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Monitoring and Comparative Analysis of NO2 and HCHO in Shanghai Using Dual-Azimuth Scanning MAX-DOAS and TROPOMI
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Monitoring and Comparative Analysis of NO2 and HCHO in Shanghai Using Dual-Azimuth Scanning MAX-DOAS and TROPOMI
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Journal Article
Monitoring and Comparative Analysis of NO2 and HCHO in Shanghai Using Dual-Azimuth Scanning MAX-DOAS and TROPOMI
2025
Overview
This study employed dual-azimuth scanning MAX-DOAS to monitor vertical column densities of NO2 and HCHO in Shanghai during the summer and winter of 2023, and compared the results with Sentinel-5P TROPOMI data. Dual-azimuth scanning revealed a generally consistent trend in gas concentrations (r > 0.95), but concentrations at 90° were higher than those at 0°, especially near the surface. This suggests that averaging multiple azimuth angles is necessary to better represent regional pollution levels. During the observation period, diurnal patterns revealed that NO2 exhibited a “double peak” in the morning and evening, which was more pronounced in the summer, while HCHO peaked between 13:00 and 15:00. Comparisons with the TROPOMI data demonstrated overall good agreement. However, the probability of TROPOMI’s NO2 and HCHO measurements being lower than those of MAX-DOAS was 80% and 62.5%, respectively. Furthermore, TROPOMI tended to overestimate at high concentrations, with overestimation reaching 41.14% for NO2 when exceeding 9.54 × 1015 molecules/cm2 and 25.93% for HCHO when exceeding 1.26 × 1016 molecules/cm2. Sensitivity analysis of the sampling distance (0–40 km) between TROPOMI samples and the ground-based site, and the sampling time (±5 to ±60 min) relative to the TROPOMI overpass, revealed that using a sampling distance of 15–25 km for NO2 and 10–20 km for HCHO, along with appropriately shortening sampling times in the winter and extending them in the summer, can effectively enhance the consistency between satellite and ground-based observations. These findings not only reveal the spatiotemporal distribution characteristics of regional pollutants but optimize the sampling time and distance parameters for satellite–ground observation validation, providing data support for improving and enhancing the accuracy of satellite retrieval algorithms.
