Characteristics of Dust Events in China from 2015 to 2020

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.

Changes of water-soluble inorganic sulfate and nitrate during severe dust storm episodes in a coastal city of North China

Dust storms are one of the largest sources of non-exhaust emissions in China, which can adversely affect air quality and human health during long-distance transportation. To study the influence of dust storms on aerosol particle composition, samples of fine aerosol (PM2.5) were collected before, during, and after the severe dust storm episodes in a coastal city of North China. Then the water-soluble inorganic ions in the filters were analyzed. The results showed that the chemical composition varied significantly in different sampling periods. Before the dust storm periods (Phase 1), the weather was characterized by high relative humidity. NO3- was the main water-soluble inorganic ion, accounting for about 1/3 of the total mass of PM2.5, which is very different from the situation a few years ago when sulfate was the dominant. The results indicated that the chemical composition of the atmosphere in China has changed significantly after the implementation of strict air pollution control measures. During the severe dust storm periods (within a few hours after the dust invasion, Phase 2), the proportion of Ca2+ in PM2.5 was high; the sulfate formation was limited due to adiabatic air mass affected by the cold front, and the sulfate content might be mainly from desert soil. However, a small amount of nitrate can be formed during their long-distance transportation. After the dust storm periods (Phase 3), dust plums and local polluted air mass mixed well. The proportion of secondary inorganic ions increased, and nitrate formation was still the main. The changes in the chemical composition from a few years ago during Phase 1 and the sharp changes in different water-soluble inorganic ions during different Phases should be carefully considered to evaluate their implications for air quality and human health.

Classification and sources of extremely severe sandstorms mixed with haze pollution in Beijing

Air quality has significantly improved in China; however, new challenges emerge when dust weather is combined with haze pollution during spring in northern China. On March 15, 2021, an extremely severe sandstorm occurred in Beijing, with hourly maximum PM₁₀ and PM_2.5 concentrations reaching 5267.7 μg m^-3 and 963.9 μg m^-3, respectively. Continuous sandstorm events usually lead to complicated pollution status in spring. Three pollution types were identified disregarding the time sequence throughout March. The secondary formation type was dominant, with high ratios of PM_2.5/PM₁₀ (mean 74%) and PM₁/PM_2.5 (mean 52%). This suggests that secondary transformations are the primary cause of heavy pollution, even during the dry seasons. Sandstorm type resulted in dramatic PM₁₀ levels, with a noticeable decrease in PM_2.5/PM₁₀ levels (27%), although PM_2.5 levels remain high. The transitional pollution type was distinguished by an independent increase in PM₁₀ levels, although PM_2.5 and PM₁ levels differed from the PM₁₀ levels. Throughout March, the sulfur oxidation rate varied considerably, with high levels during most periods (mean 0.52). A strong correlation indicated that relative humidity was the primary variable promoting the formation of secondary sulfate. Sandstorms promote heterogeneous reactions by providing abundant reaction surfaces from mineral particles, therefore aggravating secondary pollution. The sandstorm air mass from the northwest passing through the sand sources of Mongolia carried not only crustal matter but also organic components, such as bioaerosols, resulting in a sharp increase in the organic carbon in PM_2.5.

High spatiotemporal resolution estimation of AOD from Himawari-8 using an ensemble machine learning gap-filling method

The data incompleteness of aerosol optical depth (AOD) products and their lack of availability in highly urbanized areas limit their great potential of application in air quality research. In this study, we developed an ensemble machine-learning approach that integrated random forest-based Space Interpolation Model (SIM) and deep neural network-based Time Interpolation Model (TIM) to achieve high spatiotemporal resolution dataset of AOD. The spatial interpolation model first filled the spatial gaps in the Level-2 Himawari-8 hourly AOD product in 0.05° (∼5 km) spatial resolution, while the time interpolation model further improved the temporal resolution to 10 min on its basis. A full-coverage AOD dataset of Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) in 2020 was obtained as a practical implementation. The validation against in-situ AOD observations from AERONET and SONET indicated that this new dataset was satisfactory (R = 0.80), and especially in spring and summer. Overall, our ensemble machine-learning model provided an effective scheme for reconstruction of AOD with high spatiotemporal resolution of 0.05° and 10 min, which may further advance the near-real-time monitoring of air-quality in urban areas.

Impacts of dust events on chemical characterization and associated source contributions of atmospheric particulate matter in northern China

Sand and dust have significant impacts on air quality, climate, and human health. To investigate the influences of dust storms on chemical characterization and source contributions of fine particulate matter (PM_2.5) in areas with different distances from dust source regions, PM_2.5 and associated chemical composition were measured in two industrial cities with one near sand sources (i.e., Wuhai) and the other far from sand sources (i.e., Jinan) in northern China in March 2021. Results showed that PM mass concentrations significantly increased and exceeded the Chinese National Ambient Air Quality standard during the dust events, with absolute concentrations and fractional contributions of PM_2.5-bound crustal and trace elements increased while secondary inorganic ions decreased at both sites. Crustal materials dominated the increased PM_2.5 mass from non-dust period to dust period in both cities. These were further evidenced by PM_2.5 source apportionment results from positive matrix factorization model. During the dust events, dust sources contributed up to 88% of PM_2.5 mass in Wuhai and ∼38% of PM_2.5 mass in Jinan, a city about thousands of kilometers away from the sand source. Besides, the measurement data indicated that dust from northwest China may also bring along with high abundance of organic matter and vanadium. Secondary and traffic sources were two of the most important source contributors to PM_2.5 in both cities during the non-dust periods. However, the near sand source city was more susceptible to the aggravating effects of dust and minerals, with much higher contributions by crustal materials (∼47%, from the aspect of chemical components) and dust-related sources (∼26%, from the aspect of sources) to PM_2.5 mass even during non-dust periods. This study highlighted the urgent need for more action and effective control of sand sources to reduce the impact on air quality in downstream regions.

Analyses of the Dust Storm Sources, Affected Areas, and Moving Paths in Mongolia and China in Early Spring

Dust storms are common in Mongolia and northern China, this is a serious threat to the ecological security and socioeconomic development of both countries and the surrounding areas. However, a complete quantitative study of the source area, affected area, and moving path of dust storm events (DSEs) in Mongolia and China is still lacking. In this study, we monitored and analyzed the spatiotemporal characteristics of the source area and affected areas of DSEs in Mongolia and China using the high-spatiotemporal-resolution images taken by the Himawari-8 satellite from March to June 2016–2020. In addition, we calculated the moving path of dusty weather using the HYSPLIT model. The results show that (1) temporality, a total of 605 DSEs occurred in the study area, with most of them occurring in April (232 DSEs), followed by May (173 DSEs). Spatially, the dust storm sources were concentrated in the arid inland areas such as the Taklimakan Desert (TK, 138 DSEs) and Badain Jaran Desert (BJ, 87 DSEs) in the western, and the Mongolian Gobi Desert (GD, 69 DSEs) in the central parts of the study area. (2) From the affected areas of the DSEs, about 60% of the DSEs in Mongolia started locally and then affected downwind China, as approximately 55% of the DSEs in the Inner Mongolia Desert Steppe and Hunshandake Sandy Land came from Mongolia. However, the DSEs in the TK located in the Tarim Basin of northwest China affected the entire study area, with only 31.3% belonging to the local dust. (3) From the moving path of the dusty weather, the dusty weather at the three meteorological stations (Dalanzadgad, Erlian, and Beijing), all located on the main transmission path of DSEs, was mainly transported from the windward area in the northwest, accounting for about 65.5% of the total path. This study provides a reliable scientific basis for disaster prevention and control, and has practical significance for protecting and improving human settlements.