Toward a Solution of the “Peruvian Puzzle”: Pelagic Food-Web Structure and Trophic Interactions in the Northern Humboldt Current Upwelling System Off Peru

The northern Humboldt Current upwelling system (HCS) belongs to the most productive marine ecosystems, providing five to eight times higher fisheries landings per unit area than other coastal upwelling systems. To solve this “Peruvian puzzle”, to elucidate the pelagic food-web structure and to better understand trophic interactions in the HCS, a combined stable isotope and fatty acid trophic biomarker approach was adopted for key zooplankton taxa and higher trophic positions with an extensive spatial coverage from 8.5 to 16°S and a vertical range down to 1,000 m depth. A pronounced regional shift by up to ∼5‰ in the δ15N baseline of the food web occurred from North to South. Besides regional shifts, δ15N ratios of particulate organic matter (POM) also tended to increase with depth, with differences of up to 3.8‰ between surface waters and the oxygen minimum zone. In consequence, suspension-feeding zooplankton permanently residing at depth had up to ∼6‰ higher δ15N signals than surface-living species or diel vertical migrants. The comprehensive data set covered over 20 zooplankton taxa and indicated that three crustacean species usually are key in the zooplankton community, i.e., the copepods Calanus chilensis at the surface and Eucalanus inermis in the pronounced OMZ and the krill Euphausia mucronata, resulting in an overall low number of major trophic pathways toward anchovies. In addition, the semi-pelagic squat lobster Pleuroncodes monodon appears to play a key role in the benthic-pelagic coupling, as indicated by highest δ13C’ ratios of −14.7‰. If feeding on benthic resources and by diel vertical migration, they provide a unique pathway for returning carbon and energy from the seafloor to the epipelagic layer, increasing the food supply for pelagic fish. Overall, these mechanisms result in a very efficient food chain, channeling energy toward higher trophic positions and partially explaining the “Peruvian puzzle” of enormous fish production in the HCS.

Quantitative Analysis on Coastline Changes of Yangtze River Delta Based on High Spatial Resolution Remote Sensing Images

The coastline situation reflects socioeconomic development and ecological environment in coastal zones. Analyzing coastline changes clarifies the current coastline situation and provides a scientific basis for making environmental protection policies, especially for coastlines with significant human interference. As human activities become more intense, coastline types and their dynamic changes become more complicated, which needs more detailed identification of coastlines. High spatial resolution images can help provide detailed large spatial coverage at high resolution information on coastal zones. This study aims to map the position and status of the Yangtze River Delta (YRD) coastline using an NDWI threshold method based on 2 m Gaofen-1/Ziyuan-3 imagery and analyze coastline change and coastline type distribution characteristics. The results showed that natural and artificial coastlines in the YRD region accounted for 42.73% and 57.27% in 2013 and 41.56% and 58.44% in 2018, respectively. The coastline generally advanced towards the sea, causing a land area increase of 475.62 km2. The changes in the YRD coastline mainly resulted from a combination of large-scale artificial construction and natural factors such as silt deposition. This study provides a reference source for large spatial coverage at high resolution remote sensing coastline monitoring and a better understanding of land use in coastal zone.

Assessing Spatial Vulnerability of Bangladesh to Climate Change and Extremes: A Geographic Information System Approach

Regarding climate change, the world’s most discussed issue for the last few decades, countries like Bangladesh are always noteworthy due to its susceptibility resulting from its geography, hazard proneness, and socioeconomic condition. Thus, this aimed to justify the hypothesis that Bangladesh has spatial diversity in sectors of Climate Change Vulnerability (CCV) by identifying the sectors of vulnerability and visualizing the spatial distribution of vulnerability through multivariate geospatial analysis in the GIS environment. For an integrated assessment of CCV, 38 indicators (socio-economic and biophysical) have been incorporated in the IPCC framework in raster form. Test statistics have shown Kiser-Meyer-Olkin (KMO) value is 0.73 and the p-value of Bartlett’s sphericity is 0. The principal component analysis resulted in 6 principal components with 73.52% total explained variance. Sectors of CCV are the Climatic extreme event vulnerability (PC1), Meteorological shift vulnerability (PC2), Infrastructure and demographic vulnerability (PC3), Ecological vulnerability (PC4), Flood vulnerability (PC5), and Economic vulnerability (PC6) with Cronbach’s alpha 0.90, 0.81, 0.88, 0.72, 0.72, and 0.66 respectively. Among 3 clusters (Jenk’s Natural break) of weighted averaged indices, the highly vulnerable cluster has shown that the PC1 has the highest magnitude with a score of 0.53–0.87, while the PC5 has the highest spatial coverage with 24 districts. The present study however is a new edition in climate vulnerability assessment in Bangladesh since it encompasses multivariate spatial analysis to demonstrate countrywide CCV. This study should be an important tool for setting adaptation and mitigation strategies from the root level to policymaking platforms of Bangladesh.

Deep Neural Networks for Aerosol Optical Depth Retrieval

Aerosol Optical Depth (AOD) is a measure of the extinction of solar radiation by aerosols in the atmosphere. Understanding the variations of global AOD is necessary for precisely determining the role of aerosols. Arctic warming is partially caused by aerosols transported from vast distances, including those released during biomass burning events (BBEs). However, measuring AODs is challenging, typically requiring active LIDAR systems or passive sun photometers. Both are limited to cloud-free conditions; sun photometers provide only point measurements, thus requiring more spatial coverage. A more viable method to obtain accurate AOD may be found through machine learning. This study uses DNNs to estimate Svalbard’s AODs using a minimal set of meteorological parameters (temperature, air mass, water vapor, wind speed, latitude, longitude, and time of year). The mean absolute error (MAE) between predicted and true data was 0.00401 for the entire set and 0.0079 for the validation set. It was then shown that the inclusion of BBE data improves predictions by 42.167%. It was demonstrated that AODs may be accurately estimated without the use of expensive instrumentation, using machine learning and minimal data. Similar models may be developed for other regions, allowing immediate improvement of current meteorological models.

Mosaicking Weather Radar Retrievals from an Operational Heterogeneous Network at C and X Band for Precipitation Monitoring in Italian Central Apennines

Meteorological radar networks are suited to remotely provide atmospheric precipitation retrieval over a wide geographic area for severe weather monitoring and near-real-time nowcasting. However, blockage due to buildings, hills, and mountains can hamper the potential of an operational weather radar system. The Abruzzo region in central Italy’s Apennines, whose hydro-geological risks are further enhanced by its complex orography, is monitored by a heterogeneous system of three microwave radars at the C and X bands with different features. This work shows a systematic intercomparison of operational radar mosaicking methods, based on bi-dimensional rainfall products and dealing with both C and X bands as well as single- and dual-polarization systems. The considered mosaicking methods can take into account spatial radar-gauge adjustment as well as different spatial combination approaches. A data set of 16 precipitation events during the years 2018–2020 in the central Apennines is collected (with a total number of 32,750 samples) to show the potentials and limitations of the considered operational mosaicking approaches, using a geospatially-interpolated dense network of regional rain gauges as a benchmark. Results show that the radar-network pattern mosaicking, based on the anisotropic radar-gauge adjustment and spatial averaging of composite data, is better than the conventional maximum-value merging approach. The overall analysis confirms that heterogeneous weather radar mosaicking can overcome the issues of single-frequency fixed radars in mountainous areas, guaranteeing a better spatial coverage and a more uniform rainfall estimation accuracy over the area of interest.

Evaluating the Utility of High-Resolution Spatiotemporal Air Pollution Data in Estimating Local PM2.5 Exposures in California from 2015–2018

Air quality management is increasingly focused not only on across-the-board reductions in ambient pollution concentrations but also on identifying and remediating elevated exposures that often occur in traditionally disadvantaged communities. Remote sensing of ambient air pollution using data derived from satellites has the potential to better inform management decisions that address environmental disparities by providing increased spatial coverage, at high-spatial resolutions, compared to air pollution exposure estimates based on ground-based monitors alone. Daily PM2.5 estimates for 2015–2018 were estimated at a 1 km2 resolution, derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in order to assess the utility of highly refined spatiotemporal air pollution data in 92 California cities and in the 13 communities included in the California Community Air Protection Program. The identification of pollution hot-spots within a city is typically not possible relying solely on the regulatory monitoring networks; however, day-to-day temporal variability was shown to be generally well represented by nearby ground-based monitoring data even in communities with strong spatial gradients in pollutant concentrations. An assessment of within-ZIP Code variability in pollution estimates indicates that high-resolution pollution estimates (i.e., 1 km2) are not always needed to identify spatial differences in exposure but become increasingly important for larger geographic areas (approximately 50 km2). Taken together, these findings can help inform strategies for use of remote sensing data for air quality management including the screening of locations with air pollution exposures that are not well represented by existing ground-based air pollution monitors.

Estimating Hourly Full-Coverage PM2.5 Concentrations Based On MODIS Data Over The Northeast of Thailand.

Particulate matter (PM2.5) pollutants are a significant health issue with impacts on human health; however, monitoring of PM2.5 is very limited in developing countries. Satellite remote sensing can expand spatial coverage, potentially enhancing our ability in a specific area for estimating PM2.5; however, some have reported poor predictive performance. An innovative combination of MODIS AOD was developed to fulfill all missing aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Therefore, hourly PM2.5 concentrations were obtained in Northeastern Thailand. A Linear mixed-effects (LME) model was used to predict location-specific hourly PM2.5 levels. Hourly PM2.5 concentrations measured at 20 PM2.5 monitoring sites and 10- fold cross-validation were addressed for model validation. The observed and predicted concentrations suggested that LME obtained from MODIS AOD data and other factors are a potentially useful predictor of hourly PM2.5 concentrations (R2 >0.70), providing more detailed spatial information for local scales studies. Interestingly, PM2.5 along the Mekong River area was observed higher than in the plain area. The finding can infer that the monsoon wind brings polluted air into the province from sources outside the region. The results will be helpful to analyze air pollution-related health studies.

Constructed Floating treatment wetlands: can they improve water quality in a northern USA climate?

Constructed floating treatment wetlands (FTWs) are a best management practice (BMP) applied in aquatic environments to improve water quality by mitigating nutrient pollution. We evaluated the efficacy of FTWs in Minnesota, USA as a tool for the removal of excess nutrients in surface water to enhance water quality. We began with a 2015 outdoor mesocosm study to quantify the removal efficiency of total phosphorus (TP), ortho-phosphate-P (PO4-P), nitrate-N, and ammonia-N. The FTWs were each planted with wetland plants Juncus effusus, Eleocharis acicularis, and Glyceria canadensis. A paired controlled TP budget was prepared to identify mesocosm sources and sinks. Mesocosm FTWs showed higher PO4-P reduction efficiencies than the control mesocosms. Mesocosms with FTWs had significantly lower pH and dissolved oxygen (DO) concentrations. Water quality measurements were made along with qualitative observations, such as durability, at two different field scales where FTWs were installed in a pond and lake in 2016. Field deployed FTWs showed measurable changes in several water quality parameters over the study period. Statistically significant reductions were observed in PO4-P, DO, and pH for the pond site but not at the lake site. Though positive results were observed, factors other than FTWs may better explain the field deployed FTW results. Overall, the high FTW spatial coverage (15%) in the mesocosms showed clear PO4-P removal, whereas low FTW spatial coverage (<1%) of the field scale surface water was likely the most limiting factor to achieving optimal water quality at the study sites and rather than individual FTW performance.

Quantitative Precipitation Estimation over Antarctica Using Different Ze-SR Relationships Based on Snowfall Classification Combining Ground Observations

Snow plays a crucial role in the hydrological cycle and energy budget of the Earth, and remote sensing instruments with the necessary spatial coverage, resolution, and temporal sampling are essential for snowfall monitoring. Among such instruments, ground-radars have scanning capability and a resolution that make it possible to obtain a 3D structure of precipitating systems or vertical profiles when used in profiling mode. Radars from space have a lower spatial resolution, but they provide a global view. However, radar-based quantitative estimates of solid precipitation are still a challenge due to the variability of the microphysical, geometrical, and electrical features of snow particles. Estimations of snowfall rate are usually accomplished using empirical, long-term relationships between the equivalent radar reflectivity factor (Ze) and the liquid-equivalent snowfall rate (SR). Nevertheless, very few relationships take advantage of the direct estimation of the microphysical characteristics of snowflakes. In this work, we used a K-band vertically pointing radar collocated with a laser disdrometer to develop Ze-SR relationships as a function of snow classification. The two instruments were located at the Italian Antarctic Station Mario Zucchelli. The K-band radar probes the low-level atmospheric layers, recording power spectra at 32 vertical range gates. It was set at a high vertical resolution (35 m), with the first trusted range gate at a height of only 100 m. The disdrometer was able to provide information on the particle size distribution just below the trusted radar gate. Snow particles were classified into six categories (aggregate, dendrite aggregate, plate aggregate, pristine, dendrite pristine, plate pristine). The method was applied to the snowfall events of the Antarctic summer seasons of 2018–2019 and 2019–2020, with a total of 23,566 min of precipitation, 15.3% of which was recognized as showing aggregate features, 33.3% dendrite aggregate, 7.3% plates aggregate, 12.5% pristine, 24% dendrite pristine, and 7.6% plate pristine. Applying the appropriate Ze-SR relationship in each snow category, we calculated a total of 87 mm water equivalent, differing from the total found by applying a unique Ze-SR. Our estimates were also benchmarked against a colocated Alter-shielded weighing gauge, resulting in a difference of 3% in the analyzed periods.