Abstract

Abstract. Emission datasets of nitrogen oxides (NOx) at high horizontal resolutions (e.g., 0.05∘×0.05∘) are crucial for understanding human influences at fine scales, air quality studies, and pollution control. Yet high-resolution emission data are often missing or contain large uncertainties especially for the developing regions. Taking advantage of long-term satellite measurements of nitrogen dioxide (NO2), here we develop a computationally efficient method of estimating NOx emissions in major urban areas at the 0.05∘×0.05∘ resolution. The top-down inversion method accounts for the nonlinear effects of horizontal transport, chemical loss, and deposition. We construct a two-dimensional Peking University High-resolution Lifetime-Emission-Transport model (PHLET), its adjoint model (PHLET-A), and a satellite conversion matrix approach to relate emissions, lifetimes, simulated NO2, and satellite NO2 data. The inversion method is applied to the summer months of 2012–2015 in the Yangtze River Delta (YRD; 29–34∘ N, 118–123∘ E) area, a major polluted region of China, using the NO2 vertical column density data from the Peking University Ozone Monitoring Instrument NO2 product (POMINO). A systematic analysis of inversion errors is performed, including using an independent test based on GEOS-Chem simulations. Across the YRD area, the summer average emissions obtained in this work range from 0 to 15.3 kg km−2 h−1, and the lifetimes (due to chemical loss and deposition) range from 0.6 to 3.3 h. Our emission dataset reveals fine-scale spatial information related to nighttime light, population density, road network, maritime shipping, and land use (from a Google Earth photo). We further compare our emissions with multiple inventories. Many of the fine-scale emission structures are not well represented or not included in the widely used Multi-scale Emissions Inventory of China (MEIC).

Highlights

  • Nitrogen oxides (NOx = NO + NO2) are a main precursor of particulate matter, ozone, and other atmospheric pollutants

  • The satellite conversion matrix (SCM) is applied to Peking University High-resolution Lifetime-Emission-Transport model (PHLET)-simulated vertical column densities (VCDs) to mimic how each satellite pixel averages the spatial distribution of NO2, in order to ensure the spatial sampling consistency between PHLET and POMINO

  • This study presents a satellite-based top-down method to estimating nitrogen oxides (NOx) emissions over urban and surrounding areas at a high horizontal resolution

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Summary

Introduction

Gridded bottom-up emission inventories typically use spatial proxies (like population and GDP) to allocate provinciallevel emission values, which are derived from activity statistics and emission factor data, to individual locations (Zhao et al, 2011, 2015; Janssens-Maenhout et al, 2015) Such a gridding method may lead to large uncertainties at high resolutions (Geng et al, 2017), because the mismatch between proxies and emissions becomes more significant and emitting facilities are harder to allocate accurately as the resolution increases (Zheng et al, 2017).

A general framework to retrieve NOx emissions at a high resolution
Tropospheric NO2 VCDs retrieved from OMI
The PHLET model simulation
Governing equation of PHLET
Vertical shape and regional background of NO2
Application of SCM
Summary of model errors
PHLET-A: the adjoint model of PHLET
Deriving emission and loss from the local net source term
Uncertainty estimate for top-down emissions
Spatial distribution of emissions
Comparison between our top-down emissions and spatial proxies
Comparison between our emission dataset and other inventories
Findings
Concluding remarks
Full Text
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