Particulate matter (PM) is a common air pollutant linked to many human deaths and illnesses worldwide. Once emitted in the air, PM is most e ectively removed by plants in phytoremediation. While the physical mechanics and factors of phytoreme- diation have been well-studied, there has been little attention regarding its biological mechanisms. In this empirical study, we measure the di erence in PM phytoremedia- tion ability of live Sansevieria trifasciata specimens versus dried and pressed ones. Our results show that live specimens are in fact worse at removing PM from an enclosed space as compared to the dried and pressed specimens. Further research is needed to investigate the factors underlying this di erence. 1 Keywords| phytoremediation, air pollution, particulate matter, Sansevieria trifasciata , Stefan ow, dry deposition List of Figures 1 Layout of the experimental set-up. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Scatter plot of PM concentration against time. . . . . . . . . . . . . . . . . . . . . . 7 List of Tables 1 Summary of coecient estimates from the regression. . . . . . . . . . . . . . . . . . 8 2 Esimated half-lives of PM for each condition, in each chamber. . . . . . . . . . . . . 8 1 Introduction Particulate matter (PM) is a signi cant component of air pollution.1Human exposure to PM is associated with cardiovascular, respiratory, and other detrimental health conditions.2PM 2.5, a subset of PM with diameter smaller than 2 :5µm, has alone been estimated to cause 3.3 million premature deaths per year, worldwide.3 Given its highly varied origins,4;5;6;7PM is dicult to tackle at its sources of emission, and has to be complemented by remediation measures after its release into the atmosphere8. Then, phytoremediation|the mitigation of pollutants by plants|is the one of the few cost-e ective meth- ods of removing PM from the air.8In particular, these phytoremediation methods include the use of urban trees and forests,9;10;11;12;13indoor potted plants,14;15green walls,16;17or active bio l- ters.18;19 To improve the eciency of phytoremediation and thus PM removal from the atmosphere, it is important to identify exactly which factors have a direct impact on a plant's ability to remove PM from the air. In terms of physical mechanisms, PM is believed to be captured by plants through the process 2 of dry deposition, in which PM is transferred to the surface of the plant in three stages.20First, PM is transported from the atmosphere to the quasi-laminar sublayer (a thin millimetre or smaller layer around the plant with minimal turbulence) via eddy di usion and gravitational sedimentation e ects. Then, the PM are transported from the sublayer to the surface of the leaf through di usion, interception, impaction and sedimentation. Lastly, PM interacts with the surface of the plant and may adhere to it by either depositing directly on the plant surface, or by embedding into the plant wax over time.21PM deposited on leaves may also eventually be washed o by rainfall,22 while a certain percentage of incident PM may undergo resuspension into the surrounding air.23 Therefore, external morphological characteristics which a ect the rate of dry deposition such as the root structure,19leaf shape,24;25arrangement,26and surface microstructure21;27;23;28consequently in uence a plant's ability to capture PM from the air. By identifying these factors and the nature of their e ects, we can make better decisions about which species of plant, structure of green wall, or form of bio lter would most eciently remove PM from the air. It may even be possible to genetically engineer plants in order to maximise these factors for PM removal. Research about the e ects of biological processes on PM phytoremediation is scarce. While direct biological mechanisms of PM removal a la metabolism are unlikely, some studies suggest that there may be secondary e ects at play, by showing that light intensity is positively correlated with PM removal in an enclosed space29;30. To our knowledge, however, there are no existing studies investigating the source of these e ects, and the magnitudes or nature of their contribution to PM removal. For example, we could hypothesise that increased light intensity induces the widening of stomatal pores in plants, increasing the e ective surface area available for dry deposition. But how does this compare to the negative e ect of a stronger Stefan ow pushing PM away from the leaf surface31? To begin answering these questions, it is important to rst determine the net marginal e ect of biological processes on PM removal rates. Then, further studies of these biological processes can help us maximise the eciency of PM phytoremediation by informing our selection of plant species. To this end, we designed and conducted an empirical study to investigate the net e ect of biological processes on PM removal in plants. We do so by preparing a control consisting of a set of dried and pressed plant specimens, then comparing its eciency of PM removal in an enclosed 3 experimental chamber against a set of live specimens. 2 Materials and Methods 2.1 Materials In this study, we quanti ed PM removal by measuring the rate of decrease of both PM 2.5and PM 10 of air in a small enclosed experimental chamber. We formed 3 treatment and control groups: (i) a pot with living specimens, (ii) a pot with dried and pressed specimens, and (iii) an empty control group. These groups would allow us to estimate the marginal e ects of biological processes and surface area provided by the plant leaves on PM removal rate. The experimental chambers was formed by 3 `open-bottom' acrylic enclosures of size 40 4060 cm (LWH) taped onto a large rubber sheet. Within each chamber is the control or treatment, a PM sensor, a ceramic bowl to contain the PM source, and a small USB-powered fan for air circu- lation. Three 2 :5 mm diameter holes were drilled into the walls of each chamber: one was used as a cable pass-through for the PM sensor and fan, sealed using a silicone sealant; the other two were used as venting ports for excess PM (Figure 1). The plant specimens used are individuals of the species Sansevieria trifasciata , a common or- namental plant. A total of three potted S. trifasciata of similar size were obtained from a local distributor. Each pot contained multiple individual plants chosen by the distributor so that the leaf areas of each pot were roughly equivalent. To form the two treatment groups, one set of spec- imens was removed from its pot, dried while pressed under pressure (a common plant preservation technique), then placed back into the pot; the other set was left unmodi ed. We used a low-cost laser scattering sensor to measure the levels of PM 2.5and PM 10in the range 0:0µg m3to 999:9µg m3.32The sensors were connected to a Raspberry Pi Zero, a small single-board computer, to collect readings every minute. Small quantities of dried plant matter were burnt in the ceramic bowls in order to generate PM. 4 Figure 1: Left to right within each experimental chamber: a ceramic bowl to contain the PM source, the treatment or control, a small USB-powered fan, and a PM sensor. Each chamber has three 2 :5 mm diameter holes: one was used as a cable pass-through, sealed using a silicone sealant; the other two were used as venting ports for excess PM (shown covered in black-coloured tape). The PM sensors were all connected to a Raspberry Pi Zero, a small single-board computer, to collect readings. 5 2.2 Experimental Procedure For each trial, we placed and ignited a small quantity of dried plant matter in the ceramic bowl. The two venting holes were then left open to allow the PM to escape until the concentration of PM 2.5fell to 950:0µg m3, within range of the sensors. Then, we sealed the venting holes with tape, ensuring that each chamber is fully sealed before the concentration of PM 2.5dropped to 900:0µg m3. The trial was then left to run for at least 6 hours in the same lighted room, with about 24 hours in-between each trial. We rotated the 3 treatment groups evenly about the 3 chambers for a total of n= 45 trials|15 trials for each treatment or control, each set of 15 is split equally about each chamber. 2.3 Statistical Methods Statistical analyses and plots were generated using Rand its related packages33;34. Each PM 2.5 and PM 10concentration in the experimental chamber Ywas modelled to exponentially decay with timet, modi ed with e ects due to the presence of additional surface area provided by leaves of the live and dead plants (represented by Xs), and the presence of some biological activity in the live plant only (represented by Xb); and e ects due to di erence in experimental chambers and sensor calibrations (represented by control variables XB;XC). For example, the trials with dead plants was encoded with Xs= 1 andXb= 0, while those with live plants are encoded with Xs=Xb= 1. All explanatory and control \ X" variables are binary indicator variables. Suitability of the model was checked with the Bayesian information criterion. Y= expf 0+ 1t+ 2XB+ 3XC+ 4tXB+ 5tXC+ 6tXs+ 7tXbg The data was t using a log-linked generalized linear model with Gamma random component. Of interest are the marginal e ect of additional surface area provided by leaves of the live and dead plants, 6; and the marginal e ect of biological activity in the live plant only, 7. We can also obtain the estimated half-life of PM in each condition with (ln 2) =, where= 1+ 4XB+ 5XC+ 6Xs+ 7Xb(obtained from the terms with time t). 6 Figure 2: Scatter plots of PM concentration against time, after controlling for di erences due to experimental chambers and sensor calibrations. Grey-coloured points in the background are readings from other conditions within the same PM type. 3 Results and Discussion 3.1 Results All coecient estimates are signi cantly non-zero (Table 1). As compared to the empty control, trials using the dead plant reduce the half-life of airborne PM in the experimental chamber; com- pared to using the dead plant, trials using the live plants increase the half-life of airborne PM. This result is true and the e ect sizes are non-trivial across all experimental chambers, for both PM 2.5 and PM 10(Figure 2 and Table 2). 7 Coecient Estimate Std. Error 95% con dence interval PM 2.5 0 7:077 0:005 820 [ 7 :066 , 7 :088 ] 10:012 76 0:000 032 11 [0:012 83 ,0:012 70 ] 2 0:054 00 0:009 023 [ 0 :036 03 , 0 :071 97 ] 3 0:1127 0:007 823 [ 0 :097 16 , 0 :1283 ] 40:006 306 0:000 063 74 [0:006 435 ,0:006 177 ] 5 0:002 750 0:000 039 90 [ 0 :002 670 , 0 :002 830 ] 60:004 961 0:000 025 15 [0:005 011 ,0:004 910 ] 7 0:001 932 0:000 027 04 [ 0:001 879 , 0:001 985 ] PM 10 0 7:192 0:005 814 [ 7 :181 , 7 :203 ] 10:012 78 0:000 031 67 [0:012 84 ,0:012 72 ] 2 0:063 64 0:009 038 [ 0 :045 78 , 0 :081 51 ] 3 0:1576 0:007 910 [ 0 :1419 , 0 :1733 ] 40:006 396 0:000 063 18 [0:006 523 ,0:006 270 ] 5 0:002 558 0:000 039 65 [ 0 :002 479 , 0 :002 637 ] 60:005 015 0:000 024 21 [0:005 063 ,0:004 967 ] 7 0:001 944 0:000 025 96 [ 0:001 893 , 0:001 995 ] Table 1: Summary of coecient estimates from the regression. Highlighted rows are statis- tics for the coecients related to Xs, encoding the presence of surface area provided by leaves of either the dead or live plants; and Xb, encoding the presence of some biological activity. Chamber Control Dead plants Live plants PM 2.5A 54:3; [ 54:0, 54:6 ] 39:1; [ 38:9, 39:4 ] 43:9; [ 43:4, 44:4 ] B 36:3; [ 36:0, 36:7 ] 28:8; [ 28:6, 29:1 ] 31:4; [ 31:0, 31:8 ] C 69:2; [ 68:2, 70:2 ] 46:3; [ 45:7, 46:9 ] 53:1; [ 52:2, 54:2 ] PM 10A 54:2; [ 54:0, 54:5 ] 39:0; [ 38:7, 39:2 ] 43:7; [ 43:3, 44:2 ] B 36:1; [ 35:8, 36:5 ] 28:7; [ 28:4, 28:9 ] 31:2; [ 30:8, 31:6 ] C 67:8; [ 66:9, 68:8 ] 45:5; [ 44:9, 46:1 ] 52:1; [ 51:2, 53:1 ] Table 2: Estimated half-lives (in minutes) of PM for each condition, in each chamber. Intervals shown are the 95% con dence intervals. 8 3.2 Possible Mechanisms As expected, the introduction of additional surface area from leaves of either dead or live plants reduces the half-life of airborne PM in the experimental chambers, likely since there is more area available for dry deposition to occur. What is unexpected, however, is the marginal increase in half-life due to the net e ects of biological activity in the live plants. We consider these biological e ects to be either due to primary or secondary e ects. Primary biological e ects refer to any direct interaction between plant cells and PM such that the PM is degraded, absorbed, or otherwise removed from consideration. Such primary e ects are unlikely, since PM is usually much larger than the pore sizes of plant cell walls35, so that the cells cannot interact directly with PM. It must also be emphasised that in order for these primary biological e ects to take place, the PM must have already come into physical contact with the leaf surface. This implies rstly that these e ects could not have been measured in our experiment, because our PM sensors only estimate how much PM is in the air, which we use to infer how much PM has been deposited onto the leaves|any additional e ects after that point are not re ected in airborne PM readings. Secondly, the importance of these primary e ects depend largely on the propensity for already deposited PM to be resuspended into the air. More likely are secondary e ects, in which biological processes a ect the ecacy of physical mechanisms of PM removal. For example, transpiration increases surrounding humidity, promoting PM coagulation36;37;30; photosynthesis and exposure to light induces opening of the stomata in some plants, making the intercellular air space (IAS) surfaces within leaves available for dry deposition38. Negative secondary e ects are possible too, and are likely the cause for the marginal increase in PM half-life we can attribute to the live plants in this experiment. In particular, we suspect that transpiration in the live plants produced a signi cant magnitude of Stefan ow, where gas molecules ow away from an evaporating surface31; thereby pushing PM away from the quasi- laminar sublayer, interfering with the process of dry deposition and thus reducing PM removal eciency. 9 3.3 Implications Previous studies have shown that increasing light intensity improves PM phytoremediation in cer- tain plant species29;30. In this article, we show that in Sansevieria trifasciata , secondary e ects of biological processes (including photosynthesis) have a net negative e ect on PM phytoremediation. These seemingly contradictory results suggest that the biological factors of PM phytoremediation are complex, and still need to be elucidated. Then, a deeper understanding of the biological factors at play can better inform our choice of plant species to maximise PM phytoremediation. For example, plants which utilise the Crassulacean acid metabolism (CAM) pathway, typically understood of as an adaptation to arid environments, tend to have smaller IAS39, and transpire less in the day than at night40. Indeed, the Sansevieria trifasciata plants we use in this study utilise this CAM pathway. Does this imply that CAM plants are worse PM phytoremediators because they do not release much water vapour for PM coagulation and have less available IAS for dry deposition? Or are they better because a reduced rate of transpiration implies a weaker Stefan ow? How do the answers change from day-time to night-time? As far as we know, these questions of secondary biological mechanisms in PM phytoremediation have received little attention in the literature. In showing the net negative e ect of biological activity on PM phytoremediation, taken in contrast with prior literature demonstrating a positive e ect of light intensity, we hope that our study will motivate and contribute towards the further study of these complex biological e ects. 4 Data Availability Statement All data, models, and programs generated and used during the study are available online at https: //github.com/ning-y/plants-pm-data . 5 Acknowledgments This work was funded by the National University of Singapore's University Scholars Programme Student Grant. 10 6 Declaration of Con icting Interests The authors declare no con ict of interest.