NCAR Technical Notes NCAR/TN-538+STR

Transcription

1 Ozone Concentration and Foliar Injury Analysis at Purchase Knob Garden Sophia Chen Tagleet Geltser Kaden Hawley Dorit Hammerling Danica Lombardozzi NCAR Technical Notes NCAR/TN-538+STR National Center for Atmospheric Research P. O. Box 3000 Boulder, Colorado NCAR IS SPONSORED BY THE NSF

2 NCAR TECHNICAL NOTES The Technical Notes series provides an outlet for a variety of NCAR Manuscripts that contribute in specialized ways to the body of scientific knowledge but that are not yet at a point of a formal journal, monograph or book publication. Reports in this series are issued by the NCAR scientific divisions, serviced by OpenSky and operated through the NCAR Library. Designation symbols for the series include: EDD Engineering, Design, or Development Reports Equipment descriptions, test results, instrumentation, and operating and maintenance manuals. IA Instructional Aids Instruction manuals, bibliographies, film supplements, and other research or instructional aids. PPR Program Progress Reports Field program reports, interim and working reports, survey reports, and plans for experiments. PROC Proceedings Documentation or symposia, colloquia, conferences, workshops, and lectures. (Distribution maybe limited to attendees). STR Scientific and Technical Reports Data compilations, theoretical and numerical investigations, and experimental results. The National Center for Atmospheric Research (NCAR) is operated by the nonprofit University Corporation for Atmospheric Research (UCAR) under the sponsorship of the National Science Foundation. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. National Center for Atmospheric Research P. O. Box 3000 Boulder, Colorado

3 NCAR/TN-538+STR NCAR Technical Note Ozone Concentration and Foliar Injury Analysis at Purchase Knob Garden Sophia Chen Student assistant, National Center for Atmospheric Research, Boulder, CO Tagleet Geltser Student assistant, National Center for Atmospheric Research, Boulder, CO Kaden Hawley Student assistant, National Center for Atmospheric Research, Boulder, CO Dorit Hammerling Institute for Mathematics and Applied Geosciences, National Center for Atmospheric Research, Boulder, CO Danica Lombardozzi Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO Computational and Information Systems Laboratory (CISL) Institute for Mathematics and Applied Geosciences (IMAGe) Climate and Global Dynamics Laboratory (CGD) Terrestrial Science Section (TSS) NATIONAL CENTER FOR ATMOSPHERIC RESEARCH P. O. Box 3000 BOULDER, COLORADO ISSN Print Edition ISSN Electronic Edition

4 Ozone Concentration and Foliar Injury Analysis at Purchase Knob Garden Sophia Chen 1, Tagleet Geltser 2, Kaden Hawley 1, Dorit Hammerling 3, and Danica Lombardozzi 3 1 Fairview High School 2 Cherry Creek High School 3 National Center for Atmospheric Research August 29, 2017 Abstract Tropospheric ozone (O 3 ) is a common greenhouse gas, which in high concentrations is harmful to the respiratory systems of humans, animals, and certain plants. Some of these bioindicator plants have observable symptoms that arise from exposure to high ozone concentrations. We observe these plants in ozone bioindicator gardens, which have been specifically created for the purpose of observing ozone damage. In this analysis we use data from the Purchase Knob ozone garden site in the Great Smoky Mountains National Park in North Carolina with the goal of discovering the relationship between plant damage and ozone concentration. Starting with hourly O 3, we calculated the cumulative W126 index, which is a metric used to summarize hourly ozone concentrations and gives higher, more damaging ozone concentrations more weight. We then used a simple linear regression, modeling percentage of damaged leaves versus cumulative W126 index. We also tested a second regression model, modeling the percentage of damaged leaves against the logarithm of cumulative W126. The logarithmic model performed better, explaining 36% of the inter-annual variability. Keywords: Ozone Concentration, Ozone Garden, W126 Sophia Chen is a student assistant, National Center for Atmospheric Research, PO Box 3000, Boulder CO (schen9981@gmail.com), Tagleet Geltser is a student assistant, National Center for Atmospheric Research, PO Box 3000, Boulder CO (leeta@geltser.com), Kaden Hawley is a student assistant, National Center for Atmospheric Research, PO Box 3000, Boulder CO (kaden.hawley@gmail.com), Dorit Hammerling is a Project Scientist II, National Center for Atmospheric Research, PO Box 3000, Boulder CO (dorith@ucar.edu), Danica Lombardozzi is a Project Scientist I, National Center for Atmospheric Research, PO Box 3000, Boulder CO (dll@ucar.edu). 1

5 Contents 1 Introduction 5 2 Ozone Gardens 6 3 Data Ozone Concentration Data The W126 Index Foliar Damage Data Relationship between ozone concentration and foliar damage Simple Linear Regression Model Logarithmic Regression Model Summary 18 6 Future Work 19 2

6 List of Figures 1 Stippling on Crown Beard and Milkweed Leaves Existing and target future ozone garden locations in the United States as of Hourly ozone concentrations for years 2003 to The EPA limit is based on the 4 th highest 8-hour average, so hourly values above the EPA limit in Figure 3 do not necessarily imply that the EPA limit is exceeded Cumulative daily W126 indices for years 2003 to The x s and triangles indicate the highest and lowest ozone exposure years, respectively A visual of the 16 leaves measured for each plant.[1] Percent frequency of injury scores for the last fully developed leaves on Crown Beard plants during the growing season (July - September) Simple linear regression for percentage of leaves with injury scores 2 or higher for the month September versus the mid-september values for cumulative W126 indices for all years Simple linear regression for percentage of leaves with injury scores 3 or higher for the month September versus the mid-september values for cumulative W126 indices for all years Logarithmic regression for percentage of leaves with injury scores 2 or higher for the month September versus the mid-september values for cumulative W126 indices for all years. The predicted values were plotted against the original mid-september W126 indices Logarithmic regression for percentage of leaves with injury scores 3 or higher for the month September versus the mid-september values for cumulative W126 indices for all years. The predicted values were plotted against the original mid-september W126 indices

7 List of Tables 1 Summary of monthly weather from April to October at Purchase Knob. [2] 7 2 Summary of yearly ozone concentration data Rating system for leaf damage Linear regression model output for plants damaged with injury score 2 or higher Linear regression model output for plants damaged with injury score 3 or higher Logarithmic regression model output for plants damaged with injury score 2 or higher Logarithmic regression model output for plants damaged with injury score 3 or higher

8 1 Introduction There are two types of ozone: stratospheric and tropospheric. Whereas stratospheric ozone blocks ultraviolet radiation from hitting Earth s surface, tropospheric ozone is a common greenhouse gas formed through ultraviolet radiation catalysis. Tropospheric ozone catalyzes the reactions of reactive nitrogen (NOx) and volatile organic compounds (VOCs). As a result, tropospheric ozone is typically found downwind of industrial areas and large cities with an abundance of pollutants, which contain high amounts of NOx or VOCs. Because it is a powerful oxidant, tropospheric ozone is a leading cause of damage to the respiratory systems of humans, animals, and plants, and can have significant impacts on crop yields. The EPA standard for tropospheric ozone concentrations is 70 ppb. Plant damage starts, according to the EPA, at an ozone concentration of 40 ppb. Ozone can cause plants to close their stomata and can also damage biochemical components of photosynthesis, thereby inhibiting photosynthesis. When leaves are damaged by ozone, some plant show visible signs of damage in the form of purpling, also known as stippling, as seen in Figure 1. Necrosis (cell death), and chlorosis (yellow or discolored leaves), are also visual signs of plant damage that could arise from high levels of ozone, but could arise from other factors as well. Thus, purpling/stippling is the only form of plant damage that is exclusively caused by ozone, and can be used to indicate leaf damage caused by ozone. (a) Crown Beard (Verbesina occidentalis) (b) Milkweed (Asclepias syriaca) Figure 1: Stippling on Crown Beard and Milkweed Leaves. 5

9 2 Ozone Gardens Due to the presence of visible foliar damage as a result of high ozone concentrations, several ozone gardens have been established throughout the United States in an attempt to track visible ozone damage. Ozone gardens consist of various types of ozone sensitive plants, such as Milkweed, Coneflower, and Crown Beard, which are analyzed in this study. There are currently 11 existing ozone gardens in the United States and 10 other locations where ozone gardens may be established in the future, as seen in Figure 2. The National Center for Atmospheric Research (NCAR) Mesa Lab has one of these existing ozone gardens, with four types of bioindicator plants. Figure 2: Existing and target future ozone garden locations in the United States as of The Purchase Knob garden contains several ozone sensitive plants. It is located in the Great Smoky Mountains National Park in North Carolina at an elevation of 5,086 feet. The climatic zone is perhumid microthermal, and the geographical coordinates of the site are N, W. Table 1 contains the monthly climatology from April to October, when plants grow. The Purchase Knob garden contains three different types of ozone sensitive plants. These three plants - Crown Beard, Coneflower, and Milkweed - exhibit visible signs of the presence of ozone through stippling, as seen in Figure 1. 6

10 Table 1: Summary of monthly weather from April to October at Purchase Knob. [2] Month Min Temp ( F) Max Temp ( F) Average Rainfall (inches) April May June July August September October Data We examined two types of data: ozone concentration data and leaf injury scores, both collected at the Purchase Knob ozone garden. 3.1 Ozone Concentration Data The ozone concentration data were collected in the Purchase Knob garden from 2003 to Each year of data contains hourly averages from April 1st at 0:00 to October 31st at 23:00. In addition, all years have at least 1 missing hour of data per day due to daily auto-calibration checks. Some days have more than one missing data point which can be for a variety of reasons, the most common being maintenance, routine repairs, and shelter temperatures that are too high for monitor operation. The number of observations is very similar for years 2003 to 2015, averaging about 4,795 observations per year, and somewhat lower for 2016 with 4,561 observations. The hourly ozone concentrations were plotted for each year from 2003 to 2016, along with the EPA limit of 70 ppb, as seen in Figure 3. One interesting feature is the decrease in the number of observations exceeding 70 ppb for the latter years of the investigated time period, as seen in Table 2. From 2003 to 2008, there are 973 observations exceeding 70 ppb, while from 2009 to 2016 there are only 136 observations exceeding 70 ppb. It is also noteworthy that the maximum values for 5 of 6 years from the 2003 to 2008 exceed 90 ppb, while none of the maximum values from 2009 to 2016 exceed 90 ppb. There is an overall decrease in the 1-hour maximum from 2003 to 2016, and there are slight drops in the mean and median as well. The large number of observations over 70 ppb in the earlier years could be attributed to sunny hot days with little circulation, but may also be due to changes in NOx and VOC emissions in the region. The year with the highest number of observations over 70 ppb, 2005, also has the highest mean ozone concentration, the second highest maximum ozone concentration, and is tied with 2003 and 2007 for the highest median ozone concentration. Additionally, the year 7

11 with the lowest mean ozone concentration of 39.3 ppb, 2009, also has the lowest number of observations over 70 ppb and is tied with 2014 for the lowest median ozone concentration. Table 2: Summary of yearly ozone concentration data. Year Mean Median Max Min Number Observed Number Observed Over 70 ppb All Years

12 Figure 3: Hourly ozone concentrations for years 2003 to The EPA limit is based on the 4 th highest 8-hour average, so hourly values above the EPA limit in Figure 3 do not necessarily imply that the EPA limit is exceeded. 9

13 There were several years in which hourly ozone concentrations were above the EPA limit multiple times, namely 2003, 2005, 2006, 2007, and While there were other years where concentration generally remained below the limit at all times, namely 2009, 2013, 2015, However, the EPA limit is based on the 4 th highest 8-hour average, so individual hourly values above the EPA limit do not necessarily imply that the EPA limit is exceeded and we did not investigate actual EPA limit exceedances. Ozone concentrations are highly dependent on local weather conditions. Hot, sunny days are conducive to ozone formation where as cool, rainy days are not. Overall, 2005 saw the highest ozone exposure and 2009 saw the lowest ozone exposure, with the maximum ozone concentration reached being 72 ppb. [3] The W126 Index The W126 index [4] is a cumulative metric used to summarize the hourly ozone concentrations. This index highlights the evolution of ozone concentrations throughout the growing season, but uses a weighted scale to place emphasis on higher ozone concentrations, which might be more damaging to plants. D.I. = 23:00 i=0:00 1 O 3i ( 1 + (4403 e 126 O ) 3i ) Daily W126 index is calculated from the hourly ozone concentrations using the equation above, where O 3i is the hourly ozone concentration, in ppm. The highest ozone concentration within a day is given a weight of close to 1, while the lowest is given a weight of close to 0. This weighting system gives more weight to the ozone concentrations that are most likely to negatively affect plants, and therefore, is a potentially more useful metric than hourly ozone concentrations. Furthermore, this metric distinguishes between the impact from high ozone concentrations and the impact from long exposure duration. [5] The periods of gradual increase in W126, as shown in Figure 4, correspond to relatively low ozone concentrations. Conversely, periods of steep increase on the graph are periods of time with high ozone concentrations. Thus, similar to the results seen in Figure 3, 2005 (marked with x s) saw the highest cumulative ozone exposure, with the steepest increase in cumulative W126, while 2009 (marked with triangles) saw the lowest ozone exposure, with the most gradual increase in cumulative W

14 Figure 4: Cumulative daily W126 indices for years 2003 to The x s and triangles indicate the highest and lowest ozone exposure years, respectively. 3.2 Foliar Damage Data The foliar damage data contain injury scores for 24 Crown Beard plants, 20 Coneflower plants, and 20 Milkweed plants from 2003 to Sixteen leaves on each plant were analyzed; 8 on the right side and 8 on the left. A detailed visualization of the leaves positions is shown in Figure 5. Our analysis focuses on the 24 Crown Beard plants. Though damage scores were recorded throughout all years for each growing season, which starts in late April to early May and ends late September to early October, we selectively choose to examine the two top leaves of each plant. The last fully developed (Figure 5, leaves 8a and 8b) leaves typically emerge around late June to early July. These leaves are expected to respond similarly to different levels of ozone exposure because they are similar in age. Thus, all injury scores for the top two leaves on each of the Crown Beard plants were averaged each day. Injury scores were used to record the percentage of each leaf that showed sign of ozone damage through stippling. Table 3 displays each damage score and the respective damage impact on the leaf. Because this study focuses on damage caused exclusively by ozone, any ratings higher than 6 were disregarded, as scores from 7 to 10 are leaf death codes. Visible ozone damage accumulates on a leaf throughout each growing season, so injury scores should always stay the same or increase. At times some leaves appear to have decreasing damage scores, which suggests errors in the data collection. Thus, in an effort 11

15 to maintain the quality of the data, plants which had leaves with decreasing severity scores were excluded from analysis. Figure 5: A visual of the 16 leaves measured for each plant.[1] Rating Table 3: Rating system for leaf damage. Description 1 0% of leaf is affected 2 1-6% of leaf is affected % of leaf is affected % of leaf is affected % of leaf is affected % of leaf is affected 7 Leaf fell off with no rating higher than 1 8 Leaf fell off, had prior chlorosis but no purpling 9 Leaf fell off, had prior purpling but no chlorosis 10 Leaf fell off, had prior purpling and chlorosis 12

16 Figure 6: Percent frequency of injury scores for the last fully developed leaves on Crown Beard plants during the growing season (July - September). 13

17 One problem with plotting averages of leaf scores is that such interpretation is vulnerable to noise present in sampling. Each day, data collectors did not collect data on all of the 24 Crown Beard plants. It is possible that while on one day the data collectors observed plants where a majority of the leaves had injury score 2, the next day they observed the plants with leaves of injury score 1. Thus, to alleviate the sampling effect, we look at frequency of leaf damage over a given month, as is done in Figure 6. In July of most years other than 2005, the majority of the injury scores fall in a rating of 1. This behavior is expected as ozone damage takes time to accrue and the leaves typically emerge in early July, so ozone damage has not accumulated much in July. By August in many years, such as in 2005, 2010, 2011, and 2012, higher damage scores of rating 3 and 4 start to increase in frequency. Finally, by September, the highest damage scores emerge, with scores of 5 and 6 becoming more prominent. For all 14 years, the frequency of high foliar damage scores increases as a function of time, thus imply increasing severity of damage from July to September of each year. Growth of the last fully developed leaves usually starts in late June to early July, and in 2005 the last fully developed leaf was already showing signs of severe damage by July, whereas in all other years, there was no severe damage in July. By September, the year 2005 also had greater frequency of the damage score 6, while the year 2009, with the lowest W126, had little to no leaves with damage score 6. 4 Relationship between ozone concentration and foliar damage To more accurately compare leaf damage with cumulative W126 indices, we modeled the percent frequency of leaves with injury scores greater than 2 for the month September and the percent frequency of leaves with injury scores greater than 3 for the month September against the mid-september values for cumulative W126 indices for all years. We then use simple linear regressions and logarithmic regressions to model the relationship between foliar leaf damage and ozone concentrations expressed as mid-september W126. The resulting models can approximate predictions regarding how much damage is expected to be seen at certain cumulative W126 values. 4.1 Simple Linear Regression Model Figure 7 considers the percentage of leaves with damage scores greater than 2 in September and fits a simple linear regression model with W126 in September as the predictor. This model is based on a direct correlation between the cumulative W126 values and the percentage of leaves with damage scores greater than 2. The model has an R 2 value of 0.29 and a p-value of , suggesting a statistically significant relationship. Further 14

18 information regarding the model s output is highlighted in Table 4. Figure 8 considers the same model for leaves with damage scores greater than 3. It also suggests a direct positive correlation between cumulative W126 values and percentage of leaves with damage scores greater than 3. However, the model outputs a p-value of and a R 2 value of 0.04; and thus, it cannot be considered statistically significant. Considering the percentage of leaves with damage scores greater than 2 gave us a model that explained more of the variability between the years, namely 30%. This suggests that as W126 increases, a higher proportion of leaves show visible signs of damage Figure 7: Simple linear regression for percentage of leaves with injury scores 2 or higher for the month September versus the mid-september values for cumulative W126 indices for all years. Table 4: Linear regression model output for plants damaged with injury score 2 or higher. R-squared 0.29 Intercept 0.28 Slope 0.01 P-value Significance

19 Figure 8: Simple linear regression for percentage of leaves with injury scores 3 or higher for the month September versus the mid-september values for cumulative W126 indices for all years. Table 5: Linear regression model output for plants damaged with injury score 3 or higher. R-squared 0.04 Intercept 0.21 Slope P-value Significance N/A 4.2 Logarithmic Regression Model We also performed a logarithmic regression model, comparing proportion of injury scores against the log of the mid-september cumulative W126 index. In Figure 9, the model outputs a p-value of , suggesting a statistically significant correlation. Furthermore, with a R 2 value of 0.36; this model explains 36% of the variability between years. For Figure 10, the model outputs a p-value of , which is not statistically significant. Thus, a logarithmic regression model considering proportion of injury scores 2 or higher yielded a better model. Overall,the logarithmic model considering percentage of injury scores 2 or higher gave the best fit of the data, slightly outperforming the simple linear regression. 16

20 Figure 9: Logarithmic regression for percentage of leaves with injury scores 2 or higher for the month September versus the mid-september values for cumulative W126 indices for all years. The predicted values were plotted against the original mid-september W126 indices. Table 6: Logarithmic regression model output for plants damaged with injury score 2 or higher. R-squared 0.36 Intercept Slope 0.39 P-value Significance

21 Figure 10: Logarithmic regression for percentage of leaves with injury scores 3 or higher for the month September versus the mid-september values for cumulative W126 indices for all years. The predicted values were plotted against the original mid-september W126 indices. Table 7: Logarithmic regression model output for plants damaged with injury score 3 or higher. R-squared 0.07 Intercept Slope 0.17 P-value Significance N/A 5 Summary Between the simple linear regression model and the logarithmic regression model, the logarithmic regression model considering percentage of plants with injury score of 2 or higher performed best. This model suggests a statistically significant relationship between the presence of foliar leaf damage and the logarithm of W126. This model can be used to predict approximate proportions of Crown Beard plants that will show signs of damage at given W126 values in the future. 18

22 While the logarithmic model suggests a statistically significant relationship between cumulative W126 and proportion of foliar damage, it cannot robustly predict exact proportions of foliar damage as there are a variety of environmental, biological, and physical factors that affect plant reaction to ozone concentration. For instance, drought stress could have caused plants to close stomata despite high ozone. Nutrient availability may also change the response of plants to ozone. We must also consider the length of time that plants were exposed to ozone versus the intensity of the ozone concentration. However, even with these external factors, we were still able to see a statistical relationship between foliar leaf damage and ozone concentration. The models suggest that W126 can be used to predict the proportion of leaves that show signs of foliar injury, but not necessarily the increasing severity of the injury. 6 Future Work The goal of this analysis is to determine whether there is a relationship between foliar damage and ozone concentration. In the biological world there are a myriad of factors which influence one outcome. Based on our simple linear regression analysis, we cannot pin point ozone concentration as the only factor affecting plant damage. To determine the strength and consistency of the relationship between visible foliar damage and ozone concentration, data from other ozone gardens can be examined with a similar model to see if similar results are found. Another point of interest would be to investigate more complex models, such as a multinomial model, and using more predictors. We only used one type of plant, Crown Beard, although three different types of ozone sensitive plants were available. Hence, a study into which type of plant most effectively and accurately indicates damage in response to ozone concentration could also be pursued. Lastly, by considering exclusively crop plants, this analysis could be extended to determine the quantitative effect of ozone on crop yields. Acknowledgements All data from the Purchase Knob garden used in this study was taken from the Hands on the Land website. Thanks to Dorit Hammerling and Danica Lombardozzi for their help in this project. Thanks to Susan Sachs for the foliar damage data and assistance in its interpretation. We also thank Steve Ensley from North Carolina Department of Environmental Quality for the ozone concentration data. References [1] Hands on the Land. Ozone plant visualization. 19

23 [2] National Parks Service. Great Smoky Mountains National Park Weather, [3] Environmental Protection Agency. Trends in ozone adjusted for weather conditions. [4] Environmental Protection Agency. Ozone W126 Index. [5] Allen S. Lefohn Robert C. Musselman, Patrick M. McCool. Ozone descriptors for an air quality standard to protect vegetation. 20