Community-based adaptation in low-lying islands in the Philippines: challenges and lessons learned

Community-based adaptation (CBA) seeks to address climate risks and socio-economic drivers of vulnerability simultaneously. However, as CBA activities appear very similar to standard development work, difficulties in identifying good practices arise. To clarify the role of CBA, this study elucidated how climate change can impact pre-existing development problems by investigating the experiences of four low-lying island communities in central Philippines. The islands currently suffer from frequent and extreme tidal flooding (following an earthquake-induced land subsidence in 2013, with a magnitude that is broadly similar to sea-level rise projections under a 1.5 to 2 °C global warming scenario), and endured a dry spell in 2016. The study also identified various publicly and privately initiated adaptation strategies, and evaluated their resilience against actual biophysical events. The study conducted focus group discussions with local leaders and in-depth interviews with government officials and residents in March 2016. Results show that tidal flooding impacted almost all aspects of daily life on the islands, while the dry spell completely depleted their limited water supplies. The strategies implemented by governments and NGOs (e.g., seawalls, rainwater collectors) were found to be inadequate in preventing tidal flooding and compensating for the dry spell. Also, communities used coral stones and plastic waste for raising the floors of their homes, which have an erosive effect on their capacity to adapt in the long term. Lack of community participation in publicly initiated projects and lack of adaptation funding for community-based strategies were the greatest obstacles to implementing climate-resilient solutions.     

Ferulic acid supplementation for 40 days in hair ewe lambs experiencing seasonal heat stress: short-term effects on physiological responses,growth, metabolism,and hematological profile

Environmental Science and Pollution Research - Free ferulic acid (FA) is a natural compound with antioxidant properties which mitigates the negative effects of cold stress in sheep; however, its...     

Validation of a multiclass multiresidue method and monitoring results for 210 pesticides in fruits and vegetables by gas chromatography-triple quadrupole mass spectrometry

A rapid, sensitive, accurate and reliable multiresidue method for the identification and quantification of 210 relevant pesticides in four representative fruit and vegetable commodities (tomato, potato, spring onion and orange) has been developed and validated by gas chromatography in tandem with triple quadrupole mass spectrometry. The method has been fully validated and applied to 292 samples from different countries. Prior to instrumental analysis, an extraction procedure based on a sample extraction of multiclass analytes, using the ethyl acetate method was employed. Mass spectrometric conditions were individually optimized for each compound in the selected reaction monitoring (SRM) mode to achieve maximum sensitivity. The pesticides were separated in less than 25 min. This was followed by an exhaustive control of the retention times. The Retention Time Locking Method was applied, working at a constant pressure throughout the analysis. System maintenance was reduced by using a purged capillary flow device that provided backflush capabilities by reversing column flow immediately after elution of the last compound of interest. Istotopically labelled internal standards were employed to improve the quality of the analytical results.     

Model-driven development of covariances for spatiotemporal environmental health assessment

Known conceptual and technical limitations of mainstream environmental health data analysis have directed research to new avenues. The goal is to deal more efficiently with the inherent uncertainty and composite space-time heterogeneity of key attributes, account for multi-sourced knowledge bases (health models, survey data, empirical relationships etc.), and generate more accurate predictions across space-time. Based on a versatile, knowledge synthesis methodological framework, we introduce new space-time covariance functions built by integrating epidemic propagation models and we apply them in the analysis of existing flu datasets. Within the knowledge synthesis framework, the Bayesian maximum entropy theory is our method of choice for the spatiotemporal prediction of the ratio of new infectives (RNI) for a case study of flu in France. The space-time analysis is based on observations during a period of 15 weeks in 1998–1999. We present general features of the proposed covariance functions, and use these functions to explore the composite space-time RNI dependency. We then implement the findings to generate sufficiently detailed and informative maps of the RNI patterns across space and time. The predicted distributions of RNI suggest substantive relationships in accordance with the typical physiographic and climatologic features of the country.