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AbstractThe purpose of this study was to measure the self‐reported willingness to respond (WTR) to 12 disaster scenarios for Louisiana based Law Enforcement Officials (LEO) and Emergency Medical Services (EMS) personnel. The study analyzed the demographic traits, facilitators, barriers, and potential incentives to determine which variables had a significant influence on WTR. The overall WTR of Louisiana EMS and LEO personnel was 69.9%. The WTR decreased as perceived threat increased. Traits found in those responders who were more willing to respond were males, under 44 years old, no children, military veterans, with prior disaster experience, with less than 5 years experience, and those whose significant other does not have a disaster response role. The statistically significant influences on WTR were fear of working an unfamiliar role, concern for family, self‐safety, feeling well‐prepared to respond, duty to colleagues, and increasing the frequency of training. Findings from this study provide insights into interventions for improving EMS and LEO workers' willingness to respond to duty.

Malaysia is the third largest country in the world that had lost forest cover. Therefore, timely information on forest cover is required to help the government to ensure that the remaining forest resources are managed in a sustainable manner. This study aims to map and detect changes of forest cover (deforestation and disturbance) in Iskandar Malaysia region in the south of Peninsular Malaysia between years 1990 and 2010 using Landsat satellite images. The Carnegie Landsat Analysis System-Lite (CLASlite) programme was used to classify forest cover using Landsat images. This software is able to mask out clouds, cloud shadows, terrain shadows, and water bodies and atmospherically correct the images using 6S radiative transfer model. An Automated Monte Carlo Unmixing technique embedded in CLASlite was used to unmix each Landsat pixel into fractions of photosynthetic vegetation (PV), non photosynthetic vegetation (NPV) and soil surface (S). Forest and non-forest areas were produced from the fractional cover images using appropriate threshold values of PV, NPV and S. CLASlite software was found to be able to classify forest cover in Iskandar Malaysia with only a difference between 14% (1990) and 5% (2010) compared to the forest land use map produced by the Department of Agriculture, Malaysia. Nevertheless, the CLASlite automated software used in this study was found not to exclude other vegetation types especially rubber and oil palm that has similar reflectance to forest. Currently rubber and oil palm were discriminated from forest manually using land use maps. Therefore, CLASlite algorithm needs further adjustment to exclude these vegetation and classify only forest cover.

Malaysia is the third largest country in the world that had lost forest cover. Therefore, timely information on forest cover is required to help the government to ensure that the remaining forest resources are managed in a sustainable manner. This study aims to map and detect changes of forest cover (deforestation and disturbance) in Iskandar Malaysia region in the south of Peninsular Malaysia between years 1990 and 2010 using Landsat satellite images. The Carnegie Landsat Analysis System-Lite (CLASlite) programme was used to classify forest cover using Landsat images. This software is able to mask out clouds, cloud shadows, terrain shadows, and water bodies and atmospherically correct the images using 6S radiative transfer model. An Automated Monte Carlo Unmixing technique embedded in CLASlite was used to unmix each Landsat pixel into fractions of photosynthetic vegetation (PV), non photosynthetic vegetation (NPV) and soil surface (S). Forest and non-forest areas were produced from the fractional cover images using appropriate threshold values of PV, NPV and S. CLASlite software was found to be able to classify forest cover in Iskandar Malaysia with only a difference between 14% (1990) and 5% (2010) compared to the forest land use map produced by the Department of Agriculture, Malaysia. Nevertheless, the CLASlite automated software used in this study was found not to exclude other vegetation types especially rubber and oil palm that has similar reflectance to forest. Currently rubber and oil palm were discriminated from forest manually using land use maps. Therefore, CLASlite algorithm needs further adjustment to exclude these vegetation and classify only forest cover.

The international airport of Heathrow is a major source of nitrogen oxides, but its contribution to the levels of sub-micrometre particles is unknown and is the objective of this study. Two sampling campaigns were carried out during warm and cold seasons at a site close to the airfield (1.2 km). Size spectra were largely dominated by ultrafine particles: nucleation particles ( < 30 nm) were found to be ∼ 10 times higher than those commonly measured in urban background environments of London. Five clusters and six factors were identified by applying k means cluster analysis and positive matrix factorisation (PMF), respectively, to particle number size distributions; their interpretation was based on their modal structures, wind directionality, diurnal patterns, road and airport traffic volumes, and on the relationship with weather and other air pollutants. Airport emissions, fresh and aged road traffic, urban accumulation mode, and two secondary sources were then identified and apportioned. The fingerprint of Heathrow has a characteristic modal structure peaking at < 20 nm and accounts for 30–35 % of total particles in both the seasons. Other main contributors are fresh (24–36 %) and aged (16–21 %) road traffic emissions and urban accumulation from London (around 10 %). Secondary sources accounted for less than 6 % in number concentrations but for more than 50 % in volume concentration. The analysis of a strong regional nucleation event showed that both the cluster categorisation and PMF contributions were affected during the first 6 h of the event. In 2016, the UK government provisionally approved the construction of a third runway; therefore the direct and indirect impact of Heathrow on local air quality is expected to increase unless mitigation strategies are applied successfully.