Constraining emission estimates of carbon monoxide using a perturbed emissions ensemble with observations: a focus on Beijing

Funder: Tsinghua University Initiative Scientific Research ProgramFunder: National Centre for Atmospheric Science; doi: http://dx.doi.org/10.13039/501100000662Funder: Met Office; doi: http://dx.doi.org/10.13039/501100000847Abstract: The reliability of air quality simulations has a strong dependence on the input emissions inventories, which are associated with various sources of uncertainties, particularly in regions undergoing rapid emission changes where inventories can be ‘out of date’ almost as soon as they are compiled. This work provides a new methodology for updating emissions inventories by source sector using air quality ensemble simulations and observations from a dense monitoring network. It is adopted to determine the short-term trends in carbon monoxide (CO) emissions, an important pollutant and precursor to tropospheric ozone, in a study area centred around Beijing following the implementation of clean air policies. We sample the uncertainties associated with using an a priori emissions inventory for the year 2013 in air quality simulations of 2016, using an atmospheric dispersion model combined with a perturbed emissions ensemble (PEE), which is constructed based on expert-elicited uncertainty ranges for individual source sectors in the inventory. By comparing the simulation outputs with observational constraints, we are able to constrain the emissions of key source sectors relative to those in the a priori emissions inventory. From 2013 to 2016, we find a 44–88% reduction in the transport sector emissions (0.92–4.4×105 Mg in 2016) and a minimum 61% decrease in residential sector emissions (<3.5×105 Mg in 2016) within the study area. We also provide evidence that the night-time fraction of traffic sources in 2016 was higher than that in the 2013 emissions inventory. This study shows the applicability of PEEs and high-resolution observations in providing timely updates of emission estimates by source sector

Explaining decision-making in government: the neo-Durkheimian institutional framework

In understanding styles of political judgement in government decision-making, explanatory limitations of rational choice, prospect theoretic, historical institutional, groupthink, and other approaches suggest that there is space for developing other frameworks. This article argues that the neo-Durkheimian institutional theoretical framework deserves serious consideration. It shows that it offers a powerful causally explanatory framework for generating theories of decision-making in government which can be examined using historical comparative research designs. The value of the concept of a ‘thought style’ for understanding political judgement is demonstrated, and contrasted sharply with ideology. The theory argues that informal institutions explain thought styles. Well-known cases from the Cuban missile crisis, and the Wilson and Heath governments illustrate the argument. The article rebuts criticisms offered of the neo-Durkheimian institutional framework in the literature. Finally, it identifies recent developments and innovations in the approach that make it especially suited to explaining political judgement in government decision-makingThis work was supported by the Leverhulme Trust (grant number: F01374I

Varieties of living things: Life at the intersection of lineage and metabolism

publication-status: Publishedtypes: Articl

Some behaviour problems and their treatment

[No abstract available]Arts, Faculty ofPhilosophy, Department ofGraduat

Improving NO<inf>x</inf>emission estimates in Beijing using network observations and a perturbed emissions ensemble

Emissions inventories are crucial inputs to air quality simulations and represent a major source of uncertainty. Various methods have been adopted to optimise emissions inventories, yet in most cases the methods were only applied to total anthropogenic emissions. We have developed a new approach that updates a priori emission estimates by source sector, which are particularly relevant for policy interventions. At its core is a perturbed emissions ensemble (PEE), constructed by perturbing parameters in an a priori emissions inventory within their respective uncertainty ranges. This PEE is then input to an air quality model to generate an ensemble of forward simulations. By comparing the simulation outputs with observations from a dense network, the initial uncertainty ranges are constrained and a posteriori emission estimates are derived. Using this approach, we were able to derive the transport sector NOX emissions for a study area centred around Beijing in 2016 based on a priori emission estimates for 2013. The absolute emissions were found to be 1.5–9 × 104 Mg, corresponding to a 57–93 % reduction from the 2013 levels, yet the night-time fraction of the emissions was 67–178 % higher. These results provide robust and independent evidence of the trends of traffic emission in the study area between 2013 and 2016 reported by previous studies. We also highlighted the impacts of the chemical mechanisms in the underlying model on the emission estimates derived, which is often neglected in emission optimisation studies. This work paves forward the route for rapid analysis and update of emissions inventories using air quality models and routine in situ observations, underscoring the utility of dense observational networks. It also highlights some gaps in the current distribution of monitoring sites in Beijing which result in an underrepresentation of large point sources of NOX