Uncertainties in estimating mercury emissions from coal-fired power plants in China

Abstract

Abstract. A detailed multiple-year inventory of mercury emissions from anthropogenic activities in China has been developed. Coal combustion and nonferrous metals production continue to be the two leading mercury sources in China, together contributing ~80% of total mercury emissions. Within our inventory, a new comprehensive sub-module for estimation of mercury emissions from coal-fired power plants in China is constructed for uncertainty case-study. The new sub-module integrates up-to-date information regarding mercury content in coal by province, coal washing and cleaning, coal consumption by province, mercury removal efficiencies by control technology or technology combinations, etc. Based on these detailed data, probability-based distribution functions are built into the sub-module to address the uncertainties of these key parameters. The sub-module incorporates Monte Carlo simulations to take into account the probability distributions of key input parameters and produce the mercury emission results in the form of a statistical distribution. For example, the best estimate for total mercury emissions from coal-fired power plants in China in 2003 is 90.5 Mg, with the uncertainty range from 57.1 Mg (P10) to 154.6 Mg (P90); and the best estimate for elemental mercury emissions is 43.0 Mg, with the uncertainty range from 25.6 Mg (P10) to 75.7 Mg (P90). The results further indicate that the majority of the uncertainty in mercury emission estimation comes from two factors: mercury content in coal and mercury removal efficiency.

Understanding factors influencing the detection of mercury policies in modelled Laurentian Great Lakes wet deposition

“…The large epistemic uncertainties-that is, uncertainty due to imperfect knowledge [84][85][86] -in anthropogenic emissions inventories (in the range of 30%) are widely acknowledged to be a challenge for mercury modelling, monitoring, and policy evaluation. 14 However, our simulations demonstrate that even if "true" emissions values are known, year-to-year variability in these emissions-in our simulations, driven by variability in air pollution control technology performance, 22,70,71 but potentially also from other sources, like fluctuations in economic activity-can dampen a policy effect. Because they are labor-intensive to produce, many emissions inventories are released at multi-year intervals, with users linearly interpolating between these years.…”

“…Variability in the performance of air pollution control devices can be due to variabilities in fuel characteristics and operating conditions. 22,70,71 To investigate the potential impact of such variability on the policy signal, we treat the removal fraction of each air pollution control configuration probabilistically each year. Rather than assuming a static removal fraction for each air pollution control configuration, we bootstrap a normal distribution for the population mean from the sample data from Bullock and Johnson, 45 described in Section 2.4.1, and randomly select the removal fraction for each year from this bootstrapped distribution.…”

Unit-based emission inventory and uncertainty assessment of coal-fired power plants

“…Energy consumption by the power sector in national statistics is usually regarded as accurate with a small discrepancy, less than ±5% for coal consumption (Wu et al, 2010). Normal distribution with coefficients of variation (CV, the standard deviation divided by the mean) of 10% for unit-based coal consumption was assumed in the paper because errors can occur when the coal consumption of each plant is divided into unit level by unit capacity.…”

How Green Transition of Energy System Impacts China's Mercury Emissions

“…There are a few leading factors related to the uncertainties in this study, including but not limited to mercury emission inventory, disaggregation of the power sector, the inherited uncertainties from IO table compilation, and the choice of SDA method. For mercury emission inventory, this study uses Monte Carlo simulations to generate the probabilistic emissions by taking into account the probability distribution of key parameters, including activity level, mercury content in coal, and APCD combination removal efficiencies (Wu et al, , ). A normal distribution with a coefficient of variation is set to be 5% for energy consumption data (Liu et al, ), while mercury content of consumed coal is assumed to fit a log‐normal distribution curve (Wu et al, ; Zhang et al, ).…”

“…For mercury emission inventory, this study uses Monte Carlo simulations to generate the probabilistic emissions by taking into account the probability distribution of key parameters, including activity level, mercury content in coal, and APCD combination removal efficiencies (Wu et al, , ). A normal distribution with a coefficient of variation is set to be 5% for energy consumption data (Liu et al, ), while mercury content of consumed coal is assumed to fit a log‐normal distribution curve (Wu et al, ; Zhang et al, ). In terms of APCD removal efficiencies, different types of APCD combinations are considered to fit normal distribution or Weibull distribution, and the average and coefficient of variation are collected from previous studies (Liu et al, , ; Wu et al, ; Zhang et al, ).…”