Introduction
The effect of air pollution on the metabolic processes of vegetation is becoming a well-documented and researched part of the ongoing combating of climate change. The research and studies surrounding air pollution is a key element in determining how areas of the world are being affected by the presence of toxic airborne particles. This topic is important to research and understand because the premature deaths of species of vegetation could have drastic consequences. The loss of vegetation biodiversity would lead to a cascade of species loss due to those species depending on that vegetation that is being affected by air pollution (Vizzari et al. 2015). Vegetation in particular, provides insight into how the presence of pollutants in the atmosphere can result in the interruption or even halting of metabolic processes that are shared with humans, such as cellular respiration. In addition to the effect that air pollutants have on the shared metabolic processes of most biotic organisms, the presence of air pollutants can affect metabolic processes that are unique to plants and significant for their growth. These processes allow the plants to keep the energy cycle from stopping which prevents entire ecosystems from collapsing. This is due to the fact that plants are primary producers who transfer solar energy into chemical energy that can be consumed by other organisms.
Photosynthesis is a biochemical process which allows plants to turn light energy from the sun into glucose which can then be utilized by the plant as a structural element or turned into energy via cellular respiration. The photoreceptor that is responsible for absorbing the photons emitted by the sun is the chlorophyll located in the cells of the plant. These photoreceptors help capture that solar energy and use it to create the structural components of glucose. Literature shows that the purpose of the laminae of the leaves increases the surface area for absorption of that solar energy by that chlorophyll in the leaves (Tabassum et al. 2016). It is known that chlorophyll a and chlorophyll b absorb violet, orange, blue and yellow wavelengths of light and reflect green wavelengths (Milne et al. 2014) which is the color one sees most healthy leaves as.
The reason that this research topic focuses on the color change of the plants as one of the factors of how construction site air pollution affects the metabolic activities of plants is due to the fact that those leaves act as an indicator for the amount of air pollution that the trees have been exposed to. Literature shows that the amount of air pollution that a tree is exposed to can be observed through the color of the leaves which would include bleaching or chlorosis of the leaves. Previous research also reveals that this color change indicates a level of chlorophyll destruction because normal chlorophyll concentrations in leaves gives those leaves the green color that everyone is familiar with (Giri et al. 2013). Any drastic or abnormal color change would indicate a change in metabolic behavior in those plant cells, specifically in chlorophyll which is a measure of overall plant health (Pavlovic et al. 2014). Subsequently, if a larger area of the leaf has experienced that color change, then more chlorophyll destruction has occurred and thus the health of the plant is decreasing.
In addition to the level of color change being used as an indicator for the absorption of air pollution, the mass of the leaves as well as general leaf fall will also be used as an indicator. According to research done, PM2.5 is an air-borne pollutant that is emitted from internal combustion engines and can be used as a general indicator of air pollution within a certain area. In this topics case, the area in question is multiple construction sites as shown in Figure 1, Figure 2 and Figure 3. to determine the effect of their emissions on vegetation.
Figure 1. Site 1 at the beginning of observations/measurements
Figure 2. Site 2 at the beginning of observations/measurements
Figure 3. Site 3 at the beginning of observations/measurements
PM2.5 can often be formed from other toxic precursor emissions such as NOx and SO2 (Hodan et al. 2004) which are air pollutants that are known to cause color changes in the leaves of plants (Swami et al. 2018). Therefore a high concentration of PM2.5 would seem to indicate a high concentration of those precursors as well. Similarly to previous studies, the recording of the masses of the leaves would allow for the determining if PM2.5 has deposited on the leaves (Luo et al. 2018) as well as if the leaves have absorbed the previously mentioned precursor pollutants (SO2 and NOx). Finally, leaf fall will also be used as an indicator because as leaves absorb those toxic molecules, the plant must get rid of them somehow, so those molecules do not damage the rest of it. The best mechanism for plants to do this is to get rid of the part of the plant that has absorbed most of the pollutant which would be the leaf. Therefore a greater amount of leaf fall under a plant that is near a construction site could be another indicator that that plant is being affected by the construction equipment emissions.
The overall objective of this research topic is to measure the amount of PM2.5 around multiple construction sites and then compare that level of air pollution to the amount of color change in the leaves of trees in that area. In addition to the observations of color change. The amount of leaf fall from those trees near the construction site as well as the mass of those leaves themselves will be compared to the leaf fall and mass of leaves on trees that are found farther from the site. The culmination of all the gathered data will then allow for determining the effect of emissions of internal combustion engines present in construction equipment on the metabolic processes of trees near those construction sites.
Methods
The leaves of the plant are important for how the plant acquires necessary nutrients/energy such as water or sunlight. However, as previously mentioned, it does increase the surface area for catching non necessary and even harmful molecular compounds such as SO2, NOx, or PM2.5. These pollutants are commonly emitted by internal combustion engines such as the ones present in construction equipment. As previously mentioned, these toxic molecules can be absorbed by the vegetation around where they are emitted and can cause drastic changes in metabolism and structure that can severely damage the plant. Therefore the concentration of these molecules around certain areas will help determine a general amount of pollution a plant has been around and even absorbed through due to their exposure to these types of pollutants.
The design of this study involved the investigation of flora over and around 3 different sites where construction equipment and construction practices, such as ground disturbance, kicking up of dust, etc., were present. These sites were ranked in size via the amount of construction equipment present, number of workers on site and how much land the site took up. The sizes were ranked with either small, medium, or large. Through the use of a PM2.5 detector, minimum and maximum values of PM2.5 (in ug/m3) were collected as well as observations of the color of the leaves of trees close to the source of pollution (the construction site) relative to trees farther from the source of pollution. Secondly, observations about the amount of leaf fall of a tree/group of trees close in proximity to the source of pollution were made and were again compared to trees/groups of trees located farther away from the source. Finally, using an analytical balance, the mass of leaves of trees close to the construction site were compared to leaves of the same species located farther away.
The measuring of the concentration of PM2.5 allowed for determining how much pollution the construction site was contributing to its surroundings. Studies have found that there is a correlation between the absorption capacity of PM2.5 by the leaf and the level of pollution around the leaf itself (Lu et al. 2018). This, coupled with the fact that PM2.5 is a good general marker for air pollution, especially from internal combustion engines, were both deciding factors in regard to choosing which type of pollutant to focus on when gathering data. Additionally, the time of day that the levels were being recorded was taken into consideration as well to reduce the amount of time between the machine being used and the measurements being taken in order to get accurate emission results (Cheng et al. 2015). In terms of findings, a good range of concentrations, the PM2.5 detector, as shown in Figure 4. was turned on and the perimeter of the
construction sites were circled multiple times in order to get an accurate reading of the levels of PM2.5.
Figure 4. PM2.5 detector
Additionally, the detector was moved away from the site (at least 100m) to make sure that the levels decreased to see if the concentration was closer near the location of interest. These PM2.5 values were also compared to the local PM2.5 conditions measured by the Indiana Department of Environmental Management to see if the ranges found were higher or lower than the average local PM2.5 concentration over the same time frame. For the purpose of the study, the leaves of the trees closest to the site were observed in regard to chlorosis as well as leaf fall. Leaves of a certain tree species closest to one of the chosen sites were taken as well and measured in regard to their mass.
Chlorosis/browning is another one of the observations documented in order to determine the level of absorption of the PM2.5 pollutant. The intensity of the yellowing and/or browning of leaves is indicative of how much cellular damage that the leaf has been exposed to. PM2.5 is one of many pollutants that can damage the chlorophyll present in the plant cells of the leaf (Li et al. 2021, Iqbal et. al 2015). When the leaf of the tree absorbs the PM2.5, the pollutant will cause the destruction of the chlorophyll (Joshi et al. 2009, Guderian 2012) and, as previously explained, a changing of leaf color. The leaves on the trees nearest to the site of construction will be the ones that will the focus of the observational data and will be ranked on a scale of mild to moderate to high depending on the area of the leaf that has suffered from chlorosis as well as the amount of the leaves on the tree that are yellowing/browning.
The second method of measuring the amount of pollution affecting the leaves on the tree would be the amount of leaf fall under the tree themselves. The degree of leaf fall (mild, moderate or high) will be concluded by looking at the number of leaves on the ground or on the tree and then relate those observations to the proximity of the tree near the site. In addition, multiple studies in the past 30 years have found that the increased amounts of air pollution can cause leaves to age faster and then subsequently fall from the tree (Prajapati et al. 2012, Sawidis et al 1995, Winner et al 1994) which emphasizes this research studies methodology of using the amount of leaf fall as an indicator.
The final method of determining the concentration of air pollution that leaves of trees have absorbed would be through the use of measuring the mass of the leaves on trees closest to the construction site. It was previously mentioned that the large surface area of leaves allows for the absorption of sunlight and water, however, it also increases the amount of surface area for the absorption of pollutants. The leaves of trees, and plants in general, act as a sponge for those toxic materials (Nguyen et al. 2015) and if too much of those particles are absorbed, the leaf falls from the tree. Through the use of analytical balance as shown in Figure 5., the mass of leaves of the Eastern Redbud tree will be recorded. This tree was chosen due to its abundance around the chosen site and its proximity to the site. The masses of these leaves, shown in Figure 6., will then be compared against the masses of leaves of the same species, to ensure consistency, to determine if there is a difference in mass as a result of the deposition or absorption of the PM2.5 into the leaves.
Figure 5. Analytical balance used to measure the mass of collected leaves
Figure 6. Taking the mass of the Eastern Redbud leaves
The collection of this data is critical to the objective of the research project which is to observe the effect of air pollution on the leaves of trees near construction sites. The final report will include the above mentioned data, which includes the measurements of air pollution, difference in leaf mass, observations of chlorosis and leaf fall, as well as an analysis of the data/observations. The data will be used to determine the effect of emissions from internal combustion engines on the metabolic and structural integrity of trees through the analysis of their leaves.
Results
Over the course of 7 weeks 10/9/21-11/20/21, measurements of the PM2.5 values were gathered and observations of the chlorosis and leaf fall of the leaves of trees at each site were recorded. Site 1, the medium sized construction site, had the perimeter of the site exposed which allowed for circling of the site multiple times in order to get the most accurate results. This site was located at the crossing of E 7th St. and N Jordan Ave on the IU Bloomington Campus. The minimum and maximum values were recorded and can be seen in Table 1. The minimums and maximums for each were averaged to find an average range of pollution around the site which was calculated to be 5.8-8.0ug/m3.
| PM2.5 Concentration at Site 1 | ||||
| Week # | Min PM2.5 Value (ug/m3) | Max PM2.5 Value (ug/m3) | Chlorosis Level | Leaf Fall Level |
| 1 | 15.8 | 17.3 | Mild | Mild |
| 2 | 2.3 | 4 | Mild | Mild |
| 3 | 4.4 | 7.5 | Moderate | Mild |
| 4 | 1.7 | 3.2 | Moderate | Mild |
| 5 | 5.5 | 6.7 | High | Moderate |
| 6 | 2.3 | 4.4 | High | High |
| 7 | 8.5 | 12.8 | High | High |
| Average | 5.8 | 8.0 | ||
Table 1.
In regard to qualitative observations at the site, the chlorosis of the leaves of the trees near the site started at around week 3. As time passed the leaves became more and more chlorotic until week 5 where a majority of the leaves still on the trees (and the leaves on the ground) had succumbed to chlorosis. This can be seen in Figure 7.
Figure 7. High Levels of Chlorosis on trees near/over the site when visited on Week 6.
In regard to leaf fall, leaf fall was slow to onset but after it was onset, leaf fall was rapid. Leaf fall was recorded to be mild from weeks 1 to 4 however by week 5 the leaf fall had rapidly changed to moderate and by week 6, leaf fall was high which can be seen in Figure 8.
Figure 8. Noticeable leaf fall of trees around Site 1.
Both chlorosis and leaf fall were quickly onset for the trees nearest to Site 1. However for trees greater than 100m away from the site, such as the trees in Indiana University’s Arboretum, chlorosis and leaf fall took much longer to be present with it taking until week 6 for those trees be similar in appearance to the trees near the construction site.
| PM2.5 Concentration at Site 2 | ||||
| Week # | Min PM2.5 Value (ug/m3) | Max PM2.5 Value (ug/m3) | Chlorosis Level | Leaf Fall Level |
| 1 | 15.6 | 17.8 | Mild | Mild |
| 2 | 2.6 | 3.7 | Mild – Moderate | Mild – Moderate |
| 3 | 11.3 | 16.9 | Moderate | High |
| 4 | 1.8 | 2.6 | High | High |
| 5 | 15.3 | 18.5 | High | High |
| 6 | 7.4 | 9.1 | High | High |
| 7 | 12.1 | 15 | High | High |
| Average | 9.4 | 11.9 | ||
Table 2.
Site 2, the smaller site of the three surveyed, was located on State Road 37 and involved minor road construction compared to the extensive construction at Site 1. The perimeter of the site was circled multiple times and the minimum and maximum PM2.5 concentrations were measured around the site. The average of these minimum and maximum values from across the seven weeks that the site was visited, was then calculated. This average range was calculated to be 9.4-11.9 ug/m3 as seen in Table 2.
Chlorosis was much faster onset for the leaves of trees around site 2 when compared to Site 1 with a moderate amount of chlorosis noted at Week 3 and a high amount of chlorosis by week 4. This is also reflected in the amount of leaf fall as well as high leaf fall was observed at week 3 and the subsequent weeks was high to N/A (due to there being little to no leaves present on the trees). This fast onset might be explained by the high PM2.5 minimum and maximum concentrations. These high values would be reflected in the rapid chlorosis and large amount of leaf fall in such a short period of time compared to site 1. Similar to site 1, trees that were greater than 100m away from the site, had smaller amounts of leaf fall and chlorosis over a longer period of time with it taking almost 4 weeks for those trees to show similar appearances of those trees near the site.
The final construction site, site 3, was the largest of all the sites surveyed in terms of physical size as well as the amount of machinery present and amount of people working on the site. Measuring the concentration at this site was different from sites 1 and 2 because the nearest tree line was farther away from the site. To account for this larger distance, the minimum and maximum PM2.5 concentrations were taken from both the perimeter of the site as well as taken from the tree line closest to the construction. The minimum and maximum PM2.5 concentrations around the perimeter of the site were averaged as well as the concentrations at the nearest tree line. The average PM2.5 range around the perimeter of the site was calculated to be 8-11.5 ug/m3 and the average range of PM2.5 at the tree line was calculated to be 9.7-13.6 ug/m3. The culmination of the data from both areas can be seen in Table 3 and Table 4. It was expected that this site would have the highest concentrations due to the amount of construction equipment present at the site and the overall extensiveness of construction.
| PM2.5 Concentration at perimeter of Site 3 | ||
| Week # | Min PM2.5 Value | Max PM2.5 Value |
| 1 | 18.3 | 25.5 |
| 2 | 3.1 | 6 |
| 3 | 15.5 | 20.2 |
| 4 | 2 | 4.3 |
| 5 | 9.3 | 13.1 |
| 6 | 1.7 | 2.9 |
| 7 | 6.1 | 8.5 |
| Average | 8 | 11.5 |
Table 3.
| PM2.5 Concentration at tree line of Site 3 | ||||
| Week # | Min PM2.5 Value | Max PM2.5 Value | Chlorosis level | Leaf fall level |
| 1 | 21.2 | 28.1 | Mild | Mild |
| 2 | 5.3 | 10.3 | Mild | Mild – moderate |
| 3 | 15.4 | 16.2 | Mild | Moderate |
| 4 | 3.9 | 8.5 | Mild – moderate | Moderate |
| 5 | 14 | 18.6 | Moderate | Moderate |
| 6 | 2.7 | 5.5 | Moderate | Moderate-high |
| 7 | 5.2 | 8.3 | Moderate | Moderate-high |
| Average | 9.7 | 13.6 | ||
Table 4.
The chlorosis of the leaves on the trees nearest Site 3 was slower onset compared to sites 1 and 2 with it being noticeable by week 4 and only getting a moderate level by week 7. Leaf fall was slightly faster onset than the chlorosis by week 3 but was only ranked between moderate and high at the end of week 7. These observations stand out compared to those seen at site 1 and 2 which were smaller than site 3 so it would be expected that the trees near this site would suffer from chlorosis and leaf fall faster than those trees near sites 1 and 2 but this is not the case. However, similarly to sites 1 and 2, the trees near site 3 suffered from chlorosis and leaf fall faster compared to trees greater than 100m away from the site with those trees taking until week 6 to show significant leaf fall/chlorosis. In addition the level of chlorosis and leaf fall, the difference in mass of leaves near the site and the mass of leaves greater than 100m from the site were compared. As shown in Figure 6. 15 leaves from an Eastern Redbud tree were taken from trees near the site area and their sum mass was taken. On the same day, 15 leaves from another Eater Redbud were taken from a tree farther away and had their sum mass taken as well. These sum masses were then divided by 15 in order to get the mass of each individual leaf. The mass of the leaf closest to the site was calculated to be 0.462 + 0.001g and the mass of the leaf farther away from the site was 0.445 + 0.001g.
| Date | PM2.5 concentration |
| October 9th | 9.6 |
| October 12th | 4 |
| October 15th | 3.9 |
| October 18th | 7.7 |
| October 21st | 7 |
| October 24th | 2.2 |
| October 27th | 11.7 |
| October 30th | 3.3 |
| November 2nd | 4.7 |
| November 5th | 10.6 |
| November 8th | 8.9 |
| November 11th | 3.7 |
| November 14th | 8.9 |
| November 17th | 5.1 |
| November 20th | 10.7 |
| Average | 6.8 |
Table 5.
Discussion
The collected concentration ranges from each of the sites as well as the qualitative observations, allows for the comparison of the damage those trees nearest the sites suffered compared to those farther away from the site. Data from the Indiana Department of Environmental Management included local conditions of PM2.5 from 10/9/20-11/20/21. These values were averaged to find the mean PM2.5 concentration over that same time period which was calculated to be 6.8 ug/m3 as seen in Table 5. above . Sites 2 and 3 had ranges of PM2.5 concentration that were greater than that average with the average site 1 range having its minimum value below that local average (5.8 < 6.8) and its maximum above that local average (8.0 > 6.8). These larger values of PM2.5 near the sites would indicate that the trees surrounding the sites were exposed to more PM2.5 compared to those trees in the local area that are farther away from the sites themselves. This can be seen in the qualitative observations as well with all three of the sites having a faster onset of chlorosis and leaf fall compared to those trees greater than 100m away from the site. However, there is a degree of uncertainty in these measurements because a majority of the sites were near roads. Emissions from cars on these would interfere with the detector and could give it higher or lower readings than what is accurate. Additionally, the initial health of the trees was not considered which may also be a reason why the trees nearest the site lost their leaves faster than other trees because they could have already been suffering from a previous affliction.
In addition to the ranges of PM2.5, the deposition of the PM2.5 on the leaves, as indicated by the calculated masses, show that the leaves closest to the site have a greater mass of about 0.017g. This difference could be explained by the leaves closest to the site having absorbed the PM2.5 as well as other pollutants. There is some uncertainty in this however, as it is known that rain and wind can carry away PM2.5 from a leaf’s surface(Luo et al. 2020). This could affect the concentration of pollutants on both the leaves near the site as well as the leaves farther away from the site which would affect the accuracy of the masses taken.
Conclusion
The results of the research project emphasizes the effect that PM2.5 emissions from internal combustion engines can have on the vegetation that are in proximity to those emissions. As the population of humans is exponentially increasing, the amount of housing needed is also increasing (Rivera et al. 2016). This will require a large amount of clearing of land and construction projects in order to be able to fit the housing needs of an increasing population. This means that not only will more vegetation biodiversity could be lost from the physical clearing, but the surrounding vegetation that was not cleared could also be damaged from the emissions from the construction equipment.
This loss of biodiversity can have a long term effect on human health however the emissions themselves can have an acute impact on the construction workers. Studies have found that there is a link between air pollution at a job site and risks for chronic diseases such as ischemic heart disease (Torén et al. 2007). This could also affect those living or working near sites of construction which might be more commonplace as humanity will require an increase in industrialization to meet the needs of a growing population.
The overall goal of this project was to look at the effect of PM2.5 on the health of trees near construction sites. For this project, the level of damage done to the trees was assumed via measurements and observations of the leaves of trees near and far from the construction site. The data collected showed that there is a higher concentration of PM2.5 near the sites surveyed compared to the local PM2.5 conditions measured by the Indiana Department of Environmental Management. The accuracy of these measurements and observations, however, could be affected by varying weather conditions so they cannot be 100% conclusive. Nevertheless, it is important to understand that previous studies have already emphasized the effect of PM2.5 as well as other types of pollutants have on the metabolic processes of plants. It should be acknowledged the role that plants have as producers in the global ecosystem, as well as the threats humans pose to this role. If not considered, the loss of the biodiversity plants provide could lead to drastic effects on, not only human health, but the overall health of the planet as well.
Works Cited
Cheng, Y., Lee, S. C., Gao, Y., Cui, L., Deng, W., Cao, J., … & Sun, J. (2015). Real-time measurements of PM2. 5, PM10–2.5, and BC in an urban street canyon. Particuology, 20, 134-140.
Giri S, Shrivastava D, Deshmukh K, Dubey P. 2013. Effect of Air Pollution on Chlorophyll Content of Leaves. Curr Agri Res;1(2). doi : http://dx.doi.org/10.12944/CARJ.1.2.04
Guderian, R. (2012). Air pollution: phytotoxicity of acidic gases and its significance in air pollution control. Vol. 22. Springer Science & Business Media.
Hodan W, Barnard W,. Evaluating the Contribution of PM2.5 Precursor Gases and Re-entrained Road Emissions to Mobile Source PM2.5 Particulate Matter Emissions. (2004). MACTEC Federal Programs. https://www3.epa.gov/ttnchie1/conference/ei13/mobile/hodan.pdf
Iqbal, M., Shafiq, M., Zaidi, S., & Athar, M. (2015). Effect of automobile pollution on chlorophyll content of roadside urban trees. Global Journal of Environmental Science and Management, Vol .1(4), 283-296.
Joshi P. C., and Swami A,. (2009). Air pollution induced changes in the photosynthetic pigments of selected plant species. Journal of Environmental Biology Vol. 30.2: 295-298.
Li, M., Huang, D., Zhou, Y., Zhang, J., Lin, X., & Chen, J. (2021). The legacy effects of PM2. 5 depositon on Nerium Oleander L. Chemosphere, 281, 130682.
Lu S, Yang X, Li S, Chen B, Jiang Y, Wang D, Xu L,. (2018). Effects of plant leaf surface and different pollution levels on PM2.5 adsorption capacity. Urban Forestry & Urban Greening. Vol 34, 64-70,
Luo J, Zhou X, Tian Y, Zhang M,. (2018). Research report of particulate matter deposited on leaf surface of major ecological tree species. IOP Conference Series: Earth and Environmental Science. 170(5). 052002. 10.1088/1755-1315/170/5/052002.
Luo, J., Niu, Y., Zhang, Y., Zhang, M., Tian, Y., & Zhou, X. (2020). Dynamic analysis of retention PM2. 5 by plant leaves in rainfall weather conditions of six tree species. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42(8), 1014-1025.
Milne B, Toker Y, Rubio A, Nielsen S,. (2014). Unraveling the Intrinsic Color of Chlorophyll. Angewandte Chemie. 127. 10.1002/ange.201410899.
Nguyen, T., Yu, X., Zhang, Z., Liu, M., & Liu, X. (2015). Relationship between types of urban forest and PM2. 5 capture at three growth stages of leaves. Journal of Environmental Sciences, Vol. 27, 33-41.
Novak K, Skelly J, Schaub M, Kräuchi N, Hug C, Landolt W, Bleuler P,. (2003). Ozone air pollution and foliar injury development on native plants of Switzerland. Environmental Pollution.125(1):41-52. https://doi.org/10.1016/S0269-7491(03)00085-X.
Pavlovic D, Nikolic B, Đurović S, Waisi H, Anđelković A, Dragana M,. (2014). Chlorophyll as a measure of plant health: Agroecological aspects. Pesticidi i fitomedicina. 29. 21-34. 10.2298/PIF1401021P.
Prajapati, S. K. (2012), Ecological effect of airborne particulate matter on plants, Environ. Skep. Crit., Vol. 1, 12– 22.
Rivera, A., Le, N., Kashiwagi, J., & Kashiwagi, D. (2016). Identifying the global performance of the construction industry. Journal for the Advancement of Performance Information & Value, 8(2), 7-19.
Sawidis, T., Marnasidis, A., Zachariadis, G. et al.(1995) A study of air pollution with heavy metals in Thessaloniki city (Greece) using trees as biological indicators. Arch. Environ. Contam. Toxicol. Vol 28, 118–124 https://doi.org/10.1007/BF00213976
Swami A,. (2018). Impact of Automobile Induced Air Pollution on roadside vegetation: A Review. ESSENCE International Journal for Environmental Rehabilitation and Conservation. 9:101-116. 10.31786/09756272.18.9.1.113.
Tabassum MA, Zhu G, Hafeez A, Wahid MA, Shaban M, Li Y. (2016). Influence of leaf vein density and thickness on hydraulic conductance and photosynthesis in rice (Oryza sativa L.) during water stress. Sci Rep. 6:36894.doi:10.1038/srep36894
Torén, K., Bergdahl, I. A., Nilsson, T., & Järvholm, B. (2007). Occupational exposure to particulate air pollution and mortality due to ischaemic heart disease and cerebrovascular disease. Occupational and environmental medicine, 64(8), 515-519.
Vizzarri M, Tognetti R, Marchetti M,. (2015) Forest Ecosystem Services: Issues and Challenges for Biodiversity, Conservation, and Management in Italy. Forests. 6(6):1810-1838. https://doi.org/10.3390/f6061810
Winner, W.E. (1994). Mechanistic Analysis of Plant Responses To Air Pollution. Ecological Applications, Vol 4, 651-661.
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