# Groundwater Vulnerability and Nitrate Contamination Assessment and Mapping Using DRASTIC and Geostatistical Analysis

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

^{2}and, with about 2 million inhabitants, is one of the most densely populated areas in the world. The Gaza Strip is in a chronic state of water shortage due to the increasing water demand for domestic, agricultural and industrial use. The underlying coastal aquifer is the only freshwater source and is increasingly depleted and polluted [1,2]. Major threats to groundwater quality are seawater intrusion, wastewater leakage and seepage from fertilization leading to high levels of chloride and nitrate. Most wells have nitrate levels above the WHO standard and 96% percent of groundwater abstraction does not meet drinking water quality standards, posing a serious threat to public health [1].

## 2. Materials and Methods

#### 2.1. Study Area and Data

#### 2.2. Data Sampling and Analysis

#### 2.3. DRASTIC Model

#### 2.4. Geostatistical Analysis

## 3. Results

#### 3.1. Groundwater Vulnerability

_{41}represents the soils with a sandy clay loam texture but, because this category is not available in the DRASTIC procedure, we opted for the loam class. The topographic factor map (Figure 5D) shows slope classes derived from the topographic map (Figure 1A). There are five topography slope factor classes, but only two are important: slopes less than 2% that correspond to flat areas, and slopes in the range of 2–6% that indicate dunes and ridges. The impact of the vadose zone factor map (Figure 5E) is very patchy due to the nearest neighbor interpolation of the well logs as explained earlier, but the clay class is dominant.

_{16}) and 0–1.55 m (C

_{17}), recharge class > 250 mm/year (C

_{25}) and topography class 6–12% slope (C

_{51}); in the latter case topography class 2–6% slope (C

_{52}) had to be included instead in the intercept. The significance of the regression predictors is expressed by their t-value and associated p-value, which should normally be less than 5%. It follows that only the intercept and factor classes C

_{42}and C

_{53}are statistically significant, while C

_{13}, C

_{24}and C

_{82}are almost significant. In particular, there are no real significant factor classes for impact of the vadose zone. However, it has to be taken into account that 15 regression coefficients are estimated, making the regression model somewhat overfitted. In addition, there may be few observations in small-sized factor classes, leading to more uncertainty in the estimated regression coefficients.

#### 3.2. Mapping of Nitrate Concentration

_{24}), soil class sandy loam (C

_{42}), topography class slope < 2% (C

_{53}) and land-use class built-up area (C

_{81}). Note that the land-use class built-up area (C

_{81}) is very significant with a p-value of about 1 × 10

^{−5}, which is a result of deleting the less significant recharge classes (C

_{21}, C

_{22}and C

_{23}) that interfered with the built-up land-use class. Also recharge class 180–250 mm/year (C

_{13}) is now statistically significant with a p-value of 0.001, because other less significant classes that interfered with this class are ignored. The correlation between nitrate predictions and observations is 0.46, which is the same as obtained with the full linear regression. Figure 8A shows the empirical variogram and the fitted spherical model with estimated coefficients given in Table 5 in case of regression kriging. The variogram model and its parameters are very close to what is obtained for ordinary kriging, except that the sill is somewhat smaller. This shows that by including external drift components, the interpolation of the nitrate concentration becomes more accurate and the spatial variability of the nitrate distribution becomes less uncertain. The accuracy of the variogram model is also verified by cross-validation with similar results as for ordinary kriging (Figure 8B).

## 4. Discussion

#### 4.1. DRASTIC Groundwater Vulnerability

_{ij}, the resulting nitrate level is obtained by multiplying exp(${\lambda}_{0}$) and the exponential of the regression coefficient of that factor class, exp(${\lambda}_{ij}$). So, exp(${\lambda}_{ij}$) values larger than one increase the nitrate level and vice-versa. Factor classes that promote groundwater contamination by nitrate are high recharge (C

_{23}and C

_{24}), sandy loam soil (C

_{42}) and flat areas (C

_{53}), and factor classes associated with less nitrate contamination are small depth to groundwater (C

_{12}–C

_{15}), less recharge (C

_{22}) and cultivated or natural land-use (C

_{82}and C

_{83}). Note that the vadose zone has almost no impact. Not all of these findings are statistically significant considering their p-value, but this is mainly due to lack of data. The physical interpretation is mostly obvious. High recharge (C

_{24}), sandy loam soil (C

_{42}) and flat areas (C

_{53}) increase nitrate in groundwater because they promote and increase seepage from sewage and fertilization, and the inverse applies to less recharge (C

_{22}). The effect of a smaller depth to groundwater (C

_{12}–C

_{15}) is less clear. One would expect a positive effect on nitrate content in groundwater, but the opposite is true. The reason must be found in the spatial appearance of these classes; they are all located along the coast and in the Gaza Wadi (Figure 5A), where observed nitrate concentrations are generally low, which may be due to less agricultural activity in dunes and on beaches and on the sandy soils along the coast. It is also necessary to clarify the conclusion that cultivated or natural land leads to less nitrate pollution. This finding should be seen in contrast to the land-use class in the predictor, which is built-up area. Our results thus show that nitrate in groundwater in urban areas is higher than in other areas, which is in line with what is observed in practice and indicates more nitrate hazard in urban areas.

#### 4.2. Nitrate Concentration Maps

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**Maps showing observed groundwater level in 2015 (

**A**), estimated groundwater recharge (adapted from Zomlot [34]) (

**B**), and observed groundwater nitrate concentration in 2016 (

**C**).

**Figure 4.**Cumulative frequency distribution of the observed groundwater nitrate concentration and fitted lognormal distribution.

**Figure 5.**Spatial distribution of the DRASTIC factors: depth to the groundwater (

**A**), groundwater recharge (

**B**), soil texture (

**C**), topographic slope (

**D**), and impact of the vadose zone (

**E**); also shown is the color scale for the rating values.

**Figure 6.**Maps of DRASTIC index obtained by the standard procedure and ratings (

**A**), and DRASTIC-N index obtained by multiple linear regression of observed log-transformed nitrate concentration with DRASTIC factors and land-use (

**B**).

**Figure 7.**Observed nitrate concentration versus nitrate concentration predicted by linear regression with all DRASTIC factors and land-use classes (A), and with statistically significant DRASTIC factors and land-use classes (B).

**Figure 8.**Empirical variograms of log-transformed nitrate concentration and fitted spherical models (

**A**), and cross-validation of the variogram models (

**B**), for ordinary kriging (OK) and regression kriging (RK).

**Figure 9.**Spatial distribution of nitrate concentration (

**A**) and the corresponding relative error (

**B**) obtained by ordinary kriging interpolation, and nitrate concentration (

**C**) and corresponding relative error (

**D**) obtained by regression kriging interpolation.

**Table 1.**DRASTIC factors with default weights (${a}_{i}$), and factor classes with symbols (${C}_{ij}$) and defaults ratings (${b}_{ij}$) used to predict the groundwater vulnerability in the Gaza Strip.

Factor | $\mathbf{Weight}\left({\mathit{a}}_{\mathit{i}}\right)$ | Class | $\mathbf{Symbol}\left({\mathit{C}}_{\mathit{i}\mathit{j}}\right)$ | $\mathbf{Rating}\left({\mathit{b}}_{\mathit{i}\mathit{j}}\right)$ |
---|---|---|---|---|

Depth (m) | 5 | >30.5 | C_{11} | 1 |

23–30.5 | C_{12} | 2 | ||

15–23 | C_{13} | 3 | ||

9–15 | C_{14} | 5 | ||

4.5–9 | C_{15} | 7 | ||

1.5–4.5 | C_{16} | 9 | ||

0–1.5 | C_{17} | 10 | ||

Recharge (mm/year) | 4 | 0–50 | C_{21} | 1 |

50–100 | C_{22} | 3 | ||

100–180 | C_{23} | 6 | ||

180–250 | C_{24} | 8 | ||

>250 | C_{25} | 9 | ||

Aquifer | 3 | Sandstone | C_{31} | 6 |

Soil | 2 | Loam | C_{41} | 5 |

Sandy loam | C_{42} | 6 | ||

Sand | C_{43} | 9 | ||

Topography (slope %) | 1 | 6–12 | C_{51} | 5 |

2–6 | C_{52} | 9 | ||

<2 | C_{53} | 10 | ||

Impact vadose zone | 5 | Clay | C_{61} | 3 |

Clayey sand | C_{62} | 6 | ||

Sand | C_{63} | 8 | ||

Conductivity (m/day) | 3 | 20–80 | C_{71} | 7 |

DRASTIC Factor | Relative Weight (%) | |
---|---|---|

Theoretical | Effective | |

Depth to groundwater | 21.7 | 8.0 |

Recharge | 17.4 | 6.6 |

Aquifer type | 13.0 | 19.0 |

Soil type | 8.7 | 13.8 |

Topography (slope) | 4.3 | 10.3 |

Impact vadose zone | 21.7 | 20.2 |

Conductivity | 13.0 | 22.1 |

**Table 3.**DRASTIC factors and land-use classes with estimated coefficients (λ

_{ij}) obtained by linear regression of log-transformed observed nitrate concentration (mg/L); also given are the standard deviation (StD), the t-statistic (t-value) and the probability (p-value) of the estimates; the last column gives the natural exponential of the regression coefficients.

Factor | Class | Symbol | Estimate (λ_{ij}) | StD | t-Value | p-Value | Exp(λ_{ij}) |
---|---|---|---|---|---|---|---|

Intercept | λ_{0} | 4.66 | 0.09 | 54.61 | <2 × 10^{−16} | 106. | |

Depth (m) | 23–30.5 | C_{12} | −0.08 | 0.15 | −0.57 | 0.57 | 0.92 |

15–23 | C_{13} | −0.31 | 0.18 | −1.69 | 0.09 | 0.74 | |

9–15 | C_{14} | −0.26 | 0.33 | −0.80 | 0.42 | 0.77 | |

4.5–9 | C_{15} | −0.12 | 0.59 | −0.21 | 0.84 | 0.88 | |

Recharge (mm/year) | 50–100 | C_{22} | −0.02 | 0.29 | −0.07 | 0.95 | 0.98 |

100–180 | C_{23} | 0.27 | 0.30 | 0.90 | 0.37 | 1.31 | |

180–250 | C_{24} | 0.58 | 0.33 | 1.76 | 0.08 | 1.78 | |

Soil | Sandy loam | C_{42} | 0.36 | 0.11 | 3.17 | 1.7 × 10^{−3} | 1.44 |

Sand | C_{43} | −0.09 | 0.09 | −0.98 | 0.33 | 0.92 | |

Topography (slope %) | <2 | C_{53} | 0.28 | 0.08 | 3.67 | 2.9 × 10^{−4} | 1.32 |

Vadose zone | Clayey sand | C_{62} | −0.04 | 0.13 | −0.29 | 0.77 | 0.96 |

Sand | C_{63} | −0.01 | 0.21 | −0.03 | 0.97 | 0.99 | |

Land-use | Cultivated | C_{82} | −0.42 | 0.28 | −1.48 | 0.14 | 0.66 |

Natural | C_{83} | −0.37 | 0.30 | −1.21 | 0.23 | 0.69 |

**Table 4.**List of the estimated geostatistical parameters for the spherical variogram model of the log-transformed nitrate concentration in case of ordinary kriging (OK) and regression kriging (RK).

Parameter | OK | RK |
---|---|---|

Nugget | 0.100 | 0.112 |

Sill | 0.345 | 0.312 |

Range (m) | 1972 | 1791 |

**Table 5.**Significant DRASTIC-N factor classes and estimated coefficients (λ

_{ij}) obtained by parsimonious linear regression of log-transformed observed nitrate concentration (mg/L); also given are the standard deviation (StD), the t-statistic (t-value) and the probability (p-value) of the estimates; the last column gives the natural exponential of the coefficient.

Factor | Class | Symbol | Estimate (λ_{ij}) | StD | t-Value | p-Value | Exp(λ_{ij}) |
---|---|---|---|---|---|---|---|

Intercept | λ_{0} | 4.53 | 0.06 | 73.48 | <2 × 10^{−16} | 93.6 | |

Recharge (mm/year) | 180–250 | C_{24} | 0.45 | 0.18 | 2.53 | 1.2 × 10^{−2} | 1.57 |

Soil | Sandy loam | C_{42} | 0.53 | 0.09 | 6.09 | 3.5 × 10^{−9} | 1.69 |

Topography (slope %) | <2 | C_{53} | 0.24 | 0.07 | 3.27 | 1.1 × 10^{−3} | 1.28 |

Land-use | Built-up area | C_{81} | 0.32 | 0.07 | 4.52 | 9.1 × 10^{−6} | 1.38 |

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**MDPI and ACS Style**

El Baba, M.; Kayastha, P.; Huysmans, M.; De Smedt, F.
Groundwater Vulnerability and Nitrate Contamination Assessment and Mapping Using DRASTIC and Geostatistical Analysis. *Water* **2020**, *12*, 2022.
https://doi.org/10.3390/w12072022

**AMA Style**

El Baba M, Kayastha P, Huysmans M, De Smedt F.
Groundwater Vulnerability and Nitrate Contamination Assessment and Mapping Using DRASTIC and Geostatistical Analysis. *Water*. 2020; 12(7):2022.
https://doi.org/10.3390/w12072022

**Chicago/Turabian Style**

El Baba, Moustafa, Prabin Kayastha, Marijke Huysmans, and Florimond De Smedt.
2020. "Groundwater Vulnerability and Nitrate Contamination Assessment and Mapping Using DRASTIC and Geostatistical Analysis" *Water* 12, no. 7: 2022.
https://doi.org/10.3390/w12072022