nasa-gesdisc/es-publications-researchareas huggingface dataset:
Estimating the spatiotemporal variation of NO2 concentration using an adaptive neuro-fuzzy inference system (2018)

Statistical modelling has been successfully used to estimate the variations of NO2 concentration, but employing new modelling techniques can make these estimations far more accurate. To do so, for the first time in application to spatiotemporal air pollution modelling, we employed a soft computing algorithm called adaptive neuro-fuzzy inference system (ANFIS) to estimate the NO2 variations. Comprehensive data sets were investigated to determine the most effective predictors for the modelling process, including land use, meteorological, satellite, and traffic variables. We have demonstrated that using selected satellite, traffic, meteorological, and land use predictors in modelling increased the R2 by 21%, and decreased the root mean square error (RMSE) by 47% compared with the model only trained by land use and meteorological predictors. The ANFIS model found to have better performance and higher accuracy than the multiple regression model. Our best model, captures 91% of the spatiotemporal variability of monthly mean NO2 concentrations at 1 km spatial resolution (RMSE 1.49 ppb) in a selected area of Australia.

Research areas: Air Quality
Simulation and analysis of synoptic scale dust storms over the Arabian Peninsula (2018)

Dust storms are among the most severe environmental problems in arid and semi-arid regions of the world. The predictability of seven dust events, viz. D1: April 24, 2014; D2: February 2324, 2015; D3: April 13, 2015; D4: March 2628, 2016; D5: August 35, 2016; D6: March 1314, 2017 and D7:March 1921, 2017, are investigated over the Arabian Peninsula using a regionally adapted chemistry transport model CHIMERE coupled with the Weather Research and Forecast (WRF) model. The hourly forecast products of particulate matter concentrations (PM10) and aerosol optical depths (AOD) are compared against both satellite-based (MSG/SEVRI RGB dust, MODIS Deep Blue Aerosol Optical Depth: DB-AOD, Ozone Monitoring Instrument observed UV Aerosol Absorption Index: OMI-AI) and ground-based (AERONET AOD) remote sensing products. The spatial pattern and the time series of the simulations show good agreement with the observations in terms of the dust intensity as well as the spatiotemporal distribution. The causative mechanisms of these dust events are identified by the concurrent analyses of the meteorological data. From these seven storms, five are associated with synoptic scale meteorological processes, such as prefrontal storms (D1 and D7), postfrontal storms of short (D2), and long (D3) duration types, and a summer shamal storm (D6). However, the storms D4 and D6 are partly associated with mesoscale convective type dust episodes known as haboobs. The socio-economic impacts of the dust events have been assessed by estimating the horizontal visibility, air quality index (AQI), and the dust deposition flux (DDF) from the forecasted dust concentrations. During the extreme dust events, the horizontal visibility drops to near-zero values co-occurred withhazardous levels of AQI and extremely high dust deposition flux (250 g cm 2 day 1).

Research areas: Air Quality
High-resolution mapping of the NO2 spatial distribution over Belgian urban areas based on airborne APEX remote sensing (2017)

Abstract. We present retrieval results of tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs), mapped at high spatial resolution over three Belgian cities, based on the DOAS analysis of Airborne Prism EXperiment (APEX) observations. APEX, developed by a Swiss-Belgian consortium on behalf of ESA (European Space Agency), is a pushbroom hyperspectral imager characterised by a high spatial resolution and high spectral performance. APEX data have been acquired under clear-sky conditions over the two largest and most heavily polluted Belgian cities, i.e. Antwerp and Brussels on 15 April and 30 June 2015. Additionally, a number of background sites have been covered for the reference spectra. The APEX instrument was mounted in a Dornier DO-228 aeroplane, operated by Deutsches Zentrum fur Luft- und Raumfahrt (DLR). NO2 VCDs were retrieved from spatially aggregated radiance spectra allowing urban plumes to be resolved at the resolution of 60  80 m2. The main sources in the Antwerp area appear to be related to the (petro)chemical industry while traffic-related emissions dominate in Brussels. The NO2 levels observed in Antwerp range between 3 and 35  1015 molec cm2, with a mean VCD of 17.4  3.7  1015 molec cm2. In the Brussels area, smaller levels are found, ranging between 1 and 20  1015 molec cm2 and a mean VCD of 7.7  2.1  1015 molec cm2. The overall errors on the retrieved NO2 VCDs are on average 21 and 28 % for the Antwerp and Brussels data sets. Low VCD retrievals are mainly limited by noise (1 slant error), while high retrievals are mainly limited by systematic errors. Compared to coincident car mobile-DOAS measurements taken in Antwerp and Brussels, both data sets are in good agreement with correlation coefficients around 0.85 and slopes close to unity. APEX retrievals tend to be, on average, 12 and 6 % higher for Antwerp and Brussels, respectively. Results demonstrate that the NO2 distribution in an urban environment, and its fine-scale variability, can be mapped accurately with high spatial resolution and in a relatively short time frame, and the contributing emission sources can be resolved. High-resolution quantitative information about the atmospheric NO2 horizontal variability is currently rare, but can be very valuable for (air quality) studies at the urban scale.

Research areas: Air Quality, Energy Production/Use
Differential Impact of Anniversary-Severe Cyclones on the Water Quality of a Tropical Coastal Lagoon (2017)

Mapping spatio-temporal dynamics of suspended load in a lagoon before-during-after a cyclone is crucial for monitoring sudden nutrient enrichment and associated processes such as algal blooms and siltation. However, not all cyclones produce similar impact on a coastal lagoon, some trigger algal blooms after passage while others just increase the overall turbidity. Asias largest brackish water lagoon, Chilika Lagoon, India was hit by two anniversary-severe cyclones Phailin (12 October 2013) and Hudhud (12 October 2014) recently. Their impacts were analysed with respect to physical, biological and meteorological factors which favour or restrict a phytoplankton bloom after the passage of a storm. Moderate-resolution imaging spectroradiometer surface reflectance data were used to examine the spatio-temporal variability in total suspended sediment (TSS) and chlorophyll-a (Chl-a) concentration pre- and post-cyclone. Comparative results revealed that Phailin was associated with higher rainfall, wind speed and surface runoff compared with Hudhud. These factors contributed to higher TSS concentration in all sectors of the lagoon post-Phailin compared with post-Hudhud. Extreme TSS, limited light, high rainfall and runoff and increased flushing rate post-Phailin restricted the likelihood of a phytoplankton bloom in the lagoon, a commonly reported phenomenon after the passage of a cyclone. In contrast, sufficient light availability due to lower TSS, low runoff and flushing and stable wind supported a phytoplankton bloom post-Hudhud. The observed differential impacts were linked to the individual cyclones characteristics such as differences in landfall locations, wind speed, trajectory after the landfall, differences in rainfall rate and surface runoff and speed of passage.

Research areas: Water Quality, Severe Storms
Detecting Areas Vulnerable to Sand Encroachment Using Remote Sensing and GIS Techniques in Nouakchott, Mauritania (2018)

Sand dune advances poses a major threat to inhabitants and local authorities in the area of Nouakchott, Mauritania. Despite efforts to control dune mobility, accurate and adequate local studies are still needed to tackle sand encroachment. We have developed a Sand Dune Encroachment Vulnerability Index (SDEVI) to assess Nouakchotts vulnerability to sand dune encroachment. Said index is based on the geo-physical characteristics of the area (wind direction and intensity, slope and surface height, land use, vegetation or soil properties) with Geographic Information System (GIS) techniques that can support local authorities and decision-makers in implementing preventive measures or reducing impact on the population and urban infrastructures. In order to validate this new index, we use two remote sensing approaches: optical-Sentinel 2 and Synthetic Aperture Radar (SAR)Sentinel 1 data. Results show that the greatest vulnerability is located in the north-eastern part of Nouakchott, where local conditions favor the advance of sand in the city, although medium to high values are also found in the eastern part. Optical images enabled us to distinguish desert sand using the ratio between near infrared/blue bands, and SAR Coherence Change Detection (CCD) imagery was used to assess the degree of stability of those sand bodies. The nature of the SDEVI index allows us to currently assess which areas are vulnerable to sand encroachment since we use long data records. Nevertheless, optical and SAR remote sensing allow sand evolution to be monitored on a near real-time basis.

Research areas: Land Surface/Agriculture Indicators
Determination and prediction of standardized precipitation index (SPI) using TRMM data in arid ecosystems (2018)

Drought over a period threatens the water resources, agriculture, and socioeconomic activities. Therefore, it is crucial for decision makers to have a realistic anticipation of drought events to mitigate its impacts. Hence, this research aims at using the standardized precipitation index (SPI) to predict drought through time series analysis techniques. These adopted techniques are autoregressive integrating moving average (ARIMA) and feed-forward backpropagation neural network (FBNN) with different activation functions (sigmoid, bipolar sigmoid, and hyperbolic tangent). After that, the adequacy of these two techniques in predicting the drought conditions has been examined under arid ecosystems. The monthly precipitation data used in calculating the SPI time series (SPI 3, 6, 12, and 24 timescales) have been obtained from the tropical rainfall measuring mission (TRMM). The prediction of SPI was carried out and compared over six lead times from 1 to 6 using the model performance statistics (coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE)). The overall results prove an excellent performance of both predicting models for anticipating the drought conditions concerning model accuracy measures. Despite this, the FBNN models remain somewhat better than ARIMA models with R  0.7865, MAE  1.0637, and RMSE  1.2466. Additionally, the FBNN based on hyperbolic tangent activation function demonstrated the best similarity between actual and predicted for SPI 24 by 98.44%. Eventually, all the activation function of FBNN models has good results respecting the SPI prediction with a small degree of variation among timescales. Therefore, any of these activation functions can be used equally even if the sigmoid and bipolar sigmoid functions are manifesting less adjusted R2 and higher errors (MAE and RMSE). In conclusion, the FBNN can be considered a promising technique for predicting the SPI as a drought monitoring index under arid ecosystems.

Research areas: Droughts
The deterioration of materials as a result of air pollution as derived from satellite and ground based observations (2018)

Dose Response Functions (DRFs) are widely used in estimating corrosion and/or soiling levels of materials used in building constructions and cultural monuments. These functions quantify the effects of air pollution and environmental parameters on different materials through ground based measurements of specific air pollutants and climatic parameters. Here, we propose a new approach where available satellite observations are used instead of ground-based data. Through this approach, the use of DRFs is expanded to cover situations where there are no in situ measurements, introducing also a totally new field where satellite data can be shown to be very helpful. In the present work satellite observations made by MODIS (MODerate resolution Imaging Spectroradiometer) on board Terra and Aqua, OMI (Ozone Monitoring Instrument) on board Aura and AIRS (Atmospheric Infrared Sounder) on board Aqua have been used.

Research areas: Air Quality
Extended database of SO2 column densities over Pakistan by exploiting satellite observations (2019)

Increasing levels of atmospheric SO2 is a major threat and is now widely included in health risk assessment studies. An annual estimation of SO2 has been conducted for both anthropogenic and natural activities at country level. Satellite observations of atmospheric sulfur dioxide (SO2) are obtained over Pakistan during the time span of 20052016. The data was acquired from the Ozone Monitoring Instrument (OMI) onboard NASA's Aura satellite. DOAS (Differential Optical Absorption Spectroscopy) based algorithm was utilized to retrieve SO2 column densities. In addition to spatio-temporal analysis of SO2 column, seasonal cycles over major areas of Pakistan are evaluated and discussed. Year 2007, 2011 exhibited anomalous high levels of SO2 attributed to trans-boundary volcanic plumes observed in Pakistan. SO2 plumes were caused by Dallafilla and Nabro volcanic eruption and spread over East African, the Middle Eastern and South Asian regions. A temporal increase of about 78% was observed in anthropogenic SO2 levels for a period of 20052016 across Pakistan. Further, the relative change was maximum in Khyber Pakhtoon Khwa (KPK region 46%) followed by Punjab (38%), Sindh (28%) and Baluchistan (18%), respectively. The economic prosperity in recent years resulted in increased demand for energy by both the industrial and domestic sector, and consequent increase in SO2 emissions in Pakistan.

Research areas: Air Quality, Volcanic Eruptions, Energy Production/Use
Performance Evaluation of CCAM-CTM Regional Airshed Modelling for the New South Wales Greater Metropolitan Region (2018)

A comprehensive evaluation of the performance of the coupled Conformal Cubic Atmospheric Model (CCAM) and Chemical Transport Model (CTM) (CCAM-CTM) for the New South Wales Greater Metropolitan Region (NSW GMR) was conducted based on modelling results for two periods coinciding with measurement campaigns undertaken during the Sydney Particle Study (SPS), namely the summer in 2011 (SPS1) and the autumn in 2012 (SPS2). The model performance was evaluated for fine particulate matter (PM2.5), ozone (O3) and nitrogen dioxide (NO2) against air quality data from the NSW Governments air quality monitoring network, and PM2.5 components were compared with speciated PM measurements from the Sydney Particle Studys Westmead sampling site. The model tends to overpredict PM2.5 with normalised mean bias (NMB) less than 20%, however, moderate underpredictions of the daily peak are found on high PM2.5 days. The PM2.5 predictions at all sites comply with performance criteria for mean fractional bias (MFB) of 60%, but only PM2.5 predictions at Earlwood further comply with the performance goal for MFB of 30% during both periods. The model generally captures the diurnal variations in ozone with a slight underestimation. The model also tends to underpredict daily maximum hourly ozone. Ozone predictions across regions in SPS1, as well as in Sydney East, Sydney Northwest and Illawarra regions in SPS2 comply with the benchmark of MFB of 15%, however, none of the regions comply with the benchmark for mean fractional error (MFE) of 35%. The model reproduces the diurnal variations and magnitudes of NO2 well, with a slightly underestimating tendency across the regions. The MFE and normalised mean error (NME) for NO2 predictions fall well within the ranges inferred from other studies. Model results are within a factor of two of measured averages for sulphate, nitrate, sodium and organic matter, with elemental carbon, chloride, magnesium and ammonium being underpredicted. The overall performance of CCAM-CTM modelling system for the NSW GMR is comparable to similar model predictions by other regional airshed models documented in the literature. The performance of the modelling system is found to be variable according to benchmark criteria and depend on the location of the sites, as well as the time of the year. The benchmarking of CCAM-CTM modelling system supports the application of this model for air quality impact assessment and policy scenario modelling to inform air quality management in NSW.

Research areas: Validation, Air Quality
Long-term distribution patterns of remotely sensed water quality variables in Pearl River Delta, China (2019)

Pearl River Delta (PRD) is the third largest and one of the most productive Deltas in south China, where long-term monitoring and its assessment of water quality are necessary to understand the distribution patterns to infer the coastal zone management. The studies on long-term monitoring in water quality variable concerning the changing climate and anthropogenic effects can be made possible by continuous coverage of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This study attempts to study the long-term trends, quantitative measurements, and assessment of water quality variables including total suspended solids (TSS), diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd (490)), and sea surface temperature (SST) using 8-day composite and monthly data products of MODIS data for the period between July 2002 and August 2018 in the turbid waters of the PRD. Monthly products are used to quantify the variability of these water quality properties on long-term climatology, seasonal and as well as spatial distributions to interannual timescales. We observed a decreasing trend of TSS and Kd (490) between 2002 and 2018, though not significant, whereas, an increasing trend of SST has recorded for the PRD region during the same time. Seasonal patterns of TSS, Kd (490) and SST showed significant differences between the seasons. The high values of TSS (>15 gm3) and Kd (490) (>2 m1) were found in summer, autumn followed by winter, and low values (<0.5 gm 3 and 0.1 m-1) in spring. Consequently, high SSTs were noticed in the in summer-Autumn season and low SSTs in winter. The spatial concentrations of TSS, Kd (490) and SST were found high in the northern region and low values in the offshore and the southern part of the PRD. The increased values of TSS and Kd (490) in summer can be attributed to the high amount of precipitation, and strong monsoonal winds lead to the significant amounts of highly turbid waters in the water column. Based on the MODIS derived time series of TSS and Kd (490), a significant correlation with wind speed was found between these variables, suggesting a critical role of precipitation, river discharge and wind forcing in the variability of ocean variables in the PRD region.

Research areas: Water Quality
Comparative analysis of two rainfall retrieval algorithms during extreme rainfall event: a case study on cloudburst, 2010 over Ladakh (Leh), Jammu and Kashmir (2019)

Intense rainfall events lead to floods and landslides in the Western Himalayas (WH). These rainfall amounts are considered comparatively moderate over the plains. These events, called cloudbursts, are convective triggered followed by orographically locked phenomena producing sudden high-intensity rainfall over a small area. Early warning and prediction of such severe local weather systems is important to mitigate societal impacts arising from the accompanying flash floods, landslides, etc. Due to lack of ground-based observations, cloudbursts over remote and unpopulated hilly areas often go unreported. Present study examines a cloudburst occurred at Ladakh (Leh) in the WH in the early hours of August 5, 2010, using remotely sensed rainfall data from Tropical Rainfall Measuring Mission (TRMM) and Kalpana-1. The storm lasted for 2 days starting from August 3, 2010, followed by flash floods. Rain-band propagation over the region is studied from Kalpana-1 3-hourly rainfall estimates using Indian Satellite (INSAT) multi-spectral rainfall algorithm (IMSRA) and TRMM rainfall estimates using TRMM 3B42 algorithm. Quantitative and qualitative assessment and comparison of these two products is made. It is observed that there is decrease in satellite brightness temperature (BT) during the rainfall event. Initiation of rainfall occurs at about 

Research areas: Atmospheric/Ocean Indicators, Floods
Influence of Oceanographic Variability on the Life Cycle and Spawning Period of Sardinella fimbriata in the Northern Part of Bay of Bengal (2020)

Reproduction and distribution patterns of Sardinella fimbriata had responded differently to oceanographic changes in various parts of the world. This is the first report investigating the effects of oceanographic parameters on the maturation and spawning of S. fimbriata in the northern part of Bay of Bengal. Monthly precipitation rate, sea surface temperature (SST) and chlorophyll-a data were analyzed for possible correlation with Catch per Unit Effort (CPUE) of the fish species under study. Gonado-somatic Index, Gastro-somatic Index, and histology of the gonadal maturity stages were also studied. The peak spawning activity was found to take place during the period with the highest precipitation rate (r2 = 0.5006, p 

Research areas: Ecosystems, Atmospheric/Ocean Indicators
The satellite derived environmental factors and their relationships with dimethylsulfide in the East Marginal Seas of China (2020)

Biogenic dimethylsulfide (DMS) sea-to-air flux and its closely linked environment factors are studied in the East Marginal Seas of China (EMSC) (25N-40N, 120E-130E) from 2011 to 2015. Higher chlorophyll-a (CHL) up north during blooming season is due to the southeasterly wind brought more nutrients from the Changjiang River waters. The elevated CHL during autumn and winter periods down south is more related with the higher sea surface temperature (SST). The lower CHL in the middle region in the later half year was mainly influenced by terrestrial runoff and the diluted Changjiang River waters. The enriched Changjiang River discharges brought more nutrients to the Zhejiang coastal waters and enriched the local phytoplankton, where aerosol optical depth (AOD) had the highest correlation with CHL (0.96). Peak SST was in August and had a negative correlation with wind speed (WIND). With increased WIND up north, AOD reduced accordingly. Lower CHL in 2015 was mainly related to the southeastern wind brought less nutrient water from Open Sea during blooming season. DMS sea-to-air fluxes are calculated and compared with OUC (Ocean University of China) field data. The reasonable agreements are made. DMS concentrations ranged from 0.32 to 5.64 nM with a mean of 2.19 nM, it had high positive correlations with CHL in the summer and autumn periods. DMS sea-to-air fluxes ranged within 2.1459.06 molm2d1 with a mean of 18.96 molm2d1. The elevated DMS sea-to-air fluxes in ECMS indicate the contributions of DMS emission in the study region cannot be ignored on the global climate evaluation.

Research areas: Ecosystems