nasa-gesdisc/es-publications-researchareas huggingface dataset:
Radiative effects of absorbing aerosol types over South Asia (2023)

A comprehensive study on classifying the aerosol types and absorbing aerosol types, and quantifying the effect of absorbing aerosols on aerosol optical and radiative properties using four years (20152016, 20182019) of high-quality Aerosol Robotic Network (AERONET) datasets over Kanpur (urban) and Gandhi College (rural) in the Indo-Gangetic Plain (IGP) region is conducted on a seasonal scale, for the first time. Biomass burning (BB), urban-industrial, and mixed aerosol types are always present, whereas dust aerosol and mostly dust absorbing aerosol types are only present in pre-monsoon and monsoon seasons. During winter and post-monsoon seasons, BB aerosols and mostly black carbon (MBC) absorbing aerosols dominate, and the contribution of aerosol optical depth (AOD) and single scattering albedo (SSA) corresponding to MBC to total AOD and SSA are higher. SSA for MBC varies over a broader range due to mixing of BC with water-soluble aerosols. During pre-monsoon and monsoon seasons, mixing of dust with anthropogenic aerosols increases the amount of mixed aerosol type. Surface cooling and atmospheric heating efficiency for mixed aerosols are higher than MBC and dust aerosols due to enhancement in aerosol absorption over both locations. Seasonal analysis of aerosol radiative properties showed that during winter and post-monsoon, MBC absorbing aerosols are the major contributor in controlling/influencing the total aerosol radiative forcing (ARF) and heating rate (HR). During the other seasons, each absorbing aerosol type significantly influences ARF depending on their AOD and SSA values. In addition to Kanpur and Gandhi College, data from seven other AERONET sites located at Karachi, Lahore, Jaipur, Lumbini, Pokhara, Bhola, and Dhaka in South Asia are analysed to conduct a regional-scale examination of aerosol optical parameters and radiative effects due to different absorbing aerosol types. As the aerosol characteristics and trends are similar over these sites, the findings from such a regional-scale analysis can be an appropriate representative for the South Asian region. The regional analysis revealed that the annual mean atmospheric ARF (ARFATM) and ARF efficiency (ARFEATM), and HR are higher for MBC, followed by mixed and MD aerosols over South Asia due to higher AOD, and higher absorbing efficiency of MBC aerosols. In comparison, mixed aerosols exhibit higher ARFATM over East Asia. This quantification of absorbing aerosol types over a global aerosol hotspot will be useful for an accurate quantification of climate impacts of aerosols.

Research areas: Atmospheric/Ocean Indicators
Functional Genes Profile of Atmospheric Dust in the East Mediterranean Suggests Widespread Anthropogenic Influence on Aerobiome Composition (2022)

The microbiome of atmospheric dust events has raised increasing interest in the last decade, resulting in numerous studies that characterized the different parameters affecting the composition of the atmospheric microbiome, that is, the aerobiome. However, less is known about the functional profile of the aerobiome and how it compares with other environments. Here, we describe the results of shotgun metagenome analysis conducted on a representative set of particulate matter (PM) samples taken in Israel under dusty and nondusty conditions. We compared the functional profiles of these samples to local metagenomes collected from soils, sea, and leaf surfaces and to PM collected in Saudi Arabia, in order to link between the sampled aerosols and potential sources that contribute to the aerobiome. We found that PM samples collected in Israel most resembled Saudi Arabian dust and Israeli soils in both community composition and functional genes profile. In addition, we found significant differences in the abundances of genes associated with anthropogenic activity. Specifically, the examined dust exhibited a significantly higher abundance of genes associated with the biodegradation of organic contaminants, mostly benzoate and aminobenzoate, compared with all other examined environments. These preliminary results suggest that an anthropogenic impact on the aerobiome composition and functional profile is widespread, and pave the path to understanding the role of dust storms in disseminating microorganisms in various environments, spreading various traits, and affecting humans, livestock, plants, and ecosystem health.

Research areas: Ecosystems
Earthquake-induced landslides in Haiti: analysis of seismotectonic and possible climatic influences (2022)

Abstract. First analyses of landslide distribution and triggering factors are presented for the region affected by the 14 August 2021 earthquake (Mw=7.2) in the Nippes Department, Haiti. Landslide mapping was mainly carried out by comparing pre- and post-event remote imagery (0.51 m resolution) available on Google Earth Pro and Sentinel-2 (10 m resolution) satellite images. The first covered about 50 % of the affected region (for post-event imagery and before completion of the map in January 2022), and the latter were selected to cover the entire potentially affected zone. On the basis of the completed landslide inventory, comparisons are made with catalogs compiled by others both for the August 2021 and the January 2010 seismic events, including one open inventory (by the United States Geological Survey) that was also used for further statistical analyses. Additionally, we studied the pre-2021 earthquake slope stability conditions. These comparisons show that the total number of landslides mapped for the 2021 earthquake (7091) is larger than the one recently published by another research team for the same event but slightly smaller than the number of landslides mapped by a third research team. It is also clearly smaller than the one observed by two other research teams for the 2010 earthquake (e.g., 23 567, for the open inventory). However, these apparently fewer landslides triggered in 2021 cover much wider areas of slopes (>80 km2) than those induced by the 2010 event (25 km2  considering the open inventory). A simple statistical analysis indicates that the lower number of 2021 landslides can be explained by the missing detection of the smallest landslides triggered in 2021, partly due to the lower-resolution imagery available for most of the areas affected by the recent earthquake; this is also confirmed by an inventory completeness analysis based on sizefrequency statistics. The much larger total area of landslides triggered in 2021, compared to the 2010 earthquake, can be related to different physical reasons: (a) the larger earthquake magnitude in 2021, (b) the more central location of the fault segment that ruptured in 2021 with respect to coastal zones, (c) and possible climatic preconditioning of slope instability in the 2021 affected area. These observations are supported by (1) a new pre-2021 earthquake landslide map; (2) rainfall distribution maps presented for different periods (including October 2016  when Hurricane Matthew had crossed the western part of Haiti), covering both the 2010 and 2021 affected zones; and (3) shaking intensity prediction maps.

Research areas: Land Surface/Agriculture Indicators, Earthquakes
Climatology and model prediction of aerosol optical properties over the Indo-Gangetic Basin in north India (2022)

The current research focuses on the use of different simulation techniques in the future prediction of the crucial aerosol optical properties over the highly polluted Indo-Gangetic Basin in the northern part of India. The time series model was used to make an accurate forecast of aerosol optical depth (AOD) and angstrom exponent (AE), and the statistical variability of both cases was compared in order to evaluate the effectiveness of the model (training and validation). For this, different models were used to simulate the monthly average AOD and AE over Jaipur, Kanpur and Ballia during the period from 2003 to 2018. Further, the study was aimed to construct a comparative model that will be used for time series statistical analysis of MODIS-derived AOD550 and AE412470. This will provide a more comprehensive information about the levels of AOD and AE that will exist in the future. To test the validity and applicability of the developed models, root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), fractional bias (FB), and Pearson coefficient (r) were used to show adequate accuracy in model performance. From the observation, the monthly mean values of AOD and AE were found to be nearly similar at Kanpur and Ballia (0.62 and 1.26) and different at Jaipur (0.25 and 1.14). Jaipur indicates that during the pre-monsoon season, the AOD mean value was found to be highest (0.32  0.15), while Kanpur and Ballia display higher AOD mean values during the winter season (0.72  0.26 and 0.83  0.32, respectively). Among the different methods, the autoregressive integrated moving average (ARIMA) model was found to be the best-suited model for AOD prediction at Ballia based on fitted error (RMSE (0.22), MAE (0.15), MAPE (24.55), FB (0.05)) and Pearson coefficient r (0.83). However, for AE, best prediction was found at Kanpur based on RMSE (0.24), MAE (0.21), MAPE (22.54), FB (-0.09) and Pearson coefficient r (0.82).

Research areas: Validation
Albedo estimation of finite-sized concrete specimens (2018)

Albedo in the context of the Urban Heat Island contributes to the environmental impact of a pavement. Measuring the pavement albedo is difficult as it depends on the size of the specimen, background interference, and variations in the incoming solar spectrum. For this study, the albedo was determined for a 1-m2 concrete slab cast with white cement containing titanium dioxide nanoparticles. The size of the exposed surface of the slab was varied using black paper. To calculate the albedo of these nonstandard specimen sizes, a new testing technique was proposed that uses an albedometer and various geometric conditions, including the exposed slab surface and measurement height. The albedo of the finite-sized concrete specimen was found to range from 0.50 to 0.55, depending on the number of unknowns assumed, with the most reasonable estimate of 0.54. The measured slab albedo from the new method was also verified using a laboratory spectrophotometer. With this proposed methodology to account for background surfaces, the albedo of laboratory-sized concrete specimens of any shape can now be easily measured at various test site locations.

Research areas: Heat
Forest Fire Susceptibility Mapping by Integrating Remote Sensing and Machine Learning Algorithms (2022)

Forest fires are a very common in India, especially in the hilly regions of the western and northeastern Himalayas, which puts adverse impacts on the environment and society. Himachal Pradesh and Uttarakhand are the states most prone to forest fires; therefore, this research is intended to map forest fire occurrence in both states using geospatial techniques and machine learning algorithms (MLAs). To fulfill this objective, we used the meteorological data such as evapotranspiration, precipitation, temperature and wind speed data regarding aridity, elevation, slope, aspect, curvature and land use/land cover (LULC) data; and three MLAs: support vector machine (SVM), random forest (RF), and logistic regression (LR) along with an ensemble learning model were used for the modeling of susceptibility of forest fires. This produced forest fire susceptible maps that were finally validated using the global fire atlas of the NASA Earth DATA. The result showed that more than 50% of the area of Uttarakhand and Himachal Pradesh states lies under the moderate, high, and very high forest fire susceptible zones. Further, most of the low hilly and foothill regions of both the states lie under the high and very high forest fire susceptible zones. The validation of the models used was completed using the ROC curve, which showed that the area under curve (AUC) of the ROC (experimental and binormal) was highest for ensemble model, i.e. 0.923 and 0.989, respectively followed by SVM (0.89 and 0.97), RF (0.887 and 0.96), and LR (0.85 and 0.92). This indicates that, although all the models produced satisfactory results, the accuracy of the ensemble model was highest. The output of this research can be utilized for the effective mitigation of forest fires, as well as forest management in the region. Further, similar approaches may be used in other parts of the world for forest fire susceptibility mapping using additional parameters.

Research areas: Wildfires
Impact of Relative Humidity on the vertical distribution of aerosols over India (2023)

Water uptake by aerosol particles significantly changes its light-scattering characteristics, alters the physical and optical properties of aerosols. In this article, we present the effect of ambient relative humidity (RH) on the optical properties of surface and columnar aerosols under different RH conditions during different seasons over India. In particular, relation between surface RH and surface aerosol properties, surface RH and aerosol optical depth (AOD) and profile of RH on the AOD is presented. An increase in the surface aerosol scattering coefficient (scat) and total aerosol number concentration (N(d)) is observed under high humidity (50100%) condition than low humidity (030%). This is more prominent in the winter and pre-monsoon seasons. An increase in the AOD is observed as surface RH increase in all the seasons except in monsoon season where it showed an inverse relation. The profile measurements (Lidar and radiosonde) also showed a consistent increase in AOD (0-6 km) from low to high RH (0-6 km) conditions. This relationship is significant during winter followed by the post-monsoon, pre-monsoon, and monsoon seasons. An increase in AOD with RH is observed regardless of altitude in all seasons, except during the monsoon at higher altitudes (2-6 km). Our findings clearly demonstrated that during the winter and monsoon (pre- and post-monsoon) seasons, aerosols within (above) the boundary layer are more sensitive to the RH. This relationship has been tested in different surface conditions across India using CALIPSO and IMD radiosonde measurements. These measurements also confirmed an increase in AOD with increasing RH in all the regions except East Coast (EC) region. Irrespective of the region, this feature is more prominent at lower altitudes (01.5 km) during winter and higher altitudes (1.5-6 km) during other seasons. At higher altitudes, only a few regions (Central India, Indo-Gangetic Plain, and West Coast) showed an inverse relationship between AOD and RH during the monsoon and post-monsoon seasons. Further, our results illustrated the contribution of hygroscopic particles to the RH increases the AOD in a given season. This finding implies that hygroscopic growth caused by water-soluble particles in the lower troposphere can significantly alter the magnitude of aerosol radiative forcing both at the surface and at the top of the atmosphere.

Research areas: Validation
Long Term Study of Surface Gases Variability Trends Using Satellite Based Data at Southern Region of Rajasthan (2022)

In this study, we present long-term total column measurements of carbon monoxide (CO), Nitrogen dioxide (NO2), Sulfur dioxide (SO2), ozone (O3) at an urban location, Udaipur (24.59N, 73.73E, 423 m above mean sea level), in Rajasthan from January 2010 to December 2020. The long-term mean total column concentrations of CO, NO2, SO2, and O3 were 2.09 (x 10 18 mol/cm 2), 1.9 (x 10 15 mol/cm 2), 0.12 DU, and 270.4 DU, respectively. CO, NO2, and SO2 were the lowest during the monsoon season, whereas O3 concentration peaked during summer. The former could be attributed mainly to the near-surface anthropogenic sources (e.g. automobiles, residential cooking, agricultural land-clearing, and biomass burning) and increased humidity. In contrast, the latter was clearly due to enhanced chemical production of O3 during the pre-monsoon (i.e. summer) season. The mean lowest concentration of all gases was observed during the post-monsoon season due to efficient wet scavenging by precipitation. The averaged decadally and multi-annual patterns showed mixed trends, declining in 2020 due to movement restriction. Total column concentrations of CO, NO2, SO2, and O3 register 1.8%, 6.9%, 7.7%, and 1.3% enhancement since 2010 in the Udaipur region. From the interannual comparison, the column CO and O3 concentrations have increased by 1.5% and 3.5%, respectively, from 2019, despite the imposed movement restrictions. Upon the investigation of atmospheric parameters, average Temperatures are linked to the boundary layer height variability. The correlation between CO, NO2, SO2 and O3 was average. Contrarily, O3 depicted a reverse pattern with the highest concentrations during summer and monsoon and lowest in the winters. The mean wind speeds, PBL height and RH at Udaipur during 2010-2020 were five m/s, 820 m, and 38%, respectively. O3 followed a positive correlation with Temperature, the negative with CO, NO2 and SO2.

Research areas: Air Quality, Environmental Impacts
Variation in Fish Abundance, Diversity and Assemblage Structure in Seagrass Meadows across the Atlanto-Mediterranean Province (2022)

Seagrasses worldwide provide key habitats for fish assemblages. Biogeographical disparities in ocean climate conditions and seasonal regimes are well-known drivers of the spatial and temporal variation in seagrass structure, with potential effects on associated fish assemblages. Whether taxonomically disparate fish assemblages support a similar range of ecological functions remains poorly tested in seagrass ecosystems. In this study, we examined variation in the abundance, diversity (from a taxonomic and functional perspective), and assemblage structure of fish community inhabiting nine meadows of the seagrass Cymodocea nodosa across three regions in the Mediterranean (Mallorca and Alicante) and the adjacent Atlantic (Gran Canaria), and identified which attributes typifying the structure of meadows, and large-scale variability in ocean climate, contributed most to explaining such ecological variation. Despite a similar total number of species between Mallorca and Gran Canaria, the latter region had more taxonomically and functionally diverse fish assemblages relative to the western Mediterranean regions, which translated into differences in multivariate assemblage structure. While variation in the abundance of the most conspicuous fish species was largely explained by variation in seagrass structural descriptors, most variation in diversity was accounted for by a descriptor of ocean climate (mean seasonal SST), operating at regional scales. Variation in fish assemblage structure was, to a lesser extent, also explained by local variability in seagrass structure. Beyond climatic drivers, our results suggest that lower temporal variability in the canopy structure of C. nodosa meadows in Gran Canaria provides a more consistent source of food and protection for associated fish assemblages, which likely enhances the more abundant and diverse fish assemblages there.

Research areas: Ecosystems
Relationship between Lightning and Aerosol Optical Depth over the Uttarakhand Region in India: Thermodynamic Perspective (2022)

The current study is mainly focused on the monthly variation in the lightning flash rate (LFR) and related thermodynamic parameters using the data for the years 20002013, and the trend of lightning variation is explored. Lightning data are used from a lightning imaging sensor (LIS) and an optical transient detector (OTP) boarded on the tropical rainfall measuring mission (TRMM). Additionally, aerosol optical depth (AOD) data at 550 nm for the same period were considered from a Moderate Resolution Imaging Spectroradiometer (MODIS). The assessment of lightning and AOD using monthly data makes it difficult to study seasonal contributions, and higher-resolution (hourly) data may be more appropriate, but unfortunately, no data were available with a higher resolution than monthly. The dependency of LFR is also investigated using thermodynamic/dynamic parameters. The LFR shows a moderate correlation with a correlation coefficient of 0.56, 0.62, and 0.63 for AOD, CAPE, and vertical velocity, respectively. The increasing AOD in the pre-monsoon season is associated with higher lightning flash rates over this region. The possible sources of aerosols that cause an increase in lightning activities are identified from the classification of aerosols based on the characteristic values of the AOD and the Angstrom exponent. The thermodynamic relation of the Product of Bowen ratio with the sum of the precipitation rate and evaporation rate has been used as a proxy to evaluate the lightning flash rate density over Srinagar, Uttarakhand region (78.55 E79.05 E, 29.97 N30.47 N), with nine models from the Coupled Model Inter-comparison Project-Phase 5 (CMIP5). The model-simulated LFR has also been used for the projection of lightning in the late 21st century, and the projected LFR over the study area shows a 7.41% increase during the (20792088) period as compared to the historic period (19962005). The results of the study region indicate caution in using any single climate variable as a proxy for projecting a change in the lightningclimate relationships in the scenario of global warming.

Research areas: Atmospheric/Ocean Indicators
Multiple sampling methods to develop indices of mid-trophic levels abundance in open ocean ecosystems (2022)

Mid-trophic level (MTL) organisms play a key role in the sub-Antarctic ecosystem food web, linking primary producers and tertiary consumers transferring energy across trophic levels. In this region, the relative abundance of MTLs has been monitored using single-frequency acoustic data collected opportunistically in a time series of trawl surveys carried out in the austral late spring/early summer. To advance this approach, we developed a methodology that enabled us to disaggregate multifrequency acoustic data into three different MTL groups to assess their temporal and spatial patterns: mesopelagic fish, gelatinous zooplankton (excluding pyrosomes), and euphausiids. We used acoustic data and biological sampling information collected with two midwater trawl nets from a dedicated voyage, to train a classification tree algorithm. We complemented these data with footage from an in-trawl camera system that gave us new insights on vertical distribution and presence of pelagic taxa. The testing dataset used to implement our classification algorithm was a time series of acoustic data from the sub-Antarctic trawl survey. This approach was successful at disaggregating acoustic data and provided useful information to study the relative abundance and temporal and spatial patterns of pelagic taxa. Our analysis revealed the presence of dense shallow layers of gelatinous zooplankton which showed a statistically significant increasing trend over the time series. Although we were unable to detect pyrosomes acoustically, we encountered large biovolumes of these organisms during our biological sampling, which was supported by the in-trawl camera system.

Research areas: Ecosystems