Assessment of satellite-estimated near-surface sulfate and nitrate concentrations and their precursor emissions over China from 2006 to 2014

Yidan Si; Chao Yu; Luo Zhang; Wende Zhu; Kun Cai; Liangxiao Cheng; Liangfu Chen; Shenshen Li

doi:10.1016/j.scitotenv.2019.02.180

Assessment of satellite-estimated near-surface sulfate and nitrate concentrations and their precursor emissions over China from 2006 to 2014

Sci Total Environ. 2019 Jun 15:669:362-376. doi: 10.1016/j.scitotenv.2019.02.180. Epub 2019 Mar 5.

Authors

Yidan Si¹, Chao Yu², Luo Zhang¹, Wende Zhu³, Kun Cai³, Liangxiao Cheng¹, Liangfu Chen⁴, Shenshen Li²

Affiliations

¹ State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; University of the Chinese Academy of Sciences, Beijing 100049, China.
² State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China.
³ School of Computer and Information Engineering, Henan University, Kaifeng 475004, China.
⁴ State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China. Electronic address: chenlf@radi.ac.cn.

PMID: 30884261
DOI: 10.1016/j.scitotenv.2019.02.180

Abstract

China is the largest anthropogenic aerosol-generating country worldwide; however, few studies have analyzed the PM_2.5 chemical components and their underlying precursor emissions over long periods and across the national domain. First, global 3-D tropospheric chemistry and transport model (GEOS-Chem)-integrated satellite-retrieved aerosol optical depth (AOD) and vertical profiles were used to estimate near-surface sulfate and nitrate levels at 10-km resolution over China from 2006 to 2014. Ground measurement validation of our satellite model yielded correlation coefficients (r) of 0.7 and 0.73 and normalized mean bias (NMB) values of -37.96% and - 32.73% for sulfate and nitrate, respectively. Second, analyses of the spatiotemporal distributions of sulfate and nitrate as well as the vertical density Ozone Monitoring Instrument (OMI)-measured SO₂ (PBL_SO₂) and NO₂ (TVCD_NO₂) indicated that the highest nitrate and sulfate levels occurred in the North China Plain (~25 μg/m³) and Sichuan Basin (SCB) (~30 μg/m³), respectively. The long-term variations in the estimated components and precursor gases indicated that the large sulfate decline was positively correlated with the SO₂ emission reduction due to the mandatory desulfurization implemented in 2007. The annual growth rate of sulfate relative to the national mean was -6.19%/yr, and the concentration decreased by 17.10% from 2011 to 2014. Energy consumption increases and a lack of control measures for NO₂ resulted in persistent increases in NO₂ emissions and nitrate concentrations from 2006 to 2010, particularly in the SCB. With energy consumption structure advancements, reductions in NO₂ emissions and corresponding nitrate levels over three typical regions were prominent after 2012. Third, the estimated national-scale uncertainties of satellite datasets at 0.1° × 0.1° were 26.88% for sulfate and 25.55% for nitrate. Differences in the spatial distributions and temporal trends between our estimated components and precursor gases were mainly attributed to the dataset accuracy, the data pre-processing strategy, inconsistent column density and near-surface mass concentration, meteorological variables and complex chemical reactions.

Keywords: NO(2); Nitrate; SO(2); Spatial distribution; Sulfate; Temporal variation.