Phase I—Results

Particle Concentrations

The leaves had a wide range of particle concentrations. These ranged from a minimum of 55.3 particles per mm2 (leaf 8a, run 88), to 4166.7 particles per mm2 (Leaf 9a, run 89). As is visible in the graph in fig. 3, the particle concentration values on each of the leaves were fairly inconsistent. The range of concentrations, even on a single leaf, was very wide. However, when shown in the average concentrations graph in fig.4 (which is the average concentration of each leaf sample from both sides combined), it is shown that leaf 9, the urban leaf, had the highest concentration (1573.0 part./mm2), followed by leaf 7, the suburban leaf (884.6 part./mm2), and then by leaf 8, the rural leaf, with the lowest concentration (572.5 part./mm2). These results support the hypothesis made earlier, that the particle concentrations would decrease from urban to suburban to rural locations.

The graph in fig.5 shows the average concentrations of particles on the tops (a) and bottoms (b) of each leaf. It is clear from this graph that the concentrations of particles on the tops of leaves are significantly higher than those of the bottom of the leaves. This contradicts the hypothesis made earlier, that concentrations on the bottom surface would be higher than those on the top. Apparently, the amount of particles caught on the bottom surface from the flow of air through the stomata is not as high as the amount depositing on the top surface as dust.

Particle Sizes

The median diameter statistics of particles on the leaf surfaces also showed a fairly wide distribution, although these were more consistent than the particle concentrations (see fig.6). The lowest median particle diameter was 0.41 µm (leaf 8b, run 124). The highest was 1.68 µm (leaf 7b, run 93). As is visible in the graph in fig.6, each run seemed to have different ranges. Run 93 appears to have higher diameters overall, and run 124 appears to have the lowest.

The graph in fig.7 shows the averages of all the median diameters on each leaf. The particle diameters on the bottoms of the leaves are visibly lower than those on the tops of the leaves. On leaf 7a, the average particle diameter is 1.17 µm, while on leaf 7b it was 1.08 µm. On leaf 8a, it was 1.11 µm, while on leaf 8b it was 0.63 µm. On leaf 9a, it was 1.12 µm, while on leaf 9b it was only 0.97 µm. This is supportive of the hypothesis made earlier that particle diameters on the bottom of leaves would be smaller than those on the top.

The average particle diameters of leaf 8, the rural leaf, do not seem to fit in with the other leaves. As shown in the graph in fig.7, its average diameters are visibly smaller than those of the other leaf samples. Because the particles found in the rural area were expected to be mostly from natural sources and particles from natural sources are usually larger than those from anthropogenic sources, the particles on the rural leaf would be expected to be larger than those on the suburban and urban leaves. However, there is a possible explanation for this. Smaller particles can travel very far before depositing on the ground. Therefore, small particles from the city may have traveled out to the rural location and deposited on the leaves there, while the larger particles deposited in the suburban and areas closer to the city and in the city itself. The reason the urban leaf average is in the middle is probably because it has a wider range of sizes than either rural or suburban.

Sources of Errors

The first source of error that was recognized was the fact that the leaves used were of different species. Different species of leaves have different anatomical characteristics, such as the presence of leaf hairs and differences in surface stickiness. This could have had a fairly big effect on the results, because leaves with many leaf hairs or a stickier surface could collect more particles than other leaves without these characteristics, causing the different leaves to have inaccurate comparisons. The best solution to this problem is probably the most obvious one, which is to collect samples from the same species of tree. This may be hard if there are not any of the same species in the two locations which are being sampled. However, the different species problem may not be a very big problem. In the graphs shown in fig.8 and fig.9, one can see that run 88/89 and run 124, which used different species of leaves, showed very similar results. The big differences such as the high concentration of leaf 8a, run 124, are probably caused by one of the other sources of error, such the detection threshold or the leaf storage, or the ages of the leaves. Therefore if this is a source of error, it is most likely not a very big one.

The second source of error, also regarding leaf collection, is the ages of the leaves. The ages of the leaves collected was unable to be determined. Some of the leaves may have sprouted earlier than others. The earlier the leaf sprouts, the more time it has to collect particles and therefore the higher the concentration of particles it would have. Therefore, a comparison between an older leaf and a newer leaf would be inaccurate. There is not much to be done to avoid this.

The third source of error is the particle detection threshold. This was a very big problem, resulting in the need to rerun several automated analyses. The reason this is such a big problem is because the threshold has to be set nearly perfectly, or it will affect the results. If the threshold is too sensitive, it will record background “noise,” such as the leaf’s epidermal cells, as particles, causing a higher concentration to be found. If the threshold is not sensitive enough, the very small and the less dense particles will not be recorded as particles, causing a lower concentration to be found. What makes this even more of a problem is that only a single threshold can be set up for an entire automated analysis, which means if the threshold needed for one leaf is different from that needed for another leaf, the threshold has to be compromised to fit both. This means it will lose particles from one leaf and record more from the other. This is especially hard when there are more than two leaves present in the analysis, in which case the threshold would have to be compromised to have the best fit for all of the leaves. The best way to avoid this problem would be to perform a manual analysis of the particles, so that the operator is able to decide what particles are analyzed and recorded. This would take more time, and measuring the size of the particles would be harder, but it would eliminate the threshold problem completely.

The fourth source of error was the method of leaf storage. This caused several problems, as it was probably not the best method for leaf storage. The first problem regarding storage was the wrapping of leaves together. Although the leaves were only stored with other leaves from the same location, when the leaves rub together it could cause particles on the surface of one leaf to rub off onto the other leaf. This would create a higher concentration on the leaf that the particles rubbed onto and a lower concentration on the leaf that they were rubbed off of. Also, particles that are part of one leaf may rub off onto another leaf, causing a higher concentration on the receiving leaf. The obvious solution to this would be to individually wrap each leaf preventing them from rubbing together.

However, the wrapping of leaves is also a problem. In this experiment, leaves were wrapped in aluminum foil. The problem with this is that small aluminum particles may rub off of the foil and onto the leaves. This would raise the concentration of particles on the leaf and therefore change the results of the experiment. There were many particles of aluminum on the leaf surfaces, which may be the result of the rubbing off of particles from the aluminum foil. Also, particles may have rubbed off of the leaf surfaces and onto the foil, causing a lower concentration on the leaf. A possible solution to this would be to store the leaves in a container that does not rub against them as much, or, to prevent the raising of the concentration of particles found, the leaves could be wrapped in a material composed of an element such as carbon which does not show up in the SEM.

The third problem regarding leaf storage is the fact that the leaves were stored in the freezer. When the leaves are taken out of the freezer, water condenses on them because of their cold temperature. Water has been shown to leave particles on the surface of yew needles. Therefore it is likely that the water on the leaves also left particles on the surfaces. This would raise the concentration of particles on the surface of the leaf, which is very likely what caused the concentration of leaf 8a in run 124 to be so high. Leaf 8a in runs 99 and 124 seemed to be affected most by the condensation, as the leaf had many frozen drops of water on it when it was taken out of the freezer.

Water has also been shown to remove particles from yew needle surfaces. This may be true for leaves as well, and may have therefore lowered particle concentrations on several of the leaves. The only way to solve these problems would be to find a new method of storage. This could be a possible subject for a future experiment.

The fifth source of error is particle deposition to the leaf samples after collection. There were times when the samples were exposed to the air, although for a very short time. This could have exposed the samples to more particle deposition, which would have raised the particle concentrations on the leaves. This was probably not a very big factor, however, because the amount of time that the samples were exposed for (such as transfer to the SEM) was not long enough for much particle deposition to occur. However, to completely avoid this, it is necessary to make sure that the samples are covered at all times.

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