A field campaign was conducted from November to December 2017 at the campus of Peking University (PKU) to investigate the formation mechanism of the winter air pollution in Beijing with the measurement of hydroxyl and hydroperoxyl radical (OH and HO2) with the support from comprehensive observation of trace gases compounds. The extent of air pollution depends on meteorological conditions. The daily maximum OH radical concentrations are on average 2.0 × 106 cm−3 and 1.5 × 106 cm−3 during the clean and polluted episodes, respectively. The daily maximum HO2 radical concentrations are on average 0.4 × 108 cm−3 and 0.3 × 108 cm−3 during the clean and polluted episodes, respectively (diurnal averaged for one hour bin). A box model based on RACM2-LIM1 mechanism can reproduce the OH concentrations but underestimate the HO2 concentrations by 50% during the clean episode. The OH and HO2 concentrations are underestimated by 50% and 12 folds during the polluted episode, respectively. Strong dependence on nitric oxide (NO) concentration is found for both observed and modeled HO2 concentrations, with the modeled HO2 decreasing more rapidly than observed HO2, leading to severe HO2 underestimation at higher NO concentrations. The OH reactivity is calculated from measured and modeled species and inorganic compounds (carbon monoxide (CO), NO, and nitrogen dioxide (NO2)) make up 69%–76% of the calculated OH reactivity. The photochemical oxidation rate denoted by the OH loss rate increases by 3 times from the clean to polluted episodes, indicating the strong oxidation capacity in polluted conditions. The comparison between measurements at PKU site and a suburban site from one previous study shows that chemical conditions are similar in both urban and suburban areas. Hence, the strong oxidation capacity and its potential contribution to the pollution bursts are relatively homogeneous over the whole Beijing city and its surrounding areas.
The US-China relationship is one of the most important bilateral relationships in today’s world politics. Conflict and cooperation between the two major powers affect regional and global stability and the dynamics of the international system. What are the patterns of the ebbs and flows of this relationship? And how can the US-China conflict and cooperation be explained by reciprocity, policy inertia, and influence of an important third party? Scholars have used machine-code event data and time-series tools to analyze the three factors in American-Soviet relations. This research extends the existing research and applies new data and methods to trace and explain the variations in US-China interactions.
We rely on data from event data from the Global Data on Events Location and Tone (GDELT) and obtain a sample of directed actions of China, the United States, and Russia/U.S.S.R. towards one another between 1979 and 2017, totaling 3,957,479 daily records. We first apply multivariate change-point analysis and locate three structural breaks in the 38 years. For each of the four sub-periods, we specify a Vector Autoregressive Model and utilize the Impulse Response Functions to estimate the mutual effects of six time series of directed actions among China, the U.S., and Russia. Then we build signed and directed networks using country dyads as nodes and estimates as edges to summarize and interpret the interdependence among dyadic interactions in this triangle.
The paper has three major empirical findings. First, the most important factor behind the US actions toward China is policy inertia that could reflect the effect of domestic politics on American foreign policy decision- making. But China’s action towards the US are equally determined by reciprocity, policy inertia, and Russia as the third party. Second, we find mutual reciprocity in the China-US interactions, but reciprocity is highly assymmetric---the responses of the US to China’s actions are much weaker than China’s responses to the US actions, which could explain why US-China cooperation is often difficult to reach. Thirdly, the China- US-Russia triangle is featured by a logic of “balance of power” --- Russia and China are more cooperative to each other when their relations with the US get more conflictual, and vice versa. The pattern shows that the cooperation between China and Russia is a tactic to increase their leverage to settle conflicts with the US. Furthermore, there is no stable reciprocity between China and Russia, which means that a China-Russia alliance is unlikely to become true despite that the foreign policy rhetoric and diplomatic gestures seem to suggest an emergence of such an alliance.