Suchergebnisse

At present, the air environment in China is characterized by complex pollution. In this paper, the pollutant sources, transport paths and aerosol optical properties during the dust pollution was conducted to analyse based on ground-based lidar, space-borne sensor and atmospheric transmission model. Firstly, the NMMB/BSC-Dust model, the VIIRS-Suomi NPP date and HYSPLIT were carried out to analyse the dust transport paths and the dust particle size, and then the concentration of particles was analysed. Finally, the optical properties of aerosol particles in the dust weather were studied. During the formation of this weather, there is high dust in the Gobi and Taklamakan deserts. With the influence of wind direction, the dust moves from north to south, and the dust load significantly increased in southern China. Dust at the low altitude is generally transported from the Taklamakan Desert, while dust at the high altitude is generally transported from the Gobi Desert. The hourly average change of PM 10 is from 36 μg/m 3 to 818 μg/m 3 , while the hourly average change of PM 2.5 is from 15 μg/m 3 to 197 μg/m 3 . The dust was the main cause of the pollution weather. In this study, the formation process of the dust pollution revealed which can be used to provide guidance for government for the prevention work of dust pollution.

At present, the air environment in China is characterized by complex pollution. In this paper, the pollutant sources, transport paths and aerosol optical properties during the dust pollution was conducted to analyse based on ground-based lidar, space-borne sensor and atmospheric transmission model. Firstly, the NMMB/BSC-Dust model, the VIIRS-Suomi NPP date and HYSPLIT were carried out to analyse the dust transport paths and the dust particle size, and then the concentration of particles was analysed. Finally, the optical properties of aerosol particles in the dust weather were studied. During the formation of this weather, there is high dust in the Gobi and Taklamakan deserts. With the influence of wind direction, the dust moves from north to south, and the dust load significantly increased in southern China. Dust at the low altitude is generally transported from the Taklamakan Desert, while dust at the high altitude is generally transported from the Gobi Desert. The hourly average change of PM10 is from 36 μg/m3 to 818 μg/m3, while the hourly average change of PM2.5 is from 15 μg/m3 to 197 μg/m3. The dust was the main cause of the pollution weather. In this study, the formation process of the dust pollution revealed which can be used to provide guidance for government for the prevention work of dust pollution.

30 pags., 13 figs., 4 tabs. ; We present the inter-comparison of delta slant column densities (SCDs) and vertical profiles of nitrous acid (HONO) derived from measurements of different multiaxis differential optical absorption spectroscopy (MAXDOAS) instruments and using different inversion algorithms during the Second Cabauw Inter-comparison campaign for Nitrogen Dioxide measuring Instruments (CINDI- 2) in September 2016 at Cabauw, the Netherlands (51.97° N, 4.93° E). The HONO vertical profiles, vertical column densities (VCDs), and near-surface volume mixing ratios are compared between different MAX-DOAS instruments and profile inversion algorithms for the first time. Systematic and random discrepancies of the HONO results are derived from the comparisons of all data sets against their median values. Systematic discrepancies of HONO delta SCDs are observed in the range of ±0:3×1015 molec. cm2, which is half of the typical random discrepancy of 0:6× 1015 molec. cm2. For a typical high HONO delta SCD of 2×1015 molec. cm2, the relative systematic and random discrepancies are about 15% and 30 %, respectively. The inter-comparison of HONO profiles shows that both systematic and random discrepancies of HONO VCDs and nearsurface volume mixing ratios (VMRs) are mostly in the range of ∼ ±0:5×1014 molec. cm2 and ∼ ±0:1 ppb (typically ∼ 20 %). Further we find that the discrepancies of the retrieved HONO profiles are dominated by discrepancies of the HONO delta SCDs. The profile retrievals only contribute to the discrepancies of the HONO profiles by ∼ 5 %. However, some data sets with substantially larger discrepancies than the typical values indicate that inappropriate implementations of profile inversion algorithms and configurations of radiative transfer models in the profile retrievals can also be an important uncertainty source. In addition, estimations of measurement uncertainties of HONO dSCDs, which can significantly impact profile retrievals using the optimal estimation method, need to consider not only DOAS fit errors, but also atmospheric variability, especially for an instrument with a DOAS fit error lower than ∼ 3×1014 molec. cm2. The MAX-DOAS results during the CINDI-2 campaign indicate that the peak HONO levels (e.g. near-surface VMRs of ∼ 0:4 ppb) often appeared in the early morning and below 0.2 km. The near-surface VMRs retrieved from the MAXDOAS observations are compared with those measured using a co-located long-path DOAS instrument. The systematic differences are smaller than 0.15 and 0.07 ppb during early morning and around noon, respectively. Since true HONO values at high altitudes are not known in the absence of real measurements, in order to evaluate the abilities of profile inversion algorithms to respond to different HONO profile shapes, we performed sensitivity studies using synthetic HONO delta SCDs simulated by a radiative transfer model with assumed HONO profiles. The tests indicate that the profile inversion algorithms based on the optimal estimation method with proper configurations can reproduce the different HONO profile shapes well. Therefore we conclude that the features of HONO accumulated near the surface derived from MAX-DOAS measurements. ; Funding for this study was provided by ESA through the CINDI-2 (ESA contract no. 4000118533/16/I-Sbo) and FRM4DOAS (ESA contract no. 4000118181/16/I-EF) projects, by the NSFC (grant no. 41805027), the Russian Foundation for Basic Research (grant no. 18-35-00682), the Russian Academy of Sciences (grant nos. 0150-2018-0052 and 0129-2019- 0002), NASA's Atmospheric Composition Program (grant no. NASA-16-NUP2016-0001), the US National Science Foundation (AGS-1620530 award), and the European Union's Horizon 2020 research and innovation programme through the ACTRIS-2 transnational access programme (grant no. 654109). The AIOFM group is grateful for the support by the NSFC (grant no. 41530644). The article processing charges for this open-access publication were covered by the Max Planck Society