Air monitoring: Research needs.
Sampling and analyses difficulties encountered in the ambient, source and exposure monitoring areas of environmental data sampling of inorganic particulate matter, inorganic gases, organic particulate matter, and organic gases are discussed. Attention is given to instrumentation such as tunable lasers, ion chromatography, optical emission spectroscopy, fiber glass filters, dichotomous samplers, Teflon and other polymeric membrane filters, and electrostatic samplers. A lack of species-tailored devices is noted, as is a need to develop numerical and statistical models that will decrease the required sampling volume for reliable estimates. Finally, better characterizations of the effects that sampling has on the medium sampled are recommended. 8 references.
Ability of Fixed Monitoring Stations to Represent Personal Carbon Monoxide Exposure
This study investigates the ability of fixed location ambient air quality monitoring stations to represent personal environmental exposures to carbon monoxide (CO) accurately. The relationship between residential location, commuting transportation mode, employment location, and time-weighted average human exposure to CO in the metropolitan area was also explored. Personal exposure was measured by equipping 66 nonsmoking volunteers in the Boston area with portable CO samplers which employ an electrochemical sensing method. Measurements at six fixed monitoring stations operated by the Massachusetts Bureau of Air Quality control underestimated mean 1 hour personal exposure by a factor of 1.3 to 2.1. However, alveolar air samples showed no significant increase in blood carboxyhemo-globin (COHb) levels due to commuting exposure since only 1 % of the commuting exposures approached the national ambient air quality standard (NAAQS) of 35 ppm for 1 hour. The underestimation of personal exposure by fixed stations i...
Integrating Local CNN and Global CNN for Script Identification in Natural Scene Images
Script identification in natural scene images is a key pre-step for text recognition and is also an indispensable condition for automatic text understanding systems that are designed for multi-language environments. In this paper, we present a novel framework integrating Local CNN and Global CNN both of which are based on ResNet-20 for script identification. We first obtain a lot of patches and segmented images based on the aspect ratios of the images. Subsequently, these patches and segmented images are used as inputs to Local CNN and Global CNN for training, respectively. Finally, to get the final results, the Adaboost algorithm is used to combine the results of Local CNN and Global CNN for decision-level fusion. Benefiting from such a strategy, Local CNN fully exploits the local features of the image, effectively revealing subtle differences among the scripts that are difficult to distinguish such as English, Greek, and Russian. Moreover, Global CNN mines the global features of the image to improve the accuracy of script identification. The experimental results demonstrate that our approach has a good performance on four public datasets.