Bentonite, biotite, illite, kaolin, vermiculite and zeolite were acidified or alkalized with hydrochloric acid or sodium hydroxide at concentrations of 0.1, 1.0 and 5.0 mole dm⁻³ at room temperature for two weeks. In acid treatments, dissolution of Al prevailed over Si and the opposite was observed in alkali treatments. The XRD patterns showed severe alteration of the crystal structure after acid treatments, whereas sharpening of the XRD peaks after alkali treatments was observed. Illite and kaolin were most resistant to acid attack. with a few exceptions, the surface areas of the minerals computed from both water and nitrogen adsorption isotherms increased with acid and alkali treatments. with increasing reagent concentration, the nitrogen surface area increased faster than the water surface area. well-defined trends were not noted in either changes of average water or nitrogen adsorption energies or in relative amounts of adsorption sites, indicating that the effects of acid and alkali attack are controlled by the individual character of the minerals.

Thousands of tons of zeolitic materials are used yearly as soil conditioners and components of slow-release fertilizers. A positive influence of application of zeolites on plant growth has been frequently observed. Because zeolites have extremely large cation exchange capacity, surface area, porosity and water holding capacity, a paradigm has aroused that increasing plant growth is caused by a long-lasting improvement of soil physicochemical properties by zeolites. In the first year of our field experiment performed on a poor soil with zeolite rates from 1 to 8 t/ha and N fertilization, an increase in spring wheat yield was observed. Any effect on soil cation exchange capacity (CEC), surface area (S), pH-dependent surface charge (Qv), mesoporosity, water holding capacity and plant available water (PAW) was noted. This positive effect of zeolite on plants could be due to extra nutrients supplied by the mineral (primarily potassium—1 ton of the studied zeolite contained around 15 kg of exchangeable potassium). In the second year of the experiment (NPK treatment on previously zeolitized soil), the zeolite presence did not impact plant yield. No long-term effect of the zeolite on plants was observed in the third year after soil zeolitization, when, as in the first year, only N fertilization was applied. That there were no significant changes in the above-mentioned physicochemical properties of the field soil after the addition of zeolite was most likely due to high dilution of the mineral in the soil (8 t/ha zeolite is only ~0.35% of the soil mass in the root zone). To determine how much zeolite is needed to improve soil physicochemical properties, much higher zeolite rates than those applied in the field were studied in the laboratory. The latter studies showed that CEC and S increased proportionally to the zeolite percentage in the soil. The Qv of the zeolite was lower than that of the soil, so a decrease in soil variable charge was observed due to zeolite addition. Surprisingly, a slight increase in PAW, even at the largest zeolite dose (from 9.5% for the control soil to 13% for a mixture of 40 g zeolite and 100 g soil), was observed. It resulted from small alterations of the soil macrostructure: although the input of small zeolite pores was seen in pore size distributions, the larger pores responsible for the storage of PAW were almost not affected by the zeolite addition.

The possibility of detecting low levels of soil pollution by petroleum fuel using an electronic nose (e-nose) was studied. An attempt to distinguish between pollution caused by petrol and diesel oil, and its relation to the time elapsed since the pollution event was simultaneously performed. Ten arable soils, belonging to various soil groups from the World Reference Base (WRB), were investigated. The measurements were performed on soils that were moistened to field capacity, polluted separately with both hydrocarbons, and then allowed to dry slowly over a period of 180 days. The volatile fingerprints differed throughout the course of the experiment, and, by its end, they were similar to those of the unpolluted soils. Principal component analysis (PCA) and artificial neural network (ANN) analysis showed that the e-nose results could be used to detect soil contamination and distinguish between pollutants and contamination levels.