Mahmoud M. Abu-Allaban This email address is being protected from spambots. You need JavaScript enabled to view it.1, Salahuddin M. Jaber1, Aiman Soleiman2, Hasan Almarayeh2

1 Department of Water Management and Environment, Faculty of Natural Resources and Environment, Hashemite University, Zarqa, Jordan
2 Environment Commissionerate, Aqaba Special Economic Zone Authority, Aqaba, Jordan


Received: February 17, 2020
Revised: May 15, 2020
Accepted: May 21, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.2020.02.0053  

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Cite this article:

Abu-Allaban, M.M., Jaber, S.M., Soleiman, A. and Almarayeh, H. (2020). Quantifying Emissions of PM10 Generated by the Implosion of Concrete Grain Silos. Aerosol Air Qual. Res. 20: 2548–2556. https://doi.org/10.4209/aaqr.2020.02.0053


HIGHLIGHTS

  • PM10 exceeded baseline values by 26-times due to implosion of concrete grain silos.
  • Post-implosion PM10 values decayed logarithmically with distance.
  • Water spraying suppressed dust generation at the implosion site.
  • Emission rate of PM10 from implosion was found to be 17 ± 2 mg m2 s–1.
  • Mixing height during the implosion was found to be 613 ± 72 m.
 

ABSTRACT


This study quantified the effect of imploding old concrete grain silos in Aqaba, Jordan, on the eastern side of the Gulf of Aqaba, an arid region, on air quality by measuring the PM10 concentrations before and after the implosion at four monitoring locations. The implosion of the silos forms part of a comprehensive plan to relocate and upgrade the Port of Aqaba, which is situated on the coast of the Red Sea, with the goal of freeing space for development and improving the infrastructure in the heart of the city. The demolition, which occurred at 11:00 a.m. (local time) on 13 January 2019, generated a massive cloud of dust that was transported to nearby areas. To characterize these emissions, descriptive statistics, graphical methods, inverse distance weighting interpolation, decision trees constructed with recursive partitioning, the Gaussian dispersion model, the modified box model, and regression analysis were applied. The PM10 concentrations were in compliance with the Jordanian 24-h standard of 120 µg m–3 prior to the implosion but substantially increased (although still varied by distance from the demolition site) at all four stations afterward, with the maximum values (259–587 µg m–3) exceeding the pre-implosion ones by as much as 26 times. However, these high concentrations were short-lived, and the majority of the stations returned to background levels within 30–33 hours. According to our calculations on the implosion, the PM10 emission rate was 17 ± 2 mg m–2 s–1, which is equivalent to 215 ± 22 kg silo–1, and the air mixing height was 613 ± 72 m, or approximately eight times the height of the silos.


Keywords: PM10; Demolition; Implosion; Emission rate; Air pollution; Aqaba.


INTRODUCTION


It is commonly known that air pollution by particulate matter (PM) has chronic and acute health impacts, including cough, wheezing, asthma attacks, bronchitis, skin and eye irritation, high blood pressure, heart attack, strokes and increased mortality rate (Eggleston et al., 1999; Rand et al., 2000; Castro et al., 2001; Peters et al., 2001; Samoli et al., 2008). Sources of dust in urban areas are various, including construction and building demolition resulting in the release of a huge quantity of fugitive dust leading to severe deterioration of air quality that affects the quality of life of local people and their businesses (Muleski et al., 2005; Joseph et al., 2009; Font et al., 2014; Azarmi et al., 2015). Rabito et al. (2007) argued that demolition of old structures having lead-based paint are potential sources of environmental lead exposure.

Building demolition is carried out either by implosion or mechanical means that involve excavators or wrecking balls. Mechanical means have longer impact on air quality at the demolition site and its surroundings (Dorevitch et al., 2006) as compared to implosion which seems to have a short-lived but severe impact. Beck et al. (2003) studied air quality immediately after the implosion of a 22-story building within a four-block radius in east Baltimore and reported a fugitive dust increase by up to 3,000 times the amount before the implosion. Similarly, the implosion of a hospital in Calgary, Alberta, Canada, caused a massive increase in total suspended particulates (TSPs) in areas surrounding the implosion site (Stefani et al., 2005).

Although there is a wealth of studies that have tackled air pollution from diverse sources such as road vehicles (Kumar et al., 2014; Goel and Kumar, 2015), road works (Fuller and Green, 2004; Tian et al., 2007), non-vehicular activities (Saliba et al., 2010; Kumar and Morawska, 2014), and industrial works (Rodríguez et al., 2004; Toledo et al., 2008; Diapouli et al., 2013; Kumar et al., 2014), there is a lack of studies that address PM emanating from demolition sites. Consequently, the focus of this paper is related to (1) quantifying particulate matter with aerodynamic diameter less than or equal to 10 µm (PM10) emissions during the implosion of concrete grain silos located at the old seaport in Aqaba off the Gulf of Aqaba that took place on 13 January 2019 and (2) estimating particle mass emission rate of PM10 produced by the collapse of imploded silos.

 
DATA AND METHODS



Study Site and PM10 Monitoring Stations and Data

The study site (Fig. 1) has 75 grain silos arranged in five parallel rows occupying a rectangular area of 8,246 m2 (133 m × 62 m) on the eastern coast of the Gulf of Aqaba in Aqaba. It is located between longitudes 34°57ʹE and 35°02ʹE and latitudes 29°21ʹN and 29°33ʹN. The silos were constructed in 1975 and made of reinforced concrete. Diameter and height of each silo are 9 m and 75 m, respectively. The silos were demolished by implosion, which took place at 11:00 a.m. (local time) on 13 January 2019, by deploying 1,000 kg of explosives. Jordanian Department of Civil Defense (firefighting) placed several firefighting tankers and a jet to spray water at the collapsing silos in order to reduce dust emission. A square block surrounding the implosion site was fenced off and guarded by police and city officials on the morning of the implosion. Public access was restricted preceding the implosion as well as for two hours following the event.

Fig. 1. Location map of the study site.
Fig. 1.
 Location map of the study site.

To differentiate implosion-generated PM10 from background values, four MP101M analyzers (ENVEA, Poissy, France), which use the standard beta gauge measurement method, were installed at four monitoring stations (Fig. 1). The monitoring stations were also equipped with combined wind speed and direction anemometers DNA021-022 (LSI SpA, Milano, Italy). The first station is City Station located in downtown Aqaba about 3,830 m north of the silos. The other three stations are Mobile Station, South Station, and New Port Station. They are located south of the silos along the Gulf of Aqaba about 3,059 m, 12,080 m, and 16,005 m away, respectively. Monitoring continued for 72 hours starting at 9:15 a.m. (local time) on 12 January 2019 and ending at 9:00 a.m. (local time) on 15 January 2019.


Descriptive Statistical Analysis and Spatial Visualization

Measures of descriptive statistics (represented by mean, minimum, and maximum) in addition to graphical methods (represented by time-series scatter plots) were produced for the PM10 time-series data before and after imploding for the four stations (Freedman et al., 2007).

For quick visualization of the spatial and temporal variability of PM10 time-series data at the four stations before and after the imploding, inverse distance weighting (IDW) interpolation technique was applied (Borga and Vizzaccaro, 1997).


Recursive Partitioning Analysis

Decision trees were constructed from the PM10 time-series data for the four stations by applying recursive partitioning (Zhang and Singer, 2010). The aim is to partition the time series into homogeneous meaningful clusters (nodes). These clusters should describe the expected behavior of PM10 in the study area pre-implosion, post-implosion, in addition to decaying after the dispersal of the impact resulting from implosion. Decision trees consist of root nodes, internal nodes, and terminal nodes. Root nodes contain the entire observations from which the trees are grown.

In the present study, decision tree size (i.e., number of cut-off points) was determined interactively by first growing the tree to a large enough size one split at a time and then pruning so that subgroups that cannot increase significantly the explanation power of the tree (measured by the coefficient of determination (R2)) were eliminated (Izenman, 2008).

 
PM10 Emission Rate

PM10 emission rate from imploding of the silos were calculated by employing the Gaussian plume dispersion formula:

where C is steady state concentration at point (x, y, z) (µg m–3), Q is emission rate (µg s–1), u is wind speed at stack height (m s–1), σy and σz are dispersion coefficient functions, and heff is elevation of emission point (m).

When calculating ground-level PM10 concentration directly below the centerline of a dust plume that emanates from a ground-level source, y, z, and heff were all assumed to be zero. In this case Eq. (1) becomes:

 

Solving Eq. (2) for Q yields:

 

PM10 emission rate could also be calculated by applying the modified box model (Fig. 2) that has been developed and used by previous studies (Jamriska and Morawska, 2001; Font et al., 2014; Azarmi et al., 2016):

Fig. 2. Schematic diagram of the box model showing all dimensions: L and W refer to length and width of the box, respectively, H refers to air mixing height, and  refers to wind velocity (adapted from Jamriska and Morawska, 2001; Font et al., 2014; Azarmi et al., 2016).Fig. 2. Schematic diagram of the box model showing all dimensions: L and W refer to length and width of the box, respectively, H refers to air mixing height, and  refers to wind velocity (adapted from Jamriska and Morawska, 2001; Font et al., 2014; Azarmi et al., 2016).

                                                         

where q is PM10 emission rate from unit area (Q/8,246 m–2) (in µg m–2 s–1), u is wind speed, W is the box width parallel to wind velocity , H is mixing height (box height), and B is average background concentration. Mixing height (how high the dust spewing by the implosion can reach during the implosion) depends on atmospheric stability. It plays important role in dust dilution and dispersion. Therefore, it can be calculated by solving Eq. (4) for H, which yields the following expressions:

where ΔPM10 is the difference between average PM10 concentration during the spread of the dust plume resulting from the implosion and the average PM10 before the implosion (background concentration). It had to be calculated at the sides of the hypothetical box that surrounds the collapsing silos. This was achieved by developing a logarithmic decay profile for ΔPM10 in order to back-calculate PM10 concentrations at the box faces about 31 m away from the silos’ center (Azarmi et al., 2016):

where d is the distance from the implosion site, a and b are regression coefficients.

 
RESULTS AND DISCUSSIONS


Table 1 and Fig. 3 show some basic descriptive statistics and scatter plots for the PM10 time-series data recorded at the four monitoring stations. It can be observed that before implosion, all PM10 values (background) were in compliance with the Jordanian 24-h standard of 120 µg m–3 but have increased substantially after the implosion and exceeded background values by several times. The lowest pre-implosion values of PM10 were recorded at South Station where PM10 varied from 23 µg m–3 to 85 µg m–3 with mean of 51 µg m–3, whereas the highest pre-implosion PM10 values were recorded at Mobile Station where PM10 varied from 58 µg m–3 to 119 µg m–3 with a mean of 82 µg m–3. However, the lowest post-implosion PM10 values were recorded at New Port Station, which is the farthest station from the silos, where PM10 varied from 34 µg m–3 to 259 µg m–3 with a mean of 193 µg m–3. The highest post-implosion PM10 values were recorded at Mobile Station, which is the closest station to the silos, where PM10 varied from 101 µg m–3 to 587 µg m–3 with a mean of 292 µg m–3. These observations indicate that post-implosion PM10 values were up to 26 times higher than the background values. This is higher than those obtained by other studies (Dorevitch et al., 2006; Azarmi et al., 2016) who reported up to 11-fold-higher PM10 values during demolition than background levels, but less than the 3,000-fold-higher post-implosion PM10, which is reported by Beck et al. (2003). It is worth mentioning that Beck et al. (2003) measurements were carried out at only four blocks away from the implosion site which is much closer than our monitoring locations which were located between 3.1 km and 16 km away from the imploded silos. Furthermore, mitigation measures (represented by spraying water at the collapsing silos) that have been implemented during implosion in our case have likely reduced dust emissions; otherwise post-implosion PM10 values would have been more than 26 times higher than pre-implosion PM10. Windrows of wind speed and direction recorded at the four stations indicate that wind speed was relatively low (0–7.5 m s–1) during the monitoring period with continuous shift of wind direction. This would have enhanced the chance to transport dust evenly to surrounding areas. Nevertheless, values of PM10 recorded in the present study before and after the implosion show that the amount of dust emitted from the implosion site was massive, but it decayed rapidly as it dispersed away from the site.

 Table 1. Basic descriptive statistics for PM10 data for the four stations.

 Fig. 3. Scatter plots for the PM10 (µg m–3) time-series data for the four stations showing the Jordanian standard (horizontal dashed line) and the time of implosion (vertical solid line).Fig. 3. Scatter plots for the PM10 (µg m–3) time-series data for the four stations showing the Jordanian standard (horizontal dashed line) and the time of implosion (vertical solid line).

wo major peaks of PM10 values (Fig. 3) can be noticed after the imploding in all monitoring stations. For City Station, which lies to the north of the silos, these two peaks appeared about 6 hours (PM10 of ~433 µg m–3) and 21 hours (PM10 of ~442 µg m–3) after the implosion. For Mobile Station, South Station, and New Port Station, the first peaks existed about 4 hours (PM10 of ~587 µg m–3), 6 hours (PM10 of ~346 µg m–3), and 8 hours (PM10 of ~259 µg m–3), respectively, after implosion while the second peaks existed at about 21 hours (PM10 of ~439 µg m–3), 23 hours (PM10 of ~355 µg m–3), and 25 hours (PM10 of ~253 µg m–3), respectively, after implosion. Hence, for monitoring stations that lie to the south of the silos, it can be concluded that the times at which these two major peaks are noticed increase as the distance between the monitoring station and silos increases, while their magnitudes decrease as the distance between the monitoring station and silos increases. A third minor peak of PM10 values can be detected after the implosion in New Port Station (33 hours after implosion with PM10 of ~204 µg m–3) and City Station (33 hours after implosion with PM10 of ~281 µg m–3). This third minor peak of PM10 values can hardly be detected at Mobile Station (29 hours after implosion with PM10 of ~331 µg m–3) and South Station (30 hours after implosion with PM10 of ~212 µg m–3).

Fig. 4 shows snapshots of the spatial and temporal distribution of PM10 values before and after implosion at 10 distinct times resulting from applying IDW. This figure supports our previous stated observation. That is, before implosion all areas at different times had PM10 values below the Jordanian standard. After implosion, areas with PM10 values above the standard by several times appeared first in locations centered on Mobile Station, then gradually covered the whole study area, and then air returned to its background values as it was before implosion. The last traces of PM10 values above the standard after implosion were seen around New Port Station.

Fig. 4. The results of applying inverse distance weighting (IDW) on the PM10 time-series data for the four stations before and after implosion. Areas with PM10 values greater than the Jordanian standard are also shown.Fig. 4. The results of applying inverse distance weighting (IDW) on the PM10 time-series data for the four stations before and after implosion. Areas with PM10 values greater than the Jordanian standard are also shown.

The results of applying recursive partitioning on PM10 time-series data for the four monitoring stations are shown in Table 2 and Figs. 56. Regarding all monitoring stations, the decision trees explained about 80% of the variability in PM10 data (Table 2). For City Station, Mobile Station, and South Station, the analysis partitioned the data into three distinct homogeneous clusters with two cut-off points, while for New Port Station, four distinct homogeneous clusters were constructed with three cut-off points (Figs. 5–6). The first cut-off point for City Station is located 1 hour and 45 minutes after the implosion. For Mobile Station, South Station, and New Port Station, the first cut-off points are located at 1 hour and 15 minutes, 2 hours and 45 minutes, and 4 hours and 45 minutes after the implosion, respectively. The last cut-off point for City Station is located 34 hours and 30 minutes after the implosion. For Mobile Station, South Station, and New Port Station, the last cut-off points are located at 32 hours and 15 minutes, 32 hours and 45 minutes, and 37 hours and 45 minutes after the implosion, respectively. Hence, it is obvious that the times at which the first and last cut-off points are located are directly proportional to the distances between the monitoring stations and silos. That is, the longer the distance between the monitoring station and the silos, the longer the time at which the cut-off point is observed.

Table 2. Predictive powers of the decision trees constructed from applying recursive partitioning technique on the PM10 time-series data for the four stations and the associated decision rules.

Fig. 5. Scatter plots for the R2 values resulting from interactively growing the decision trees constructed from applying recursive partitioning technique on the PM10 time-series data for the four stations to large enough sizes one split at a time and then pruningFig. 5. Scatter plots for the R2 values resulting from interactively growing the decision trees constructed from applying recursive partitioning technique on the PM10 time-series data for the four stations to large enough sizes one split at a time and then pruning.

Fig. 6. Plots for the decision rules constructed from applying recursive partitioning technique on the PM10 time-series data for the four stations showing the Jordanian standard (horizontal dashed line) and the time of implosion (vertical solid line).Fig. 6. Plots for the decision rules constructed from applying recursive partitioning technique on the PM10 time-series data for the four stations showing the Jordanian standard (horizontal dashed line) and the time of implosion (vertical solid line).

Furthermore, the predicted means of PM10, which were calculated for the clusters, obtained for City Station and Mobile Station are almost similar. They increased from 81 µg m–3 to 305 µg m–3, and then decreased to 141 µg m–3 for City Station, and increased from 84 µg m–3 to 366 µg m–3, and then decreased to 143 µg m–3 for Mobile Station. The distances between these two stations and the silos are very similar; however, City Station is located to the north of the silos while Mobile Station is in the south. For South Station, the predicted means of PM10, which were associated with the clusters, increased from 55 µg m–3 to 218 µg m–3 and then decreased to 113 µg m–3. For New Port Station, the predicted means of PM10 associated with the clusters increased from 168 µg m–3 to 227 µg m–3, and then decreased to 185 µg m–3, and finally went down to 117 µg m–3. Again, it is clear that (excluding New Port Station) the magnitudes of the predicted means of PM10 data associated with different clusters, which were obtained for the monitoring stations, are inversely proportional to the distances between the monitoring stations and silos. This means that the longer the distance between the monitoring station and the silos is, the smaller the magnitude of the predicted mean of PM10 is.

For City Station, Mobile Station, and South Station, the first and last cut-off points can be regarded as representing the times at which these monitoring stations started and ended recording the impact of imploding on PM10 values, respectively. Similarly, the first clusters and the associated predicted means of PM10, the second clusters and the associated predicted means of PM10, and the third clusters and the associated predicted means of PM10 can be regarded as representing the background for PM10 before the implosion, the change in values of PM10 resulting from the implosion, and the change in values of PM10 after the dispersal of the impact resulting from the implosion, respectively.

Emission rate of PM10 (Q) was estimated by applying Eq. (3). It was found to be 140 ± 14 g s–1 (Table 3). Therefore, emission rate of PM10 per unit area per second (q) was attained by dividing this value by 8,246 (the total area occupied by the silos), which yields 17 ± 2 mg m–2 s–1. This value is about 500-fold greater than that observed by Azarmi et al. (2016) who reported emission rates of about 0.035 mg m–2 s–1 for PM10 from a building demolition that took place in south England using an excavator. This finding clearly demonstrates that implosion releases a massive amount of dust that severely deteriorates air quality at the surroundings of the implosion site. Weather, soil type, and absence of green areas in Aqaba whose climate is hot and dry have likely sustained dust re-entrainment, which is contrasted to south England, whose wet climate that supports a lush and green landscape, lowers dust generation considerably.

Table 3. PM10 emission rate. Values of σy, σz are calculated by following Martin (1976) method, assuming slightly unstable air (Class C).

Total mass of fugitive dust emanating from the demolished silos in 32 hours (the average time that took PM10 to decrease to background value as determined from recursive partitioning) is about 16 ± 2 t. PM10 emission rate expressed in kilograms per silo was calculated by dividing the total mass of generated PM10 by 75 (number of silos), which yields 215 ± 22 kg silo–1. Air mixing height (H) was calculated by substituting the value of PM10 emission rate per unit area (q) along with the values of a and b from Fig. 7, which are –73.1 µg m–3 and 824 µg m–3, respectively in Eq. (7), and found to be 613 ± 72 m, which is about eight times the original silos’ height. The calculation of H is important for determining the turbulent domain in which dispersion takes place. In addition, it could be used as a scaling factor to describe the vertical profiles of the atmospheric boundary layer variables (Beyrich et al., 1998).

Fig. 7. Logarithmic decay profile of PM10 at the implosion site.
Fig. 7.
 Logarithmic decay profile of PM10 at the implosion site.

 
SUMMARY AND CONCLUSION 


The Aqaba Special Economic Zone Authority has been implementing a comprehensive plan to relocate and upgrade the Port of Aqaba, which is situated on the coast of the Red Sea, with the goal of freeing space for development and improving the infrastructure in the heart of the city. The new site is set approximately 20 km south of the current location, near the border of Jordan and Saudi Arabia. The project involves massive amounts of construction and demolition, which will potentially elevate the airborne particulate matter concentrations in the vicinities of the work areas. Most of the old facilities, which contain asbestos, have been dismantled by selective excavation in order to suppress fugitive dust. However, the asbestos-free grain silos that were built south of Aqaba next to the Gulf of Aqaba in the 1970s have been demolished by implosion. The demolition, which occurred at 11:00 a.m. (local time) on 13 January 2019, generated a massive cloud of dust that was transported to nearby areas. The PM10 levels were measured at four monitoring stations surrounding the demolition site before and after the implosion.

The monitoring period can be divided into three phases, namely, the stage preceding the implosion, the implosion and its aftermath, and the return to pre-implosion conditions. The PM10 concentrations during the first phase were below the Jordanian 24-h limit of 120 µg m–3 and represent the areal background levels. The second phase commenced with the implosion and lasted 30–33 hours, depending on the location of the station. During this stage, the concentrations increased substantially, resulting in maximum values (259–587 µg m–3) that exceeded the minimum background values (23–58 µg m–3) by up to 26 times.µµ Mitigation measures (spraying the imploding silos with water) in addition to favorable meteorological conditions (low wind speed and continual wind shifts) likely reduced the emission of dust and enhanced the evenness of the dispersion to surrounding areas; otherwise, the post-implosion values would have been even higher. Hence, mitigation and timing (with respect to meteorological conditions, specifically, the wind speed and direction) are critical factors in minimizing the adverse effects of such projects. During the third phase, the PM10 emitted by the implosion dispersed, and the concentrations returned to their background values. The results indicate that the concentrations associated with an implosion decay logarithmically with distance; in this case, the PM10 emission rate was 17 ± 2 mgµ m–2 s–1, which is equivalent to 215 ± 22 kg silo–1, and the air mixing height was 613 ± 72 m, or approximately eight times the height of the silos.

After World War II, the need for large-scale reconstruction in dense urban areas led to the emergence of the demolition industry, which, employing controlled implosion among other methods, grew and matured throughout the world during the latter half of the twentieth century (Blanchard, 2002). The current study, however, is unique, as no demolition projects using implosion have been previously investigated in Jordan. Furthermore, the lack of regulations in this sector demonstrates a significant gap in the regulatory system of the nation, which must be filled by establishing well-defined and scientifically based rules. Mitigation measures, as mentioned previously, can reduce the risks to surrounding communities and the environment.

Finally, future studies should extend the third phase of the monitoring period in consideration of the relatively long dispersal time of PM10. Additional time-series data will enable us to examine the time lapse of the decrease in concentration and determine exactly when the background value is reached, which is the most important question in terms of health.

 
ACKNOWLEDGEMENT


Immense gratitude is due to Aqaba Special Economic Zone Authority for permitting the authors to use meteorological and air quality data in preparing this paper.


REFERENCES


  1. Azarmi, F., Kumar, P., Marsh, D. and Fuller, G.W. (2015). Assessment of long-term impacts of PM10 and PM2.5 particles from construction works on surrounding areas. Environ. Sci. Processes Impacts 18: 208–21. https://doi.org/10.1039/c5em00549c

  2. Azarmi, F. and Kumar, P. (2016). Ambient exposure to coarse and fine particle emissions from building demolition. Atmos. Environ. 137: 62–79. https://doi.org/10.1016/j.atmosenv.2016.04.029

  3. Beck, C.M., Geyh, A., Srinivasan, A., Breysse, P.N., Eggleston, P.A. and Buckley, T.J. (2003). The impact of a building implosion on airborne particulate matter in an urban community. J. Air Waste Manage. Assoc. 53: 1256–1264. https://doi.org/10.1080/10473289.2003.10466275

  4. Beyrich, F., Gryning, S.E., Joffre, S., Rasmussen, A., Seibert, P. and Tercier, P. (1998). Mixing height determination for dispersion modelling – A test of meteorological pre-processors. In Air pollution modeling and its application XII, Gryning, S.E. and Chaumerliac, N. (Eds.), Air Pollution Modeling and Its Application XII. NATO. Challenges of Modern Society. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9128-0_55

  5. Blanchard, B. (2002). A history of explosive demolition in America. Proceedings of the Annual Conference on Explosives and Blasting Technique. International Society of Explosives Engineers. pp. 27–44.

  6. Borga, M. and Vizzaccaro, A. (1997). On the interpolation of hydrologic variables: Formal equivalence of multiquadratic surface fitting and kriging. J. Hydrol. 195: 160–171. https://doi.org/10.1016/S0022-1694(96)03250-7

  7. Castro, M., Schechtman, K.B., Halstead, J. and Bloomberg, G. (2001). Risk factors for asthma morbidity and mortality in a large metropolitan city. J. Asthma 38: 625–635. https://doi.org/10.1081/JAS-100107540

  8. Diapouli, E., Papamentzelopoulou, A. and Chaloulakou, A. (2013). Survey of airborne particulate matter concentration at a marble processing facility - workers’ exposure assessment. Global NEST J. 15: 204–208. https://doi.org/10.30955/gnj.000953

  9. Dorevitch, S., Demirtas, H., Perksy, V.W., Erdal, S., Conroy, L., Schoonover, T., and Scheff, P.A. (2006). Demolition of high-rise public housing increases particulate matter air pollution in communities of high-risk asthmatics. J. Air Waste Manage. Assoc. 56: 1022–1032. https://doi.org/10.1080/10473289.2006.10464504

  10. Eggleston, P.A., Buckley, T.J., Breysse, P.N., Wills-Karp, M., Kleeberg, S.R.,and Jaakkola, Y. (1999). The environment and asthma in U.S. inner cities. Environ. Health Perspect. 107: 439–450. https://doi.org/10.1289/ehp.99107s3439

  11. Font, A., Baker, T., Mudway, I.S., Purdie, E., Dunster, C., and Fuller, G.W. (2014). Degradation in urban air quality from construction activity and increased traffic arising from a road widening scheme. Sci. Total Environ. 497–498: 123–132. https://doi.org/10.1016/j.scitotenv.2014.07.060

  12. Freedman, D., Pisani, R. and Purves, R. (2007). Statistics. 4th Edition. W. W. Norton and Company, Inc.

  13. Fuller, G.W. and Green, D. (2004). The impact of local fugitive from building works and road works on the assessment of the European Union limit value. Atmos. Environ. 38: 4993–5002. https://doi.org/10.1016/j.atmosenv.2004.06.024

  14. Goel, A. and Kumar, P. (2015). Characterisation of nanoparticle emissions and exposure at traffic intersections through fast–response mobile and sequential measurements. Atmos. Environ. 107: 374–390. https://doi.org/10.1016/j.atmosenv.2015.02.002

  15. Izenman, A.J. (2008). Modern multivariate statistical techniques: Regression, classification, and manifold learning. Springer Science + Business Media, LLC.

  16. Jamriska, M. and Morawska, L. (2001). A model for determination of motor vehicle emission factors from on-road measurements with a focus on submicrometer particles. Sci. Total Environ. 264: 241–255. https://doi.org/10.1016/S0048-9697(00)00720-8

  17. Joseph, J., Patil, R.S., and Gupta, S.K. (2009). Estimation of air pollutant emission loads from construction and operational activities of a port and harbour in Mumbai, India. Environ. Monit. Assess. 159: 85. https://doi.org/10.1007/s10661-008-0614-x

  18. Kumar, P. and Morawska, L. (2014). Recycling concrete: An undiscovered source of ultrafine particles. Atmos. Environ. 90: 51–58. https://doi.org/10.1016/j.atmosenv.2014.03.035

  19. Kumar, P., Morawska, L., Birmili, W., Paasonen, P., Hu, M., Kulmala, M., Harrison, R.M., Norford, L. and Britter, R. (2014). Ultrafine particles in cities. Environ. Int. 66: 1–10. https://doi.org/10.1016/j.envint.2014.01.013

  20. Martin, D.O. (1976). Comment on the change of concentration standard deviation with distance. J. Air Pollut. Control Assoc. 26: 145–147. https://doi.org/10.1080/00022470.1976.10470238

  21. Muleski, G.E., Cowherd, Jr. C. and Kinsey, J.S. (2005). Particulate emissions from construction activities. J. Air Waste Manage. Assoc. 55: 772–783. https://doi.org/10.1080/10473289.2005.10464669

  22. Peters, A., Dockery, D.W., Muller, J.E. and Mittleman, M.A. (2001). Increased particulate air pollution and the triggering of myocardial infarction. Circulation 103: 2810–2815. https://doi.org/10.1161/01.CIR.103.23.2810

  23. Rabito, F.A., Iqbal, S., Shorter, C.F., Osman, P., Philips, P.E., Langlois, E. and White, L.E. (2007). The association between demolition activity and children's blood lead levels. Environ Res. 103: 345–351. https://doi.org/10.1016/j.envres.2006.09.011

  24. Rand, C.S., Butz, A.M., Kolodner, K., Huss, K., Eggleston, P. and Malveaux, F. (2000). Emergency department visits by urban african American Children with asthma. J. Allergy Clin. Immunol. 105: 83–90. https://doi.org/10.1016/s0091-6749(00)90182-9

  25. Rodríguez, S., Querol, X., Alastuey, A., Viana, M.M., Alarcón, M., Mantilla, E. and Ruiz, C.R. (2004). Comparative PM10–PM2.5 source contribution study at rural, urban and industrial sites during PM episodes in Eastern Spain. Sci. Total Environ. 328: 95–113. https://doi.org/10.1016/S00489697(03)00411-X

  26. Saliba, N.A., El Jam, F., El Tayar, G., Obeid, W., and Roumie, M. (2010). Origin and variability of particulate matter (PM10 and PM2.5) mass concentrations over an Eastern Mediterranean city. Atmos. Res. 97: 106–114. https://doi.org/10.1016/j.atmosres.2010.03.011

  27. Samoli, E., Peng, R., Ramsay, T., Pipikou, M., Touloumi, G., Dominici, F., Burnett, R., Cohen, A., Krewski, D., Samet, J. and Katsouyanni, K. (2008). Acute effects of ambient particulate matter on mortality in Europe and North America: Results from the APHENA study. Environ. Health Perspect. 116: 11. https://doi.org/10.1289/ehp.11345

  28. Stefani, D., Wardman, D. and Lambert, T. (2005). The implosion of the Calgary General Hospital: Ambient air quality issues. J. Air Waste Manage. Assoc. 55: 52–59. https://doi.org/10.1080/10473289.2005.10464605

  29. Tian, G., Fan, S., Li, G. and Qin, J. (2007). Characteristics of fugitive dust emission from paved road near construction activities. Huanjing Kexue 28: 2626–2629.

  30. Toledo, V.E., de Almeida Junior, P.B., Quiterio, S.L., Arbilla, G., Moreira, A., Escaleira, V., and Moreira, J.C. (2008). Evaluation of levels, sources and distribution of toxic elements in PM10 in a suburban industrial region, Rio de Janeiro, Brazil. Environ. Monit. Assess. 139: 49–59. https://doi.org/10.1007/s10661-007-9815-y

  31. Zhang, H. and Singer, B.H. (2010). Recursive partitioning and applications. Springer Science + Business Media, LLC.

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