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Using Data Mining to Predict Soil Quality after Application of Biosolids in Agriculture

Jérôme Cortet (1), Dragi Kocev (2), Caroline Ducobu (1), Sašo Džeroski (2), Marko Debeljak (2), and Christophe Schwartz (1)

Cortet & al., 2011
Cortet & al., Journal: J Environ Qual., (2011), 40(6):1972-82

The amount of biosolids recycled in agriculture has steadily increased during the last decades. However, few models are available to predict the accompanying risks, mainly due to the presence of trace element and organic contaminants, and benefits for soil fertility of their application. This paper deals with using data mining to assess the benefits and risks of biosolids application in agriculture.

Main results

The analyzed data come from a 10-yr field experiment in northeast France (La Bouzule experimental farm) focusing on the effects of biosolid application and mineral fertilization on soil fertility and contamination (figure 1).

fig.1 Cortet 2011

Figure 1 : Biosolid characteristics, trace elements and organic pollutants content. Mean of four values for each parameter (one for each spreading during the study). Values in italics are close to or above the threshold values. † LSS, liquid sewage sludge; LDSS, lightly dehydrated sewage sludge; LDCSS, lightly dehydrated composted sewage sludge; LDCSSO, lightly dehydrated composted sludge added with organic pollutants; LDCSSM, lightly dehydrated composted sludge added with metals; MPS, mixed paper sludge; CA, coal ashes; HWA, household waste ashes. ‡ French Council decision of 8 Jan. 1998 (Voynet et al., 1998). § PCB, polychlorinated biphenyl.

Biosolids were applied at agriculturally recommended rates. Biosolids had a significant effect on soil fertility, causing in particular a persistent increase in plant-available phosphorus (P) relative to plots receiving mineral fertilizer. However, soil fertility at seeding and crop management method had greater effects than biosolid application on soil fertility at harvest, especially soil nitrogen (N) content. Levels of trace elements and organic contaminants in soils remained below legal threshold values. Levels of extractable metals correlated more strongly than total metal levels with other factors. Levels of organic contaminants, particularly polycyclic aromatic hydrocarbons, were linked to total metal levels in biosolids and treated soil (figure 2).

fig.2 Cortet 2011

Figure 2: Regression trees predicting values for total polycyclic aromatic hydrocarbons (PAH) and polychlorinated biphenyl (PCB) (mg kg−1 dry matter [DM]) at harvest time (Scenario 4). MI = minimum instances in a leaf (i.e., minimum number of samples from which the predicted value is calculated); MD = maximum tree depth (i.e., number of hierarchical levels of the tree); “(before)” = content in soil at sowing time; “w” = content in biosolids; “tot” = total metal content (mg kg−1 DM); “f” = content in fertilizers. Trees should be read like “what-if” questions from the root of each tree. The closer the attribute is to the root of the tree, the more it is statistically infl uent for the considered predicted attribute.

This study confirmed that biosolid application at rates recommended for agriculture is a safe option for increasing soil fertility. However, the quality of the biosolids selected has to be taken into account. The results also indicate the power of data mining in examining links between parameters in complex data sets.

Affiliations

1. Nancy Université, UMR INPL/INRA 1120, Laboratoire Sols et Environnement, 2 av. de la Forêt de Haye, 54505 Vandoeuvre-lès- Nancy, France;

2. Jozef Stefan Institute, Dep. Of Knowledge Technologies, Jamova 39, 1000 Ljubljana, Slovenia. Assigned to Associate Editor César Plaza.