Abstract

Improving air quality and reducing human exposure to unhealthy levels of airborne chemicals are important global missions, particularly in China. Satellite remote sensing offers a powerful tool to examine regional trends in NO2, thus providing a direct measure of key parameters that strongly affect surface air quality. To accurately resolve spatial gradients in NO2 concentration using satellite observations and thus understand local and regional aspects of air quality, a priori input data at sufficiently high spatial and temporal resolution to account for pixel-to-pixel variability in the characteristics of the land and atmosphere are required. In this paper, we adapt the Berkeley High Resolution product (BEHR-HK) and meteorological outputs from the Weather Research and Forecasting (WRF) model to describe column NO2 in southern China. The BEHR approach is particularly useful for places with large spatial variabilities and terrain height differences such as China. There are two major objectives and goals: (1) developing new BEHR-HK v3.0C product for retrieving tropospheric NO2 vertical column density (TVCD) within part of southern China, for four months of 2015, based upon satellite datasets from Ozone Monitoring Instrument (OMI); and (2) evaluating BEHR-HK v3.0C retrieval result through validation, by comparing with MAX-DOAS tropospheric column measurements conducted in Guangzhou. Results show that all BEHR-HK retrieval algorithms (with R-value of 0.9839 for v3.0C) are of higher consistency with MAX-DOAS measurements than OMI-NASA retrieval (with R-value of 0.7644). This opens new windows into research questions that require high spatial resolution, for example retrieving NO2 vertical column and ground pollutant concentration in China and other countries.

Highlights

  • Corresponding Air Mass Factor (AMF) spatial distributions within Southern China for Ozone Monitoring Instrument (OMI)-NASA and BEHR-HK retrieval for all four months are presented as part of the Supplementary Materials (Figures S1 and S2)

  • BEHR-HK vertical column density (VCD) were computed with Equation (9) and Community Multiscale Air Quality (CMAQ) VCDs with Equation (10)

  • Corresponding plots of the AMFs from the OMI-NASA and BEHR-HK products are available in Figures S1 and S2

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Summary

Introduction

Beijing, Guangzhou, Nanjing, Shanghai and a few more developed cities in China had ground monitoring stations for air quality research studies before 2013 [15], so there are no long-term and large-scale monitoring datasets from which to assess changes in pollutant emissions throughout previous years. Satellite datasets and remote sensing techniques monitor the column density of pollutants within the lower troposphere, and ground pollutant concentrations are derived from inverse formulations [19] and nonlinear statistical models [20] These data provide a long record (more than 20 years) that can be used to evaluate changes in emissions and chemistry.

Principles of NO2 Remote Sensing
BEHR Algorithm for NO2 Column Retrieval
Calculation of AMF and Tropospheric VCDs
CMAQ Tropospheric VCD Simulation
Study Areas and Datasets
Datasets
Grid Formation and Decomposition
Results
Discussion
MAX-DOAS Validation in Guangzhou
Conclusions
Full Text
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