Spatial-Temporal resolution implementation of cloud-aerosols data through satellite cross-correlation

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard Terra and Aqua satellites provides measurements of several atmospheric parameters. This paper focuses on the cloud fraction data representing the number of cloudy pixels divided by the total number of pixels, and available through 1° x 1° grids spatial resolution with daily or monthly temporal resolution. The aim of the study is to propose a novel method called The Spatial-Temporal Implementation Algorithm (STIA) for analysing satellite daily 1° x 1°grid cloud fraction average values for•Comparing two datasets retrieved by MODIS aboard Aqua and Terra satellites to obtain information on the cloud formation in the afternoon and morning, respectively, thus enhancing the temporal resolution.•Comparing the actual parameter with others retrieved by instruments aboard of different satellites characterized by a better resolution. As an example of STIA application, this study uses the Aerosol Optical Depth (AOD) collected by the Ozone Monitoring Instrument (OMI) on board of Aura satellite for comparison with MODIS instrument to achieve and enhanced spatial resolution of the grid-cell.

Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30 m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of - 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.

Unmasking the sky: high-resolution PM2.5 prediction in Texas using machine learning techniques

BACKGROUND

Although PM2.5 (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies.

OBJECTIVE

This study aimed to predict PM2.5 concentrations at a fine spatial scale on a daily basis by using novel machine learning approaches and incorporating satellite-derived Aerosol Optical Depth (AOD) and a variety of weather and land use variables.

METHODS

We compiled a comprehensive dataset in Texas from 2013 to 2017, including ground-level PM2.5 concentrations from regulatory monitors; AOD values at 1-km resolution based on images retrieved from the MODIS satellite; and weather, land-use, population density, among others. We built predictive models for each year separately to estimate PM2.5 concentrations using two machine learning approaches called gradient boosted trees and random forest. We evaluated the model prediction performance using in-sample and out-of-sample validations.

RESULTS

Our predictive models demonstrate excellent in-sample model performance, as indicated by high R2 values generated from the gradient boosting models (0.94-0.97) and random forest models (0.81-0.90). However, the out-of-sample R2 values fall within a range of 0.52-0.75 for gradient boosting models and 0.44-0.69 for random forest models. Model performance varies slightly across years. A generally decreasing trend in predicted PM2.5 concentrations over time is observed in Eastern Texas.

IMPACT STATEMENT

We utilized machine learning approaches to predict PM2.5 levels in Texas. Both gradient boosting and random forest models perform well. Gradient boosting models perform slightly better than random forest models. Our models showed excellent in-sample prediction performance (R2 > 0.9).

Interactions between aerosols and surface ozone in arid and semi-arid regions of China

Atmospheric aerosols affect surface ozone concentrations by influencing radiation, but the mechanism and dominant factors are unclear. Therefore, this paper analyses the changes in aerosol-radiative-surface ozone in China's arid and semi-arid regions with the help of the Atmospheric Radiative Transfer (SBDART) model. The results suggest that Aerosol Optical Depth (AOD) and coarse Particulate Matter (PM10) have the same trend, with high values in spring and winter and low values in summer and autumn. Surface ozone is high in spring and summer and low in autumn and winter. Surface ozone is higher in spring and summer and lower in autumn and winter. In winter, mainly secondary pollutants are dominated by high pollution levels. In the rest of the seasons, a mixture of dust, motor vehicle exhaust, and soot is dominated by low pollution levels. Surface ozone is positively correlated with fine particles and negatively correlated with coarse particles. Temperature is positively correlated with surface ozone in all seasons and negatively correlated with PM10 in summer, autumn, and winter. Precipitation negatively correlates with PM10 each season and surface ozone in winter and spring. Analysis of surface ozone and PM10 sources in the more polluted city of Hohhot based on the back-line trajectory model showed that airflow trajectories mainly transported surface ozone and PM10 pollution from northwestern Inner Mongolia and western Mongolia. During dusty solid weather, the decrease in radiation reaching the Earth's surface and the cooling effect of aerosols lead to lower temperatures, which slows down the rate of chemical reactions of precursors of surface ozone, resulting in lower ozone concentrations at the surface. This study can provide a theoretical reference for aerosol and surface ozone control in arid and semi-arid areas of China.

Retrieval of aerosol optical properties from GOCI-II observations: Continuation of long-term geostationary aerosol monitoring over East Asia

Since the Geostationary Ocean Color Imager (GOCI) was successfully launched in 2010, the GOCI Yonsei aerosol retrieval (YAER) algorithm has been continuously updated to retrieve hourly aerosol optical properties. GOCI-II has 4 more channels including UV, finer spatial resolution (250 m), and daily full disk coverage as compared to GOCI, and was launched in February 2020, onboard the GEO-KOMPSAT-2B (GK-2B) satellite. In this study, we extended the YAER algorithm to GOCI-II data based on its improved performance in many aspects and present the first results of aerosol optical properties retrieved from GOCI-II data. Utilizing the overlapping period between the GOCI-II and GOCI in geostationary Earth orbit, we present GOCI-II aerosol retrievals for high aerosol-loading cases over East Asia and show that these have a consistent spatial distribution with those from GOCI. Furthermore, GOCI-II provides AOD at an even higher spatial resolution, revealing finer changes in aerosol concentrations. Validation results for one year data show that the GOCI-II AOD has a correlation coefficient of 0.83 and a ratio within the expected error (EE) of 59.4 % when compared with the aerosol robotic network (AERONET) data. We compared statistical metrics for the GOCI and GOCI-II AODs to assess the consistency between the two datasets. In addition, it was found that there is a strong correlation between the two datasets from the comparison of gridded GOCI and GOCI-II AOD products. It is expected that data from GOCI-II will continue long-term aerosol records with high accuracy that can be used to address air-quality issues over East Asia.

Bacteria Associated with Acute Oak Decline: Where Did They Come From? We Know Where They Go

Acute oak decline is a high-impact disease causing necrotic lesions on the trunk, crown thinning and the eventual death of oak. Four bacterial species are associated with the lesions-Brenneria goodwinii, Gibbsiella quercinecans, Rahnella victoriana and Lonsdalea Britannica-although an epi-/endophytic lifestyle has also been suggested for these bacteria. However, little is known about their environmental reservoirs or their pathway to endophytic colonisation. This work aimed to investigate the ability of the four AOD-associated bacterial species to survive for prolonged periods within rhizosphere soil, leaves and acorns in vitro, and to design an appropriate method for their recovery. This method was trialled on field samples related to healthy and symptomatic oaks. The in vitro study showed that the majority of these species could survive for at least six weeks within each sample type. Results from the field samples demonstrated that R. victoriana and G. quercinecans appear environmentally widespread, indicating multiple routes of endophytic colonisation might be plausible. B. goodwinii and L. britannica were only identified from acorns from healthy and symptomatic trees, indicating they may be inherited members of the endophytic seed microbiome and, despite their ability to survive outside of the host, their environmental occurrence is limited. Future research should focus on preventative measures targeting the abiotic factors of AOD, how endophytic bacteria shift to a pathogenic cycle and the identification of resilient seed stock that is less susceptible to AOD.

Changing patterns in the highly contributing aerosol types/species across the globe in the past two decades

With the rapidly changing aerosol emissions due to the increase in urbanization, energy consumption, population density, and industrialization in the past two decades across the globe, there is an evolution of different chemical properties of aerosols that are yet not quantified properly. Therefore, a rigorous attempt is made in this study to obtain the long-term changing patterns in the contribution of different aerosol types/species, to the total aerosol loading. This study is carried out only over those regions exhibiting either increasing or decreasing trends in the aerosol optical depth (AOD) parameter on a global scale. Applying the multivariate linear regression trend analysis on Modern-Era Retrospective Analysis for Research and Application version 2 (MERRA-2) aerosol species dataset obtained between 2001 and 2020, we found that despite the overall statistically significant decrease in total columnar AOD trend values over North-Eastern America, and Eastern and Central China regions, an increase in the dust and organic carbon aerosols is observed, respectively. As the uneven vertical distribution of aerosols can alter the direct radiative effects, the extinction profiles of different aerosol types obtained using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) dataset between 2006 and 2020, are further partitioned, for the first time, based on their presence in different altitudes (i.e., within the atmospheric boundary layer and free-troposphere) as well as measurement timing (i.e., daytime and night-time) regimes. The detailed analysis showed that there exists an overall higher contribution of aerosols persisting in the free troposphere region which in turn can have a long-term effect on climate due to their higher residence time, particularly absorbing aerosols. As the trends are mostly associated with the changes in energy use, regional regulatory policies, and/or changing background meteorology conditions, therefore this study also elaborates on the effectiveness of these factors with the changes obtained in different aerosol species/types over the region.

Analysis of aerosol optical depth and relation to covariates during pre-monsoon season (2002-2019) over Pakistan using ARIMAX model and cross-wavelet analysis

The pre-monsoon season heavily influences the precipitation amount in Pakistan. When hydrometeorological parameters interact with aerosols from multiple sources, a radiative climatic response is observed. In this study, aerosol optical depth (AOD) space-time dynamics were analyzed in relation to meteorological factors and surface parameters during the pre-monsoon season in the years 2002-2019 over Pakistan. Level-3 (L3) monthly datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectroradiometer (MISR) were used. Tropical Rainfall Measuring Mission (TRMM) derived monthly precipitation, Atmospheric Infrared Sounder (AIRS) derived air temperature, after moist relative humidity (RH) from Modern-Era Retrospective analysis for Research and Applications, Version-2 (MERRA-2), near-surface wind speed, and soil moisture data derived from Global Land Data Assimilation System (GLDAS) were also used on a monthly time scale. For AOD trend analysis, Mann-Kendall (MK) trend test was applied. Moreover, Autoregressive Integrated Moving Average with Explanatory variable (ARIMAX) technique was applied to observe the actual and predicted AOD trend, as well as test the multicollinearity of AOD with covariates. The periodicities of AOD were analyzed using continuous wavelet transformation (CWT) and the cross relationships of AOD with prevailing covariates on a time-frequency scale were analyzed by wavelet coherence analysis. A high variation of aerosols was observed in the spatiotemporal domain. The MK test showed a decreasing trend in AOD which was most significant in Baluchistan and Punjab, and the overall trend differs between MODIS and MISR datasets. ARIMAX model shows the correlation of AOD with varying meteorological and soil parameters. Wavelet analysis provides the abundance of periodicities in the 2-8 months periodic cycles. The coherency nature of the AOD time series along with other covariates manifests leading and lagging effects in the periodicities. Through this, a notable difference was concluded in space-time patterns between MODIS and MISR datasets. These findings may prove useful for short-term and long-term studies including oscillating features of AOD and covariates.

Investigating changes in atmospheric aerosols properties over the Indo-Gangetic Plain during different phases of COVID-19-induced lockdowns

Impact of COrona VIrus Diseases 2019 (COVID-19) restrictive measures on aerosol optical depth (AOD) and black carbon (BC) concentration is investigated for the western, central, and eastern Indo-Gangetic Plain (IGP) using satellite-based observations. Due to COVID-19-induced lockdown measures, a noticeable decline in AOD and BC concentrations was observed across the IGP when compared to pre-lockdown period of 2020 and the lockdown concurrent period of 2015-2019. During the total lockdown period, a maximum drop in AOD and BC was observed in the central IGP (26.5 % and 10.1 %), followed by western IGP (24.9% and 5.2%) and eastern IGP (23.2 % and 4.9 %) with respect to the same period of 2015-2019. We have removed seasonal influences on aerosol properties during the COVID-19 lockdown, by taking average seasonal variations during the period of 2015-2019 as reference and projecting the hypothetical AOD and BC for the lockdown period under normal scenario. The difference between the hypothetical AOD and BC (under normal scenario) and the retrieved AOD and BC for the lockdown period is the absolute percentage change in AOD and BC concentration due to the lockdown alone. This elimination of seasonal influence is a novel approach. Central IGP showed an absolute decrease in AOD and BC of 38.5% and 18.2% during the lockdown period followed by western IGP (34.6% and 7.7%) and eastern IGP (25.9% and 11.5%). The observed absolute reduction in AOD, 26-39 %, is significantly higher than the global average reduction in AOD of 2-5%. CALIPSO-derived aerosol sub-types over major location of the western, central, and eastern IGP suggests prevalence of anthropogenic activities during pre- and post-lockdown periods. During the lockdown, IGP was influenced by aerosols from natural sources, with mineral dust and polluted dust in the western and central IGP, and aerosols from marine regions in the eastern IGP. Replenishment of aerosols within the boundary layer were far quicker when compared to total column during post-lockdown. Overall, the study reveals a reduction in anthropogenic emissions during the COVID-19-induced lockdowns, leading to temporary improvements in air quality over the IGP. Our study presents a comprehensive analysis of COVID-19 lockdown impact on aerosols properties over the IGP and highlights unprecedented reductions in AOD (~ 40 %) and BC (~ 20 %), due to imposition of lockdown and subsequent cessation of aerosol sources, by removing seasonal influences.

Long-term spatiotemporal variation in atmospheric aerosol properties over Türkiye based on MERRA-2 reanalysis data: aerosol classification based on city type

Due to their complex aerosol characteristics, it is crucial to analyze the trends and properties of atmospheric aerosols over the eastern Mediterranean countries. This study comprehensively evaluates Aerosol Optical Depth (AOD) and Angström Exponent (AE) trends and aerosol classification over Türkiye, using the MERRA-2 reanalysis data from 1980 to 2019. The spatial distributions of AOD and AE were determined across various temporal scales, including multiannual, 5-year intervals, seasonal, and monthly periods. The analysis of the spatial distribution of AOD values revealed that the mean values in the northwestern areas, ranging from 0.20 to 0.25, were comparatively higher than those observed in the eastern regions, which ranged from 0.10 to 0.15. Between 1980 and 1994, the AOD values gradually increased, followed by a subsequent decline from 1995 to 2019. Based on 5-year intervals between 1980 and 2019, the coastal regions exhibited higher AOD values than the inland areas. Specifically, higher AOD values were noted between May and August, whereas lower values were observed during autumn and winter. Additionally, higher AE values were detected over the northwestern region, while the southeastern region had the lowest AE values, particularly during spring, attributed to the frequent occurrence of dust transport events in this area. The AOD and AE values were also examined in different city types, using the population thresholds of the European Commission. The global city category consisting only of Istanbul showed the highest AOD values across all seasons, while the category of very small cities, which includes 12 cities, had the lowest AOD values. Furthermore, this study investigated the contributions of dominant aerosol categories across various city types based on multiannual and seasonal variations of AOD and AE. The results showed that mixed and continental aerosols had higher portions across all city types. However, biomass burning/industrial and mixed aerosol categories were more prominent in global and large cities. Overall, this study provides a comprehensive overview of the atmospheric aerosol properties in Türkiye and can serve as a useful guide for researchers intending to conduct future studies utilizing AOD and AE data obtained through MERRA-2 aerosol diagnosis.

Evaluation of MERRA-2 data for aerosols patterns over the Kingdom of Saudi Arabia

Aerosol is one of the major climate-forcing parameters which affect the Kingdom of Saudi Arabia in particular. The most relevant consideration that characterizes the aerosol properties and distribution is the Aerosol Optical Depth (AOD). In this study Modern Era Retrospective Analysis for Research and Applications (MERRA-2) AOD product from the year 1980-2021 is used to investigate aerosols pattern over the Kingdom of Saudi Arabia. The validation of the MERRA-2 AOD product is made by using AOD data retrieved from Aerosol Robotic Network (AERONET) stations located at Solar Village (SV) and at King Abdullah University of Science and Technology (KAUST). Various statistical analyses are performed to test the reliability of MERRA-2 data in the study region. The results of the statistical analysis indicate that MERRA-2 is highly correlated with both AERONET stations data. Thus, annual and seasonal aerosol climatology maps based on 41 years of MERRA-2 data are prepared and analyzed over the study region. The annual and seasonal aerosol climatology analysis of MERRA-2 data shows high density of AOD at southern and eastern regions while the low density emerges over the western and northern regions of the country during the study period. The results of the study are very encouraging, which increases our confidence level to use historical MERRA-2 AOD product to improve the knowledge on aerosols distribution over the region in future.

Retrieval uncertainty and consistency of Suomi-NPP VIIRS deep blue and dark target aerosol products under diverse aerosol loading scenarios over South Asia

Retrieval accuracy and stability of two operational aerosol retrieval algorithms, Deep Blue (DB) and Dark Target (DT), applied on Visible Infrared Imaging Radiometer Suite (VIIRS) on-board Suomi National Polar-orbiting Partnership (S-NPP) satellite were evaluated over South Asia. The region is reported to be highly challenging to accurate estimation of satellite-based aerosol optical properties due to variations in surface reflectance, complex aerosol system and regional meteorology. Performance of both algorithms were initially evaluated by comparing their ability to retrieve aerosol signal over the complex geographical region under specific air pollution emission scenario. Thereafter, retrieval accuracy was investigated against 10 AERONET sites across South Asia, selected based on their geography and predominance aerosol types, from year 2012-2021. Geo-spatial analysis indicates DB to efficiently retrieve fine aerosol features over bright arid surfaces, and for smoke/dust dominating events whereas DT was better to identify small fire events under dark vegetated surface. Both algorithms however, indicate unsatisfactory retrieval accuracy against AERONET having 56-59% of valid retrievals with high RMSE (0.30-0.33) and bias. Overall, DB slightly underpredicted AOD with -0.02 mean bias (MB) whereas DT overpredicted AOD (MB: 0.13), with seasonality in their retrieval efficiency against AERONET. Time-series analysis indicates stability in retrieving AOD and match-up number for both algorithms. Retrieval bias of DB and DT AOD against AERONET AOD under diverse aerosol loading, aerosol size, scattering/absorbing aerosol, and surface vegetation coverage scenarios revealed DT to be more influenced by these conditions. Error analysis indicates at low AOD (≤0.2), accuracy of both DB and DT were subject to underlying vegetation coverage. At AOD>0.2, DB performed well in retrieving coarse aerosols whereas DT was superior when fine aerosols dominated. Overall, accuracy of both VIIRS algorithms require further refinement to continue MODIS AOD legacy over South Asia.

72-hour real-time forecasting of ambient PM2.5 by hybrid graph deep neural network with aggregated neighborhood spatiotemporal information

The observation-based air pollution forecasting method has high computational efficiency over traditional numerical models, but a poor ability in long-term (after 6 h) forecasting due to a lack of detailed representation of atmospheric processes associated with the pollution transport. To address such limitation, here we propose a novel real-time air pollution forecasting model that applies a hybrid graph deep neural network (GNN_LSTM) to dynamically capture the spatiotemporal correlations among neighborhood monitoring sites to better represent the physical mechanism of pollutant transport across the space with the graph structure which is established with features (angle, wind speed, and wind direction) of neighborhood sites to quantify their interactions. Such design substantially improves the model performance in 72-hour PM_2.5 forecasting over the whole Beijing-Tianjin-Hebei region (overall R² increases from 0.6 to 0.79), particularly for polluted episodes (PM_2.5 concentration > 55 µg/m³) with pronounced regional transport to be captured by GNN_LSTM model. The inclusion of the AOD feature further enhances the model performance in predicting PM_2.5 over the sites where the AOD can inform additional aloft PM_2.5 pollution features related to regional transport. The importance of neighborhood site (particularly for those in the upwind flow pathway of the target area) features for long-term PM_2.5 forecast is demonstrated by the increased performance in predicting PM_2.5 in the target city (Beijing) with the inclusion of additional 128 neighborhood sites. Moreover, the newly developed GNN_LSTM model also implies the "source"-receptor relationship, as impacts from distanced sites associated with regional transport grow along with the forecasting time (from 0% to 38% in 72 h) following the wind flow. Such results suggest the great potential of GNN_LSTM in long-term air quality forecasting and air pollution prevention.

A study on optical properties, classification, and transport of aerosols during the smog period over South Asia using remote sensing

Over the past few years, South Asian region has experienced frequent and thick smog events because of rapid population growth and enhanced anthropogenic activities, particularly in the Indo-Gangetic Plain (IGP). Therefore, the present study investigates aerosol properties such as aerosol optical depth (AOD) (500 nm), Angstrom exponent (AE) (440-870 nm), single scattering albedo (SSA), fine-mode fraction (FMF), absorption aerosol optical depth (AAOD), and absorption aerosol exponent (AAE) over selected AERONET sites namely Bhola (2012-2021), Dhaka (2012-2021), Jaipur (2011-2021), Kanpur (2011-2021), Karachi (2011-2021), Lahore (2011-2021), and Pokhara (2011-2021) in the IGP during the smog period (October, November, and December). Additionally, different aerosol types were categorized using AERONET direct sun (AOD, AE) and inversion products (VSD, SSA, RI, FMF, and ASY). The monthly mean AOD, AE, and FMF varied from ⁓0.33 to 1.07, ⁓0.3 to 1.4, and 0.6-0.9 µm over all selected AERONET sites during the smog period. Moreover, the outcomes revealed the dominance of biomass-burning and urban/ industrial aerosols over Lahore, Karachi, Dhaka, and Bhola during the smog period. Contrary to this, dust and mixed aerosols were abundant over Jaipur and Karachi, respectively. Furthermore, HYSPLIT cluster analysis is used to trace the transmission paths and potential sources of aerosols over selected sites.

Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak

Atmospheric pollutant data retrieved through satellite sensors are continually used to assess changes in air quality in the lower atmosphere. During the COVID-19 pandemic, several studies started to use satellite measurements to evaluate changes in air quality in many different regions worldwide. However, although satellite data is continuously validated, it is known that its accuracy may vary between monitored areas, requiring regionalized quality assessments. Thus, this study aimed to evaluate whether satellites could measure changes in the air quality of the state of São Paulo, Brazil, during the COVID-19 outbreak; and to verify the relationship between satellite-based data [Tropospheric NO₂ column density and Aerosol Optical Depth (AOD)] and ground-based concentrations [NO₂ and particulate material (PM; coarse: PM₁₀ and fine: PM_2.5)]. For this purpose, tropospheric NO₂ obtained from the TROPOMI sensor and AOD retrieved from MODIS sensor data by using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm were compared with concentrations obtained from 50 automatic ground monitoring stations. The results showed low correlations between PM and AOD. For PM₁₀, most stations showed correlations lower than 0.2, which were not significant. The results for PM_2.5 were similar, but some stations showed good correlations for specific periods (before or during the COVID-19 outbreak). Satellite-based Tropospheric NO₂ proved to be a good predictor for NO₂ concentrations at ground level. Considering all stations with NO₂ measurements, correlations >0.6 were observed, reaching 0.8 for specific stations and periods. In general, it was observed that regions with a more industrialized profile had the best correlations, in contrast with rural areas. In addition, it was observed about 57% reductions in tropospheric NO₂ throughout the state of São Paulo during the COVID-19 outbreak. Variations in air pollutants were linked to the region economic vocation, since there were reductions in industrialized areas (at least 50% of the industrialized areas showed >20% decrease in NO₂) and increases in areas with farming and livestock characteristics (about 70% of those areas showed increase in NO₂). Our results demonstrate that Tropospheric NO₂ column densities can serve as good predictors of NO₂ concentrations at ground level. For MAIAC-AOD, a weak relationship was observed, requiring the evaluation of other possible predictors to describe the relationship with PM. Thus, it is concluded that regionalized assessment of satellite data accuracy is essential for assertive estimates on a regional/local level. Good quality information retrieved at specific polluted areas does not assure a worldwide use of remote sensor data.

Description of Dryocola gen. nov. and two novel species, Dryocola boscaweniae sp. nov. and Dryocola clanedunensis sp. nov. isolated from the rhizosphere of native British oaks

While investigating the role of the rhizosphere in the development of Acute Oak Decline, bacterial strains belonging to the family Enterobacteriaceae were isolated from rhizosphere soil following enrichment for the Enterobacterales. Partial sequencing of several housekeeping genes showed that these strains could not be assigned to an existing genus. Overall, 16 strains were investigated using a polyphasic approach to determine their taxonomic status. This involved phenotypic testing and fatty acid analysis paired with phylogenetic analyses of 16S rRNA and housekeeping gene sequences, as well as phylogenomic analysis of whole genome sequences. Phylogenomic and phylogenetic analyses consistently demonstrated that the 16 isolates could be separated into two distinct clusters in a monophyletic clade situated between the genera Cedecea and Buttiauxella. The two clusters could be genotypically and phenotypically differentiated from each other and from their closest neighbours. As such we propose the description of Dryocola boscaweniae gen. nov. sp. nov. (type strain H6W4^T = CCUG 76177^T = LMG 32610^T) and Dryocola clanedunesis sp. nov. (type strain H11S18^T = CCUG 76181^T = LMG 32611^T).

Climatology and landscape determinants of AOD, SO2 and NO2 over Indo-Gangetic Plain

Indo-Gangetic Plains (IGP) experiences high loading of particulate and gaseous pollutants all year around and is considered to be the most polluted regions of India. Understanding the effect of landscape determinants on air pollution in IGP regions is crucial to make its environment sustainable. We examined satellite retrievals of OMI NO₂ and SO₂, and MODIS AOD to analyse the long-term trend, spatio-seasonal pattern and dynamics of aerosols, NO₂ and SO₂ over three IGP regions, namely Upper Indo-Gangetic plain (UIGP), Middle Indo-Gangetic plain (MIGP) and Lower Indo-Gangetic plain (LIGP) over the period 2005-2019. IGP experienced an overall increment in AOD (R² = 0.63) and SO₂ (R² = 0.67) values, with LIGP (AOD, R² = 0.8 & SO₂, R² = 0.8) experiencing the largest rate of enhancement. The levels of NO₂ (R² = 0.2) experienced a decrement after 2012 (owing to implementation of vehicle emission policy) except in MIGP, with UIGP (R² = 0.23) exhibiting the largest rate of decrement. Seasonal heterogeneity in the nature of sources was observed over IGP regions. AOD (0.61 ± 0.1) and NO₂ value (3.82 ± 0.98 × 10¹⁵ molecules/cm²) were found highest during post-monsoon in UIGP owing to crop residue burning activity. The value of NO₂ (3.8 ± 1.4 × 10¹⁵ molecules/cm²) in MIGP was found highest during pre-monsoon due to high consumption of coal in power plants for summer cooling demand. The highest SO₂ level (0.09 ± 0.06 DU) was observed during post-monsoon in UIGP, as a large number of brick kilns are fired during this period. Correlations among landscape determinants and pollutants revealed that topography is the dominant variable that affect the spatial pattern of AOD compared to vegetation and land use. Lower elevation tends to have high AOD values compared to higher elevation. Vegetation-AOD relationship showed an inverse association in IGP regions and is influenced by factors such as seasonal meteorology and size of the airborne particles. Vegetation possesses positive relationship with SO₂ and NO₂, implying no pollution abatement effect on SO₂ and NO₂ pollutants. Built-up change has deteriorating effect as well as quenching effect on pollutants. Increase in built terrain have deteriorated the air quality in UIGP whereas it favored in suppressing the aerosol level in LIGP.

Accounting for the aerosol type and additional satellite-borne aerosol products improves the prediction of PM2.5 concentrations

Fine airborne particles (diameter <2.5 μm; PM_2.5) are recognized as a major threat to human health due to their physicochemical properties: composition, size, shape, etc. However, normally only size-fraction-specific particle concentrations are monitored. Interestingly, although the aerosol type is reported as part of the aerosol optical depth retrieval from satellite observations, it has not been utilized, to date, as an auxiliary information/co-variate for PM_2.5 prediction. We developed Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models that account for this information when predicting surface PM_2.5. The models take as input only widely available data: satellite aerosol products with full cover and surface meteorological data. Distinct models were developed for AOD of specific aerosol types. Both the RF and XGBoost models performed well, showing moderate-to-high cross-validated adjusted R² (RF: 0.753-0.909; XGBoost: 0.741-0.903), depending on the aerosol type and other covariates. The weighted performance of the specific aerosol-type models was higher than of the RF and XGBoost baseline models, where all the AOD retrievals were used together (the common practice). Our approach can provide improved risk estimates due to exposure to PM_2.5, better resolved radiative forcing calculations, and tailored abatement surveillance of specific pollutants/sources.

An assessment of aerosol optical depth over three AERONET sites in South Africa during the year 2020

It is important to notice that the world health organization (WHO) on  the 11th of March 2020,  declared COVID-19 a global pandemic and in response governments around the world introduced lockdowns that restricted human and traffic movements including South Africa. This pandemic resulted in a total lockdown from 26 March until 16 April 2020 in South Africa with expected  decrease in atmospheric aerosols. In this present study,  the aerosol optical depth (AOD) over Southern Africa based on ground-based remotely sensed data derived from three AERONET sites (Durban, Skukuza and Upington) during 2020 were used to detrermine the restriction resopnse on atmospheric aerosol pollution  The study used data from 2019, 2018 and 2017  as base years. The AERONET derived data was complemented with the HYSPLIT Model and NCEP/NCAR Reanalysis data. The study findings show that peak increase of AOD corresponds to Angstrom exponent (AE) enhancement for two sites Durban and Skukuza during winter (JJA) while the Upington site showed a different trend where peak AOD were observed in spring (SON). The study also observed the influence of long transport airmasses particularly those originating from the Atlantic and Indian ocean moreso for the Durban and Skukuza sites (summer and autumn) thus these sites received fresh marine aerosols however this was not the case for Upington which fell under the influence of short-range inland airmasses and was likely to receive anthropogenic and dust aerosols. The major results  suggest that the lockdowns did not translate into a significant decrease in AOD levels compared to previous immediate years. The results has presented restriction response of AOD over South Africa but additional analysis is required using more locations to compare results.

Evaluation of different machine learning approaches and aerosol optical depth in PM2.5 prediction

Atmospheric Aerosol Optical Depth (AOD), derived from polar-orbiting satellites, has shown potential in PM_2.5 predictions. However, this important source of data suffers from low temporal resolution. Recently, geostationary satellites provide AOD data in high temporal and spatial resolution. However, the feasibility of these data in PM_2.5 prediction needs further study. In this paper, we analyzed the impact of AOD derived from Himawari-8 in PM_2.5 predictions. Moreover, by combining wavelet, machine learning techniques, and minimum redundancy maximum relevance (mRMR), a novel hybrid model was proposed. The results showed that AOD missing rate over Yangtze River Delta region is the highest in Nanjing, Hefei, and Maanshan. In addition, missing rates are the lowest in winter and summer (∼80%). Moreover, we found that considering AOD, as an auxiliary variable in the model, could not improve the accuracy of PM_2.5 predictions, and in some cases decreased it slightly. In comparison with other models, our proposed hybrid model showed higher prediction accuracy, R² is improved by 11.64% on average, and root mean square error, mean absolute error, and mean absolute percentage error is reduced by 26.82%, 27.24%, and 29.88% respectively. This research provides a general overview of the availability of Himawari-8 AOD data and its feasibility in PM_2.5 predictions. In addition, it evaluates different machine learning approaches in PM_2.5 predictions. Our proposed framework can be used in other regions to predict different air pollutants concentrations and can be used as an aid for air pollution controlling programs.

Evolution of Pollution Levels from COVID-19 Lockdown to Post-Lockdown over India

The spread of the COVID-19 pandemic forced the administration to lock down in many countries globally to stop the spread. As the lockdown phase had only the emergency use of transportation and most of the industries were shut down, there was an apparent reduction in pollution. With the end of the lockdown period, pollution is returning to its regular emission in most places. Though the background was abnormally low in emissions (during the lockdown phase) and the reduced pollution changed the radiation balance in the northern hemispheric summer period, a modified pollution pattern is possible during the unlock phases of 2020. The present study analysed the unlock 1 and 2 stages (June-July) of the COVID-19 lockdown over India. The rainfall, surface temperature and cloud cover anomalies of 2020 for understanding the differences in pollutants variation were also analysed. The unlock phases show remarkable differences in trends and mean variations of pollutants over the Indian region compared to climatological variations. The results indicated changing high-emission regions over India to climatological variations and identified an AOD dipole with future emissions over India.

Survival rates in atlanto-occipital dissociation: a look at the past 20 years

BACKGROUND CONTEXT

Atlanto-occipital dissociation (AOD) has historically been considered a fatal injury. Recent small case series, however, have suggested that AOD injuries have become increasingly survivable. There has not been an adequately powered study that confirms this.

PURPOSE

The aim of this study is to assess whether the survival rate for patients with AOD increased over time.

STUDY DESIGN/SETTING

Retrospective case series.

PATIENT SAMPLE

Patients with traumatic AOD identified from our Level 1 Trauma Center database.

OUTCOME MEASURES

Mortality following traumatic AOD.

METHODS

Patients with traumatic AOD from 1996 to 2019 were retrospectively identified from our Level 1 Trauma Center database using International Classification of Diseases 9 and 10 codes. Patients were stratified into two cohorts- those diagnosed before August 1, 2015 and after.

RESULTS

A total of 52 patients met our inclusion criteria and were analyzed. Mean age was 34.41 (11.71), with 34 (65.4) females, and 26 (50) Hispanics. Mean BMI was 28.13 (7.30), mean injury severity score was 40.79 (21.72), and mean Glasgow coma scale was 5.91 (4.72). Overall, 33 patients died (63.5%). The mortality rate before 2015 was 81.80%, this number dropped down to 50% for those who were treated post 2015 (p=.01).

CONCLUSIONS

This study demonstrates that patients treated recently for AOD at a level 1 trauma center were more likely to survive than patients treated in the past at the same center. Possible reasons for the improved survival rate seen in this study include: increased awareness of AOD, improved diagnostic protocols with more uniform computed tomography based imaging, and advances in the care of these patients.

Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 μm (PM₁₀) and <2.5 μm (PM_2.5) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM₁₀ and PM_2.5 were R² = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM₁₀ and PM_2.5 were R² = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.

Long-term climatology and spatial trends of absorption, scattering, and total aerosol optical depths over East Africa during 2001-2019

The unprecedented increase in anthropogenic activities, coupled with the prevailing climatic conditions, has increased the aerosol load over East Africa (EA). Given this, the present study examined the trends in total, absorption, scattering, and total aerosol extinction optical depth (TAOD, AAOD, SAOD, and TAEOD) over EA, alongside trends in single scattering albedo (SSA). For this purpose, the AOD of different optical properties retrieved from multiple sensors and the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) model between January 2001 to December 2019 were utilized to estimate trends and assess their statistical significance. The spatial patterns of seasonal mean AOD from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor and MERRA-2 model were generally characterized with high (>0.35) and low (<0.2) AOD centers over EA observed during the local dry and wet seasons, respectively. Also, the spatial trend analysis revealed a general increase in TAOD, being positive and significant over the arid and semi-arid zones of the northeastern part of EA, which is majorly dominated by locally derived dust. The local dry (wet) months generally experienced positive (negative) trends in TAOD, associated with seasonal cycles of rainfall. High and significant positive trends in AAOD were dominated over the study domain, attributed to an increased amount of biomass burning, variations in soil moisture, and changes in the rainfall pattern. The trends in TAEOD showed a distinct pattern, except over some months that depicted significant increasing trends attributed to changes in climatic conditions and anthropogenic activities. At last, the study domain exhibited decreasing trends in SSA, signifying strong absorption of direct solar radiation resulting in a warming effect. The study revealed patterns of trends in aerosol optical properties and forms the basis for further research in aerosols over EA.

Interaction of aerosol with meteorological parameters and its effect on the cash crop in the Vidarbha region of Maharashtra, India

Regional weather variability depends on various meteorological variables such as temperature and rainfall. The current research focuses on the variability and trends in annual aerosol optical depth (AOD), temperature (T), and rainfall (RF) in 11 Vidarbha districts. The annual trend analysis of AOD, T, and R is determined using the non-parametric Sen slope and Mann-Kendall (MK) test at a 5% significant level from 1980 to 2019. Annual T and AOD indicate a substantial increase in this study, whereas rainfall shows a non-significant trend (MK, test) over the study period. According to Sen's slope trends, the relatively high rainfall area (Chandrapur = 1.273 and Garchiroli = 4.06) got positive trends, but Gondia and Bhandara districts have negative (Sen's slope =  - 2.79 and - 2.56) trends. The moderate rainfall areas are showing a less negative Sen slope (Wardha =  - 0.21, Washim =  - 1.13 and Yavatmal =  - 2.75), whereas Nagpur districts' Sen's slope shows a positive value (Sens's slope = 0.72). The assured rainfall area districts show Sen's slope trends are positive (Akola = 0.45, Amravati = 1.17 and Buldana = 0.42). Sen's slope trend indicates rising rainfall, whereas negative trends indicate decreasing rainfall in the time series. This study has also looked at the effect of RF, AOD, and T on the last two decades' cash crop production (2000-2019) for Vidarbha districts. The relationship between rainfall departure (DRF) and cash crop yield has also been highlighted. Five cash crops, such as cotton (Ct), total cereals (TCrl), total oilseeds (TOsd), total pulses (TPS), and sugarcane (Sc), are selected for the present study.

The impact of meteorological parameters on PM10 and visibility during the Middle Eastern dust storms

Air pollution is one of the most pressing issues in populated Middle Eastern cities, in particular for the city of Ahvaz, Iran, imposing deleterious effects on the environment, public health, economy, culture, and other sectors. In this study, we investigate the relationship between meteorological parameters, PM₁₀, AOD, air mass source origin, and visibility during severe desert dust storms (Average_3h PM₁₀ > 3200 µg m^-3) between 2009 and 2012. Six of seven such events occurred between February and March. Interestingly, for the seven cases there was always an alarming PM₁₀ mass concentration peak (137-553 µg m^-3) between 12:00-18:00 (local time) that was 18-24 h before the dominant peak of the storm (3279-4899 µg m^-3). The maximum wind speed over the multi-day periods examined for the dust storms is usually observed 6 h before the alarming PM₁₀ peak. The minimum relative humidity, dew point temperature and air pressure occurred ± 3 h around the time of the alarming PM₁₀ peak. Wind speed was the meteorological parameter that was consistently higher around the time of the first peak as compared to the second peak, with the reverse being true for sea level pressure. Based on four years of daily data in Ahvaz, PM₁₀ was positively correlated with wind speed and air temperature and inversely correlated with sea level pressure and RH. An empirically-derived equation with R² = 0.95 is reported to estimate the maximum PM₁₀ concentration for severe desert dust events in the study region based on meteorological parameters. Finally, AOD is shown to correlate strongly (R² = 0.86) with PM₁₀ during periods with severe desert dust storms in the region.

Exploring the relationships between ground-measured particulate matter and satellite-retrieved aerosol parameters in China

In this study, the PM_2.5 and PM₁₀ concentrations from 367 cities in China were integrated with MODIS-retrieved aerosol optical depth (AOD) and Angstrom exponent (AE) data to explore the relationship between ground-measured surface particle concentrations and remote-sensing aerosol parameters. The impact of meteorological and topographical factors and seasonality were also taken into consideration and the partial least squares (PLS) regression model was adopted to evaluate the effects of surface particulate matter (PM) concentration and meteorological factors on the variation of aerosol parameters. PM concentrations and aerosol parameters all presented strong spatial disparity and seasonal patterns in China. After implementation of stringent clean air actions and policies, both the ground-measured and satellite-retrieved aerosol parameters revealed that the concentrations of suspended particles in China's cities declined dramatically from 2015 to 2018. The PM/AOD ratio showed conspicuous south-north and west-east differences. The ratio was strongly correlated to meteorological and topographic factors, and it tended to be higher in arid and less polluted regions. Moreover, the dominant factors affecting seasonal PM/AOD ratios varied among China's five regions. The correlations of daily PM-AOD were always strong in southwest China and in basin terrain (e.g., Sichuan Basin and Tarim Basin). In contrast, the PM-AOD correlation was found to be negative in some cities on the Tibetan Plateau because local relative humidity makes a greater contribution to AOD variation. Since the climate is arid and the ratio of coarse particles (e.g., PM₁₀) is much higher, PM tended to have a significantly negative correlation with AE in northwestern cities. Whereas in many southern cities, PM was positively correlated with AE because of the area's high relative humidity and aerosol hygroscopic properties.

Assessment of the association between dust storms and COVID-19 infection rate in southwest Iran

This study assesses a plausible correlation between a dust intrusion episode and a daily increase in COVID-19 cases. A surge in COVID-19 cases was observed a few days after a Middle East Dust (MED) event that peaked on 25^th April 2020 in southwest Iran. To investigate potential causal factors for the spike in number of cases, cross-correlations between daily combined aerosol optical depths (AODs) and confirmed cases were computed for Khuzestan, Iran. Additionally, atmospheric stability data time series were assessed by covering before, during, and after dust intrusion, producing four statistically clustered distinct city groups. Groups 1 and 2 had different peak lag times of 10 and 4-5 days, respectively. Since there were statistically significant associations between AOD levels and confirmed cases in both groups, dust incursion may have increased population susceptibility to COVID-19 disease. Group 3 was utilized as a control group with neither a significant level of dust incursion during the episodic period nor any significant associations. Group 4 cities, which experienced high dust incursion levels, showed no significant correlation with confirmed case count increases. Random Forest Analysis assessed the influence of wind speed and AOD, showing relative importance of 0.31 and 0.23 on the daily increase percent of confirmed cases, respectively. This study may serve as a reference for better understanding and predicting factors affecting COVID-19 transmission and diffusion routes, focusing on the role of MED intrusions.

Effects of the COVID-19 lockdown and recovery on People's mobility and air quality in the United Arab Emirates using satellite and ground observations

The stringent COVID-19 lockdown measures in 2020 significantly impacted people's mobility and air quality worldwide. This study presents an assessment of the impacts of the lockdown and the subsequent reopening on air quality and people's mobility in the United Arab Emirates (UAE). Google's community mobility reports and UAE's government lockdown measures were used to assess the changes in the mobility patterns. Time-series and statistical analyses of various air pollutants levels (NO₂, O₃, SO₂, PM₁₀, and aerosol optical depth-AOD) obtained from satellite images and ground monitoring stations were used to assess air quality. The levels of pollutants during the initial lockdown (March to June 2020) and the subsequent gradual reopening in 2020 and 2021 were compared with their average levels during 2015-2019. During the lockdown, people's mobility in the workplace, parks, shops and pharmacies, transit stations, and retail and recreation sectors decreased by about 34%-79%. However, the mobility in the residential sector increased by up to 29%. The satellite-based data indicated significant reductions in NO₂ (up to 22%), SO₂ (up to 17%), and AOD (up to 40%) with small changes in O₃ (up to 5%) during the lockdown. Similarly, data from the ground monitoring stations showed significant reductions in NO₂ (49% - 57%) and PM₁₀ (19% - 64%); however, the SO₂ and O₃ levels showed inconsistent trends. The ground and satellite-based air quality levels were positively correlated for NO₂, PM₁₀, and AOD. The data also demonstrated significant correlations between the mobility and NO₂ and AOD levels during the lockdown and recovery periods. The study documents the impacts of the lockdown on people's mobility and air quality and provides useful data and analyses for researchers, planners, and policymakers relevant to managing risk, mobility, and air quality.

Visibility, aerosol optical depth, and low-visibility events in Bangkok during the dry season and associated local weather and synoptic patterns

Visibility and aerosol optical depth (AOD) characterization, and their relationship with PM₁₀ and local and synoptic meteorology, were studied for January-March in 2014 and 2015 over Bangkok. Visibility degradation intensifies in the dry season as compared to the wet season due to increase in PM₁₀ and unfavorable meteorological conditions. The average visibility is lower in January and February as compared to the other months. Relatively higher AOD in March despite lower PM₁₀ is attributed to the synergetic effect of moderate relative humidity, secondary aerosols, elevated aerosol layer due to summertime convection, and biomass burning. Larger variability in visibility and PM₁₀ in winter months is due to more synoptic weather fluctuations while AOD shows similar variability for all months attributed partly to fires. Higher PM₁₀ and moderate-to-high relative humidity cause lower visibility in the morning while it improves in afternoon as PM₁₀ and relative humidity decrease. AOD is higher in the afternoon as compared to that in the morning and evening as it is less sensitive to diurnal change in aerosols and meteorology at the surface level. Visibility and AOD relationships with PM₁₀ are dependent on relative humidity. Weaker winds lead to lower visibility, higher PM₁₀, and higher AOD irrespective of wind direction. Stronger winds improve visibility and decrease PM₁₀ for all directions while AOD is higher for all directions except eastern and northeastern. The back-trajectory results show that the transport of pollutant and moist air is coupled with the synoptic weather and influence visibility and AOD. Two low-visibility events were investigated. The first event is potentially caused by the combined effect of local emissions and their accumulation due to stagnant weather conditions, secondary aerosols, and forest fires in the nearby regions. The second event can be attributed to the local emission and fires in the nearby area with hygroscopic growth of aerosols due to moist air from the Gulf of Thailand. Based on these findings, some policy implications have also been given.

Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China

PM_2.5 (fine particulate matter with aerodynamics diameter <2.5 μm) is the most important component of air pollutants, and has a significant impact on the atmospheric environment and human health. Using satellite remote sensing aerosol optical depth (AOD) to explore the hourly ground PM_2.5 distribution is very helpful for PM_2.5 pollution control. In this study, Himawari-8 AOD, meteorological factors, geographic information, and a new deep forest model were used to construct an AOD-PM_2.5 estimation model in China. Hourly cross-validation results indicated that estimated PM_2.5 values were consistent with the site observation values, with an R² range of 0.82-0.91 and root mean square error (RMSE) of 8.79-14.72 μg/m³, among which the model performance reached the optimum value between 13:00 and 15:00 Beijing time (R² > 0.9). Analysis of the correlation coefficient between important features and PM_2.5 showed that the model performance was related to AOD and affected by meteorological factors, particularly the boundary layer height. Deep forest can detect diurnal variations in pollutant concentrations, which were higher in the morning, peaked at 10:00-11:00, and then began to decline. High-resolution PM_2.5 concentrations derived from the deep forest model revealed that some cities in China are seriously polluted, such as Xi 'an, Wuhan, and Chengdu. Our model can also capture the direction of PM_2.5, which conforms to the wind field. The results indicated that due to the combined effect of wind and mountains, some areas in China experience PM_2.5 pollution accumulation during spring and winter. We need to be vigilant because these areas with high PM_2.5 concentrations typically occur near cities.

Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM2.5 levels during the Camp Fire episode in California

Wildland fire smoke contains large amounts of PM2.5 that can traverse tens to hundreds of kilometers, resulting in significant deterioration of air quality and excess mortality and morbidity in downwind regions. Estimating PM2.5 levels while considering the impact of wildfire smoke has been challenging due to the lack of ground monitoring coverage near the smoke plumes. We aim to estimate total PM2.5 concentration during the Camp Fire episode, the deadliest wildland fire in California history. Our random forest (RF) model combines calibrated low-cost sensor data (PurpleAir) with regulatory monitor measurements (Air Quality System, AQS) to bolster ground observations, Geostationary Operational Environmental Satellite-16 (GOES-16)'s high temporal resolution to achieve hourly predictions, and oversampling techniques (Synthetic Minority Oversampling Technique, SMOTE) to reduce model underestimation at high PM2.5 levels. In addition, meteorological fields at 3 km resolution from the High-Resolution Rapid Refresh model and land use variables were also included in the model. Our AQS-only model achieved an out of bag (OOB) R2 (RMSE) of 0.84 (12.00 μg/m3) and spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 μg/m3) and 0.73 (16.58 μg/m3), respectively. Our AQS + Weighted PurpleAir Model achieved OOB R2 (RMSE) of 0.86 (9.52 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.75 (14.93 μg/m3) and 0.79 (11.89 μg/m3), respectively. Our AQS + Weighted PurpleAir + SMOTE Model achieved OOB R2 (RMSE) of 0.92 (10.44 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.84 (12.36 μg/m3) and 0.85 (14.88 μg/m3), respectively. Hourly predictions from our model may aid in epidemiological investigations of intense and acute exposure to PM2.5 during the Camp Fire episode.

Comparative observation of aerosol vertical profiles in urban and suburban areas: Impacts of local and regional transport

Ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments were used to carry out observation of aerosol in the urban and suburban areas of Shanghai from October 17 to November 21, 2019. Fudan University (FDU) site is a typical urban environment, surrounded by residential areas, commercial areas and arterial roads, while Dianshan Lake (DSL) site is a suburban environment with high vegetation coverage and no pollutant emission sources. The aerosol retrieved by MAX-DOAS was in good correlation with the observation of sun photometer and the PM_2.5 concentration of the corresponding site, which demonstrates that the aerosol retrieved by MAX-DOAS is reliable and feasible. Comparing the mean aerosol extinction coefficient (AEC) profiles during the observation period between urban and suburban areas, it was found that the occurrence of high aerosol concentration at FDU was nearly 3 h later than that of DSL at suburban site. And the aerosol at DSL was concentrated at an altitude of 0.3- 0.5 km, with a mean peak value of 0.486 km^-1, which was slightly higher than the peak AEC of 0.453 km^-1 at FDU of 0.2- 0.4 km. The difference in aerosol characteristics between the two sites may be due to the fact that the influences of aerosol transport and boundary layer dynamics are different between the two sites. The backward trajectories analysis also presents that there were mutual transports of aerosol between urban and suburban areas, which affect the optical properties of the aerosol in these two sites. In a case of aerosol pollution, we visualized the transport pathway of aerosol from the western part of the North China Plain to Shanghai using AEC profiles and backward trajectories, providing the evidence that the local aerosol pollution in Shanghai was affected by long-distance transport.

Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms

Aerosol optical depth (AOD) has been widely used to estimate the near-surface PM_2.5 (fine particulate matter with particle size less than 2.5 μm). However, the total-column AOD obtained by passive remote sensing instruments can neither distinguish the contribution of AOD in various altitude layers nor obtain the vertical PM_2.5 concentration. In this study, we compared several AOD-PM_2.5 models including Extra Trees (ET), Random Forest (RF), Deep Neural Network (DNN), and Gradient Boosting Regression Tree (GBRT), and analyzed the corresponding results using AOD of different altitudes and auxiliary data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The results indicate that the ET model performs best in terms of the model effectiveness and feature interpretation on the training dataset. We conclude that the feature importance of the bottom layer AOD is higher than that of the upper and total column AOD. The results showed that regional differences existed in the optimal height of the AOD-PM_2.5 correlation in study area. The results of cross-validation indicate that ET manages the most appealing overall performance with an R² (RMSE) of 0.85 (17.77 μg/m³). Regarding the 729 sites involved in this study, 73% had R² > 0.7, and the region or season with higher AOD feature importance achieves better model performance. The results of the AOD-PM_2.5 model in each layer were corrected using the AOD weight, to obtained the PM_2.5 vertical concentrations from 2015 to 2019. The results highlight that the high PM_2.5 concentration area is primarily near the ground and decreases with height. Additionally, the PM_2.5 vertical concentration in Beijing-Tianjin-Hebei (-1.80 μg/m³, P < 0.001), Central China (-1.62 μg/m³, P < 0.001), and Pearl River Delta (-0.66 μg/m³, P < 0.001) show an apparent downward trend. We believe that the vertical distribution analysis of PM_2.5 can provide meaningful information for studying air pollution.

Synergistic data fusion of multimodal AOD and air quality data for near real-time full coverage air pollution assessment

Data gaps in satellite aerosol optical depth (AOD) retrievals pose a huge challenge in near real-time air quality assessment. Here, we present a multimodal aerosol data fusion approach to integrate multisource AOD and air quality data for the generation of full coverage AOD maps at hourly resolution. Specifically, data gaps in each Himawari-8 AOD snapshot were partially filled by merging all available daytime AOD snapshots, and these partially gap-filled AOD maps were then fused with coarse yet spatially complete numerical AOD simulations to generate full coverage AOD imageries. Ground-based air quality measurements, including concentrations of PM_2.5, PM₁₀, NO₂, and SO_2, were simultaneously assimilated into gridded AOD fields to enhance the overall data accuracy. A practical implementation of the proposed method was illustrated by generating hourly full-coverage AOD maps in China from 2015 to 2020, and the validation results indicate this new AOD dataset agreed well with ground-based AOD measurements (R = 0.83), from which a ubiquitous AOD decreasing trend was revealed, especially during the noontime. Moreover, the hourly resolution and full-coverage advantages of this AOD dataset allow us to better assess spatiotemporal variations of PM₁₀ and PM_2.5 pollution that occurred in China. Overall, the proposed method paves a new way as big data analytics to advance regional air pollution assessment given the full coverage capacity and enhanced accuracy of the resulting AOD and PM concentration data.

Brenneria goodwinii growth in vitro is improved by competitive interactions with other bacterial species associated with Acute Oak Decline

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Mutually competitive interactions prevail in pairwise cultures of AOD bacteria.

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In vitro growth of brenneria goodwinii is improved by bacterial competition.

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Co-culturing of AOD bacteria indicates evolving improved fitness of B. goodwinii.

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B. goodwinii and R. victoriana can collaborate in vitro to outcompete G. quercinecans.

Brenneria goodwinii, Rahnella victoriana and Gibbsiella quercinecans are three bacterial species frequently isolated together from oak displaying symptoms of Acute Oak Decline (AOD), which include weeping patches on trunks. All three bacterial species play a role in lesion formation in the current episode of AOD in Britain, although B. goodwinii is the most dominant. The ongoing research into stem lesion formation characteristic of this polybacterial syndrome has been focussed primarily on the pathogenicity, identification and taxonomy of these bacteria. As all three species were newly classified within the past ten years, there are many unanswered questions regarding their ecology and interactions with each other. To determine the effect of bacterial interactions on fitness in vitro, we examined pairwise (diculture) and multispecies (triculture) interactions between B. goodwinii, R. victoriana and G. quercinecans in oak leaf media microcosms. Additionally, the effect of co-culturing on the evolution of these species was determined and the evolved B. goodwinii strains were examined further by whole genome sequencing. Our results indicate that B. goodwinii thrived in monoculture with significantly higher viable cell counts than the other two species. Additionally, B. goodwinii performed well in pairwise culture with mutually competitive interactions observed between B. goodwinii and R. victoriana, and between B. goodwinii and G. quercinecans. In the multispecies triculture, B. goodwinii and R. victoriana appeared to exhibit co-ordinated behaviour to outcompete G. quercinecans. After four weeks B. goodwinii grown in co-culture with the other two species developed greater evolved fitness than the strain grown in monoculture as reflected by the increased viable cell counts. The competitive interactions taking place between the threes species indicated evolving improved fitness of B. goodwinii in vitro, that gave it a growth advantage over both R. victoriana and G. quercinecans which showed no significant changes in fitness. Overall, B. goodwinii gains greater benefit in terms of fitness from in vitro competitive interaction with the other two species.

Is the atmospheric boundary layer altitude or the strong thermal inversions that control the vertical extent of aerosols?

It is well known that the atmospheric boundary layer (ABL) plays a significant role in controlling the variability of atmospheric constituents such as aerosols and trace-gases. Hence, significant diurnal and seasonal variation in these will be observed as the ABL altitude does. However, on several occasions, high aerosol concentration in the lidar measurements is observed even above the ABL altitude. This raised a question that up to what extent ABL altitude acts as a capping layer for these pollutants? From the detailed analysis carried out using long-term (2010-2018) lidar observations and simultaneous radiosonde profiles obtained from Gadanki, India, we show that 'there exist thermal inversions (TI), which are stronger than the ABL inversions, that fully control the vertical extent'. The detailed characteristics of TI (inversion strength (IS) and inversion depth (ID)) are also obtained. The results revealed that aerosol concentrations below the TI altitude increases with IS (ID) up to 3-4 K (300-400 m) during winter whereas in pre-monsoon it increases up to 2-3 K (100-200 m). Thus, IS of up to 2-4 K is required to fully trap the aerosol concentrations and this TI coincide with the ABL inversions for 51.7% only, particularly during the winter and pre-monsoon seasons. This analysis is further extended to different geographical locations of India using the aerosol profiles obtained from CALIPSO and a network of 23 radiosonde stations. The observed results provided further evidence that the vertical distribution of aerosols is restricted to the maximum extent by the TI but not the ABL altitude. These observations lead us to propose a hypothesis that 'trapping of aerosols fully occurs up to particular IS and ID only and the ABL altitude is not the deciding factor most of the time for capping the aerosol vertical distribution'. These findings will greatly help in modeling the diffusion and transport of air pollutants in the lower troposphere.

High-Speed Optical Traps Address Dynamics of Processive and Non-Processive Molecular Motors

Interactions between biological molecules occur on very different time scales, from the minutes of strong protein-protein bonds, down to below the millisecond duration of rapid biomolecular interactions. Conformational changes occurring on sub-ms time scales and their mechanical force dependence underlie the functioning of enzymes (e.g., motor proteins) that are fundamental for life. However, such rapid interactions are beyond the temporal resolution of most single-molecule methods. We developed ultrafast force-clamp spectroscopy (UFFCS), a single-molecule technique based on laser tweezers that allows us to investigate early and very fast dynamics of a variety of enzymes and their regulation by mechanical load. The technique was developed to investigate the rapid interactions between skeletal muscle myosin and actin, and then applied to the study of different biological systems, from cardiac myosin to processive myosin V, microtubule-binding proteins, transcription factors, and mechanotransducer proteins. Here, we describe two different implementations of UFFCS instrumentation and protocols using either acousto- or electro-optic laser beam deflectors, and their application to the study of processive and non-processive motor proteins.

Effects of COVID-19 pandemic lockdown: A satellite data-based appraisal of air quality in Guwahati, Assam

Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) based data are used to evaluate the effects of the COVID-19 lockdown on the concentrations of pollutants such as aerosol optical depth (AOD) and tropospheric columns of nitrogen dioxide (NO₂) along with sulfur dioxide (SO₂) respectively for the period of January 2017 to September 2021 over the capital city of Assam, Guwahati. In India lockdown due to COVID-19 was first imposed from 24th March to 14th April as phase I and then it extended from 15th April to 3rd May as phase II in the year 2020. The concentration of all pollutants was usually fall during the lockdown period as compared to their average during the 5-year period over the study area. The results showed that Pre-monsoon (March-May) seasonal AOD for the pandemic year 2020 was decreased by ∼ 23% after lockdown as compared to same season of normal years over the study location. The seasonally averaged AOD reached its peak value in pre-monsoon (0.78 ± 0.09), followed by winter (0.59 ± 0.10) and monsoon (0.52 ± 0.05), with the minimum taking place in post-monsoon (0.38 ± 0.08) season. The monthly average AOD varies from its highest value (0.82 ± 0.18) in May to its lowest value (0.36 ± 0.10) in October for the study period over Guwahati. Tropospheric column NO₂ exhibits same seasonality as AOD with highest value (0.21 × 10¹⁶ molecules cm^-2) in pre-monsoon and lowest value (0.13 × 10¹⁶ molecules cm^-2) in post-monsoon season which may be due to same source of origination of both NO₂ and AOD. Conversely, SO₂ does not vary much from the five-year average value during the lockdown period. Significant reduction in PM_2.5 mass concentration value during Covid-19 lockdown months has been observed which indicates short term improvement of air quality over Guwahati.

Changes in ambient particulate matter during the COVID-19 and associations with biomass burning and Sahara dust in northern Colombia

The restriction of mobility due to preventive social isolation has improved air quality in many regions of the world. At the same time, global and regional atmospheric phenomena such as biomass burning or dust transport from Sahara can exacerbate particulate matter (PM) mass. In this study, PM₁₀ and PM_2.5 concentrations were evaluated in industrial and urban areas during the lockdown period due to COVID-19 in northern Colombia. Aerosol Optical Depth (AOD) observations obtained from the spaceborne MODIS (MOD04-3k) and the active fire data was obtained from VIIRS Active Fire. We measured surface contamination at several stations to quantify the PM₁₀ and PM_2.5 changes associated with the general closure of anthropogenic and industrial activities driven by COVID-19 and by the macroscale and/or mesoscale contributions. In the industrial zone, a slight decrease in daily concentrations was detected at the stations located near the mining operations. In the urban area, the decrease is more salient in COVID-19 lockdown. A reduction rate in the daily averages of PM10 of 23.3%, 6.0%, and 19.0% was observed in the SCa, SBi, and SUn stations, respectively. The biomass burning episode has contributed 52% to the daily average of PM₁₀ and 45% to the daily average of PM_2.5. The episode due to the passage of Saharan dust through the Caribbean Sea has contributed 79% to the daily average of PM₁₀ (150.75 μg/m³) and on 57% to the daily average of PM_2.5.

PM2.5 and PM10 during COVID-19 lockdown in Kuwait: Mixed effect of dust and meteorological covariates

This study investigated the impact of COVID-19 lockdown on particulate matter concentrations, specifically PM2.5 and PM10, in Kuwait. We studied the variations in PM2.5 and PM10 between the lockdown in 2020 with the corresponding periods of the years 2017-2019, and also investigated the differences in PM variations between the 'curfew' and 'non curfew' hours. We applied mixed-effect regression to investigate the factors that dictate PM variability (i.e., dust and meteorological covariates), and also processed satellite-based aerosol optical depths (AOD) to determine the spatial variability in aerosol loads. The results showed low PM2.5 concentration during the lockdown (33 μg/m3) compared to the corresponding previous three years (2017-2019); however, the PM10 concentration (122.5 μg/m3) increased relative to 2017 (116.6 μg/m3), and 2019 (92.8 μg/m3). After removing the 'dust effects', both PM2.5 and PM10 levels dropped by 18% and 31%, respectively. The mixed-effect regression model showed that high temperature and high wind speed were the main contributors to high PM2.5 and PM10, respectively, in addition to the dust haze and blowing dust. This study highlights that the reductions of anthropogenic source emissions are overwhelmed by dust events and adverse meteorology in arid regions, and that the lockdown did not reduce the high concentrations of PM in Kuwait.

Fine resolution air quality dynamics related to socioeconomic and land use factors in the most polluted desert metropolitan in the American Southwest

Air pollution kills approximately 4.2 million people every year. As air pollution varies significantly in different urban areas, the assessment of urban emissions is key to taking appropriate actions and formulating policies for sustainable built environments and to promote the wellbeing of people. The overarching goal of this study was to generate fine resolution aerosol optical depth (AOD) using Landsat imagery and examine both the socioeconomic inequalities of air pollution exposure and the air quality variation related to different land-use categories. This study has focused on a period of unusual population growth, 2000-2010, in the Phoenix Metropolitan Area. It was found that socioeconomic factors, vegetation indexes, and land use land cover types are all strong predictors of AOD, and the negative coefficients of socioeconomic values reveal insight into the social inequality of air pollution exposure. Results suggest that the government regulation on air pollution during the study period helped to improve air quality. Meanwhile, planting vegetation to mitigate air pollution should be carefully examined in order to find the right vegetation species and spatial configuration of vegetation cover in urban settings.

Improvement of atmospheric pollution in the capital cities of US during COVID-19

The spread of COVID-19 during 2020 impacted the whole world and still affecting the lives of people living in some parts of the world. The spread of this epidemic started in the US in late March 2020 and became a major issue in April due to an outburst of COVID-19 cases. Most of the countries in the world imposed complete to partial lockdown, but in the US, few states imposed lockdowns. Even after the advisory of the various Government department, the mobility data suggest that there was an enhancement (10–15%) in mobility during March 2020. Later sudden drop in mobility was observed during April 2020. The fall in aerosols optical depth (AOD), particulate matter concentration, NO_2, and Ozone are observed along with the positive shifts in the SO₂. In some of the states, AOD shows pronounced decline during May and June (5–40.90%), in the month of  May more than 80% decline was observed compared to the month of June 2020. In the month of April 2020, up to 73.64% decline was observed in NO_2, and 70–99% in the months of May and June 2020. We found a good relationship between the mobility data and improvement in the air quality of the US. The changes were not significant compared to other countries in the world due to scattered lockdown policy, but in the US a pronounced change is observed during April month compared to March and May.

Analysis of some factors related to dust storms occurrence in the Sistan region

Dust storms over the Sistan region in East Iran are associated with predominant northwest winds (called 120-day winds) which promote desertification, including drying of the Hamoun wetlands. These storms are more frequent in spring and summer seasons in the Sistan region. The study aims to examine the relationship between vegetation cover and wind speed with dust storms intensity in order to understand the behavior of dust sources using satellite remote sensing data (AOD) between 2000 and 2019. Based on the time series, the study period can be divided into three parts based on the following characteristics: high dust intensity (2004), moderate relative intensity of value in all parameters studied (2005 to 2014), and dust reduction (2015-2019). Time series analysis shows a negative relationship between AOD and wind speed owing presumably to vegetative cover changes during years that wind speed has increased. Based on multiple regression analysis by monthly time scales that conforms time series result, monthly NDVI is significantly related to AOD. Analysis of the 3 hourly wind data suggests a positive relationship between wind and dust, and effective thresholds for dust erosion based on wind speeds are proposed for the Sistan region.

Improvement in air quality and its impact on land surface temperature in major urban areas across India during the first lockdown of the pandemic

The SARS CoV-2 (COVID-19) pandemic and the enforced lockdown have reduced the use of surface and air transportation. This study investigates the impact of the lockdown restrictions in India on atmospheric composition, using Sentinel-5Ps retrievals of tropospheric NO₂ concentration and ground-station measurements of NO₂ and PM_2.5 between March-May in 2019 and 2020. Detailed analysis of the changes to atmospheric composition are carried out over six major urban areas (i.e. Delhi, Mumbai, Kolkata, Chennai, Bangalore, and Hyderabad) by comparing Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) and land surface temperature (LST) measurements in the lockdown year 2020 and pre-lockdown (2015-2019). Satellite-based data showed that NO₂ concentration reduced by 18% (Kolkata), 29% (Hyderabad), 32-34% (Chennai, Mumbai, and Bangalore), and 43% (Delhi). Surface-based concentrations of NO₂, PM_2.5, and AOD also substantially dropped by 32-74%, 10-42%, and 8-34%, respectively over these major cities during the lockdown period and co-located with the intensity of anthropogenic activity. Only a smaller fraction of the reduction of pollutants was associated with meteorological variability. A substantial negative anomaly was found for LST both in the day (-0.16 °C to -1 °C) and night (-0.63 °C to -2.1 °C) across select all cities, which was also consistent with air temperature measurements. The decreases in LST could be associated with a reduction in pollutants, greenhouse gases and water vapor content. Improvement in air quality with lower urban temperatures due to lockdown may be a temporary effect, but it provides a crucial connection among human activities, air pollution, aerosols, radiative flux, and temperature. The lockdown for a shorter-period showed a significant improvement in environmental quality and provides a strong evidence base for larger scale policy implementation to improve air quality.

Influence of Super Cyclone "Amphan" in the Indian Subcontinent amid COVID-19 Pandemic

Tropical cyclone "Amphan" developed as a super cyclone on 19 May 2020 and caused severe impact on the landmass with very high torrential precipitation (>250 mm day^-1), and extremely high wind speed (>150 km h^-1) after landfall on 20 May 2020. The tropical cyclone Amphan largely affected agricultural land (78.2%) and forest, including mangroves (10.8%) in eastern India and Bangladesh. The built-up area over the trajectory of the cyclone and its proximity, including eastern parts of the Kolkata metropolitan area, was considerably affected by the cyclone due to the high population density and poor structural and community planning. Although the regions with close proximities to cyclones' trajectory (2033 km² area under <2 km proximity) were affected severely, the presence of mangrove forest in Sundarban substantially reduced the magnitude of the tropical cyclone. A considerable decrease (~30%) in aerosol optical depth (AOD) in April-May 2020 as compared to that in 2019 is considered one of the major causes of the development of the warm pool and cyclogenesis in the Bay of Bengal. The number of COVID-19 cases increased by ~70% in the post-cyclonic period (29 May 2020) compared to that in the pre-cyclonic period (19 May 2020) illustrating the impact of the cyclonic hazard.

Aerosols optical depth and Ångström exponent over different regions in Garhwal Himalaya, India

Aerosol optical depth (AOD) and Ångström exponent (AE) are observed to be important parameters in understanding the status of ambient aerosol concentration over a particular location and depend not only upon the local but also on the large-scale dynamics of the atmosphere. The present article analyses the AOD and AE parameters retrieved with Moderate Resolution Imaging Spectrometer (MODIS) and Multi-angle Imaging Spectro-Radiometer (MISR) instruments onboard satellites, for the upper (Chamoli) and foothill (Dehradun) regions of Garhwal Himalaya in Uttarakhand, India, from 2006 to 2015. Aerosol properties are investigated at monthly, seasonal, and annual scales. The monthly mean values of MODIS-derived AOD and AE were observed to be 0.18 (± 0.14) and 1.05 (± 0.43) respectively over the Dehradun region. The seasonal maximums in AOD with MODIS and MISR were observed as 0.23 ± 0.06 and 0.29 ± 0.07 respectively in the pre-monsoon season, and the minimum values (0.099 ± 0.02) were observed in the post-monsoon season, over the Dehradun region. In contrast, in the Chamoli region, the maximum AOD (MODIS) was 0.21 ± 0.06 observed in the monsoon season and the minimum was 0.036 ± 0.007 in the post-monsoon season. Over a decade, the AE for Chamoli and Dehradun was found to vary from 0.07 to 0.17 and from 0.14 to 0.20 respectively. The median AE for Chamoli and Dehradun was found to be 1.49 and 1.47 respectively, marking the dominance of fine mode particles of anthropogenic origin. Observations show the presence of dust and polluted dust resulting from the long-range transport from the west. The comparison of AOD values from the two sensors shows a significant correlation (0.73) with slightly higher values from MISR over the year. The results obtained are important in understanding the climatic implications due to the atmospheric aerosols over the abovementioned Himalayan region of Uttarakhand, India.

Impact of COVID-19 lockdown upon the air quality and surface urban heat island intensity over the United Arab Emirates

The 2019 pandemic of Severe Acute Respiratory Syndrome-Corona Virus Diseases (COVID-19) has posed a substantial threat to public health and major global economic losses. The Northern Emirates of the United Arab Emirates (NEUAE) had imposed intense preventive lockdown measures. On the first of April 2020, a lockdown was implemented. It was assumed, due to lower emissions, that the air quality and Surface Urban Heat Island Intensity (SUHII) had been strengthened significantly. In this research, three parameters for Nitrogen Dioxide (NO2), Aerosol Optical Depth (AOD), and SUHII variables were examined through the NEUAE. we evaluated the percentage of the change in these parameters as revealed by satellite data for 2 cycles in 2019 (March 1st to June 30th) and 2020 (March 1st to June 30th). The core results showed that during lockdown periods, the average of NO2, AOD, and SUHII levels declined by 23.7%, 3.7%, and 19.2%, respectively, compared to the same period in 2019. Validation for results demonstrates a high agreement between the predicted and measured values. The agreement was as high as R2=0.7, R2=0.6, and R2=0.68 for NO2, AOD, and night LST, respectively, indicating significant positive linear correlations. The current study concludes that due to declining automobile and industrial emissions in the NEUAE, the lockdown initiatives substantially lowered NO2, AOD, and SUHII. In addition, the aerosols did not alter significantly since they are often linked to the natural occurrence of dust storms throughout this time of the year. The pandemic is likely to influence several policy decisions to introduce strategies to control air pollution and SUHII. Lockdown experiences may theoretically play a key role in the future as a possible solution for air pollution and SUHII abatement.

Impact of lockdown on particulate matter concentrations in Colombia during the COVID-19 pandemic

The first confirmed case of COVID-19 in Colombia was reported on March 6, 2020. For this reason, on March 25, preventive isolation was declared mandatory. These measures involved the suspension of economic activities and drastically reduced the number of vehicles on the road. The objective of this study is to evaluate the impact of the lockdown due to the COVID-19 pandemic on PM_2.5 concentrations at 5 monitoring stations and aerosol optical depth values of the Terra/MODIS satellite. We analyzed and compared the weekly and monthly concentrations of PM_2.5 before and during the lockdown between the week of January 6 to June 22, 2020, and compared the daily values obtained from the Terra/MODIS satellite for the months of January-June during the years 2018-2020 to elucidate the effects of the lockdown. Similar to other monitored sites in the world, we observed substantial reductions in weekly PM_2.5 concentrations, from 41 to 84% (Bogotá), from 13 to 66% (Funza), from 17 to 57% (Boyacá), from 35 to 86% (Valledupar) and 31 at 60% (Risaralda). Unlike other studies, the aerosol optical depth values increased up to 59% during the months of lockdown compared to previous years and up to 70% of the weekly mean when compared to before the lockdown. These spatiotemporal behaviors of PM_2.5 and the aerosol optical depth in Colombia are influenced by reductions in vehicular flow during quarantine, regional rainfall, and height of the planetary boundary layer. Emissions from economic activities affect pollutant levels in the area. The analysis of the levels of pollutants during the lockdown provides a baseline for regulatory agencies to establish mitigation plans.

Quantifying COVID-19 enforced global changes in atmospheric pollutants using cloud computing based remote sensing

Global lockdowns in response to the COVID-19 pandemic have led to changes in the anthropogenic activities resulting in perceivable air quality improvements. Although several recent studies have analyzed these changes over different regions of the globe, these analyses have been constrained due to the usage of station based data which is mostly limited up to the metropolitan cities. Also the quantifiable changes have been reported only for the developed and developing regions leaving the poor economies (e.g. Africa) due to the shortage of in-situ data. Using a comprehensive set of high spatiotemporal resolution satellites and merged products of air pollutants, we analyze the air quality across the globe and quantify the improvement resulting from the suppressed anthropogenic activity during the lockdowns. In particular, we focus on megacities, capitals and cities with high standards of living to make the quantitative assessment. Our results offer valuable insights into the spatial distribution of changes in the air pollutants due to COVID-19 enforced lockdowns. Statistically significant reductions are observed over megacities with mean reduction by 19.74%, 7.38% and 49.9% in nitrogen dioxide (NO₂), aerosol optical depth (AOD) and PM_2.5 concentrations. Google Earth Engine empowered cloud computing based remote sensing is used and the results provide a testbed for climate sensitivity experiments and validation of chemistry-climate models. Additionally, Google Earth Engine based apps have been developed to visualize the changes in a real-time fashion.