Appl Geomat (2012) 4:55–64 DOI 10.1007/s12518-012-0078-0
ORIGINAL PAPER
Method validation for the identification of asbestos–cement roofing Lorenza Fiumi & Antonella Campopiano & Stefano Casciardi & Deborah Ramires
Received: 28 March 2011 / Accepted: 5 January 2012 / Published online: 31 January 2012 # Società Italiana di Fotogrammetria e Topografia (SIFET) 2012
Abstract The aim of this multidisciplinary work is to assess the potentiality of remote sensing multispectral infrared visible imaging spectrometer (MIVIS) data classified for mapping asbestos–cement roofing. In order to validate the methodology, measurement were carried out on the ground in order to later verify the results between the processed data and reality. All roofs classified as asbestos–cement were then sampled and analysed by phase contrast optical microscopy and/or scanning electron microscopy. The average classification accuracy obtained corresponds to 89.1%, and the classification accuracy of the test pixels of asbestos– cement is equal to 94.3%. Only 5.7% of pixels were misclassified. Information about the presence of asbestos–cement in the studied area has been also collected. The asbestos– cement surfaces of buildings vary from 100 to 5,000 m2, totalling to 30,800 m2, which is approximately 400,400 kg of asbestos surfaces in an area of 5.2 km2. The integration of these techniques, resulting from both MIVIS data classification and the results provided by laboratory analyses of the roofs samples, in particular from those not detected by processing MIVIS data, allowed the validation and improvement of this method, and the possibility to develop researches specifically aimed at highlighting the state of alteration of asbestos-cement surfaces. Regardless of these encouraging L. Fiumi (*) National Research Council, Institute of Atmospheric Pollution Research, c/o Consorzio per l’Università di Pomezia, Rome, Italy e-mail:
[email protected] A. Campopiano : S. Casciardi : D. Ramires Department of Occupational Hygiene, National Institute for Occupational Prevention and Safety, Monte Porzio Catone, Rome, Italy
results, further testing in different areas is still needed in order to improve the methodology developed. Keywords Asbestos-cement roofing . Remote sensing . Measurement campaigns . Data validation
Introduction The complexity of urban reality implies the use of new methods of investigation connected in multidisciplinary studies. In the meanwhile, applied and fundamental researches have encouraged the development of new technologies in order to understand these changes. “Remote sensing” is the term used when indicating a technology used in observing Earth at different altitudes and in different regions of the electromagnetic spectrum while taking repeated shots over time. Remote sensing allows to better understand and study pollution phenomena that otherwise are only detectable with great difficulty. Data obtained through remote sensing techniques is sometimes extremely significant and unique. The sensors of different satellite systems used in observing the Earth are able to pick up images on various spectral beams (0.35–14.0 μm) with a spectral resolution from 0.06 μm (Landsat, Thematic Mapper, channel 3) to 0.21 μm (Landsat, Thematic Mapper, channel 6) and a spatial resolution from 0.61×0.61 m (panchromatic/colour-sensitive Quik Bird) to 60×60 m (Landsat, Thematic Mapper, channel 6) (Fiumi et al. 2004). Nowadays a great potential in remote sensing is offered by the most recent hyperspectral systems (or systems of imaging spectrometry; Roessner et al. 2001). These systems are usually able to pick up an entire spectrum of solar energy reflected by the earth’s surface for each element of the
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picture (pixel) in the region of the electromagnetic spectrum between 0.40 and 2.50 μm in numerous tight beams (Ben-Dor et al. 2001). With the purpose to transfer the scientific knowledge in the field of remote sensing, the National Research Council gave the go-ahead to the Airborne Laboratory for Environmental Research (LARA), which is now a detached section of the Institute of Pollution aimed at getting remotely sensed data from aerial platforms (Bianchi et al. 1996). LARA owns and manages an advanced system of electronic shooting equipped with multispectral infrared visible imaging spectrometer (MIVIS) AA5000 scanning (the founder of a new generation of hyperspectral sensory equipment that works with a high spatial and spectral resolution) installed in a twin-engine turbo propeller CASA C 212/200 (Construcciones Aeronáuticas, S.A.). MIVIS is a modular instrument made up of four spectrometers, which are able to produce a spectral sampling in 102 channels in the range between 0.433 and 12.70 μm. MIVIS sensor characteristics are shown in Table 1. Additional technical details can be found in Bianchi et al. (1996). The innovative aspect of the hyperspectral remote sensing methodology is that the device allows the detection and census of an infinite amount of surfaces and roofing materials for a range of square kilometres with an accurate sensibility (metres) unique in its field of study, and at a reduced cost. With hyperspectral remote sensing, it is possible to recognise roads (asphalt or covered with porphyry cubes, travertine pavements, or cement-marble tiled terraces), different types of arboreal species (and their state of health), asbestos–cement roofing, and the temperature of roads, squares, and buildings. The aim of this study is to demonstrate the potentiality of MIVIS data to characterise the asbestos–cement roofing. Figure 1 shows the activity diagram. Unlike the USA, where the use of asbestos is still legal but tightly controlled, as of the 1 January 2005, the marketing and the use of asbestos containing products was banned throughout the European Union (as a consequence of the European Directive 76/769/CEE (1976)). The prohibition is related to the asbestos-related diseases. Asbestos is the main recognised etiological factor for malignant mesothelioma and the only factor for asbestosis. Moreover, the malignant mesothelioma is not only an occupational disease but also Table 1 MIVIS sensor characteristics Spectrometer Spectral coverage Channels Bands ranging (micron) I
Visible
20
0.43–0.83
II III
Near infrared Shortwave infrared Thermal infrared
8 64
1.15–1.55 2.0–2.5
10
8.2–12.7
IV
Fig. 1 Diagram of the various activity and elaborations needed to verify method. Figures show different steps: Data acquisition (top left), Data processing (top right), Field surveys (at the center of the image) and Laboratory Analyses (at the bottom side)
several environmental and household case lists were also reported (Bourdes et al. 2000; Hillerdal 1999). Italy was a great producer of asbestos until the ban in 1992 with 3,748,550 t of raw asbestos mined and processed since the end of the Second World War to the ban; more than 160,000 t/year between 1976 and 1979 continuing with more than 100,000 t/year until 1987 (Marinaccio et al. 2008). There are still 25,109 m2 of corrugated asbestos– cement roofing sheets equivalent to 32,106 t of mineral asbestos in Italian urban areas (Marabini et al. 2002). Due to the large-scale industrial use of asbestos, the Italian Law D.M. 101/ 2003 (2003), introduced the importance of the mapping of asbestos-containing materials focused on abatement programmes such as removal or encapsulation. Up to now, a defined timing schedule of the asbestos–cement roofs mapping is still not available in Italy. At present time, monitoring of asbestos–cement roofing is done by on-site direct survey usually carried out by experts of Local Health Authorities and Regional Agencies for the Environment, namely the Italian public local sanitary organisations. However, locating cement–asbestos through on-site surveillance has its complications; one of
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them being that it is very costly especially when covering vast areas. On-site monitoring consists in a visual inspection useful when assessing risk of asbestos fibre release. The presence of cracks and other mechanical damages are taken into consideration as important factors and bulk samples are collected in order to verify the presence and type of asbestos. This survey classifies the asbestos–cement findings into three groups: (1) intact materials not susceptible to damage, (2) materials susceptible to damage, and (3) damaged materials. In order to verify the release of asbestos fibres, the surveying technicians can use various procedures consisting in either sampling the surrounding air or use a “point system” which can be summarized in a mathematical algorithm. However, whatever method is used, it will always be conditioned by the competency and expertise of the operators. In other words, these methods alone cannot assess the true risk of asbestos exposure (Campopiano et al. 2009). An interesting alternative to traditional on site surveillance is aerospace remote sensing.
Materials and methods The area studied is the Magliana neighbourhood located in the southwest region of Rome. This area, initially developed as suburbs in the 1960s and presents problems related to absence of urban planning and analysis; common dwelling houses, industrial sheds, sport structures, open-sky depots and vegetable gardens are randomly scattered. The images used for this study were taken by the MIVIS over Rome (Fig. 2). The spatial resolution of the images is 4×4 m. The study area taken into consideration corresponds to a subscene with 755 columns×430 lines. Fig. 2 MIVIS scene over Rome. The strip represents a synthesis in red, green, and blue of the channels 13 (0.673– 0.693 μm), 7 (0.553–0.573 μm), and 1 (0.433–0.453μm) of the sensor
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The analysis of data was carried out by a PC provided with an image digital processing and spectral analysis, software ENVI (ITT 2011). The radiometric calibration is necessary to correct the error generated by the sensor and the influence of the layer of atmosphere between the sensor and the investigated scene. The general criterion is that the signal produced by the sensor is always compared with a standard source of radiance as a reference. Since the reflectance measurements at ground level and the information about the characterisation of the air column between the sensor and the ground were not available, a calibration method known as International Average Relative Reflectance (Kruse et al 1985, 1993) was used. This method consists on the division of the radiance spectrum of each pixel of the flight line by the average spectrum of the whole scene. This procedure is a variance of the principle called "flat field calibration" (Kruse et al 1985; Heiden et al. 2007), which approximately removes the solar radiance, the atmospheric absorption, the scattering effects and any additional residual noise of the instruments. The thermal channels (from 93 to 102) have not been used. The authors intend to assess the utility of thermal channels by specific investigations in the future.
Classification of remote sensing MIVIS data With the technique classification, the different classes are identified in the surface digital image on the spectral characteristics consistent with existing soil. The thematic map consists of a set of elements (pixels) that are associated, as well as the type of spatial information, with information on the class of spectral type.
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The radiometrically corrected data was classified using the Spectral Angle Mapper (SAM); this method enables to quickly map the similarities between image spectra and reference spectra (ITT 2011; Yuhas et al. 1992). The reference spectra can be determined either in laboratory, on site, or they can be retrieved from the image. In this study, the reference spectrum was obtained from the image MIVIS, with the support of 1:12,000 scale colour photos (courtesy of Enel spa granted). The SAM algorithm gives the spectral similarity between the two spectra by the calculation of the "angle" formed between two spectra. These are considered as vectors in a space where dimensionality is equal to the number of bands (additional technical details can be found in ITT; 2011). The similarity between an unknown spectrum t and a reference spectrum r comes out from the following equation: 0
!
!
That can also be expressed as follows: 0 1 nb P B C ti ri B C i¼1 C a ¼ cos1 B B nb 12 nb 12 C @ P 2 P 2 A ti ri i¼1
Results
1
B t r C a ¼ cos1 @ ! ! A t r
the study area have been carefully examined to this end. Furthermore, the analysis of bulk samples permitted to verify the presence and identify the type of asbestos. The bulk samples were analysed by means of phase contrast optical microscopy (LEICA DM2500P) and/or scanning electron microscopy (LEO 440) combined with an energy dispersive X-ray spectroscopy (Oxford Instrument INCA), which allows the chemical identification of asbestos fibres. In some cases, air sampling was carried out using static samplers (Analitica Airflow 300 T) with a flow rate of 10–12 l/min. The airborne particulate was sampled by means of 25 mm diameter and 0.8 μm porosity polycarbonate membrane filters (D.M.6/9/1994; AIA 1984).
ð1Þ
ð2Þ
i¼1
where, ! is the angle between vectors and nb is the number if bands in the image. For each reference spectrum chosen in the analysis of hyperspectral images, “the angle” is determined for every element of the image (pixel). This value in radiant is assigned to the corresponding spectrum in the output SAM image, one for each reference spectrum. The maps of the spectral angle produced show a new cube of data providing a band number equal to that of the reference spectra used for mapping. The SAM algorithm implemented in the software ENVI requires as input a number of training areas or reference spectra resulting from specific "Regions of Interest" (ROI) or spectral databank (ITT 2011). As far as our study is concerned, the input spectra taken from ROI, were carefully picked out in the scene through the visual analysis of stereo colour aerial photos at a scale of 1:12,000 (concession kindly granted by ENEL), and integrated with a series of accurate observations of the areas involved with the visual analysis of additive syntheses in red, green, and blue (RGB, ch. 13-7-1) which displays the picture in natural colours. Specific ROI were found for as many materials briefly described in Table 2. Several surveys were performed in situ in order to identify the asbestos–cement roofing, and all roofs of
Figure 3 shows the results of the classification of the MIVIS image referred to the study area in 13 spectral classes achieved by the SAM. Figure 4 shows the roofs identified as asbestos–cement roofing. The confusion matrix is a standard method to accumulate information given by the comparison of pixel to pixel in the map to evaluate (classification) compared to the reference map (truth; Yuhas et al. 1992; Story and Cangalton1986). The confusion matrix of this classification is indicated in Table 3. It shows how the verification pixel set (obtained by means of direct and accurate observations of the study area, and then integrated with the analysis of stereo colour photos at the scale of about 1:1.000 and some synthesis of MIVIS images in RGB) was assigned to the different classes. It then provides the classification accuracy for each class (Story and Congalton 1986). The numbers in the confusion matrix represent the number of pixels that belong to a class after grading it in relation to the ROI (ground truth). The elements on the main diagonal are the cases of agreement between classification and truth, while the elements outside the diagonal represent the pixels classified incorrectly. The validity of the classification results obtained was further verified by making measurements directly on ground. By analysing Table 3, it is possible to notice that the average classification accuracy obtained corresponds to 89.1%, whereas most classes were found with an accuracy ranging from 82.7% to 100%, except for the class "bituminous surfaces" (accuracy 59.6%). The accuracy of the test pixels of asbestos–cement was equal to 94.3%. The small differences between bituminous surfaces and roads (protective covers, sheathings, etc.), depends on the fact that both are mainly composed of bituminous materials (mixtures of hydrocarbons of natural or pyrogenic nature). These materials are mixed with a consistent amount of
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Table 2 Region of Interest (ROI) Bricks Grits–travertine
Asbestos–cement
Cement surfaces or ecological cement Metallic surfaces
Bituminous surfaces
Pozzolan surfaces Other surfaces Roads Treed surfaces Bushed surfaces Grassy surfaces Water
They are made of a natural material, namely clay, with the addition of colouring substances. They represent the older roofing material mainly used for civil buildings (Bruno 1981) This material is an "artificial stone" obtained by mixing in appropriate proportions cement, other stony materials like gravel or common stones, water and additive substances. Grits are generally used for outside surface, such as horizontal covers (Bruno 1981) This material is composed of 90% cement and water and 10% of asbestos fibres. It has been largely used for its lightness combined with its remarkable strength and easy workability. However special provisions regulate at present its use since it represents a serious danger for human health due to its progressive staggering and pulverisation over time (Chandra and Berntsson 2003) It is a new building material manufactured in the place of asbestos-cement. It is mainly composed of 90% silicon calcium matrix and the remaining 10% of organic fibres like cellulose and polyvinyl alcohol. It has the same characteristics of asbestos–cement as regards both shape and colours (data provided by Società Italiana Lastre) Unlike other metallic materials known since ancient times, aluminium and in particular aluminium alloys have been only recently used specially to roof industrial buildings. It is a silver white material well withstanding atmospheric corrosion; it widely reflects radiant energy in the whole spectral field, from ultraviolet to infrared They are covering waterproofing membranes made of sheets whose composition in bitumen-polymer makes them waterproofing and not much alterable. They at present represent one of the systems used to cover industrial buildings due to the cheap cost, the quickness and the easiness of their installation (Bruno 1981) They are composed of volcanic materials, incoherent facies, mainly ashen, of Neapolitan yellow tuff. They are often used to build soccer grounds They are synthetic surfaces made of various materials used to floor both small and medium sports grounds (tennis courts, soccer grounds, lawn bowling grounds, etc.) (Bruno 1981) They are covered by a bituminous material made of a mixture of hydrocarbons having natural or pyrogenic origin; this material plays the role of binder, as it joins inert elements to give cohesion and stability to road surfaces (Bruno 1981) Areas mostly covered by trees with deciduous leaves or evergreen (planes, pines and ilexes) Areas covered by vegetation with the prevailing presence of wooden plants not higher than 1.50 m (Wheat) and meadows Tiber river
synthetic products (atactic polypropylene). As far as roads are concerned, bitumen is instead blended with inert matters as a binding compound (Bruno 1981). As a consequence, when the road cover is in good condition, the asphalt that emerges and the above classes tend to mingle spectrally.
However, when the road is not in good condition, the inert matter emerges due to wear and the two classes tend to separate. Therefore the 92.8% classification accuracy of the class "roads" reveals the spectral predominance of the compound "inert matter" over the compound "bitumen"
Fig. 3 Classification of materials by the SAM method. Thirteen classes of different materials were highlighted, among which the vegetation and the River Tiber, which represent different spectral typologies present in the view (the lack of a scanning line in the view is due to a recording error)
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Fig. 4 Roofs identified as asbestos–cement, shown in red, are in the classification of Fig. 3, obtained through the spectral identification of the material present in the view
indicating the asphalt’s state of wear of the road cover in the area investigated. According to the data shown in Table 1, water, metallic surfaces, and bricks, as well as the two types of bushy and dry vegetation, are easily distinguishable within the environmental framework of the area studied by means of MIVIS data processed according to SAM method. The classification accuracy of travertine (88.9%) proves remarkable, whereas that of surfaces covered by pozzolana (82.7%) and arboreal vegetation (76.6%) is slightly less precise even though good. Asbestos–cement surfaces (94.3%) are remarkable as well (Fig. 4). The error percentage in the identification of the asbestos cement roofing is equal to 5.7%. The confusion class with other surfaces is due to anthropic environments of small surfaces made of many different materials; therefore, the spectra of the selected reference do not represent the complexity of the surfaces.
Surveys in situ In order to validate the methodology that characterises asbestos–cement roofings through remote-sensed MIVIS data, surveys in situ were carried out with direct and accurate observations. Small samples of roofs were taken from roofings recognised as asbestos–cement by data processing. Additional information, as shown in Table 4, was collected for each building within the study area. Table 4 shows some data regarding the buildings (year of construction, their typology or use, the number of employees working indoor, the roofing surface, the presence of friable asbestos inside the construction, the presence of false ceilings, the method of asbestos abatement, and the results of laboratory analysis) (Fiumi et al 2004). It was not possible to carry out laboratory analyses for all the roofs present in the investigated area. In some cases, the
Table 3 Confusion matrix Class of roofing
Spectral classes 1
Bricks Grit–travertine Asbestos–cement Cement surfaces Metallic surfaces Bituminous surfaces Pozzolan surfaces Other surfaces Roads Treed surfaces Bushed surfaces Grassy surfaces Water (Tiber)
2
3
4
5
6
7
8
9
2
3 1 3
10
11
12
Classification accuracy
error
13
(%)
(%)
36
100 88.9 94.3 92.8 100 59.6 82.7 91.3 92.8 76.6 98.9 92.9 100
0.00 11.1 5.7 7.2 0.00 40.4 17.3 8.7 7.2 23.4 1.1 7.1 0.00
21 24 51 39 15 2
19
11 4
19 21 1
1
2 26
3
99 1 1
30 89 2
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Table 4 Identification of the asbestos–cement roofs Roofs Accessible Building area year
Use
Friable asbestos Presence of Asbestos PCOM and SEM Workers/ Roofing residents surface (m2) inside building false ceilings abatement analysis of asbestos
1 2 3 4 5 6 7 8 9 10 11 12 13
Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes
– 1973 1965 1960 – 1960 – 1960 1983 1970 1970 1960 1965/1966
Industry Industry Dwelling Store Store Store – Industry Industry Industry Industry Store Industry
10 5 2 90 8 2 – 17 2 3 2 10 3
575 250 100 5,000 1,500 5,000 300 5,000 300 100 250 1,100 300
No No No No No No – No No No No No No
Yes No Yes Yes No Yes – No – No No No Yes
No Enclosure No No No No – No – No No No No
Chrysotile Chrysotile Chrysotile Chrysotile – Chrysotile – Chrysotile – – Chrysotile Chrysotile Chrysotile
14 15 16 17 18 19
Yes Yes Yes Yes Yes Yes
1965/1966 1960 1988/1990 1988/1990 1957 1960
Industry Garage Store – Industry Store
– – 15 20–25 15 3
– 250 2,900 2,860 3,000 150
– No No No No No
– enclosure No No Yes Enclosure No No Yes, partially Enclosure No Enclosure
Chrysotile Chrysotile Chrysotile – Chrysotile Chrysotile
roofs were not easily accessible because of the height of the roof or absence of owner. The last column of Table 4 shows the concentrations of airborne asbestos fibres measured in the areas adjacent to the buildings where extensive and more deteriorated asbestos–cement roofing was found. The roofs of Table 4 can be identified in Fig. 5. Table 5 shows the surfaces classified by remote sensing as asbestos–cement roofing but substituted with asbestosfree cement roofing after the flight and before the surveys in situ. The data concerning asbestos abatement was collected
Fig. 5 Naming by numbers of the asbestos–cement roofs listed in Table 4
Mean C (f/l)
crocidolite crocidolite crocidolite 0.1 – crocidolite – crocidolite 0.3 – – crocidolite crocidolite 0 crocidolite crocidolite crocidolite 0 – crocidolite crocidolite
by the Local Health Service and reported in Table 5. The roofs of Table 5 can be identified in Fig. 6.
Discussion The classification of MIVIS generated 1,925 pixels recognised as asbestos–cement, identified in the MIVIS image as red, corresponding to 30,800 m2, which make up 4.2% of building roofs. This information allows quantifying the asbestos-containing materials in the area and to provide accurate economic investments so that the material is land filled. The roofs classified as asbestos–cement material are 32. All 32 buildings were counted both in the field through surveys or documents obtained from archives of the Local Health Service. The census results are reported in Tables 4 and 5. All 32 buildings in the area of study at the time of data acquisition MIVIS had asbestos–cement roofings. The error in the recognition of this material is equivalent to 5.7%, reported in Table 3. Confusion matrix is due to the presence of mixed pixels, e.g., roofs made of pixels falling within different materials (skylights, dormers, etc.). From the surveys in situ, only 19 of the 32 buildings had coverage in asbestos–cement. The information collected is shown in Table 4. The remaining 13 buildings, at the time of site inspection, had the asbestos–cement roofing replaced.
62 Table 5 Identification of the asbestos–cement roofs “substituted”
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Roofs
Use
Roofing surface (m2)
Type of asbestos
Amount of waste (Kg)
20 21 22 23 24 25 26 27 28 29 30 31 32
Industry Industry Industry Industry Industry Industry Dwelling Industry Military area Military area Store Store Industry
550 100 220 2,650 190 350 35 100 150 450 500 2,000 –
Chrysotile – – – – – – Chrysotile Chrysotile Chrysotile Chrysotile – Chrysotile
7,150 1,300 2,860 34,450 2,470 4,550 455 1,300 1,950 5,850 6,500 26,000 –
The data is presented in Table 5. In fact, the sampling of the roofs and environmental surveys were carried out 2 years after the acquisition of the data MIVIS. The asbestos–cement surfaces identified by MIVIS and assessed using the forms filled in by the owners/tenants, vary from a minimum of 100 m2 to a maximum of 5,000 m2, with an average presence of about 1,500 m2. In total, we calculated 30,800 m2 of asbestos surfaces in the study area. This is equal to about 400,400 kg, most of which were not particularly deteriorated. Furthermore, asbestos in friable matrix was not detected inside the buildings examined. Asbestos was usually encountered in one-storey buildings; the one-storey structures are equal to 90.5% of the buildings present in the MIVIS scene. The buildings built between 1950 and 1960 are 4.75%, between 1961 and 1970 are 31.25%, between 1971 and 1980 are 6.25% and between 1981 and 1990 are 18.75%.
Fig. 6 Naming by numbers of the asbestos–cement roofs listed in Table 5
crocidolite crocidolite crocidolite crocidolite
During the decade between the years 1950 and 1960, the city of Rome was characterised by an intense building activity. Even the area of this study was developed during this period. Of these buildings, 56.6% are used for production activities, while 26.6% are stores and the remaining percentages are divided between military or dwelling areas, and one being used as a garage. At present time, there are 242 workers employed in the buildings with asbestos–cement roofing, ranging from a maximum of 90 and a minimum of 1, depending on the day and the hour considered. The presence of ceilings inside buildings is limited to 43.75%, while 56.25% of the buildings with asbestos–cement roofing do not have a ceiling that protects the occupants from a possible exposure to asbestos. Only 4.8% of the roofs inspected were encapsulated. The results of the analysis of samples by phase contrast optical microscopy performed on 20 samples, including 14 in Table 4 and six in Table 5, permitted to verify the presence of asbestos in roofs recognised by MIVIS data and identify the type of asbestos. The results show that 80% of roofs are made up of chrysotile and crocidolite, while 20% are made up of only chrysotile. It should be remembered that since 1986, the use of crocidolite in asbestos–cement products has been forbidden (Italian Ministry of Health D. M. 26 Giugno 1986) due to the increased danger if these fibres compared with other varieties of asbestos. This explains the presence of asbestos–cement sheets containing only chrysotile, introduced afterwards on the market, instead of sheets with both varieties of asbestos (serpentine and amphibole). The analysis done by microscope showed that most roofs presented chrysotile and crocidolite. Despite the age of roofing’s, the levels of airborne asbestos are low and comparable with those proposed by World Health Organization in urban areas. In rural areas, for example in sites far away from sources of emission by human activity, the concentrations are less than
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0.1 f/l, while in urban areas, the pollution levels vary from values lower than 0.1 f/l up to approximately 1 f/l. The latter value corresponds to areas with heavy traffic (WHO 2000). Only one case gave a value of airborne asbestos fibre concentration higher, due to the greater deterioration of the roofing.
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with an efficient, rapid environmental mapping procedure that can provide information about the location of asbestos cement roofings. Acknowledgments The authors wish to thank Inspector Gioacchino Ruocco, manager of Surveillance Workers of the Local Health Service, for his active cooperation in the investigations in situ.
Conclusions References The final results demonstrate that the potentialities of MIVIS hyperspectral data can monitor urban and suburban covering surfaces and particularly those in asbestos–cement. The use of MIVIS data could be particularly interesting for assessing wear condition of roads’ asphalt. Although this study has produced interesting results, further investigations seem to be nevertheless necessary, especially with regards to: (a) more detailed analysis of the spectral behaviour of each material (based on spectral-radiometric measures in situ); (b) more careful radiometric corrections (which could be easily realised if reflectance data achieved by the above spectral-radiometric measures are available); (c) validation of additional methodologies of image analysis (for ex. spectral mixture analysis), and (d) assessment of the potentialities of thermal channels of MIVIS system not taken into consideration in this study. If future applications confirmed these results, involving possibly wider areas, the operative use of this kind of data could be considered as a substitution, or at least an integration of the more conventional systems, in order to locate the areas where asbestos–cement roofings are present and to provide information on possible pollution relating to their state of deterioration. The data MIVIS can give important information: quantify the asbestos-containing materials that will be landfilled. This will allow for a development of effective intervention policies (i.e., for selecting roofs to be removed or treated) and monitoring programmes. In addition, the georeferencing of the MIVIS data information, inserted in the geographic information system (GIS), enables spatial queries and logic to help evaluate the different interactions depending on the scale of analysis. In fact, a specifically designed GIS can become a modern and effective business tool to be used by the Local health Service storage and for the cataloguing of current activities. The results of this first study have shown the encouraging potential of hyperspectral data of the MIVIS system for mapping roofing surfaces. The integration of the technique of MIVIS data classification with the results provided by environmental study and by laboratory analysis allowed to validate and to improve the method, to identify possible development of researches aimed at highlighting the state of alteration of asbestos–cement surfaces. Therefore, this technique can furnish government authorities
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