Updated emission inventories of power plants in simulating air quality during haze periods over East China

Abstract

Abstract. Air pollutant emissions play a determinant role in deteriorating air quality. However, an uncertainty in emission inventories is still the key problem for modeling air pollution. In this study, an updated emission inventory of coalfired power plants (UEIPP) based on online monitoring data in Jiangsu Province of East China for the year of 2012 was implemented in the widely used Multi-resolution Emission Inventory for China (MEIC). By employing the Weather Research and Forecasting model with Chemistry (WRF-Chem), two simulation experiments were executed to assess the atmospheric environment change by using the original MEIC emission inventory and the MEIC inventory with the UEIPP. A synthetic analysis shows that power plant emissions of PM 2.5 , PM 10 , SO 2 , and NO x were lower, and CO, black carbon (BC), organic carbon (OC) and NMVOCs (non-methane volatile organic compounds) were higher in UEIPP relative to those in MEIC, reflecting a large discrepancy in the power plant emissions over East China. In accordance with the changes in UEIPP, the modeled concentrations were reduced for SO 2 and NO 2 , and increased for most areas of primary OC, BC, and CO. Interestingly, when the UEIPP was used, the atmospheric oxidizing capacity significantly reinforced. This was reflected by increased oxidizing agents, e.g., O 3 and OH, thus directly strengthening the chemical production from SO 2 and NO x to sulfate and nitrate, respectively, which offset the reduction of primary PM 2.5 emissions especially on haze days. This study indicates the importance of updating air pollutant emission inventories in simulating the complex atmospheric environment changes with implications on air quality and environmental changes.

Constraining the aerosol influence on cloud liquid water path

“…Other possibilities include variations in the vertical distribution of cloud water, assumptions about the droplet size spectrum, a dependence on satellite and solar zenith angle (Eastman and Wood, 2016;Grosvenor and Wood, 2014), and non-linearities in the retrieval (Zhang and Platnick, 2011).…”

“…Pixels with a 5 km cloud fraction less than 0.9 are excluded to remove pixels close to cloud edges, and only pixels with a solar zenith angle of less than 65 • and a sensor zenith angle of less than 41.4 • are used to reduce the impact of known biases (Grosvenor and Wood, 2014;Eastman and Wood, 2016;Grosvenor et al, 2018). Finally, only pixels with an inhomogeneity index (Cloud_Mask_SPI) of less than 30 are used to account for biases in the effective radius (r e ) in inhomogeneous scenes (Zhang and Platnick, 2011). Trials using a more stringent upper limit of 10 show little difference to the results presented here (not shown).…”

Recommendations on benchmarks for photochemical grid model applications in China: Part I – PM<sub>2.5</sub> and chemical species

“…While most of the studies only performed model evaluation for one modelling domain (usually the finest domain), four studies (X. Qiao et al, 2015; L. X. Liu et al, 2010;S. Liu et al, 2018) calculated statistical results for multiple domains.…”

High-resolution spatiotemporal patterns of China’s FFCO2 emissions under the impact of LUCC from 2000 to 2015

“…This application of top-down measurements has led to an accuracy of global CO 2 concentrations estimated from space-borne observations better than 1% (less than 4 ppmv) and could decrease the uncertainty in regional estimates of CO 2 sources and sinks (Rayner and O'Brien 2001). However, although Hakkarainen et al (2018) showed how global and local satellite-based CO 2 anomalies can be used to study anthropogenic CO 2 emission sources, Zhang et al (2018) concluded the existing discrepancies between inventories and models based on different methods and data sources, result in significant uncertainties and gaps associated with these emission inventories (Guevara et al 2017, Zhao et al 2017. Presently, the community has not found a sufficient way to comprehensively monitor and verify anthropogenic CO 2 emissions.…”