Yeyuan Huang’s research while affiliated with Chinese Academy of Sciences and other places

The research on the mechanism of combined air pollution in the Yangtze–Huaihe region, which is characterized by unique meteorological and geographical conditions and pollution emission characteristics, is still insufficient. We performed an experiment on key pollutants and an ozone formation study in Hefei, which is a pivotal city in the Yangtze–Huaihe region, from September 1 to 20, 2020. The aerosols retrieved via two-dimensional Multi-axis Differential Optical Absorption Spectroscopy (2D-MAX-DOAS) with a Boltzmann-shaped a priori profile had the best agreement with the results of Light Detection and Ranging (LIDAR) and sun-photometer measurements among the three typical a priori profiles (Gaussian, Boltzmann, and exponential shapes). The correlation coefficients of the near-surface gas concentrations retrieved using both 2D-MAX-DOAS and in situ measurements were 0.86 (NO2) and 0.61 (HCHO). The high NO2 and HCHO concentrations were observed at azimuths of 180° and 315° at heights of 0.8–1.5 km, and they may have been emitted by aircrafts. Importantly, the ratio of HCHO to NO2 during a typical pollution episode revealed that the factors controlling the O3 formation changed with altitude: VOCs (surface) to NOx (0.4 km) to transition (1.0 km) to VOCs (1.6 km). Moreover, the effect of VOCs on the O3 generation was stronger than that of NOx, especially in the downtown area of Hefei. When the ratio of HCHO to NO2 was 3.55–7.46, the ozone concentration in Hefei could be controlled well, especially at the optimal value of 5.50.

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

Multi‐AXis Differential Optical Absorption Spectroscopy (MAX‐DOAS) observations were carried out from March 1, 2019 to February 29, 2020 in Qingdao, China, on the coast of the Yellow Sea (120.67°E, 36.35°N). Atmospheric parameters retrieved from the MAX‐DOAS instrument included vertical profiles of aerosol extinction, gaseous aerosol precursors (NO2, SO2), and H2O. First, the seasonal relationship between aerosol optical depth (AOD) and the H2O vertical column density (VCD) was analyzed. The correlation coefficients (r) between AOD and H2O VCD were largest in the winter, followed by spring, autumn, and summer. In addition, the correlation during heavily polluted weather was much higher than that during clean weather or slightly polluted weather. Then, an analysis of the wind speed and direction, and trace gas VCDs during the months with frequent pollution (December 2019 and January 2020) found that heavy pollution episodes easily occurred in Qingdao due to a combination of high H2O concentrations and low wind speeds. Out of four pollution events (P1, P2, P3, P4), both the H2O concentration and pollution were highest during the first event (P1). The H2O VCD, AOD, sulfate AOD, and total column HNO3 simultaneously increased during P1, while the NO2 and SO2 VCDs decreased. These trends are consistent with liquid phase reactions. Finally, the 1,000 m backward trajectory for the wind and the gas profiles during P1 revealed that the pollutants increased in Qingdao due to the influence of a polluted air mass from the western inland regions.

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.

Although the government of Nanjing implemented the more stringent atmospheric control measures during the second half of 2019, compared with the same period in 2018, the air pollution has increased unexpectedly, such as the O3 pollution and the acid rain. In this study, therefore, we performed observations of the vertical structures of the precursors (NO2, SO2, and HCHO) that caused the pollutions in Nanjing using the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) instrument during the daytime of 2019. The concentration of NO2 was found to be concentrated primarily within 200 m of the surface and decayed sharply with height, although the profiles of SO2 and HCHO could extend to higher altitudes than NO2. Then, the potential source contribution function (PSCF) was used to analyze the source distributions in different atmospheric layers of Nanjing’s pollutants during different seasons. The results reveal that the downtown and north of Nanjing with the WPSCF is greater than 0.6 constitute the most important potential source areas. Moreover, potential sources in south-central Jiangsu Province (in the summer and autumn) and east-central Anhui Province (in the winter) are also non-negligible (0.4 < WPSCF < 0.6). The transports of SO2 and HCHO from southeastern Anhui and south-central Jiangsu become stronger in the middle and upper layers (WPSCF > 0.5) than transports in the lower layer (0.3 < WPSCF < 0.5). In terms of altitude, the NO2 (SO2) contribution of long-range transport to the research area in the lower, middle, and upper layers accounted for 13% (20%), 11% (32%), and 8% (45%) of the averaged concentration, respectively. The results indicate that there are the significant SO2 transports from the NW (19.08%) and W (18.64%) orientations, while the most of the NO2 transports are approximately 13% in the lower layer, with the main orientations of W (7.29%) and N (6.40%).

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 PM2.5 measured by the in situ instrument and the PM2.5 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. PM2.5 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.

Mobile differential optical absorption spectroscopy (mobile DOAS) has become an important tool for the quantification of emission sources, including point sources (e.g., individual power plants) and area emitters (e.g., entire cities). In this study, we focused on the error budget of mobile DOAS measurements from point sources, and we also offered recommendations for the optimum settings of such measurements via a simulation with a modified Gaussian plume model. Following the analysis, we conclude that (1) the proper sampling resolution should be between 5 and 50 m. (2) When measuring far from the source, undetectable flux (measured slant column densities (SCDs) are under the detection limit) resulting from wind dispersion is the main error source. The threshold for the undetectable flux can be lowered by larger integration time. When measuring close to the source, low sampling frequency results in large errors, and wind field uncertainty becomes the main error source of SO2 flux (for NOx this error also increases, but other error sources dominate). More measurement times can lower the flux error that results from wind field uncertainty. The proper wind speed for mobile DOAS measurements is between 1 and 4 m s-1. (3) The remaining errors by [NOx] / [NO2] ratio correction can be significant when measuring very close. To minimize the [NOx] / [NO2] ratio correction error, we recommend minimum distances from the source, at which 5 % of the NO2 maximum reaction rate is reached and thus NOx steady state can be assumed. (4) Our study suggests that emission rates < 30 g s-1 for NOx and < 50 g s-1 for SO2 are not recommended for mobile DOAS measurements. Based on the model simulations, our study indicates that mobile DOAS measurements are a very well-suited tool to quantify point source emissions. The results of our sensitivity studies are important to make optimum use of such measurements.

NOX (NOX = NO + NO2) emissions measurements in Beijing are of great significance because they can aid in understanding how NOX pollution develops in mega-cities throughout China. However, NOX 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 NOX 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 NOX emission flux of different ring roads using wind field averages from measured wind data. The results showed that the NOX 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, NOX 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 NOX 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 NOX 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 NOX emission flux error sensitivity study indicated that more accurate measurements of the wind field are crucial for effective NOX emission flux measurements in Chinese mega-cities. Obtaining actual time and high resolved wind measurements is an advantage for mega-cities’ NOX emission flux measurements. The emission flux errors caused by wind direction and wind speed uncertainties were clearly distinguished. Other sensitivity studies indicated that NOX/NO2 ratio uncertainty dominated flux errors when the NOX/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.

Abstract. Mobile differential optical absorption spectroscopy (mobile DOAS) has become an important tool for the quantification of emission sources, including point sources (e.g., individual power plants) and area emitters (e.g., entire cities). In this study, we focused on the error budget of mobile DOAS measurements from point sources, and we also offered recommendations for the optimum settings of such measurements. First we established a Gaussian plume model from which the NO _x and SO₂ distribution from the point source was determined. In a second step the simulated distributions are converted into vertical column densities of NO _x and SO₂ according to the mobile DOAS measurement technique. With assumed parameters, we then drove the forward model in order to simulate the emissions, after which we performed the analysis. Following this analysis, we conclude that: (1) Larger sampling resolution clearly results in larger flux error. The proper resolution we suggest is between 5 m and 50 m. Even larger resolutions may also be viable, but > 100 m is not recommended. (2) Error effects vary with measurement distance from the source. We found that undetectable flux (measured VCDs are under the detection limit) is the main error source when measuring far from the source, for both NO _x and SO₂. When measuring close to the source, low sampling frequency results in large flux error. (3) The wind field primarily affects 2 aspects of the flux measurement error. When measuring far from the source, dispersion results in more undetectable flux, which is the main error source. When measuring close to the source, wind field uncertainty becomes the main error source of SO₂ flux, but not of NO _x . We suggested that the proper wind speed for mobile DOAS measurements is between 1 m/s and 4 m/s. (4) The study of NOx atmospheric chemistry reactions indicated that a [NO _x ]/[NO₂] ratio correction has to be applied when measuring very close to the emission source. But even when such a correction is applied, the remaining errors can be significant. To minimize the [NO _x ]/[NO₂] ratio correction error, we recommended 0.05 NO₂ maximum reaction rate as the accepted NO _x steady-state thus to determine the proper starting measurement distance. (5) The error of the spectral retrieval is not a main emission flux error source and its error budget varies with the measuring distance. (6) Increasing the number of measurements can lower the flux error that results from wind field uncertainty and retrieval error. This directly indicates that SO₂ flux error could be lowered if the measurements are repeated when not too far from the emission source. With regard to NO _x , more measurement times can only work effectively when not very close or too far from the source. (7) Also the effects of the temporal and spatial sampling are investigated. When the sampling resolution is prescribed, the integration depends on the driving speed and the corresponding flux error is mainly determined by the undetectable flux. When the car speed is prescribed, the integration time is determined by the sampling resolution for measuring near the source, while undetectable flux predominates when far away. (8) As a general recommendation, our study suggests that emission rates < 30 g/s for NOx and < 50 g/s for SO₂ are not recommended for mobile DOAS measurements. The source height affects the undetectable flux, but has little influences on the total error. Based on the model simulations our study indicates that mobile DOAS measurements are very well suited tool to quantify point source emissions. The results of our sensitivity studies are important to make optimum use of such measurements.