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Hydrogen sulphide emissions and dispersion modelling from a wastewater reservoir using flux chamber measurements and AERMOD® simulations
Atmospheric Environment 224 (2020) 117263
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Hydrogen sulphide emissions and dispersion modelling from a wastewater reservoir using flux chamber measurements and AERMOD® simulations �n, L. Gonza �lez, P. Rinco �n, C. Moreno-Silva *, D.C. Calvo, N. Torres, L. Ayala, M. Gaita M. Rodríguez Susa Environmental Engineering Research Centre, Universidad de los Andes, Carrera 1Este No. 19A–40, Bogot� a, Colombia
H I G H L I G H T S
� The average H2S emission rate from Mu~ na reservoir is 1,886 μg/(min⋅m2). � Critical conditions could lead to 4 ppm H2S atmospheric concentration in urban areas. � AERMOD® model predicts H2S behaviour in a reservoir-surrounding region. � Wind direction is most relevant variable in H2S atmospheric concentration. A R T I C L E I N F O
A B S T R A C T
Keywords: AERMOD® Flux chamber Dispersion Hydrogen sulphide
Odours associated with hydrogen sulphide (H2S) have been a concern in the municipality of Sibat�e (Colombia) over the last decades. With the aim of determining the odour impact of Mu~ na’s reservoir, different H2S emission sources were evaluated and simulated. H2S surface emission rates from Mu~ na’s reservoir were estimated using the Flux Chamber experimental method. Meteorological conditions and emission rates were then fed to the AERMOD® model to identify the contribution of Mu~ na’s reservoir on H2S concentration in Sibat� e’s urban area. These results were compared and calibrated with real time H2S atmospheric concentration data. The average H2S emission rate was 1,886 μg/(min⋅m2), which primarily affects the municipality when wind direction is towards the south. The results of this study demonstrated the applicability of the AERMOD® model and flux chamber measurements in predicting H2S behaviour in the Sibat�e region.
1. Introduction An odour problem has affected Sibat�e’s population for the past de cades. Research combined with community fieldwork identified sul phuric compounds as accountable for the issue. Additionally, previous studies on odour problems have identified hydrogen sulphide (H2S) as responsible for annoying odours that negatively affect the quality of life in inhabited areas (Agency for Toxic Substances and Disease Registry (ATSDR), 2016; Juliusson et al., 2015). In order to establish the main contributors to the odour problem in Sibat� e’s area, five possible sources of sulphuric compounds emissions were assessed: a. Sibat�e’s sewer ~ a’s Reservoir, d. a wastewater system, b. Sibat�e’s industrial area, c. Mun pumping station (Alicachín), and e. the water discharge from a box-culvert structure in the reservoir. After an initial assessment, only ~ a’s Reservoir and two sources were categorised as main emitters, Mun
the discharge from a box-culvert structure. ~ a’s Reservoir is a 7 km2 water body that has received Bogota’s Mun industrial and residential wastewater since 1967. Water is pumped from the Bogota River at Alicachín pumping station to the reservoir by a boxculvert structure, and then sent through turbines to produce energy (Calvo et al., 2009). The wastewater used for energy generation has a high organic matter load and low oxygen content (<1 mg/L), conferring an anaerobic state to the reservoir. A direct consequence is the pro duction and emission of hydrogen sulphide (H2S), methane (CH4), mercaptans, and other compounds from the water surface. Hydrogen sulphide, associated with a rotten egg smell (ATSDR, 2016), is produced by the mineralisation of organic compounds, and the reduction of inorganic compounds by archaea and bacteria under anaerobic conditions (Muyzer and Stams, 2008; Blunden and Aneja, 2008; Reinhart et al., 2018). According to the Agency for Toxic
* Corresponding author. E-mail addresses: [email protected], [email protected] (C. Moreno-Silva). https://doi.org/10.1016/j.atmosenv.2020.117263 Received 28 August 2019; Received in revised form 4 December 2019; Accepted 4 January 2020 Available online 31 January 2020 1352-2310/© 2020 Elsevier Ltd. All rights reserved.
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Substances and Disease Registry (ATSDR, 2016) and the Occupational Health and Safety Administration (OSHA, 2018), H2S human odour threshold ranges from 0.0005 to 1.5 ppm. Moreover, H2S is not only recognised by its bad smell, but also for its toxicity (OSHA, 2018; WHO, 2000). Prolonged exposure to concentrations between 2 and 5 ppm produce health issues such as nausea, headache, and tearing of eyes, and concentrations of 50 ppm can produce respiratory tract irritation (OSHA, 2018; WHO, 2000). The purpose of this study was to determine the responsibility asso ~ a’s Reservoir regarding Sibat�e’s odour problems. This is ciated to Mun assessed by modelling gas emissions from the water body and boxculvert structure, which were selected as the major sources of H2S. Previous studies in odour annoyances have used diverse techniques such as models, surveys and chamber monitoring to estimate the degree of odours’ impact from wastewater anaerobic processes (Beghi et al., 2012). In this study, emissions from the waterbody were calculated using flux chamber monitoring. Emissions from the box-culvert were also assessed using gas speed head and concentration, taking into ac count the turbulence from the water discharge as a factor that increases the emission rate of H2S (Dai and Blanes-Vidal, 2013). Gas dispersion in Mu~ na’s and Sibat�e’s area was modelled using the emission rates from these two sources in the AERMOD® model. Five modelling scenarios were proposed to analyse Mu~ na’s emission impact on Sibat� e’s H2S measured concentration. It has been argued that the main factors affecting the distribution of contaminants are the landscape and the thermal inversion of air (Juliusson et al., 2015). Firstly, AERMOD® accounts for turbulence effects generated by topo graphic changes; however, the landscape in Sibat� e’s scenario does not represent a significant factor. There are no natural or anthropogenic barriers that could affect dispersion in the analysed area. Secondly, thermal inversion is analysed in one of the scenarios proposed in the study, as well as changes in wind speed and emission rates. This study is different to most of the studies previously reported in the literature as it addresses an odour problem generated by a large reservoir. This is similar to the odour problems presented in the Wastewater Treatment Plants (WWTP) (Beghi et al., 2012; Colomer �s Morato � and Mantilla Iglesias, et al., 2011; Llavador Colomer, Espino 2012). However, the dimensions of the reservoir are larger than the dimensions of the operation units at the WWTP studied and the nature of the water body is not of a controlled industrial process. These elements captured the attention from a research point of view. Additionally, the applicability of H2S monitoring using flux chamber have not been combined with the simulation with AERMOD® model before. Previous attempts to measure H2S emission in the reservoir have provided an initial estimate but have not delivered a whole description of the problem. Calvo, Behrentz, and Rodríguez (2009) provided the first ~ a’s reservoir. This approach to measuring the H2S emission from Mun approach used a theoretical stoichiometric technique that assessed the emission from two components of the reservoir, the water matrix and the benthos; an experimental method using a water column and benthos; and a simulation tool. The results revealed that improvements were required to provide experimental methodologies to quantify the emis sion and in using a better modelling tool that could predict H2S con centrations at the measured vales. The flux chamber and the AERMOD® model then emerged as an alternative.
to measure emission rates from surface sources. The tool consists of a one-sided open camera (Fig. 1) and was designed to measure Volatile Organic Compounds (VOCs) from a land surface (Klenbusch, 2004). However, it has been applied previously to different compound emis sions and modifications have been proposed according to the emissions condition. EPA Flux Chamber design works as a continuous reactor (Schmidt, 2004). The camera has an input of air flow which dilutes the emission (Fig. 1b). The concentration of the compound of interest inside the camera is measured in the air flowing out. According to the application and the availability of tools, the flux chamber has also been used without any air flux. The camera with air flow inside is known as a dynamic flux chamber, whereas the camera without any flux is known as a static chamber. Both cameras provide reliable results; however, they have advantages and disadvantages regarding the application (Hartman, 2003). The flux of air used for the dynamic flux chamber must be an inert gas that does not react with the components being monitored (Dai and Blanes-Vidal, 2013). The equation to obtain the emission for dynamic chamber is (Hart man, 2003): E ¼ Y.Q/A
(1)
where E is the emission rate (mass/(area⋅time)), Y is component con centration in the out-going flow (mass/volume), Q is the sweeping air flow inside the camera (volume/time), and A is the surface area covered by the camera (area). The equation for static chamber is (Eun, 2004): E¼(V/A)(dC/dt)
(2)
where V is the volume of the chamber (volume) and dC/dt is the rate of change of the component concentration inside the camera (mass/ (volume⋅time)). In this case, dynamic chamber was used for high emission rates, and static chamber for low rates. The materials used to construct the camera were acrylic, reflecting film, Tygon® and high-density polyethylene (HDPE) piping lines. The flux chamber designed and constructed varies from EPA protocol in the chamber’s material and reflecting film. The reflective film was incorporated to minimise the impact of the sun rays on the gases in the chamber. Previous studies have determined that radiation could produce significant errors in the emission rates measured; reflecting film decrease radiation effect even though other techniques have been applied (Carpi and Lindberg, 1998; Gillis and Miller, 2000). The analyser in flux chamber measurements was Jerome 631-X®. This analyser has been used to measure H2S concentrations by Dai and Blanes-Vidal (2013). The emission from the reservoir was monitored over a period of 19 days, with a severe climatological change. Experimentation started in dry conditions and it finalised with the presence of precipitation. The flux chamber creates a microenvironment under the chamber with different meteorology to the atmospheric conditions. This behavior masks the external wind speed (Tran et al., 2018; Mansfield et al., 2018). Correlation between the wind speed and the mass transfer have been standardized by equations such as the Ro and Hunt (2006) correlation. Transfer coefficients are not significantly influenced by low wind speeds. The data used to obtain these correlations demonstrates that the impact of the wind on the mass transfer is evident on wind speeds over 4 m/s, and almost ineligible under this speed (Ro and Hunt, 2006). Additionally, inverse models have been applied to validate the flux chamber. These models overestimate the emissions, while correction coefficients provide a better estimate of the emission rate (Tran et al., 2018). Correction coefficients are 1.1 for alkanes and aromatic com pounds, which means that the corrected emission rate is 10% higher than the measured emission rate (Tran et al., 2018). Most recent Computational Fluid Dynamics (CFD) simulation of H2S in a flux chamber demonstrated that although this device reduces the incidence
2. Methods 2.1. Emission component Two emissions sources were considered in AERMOD® modelling of ~ a’s reservoir: the reservoir’s surface and the box-culvert structure. Mun 2.1.1. Reservoir’s surface emission US EPA Flux Chamber EPA/600/8-8E/008 method was used to es timate emissions from the reservoir. This method has been widely used 2
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Atmospheric Environment 224 (2020) 117263
Fig. 1. Static (a) and dynamic (b) flux chamber’s designs following EPA’s nonofficial protocol.
of the wind, the emission is only 1.18% lower for the dynamic flux chamber (Prata et al., 2016). Based on our experimentation and research of the phenomena at the Mu~ na reservoir, the flux chamber method is the best alternative to monitor the emission from the reservoir. Due to the low percentage of impact from wind on H2S emissions estimated by Prata et al. (2016), and the fact that wind speed in this study is under 4 m/s, the wind impact has not been considered in the calculations.
concentration, the H2S total emission was calculated. 2.2. Dispersion component 2.2.1. Ambient concentration API analysers (model 101E®) were employed to measure H2S ~ a’s reservoir area. This equipment gives ambient concentration on Mun H2S concentration in ppb units. Ambient concentration was monitored over a period of two years. The average hourly concentration in the monitoring stations was used as a background concentration for modelling tool.
2.1.2. Box-culvert emissions To determine the H2S emission inside this structure, a micro manometer (Dwyer®) was used. This device measures speed head and determines gas flow (7.42 m3/s). Using this parameter and H2S
Fig. 2. Sampling points on Mu~ na’s reservoir (a) and H2S average emission profile (b). 3
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2.2.2. Modelling tool Modelling was done using the AERMOD® tool. This is an improved version of the ISC3 model (Industrial Source Complex Model) encom passed with an advanced graphic format. AERMOD® was chosen for its wide industrial application and has been approved by the EPA since 1996. Moreover, AERMOD® has a meteorological analysis tool, AER MET®. ISC3 which has been developed on the classical Gaussian model base. The model works in a steady state and is designed for complex landscapes. It can be used for different sources (e.g. lineal and surface). Time basis can be modified according to the needs of the simulation.
3.2. Dispersion component 3.2.1. Ambient concentration H2S ambient concentration was measured in six different points related to historic complaints of odour problem. Sampling points are shown in Fig. 3, while statistical information of H2S concentration measured is presented in Table 2. Table 2 shows that the maximum concentration of 17344 ppb (17.3 ppm) was measured in Alicachín (point 2). However, the maximum average concentration (414.1 ppb) was reported in the box-culvert area (point 4), where high emission rates from the reservoir were obtained, and emission from the turbulence of the inflow flux from the box culvert influences the atmospheric concentration. H2S concentration behaviour is consistent with the continuous emission from the reservoir and batch emissions from the water discharge in Alicachín (point 2). In contrast, the mid-east point (point 5), closest to the highest measured emissions, did not evidence as high ambient concentration as the box-culvert or Alicachín measurements. This is attributed to the climatic conditions that influence the dispersion behaviour. A decreasing concentration was obtained from the box-culvert structure (point 4) towards the south of the reservoir (Tails-Point 3), with the minimum average concentration of 3.9 ppb (0.0039 ppm) measured in the municipality of Sibat� e. This concentration is in the range of odour perception from 0.0005 to 1.5 ppm (ATSDR, 2016; OSHA, 2018). The average measured concentrations are indicative of the emission behaviour during regular operation conditions of the reservoir, while measured emission (Fig. 2) represents the particular behaviour during monitoring conditions.
3. Results and discussion 3.1. Emission component 3.1.1. Reservoir’s surface emission Forty sampling areas were selected to measure the whole reservoir emission (Fig. 2a). More importance was given to the closest points to the box-culvert and the water outflow structure, where more organic matter is accumulated and pilot experiments showed the highest emis sion rates. Each sampling point was associated to a uniform emission area determined before experimentation, based on the bathymetry of the reservoir. Fig. 2 shows the sampling points and the emission rates results. The highest emission points are related to organic matter accumulation. As predicted, high emission rates were obtained close to the box-culvert and the outflow structures located in the north part of the reservoir. However, the highest emission rates were measured in the eastern sec tion of the reservoir, where inactive water and sediment storage has been identified. Lower emission rates were observed in the southern section, where water circulation and particulate matter deposition is lower. Table 1 shows maximum and average measured emission rates ~ a’s reservoir. for Mun The highest emission rates in the eastern section of the reservoir could be attributed to the high sludge retention times and anaerobic conditions present during the monitoring in that area, which favoured the production of H2S. This region receives less amount of organic sediments than the area between the box-culvert and the water outflow structure (Fig. 2a), where shortcut flow has been identified when both structures operate simultaneously. However, the accumulation of sedi ments is high in this area, in particular when only the box-culvert inflow operates but not the water outflow. The water and sediment retention time is usually longer in this section of the reservoir. Considering that the monitoring started from dry season to wet season, initial pumping from the reservoir was required for energy generation and water inflow was limited due the dry climatologic conditions. These conditions fav oured longer retention times in shallow areas, such as the eastern area of the reservoir, where highest emission were observed.
3.2.2. Modelling tool. The AERMOD® model was used to simulate hydrogen sulphide concentration in the area of interest. Calibration of the model was conducted before scenario’s evaluation. The meteoro logical conditions were fed to the model using AERMET®. This infor mation was collected simultaneously with H2S concentration at the six sampling points. H2S emission from water body and box-culvert H2S turbulence variables were also input information for the model. Mete orological conditions, apart from H2S real time concentration, were simulated for specific dates. In this way, model parameters were found. From the implementation of these parameters, model simulations were obtained and compared with H2S measurements in the sampling points. The background H2S concentration used for the model was the average concentration measured by the monitoring stations, and the altitude implemented was 2600 m s.n.m. Table 3 compares H2S measured con centration with model simulations for the four main points of interest. The error presented in Table 3 was calculated from the differences between measured and modelled concentrations. Although the per centage of error may seem high, the values obtained from the model are in the same range of values of the measured concentrations. Moreover, model estimations provide a useful predictive approximation to the real H2S concentration. Fluctuations in emission measurement from the box-culvert and the reservoir surface are sources of error, as well as the microclimates in each point of monitoring that affect the dispersion behaviour of H2S. Daily variations in temperature, radiation, wind speed, among other variables, influence emission and mass transfer behaviour and moni toring measurements. The higher percentage of error (388%) was esti mated for the box-culvert area (point 4), where turbulence generates H2S emissions due to water flow at variable rates. At this point, the highest differences between average and maximum measured concen tration were registered (Table 2). The water inflow rate is calculated based on the daily plant operation requirements. This makes difficult to predict and incorporate this variable with accuracy in the model. Other source of error is the H2S emission from Alicachín (point 2). This loca tion has the highest H2S ambient concentration, as presented in Table 2; however, the emission from this point was not considered as an
3.1.2. Box-culvert emissions Using the results of the gas flux and the H2S concentration, H2S concentration in the gas flow from the Box-culvert was between 40 ppm and 80 ppm.
Table 1 H2S Emission results for flux chamber experiments on Mu~ na’s reservoir. H2S Emission
μg/(min*m2) Maximum Average
6400 1886
4
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Fig. 3. H2S sampling points.
of highest ambient concentration measurement. This result can be attributed to the hydrodynamic behaviour of the reservoir at the time of monitoring, which indicates a temporary emission rather than general behaviour of the reservoir. To analyse different climatological conditions, five scenarios were simulated and are listed as follows. Simulation results are presented in Fig. 4.
Table 2 Statistic parameters for H2S ambient concentration. Sampling point Point 1 Point 2 Point 3 Point 4 Point 5 Point 6
Middle west Alicachín Tails Box-culvert Middle east Sibat�e
Max.
Min.
Monthly average
ppb
ppb
ppb
416.7 17344.0 405.1 8738.8 357.3 93.3
0 0.9 0 0 0 0
25.7 155.3 15.5 414.1 36.1 3.9
Scenario A: Simulated average concentration in an hour. Scenario B: Simulated average concentration per hour of maximum daily concentration. Scenario C: Simulated average concentration per hour of maximum daily concentration if there were a H2S emission reduction in the box-culvert as a result of chemical or biological treatment of the gas. This option was considered because five treatment alternatives were evaluated as reaching satisfactory H2S removing rates from the gas in the box-culvert. Scenario D: Simulated average concentration in an hour using hydroclimatological conditions between 8:00 am to 9:00 am. This hour was chosen as it represents the time with the highest concen tration of H2S measured in Sibat� e. During this hour the wind direc tion was towards the south west. Scenario E: Simulated critical condition in Sibat�e. The wind direc tion was towards the south and the wind speed was higher than normal. Concentrations in Sibat�e reached a peak of 4000 ppb.
Table 3 Model calibration.
Point 1 Point 3 Point 4 Point 6 a
Measured concentrationa
Model prediction
Error
ppb
ppb
%
Ave.
Ave.
85.9 13.5 442.0 7.0
112.1 23.3 703.0 13.0
39.5 19.6 388.7 18.5
H2S hourly average measured concentration.
independent source of emission in the model. Error percentages in the other monitoring locations range from 18.5% to 39.5%. This range of values evidences that it is possible to predict concentrations in the magnitude order of the real concentration measured for the area. Additionally, the highest emission was not observed in the same area
The climatological parameters used for model simulation are pre sented in Table 4. Scenarios were modelled under normal and critical conditions in the area of study. Simulation results are presented in Fig. 4, as well as the 5
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Fig. 4. Simulation results and compass rose associated.
9:00 am to assess the thermic inversion phenomena. Fig. 4-D shows that ~ a’s reservoir, compared to sce critical concentration decreases on Mun nario A and B, and the concentration increases in Sibat�e as a conse quence of the change in wind direction. Scenario D and E, which implemented changes in wind direction, evidenced wider dispersion of the pollutant. These two scenarios are critical conditions for the mu nicipality of Sibat�e, where higher concentrations were predicted. A concentration of 4000 ppb (4 ppm) in the municipality was predicted in Scenario E. This concentration could increase the risk of health problems for the population.
Table 4 Hourly average climatic parameters used in simulation. Scenario
Scenario A Scenario B Scenario C Scenario D Scenario E
Wind speed
Wind direction
Temperature
Precipitation
Radiation
m/s
-
�
C
mm
W/m2
1.89
N
13.1
0.1
158
1.89
N
13.1
0.1
158
1.89
N
13.1
0.1
158
1.97
SW
14.9
0.01
411
4. Discussion
2.34
S
14.8
0.03
326
The emission rates obtained in this study behave similarly to the rates estimated by the previously reported stoichiometric analysis. The theoretical rates were predicted between 3 and 16 μg/(min*m3) for emission from the water matrix, and between 1527 and 3542 μg/ (min*m3) for the emission from the benthos (Calvo et al., 2009). These results indicate that the emission from the benthic matrix is higher than from the water matrix. In this study, lower emission was observed where less sediment accumulation occurred, and higher emission was reported in the areas where more accumulation of sediments happened. Although the emission rate reported in this paper is per square meter, these rates are in the same magnitude order to the theoretical rates presented by Calvo et al. (2009) if an assumption of a benthos depth between 1 (one)
wind rose associated to each simulation. As expected, the concentration of H2S increases in Sibat� e municipality as the atmospheric concentration raises in the identified sources of emission. According to simulation results in Fig. 4-B, concentration increased compared to scenario A, but dispersion remained similar. Comparison between scenario B and C evidenced that reduction in the box-culvert emissions does not dramatically affect the concentration in Sibat� e. In contrast, wind di rection is a key variable of analysis in the dispersion of H2S. Scenario D simulation uses the hydroclimatological condition between 8:00 am to 6
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to a few meters is considered for the average emission of 1886 μg/(min*m2). Similar results have been obtained for H2S emissions from WWTP. Emission rates in WWTP have been estimated using passive samplers, with values between 0.09 and 2.47 g/s for WWTP inflow rates between 38962 and 61821 m3/day. These rates are equivalent to a daily release of H2S from 50 to 100 kg (Llavador Colomer et al., 2012). These results ~ a’s are comparable with approximately 70 kg emission from the Mun reservoir considering the average emission rate. Baawain et al. (2019) estimated the emission between 0.1 g/s to 3 g/s from a tanker facility for a 55000 m3/day treatment plant, and H2S emission from an aeration tank has been estimated in 0.22 g/s (Beghi et al., 2012). Although these emission rates are within the range of our estimations, the dimension of the WWTP analysed are smaller than the reservoir. Prata et al. (2016) validated the measurements of the dynamic chamber producing H2S emission in the laboratory, identifying similar emission rates of 1663 μg/(min*m2). Regarding the simulation tool, AERMOD® model has been applied successfully providing an alternative to assess the dispersion in the area of analysis. This model has been applied previously to produce reverse modelling of H2S from swine plants, and to provide estimates of dispersion of H2S from geothermal power plants (O’Shaughnessy and Altmaier, 2011; Peralta et al., 2013). In these cases, a possible over estimation has been identified in specific scenarios, such as night and early morning modelling. However, AERMOD® have been applied with satisfactory results to predict H2S concentration behaviour.
CRediT author statement C. Moreno-Silva: Conceptualization, Writing- Reviewing and Edit ing, Investigation; D.C. Calvo: Writing- Reviewing and Editing; M. �n: Software; N. Torres: Investigation; L. Ayala & L. Gonza �lez: Gaita � n: Investigation; M. Rodríguez Susa: Supervision. Resources; P. Rinco Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.atmosenv.2020.117263. References Agency for Toxic Substances and Disease Registry (ATSDR), 2016. Toxicological Profile for Hydrogen Sulfide/Carbonyl Sulfide. Retrieved from. http://www.atsdr.cdc. gov/toxprofiles/tp114-c4.pdf. Baawain, M., Al-Mamun, A., Omidvarborna, H., Al-Sulaimi, I., 2019. Measurement, control, and modeling of H 2 S emissions from a sewage treatment plant. Int. J. Environ. Sci. Technol. 16 (6), 2721–2732. https://doi.org/10.1007/s13762-0181997-z. Beghi, S.P., Santos, J.M., Reis, N.C., de Sa, L.M., Goulart, E.V., de Abreu Costa, E., 2012. Impact assessment of odours emitted by a wastewater treatment plant. Water Sci. Technol. 66 (10), 2223–2228. https://doi.org/10.2166/wst.2012.409. Blunden, J., Aneja, V.P., 2008. Characterizing ammonia and hydrogen sulfide emissions from a swine waste treatment lagoon in North Carolina. Atmos. Environ. 42 (14), 3277–3290. https://doi.org/10.1016/j.atmosenv.2007.02.026. Calvo, D.C., Behrentz, E., Rodríguez, M.S., 2009. Emissions and dispersion of hydrogen sulphide originated from a highly polluted waterbody. In: Odours and VOCs: Measurement, Regulation and Control Techniques. Kassel University Press, pp. 115–122. Carpi, A., Lindberg, S.E., 1998. Application of a teflon™ dynamic flux chamber for quantifying soil mercury flux: tests and results over background soil. Atmos. Environ. 32 (5), 873–882. https://doi.org/10.1016/S1352-2310(97)00133-7. Colomer, F., Espinos Morato, H., Iglesias, E., 2011. Estimating Hydrogen Sulfide Emission Rates by Combining Experimental Immission Measurements and Dispersion Models at Various Wastewater Treatment Plants in the Valencian Community. Spain. Dai, X.R., Blanes-Vidal, V., 2013. Emissions of ammonia, carbon dioxide, and hydrogen sulfide from swine wastewater during and after acidification treatment: effect of pH, mixing and aeration. J. Environ. Manag. 115, 147–154. https://doi.org/10.1016/j. jenvman.2012.11.019. Eun, S., 2004. Hydrogen sulfide flux measurements and dispersion modeling from construction and demolition (C&D) debris landfills. Master Sci. Dep. Civil Environ. Eng. University of Central Florida, Retrieved from http://stars.library.ucf.edu/cgi /viewcontent.cgi?article¼1185&context¼etd. Gillis, A., Miller, D.R., 2000. Some potential errors in the measurement of mercury gas exchange at the soil surface using a dynamic flux chamber. Sci. Total Environ. 260 (1), 181–189. https://doi.org/10.1016/S0048-9697(00)00562-3. Hartman, B., 2003. How to collect reliable soil-gas data for upward risk assessments. Part 2: surface flux-chamber method. Paper presented at the H&P Mobile Geochemistry Inc Vap. Intrusion Spec. http://www.handpmg.com/articles/lustline44-flux-cha mbers-part-2.html. Juliusson, B.M., Gunnarsson, I., Matthiasdottir, K.V., Bjarnason, B., Sveinsson, O.G., Gislason, T., Thorsteinssson, H., 2015. Tackling the challenge of H2S emissions. Paper presented at the. In: Proceedings World Geothermal Congress 2015. Melbourne, Australia. https://pangea.stanford.edu/ERE/db/WGC/papers/WGC/ 2015/02062.pdf. Klenbusch, M., 2004. Measurement of Gaseous Emission Rates from Land Surfaces Using an Emission Isolation Flux Chamber. User’s Guide. U.S. Environmental Protection Agency. EPA/600/8-86/008. Llavador Colomer, F., Espin� os Morat� o, H., Mantilla Iglesias, E., 2012. Estimation of hydrogen sulfide emission rates at several wastewater treatment plants through experimental concentration measurements and dispersion modeling. J. Air Waste Manag. Assoc. 62 (7), 758–766. https://doi.org/10.1080/10962247.2012.674008. Mansfield, M.L., Tran, H.N.Q., Lyman, S.N., Bowers, R.L., Smith, A.P., Keslar, C., 2018. Emissions of organic compounds from produced water ponds III: mass-transfer coefficients, composition-emission correlations, and contributions to regional emissions. Sci. Total Environ. 627 (C), 860–868. https://doi.org/10.1016/j. scitotenv.2018.01.242. Muyzer, G., Stams, A., 2008. The ecology and biotechnology of sulphate-reducing bacteria. Nat. Rev. Microbiol. 6, 441–454. https://doi.org/10.1038/nrmicro1892. Retrieved from.
5. Conclusions ~ a’s reservoir in This study demonstrates that emissions from Mun fluences the presence of odours in the municipality of Sibat� e. The highest ambient concentrations were measured in the box-culvert area (point 4) and at Alicachín (point 2), with values of 8738 ppb (8.7 ppm) and 17344 ppb (17.3 ppm) respectively. The emission of these two points is associated to water turbulence; additionally, the box-culvert area and the eastern region (point 5) of the reservoir evidenced the highest emission rates from the water body. High H2S emission in this area is a likely consequence of bathymetry and kinetics of the reservoir which leads to retention and anaerobic degradation of organic matter from the river. Contrarily, the lowest average concentrations of H2S were measured at the municipality (3.9 ppb). Reservoir emissions were successfully measured using the Flux chamber experimental tool, obtaining real water surface emission rates, and delivering the required measurements for model calibration and simulation verification. How ever, experimental error in the sampling method and hydro climatological conditions could contribute to the error measured in the simulations. The AERMOD® model utilised real meteorological conditions, the flux chamber measured emission rates, and estimated box-culvert emission rates to generate H2S concentrations in Sibat� e under different scenarios. AERMOD® was calibrated with H2S real time con centrations in different points surrounding Mu~ na’s reservoir. As a result, the model predicted H2S concentration values within one order of magnitude of the measured concentrations when simulation was con ducted under average meteorological conditions. Low concentrations are predicted for the municipality under simulation of conservative scenarios. However, when critical wind conditions were used, concen trations of 4 ppm were predicted in Sibat�e. This concentration is not only higher than the odour threshold, but could also represent a health hazard. Additionally, the results obtained evidenced the impact of wind in the dispersion of the H2S. In summary, this model could be implemented in future projects to predict H2S concentrations in affected areas. Nevertheless, the analysis should be conservative taking into account the percentage of error of 18.5% in predicting H2S concentrations in the area of interest.
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Atmospheric Environment 224 (2020) 117263 Ro, K.S., Hunt, P.G., 2006. A new unified equation for wind-driven surficial oxygen transfer into stationary water bodies. Trans. ASABE (Am. Soc. Agric. Biol. Eng.) 49 (5), 1615–1622. https://doi.org/10.13031/2013.22020. Schmidt, C.E., 2004. In: Source Test Protocol. Biofilter Facility Baseline Emissions Testing at the Yolo County Research Facility Located in Woodland, California. Using the USEPA Flux Chamber for fugitive emissions, Woodland, California. Appendix H – Biofilter Emission Results. Tran, H.N.Q., Lyman, S.N., Mansfield, M.L., O’Neil, T., Bowers, R.L., Smith, A.P., Keslar, C., 2018. Emissions of organic compounds from produced water ponds II: evaluation of flux chamber measurements with inverse-modeling techniques. J. Air Waste Manag. Assoc. 68 (7), 713–724. https://doi.org/10.1080/ 10962247.2018.1426654. World Health Organization (WHO), 2000. Hydrogen sulphide In: Air Quality Guidelines, second ed., vol. 91. World Health Organization, Copenhagen.
O’Shaughnessy, P.T., Altmaier, R., 2011. Use of AERMOD to determine a hydrogen sulfide emission factor for swine operations by inverse modeling. Atmos. Environ. 45 (27), 4617–4625. https://doi.org/10.1016/j.atmosenv.2011.05.061. Occupational Health and Safety Administration (OSHA), 2018. Hydrogen sulfide. Retrieved from. https://www.osha.gov/SLTC/hydrogensulfide/index.html. Peralta, O., Castro, T., Dur� on, M., Salcido, A., Celada-Murillo, A.-T., NavarroGonz� alez, R., Torres-Jaramillo, A., 2013. H2S emissions from Cerro Prieto geothermal power plant, Mexico, and air pollutants measurements in the area. Geothermics 46, 55–65. https://doi.org/10.1016/j.geothermics.2012.12.001. Prata, A.A., Santos, J.M., Beghi, S.P., Fernandes, I.F., Vom Marttens, L.L.C., Pereira Neto, L.I., Stuetz, R.M., 2016. Dynamic flux chamber measurements of hydrogen sulfide emission rate from a quiescent surface – a computational evaluation. Chemosphere 146, 426–434. https://doi.org/10.1016/j.chemosphere.2015.11.123. Reinhart, D., Townsend, T.G., Eun, S., Xu, Q., 2018. Control of Odors from Construction and Demolition (C&D) Debris Landfills. State University System of Florida Florida Center for Solid and Hazardous Waste Management.
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Short communication
Hydrogen sulphide emissions and dispersion modelling from a wastewater reservoir using flux chamber measurements and AERMOD® simulations �n, L. Gonza �lez, P. Rinco �n, C. Moreno-Silva *, D.C. Calvo, N. Torres, L. Ayala, M. Gaita M. Rodríguez Susa Environmental Engineering Research Centre, Universidad de los Andes, Carrera 1Este No. 19A–40, Bogot� a, Colombia
H I G H L I G H T S
� The average H2S emission rate from Mu~ na reservoir is 1,886 μg/(min⋅m2). � Critical conditions could lead to 4 ppm H2S atmospheric concentration in urban areas. � AERMOD® model predicts H2S behaviour in a reservoir-surrounding region. � Wind direction is most relevant variable in H2S atmospheric concentration. A R T I C L E I N F O
A B S T R A C T
Keywords: AERMOD® Flux chamber Dispersion Hydrogen sulphide
Odours associated with hydrogen sulphide (H2S) have been a concern in the municipality of Sibat�e (Colombia) over the last decades. With the aim of determining the odour impact of Mu~ na’s reservoir, different H2S emission sources were evaluated and simulated. H2S surface emission rates from Mu~ na’s reservoir were estimated using the Flux Chamber experimental method. Meteorological conditions and emission rates were then fed to the AERMOD® model to identify the contribution of Mu~ na’s reservoir on H2S concentration in Sibat� e’s urban area. These results were compared and calibrated with real time H2S atmospheric concentration data. The average H2S emission rate was 1,886 μg/(min⋅m2), which primarily affects the municipality when wind direction is towards the south. The results of this study demonstrated the applicability of the AERMOD® model and flux chamber measurements in predicting H2S behaviour in the Sibat�e region.
1. Introduction An odour problem has affected Sibat�e’s population for the past de cades. Research combined with community fieldwork identified sul phuric compounds as accountable for the issue. Additionally, previous studies on odour problems have identified hydrogen sulphide (H2S) as responsible for annoying odours that negatively affect the quality of life in inhabited areas (Agency for Toxic Substances and Disease Registry (ATSDR), 2016; Juliusson et al., 2015). In order to establish the main contributors to the odour problem in Sibat� e’s area, five possible sources of sulphuric compounds emissions were assessed: a. Sibat�e’s sewer ~ a’s Reservoir, d. a wastewater system, b. Sibat�e’s industrial area, c. Mun pumping station (Alicachín), and e. the water discharge from a box-culvert structure in the reservoir. After an initial assessment, only ~ a’s Reservoir and two sources were categorised as main emitters, Mun
the discharge from a box-culvert structure. ~ a’s Reservoir is a 7 km2 water body that has received Bogota’s Mun industrial and residential wastewater since 1967. Water is pumped from the Bogota River at Alicachín pumping station to the reservoir by a boxculvert structure, and then sent through turbines to produce energy (Calvo et al., 2009). The wastewater used for energy generation has a high organic matter load and low oxygen content (<1 mg/L), conferring an anaerobic state to the reservoir. A direct consequence is the pro duction and emission of hydrogen sulphide (H2S), methane (CH4), mercaptans, and other compounds from the water surface. Hydrogen sulphide, associated with a rotten egg smell (ATSDR, 2016), is produced by the mineralisation of organic compounds, and the reduction of inorganic compounds by archaea and bacteria under anaerobic conditions (Muyzer and Stams, 2008; Blunden and Aneja, 2008; Reinhart et al., 2018). According to the Agency for Toxic
* Corresponding author. E-mail addresses: [email protected], [email protected] (C. Moreno-Silva). https://doi.org/10.1016/j.atmosenv.2020.117263 Received 28 August 2019; Received in revised form 4 December 2019; Accepted 4 January 2020 Available online 31 January 2020 1352-2310/© 2020 Elsevier Ltd. All rights reserved.
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Substances and Disease Registry (ATSDR, 2016) and the Occupational Health and Safety Administration (OSHA, 2018), H2S human odour threshold ranges from 0.0005 to 1.5 ppm. Moreover, H2S is not only recognised by its bad smell, but also for its toxicity (OSHA, 2018; WHO, 2000). Prolonged exposure to concentrations between 2 and 5 ppm produce health issues such as nausea, headache, and tearing of eyes, and concentrations of 50 ppm can produce respiratory tract irritation (OSHA, 2018; WHO, 2000). The purpose of this study was to determine the responsibility asso ~ a’s Reservoir regarding Sibat�e’s odour problems. This is ciated to Mun assessed by modelling gas emissions from the water body and boxculvert structure, which were selected as the major sources of H2S. Previous studies in odour annoyances have used diverse techniques such as models, surveys and chamber monitoring to estimate the degree of odours’ impact from wastewater anaerobic processes (Beghi et al., 2012). In this study, emissions from the waterbody were calculated using flux chamber monitoring. Emissions from the box-culvert were also assessed using gas speed head and concentration, taking into ac count the turbulence from the water discharge as a factor that increases the emission rate of H2S (Dai and Blanes-Vidal, 2013). Gas dispersion in Mu~ na’s and Sibat�e’s area was modelled using the emission rates from these two sources in the AERMOD® model. Five modelling scenarios were proposed to analyse Mu~ na’s emission impact on Sibat� e’s H2S measured concentration. It has been argued that the main factors affecting the distribution of contaminants are the landscape and the thermal inversion of air (Juliusson et al., 2015). Firstly, AERMOD® accounts for turbulence effects generated by topo graphic changes; however, the landscape in Sibat� e’s scenario does not represent a significant factor. There are no natural or anthropogenic barriers that could affect dispersion in the analysed area. Secondly, thermal inversion is analysed in one of the scenarios proposed in the study, as well as changes in wind speed and emission rates. This study is different to most of the studies previously reported in the literature as it addresses an odour problem generated by a large reservoir. This is similar to the odour problems presented in the Wastewater Treatment Plants (WWTP) (Beghi et al., 2012; Colomer �s Morato � and Mantilla Iglesias, et al., 2011; Llavador Colomer, Espino 2012). However, the dimensions of the reservoir are larger than the dimensions of the operation units at the WWTP studied and the nature of the water body is not of a controlled industrial process. These elements captured the attention from a research point of view. Additionally, the applicability of H2S monitoring using flux chamber have not been combined with the simulation with AERMOD® model before. Previous attempts to measure H2S emission in the reservoir have provided an initial estimate but have not delivered a whole description of the problem. Calvo, Behrentz, and Rodríguez (2009) provided the first ~ a’s reservoir. This approach to measuring the H2S emission from Mun approach used a theoretical stoichiometric technique that assessed the emission from two components of the reservoir, the water matrix and the benthos; an experimental method using a water column and benthos; and a simulation tool. The results revealed that improvements were required to provide experimental methodologies to quantify the emis sion and in using a better modelling tool that could predict H2S con centrations at the measured vales. The flux chamber and the AERMOD® model then emerged as an alternative.
to measure emission rates from surface sources. The tool consists of a one-sided open camera (Fig. 1) and was designed to measure Volatile Organic Compounds (VOCs) from a land surface (Klenbusch, 2004). However, it has been applied previously to different compound emis sions and modifications have been proposed according to the emissions condition. EPA Flux Chamber design works as a continuous reactor (Schmidt, 2004). The camera has an input of air flow which dilutes the emission (Fig. 1b). The concentration of the compound of interest inside the camera is measured in the air flowing out. According to the application and the availability of tools, the flux chamber has also been used without any air flux. The camera with air flow inside is known as a dynamic flux chamber, whereas the camera without any flux is known as a static chamber. Both cameras provide reliable results; however, they have advantages and disadvantages regarding the application (Hartman, 2003). The flux of air used for the dynamic flux chamber must be an inert gas that does not react with the components being monitored (Dai and Blanes-Vidal, 2013). The equation to obtain the emission for dynamic chamber is (Hart man, 2003): E ¼ Y.Q/A
(1)
where E is the emission rate (mass/(area⋅time)), Y is component con centration in the out-going flow (mass/volume), Q is the sweeping air flow inside the camera (volume/time), and A is the surface area covered by the camera (area). The equation for static chamber is (Eun, 2004): E¼(V/A)(dC/dt)
(2)
where V is the volume of the chamber (volume) and dC/dt is the rate of change of the component concentration inside the camera (mass/ (volume⋅time)). In this case, dynamic chamber was used for high emission rates, and static chamber for low rates. The materials used to construct the camera were acrylic, reflecting film, Tygon® and high-density polyethylene (HDPE) piping lines. The flux chamber designed and constructed varies from EPA protocol in the chamber’s material and reflecting film. The reflective film was incorporated to minimise the impact of the sun rays on the gases in the chamber. Previous studies have determined that radiation could produce significant errors in the emission rates measured; reflecting film decrease radiation effect even though other techniques have been applied (Carpi and Lindberg, 1998; Gillis and Miller, 2000). The analyser in flux chamber measurements was Jerome 631-X®. This analyser has been used to measure H2S concentrations by Dai and Blanes-Vidal (2013). The emission from the reservoir was monitored over a period of 19 days, with a severe climatological change. Experimentation started in dry conditions and it finalised with the presence of precipitation. The flux chamber creates a microenvironment under the chamber with different meteorology to the atmospheric conditions. This behavior masks the external wind speed (Tran et al., 2018; Mansfield et al., 2018). Correlation between the wind speed and the mass transfer have been standardized by equations such as the Ro and Hunt (2006) correlation. Transfer coefficients are not significantly influenced by low wind speeds. The data used to obtain these correlations demonstrates that the impact of the wind on the mass transfer is evident on wind speeds over 4 m/s, and almost ineligible under this speed (Ro and Hunt, 2006). Additionally, inverse models have been applied to validate the flux chamber. These models overestimate the emissions, while correction coefficients provide a better estimate of the emission rate (Tran et al., 2018). Correction coefficients are 1.1 for alkanes and aromatic com pounds, which means that the corrected emission rate is 10% higher than the measured emission rate (Tran et al., 2018). Most recent Computational Fluid Dynamics (CFD) simulation of H2S in a flux chamber demonstrated that although this device reduces the incidence
2. Methods 2.1. Emission component Two emissions sources were considered in AERMOD® modelling of ~ a’s reservoir: the reservoir’s surface and the box-culvert structure. Mun 2.1.1. Reservoir’s surface emission US EPA Flux Chamber EPA/600/8-8E/008 method was used to es timate emissions from the reservoir. This method has been widely used 2
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Fig. 1. Static (a) and dynamic (b) flux chamber’s designs following EPA’s nonofficial protocol.
of the wind, the emission is only 1.18% lower for the dynamic flux chamber (Prata et al., 2016). Based on our experimentation and research of the phenomena at the Mu~ na reservoir, the flux chamber method is the best alternative to monitor the emission from the reservoir. Due to the low percentage of impact from wind on H2S emissions estimated by Prata et al. (2016), and the fact that wind speed in this study is under 4 m/s, the wind impact has not been considered in the calculations.
concentration, the H2S total emission was calculated. 2.2. Dispersion component 2.2.1. Ambient concentration API analysers (model 101E®) were employed to measure H2S ~ a’s reservoir area. This equipment gives ambient concentration on Mun H2S concentration in ppb units. Ambient concentration was monitored over a period of two years. The average hourly concentration in the monitoring stations was used as a background concentration for modelling tool.
2.1.2. Box-culvert emissions To determine the H2S emission inside this structure, a micro manometer (Dwyer®) was used. This device measures speed head and determines gas flow (7.42 m3/s). Using this parameter and H2S
Fig. 2. Sampling points on Mu~ na’s reservoir (a) and H2S average emission profile (b). 3
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2.2.2. Modelling tool Modelling was done using the AERMOD® tool. This is an improved version of the ISC3 model (Industrial Source Complex Model) encom passed with an advanced graphic format. AERMOD® was chosen for its wide industrial application and has been approved by the EPA since 1996. Moreover, AERMOD® has a meteorological analysis tool, AER MET®. ISC3 which has been developed on the classical Gaussian model base. The model works in a steady state and is designed for complex landscapes. It can be used for different sources (e.g. lineal and surface). Time basis can be modified according to the needs of the simulation.
3.2. Dispersion component 3.2.1. Ambient concentration H2S ambient concentration was measured in six different points related to historic complaints of odour problem. Sampling points are shown in Fig. 3, while statistical information of H2S concentration measured is presented in Table 2. Table 2 shows that the maximum concentration of 17344 ppb (17.3 ppm) was measured in Alicachín (point 2). However, the maximum average concentration (414.1 ppb) was reported in the box-culvert area (point 4), where high emission rates from the reservoir were obtained, and emission from the turbulence of the inflow flux from the box culvert influences the atmospheric concentration. H2S concentration behaviour is consistent with the continuous emission from the reservoir and batch emissions from the water discharge in Alicachín (point 2). In contrast, the mid-east point (point 5), closest to the highest measured emissions, did not evidence as high ambient concentration as the box-culvert or Alicachín measurements. This is attributed to the climatic conditions that influence the dispersion behaviour. A decreasing concentration was obtained from the box-culvert structure (point 4) towards the south of the reservoir (Tails-Point 3), with the minimum average concentration of 3.9 ppb (0.0039 ppm) measured in the municipality of Sibat� e. This concentration is in the range of odour perception from 0.0005 to 1.5 ppm (ATSDR, 2016; OSHA, 2018). The average measured concentrations are indicative of the emission behaviour during regular operation conditions of the reservoir, while measured emission (Fig. 2) represents the particular behaviour during monitoring conditions.
3. Results and discussion 3.1. Emission component 3.1.1. Reservoir’s surface emission Forty sampling areas were selected to measure the whole reservoir emission (Fig. 2a). More importance was given to the closest points to the box-culvert and the water outflow structure, where more organic matter is accumulated and pilot experiments showed the highest emis sion rates. Each sampling point was associated to a uniform emission area determined before experimentation, based on the bathymetry of the reservoir. Fig. 2 shows the sampling points and the emission rates results. The highest emission points are related to organic matter accumulation. As predicted, high emission rates were obtained close to the box-culvert and the outflow structures located in the north part of the reservoir. However, the highest emission rates were measured in the eastern sec tion of the reservoir, where inactive water and sediment storage has been identified. Lower emission rates were observed in the southern section, where water circulation and particulate matter deposition is lower. Table 1 shows maximum and average measured emission rates ~ a’s reservoir. for Mun The highest emission rates in the eastern section of the reservoir could be attributed to the high sludge retention times and anaerobic conditions present during the monitoring in that area, which favoured the production of H2S. This region receives less amount of organic sediments than the area between the box-culvert and the water outflow structure (Fig. 2a), where shortcut flow has been identified when both structures operate simultaneously. However, the accumulation of sedi ments is high in this area, in particular when only the box-culvert inflow operates but not the water outflow. The water and sediment retention time is usually longer in this section of the reservoir. Considering that the monitoring started from dry season to wet season, initial pumping from the reservoir was required for energy generation and water inflow was limited due the dry climatologic conditions. These conditions fav oured longer retention times in shallow areas, such as the eastern area of the reservoir, where highest emission were observed.
3.2.2. Modelling tool. The AERMOD® model was used to simulate hydrogen sulphide concentration in the area of interest. Calibration of the model was conducted before scenario’s evaluation. The meteoro logical conditions were fed to the model using AERMET®. This infor mation was collected simultaneously with H2S concentration at the six sampling points. H2S emission from water body and box-culvert H2S turbulence variables were also input information for the model. Mete orological conditions, apart from H2S real time concentration, were simulated for specific dates. In this way, model parameters were found. From the implementation of these parameters, model simulations were obtained and compared with H2S measurements in the sampling points. The background H2S concentration used for the model was the average concentration measured by the monitoring stations, and the altitude implemented was 2600 m s.n.m. Table 3 compares H2S measured con centration with model simulations for the four main points of interest. The error presented in Table 3 was calculated from the differences between measured and modelled concentrations. Although the per centage of error may seem high, the values obtained from the model are in the same range of values of the measured concentrations. Moreover, model estimations provide a useful predictive approximation to the real H2S concentration. Fluctuations in emission measurement from the box-culvert and the reservoir surface are sources of error, as well as the microclimates in each point of monitoring that affect the dispersion behaviour of H2S. Daily variations in temperature, radiation, wind speed, among other variables, influence emission and mass transfer behaviour and moni toring measurements. The higher percentage of error (388%) was esti mated for the box-culvert area (point 4), where turbulence generates H2S emissions due to water flow at variable rates. At this point, the highest differences between average and maximum measured concen tration were registered (Table 2). The water inflow rate is calculated based on the daily plant operation requirements. This makes difficult to predict and incorporate this variable with accuracy in the model. Other source of error is the H2S emission from Alicachín (point 2). This loca tion has the highest H2S ambient concentration, as presented in Table 2; however, the emission from this point was not considered as an
3.1.2. Box-culvert emissions Using the results of the gas flux and the H2S concentration, H2S concentration in the gas flow from the Box-culvert was between 40 ppm and 80 ppm.
Table 1 H2S Emission results for flux chamber experiments on Mu~ na’s reservoir. H2S Emission
μg/(min*m2) Maximum Average
6400 1886
4
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Fig. 3. H2S sampling points.
of highest ambient concentration measurement. This result can be attributed to the hydrodynamic behaviour of the reservoir at the time of monitoring, which indicates a temporary emission rather than general behaviour of the reservoir. To analyse different climatological conditions, five scenarios were simulated and are listed as follows. Simulation results are presented in Fig. 4.
Table 2 Statistic parameters for H2S ambient concentration. Sampling point Point 1 Point 2 Point 3 Point 4 Point 5 Point 6
Middle west Alicachín Tails Box-culvert Middle east Sibat�e
Max.
Min.
Monthly average
ppb
ppb
ppb
416.7 17344.0 405.1 8738.8 357.3 93.3
0 0.9 0 0 0 0
25.7 155.3 15.5 414.1 36.1 3.9
Scenario A: Simulated average concentration in an hour. Scenario B: Simulated average concentration per hour of maximum daily concentration. Scenario C: Simulated average concentration per hour of maximum daily concentration if there were a H2S emission reduction in the box-culvert as a result of chemical or biological treatment of the gas. This option was considered because five treatment alternatives were evaluated as reaching satisfactory H2S removing rates from the gas in the box-culvert. Scenario D: Simulated average concentration in an hour using hydroclimatological conditions between 8:00 am to 9:00 am. This hour was chosen as it represents the time with the highest concen tration of H2S measured in Sibat� e. During this hour the wind direc tion was towards the south west. Scenario E: Simulated critical condition in Sibat�e. The wind direc tion was towards the south and the wind speed was higher than normal. Concentrations in Sibat�e reached a peak of 4000 ppb.
Table 3 Model calibration.
Point 1 Point 3 Point 4 Point 6 a
Measured concentrationa
Model prediction
Error
ppb
ppb
%
Ave.
Ave.
85.9 13.5 442.0 7.0
112.1 23.3 703.0 13.0
39.5 19.6 388.7 18.5
H2S hourly average measured concentration.
independent source of emission in the model. Error percentages in the other monitoring locations range from 18.5% to 39.5%. This range of values evidences that it is possible to predict concentrations in the magnitude order of the real concentration measured for the area. Additionally, the highest emission was not observed in the same area
The climatological parameters used for model simulation are pre sented in Table 4. Scenarios were modelled under normal and critical conditions in the area of study. Simulation results are presented in Fig. 4, as well as the 5
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Fig. 4. Simulation results and compass rose associated.
9:00 am to assess the thermic inversion phenomena. Fig. 4-D shows that ~ a’s reservoir, compared to sce critical concentration decreases on Mun nario A and B, and the concentration increases in Sibat�e as a conse quence of the change in wind direction. Scenario D and E, which implemented changes in wind direction, evidenced wider dispersion of the pollutant. These two scenarios are critical conditions for the mu nicipality of Sibat�e, where higher concentrations were predicted. A concentration of 4000 ppb (4 ppm) in the municipality was predicted in Scenario E. This concentration could increase the risk of health problems for the population.
Table 4 Hourly average climatic parameters used in simulation. Scenario
Scenario A Scenario B Scenario C Scenario D Scenario E
Wind speed
Wind direction
Temperature
Precipitation
Radiation
m/s
-
�
C
mm
W/m2
1.89
N
13.1
0.1
158
1.89
N
13.1
0.1
158
1.89
N
13.1
0.1
158
1.97
SW
14.9
0.01
411
4. Discussion
2.34
S
14.8
0.03
326
The emission rates obtained in this study behave similarly to the rates estimated by the previously reported stoichiometric analysis. The theoretical rates were predicted between 3 and 16 μg/(min*m3) for emission from the water matrix, and between 1527 and 3542 μg/ (min*m3) for the emission from the benthos (Calvo et al., 2009). These results indicate that the emission from the benthic matrix is higher than from the water matrix. In this study, lower emission was observed where less sediment accumulation occurred, and higher emission was reported in the areas where more accumulation of sediments happened. Although the emission rate reported in this paper is per square meter, these rates are in the same magnitude order to the theoretical rates presented by Calvo et al. (2009) if an assumption of a benthos depth between 1 (one)
wind rose associated to each simulation. As expected, the concentration of H2S increases in Sibat� e municipality as the atmospheric concentration raises in the identified sources of emission. According to simulation results in Fig. 4-B, concentration increased compared to scenario A, but dispersion remained similar. Comparison between scenario B and C evidenced that reduction in the box-culvert emissions does not dramatically affect the concentration in Sibat� e. In contrast, wind di rection is a key variable of analysis in the dispersion of H2S. Scenario D simulation uses the hydroclimatological condition between 8:00 am to 6
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to a few meters is considered for the average emission of 1886 μg/(min*m2). Similar results have been obtained for H2S emissions from WWTP. Emission rates in WWTP have been estimated using passive samplers, with values between 0.09 and 2.47 g/s for WWTP inflow rates between 38962 and 61821 m3/day. These rates are equivalent to a daily release of H2S from 50 to 100 kg (Llavador Colomer et al., 2012). These results ~ a’s are comparable with approximately 70 kg emission from the Mun reservoir considering the average emission rate. Baawain et al. (2019) estimated the emission between 0.1 g/s to 3 g/s from a tanker facility for a 55000 m3/day treatment plant, and H2S emission from an aeration tank has been estimated in 0.22 g/s (Beghi et al., 2012). Although these emission rates are within the range of our estimations, the dimension of the WWTP analysed are smaller than the reservoir. Prata et al. (2016) validated the measurements of the dynamic chamber producing H2S emission in the laboratory, identifying similar emission rates of 1663 μg/(min*m2). Regarding the simulation tool, AERMOD® model has been applied successfully providing an alternative to assess the dispersion in the area of analysis. This model has been applied previously to produce reverse modelling of H2S from swine plants, and to provide estimates of dispersion of H2S from geothermal power plants (O’Shaughnessy and Altmaier, 2011; Peralta et al., 2013). In these cases, a possible over estimation has been identified in specific scenarios, such as night and early morning modelling. However, AERMOD® have been applied with satisfactory results to predict H2S concentration behaviour.
CRediT author statement C. Moreno-Silva: Conceptualization, Writing- Reviewing and Edit ing, Investigation; D.C. Calvo: Writing- Reviewing and Editing; M. �n: Software; N. Torres: Investigation; L. Ayala & L. Gonza �lez: Gaita � n: Investigation; M. Rodríguez Susa: Supervision. Resources; P. Rinco Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.atmosenv.2020.117263. References Agency for Toxic Substances and Disease Registry (ATSDR), 2016. Toxicological Profile for Hydrogen Sulfide/Carbonyl Sulfide. Retrieved from. http://www.atsdr.cdc. gov/toxprofiles/tp114-c4.pdf. Baawain, M., Al-Mamun, A., Omidvarborna, H., Al-Sulaimi, I., 2019. Measurement, control, and modeling of H 2 S emissions from a sewage treatment plant. Int. J. Environ. Sci. Technol. 16 (6), 2721–2732. https://doi.org/10.1007/s13762-0181997-z. Beghi, S.P., Santos, J.M., Reis, N.C., de Sa, L.M., Goulart, E.V., de Abreu Costa, E., 2012. Impact assessment of odours emitted by a wastewater treatment plant. Water Sci. Technol. 66 (10), 2223–2228. https://doi.org/10.2166/wst.2012.409. Blunden, J., Aneja, V.P., 2008. Characterizing ammonia and hydrogen sulfide emissions from a swine waste treatment lagoon in North Carolina. Atmos. Environ. 42 (14), 3277–3290. https://doi.org/10.1016/j.atmosenv.2007.02.026. Calvo, D.C., Behrentz, E., Rodríguez, M.S., 2009. Emissions and dispersion of hydrogen sulphide originated from a highly polluted waterbody. In: Odours and VOCs: Measurement, Regulation and Control Techniques. Kassel University Press, pp. 115–122. Carpi, A., Lindberg, S.E., 1998. Application of a teflon™ dynamic flux chamber for quantifying soil mercury flux: tests and results over background soil. Atmos. Environ. 32 (5), 873–882. https://doi.org/10.1016/S1352-2310(97)00133-7. Colomer, F., Espinos Morato, H., Iglesias, E., 2011. Estimating Hydrogen Sulfide Emission Rates by Combining Experimental Immission Measurements and Dispersion Models at Various Wastewater Treatment Plants in the Valencian Community. Spain. Dai, X.R., Blanes-Vidal, V., 2013. Emissions of ammonia, carbon dioxide, and hydrogen sulfide from swine wastewater during and after acidification treatment: effect of pH, mixing and aeration. J. Environ. Manag. 115, 147–154. https://doi.org/10.1016/j. jenvman.2012.11.019. Eun, S., 2004. Hydrogen sulfide flux measurements and dispersion modeling from construction and demolition (C&D) debris landfills. Master Sci. Dep. Civil Environ. Eng. University of Central Florida, Retrieved from http://stars.library.ucf.edu/cgi /viewcontent.cgi?article¼1185&context¼etd. Gillis, A., Miller, D.R., 2000. Some potential errors in the measurement of mercury gas exchange at the soil surface using a dynamic flux chamber. Sci. Total Environ. 260 (1), 181–189. https://doi.org/10.1016/S0048-9697(00)00562-3. Hartman, B., 2003. How to collect reliable soil-gas data for upward risk assessments. Part 2: surface flux-chamber method. Paper presented at the H&P Mobile Geochemistry Inc Vap. Intrusion Spec. http://www.handpmg.com/articles/lustline44-flux-cha mbers-part-2.html. Juliusson, B.M., Gunnarsson, I., Matthiasdottir, K.V., Bjarnason, B., Sveinsson, O.G., Gislason, T., Thorsteinssson, H., 2015. Tackling the challenge of H2S emissions. Paper presented at the. In: Proceedings World Geothermal Congress 2015. Melbourne, Australia. https://pangea.stanford.edu/ERE/db/WGC/papers/WGC/ 2015/02062.pdf. Klenbusch, M., 2004. Measurement of Gaseous Emission Rates from Land Surfaces Using an Emission Isolation Flux Chamber. User’s Guide. U.S. Environmental Protection Agency. EPA/600/8-86/008. Llavador Colomer, F., Espin� os Morat� o, H., Mantilla Iglesias, E., 2012. Estimation of hydrogen sulfide emission rates at several wastewater treatment plants through experimental concentration measurements and dispersion modeling. J. Air Waste Manag. Assoc. 62 (7), 758–766. https://doi.org/10.1080/10962247.2012.674008. Mansfield, M.L., Tran, H.N.Q., Lyman, S.N., Bowers, R.L., Smith, A.P., Keslar, C., 2018. Emissions of organic compounds from produced water ponds III: mass-transfer coefficients, composition-emission correlations, and contributions to regional emissions. Sci. Total Environ. 627 (C), 860–868. https://doi.org/10.1016/j. scitotenv.2018.01.242. Muyzer, G., Stams, A., 2008. The ecology and biotechnology of sulphate-reducing bacteria. Nat. Rev. Microbiol. 6, 441–454. https://doi.org/10.1038/nrmicro1892. Retrieved from.
5. Conclusions ~ a’s reservoir in This study demonstrates that emissions from Mun fluences the presence of odours in the municipality of Sibat� e. The highest ambient concentrations were measured in the box-culvert area (point 4) and at Alicachín (point 2), with values of 8738 ppb (8.7 ppm) and 17344 ppb (17.3 ppm) respectively. The emission of these two points is associated to water turbulence; additionally, the box-culvert area and the eastern region (point 5) of the reservoir evidenced the highest emission rates from the water body. High H2S emission in this area is a likely consequence of bathymetry and kinetics of the reservoir which leads to retention and anaerobic degradation of organic matter from the river. Contrarily, the lowest average concentrations of H2S were measured at the municipality (3.9 ppb). Reservoir emissions were successfully measured using the Flux chamber experimental tool, obtaining real water surface emission rates, and delivering the required measurements for model calibration and simulation verification. How ever, experimental error in the sampling method and hydro climatological conditions could contribute to the error measured in the simulations. The AERMOD® model utilised real meteorological conditions, the flux chamber measured emission rates, and estimated box-culvert emission rates to generate H2S concentrations in Sibat� e under different scenarios. AERMOD® was calibrated with H2S real time con centrations in different points surrounding Mu~ na’s reservoir. As a result, the model predicted H2S concentration values within one order of magnitude of the measured concentrations when simulation was con ducted under average meteorological conditions. Low concentrations are predicted for the municipality under simulation of conservative scenarios. However, when critical wind conditions were used, concen trations of 4 ppm were predicted in Sibat�e. This concentration is not only higher than the odour threshold, but could also represent a health hazard. Additionally, the results obtained evidenced the impact of wind in the dispersion of the H2S. In summary, this model could be implemented in future projects to predict H2S concentrations in affected areas. Nevertheless, the analysis should be conservative taking into account the percentage of error of 18.5% in predicting H2S concentrations in the area of interest.
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O’Shaughnessy, P.T., Altmaier, R., 2011. Use of AERMOD to determine a hydrogen sulfide emission factor for swine operations by inverse modeling. Atmos. Environ. 45 (27), 4617–4625. https://doi.org/10.1016/j.atmosenv.2011.05.061. Occupational Health and Safety Administration (OSHA), 2018. Hydrogen sulfide. Retrieved from. https://www.osha.gov/SLTC/hydrogensulfide/index.html. Peralta, O., Castro, T., Dur� on, M., Salcido, A., Celada-Murillo, A.-T., NavarroGonz� alez, R., Torres-Jaramillo, A., 2013. H2S emissions from Cerro Prieto geothermal power plant, Mexico, and air pollutants measurements in the area. Geothermics 46, 55–65. https://doi.org/10.1016/j.geothermics.2012.12.001. Prata, A.A., Santos, J.M., Beghi, S.P., Fernandes, I.F., Vom Marttens, L.L.C., Pereira Neto, L.I., Stuetz, R.M., 2016. Dynamic flux chamber measurements of hydrogen sulfide emission rate from a quiescent surface – a computational evaluation. Chemosphere 146, 426–434. https://doi.org/10.1016/j.chemosphere.2015.11.123. Reinhart, D., Townsend, T.G., Eun, S., Xu, Q., 2018. Control of Odors from Construction and Demolition (C&D) Debris Landfills. State University System of Florida Florida Center for Solid and Hazardous Waste Management.
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