Evaluated Weather Research and Forecasting model inline with chemistry (WRF/Chem) simulations of the 2009 Crazy Mountain Complex wildfire in Interior Alaska served as a testbed for typical Alaska wildfire-smoke conditions. A virtual unmanned air vehicle (UAV) sampled temperatures, dewpoint temperatures, primary inert and reactive gases and particular matter of different sizes as well as secondary pollutants from the WRF/Chem results using different sampling patterns, altitudes and speeds to investigate the impact of the sampling design on obtained mean distributions. In this experimental design, the WRF/Chem data served as the “grand truth” to assess the mean distributions from sampling. During frontal passage, the obtained mean distributions were sensitive to the flight patterns, speeds and heights. For inert constituents mean distributions from sampling agreed with the “grand truth” within a factor of two at 1000 m. Mean distributions of gases involved in photochemistry differed among flight patterns except for ozone. The diurnal cycle of these gases’ concentrations led to overestimation (underestimation) of 20 h means in areas of high (low) concentrations as compared to the “grand truth.” The mean ozone distribution was sensitive to the speed of the virtual UAV. Particulate matter showed the strongest sensitivity to the flight patterns, especially during precipitation.
In recent years, unmanned air vehicles (UAVs) have attained increasing attention from environmental scientists as UAVs permit measurements in hazardous air space (e.g. over wildfires) and/or over difficult to access remote areas [
Over the last decades, communities in the boreal taiga have grown, and they are expected to grow further in the future [
Due to its continental location, Interior Alaska summers are dry and warm with calm winds. Most of its summer precipitation is from convection and thunderstorms [
In atmospheric sciences, UAVs have been deployed for measuring meteorological fields. A recent study [
In geology, some studies focused on volcanic heat and gas emissions [
In the above studies, the UAVs collected data over a small area and for phenomena that lasted comparatively short in time (e.g. formation of inversion, downdraft, gust front) and/or with the purpose of research. However, when the intent is to use data for air-quality advisory and to fly UAVs instead of installing and maintaining a monitoring network, it has to be examined whether UAVs can provide reliable mean spatial distributions of air-quality relevant quantities. Air-quality advisory namely relates to the National Ambient Air Quality Standard (NAAQS) that is defined for time ranges of 1 h to 24 h depending on the pollutant [
Our feasibility study examined whether UAVs could provide the spatial distributions of air-quality relevant information desired for air-quality advisories. To achieve our goal, we turned to numerical modeling and applied the analysis method by [
To investigate the impact of UAV flight patterns, speeds and altitudes on temporal mean spatial distributions derived from the sampled data, we ran WRF/Chem [
A modified version of the Grell-Dévényi cumulus ensemble scheme [
The processes in the ABL were parameterized using the Eta model Mellor-Yamada-Janjić schemes [
The Rapid Update Cycle land-surface model [
The gas-phase chemistry mechanism [
The Modal Aerosol Dynamics for Europe [
Emissions from biomass burning were created with the so-called PREP-CHEM-SRC emission processor [
Anthropogenic emissions were generated from the Emission Database for Global Atmospheric Research (EDGAR) emission inventory, which provides annual emissions of greenhouse and precursor gases on a 1˚ × 1˚ grid [
Biogenic emissions were calculated inline depending on land-use/cover following [
The model domain of interest covered the atmosphere over Interior Alaska centered at 65.57˚N, 145.9˚W with 110 ´ 100 grid-points of 4 km increment to 100 hPa (
The simulation started on August 3, 2009 0000 UTC with Alaska background concentrations. The chemical concentrations from the first two days of the simulation were excluded from the UAV sampling to allow for spin-up of the chemical fields. WRF/Chem was run in forecast mode for August 3 to 10, 2009. The meteorology was re-initialized every five days, while the chemical fields of the previous day served as initial data for the next.
To assess whether the WRF/Chem data represented a realistic dataset, we used data from 33 surface meteorological sites. The performance in predicting 2 m air temperatures, 2 m dewpoint temperatures, 10 m wind speeds and directions was quantified in terms of bias (simulated vs. observed), root-mean-square error (RMSE), standard deviation of error (SDE), and correlation-skill score (R).
Data of PM2.5 from three sites in the Fairbanks metropolitan area served to assess WRF/Chem’s performance in capturing the temporal evolution in this area and in case of 1-in-3-days data, the order of magnitude. We omitted calculation of spatio-temporal means for the following reasons: (a) All data were from the same area in the domain. (b) In this area, notable anthropogenic emissions occurred which was not the case anywhere else. (c) Too few data existed for a meaning full statistic. (d) Our study focused on wildfire smoke.
To assess the performance in predicting the height and vertical extension of the smoke plume, cross-sections of WRF/Chem predicted PM10 were compared qualitatively to Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) level 1B backscatter and depolarization data. We used the backscatter data to assess orientations, sizes, and shapes of aerosols through the linear depolarization ratio (LDR) following [
In the interpretation of the CALIPSO data, we used the same considerations and thresholds as [
The Crazy Mountain Complex fire of 2009 served as a testbed (
Following [
The area that the virtual Scan Eagle can cover within 20 h of sampling encompasses about 60 km × 60 km [
The authors are well aware that sampling frequency, i.e. the number of readings per time unit, differ among instruments for the various quantities mentioned above. For simplicity of our theoretical investigations, we assumed a frequency of one reading per second for all instruments mounted on the virtual UAV.
Since the WRF/Chem output data were recorded at one-hour intervals on a lattice with 4 km increment, the sampled quantities were interpolated in time and space between available WRF/Chem data. The field quantities were collected under consideration of the UAV’s speed and wind speed as a distance-time weighted mean between the values at the grid-cell in which the UAV was located and the nearest grid-cells along the flight path, and between the values of the past and next WRF/Chem recording at these grid-cells. A grid-cell was sampled for the duration that the UAV flew in the grid-cell. Consequently, more data were collected within a grid-cell where the UAV faced headwind than in a grid-cell with tailwind. For instance, at zero wind speed and cruise speed, for a grid-cell that is not located at the boundaries of the 60 km ´ 60 km sampling domain, about 130 data were collected on the outbound and inbound paths each, i.e. in total 260 data. As it is obvious from
Based on all the data sampled during the flight, a 20 h mean was calculated for the 60 km ´ 60 km sampling area. Furthermore, based on all data sampled within 20 h in a 4 km ´ 4 km grid-cell, a 20 h mean was calculated for each grid-cell to obtain the 20 h mean distribution within the sampling area. Such 20 h distributions were calculated for each day from August 5 to 10 for each sampled quantity.
We compared these distributions to the distribution of 20 h means calculated from the WRF/Chem data (“grand truth”). Differences in chemical field quantities were expressed in terms of normalized mean bias (NMB), and fractional mean bias (FB) in accord with [
In case of flying at maximum and stall speed, the UAV finished sampling the area in less and more than 20 h, respectively. In the plots, we showed the mean distributions obtained for sampling at different speeds no matter of how long it took the virtual UAV to cover the entire sampling area. Thus, be aware that plots show 20 h mean distributions for all sampling designs except the minimum and maximum speed scenarios. In the calculation of the statistics, we used sampled and “grand truth” 20 h mean distributions no matter whether the entire domain was already sampled (in case of stall speed) or sampling took less than 20 h (in case of maximum speed).
Capturing the meteorology is central to air-quality forecasts [
The obtained skill scores have similar magnitude as those of other WRF studies in high latitudes [
WRF/Chem captured the temporal behavior of 2 m dewpoint temperatures acceptably (
On average over all sites, WRF/Chem captured the temporal evolution of 10 m wind speed acceptably (
fire-related winds led to huge variability in observed wind speeds. During frontal passage, simulated and observed wind speeds were slightly offset, but agreed better in magnitude than during calm conditions. WRF/ Chem overestimated 10 m wind speeds during the stagnant conditions in the middle of the episode. Overall, these shortcomings yielded a positive bias of 0.1 m∙s−1 with RMSE and SDE of 6.2 m∙s−1 and low correlation.
The complex terrain caused the high variability of wind directions (
Simulated PM2.5 at the State Office building in Fairbanks showed similar general temporal behavior as the observations (
The episode was relatively cloudy (e.g.
Comparison of WRF/Chem vertical-integrated horizontal distributions of smoke extend with MODIS data (e.g.
Comparison of simulated and observed cloud distributions revealed that in mountainous terrain, WRF/Chem underestimated convection related to slope winds. This shortcoming was because WRF/Chem used the mean terrain height as representative for the terrain height within each grid-cell. Consequently, steep or small valleys were of subgrid-scale. Furthermore, WRF/Chem had difficulties capturing some of the cirrus seen in MODIS and/or CALIPSO data due to the coarse vertical grid resolution at these heights.
Above high-level clouds and in the upper troposphere, simulated PM10 concentrations showed a homogeneous distribution with marginal changes over time. On the contrary, the CALIPSO data suggested more heterogenous distributions. This discrepancy was due to the coarse resolution of WRF/Chem in the upper troposphere. For the same reason, WRF/Chem failed to simulate some of the cirrus clouds and the full vertical extent of high reaching convection (e.g.
The virtual sampling focused on the 60 km ´ 60 km area centered over the Crazy Mountain fires complex (
The 20 h mean distributions from sampling at three altitudes were able to capture the vertical gradients for air and dewpoint temperature (O3) that naturally increase (decrease) with height in the ABL. These distributions also captured pertubations of these general features when the perturbations occurred due to advection.
After the cold front passed on August 6, concentrations of particulate matter were quasi-uniform at all heights. Thereafter, heterogeneity increased as time progressed.
The following applied to all sampling heights, flight patterns, and flight speeds:
• For all constituents, 20 h mean concentration distributions differed stronger from the “grand truth” on the day with the frontal passage than on the days prior to or after the event.
• The 20 h mean distributions from sampling differed strongest among each other and from the “grand truth” for field quantities with a distinct diurnal course.
• The virtual UAV may sample in areas of extreme values. However, the 20 h means smoothed the distributions due to changes in wind directions and because the magnitudes of minima and maxima varied in time.
• The likelihood for sampling in the region of maximum values decreased as the spatial-temporal varibility in plume location increased.
• Correlation between sampled and “grand truth” means decreased, and errors (e.g. RMSE, NMB, FB) increased with increasing natural spatio-temporal heterogeneity of the field quantity.
Our discussion focused on an area that a Scan Eagle could cover within 20 h of sampling (
On average (August 3 to 10), temperatures differed about 1˚C∙100 m−1 between the three flight levels, i.e. the lower ABL was nearly dry-adiabatic except for August 6, the day of the cold front passage. The virtual sampling reflected this vertical behavior well in all cases.
Typically, distributions derived from sampled temperatures differed the strongest from the “grand truth” where the virtual UAV sampled at times around the daily maximum temperature. Sampled and “grand truth” mean temperatures agreed best on days without frontal activity in the sampling domain. On these days, the design of the flight patterns barely played a role for the differences between sampling-derived and “grand truth” distributions of 20 h mean temperatures.
Discepancies between sampling-derived and “grand truth” distributions of air temperatures at low altitude exceeded those at high altitude in the ABL (
On days without any changes in the synoptic conditions, mean differences between the temperature distributions obtained for the three flight patterns and the “grand truth” were less than 2˚C. However, local differences reached up to 7˚C. Typically, at the same height, sampled distributions of air temperatures differed least among each other in the middle, and largest along the boundaries of the sampling domain (
On August 6, the passage of a cold front led to huge discrepancies between sampling-derived distributions in areas affected by the front during the 20 h. Since the cold front sloped backward with height, notable discrepancies occurred over a smaller region at 1000 m height than at 500 m or 200 m (
The results from the default and third sampling patterns only slightly differed as both sampled the front at similar times in about the same location. Areas of small and large differences were rotated among the default and third patterns reflecting the relationship of the two sampling patterns (
flight patterns can affect the mean distributions derived therefrom. Consequently, one has to choose the sampling pattern that is most suitable for the research question/task for which the data are needed. This means, applying a virtual sampling using the forecast data can be of help in the flight planning and decision-making process.
According to the “grand truth”, episode-mean temperatures over the sampling domain were 20˚C ± 2.6˚C, 19.7˚C ± 2.7˚C, and 13.9˚C ± 2.5˚C at 200 m, 500 m and 1000 m height, respectively. On this spatio-temporal mean, the best agreement between sampling-derived and “grand truth” mean temperatures according to the RMSE and correlation were flight pattern 2 at cruise-speed, flight pattern 1 at maximum speed, and flight pattern 3 at cruise-speed at 200 m, 500 m and 1000 m height, respectively. On average over all days of the episode and the sampling domain, RMSE, and SDE were highest (3.2˚C - 3.5˚C, 1.6˚C - 1.9˚C) at 200 m, and lowest (0.6˚C - 0.7˚C, 0.7˚C - 0.8˚C) at 1000 m for all sampling patterns when flying at cruise speed. The mean biases were of the order of measurement accuracy independent of altitude and sampling patterns at cruise speed. Except for the day with the frontal passage, correlations between the distribution of 20 h mean temperatures from sampling and the “grand truth” were highest at 1000 m (0.8 - 0.9) for the various sampling patterns at cruise speed [
The investigations on flight speeds suggested that the signature of the diurnal cycle became more obvious in the mean temperature distributions at stall speed than at maximum speed (e.g.
Looking at the various flight patterns and cruising speeds revealed that the sampling underestimated the mean temperatures on average over all days and the sampling domain by 0.9˚C - 2.6˚C, and 1.5˚C - 2˚C at 200 m and 500 m height, respectively, but overestimated it up to 0.4˚C at 1000 m height. The sampling suggested about twice as high spatial variation at all altitudes than was present in the “grand truth.” Overall, the above findings suggested that determining area-temporal mean values for an area of 60 km ´ 60 km, about the size of high- resolution climate models, provided similar uncertainty than deriving them for small areas, i.e. in our case 4 km ´ 4 km areas. However, providing area means for large areas (e.g. the entire Interior Alaska) would require flying several UAVs in adjacent areas at the same time.
In Interior Alaska summer, moist flux densities are not large, for which dewpoint temperatures do not change quickly except when a front moves in. Dewpoint temperatures showed little diurnal variability in the sampled values and the “grand truth” (therefore not shown).
In the U.S. standard atmosphere, dewpoint temperature decreases at a rate of 0.172˚C・100 m−1 [
On average over the episode and sampling domain, “grand truth” dewpoint temperatures were 1˚C ± 0.3˚C, 0.5˚C ± 1.5˚C, and −0.3˚C ± 1.6˚C at 200 m, 500 m, and 1000 m, respectively. Over the episode and sampling domain, spatio-temporal variability obtained from sampling dewpoint temperatures at 200 m exceeded that of the “grand truth” by threefold for all three sampling patterns. However, the episode sampling domain mean temperature was captured independent of the flight pattern. In contrast to the 200 m level, sampling well captured the spatio-temporal variability of dewpoint temperatures at 500 m and 1000 m height. The virtual UAV’s speed had marginal impact on the differences between distributions of mean dewpoint temperatures from sampling and those derived from the “grand truth” except for August 6 when the cold front went through.
On 6 August, sampling at stall speed showed increases in dewpoint temperatures up to 4˚C in the western part of the sampling domain. At cruise and maximum speeds, most of the virtual sampling occurred in front of the cold front where dewpoint temperatures were still low.
On average over the episode and sampling domain, discrepancies between sampled and “grand truth” temperatures decreased with height. Sampled mean dewpoint temperature distributions and the “grand truth” correlated the least for sampling at stall speed as part of the area was not sampled within the 20 h flight duration.
Even though CO is part of a series of chemical reactions that form photochemical smog, its mean atmospheric lifetime is about 60 days [
During a wildfire, CO concentrations increase downwind of the fire due to transport. The NAAQS for CO is 35 ppm on 1 h and 9 ppm on 8 h average [
For each day of August 5 to 10, we determined the distribution of 20 h means from the WRF/Chem data over the sampling domain as the reference (“grand truth”). August 3 and 4 were discarded from the analysis to permit the chemical fields to spinup. This procedure was applied for all chemical species and particulate matter as well.
Prior to the frontal passage, locally, CO concentrations exceeded the 8 h average in the sampling domain [
In the sampling domain, CO concentrations decreased about 13% at most between 200 m and 1000 m in both the distributions from virtual sampling and the “grand truth” (
At 200 m, for the first and second flight patterns, the 20 h mean CO distributions based on virtual sampling showed larger spatio-temporal variability than the “grand truth”. On the contrary, using the third flight pattern underestimated the spatio-temporal variability on average over the period and sampling domain.
At 500 m height, the second flight pattern suggested twice as high spatio-temporal variability than the “grand truth.” The default and third flight patterns showed the same spatio-temporal variability as the “grand truth.” This finding differs from that of temperature and dewpoint temperature due to the stronger spatial (horizontal and vertical) heterogeneity of CO. On average over the episode, sampling at 500 m represented the distribution of relatively higher and relatively lower CO concentrations the best because the wind field was less turbulent at this height than at 200 m. Compared to CO concentrations at the 200 m height, the differences between high and low concentrations were smaller at 500 m height.
Since the height of the ABL varied during the 20 h, virtual sampling at 1000 m was sometimes above the inversion where concentrations were lower than below the top of the ABL. At 1000 m, all sampling patterns underestimated the spatio-temporal variability by a factor of two. Air-quality models are considered to have high preformance when predicted concentrations agree within a factor of two with the observations [
applying the same quality criterion for good agreement to the 20 h mean CO distributions derived from sampling vs. those of the “grand truth” like in air-quality modeling our findings mean that the calculation of 20 h mean distributions from UAV data will provide valuable results.
These findings for the sensitivity of derived CO distributions to flight patterns differed from that of temperature and dewpoint temperature. Recall for both air and dewpoint temperatures, the first and third patterns captured the 20 h mean distributions of the “grand truth” in a similar way. Obviously, which sampling pattern is the most suitable depends on the vertical profiles and horizontal distributions of the sampled quantities. These distributions were quite different for CO and air temperature/dewpoint temperature.
The injection height for all wildfire-released species was calculated inline by WRF/Chem. Injection height varied with time reaching up to 4 km above ground level at some times. This means species and temperature distributions were not collocated in space and time.
These differences in the distributions of the sampled quantities suggest that a numerical forecast and virtual sampling of the forecasted quantities may be needed to decide on flight levels and sampling patterns for the various quantities to be observed by a cohort of UAVs. In other words, our findings mean that different flight patterns are to be considered for UAVs depending on the mounted instrument.
Among other things, wildfires release SO2 and NO [
Prior to the frontal passage, 20 h mean SO2 concentrations ranged between 2 ppb outside the plume and 18 ppb in the plume at 200 m (
The 20 h mean distributions of SO2 constructed from sampling showed locally positive and negative biases as compared to the “grand truth.” Investigations showed that SO2 concentrations decreased at onset of twilight (~0004 Alaska Daylight Time (AKDT = UTC − 8 h)) hinting at photolytic reactions being involved. During the episode of this study, sunrise occurred between 0430 and 0500 AKDT and sunset was between 2300 and 2230 AKDT. The decrease in SO2 and sulfate particulate matter showed no correlation [
The virtual UAV collected data for 20 h. Hence, it took samples at different times of the diurnal course of SO2 concentrations. In areas where the virtual UAV sampled when concentrations were low in the diurnal course, the 20 h means derived therefrom underestimated the “grand truth” 20 h mean SO2 concentrations (cf.
The cold front reset the SO2 concentrations to clean air background concentrations. Thus, on August 7, the distributions of 20 h mean SO2 concentrations from sampling and the “grand truth” agreed well independent of the flight patterns in most of the sampling domain except for its southeast corner (
On August 8, wind direction shifted, for which the highest SO2 concentrations occurred farther north and in the center of the sampling domain (not shown). Due to the calming of the winds, the plume dispersed more strongly than the day before. Nevertheless, on all days, the largest differences between the distribution of 20 h means from SO2 sampling and the 20 h “grand truth” means occurred along the corners of the sampling domain for all
flight patterns (e.g.
Typically, independent of the virtual UAV’s speed, 20 h mean SO2 concentrations were overestimated and underestimated within areas of high and low concentrations, respectively (e.g.
However, at 200 m altitude, 20 h SO2 concentration means from sampling captured the spatio-temporal variability best when flying at stall speed. Flying at maximum speed suggested 50% higher spatio-temporal variability than existed according to the “grand truth” at 200 m. On average, at 500 m altitude, spatio-temporal variability was underestimated at all speeds by 44% to 77%. The strongest (least) underestimation occurred at cruising (stall) speed. At 1000 m, on average, the largest underestimation of spatio-temporal variability occurred at stall speed, while flying at maximum speed provided the best results and highest correlation of sampled and “grand truth” 20 h mean SO2 concentrations. Based on these findings, one has to conclude that sampling SO2 concentrations at high speeds minimizes errors. This finding is because the virtual UAV needs less time to cover the entire sampling domain. Thus, signals of extremes in the diurnal course have less impact at highest than at slower speeds.
Due to the sparse population and synoptic situation, the main source of NO in the sampling domain was the Crazy Mountain fires. Due to the reactivity of NO, the NAAQS considers NO2 with a 1 h average of 100 ppb [
Like for SO2, NO has a diurnal cycle due to photochemical reactions [
On episode (August 5 to 10) and sampling domain average, sampled and “grand truth” NO concentrations agreed best with respect to the combined RMSE and correlations scores for flight pattern 1 at maximum cruise speed at all heights.
In summary, the virtual sampling showed that due to the diurnal cycle of pollutants involved in photochemistry 20 h mean distributions locally fail to capture the 20 h mean of the “grand truth” undoubtly. For all days after spinup, i.e. also on the days without frontal passage, the obtained distributions were sensitive to when the UAV passed an area. Analysis suggested that data should be separated for daylight and dark hours to determine daylight and nighttime mean distributions instead of 20 h mean distributions [
Secondary pollutants like O3 form by reactions involving primary pollutants. The NAAQS for O3 is an 8 h average concentration of 75 ppb [
Recall that during the episode of our study, complete darkness occurred only for about 4 to 5 hours. According to the WRF/Chem data, O3 concentrations showed no distinct minimum during daylight despite the reactions with NO and VOCs. The distributions of 20 h mean O3 concentrations showed an increase of O3 with increasing height. Typically, 20 h mean O3 concentrations ranged between 36 and 44 ppb, 40 and 50 ppb, and 42 and 52 ppb at 200 m, 500 m and 1000 m height, respectively. Overall, the O3 distributions showed low spatial features at the three heights and on all days. Comparison of the O3 concentrations prior to and after the cold front indicated some ozone formation due to the wildfire emissions.
The low spatial and temporal changes in O3 concentrations yielded for the default and second flight patterns provided broadly similar distributions of 20 h means (
Obviously, sampling of O3 concentrations is sensitive to the UAV’s speed (cf.
UAV flew at cruise speed. Since the obtained 20 h mean O3 distributions were best at cruise speed, an optimum sampling speed may exist that could be determined by virtual sampling.
Particulate matter (PM) can form in the atmosphere from precursor gases by gas-to-particle conversion [
According to the “grand truth” during the episode, 20 h mean PM10concentrations were highest at the 200 m flight level except for August 6 (
The obtained 20 h mean distributions of PM10 concentrations depended much more on the flight patterns than the meteorological or gaseous quantities (cf.
Sampling at maximum speed permitted capturing high concentrations better than sampling at the other speeds (
According to the WRF/Chem data, like for PM10, the 20 h mean horizontal distributions of PM2.5 varied typically the strongest at the 200 m flight level (
Overall, the 20 h mean PM2.5 distributions derived from sampling followed those of PM10 throughout the episode at 200 m height (
Generally, 20 h means of PM2.5 from sampling overestimated the 20 h means of the “grand truth” (
The largest differences among the 20 h mean PM2.5 concentrations from sampling among each other and the “grand truth” occurred for stall speed (
PM10 also encompasses PM2.5. Comparison of PM10 and PM2.5 concentrations showed that PM with diameters between 2.5 mm and 10 mm occurred at all three flight levels at all times. Due to the size dependency of settling velocities and of thermodynamic behavior under high relative humidity conditions, PM can experience stratification [
Typically, for both PM2.5 and PM10 concentrations decreased with increasing height except for four cases when PM accumulated at the top of the ABL due to inversions. PM10 and PM2.5 concentrations differed by about 0.1 μg∙m−3 which is about the uncertainty of current state-of-the-art measurements on the ground. This finding means that (a) the majority of the particulate matter was PM2.5 which is health adverse [
Our feasibility study theoretically examined whether UAVs could provide spatial distributions of mean pollutant concentrations suitable for public air-quality advisory. We used an episode during the 2009 Crazy Mountain fires in Interior Alaska as a test case.
Evaluated WRF/Chem data served to represent the conditions in the ABL and, hence, as “grand truth” in this study. A virtual Scan Eagle travelling at different heights, speeds and different patterns collected data from the WRF/Chem results along its flight path. We assumed optimum conditions with respect to flight duration, i.e. zero-weight payload and full fuel tank. Under such conditions, the Scan Eagle can fly about 20 h. The mean distributions derived from the sampled data were compared to the mean distributions according to the “grand truth.” We examined a polluted situation with a fully developed wildfire-smoke plume, the removal of pollutants by a cold front passage, and the re-development of the smoke plumes. All quantities showed strong sensitivity to the flight patterns and heights on the day of the cold front passage.
Comparison of 20 h mean distributions obtained from sampling at different altitudes revealed the following: For air and dewpoint temperatures, differences were related to the environmental temperature lapse rate and the dewpoint temperature lapse rate, respectively. Concentrations of gases were nearly uniform with height under conditions of strong vertical mixing within the ABL.
In general, the virtual UAV captured the concentrations’ reset to clean air background values after the cold front had passed the sampling domain, and the re-development of the plume thereafter. However, on the day of the frontal passage, 20 h mean distributions from sampling at different speeds and/or with different patterns led to different results and greater discrepancies from the 20 h means of the “grand truth” than found on the days prior to and after the frontal passage.
In the case of CO, the 20 h spatio-temporal variability obtained from sampling agreed with the “grand truth” within a factor of two at 1000 m, i.e. UAV sampling can provide good 20 h mean distributions of CO at 1000 m for 60 km ´ 60 km and retrieve information on smoke-plume propagation. Based on the results for CO, one may conclude, that in general, some of the 20 h mean pollutant concentrations obtained by the different flight patterns were due to changes in wind direction.
For primary pollutants involved in photochemical reaction chains (SO2, NO) it is necessary to derive separate mean distributions for daytime and nighttime. The diurnal cycle of their concentrations led to overestimation (underestimation) of 20 h means in areas of high (low) concentrations as compared to the “grand truth.” The unequal amount of daylight and dark hours and the time differences when the virtual UAV scanned a location caused that spatio-temporal biases did not cancel out.
Given the relative short darkness at high latitudes in late summer, the collection of enough nighttime data would require more than one UAV for coverage of the same area. Each UAV would have to fly a different sampling pattern for 20 h. The choice of sampling patterns must ensure that during darkness, the different UAVs would collect data in different areas of the sampling domain. The shorter the darkness, the more UAVs would be required which might cause logistic and personnel difficulties. The collected data would have to be sorted to create separate daylight and nighttime mean distributions.
The 20 h mean distributions of gases involved in photochemistry differed among flight patterns except for O3. However, the 20 h mean O3 distribution obtained from sampling depended on the speed of the virtual UAV.
The lowest possible safe flight height and cruising speed would provide information on how the underlying landscape modulates the smoke plume. Sampling around the top of the ABL would provide information on the plume’s dispersion and would be helpful for aviation advisory for small aircrafts when satellite imagery cannot provide this information due to a closed cloud cover in the mid- and upper troposphere. When deciding on the flight pattern, it is critical to consider wind speed, direction and precipitation, and their forecasted spatio- temporal evolutions during the planned flight duration. The sampling pattern must be designed to capture the conditions of interest (e.g. severity of pollution, washout due to frontal passage).
Sampling strategies for meteorological and chemical quantities might differ. Thus, air-quality forecasts and the virtual sampling technique introduced here may be an asset in effective, optimized flight planning, and collecting the data needed to answer the research question(s) at hand.
Our theoretical study assumed zero payload, i.e. a full tank. The heavier the payload the less fuel can be added, which reduces flight duration. While flight duration may be of low relevance for research questions related to short-term processes, deployment of UAVs for use in air-quality advisories requires a long flight duration to cover a large area in the downwind of the wildfire and for calculation of multi-hour means depending on the sampled species and its NAAQS averaging requirements. For some of the examined quantities, instruments light and small enough to fit in the UAV and that can sample at high enough frequency still have to be developed.
The authors wish to thank Uma S. Bhatt, R.L. Collins, M.C. Hatfield, D. Thorsen and the anonymous reviewers for fruitful discussion and helpful comments. The Research Support Center located at the Geophysical Institute of the University of Alaska Fairbanks provided CPU time and data storage. The National Aeronautics and Space Administration provided funding (Grant NASA-NNX11AQ27A).
NicoleMölders,Mary K.Butwin,James M.Madden,Huy N. Q.Tran,KennethSassen,GerhardKramm, (2015) Theoretical Investigations on Mapping Mean Distributions of Particulate Matter, Inert, Reactive, and Secondary Pollutants from Wildfires by Unmanned Air Vehicles (UAVs). Open Journal of Air Pollution,04,149-174. doi: 10.4236/ojap.2015.43014