The presented paper reports data from malacological and pedological studies carried out at sites representing diverse biotopes (beech wood, coniferous forest, and meadow) located 2 km away from the Dyckerhoff Cement Plant in Sitkówka-Nowiny in 1992 and in 2008–2009. The studies aimed to determine physicochemical properties of soils exposed to cement and limestone dust emission and to identify composition of snail communities inhabiting three different biotopes in relation to physicochemical properties of soils, and to grasp the dynamics of the alkalization-dependent changes in physicochemical properties of soils and their impact on the composition and ecological structure of malacofauna.
Infiltration process plays important role in water balance concept particularly in runoff analysis, groundwater re-charged, and water conservation. Hence, increasing knowledge concerning infiltration process becomes essential for water manager to gain an effective solution to water resources problems. This study employed multiple linear regression for esti-mating infiltration rate where the soil properties used as the predictor variable and measured infiltration rate as the response variable. Field measurement was conducted at sixteen points to obtain infiltration rate using double ring infiltrometer and soil properties namely soil porosity, silt, clay, sand content, degree of saturation, and water content. The result showed that measured infiltration rate had an average initial infiltration rate (f0) of 6.92 mm∙min–1 and final infiltration rate (fc) of 1.49 mm∙min–1. Soil porosity and sand content showed a positive correlation with infiltration rate by 0.842, 0.639, respectively, while silt, clay, water content, and degree of saturation exhibited a negative correlation by –0.631, –0.743, –0.66 and –0.49, respectively. Three types of regression equations were established based on type of soil properties used as predictor varia-bles. The model performance analysis was conducted for each equation and the result shows that the equation with five predictor variables fMLR_3 = – 62.014 + 1.142 soil porosity – 0.205 clay, – 0.063 sand – 0.301, silt + 0.07 soil water content with R2 (0.87) and Nash–Sutcliffe (0.998) gave the best result for estimating infiltration rate. The study found that soil po-rosity contributes mostly to the regression equation that indicates great influence in controlling soil infiltration behavior.