Inverse modeling of the 2021 spring super dust storms in East Asia

This spring, super dust storms reappeared in East Asia after being absent for a (two) decade(s). The event caused enormous losses both in Mongolia and in China. Accurate simulation of such super sandstorms is valuable for the quantification of health damages, aviation risks, and profound impacts on the Earth system, but also to reveal the driving climate and the process of desertification. However, accurate simulation of dust life cycles is challenging mainly due to imperfect knowledge of emissions. In this study, the emissions that lead to the 2021 spring dust storms are estimated through assimilation of MODIS AOD and ground-based PM10 concentration data. To be able to use the AOD observations to represent the dust load, an Angstrom-based data screening is designed to select only observations that are dominated by dust. In addition, a non-dust AOD bias correction has been designed to remove the part of the AOD that could be attributed to other aerosols than dust. With this, the dust concentrations during the 2021 spring super storms could be reproduced and validated with concentration observations. The emission inversion results reveal that wind blown dust emissions originated from both China and Mongolia during spring 2021. Specifically, 18.3M and 27.2M ton of particles were released in Chinese desert and Mongolia desert respectively during these severe dust events. By source apportionment it has been estimated that 58 % of the dust deposited in the densely populated Fenwei Plain (FWP) in the northern China originate from transnational transport from Mongolia desert. For the North China Plain (NCP), local Chinese desert play a less significant roles in the dust affection; the long-distance transport from Mongolia contributes for about 69 % to the dust deposition in NCP, even if it locates more than 1000 km away from the nearest Mongolian desert.

Estimating Hourly Full-Coverage PM2.5 Concentrations Based On MODIS Data Over The Northeast of Thailand.

Particulate matter (PM2.5) pollutants are a significant health issue with impacts on human health; however, monitoring of PM2.5 is very limited in developing countries. Satellite remote sensing can expand spatial coverage, potentially enhancing our ability in a specific area for estimating PM2.5; however, some have reported poor predictive performance. An innovative combination of MODIS AOD was developed to fulfill all missing aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Therefore, hourly PM2.5 concentrations were obtained in Northeastern Thailand. A Linear mixed-effects (LME) model was used to predict location-specific hourly PM2.5 levels. Hourly PM2.5 concentrations measured at 20 PM2.5 monitoring sites and 10- fold cross-validation were addressed for model validation. The observed and predicted concentrations suggested that LME obtained from MODIS AOD data and other factors are a potentially useful predictor of hourly PM2.5 concentrations (R2 >0.70), providing more detailed spatial information for local scales studies. Interestingly, PM2.5 along the Mekong River area was observed higher than in the plain area. The finding can infer that the monsoon wind brings polluted air into the province from sources outside the region. The results will be helpful to analyze air pollution-related health studies.

An empirical relationship between PM2.5 and aerosol optical depth from MODIS satellite images for spatial simulation over Ho Chi Minh city

Air quality in megacities has been a pressing concern of environmental managers and scientists for decades. Indeed, particulate matter (PM), especially PM2.5, is considered a dangerousparticle that is harmful to human health. The current sparse monitoring network in Ho Chi Minh city (HCMC) does not accurately reflect the spatial distribution of fine particles in ambient air. Therefore, this research examines the relationship between ground-based station data and aerosol optical depth (AOD) imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra/Aqua satellite to establish a PM2.5 distribution map of HCMC. PM2.5 concentration values monitored from two ground stations were collocated by time and space with Terra/MODIS AOD data from the period of 2016-2020. Pairs of values were checked for correlation and then fit to several regression functions. The most suitable function was chosen to simulate the quantified PM2.5distributions in the study area. A high correlation between PM2.5 concentrations and AOD at the wavelength of green light (R2=0.810) was found with a linear regression model. The results showed that the highest concentration of PM2.5 was in February, and the mean value was higher than QCVN 05:2013 (32.5 μg/m3compared with 25 μg/m3, annual mean). These results support the need for essential air quality monitoring in HCMC.

Estimating PM2.5 concentrations in contiguous eastern coastal zone of China using MODIS AOD and a two-stage random forest model

The coarse moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) product (spatial resolution: 3 km) retrieved by dark-target algorithm always generates the missing values when being adopted to estimate the ground-level PM2.5 concentrations. In this study, we developed a two-stage random forest using MODIS 3 km AOD to obtain the PM2.5 concentrations with full-coverage in a contiguous coastal developed region, i.e., Yangtze River Delta-Fujian-Pearl River Delta region of China (YRD-FJ-PRD). A first-stage random forest integrated six meteorological fields was employed to predict the missing values of AOD product, and the combined AOD (i.e., random forest derived AOD and MODIS 3 km AOD) incorporated with other ancillary variables were developed for predicting PM2.5 concentrations within a second-stage random forest model. The results showed that the first-stage random forest could explain 94% of the AOD variability over YRD-FJ-PRD region, and we achieved a site-based cross validation (CV) R2 of 0.87 and a time-based CV R2 of 0.85, respectively. The full-coverage PM2.5 concentrations illustrated a spatial pattern with annual-mean PM2.5 of 46, 40 and 35 μg/m3 in YRD, PRD and FJ, respectively, sharing the same trend with previous studies. Our results indicated that the proposed two-stage random forest model could be effectively used for PM2.5 estimation in different areas.

Long-Term Variability of Dust Events in Southwestern Iran and Its Relationship with the Drought

Dust storms represent a major environmental challenge in the Middle East. The southwest part of Iran is highly affected by dust events transported from neighboring desert regions, mostly from the Iraqi plains and Saudi Arabia, as well as from local dust storms. This study analyzes the spatio-temporal distribution of dust days at five meteorological stations located in southwestern Iran covering a period of 22 years (from 1997 to 2018). Dust codes (06, 07, 30 to 35) from meteorological observations are analyzed at each station, indicating that 84% of the dust events are not of local origin. The average number of dust days maximizes in June and July (188 and 193, respectively), while the dust activity weakens after August. The dust events exhibit large inter-annual variability, with statistically significant increasing trends in all of five stations. Spatial distributions of the aerosol optical depth (AOD), dust loading, and surface dust concentrations from a moderate resolution imaging spectroradiometer (MODIS) and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) retrievals reveal high dust accumulation over southwest Iran and surrounding regions. Furthermore, the spatial distribution of the (MODIS)-AOD trend (%) over southwest Iran indicates a large spatial heterogeneity during 2000–2018 with trends ranging mostly between −9% and 9% (not statistically significant). 2009 was the most active dust year, followed by 2011 and 2008, due to prolonged drought conditions in the fertile crescent and the enhanced dust emissions in the Iraqi plains during this period. In these years, the AOD was much higher than the 19-year average (2000 to 2018), while July 2009 was the dustiest month with about 25–30 dust days in each station. The years with highest dust activity were associated with less precipitation, negative anomalies of the vegetation health index (VHI) and normalized difference vegetation index (NDVI) over the Iraqi plains and southwest Iran, and favorable meteorological dynamics triggering stronger winds.

Long-Term Changes in Aerosol Loading and Observed Impacts on Radiative Budget over the Middle-East

Atmospheric aerosols play essential roles in regional energy balance, hydrological cycle, and air quality, thus greatly influencing the global climate and public health. Rapid economic expansion, industrialization, urbanization, and energy demand have significantly enhanced anthropogenic emissions over the Middle East (ME) that received the utmost scientific attention. Therefore, we present the temporal variability of atmospheric aerosols over the ME for a period of 15 years (2005–2019). Here, the long-term measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua, Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Clouds and the Earth’s Radiant Energy System (CERES) on Aqua are analyzed in order to understand the spatio–temporal variability of aerosols and their impacts on radiation budget over the ME. On average, a significant increase in aerosol optical depth (AOD) trend is observed by ~0.01 per year over ME. The peak aerosol loading was observed in summer (March–September) followed by the winter (October–February). A similar trend was observed in the CALIOP-derived extinct aerosol coefficients over ME. In addition, MODIS retrievals are validated against the AErosol RObotic NETwork (AERONET)’s ground-based sun photometers. Overall, MODIS AOD showed good agreement against AERONET AOD, with ~70% of the retrievals falling within the expected error and high correlation coefficient (R > 0.8). Furthermore, the associated changes in clear-sky Shortwave (SW) radiative flux indicates the enhanced aerosol loading over ME further increases the surface cooling (~1.2 W m−2 per year) and atmospheric warming (~1.8 W m−2 per year). Overall, the results suggest that enhanced aerosol emissions have significantly impacted the regional energy budget over ME during 2005–2019. The assessment also demonstrates the potential of synergetic use of multi-platform measurements for climate system studies.