Source contributions to two super dust storms over Northern China in March 2021 and the impact of soil moisture.

Two extremely devastating super dust storms (SDS) hit Mongolia and Northern China in March 2021, causing many deaths and substantial economic damage. Accurate forecasting of dust storms is of great importance for avoiding or mitigating their effects. One of the most critical factors affecting dust emissions is soil moisture, but its value in desert exhibits significant uncertainty. In this study, model experiments were conducted to simulate dust emissions using four soil moisture datasets. The results were compared with observations to assess the effects of soil moisture on the dust emission strength. The Integrated Source Apportionment Method (ISAM) was used to track the dust sources and quantify the contribution from each source region to the dust load over the North China Plain (NCP), Korea peninsula, and western Japan. The results show large differences in the dust load depending on the soil moisture datasets used. The high soil moisture in the NCEP dataset results in substantial underestimation of the dust emission flux and PM10 concentration. Despite a minor overestimation of PM10 concentrations in many Northern China cities, the ERA5 dataset yields the best simulation performance. During the two SDS events, about 7.5 Mt dust was released from the deserts in Mongolia and 2.8 Mt from the deserts in China. Source apportionment indicates that the Mongolian Gobi Desert is the dominant source of PM10 in the NCP, Korea peninsula, and western Japan, accounting for 60 %-80 %, while Inner Mongolia contributed 10 %-20 %.

An improved meteorological variables-based aerosol optical depth estimation method by combining a physical mechanism model with a two-stage model.

A two-stage model integrating a spatiotemporal linear mixed effect (STLME) and a geographic weight regression (GWR) model is proposed to improve the meteorological variables-based aerosol optical depth (AOD) retrieval method (Elterman retrieval model-ERM). The proposed model is referred to as the STG-ERM model. The STG-ERM model is applied over the Beijing-Tianjin-Hebei (BTH) region in China for the years 2019 and 2020. The results show that data coverage increased by 39.0% in 2019 and 40.5% in 2020. Cross-validation of the retrieval results versus multi-angle implementation of atmospheric correction (MAIAC) AOD shows the substantial improvement of the STG-ERM model over earlier meteorological models for AOD estimation, with a determination coefficient (R2) of daily AOD of 0.86, root mean squared prediction error (RMSE) and the relative prediction error (RPE) of 0.10 and 36.14% in 2019 and R2 of 0.86, RMSE of 0.12 and RPE of 37.86% in 2020. The fused annual mean AOD indicates strong spatial variation with high value in south plain and low value in northwestern mountainous areas of the BTH region. The overall spatial seasonal mean AOD ranges from 0.441 to 0.586, demonstrating strongly seasonal variation. The coverage of STG-ERM retrieved AOD, as determined in this exercise by leaving out part of the meteorological data, affects the accuracy of fused AOD. The coverage of the meteorological data has smaller impact on the fused AOD in the districts with low annual mean AOD of less than 0.35 than that in the districts with high annual mean AOD of greater than 0.6. If available, continuous daily meteorological data with high spatiotemporal resolution can improve the model performance and the accuracy of fused AOD. The STG-ERM model may serve as a valuable approach to provide data to fill gaps in satellite-retrieved AOD products.

Long-term PM2.5 pollution over China: Identification of PM2.5 pollution hotspots and source contributions.

Fine particulate matter, with an aerodynamic diameter ≤ 2.5 µm (PM2.5), is a severe problem in China. The lack of ground-based measurements and its sparse distribution obstruct long-term air pollution impact studies over China. Therefore, the present study used newly updated Global Estimates (V5. GL.02) of monthly PM2.5 data from 2001 to 2020 based on Geographically Weighted Regression (GWR) by Washington University. The GWR PM2.5 data were validated against ground-based measurements from 2014 to 2020, and the validation results demonstrated a good agreement between GWR and ground-based PM2.5 with a higher correlation (r = 0.95), lower error (8.14), and lower bias (-3.10 %). The long-term (2001-2020) PM2.5 data were used to identify pollution hotspots and sources across China using the potential source contribution function (PSCF). The results showed highly significant PM2.5 pollution hotspots in central (Henan, Hubei), North China Plain (NCP), northwest (Taklimakan), and Sichuan Basin (Chongqing, Sichuan) in China, with the most severe pollution occurring in winter compared to other seasons. During the winter, PM2.5 was in the range from 6.08 to 93.05 µg/m3 in 33 provinces, which is 1.22 to 18.61 times higher than the World Health Organization (WHO) Air Quality Guidelines (AQG-2021; annual mean: 5 µg/m3). In 26 provinces, the reported PM2.5 was 1.07 to 2.66 times higher than the Chinese Ambient Air Quality Standard (AAQS; annual mean: 35 µg/m3). Furthermore, provincial-level trend analysis shows that in most Chinese provinces, PM2.5 increased significantly (3-43 %) from 2001 to 2012, whereas it decreased by 12-94 % from 2013 to 2020 due to the implementation of air pollution control policies. Finally, the PSCF analysis demonstrates that China's air quality is mainly affected by local PM2.5 sources rather than by pollutants imported from outside China.

Comment on Abdul Jabbar et al. Air Quality, Pollution and Sustainability Trends in South Asia: A Population-Based Study. Int. J. Environ. Res. Public Health 2022, 19, 7534.

This comment discusses the use of PM2.5 (mass concentration of fine particulate matter with an aerodynamic diameter less than 2.5 microns) data in the recently published article entitled "Air Quality, Pollution and Sustainability Trends in South Asia: A Population-Based Study" by Abdul Jabbar et al. [...].

Spatiotemporal changes in aerosols over Bangladesh using 18 years of MODIS and reanalysis data.

In this study, combined Dark Target and Deep Blue (DTB) aerosol optical depth at 550 nm (AOD550 nm) data the Moderate Resolution Imaging Spectroradiometer (MODIS) flying on the Terra and Aqua satellites during the years 2003-2020 are used as a reference to assess the performance of the Copernicus Atmosphere Monitoring Services (CAMS) and the second version of Modern-Era Retrospective analysis for Research and Applications (MERRA-2) AOD over Bangladesh. The study also investigates long-term spatiotemporal variations and trends in AOD, and determines the relative contributions from different aerosol species (black carbon: BC, dust, organic carbon: OC, sea salt: SS, and sulfate) and anthropogenic emissions to the total AOD. As the evaluations suggest higher accuracy for CAMS than for MERRA-2, CAMS is used for further analysis of AOD over Bangladesh. The annual mean AOD from both CAMS and MODIS DTB is high (>0.60) over most parts of Bangladesh except for the eastern areas of Chattogram and Sylhet. Higher AOD is observed in spring and winter than in summer and autumn, which is mainly due to higher local anthropogenic emissions during the winter to spring season. Annual trends from 2003-2020 show a significant increase in AOD (by 0.006-0.014 year-1) over Bangladesh, and this increase in AOD was more evident in winter and spring than in summer and autumn. The increasing total AOD is caused by rising anthropogenic emissions and accompanied by changes in aerosol species (with increased OC, sulfate, and BC). Overall, this study improves understanding of aerosol pollution in Bangladesh and can be considered as a supportive document for Bangladesh to improve air quality by reducing anthropogenic emissions.

Investigating primary marine aerosol properties: CCN activity of sea salt and mixed inorganic-organic particles.

Sea spray particles ejected as a result of bubbles bursting from artificial seawater containing salt and organic matter in a stainless steel tank were sampled for size distribution, morphology, and cloud condensation nucleus (CCN) activity. Bubbles were generated either by aeration through a diffuser or by water jet impingement on the seawater surface. Three objectives were addressed in this study. First, CCN activities of NaCl and two types of artificial sea salt containing only inorganic components were measured to establish a baseline for further measurements of mixed organic-inorganic particles. Second, the effect of varying bubble residence time in the bulk seawater solution on particle size and CCN activity was investigated and was found to be insignificant for the organic compounds studied. Finally, CCN activities of particles produced from jet impingement were compared with those produced from diffuser aeration. Analyses indicate a considerable amount of organic enrichment in the jet-produced particles relative to the bulk seawater composition when sodium laurate, an organic surfactant, is present in the seawater. In this case, the production of a thick foam layer during impingement may explain the difference in activation and supports hypotheses that particle production from the two methods of generating bubbles is not equal.

Marine aerosol production: a review of the current knowledge.

The current knowledge in primary and secondary marine aerosol formation is reviewed. For primary marine aerosol source functions, recent source functions have demonstrated a significant flux of submicrometre particles down to radii of 20 nm. Moreover, the source functions derived from different techniques up to 10 microm have come within a factor of two of each other. For secondary marine aerosol formation, recent advances have identified iodine oxides and isoprene oxidation products, in addition to sulphuric acid, as contributing to formation and growth, although the exact roles remains to be determined. While a multistep process seems to be required, isoprene oxidation products are more likely to participate in growth and sulphuric acid is more likely to participate in nucleation. Iodine oxides are likely to participate in both nucleation and growth.

Atmospheric refraction effects on optical-infrared sensor performance in a littoral-maritime environment.

During a number of transmission experiments over littoral waters, quantitative measurements of atmospheric refraction phenomena were carried out to determine the range performance of optical-IR sensors. Examples of distortion and intensity gain generated by spatial variations of the atmospheric refractive index are shown. A high-precision ray-tracing model has been developed for better understanding of the phenomena and to satisfy the requirements for accuracy of the meteorological data used in refraction models. The output of the model includes the propagation function, the intensity gain, and details of the ray curvature and of the optical phase behavior along the path between the target and the observer. Examples of measured transmission data and their interpretation are presented.

Low-altitude infrared propagation in a coastal zone: refraction and scattering.

Midwave and long-wave infrared propagation were measured in the marine atmosphere close to the surface of the ocean. Data were collected near San Diego Bay for two weeks in November 1996 over a 15-km horizontal path. The data are interpreted in terms of effects expected from molecules, aerosol particles, and refraction. Aerosol particles are a dominant influence in this coastal zone. They induce a diurnal variation in transmission as their character changes with regular changes in wind direction. A refractive propagation factor calculation is introduced, and it is systematically applied to the model and to the data analysis. It is shown that this refractive propagation factor is a necessary component of a complete near-sea-surface infrared transmission model.