In this paper, we investigate the statistical correlation of the time series for temperature measured at the heat box in the automobile drying process. We show, in terms of the sample variance, that a significant non-linear correlation exists in the time series that consist of absolute temperature changes. To investigate further the non-linear correlation, we utilize the volatility, an important concept in the financial market, and induce volatility time series from absolute temperature changes. We analyze the time series of volatilities in terms of the de-trended fluctuation analysis (DFA), a method especially suitable for testing the long-range correlation of non-stationary data, from the correlation perspective. We uncover that the volatility exhibits a long-range correlation regardless of the window size. We also analyze the cross correlation between two (inlet and outlet) volatility time series to characterize any correlation between the two, and disclose the dependence of the correlation strength on the time lag. These results can contribute as important factors to the modeling of forecasting and management of the heat box’s temperature.

At riverbank filtration sites, groundwater levels of alluvial aquifers near rivers are sensitive to variation in river discharge and pumping quantities. In this study, the groundwater level fluctuation, pumping quantity, and streamflow rate at the site of a riverbank filtration plant, which produces drinking water, in the lower Nakdong River basin, South Korea were interrelated. The relationship between drawdown ratio and river discharge was very strong with a correlation coefficient of 0.96, showing a greater drawdown ratio in the wet season than in the dry season. Autocorrelation and cross-correlation were carried out to characterize groundwater level fluctuation. Autoregressive model analysis of groundwater water level fluctuation led to efficient estimation and prediction of pumping for riverbank filtration in relation to river discharge rates, using simple inputs of river discharge and pumping data, without the need for numerical models that require data regarding several aquifer properties and hydrologic parameters.

This study examined the effects of chemical blends (a combination of alum and aluminum chloride) on pH, N, and pathogens in duck litter during a six-week experiment. In total, 240 Pekin ducks (160 males and 80 females) were individually distributed into 16 pens, in a randomized experimental design consisting of four treatments and four replicate pens per treatment. Our treatments included a control, T1 (75 g alum + 75 g aluminum chloride/kg duck litter), T2 (100 g alum + 100 g aluminum chloride/kg duck litter), and T3 (150 g alum + 150 g aluminum chloride/kg duck litter). There was no difference among treatments in pH and Total N (TN) at weeks 2, 4, and 6 and weeks 1, 4, 5, and 6, respectively. However, there were significant differences in both pH and TN among treatments at weeks 1, 3, and 5 and weeks 2 and 3, respectively. Regarding pathogens, we found small differences in all treatments in Escherichia coli populations from weeks 1 to 5 and in Salmonella enterica populations from weeks 1 to 3. In conclusion, the addition of chemical blends to duck litter increased TN, which resulted in a lower litter pH, but did not significantly affect pathogen populations.

Groundwater recharge characteristics in a fractured granite area, Mt. Geumjeong, Korea. was interpreted using bedrock groundwater and wet-land water data. Time series analysis using autocorreclation, cross-correlation and spectral density was conducted for characterizing water level variation and recharge rate in low water and high water seasons. Autocorrelation analysis using water levels resulted in short delay time with weak linearity and memory. Cross-correlation function from cross-correlation analysis was lower in the low water season than the high water season for the bedrock groundwater. The result of water level decline analysis identified groundwater recharge rate of about 11% in the study area.