Implementation of satellite-based data for improving predictions of arsenic contamination in groundwater in the Red River Delta in Vietnam

Natural arsenic contamination of groundwater aquifers is globally widespread, and particularly poses a problem in regions where groundwater is the main source of drinking and cooking water. Arsenic poisoning can lead to a myriad of serious health effects such as diseases of blood vessels, diabetes and cancers. The aquifers of the Red River Delta in Vietnam are highly contaminated with arsenic and it has been estimated that in this area, around 3 million people are affected by high arsenic concentrations (> 10 \ub5g/L, WHO guideline value; Winkel et al., 2011). Previously, predictions of arsenic contamination in the Red River Delta were established via geospatial modelling using arsenic measurements, as well as surface and 3D-geology. Based on these predictions, probability maps of arsenic at specific depths were created. By comparing these depthresolved probabilities to measured arsenic concentrations, a drawdown of arsenic-enriched waters from Holocene aquifers to previously uncontaminated Pleistocene aquifers was observed. This finding indicated that arsenic contamination has been exacerbated by excessive groundwater pumping rates (Winkel et al., 2011). Furthermore, in a study conducted in the Mekong delta, it was hypothesized that groundwater extraction causes interbedded clays to compact, thereby releasing water containing dissolved arsenic that is subsequently transported to deeper aquifers (Erban et al., 2013). Such human-induced changes cannot be captured by the previous predictive models based on natural predictive parameters mentioned above, leading to erroneous predictions of the arsenic content in areas affected by urbanization, especially in deeper aquifers. To improve predictions in human-affected regions we are using satellite data and remote sensing techniques that enable detection of changes of urban and suburban extents (Nghiem et al., 2009) and vertical build-up (Mathews et al., 2019). Those data and techniques in combination with geochemical and environmental data can help in i) resolving mechanisms behind arsenic mobilization in aquifers due to increased pumping rates and ii) making predictions of arsenic contamination more accurate, especially in areas characterized by increased groundwater pumping

Complete breeding failures in ivory gull following unusual rainy storms in North Greenland

Natural catastrophic events such as heavy rainfall and windstorms may induce drastic decreases in breeding success of animal populations. We report the impacts of summer rainfalls on the reproductive success of ivory gull (Pagophila eburnea) in north-east Greenland. On two occasions, at Amdrup Land in July 2009 and at Station Nord in July 2011, we observed massive ivory gull breeding failures following violent rainfall and windstorms that hit the colonies. In each colony, all of the breeding birds abandoned their eggs or chicks during the storm. Juvenile mortality was close to 100% at Amdrup Land in 2009 and 100% at Station Nord in 2011. Our results show that strong winds associated with heavy rain directly affected the reproductive success of some Arctic bird species. Such extreme weather events may become more common with climate change and represent a new potential factor affecting ivory gull breeding success in the High Arctic

Urban impacts on air quality observed with remote sensing and ground station data from the PO plain field campaign

The Po Plain area is one of the most urbanized and polluted regions in Europe, with the city of Milan being a major \u201chot spot\u201d of nitrogen dioxide (NO2) in the world. The Po Plain Experiment Field Campaign has been carried out to identify and understand impacts of urban characteristics on the environment across the Po Plain in Northern Italy. Air quality is investigated with both remote sensing and ground station data. Preliminary results show a moderate correlation between satellite observations and ground-based measurements, highlighting the close relationship between the urban pattern and the distribution of NO2 all over the region

New approaches to integrate the time dimension in groundwater vulnerability assessments

European Union Directives (2000/60/EC, 2006/118/EC) require the identification of areas showing groundwater quality deterioration, as well as groundwater contamination by anthropogenic pollutants. Nitrate is one of the most common contaminants that affects groundwater in Northern Italy and all over the world. We present different approaches of groundwater vulnerability assessment to nitrate contamination, of the Po Plain area in Lombardy region, in order to meet the European Union requirements. Groundwater vulnerability is assessed through the spatial statistical method Weights of Evidence (WofE). WofE is a method based on the Bayesian conditional probability, which enables observations of the individual role and the combined effect of both anthropogenic and natural factors in relation to contamination in groundwater. Three scenarios have been evaluated: a) the status of nitrate contamination and anthropogenic sources in 2009; b) the change in nitrate contamination and the evolution of anthropogenic sources in the period 2000-2009; c) the combination of a) and b). Anthropogenic sources of nitrate contamination are fertilizer and manure from agricultural activities, and sewer leakages in urban areas. The first source is represented by the amount of nitrate loadings, while the second by the urbanization derived from satellite scatterometer data (QuikSCAT-DSM data). Natural factors representing geological and hydrogeological conditions (e.g., soil protective capacity and groundwater depth) are considered not time dependent in the analyses. The analyses show the differences and the similarities of the distribution of the vulnerable areas among the three scenarios. In these areas, the combination of natural and anthropogenic factors involves a given absolute level of contamination in the aquifer and a deterioration of groundwater quality. Moreover, these approaches and the available data sets enable the development of future scenarios that consider land use changes and their impact on groundwater quality

Use of scatterometer data in groundwater vulnerability assessment

Lombardy in Italy has been selected as a case study to evaluate the capability of the QuikSCAT - Dense Sampling Method (QSCAT-DSM) data in delineating urban extent, estimating rate of urban changes, and assessing aquifer vulnerability, in particular to investigate the relationship between land-use changes and groundwater contamination. QSCAT-DSM data represent an innovative approach to delineate urban and interurban areas with satellite scatterometer data. Radar backscatter acquired by the SeaWinds scatterometer aboard the QSCAT satellite together with the DSM are used to identify and map surface features at a posting scale of about 1 km2. Through the spatial statistical methods Weight of Evidence (WofE), both urban changes given by QSCAT-DSM data and population changes in the decade of the 2000's have been correlated to nitrate concentration trend in groundwater in the same time period. Both analyses based on urban change and on population change lead to the same result: urban nitrate sources in Lombardy increase the level of nitrate concentration in groundwater, indicating a degradation of the water quality. Moreover, QSCAT-DSM data proved to be a reliable tool for evaluating urban changes continuously without a temporal or spatial gap, and to be a strategic variable allowing the assessment of groundwater vulnerability consistently throughout the decadal time scale

Groundwater vulnerability maps derived from a time-dependent method using satellite scatterometer data

Introducing the time variable in groundwater vulnerability assessment is an innovative approach to study the evolution of contamination by non-point sources and to forecast future trends. This requires a determination of the relationship between temporal changes in groundwater contamination and in land use. Such effort will enable breakthrough advances in mapping hazardous areas, and in assessing the efficacy of land-use planning for groundwater protection. Through a Bayesian spatial statistical approach, time-dependent vulnerability maps are derived by using hydrogeological variables together with three different time-dependent datasets: population density, high-resolution urban survey, and satellite QuikSCAT (QSCAT) data processed with the innovative dense sampling method (DSM). This approach is demonstrated extensively over the Po Plain in Lombardy region (northern Italy). Calibrated and validated maps show physically consistent relations between the hydrogeological variables and nitrate trends. The results indicate that changes of urban nitrate sources are strongly related to groundwater deterioration. Among the different datasets, QSCAT-DSM is proven to be the most efficient dataset to represent urban nitrate sources of contamination, with major advantages: a worldwide coverage, a continuous decadal data collection, and an adequate resolution without spatial gaps. This study presents a successful approach that, for the first time, allows the inclusion of the time dimension in groundwater vulnerability assessment by using innovative satellite remote sensing data for quantitative statistical analyses of groundwater quality changes