Asphalt 8 Airborne 2013 _TOP

We studied exposure of 151 workers to polycyclic aromatic compounds and asphalt emissions during the manufacturing of asphalt roofing products-including 64 workers from 10 asphalt plants producing oxidized, straight-run, cutback, and wax- or polymer-modified asphalts, and 87 workers from 11 roofing plants producing asphalt shingles and granulated roll roofing. The facilities were located throughout the United States and used asphalt from many refiners and crude oils. This article helps fill a gap in exposure data for asphalt roofing manufacturing workers by using a fluorescence technique that targets biologically active 4-6 ring polycyclic aromatic compounds and is strongly correlated with carcinogenic activity in animal studies. Worker exposures to polycyclic aromatic compounds were compared between manufacturing plants, at different temperatures and using different raw materials, and to important external benchmarks. High levels of fine limestone particulate in the plant air during roofing manufacturing increased polycyclic aromatic compound exposure, resulting in the hypothesis that the particulate brought adsorbed polycyclic aromatic compounds to the worker breathing zone. Elevated asphalt temperatures increased exposures during the pouring of asphalt. Co-exposures in these workplaces which act as confounders for both the measurement of total organic matter and fluorescence were detected and their influence discussed. Exposures to polycyclic aromatic compounds in asphalt roofing manufacturing facilities were lower than or similar to those reported in hot-mix paving application studies, and much below those reported in studies of hot application of built-up roofing asphalt. These relatively low exposures in manufacturing are primarily attributed to air emission controls in the facilities, and the relatively moderate temperatures, compared to built-up roofing, used in these facilities for oxidized asphalt. The exposure to polycyclic aromatic compounds was a very small part of the overall worker exposure to asphalt fume, on average less than 0.07% of the benzene-soluble fraction. Measurements of benzene-soluble fraction were uniformly below the American Conference of Governmental Industrial Hygienists' Threshold Limit Value for asphalt fume.

The characteristic absorption bands of silicates and limestone that outcrop when the asphalt pavements show surface defects, have been studied by many authors in the 8 to 12m thermal infrared spectral region (TIR) [32-34,53-55,65,66].

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In this framework, the research will be focused on developing, implementing and validating the effectiveness of emission spectroscopy, in the TIR spectral range from 8.18m to 12.70m, to provide a rapid assessment of the asphalt surface distress. Fast and non destructive methods, such as emission spectroscopy, offer potentially useful alternatives to time-consuming chemical methods of asphalt analysis. The characteristics of asphalt pavement emissivity spectra are controlled by mineral composition, water (hydration, hygroscopic, and free pore water) and particle size distribution.

Nowadays, the most common sensors that operate in the TIR range are: the TIMS instrument (8.2-12.6m with 6 bands) [33], the SEBASS airborne sensor (7.57-13.5m with 128 bands) [36-64], the ARES instrument (8.32-12.97m with 32 bands) [52] and the AHI airborne sensor (7.5-11.7m with 256 bands) [7]. Such spectral range is covered by sensors functioning also in other spectrum regions: the DAIS-7915, the Multispectral Infrared Visible Image Spectrometer (MIVIS), the AHS-160 and the MASTER simulator (0.46-2.39m with 25 bands; 3.14-5.26m with 15 bands; 7.76-12.87m with 10 bands) [28].

Since the asphalt pavements aging can be related to the loss of oily components [60] and to the sealing tar surface [25], and the decrease of oily components leads to an increase of several types of limestone deposits that are identifiable in the TIR range [36], a simple and fast method was developed in order to define a threshold on the basis of the band depth analysis [9] at 11.2m (i.e., the limestone absorption peak in the TIR range).

The radiometric calibration of the airborne MIVIS TIR raw data was performed using a two-point calibration technique that is based on the linearity of the detector response over the dynamic range of the instrument [47]. To this goal, the maximum and minimum reference values of the radiance were acquired at the beginning and end of each scansion line to satisfy the calibration accuracy requirements.

For this study, according to Jensen [30], we used an object-oriented approach with a segmentation procedure followed by classification as implemented in the Feature Extraction module of the ENVI 4.4 software package [42]. In more detail, the procedure consists of a combined process of segmenting the image into regions of pixels, computing attributes for each region to create objects, and last classifying the objects. In order to identify only road asphalt pavements with the requirement of a sufficient neighboring number of pixels showing a homogeneous asphalt mixture, we chose only highways and exits asphalt pavements for the further band-depth analysis. For this purpose, a workflow consisting of two main tasks was adopted.

The applied procedure consists, first, of the analysis of the available John Hopkins University (JHU) spectral library [55] information allowed for retrieving the emissivity spectral features (8-13m TIR range) of the asphalt paving material. An absorption band can be described by characteristics, such as the position, depth, width and asymmetry [8-10,68]. The presence of an absorption feature and its position in the reflectance/emissivity spectrum provides valuable information about the chemical composition of a material [21].

Figure 4 depicts the JHU emissivity spectra convolved to MIVIS bandpasses in order to show how their occurrence would affect MIVIS detectability of the major limestone absorption feature. Looking at Figure 4a, it is evident that the main difference between the emissivity spectral feature, in the 8-13m range, of new and old pavement asphalts is the spectral contrast centered at 11.2m. Moreover, the study of Kirkland et al. [36] confirms that the 11.2m is the diagnostic emissivity band for the limestone (Figure 4b).

Examples (a) of new and old emissivity spectra of paving asphalt from the JHU spectral library and (b) of limestone band-depth analysis (intervals 9.59-11.94m): emissivity continuum-removed absorption peak of a pure limestone spectrum (JHU spectral library), both convolved to MIVIS bandpasses in order to show how its occurrence would affect MIVIS detectability.

Therefore, once selected the highway/exits asphalt roads by means of the object-oriented approach, a Band-Depth (BD) analysis was performed on MIVIS emissivity data to measure the spectral contrast of the limestone peak at 11.2m that is centered, if convolved to MIVIS bandpasses, at 10.93m (Figure 4b).

The MIVIS sensor DN values, acquired in-flight on the internal blackbodies, were converted to emissivity following the same procedure used in 3.1 [26,35], except for atmospheric correction. Atmospheric correction of the blackbody data was not taken into account, because the distance between the sensors and the blackbodies was negligible. The SNR of the TIR bands was, consequently, estimated by dividing the emissivity mean spectra of all masked asphalt pavement pixels (i.e., signal) by the standard deviation of blackbody emissivity (i.e., noise). Figure 6 shows MIVIS SNR calculated for the TIR spectral range on the masked asphalt pavements of the study area.

Once the BDL analysis confirms that MIVIS characteristics allows for recognizing the limestone diagnostic emissivity peak, the MIVIS emissivity data of the masked highways/exits pavements were analyzed by means of the band-depth procedure to establish a suitable threshold level for discriminating those asphalt pavements to be checked for maintenance intervene.

For this purpose, an extensive field survey was carried out on all masked asphalt roads to visually check the asphalt pavements conditions. Figure 7a shows the two selected areas with certainly surface defects on which retrieving the BD threshold level that identifies those asphalt pavements (i.e., pixels) where to check for an asphalt maintenance intervention. On the two selected test areas the distribution function of the BD (at 10.93m) analysis was calculated. Next, a threshold, which is based on the BD values distribution and that allows for discriminating in both test areas distressed asphalt pavements pixels (visually checked by field surveys), was assessed by using:

(a) In yellow are depicted the two test areas, selected for training the band-depth analysis; (b) Image showing the band-depth analysis results: in red are depicted the detected asphalt pavements showing surface defects thus to be checked for maintenance. Both images are overlaid on MIVIS channel 13 only for visualization purposes.

where, is the mean value of the test areas BD distribution and is the corresponding standard deviation. As a consequence, the BD value of 0.020 is the identified threshold for determining on all masked pixels (i.e., the highways/exits) the asphalt pavements to be checked for maintenance work. Figure 7b shows in red the distressed asphalt pixels individuated using the above mentioned threshold of 0.020. For example, in Figure 8, asphalt pavements of a highway and the relative exits, different from the two chosen as test areas, are shown as a particular case as they demonstrate the ability of the proposed procedure in detecting the different surface defects of the same asphalt.

Images showing an example of asphalt pavements with different surface defects within the study area. Image (b) shows MIVIS emissivity BD classification results. Both images are overlaid on MIVIS channel 13 only for visualization purposes. ff782bc1db