This is the accepted version of the article: Torrente, Jorge M.; Giampietro, Mario; Ripa, Maddalena; [et al.]. «Landfill reactions to society actions : the case of local and global air pollutants of Cerro Patacón in Panama». Science of the total environment, (December 2019), art. 135988. DOI 10.1016/j.scitotenv.2019.135988 This version is avaible at https://ddd.uab.cat/record/215653 under the terms of the license Authors' accepted manuscript. The published version of this paper is available at publisher's website: https://doi.org/10.1016/j.scitotenv.2019.135988 1 Landfill reactions to society actions: The case of local and global air pollutants of Cerro 2 Patacón in Panama 3 Jorge M. Torrente V. 1,3, Mario Giampietro1,2, Maddalena Ripa1, Rosaria Chifari4 1 4 Institut de Ciència i Tecnologia Ambientals, Universitat Autònoma de Barcelona, Bellaterra, 5 Spain, 2 6 Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain, 3 7 Universidad Tecnológica de Panamá, Panamá. 4 8 Fundació ENT, Barcelona, Spain. 9 Corresponding author: Jorge Miguel Torrente Velásquez – jorgemiguel.torrente@uab.cat 10 11 Abstract 12 This paper studies landfill emissions and the related environmental and health risks in Panama 13 City, with the aim to sensitize the population about the harmful effects of irresponsible resource 14 consumption and non-deliberate solid waste generation that it is disposed of in an uncontrolled 15 manner in landfills. Empirical data on Cerro Patacón, Panama City’s landfill was obtained to 16 describe the status of municipal waste disposal. Ten known methane generation models were 17 used to estimate the yearly emission rate of methane from the landfill for a 100-year period 18 starting from its inception in 1986. From the models used, the GasSIM model was chosen to 19 estimate emission rates of six long-term hazardous air pollutants. The AERMOD source 20 dispersion model was used to simulate their atmospheric downwind dispersion by levels of 21 concentration over nearby affected communities; results were mapped in Google Earth. The 22 relative contributions by population of the 32 towns making up Panama City to the forecasted 23 waste generation in 2022 and related hazardous air pollutants emission rates from the landfill 1 24 were assessed. It was found that Cerro Patacón will generate 45% of the countrywide methane 25 generation by 2022; an average of 47 Gg. The solid waste generated by the 1.5 million 26 inhabitants of Panama City impacts the health of ~73,600 inhabitants in nearby communities 27 through the dispersion of hazardous atmospheric pollutants derived from the landfill. The 28 highest emission rates were from hydrogen sulfide and dichloromethane, which can be largely 29 attributed to the waste generated by the communities of Juan Diaz and Tocúmen. The 30 concentration of Hydrogen Sulfide and Benzene was over the reference concentration 31 (uncertainty factor spanning three orders of magnitude) for all communities and years 32 simulated. The concentration of Vinyl Chloride was over the RfC for all communities and years 33 simulated, except in 2018 for 12 communities. 34 Keywords: Uncontrolled municipal waste disposal; landfill emissions; air pollution; 35 environmental pressure; Panama City 36 37 Graphical abstract 38 2 39 Caption: Interactions between actors of the waste management system and responsibilities for 40 resulting environmental pressures and social impacts. 41 42 Highlights 43  The anthroposphere non-deliberately generates waste without responsibility allocation 44  Uncontrolled waste disposal exerts pressure over the biosphere and atmosphere 45  Local and global environmental pressures from Panama City landfill were identified 46  Local societal impacts were allocated to 5% of communities affected by the rest 95% 47  Data becomes useful when people acknowledge mutual impacts through waste generation 48 Abbreviations Phrase Abbreviation Municipal Solid Waste MSW Developing country DC Non-methane Organic Compound NMOC Hazardous Air Pollutant HAP Panama District PD San Miguelito District SMD Cerro Patacón CP Reference Concentration RfC Degradable organic carbon DOC Zero Order Decay ZOD First Order Decay FOD 49 3 50 1. Introduction 51 Landfills require land availability and have negative side effects on the environment and for 52 this reason their placement is often opposed by surrounding residents (Hoornweg and Bhada, 53 2012). Nonetheless, it remains the most common method for waste disposal worldwide and is 54 considered a reliable and low-cost alternative to final MSW disposal (Caprile and Ripa, 2014; 55 Powrie and White, 2004; Zacharof and Butler, 2004). 56 In most countries, the costs of using landfills for MSW disposal and household waste collection 57 and transport are covered by public fees that are defined by the (local) authorities based on 58 many different factors, such as household income level, household area, water usage or waste 59 generation. However, the operation and maintenance of landfill sites are often left to private 60 companies who establish a fee per ton of waste, its size depending on the landfill’s engineering 61 complexity, that is intended to not only cover the costs but also generate profit (Kinnaman, 62 2009). 63 In an effort to promote sustainable waste management, in developed countries more stringent 64 limits on the proportion of organic waste in landfills have driven advanced engineering 65 solutions for landfills sites (Pan et al., 2014). As a result, MSW landfills in developed countries 66 are now mostly used to dispose of inert materials resulting from pre-treated MSW under strict 67 conditions (DEFRA, 2010). This allows the establishment of fixed and transparent landfill 68 operation and maintenance costs due to the relatively homogeneous physicochemical 69 characteristics of the waste (Rigamonti et al., 2016). 70 DCs, on the other hand, face serious problems of uncontrolled waste disposal due to the 71 absence of separated collection practices and waste treatment before it is disposed of in 72 landfills. In addition to accepting untreated MSW, most landfills also accept sludge, hospital 73 and industrial waste without any pre-treatment and thus retaining bacteriologic activity. This 74 leads to unpredictable landfill operation and maintenance costs, limits the pre-treatment 4 75 technologies that can be deployed on-site and encourages irresponsible resource consumption 76 (Wilson et al., 2012). 77 These irregularities affect the local and global environment throughout the landfill’s active life, 78 and eventually derive in societal impacts (OECD, 2008). Indeed, landfills are the final stage of 79 the waste management system where uncontrolled waste disposal, combined with local 80 weather conditions—temperature and humidity—and inappropriate management, creates 81 environmental pressures (Sarptaş, 2016). The shifting of landfill costs to the environment is the 82 central concern of the opposition to landfill sites, since such environmental pressures are the 83 chronicle of foretold societal impact. 84 When the society resents the impacts, claims arise to the waste management authorities for 85 solutions. The authorities need a source of information that will allow them to describe and 86 show society that these impacts are partly caused by the population’s own irresponsible 87 resource consumption and non-deliberate waste generation. In this regard, there is an urgent 88 need for reliable estimates and non-steady-state assumptions about the environmental 89 pressures caused by landfill emissions, and there is a general lack of such data in DCs (Bogner 90 and Matthews, 2003). 91 More in general, there is a need for a holistic perspective of the MSW management system 92 (Chifari et al., 2016; Ziout et al., 2014) in order to understand the extent to which the 93 environmental pressures exerted by landfills is coupled to other aspects of the MSW 94 management system and resource consumption. These insights are also necessary to assess 95 the extent to which claims by the society are caused by uncontrolled waste disposal and 96 inappropriate landfill management, or conversely, are derived from non-deliberate waste 97 generation and irresponsible resource consumption (Chifari et al., 2016; Ziout et al., 2014). 98 With this information in hand, authorities will be better equipped to create meaningful waste 99 acceptance criteria (DEFRA, 2010) and impose effective operation and maintenance policies on 5 100 the landfill managers, giving the latter a threshold with which to balance economic profit and 101 socio-ecological interests. Policies to reduce waste generation and campaigns for responsible 102 resource consumption could be fostered in society, and based on the relevant technical, 103 economic and socio-ecological aspects of the entire mechanism of landfill environmental 104 pressure, thus giving the population a reference point for the level of responsibility they have 105 for their own claims. 106 This paper studies landfill gas emissions in Panama City and the related local and global 107 environmental pressures and human health effects with the aim to raise awareness among the 108 population about the responsibility they bear for the disproportionate impacts mutually 109 exerted through irresponsible resource consumption and non-deliberate solid waste 110 generation that is disposed of in landfills in an uncontrolled manner. 111 Empirical data on CP, Panama´s main city landfill, was obtained to describe the status of waste 112 disposal. Ten known methane generation models were used to estimate the yearly emission 113 rate of methane from CP for a 100-year period starting in 1986 to generate a picture of the 114 environmental pressure exerted. From the ten models used, the GasSIM model was chosen to 115 estimate also NMOC emission rates, specifically of the six long-term HAPs that presented the 116 higher dispersion rates from CP. The AERMOD source dispersion model, local meteorological 117 conditions data were used to simulate HAP´s atmospheric downwind dispersion by their levels 118 of concentration over nearby affected communities and results were mapped in Google Earth. 119 To gain insight in the environmental pressure exerted on local communities, the local 120 concentration of HAPs was compared with the inhalation chronic reference concentration RfC 121 [μg m-3], which is an estimate of the level of human exposure through chronic inhalation 122 throughout life that is unlikely to have an appreciable deleterious effect (US EPA, 2011). 123 Relative contribution to forecasted (2022) MSW generation and HAPs emission rates from the 124 landfill by the 32 towns making up Panama City was assessed to sensitise the population about 6 125 (potential) environmental and health effects of irresponsible resource consumption and 126 uncontrolled MSW disposal. 127 128 2. Case Study 129 Panama is an upper-middle income DC (The World Bank, 2018) with the most rapid economic 130 growth of all Latin-American cities (Coleman et al., 2014). It is also the world’s highest-ranking 131 city in terms of well-being, based on purpose, social, community, physical and financial 132 elements, with 61% of Panamanians thriving in three or more elements (GALLUP, 2014). 133 The metropolitan area of Panama City consists of two districts, PD and SMD. PD is made up of 134 23 towns and SMD of 9 towns; a total of 276 communities make up the 32 towns of PD and 135 SMD (INEC, 2000). According to the last census, the population of the metropolitan area was 136 ~1.4 million people, representing 35% of the national population (total land area 75,517 km 2). 137 The national population density is 53 people/km2, while that of the metropolitan area is 673 138 people/ km 2; the most crowded in the country (Weitz et al., 2008). Recent studies estimated 139 the MSW generation per capita for PD in 1.55 and for SMD in 1.28 kg inh-1day-1 (INECO, 2017). 140 2.1. Panama City landfill: Cerro Patacón 141 CP is the landfill for the metropolitan area of Panama City. Its total area is about 130 ha 142 including administration, operation and other non-disposal zones, of which the net disposal 143 area was 53 ha in 2002 (JICA, 2003) and 63 ha in 2016 (AAUD, 2016a). CP receives various 144 waste fractions: MSW (including street sweeping, household waste, and commercial waste 145 assimilable to household waste), sewage and industrial sludge, used tires, clinical waste and 146 construction/demolition (C&D) waste; approximately 60% of this waste is biodegradable 147 (AAUD, 2016b). 7 148 Waste disposal has increased by an average of 3.5% per year since CP opened in 1986. From 149 1986 to 2007, the increment estimates were based on the design stage for achieving its 150 planned lifespan: an average of 1% per year (Weitz et al., 2008). The accelerated immigration 151 and economic growth that began in late 2007/early 2008 (CEPAL, 2009; INEC, 2008) caused a 152 rise in resource consumption that increased the MSW generation rate to an average of 7%. By 153 2016, waste reception had reached approximately 2,300 tons per day, 40% more than 154 expected for the same year according to its initial design (Figure 1). 155 The initially planned closure date by design was 2037, but actual waste deposited in CP 156 exceeds the design forecast and this rate is expected to continue until at least 2022, the date 157 to which CP closure has been advanced following recent studies of its capacity (AAUD, 2016a). 158 The degree of saturation of CP is evident from a comparison of the maximum elevation of 159 waste slopes reached in 2002 (106 m) to that reached in 2016 (126 m) (JICA, 2003). This was 160 the maximum permitted until a new maximum of 145 m was approved for the same year in 161 order to fulfill the city’s increasing needs for landfill capacity (AAUD, 2016a), despite the 162 augmented risk of hazardous gas dispersion for nearby communities (Paraskaki and Lazaridis, 163 2005). 8 Greenwaste Tires, 0.31% 3000 (parks), 0.25% Sewage sludge, Street sweeping, 6.48% 5.27% Industrial, 0.23% Construction & 2500 demolition, 0.16% Clinical, 0.45% 2000 Average tons/day 1500 1000 500 USW, 86.85% 0 2001 1986 1989 1992 1995 1998 2004 2007 2010 2013 2016 Operation years Real Expected 164 165 Figure 1. Expected (red line) vs. real data (green line) of the daily waste disposal in tons 166 throughout the lifespan of CP (left), and waste composition in 2016 (right) 167 2.2. Local and global environmental pressures derived from Cerro Patacón 168 Biodegradable materials in landfills are decomposed by microbes under anaerobic conditions. 169 This microbial action is highly complex due to unpredictable differences in the degradation 170 rates of the materials that make up solid wastes (Farquhar and Rovers, 1973). The degradation 171 process starts with hydrolysis of solid materials, such as hemicellulose and cellulose, into larger 172 soluble organic molecules, fermentation of which yields organic acids that give rise to 173 methanogenesis. Simple sugars, fats and hemicellulose are easily degraded; cellulose has a 174 moderate degradability, while lignin is resistant to biodegradation under anaerobic conditions. 175 Depending on its availability to bacteria, it can also influence cellulose degradation (Chandler 176 and Jewell, 1980). The intermediate products of landfill waste biodegradation, such as 177 carboxylic acids (R–COOH), carbon dioxide (CO2) and hydrogen (H2), generate liquid and 178 gaseous emissions, leachate, and Landfill Gas (LFG) respectively, producing negative 179 environmental and societal impacts (Bogner and Matthews, 2003). 9 180 2.2.1.Global Environmental Pressures 181 182 2.2.1.1. Methane emissions 183 The atmosphere is being polluted by LFG acting as a greenhouse gas (GHG). LFG is a 184 combination of methane (CH4) and carbon dioxide in approximately equal proportions, such 185 that total LFG flow can be assumed to be twice the methane flow. However, traces of other 186 gases have also been found, which constitute no more than 2% of the flow but sum to more 187 than 160 compounds, such as non-methane organic compounds (NMOC), reduced sulfur, and 188 speciated organics (US EPA, 2008). Some NMOCs contain volatile organic compounds (VOC) 189 that can be organic HAPs. Notably, methane and the NMOC nitrous oxide have a Global 190 Warming Potential (GWP) of 21- and 298-times that of CO2 (Frischknecht et al., 2007; 191 Majdinasab et al., 2017). 192 2.2.2.Local Environmental Pressures 193 194 2.2.2.1. Diffusion of toxic gases traces 195 Local environmental pollution by the waste management sector is now one of the most 196 sensitive social issues in many DCs including Panama (US EPA, 2006a). However, throughout 197 the lifetime of CP, the surrounding community has only been aware of the impacts that their 198 senses can perceive, such as bad smells, noise, vibrations from machines, and surface fires 199 (ATSDR, 2004). The proportion of HAPs present in LFG varies with the local weather conditions 200 and the characteristics of the disposed waste such as the quantity, age, and organic waste 201 content (Paraskaki and Lazaridis, 2005; Sarptaş, 2016). Several NMOCs have been studied to 202 evaluate their HAP properties at the low concentrations at which they are emitted from 203 landfills (Saral et al., 2009). These compounds contribute to air quality deterioration (US EPA, 10 204 2006b, 2008), and long–term inhalation throughout life is harmful but imperceptible to people 205 in nearby communities (US EPA, 2011). 206 2.2.2.2. Landfill surface fires 207 The lack of proper waste acceptance policies observed in CP is a common problem in DCs 208 (Powell et al., 2016). The combination of uncontrolled waste disposal and inappropriate landfill 209 management (Blais et al., 2010) derives in undesired events like that which occurred in March 210 2013, when LFG and stockpiles of waste tires combined to produce a heat-generating reaction 211 that caused the worst surface fire seen to date at CP. LFG accumulated in the void spaces in 212 the waste tires (75% volume) and boosted the combustion energy to 28% higher than that of 213 coal, which made the fire difficult to quench both with water and by suffocation (Islam et al., 214 2009; Pennington, 1996). This fire occupied an area of 30 ha and burned continuously for 10 215 days, releasing airborne fumes to a radius of 13 km (La Prensa, 2013). No report has yet been 216 published on the environmental damage that resulted from this fire, but studies of similar 217 events have reported increased levels of NMOC, particulate matter, nitrogen oxide, sulfur 218 dioxide, carbon monoxide, polycyclic aromatic hydrocarbon, benzene and dioxin/furan (US 219 EPA, 2008); for the latter, levels reached up to 66 times higher during burning (Weichenthal et 220 al., 2015). 221 2.2.2.3. Nearby infrastructure explosion risks 222 As a result of this fire, local authorities concluded that there was a need to assess various 223 issues, including the topography of the landfill's high slopes, where the waste was neither 224 compacted nor covered (AAUD, 2016a). This issue highlighted an environmental pressure 225 resulting from the inappropriate landfill management: risk of explosion by methane migration 226 because the waste was not properly covered with soil. 11 227 When LFG is generated, it can be emitted directly into the atmosphere, oxidized to carbon 228 dioxide via an aerobic soil cover, retained within the landfill volume, or migrated laterally to 229 the subsurface. As for the latter case, migration can extend to >300 m in poorly engineered 230 landfills (Kjeldsen, 1996). The most important parameter that controls LFG migration to the 231 surrounding zones is soil permeability to air, which has been found to be strongly influenced 232 by the soil’s permeability to water: higher water content results in lower gas migration due to 233 reduced soil porosity (Poulsen et al., 2001). 234 The soil in CP has a low water permeability (AAUD, 2016a), so despite the high levels of rainfall 235 risk of LGF migration increases during the 3-month dry season (Poulsen et al., 2003). CP is 236 located adjacent to a 4,000-ha protected national park, and the closest residential community 237 is at 50 m distance (Calle 50 squatter settlement) (Figure 2). Migration of LFG through soil may 238 result in societal impacts such as explosion hazards in nearby civilian structures, and damage 239 to vegetation due to high concentrations of LFG (Blais et al., 2010; Poulsen et al., 2003). 240 12 241 Figure 2. CP landfill area is located adjacent to a protected national park (green zones) and 50 242 meters from the nearest community. 243 2.2.2.4. Sludge spills and leachate infiltration in ground and surface water 244 CP lies in a tropical weather zone, with annual rainfall of 3000 mm and evaporation of 1500 245 mm. These characteristics are important for CP’s waterproofing system, which is not adapted 246 to avoid leachate infiltration to groundwater. Older areas are covered by a 40 to 80 cm layer of 247 clay, while recently constructed areas have a geomembrane and a geotextile, which lacks 248 appropriate systems for gas collection and leachate recirculation (AAUD, 2016a). 249 Sludge is directly disposed of in a pit, from which a stream flows into a nearby river located no 250 more than 200 m from CP, and from which some communities are supplied with daily-use 251 water. Recent field studies found annual leachate accumulation of >450,000 m 3 in the ponds, 252 with concentrations well above legal limits for various water quality control parameters such 253 as cadmium, lead, nitrate/nitrite, and chlorides in groundwater, and Biological/Chemical 254 Oxygen Demand (BOD5, COD), cadmium, copper, iron, chromium, lead, and sodium in surface 255 water (AAUD, 2016a). The amount and composition of landfill leachate depend on waste type 256 and compaction, landfill hydrology, climate and landfill age, and treatment methods are 257 chosen accordingly (Aziz et al., 2018; Roudi et al., 2018). 258 2.3. DOC in the waste disposed in CP 259 260 As mentioned above, MSW represents approximately 87% of the total waste in CP (Figure 1). It 261 is composed of commingled waste, i.e. bulk waste, collected in PD and SMD. The overall 262 composition of MSW when landfilled is 4% metals, 16% paper/cardboard, 18% plastics, 27% 263 organics and 35% others (AAUD, 2016b). It has 80% humidity and 45% carbon content (AAUD, 264 2016a). The fractions of MSW that contain DOC, which ultimately generates LFG and leachate 265 (IPCC, 2006a), are paper/cardboard, organics (food waste and green-waste) and some 13 266 materials of the “others” fraction. This is composed of 32 different identifiable fractions, 267 including wood, cellulose, diapers, textiles, glass, etc. 268 In addition to MSW, DOC containing waste also includes sludge, non-hazardous industrial 269 waste, and street sweeping waste (Sarptaş, 2016). Of the total waste in CP, 73% contains DOC, 270 of which 82% comes from MSW, and the rest from other fractions. Local conditions such as soil 271 cover and weather conditions determine how much of the DOC contained in the waste is 272 available for degradation as methane, as not all biodegradable material can be converted to 273 LFG, and methane accounts for approximately 50% (Oonk, 2010). 274 3. Materials and methods 275 Figure 3 shows the methodology applied in this study. 276 277 Figure 3. Methodological pathway of this study 14 278 3.1. Methane emission modelling parameters 279 Methane emissions are generally calculated from the methane mass-balance, which is the 280 difference between methane generated and that recovered plus oxidized methane. Oxidation 281 is ~10% of the total methane generation (IPCC, 2006b), and most landfills in DC, including CP, 282 do not have LFG recovery (JICA, 2002; Machado et al., 2009). 283 Since the extreme complexity of landfill processes makes it impossible to obtain precise data 284 on their outputs, methane emission models are used to assess landfill outputs using field- 285 collected data on a yearly basis, such as the waste disposal volumes and landfill conditions. By 286 modelling the behavior of a landfill site, we can attempt to interpret its environmental 287 pressure (Powrie and White, 2004). Models are constructed using data from different waste 288 categories, which, depending on the country, can differ depending on local definitions of the 289 various waste fractions –e.g. MSW- (Scharff and Jacobs, 2006). There is no perfect model that 290 accurately predicts methane emissions within narrow limits (Oonk, 2010). 291 Existing models can be empirical, mathematical or numerical. The most well developed and 292 widely used models are the ZOD and FOD empirical models. Second Order Decay empirical 293 models are much less widely used because they are less accurate regarding their complexity 294 (Oonk, 1994), and the reliability of the available mathematical and numerical models (El-Fadel 295 et al., 1989) depends on the availability of input data which is very specific (Majdinasab et al., 296 2017). 297 ZOD models are generally used in DC, where there is little or no data on the amount, age and 298 composition of the waste (Oonk, 2010). FOD models are often used in developed countries 299 (Zacharof and Butler, 2004). ZOD models assume that waste produces a fixed amount of LFG 300 for a fixed number of years (Peer et al., 1992), while FOD models assume that LFG forms after 301 waste deposition and that emissions begin to decrease by a given methane generation rate k 302 [yr-1] per half-life t1/2 [yr] over a fixed number of years. In this sense, data temporal resolution 15 303 in this work will be given in a yearly basis. The assumed value of t1/2 is 0.693 k-1 [yr], in which 304 50% of the original amount of DOC in waste is biodegraded as methane, depending on the 305 level of moisture, pH, local weather conditions, and the availability of nutrients for methane- 306 generating bacteria, etc. (Ludwig, 2007). 307 The input for FOD models is usually a “single-phase” or bulk waste amount, -e.g. MSW with 308 homogenous characteristics (Scharff and Jacobs, 2006)-, and thanks to the increasing 309 availability of this type of data, FOD models are increasingly preferred over ZOD models in DCs 310 landfills (Bogner and Matthews, 2003). Moreover, by conducting waste characterization 311 studies, FOD “multi-phase” models can be used to assign parametric values for k and methane 312 generation potential L0 for each waste fraction characterized (Mou et al., 2015). In this case, 313 depending on the model, both parameters can be assigned for different material decay rates, 314 for which the characterized waste proportions must be adapted according to the model’s 315 specific waste input categories -e.g. slow-, medium- and fast-degrading-, and local weather 316 conditions -i.e. wet, dry, tropical, temperate, etc.- (Krause et al., 2016). 317 The methane generation potential L0 [m3 CH4 Mg-1 waste] indicates the total amount of 318 methane emitted from DOC during the methanogenesis phase of waste degradation for bulk 319 waste with a particular composition (i.e. ZOD and FOD models), or for different fractions of 320 waste -i.e. FOD multi-phase models-. Some models express L0 in terms of mass as Biochemical 321 Methane Potential (BMP) [Kg CH4 Mg-1 waste] (Mou et al., 2014). Others express methane 322 yield as a variable that describes the percentage of carbon actually degraded depending on 323 waste composition, the Biodegradable Carbon (BDC) [kg C Mg-1 waste]. In this study, methane 324 generation potential for all models used is expressed as L0 [m3 CH4 Mg-1 waste] using a 325 conversion factor of 1.33 kg CH4 kg C-1 to convert carbon mass to methane mass and 0.714 kg 326 CH4 m-3 CH4 to convert methane mass to methane volume (Krause et al., 2016). 16 327 In CP, ~73% of DOC contained in disposed waste is biodegradable from which ~10% is 328 aerobically degraded by oxidation occurring in landfill soil cover, and ~90% is anaerobically 329 degraded through the methanogenesis process (IPCC, 2006b). Not all anaerobically converted 330 DOC is bioavailable, it is defined by landfill conditions (Krause et al., 2016). However, suitable 331 landfill conditions in CP allow all anaerobically degraded DOC to be considered bioavailable. 332 Only ~2% of bioavailable anaerobically biodegraded DOC from disposed waste is trace gases, 333 the rest is methane and carbon dioxide in equivalent proportions (Figure 4). ZOD and FOD 334 models use the Bioavailable Carbon Factor (BACf), dissimilation factor (Ϛ) or the Methane 335 Conversion Factor (MCF), all of which account for the availability of the degradable material 336 due to external conditions (Krause et al., 2016). This value is taken as 100% for anaerobic 337 unmanaged solid waste wet disposal sites (IPCC, 2006a; Scharff and Jacobs, 2006), which is the 338 case of CP. 339 340 Figure 4. Conditions defining methane emission proportions according to the DOC content of 341 waste disposed in Cerro Patacón. 342 3.2. Methane emission models 17 343 In this study, 2 ZOD, 4 FOD and 4 FOD multi-phase models were used to estimate the average 344 methane generation for CP (Table 1). The waste characterization study performed in 2016 was 345 used to benchmark the waste composition for all years modelled since 1986; no other 346 characterization study had previously been carried out in CP (AAUD, 2016b). 347 For one FOD model (the TNO-model) and all four FOD multi-phase models, waste categories 348 were re-coded to match the input format required by each model and are given as a 349 proportion of the total disposed waste. Parameters k and L0 were obtained from default values 350 according to each model (Krause et al., 2016; Majdinasab et al., 2017; Mou et al., 2014), for 351 which a weighted-average has been obtained for bulk waste representing 100% of the waste 352 disposed in CP –i.e. commingled waste-; the value obtained serves as a guide for future 353 modeling with no characterization study available. 354 We computed the yearly average L0 for the weighted-averages of the bulk waste input values 355 of the five models mentioned above (97 m3 CH4 Mg-1 of waste), and used this as the L0 value 356 for the remaining models (T&R, LandGem, SWANA FOD, SWANA ZOD, and EPER Germany), as 357 their input is a bulk waste value. Others studies have reported similar L 0 values for bulk waste 358 in wet/tropical landfills (Bentley et al., 2005; Faour et al., 2007; Machado et al., 2009); their k 359 values are the default (Krause et al., 2016; Oonk, 2010; SWANA, 1997). 360 Table 1. Methane emission models and parameters used for CP Model Waste Parameters L0 (m3 Name and Proportion k (y- CH4/M Type Categories reference s 1) g Waste) Weighted-average for bulk waste 100% 0.28 78 18 Paper/Cardboard 19% 0.12 116 FOD Textiles 6% 0.12 113 GasSim multi Miscellaneous 6% 0.12 113 (Attenboroμgh et - Putrescible 28% 0.69 115 al., 2002) phas Fines 8% 0.08 111 e Sludge 6% 0.69 34 Non-degradable 27% 0.00 0 Weighted-average for bulk waste 100% 0.13 80 Food 18% 0.40 70 Garden 6% 0.17 93 FOD Paper 19% 0.07 187 multi IPCC (Pipatti et al., Wood 5% 0.04 201 - 2006) Textiles 6% 0.07 112 phas Disposable nappies 4% 0.17 112 e Sludge 12% 0.17 23 Industrial 1% 0.17 70 Plastics, other inert 28% 0.00 0 Weighted-average for bulk waste 100% 0.13 78 C&D waste 1% 0.03 15 FOD Street cleaning 5% 0.03 27 multi Coarse household waste 1% 0.03 112 Afvalzorg (Mou et - Sludge and composting waste 7% 0.21 35 al., 2015) phas Refuse 2% 0.03 88 e Household waste 46% 0.21 127 Vegetable, fruit and garden 6% 0.21 66 waste 19 Wood 5% 0.10 177 Inorganic 27% 0.00 0 Weighted-average for bulk waste 100% 0.12 84 Food Waste 16% 0.23 68 FOD Fast-decaying green waste 6% 0.23 68 multi Other fast-decay organic waste 25% 0.23 68 Central America - Slower-decay green waste 2% 0.03 207 (Weitz et al., 2008) phas Paper and Cardboard 14% 0.03 207 e Wood Waste 3% 0.03 207 Rubber, Leather, Textiles, Bones 7% 0.03 207 Inorganic waste 28% 0.00 0 Weighted-average for bulk waste 100% 0.07 164 C&D waste 1% 0.10 20 Street cleaning 5% 0.10 168 Coarse household waste 1% 0.10 242 Sludge and composting waste 7% 0.10 168 TNO (Oonk, 1994) FOD Refuse 2% 0.10 168 Household waste 46% 0.10 242 Vegetable, fruit and garden 6% 0.10 242 waste Wood 5% 0.10 242 Inorganic 27% 0.00 0 T & R (Tabasaran and Rettenberger, FOD Bulk waste 100% 0.03 97 1987) 20 LandGem (US EPA, FOD Bulk waste 100% 0.70 97 2001) SWANA (SWANA, FOD Bulk waste 100% 0.15 97 1997) SWANA (SWANA, ZOD Bulk waste 100% 97 1997) EPER Germany (Scharff and Jacobs, ZOD Bulk waste 100% 97 2006) 361 362 3.3. HAP atmospheric dispersion modelling and relative share 363 The Gaussian Plume Model of Atmospheric Dispersion AERMOD (US EPA, 2018) was used to 364 simulate NMOC behavior and atmospheric downwind dispersion based on local meteorological 365 data according to local weather conditions up to 10 km around CP, which is considered as an 366 area-shaped air polluting source. The annual average concentrations of six long-term standard 367 hazardous landfill-derived NMOCs that are considered VOC and HAP are reported in Table 2: 368 Hydrogen Sulphide (H2S), Vinyl Chloride (H2C=CHCl), trichloroethene or trichloroethylene 369 (TCE)(C2HCl3), Benzene (C6H6), Dichloromethane or methylene chloride (CH2Cl2) and 370 Chloroform (CHCl3) (Gioia et al., 1995; Kanabkaew et al., 2014; Paraskaki and Lazaridis, 2005; 371 US EPA, 1991, 1995, 2000). 372 HAP emission rates from the FOD model GasSim [g s-1] were used as input for AERMOD per m 2 373 of net waste disposal area at CP for the years 2002, 2018 and 2022. GasSim was preferred over 374 other models because it has been designed with a special inclination toward the potential 375 health effects on the population living near and working on landfills (Golder Associates, 2012). 376 This is of interest given the reported statistical relationship between landfill proximity and 21 377 adverse effects on human health (Elliott et al., 2001). The resulting concentrations were 378 compared to exposure threshold values of RfC (Dankovic et al., 2015; NITE, 2017; US EPA, 379 2003) (Table 2). 380 Table 2. Name, formula, RfC [μg m-3] (uncertainty factor spanning three orders of magnitude), 381 relative proportion of the total landfill HAPs emitted and emission rates [g s-1] (at 1 Atm and 382 25ºC) for the years 2002, 2018 and 2022 of simulated HAPs at Cerro Patacón. Year of simulation HAP 2002 2018 2022 Name Formula RfC Proportion Emission rates Hydrogen Sulphide H2S 10 5% 0.1071 0.2 0.262 Vinyl Chloride H2C=CHCl 100 2% 0.0398 0.075 0.097 Trichloroethene C2HCl3 600 2% 0.0321 0.06 0.078 Benzene C6H6 30 4% 0.075 0.14 0.183 Dichloromethane CH2Cl2 600 5% 0.1038 0.194 0.254 Chloroform CHCl3 100 0.01% 0.0003 6E-04 8E-04 383 384 385 Meteorological data input for AERMOD was obtained from the Albrook station (WMO, 2001); 386 others parameters are described in Table 3. 387 Table 3. Site-specific and meteorological parameters for AERMOD simulation Parameter Value CP approximate location (UTM) 17P 656961E 1000248N Sensible heat flux (W/m2) 40 (Hamza and Muñoz, 1996) Surface friction velocity (m/s) 0.72 (Cheng and Georgakakos, 2011) 22 Bowen ratio 0.48 (Lewis, 1995) Albedo 0.17 (McEvoy et al., 2012) Average wind speed (m/s) 1.67 (Hidromet, 2017) Average wind direction (degrees) 315 (Hidromet, 2002) Average relative humidity (%) 90 (Hidromet, 2017) Average cloud cover (tenths) 5 (Hidromet, 2017) Precipitation (mm/hr) 0.34 (Hidromet, 2017) Monin-Obukhov length (m) 92.39 (Pino et al., 2006) 388 389 Relative contributions to MSW generation and related HAPs emission rates from CP by the 32 390 towns making up Panama City were assessed. HAPs concentrations within affected 391 communities close to CP were allocated by population relative share to assess environmental 392 pressures within the same year. 393 4. Results and discussion 394 Results on methane emission behaviour are shown for the 10 models used from the opening 395 of CP in 1986 to 2086; an overall model average behaviour is also presented (Figure 5). The 396 behaviour of the total NMOC emissions (Figure 6) and 6 major HAPs (Figure 7) is shown for a 397 period of 46 years (1986-2032). Simulations of HAP atmospheric dispersion over the area 398 surrounding CP are shown (Figure 8) for the years 2002, 2018 and 2022, along with the total 399 population at risk and the HAP concentrations in the affected communities. Radar plots to 400 allocate the MSW generation and HAP emission rates from CP by towns and to allocate EP 401 through HAP concentrations by towns and affected communities were forecasted to 2022 402 (Figures 9 and 10). 403 4.1. Methane emissions 23 404 Oonk (2010) reported differences between model estimations of more than 10-fold. The 405 Central America model reached its highest methane levels by 2023, 92 Gg CH4, while the TNO 406 model reached its highest value by 1998, 9 Gg CH4 (Figure 5). The LandGEM model behaved in 407 a similar way to that reported by Plocoste et al., (2016) when applied to a tropical area. 408 The methane emissions of the TNO, GasSIM and LandGEM models had the same order of 409 magnitude to the results reported by Scharff and Jacobs, (2006) for the same landfill; the same 410 is observed for the AFVALZORG, IPCC and LandGEM models in Mou et al., (2015), and for the 411 T&R, TNO, IPCC and LandGEM models in Sarptaş (2016). 412 Since EPER Germany estimates methane emissions independent of the amount of the methane 413 already generated (Majdinasab et al., 2017), the methane emission curve falls to 0 Gg CH 4 by 414 2022, the year reported for landfill closure. Similar behavior can be observed in another ZOD 415 model, SWANA, although methane emissions did not fall so dramatically, but rather remained 416 almost constant after the highest peak of 43 Gg CH 4 in 2022, which is the typical behavior of 417 ZOD models (Oonk, 2010). 418 The SWANA FOD model reaches its peak long before the other models because it considers the 419 effect of waste age on methane emissions by simulating a direct relationship between the 420 methane generation potential (L 0) and the waste decay rate (k). Thus, L0 becomes over- 421 sensitive to k, and reaches its maximum in the last year in which the interaction between L 0 422 and k shows a curve-increasing result (Majdinasab et al., 2017). 423 The behavior of the average curve is similar to that of the LandGEM model, and the peak value 424 by 2022 is very close to the last peak (46.81 Gg CH4) reported in 2011 by the National 425 Environmental Authority of Panama in their Second National Communication to the United 426 Nations Framework Convention on Climate Change (UNFCCC); however, there is difference of 427 eleven years between the results (ANAM, 2011). 24 428 Globally, over 60% of methane emissions comes directly from human activities such as coal 429 mining, the petroleum industry, and MSW landfills, with the remainder being accounted for by 430 other indirect activities and natural sources (US EPA, 2010, 2002). In Panama, cattle raising, 431 manure management, and wetlands account for 40% (80 Gg year-1) of methane emissions 432 (Dennehy et al., 2017; Goopy et al., 2018; INEC, 2014a, 2014b; Philippe and Nicks, 2015). There 433 is no coal mining or petroleum industry, so direct anthropogenic methane emissions, which 434 represent 60% (120 Gg year-1), come from landfills alone; ~45% is emitted by the CP 435 metropolitan landfill (AAUD, 2016b). 436 100 90 80 70 Methane emission (Gg) 60 50 40 30 20 10 0 1992 2013 2028 2049 2064 2085 1986 1989 1995 1998 2001 2004 2007 2010 2016 2019 2022 2025 2031 2034 2037 2040 2043 2046 2052 2055 2058 2061 2067 2070 2073 2076 2079 2082 Year GasSIM IPCC AFVALZORG CentralAmerica TNO T&R LANDGEM SWANA FOD SWANA ZOD EPER Germany Average 437 438 Figure 5. Annual methane emission rates for the ten models used and the average curve 439 4.2. HAP emissions 25 440 According to the US EPA (2005), landfills with NMOC emissions of >50 Mg yr-1 require an LFG 441 capturing mechanism. At CP, the total HAP emission per year, which represent 52% of all 442 NMOC generated, reaches a peak of 194 Mg by 2022 (Figure 6), so CP should have had an LFG 443 capture system since its opening in 1986. The 6 HAPs simulated in this study account for more 444 than 18% of the total HAP per year, and less than 1% of the total LFG. Hydrogen Sulphide and 445 Dichloromethane had the highest emissions in 2022, both with a peak of ~9 Mg, followed by 446 Benzene, Vinyl Chloride and Trichloroethene, with 13, 4 and 2 Mg, respectively. Chloroform 447 had the lowest emission for 2022 (26 kg, Figure 7). 448 250 200 Total HAP emission (Mg) 150 100 50 0 198719891991199319951997199920012003200520072009201120132015201720192021202320252027202920312033 Year 449 450 Figure 6. Total HAP emission (Mg) per year in Cerro Patacón. 26 10 30 9 25 8 HAP emission (Mg) except CHCL3 7 20 CHCL3 emission (Kg) 6 5 15 4 10 3 2 5 1 0 0 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 Year H2S H2C=CHCl C2HCl3 C6H6 CH2Cl2 CHCl3 451 452 Figure 7. Emissions per year in Cerro Patacón for the six HAPs simulated for the period 1986- 453 2032. 454 4.2.1.HAP concentrations 455 A total of 16 communities close to CP, with ~73,600 inhabitants, are affected by low- 456 concentration, long-term HAP emissions (Table 4). Communities 1 and 2 are squatter 457 settlements very close to CP, while community 3 is an old low-income rural community that 458 was settled before CP came into existence. Communities 4–8 are recently settled middle-high 459 income condos, while the remainder are older middle-income communities. 460 Table 4. Approximate population size of nearby communities, and their distance from Cerro 461 Patacón (INEC, 2010) # Community Population Distance range (m) 27 1 Kuna Nega 2000 <250 2 Calle 50 100 <250 3 El Valle de San Francisco 3000 <250 4 P.H. Altamira Gardens 500 1000-2000 5 P.H. Las Huacas 500 2000-4000 6 P.H. 4 Horizontes 500 5000-7000 7 Paseo Dorado 500 2000-4000 8 Dorado Lakes 500 5000-7000 9 Carrasquilla 5000 5000-7000 10 La Loma 1000 5000-7000 11 El Ingenio 5000 5000-7000 12 Club X 5000 5000-7000 13 Betania 40000 5000-7000 14 Hato Pintado 5000 5000-7000 15 Panacasa 3000 5000-7000 16 Villa de las Fuentes 1 2000 5000-7000 Total 73600 462 463 Table 5 show the concentration of the 6 HAPs studied in the 16 communities close to CP. 464 Community numbering refers to Table 4. 465 Figure 8 shows simulations of the HAP atmospheric dispersion derived from CP (red spotted 466 area) up to 10 km around for the years 2002, 2018 and 2022. HAP dispersion is shown from 467 the highest to the lowest concentration according to average air direction patterns, where the 468 orange pattern is the highest concentration and the blue one is the lowest. 28 469 In 2002, the waste disposed of in CP emitted HAPs that affected communities 1 and 3 (these 470 were the only communities existing at that time, Table 5), with HAP dispersion extending as far 471 as 4 km from CP to the inhabited areas (Figure 8a). In 2018, there was a well-marked 472 difference in population growth around this area (Figure 8b), and in this year the landfill 473 produced HAPs emissions that dispersed within the same distance as in 2002; although the 474 concentrations were higher in some of the earlier populated areas (Table 5). By 2022, HAP 475 emissions may extend as far as 10 km from CP (Figure 8c), thus affecting many more 476 communities (Table 5). 477 478 a) 29 479 480 b) 481 482 c) 483 Figure 8. AERMOD Dispersion patterns of HAP according to average local meteorological 484 conditions for the years 2002 (a), 2018 (b), and 2022 (c). 30 485 The concentration of Hydrogen Sulfide in 2002 is consistent with the field measurement 486 results from JICA (2003b) and is over the RfC (Table 2) for all communities and years simulated 487 (Table 5). The concentration of Vinyl Chloride is over the RfC for all communities and years 488 simulated, except for communities 5, 6, 7 and 8 to 16 by 2018. The concentration of Benzene is 489 over the RfC for all communities and years simulated. The concentration of Dichloromethane is 490 over the RfC for community 2 in 2018 and 2022. The concentration of Trichloroethene and 491 Chloroform are under RfC for all communities and years simulated. Risk of falling over RfC 492 values beyond 2022 for some HAPs increases with rapid population growth and unpredictable 493 political decision-making that could postpone the closure of CP or continue its operation until 494 the closure year planned under the original design (2037). 495 496 Table 5. HAP concentrations in communities close to CP per year [μg m-3]. Hydrogen Vinyl HAP Sulphide Chloride Trichloroethene Benzene Dichloromethane Chloroform Communit y# Concentrations 2002 1/3 0.4129 0.1535 0.1376 0.3213 0.4446 0.0134 2018 1/3 0.5834 0.2169 0.1749 0.4086 0.5655 0.0015 2 0.7001 0.2603 0.2099 0.4903 0.6786 0.0020 4 0.2917 0.1085 0.0875 0.2043 0.2827 0.0009 5/7 0.1750 0.0651 0.0525 0.1226 0.1696 0.0005 6/8-16 0.1167 0.0434 0.0350 0.0817 0.1131 0.0003 2022 31 1/3 0.6114 0.2274 0.1834 0.4282 0.5926 0.0018 2 0.7337 0.2729 0.2200 0.5138 0.7112 0.0021 4 0.3057 0.1137 0.0917 0.2141 0.2963 0.0009 5/7 0.1834 0.6822 0.0550 0.1285 0.1778 0.0005 6/8-16 0.1223 0.4548 0.0367 0.0856 0.1185 0.0004 497 498 4.2.2. Attribution of landfill emission rates to MSW generation by town and town 499 exposure to dispersed HAPs. 500 Figure 9 shows the forecasted MSW generation from PD and SMD in 2022 and the 501 corresponding HAPs emission rates from CP by town. Approximately 95% of the MSW 502 expected to be generated by the 32 towns from PD and SMD (1.5 million inhabitants) in 2022 503 will exert environmental pressure on 16 communities belonging to 4 towns (73,600 504 inhabitants) of the PD through HAPs atmospheric dispersion. By weighing the HAPs emission 505 rates with the expected MSW generated by town, it is observed that the highest emission rates 506 are for Hydrogen Sulphide and Dichloromethane from the MSW generation of Juan Diaz and 507 Tocúmen followed by Chilibre, Pacora, 24 de Diciembre and Ernesto Córdoba Campos. Within 508 the affected communities, the HAP emission rates from the MSW generated by community 13 509 exert the highest EP. 32 510 511 33 512 Figure 9. Expected MSW generation in 2022 (ton town-1 day-1) (upper graph) and 513 corresponding HAPs emission rates (g s-1) from CP by Panama City towns. Towns marked with 514 SMD in parenthesis belong to the San Miguelito District, all the others to the Panama District. 515 The reddish zone marks communities (AC) closest to the landfill. 516 517 518 Figure 10. Expected community exposure to dispersed HAPs (in µg m-3) (upper graph) in 2022. 519 Only towns with an exposure exceeding the RF are shown. Towns with SMD in parenthesis 520 belong to the SMD, all the others to the PD. 521 Figure 10 shows expected exposure to dispersed HAPs emitted from the landfill in 2022 for 522 affected communities of the PD (HAPs concentration exceeding RfC). Community 2, located in 523 the town Ancón, is subject to the highest environmental pressure from exposure to dispersed 524 Hydrogen Sulphide and Dichloromethane, while the communities 5 and 7, located in the same 34 525 town, are exposed to the highest concentration of Vinyl Chloride. The community 2 is also 526 exposed to the highest concentration of Benzene. 527 5. Conclusions and recommendations 528 To reduce global environmental pressures, many DCs including Panama, ratified the Paris 529 Agreement with an action plan based on a carbon pricing mechanism mostly applied to energy 530 generation (Elkahwagy et al., 2017), but waste generation is often left aside because, contrary 531 to energy, measurement mechanisms are rare. Climate change awareness is high but it works 532 in a short life natural attention cycle difficult to engage with for long time because there are 533 not sensible strategies for engagement (Spence et al., 2012). Taking actions on landfills will be 534 necessary to reduce the global environmental pressures exerted. 535 People engage better with health than with environmental issues, this study presents 536 quantitative results to relate societal impacts with global environmental impacts –e.g. Climate 537 Change- through local environmental pressures –e.g. health hazards- as an alternative to raise 538 awareness in population on the global harms their lifestyles contribute to. With the right 539 engagement tools, awareness on the personal repercussion could contribute to long-term 540 global environmental awareness. 541 It was found that CP will average a methane emission rate of ~47 Gg by 2022, thus generating 542 ~45% of the total countrywide methane emissions. Waste generated by 1.5 million inhabitants 543 directly impacts 73,600 inhabitants of the Panama district through the dispersion of HAPs 544 derived from landfill. The highest emission rates were from Hydrogen Sulfide and 545 Dichloromethane, allocated to the waste generated by the communities of Juan Diaz and 546 Tocúmen. Calle 50, a squatter settlement nearby CP, bears the highest environmental pressure 547 with the highest concentration of Hydrogen Sulphide and Dichloromethane; it also receives 548 environmental pressures from the highest concentration of Benzene. 35 549 Landfill sites will become even more necessary during the coming years due to rapid 550 urbanization in DCs. However, as an alternative to open dumping, restructuring entire waste 551 management systems by introducing waste separated collection, treatment before final 552 disposal, efficient waste transport logistic and better engineered landfills and their appropriate 553 management are required to support healthy socio-economic growth (Bogner and Matthews, 554 2003). 555 Despite the fact that local environmental pressures and societal impacts derived are directly 556 proportional to the waste generating society; the path to reliable waste management systems 557 in DCs is a technical and political issue that hardly depends on the society but on the 558 government Will to solve the issue. However, waste generation is it source; the part of the 559 waste management system that society bear responsibility for. 560 Same way, landfills are the sink of waste management systems, perceived by society as the 561 main cause of environmental pressures, whereas it is merely the source. Societal impacts- 562 mitigating actions are likely to be ineffective if they only target the source of the 563 environmental pressure, rather than its underlying cause. 564 Environmental justice (Martínez-Alier, 1997) is possible with information mechanisms in place 565 for each inhabitant to acknowledge from their personal perspective the impacts they exert 566 each other with their non-deliberate waste generation. Deep understanding of impacts each 567 inhabitant receive will contribute to opening informed consultation spaces between society 568 and government to foster a solution-oriented decision-making process on the waste 569 management systems. 570 This study is limited to Panama City, more case studies will be necessary to explore the issue of 571 societal impacts caused by environmental pressures produced in landfills of DCs. It is 572 recommended further research on methods to quantify societal impacts produced by landfill 573 surface fires and nearby infrastructure explosions due to the discrete occurrence of these 36 574 events. Also, on societal impacts caused by leachate infiltration in groundwater since only 575 communities close to surface water bodies will be affected and different approaches will be 576 necessary. Further research is needed to understand societal impacts from landfills of DCs, not 577 only from an environmental pressure standpoint but multidimensionally, where other criteria 578 to assess societal impacts can be taken into account -e.g. economic, socio-political, cultural-. 579 This study lacks an uncertainty and sensitivity analysis to verify the quality of the results of the 580 models used, this limitation could be approached in further studies by using only open models 581 allowing variables to be manipulated and understanding their interactions. RfC values 582 uncertainty factors present great variability as per source and reference update. This could 583 affect the interpretation of results, especially when studies are based on models, like in this 584 case. LFG direct field data collection and analysis is recommended in further studies to 585 decrease result uncertainty and increase the reliability degree of the present study. 586 587 Acknowledgement 588 Author Jorge M. Torrente V. acknowledge financial support from the Technological University of 589 Panama. The Institute for Environmental Sciences and Technology (ICTA) has received funding 590 from the Spanish Ministry of Science, Innovation and Universities, through the “María de 591 Maeztu” program for Units of Excellence (MDM-2015-0552). 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