In the present study, hydroxyapatite powders were prepared by modified precipitation method and characterized by XRD, FT-IR and N 2 adsorption-desorption techniques. The prepared nonporous particles were organized to agglomerate with mesoporous structure and consisted of low crystallinity Ca-deficient hydroxyapatite and in amorphous phase. The commercial direct yellow 27 was selected as a model dye in order to examine the adsorption capacity of hydroxyapatite at room temperature. The adsorption isotherms are transformed from L-type to S-type curve, in Giles classification, by increasing the pH values. Equilibrium data fitted very well to the Langmuir model, signifying the energetic homogeneity of hydroxyapatite surface adsorption sites. The dye sorption kinetics was fairly described by the pseudo-first-order kinetic model.

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Finding significant trends in hydroclimate time series has been deemed an essential task in numerous studies. Despite the existence of various trend detection methods, statistical significance is mostly examined for linear trends and related to the meaningfulness of the found trends. We wish to draw attention to a more general definition of meaningful trends by cross-referencing not only linear but also smoothing techniques. We apply linear regression (LR) and two smoothing techniques based on regularized minimal-energy tensor-product B-splines (RMTB) to the trend detection of standardized precipitation index (SPI) series over Taiwan. LR and both RMTB-based methods identify an overall upward (wetting) trend in the SPI series across the time scales in Taiwan from 1960 to 2019. However, if dividing the entire time series into the earlier (1960–1989) and later (1990–2019) sub-series, we find that some downward (drying) trends at varied time scales migrate from southcentral–southwestern to eastern regions. Among these significant trends, we have more confidence in the recent drying trend over eastern Taiwan since all the methods show trend patterns in highest similarity. We also argue that LR should be used with great caution, unless linearity in data series and independence and normality in residuals can be assured.

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Research on temperature extremes deserves more importance because it reacts sensitively to climate change. As elsewhere across the world, Bangladesh has already become a victim of temperature extremes. Hence, this study was conducted to assess the trends and variability of 11 temperature-related extreme indices based on daily maximum (TX) and daily minimum (TN) temperature recorded at Rajshahi and Barisal over the period 1976–2015. The indices were calculated on annual basis and their average annual and decadal trends were evaluated by non-parametric Mann-Kendall test and Sen’s slope estimate. Significant (p ≤ 0.01) upward trend was observed in some of the hot extremes, such as SU35: number of days with TX > 35°C and TR25: number of days with TN > 25°C, indicating that the number of days and nights with extreme hot temperature are increasing in both sites. Significant decreasing rate (-0.308 day/year) of SU25: number of days with TX > 25°C and increasing rate (1.00 day/year) of SU35 demonstrate that moderate hot days are converting to extreme hot days at Rajshahi. All cold indices showed significant (p ≤ 0.05) variations at Rajshahi implying that cold extremes are becoming severe in this area. Significant rising trend of diurnal temperature range (DTR) indicated the higher rate of increase in TX than in TN at Rajshahi. The increasing trend of all hot indices at Barisal, close to the coast, reveals more warming in hot extremes. However, no significant trends of cold indices were observed at Barisal. Significant average decadal variations of temperature indices were only observed for hot index TNx: annual maximum TN (0.372 °C/decade) and cold index CD25: number of days with TX < 25°C (4.70 days/decade) at Rajshahi and hot index SU35 (5.650 days/decade) at Barisal. So, the relatively dry western region of the country is vulnerable to both hot and cold extremes, whereas coastal area is susceptible to only hot extremes.J. Bangladesh Agril. Univ. 16(2): 283-292, August 2018