This paper presents a bottom-up methodology based on the local emission factors, complemented with the widely used emission factors of Computer Programme to Calculate Emissions from Road Transport (COPERT) model and near-real-time traffic data on road segments to develop a vehicle emission inventory with high temporal–spatial resolution (HTSVE) for the Beijing urban area. To simulate real-world vehicle emissions accurately, the road has been divided into segments according to the driving cycle (traffic speed) on this road segment. The results show that the vehicle emissions of NO_x, CO, HC and PM were 10.54 × 10⁴, 42.51 × 10⁴ and 2.13 × 10⁴ and 0.41 × 10⁴ Mg respectively. The vehicle emissions and fuel consumption estimated by the model were compared with the China Vehicle Emission Control Annual Report and fuel sales thereafter. The grid-based emissions were also compared with the vehicular emission inventory developed by the macro-scale approach. This method indicates that the bottom-up approach better estimates the levels and spatial distribution of vehicle emissions than the macro-scale method, which relies on more information. Based on the results of this study, improved air quality simulation and the contribution of vehicle emissions to ambient pollutant concentration in Beijing have been investigated in a companion paper (He et al., 2016).

A companion paper developed a vehicle emission inventory with high temporal–spatial resolution (HTSVE) with a bottom-up methodology based on local emission factors, complemented with the widely used emission factors of COPERT model and near-real-time (NRT) traffic data on a specific road segment for 2013 in urban Beijing (Jing et al., 2016), which is used to investigate the impact of vehicle pollution on air pollution in this study. Based on the sensitivity analysis method of switching on/off pollutant emissions in the Chinese air quality forecasting model CUACE, a modelling study was carried out to evaluate the contributions of vehicle emission to the air pollution in Beijing's main urban areas in the periods of summer (July) and winter (December) 2013. Generally, the CUACE model had good performance of the concentration simulation of pollutants. The model simulation has been improved by using HTSVE. The vehicle emission contribution (VEC) to ambient pollutant concentrations not only changes with seasons but also changes with time. The mean VEC, affected by regional pollutant transports significantly, is 55.4 and 48.5 % for NO₂ and 5.4 and 10.5 % for PM_2.5 in July and December 2013 respectively. Regardless of regional transports, relative vehicle emission contribution (RVEC) to NO₂ is 59.2 and 57.8 % in July and December 2013, while it is 8.7 and 13.9 % for PM_2.5. The RVEC to PM_2.5 is lower than the PM_2.5 contribution rate for vehicle emission in total emission, which may be due to dry deposition of PM_2.5 from vehicle emission in the near-surface layer occuring more easily than from elevated source emission.

A comprehensive statistical study related to biochar was conducted by using the scientometric method. The publications are mainly in the form of articles (over 16 000), accounting for 87.7% of the total, which demonstrates that researchers have great interest in this research field. Among these articles, 96.8% were written and published in English and came from 2655 different journals. The rate of increase in the annual number of publications was rapid from 2010 to 2017, and it was predicted that the cumulative number of articles concerning biochar will exceed 20 000 by the year 2020. At least one article from 154 countries or regions has been published, and every continent except Antarctica has had articles published over the past 20 years period. The percentage of collaborative articles was 71.9% and the collaboration between the USA and China has been the most fruitful. In addition, the Chinese Academy of Sciences is the research institute with the most publications. Furthermore, over 60% of the articles were published as a result of the cooperation and connection between the Chinese Academy of Sciences and the University of Chinese Academy of Sciences. The article published in Nature had the highest citation numbers, while Environmental Science & Technology had the most articles (4) that were selected as the top 20 for the most-cited articles. The agriculture research category had the highest average citations among the top four categories (i.e., environmental sciences and ecology, agriculture, chemistry and engineering).