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Activated carbon has been extensively utilized to adsorb pollutants generated by industrial activities. There have been many attempts to efficiently produce activated carbon from spent coffee grounds in the field of environmental technology. In this study, the feasibility of the novel production of activated carbon from coffee ground waste using a plasma jet was evaluated. A rotating gliding arc generator was designed that used an N2 plasma jet for the carbonization process and a CO2 plasma jet for the activation process. It was confirmed that the coffee ground waste could be carbonized and activated by the two plasma jets in the same reactor. The characteristics of the surface morphologies of the activated carbon samples varied depending on the plasma treatment conditions, such as the electric power of the plasma jet and the treatment time. The results implied that the adsorption capacity of the activated carbon could be optimized by regulating the pore size and distribution based on the plasma treatment conditions with regard to the molecular size of the target adsorbate.

Abstract Reliable projection of evapotranspiration (ET) is important for planning sustainable water management for the agriculture field in the context of climate change. A global dataset of monthly climate variables was generated to estimate potential ET (PET) using 14 General Circulation Models (GCMs) for four main shared socioeconomic pathways (SSPs). The generated dataset has a spatial resolution of 0.5° × 0.5° and a period ranging from 1950 to 2100 and can estimate historical and future PET using the Penman-Monteith method. Furthermore, this dataset can be applied to various PET estimation methods based on climate variables. This paper presents that the dataset generated to estimate future PET could reflect the greenhouse gas concentration level of the SSP scenarios in latitude bands. Therefore, this dataset can provide vital information for users to select appropriate GCMs for estimating reasonable PETs and help determine bias correction methods to reduce between observation and model based on the scale of climate variables in each GCM.

Abstract This study estimated global climate change signals at different latitudes for four main Shared Socioeconomic Pathways (SSPs). Five evaluation metrics were integrated using the Technique for Order of Preference by Similarity to Ideal Solution to quantify the historical reproducibility of 25 CMIP6 General Circulation Models (GCMs) with Global Precipitation Climatology Centre precipitation and Climatic Research Unit temperature as the reference. The most suitable GCMs for simulating climate over different latitudes, selected based on evaluation metrics, were used to prepare a multimodel ensemble and project the future annual and seasonal precipitation and temperature in the near (2031–2065) and far future (2066–2100). The results showed that GCMs estimated the historical mean temperature efficiently but underestimated the monthly precipitation compared to the reference data. The changes in precipitation and temperature at mid‐latitudes (N45.5°–60°) showed the highest variability for all scenarios. The maximum increases in both climate variables for SSP5‐8.5 were 80.5% and 4.8% at N45.5°–60°, respectively. In contrast, the temperature and precipitation at S30.5°–45° revealed a decreasing pattern. Mid‐latitude winter (S30.5°–45°) would be drier in the future than in the base period (1980–2014). This study showed that precipitation variability and the mean temperature in the northern hemisphere would be larger for SSPs with higher radiative forcing. Therefore, the results of this study help improve knowledge of global future climate change by latitudes.