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In this work, 2-Aminopyridine functionalized partially reduced graphene oxide hydrogels (APGHs) have been prepared via a simple hydrothermal method. During the reaction process, graphene oxide was used as the reactant and 2-Aminopyridine was used as both reductant and N dopant. Furthermore, pyridine ring can not only act as a spacer to prevent the restacking of graphene sheets, but also enhance the pseudocapacitance of the material through its redox activity. The characterization results showed that APGHs possessed high content of heteroatom functional groups (N, O), high surface area and 3D interconnected structure, respectively. Thus, the assembled APGH-40-based symmetric supercapacitor delivers a high-specific capacitance of 266.7 F g−1 at 0.3 A g−1, even at 10 A g−1, this value can still maintain at f 215.9 F g−1. In addition, the device also possesses a high energy density (9.3 Wh kg−1 at 75.0 W kg−1) and outstanding capacitance retention of 102.9% after 10,000 cycles at 10 A g−1. Therefore, the synthesized APGHs display a promising prospect in supercapacitor application.

N/O co-enriched graphene hydrogel based supercapacitors were assembled. Owing to the synergistic effect between the heteroatoms (N, O), 3D porous structures and high specific surface area, they present excellent electrochemical properties.

With urbanization occurring around weather stations, its impact on the observed air temperature has been widely recognized. However, its assessments were varied partially due to the poor understanding of its underlying mechanism. Here, we analyzed the effect of urbanization using observations obtained from ~2200 weather stations in China from 1960 to 2014. The results showed that the urbanization effect increased from 1960 to 1984 but slowed after 1995 with rapid urbanization in China, particularly in terms of the daily minimum temperature Tmin and daily mean air temperature Tmean. The urbanization effect is nearly linearly related to the urban–rural contrast of effective cloud cover (including the impact of atmospheric aerosols) derived from the observed sunshine duration. Aerosols increase atmospheric downward longwave radiation Ld through their absorption of solar radiation during the daytime, and they trap longwave radiation emitted from the surface during the nighttime. Increased anthropogenic aerosols caused the urban–rural contrast of effective cloud cover to increase from 1960 to 2014. However, the urban–rural contrast of cloud cover remained stable from 1960 to 1984 but substantially decreased due to the “urban dry island effect” after 1995, which compensated for the impact of anthropogenic aerosols on Ld and resulted in a stable urbanization effect after 1995. The urban–rural difference in Ld increased by 0.57 W m−2 (10 yr)−1, which resulted in warming of 0.074°C (10 yr)−1 (78.2%) for Tmin, 0.037°C (10 yr)−1 (151.5%) for Tmax, and 0.056°C (10 yr)−1 (96.6%) for Tmean.

China’s transport industry has made rapid progress, which has led to a great amount of carbon emissions. However, it is still unclear how carbon emissions from the transport sector are punctuated by shifts in underlying factors. This paper aims to examine the process of China’s carbon emissions from the transport sector as well as its major driving forces at the provincial level during the period of 2000 to 2015. We first estimate the carbon emissions from the transport sector at the provincial level based on the fuel and electricity consumption using a top-down method. We find that the carbon emission per capita is steadily increasing across the country, especially in the provinces of Chongqing and Inner Mongolia. However, the carbon emission intensity is decreasing in most provinces, except in Yunnan, Qinghai, Chongqing, Zhejiang, Heilongjiang, Jilin, Inner Mongolia, Henan and Anhui. We then quantify the effect of socioeconomic factors and their regional variations on carbon emissions using a panel model. The results show that the development of secondary industry is the most significant variable for carbon intensity at both the national and regional levels, while the effects of the other variables vary across regions. Among these factors, population density is the main factor of the increasing carbon emissions per capita from the transport sector for both the whole country and the western region, whereas the consumption level per capita of residents and the development of tertiary industry are the primary drivers of per capita carbon emissions for the eastern and central regions.

Diurnal cycle of surface air temperature T is an important metric indicating the feedback of land–atmospheric interaction to global warming, whereas the ability of current reanalyses to reproduce its variation had not been assessed adequately. Here, we evaluate the daily maximum temperature Tmax, daily minimum temperature Tmin, and diurnal temperature range (DTR) in five reanalyses based on observations collected at 2253 weather stations over China. Our results show that the reanalyses reproduce Tmin very well; however, except for Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), they substantially underestimate Tmax and DTR by 1.21°–6.84°C over China during the period of 1980–2014. MERRA-2 overestimates Tmax and DTR by 0.35° and 0.81°C, which are closest with observation. The reanalyses are skillful in reproducing the interannual variability of Tmax and Tmin but relatively poor for DTR. All reanalyses underestimate the warming trend of Tmin by 0.13°–0.17°C (10 yr)−1 throughout China during 1980–2014, and underestimate the warming trend of Tmax by 0.24°–0.40°C (10 yr)−1 in northwestern China while overestimating this quantity by 0.18°–0.33°C (10 yr)−1 in southeastern China. These trend biases in Tmax and Tmin introduce a positive trend bias in DTR of 0.01°–0.26°C (10 yr)−1 within China, especially in the north China plain and southeastern China. In the five reanalyses, owing to the sensitivity discrepancies and trend biases, the surface solar radiation Rs and precipitation frequency (PF) are notable deviation sources of the diurnal cycle of air temperature, which explain 31.0%–38.7% (31.9%–37.8%) and 9.8%–22.2% (7.4%–15.3%) of the trend bias in Tmax (DTR) over China, respectively.

Although global warming has been attributed to increases in atmospheric greenhouses gases, the mechanisms underlying spatiotemporal patterns of warming trends remain under debate. Herein, we analyzed surface and air warming observations recorded at 1977 stations in China from 1960 to 2003. Our results showed a significant spatial pattern for the warming of the daily maximum surface (Ts-max) and air (Ta-max) temperatures, and the pattern was stronger in northwest and northeast China and weaker or negative in South China and the North China Plain. These warming spatial patterns were attributed to surface shortwave solar radiation (Rs) and precipitation (P), which play a key role in the surface energy budget. During the study period, Rs decreased by −1.50 ± 0.42 W m−2 10 yr−1 in China, which reduced the trends of Ts-max and Ta-max by about 0.139 and 0.053 °C 10 yr−1, respectively. More importantly, the decreasing rates in South China and the North China Plain were stronger than those in other parts of China. The spatial contrasts in the trends of Ts-max and Ta-max in China were significantly reduced after adjusting for the effect of Rs and P. For example, after adjusting for the effect of Rs and P, the difference in the Ts-max and Ta-max values between the North China Plain and the Loess Plateau was reduced by 97.8 and 68.3 %, respectively; the seasonal contrast in Ts-max and Ta-max decreased by 45.0 and 17.2 %, respectively; and the daily contrast in the warming rates of the surface and air temperature decreased by 33.0 and 29.1 %, respectively. This study shows that the land energy budget plays an essential role in the identification of regional warming patterns.

This study compared the diurnal and seasonal cycles of atmospheric and surface urban heat islands (UHIs) based on hourly air temperatures (Ta) collected at 65 out of 262 stations in Beijing and land surface temperature (Ts) derived from Moderate Resolution Imaging Spectroradiometer in the years 2013–2014. We found that the nighttime atmospheric and surface UHIs referenced to rural cropland stations exhibited significant seasonal cycles, with the highest in winter. However, the seasonal variations in the nighttime UHIs referenced to mountainous forest stations were negligible, because mountainous forests have a higher nighttime Ts in winter and a lower nighttime Ta in summer than rural croplands. Daytime surface UHIs showed strong seasonal cycles, with the highest in summer. The daytime atmospheric UHIs exhibited a similar but less seasonal cycle under clear-sky conditions, which was not apparent under cloudy-sky conditions. Atmospheric UHIs in urban parks were higher in daytime. Nighttime atmospheric UHIs are influenced by energy stored in urban materials during daytime and released during nighttime. The stronger anthropogenic heat release in winter causes atmospheric UHIs to increase with time during winter nights, but decrease with time during summer nights. The percentage of impervious surfaces is responsible for 49%–54% of the nighttime atmospheric UHI variability and 31%–38% of the daytime surface UHI variability. However, the nighttime surface UHI was nearly uncorrelated with the percentage of impervious surfaces around the urban stations.