Abstract
Near surface ozone is a typical secondary pollutant, and is mostly generated by a series of complex photochemical reactions of volatile organic compounds (VOCs) and nitrogen oxides (NOx) in the air under sunlight. At present, a large number of studies have applied FNR (a ratio of formaldehyde (HCHO) to nitrogen dioxide (NO2) retrieved by satellite) indicator to study the ozone formation sensitivity (OFS). OFS analysis is critical for taking targeted ozone pollution prevention and control measures. Regional OFS can be more accurately diagnosed by utilizing localized FNR threshold. In this study, localized FNR thresholds were established for four severe ozone polluted urban agglomerations in China (Beijing-Tianjin-Hebei (BTH) region, Yangtze River Delta (YRD) region, Pearl River Delta (PRD) region, and Chengdu-Chongqing (CY) region), based on the statistical analysis between FNR (obtained from OMI observation, with daily transit time of approximately 13:45 local standard time) and ΔO3/ΔNO2 (the ratio of ozone change to nitrogen dioxide change between two consecutive months, obtained from ground measurements) from 2014 to 2016. And these thresholds were verified by the statistical analysis between FNR and ΔO3/O3 (ozone change rate between two consecutive months), and between FNR and O3 concentration during the OFS significant shift months. Furthermore, the results were also compared and verified with the method proposed by previous studies. The results indicate that there are significant regional dependences in the FNR threshold, and the lower–upper limits for the four urban agglomerations are as follows: 0.65–1.21 for BTH, 0.64–1.48 for the YRD, 1.25–2.39 for the PRD, and 1.44–3.69 for CY (FNR < lower limit indicates VOCs-limited regime; lower limit < FNR < upper limit indicates transitional regime; FNR > upper limit indicates NOx-limited regime). This method eliminates the problems associated with the undifferentiated use of FNR thresholds in different regions and significantly reduces the deviations for OFS.
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