A Comparative Study of Particulate Matter Between New Delhi, India and Riyadh, Saudi Arabia During the COVID-19 Lockdown Period

Novel Coronavirus disease (COVID-19), after being identified in late December 2019 in Wuhan city of China, spread very fast and has affected all the countries in the world. The impact of lockdowns on particulate matter during the lockdown period needs attention to explore the correlation between anthropogenic and natural emissions. The current study has demonstrated the changes in fine particulate matter PM2.5, PM10 and their effect on air quality during the lockdown. The air quality before the lockdown was low in New Delhi (India) and Riyadh (Saudi Arabia), among major cities worldwide. The air quality of India is influenced by dust and sand from the desert and surrounding areas. Thus, the current study becomes important to analyse changes in the air quality of the Indian sub-continent as impacted by dust storms from long distances. The result indicated a significant reduction of PM2.5 and PM10 from 93.24 to 37.89 μg/m3 and from 176.55 to 98.87 μg/m3 during the lockdown period as compared to pre lockdown period, respectively. The study shows that average concentrations of PM10 and PM2.5 have declined by -44% and -59% during the lockdown period in Delhi. The average value of median PM10 was calculated at 33.71 μg/m3 for Riyadh, which was lower than that value for New Delhi during the same period. The values of PM10 were different for pre and during the lockdown periods in Riyadh, indicating the considerable influence on air quality, especially the concentration of PM10, from both the natural (sand and dust storms) and the anthropogenic sources during the lockdown periods. However, relatively smaller gains in the improvement of air quality in Riyadh were correlated to the imposition of milder lockdown and the predominance of natural factors over the anthropogenic factors there. The Air Quality Index (AQI) data for Delhi showed the air quality to be ‘satisfactory’ and in the green category during the lockdown period. This study attempts to better understand the impact of particulate matter on the short- and long-term air quality in Delhi during the lockdown. This study has the scope of being scaled up nationwide, and this might be helpful in formulation air pollution reduction and sustainable management policies in the future.

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.

Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas

The SEMARA approach, an integration of the Simplified and Robust Surface Reflectance Estimation (SREM) and Simplified Aerosol Retrieval Algorithm (SARA) methods, was used to retrieve aerosol optical depth (AOD) at 550 nm from a Landsat 8 Operational Land Imager (OLI) at 30 m spatial resolution, a Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 m resolution, and a Visible Infrared Imaging Radiometer Suite (VIIRS) at 750 m resolution over bright urban surfaces in Beijing. The SEMARA approach coupled (1) the SREM method that is used to estimate the surface reflectance, which does not require information about water vapor, ozone, and aerosol, and (2) the SARA algorithm, which uses the surface reflectance estimated by SREM and AOD measurements obtained from the Aerosol Robotic NETwork (AERONET) site (or other high-quality AOD) as the input to estimate AOD without prior information on the aerosol optical and microphysical properties usually obtained from a look-up table constructed from long-term AERONET data. In the present study, AOD measurements were obtained from the Beijing AERONET site. The SEMARA AOD retrievals were validated against AOD measurements obtained from two other AERONET sites located at urban locations in Beijing, i.e., Beijing_RADI and Beijing_CAMS, over bright surfaces. The accuracy and uncertainties/errors in the AOD retrievals were assessed using Pearson’s correlation coefficient (r), root mean squared error (RMSE), relative mean bias (RMB), and expected error (EE = ± 0.05 ± 20%). EE is the envelope encompassing both absolute and relative errors and contains 68% (±1σ) of the good quality retrievals based on global validation. Here, the EE of the MODIS Dark Target algorithm at 3 km resolution is used to report the good quality SEMARA AOD retrievals. The validation results show that AOD from SEMARA correlates well with AERONET AOD measurements with high correlation coefficients (r) of 0.988, 0.980, and 0.981; small RMSE of 0.08, 0.09, and 0.08; and small RMB of 4.33%, 1.28%, and -0.54%. High percentages of retrievals, i.e., 85.71%, 91.53%, and 90.16%, were within the EE for Landsat 8 OLI, MODIS, and VIIRS, respectively. The results suggest that the SEMARA approach is capable of retrieving AOD over urban areas with high accuracy and small errors using high to medium spatial resolution satellite remote sensing data. This approach can be used for aerosol monitoring over bright urban surfaces such as in Beijing, which is frequently affected by severe dust storms and haze pollution, to evaluate their effects on public health.

Detection of Martian dust storms using mask regional convolutional neural networks

Martian dust plays a crucial role in the meteorology and climate of the Martian atmosphere. It heats the atmosphere, enhances the atmospheric general circulation, and affects spacecraft instruments and operations. Compliant with that, studying dust is also essential for future human exploration. In this work, we present a method for the deep-learning-based detection of the areal extent of dust storms in Mars satellite imagery. We use a mask regional convolutional neural network, consisting of a regional-proposal network and a mask network. We apply the detection method to Mars daily global maps of the Mars global surveyor, Mars orbiter camera. We use center coordinates of dust storms from the eight-year Mars dust activity database as ground-truth to train and validate the method. The performance of the regional network is evaluated by the average precision score with $$50\%$$ 50 % overlap ($$mAP_{50}$$ m A P 50 ), which is around $$62.1\%$$ 62.1 % .

Monitoring of Land Degradation in Alluvial Plain in Iraq by Using Geomatics Techniques

Iraq faces a major environmental problem represented by severe deterioration, which threatens its food security. Many natural and human factors combine to make it, and it has dire environmental, economic, social and cultural consequences, most notably the loss of productive lands, the movement of sand dunes, severe sand and dust storms, and the resulting increase in air pollution. This study attempts to identify the development of the problem, analyze its causes and consequences, and propose a number of solutions to address it. In this article Remote Sensing techniques have been used to monitoring land degradation in ( Alluvial Plain ) of Iraq for the stage (1976 - 2021) using different sources of data such as satellite images (Landsat 1-5 MSS 1976, Landsat 5 1996 TM, Landsat8 2016 and sentinel 2 2021), also more than one software was used such as ENVI 5.3 and Erdas image 2015 to extract information from above images, Erdas imagine 2015 was use to sub set area of study, layer stack, merge resolution and classification stage, Arc GIS 10.7 use to make database and maps production), the article used supervise and unsupervised classification techniques to obtain the results, the article indicated that there is a big problem in the year 1976, this problem almost disappeared in the second station of work 1996, but it returned back after that through the results for the years 2016 and 2021. Finally, the article found a deterioration in the soil class during the stages from 2016 (988.547 Km2) to 2021(1342.398 Km2) and a decrease in the area of vegetation cover from (1931.596 Km2) in (2016) to (1632.695 Km2) in (2021).

Annual and seasonal changes of the air temperature with altitude in the Upper Dades valley, High Atlas, Morocco

The purpose of this study is to determine the size of air temperature changes with altitude in the mountains of the arid zone, on the example of the Upper Dades valley (High Atlas, Morocco). The air temperature change with altitude was determined on the basis of 5 years data from three meteorological stations. The analysis was carried out on an annual and seasonal basis. The annual and daily variations of thermal gradients between pairs of stations were also determined. It was found that the average thermal gradient in the Upper Dades valley was -1.02°C per 100 m. The highest values of the thermal gradient occur in winter and the lowest in summer. In winter, the thermal gradient was characterized by the greatest variability. Minima of the daily variation of air temperature gradients were observed in early morning hours and maxima around midday. In the lower part of the valley, air temperature inversion frequently developed between 10 AM and 3 PM UTC. The obtained results show high thermal gradients in the mountains of the arid zone, with their annual amplitude increasing in the lower parts of the valley. The instantaneous values of the gradients were significantly modified by the supply of latent heat and the occurrence of dust storms. It has been shown that the advection factor plays an important role in shaping large gradient values. The study contains novel results of thermal gradient measurements in high mountains of arid zone.

Using and Evaluating the Efficiency of GRIMs Model in Making Some Simulations of Weather Variables in Three Countries in Middle East: A Snowfall Case Study

Predicting weather by numerical models have been used extensively in research works for Middle East, mostly for dust storms, rain showers, and flash floods with a less deal of interest on snow precipitation. In this study, the Global/Regional Integrated Model System (GRIMs) that was developed in South Korea was used to predict a rare snowfall event occurred in three countries in Middle East (Syria, Jordan and Iraq) located between (25-65 oE; 12-42 oN) in year 2008. The main aim of this study was to test GRIMs efficiency, which would be used for the first time in Middle East, to make predictions of weather parameters such as pressure, temperature, and relative humidity especially in the selected area. In addition, the study would investigate the conditions that caused the snowfall event. GRIMs model was installed, compiled, and run on a Linux platform by using NCEP-NCAR reanalysis dataset as initial conditions on 0.5 × 0.5 grid resolution to make simulations for three days at intervals of three hours. The output of the model was evaluated by making comparisons with actual data obtained from the GFS Agency dataset and the model showed its efficiency. The snowfall event was synoptically discussed in details. It was found that the snowfall event was a result of fast succession systems of a strong cold high pressure and then a deep warm low pressure. The high instability in the region had led to form large cumuliform clouds with snow precipitation as a rare event in very long period.