ABSTRACT
Recent developments in sensory and communication technologies have made the development of portable air-quality (AQ) micro-sensing units (MSUs) feasible. These MSUs allow AQ measurements in many new applications, such as ambulatory exposure analyses and citizen science. Typically, the performance of these devices is assessed using the mean error or correlation coefficients with respect to a laboratory equipment. However, these criteria do not represent how such sensors perform outside of laboratory conditions in large-scale field applications, and do not cover all aspects of possible differences in performance between the sensor-based and standardized equipment, or changes in performance over time. This paper presents a comprehensive Sensor Evaluation Toolbox (SET) for evaluating AQ MSUs by a range of criteria, to better assess their performance in varied applications and environments. Within the SET are included four new schemes for evaluating sensors' capability to: locate pollution sources; represent the pollution level on a coarse scale; capture the high temporal variability of the observed pollutant and their reliability. Each of the evaluation criteria allows for assessing sensors' performance in a different way, together constituting a holistic evaluation of the suitability and usability of the sensors in a wide range of applications. Application of the SET on measurements acquired by 25 MSUs deployed in eight cities across Europe showed that the suggested schemes facilitates a comprehensive cross platform analysis that can be used to determine and compare the sensors' performance. The SET was implemented in R and the code is available on the first author's website.
ABSTRACT
The interpretation and communication of fire danger warning levels based on fire weather index values are critical for fire management activities. A number of different indices have been developed for various environmental conditions, and many of them are currently applied in operational warning systems. To select an appropriate combination of such indices to work in different ecoregions in mountainous, hilly and flat terrain is challenging. This study analyses the performance of a total of 22 fire weather indices and two raw meteorological variables to predict wildfire occurrence for different ecological regions of Austria with respect to the different characteristics in climate and fire regimes. A median-based linear model was built based on percentile results on fire days and non-fire days to get quantifiable measures of index performance using slope and intercept of an index on fire days. We highlight the finding that one single index is not optimal for all Austrian regions in both summer and winter fire seasons. The summer season (May-November) shows that the Canadian build-up index, the Keetch Byram Drought Index and the mean daily temperature have the best performance; in the winter season (December-April), the M68dwd is the best performing index. It is shown that the index performance on fire days where larger fires appeared is better and that the uncertainties related to the location of the meteorological station can influence the overall results. A proposal for the selection of the best performing fire weather indices for each Austrian ecoregion is made.
