NDLI: Improved Satellite Retrieval of Tropospheric $NO_{2}$ Column Density via Updating of Air Mass Factor (AMF): Case Study of Southern China

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Hugo Wai Leung Mak Joshua L. Laughner Jimmy Chi Hung Fung Qindan Zhu Ronald C. Cohen
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 $NO_{2}$, thus providing a direct measure of key parameters that strongly affect surface air quality. To accurately resolve spatial gradients in $NO_{2}$ 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 $NO_{2}$ 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 $NO_{2}$ 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 $NO_{2}$ vertical column and ground pollutant concentration in China and other countries.
e-ISSN	20724292
DOI	10.3390/rs10111789
Journal	Remote Sensing
Issue Number	11
Volume Number	10
Language	English
Publisher	MDPI AG
Publisher Date	2018-11-01
Publisher Place	Switzerland
Access Restriction	Open
Subject Keyword	Science Remote Sensing Techniques Tropospheric $no_{2}$ Column Retrieval Air Mass Factor (amf) Meteorological Reformulation Max-doas Measurements Satellite Informatics
Content Type	Text
Resource Type	Article

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