nasa-gesdisc/es-publications-researchareas huggingface dataset:
An unusually long Rift valley fever inter-epizootic period in Zambia: Evidence for enzootic virus circulation and risk for disease outbreak (2022)

Rift valley fever (RVF) is a mosquito-borne disease of animals and humans. Although RVF outbreaks are usually reported at 5-15-year intervals in sub-Saharan Africa, Zambia has experienced an unusually long inter-epizootic/-epidemic period of more than three decades. However, serological evidence of RVF virus (RVFV) infection in domestic ruminants during this period underscores the need for comprehensive investigation of the mechanisms of virus perpetuation and disease emergence. Mosquitoes (n = 16,778) captured from eight of the ten provinces of Zambia between April 2014 and May 2019 were pooled (n = 961) and screened for RVFV genome by a pan-phlebo RT-PCR assay. Aedes mosquito pools (n = 85) were further screened by nested RT-PCR assay. Sera from sheep (n = 13), goats (n = 259) and wild ungulates (n = 285) were screened for RVFV antibodies by ELISA while genome detection in pooled sera (n = 276) from domestic (n = 248) and wild ungulates (n = 37) was performed by real-time RT-PCR assay. To examine the association between the long inter-epizootic period and climatic variables, we examined El Nino-Southern Oscillation indices, precipitation anomalies, and normalized difference vegetation index. We then derived RVF risk maps by exploring climatic variables that would favor emergence of primary RVFV vectors. While no RVFV genome could be detected in pooled mosquito and serum samples, seroprevalence was significantly high (OR = 8.13, 95% CI [4.6314.25]) in wild ungulates (33.7%; 96/285) compared to domestic ruminants (5.6%; 16/272). Retrospective analysis of RVF epizootics in Zambia showed a positive correlation between anomalous precipitation (La Nina) and disease emergence. On risk mapping, whilst northern and eastern parts of the country were at high risk, domestic ruminant population density was low (< 21 animals/km2) in these areas compared to low risk areas (>21 animals/km2). Besides evidence of silent circulation of RVFV and the risk of disease emergence in some areas, wildlife may play a role in the maintenance of RVFV in Zambia.

Research areas: Public Health, Atmospheric/Ocean Indicators
Understanding temporary reduction in atmospheric pollution and its impacts on coastal aquatic system during COVID-19 lockdown: a case study of South Asia (2021)

The strict lockdown measures not only contributed to curbing the spread of COVID-19 infection, but also improved the environmental conditions worldwide. The main goal of the current study was to investigate the co-benefits of COVID-19 lockdown on the atmosphere and aquatic ecological system under restricted anthropogenic activities in South Asia. The remote sensing data (a) NO2 emissions from the Ozone Monitoring Instrument (OMI), (b) Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and (c) chlorophyll (Chl-a) and turbidity data from MODIS-Aqua Level-3 during JanOct (2020) were analyzed to assess the changes in air and water pollution compared to the last five years (20152019). The interactions between the air and water pollution were also investigated using overland runoff and precipitation in 2019 and 2020 at a monthly scale to investigate the anomalous events, which could affect the N loading to coastal regions. The results revealed a considerable drop in the air and water pollution (3040% reduction in NO2 emissions, 45% in AOD, 50% decline in coastal Chl-a concentration, and 29% decline in turbidity) over South Asia. The rate of reduction in NO2 emissions was found the highest for Lahore (32%), New Delhi (31%), Ahmadabad (29%), Karachi (26%), Hyderabad (24%), and Chennai (17%) during the strict lockdown period from AprJun, 2020. A positive correlation between AOD and NO2 emissions (0.230.50) implies that a decrease in AOD is attributed to a reduction in NO2. It was observed that during strict lockdown, the turbidity has decreased by 29%, 11%, 16%, and 17% along the coastal regions of Karachi, Mumbai, Calcutta, and Dhaka, respectively, while a 56% increase in turbidity was seen over the Madras during the same period. The findings stress the importance of reduced N emissions due to halted fossil fuel consumption and their relationships with the reduced air and water pollution. It is concluded that the atmospheric and hydrospheric environment can be improved by implementing smart restrictions on fossil fuel consumption with a minimum effect on socioeconomics in the region. Smart constraints on fossil fuel usage are recommended to control air and water pollution even after the social and economic activities resume business-as-usual scenario.

Research areas: Air Quality, Water Quality
Observations of Emissions and the Influence of Meteorological Conditions during Wildfires: A Case Study in the USA, Brazil, and Australia during the 2018/19 Period (2021)

Wildfires can have rapid and long-term effects on air quality, human health, climate change, and the environment. Smoke from large wildfires can travel long distances and have a harmful effect on human health, the environment, and climate in other areas. More recently, in 20182019 there have been many large fires. This study focused on the wildfires that occurred in the United States of America (USA), Brazil, and Australia using Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP) and a TROPOspheric Monitoring Instrument (TROPOMI). Specifically, we analyzed the spatial-temporal distribution of black carbon (BC) and carbon monoxide (CO) and the vertical distribution of smoke. Based on the results, the highest detection of smoke (~14 km) was observed in Brazil; meanwhile, Australia showed the largest BC column burden of ~1.5 mg/m2. The meteorological conditions were similar for all sites during the fires. Moderate temperatures (between 32 and 42 C) and relative humidity (3050%) were observed, which resulted in drier conditions favorable for the burning of fires. However, the number of active fires was different for each site, with Brazil having 13 times more active fires than the USA and five times more than the number of active fires in Australia. However, the high number of active fires did not translate to higher atmospheric constituent emissions. Overall, this work provides a better understanding of wildfire behavior and the role of meteorological conditions in emissions at various sites.

Research areas: Wildfires, Air Quality
Fire-derived phosphorus fertilization of African tropical forests (2021)

Central African tropical forests face increasing anthropogenic pressures, particularly in the form of deforestation and land-use conversion to agriculture. The long-term effects of this transformation of pristine forests to fallow-based agroecosystems and secondary forests on biogeochemical cycles that drive forest functioning are poorly understood. Here, we show that biomass burning on the African continent results in high phosphorus (P) deposition on an equatorial forest via fire-derived atmospheric emissions. Furthermore, we show that deposition loads increase with forest regrowth age, likely due to increasing canopy complexity, ranging from 0.4 kg P ha1 yr1 on agricultural fields to 3.1 kg P ha1 yr1 on old secondary forests. In forest systems, canopy wash-off of dry P deposition increases with rainfall amount, highlighting how tropical forest canopies act as dynamic reservoirs for enhanced addition of this essential plant nutrient. Overall, the observed P deposition load at the study site is substantial and demonstrates the importance of canopy trapping as a pathway for nutrient input into forest ecosystems.

Research areas: Habitat Conversion/Fragmentation, Environmental Impacts
Unraveling the spatio-temporal structure of the atmospheric and oceanic intra-seasonal oscillations during the contrasting monsoon seasons (2017)

Using remotely sensed data sets of rainfall and outgoing longwave radiation (OLR) over Indian land and adjacent oceanic regions and sea surface temperature (SST) over adjacent oceanic regions; we examine the major characteristics of the intra-seasonal oscillations of Indian summer monsoon (ISM) during the flood and drought years. Intra-seasonal oscillations of rain, OLR and SST corresponding to 3060days transpires to contribute more to the intra-seasonal variability over the Arabian Sea, whereas 1020days' mode is found to be more dominating over the Bay of Bengal during the drought years. Therefore, suggesting that both of the Seas surrounding the Indian land region respond in a different way to the below normal rainfall conditions of Indian land region. Another important finding of the present work is that during the drought years, 3060days intra-seasonal oscillations of SST over both of the seas follow the intra-seasonal oscillations of rain at 3060days' time scale over central India approximately after 26days. Conversely in the flood years, intra-seasonal oscillations of SST at 3060days over the Arabian Sea and Bay of Bengal lead the intra-seasonal oscillations of rain over central India by 6days. Present analysis also reveals that the intra-seasonal variability of ISM at two different time-scales (1020 and 3060days) possess different spatio-temporal characteristics during the contrasting monsoon conditions over the oceanic regions; therefore it is advisable to study the two modes individually for understanding the underlying physical mechanism. Results presented in this paper may be useful for improved ISM prediction.

Research areas: Atmospheric/Ocean Indicators, Droughts
Spatiotemporal assessment of CO2 emissions and its satellite remote sensing over Pakistan and neighboring regions (2017)

For the first time, anthropogenic CO2 emissions and spatiotemporal variability of mid-tropospheric CO2 has been discussed using EDGAR database and Atmospheric Infrared Sounder (AIRS) onboard Aqua satellite observations. The EDGAR data indicate an increase of 147% in anthropogenic CO2 emissions from 66,101 to 163,737 Gg for Pakistan during the period of 19902008. Dera Ghazi Khan (Pakistan) is found with the highest increase of 260% of anthropogenic CO2 emissions followed by Delhi (India) 153%, Karachi (Pakistan) 66% and Lahore (Pakistan) 59% whereas a decreasing trend of 53% is observed for Kabul (Afghanistan) during 19902008. Industrial activities, road transportation, open field crop-waste burning, and energy production have been identified as major anthropogenic emission sources of CO2 in the studied region. AIRS CO2 retrievals over Pakistan and adjoining areas of India and Afghanistan show an averaged CO2 to be 3835ppm with a positive trend of 5.05% during December 2002 to February 2012. An elevated value of CO2 has been observed over northern mountainous and high human settlement regions. The seasonal analysis shows a spring maximum 3855ppm with a secondary peak in late autumn, and the highest increasing trend of 5.5% associated with winter. May and August showed maximum and minimum mean monthly values of 3855ppm and 3825ppm respectively. HYSPLIT trajectories of air masses movement have been drawn to track CO2 transport.

Research areas: Greenhouse Gases, Environmental Impacts, Energy Production/Use
An important fingerprint of wildfires on the European aerosol load (2011)

Abstract. Wildland fires represent the major source of fine aerosols, i.e., atmospheric particles with diameters <1 m. The largest numbers of these fires occur in Africa, Asia and South America, but a not negligible fraction also occurs in Eastern Europe and former USSR countries, particularly in the Russian Federation, Ukraine and Kazakhstan. Besides the impact of large forest fires, recent studies also highlighted the crucial role played by routine agricultural fires in Eastern Europe and Russia on the Arctic atmosphere. An evaluation of the impact of these fires over Europe is currently not available. The assessment of the relative contribution of fires to the European aerosol burden is hampered by the complex mixing of natural and anthropogenic particle types across the continent. In this study we use long term (20022007) satellite-based fires and aerosol data coupled to atmospheric trajectory modelling in the attempt to estimate the wildfires contribution to the European aerosol optical thickness (AOT). Based on this dataset, we provide evidence that fires-related aerosols play a major role in shaping the AOT yearly cycle at the continental scale. In general, the regions most impacted by wildfires emissions and/or transport are Eastern and Central Europe as well as Scandinavia. Conversely, a minor impact is found in Western Europe and in the Western Mediterranean. We estimate that in spring 5 to 35% of the European fine fraction AOT (FFAOT) is attributable to wildland fires. The estimated impact maximizes in April (2035%) in Eastern and Central Europe as well as in Scandinavia and in the Central Mediterranean. An important contribution of wildfires to the FFAOT is also found in summer over most of the continent, particularly in August over Eastern Europe (28%) and the Mediterranean regions, from Turkey (34%) to the Western Mediterranean (25%). Although preliminary, our results suggest that this fires-related, continent-wide haze plays a not negligible role on the European radiation budget, and possibly, on the European air quality, therefore representing a clear target for mitigation.

Research areas: Wildfires, Air Quality
Effect of urbanization on lightning over four metropolitan cities of India (2011)

In the recent years the effect of urbanization on local convections and lightning has been studied very extensively. Here we have analyzed the last 8 years (from 2001 to 2008) data of total lightning, Aerosol Optical Depth (AOD) and rainfall over two inland and two coastal metropolitan cities of India, to study the effect of urbanization on lightning. The lightning and rainfall data are taken from TRMM satellite and AOD data is taken from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Our analysis shows that both the inland cities show considerable enhancement, where as both the coastal cities do not show enhancement in lightning, in the last 8 years. The results show that, over a inland city, where aerosol concentration is not increased appreciably in last few years the enhancement in convective activity and lightning is controlled by thermodynamic effect, where as, where aerosol concentration show increasing trend, aerosols play major roll in enhancement in lightning activity. It has been also found that over a city where aerosol concentration show increasing trend, lightning show a sharp increase due to combined positive effect of thermodynamics and aerosols, however rainfall show only small increase because of negative effect of aerosols on rainfall. The analysis of lightning, aerosols and rainfall over coastal cities do not show any increasing trend in rainfall and lightning activity which suggest that during premonsoon period all these parameters are controlled by large scale processes and therefore heat island effect or aerosol effect are not observed in both the coastal cities.

Research areas: Habitat Conversion/Fragmentation, Heat, Atmospheric/Ocean Indicators
Compositional Spatio-Temporal PM2.5 Modelling in Wildfires (2021)

Wildfires are natural ecological processes that generate high levels of fine particulate matter (PM2.5) that are dispersed into the atmosphere. PM2.5 could be a potential health problem due to its size. Having adequate numerical models to predict the spatial and temporal distribution of PM2.5 helps to mitigate the impact on human health. The compositional data approach is widely used in the environmental sciences and concentration analyses (parts of a whole). This numerical approach in the modelling process avoids one common statistical problem: the spurious correlation. PM2.5 is a part of the atmospheric composition. In this way, this study developed an hourly spatio-temporal PM2.5 model based on the dynamic linear modelling framework (DLM) with a compositional approach. The results of the model are extended using a GaussianMattern field. The modelling of PM2.5 using a compositional approach presented adequate quality model indices (NSE = 0.82, RMSE = 0.23, and a Pearson correlation coefficient of 0.91); however, the correlation range showed a slightly lower value than the conventional/traditional approach. The proposed method could be used in spatial prediction in places without monitoring stations.

Research areas: Wildfires, Air Quality
Forecasting of extreme flood events using different satellite precipitation products and wavelet-based machine learning methods (2020)

An accurate and timely forecast of extreme events can mitigate negative impacts and enhance preparedness. Real-time forecasting of extreme
flood events with longer lead times is difficult for regions with sparse rain gauges, and in such situations, satellite precipitation could be
a better alternative. Machine learning methods have shown promising results for flood forecasting with minimum variables indicating the
underlying nonlinear complex hydrologic system. Integration of machine learning methods in extreme event forecasting motivates us to
develop reliable flood forecasting models that are simple, accurate, and applicable in data scare regions. In this study, we develop a forecasting
method using the satellite precipitation product and wavelet-based machine learning models. We test the proposed approach in the flood-
prone Vamsadhara river basin, India. The validation results show that the proposed method is promising and has the potential to forecast
extreme flood events with longer lead times in comparison with the other benchmark models.

Research areas: Floods
Carbon monoxide (CO) emissions and its tropospheric variability over Pakistan using satellite-sensed data (2015)

This study presents major anthropogenic sources of carbon monoxide (CO) in Pakistan and discusses the spatio-temporal variability of tropospheric CO over Pakistan and neighboring regions of Afghanistan, India and Iran for a period from 2003 to 2012 using satellite-sensed (AIRS/AMSU) data. The results show a large spatio-temporal variability of CO over the study region mostly associated with anthropogenic activities such as crop residue burning, vehicular transport, and electricity and energy generation, and local meteorology. The annual mean value of tropospheric CO is observed to be 1152ppbv that remains almost steady during the study period with decadal increase of only 2%. Due to more anthropogenic emissions of CO and its transport, the eastern zone shows a higher average value of 1222ppbv with 2.7% decadal increase than the western zone (1113ppbv with 1.4% decadal increase). Elevated concentrations of CO have been observed over the Indo-Gangetic Basin, Lahore, Karachi, and Delhi. During the study period large fluctuations in CO mean monthly values are found ranging from 99ppbv to 131ppbv. The fact that, in spite of a large increase in the CO emissions from 2003 to 2012, its average concentration remains almost stable indicates that a large scale regional transport contributes substantially to the tropospheric CO. Carbon monoxide concentrations exhibit a strong seasonal pattern with maximum amplitude in spring and minimum in autumn. July is found to have the highest decadal increasing trend of 13% followed by August at 8%, whereas May has the highest decreasing trend of 8% followed by November at 4.4%.

Research areas: Air Quality, Environmental Impacts
Impacts of COVID-19 Restrictions on Regional and Local Air Quality Across Selected West African Cities (2022)

The emergence of COVID-19 brought with it panic and a sense of urgency causing governments to impose strict restrictions on human activities and vehicular movements. With anthropogenic emissions, especially waste management (domestic and municipal), traffic, and industrial activities, said to be a significant contributor to ambient air pollution, this study assessed the impacts of the imposed restrictions on the concentrations and size distribution of atmospheric aerosols and concentration of gaseous pollutants over West African subregion and seven major COVID-19 epicenters in the subregion. Satellite retrievals and reanalysis data sets were used to study the impact of the restrictions on Aerosol Optical Depth (AOD) and atmospheric concentrations NO2, SO2, CO, and O3. The anomalies were computed for 2020 relative to 20172019 (the reference years). In 2020 relative to the reference years, for area-averaged AOD levels, there was a consequential mean percentage change between 6.7%  21.0% and 19.2%  27.9% in the epicenters and 10.1%  15.4% over the subregion. The levels of NO2 and SO2 also reduced substantially at the epicenters, especially during the periods when the restrictions were highly enforced. However, the atmospheric levels of CO and ozone increased slightly in 2020 compared to the reference years. This study shows that a one cap fits all policy cannot reduced the level of air pollutants and that traffic and industrial processes are not the predominant sources of CO in major cities in the subregion.

Research areas: Air Quality
Browning of vegetation in efficient carbon sink regions of India during the past two decades is driven by climate change and anthropogenic intrusions (2023)

Accurate estimation of carbon cycle is a challenging task owing to the complexity and heterogeneity of ecosystems. Carbon Use Efficiency (CUE) is a metric to define the ability of vegetation to sequester carbon from the atmosphere. It is key to understand the carbon sink and source pathways of ecosystems. Here, we quantify CUE using remote sensing measurements to examine its variability, drivers and underlying mechanisms in India for the period 20002019, by applying the principal component analyses (PCA), multiple linear regression (MLR) and causal discovery. Our analysis shows that the forests in the hilly regions (HR) and northeast (NE), and croplands in the western areas of South India (SI) exhibit high (>0.6) CUE. The northwest (NW), Indo-Gangetic plain (IGP) and some areas in Central India (CI) show low (<0.3) CUE. In general, the water availability as soil moisture (SM) and precipitation (P) promote higher CUE, but higher temperature (T) and air organic carbon content (AOCC) reduce CUE. It is found that SM has the strongest relative influence (33%) on CUE, followed by P. Also, SM has a direct causal link with all drivers and CUE; reiterating its importance in driving vegetation carbon dynamics (VCD) for the cropland dominated India. The long-term analysis reveals that the low CUE regions in NW (moisture induced greening) and IGP (irrigation induced agricultural boom) have an increasing trend in productivity (greening). However, the high CUE regions in NE (deforestation and extreme events) and SI (warming induced moisture stress) exhibit a decreasing trend in productivity (browning), which is a great concern. Our study, therefore, provides new insights on the rate of carbon allocation and the need of proper planning for maintaining balance in the terrestrial carbon cycle. This is particularly important in the context of drafting policy decisions for the mitigation of climate change, food security and sustainability.

Research areas: Land Surface/Agriculture Indicators, Habitat Conversion/Fragmentation, Atmospheric/Ocean Indicators