International Journal of Environmental Health Research

ISSN: 0960-3123 (Print) 1369-1619 (Online) Journal homepage: http://www.tandfonline.com/loi/cije20

A methodology for the determination of fugitive dust emissions from landfill sites Eleftheria Chalvatzaki, Thodoros Glytsos & Mihalis Lazaridis To cite this article: Eleftheria Chalvatzaki, Thodoros Glytsos & Mihalis Lazaridis (2015) A methodology for the determination of fugitive dust emissions from landfill sites, International Journal of Environmental Health Research, 25:5, 551-569, DOI: 10.1080/09603123.2014.989491 To link to this article: http://dx.doi.org/10.1080/09603123.2014.989491

Published online: 07 Jan 2015.

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Date: 06 November 2015, At: 03:34

International Journal of Environmental Health Research, 2015 Vol. 25, No. 5, 551–569, http://dx.doi.org/10.1080/09603123.2014.989491

A methodology for the determination of fugitive dust emissions from landfill sites Eleftheria Chalvatzaki, Thodoros Glytsos and Mihalis Lazaridis* Department of Environmental Engineering, Technical University of Crete, Chania, Greece

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(Received 9 July 2014; final version received 6 October 2014) This study focuses on the development of a methodology for the determination of the contribution of fugitive dust emissions from landfill sites to ambient PM10 concentrations and the subsequent exposure to working personnel. Fugitive dust emissions in landfills mainly originate from resuspension due to truck traffic on paved and unpaved roads and from wind-blown dust from landfill cover soil. The results revealed that exposure to PM10, originating from fugitive dust emissions in the landfill site, was exceeding the health protection standards (50 μg m−3). The higher average daily PM10 concentration (average value) for weekdays was equal to 275 μg m−3 and was computed for the areas nearby the unpaved road located inside the landfill facilities that lead to the landfill cell. The percentage contributions of road and wind-blown dust to the PM10 concentrations on weekdays were equal to 76 and 1 %, respectively. The influence of the background concentration is estimated close to 23 %. Keywords: landfill site; PM10; dust; exposure; dose

Introduction Air pollution associated with aerosols has gained the concern of the scientific community and public regulatory agencies worldwide, since an additional health risk for humans has been proven to arise from exposure to particles (Samet et al. 2000; Pope et al. 2002; WHO 2011). Particles less than 10 micrometres in diameter (PM10) pose a health concern because they can be inhaled, and accumulate in the respiratory system. To evaluate a potential health threat due to PM10 inhalation, the health risk estimates are based on exposure and dose assessments for the exposed individuals. However, equally important is the estimation of the effective internal dose via lung deposition, transport and clearance mechanisms (ICRP 1994). In order to protect human health, target values for PM10 concentrations were proposed by the World Health Organization and the United States Environmental Protection Agency (US EPA 2004; WHO 2006). In the European Union (EU), the roles, goals and methods of air quality management are determined by EU directive 96/62/EC and the later daughter directives which describe the objectives for air protection policy and standards for EU member states as well as for candidates’ states. For PM10, the EU air quality standards have been established at levels of 40 μg m−3 (annual limit value for the protection of human health) and 50 μg m−3 PM10 (24-h limit value for the protection of human health). Particularly for occupational exposure, the Occupational Safety & Health *Corresponding author. Email: [email protected] © 2015 Taylor & Francis

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Administration of the US Department of Label has established an 8-h permissible exposure limit of 15,000 μg m−3 measured as total particulate and an 8-h limit of 5000 μg m−3 for respirable particulates. The PM10 are respirable particulates, and 80 % or more will deposit in the human respiratory system (Gamble & Lewis 1996). PM10 are generally able to travel into the human respiratory system. Different sizes of airborne particles affect different regions of the respiratory system including 0.4–0.7 μm (alveolar), 0.7–1.1 μm (alveo–bronchial), 1.1–2.1 μm (bronchial), 4.7–5.8 μm (trachea-bronchial) and 5.8–10 μm (extrathoraric) (Ny & Lee 2011). Dusty industrial sites such as landfill or construction sites are regarded as fugitive dust sources and may need to be monitored for regulatory emission control requirements (Datson et al. 2012). Fugitive dust emissions from landfill operations occur primarily as a result of wind erosion of the surface of the landfill and resuspension of road dust from waste transportation. Westbrook and Sullivan (2007) found that the majority of total calculated PM10 emissions from landfill sites derived from heavy truck traffic on paved and unpaved roads. In addition, Han et al. (2011) reported elevated PM10 concentrations in resuspended road dust samples collected at a landfill site, using mobile sampling methodology. Field monitoring and emission inventories revealed that fugitive emissions, particularly resuspended road dust, are significant contributors of PM10 (DeLuca et al. 2012). Exposure to traffic emissions has been associated to adverse health effects and has also been linked to increased risk of respiratory illnesses (Tsai et al. 2000; Lin et al. 2002). Furthermore, dust emanating from landfill operations contains traces of heavy metals due to the nature of materials (e.g. sludge and batteries) which have been deposited over the lifetime of the landfill (Chalvatzaki et al. 2014). The PM10 metal content is enhanced by refuse truck emissions (e.g. exhaust, tyre wear dust, brake wear dust, road surface wear dust and resuspension of deposited PM10 on a road surface). Therefore, it is important to examine the influence of landfill activities to the ambient PM10 levels. This study examines the influence of resuspended PM10 (from roads) and wind-blown dust (from landfill cover soil) at a landfill site to the ambient PM10 levels, and the subsequent human exposure and dose. The PM10 concentration at the landfill site was calculated as the sum of the fugitive dust concentration derived from roads, landfill cover and the background measured concentration which includes all other possible sources. Road dust emission rates were calculated using the proposed United States Environmental Protection Agency dust resuspension formulation (US EPA 2006), while dust emissions from wind-blown dust of landfill cover soil were calculated using wind-blown dust parameterization by Choi and Fernando (2008). The human exposure and dose of a Caucasian male worker at the outdoor weighing facility of the landfill site were computed by a lung deposition model (Aleksandropoulou & Lazaridis 2013). Materials and methods Description of the site The study area is located at a landfill which belongs to the Chania municipality on the island of the Crete (Greece). The Akrotiri landfill is situated at the north-east part of the prefecture of Chania and east of the city of Chania. The amount of municipal solid waste (MSW) managed in the landfill is approximately 89,000 metric tonnes year. Furthermore, 133 trucks travel on the landfill per day (weekdays) during summer, of which, 41 trucks travel only on paved roads, while 92 trucks travel both on unpaved and paved surfaces. Truck weighing ranges from 12 to 28 tons (full) and from 10 to 15 tons

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(empty). In addition, 34 trucks travel on the landfill per day (weekends) during summer and truck weighing ranges from 5 to 27 tons (full) and from 4 to 16 tons (empty) (Kontaksakis, personal communication). The trucks travel on the landfill during the whole day (24 h) with greater flow of trucks in the mornings. Typical truck traffic at Akrotiri landfill is composed by solid waste transport trucks, green waste transport trucks, cover material transport trucks and sludge transport trucks. The surrounding terrain is moderately hilly to the south-west direction, and there is a gorge to the northern direction which leads to the sea. There are no residential areas close to the landfill. A more detailed description of the study area can be found in Chalvatzaki and Lazaridis (2010). Estimation of resuspended dust emissions Resuspended dust emissions from unpaved roads The unpaved road dust emissions were calculated using the United States Environmental Protection Agency prescribed methodology in AP-42. The AP-42 document contains emission factors and process information for more than 200 air pollution source categories. For trucks travelling on unpaved surfaces at industrial sites, emissions for PM10 are estimated from the Equation (1) (US EPA 2006): E ¼ 1:5  ðs=12Þ0:9  ðW =3Þ0:45

(1)

−1

where E is the emission factor (lb (VMT) ), s is the surface material silt content (%), W is the mean vehicle weight (tons). The metric conversion from pounds (lb) per vehicle mile travelled (VMT) to grams (g) per vehicle kilometre travelled (VKT) is: 1 lb (VMT)−1 = 281.9 g (VKT)−1. Most unpaved roads consist of a graded and compacted roadbed usually created from the parent soil material. The rolling wheels of the vehicles impact a force to the surface that pulverises the roadbed material and ejects particles from the shearing force as well as by the turbulent vehicle wakes (Nicholson 1988). The turbulent wake behind the vehicle continues to act on the road surface after the vehicle has passed. Dust emissions from unpaved roads have been found to vary directly with the fraction of silt in the road surface materials. The surface material silt content for MSW landfills roads (unpaved) ranged from 2.2 % (minimum value) to 21 % (maximum value) with mean value equal to 6.4 % based on data published in US EPA AP-42 (US EPA 2006). Therefore, the surface material silt content was adopted to be equal to 6.4 % (mean value) in this study, since no available information exists for the Akrotiri landfill site. Resuspended dust emissions from paved roads The paved road dust emissions were also calculated using the United States Environmental Protection Agency prescribed methodology in AP-42. Generally, PM10 emissions from paved roads originate from the loose material present on the surface (US EPA 2011). The quantification of PM10 emissions from resuspension of loose material on the road surface due to truck travel, on a dry paved road, is estimated using the Equation (2) (US EPA 2011): E ¼ 0:62  ðsLÞ0:91  ðW Þ1:02

(2)

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where E is the PM10 emission factor (g (VKT)−1), sL is the road surface silt loading and W is the average weight (tons) of the trucks travelling the road. It should be noted that the size fraction is incorporated into the emission factor and that the silt loading rather than the fraction of silt is used. The surface silt loading (sL) for MSW landfills roads (paved) ranged from 1.1 g m−2 (minimum value) to 32.0 g m−2 (maximum value) with mean value equal to 7.4 g m−2 based on data published in US EPA AP-42 (US EPA 2011). Therefore, the surface silt loading was adopted to be equal to 7.4 g m−2 (mean value) in this study. There is no dependence of the paved road dust emission factor on vehicle speed or on the number of wheels incorporated in the calculations (Kuhns et al. 2001). Emissions of fugitive wind-blown dust The emission of wind-blown dust depends on the land cover, soil texture, wind friction velocity and threshold friction velocity at the study area during the study period. Dust emissions from wind-blown dust of landfill cover soil were calculated using the method presented in Choi and Fernando (2008). The landfill cover soil in the Akrotiri landfill site is composed of 62.5 % sandy soil, 34 % gravel soil and 3.5 % clay soil (Paterakis, personal communication). The vertical dust emission flux (Fa) is estimated using the formula of Westphal et al. (1987) modified by the results of Park and In (2003) and Liu and Westphal (2001). Choi and Fernando (2008) presented for predominantly sandy soils that, on average, only 13 % of the erodible lands is capable of emitting dust. For predominantly sandy soils, the vertical dust emission flux is estimated using the Equations (3) and (4): Fa ¼ 0:13  ð1  RÞ  1013  U3 when U  Ut

(3)

Fa ¼ 0 when U \Ut

(4)

where R is a reduction factor (0.1 for barren land), U*t is threshold friction velocity (cm s−1) and U* is friction velocity (cm s−1). Wind erosion only occurs when the friction velocity exceeds the threshold friction velocity of the surface. Threshold friction velocity is the minimum friction velocity that is required to initiate movement of an aggregate or particle resting on the soil surface. Movement occurs when drag and lift forces overcome gravitational and interparticle cohesive forces acting on the soil aggregate or particle (Lazaridis & Drossinos 1998). The threshold friction velocity is estimated using the following approach (Choi & Fernando 2008): Ut ¼ 0:30  e7:22z0 when w\w0

(5)

h i0:5 Ut ¼ ð0:30  e7:22z0 Þ  1 þ 1:21  ðw  w0 Þ0:68 when w [ w0

(6)

where z0 (cm) is the surface roughness length, w and w0 are the ambient and threshold volumetric soil moisture with w0 = 0.0014 × (%clay)2 + 0.17 × (%clay). Measurements at the Akrotiri landfill site reveal that the landfill cover soil is composed of 3.5 % clay soils, while the soil moisture is equal to 7 % (Paterakis, personal communication).

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The atmospheric dispersion model-ISC3-ST Air dispersion models are designed to predict the fate and transport of emissions of pollutants into the atmosphere (Silverman et al. 2007). Many dispersion models have been developed and widely used in air pollution studies (e.g. AERMOD, ISC3, and ADMS). In this work, the dispersion of road and landfill cover soil fugitive dust emissions was calculated using the ISC3-ST air quality model. The ISC3 Industrial Source Complex Model is a steady-state Gaussian plume model for modelling concentration from point, area, volume and open-pit sources. The ISC3-ST area source algorithm is used to model low-level or ground-level releases with no plume rise. Storage piles, landfills and lagoons are typical examples of area sources (US EPA 1995). Several input data are required for the implementation of the ISC model including meteorological data and source emission characteristics. The ISC3 model is widely used because of its broad applicability to multiple source types. It is most useful for analysing short-range pollutant transport within 20 km of the source (Silverman et al. 2007). Several studies have used ISC model (Kumar et al. 1999; Hanna et al. 2001; Silverman et al. 2007; Faulkner et al. 2008). The major advantages of ISC3 in comparison with other dispersion models (e.g. AERMOD and ADMS) are its relative simplicity of use and its robust predictions (i.e. the same results are obtained by different users for the same scenario) (Hanna et al. 2001). Furthermore, the amount of meteorological input data required by ISC3 is relatively small in comparison with other dispersion models (Hanna et al. 2001). In this study, the roads in the landfill site were modelled as an area source following the paved and unpaved roads with a total exposed surface of 16,310 m2. The emission rates of PM10 were calculated using the aforementioned emission factor methodology for paved and unpaved roads. The release height of road emissions was set to 2.3 m, whereas the receptor height was equal to 1.5 m, which corresponds to the typical human receptor breathing zone height. The release height (HS; m) of road emissions was estimated as a function of truck height (US EPA 2012): 1:7  Truck height (7) 2 where the average truck height was set equal to 2.7 m (Kontaksakis, personal communication). In addition, the landfill cover soil has been modelled as an area source with total exposed surface of 67,000 m2. The emission rates of PM10 were calculated using the emission factor methodology of fugitive wind-blown dust. The release height of emissions from landfill cover soil was equal to 0.5 m (average height of the cover soils), whereas the receptor height was also equal to 1.5 m. The area around the sources was considered rural during the ISC3-ST implementation. HS ¼

Exposure and dose assessment using the ExDoM model Several models have been developed to simulate the dynamics of PM10 in human airways, and subsequently to calculate the human exposure and the deposition dose (e.g. MPPD model, HRTM model MENTOR and DORIAN systems). The Exposure and Dose Model (ExDoM) was developed at the Laboratory of Atmospheric Aerosols of Technical University of Crete, and it is based on the human respiratory tract model (HRTM) of the ICRP (ICRP 1994). Several studies have used the HRTM model of the

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ICRP (Falk et al. 1999; Smith et al. 2002; Bailey et al. 2003). This model is a powerful, simple in use and reliable inhalation dosimetry estimation tool. The Exposure and Dose Model (ExDoM; Aleksandropoulou & Lazaridis 2013) can simulate the dynamics of respirable particulate matter in human airways and calculate the human exposure and the deposition, dose, clearance and finally retention of PM10 in the human respiratory tract (RT), the gastrointestinal (GI) tract and lymph nodes, and their absorption to blood capillaries. According to this model, the RT is divided in five regions, the extrathoracic airways ET1 (anterior nose) and ET2 (posterior nasal passages), the thoracic BB (bronchial), bb (bronchiolar) and the alveolar interstitial AI. The model estimates the individual’s dose of particles in different size fractions as follows: X H¼ BCi ni;j (8) where Ci is the exposure concentration (μg m−3) for particles in the size fraction i, B is the ventilation rate of the exposed individual (m3 h−1) and ni,j is the deposition fraction in region j of the RT for particles in the size fraction i. The particles mass in each compartment of the RT during and after the exposure and their mass fraction transferred to the GI tract, lymph nodes and blood are estimated by the Equation (9):  dRi X  ¼ mj;i Rj  ðmi;j þ sÞ  Ri þ Hi (9) dt j where m is the mechanical movement rate of particles from compartment i to j (mi,j ) or the opposite (mj,i), s is the rate of absorption into blood, R is the retained mass after time t in compartments i and j, and Hi is the instantaneous dose applied to the compartment i at time t. A detailed description of the model can be found in Aleksandropoulou and Lazaridis (2013). In this study, the ExDoM was applied to determine the dose for a Caucasian male worker at the outdoor weighing facility of the landfill site during working days (Monday–Friday). The worker was assumed to have the following daily schedule: sleep (indoors) 11 am–7 am, light exercise (outdoor weighing facility) 7 am–1 pm, sitting (indoors) 1 pm–4 pm, light exercise (indoors) 4 pm–6 pm, light exercise (outdoors) 6 pm–9 pm and sitting (outdoors) 9 pm–11 am. The workplace exposure is assumed to occur from 7 am to 1 pm each day. During the rest of the day, exposure to PM10 occurred indoors and outdoors in the vicinity of the background station approximately 10 km from the Akrotiri landfill. The daily activity schedule and breathing conditions determine, among other factors, the exposure and dose of the individual. The worker was assumed to be a nose breather, and his ventilation rates (volume of air inhaled per unit time) for light exercise, sitting and sleep were set equal to 1.5, 0.54 and 0.45 m3 h−1, respectively. The corresponding breathing frequencies were 20, 12 and 12 breaths per minute (reference values for adult Caucasian males; ICRP 1994). Moreover, the dose depends on the size, density and shape of particles. The particle density was considered equal to 1.5 g cm−3, which corresponds to the average density of typical ambient aerosols (Zhang et al. 2005).

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Measurements of particulate matter and meteorological parameters Meteorological parameters are necessary input data for the implementation of the ISC3ST and ExDoM models. The meteorological conditions were monitored (wind speed sensor 4034BG; wind direction sensor 4122BG; combined temperature/humidity sensor in shelter 3030BG; Theodor Friedrichs & Co, Germany) at the Akrotiri Research Station (Lazaridis et al. 2008). The station is located at a background site approximately 10 km away from the Akrotiri landfill. In addition, PM10 measurements were performed at the landfill site (locations are show in Figure 1 (in bold)) for validating of the dispersion calculations. The PM10 measurements in the landfill were performed using the TSI’s Dusttrak™ Aerosol Monitor model 8520 which is a portable, battery-operated, laser photometer that measures airborne dust concentrations (TSI 2003). Furthermore, PM10 measurements were carried out and at the background site in order to determine background values for the PM10 concentration levels in the general area of the landfill (Chalvatzaki et al. 2010). The PM10 measurements at the background station were performed using the FH 62 SEQ particulate monitor (Thermo 2003). Comparative measurements of PM10 concentrations during clear sky weather conditions by the beta attenuation monitor (FH 62 SEQ) and the Dusttrak™ Aerosol Monitor model 8520 were performed at the background station. The Dusttrak™ instrument measurements were corrected based on these

Figure 1. (a) Map of the Akrotiri area with sampling location; (b) Plan of the Akrotiri landfill site with sampling locations (in bold).

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comparative measurements by the correction formula: beta [Attenuation Monitor Concentration] (μg m−3) = 0.80 [Dusttrak Monitor Concentration] (μg m−3) + 10.4 (Chalvatzaki et al. 2010). The background station outdoor PM10 concentration was used to estimate the indoor PM10 concentration characteristics, using the indoor/outdoor PM10 concentration ratio (approximately 0.7). This ratio was obtained from Chalvatzaki et al. (2012) by the analysis of measurements, performed at an apartment located in the residential area in the vicinity of the background station. Similar sampling methodology was used by Morawska and Salthammer (2003). The indoor concentration was measured with a Dusttrak aerosol monitors (Laser photometer; ±1 μg m−3; 1-min interval; TSI Model 8520). The measurements in the apartment included long periods with no typical household activities and cooking. This result is in agreement with the study by Morawska and Salthammer (2003). Morawska and Salthammer (2003) concluded that, for naturally ventilated buildings in the absence of indoor sources, indoor/outdoor PM10 concentration ratio ranged from 0.5 to 0.98 (with a median value of 0.7). Finally, size distribution measurements of PM10 were performed with an Andersen impactor both at the background station (Chalvatzaki et al. 2012) and at the outdoor weighing facility of the landfill site. The Andersen sampler is a cascade impactor which consists of eight aluminium plates and one backup stage (non-viable, eight stage, Series 20-800, Thermo Scientific). The filters used for particle collection were dried before and after sampling in a laboratory room with constant temperature and relative humidity for a 24- h period. A Sartorius balance (Sartorius CP 225D, Sartorius AG, Goettingen, Germany) with mass resolution of 0.01 mg was used for the weighting of filters before and after sampling. In addition, measurements of PM10 chemical composition were also performed at the outdoor weighing facility of the landfill site. Chemical composition of PM10 especially in terms of heavy metals content is a matter of concern due to the both acute and chronic adverse health effects linked with heavy metals (Mishra et al. 2013). Chemical analyses show that PM10 contains heavy metals such as Cr, Mn, Zn, Ti and Pb. In particular, the concentrations of PMCr, PMMn, PMZn, PMTi and PMPb were equal to 529.87 ± 2.75, 273.60 ± 1.39, 49.04 ± 0.80, 17.62 ± 0.83 and 14.35 ± 0.27 ng m−3, respectively (unpublished work). Α detailed presentation of the results of the chemical analysis of PM10 is not in the scope of this study, which focuses on contribution of dust emissions from landfill sites to ambient PM10 concentrations and the subsequent exposure to working personnel. Results and discussion Assessment of PM10 concentration using the dispersion model ISC3-ST The methodology proposed for the determination of PM10 ambient concentration at a landfill due to fugitive dust emissions was applied during August 2008. This period was chosen, as then the PM10 field measurements in the landfill site were also available. The PM10 concentration at the landfill site was calculated as the sum of the fugitive dust concentration derived from the ISC3-ST model results and the background measured concentration. During the studied period, stable atmospheric conditions (stability classes E and F) at night and unstable atmospheric conditions at day (stability classes B and C) were observed. Furthermore, 1 h before sunset or after sunrise, the stability class was categorized as D (neutral conditions) (Mohan & Siddiqui 1998). Atmospheric stability

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is required in many dispersion studies as meteorological input parameter used to define the turbulent state of the atmosphere or to describe the dispersion capabilities of the atmosphere (Mohan & Siddiqui 1998). The average daily wind speed ranged from 1 to 4 m s−1, while average daily temperature during the simulation period varied between 24 and 30 °C. The effects of the resuspended particles from truck movement and from wind-blown dust on the ambient PM10 concentration can be seen in Figure 2(a) and (b). In particular, in Figure 2(a), the spatial distribution of the PM10 concentration (average daily values) on weekdays during August 2008 is depicted. PM10 concentrations in most locations of the landfill area (except south-west edge of the landfill area) exceeded the health protection standards (50 μg m−3). Furthermore, PM10 concentrations more than 50 μg m−3 are observed outside of landfill areas depicted in Figure 2(a) and for distances ranging close to 300–700 m from the landfill area. The highest PM10 concentration (average value) at the landfill site was computed near the unpaved road, where

Figure 2. (a) Spatial surface distribution of ambient PM10 concentrations (μg m−3) on weekdays (prevailing wind direction was NW). (b) Spatial surface distribution of ambient PM10 concentrations (μg m−3) on weekends (prevailing wind direction was SW).

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trucks travel to offload garbage in the landfill cell, and was equal to 275 μg m−3. The percentage contribution due to resuspension to the PM10 mass concentrations (275 μg m−3) was calculated to be close to 76 % (210 μg m−3), while the percentage contribution from the wind-blown dust was close to 1 % (3 μg m−3), and the contribution from the background concentration reached 23 % (62 μg m−3). These results are similar to the values reported by Westbrook and Sullivan (2007) from a large MSW landfill located in eastern Oregon. Westbrook and Sullivan (2007) found that 68 % of total calculated PM10 emissions were from heavy truck traffic on paved and unpaved roads, 4 % from wind erosion and 28 % from other activities (material handling, engine and landfill gas flare). The PM10 level at the landfill site and the contribution of the sources to the ambient PM10 concentration vary according to the location and the wind direction. In Figure 2(b), the spatial distribution of the PM10 concentration (average daily values) on weekends during August 2008 is shown. The PM10 concentration exceeds the health protection standards (50 μg m−3) at the outdoor weighing facility and at the landfill cell. Furthermore, PM10 concentrations more than 50 μg m−3 are observed outside of landfill area at distances 0–350 m from south-east edge of the landfill area and at distances 0–700 m from the east edge of the landfill area. The higher PM10 concentration on weekends was computed in the landfill cell and was equal to 135 μg m−3 (average daily value). The percentage contribution due to the resuspension to the PM10 mass concentrations (135 μg m−3) was close to 59 % (80 μg m−3), while the percentage contribution from the wind-blown dust was close to 6 % (8 μg m−3), and the contribution from the background concentration reached 35 % (47 μg m−3). Furthermore, the comparison between the higher PM10 mass concentration (average daily value) on weekdays and weekends showed that the higher PM10 mass concentration for the weekdays showed twice the value of the corresponding concentration for the weekends. Likewise, the average daily concentration at the outdoor weighing facility on weekdays presented 1.5 times the value of the corresponding concentration for the weekends. This difference between weekdays and weekends is due to the greater flow of trucks on weekdays. In particular, 133 trucks travel on the landfill per day on weekdays, while 34 trucks travel on the landfill per day on weekends as discussed earlier. Dispersion model validation and sensitivity analysis In order to validate the results of dispersion modelling, data of PM10 concentrations from field measurements at the landfill area were used. A comparison between the measured PM10 concentrations at the Akrotiri landfill (derived from field measurements) with the corresponsive PM10 concentrations calculated by the ISC3-ST model in conjunction with the background concentration is depicted in Figure 3(a). The higher PM10 concentrations were observed (for both field measurements and model results) at the main gate of the landfill site and at the weighing site due to the higher flow of trucks. The average value of the PM10 field measurements at the outdoor weighing was 156 μg m−3, while the model average value at the same site was 128 μg m−3. Furthermore, the field measurements and model results of average PM10 concentration at the main gate of the landfill site were 369 and 354 μg m−3, respectively. The agreement of the model results and field measurements was satisfactory (r2 = 0.8 for hourly results as depicted in Figure 3(b)) implying that the proposed methodology for the evaluation of fugitive dust emissions can be applied at landfill sites.

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0 01-08-08 (main gate of the landfill factory) 10:00-13:00

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Figure 3. (a) Comparison of PM10 model results (together with background concentration) at the Akrotiri landfill with field measurements. (b) Comparison of hourly PM10 model results (together with background concentration) with hourly PM10 field measurements.

A sensitivity analysis was performed by changing silt loadings (sL) and silt contents (s). In particular, Figure 4 presents the ambient PM10 concentrations at the outdoor weighing facility using different silt loadings and silt contents in the simulations. The surface silt loadings (for paved roads) selected in this study are as follows: sL = 1.1 g m−2 (minimum value), sL = 7.4 g m−2 (mean value) and sL = 32.0 g m−2 (maximum value) based on data published in US EPA AP-42 for MSW landfills roads.

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Figure 4. Ambient PM10 concentrations (μg m−3) on August 2008 at the outdoor weighing facility for different silt loadings (sL) and silt contents (s) in the simulations.

Likewise, the surface material silt contents (for unpaved roads) selected in this study are as follows: s = 2.2 % (minimum value), s = 6.4 % (mean value) and s = 21 % (maximum value) based on data published in US EPA AP-42. It is observed in Figure 4 that a reduction of the parameter sL by 85 % (the sL reduced to 1.1 g m−2 (minimum value) from 7.4 g m−2 (mean value)) and of parameter s by 66 % (the s reduced to 2.2 % (minimum value) from 6.4 % (mean value)) would reduce the average ambient PM10 concentration by 31 %. Likewise, an increase of parameter sL by 332 % (the sL increased to 32 g m−2 (maximum value) from 7.4 g m−2 (mean value)) and of parameter s by 228 % (the s increased to 21 % (maximum value) from 6.4 % (mean value)) would increase the average ambient PM10 concentration by 103 %. The calculations showed that higher silt loadings and silt contents result in elevated ambient PM10 concentrations due to the higher dust emission rates. Several studies confirm the current conclusion (Gillies et al. 1999; Fan et al. 2009). Therefore, the surface silt loading for paved roads and the surface material silt content for unpaved roads were important variables for the quantification of dust emissions. In this study, the surface silt loading and the surface material silt content used showed an agreement between the model results and the field measurements (r2 = 0.8 for hourly results). In addition, organic compounds in dust are associated with high road dust emissions. The grinding of plant detritus by vehicle tires on the hard paved surface liberates naturally occurring compounds that then admix with the road dust and can become airborne (Rogge et al. 2012). There is not enough information in the current literature allowing us to quantify the impact of organic content of PM10 on dust emissions. Effect of wind direction on PM10 concentrations The effect of the meteorological parameters, in particular wind direction, on PM10 concentrations (results of ISC3-ST model without background concentration) was examined.

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During the simulation period (on weekdays), the distribution of wind directions were 28.6 % for NW (315°), 23.8 % for W (270°), 14.3 % for WSW (247.5°), 9.5 % for SW (225°) and 4.8 % for the other wind directions (NNW (337.5°), WNW (292.5°), E (90°), S (180°) and ESE (112.5°)). Figure 5 depicts the influence of wind direction to the PM10 concentrations (due to the resuspended road dust and wind-blown dust) during the simulation period (on weekdays) at different locations (outdoor weighing, outdoor headquarters and main gate of the landfill site). In particular, the higher PM10 concentration (215 μg m−3) at the main gate of the landfill site was observed when air masses originated from east (90°) direction, while at the outdoor headquarters (89 μg m−3), it was when air masses originated from east– southeast (112.5°) direction. At the outdoor weighing facility, higher PM10 concentration (96 μg m−3) was observed when prevailing wind direction was south (180°). Therefore, wind direction is a variable with strong influence on PM10 levels (Van der Wal & Janssen 2000). On the other hand, the most frequently occurring wind directions during the simulation period was western (NW (315°) and W (270°)); western winds (NW (315°) and W (270°)) lead to lower PM10 concentration at various locations (outdoor weighing facility, outdoor headquarters and main gate of the landfill site) of the landfill. Estimation of the exposure and human RT dose of particulate matter using the ExDoM model The ExDoM model was applied to determine the dose for a Caucasian male worker at the outdoor weighing facility at the landfill site during working days (Monday–Friday). The outdoor weighing facility was selected for determining the exposure scenario due to the high PM10 concentrations caused mainly by the high flow of trucks in the vicinity area. For the implementation of the ExDoM, PM10 concentration and size distribution

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data are required as input parameters. PM10 size distribution is important because it plays a major role in determining the dose and the region affected in the human respiratory track. Size distribution measurements of PM10 were performed with an Andersen impactor. The Andersen cascade impactor consists of nine collecting stages (eight aluminium plates and one backup stage) with aerodynamic cut-off diameters equal to 10, 9, 5.8, 4.7, 3.3, 2.1, 1.1, 0.7 and 0.4 μm. Therefore, based on the cut-off diameter, fine particles are particles with diameter less than 2.1 μm, while coarse particles are particles between 2.1 and 10 μm. Figure 6 depicts the PM10 mass fraction distribution at the (a) Akrotiri Research station and (b) at the outdoor weighing facility. The analysis of measurement data showed that the mass size distribution at the background area is bimodal, while the mass size distribution at the outdoor weighing is unimodal, reflecting the resuspension of coarse particles. In particular, as regards the size distribution at the background area, the percentage contributions of fine (PM2.1) and coarse particles (PM10-2.1) to PM10 were 31 and 69 %, respectively, while at the outdoor weighing facility, they were 6 and 94 %, respectively. Fine particles penetrate deeper in the lungs and can reach the alveolar region of the exposed subjects, whereas coarse particles are mainly deposited in the upper RT regions (Milford et al. 2013). The time plot of the PM10 1-h average mass concentration under the current worker exposure scenario is depicted in Figure 7. The average PM10 concentration (on weekdays) at the outdoor weighing during working hours (07:00–13:00) was equal to 132 μg m−3. The exposure, after working hours, to PM10 occurred indoors and outdoors in the vicinity of the background station approximately 10 km from the Akrotiri landfill. The average outdoor PM10 concentration (on weekdays) was equal to 62 μg m−3 (during 18:00–23:00), while the indoor PM10 concentration (during periods without indoor activities) was equal to 30 μg m−3. Therefore, the daily averaged concentration was equal to 62 μg m−3. The hourly outdoor

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Figure 7. Average hourly concentration to the PM10 (μg m−3) for an adult Caucasian worker at the outdoor weighing facility.

PM10 values were derived from measurements in a background station, while the hourly indoor PM10 values were derived using the aforementioned indoor/outdoor concentration ratio methodology. During working hours, the PM10 values were derived from the model simulations. A sharp change in the exposure concentration at 07:00 is clearly shown, which is associated with the change of the exposure conditions of the individual worker who arrived at the landfill site. The results as regards the exposure concentration and the estimated daily dose of PM10 for an adult Caucasian male worker at the landfill site (outdoor weighing facility) are depicted in Figure 8(a). It is observed in Figure 8(a) that cumulative daily dose for workers at the landfill site ranged from 1041 to 1893 μg for PM10. However, Aleksandropoulou and Lazaridis (2013) found that at a coastal remote site in the eastern Mediterranean (Finokalia, Greece), the cumulative daily dose ranged from 345 to 761 μg, whereas in a residential background area in northern Europe (Oslo, Norway), the cumulative daily dose ranged from 133 to 212 μg. The cumulative daily dose for workers at the landfill site is higher than the cumulative daily dose of individuals in the above studies. This occurs due to high PM10 concentration at the landfill site and therefore high workplace dose. The percentage contribution of workplace dose to the cumulative daily dose ranged from 68 to 83 % for workers at the landfill site (outdoor weighing facility). Moreover, in Figure 8(b), the cumulative exposure and dose are presented along with the cumulative dose delivered to the lower head airways and to the thoracic and pulmonary regions of the RT (depicted in Figure 8(b) as four regions of the RT). The internal dose and the accumulated mass on the RT surfaces of the exposed subject were calculated together with the dose received by the GI tract through the mucociliary escalator and the amount of particles absorbed in blood. Particle absorption in blood was assumed to be moderate in the simulations. It is observed in Figure 8(b) that, of the 4.22 × 103 μg

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of particles deposited to the RT (except for ET1), 3.50 × 103 μg (83 %) was transferred to the GI tract and 2.52 × 102 μg (6 %) was absorbed in blood (moderate absorption). Approximately 11 % of the particles that were deposited to RT remained there at the end of the exposure. However, in a previous study (Chalvatzaki et al. 2012), it was shown

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that in the residential area in the vicinity of the background station (Chania, Greece), 76 % of particles deposited to RT were transferred to the GI tract, 6 % were absorbed to blood (moderate absorption) and 18 % remained at the RT at the end of the exposure (Chalvatzaki et al. 2012). These discrepancies are due to differences in the particles size distribution during the working hours. In particular, as regards to the size distribution characteristics, the percentage contribution of coarse particles to PM10 was approximately (69 %) in the residential area in the vicinity of the background station (Chania, Greece), while at the outdoor weighing facility, the percentage contribution of coarse particle to PM10 was approximately 94 % (average value). This implies that in the landfill site, more particles were deposited to the extrathoracic region of the lungs and followed mechanically clearance pathways. This is in accordance with the elevated coarse fraction due to the particle resuspension at the landfill site. Conclusions The changes in the PM10 ambient concentration at a landfill site as a result of resuspension and wind-blown dust, as well as the exposure and dose in landfill worker, were calculated using a methodology combining the ISC3-ST and ExDoM models. The implementation of the methodology was performed on August 2008 for a landfill located at Chania (Greece). The computed average daily concentration at the outdoor weighing facility of the landfill site was equal to 92 μg m−3, while the average workplace concentration (6 h average) was equal to 132 μg m−3. In order to validate the results of dispersion modelling and the values of PM10 concentrations used in the exposure assessment data, PM10 concentrations from field measurements at the landfill area were used. The PM10 concentration derived as the sum of the model results, and the background measured concentration was in good agreement with the field measurements (r2 = 0.8). In addition, the PM10 concentrations were analysed with the ExDoM model which calculates the deposition, dose and finally retention of PM10 in the RT. The cumulative exposure and dose profiles along with the cumulative dose to the four regions of the RT the dose received by the GI tract, and the amount of particles absorbed in blood, were calculated for an adult Caucasian male worker at the outdoor weighing facility. It was observed that 83 % of particles deposited in the RT were transferred to the GI tract, while 6 % were absorbed in blood and 11 % were retained in the four lower regions of the RT. The elevated PM10 levels in the landfill resulted in increased human exposure. Since the chemical composition of the waste incorporates toxic substances, it is of great importance to minimise the PM10 resuspension at landfill sites. This conclusion is in agreement with the results of the chemical analyses performed in collected samples from the landfill site, which showed that PM10 contains heavy metals (e.g. Pb, Cr, Mn, Zn and Ti).The reduction of PM10 emissions can be achieved by frequent wetting of the roads and removing dust from site roadways with rotary brush vacuum wagons, which are common PM10 reduction practices in other landfill sites. References Aleksandropoulou V, Lazaridis M. 2013. Development and application of a model (ExDoM) for calculating the respiratory tract dose and retention of particles under variable exposure conditions. Air Qual Atmos Health. 6:13–26. Bailey MR, Absoborlo E, Guilmette RA, Paquet F. 2003. Practical application of the ICRP human respiratory tract model. Radiat Prot Dosim. 105:71–76.

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Chalvatzaki E, Lazaridis M. 2010. Assessment of air pollutant emissions from the Akrotiri landfill site (Chania, Greece). Waste Manage Res. 28:778–788. Chalvatzaki E, Kopanaki I, Kontaksakis M, Glytsos T, Kalogerakis N, Lazaridis M. 2010. Measurements of particulate matter concentrations at a landfill site (Crete, Greece). Waste Manage. 30:2058–2064. Chalvatzaki E, Aleksandropoulou V, Glytsos T, Lazaridis M. 2012. The effect of dust emissions from open storage piles to particle ambient concentration and human exposure. Waste Manage. 32:2456–2468. Chalvatzaki E, Aleksandropoulou V, Lazaridis M. 2014. A case study of landfill workers exposure and dose to particulate matter-bound metals. Water Air Soil Pollut. 225:1782–1800. Choi Y, Fernando HJS. 2008. Implementation of a windblown dust parameterization into MODELS-3/ CMAQ: application to episodic PM events in the US/ Mexico border. Atmos Environ. 42:6039–6046. Datson H, Hall D, Birch B. 2012. Validation of a new method for directional dust monitoring. Atmos Environ. 50:1–8. DeLuca PF, Corr D, Wallace J, Kanaroglou P. 2012. Effective mitigation efforts to reduce road dust near industrial sites: assessment by mobile pollution surveys. J Environ Manage. 98:112–118. Falk R, Philipson K, Svartengren M, Bergmann R, Hofmann W, Jarvis N, Bailey M, Per Camner P. 1999. Assesment of long-term bronchiolar clearance of particles from measurements of lung retention and theoretical estimates of regional deposition. Exp Lung Res. 25:495–516. Fan S, Tian G, Li G, Huang Y, Qin J, Cheng S. 2009. Road fugitive dust emission characteristics in Beijing during Olympics Game 2008 in Beijing, China. Atmos Environ. 43:6003–6010. Faulkner WB, Shaw BW, Grosch T. 2008. Sensitivity of two dispersion models (AERMOD and ISCST3) to input parameters for a rural ground-level area source. J Air Waste Manage Assoc. 58:1288–1296. Gamble JF, Lewis RJ. 1996. Health and respirable particulate (PM10) air pollution: a causal or statistical association? Environ Health Perspect. 104:838–850. Gillies JA, Watson JG, Rogers CF, DuBois D, Chow JC, Langston R, Sweet J. 1999. Long-term efficiencies of dust suppressants to reduce PM10 emissions from unpaved roads. J Air Waste Manage Assoc. 49:3–16. Han S, Youn JS, Jung Y. 2011. Characterization of PM10 and PM2.5 source profiles for resuspended road dust collected using mobile sampling methodology. Atmos Environ. 45:3343–3351. Hanna SR, Egan BA, Purdum J, Wagler J. 2001. Evaluation of the ADMS, AERMOD, and ISC3 dispersion models with the OPTEX, Duke Forest, Kincaid, Indianapolis and Lovett field datasets. Int J Environ Pollut. 16:301–314. [ICRP] International Commission on Radiological Protection. 1994. Human respiratory tract model for radiological protection. Oxford: ICRP Publication 66, Pergamon Press. Kuhns H, Etyemezian V, Landwehr D, Macdougall C, Pitchford M, Green M. 2001. Testing re-entrained aerosol kinetic emissions from roads (TRAKER): a new approach to infer silt loading on roadways. Atmos Environ. 35:2815–2825. Kumar A, Bellam NK, Sud A. 1999. Performance of an industrial source complex model: predicting long-term concentrations in an urban area. Environ Prog. 18:93–100. Lazaridis M, Drossinos Y. 1998. Multilayer resuspension of small identical particles by turbulent flow. Aerosol Sci Technol. 28:548–560. Lazaridis M, Dzumbova L, Kopanakis I, Ondracek J, Glytsos T, Aleksandropoulou V, Voulgarakis A, Katsivela E, Mihalopoulos N, Eleftheriadis K. 2008. PM10 and PM2.5 levels in the Eastren Meditteranean (Akrotiri Reasearch station, Crete, Greece). Water Air Soil Pollut. 189:85–101. Lin S, Munsie JP, Hwang SA, Fitzgerald E, Cayo MR. 2002. Childhood asthma hospitalization and residential exposure to state route traffic. Environ Res. 88:73–81. Liu M, Westphal DL. 2001. A study of the sensitivity of simulated mineral dust production to model resolution. J Geophys Res. 106:18099–18112. Milford C, Castell N, Marrero C, Rodriguez S, Sanchez de la Campa AM, Fernadez-Camacho R, de la Rosa J, Stein AF. 2013. Measurements and simulation of speciated PM2.5 in south-west Europe. Atmos Environ. 77:36–50. Mishra AK, Maiti SK, Pal AK. 2013. Status of PM10 bound heavy metals in ambient air in certain parts of Jhar ia coal field, Jharkhand, India. Int J Environ Sci. 4:141–150.

Downloaded by [York University Libraries] at 03:34 06 November 2015

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Mohan M, Siddiqui TA. 1998. Analysis of various schemes for the estimation of atmospheric stability classification. Atmos Environ. 32:3775–3781. Morawska L, Salthammer T. 2003. Indoor environment. Darmstadt: Wiley-VCH, 450 pp. Nicholson KW. 1988. A review of particle resuspension. Atmos Environ. 22:2639–2651. Ny MT, Lee BK. 2011. Size distribution of airborne particulate matter and associated metallic elements in an urban area of an industrial city in Korea. Aerosol Air Qual. Res. 11:643–653. Park S, In H. 2003. Parameterization of dust emission for the simulation of the yellow sand (Asian dust) event observed in March 2002 in Korea. J Geophys Res. 108:9.1–9.21. Pope CA, Burnett RT, Thun MJ, Calle E, Krewski D, Ito K, Thurston GD. 2002. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. J Am Med Assoc. 287:1132–1141. Rogge WF, Medeiros PM, Simoneit BRT. 2012. Organic compounds in dust from rural and urban paved and unpaved roads taken during the San Joaquin Valley Fugitive dust characterization study. Environ Eng Sci. 29:1–13. Samet JM, Dominici F, Curriero FC, Coursac I, Zeger SL. 2000. Fine particulate air pollution and mortality in 20 U.S. Cities, 1987–1994. New Engl J Med. 343:1742–1749. Silverman KC, Tell JG, Sargent EV, Qiu Z. 2007. Comparison of the industrial source complex and AERMOD dispersion models: case study for human health risk assessment. J Air Waste Manage Assoc. 57:1439–1446. Smith JRH, Etherington G, Shutt AL, Youngman MJ. 2002. A study of aerosol deposition and clearance from the human nasal passage. Ann Occup Hyg. 46:309–313. Thermo ESM Andersen Instruments GmbH. 2003. Operating Manual, Particulate Monitoring Instrument, FH 62 I-R. Atlanta (GA): Andersen Instruments. Tsai FC, Apte MG, Daisey JM. 2000. An exploratory analysis of the relationship between mortality and the chemical composition of airborne particulate matter. Inhal Toxicol. 12:121–135. TSI. 2003. Model 8520 DustTRAK Aerosol monitor operation and service manual. Shoreview (MN): TSI. US EPA. 1995. User’s guide for the industrial sourcecomplex (ISC3) dispersionmodels, volume II – description of model algorithms, office of air quality planning and standards monitoring, and analysis emissions, USEPA-454/B-95-003b. Research Triangle Park (NC). US EPA. 2004. Air quality criteria for particulate matter (Report EPA/600/P-99/002aF and bF), Research Triangle Park (NC): US Environmental Protection Agency. US EPA. 2006. Emission factor documentation For AP-42, section 1322, unpaved roads, final report, Midwest Research Institute, Kansas City; [cited 2013 Nov 22]. Available from: http:// www.epa.gov/ttnchie1/ap42/ch13/final/c13s0202.pdf US EPA. 2011. Emission factor documentation For AP-42, section 1321, paved roads, final report, Midwest Research Institute, Kansas City; [cited 2013 Nov 22]. Available from: http://www. epa.gov/ttn/chief/ap42/ch13/final/c13s0201.pdf US EPA. 2012. Haul road workgroup final report submission to EPA-OAQPS; [cited 2013 Nov 22]. Available from: http://www.epa.gov/scram001/reports/Haul_Road_Workgroup Final_Report_Pack age-20120302.pdf Van der Wal JT, Janssen LHJM. 2000. Analysis of spatial and temporal variations of PM10 concentrations in the Netherlands using Kalman filtering. Atmos Environ. 34:3675–3687. Westbrook JA, Sullivan PS. 2007. Fugitive dust modeling with AERMOD for PM10 emissions from a municipal waste landfill air and waste management association – Guideline on air quality models: applications and FLAG developments 2006, An A&WMA specialty conference 164: 207-223; [cited 2013 Nov 22]. Available from: http://www.scsengineers.com/Papers/ Sullivan_Fugitive_Dust_Modeling.pdf Westphal DL, Toon OB, Carlson TN. 1987. A two-dimensional numerical investigation of the dynamics and microphysics of Saharan dust storms. J Geophys Res. 92:3027–3049. [WHO] World Health Organization. 2006. Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide, global update 2005, summary of risk assessment. Geneva: World Health Organization, WHO/SDE/PHE/OEH/06.02. [WHO] World Health Organization. 2011. Air quality and health; [Updated 2011 Sep; cited 2013 Nov 22]. Available from: http://www.who.int/mediacentre/factsheets/fs313/en/ Zhang Q, Canagaratna MR, Jayne JT, Worsnop DR, Jimenez JL. 2005. Time and size-resolved chemical composition of submicron particles in Pittsburg-implications for aerosol sources and processes. J Geophys Res D: Atmos. 110:1–19.

A methodology for the determination of fugitive dust emissions from landfill sites.

This study focuses on the development of a methodology for the determination of the contribution of fugitive dust emissions from landfill sites to amb...
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