Article pubs.acs.org/est
Statistical Approaches for Identifying Air Pollutant Mixtures Associated with Aircraft Departures at Los Angeles International Airport David M. Diez,†,* Francesca Dominici,† Darcy Zarubiak,‡ and Jonathan I. Levy§ †
Harvard School of Public Health, 655 Huntington Ave, SPH2, fourth Floor, Boston, Massachusetts 02115, United States LeighFisher Management Consultants § Boston University School of Public Health ‡
S Supporting Information *
ABSTRACT: Aircraft departures emit multiple pollutants common to other near-airport sources, making it challenging to determine relative source contributions. While there may not be unique tracers of aircraft emissions, examination of multipollutant concentration patterns in combination with flight activity can facilitate source attribution. In this study, we examine concentrations of continuously monitored air pollutants measured in 2008 near a departure runway at Los Angeles International Airport (LAX), considering single-pollutant associations with landing and takeoff (LTO) of the aircraft (LTO activity, weighted by LTO cycle fuel burn), as well as multipollutant predictors of binary LTO activity. In the singlepollutant analyses, one-minute average concentrations of carbon monoxide, carbon dioxide, nitrogen oxides, and sulfur dioxide are positively associated with fuel burn-weighted departures on the runway proximate to the monitor, whereas ozone is negatively associated with fuel burn-weighted departures. In analyses in which the flight departure is predicted by pollutant concentrations, carbon dioxide and nitrogen oxides are the best individual predictors, but including all five pollutants greatly increases the power of prediction compared to single-pollutant models. Our results demonstrate that air pollution impacts from aircraft departures can be isolated using time-resolved monitoring data, and that combinations of simultaneously measured pollutants can best identify contributions from flight activity.
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INTRODUCTION Multiple air pollution sources at airports can influence air quality in surrounding neighborhoods, including aircraft, ground support equipment, and traffic induced by the presence of the airport. Monitoring studies have used a range of methods to distinguish source contributions, leveraging potential differences in emission patterns and pollutant dispersion. For example, land use regression was used to associate nitrogen dioxide (NO2) concentrations with proximity to major roadways and the airport terminal,1 though with limited ability to ascertain specific airport sources given one-week average concentrations. Other studies leveraged wind speed and direction data to draw conclusions about flight activity and other source contributions to nitrogen oxides (NOx),2,3 carbon monoxide (CO),3 sulfur dioxide (SO2),4 or elemental carbon (EC),5 taking advantage of the fact that runways are aligned with prevailing winds and aircraft emit buoyant plumes that typically increase in concentration with wind speed, unlike mobile sources. While these studies can be used to discern contributions from various source categories over background, it is challenging to generalize the findings to other settings or to provide specific insight about how different stages of the © 2012 American Chemical Society
landing and takeoff (LTO) cycle contribute to concentrations. Regression analyses of ambient monitoring data against flight activity covariates, simultaneously accounting for meteorological and other time-varying covariates, provide an approach to better ascertain source contributions. Studies using this approach5,6 have demonstrated significant associations between departures and measured concentrations in close proximity to runways, with a smaller effect of arrivals. However, they have only considered a small number of pollutants evaluated individually, and the results are challenging to generalize. Emissions characterization studies on airport grounds are designed to develop emission factors that can be generalized and potentially applied within atmospheric dispersion models. These studies have provided insight about emissions across the LTO cycle of a number of pollutants, including NOx, CO, and carbon dioxide (CO2),7,8 illustrating how NOx emissions per unit fuel burned tend to peak during takeoff, while CO Received: Revised: Accepted: Published: 8229
February 21, 2012 June 12, 2012 June 25, 2012 June 25, 2012 dx.doi.org/10.1021/es3007172 | Environ. Sci. Technol. 2012, 46, 8229−8235
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in statistical analyses. Candidate pollutants included CO, CO2, SO2, NOx, NO2, nitric oxide (NO), ozone (O3), methane (CH4), and nonmethane hydrocarbons (NMHC). The NMHC concentrations were generally below the instrument limit of detection, and the CH4 concentrations varied little across the data set and would not have a significant expected contribution from aircraft. Additionally, it was found that the NO, NO2, and NOx measurements were highly correlated. For these reasons, NO, NO2, CH4, and NMHC were omitted from the analyses. The SR site is shown in Supporting Information (SI) Figure S1 and is located adjacent to the blast fence near Runway 25R, approximately 100 m east of the end of the runway and 900 m west (upwind) of the nearest roadway. The instruments used for sampling each pollutant and the number of valid oneminute average measurements at the SR site are provided in SI Table S1 for each pollutant. The amount of data available varied by pollutant from 16 554 to 28 462 min. Measurements were available for all pollutants of interest during July 16−28, 2008, and we focus our analysis on this subset of data. Meteorological and Flight Activity Data. One-minute average meteorological data were collected from an Automated Surface Observing Systems weather station located on the airport’s south airfield. The airport authority provided LTO data by runway, including a time stamp to the nearest second and the aircraft type. Because different aircraft will have different emission rates, for each flight, we constructed a proxy variable that represents the LTO cycle fuel consumption rates given the types and numbers of the engine of each flight.13 While emissions of different pollutants will vary across the LTO cycle and in their associations with fuel burn,7 this approach allowed us to reasonably characterize large versus small aircraft. The fuel burn-weighted flight activity covariates were aggregated to oneminute resolution to be commensurate with the air pollution monitoring data. Flight activity data were collected for eight runways (6 L, 6R, 7 L, 7R, 24 L, 24R, 25 L, 25R). However, there were few departures other than on 24 L and 25R, and few arrivals other than on 24R and 25 L. We therefore considered only these four runway/flight type combinations as candidate predictors. Data Processing. We conducted single-pollutant analyses to determine the influence of flight activity on individual air pollutants at the SR site, followed by multipollutant analyses to determine the combination of pollutants and lag times that best predict presence/absence of flight activity. Prior to the analyses, we performed data processing to account for the influence of atmospheric transport and other diurnal factors on air pollution and to reduce skew. The processing was performed in two stages: (1) apply a log-transform of the air pollutant concentrations; (2) and then remove a moving average of the data. More specifically, let Wp,t represent the raw measurement at time t for air pollutant p. Then we define the log-transformed concentration as Zp,t = log Wp,t. After removing missing data, there were a small number of zero measurements of O3 (0.03% of the data set) and CO (3.6%), with a more significant number of zero measurements for SO2 (31%), and none for NOx or CO2; in such cases, Zp,t does not exist. Examination of data files and field logs confirmed that these measurements did not correspond with equipment problems or missing observations. Because it is unlikely that concentrations were truly zero, all zero concentrations were set to half of the minimum nonzero measurement of the corresponding pollutant prior to the logtransformation. Thus, Zp,t was defined for all nonmissing Wp,t.
emissions per unit fuel burned tend to peak during idle. In general, studies have shown a potentially large pollution signal associated with the high-thrust conditions found during takeoffs in particular.9 However, there is a gap between these studies and ambient monitoring studies, with relatively little quantification of relative source contributions to concentrations in the near field. In spite of the challenges in distinguishing contributions from aircraft versus contributions from other sources, the unique source characteristics coupled with statistical approaches to analyze continuous monitoring data provide an opportunity to characterize the influence of flight activity on local air quality in a manner that can be generalized. First, because flight departures and arrivals are short-term events that occur intermittently, regression analyses of continuous monitoring data against flight activity are not likely to be confounded by other major sources, especially when considering short-term (i.e., 1 min) average concentrations at monitors close to runways. Second, while there may not be a single pollutant marker of aircraft LTO activity, consideration of many air pollutants simultaneously could allow one to leverage the correlation structure among pollutants to more uniquely identify aviation or other specific sources. This strategy has been used in numerous prior studies applying factor analysis and related techniques,10−12 though rarely in the context of continuous monitoring data or in relation to aviation emissions. In addition, the statistical techniques previously used in this context did not provide specific insight about the combination of pollutants that would best serve as a tracer of specific source activity. Analyses of both individual and multipollutant models would be useful in evaluating which pollutants are most important in tracing flight activity. In this study, we evaluate a suite of continuously monitored air pollutants collected in close proximity to a departure runway at Los Angeles International Airport (LAX), where the aircraft signal would be expected to be large relative to other monitoring locations and where the high-velocity plume likely implies that ambient meteorological conditions are less relevant as predictors. First, we apply regression-modeling techniques to estimate the association between individual pollutants and realtime flight activity data weighted by an indicator of fuel burn during the LTO cycle. In the regression modeling we also include a distribution of lag terms to isolate the timing of concentration increases in relation to departures, also considering arrivals to evaluate their relative influence at this site. Second, we also fit multipollutant models using multivariable air pollution data as predictors of binary flight activity, using statistical techniques that allow us to determine whether air pollution data improve our ability to predict whether a flight has recently departed. The combination of these models allows us to isolate the contributions due to engine emissions, especially during takeoff, from other sources near airports.
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MATERIALS AND METHODS Air Pollution Monitoring. Numerous pollutants were continuously monitored at five sites near the major runways of LAX during the summer of 2008 as part of the LAX Air Quality and Source Apportionment Study Demonstration Project (http://www.lawa.org/welcome_LAX.aspx?id=1066). In this study, we focus on multiple air pollutants measured concurrently at one-minute resolution from July 7−28 at the South Runway (SR) site, as only this site had a sufficient number of valid measurements of all key pollutants to be used 8230
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smooth function (natural cubic spline) of daily time with 24 degrees of freedom to account for the changing level of flight activity each day (e.g., low activity late at night). The value Z′p,t+i represents the processed data for pollutant p at time t + i. The double summation represents 105 variables: CO, CO2, NOx, O3, and SO2, each with 21 lagged versions (as previously, i ranges from −10 to +10). Pollutant variables were selected using backward elimination with a p-value cutoff of 0.01. However, a standard backward elimination approach may result in retaining lags k and k + 2 but not k + 1, which would complicate the interpretability of the model. For this reason − and because the single pollutant models indicate a single pollutant peak following flight activity (see Results section), at each stage in the variable elimination process, only the largest and smallest lags from each pollutant were considered for elimination (e.g., if lags −7 to 3 for CO are included in the model, then of the CO lagged variables, only lags −7 and 3 will be eligible for elimination in the next step). We compared the model described above to the following models that include (1) only daily trends and no pollution variables; (2) daily trend and lagged values of a single pollutant; and (3) and the full model above with the addition of a natural cubic spline with six degrees of freedom for wind speed. To help interpret our models with respect to their ability to discern minutes with and without flight activity, we constructed summary plots of the predicted and empirical probabilities of flight activity. First, we calculate the predictive probability of at least one flight departure at each minute (p̂t) by using the GLM model defined in eq 2. Overall we estimated roughly 16 000 probabilities corresponding to each minute of observation. Second, we binned these probabilities into 20 groups defined by the following intervals: [0, 0.05), [0.05, 0.10), ..., [0.90, 0.95), [0.95, 1.00]. For each interval j we calculate the (1) average predictive probability for the group (xj) (e.g., the group [0.05, 0.10) had an average x[0.05,0.10) = 0.0747); (2) number of times t where p̂t falls in the interval j (nj) (e.g., there were n[0.05,0.10) = 2659 observations where p̂t was in the range 0.05−0.10); (3) number of times Yt = 1, that is, the number of times there has been at least one departure (rj) (e.g., there were r[0.05, 0.10) = 237 departures in the 2659 times where p̂t was in the range [0.05, 0.10).); 4) sample proportion of times in the group that had at least one flight (yj), defined as rj/nj (e.g., a fraction y[0.05,0.10) = 237/2659 = 0.0891 of times where p̂t was in the range [0.05, 0.10) actually had flights). The point for group j was plotted at the location (xj, yj), where the size of the point represents the number of observations in that bin. In a reasonable model, we would expect to observe xj, ≈ yj for all groups j. Confidence intervals for the actual probability of a flight in each group were computed using the bootstrap to identify 95% confidence intervals for each group,14 and these intervals were included in the plot. We defined a power-of-prediction measure to compare the predictive ability of the different models. For each GLM, we constructed the predicted probability of a flight at each time unit, p̂t (modeled). Then we define the power-of-prediction measure as
Following the log-transformation, a 120 min moving average of the log-transformed concentrations was subtracted from the Zp,t time series: Z′ p , t = Zp , t − MA120(Zp , t )
This process reduces the influence of factors that may contribute to diurnal concentration patterns given atmospheric transport effects. All analyses were performed using the time series Z′p,t. Single-Pollutant Analysis. The first analysis was a singlepollutant model, where each pollutant was treated as a single outcome of lagged versions of the fuel burn-weighted flight activity (XDt−i for departing planes, XAt−i for arriving planes): i =−10
Z′ p , t = βp +
∑
i =−10
αpD, iX tD− i +
∑
10
αpA, iX tA− i + εp , t
10
(1)
Note that each departing (arriving) flight event contributes to XDt−i (XAt−i) for exactly one time unit: the time of takeoff (landing). This model was fit separately for each pollutant p and also separately for Runways 24R/L and 25R/L. Lagged versions of the flight activity variable, Xt−i, (superscript D for departure and A for arrival) were created for all times and for i = −10,−9,..., 9, 10, where Xt−i represents the flight activity variable at time t − i. The time series Z′p,t represents the processed pollution data for time t and pollutant p, and βp is the model intercept. Each coefficient αp,t describes the relationship between a fuel burn-weighted flight activity variable i minutes relative to the time of the measured pollution level, and εp,t represents an error term with mean zero and constant variance within a pollutant p. We are interested in identifying the subset of pollutants and time periods for which flight activity significantly predicted pollution concentrations. Result of these analyses can inform source attribution, through the application of regression models to predict concentrations with and without flight activity, and inform the structure of the subsequent multipollutant models. Multipollutant Analysis. After having identified the pollutants that are most significantly associated with flight activity, we conducted a second analysis to determine the multivariable combination of air pollutants that best predicted the presence/absence of flight activity. This modeling treats flight activity as the outcome variable and air pollution as the predictor. While this inverts the cause-effect relationship, it allows us to determine a “tracer” of flight activity, whether single-pollutant or multipollutant. Based on our single-pollutant model findings described below, we focused on departures from Runway 25R, adjacent to the SR monitoring site. A generalized linear model (GLM) was constructed using the outcome variable Yt, which describes whether there was one or more takeoff flights (Yt = 1) or no takeoff flights (Yt = 0) at time t in minutes. Lagged variables of the pollutants Z′p,t were created in the same way as for the single-pollutant models. The model is described below: Yt ∼ Bernoulli(Pt ) ⎛ P ⎞ log⎜ t ⎟ = β + f (t ) + ⎝ 1 − Pt ⎠
i =−10
∑ ∑ p
10
αp , iZ′ p , t + i (2)
where Bernoulli(Pt) represents the Bernoulli distribution, Pt represents the probability that at least one takeoff flight occurred on Runway 25R at time t, and f(t) represents a
1 V= n 8231
t=1
⎛
∑ ⎜⎝pt̂ n
−
y . ⎞2 ⎟ n⎠
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In this equation, y represents the total number of minutes with at least one flight (observed), n represents the total number of minutes (observed), and (y)/(n) represents the fraction of times where at least one flight occurred. When the mean of the p̂t equals (y)/(n), then this measure is the variance of the p̂t. For a model with no predictive power, all weight would occur at the average probability of a success at any time point, (y)/ (n), resulting in V = 0. A model with perfect prediction power would predict a flight with probability 1 for a fraction (y)/(n) of the times, and it would predict a probability zero for a flight for a fraction (n−y)/(n) of the times, resulting in a maximum measure of V = p(1 − p) where p = (y)/(n). Sample plots for the models with no predictive power and perfect predictive power are shown in SI Figure S2. The plot described above serves as a tool to assess whether these probability predictions are accurate. Sensitivity Analysis. A sensitivity analysis was performed to evaluate the choice of data processing and model specifications in the multipollutant analysis. Three parameters were under study: the width of the moving average used in the data processing, the number of lagged variables included in the model prior to variable selection, and the number of degrees of freedom included in the spline f(t) that accounted for daily trends in flight activity. The first part of the analysis varied the moving average width over 61, 121, 241, and 481 min under two lagged variable settings: −10 to 10 (as in the primary run) and −20 to 20. The default degrees of freedom for f(t) of 24 was used in this first set of the analysis. In the second part of the sensitivity analysis, we fit ten models, where the number of degrees of freedom of f(t) was varied (6, 12, 24, 36, and 48) and the set of lags was varied between the default setting (−10 to 10) and an expanded-lag setting (−20 to 20). In the second sensitivity analysis, the moving average in the data processing step was fixed at a width of 121, as in the main analysis.
Figure 1. Estimated change in log-transformed and detrended pollutant concentrations (standardized using the average standard error associated with a pollutant’s estimates) associated with fuel-burn weighted departures, considering associations ranging from 10 min before to 10 min after the activity.
included many lagged variables that present an evident negative association. For the pollutants (CO, CO2, NOx, SO2), coefficients indicate a slight increase of pollution concentrations prior to departure, which may be a result of idling, taxiing, or increased thrust prior to takeoff. Because all five pollutants displayed significant associations (positive or negative) with departures on Runway 25R, all were considered candidates for the multipollutant model. The GLM predicting the log-odds of a departure on Runway 25R based on air pollution and daily trends in flight activity (see eq 2) was fit using backward-elimination for pollutant variables. All pollution predictors remained in the model following variable selection, however, only the following lags remained for each variable: CO (lags 0−1), CO2 (0−3), NOx (2−10), O3 (−1− 4), and SO2 (1−2). For instance, only the processed CO measurements at times t and t − 1 would be useful in predicting whether there was a flight at time t − 1. In this multipollutant model, the individual coefficients in the model were difficult to interpret because of collinear predictor variables. Summary plots of the predicted and empirical probabilities of flight activity are shown for three models in Figure 2. Panel (i) shows the results when no pollution data was incorporated into the model for calculating the model probability at each observed time point, panel (ii) shows the results for the best single-pollutant GLM (NOx), and panel (iii) shows results for the model including all five pollutants. In each of the models, the groups closely follow the y = x line, indicating the prediction probabilities are reasonable across the spectrum of predicted probabilities. On average, there was at least one departure every 4.14 min; a model with no predictive power would group all observations in the [0.20, 0.25) predictive probability group. A perfect model would predict flights with 100% accuracy, correctly identifying 1/4.14 of the minutes to include a flight and the remaining 3.14/4.14 min to not include a flight; such a plot would include only two groups with positive mass, and they would be located at 0 and 1 (right panel in SI Figure S2). As more pollutants are added into the model, a transition is observedas one would expectfrom models with lower predictive power to those with more predictive power. The models can also be compared using the power-ofprediction measure. Table 1 includes three measures: the
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RESULTS Measurements over the entire study, which spanned July 7−28, are summarized for each pollutant in SI Table S1. These summary statistics are representative of graphical exploration of the data. SI Figure S3 shows the time series data for CO, CO2, NOx, O3, and SO2 and departure data for the morning of July 26; even over the limited time window, a relationship between departures and each pollutant is evident. The regression models defined in eq 1 were constructed for predicting pollution levels based on lagged fuel-weighted flight activity variables, separately for each pollutant. The coefficients of the lagged variable were divided by their standard errors and plotted in Figure 1 for each of the pollutants, focusing on departures for Runway 25R. Associations between arrivals and pollution measurements were weak in all single-pollutant models. Results for arrival data are therefore not presented, and arrivals were not considered in the multipollutant models. The estimated regression coefficients measure the estimated change in log-transformed and detrended pollutant concentrations Z′p,t associated with a one unit change in our flight activity proxy variable (the sum of the LTO cycle fuel burn, measured in kg, across all departing aircraft during a given minute), considering associations ranging from 10 min before to 10 min after the activity. The coefficients for departures peak at either i = 1 or i = 2 for each pollutant, indicating these pollutants peak at lag of 1−2 min following a flight departure on Runway 25R (or reach minimum levels for O3). Four of the five pollutants (CO, CO2, NOx, SO2) are positively associated with emissions, while O3 8232
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Figure 2. Summary of generalized linear model results for three GLMs, where the log-odds of a flight was predicted based on pollution data and temporal trends. Plot (i) represents the model that included no pollutants; plot (ii) represents a single pollutant model that utilized NOx; plot (iii) represents the multipollutant model. In each group, represented by a point, the empirical probability of a flight occurring in that group was regressed against the average predicted probability of a flight in the group. The size of each group is described by the size of its point, and confidence intervals for the sample proportion are also shown.
in the width of the moving average window in the data processing step, the number of lags in the models (prior to variable selection), and the degrees of freedom in the spline f(t) that accounted for daily trends in flight activity. We found that the power-of-prediction measure V changed little across these model variations, ranging from 31.8% to 34.1% of the variation explained by the model, where only two of the 16 sensitivity models had measures below 33.1%. The main model presented above had a measure of 33.5%. There were larger differences across which variables remained in the model following variable selection. SI Figure S4 shows the intervals of lagged pollutant variables remaining for each pollutant in the model at the end of each sensitivity analysis. The dotted red lines show the lags covered by the main model presented above. In general, the lags range widely from one run to another, and some sensitivity analyses included a large set of lagged variables. (Recall that only the largest or smallest lags are eligible for stepwise elimination, so statistically
original, a second that is standardized using the measure from the model that corrected for daily trends but included no pollutants, and a third that describes the amount of variance in the response that is explained by the model. These results indicate an increase of 1.57−3.09 times more predictive power in a model that included any single pollutant versus the model that accounted for daily trends alone when relying on the measure V standardized by the model with no pollutants. The pollutants CO2 and NOx provided the greatest predictive power when considering individual pollutants. Including all pollutants resulted in predictive power 4.29 times greater than that of the model with no pollutants. The model with all pollutants and also including a spline for wind speed showed no improvement over the model with all pollutants but without wind speed. Several variations of the primary GLM were fit in the sensitivity analysis to test whether parameter settings had a meaningful impact on both the power-of-prediction and which variables were included in the model. We considered variations 8233
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a model that includes all pollutants provided a notable improvement in prediction. Stated another way, this indicates that the best “tracer” of flight activity at a monitor in close proximity to a departure runway is a multivariable combination of five pollutants. While this is more challenging to interpret than a single-pollutant tracer, our findings reinforce the value of multipollutant monitoring in a context where sources only contribute a fraction of measured concentrations of any individual pollutant. This conclusion was robust to numerous model assumptions; while which particular lag variables are included in a model is based on varying several data processing and model parameters, these differences had little impact on a model’s ability to predict flight activity. Multiple limitations in our analysis should be acknowledged. First, complete multivariable data were only available within a single season at one monitor, which was located on the airport grounds in close proximity to a departure runway. Measurements in other seasons and at other sites would be required to reach generalizable conclusions about source contributions to populations in proximity to LAX, especially pertaining to the influence of arrivals. That said, the SR site would be expected to have the strongest signal from departure activity across multiple pollutants, providing an ideal test case for our methods. In addition, the short distance to the runway implies that ambient meteorology is less important (as shown by the lack of explanatory power for wind speed), enhancing the generalizability of our findings. We also lacked highly time-resolved activity data for traffic and nonaircraft sources on the airport grounds (e.g., ground support equipment), which would have allowed for comparable attribution to sources other than departures. The fact that the wind direction during our sampling period was almost entirely from the west-southwest also made it less likely that concentrations at SR were significantly influenced by roadway traffic and other nonairport sources, further enhancing the signal-to-noise ratio for departure effects. In addition, although the five-pollutant model had significantly greater predictive power than single-pollutant models or models without flight activity, the model still has significant error in predicting time periods with and without flight activity (a standardized measure of 4.29, versus the ideal of 12.81). This is not unexpected, given that there are multiple sources of pollutant concentrations other than departures on a single runway, but it clearly indicates that even the multivariable combination does not represent a unique tracer. Beyond the multiple pollution sources, this is also attributable to our use of flight activity as a binary variable rather than using a fuel burnweighted covariate, in part because of challenges in interpreting the latter model in preliminary analyses. In addition, we did not consider some of the complex interactions between flight activity and meteorological factors, which may have enhanced the explanatory power, though at the expense of interpretability. Another concern with the five-pollutant model is that the individual regression coefficients are somewhat difficult to interpret, in part due to the high correlations among the laggedpollutant variables. There is therefore a trade-off between models with greater predictive power and models with greater transparency and generalizability, and the most appropriate model will depend on the application. The monitoring location and significant correlations among pollutants also made it challenging to use indicators such as pollutant ratios (e.g., NO2/NOx 15) for source attribution; preliminary analyses
Table 1. Goodness-of-Fit Measure of Generalized Linear Models Where Larger Numbers Correspond to Better Predictive Powera model spline for daily trend only CO O3 SO2 CO2 NOx all pollutants all pollutants, wind speed
measure (V)
measure standardized by spline only
measure of the explained variance
0.0143
1.00
7.8%
0.0224 0.0335 0.0369 0.0433 0.0442 0.0613 0.0610
1.57 2.34 2.58 3.03 3.09 4.29 4.27
12.2% 18.3% 20.1% 23.6% 24.1% 33.5% 33.3%
a
For these data, a perfect model would earn a measure of 0.1832 (100%). All models listed above also included a spline to account for daily trends in flight activity.
significant terms for both a small and a large lag in a single pollutant would result in a large interval of included lags.) However, as noted above, the variations in which variables were included ultimately had little impact on the power-of-prediction measure V. This flexibility indicates that a smaller reduced model would perform nearly as well as a model incorporating a wide set of lags, allowing for some flexibility in the precise size of the model.
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DISCUSSION Our single-pollutant and multipollutant analyses provide useful insights for which air pollutants are the best tracers of flight activity at a monitor in close proximity to a departure runway. The single-pollutant models showed that there is a single, prominent peak in CO, CO2, NOx, and SO2 and a prominent drop in O3 approximately 1−2 min following flight departures on an adjacent runway (25R), which is located approximately 100 m from the monitor. In contrast, there was little evidence of any association between pollution concentrations and arrival flights. This is consistent with studies showing elevated CO2 and NOx emissions or concentrations during takeoff relative to other phases of the LTO cycle,7,8 and studies arguing that SO2 is a reasonable tracer for flight activity given the relatively high sulfur content of jet fuel.4 Interestingly, CO emissions are generally considered to be lower during high-thrust conditions and elevated during idling or taxiing when combustion is incomplete.3,7 Our finding of elevated CO immediately following departure is likely explained in part by the location of our monitor, which was in close proximity to the departure runway. The differences in lags between pollutants may be indicative of differential responsiveness of monitors to changing concentrations (as the monitors used to measure NOx and SO2 report a greater delay in response), but could also be indicative of relative contributions from different stages of the LTO cycle. In general, the differences in lags reinforce the value of a lag modeling approach, both to account for possible betweeninstrument variability and to pinpoint the stage of the LTO cycle contributing to concentrations. The multipollutant models, where the levels of several pollutants at several lags were used to predict the probability of at least one flight departure, provided insights into which pollutants were most closely associated with departures. We found that CO2 and NOx are the best single predictors of flight activity, consistent with the literature described above, but that 8234
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indicated that NO2/NOx was so highly correlated with NO that it did not provide a unique signal. In spite of these limitations, our analysis provides evidence of a significant contribution of departure activity to concentrations of multiple air pollutants measured in close proximity to the runway. The single-pollutant models could be applied to predict incremental contributions of flight activity to near-field ambient concentrations, which could allow for comparisons with outputs from chemistry-transport models and ultimately provide key inputs to policy decisions and assessments of public health risk. Because of the orientation of runways toward prevailing winds at all airports, and the limited influence of ambient meteorology in close proximity to a departure runway, our methods and core findings would readily generalize to other airports. Our multipollutant modeling offers a novel approach by which the presence of emission sources can be ascertained, and these techniques can be extended to other data sets in which continuous source-related covariates are available. In combination, these analyses facilitate understanding the composition of air pollution attributable to individual source categories, which will help in identifying the impact flight activity has on public health.
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REFERENCES
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ASSOCIATED CONTENT
S Supporting Information *
Table S1 lists summary statistics for pollution measurements. Figure S1 shows a satellite view of the study area with a photo of the pollution monitor adjacent to the runway. Figure S2 shows plots for two predictive models at the extremes where the model has perfect predictive power and a second plot for a model with no predictive power. Figure S3 shows sample pollution data for one morning of the study. An overview of the quality assurance steps taken during the data collection process is reported. This material is available free of charge via the Internet at http://pubs.acs.org.
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Article
AUTHOR INFORMATION
Corresponding Author
*Phone: 507-254-2695; e-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This study was supported by the Federal Aviation Administration (FAA) through the Partnership for Air Transportation Noise and Emissions Reduction (PARTNER) under Cooperative Agreements No. 07-C-NE-HU and 09-C-NE-HU, the U.S. EPA (grant RD83479801), and the National Institute of Environmental Health Sciences (R01ES012054, ES019560). We thank Los Angeles World Airports (LAWA) for providing us with access to the demonstration project’s data, and we thank Gary Adamkiewicz and Hsiao-Hsien Leon Hsu for their help with data processing. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the FAA, LAWA, U.S. EPA, or the National Institute of Environmental Health Sciences of the National Institutes of Health. Further, U.S. EPA does not endorse the purchase of any commercial products or services mentioned in the publication. 8235
dx.doi.org/10.1021/es3007172 | Environ. Sci. Technol. 2012, 46, 8229−8235
