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In 2014, China introduced an ultra-low emissions (ULE) standards policy for renovating coal-fired power-generating units to limit SO2, NOX and PM emissions to 35, 50 and 10 mg m-3 , respectively. The ULE standard policy had ambitious levels (surpassing those of all other countries) and implementation timeline. We estimate emission reductions associated with the ULE policy by constructing a nationwide, unit-level, hourly-frequency emissions dataset using data from a continuous emission monitoring systems network covering 96-98% of Chinese thermal power capacity during 2014-2017. We find that between 2014 and 2017 China's annual power emissions of SO2, NOX and PM dropped by 65%, 60% and 72%, respectively. Our estimated emissions using actual monitoring data are 18-92% below other recent estimates. We detail the technologies used to meet the ULE standards and the determinants of compliance, underscoring the importance of ex-post evaluation and providing insights for other countries wishing to reduce their power emissions.

Experimental and numerical results concerning solid particle motion in a plane wake are presented that demonstrate the importance of large-scale vortex structures in self-organizing dispersion processes. Previous studies have demonstrated that a time scale ratio involving the aerodynamic response time of the particles and a characteristic time of the vortex structures is an important parameter for indicating the qualitative and quantitative nature of the dispersion process. A stretching and folding mechanism associated with vortex development and merging interactions has been suggested as a description for characterizing particle dispersion in plane mixing layers at intermediate time scale ratios. For plane wakes where large-scale vortex mergers rarely occur, a highly organized particle dispersion process focuses intermediate time scale ratio particles along the boundaries of the large-scale vortices. The fractal correlation dimension associated with chaotic systems is found to be a useful parameter for quantifying the relative organization of the dispersion patterns as a function of the particle time scale ratio.

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To meet the growing electricity demand, China’s power generation sector has become an increasingly large source of air pollutants. Specific control policymaking needs an inventory reflecting the overall, heterogeneous, time-varying features of power plant emissions. Due to the lack of comprehensive real measurements, existing inventories rely on average emission factors that suffer from many assumptions and high uncertainty. This study is the first to develop an inventory of particulate matter (PM), SO2 and NOX emissions from power plants using systematic actual measurements monitored by China’s continuous emission monitoring systems (CEMS) network over 96–98% of the total thermal power capacity. With nationwide, source-level, real-time CEMS-monitored data, this study directly estimates emission factors and absolute emissions, avoiding the use of indirect average emission factors, thereby reducing the level of uncertainty. This dataset provides plant-level information on absolute emissions, fuel uses, generating capacities, geographic locations, etc. The dataset facilitates power emission characterization and clean air policy-making, and the CEMS-based estimation method can be employed by other countries seeking to regulate their power emissions.