge et al. 2016; Werkenthin et al. 2016). In some scientific papers, relations between trace elements and boundary conditions such as pH, soil solution EC and climatic factors are mentioned for interpreting transport and leaching, but the temporal and spatial variations were not yet in the scientific focus, because such complex and long-term data is rare and not easily available.

Due to the soil profile heterogeneity, some experimenters found that it is more desirable to use stochastic models rather than constant values in predicting the future concentration of soil solutes and soil moisture. For this, the parameters of stochastic transport models are treated as random variables with discrete values assigned according to a given probability distribution (Zhang et al., 2009; Fortin et al., 2010, Park et al., 2005; Green et al., 2007; Khazaei et al., 2008; Aljoumani et al., 2012; Aljoumani et al., 2015; Aljoumani et al., 2017).

**Aim**

Within this context three objectives are planned:

- Understanding and predicting temporal trends and patterns of trace elements of the soil solution of roadside soils, taking into account: rainfall intensities, Humidity, evapotranspiration, infiltration, soil moisture, runoff concentrations and volumes, soil solution concentrations in different depths, soil temperatures, soil solution EC (
*σ*), pH, and data from a climate station._{p} - Estimating the pore water EC (
*σ*) from TDR/FDR probes. To get_{p}*σ*from the TDR/FDR probes, we need to covert the bulk electrical conductivity (_{p}*σ*) that will be measured by probes from the lysimeter to_{b}*σ*by evaluating the linear relationship between soil dielectric constant (_{p}*ε*) and_{b}*σ*under urban soil conditions._{b} - Evaluating the linear changes of soil temperature (∆
*t*) - logarithm of time (lnT) model using single heat probe to estimate precisely soil thermal conductivity in laboratory conditions taking into account the effect of salt on the soil thermal conductivity, and predicting the soil temperature at deeper depths by measuring upper depths in lysimeter conditions.

**Methods**

The above-mentioned objectives are based on the following hypothesis:

- To evaluate temporal trends and variations of metal concentrations in the soil solution of roadside soils, we test the hypothesis that metals concentration follow linear and non-linear relationships based on the following data: rainfall intensities and volumes, runoff concentrations and volumes, soil depth, soil temperature, soil solution EC (
*σ*), pH, hydraulic conductivity, and climate data. Dissolved metals sampled from Highway lysimeter (Fig 1)._{p} - To estimate precisely the
*σ*by TDR/FDR data obtained from urban soils, this study will test the hypothesis that there is a stochastic component in the linear relationship between_{p}*ε*and_{b}*σ*. The_{b}*σ*of a soil water system is ascertained by measuring three conductance pathways in the system. They are: (1) solid–liquid interphase; (2) solid phase; and (3) liquid phase. These pathways are demonstrated in Fig. 2. For ecological application, we are interested in the electrical conductivity of liquid phase_{b}*σ*_{p.} - To estimate the thermal conductivity by single heat probe using the De Vries model, we test the hypothesis that there is a stochastic component in the linear relationship between ∆
*t*and lnT(Fig 3). - To predict the time series of soil temperature and solute transport, we test the hypothesis that the observations are correlated to each another (soil temperature data collected from lysimeter –T1 project, Fig 4).

- Figure 1. Lysimeter besides highway (Research Project FE 05.0160/2010/MGB (BASt), collaboration with Dr. Bjöern Kluge)
- © B. Aljoumani

- Figure 4. Two lysimeters with partially sealed urban surfaces studied in projects T1 and N2
- © B. Aljoumani

The following working steps need to be done for proving the hypothesis:

(i) Using the Generalized Additive Mixed Models (GAMM) to analyze the above mentioned field data from lysimeter, lab and climate

(ii) Using of a Dynamic Linear Model (DLM9 and a Kalman Filter to convert *σ _{b }*to

*σ*by studying the linear relationship between soil dielectric constant (

_{p}*ε*) and

_{b}*σ*to data obtained from urban soil conditions, then programing these stochastic models and inserting them into Hydrus1D - which is widely used for simulating water flow and solute transport in variably 1 saturated soils and groundwater (Šimůnek et al. 2008). Moreover, the study will use also DLM and Kalman Filter to estimate soil thermal conductivity by evaluating the linear changes of temperature - logarithm of time model, and

_{b}(iii) Using of Time Series Analysis, Outlier detection and Transfering Function Model to predict the soil temperature and soil solution EC at deeper depths by measuring upper depths.

**Connections to interfaces and other doctoral theses**

The research will be closely linked with project T1 and N2 of the UWI. By working on the lysimeter soil temperature data obtained from project T1, we will develop a model to predict the soil temperature at deeper depths by measuring the temperature at lower depths using Time Series Analysis and Transfer Function techniques. This contribution will be strongly linked to the project N2.

**Reference**

Aljoumani, B., Sànchez‐Espigares, J.A., Cañameras, N., Josa, R. (2014): An advanced process to evaluate the linear dielectric constant –bulk electrical conductivity model using capacitance sensor in field conditions. Hydrological Science Journal,doi:10.1080/02626667.2014.932053, http://dx.doi.org/10.1080/02626667.2014.932053

Aljoumani, B., Sànchez‐Espigares, J.A., Cañameras, N., Josa, R., Monserrat, J. (2012): Time series outlier and intervention analysis: irrigation management influences on soil water 22 content in silty loam soil. Agricultural Water Management 111, 105‐114

Aljoumani, B., Sànchez-Espigares, J.A., Cañameras, N., Wessolek, G., Josa, R. (2018):Transfer Function and Time Series Outlier Analysis: Modelling Soil Salinity in Loamy Sand Soil by Including the Influences of Irrigation Management and Soil Temperature. Irrigation and Drainage. 67(2). p.282

Dierkes, C., Holte, A., Geiger, W.F. (1999): Heavy metal retention within a porous pavement structure. - 8th International Conference on Urban Storm Drainage, 30.8.-3.9.1999, Proceedings IV: 1955-1962; Sydney for applications in fisheries research. Fisheries Research, 70, 319–337, 2004

Fortin, J.G., Anctil, F., Parent, L., Bolinder, M.A. (2010): Aneural network experiment on the site-specific simulation of potato tuber growth in Eastern Canada. Comput. Electron. Agric. 73, 126–132

Green, T.R., Salas, J.D., Martinez, A., Erskine, R.H. (2007): Relating crop yield to topographic attributes using spatial analysis neural networks and regression. Geoderma 139, 23–37

Hjortenkrans, D.S.T., Bergbäck, B.G., Häggerud, A.V. (2008): Transversal immission patterns and leachability of heavy metals in road side soils. Journal of Environmental Monitoring 10, 739-746 http://scholarsarchive.byu.edu/iemssconference/2010/all/584

Khazaei, J., Naghavi, M.R., Jahansouz, M.R., Salimi-Khorshidi, G. (2008): Yield estimation and clustering of chickpea genotypes using soft computing techniques. Agron. J. 100, 1077–1087

Kluge, B., Markert, A. & Facklam, M. (2016): Metal accumulation and hydraulic performance of bioretention systems after long-term operation. J Soils Sediments. doi:10.1007/s11368-016-1533-z

Kluge, B., Werkenthin, M. & Wessolek, G. (2014): Metal leaching in a highway embankment on field and laboratory scale. Science of the Total Environment, 493, 495-504

Kluge, B., Wessolek, G. (2011): Heavy metal pattern and solute concentration in soils along the oldest highway of the world e the AVUS Autobahn. Environ. Monit. Assess. 184 (11), 6469e6481. http://dx.doi.org/10.1007/s10661-011- 2433-8

Kocher, B., Wessolek, G., Stoffregen, H. (2005): Water and heavy metal transport in roadside soils. Pedosphere 15, 746e753

Markert, A., Andre, P., & G. Wessolek, G. (2016): Analysis of the evaporation method to obtain soil thermal conductivity data in the full moisture range, Soil Science Society of America Journal, 80, 275-283

Park, S.J., Hwang, C.S., Vlek, P.L.G. (2005): Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions. Agric. Syst. 85, 59–81

Rhoades, J.D., Manteghi, N.A., Shouse, P.J., Alves, W.J. (1989): Soil electrical conductivity and soil-salinity - new formulations and calibrations. Soil Science Society of America Journal 53, 433-439

Richards, R.G., Tomlinson, R. & Chaloupka, M. (2012): Using Generalized Additive Models to Assess, Explore and Unify Environmental Monitoring Datasets. International Congress on Environmental Modelling and Software. 584

Werkenthin, M., Kluge, B. & Wessolek, G. (2016): Assessment of metal retention in newly constructed highway embankments. Environ Sci. Pollut. Res. doi: 10.1007/s11356-016-7526-z

Zhang, J.Q., Zhang, L.X., Zhang, M.H., Watson, C. (2009): Prediction of soybean growth and development using artificial neural network and statistical models. Acta Agron. Sin. 35 (2), 341–34