Changing storminess and global capture fisheries

This is the author accepted manuscript. The final version is available from Nature Publishing Group via the DOI in this record.Climate change-driven alterations in storminess pose a signifcant threat to global capture fsheries. Understanding how storms interact with fshery social-ecological systems can inform adaptive action and help to reduce the vulnerability of those dependent on fisheries for life and livelihood.N.C.S. acknowledges the financial support of the UK Natural Environment Research Council (NERC; GW4+ studentship NE/L002434/1), Centre for Environment, Fisheries and Aquaculture Science and Willis Research Network

Spatial variation of PM2.5, PM10, PM2.5 absorbance and PMcoarse concentrations between and within 20 European study areas and the relationship with NO2 - Results of the ESCAPE project

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Variation of NO2 and NOx concentrations between and within 36 European study areas: Results from the ESCAPE study

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Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas: The ESCAPE project

Land use regression models (LUR) frequently use leave-one-out-cross- validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R2s were 0.83, 0.81, and 0.76 whereas the median HEV R 2 were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R2 and HEV R2 for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R2s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites. © 2013 American Chemical Society

Performance of multi-city land use regression models for nitrogen dioxide and fine particles.

BACKGROUND: Land use regression (LUR) models have mostly been developed to explain intra-urban variations in air pollution based on often small local monitoring campaigns. Transferability of LUR models from city to city has been investigated, but little is known about the performance of models based on large numbers of monitoring sites covering a large area. OBJECTIVES: To develop European and regional LUR models and to examine their transferability to areas not used for model development. METHODS: We evaluated LUR models for nitrogen dioxide (NO2) and Particulate Matter (PM2.5, PM2.5 absorbance) by combining standardized measurement data from 17 (PM) and 23 (NO2) ESCAPE study areas across 14 European countries for PM and NO2. Models were evaluated with cross validation (CV) and hold-out validation (HV). We investigated the transferability of the models by successively excluding each study area from model building. RESULTS: The European model explained 56% of the concentration variability across all sites for NO2, 86% for PM2.5 and 70% for PM2.5 absorbance. The HV R(2)s were only slightly lower than the model R(2) (NO2: 54%, PM2.5: 80%, absorbance: 70%). The European NO2, PM2.5 and PM2.5 absorbance models explained a median of 59%, 48% and 70% of within-area variability in individual areas. The transferred models predicted a modest to large fraction of variability in areas which were excluded from model building (median R(2): 59% NO2; 42% PM2.5; 67% PM2.5 absorbance). CONCLUSIONS: Using a large dataset from 23 European study areas, we were able to develop LUR models for NO2 and PM metrics that predicted measurements made at independent sites and areas reasonably well. This finding is useful for assessing exposure in health studies conducted in areas where no measurements were conducted