An analysis of effects of San Diego wildfire on ambient air quality

The impact of major gaseous and particulate pollutants emitted by the wildfire of October 2003 on ambient air quality and health of San Diego residents before, during, and after the fire are analyzed using data available from the San Diego County Air Pollution Control District and California Air Resources Board. It was found that fine particulate matter (PM) levels exceeded the federal daily 24-hr average standard during the fire. There was a slight increase in some of the gaseous pollutants, such as carbon monoxide, which exceeded federal standards. Ozone (O3) precursors, such as total hydrocarbons and methane gases, experienced elevated concentration during the fire. Fortunately, the absence of sunlight because of the cloud of thick smoke that covered most of the county during the fire appears to have prevented the photochemical conversion of the precursor gases to harmful concentrations of O3. Statistical analysis of the compiled medical surveillance data has been used to establish correlations between pollutant levels in the region and the resultant health problems experienced by the county citizens. The study shows that the increased PM concentration above the federal standard resulted in a significant increase in hospital emergency room visits for asthma, respiratory problems, eye irritation, and smoke inhalation. On the basis of the findings, it is recommended that hospitals and emergency medical facilities engage in pre-event planning that would ensure a rapid response to an impact on the healthcare system as a result of a large wildfire and appropriate agencies engage in the use of all available meteorological forecasting resources, including real-time satellite imaging assets, to accurately forecast air quality and assist firefighting efforts.     

Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix.     

Critical factors in assessing exposure to PBDEs via house dust

Assessment of indoor exposure to polybrominated diphenyl ethers (PBDEs) requires a critical examination of methods that may influence exposure estimates and comparisons between studies. We measured PBDEs in residential dust collected from 20 homes in Boston, MA, to examine 5 key questions: 1) Does the choice of dust exposure metric-e.g., concentration (ng/g) or dust loading (ng/m2)-affect analysis and results? 2) To what degree do dust concentrations change over time? 3) Do dust concentrations vary between rooms? 4) Is the home vacuum bag an acceptable surrogate for researcher-collected dust? 5) Are air and dust concentrations correlated for the same room? We used linear mixed-effects models to analyze the data while accounting for within-home and within-room correlations. We found that PBDE dust concentration and surface loading were highly correlated (r=0.86-0.95, p<0.001). Average dust concentrations did not significantly differ over an 8-month period, possibly because home furnishings changed little over this time. We observed significant differences between rooms in the same home: PBDE concentrations in the main living area were 97% higher than the bedroom for decaBDE (p=0.02) and 72% higher for pentaBDE (p=0.05). Home vacuum bag dust concentrations were significantly lower than researcher-collected dust and not strongly correlated. Air (vapor and particulate phase) and dust concentrations were correlated for pentaBDE (p=0.62, p<0.01), but not for decaBDE (p=0.25). In addition, potential markers of BDE 209 debromination (BDE 202 and the BDE197:BDE201 ratio) were also observed in household dust samples. One vacuum bag sample contained the highest concentrations of BDE 209 (527,000 ng/g) and total PBDEs (544,000 ng/g) that have been reported in house dust.     

What factors control soil profile nitrogen storage?

Factors influencing soil profile nitrogen storage (SPNS) in GB have been investigated. The SPNS values of moorland peats and podzols in the South Pennines, of permanent grassland in Yorkshire, and of adjacent arable-, semi-permanent grassland- and ancient woodland-soils in Worcestershire have been compared to assess land use effects. The soils exhibited similar SPNS values, because changes in bulk density and organic matter composition offset increases in N concentrations in highly organic soils. Data from the Soil Survey of Scotland were used to show effects of soil parent material on SPNS were also small. Data on Scottish podzol soils under heather moorland and derived from sandstone or quartzite were used to show that SPNS declined with increasing precipitation, but increased with the amount of pollutant N deposited from the atmosphere.     

Nation‐building policies in Timor‐Leste: disaster risk reduction,including climate change adaptation

Few studies have explored the relationships between nation‐building, disaster risk reduction and climate change adaptation. Focusing on small island developing states, this paper examines nation‐building in Timor‐Leste, a small island developing state that recently achieved independence. Nation‐building in Timor‐Leste is explored in the context of disaster risk reduction, which necessarily includes climate change adaptation. The study presents a synopsis of Timor‐Leste's history and its nation‐building efforts as well as an overview of the state of knowledge of disaster risk reduction including climate change adaptation. It also offers an analysis of significant gaps and challenges in terms of vertical and horizontal governance, large donor presence, data availability and the integration of disaster risk reduction and climate change adaptation for nation‐building in Timor‐Leste. Relevant and applicable lessons are provided from other small island developing states to assist Timor‐Leste in identifying its own trajectory out of underdevelopment while it builds on existing strengths.     

What factors control soil profile nitrogen storage?

Factors influencing soil profile nitrogen storage (SPNS) in GB have been investigated. The SPNS values of moorland peats and podzols in the South Pennines, of permanent grassland in Yorkshire, and of adjacent arable-, semi-permanent grassland- and ancient woodland-soils in Worcestershire have been compared to assess land use effects. The soils exhibited similar SPNS values, because changes in bulk density and organic matter composition offset increases in N concentrations in highly organic soils. Data from the Soil Survey of Scotland were used to show effects of soil parent material on SPNS were also small. Data on Scottish podzol soils under heather moorland and derived from sandstone or quartzite were used to show that SPNS declined with increasing precipitation, but increased with the amount of pollutant N deposited from the atmosphere.