Development of a Reactive Plume Model for the Consideration of

Article pubs.acs.org/est

Development of a Reactive Plume Model for the Consideration of Power-Plant Plume Photochemistry and Its Applications Yong H. Kim, Hyun S. Kim, and Chul H. Song* School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea S Supporting Information *

ABSTRACT: A reactive plume model (RPM) was developed to comprehensively consider power-plant plume photochemistry with 255 condensed photochemical reactions. The RPM can simulate two main components of power-plant plumes: turbulent dispersion of plumes and compositional changes of plumes via photochemical reactions. In order to evaluate the performance of the RPM developed in the present study, two sets of observational data obtained from the TexAQS II 2006 (Texas Air Quality Study II 2006) campaign were compared with RPM-simulated data. Comparison shows that the RPM produces relatively accurate concentrations for major primary and secondary in-plume species such as NO2, SO2, ozone, and H2SO4. Statistical analyses show good correlation, with correlation coefficients (R) ranging from 0.61 to 0.92, and good agreement with the Index of Agreement (IOA) ranging from 0.76 to 0.95. Following evaluation of the performance of the RPM, a demonstration was also carried out to show the applicability of the RPM. The RPM can calculate NOx photochemical lifetimes inside the two plumes (Monticello and Welsh power plants). Further applicability and possible uses of the RPM are also discussed together with some limitations of the current version of the RPM.

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INTRODUCTION Primary air pollutants emitted from large-scale point sources are closely linked to a series of atmospheric pollution issues such as acid deposition, atmospheric haze, and photochemical ozone events.1−3 Primary air pollutants such as SO2, NOx, CO, and black carbon (BC) are chemically transformed via atmospheric photochemical reactions, producing secondary air pollutants. Secondary air pollutants such as ozone, peroxy acetyl nitrates (PANs), HCHO, sulfuric acid (H2SO4), and nitric acid (HNO3) are believed to have potentially more harmful effects on human health and crop growth/yields than primary air pollutants.4−6 In particular, significant amounts of SO2 and NOx are being emitted from large-scale point sources (e.g., power plants, steel manufacturing, and petrochemical factories), accounting for approximately 70% and 38% in South Korea for 20117 and 64% and 16% in U.S. for 2010,8 respectively. Thus, it is of great importance to precisely describe the atmospheric photochemical conversion from primary to secondary air pollutants inside the plumes emitted from large-scale point sources. Generally, photochemical conversion inside the plumes is quite dynamic and highly nonlinear, which is driven mainly by elevated levels of NOx.9,10 For instance, in close proximity to the stacks of large-scale point sources, the elevated levels of NO result in ozone depletion (ozone titration) via active NO+O3 reaction. The depleted ozone subsequently leads to OH depletion, since ozone is a main precursor of OH radicals in the atmosphere. Due to the OH depletion, near the stacks of power-plants, the reactions are almost dead. However, as the © XXXX American Chemical Society

power-plant plumes develop, the levels of NOx decrease and the levels of ozone and OH are recovered. As the power-plant plumes are further diluted (the final stage of plume chemical evolution), photochemistry inside the plumes becomes extremely vigorous. Such dynamic and nonlinear changes in the in-plume composition have been observed via aircraft measurements during several campaigns such as SOS, ITCT 2K2, and TexAQS II 2006.11−13 However, efforts to more comprehensively describe or simulate such nonlinear changes in the in-plume composition have not been actively made. In this context, the present study attempted to combine a plume dispersion scheme with plume photochemistry, with a view to being able to more comprehensively describe the dynamic changes in the plume composition. The model is hereafter referred to as the “GIST-reactive plume model (GIST-RPM)”, since it can simulate the plume composition changes due to photochemistry in addition to turbulent dispersion. Following development of the GIST-RPM, its performance was assessed via comparison of the RPM-simulated plume composition with an aircraft-observed plume composition, using TexAQS II 2006 aircraft plume data. During the TexAQS II 2006 campaign, NOAA WP-3D aircraft was flown to the northeastern part of Texas on 16th September, 2006, measuring the mixing Received: Revised: Accepted: Published: A

August 4, 2016 December 12, 2016 January 9, 2017 January 9, 2017 DOI: 10.1021/acs.est.6b03919 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology

Figure 1. Four power-plant plume transects (I−IV) made by NOAA WP-3D flight for Monticello and Welsh power-plant plumes during the TexAQS II 2006 aircraft campaign.

ratios of atmospheric species. In particular, the aircraft traversed two plumes emitted from Monticello and Welsh power plants four times between 11:00−15:00 LST (refer to Figure 1; hereafter, the two plumes are referred to as the Monticello and Welsh plumes, respectively). From this comparison, the performance of the GIST-RPM was evaluated. While ordinary plume dispersion model can consider only turbulent dispersion (horizontal and vertical dilution) of primary pollutants such as NOx and SO2, the GIST-RPM can take account of photochemical reactions as well as turbulent dispersion, being able to deal with the formation and destruction of secondary atmospheric species such as ozone, H2SO4, HNO3, HCHO, and PANs. This is the biggest advantage of GIST-RPM. It allows us to utilize GIST-RPM for more comprehensive environmental impact assessment (EIA) of large-scale point sources such as power plants. In addition, GIST-RPM is a crucial part in developing an integrative plume monitoring system, in which it is combined with a drone plume monitoring system. These efforts are now under way. Although GIST-RPM is newly developed in this study to simultaneously take account of plume photochemistry and

turbulent dispersion, several photochemical plume models have already been developed in the last three decades. However, these photochemical plume models are only equipped with relatively simple photochemistry,14−17 or their performances are not fully validated with intensive in-plume observations.15,18,19 Moreover, several RPMs have incorporated lumped photochemical mechanisms (such as CBM 4 or 5)16 with limited considerations of heterogeneous processes on aerosol surfaces. In addition, source codes of several models have not been released.18,20 These problems are the main motivations for us to develop our own RPM. The structure of GIST-RPM is also designed to be easily switched to different plume modes to consider different plume situations, including ship-plume mode,9,10 large-scale point source-derived plumes (current study), biomass burning plumes (work in progress), and aircraft-exhaust plumes (work in progress). The present manuscript is organized in the following order. In the next section, the main concept and detailed procedures of the GIST-RPM development are explained. This section is then followed by model validation using TexAQS II 2006 aircraft observations, in addition to some scientific discussion. After the B

DOI: 10.1021/acs.est.6b03919 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology discussion, a possible application of the RPM is demonstrated with an example calculation of NOx lifetimes inside two powerplant plumes (Monticello and Welsh power plants). Subsequently, further uses and limitations of the current version of RPM are also discussed, along with the future outlook.

Table 1. Conditions for Monticello and Welsh Power-Plant Plume Simulations variable

value Monticello

Emission ratea NOx (g/s) 147.55 SO2 (g/s) 1206.32 CO (g/s) 965.12 Meteorological and Topographical Condition stability class slightly unstable (C) − neutral (D) wind speed (m/s) 6.55−9.42 mixing height (km) 1.50 aerosol pH 4.00 surface roughness (m) 0.12−0.50 Location latitude (°N) 33.09 longitude (°E) −95.04 Background Concentration (ppbv) [NOx] 0.07 [O3] 33.00 [CO] 96.40 [SO2] 0.39 [HNO3] 0.60 [NOy] 1.71 [HCHO] 2.88 [ETHE]b 0.54 [ISOP]c 0.54 [BENZ]d 0.11 [PAN]e 0.27

2. EXPERIMENTAL METHODS The compositional changes inside the plumes are due to two main processes: turbulent dispersion and photochemical reactions. Therefore, how to account for these two processes in the GIST-RPM is described in order in the following sections. 2.1. Turbulent Plume Dispersion. To run the RPM model, meteorological information is necessary. Therefore, to acquire these data for the GIST-RPM simulations, the Weather Research and Forecast (WRF) v3.5.1 model was run with 1 × 1 km2 spatial-resolution and 1 min time-resolution over the plumedeveloping area in northeastern Texas (refer to Figure 1), using NOAA NCEP reanalyzed data.21 The reanalysis data sets, which have produced global meteorological parameters from 1948 to the present through data assimilation system, have been used for diagnostic meteorological analyses. The detailed methods to run the WRF model have been described elsewhere.22−24 The variables obtained from the WRF model simulation were wind velocity and direction, pressure, temperature, solar zenith angle (SZA), solar radiation, planetary boundary layer height (PBLH), and friction velocity (u*). These variables were used to determine the stability class, plume track, and dispersion coefficients of the plumes. Although the detailed discussion is carried out in the latter parts of this manuscript, the MET information used in the GIST-RPM simulation is shown briefly in Table 1, along with topographical information. Topographical information was also obtained from the WRF model, and used to determine surface roughness factors (z0). In order to evaluate the performance of the WRF model simulation, modeled metrological parameters (wind velocity, temperature, and pressure) were compared with aircraft observations. The WRF-modeled MET results showed reasonable agreements with observed values, as shown in Supporting Information (SI) Table S1. Turbulent dispersion of plumes is mainly governed by two factors: (1) stability class and (2) dispersion coefficients. The former was first determined via two independent methods based on the WRF modeling outputs (refer to Figure 2). The latter parameters were determined as a function of stability classes, plume-travel distances, surface roughness, and averaging time. The details are discussed in Section 3.1 of this manuscript. 2.2. Plume Photochemistry and Emission Rates. A total of 255 condensed photochemical reactions were incorporated into the GIST-RPM. Photochemical reactions are composed of four types of reactions; first-order photolysis reactions, bimolecular and three-body thermal reactions, and heterogeneous reactions. Of the 255 reactions, 71 are related to O3− NOy−CH4−CO−HOx reactions, whereas 184 are NMVOCrelated.9,10 The reactions are based on the Lurmann mechanism,25 however, both thermal reaction-rate coefficients and photodissociation coefficients have been continuously updated whenever new experimental data has become available.26−28 The modified reaction-rate coefficients are summarized in SI Table S2. For the purpose of calculating the photodissociation coefficients, the TUV radiation module, developed by Madronich,29 was employed in the GIST-RPM. GIST-RPM was developed to evaluate the atmospheric impacts of large-scale point sources with the choice of a surrogate photochemical mechanism, not lumped chemical

Welsh 96.00 405.89 153.23

33.06 −94.84 0.10 33.10 96.40 0.71 0.20 2.01 2.88 0.54 0.54 0.11 0.27

These emission rates are not real emissions but “virtual” ones, since they were estimated from the aircraft observations under the assumed conditions that the NOAA WP-3D aircraft traversed two plumes at the plume centerlines. bEthene, C2H4; cisoprene, C5H8; dbenzene, C6H6; e peroxy acetyl nitrate, CH3CO3NO2 a

mechanisms used in other RPMs.16,19,20,30 With the choice of surrogate photochemical mechanism (in this study, modified Lurmann mechanism), one can easily keep track of chemical fates of NMVOC species, which is relevant to policy issues on air pollution in Korea where the levels of many NMVOCs have been found to be very high.7,57 Heterogeneous partitioning of condensable species such as H2SO4, N2O5, NO3, and HNO3 is also an important atmospheric microphysicochemical process. Considering this heterogeneous condensation into atmospheric particles, the following formula was introduced:31 k mt, i

⎛ d̂ ⎞−1 4 ⎟ p ⎜ A =⎜ + γivi̅ ⎟⎠ ⎝ 2Di

(1) −1

where kmt,i is the mass-transfer coefficient of species i (s ); and d̂p (=V/A) the effective particle aerodynamic diameter (cm). dp̂ is calculated by the ratio of aerosol volume density (V, cm3 cm−3) to aerosol surface density (A, cm2 cm−3), both of which were measured during the TexAQS II 2006 campaign. Di denotes the diffusivity coefficient of species i (cm2 s−1); γi the reaction probability; and vi̅ the molecular mean velocity of species i (cm s−1). On the right-hand side of eq 1, the second term is related to partitioning of condensable species into fine particles (say, dp < ∼ 2 μm), whereas the first term is related to partitioning of condensable species into coarse particles (dp > 2 μm). As mentioned above, aerosol surface densities (A) were measured at C


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Figure 2. Meteorological parameters with plume-travel times obtained from WRF model simulations: (a) wind speed; (b) wind direction; (c) surface wind speed; (d) SZA; (e) stability class; and (f) surface roughness.

background concentrations. The background mixing ratios were measured outside the plumes during the NOAA WP-3D aircraft campaign. Only slight changes in the background concentrations were reported, thus, constant background mixing ratios were used in the simulation (refer to Table 1). Estimations of the emission rates from two power plants during TexAQS II 2006 were not based on direct emissions from the stacks, but instead on flight observations. This is due to the fact that the NOAA WP-3D possibly did not pass through the plume centerlines. When the fresh plume is emitted into the atmosphere, it reaches effective stack height, which is the sum of the actual stack height and plume rise, due to thermal buoyant force and mechanical inertial force.34,35 This plume rise is governed by four main factors (stack exit velocity, temperature

four plume transects. These aerosol surface densities were interpolated between four transects. They were then extrapolated after transect IV. In this way, the GIST-RPM can be run in an observation-constrained mode to carry out more realistic plume simulations. This is another advantage of the GIST-RPM developed in this study. In eq 1, the reaction probabilities of H2SO4 and N2O5 are of particular importance in the present study, since the former and the latter have much to do with gaseous mixing ratios of H2SO4 and NOx, respectively, inside the plumes. In the present study, γH2SO4 of 0.79 and γN2O5 of 0.017 were used on the basis of the works of Jefferson et al.32 and Brown et al.33 The photochemical component of the GIST-RPM is driven by emission rates of primary pollutants (NOx, SO2, and CO) and D


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then followed by discussions on application of the GIST-RPM and the limitations of the current GIST-RPM. 3.1. Determination of Meteorological Factors. In order to drive the GIST-RPM, meteorology-derived plume variables such as plume track, stability classes, and plume-dispersion coefficients should first be determined. As mentioned previously, basic meteorological data was generated via the WRF model simulation using NOAA NCEP reanalyzed data. First, in order to determine the plume tracks for Monticello and Welsh plumes, wind speeds and directions were investigated using the horizontal vector components of wind (u and v) that were obtained from the WRF-simulated outputs. As presented in Figure 2a and 2b, the wind speeds and directions with the plumetravel (or plume-aging) times range from approximately 6.5 to 9.0 (m s−1) and from 179 to 180° (southerly winds), respectively. The variable wind speeds were used in the calculations of plumedispersion coefficients. Second, the determination of time-dependent changes in the atmospheric stability is necessary to correctly describe the variations in turbulent dispersion. Turner’s37 method for the determination of Pasquill-Gifford stability categories was chosen for the present study. The atmospheric stability classes estimated along the plume-travel tracks exist between the slightly unstable (C) and neutral (D) conditions (see Figure 2e). The details for determining the plume track and stability classes are provided in the Supporting Information. Third, to account for atmospheric turbulent dispersion of plumes, Briggs’s38 dispersion coefficients were used and corrected. As shown in SI Table S3, lateral (σy) and vertical (σz) dispersion parameters can be expressed as a function of plume-travel distance (x) and atmospheric stability class. Since Briggs’s turbulent-dispersion parameters are known to be based on an averaging time of 30 min and a surface roughness (z0) of 0.006 m, certain corrections to the turbulent-dispersion coefficients should be made in order to consider the actual turbulent dispersions of Monticello and Welsh plumes. To correct z0, the following formulas were introduced:39,40

difference, wind velocity, and ambient temperature), as shown in eq 2: ⎛ v ⎞1.4 ⎛ ΔT ⎞ ΔH = d⎜ s ⎟ ⎜1 + ⎟ ⎝u⎠ ⎝ Ts ⎠

(2)

where, ΔH represents the rise of the plume above the stack (m); d the stack diameter (m); vs the stack exit gas velocity (m s−1); u the wind speed (m s−1); ΔT the temperature difference between stack gas and ambient air (K); and TS the temperature of stack gas (K). The effective stack heights estimated for the Monticello and Welsh power plants were between 290 and 340 m, and these values are approximately 2.1 times smaller than aviation altitudes of WP-3D (600−700 m). Thus, in order to minimize the uncertainties in the power-plant emission rates, these rates were estimated from aircraft-derived emission ratios for the Monticello and Welsh power plants. It is generally assumed that the plumes emitted into the atmosphere cannot be dispersed above mixing height, and that they spread in a conical shape, starting from the effective height. The emission rates for the primary pollutants such as NOx, SO2, and CO were determined by the aircraft-derived emission ratios of NOx, SO2, and CO to coemitted CO2. The CO2 emission rate was estimated as a function of observed CO2 concentrations, volumes, and travel times. Peischl et al.36 reported that the aircraft-derived emission ratios for [NOx]/[CO2], [SO2]/[CO2], and [CO]/[CO2] were 0.77 ± 0.10, 2.81 ± 0.28, and 5.44 ± 0.27 (ppbv/ppmv), respectively, for Monticello, and 1.19 ± 0.14, 1.50 ± 0.15, and 1.68 ± 0.11 (ppbv/ppmv), respectively, for Welsh. On the basis of these emission ratios and the calculated CO2 emission rates, the emission rates of NOx, SO2, and CO for Monticello and Welsh power plants were estimated, and are summarized in Table 1. The details on how to estimate the emission rates are discussed further in the Supporting Information. In addition, the time-steps for integration of photochemical reactions are variable with mathematical stiffness. It is very short (e.g., subseconds) in very close proximity to the stack, but is the order of several minutes for fully developed plumes.9 2.3. GIST-RPM Validation. To evaluate the performance of the GIST-RPM simulation, the measurement data obtained during the TexAQS II 2006 aircraft campaign were compared with the RPM-simulated concentrations inside the two plumes from the Monticello and Welsh power plants. To evaluate the performance of the GIST-RPM, the measured mixing ratios of four species (NOx, SO2, ozone, and H2SO4) were compared with the RPM-simulated values. It should be noted here that two species (NOx and SO2) are primary pollutants and the other two species (ozone and H2SO4) are secondary pollutants. In addition, NOx and SO2 are the main precursors of ozone and H2SO4, respectively.

⎛ z 0′ ⎞ p σz′0 = σz 0⎜ ⎟ ⎝ z0 ⎠

(3)

p = 0.53x−0.22

(4)

where z0 is from Briggs’s experiment (0.006 m); z′0 denotes the time-varying surface roughness over the plume-developing areas obtained from WRF output (m; refer to Figure 2f); σ′z0 and σz0 the corrected and Briggs’s vertical dispersions parameters (m); x the plume-travel distance (m); and p the exponent of surface roughness and distance. In order to correct the averaging time, the following formula was applied:41 ⎛ t ′ ⎞q σt′ = σt ⎜ ⎟ ⎝t⎠

3. RESULTS AND DISCUSSION Similar to large-scale Eulerian grid-based model simulations, Lagrangian RPM is also driven by meteorological information, primary pollutant emissions, and initial-boundary (IC and BC) conditions. Preparation of the emissions and IC-BC conditions to run the RPM was explained previously. In this section, how to determine the meteorological factors to drive the GIST-RPM is first discussed, and subsequently the performance of the GISTRPM is evaluated with TexAQS II 2006 aircraft observations. The model evaluation is performed, along with some scientific discussion regarding power-plant plume analysis. The analysis is

(5)

where σ′t represents the corrected dispersion parameter (m); σt is the Briggs’s dispersion parameter (m); t′ the actual averaging time (min); t the averaging time from Briggs’s experiment (i.e., 30 min); and q the exponent of averaging time. The value of q is governed by the values of averaging time. q is typically 0.2 when the averaging time is between 3 min and 1 h, and 0.25−0.30 when the averaging time is between 1 and 100 h. The turbulence−dispersion parameters are important in the estimation of turbulent plume dispersion, since they determine E


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Figure 3. Aircraft-observed vs RPM-modeled concentrations of four species for Monticello power-plant plume: (a) NOx; (b) SO2; (c) O3; (d) H2SO4. Red-dashed lines represent the modeled concentrations of primary pollutants without the consideration of nonlinear photochemistry. Also, shown are both mean bias (MB) and root-mean-square error (RMSE).

their vertical distributions becoming flatter. This situation was also considered in the GIST-RPM simulations. 3.2. Evaluation of RPM Performance. As discussed previously, the RPM-simulated air pollutant mixing ratios were compared with the WP-3D-measured mixing ratios at the four aircraft-plume transects (I−IV) to evaluate the performance of the GIST-RPM. Figure 3 and SI Figure S1 show the comparison between the simulated and observed mixing ratios of major primary and secondary in-plume species for Monticello and Welsh plumes, respectively. The mixing ratios of NOx and SO2 continued to decrease as both atmospheric turbulent dispersion and photochemical transformation proceeded from transects I to IV. From the comparisons, it was found that, in general, the newly developed RPM produced relatively accurate mixing ratios compared with the aircraft-observed mixing ratios (0.61 ≤ R ≤ 0.92; 0.76 ≤ IOA ≤ 0.95). In addition, the mixing ratios of NOx and SO2 simulated without the consideration of in-plume photochemistry were also shown in Figure 3 and SI Figure S1 (see red-dashed lines). Obviously, the mixing ratios of ozone and H2SO4 cannot be generated from the “transport-only” RPM mode, since both are secondary chemicals. For example, the mixing ratios of NOx with and without in-plume photochemistry at the plume centerlines of Transect IV are 863 pptv and 1077 pptv for Monticello plume, and are 825 pptv and 682 pptv for Welsh plumes. Furthermore, the in-plume photochemistry can take account of the significant changes in the NOx composition (i.e., ratios of [NO] to [NO2]) along the plume travel times. This is also presented in SI Figure S2.

the plume widths. They are mainly a function of stability class, surface roughness, and averaging time. Historically, an averaging time ranging from 10 min to 1 h has been considered in various dispersion experiments.42−46 Since Briggs’s turbulent-dispersion formulas are averaged over a relatively long period of time (30 min), a shorter averaging time of 10 min was chosen for the current study. A short averaging time (say, less than 10 min) has been used for simulation of the dispersion of toxic or odorous materials, for instance, those released during chemical accidents.44,47 An averaging time of 10 min was chosen for the present study, partly due to the fact that the meteorological parameters (such as surface wind speed and incoming solar radiation) used in the determination of the atmospheric stability class were also based on a sampling time of 10 min. This choice of 10 min was made rather arbitrarily, nevertheless, it was believed to be more accurate for the comparison with transient plumemode observations made by an aircraft. In the developed RPM model, the averaging times can be easily switched from one to another in accordance with research purposes. The concentration changes by turbulent dispersion are estimated using dilution factor. The definition of dilution factor is the concentration ratio between two time steps. It was assumed that the concentrations of primary pollutants (e.g., NOx, SO2) are distributed along the lateral and vertical cross-section with Gaussian shapes during the plume travel time. The concentrations of secondary pollutants (e.g., O3, H2SO4) are determined by both plume photochemistry and dilution. Details are discussed in Song et al.9 and Kim et al.10 In addition, when the vertical plume edges reach mixing height and/or land surfaces, the pollutants are considered to be completely reflected, with F


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Figure 4. Distributions of OH radical concentrations and instantaneous NOx lifetimes across the transects (I−IV) with plume-aging time for (a) Monticello and (b) Welsh plumes.

However, as shown in Figure 3 and SI Figure S1, the GISTRPM simulated the mixing ratios of NOx and SO2 reasonably well, thus, the uncertainties from the dispersion parameters are likely negligible. It is well-known that many petrochemical industries and factories are located in the state of Texas. Both NOx and NMVOC emissions from these industries and factories can affect the mixing ratios of ozone. In addition, biogenic VOC emissions can also influence the mixing ratios of in-plume ozone. According to geographical information, the majority of the land around transect IV was covered with short trees (refer to Figure 2f). The possible reason should be investigated further with more plume cases in the future. In the case of H2SO4 in Figure 3d and SI Figure S1d, the model-predicted results agree reasonably well with the observations (0.61 ≤ R ≤ 0.68; 2.39 ≤ RMSE ≤ 2.75, as shown in SI Table S4). Again, it should be noted that γH2SO4 of 0.79 related to gaseous H2SO4 mixing ratios inside plumes was used in the model simulation. The range of γH2SO4 measured by Pöschl et al.48 is also between 0.43 and 1.0. As discussed previously, the comparison between modeled and observed mixing ratios of the four species shows good correlation and agreement, with correlation coefficients (Rs) and indices of agreement (IOAs) ranging from 0.61 to 0.92 and from 0.76 to 0.95, respectively. Further statistical investigations were

In the early plume-development stage, the mixing ratios of ozone were depleted around the centerlines of the Monticello and Welsh plumes (see Figure 3c-1 and SI Figure S1c-1). These depletions of ozone mixing ratios are due to the following reaction, (R1): NO + O3 → NO2 + O2

(R1)

However, as the two plumes developed further, ozone was actually produced due to the conversion of NO2 to ozone (see R2 and R3): NO2 + hv → O(3P) + NO

(R2)

M

O(3P) + O2 → O3

(R3)

Due to these two reactions, (R2) and (R3), NO2 is referred to as a precursor of ozone. Alternatively, it can be said that ozone is stored temporarily in the form of NO2 due to (R1)-(R3). Although the comparison between the observed and modeled ozone concentrations showed reasonable agreement (0.76 ≤ R ≤ 0.77; 3.51 ≤ RMSE ≤ 3.84, see SI Table S4), there were relatively large discrepancies in transect IV for the Monticello and Welsh plumes. There are three possible causes for these discrepancies: (i) uncertainties in turbulent-dispersion parameters; (ii) interruption of other industrial plumes; and (iii) influences of biogenic emission sources during the aerial observations. G


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again confirmed in the power-plant plume simulations (refer to SI Figure S3c). Figure 4 presents the variation in OH radical mixing ratios and τiNOx during the course of the development of the power-plant plumes. Since the chemical NOx loss is mainly governed by the formation reaction of HNO3 during the daytime (i.e., NO2 + OH + M, here M denotes the third body), the distributions of τiNOx (denoted by red lines) at the cross sections of the plumes show reverse shapes to the distribution of OH radical concentrations (denoted by blue lines). The OH radical concentrations are a major factor in the determination of NOx chemical lifetimes. The i high [OH] reduces NOx chemical lifetimes. The estimated τNO x for the Monticello and Welsh plumes ranges from 4.38 to 15.39 h and from 3.25 to 14.23 h, corresponding to [OH] of 1.05 × 107 to 2.28 × 106 molecules cm−3, and from 1.33 × 107 to 2.44 × 106 molecules cm−3, respectively. In the early stages of the two i and [OH] plumes (e.g., at transect I), the distributions of τNO x had a non-Gaussian shape due to the nonlinear characteristics of i the in-plume composition. In addition, τNO around the plume x

carried out for the four species, using four statistical metrics. For absolute differences, the root-mean square error (RMSE) and mean bias (MB) were analyzed, and for relative differences, the mean normalized gross error (MNGE) and mean normalized bias (MNB) were examined. The results from the statistical analyses are summarized in SI Table S4. The NOAA WP-3D aircraft also measured the mixing ratios of HNO3, HCHO, PAN, and other atmospheric species. However, with the exception of the above-mentioned four species, the time-resolution of the measurement is too large to provide a sufficient number of data inside the plumes, or due to several technical difficulties, the data is too sparse or has many missing values inside the plumes.13 Thus, mainly due to the paucity of inplume data, other species were excluded from the comparison analysis in Figure 3 and SI Figure S1. Despite the paucity of in-plume data, the concentration changes of HCHO, HNO3, N2O5, PAN, and HONO are presented with some observed mixing ratios in SI Figure S3. As mentioned previously, γHNO3 and γH2O5 of 0.05 and 0.017 were used, respectively. However, it appears that these values should be smaller, given that the observed HNO3 and N2O5 mixing ratios are larger than the simulated mixing ratios. The details should be further investigated. Also, it is shown that HONO mixing ratios built up near the stack (around the plume-travel time of 10 min). This is certainly related to high NO mixing ratios and concurrent build-ups of OH mixing ratios, but this appears to be almost null formation, since HONO is again photodissociated back into NO and OH. 3.3. NOx Photochemical Lifetimes. In this section, the value that may be able to represent the degree of photochemical activity of a plume is introduced. This value is the NOx chemical lifetimes (τNOx) for the two plumes (Monticello and Welsh). The purpose of τNOx analysis is 2-fold: (1) to demonstrate the applicability of the newly developed GIST-RPM, and (2) to further evaluate the performance of the GIST-RPM in terms of τNOx via comparison of the modeled τNOx with observed τNOx. Quantitatively estimating the NOx chemical lifetimes is important in the examination of the chemical evolution of atmospheric NOx, and is also important in the interpretation and understanding of NOx-related chemistry in the power-plant plumes. Several previous studies have investigated the NOx chemical lifetimes in order to assess the photochemical activities in power-plant plumes.49−54 First, instantaneous chemical NOx loss rates (LiNOx) are defined by eq 6, and subsequently τNOx is estimated via eq 7:

centerline was reduced, due to the increased [OH] at the plume centerline, compared with the background situation (at the edges i represents the chemical NOx lifetime of the plumes). Since τNO x inside the power-plant plume only at the time of interest, the concept of “equivalent NOx lifetime (τeq NOx)” is introduced to estimate the chemical NOx lifetime throughout the entire volume of the two power-plant plumes:10,49 i τNO = x

eq τNO = x

(6)

[NO] + [NO2 ] i L NO x

2σ

x

x

x

(8)

Δt

∫t

t +Δt

1 i τNO x

dt

(9)

i l where τNO represents the cross-sectional averaged τNO ; f NOx x x (y) and f NOx (z) the dimensionless frequency functions for the NOx distribution over the power-plant plume cross-section in lateral (y) and vertical (z) directions, and Δt the plume-travel eq period of interest. τeq NOx was estimated from transects I to IV. τNOx of 6.98 and 6.88 h were finally estimated for the Monticello and Welsh plumes, respectively. These values are a factor of ∼2.40 shorter than the background (out-plume) NOx chemical lifetime of ∼16.61 h, which indicates that in-plume photochemistry is much more active than out-plume photochemistry. For the sake of further indirect comparison, the equivalent NOx chemical lifetimes estimated for the two plumes were compared with other chemical lifetimes estimated from powerplant plume measurements and modeling. The NOx chemical lifetimes of power-plant plumes were estimated based on aircraft observations,49,50,52,55 2-D Lagrangian modeling,51 3-D photochemical modeling,54 and satellite observations.56 The estimates are summarized in SI Table S5. In general, such NOx chemical lifetimes are comparable to those estimated in the present study (τeq NOx = ∼ 6.9 h), although they were measured in different plume locations and situations. The short lifetimes of NOx estimated from 3-D photochemical modeling may be due to instantaneous dilution of NOx inside the 3-D Eulerian grids. The instantaneous dilution tends to cause the high levels of OH radicals, which shorten the lifetimes of NOx.

i L NO = k1[OH][NO2 ] + k mt,NO3[NO3] + 2k mt,N2O5[N2O5] x

i τNO = x

2σ

i (y , z)dfNO (y)dfNO (z) ∫−2σ ∫−2σ τNO

(7)

where, k1 represents the thermal reaction coefficient (s−1); kmt,NO3 and kmt,N2O5 the mass transfer coefficients (s−1); and τiNOx the instantaneous chemical NOx lifetimes (s). In particular, both NO3 and N2O5 radicals are known to be “nighttime” species, thus, the heterogeneous reactions of both species (i.e., the second and the third terms at the right-hand side of eq 6) are thought to be nonsignificant during the “daytime”. However, it has also been reported that in the highly concentrated plumes, the mixing ratios of N2O5 can build up even during the daytime. This can be H


Article


Figure 5. An example of comprehensive environmental impact assessment for Monticello plume using reactive plume model developed in this study. Surface concentrations of the following species are reported: (a) NOx; (b) SO2; (c) O3; (d) H2SO4; (e) HCHO; (f) HNO3; (g) N2O5; (h) PAN; and (i) HONO. The calculations were made using “real emission rates” of pollutants from Monticello power-plant.

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of South Korea, in May and June, 2016.57 After the validation, the GIST-RPM will be applied to evaluate the environmental impacts of the emissions from such large point-sources in South Korea. The GIST-RPM may be able to find many areas where it can be effectively utilized, however, there are also several limitations to the current version of GIST-RPM. For instance, the current version of RPM cannot consider complex terrain effects, and the GIST-RPM should be further improved in order to take such complex situations into account. These improvements will be carried out in the future.

OUTLOOK The GIST-RPM developed in the present study is expected to be utilized in policy-making as well as in a more comprehensive environmental impact assessment (EIA). As mentioned previously, the impact of emissions from a large-scale point source on the surrounding atmosphere and surface areas can be assessed using the GIST-RPM in a more accurate and comprehensive way, with the consideration of both turbulent dispersion and photochemical reactions. Figure 5 is an example that demonstrates the spatial distributions of primary and secondary pollutants on the downwind areas of Monticello power-plant. Three villages in which more than 1000 populations are living are also denoted. Figure 5 shows what levels of air pollutants people and ecosystem downwind of the plume can be exposed to. With different wind directions, people in the villages can be exposed to the high levels of ozone, HCHO, and PAN. In future, the GIST-RPM will be further validated with powerplant and petrochemical plant-plume data measured during the NASA DC8 aircraft campaign carried out over the western coast

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.6b03919. Additional information is provided as noted in the text (PDF) I


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AUTHOR INFORMATION

Corresponding Author

*Phone: +82-62-715-3276; fax: +82-62-715-3404; e-mail: [email protected]. ORCID

Yong H. Kim: 0000-0001-6297-2727 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was supported by the GEMS program of the Ministry of Environment, South Korea, as part of the Eco Innovation Program of KEITI (2012000160004). This work was also funded by the Korea Meteorological Administration Research and Development Program (KMIPA2015-5010), and by the GIST Research Institute (GRI) in 2016. We obtained all the TexAQS II 2006 airborne data sets from the official NOAA data archive at http://esrl.noaa.gov/csd/groups/csd7/ measurements/2006TexAQS/P3/DataDownload/index.php.

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Development of a Reactive Plume Model for the Consideration of

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