Hongjun Mao

This study aimed to investigate the association between air pollution and gestational diabetes mellitus (GDM) in small- and medium-sized cities, identify sensitive periods and major pollutants, and explore the effects of air pollution on different populations. A total of 9820 women who delivered in Handan Maternal and Child Health Hospital in the Hebei Province from February 2018 to July 2020 were included in the study. Logistic regression and principal component logistic regression models were used to assess the effects of air pollution exposure during preconception and pregnancy on GDM risk and the differences in the effects across populations. The results suggested that each 20 μg/m³ increase in PM_2.5 and PM₁₀ exposure during preconception and pregnancy significantly increased the risk of GDM, and a 10 μg/m³ increase in NO₂ exposure during pregnancy was also associated with the risk of GDM. In a subgroup analysis, pregnant women aged 30–35 years, nulliparous women, and those with less than a bachelor’s education were the most sensitive groups. This study provides evidence for an association between air pollution and the prevalence of GDM, with PM_2.5, PM₁₀, and NO₂ as risk factors for GDM.

Background: There is a lack of epidemiological evidence on the association between air pollution exposure and HDP in pregnant women in small and medium-sized cities, and the air pollution situation in small and medium-sized cities cannot be ignored and the health of their inhabitants deserves to be considered. Objective: To explore pollutants affecting the risk of HDP in small and medium-sized cities and to explore differences in the effects of air pollution on GH and PE. Methods: A total of 9,820 women who delivered at Handan Maternal and Child Health Hospital in Hebei Province from February 2018 to July 2020 were included in the study. The effects of air pollution exposure on the risk of HDP during preconception and pregnancy were assessed using logistic regression models and principal component logistic regression models. Results: In multi-pollutant models adjusted for covariates, each 20 μg/m³ increase in PM_2.5 and PM₁₀ and each 10 μg/m³ increase in NO₂ during the Pre_T period was associated with a 2.4% (OR=1.024, 95% CI: 1.010-1.039), 2.5% (OR=1.025, 95% CI: 1.012-1.037), and 2.0% (OR=1.020, 95% CI: 1.005-1.037) increase in the risk of HDP, respectively. PM_2.5, PM₁₀ and NO₂ exposure during the Pre_T period also increased the risk of GH and PE in pregnant women, and the risk of each pollutant to GH was lower than that of PE. In addition, O₃ exposure per 20 μg/m³ increment during the T period increased the risk of GH with an OR of 1.026 (95% CI: 1.002 to 1.050). Conclusions: PM_2.5, PM₁₀, NO₂ and O₃ exposure had a significant effect on the increased risk of developing HDP in pregnant women, and the effects of pollutants on the risk of GH were different from those on PE.

The effect of air staging strategies on NO_x control was investigated on a 210-kW small-scale biomass boiler (SBB) and a 1.4-MW medium-scale biomass boiler (MBB). Considering the de-NO_x effect, as well as the convenience and economy for future wide use, the structures of the secondary air duct and the fuel feed tube were innovatively designed to solve the problems of the traditional prototype. The preliminary experiment showed that the lowest NO_x emission was achieved when the air excess (ε) was equal to 2.04. Then, additional operating modes were conducted on the MBB to further optimize the air staging strategies. The optimal air staging strategy of the MBB (the secondary to primary air flow ratio (λ) and the ε were equal to 0.13 and 0.76, respectively) could decrease the NO_x emission from 338.12 to 148.14 mg/m³. Furthermore, the SO₂ emissions and the lowest NO_x emission of the SBB and the MBB could meet most emission standards of China and some developed countries. The thermogravimetric analysis (TG) and combustion characteristics of the wood fuel showed that the air staging was a suitable de-NO_x technology for wood combustion, and the slagging was less likely to occur under the selected condition. Hence, the air staging technology was an effective and low-cost method for the emission reduction of biomass boilers. This study provided a practical basis for future research on the gas emission control of biomass boilers.

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).

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.

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).