NO_X (NO_X = NO + NO₂) emissions measurements in Beijing are of great significance because they can aid in understanding how NO_X pollution develops in mega-cities throughout China. However, NO_X emissions in mega-cities are difficult to measure due to changes in wind patterns and moving sources on roads during measurement. To obtain good spatial coverage on different ring roads in Beijing over a short amount of time, two mobile differential optical absorption spectroscopy (DOAS) instruments were used to measure NO_X emission flux from April 18th to 26th, 2018. In addition, a wind profile radar provided simultaneous wind field measurements for altitudes between 50 m and 1 km for each ring road measurement. We first determined NO_X emission flux of different ring roads using wind field averages from measured wind data. The results showed that the NO_X emission flux of Beijing’s fifth ring road, which represented the urban part, varied from (19.29 ± 5.26) × 10²⁴ molec./s to (36.46 ± 12.86) × 10²⁴ molec./s. On April 20th, NO_X emission flux for the third ring was slightly higher than the fourth ring because the two ring roads were measured at different time periods. We then analyzed the NO_X emission flux error budget and error sensitivity. The main error source was the wind field uncertainty. For some measurements, the main emission flux error source was either wind speed uncertainty or wind direction uncertainty, but not both. As Beijing’s NO_X emissions came from road vehicle exhaust, we found that emission flux error had a more diverse sensitivity to wind direction uncertainty, which improved our knowledge on this topic. The NO_X emission flux error sensitivity study indicated that more accurate measurements of the wind field are crucial for effective NO_X emission flux measurements in Chinese mega-cities. Obtaining actual time and high resolved wind measurements is an advantage for mega-cities’ NO_X emission flux measurements. The emission flux errors caused by wind direction and wind speed uncertainties were clearly distinguished. Other sensitivity studies indicated that NO_X/NO₂ ratio uncertainty dominated flux errors when the NO_X/NO₂ ratio uncertainty was >0.4. Using two mobile-DOAS and wind profile radars to measure NOx emission flux improved the quality of the emission flux measuring results. This approach could be applied to many other mega-cities in China and in others countries.

This paper studied the method for converting the aerosol extinction to the mass concentration of particulate matter (PM) and obtained the spatio-temporal distribution and transportation of aerosol, nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and formaldehyde (HCHO) based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations in Dalian (38.85°N, 121.36°E), Qingdao (36.35°N, 120.69°E), and Shanghai (31.60°N, 121.80°E) from 2019 to 2020. The PM_2.5 measured by the in situ instrument and the PM_2.5 simulated by the conversion formula showed a good correlation. The correlation coefficients R were 0.93 (Dalian), 0.90 (Qingdao), and 0.88 (Shanghai). A regular seasonality of the three trace gases is found, but not for aerosols. Considerable amplitudes in the weekly cycles were determined for NO₂ and aerosols, but not for SO₂ and HCHO. The aerosol profiles were nearly Gaussian, and the shapes of the trace gas profiles were nearly exponential, except for SO₂ in Shanghai and HCHO in Qingdao. PM_2.5 presented the largest transport flux, followed by NO₂ and SO₂. The main transport flux was the output flux from inland to sea in spring and winter. The MAX-DOAS and the Copernicus Atmosphere Monitoring Service (CAMS) models’ results were compared. The overestimation of NO₂ and SO₂ by CAMS is due to its overestimation of near-surface gas volume mixing ratios.

Water vapor transport affects regional precipitation and climate change. The measurement of precipitable water (PW) and water vapor flux (WVF) is of great importance for the study of precipitation and water vapor transport. This study presented a new method of computing PW and estimating WVF using the water vapor vertical column density (VCD) and profile retrieved from multi-axis differential optical absorption spectroscopy (MAX-DOAS), combined with the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 wind profiles. We applied our method to MAX-DOAS observations in the coastal (Qingdao) and inland (Xi’an) cities of China from June 2019 to May 2020 and compared the results to the ERA5 reanalysis datasets. Good agreement with ERA5 datasets was found; the correlation coefficient (r) of the PW and the zonal and meridional WVFs were r ≥ 0.92, r = 0.77, and r ≥ 0.89, respectively. The comparison results showed the feasibility and reliability of estimating PW and WVF using MAX-DOAS. Then, we analyzed the seasonal and diurnal climatology of the PW and WVFs in Qingdao and Xi’an. The results indicated that the seasonal and diurnal variations of the PW in the two cities were similar. The zonal water vapor transport of the two cities mainly involved eastward transport, Qingdao’s meridional water vapor mainly involved southward transport, and that of Xi’an mainly involved northward transport. The WVFs of the two cities were higher in the afternoon than in the morning, which may be related to wind speed. The results also indicated that the WVF transmitting belts appeared at around 2 and 1.4 km above the surface in Qingdao and around 2.8, 2.6, 1.6, and 1.0 km above the surface in Xi’an. Before precipitation, the WVF transmitting belt moved from near the ground to a high level, reaching its maximum at about 2 km, and the PW and meridional vertically integrated WVF increased. Finally, the sources and transports of water vapor during continuous precipitation and torrential rain were analyzed according to a 24 h backward trajectory. The air mass from the southeast accounted for more than 84% during continuous precipitation in Xi’an, while the air mass from the ocean accounted for more than 75% during torrential rain in Qingdao and was accompanied by a high-level ocean jet stream. As an optical remote sensing instrument, MAX-DOAS has the advantages of high spatiotemporal resolution, low cost, and easy maintenance. The application of MAX-DOAS to meteorological remote sensing provides a better method for evaluating the PW and WVF.

Haze and dust pollution have a significant impact on human production, life, and health. In order to understand the pollution process, the study of these two pollution characteristics is important. In this study, a one-year observation was carried out at the Beijing Southern Suburb Observatory using the MAX-DOAS instrument, and the pollution characteristics of the typical haze and dust events were analyzed. First, the distribution of aerosol extinction (AE) and H₂O concentrations in the two typical pollution events were studied. The results showed that the correlation coefficient (r) between H₂O and AE at different heights decreased during dust processes and the correlation slope (|k|) increased, whereas r increased and |k| decreased during haze periods. The correlation slope increased during the dust episode due to low moisture content and increased O₄ absorption caused by abundant suspended dry crustal particles, but decreased during the haze episode due to a significant increase of H₂O absorption. Secondly, the gas vertical column density (VCD) indicated that aerosol optical depth (AOD) increased during dust pollution events in the afternoon, while the H₂O VCD decreased; in haze pollution processes, both H₂O VCD and AOD increased. There were significant differences in meteorological conditions during haze (wind speed (WD) was 60%) and dust pollution (WD was >4 m/s, and RH was <60%). Next, the vertical distribution characteristics of gases during the pollution periods were studied. The AE profile showed that haze pollution lasted for a long time and changed slowly, whereas the opposite was true for dust pollution. The pollutants (aerosols, NO₂, SO₂, and HCHO) and H₂O were concentrated below 1 km during both these typical pollution processes, and haze pollution was associated with a strong temperature inversion around 1.0 km. Lastly, the water vapor transport fluxes showed that the water vapor transport from the eastern air mass had an auxiliary effect on haze pollution at the observation location. Our results are of significance for exploring the pollution process of tropospheric trace gases and the transport of water vapor in Beijing, and provide a basis for satellite and model verification.