Diagnóstico del ciclo de vida de Bahías de Huatulco

The Dutch environmental policy focuses on specific pollution issues, such as agricultural pollution, acidification and trace element dispersion (including both organic and anorganic contaminants). Indicators are used for each environmental issue to define the state of groundwater contamination. Indicators for agricultural pollution are nitrate, potassium and total-phosphate. For acidification, the pH and the aluminum concentration are normally evaluated (for example, Reijnders 1998, Pebesma 1997). The disadvantage of the sole use of these indicators is that the masking influence of geochemical reactions on groundwater quality is not easily recognised. Four extra indicators were used for the data analysis of the two regional networks, which are based on geochemical knowledge of probable subsurface reactions. The oxidation capacity (OXC) was used as an extra indicator for agricultural pollution (see Appendix I for details). The hardness/alkalinity ratio and the calcite- and siderite-saturation indices are used as extra indicators for acidification (Appendix I). These indicators provide indications of important subsurface buffering mechanisms, such as the reduction of nitrate by the oxidation of pyrite and the neutralizing of acidification by carbonate dissolution.

The monitoring results of the annual sampling rounds of 1995 to 1998 were used for data analysis. The dataset of 1995-1998 was chosen after quality checks on the monitoring data, such as checks on electro-neutrality and a comparison of field EC and field pH versus lab EC and lab pH, respectively. The concentrations of each monitoring screen were averaged over the 4 monitoring years and then used for further data-analysis. This reduces the effect of outliers in the time series of the individual monitoring wells. All concentrations below the detection limit have been given the value of 0.5 times the detection limit to enable the evaluation of summary statistics for all the monitoring data. This is sensible because the monitoring information goals have no special focus on the very low part of the frequency distribution.

As a first step in the data analysis, the frequency distribution of the indicator is presented in box plots using Tukey’s hinges for the top and bottom of the boxes (Helsel & Hirsch 1992). The box plots show the complete range of the frequency distribution, including outliers and extreme values. Because outliers and extremes are common in the groundwater quality data sets, non-parametrical methods were preferred for the estimation of typical values (statistical information goal A), the evaluation of differences between areas (statistical information goal B) and proportions of contaminated groundwater (statistical information goal D). Outliers have only been removed from the data set if there was strong evidence that the data were not representative for the respective homogeneous area.

The median was used as a measure for the typical value of an indicator for a homogeneous area (statistical information goal A). The uncertainty of the estimated median value was assessed by computing a 95% two-sided non-parametric confidence interval, using the binomial distribution (Helsel & Hirsch, 1992, appendix III.1). These confidence intervals are non-symmetric if the frequency distribution of the sample is skewed.

To evaluate statistical information goal B, the differences between the median values in the homogeneous areas were evaluated using a multiple-comparison test on ranks (Tukey method, two-sided, α <0.05, appendix III.2). The multiple comparison test was only performed if a Kruskall-Wallis one-way Analysis of Variance (ANOVA) test indicated that the within-group variance was smaller than the inter-group variance. Significant differences between the

concentrations of the homogeneous areas are only found if sample sizes in both the compared areas are large enough and limited variation within the areas is observed.

For statistical information goal D, the proportion (or percentage) of contaminated groundwater was defined by comparing the measured concentration with a relevant water quality standard. For instance, the EU drinking water standard of 50 mg NO3l-1was used as

threshold value for nitrate contamination. The estimated proportion pˆ_xcis defined as:

(3.1) where n is number of observations in the specific homogeneous area and u is the number of observations above the environmental standard. The precision of the estimates was determined using a 95% confidence interval for the estimated proportion (α=0.05, two-sided) using the method of Blyth & Still (Gilbert 1987, see Appendix III.5 for details).

Estimates and confidence intervals of the proportion of young, post-1950 groundwater have been calculated similarly. Groundwater was classified as post-1950 if the measured

concentrations exceed 5 TU (1983) or 2 TU (1992) (section 2.3, Chapter 2). The proportions were determined for each homogeneous area.

The estimates and the calculated uncertainty of the estimates were made on the assumption of random sampling from the homogeneous areas. However, this condition is not strictly fulfilled by the national and provincial networks. Although a first selection of the monitoring locations was made with GIS, the locations had to fulfil conditions of field accessibility and avoidance of upstream point sources. Subsequently, a hydrological expert guess was made on the contributing area of the well to assure homogeneous land use in the upstream area. The presented estimates and precision should therefore be considered to be indicative and not absolute.

Data from several homogeneous areas were combined to enable the comparison of concen- trations in agricultural areas in the provinces of Noord-Brabant and Drenthe. The proportion of contaminated groundwater in those combined areas must be corrected for the diverging monitoring density in the individual areas (Table 3.1). The original homogeneous areas are considered to be strata with different weights. The proportion of the combined area was calculated using the spatial percentage of the h original areas as weight-factors Wicalculating:

(3.2) where Wi= weight of homogeneous area i, pˆi= proportion in homogeneous area i and pˆ_xc(st) is the estimated proportion in the combined strata and:

(3.3) The confidence interval on the estimated proportion pˆ_xc(st) must also be corrected to account for the non-proportional allocation of observations in the individual strata. This correction is explained in Appendix III.6.

The proportions of contaminated groundwater were mapped to show the spatial

differentiation of groundwater contamination. The method described by Reijnders et al. (1998) was adopted, which was used for the national monitoring network. They defined classes for the proportion of contaminated groundwater, taking the 95% confidence intervals into account. The advantage of the method is that areas are only highlighted where the estimates have a specified precision. The following classes were defined: very high; whole confidence interval above 20%, high; whole confidence interval above 10%, low; whole confidence interval entirely

under 30%, and indifferent; miscellaneous confidence intervals (Figure 3.9). Here, the 30% threshold, instead of 20%, is used for classification low. The 30% threshold better reflected the statistical information goals for low-risk areas than the 20% threshold; estimating the exact proportion of contaminated groundwater was not considered as a relevant statistical

information goal in low-risk areas (Table 3.4). A large number of wells, and high costs, would be required to achieve the confidence interval entirely under 20%. For instance, in an area with 0% contamination, 17 wells would be needed to meet this criterion (Gilbert 1987). The class

indifferent corresponds to areas with estimated proportions near the thresholds of 10 and 20%,

or with too few observations to be classified low or high (Figure 3.9).