Spatial Risk Index Research Articles

Barau's petrel, Pterodroma baraui, as a bioindicator of plastic pollution in the South-West Indian Ocean: A multifaceted approach

Marine plastic pollution is well described by bioindicator species in temperate and polar regions but remains understudied in tropical oceans. We addressed this gap by evaluating the seabird Barau's petrel as bioindicator of plastic pollution in the South-West Indian Ocean. We conducted a multifaceted approach including necropsies of birds to quantify plastic ingestion; GPS tracking of breeding adults to identify their foraging areas; manta trawling of plastic debris to measure plastic pollution at sea and modelling of plastic dispersal. We developed a spatial risk index of seabird exposure to plastic ingestion. Seventy-one percent of the analysed birds had ingested plastic. GPS tracking coupled with manta trawling and dispersal modelling show that adults consistently foraged at places with high level of plastic concentration. The highest ingestion risk occurred in the northwest of Reunion Island and at latitude 30°S. Our findings confirm that Barau's petrel is a reliable bioindicator of plastic pollution in the region.

Unraveling the Impact of COVID-19 Pandemic Dynamics on Commercial Water-Use Variation

Water use was impacted significantly by the COVID-19 pandemic. Although previous studies quantitatively investigated the effects of COVID-19 on water use, the relationship between water-use variation and COVID-19 dynamics (i.e., the spatial-temporal characteristics of COVID-19 cases) has received less attention. This study developed a two-step methodology to unravel the impact of COVID-19 pandemic dynamics on water-use variation. First, using a water-use prediction model, the water-use change percentage (WUCP) indicator, which was calculated as the relative difference between modeled and observed water use, i.e., water-use variation, was used to quantify the COVID-19 effects on water use. Second, two indicators, i.e., the number of existing confirmed cases (NECC) and the spatial risk index (SRI), were applied to characterize pandemic dynamics, and the quantitative relationship between WUCP and pandemic dynamics was examined by means of regression analysis. We collected and analyzed 6-year commercial water-use data from smart meters of Zhongshan District in Dalian City, Northeast China. The results indicate that commercial water use decreased significantly, with an average WUCP of 59.4%, 54.4%, and 45.7%during the three pandemic waves, respectively, in Dalian. Regression analysis showed that there was a positive linear relationship between water-use changes (i.e., WUCP) and pandemic dynamics (i.e., NECC and SRI). Both the number of COVID-19 cases and their spatial distribution impacted commercial water use, and the effects were weakened by restriction strategy improvement, and the accumulation of experience and knowledge about COVID-19. This study provides an in-depth understanding of the impact of COVID-19 dynamics on commercial water use. The results can be used to help predict water demand under during future pandemic periods or other types of natural and human-made disturbance.

A GIS analysis approach for flood vulnerability and risk assessment index models at sub-district scale

This paper presents flood vulnerability and risk assessment index models, with the aim to establish a spatial risk index at sub-district scales for urban flood scenarios in the Makassar region. Firstly, the overall vulnerability assessment to floods based on the local framework analysis of the BNPB (The Indonesian National Board for Disaster Management) has been developed using Geographical Information System (GIS) analysis. These indicators were composed of various social, physical, economic, and environmental factors. Second, GIS analysis conducts grid index modeling of flood hazard model by incorporating the measurement of floods in 2013 as a flood hazard scenario. Finally, by combining the spatial factors of flood hazard and flood vulnerabilities, a flood risk assessment model has been simulated at sub-district scales to evaluate the potential impacts on social, physical, economic, and environmental aspects.

A network based spatial risk index indicator to guide active surveillance

A network based spatial risk index indicator to guide active surveillance

Assessment of coastal risks to climate change related impacts at the regional scale: The case of the Mediterranean region

This paper presents a Coastal Risk Index (CRI-MED) developed to assess coastal risks and vulnerabilities associated with the physical and socio-economic impacts of climate change in all Mediterranean coastal zones. CRI-MED is a spatial risk index, which combines variables (multiple data layers) representing different aspects of risk in such a way that coastal areas of relatively higher risk emerge from the integration of the variables. It creates an interface between theoretical concepts of risk and the decision-making process relating to disaster risk reduction. Based on a GIS application, CRI-MED provides relative hazard, exposure, vulnerability and risk maps of the Mediterranean region that allow researchers and policy-makers to identify coastal areas most at risk from coastal erosion and coastal flooding, the so-called “hot-spots”. Through the application of CRI-MED on 21 Mediterranean countries, coastal hot-spots are found to be predominantly located in the south-eastern Mediterranean region. Countries with the highest percentage of extremely high risk values are Syria (30.5%), Lebanon (22.1%), Egypt (20.7%), and Palestine (13.7%). The CRI-MED method is intended as a scientific tool which produces easily understandable outcomes, to support international organizations and national governments to enhance and mainstream decision-making based on information that is accessible and useful. The definition of coastal hot-spots aims to support the prioritization of policies and resources for adaptation and Integrated Coastal Zone Management (ICZM). In particular, the resulting risk maps enable identification of suitable and less suitable areas for urban settlements, infrastructures and economic activities.

Development of an active risk-based surveillance strategy for avian influenza in Cuba

The authors designed a risk-based approach to the selection of poultry flocks to be sampled in order to further improve the sensitivity of avian influenza (AI) active surveillance programme in Cuba. The study focused on the western region of Cuba, which harbours nearly 70% of national poultry holdings and comprise several wetlands where migratory waterfowl settle (migratory waterfowl settlements – MWS). The model took into account the potential risk of commercial poultry farms in western Cuba contracting from migratory waterfowl of the orders Anseriformes and Charadriiformes through dispersion for pasturing of migratory birds around the MWS. We computed spatial risk index by geographical analysis with Python scripts in ESRI® ArcGIS 10 on data projected in the reference system NAD 1927–UTM17. Farms located closer to MWS had the highest values for the risk indicator pj and in total 31 farms were chosen for targeted surveillance during the risk period. The authors proposed to start active surveillance in the study area 3 weeks after the onset of Anseriformes migration, with additional sampling repeated twice in the same selected poultry farms at 15 days interval (Comin et al., 2012; EFSA, 2008) to cover the whole migration season. In this way, the antibody detectability would be favoured in case of either a posterior AI introduction or enhancement of a previous seroprevalence under the sensitivity level. The model identified the areas with higher risk for AIV introduction from MW, aiming at selecting poultry premises for the application of risk-based surveillance. Given the infrequency of HPAI introduction into domestic poultry populations and the relative paucity of occurrences of LPAI epidemics, the evaluation of the effectiveness of this approach would require its application for several migration seasons to allow the collection of sufficient reliable data.

Vector-Borne Pathogen Spread Through Ticks on Migratory Birds: A Probabilistic Spatial Risk Model for South-Western Europe

Tick-borne pathogens can spread easily through the movements of infested birds. An important example is viruses that pose a threat to humans and that are carried in Hyalomma ticks that move from Africa into south-western Europe. This study evaluates the probability of arrival of migrating birds from Africa into Spain and the environmental suitability of different regions of Spain for the survival of tick stages introduced by these birds. This evaluation produced a spatial risk index measuring the probability that foreign tick populations will survive in the target area. Periods of highest risk were observed for large areas of Spain, from the second fortnight of April to the second fortnight of May. Although birds may arrive as early as January and massive migrations may take place in March, the environmental suitability for Hyalomma marginatum ticks is low in these periods and high mortality of the spread stages (nymphs) is expected. This study introduces new methods of objective analysis based on spatial and process-driven models for both ticks and hosts and critically evaluates the usefulness of spatial spreading methods for assessing the risk of tick-borne pathogens.