Chapter 4. Results. The results are presented in three parts. The statistical analysis of the door-to-door

Transcription

1 Chapter 4. Results The results are presented in three parts. The statistical analysis of the door-to-door journeys on each of the four modes of transport is presented first, followed by the spatial variations observed within each transport mode and the results and analysis of the dosage calculations. Concentration spikes and calculation of geometric means The 1 s and 1 s logging intervals managed to capture the numerous short-lived concentration spikes that occurred throughout the sampling period. These short-lived spikes resulted in distributions of exposure concentration that were skewed towards larger values. To illustrate the frequency of these spikes, time-series of the measurements taken on 2 May 213 are shown in Figure 4-1. Although some of these sharp peaks may be classified as extreme outliers (defined as > 3 standard deviations [SD] above the mean), they are a common occurrence and a real component of the total pollutant concentration, thus they have not been removed. To account for these values without giving them too much weight, the geometric mean (GM) is used as the main descriptive statistic for each trip. The GM is essentially the logarithmic mean of the data, obtained by applying a logarithmic transformation on the data, taking the arithmetic mean, then back-transforming the mean values by taking the antilog. Other studies have also used the GM instead of the arithmetic mean to better describe the data (e.g. Gulliver and Briggs, 24, de Nazelle et al., 212). 57

2 2 2 PM 1 (µg m -3 ) PM 2.5 (µg m -3 ) PM 1 (µg m -3 ) PN (1 3 # cm -3 ) ASA (mm 2 m -3 ) ppahs (ngm -3 ) 4 2 BC (µgm -3 ) :3 17: 17:3 18: Local Time CO (ppm) :3 17: 17:3 18: Local Time Figure 4-1: Post-processed data measured on 2 May 213. Time-series shown include one day of measurements on all transport modes. High variability and presence of spikes are evident in all measured parameters. In the rest of this thesis, the GM represents the average pollutant concentration measured during one door-to-door trip for each mode of transport or within a particular section of a trip. The arithmetic mean was only used to average HR and V E. For MRT mode, data from both trips on each day of measurement were combined for a total of 23 sets for analysis. However, for calculation of dosage, only data from the first MRT mode trip of each day were used. In the following description of results, mean refers to the arithmetic mean of the GMs for each trip 58

3 or section. The boxplots presented in this chapter are also based on the GM from each trip or section. 4.1 Commuter exposure on door-to-door trips Mean values and SDs of all measurements are summarised in Table 4-1. More detailed descriptive statistics including maximum and minimum values can be found in Appendix D.1. The ratios of transport mode to background site (BG) measurements in Table 4-2 provide a measure of the difference between concentrations in the different transport microenvironments and at the ambient level. Generally, all three PM size-fractions and PN concentrations are higher for the four transport modes than at the background site, with the exception of the MRT mode. This agrees with existing research on commuter exposure, which finds that measurements in the transport microenvironment are elevated compared to ambient readings (Kaur et al., 25b; Gulliver and Briggs, 27; de Nazelle et al., 212). The Walk (MRT) mode exhibits the highest (lowest) mean pollutant concentrations, while CO values in the transport environment and at BG are similar. The results of the individual pollutant metrics are described in more detail in the following sections. Table 4-1: Mean (SD) of pollutant metrics from all trips for 4 transport modes and measured at the background site. (N = 23 for Bus, Taxi, and MRT, N = 22 for Walk) Transport mode PM 1 (µg m -3 ) PM 2.5 (µg m -3 ) PM 1 (µg m -3 ) PN (# cm -3 ) Bus 28 (6) 29 (6) 32 (6) 28,916 (6,768) MRT 27 (4) 27 (4) 3 (4) 14,418 (2,625) Taxi 27 (7) 28 (7) 3 (7) 3,882 (13,799) a Walk 37 (9) 37 (9) 42 (9) 44,49 (6,142) BG 25 (8) 25 (8) 27 (8) 21,893 (5,369) ASA (mm 2 m -3 ) ppahs (ng m -3 ) BC (µg m -3 ) CO (ppm) Bus 133 (25) 79 (27) (2.318).8 (.7) MRT 114 (16) 3 (6) (1.345).5 (.7) Taxi 14 (36) 86 (48) (3.428) 1. (.9) Walk 137 (23) 99 (17) 7.59 (3.975).6 (.8) BG (1.1) 59

4 Table 4-2: Transport mode to BG ratios from all trips for pollutant metrics measured in both environments. Transport mode PM 1 PM 2.5 PM 1 PN CO Bus MRT Taxi Walk Statistical tests were also performed on the GM data to identify if concentrations measured on each mode of transport were significantly different from each other and BG. The results of the Kruskal-Wallis tests show a statistically significant difference across all measured metrics at the 95% confidence level (pvalue <.5) (Table 4-3). Post-hoc multiple comparisons were carried out using the Mann-Whitney U Test with Bonferroni correction to further discern which specific modes contributed to the result. The results of these multiple-comparison tests are presented in the following sections. Table 4-3: Results from the Kruskal-Wallis test validating that concentrations measured on each mode of transport were significantly different from each other and the background site. H = test statistic, df = degrees of freedom. Metric H df p-value PM * PM * PM <.1* PN <.1* ASA * ppahs <.1* BC <.1* CO * ** = p <.5 6

5 4.1.1 Particulate matter mass concentrations (PM) The results for the three PM size-fractions measured are discussed together since the trends between transport modes are similar (Figure 4-2). The highest mean concentrations were observed during the Walk mode, with values above 35 µg m -3 for all three fractions (PM 1 : 37 µg m -3, PM 2.5 : 37 µg m -3, PM 1 : 42 µg m -3 ). Maximum values (individual data points) for PM 1, PM 2.5 and PM 1 were also observed during the Walk mode. In terms of PM 1, the maximum recorded was 677 µg m -3, while PM 2.5 and PM 1 reached a maximum of 532 µg m -3 and 529 µg m -3 respectively (see Appendix D.1). The mode with the second highest mean PM concentrations was the Bus mode, with mean PM 1, PM 2.5 and PM 1 values of 29 µg m -3, 29 µg m -3, and 32 µg m -3 respectively. The mean concentration levels observed for the MRT and Taxi mode were very similar and close to the Bus mode, with a minor difference of 1 µg m -3 for PM 2.5. One difference between these two transport modes is the smaller spread of data for the MRT mode, with SDs of 4 µg m -3 for the three PM size-fractions compared to 7 µg m -3 for the Taxi mode (Table 4-1). Mean PM values were higher on the four transport modes than at the background site (Table 4-2). However, only the Walk mode exhibited a relatively high ratio of ~1.5. The other modes were approximately times higher than BG. As with the mean values presented above, all three size-fractions of PM exhibit similar patterns across the different transportation modes. 61

6 6 PM 1 (µg m -3 ) PM 2.5 (µg m -3 ) PM 1 (µg m -3 ) 4 2 BG Bus MRT Taxi Walk Transport mode Figure 4-2: Boxplots of PM 1 (top), PM 2.5 (middle) and PM 1 concentrations (bottom) measured during the four transport modes and at the background site (BG) averaged across the entire dataset. Boxes and thick horizontal line represent the 25 th to 75 th percentile (inter-quartile range [IQR]) and median, respectively, triangles are mean values, vertical lines extend to the highest or lowest value within 1.5 times the IQR, and diamonds (if present) are outliers beyond that. 62

7 Comparison with other, similar studies reveals that PM 2.5 values in the present study are the low end of values found elsewhere. The range observed on the various transport modes are most similar to those of recent studies in Sydney (Knibbs and de Dear, 21) and Barcelona (de Nazelle et al., 212), with values ranging from 2 35 µg m -3 (see Table 2-2). However, the variability across transport modes between studies is different. One possible explanation is the ventilation setting of the vehicles, which will be explained in more detail in Chapter 5. Ratios of PM 1 to PM 2.5 and PM 2.5 to PM 1 were calculated as a measure of the proportion of fine to coarse-particles (Table 4-4). The ratios are very high, demonstrating that fine particles are the dominant component of PM in the Singapore transport microenvironment. This result is consistent with the close proximity to vehicular exhaust which emits particles of <.1 µm diameter (Lighty et al., 2). The ratios at BG are similarly high indicating that that most of the pollution measured at background was of local origin, since the particles have not had time to grow to larger sizes in the atmosphere. Table 4-4: PM 1 /PM 2.5 and PM 2.5 /PM 1 ratios for each transport mode and at the background site averaged across the entire dataset. Transport mode PM 1 /PM 2.5 PM 2.5 / PM 1 Bus.99.9 MRT Taxi Walk.99.9 BG The Kruskal-Wallis test revealed a statistically significant difference (p <.5) between the four modes of transport and the background site for all three sizefractions of PM (Table 4-3). The results of the multiple-comparisons tests show that only the Walk mode is significantly different from the other three modes and the background site for all three size-fractions of PM (Tables 4-5 to 4-7). 63

8 Table 4-5: Results of multiple-comparisons tests for effect of transport mode on PM 1 concentrations. BG Bus MRT Taxi Walk BG Bus MRT Taxi Walk <.5* <.5* <.5* <.5* - * = p <.5 Table 4-6: Same as Table 4-5 but for PM 2.5. BG Bus MRT Taxi Walk BG Bus MRT Taxi Walk <.5* <.5* <.5* <.5* - * = p <.5 Table 4-7: Same as Table 4-5 but for PM 1. BG Bus MRT Taxi Walk BG Bus MRT Taxi Walk <.5* <.5* <.5* <.5* - * = p < Particle number concentration (PN) The PN data showed slightly different variability across transport modes compared to PM (Figure 4-3). The Walk mode exhibited the highest mean concentration (44,49 # cm -3 ), followed by the Taxi (3,882 # cm -3 ) and Bus modes (28,916 # cm -3 ). The MRT mode again displays the lowest values with a mean of 14,119 # cm -3, which is even lower than observed at BG (21,289 # cm -3 ). The average concentration 64

9 experienced for the MRT mode is also fairly consistent with a small standard deviation (2,625 # cm -3 ). The variability observed for the Taxi mode is considerably larger than for other transport modes, with a SD of 13,799 # cm -3. Both the highest and lowest PN values recorded during the entire sampling period were also taken on the Taxi mode (See Appendix D.1). 6 PN (1 3 # cm -3 ) 4 2 BG Bus MRT Taxi Walk Transport mode Figure 4-3: Boxplots of PN measured on the four modes of transport and background site (BG). For explanation of boxplot symbols see Figure 4-2. The PN concentrations observed in this study were lower than those reported for other cities. Mean values ranged from 14,418 # cm -3 on the MRT mode journeys to 44,49 # cm -3 on the Walk mode. In comparison, commonly reported mean PN concentrations in the literature range from upwards of 3, # cm -3 (see Table 2-2). de Nazelle et al. (212) even observed a geometric mean of 117,6 # cm -3 for car commutes in Barcelona. In comparison, the present study found a mean of 3,882 # cm -3 for the Taxi mode. Only Quiros et al. (213a) found relatively lower values of PN in a residential neighbourhood in Santa Monica, USA with concentrations inside cars as low as 3,12 # cm -3 with closed windows. The low PN for MRT mode trips is 65

10 consistent with the mean value calculated by Knibbs et al. (211) for electricpowered trains. The Walk mode still displays the greatest difference to background levels, with a ratio of 2. (Table 4-2). This is followed by the Taxi (1.47), Bus (1.32), and MRT mode (.6) modes. These results imply a strong influence of vehicular emissions on UFP levels. The Kruskal-Wallis test for PN revealed a statistically significant difference amongst the four transport modes and the background site (p-value <.5) (Table 4-3). Unlike the results for PM, the multiple comparisons tests revealed that there was a statistically significant difference between all transportation modes except between the Bus and Taxi modes (Table 4-8). The Taxi mode is also the only transport mode that does not reveal a statistically significant difference from the background site for PN. Table 4-8: Results of multiple-comparisons tests for effect of mode on PN concentrations. BG Bus MRT Taxi Walk BG Bus.3* MRT <.1* <.1* Taxi <.1* - - Walk <.1* <.1* <.1*.11* - * = p < Active surface area (ASA), particle-bound polycyclic aromatic hydrocarbons (ppah), ppah to ASA (PC/DC) ratio, and diameter of average surface (D ave,s ) The Taxi mode exhibited the highest mean concentrations of ASA at 14 mm 2 m -3, followed by Walk (137 mm 2 mm -3 ), Bus (133 mm 2 m -3 ), and MRT (114 mm 2 m -3 ) modes (Table 4-1). This differs from the pattern for PM and PN which were observed to be highest during the Walk mode trips. There was one trip on the Taxi mode that 66

11 observed an extremely high GM (266 mm 2 m -3 ), which might have raised the mean value. The Kruskal-Wallis test revealed a statistical significant difference amongst the four modes (p-value <.5) (Table 4-3), and multiple-comparisons tests show that only MRT mode trips are significantly different from the other three modes (Table 4-9), which are similar as suggested by the great degree of overlap of the interquartile ranges (IQR) (Figure 4-4). 25 ASA (mm 2 m -3 ) Bus MRT Taxi Walk Transport mode Figure 4-4: Boxplots of ASA measured on the four modes of transport. For explanation of boxplot symbols see Figure 4-2. Table 4-9: Results from multiple-comparisons tests for effect of mode on ASA concentrations. Bus MRT Taxi Walk Bus MRT.115* Taxi 1.172* - - Walk 1.75* 1 - * = p <.5 The trend between transport modes for ppahs is similar to that of PN although the range of values for the Walk mode is not much higher than the other modes (Figure 4-5). The mean values of the Walk, Taxi and Bus modes were fairly 67

12 similar ranging from ng m -3, but the MRT mode exhibited a considerably lower mean value of 3 ng m -3. This was confirmed by the multiple comparisons test which revealed statistically significant differences between MRT and the other three transport modes (Table 4-1). This result agrees with present understanding regarding ppah formation through incomplete combustion (Ravindra et al., 28). Since the MRT is powered by electricity, no major combustion sources were encountered on MRT mode trips, resulting in low ppah values. Similar to the ASA data, there was no statistically significant difference between the Bus, Taxi and Walk modes (Table 4-1). 2 ppahs (ng m -3 ) Bus MRT Taxi Walk Transport mode Figure 4-5: Boxplots of ppahs measured on the four modes of transport. For explanation of boxplot symbols see Figure 4-2. Table 4-1: Results from multiple-comparisons tests for effect of mode on ppahs concentrations. Bus MRT Taxi Walk Bus MRT <.1* Taxi 1. <.1* - - Walk.71 <.1* * = p <.5 68

13 Only few studies have measured ASA and ppahs in the transport microenvironment. Velasco et al. (24) measured ppahs and ASA concentrations in different outdoor, indoor, and street environments of Mexico City, Mexico, and found mean ASA and ppahs concentrations ranging from mm 2 m -3 and ng m -3, respectively in the outdoor environments. The mean ASA concentrations found in the present study are similar to those found in Mexico City roads with traffic lights at regular intervals and a mix of vehicle types. Such road conditions are similar to those found in the present study. The ppahs values in the present study are much lower than those observed by Velasco et al. (24) (average 173 ng m -3 ). A possible explanation is the much lower proportion of diesel-fuelled vehicles in Singapore s vehicular fleet (Land Transport Authority, 214c). The relationship between ppahs and ASA has been found to vary depending on the presence of nuclei mode particles (Bukowiecki et al., 22). All measured ppahs and ASA values were plotted against each other for each of the transport modes (Figure 4-6). The pattern of the scatter extends along the x-axis, which suggests the presence of nuclei mode particles, which are typically composed of nonphotoemitting material leading to low ppahs values (Bukowiecki et al., 22). However, this distribution can also be explained by particles that have low surface concentrations of PAH (Bukowiecki et al., 22). The low r 2 values suggest a wide range of particles sampled in terms of surface chemistry suggesting that particles measured in the present study are a mix of freshly emitted and aged particles. Strong relationships were not observed because complete trips encompassed both indoor and outdoor environments which are influenced by and have varying proximity to combustion sources. More useful is the analysis of the ppahs and ASA relationship in outdoor route sections which directly experience emissions from combustion sources, which is presented in Section

14 The mean PC/DC ratios for the three on-road transport modes are fairly low, ranging from ng mm -2 (Table 4-11). These values are slightly higher compared to those measured by Ott and Siegmann (26) on an arterial highway in California, USA, (mean: ng mm -2 ) and by Velasco et al. (24) on roads in Mexico City (.53 ng mm -2 ). The ratio is lowest on the underground MRT trips at.27 ng mm -2, which can be explained by the lack of combustion sources leading to extremely low ppahs values. Bus MRT 15 y 78.97x, r 2 =.25 y 29.25x, r 2 = ppahs (ng m -3 ) 15 Taxi y 5.31x, r 2 =.129 Walk y 11.14x, r 2 = ASA (mm 2 m -3 ) Figure 4-6: Correlation between ppahs and ASA for the entire dataset for each transport mode. 7

15 Table 4-11: Mean (SD) PC/DC ratio and D ave,s for four transport modes. (N = 23 for Bus, Taxi, and MRT, N = 22 for Walk) Transport mode PC/DC (ng mm -2 ) D ave,s (nm) Bus MRT a Taxi Walk a D ave,s values may not be representative due to the lack of combustion sources in indoor areas. In addition to exhibiting the highest PM, PN and ppah concentrations, the Walk mode also exhibits the smallest D ave,s of nm. This is followed by the Bus (38.65 nm) and Taxi modes (39.3 nm). The largest average particle diameter of 5.54 nm is observed on the MRT mode; however, this value may be an underestimation due to the lack of combustion sources during MRT mode trips (Bukowiecki et al., 22). The D ave,s indicates the approximate age of particles. Based on the results, the Walk (MRT) mode experiences the largest (smallest) amount of freshly emitted particles. Following the work by Bukowiecki et al. (22), the PC/DC ratio and D ave,s were also plotted against each other (Figure 4-7). Combined with PN concentrations, the distribution of points in these plots can indicate the presence of: (i) nuclei mode particles (low ppah, low or high ASA, and high PN), (ii) accumulation mode particles (high ppah, high ASA, and low or high PN), or (iii) accumulation mode particles that are covered in a layer of chemicals not measured by the PAS sensor (low ppah, low or high ASA, and low PN) (Bukowiecki et al., 22). Along with the plots in Figure 4-6, the clustering of points close to the origin in Figure 4-7 indicates the presence of a nuclei mode in the observations, suggesting the strong influence of freshly emitted particles. Data for the MRT mode are also presented. However, as noted above, the interpretation of these results must be done carefully since the majority of the particles are unlikely to be emitted from combustion sources. 71

16 Bus MRT PC/DC (ng mm -2 ) 15 Taxi Walk D av e,s (nm) Figure 4-7: PC/DC ratio versus D ave,s for each transport mode Black carbon (BC) Like for other pollutant metrics, the Walk mode exhibits the highest mean BC concentrations while the MRT mode exhibits the lowest (Figure 4-8). The mean values for the Bus and Taxi modes are very similar (Bus: µg m -3 and Taxi: µg m -3 ). Although similar to the PM and PN data, statistical testing revealed a significant difference between BC measured on the Walk mode and the three vehicular transport modes (Table 4-12). The Bus and MRT modes do not appear to be significantly different from the Taxi mode. 72

17 2 15 BC (µg m -3 ) 1 5 Bus MRT Taxi Walk Transport mode Figure 4-8: Boxplots of BC measured on the four modes of transport. For explanation of boxplot symbols see Figure 4-2. Table 4-12: Results from multiple-comparisons tests for effect of mode on BC concentrations. Bus MRT Taxi Walk Bus MRT.172* Taxi Walk.89* <.1*.62* - * = p <.5 Concentrations of BC and the other pollutant metrics were also compared against each other (Table 4-13). Since BC is a good indicator of vehicular activity, the strength of the correlation can indicate if vehicle traffic is the predominant source of the pollutants. The highest correlation was found between BC and ppahs on the Walk mode (Spearman correlation.58). Both CO and ASA were also found to be poorly correlated with BC regardless of transport mode (CO: and ASA:.7.14), which could point to different control processes between these pollutants. Additionally, the low correlations suggest that CO is not a suitable tracer for vehicle activity in Singapore. All three size-fractions of PM appear to be 73

18 moderately well correlated with BC (.34.46) except on the MRT mode, again likely due to the different emission sources in subways. Particle number concentrations were fairly well correlated with BC on the Bus and Walk modes (.43 and.41, respectively), but only moderately correlated for the MRT and Taxi modes (.29 and.26, respectively). The correlations in the present study are similar to those found by de Nazelle et al. (212) (PN:.3, PM 2.5 :.39). They suggested that the low correlations observed are due to the presence of different vehicle types which emit different proportions of pollutants. For example, BC is emitted primarily from dieselfuelled vehicles, whilst CO and UFPs may be predominantly emitted by gasolinefuelled vehicles (de Nazelle et al., 212). Table 4-13: Results of Spearman rank correlation between BC and other metrics on the four transport modes. Mode PM 1 PM 2.5 PM 1 PN ASA ppahs CO Bus MRT Taxi Walk Generally, stronger correlations were found on the Walk mode (Table 4-13). Poorer correlations were found between BC and pollutants on the MRT mode ( ) indicating that traffic emissions are not the dominant sources of particles for MRT commutes. This is expected given the lack of fossil fuel combustion sources throughout MRT mode trips. Correlations for Bus and Taxi modes are very similar except in terms of PN, possibly highlighting the similar mix of emission sources encountered on these two modes of transport. 74

19 4.1.5 Carbon monoxide The mean concentration levels for CO were extremely low across all modes of transport and at the background site, ranging from.5 ppm on the MRT mode to 1. ppm on the Taxi mode (Table 4-1). These values are comparable to those observed in London (Kaur et al., 25b) and Barcelona (de Nazelle et al., 212) (see Table 2-2). However, there is great variation in the measurements, with many outliers (Figure 4-9), and the corresponding SDs are of similar magnitude as the mean values (Table 4-1). As described in the data quality section in Chapter 3, approximately 17% of data from the CO measurer were reported as zero values and removed from analysis under the assumption that the concentrations fell below the sensor detection limits. This might have introduced a stronger positive skew into the data. Mean CO concentrations experienced on the Bus, MRT and Walk modes are low compared to the background site (.88,.53, and.69, respectively). Only the Taxi mode exhibited marginally higher CO levels, with a ratio of 1.9. These low readings across the transport modes suggest that Singapore has managed to curb CO emissions from vehicles as recommended by the 197 WHO report for Singapore (See Chapter 1 for details). The results from the multiple comparisons tests indicate a statistically significant difference between the MRT and the three other transport modes, as well as between the Taxi and Walk modes (Table 4-14). There was no statistically significant difference between data measured at the background site and on the four different modes of transport. 75

20 4 CO (ppm) BG Bus MRT Taxi Walk Transport mode Figure 4-9: Boxplots of CO measured on the four modes of transport and at the background site. For explanation of boxplot symbols see Figure 4-2. Table 4-14: Results from multiple-comparisons test for effect of mode on CO concentrations Background Bus MRT Taxi Walk Background Bus MRT 1.82* Taxi * - - Walk *.314* - * = p < Spatial variation within transport modes As described in Chapter 3, the door-to-door routes for each transport mode were split into several sections for a more detailed analysis of the particle pollution to which commuters are exposed. Different amounts of time were spent within the different invehicle, indoor, and outdoor spaces depending on the transport mode (Table 4-15). Average durations for an entire journey on each mode of transport were 14.6 (Bus), 14.3 (MRT), 18 (Taxi), and 25.8 (Walk) minutes, respectively. This, combined with variations in pollutant concentrations measured in each section, most likely contributed to the trends in exposure described in Section

21 The following sections describe the spatial variation of pollutant concentrations observed on Bus, MRT, Taxi, and Walk mode journeys in greater detail. In addition to descriptive statistics, time-series plots and maps of PM 2.5 and PN concentrations of one trip are presented to better visualise the spatial variability. The trips most representative across all measurements for each transport mode are used as examples. Table 4-15: Mean time spent in each section for all measurements presented in minutes and percentage of overall trip. In-vehicle Sections Time spent a (min) Time spent a (%) Bus Bus Indoor Mall Underpass Outdoor Bus-stop Sidewalk In-vehicle MRT Train Indoor Mall Platform Station Outdoor Sidewalk In-vehicle Taxi Taxi Indoor Mall.9 5. Outdoor Sidewalk Taxi-stand Outdoor Walk Sidewalk a Values rounded off to nearest decimal place. 77

22 4.2.1 Bus The spatial variation of PM 2.5 and PN concentrations for a door-to-door trip on the Bus mode on 1 June 213 is shown in Figures 4-1 and Clear differences are visible in different sections of the trip, although the patterns for PM 2.5 and PN are slightly different. There is a noticeable increase in PM 2.5 concentration towards the Underpass section, however there is no corresponding rise in PN except for a large spike halfway through the Underpass (Figure 4-1). The effect of opening and closing bus doors can be observed in the increases followed by a gradual decline of PN concentrations inside the Bus. Similar jumps in PM 2.5 concentrations were less distinct. 16 Sidewalk Mall Underpass Sidewalk/ Bus-stop Bus Sidewalk PM 2.5 (µg m -3 ) Sidewalk Mall Underpass Sidewalk/ Bus-stop Bus Sidewalk PN (1 3 # cm -3 ) :38 18:4 18:42 18:44 18:46 18:48 18:5 Local Time Figure 4-1: Time-series of PM 2.5 and PN concentrations during the Bus mode trip on 1 June 213. Vertical dashed lines delineate the different sections of the trip. Periodic increases in PN were observed within the Bus section when the bus opened its doors to drop off and pick up passengers. 78

23 Figure 4-11: Spatial variation of PM2.5 (top) and PN (bottom) concentrations during the Bus mode trip on 1 June

24 Mean PM 1, PM 2.5, PM 1, and PN concentrations measured in the five sections of Bus mode trips were slightly higher than at the background site, with outdoor sections (Bus-stop and Sidewalk) exhibiting much higher mean concentrations (Figure 4-12). Except for concentrations inside the Bus, CO concentrations in the other sections were lower than at the background site as well (Figure 4-12). This agrees with the trip-averaged results described in Section 4.1. Across the various sections of the Bus mode route, the Bus section exhibits the smallest mean values for PM 1, PM 2.5, PM 1, and ASA (Table 4-16). However, invehicle CO concentrations, as well as the PM 2.5 /PM 1 and PC/DC ratios were elevated compared to the other sections (Table 4-16 and Table 4-17). This might be due to the regular introduction of freshly emitted particles into the vehicle when bus doors open and close to pick up passengers, which is illustrated in Figure 4-1. The indoor locations of the Mall and Underpass generally observed lower values of PN and ppahs, and correspondingly smaller (larger) PC/DC (D ave,s ) (Figure 4-12 and Table 4-17). However, despite having the lowest mean PN concentrations, the Underpass exhibited the highest mean concentrations of PM 1, PM 2.5, and PM 1 (4, 4, and 5 µg m -3, respectively) (Table 4-16). The ratio of PM 2.5 /PM 1 is also lowest in the Underpass, with a value of.8. Taken together, this indicates a larger proportion of coarse particles, and a smaller amount of UFPs than in the other sections of Bus mode trips. 8

25 8 8 PM 1 (µg m -3 ) PM 2.5 (µg m -3 ) PM 1 (µg m -3 ) PN (1 3 # cm -3 ) ASA (mm 2 m -3 ) ppahs (ng m -3 ) BC (µg m -3 ) CO (ppm) Bus Mall Underpass Bus-stop Sidewalk Sections Bus Mall Underpass Bus-stop Sidewalk Sections Figure 4-12: Boxplots of 8 pollutant metrics in different sections of the Bus mode trips. For explanation of boxplot symbols see Figure 4-2. Mean background site concentrations, where available, are indicated as dashed line on the respective panel. 81

26 The outdoor areas (Bus-stop and Sidewalk) experienced higher mean PN concentrations (51,436 and 39,895 # cm -3, respectively) and smaller D ave,s values (35.18 and nm, respectively), likely the result of the close proximity to vehicular exhaust (Table 4-16 and Table 4-17). This is also supported by the comparatively higher concentrations of BC in these locations (Bus-stop: 8.54 µgm -3 and Sidewalk: 6.63 µg m -3 ). The calculated D ave,s in these sections are also consistent with that for the similar outdoor Walk mode (33.68 nm). In addition, the Bus-stop observed the highest mean concentrations of ASA and ppahs of 195 mm 2 m -3 and 124 ng m -3, respectively, as well as the second highest PM 1 concentrations of 43 µg m -3. Statistical testing revealed that the Bus-stop and Sidewalk areas were significantly different from the other sections in terms of PN (Table 4-19 and Appendix D.2.1). Table 4-16: Mean (SD) of pollutant metrics for different sections of the Bus mode journeys. (N = 23) Section PM 1 (µg m -3 ) PM 2.5 (µg m -3 ) PM 1 (µg m -3 ) PN (# cm -3 ) In-vehicle Bus 25 (7) 25 (7) 26 (7) (1479) Indoor Mall 28 (9) 28 (9) 33 (12) (1227) Underpass 4 (12) 4 (12) 5 (14) (6592) Outdoor Bus-stop 35 (7) 35 (7) 43 (12) (13639) Sidewalk 32 (9) 34 (9) 35 (1) (8664) In-vehicle ASA (mm 2 m -3 ) ppahs (ng m -3 ) BC (µg m -3 ) CO (ppm) Bus 123 (3) 98 (48) (3.279) 1. (.8) Indoor Mall 183 (77) 59 (28) (1.811).5 (.7) Underpass 145 (54) 66 (43) (2.766).8 (.8) Outdoor Bus-stop 195 (8) 124 (63) 8.54 (5.98).6 (.8) Sidewalk 141 (36) 77 (33) 6.63 (3.971).6 (.7) 82

27 Table 4-17: Mean PM 1 /PM 2.5, PM 2.5 /PM 1, PC/DC ratios and D ave,s in different sections of Bus mode journeys. (N = 23) Section PM 1 /PM 2.5 PM 2.5 /PM 1 PC/DC (ng mm -2 ) D ave,s (nm) In-vehicle Bus Indoor a Mall Underpass Outdoor Bus-stop Sidewalk a D ave,s values may not be representative due to the lack of combustion sources in indoor areas. Table 4-18: Results of Spearman rank correlation between BC and other metrics in the different sections of Bus mode trips. Section PM 1 PM 2.5 PM 1 PN ASA ppahs CO In-vehicle Bus Indoor Mall Underpass Outdoor Bus-stop Sidewalk Table 4-19: Results of the Kruskal-Wallis test for effects of the different sections on pollutant concentrations during Bus mode trips. H = test statistic, df = degrees of freedom. Metric H df P PM <.1* PM <.1* PM <.1* PN <.1* ASA * ppahs * BC * CO * * = p <.5 83

28 Similar to the analysis for entire trips (see Section 4.1.3), the ppah and ASA in the outdoor areas of Bus mode trips were plotted against each other. The results are inconclusive with only one or two days of measurement that exhibited a more distinct presence (r 2 >.5) of either a nuclei mode (horizontal to negative slope) or an accumulation mode (positive slope) (Figures 4-13 and 4-14). For the Bus-stop section, this lack of a distinct pattern may, in part, be due to the small number of sampling points. Most Sidewalk measurements exhibit a horizontal to negative slope, indicating the presence of a nuclei mode (Figure 4-14). However, the generally poor r 2 values suggest the presence of particles of different ages which have already gone through growing processes and chemical reactions that do not contribute UFPs in the nuclei mode. 84

29 /4/1 213/4/17 213/4/18 213/4/24 213/4/25 r 2 =.49 r 2 = 1 r 2 =.776 r 2 = 1 r 2 = /4/26 213/4/3 213/5/3 213/5/6 213/5/14 r 2 =.342 r 2 =.594 r 2 =.137 r 2 = r 2 = ppahs (ng m -3 ) /5/15 213/5/16 213/5/17 213/5/2 213/5/22 r 2 =.636 r 2 =.418 r 2 =.181 r 2 =.585 r 2 = /5/23 213/5/27 213/5/28 213/5/3 213/6/3 r 2 = 1 r 2 =.239 r 2 =.36 r 2 =.352 r 2 = /6/1 213/6/11 213/6/12 r 2 = r 2 = 1 r 2 = ASA (mm 2 m -3 ) Figure 4-13: ppahs and ASA data collected at Bus-stop sections plotted against each other for each day of sampling. Linear regression lines and the r 2 of the relationship are also plotted. 85

30 /4/1 213/4/17 213/4/18 213/4/24 213/4/25 r 2 =.378 r 2 =.383 r 2 =.256 r 2 =.459 r 2 = /4/26 213/4/3 213/5/3 213/5/6 213/5/14 r 2 =.53 r 2 =.191 r 2 =.67 r 2 =.596 r 2 = ppahs (ng m -3 ) /5/15 213/5/16 213/5/17 213/5/2 213/5/22 r 2 =.53 r 2 =.11 r 2 =.81 r 2 =.686 r 2 = /5/23 213/5/27 213/5/28 213/5/3 213/6/3 r 2 =.413 r 2 = 2.7e-7 r 2 =.118 r 2 =.15 r 2 = /6/1 213/6/11 213/6/12 r 2 =.381 r 2 =.172 r 2 = ASA (mm 2 m -3 ) Figure 4-14: ppahs and ASA data collected at Sidewalk sections during Bus mode trips plotted against each other for each day of sampling. Linear regression lines and the r 2 of the relationship are also plotted. 86

31 4.2.2 MRT The MRT mode trips were split into five sections as well (Figure 4-15). Since most of the travelling was done underground, only a short amount of time (< 1 minute) was spent outdoors on the Sidewalk where the start and end points of the sampling route were located (Figure 4-15). This meant that there were no GPS readings for MRT trips, thus the spatial variation for MRT mode was not plotted. Distinctions between sections of the MRT journey are not as well-defined in terms of PM 2.5. There is a gradual rise in PM 2.5 concentrations further indoors, reaching a maximum inside the Train. However, there is no similar pattern in the PN time-series, although concentrations inside the Train are higher than at the Platform section. This implies a different group of sources or processes controlling the amount of particles for MRT mode trips. 6 5 Sidewalk Station Platf orm Train Platf orm/ Station/ Mall Sidewalk PM 2.5 (µg m -3 ) PN (1 3 # cm -3 ) Sidewalk Station Platf orm Train Platf orm/ Station/ Mall Sidewalk 16:56 16:58 17: 17:2 17:4 17:6 17:8 17:1 Local Time Figure 4-15: Time-series of PM 2.5 (top) and PN (bottom) concentrations during the MRT mode trip on 2 May 213. Vertical dashed lines delineate the different sections of the trip. 87

32 Except for the Station areas, mean PM 1, PM 2.5, and PM 1 measured in all sections of the MRT mode are slightly higher than at the background site (Figure 4-16). In particular, PM concentrations appear to increase deeper inside the subway system, which agrees with the findings of Kim et al. (28) in the Seoul Metropolitan Subway. However, with the exception of the Sidewalk, mean PN in different sections of MRT journeys were well below background concentrations (Figure 4-16), contributing to the low MRT mode to background concentrations ratio of.6 presented above (Table 4-2). Mean PN concentrations observed at the Platform even dropped below 1, # cm -3. Concentrations in the Station and the Train are similarly low with mean readings just above 1, # cm -3. The relatively small SD further indicates that PN concentrations are consistently low in these spaces (Table 4-2). All spaces of the MRT mode journey exhibited mean CO concentrations that fell below background levels (Figure 4-16). However, the spread of data is extremely large, with standard deviations even larger than mean values (Table 4-16). Unlike the Bus mode, the highest mean concentrations of PM 1, PM 2.5, and PM 1 were observed inside the vehicle (i.e. Train). However, the second lowest mean PN concentrations were also observed in this location (Table 4-2), indicating a higher proportion of larger particles and less UFPs. In addition to the low PN concentrations, the Platform section also showed the lowest mean concentrations of ASA, ppahs, BC, and CO (Table 4-2). The Station section exhibited similarly low values as the Platform section except for ASA concentrations, leading to a relatively smaller D ave,s (Table 4-21). All sections of the MRT mode exhibited lower PC/DC and larger D ave,s compared to the sections of Bus mode journeys, although as mentioned, D ave,s values may be an underestimation due to the lack of combustion sources (Table 4-21). 88

33 PM 1 (µg m -3 ) 3 2 PM 2.5 (µg m -3 ) PM 1 (µg m -3 ) PN (1 3 # cm -3 ) ASA (mm 2 m -3 ) 2 1 ppahs (ng m -3 ) BC (µg m -3 ) CO (ppm) Train Mall Platform Station Sidewalk Sections Train Mall Platform Station Sidewalk Sections Figure 4-16: Boxplots of the 8 pollutant metrics measured in the different sections of MRT mode trips. For explanation of boxplot symbols see Figure 4-2. Mean background site concentrations where available are indicated as a dashed line on the respective graphs. 89

34 Mean concentrations of BC were relatively low in all four indoor and invehicle sections of MRT journeys (ranging from µg m -3 ). Only the outdoor Sidewalk section exhibited higher a concentration of µg m -3. Additionally, all pollutant variables exhibited poor correlation with BC in all sections except the outdoor areas. This further indicates that freshly emitted particles from vehicular combustion is not the major source of particle pollution encountered on MRT mode trips. Only the Station and Train spaces exhibited a significant difference from the other sections in terms of PM 1, PM 2.5, and PM 1 (see Appendix D.2.2). For PN, the Station and Train were the only two sections that were not significantly different. The Sidewalk also appeared to be significantly different from the Platform, Station, and Train sections in terms of BC, again possibly due to the absence of exhaust emissions in the indoor spaces. Table 4-2: Mean (SD) of measured pollutant metrics in different sections of MRT mode journeys. Section PM 1 (µg m -3 ) PM 2.5 (µg m -3 ) PM 1 (µg m -3 ) PN (# cm -3 ) In-vehicle Train 34 (3) 34 (3) 37 (4) 12,789 (2,577) Indoor Mall 27 (5) 27 (5) 32 (6) 2,835 (5,222) Platform 26 (4) 27 (4) 31 (5) 9,57 (1,93) Station 21(4) 22 (4) 24 (4) 13,833 (2,672) Outdoor Sidewalk 28 (7) 28 (7) 3 (8) 33,34 (9,315) In-vehicle ASA (mm 2 m -3 ) ppahs (ng m -3 ) BC (µg m -3 ) CO (ppm) Train 85 (21) 32 (6) (2.248).5 (.7) Indoor Mall 167 (48) 43 (14) (1.749).7 (.7) Platform 97 (3) 24 (7) 2.24 (.955).4 (.7) Station 142 (28) 27 (7) (1.24).5 (.8) Outdoor Sidewalk 148 (42) 44 (16) (3.421).5 (.7) 9

35 Table 4-21: Mean PM 1 /PM 2.5, PM 2.5 /PM 1, PC/DC ratios and D ave,s in different sections of MRT mode journeys. Section PM 1 /PM 2.5 PM 2.5 / PM 1 PC/DC (ng mm -2 ) D ave,s (nm) In-vehicle a Train Indoor a Mall Platform Station Outdoor Sidewalk a D ave,s values may not be representative due to the lack of combustion sources in indoor areas. Table 4-22: Results of Spearman rank correlation between BC and other metrics in the different sections of MRT mode trips. Mode PM 1 PM 2.5 PM 1 PN ASA ppahs CO In-vehicle Train Indoor Mall Platform Station Outdoor Sidewalk Table 4-23: Results of Kruskal-Wallis test for effect of the different sections on pollutant concentrations during MRT mode journeys. H = test statistic, df = degrees of freedom. Metric H df P PM <.1* PM <.1* PM <.1* PN <.1* ASA <.1* ppahs <.1* BC <.1* CO * * = p <.5 91

36 4.2.3 Taxi The spatial variation within Taxi mode trips is clearly illustrated in Figure 4-17 and Figure Both PM 2.5 and PN move in tandem, decreasing inside the Mall and Taxi, and rising at Sidewalk and Taxi-stand (Figure 4-17), which points towards a common emission source. This is also observed in the maps, with the Taxi-stand areas near the start and end points exhibiting higher PM 2.5 and PN concentrations (Figure 4-18). Such clear distinctions between sections are not observed for all pollutants measured, although statistical testing revealed significant differences between spaces for all pollutant variables (Table 4-27). Similar to the Bus and Train, PM 2.5 and PN concentrations decrease rapidly within the first 5 m of travel inside the Taxi (Figure 4-18). However, PM 2.5 concentrations inside the Taxi exhibited short-lived spikes that are not observed in the PN measurements (Figure 4-17). PM 2.5 (µg m -3 ) Sidewalk/ Mall Sidewalk/Taxi-stand Taxi Sidewalk/Mall 1 PN (1 3 # cm -3 ) Sidewalk/ Mall Sidewalk/Taxi-stand Taxi Sidewalk/Mall 16:35 16:4 16:45 16:5 16:55 Local Time Figure 4-17: Time-series of PM 2.5 (top) and PN (bottom) concentrations during the Taxi mode trip on 2 May 213. Vertical dashed lines delineate the different sections of the trip. 92

37 Figure 4-18: Spatial variation in PM2.5 (top) and PN (bottom) concentrations during the Taxi mode journey on 2 May

38 Except for the in-vehicle section, all other parts of Taxi mode trips experienced higher mean concentrations of PM 1, PM 2.5, PM 1 and PN than at the background site. Similar to the Bus mode trips, outdoor areas in particular (Taxistand and Sidewalk) observed much higher concentration than the background site (Figure 4-19). These two outdoor areas also exhibited higher concentrations for the other variables (Table 4-24). Carbon monoxide concentrations are lower in the other sections of Taxi mode trips compared to inside the vehicle. Again, CO concentrations exhibit a very large spread, with standard deviations on the same order as mean values. The Taxi sections exhibited mean CO concentrations of 1.6 ppm, which is higher than observed inside the Bus and Train sections. Despite higher CO levels, the lowest mean concentrations of PM 1, PM 2.5, PM 1, PN, ASA, and BC were found inside the Taxi (Table 4-24). The Bus and Taxi sections appear similar in terms of pollutant concentrations except for PN, which was found to be much lower in taxis than buses (18,91 # cm -3 and 27,775 # cm -3 respectively). The lower PN and ASA values also led to a relatively larger D ave,s compared to the other sections (Table 4-25). 94

39 PM 1 (µg m -3 ) 3 2 PM 2.5 (µg m -3 ) PM 1 (µg m -3 ) PN (1 3 # cm -3 ) ASA (mm 2 m -3 ) ppahs (ng m -3 ) BC (µg m -3 ) 2 1 CO (ppm) 4 2 Taxi Mall Taxi-stand Sidewalk Sections Taxi Mall Taxi-stand Sidewalk Sections Figure 4-19: Boxplots of the 8 pollutant metrics measured in the different sections of Taxi mode journeys. For explanation of boxplot symbols see Figure 4-2. Mean background site concentrations where available are indicated as a dashed line on the respective graphs. 95

40 Table 4-24: Mean (SD) of pollutant metrics for different sections of Taxi mode journeys. Section PM 1 (µg m -3 ) PM 2.5 (µg m -3 ) PM 1 (µg m -3 ) PN (# cm -3 ) In-vehicle Taxi 22 (8) 22 (8) 23 (8) 18,91 (13,79) Indoor Mall 27 (7) 27 (8) 28 (8) 27,558 (8,17) Outdoor Sidewalk 33 (7) 33 (7) 36 (7) 44,837 (11,32) Taxi-stand 33 (8) 33 (9) 36 (9) 56,22 (18,112) Section ASA (mm 2 m -3 ) ppahs (ng m -3 ) BC (µg m -3 ) CO (ppm) In-vehicle Taxi 123 (39) 97 (76) (3.875) 1.6 (1.1) Indoor Mall 13 (54) 63 (22) (4.659) 1. (.9) Outdoor Sidewalk 153 (5) 61 (26) (3.217).7 (.8) Taxi-stand 188 (66) 121 (59) (4.339).7 (.9) Table 4-25: Mean PM 1 /PM 2.5, PM 2.5 /PM 1, PC/DC ratios and D ave,s for different sections of Taxi mode journeys. Section PM 1 /PM 2.5 PM 2.5 / PM 1 PC/DC (ng mm -2 ) D ave,s (nm) In-vehicle Taxi Indoor a Mall Outdoor Sidewalk Taxi-stand a D ave,s values may not be representative due to the lack of combustion sources in indoor areas. Table 4-26: Results of Spearman rank correlation between BC and other metrics in the different sections of Taxi mode trips. Section PM 1 PM 2.5 PM 1 PN ASA ppahs CO In-vehicle Taxi Indoor Mall Outdoor Sidewalk Taxi-stand

41 The Kruskal-Wallis test revealed significant differences between the various sections of Taxi mode trips. The Taxi-stand in particular appears to be significantly different from the Mall and Taxi sections in terms of PM 1, PM 2.5, PM 1, PN, and ASA (See Appendix D.2.3). This mostly corresponds to the much higher concentrations observed at the Taxi-stand. There was no observed difference between the Taxi-stand and the Sidewalk except in terms of ppah concentrations. Table 4-27: Results of Kruskal-Wallis test for effect of the different sections on pollutant concentrations during Taxi mode journeys. H = test statistic, df = degrees of freedom. Metric H df P PM <.1* PM <.1* PM <.1* PN <.1* ASA * ppahs * BC * CO * * = p <.5 Similar to Bus mode trips, only ppahs and ASA data in outdoor areas of Taxi mode trips were analysed (Figures 4-2 and 4-21). The plots of ppahs and ASA measured at the Taxi-stand show great variation in patterns (Figure 4-2). A majority of trips exhibit a low slope, indicating the presence of a nuclei mode which is consistent with more inefficient combustion occurring when vehicles are idling. However, measurements on 18 April, and 3 and 16 May exhibited a relatively strong (r 2 >.6) positive slope (Figure 4-2), which suggests the presence of larger accumulation mode particles. Additionally, for the trips taken on 25 April, and 17 and 13 May, the ppahs and ASA data exhibit two different branches (only the overall regression is plotted), one with a positive slope, and another with a flatter more horizontal slope, indicating the presence of both nuclei and accumulation mode particles. 97

42 /4/1 213/4/17 213/4/18 213/4/24 213/4/25 r 2 =.137 r 2 =.17 r 2 =.836 r 2 =.39 r 2 = /4/26 213/4/3 213/5/3 213/5/6 213/5/14 r 2 =.183 r 2 =.11 r 2 =.636 r 2 =.133 r 2 = ppahs (ng m -3 ) /5/15 213/5/16 213/5/17 213/5/2 213/5/22 r 2 =.966 r 2 =.69 r 2 =.328 r 2 =.695 r 2 = /5/23 213/5/27 213/5/28 213/5/3 213/6/3 r 2 =.138 r 2 = r 2 =.893 r 2 =.235 r 2 = /6/1 213/6/11 213/6/12 r 2 =.222 r 2 =.465 r 2 = ASA (mm 2 m -3 ) Figure 4-2: ppahs and ASA data measured at Taxi-stands plotted against each other for each day of sampling. Linear regression lines and the r 2 of the relationship are also plotted. 98

43 /4/1 213/4/17 213/4/18 213/4/24 213/4/25 r 2 =.38 r 2 =.491 r 2 =.529 r 2 = 3.73e-5 r 2 = /4/26 213/4/3 213/5/3 213/5/6 213/5/14 r 2 =.129 r 2 =.135 r 2 =.384 r 2 =.151 r 2 = ppahs (ng m -3 ) /5/15 213/5/16 213/5/17 213/5/2 213/5/22 r 2 =.186 r 2 =.46 r 2 =.345 r 2 =.638 r 2 = /5/23 213/5/27 213/5/28 213/5/3 213/6/3 r 2 =.729 r 2 =.338 r 2 =.762 r 2 =.583 r 2 = /6/1 213/6/11 213/6/12 r 2 =.162 r 2 =.277 r 2 = ASA (mm 2 m -3 ) Figure 4-21: ppahs and ASA data collected measured at Sidewalk sections during Taxi mode trips plotted against each other for each day of sampling. Linear regression lines and the r 2 of the relationship are also plotted. 99

44 The study protocol prescribed that the first available taxi was taken. This resulted in sampling a range of car models representing the taxi fleet of Singapore (Table 4-28). Most taxis run on diesel fuel, with only one model (Toyota Wish) using CNG. The results suggest that car model is a factor contributing to differences in exposure and accounting for the larger spread of PN concentration for Taxi mode trips (see Table 4-1). Particle number concentrations appeared higher when travelling on the Toyota Crown, whilst mean PN concentrations inside Hyundai Sonata cars even dropped below 1, # cm -3 (Figure 4-22). The large outlier observed in the boxplots for ASA (see Figure 4-4) was for a trip on a Toyota Crown. Interviews with the drivers also revealed that Toyota Crown cars used in the present study were > 5 years old (Table 4-28). The older age of the engine combined with the use of diesel fuel might account for the higher pollutant concentrations measured on this particular car model. These results are consistent with the existing knowledge regarding the control factors for vehicle emissions as well as the capacity for vehicles to selfpollute (described in Chapter 2). However, the number of samples was too small to definitively conclude that car model and age are significant factors affecting pollutant concentrations. Table 4-28: List of taxi models sampled. The vehicles age was obtained from the drivers. Car model N Vehicle age (Years) Hyundai Sonata 9 < 1 5 Toyota Crown Toyota Wish a Chevrolet Epica 3 < Chrystler 1 5 KIA Magentis 1 - Mercedes Benz E a Fuelled by CNG. 1

45 Car Model Hyundai Sonata (N=9) Toyota Crow n (N=5) Toyota Wish (N=3) Chevrolet Epica (N=3) Chrystler (N=1) KIA Magentis (N=1) Mercedes Benz E-22 (N=1) PN (1 3 # cm -3 ) Figure 4-22: Geometric mean of in-vehicle PN measurements according to car model. N = number of trips Walk Although the Walk mode was located completely outdoors, the data show that the Sidewalk is not a completely homogenous environment, and there were spatial variations in pollutant concentrations observed along the route (Figures 4-23 and 4-24). Some of the variability observed can be explained by spot pollution sources such as smokers who congregate outside mall entrances since indoor smoking is banned in Singapore. References to field notes indicate that several of the more prominent short-lived spikes observed for both PM 2.5 and PN coincide with groups of smokers. Slight jumps in PM 2.5 and PN concentrations were also observed when waiting at traffic junctions along the route, although this pattern is not immediately distinguishable in the maps (Figure 4-24). 11

46 PM 2.5 (µg m -3 ) PN (1 3 # cm -3 ) PM 2.5 /PM :55 18: 18:5 18:1 18:15 Local Time Figure 4-23: Time series of PM 2.5 (top) and PN (middle) concentrations, and PM 2.5 /PM 1 ratio (bottom) during the Walk mode trip on 2 May

47 Figure 4-24: Spatial variation in PM2.5 (top) and PN (bottom) concentrations during the Walk mode journey on 2 May 213. Traffic light symbols denote traffic junctions. 13

48 There was also an approximately 2 m long stretch halfway through the route which exhibited considerably high levels of PM2.5 and PN for a sustained duration (approximately 1.4 minutes) (Figure 4-24). This was a part of the route where there was ongoing construction activity throughout the duration of the fieldwork. To segregate pedestrians from the construction site, a temporary covered walkway was set up (Figure 4-25). Both particle mass and number concentrations measured inside the walkway were substantially higher than in other parts of the route. Much of the observed increases appear to be in the coarse particle fraction illustrated by the decrease in PM2.5/PM1 ratio to approximately.5 whilst inside the passageway (Figure 4-23). Figure 4-25: Photograph of construction area on walk mode trips which exhibited unusually high pollutant concentrations. Electric fans were installed at different points along the passage presumably to improve ventilation. 14

49 4.3 Dosage In the present study, dosage is used as an estimate of the amount of pollutants that could be deposited within the respiratory track. It is a function of a person s ventilation rate as well as the exposure concentration and time spent in the microenvironment (dosage = V E exposure concentration time). The ventilation rate used in this study is the minute ventilation (V E ), which is the volume of air inhaled (or exhaled) from a person s lungs per minute Ventilation rates Ventilation rates were calculated from the heart rate measurements of 1 different volunteers, both male and female using Eq. (1). A wide range of V E was observed, probably reflecting the varying ages and fitness levels of the volunteers (Table 4-29). Due to these contributing factors, only the mean values of V E were used in the analysis. The maximum and minimum V E are presented to show the range of measurements. For example at the Taxi-stand, the lowest (highest) mean V E was 14.2 (48.5) L min -1. Whilst the selected route may be representative of a short trip in terms of the travel modes, in terms of activity levels, volunteers were required to take measurements continuously for two hours, only stopping to rest for a few minutes between the sampling on each transportation mode. It is therefore possible that the cumulative work of carrying the instruments for the consecutive measurements contributed to relatively higher HR and V E. 15

50 Table 4-29: Maximum, minimum and mean HR and V E for different sections of the four transport modes for all measurements (N = 23 for Bus, MRT, and Taxi, N = 22 for Walk). Transport mode, Section V E (L min -1 ) Max Min Mean Bus 2.8 In-vehicle Bus Indoor Mall Underpass Outdoor Bus-stop Sidewalk MRT 23. In-vehicle MRT Indoor Mall Platform Station Outdoor Sidewalk Taxi 2.8 In-vehicle Taxi Indoor Mall Outdoor Sidewalk Taxi-stand Walk 25. Mean V E was highest for the Walk, followed by MRT mode. The Taxi and Bus modes exhibited the same mean V E. This was expected since the Walk mode is an active mode. Within the individual sections of each transport mode, there are some surprising variations. Except for the in-vehicle sections, mean V E in the various sections were 2 L min -1, which is a reasonable result since volunteers were also 16

51 walking in these areas. However, the mean V E in the Mall and Station spaces of MRT journeys were on par with the Walk mode at 27.6 L min -1 and 25.1 L min -1 respectively. These higher values can be attributed to the additional effort required to climb the stairs between the different floors, leading to the greater average V E. Of the in-vehicle spaces, the Taxi was the only vehicle where volunteers could be seated for every trip. Unsurprisingly, this section exhibits the lowest mean V E at 14.3 L min -1 (Table 4-29). For both Bus and MRT mode trips, the vehicles were usually crowded with few to no empty seats. Volunteers thus stood inside buses and trains, leading to relatively higher V E of 17.9 L min -1 and 18.5 L min -1 compared to inside the Taxi. However, these values are still lower than that observed during the Walk mode. The V E values calculated for the present study are similar to those observed in other studies. Also using Eq. (1), Zuurbier et al. (21) found mean V E of 12.7 L min - 1 and 11.8 L min -1 for bus and car passengers respectively. In Belgium, Panis et al. (21) observed similar V E for male and female car passengers of 13.4 L min -1 and 11.3 L min -1 respectively. The V E values presented by Zuurbier et al. (21) and Panis et al. (21) correspond to in-vehicle V E values for the Bus and Taxi modes of the present study. Higher values of V E were found by de Nazelle et al. (212), who calculated inhalation rates using an equation based on measurements of average energy expenditures. They found V E of 34.1 L min -1, 2.1 L min -1, and 19.9 L min -1 for the Walk, Bus, and Taxi travel modes, respectively (de Nazelle et al., 211). They also noted that their relatively higher V E readings could be due to additional work involved in managing the instruments (de Nazelle et al., 211) Dosage results The average dose in each section is calculated by the product of mean V E, mean pollutant concentrations (presented above), and average time spent in the respective spaces of each trip. Only the results for PM 2.5 and PN are shown here (Table 4-3). The results for PM 1, PM 1, BC, and ppahs can be found in Appendix D.3. An 17

52 important note is that the calculations in the present study cannot be directly compared with those of other studies due to the shorter route. The patterns between transport modes for dosage are similar to those for pollutant concentrations. As expected, walking was associated with the highest inhaled dose for PM 2.5 and PN, which was twice as much as the next highest values found for the Taxi mode (Table 4-3). The latter experienced higher dosages for PM 2.5 and PN of 1.5 µg and particles, respectively compared to the Bus. This can be attributed to the longer time spent waiting for an available taxi (5 min) compared to the bus (1.2 min). The inhaled dose on the Bus and MRT modes are similar for PM 2.5 (8.8 and 8.9 µg, respectively). However, the number of particles potentially entering the human respiratory system is much lower when travelling on the MRT with particles (Table 4-3). The dosage results for PM 1, PM 1, BC, and ppahs follow the same patterns as those for PM 2.5 and PN (Appendix D.3). 18

53 Table 4-3: Inhaled dose by mode and section for PM 2.5 and PN based on all data. Mean concentrations Inhaled dose Transport mode, Section Duration (min) V E (L min -1 ) PM 2.5 (µg m -3 ) PN (# cm -3 ) PM 2.5 (µg) PN (1 9 #) Bus , In-vehicle Bus , Indoor Mall , Underpass , Outdoor Bus-stop , Sidewalk , MRT , In-vehicle MRT , Indoor Mall , Platform , Station , Outdoor Sidewalk , Taxi , In-vehicle Taxi , Indoor Mall , Outdoor Sidewalk , Taxi-stand , Walk ,

54 To estimate the impact of respiration, ratios of the three vehicle transport modes and the Walk mode were calculated for both pollutant concentrations and dosage (Table 4-31). The observed difference in ratios for concentrations versus the inhaled dose indicate that the combined effect of experiencing higher concentrations plus a longer duration and greater exertion (higher V E ) whilst walking, compounded the differences in commuter exposure by a factor of two compared to the other transport modes. Table 4-31: Ratios of PM 2.5, PN, BC, and ppah concentrations and inhaled dose between Bus, MRT, and Taxi modes and Walk mode. Concentrations Inhaled dose PM 2.5 PN BC ppah PM 2.5 PN BC ppah Bus/Walk MRT/Walk Taxi/Walk

55 Chapter 5. Discussion The key implications of the data presented in Chapter 4 are discussed in greater detail in this chapter. The main areas of discussion are: (1) comparisons of exposure concentration between transport modes and the background site, (2) spatial variation within each transport mode, and (3) dosage results. Comparisons will also be made with results from other, similar studies. Since most of the existing literature report PM 2.5 and PN concentrations and because of their relevance for human health, the bulk of the discussion will focus on these two metrics. 5.1 Comparison across overall trips and background site The present results indicate that exposure concentrations in the transport microenvironment of Singapore are slightly lower than in other cities in terms of PM 2.5 and PN. A possible explanation could be the young vehicle fleet, with > 86% of vehicles registered in Singapore under 1 years old (Land Transport Authority, 214a). Additionally, diesel-fuelled vehicles form only 18% of the total vehicle fleet (Land Transport Authority, 214c). At the time of this study, all gasoline-fuelled passenger cars complied with the Euro II emissions standard, whilst diesel-fuelled vehicles had to comply with the Euro IV standard (National Environmental Agency, 213a). All vehicles also undergo mandatory regular inspections to ensure compliance with the emission standards. The sale of vehicles is tightly managed as well, with quotas on the number of vehicles sold annually controlling the vehicle population growth. Such stringent regulations have probably led to the control of pollutant emissions in Singapore, which is commendable. Depending on the pollutant studied, transport modes appear to be significantly distinct from one another. The results revealed that pedestrians experience the highest concentrations of particle pollutants. In terms of PM 2.5 and PN exposures, the Walk mode was found to be significantly different from the other 111

56 transport modes at the 95% confidence level. This result can be explained by the sustained proximity to vehicle exhaust emissions since walking takes place next to the road. This is supported by the relatively stronger correlations between BC and other variables for the Walk mode (Table 4-13). In addition to the close proximity to motor vehicles, there were also other spot pollution sources along the Sidewalk such as cigarette smokers and construction activity. As described in Section 4.2.4, many of the sharp peaks or periods of high pollutant concentrations experienced during the Walk measurements coincided with the presence of these point sources of pollution. The high frequencies of occurrence of these sharp peaks contributed a significant amount to the concentrations observed in the present study. This also suggests that high temporal resolutions may be needed in order to capture the full extent of UFP pollution. Although this particular finding is arguably due to the study design, these types of sources are not unique to the commercial district of Singapore, and may be found in other land use types in varying degrees of occurrence. In contrast to the Walk mode, the MRT mode which is predominantly located away from the road, consistently exhibited lower pollutant concentrations in most parts of the journey. Overall, the MRT mode was found to be significantly different from the other two vehicular modes, likely due to the absence of motor vehicle exhaust, which is indicated by the low average BC concentrations (2.643 µg m -3 ) as well as low correlations between BC and the other pollutants. The much lower PC/DC values observed during MRT mode trips may be an indication that the particle pollution on this mode of transport has a different composition or age to those of the other transport modes. This agrees with the existing knowledge that the pollution sources for train systems are different from on-road transport modes (Nieuwenhuijsen et al., 27). However, more information regarding the chemical composition and surface composition of particles is needed in order to draw more concrete conclusions. The PM 2.5 exposure on the MRT in Singapore is slightly lower compared to similar 112

57 underground train systems such as the MTR in Hong Kong (Chan et al., 22) and MRT in Taipei (Tsai et al., 28) (see Table 2-2 for values). Pollutant concentrations for Bus and Taxi modes trips fell between those for the Walk and MRT modes. Mean PM 2.5 and PN values for Bus and Taxi modes were very similar, with no statistically significant differences observed across all measured variables. This is largely attributed to the similarities between these two on-road vehicular transport modes. Greater periods of time were spent outdoors for trips via the Bus and Taxi modes compared to the MRT mode. However, unlike the Walk mode, part of the travel was done in air-conditioned in-vehicle sections, which contributed to a lower overall trip average for these two modes. The general trends across transport modes in the present study are similar to the findings of Knibbs and de Dear (21) who also measured much lower PN concentrations inside trains than buses or cars, although the lowest mean PM 2.5 concentration was observed inside cars. It must be noted that the results presented by Knibbs and de Dear comprised solely of in-vehicle concentrations. Briggs et al. (28) similarly found higher exposures of PM 1, PM 2.5, PM 1, and PN while walking than for car passengers. However, the results are the reverse of those observed by Kaur et al. (25b), Zuurbier et al. (21), and de Nazelle et al. (212), who found higher PM 2.5 and PN concentrations during travel on motor vehicles (e.g. bus, car, taxi) than for walking (see Table 2-2 for values). This result is not surprising since different cities have vastly different traffic conditions and urban morphologies, which leads to unique trends in terms of commuter exposure. One explanation for the differences between transport modes might be the ventilation setting of the vehicles used in the respective studies. All on-road vehicles travelled in the present study had closed windows and air-conditioning was on, and all taxi drivers used the recirculation setting. The use of air-conditioning and 113

58 activation of the recirculation mode for ventilation has been found to result in reductions of particle concentrations (Hudda et al., 211). This is supported by the observed declines in PM 2.5 and PN concentrations inside the Bus and Taxi sections of the measurements in the present study (Figure 4-1 and Figure 4-17). The cars used by Briggs et al. (28) and Knibbs and de Dear (21) were driven under similar conditions, with closed windows and the air-conditioning switched on, although Knibbs and de Dear state that recirculation was not in operation for their study. No information regarding the ventilation settings of the study vehicles was provided by Kaur et al. (25b), but in Barcelona, de Nazelle et al. (212) mimicked the typical driving settings of locals, who usually leave driver s window open leading to higher in-vehicle concentrations. Such inconsistencies in sampling conditions compound the difficulty in making direct comparisons between studies and deriving general conclusions. However, it demonstrates the significance of designing studies that best represent local travel behaviours. Despite exhibiting lower pollutant concentrations than in other cities, the concentrations measured on the four transport modes are higher than at the background site for the three PM size-fractions and PN, with transport to background site ratios of > 1 (Table 4-2). The only exception was the MRT mode, which exhibited much lower PN concentrations. These results show that travel via the MRT is the best option to reduce commuter aerosol exposure. The results also reveal that fine (and possibly ultrafine) particles make up a large proportion of the particle pollution in Singapore. All transport modes and the background site exhibited PM 2.5 /PM 1 ratios.9, including the MRT mode which was predominantly indoors. This is much higher compared to studies in Taipei and Hong Kong, where PM 2.5 /PM 1 ranges were found to be.53.6 and respectively (Chan et al., 22; Tsai et al., 28). With PM 2.5 /PM 1 ratios at approximately.9 across all transport modes and even at the background site, this 114

59 suggests that much of the particle pollution experienced in Singapore on days not affected by transboundary pollution, has a predominantly local origin. The PM 1 to PM 2.5 ratios recorded in this study were even higher at.99, which further supports the hypothesis that particles were formed in close spatial and temporal proximity. Traffic emissions is the most likely contributor, which is supported by the findings of the local authorities that more than 5% of the local PM 2.5 ambient concentrations have their origin in vehicular traffic (National Environmental Agency, 213a). Another source of fine particles in the local atmosphere could be the ports located to the south of the main island. Like other diesel vehicles, shipping emissions are dominated by UFP (Isakson et al., 21). The Port of Singapore alone attracts more than 1, vessels every year (Maritime and Port Authority of Singapore, 29), and the waters around Singapore are one of the most heavily-travelled shipping routes in the world, acting as passage between Europe, the Persian Gulf, and East Asia (United States Energy Information Administration, 212). Such intense shipping activities around the city-state could contribute substantially to the high PM 2.5 /PM 1 ratios found at the background site in the present study. The high PM 1 /PM 2.5 ratios could point to the importance of using PN as an air quality metric. The fairly high values of PN in the outdoor areas mean UFP are an important component of the particle pollution in Singapore, adding further evidence to the problem of fine particle pollution. 5.2 Spatial variation of pollutant concentrations Aerosol concentrations are generally much higher in the outdoor street environment than in indoor locations (including inside vehicles) due to the close proximity to traffic emissions. The use of air-conditioning and air ventilation in indoor and invehicle spaces appears to have the additional effect of filtering out particles. 115

60 This distinct difference in pollutant concentrations between outdoor and indoor spaces is the main explanation for Walk mode trips experiencing the highest mean pollutant concentrations for all the eight different pollutant metrics measured in the present study. In contrast, the lowest concentrations were observed on MRT mode journeys where only 11% of the time is spent outdoors. The only exception to this general trend was in the Underpass section of the Bus mode. Notably, PM concentrations observed in the Underpass were elevated compared to other indoor locations on Bus mode trips. However, PN concentrations were low compared to the other Bus mode sections. The D ave,s was also relatively larger at 47.9 nm. Taken together, the data point towards a different source or mixture of particles rather than exhaust emissions such as from the ongoing construction work during the entire fieldwork duration. Analysis of ppah and ASA plots for outdoor sections did not show either a distinctive nuclei mode or accumulation mode. Instead, the particles observed in these areas appear to be a mixture of both. As Bukowiecki et al. (22) found, real-world on-road emissions are simply too complex and do not exhibit distinct pollutant signatures. Additionally, this method to identify particle mode works best on freshly emitted particles. This may explain the inconclusive results found in the present study since there was a short spatial and temporal distance between the point of emission (e.g. vehicle exhaust) to the point of contact (i.e. instrument inlet), during which particles may undergo transformation processes that significantly alter the surface chemistry. Day-to-day variations in the vehicle fleet composition and the presence of other sources such as cigarette smoke could also have had an effect on the overall results observed. Although the particle pollutants of interest in the present study are primarily emitted in outdoor environments (i.e. on roads), these emissions can also penetrate into and influence the air quality in indoor areas Yu et al. (29). However, the major 116

61 sources of particulate pollutants in indoor environments are mainly from cooking or smoking activities (Li et al., 21). Unlike shopping malls in Hong Kong (Li et al., 21), smoking indoors is banned in Singapore and tightly policed, with strict fines levied on perpetrators. Although there are restaurants and food centres within the two shopping malls that were a part of the present study, these areas were located on different floors. Combined with the pervasive air-conditioning, it is not surprising that indoor areas in this study exhibited lower mean concentrations. The lower spread of data indoors lend further evidence that indoor locations are tightly controlled, with few emission sources. These observed distinctions between outdoor and indoor spaces have important implications for Singapore where the discomfort of the tropical hot and humid climate has led to much climate conditioning. This has resulted in expansive air-conditioned spaces for people to live, work and play. The transport sector has not been immune to such trends, with the public transport network increasingly intersecting with indoor areas. A few bus terminals and interchanges have been transformed into enclosed air-conditioned spaces that are linked to shopping malls (Figure 5-1), with more of such integrated transport hubs being planned for the future (Land Transport Authority, 214b). Such developments mean that commutes are becoming a mix of both outdoor and indoor spaces, and the outdoor street environment is only one of the types of microenvironments that commuters would pass through. Although more energy is required to operate ventilation and airconditioning systems, this is a positive step towards reducing commuter exposure to aerosol pollution (Nieuwenhuijsen et al., 27). 117

62 Figure 5-1: Photograph of an air-conditioned bus interchange which is linked to an MRT station and a shopping mall Bus-stops and Taxi-stands The Bus-stop and Taxi-stand sections are expected to exhibit distinct pollutant signatures since these are areas where emissions are highly localized due to the stop and start driving profiles. In addition, the vast majority of buses and taxis in Singapore run on diesel fuel (Land Transport Authority, 214c), which have been found to emit more UFPs than gasoline-fuelled vehicles (Kittelson, 1998). The data revealed higher mean values of PM2.5, PN, ASA, and ppahs in these spaces (see Table 4-16 and 4-24); however, there was no statistically significant difference between the outdoor areas of Bus-stop and Taxi-stand and the Sidewalk. The higher pollutant concentrations observed in these locations are important especially if the frequency of buses or taxi availability is low. Although waiting times for buses and taxis in this study were fairly short, (2 minutes for Buses, and 5 minutes for Taxis), there were one or two occasions where volunteers had to wait up to 12 minutes for a taxi. Longer waits combined with elevated concentrations would lead to higher inhaled dose. The results of dosage calculations are discussed below. 118

63 The Bus-stop section exhibited relatively high mean PN and small D ave,s. However, the PM 1 concentrations were also relatively high, leading to a lower observed PM 2.5 /PM 1 ratio of.83. This result may be explained by the presence of two types of emissions. The first is engine exhaust and the second the suspended dust from the wear and tear of brakes (Riediker et al., 24). Particles generated via the latter process are larger in size and may contribution disproportionately to PM 1. Again, further work regarding the chemistry of particles experienced in this space is needed in order to confirm this hypothesis In-vehicle concentrations As explained in Chapter 3, logistical constraints resulted in a study route that is much shorter than the average daily commute. For a typical daily commute, longer periods of time would be spent inside vehicles. Thus, the Bus, Train, and Taxi sections were analysed in greater detail. Except for the Train, in-vehicle concentrations for the vehicles measured in this study are generally much cleaner than the outdoor street environment for most of the pollutant variables (the exceptions are ppahs and CO). This contrasts with other studies where in-vehicle concentrations of PM 2.5 and UFPs were found to be higher than the external street environment (e.g. McNabola et al., 28), which, as explained, can be attributed to the ventilation settings of the vehicle. The Train was the only in-vehicle section which exhibited higher PM concentrations than the other sections of the MRT mode with mean values >3 µg m -3 for the three size-fractions (Table 4-2). This result for trains agrees with the findings of Branis (26) who found higher PM 1 concentrations inside trains compared to other sections of subway commutes, although the actual values in the present study are much lower than those found in the Prague subway system. The differences observed between the Train and other indoor sections of MRT mode trips may be due to differences in the efficiency of air-conditioning systems at filtering out aerosols. Although the Train exhibits higher concentrations of PM 1, PM 2.5, and PM 1 than 119

64 either the Bus or Taxi (Figure 5-2), PN was found to be significantly lower in all areas of the MRT mode trip except the Sidewalk (see Section 4.2.2). This agrees with existing knowledge regarding the unique emissions sources within train systems generating particles in the larger size-fractions, which contribute towards mass concentrations rather than number concentrations. In contrast, emission sources in outdoor sections are mainly from combustion, which would generate smaller particles that contribute to PN but not PM. Amongst the three vehicles, higher mean concentrations of PN were measured inside buses than trains or taxis. As explained in Chapter 2, the opening and closing of bus doors may allow freshly emitted pollutants to enter the vehicle at regular intervals. This was supported by observed slight jumps in PN concentration when bus door opened (Figure 4-1). This may also explain the high PM concentrations inside buses compared to taxis. The highest CO concentrations were observed inside on-road vehicles (buses and taxis), with the highest mean CO concentrations found inside Taxis (see Section 4.2.3). This finding is consistent with those of previous studies (Kingham et al., 213), and is attributed to the use of air-conditioning and recirculation. Unlike particles, CO is unlikely to be removed by air filters in the ventilation system, which, combined with the absence of dilution under the recirculation mode, leads to the build-up of CO concentrations inside the vehicle (Abi Esber et al., 27). 12

65 5 5 PM 1 (µg m -3 ) PM 2.5 (µg m -3 ) PM 1 (µg m -3 ) PN (1 3 # cm -3 ) ASA (mm 2 m -3 ) ppahs (ng m -3 ) BC (µg m -3 ) 2 1 CO (ppm) 4 2 Bus Train Taxi Vehicle Bus Train Taxi Vehicle Figure 5-2: Boxplots of the 8 pollutant metrics measured inside the three vehicles (Bus, Train, and Taxi). For explanation of boxplot symbols see Figure

66 5.3 Dosage Dosage results between transport modes follow those for exposure concentrations. The highest mean inhaled dose was observed on the Walk mode, followed by the Taxi, Bus, and MRT modes. This is due to the increased (1) V E, (2) travel times, and (3) mean pollutant concentrations experienced while walking compared to the other modes of transport. As noted in the previous chapter, the combined effect of (1) and (2) increases the differences in inhaled dose between walking and the other three modes of transport. This combined effect of (1) and (2) is quantified in Table 4-3. The ratios between the Bus, Taxi, and MRT modes and the Walk mode differ by approximately a factor of two for both PM 2.5 and PN once V E and travel time are taken into account (Table 4-31). This result echoes the findings of Panis et al. (21), who argue that these factors are important when evaluating the health risk for different transport modes. In particular, (2) appears to have a stronger influence on the final dosage calculations in the present study. Mean V E values observed in the present study are strongly correlated to activity levels, ranging from a minimum of 14.3 L min -1 when seated inside a taxi to as high as 27.6 L min -1 whilst climbing stairs. Thus, as anticipated, the highest mean V E was observed during the Walk mode. In contrast, for the other modes of transport, volunteers could take a breather and sit in the taxi, or stand still inside the bus and MRT train, leading to lower heart rates and V E inside vehicles. This meant that the mean V E values observed were fairly similar for the three vehicular transport modes (Table 4-29). This has important implications on the potential dose since in-vehicle concentrations were observed to have lower PM 2.5 and PN concentrations as well (see Section 5.2.2). Amongst the vehicular transport modes, travel times for the MRT mode were fastest. Surprisingly, travel times for the Taxi mode were slower than for the Bus 122

67 mode by 3 minutes on average, despite the fact that buses stop at multiple points along the route (Figure 4-1). Part of this may be attributed to the longer average waiting times at the Taxi-stand than at the Bus-stop as well as the slightly longer distance travelled. Waiting times at the Taxi-stand can range from less than a minute to upwards of 1 minutes (Figure 5-3). The presence of a dedicated bus lane in the study area may also account for the fairly quick travel times. This variation in time spent in different sections has important implications for dosage. Since the highest mean PN concentration was observed at the Taxi-stand, a longer duration spent waiting for a taxi would lead to a higher dose for the overall trip regardless of the V E of the individual. Another example emphasizing the importance of travel duration is the result for the potential inhaled dose observed at the Bus-stop section. The potential dose calculated for the Bus-stop is extremely low despite this section exhibiting some of the highest average concentrations within the route for Bus mode trips (Table 4-3). This is likely due to the extremely short time spent in this section (average 1.2 minutes). 123

68 15 Minutes 1 5 Bus Mall Underpass Bus-stop Sidew alk Bus Sections 15 Minutes 1 5 Train Mall Platform Station Sidew alk MRT Sections 15 Minutes 1 5 Taxi Mall Sidew alk Taxi-stand Taxi Sections Figure 5-3: Boxplots of time spent in each section for the Bus, MRT and Taxi mode trips. For explanation of boxplot symbols see Figure

69 This result regarding the effect of travel duration, specifically the time spent within particular sections of the route, highlights a possible strategy for local authorities to reduce human respiratory exposure to traffic emissions. With increases in the frequency of buses or taxis, waiting times at locations that exhibit higher pollutant concentrations (i.e. Bus-stops and Taxi-stands) could be reduced, thereby lowering the overall trip dose. A caveat is that this assumes the increase in bus and taxi frequencies do not lead to significant increases in pollutant emissions. 125

70 Chapter 6. Conclusion The aim of the present study was to understand the level of aerosol pollution experienced by commuters on the different public transport modes and by walking in Singapore. Mobile measurements of PM, PN, ASA, ppah, BC, and CO were taken on four modes of transport in the commercial district of Singapore. In addition to exposure concentrations, the inhaled dose was also investigated. 6.1 Summary of key findings In general, the results from the door-to-door trips indicated that the Walk mode exposes commuters to the highest concentrations of PM 2.5 and PN, while exposure concentrations are lowest on the MRT mode. Mean concentrations of PM 2.5 and PN on the Walk mode were 37 µg m -3 and 44,49 # cm -3, respectively. This was followed by the Bus, Taxi and MRT modes, in order of decreasing mean PM 2.5 and PN concentrations. Mean concentrations of PM 2.5 and PN on the MRT mode were 27 µg m -3 and 14,418 # cm -3, respectively. Data for the MRT mode also exhibited a lower spread, with smaller SDs. This may be due to the fewer emissions sources along the route. The trends in exposure concentration across transport modes are similar for the other pollutant metrics, with the exception of ASA and CO. For these two metrics, Taxi mode trips observed higher concentrations than the Walk mode at 14 to 137 mm 2 mm -3 and 1. to.6 ppm for ASA and CO, respectively. The use of instruments capable of real-time monitoring enabled analysis of variations within door-to-door journeys. Each trip was split into several indoor, outdoor, and in-vehicle sections that each exhibited particles of different ages and from slightly different sources and ventilation processes. Generally, indoor and invehicle spaces exhibited lower aerosol concentrations than outdoor areas. This was attributed both to the filtering capability of the pervasive air-conditioning systems in indoor areas, as well as the greater spatial and temporal distance from vehicular 126

71 exhaust. Thus, the MRT mode, which was predominantly indoors and fully airconditioned, exhibited the lowest mean exposure concentrations overall. There were also interesting variations found between sections. For example, the Train section exhibited higher PM 2.5 concentrations (34 µg m -3 compared to 28 µg m -3 ), but lower PN concentrations (12,789 # cm -3 compared to 33,34 # cm -3 ) than the outdoor Sidewalk areas. This result agrees with existing knowledge that the emission sources in these areas generate different types and sizes of particles. It also highlights the importance of measuring both PM and PN concentrations. The use of recirculation in buses and taxis can also lead to accumulation of CO, which explained the elevated levels of the gas found inside the vehicle cabins. Outdoor areas such as Bus-stops and Taxi-stands which experience high levels of stop-and-go traffic were found to have higher exposure concentrations of PM, PN, ASA, and ppahs. However, no statistically significant difference was observed between these sections and the Sidewalk section, which indicates that outdoor areas in close proximity to the road would lead to similarly high exposure. This could be a concern for people who work in the outdoor transport microenvironment. The effect of commuter behaviour was also estimated using inhaled dose. The results reveal subtle differences in trends between exposure concentrations and dosages. As with the exposure concentrations summarized above, dosages were highest on the Walk mode. Dosages of PM 2.5 and PN observed during the Walk mode were 23.1 µg and particles, respectively, more than twice the next highest values found for the Taxi mode (1.5 µg and particles, respectively). These results can be attributed to the clear differences in activity levels between the Walk mode and the other three transport modes. Respiration rates, V E, were approximately 2% higher whilst walking compared to the other transport modes. The average travel duration for Walk mode trips was 25. min, longer than for the other three modes which ranged from min on average. Taking into 127

72 account the increased exertion combined with longer travel times, the exposure differences between the Walk mode and the other three transport modes increased by a factor of two. Due to the short route, travel duration appears to have a relatively larger effect on the dose calculations. The findings revealed complex relationships between spatial variations in exposure concentrations and commuter behaviour namely their activity levels and time spent in each environment and the resultant effect on pollutant dosages for trips on different modes of transport. The results also highlighted possible areas for environmental and health authorities to reduce human exposure. For example, inhaled dose in areas with high pollutant concentrations can be minimised by reducing the time spent in those areas. This is illustrated by the lower calculated dosages for Bus mode trips compared to the Taxi mode, due to the much shorter waiting times at the Bus-stop than at the Taxi-stand despite the similar exposure concentrations observed in the two sections. The trends observed in the present study also differed from those reported in previous studies. Such differences can be attributed to differences in vehicle fleet composition and age as well as commuter preferences such as the use of air-conditioning and recirculation. Hence, it is important to have studies that measure pollutant concentrations while taking into account unique local conditions. In addition to the findings about the aerosol exposure on the various modes of public transport and by walking, some conclusions about the status of Singapore s air quality can also be inferred. The high PM 2.5 /PM 1 ratio of approximately.9 observed across all transport modes and at the background site indicates that fine particle pollution is a problem in Singapore. This is despite the tight controls on the vehicle population and a major area that authorities should be concerned with due to the greater toxicology of small particles. On a more positive note, the low correlation observed between CO and BC further suggests that CO pollution from the transport sector is well controlled and not a major cause for concern in Singapore. 128

73 6.2 Final notes and suggestions for future research Based on the results described, it is evident that the MRT mode is the best mode for commuters to reduce exposure to aerosols, while walking is clearly the worst mode of transport in terms of air quality exposure. However, the data supporting the conclusions is based on exposure concentration and dosage. Although walking was found to be the dirtiest mode in terms of these two metrics, this outcome does not take into consideration emissions as a result of the trip itself. Of the four transport modes studied, only walking does not lead to emissions of air pollutants. Thus, while the results suggest that walking should be avoided to reduce human exposure to aerosol pollutants, walking should still be encouraged in order to reduce pollutant emissions in the transport microenvironment. A significant shift in travel patterns towards modes with no pollutant emissions such as walking and cycling could lead to reductions in pollutant concentration in the transport microenvironment, and consequently reduce exposure and potential inhaled dose. Urban planners can facilitate such a change in mobility patterns by redesigning areas to funnel pedestrians away from vehicular traffic. As an active mode of transport, walking can also be promoted for the potential health co-benefits (de Nazelle et al., 211). Furthermore, much of the comparatively lower concentration levels observed on the Bus, MRT and Taxi mode trips are due to the extensive use of air-conditioning. Although air-conditioning can help reduce exposure and inhaled dose levels on the various transport modes (Chan et al., 22; Nieuwenhuijsen et al., 27), these systems are highly energy intensive, and may lead to increased burning of fossil fuels for electricity generation. This may lead to other undesirable impacts, such as the urban heat island effect and climate change. Due to the study design and limited resources available, the present results need to be interpreted within the context of the particular conditions investigated. One limitation was the use of a single route along the main thoroughfare, which confined 129

74 the Walk mode trips to the pedestrian sidewalk. This did not take into account the possibility for pedestrians to use alternative routes to avoid traffic emissions, which was noted by both Kaur et al. (25b) and Dons et al. (212). Another limitation was the short route used. Thus the results, particularly in terms of inhaled dose, may not be representative of the typical commuter experience. In addition, comparisons with previous studies of aerosol pollution in Singapore revealed that the present PM values observed were lower than those found by Kalaiarasan et al. (29b) in a residential apartment block facing a heavily-trafficked highway. This further indicates that more research conducted in a wider range of traffic conditions and land uses is required to provide a more complete understanding of the pollutant concentrations that the Singapore population is exposed to. Despite these issues, the current findings provide useful insight to commuter exposure in Singapore, predominantly by drawing attention to specific locations (i.e. bus-stops and taxi-stands) where commuters come into contact with higher concentrations of pollutants. The results show that air quality in the transport microenvironment is a complex issue that requires further study. As noted, results report need to be interpreted within the context of Singapore s unique local circumstances. Thus it would be useful for urban planners if more research utilizing the mobile measurement technique is done. Notwithstanding the need for localized data, some of the conclusions in this study that were linked to lower pollutant concentrations and exposure, such as promoting MRT systems and increasing coverage of airconditioning, could be a useful reference for tropical developing cities that are experiencing massive vehicle population growth and are expanding their transport networks while trying to minimize commuter exposure to aerosol pollution. Another area for future research concerns the metric used to characterise aerosol pollution. Amongst the eight metrics used, only three (PM 1, PM 2.5, and CO) correspond to the criteria pollutants monitored by the local authorities. This is cause 13

75 for concern since the PM 2.5 /PM 1 and PM 1 /PM 2.5 ratios observed in this study were very high, even at the background site. This hints at a large proportion of UFP in the local atmosphere, which would be best quantified by PN or ASA. However, at present, there are no known thresholds for PN concentrations on which to base health advisories. This is an existing limitation of the present state of air quality knowledge, and calls for increased efforts to provide scientific evidence regarding the health impact of street-level air pollution that can be used by regulatory authorities. 131

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85 Appendix A DustTrak Calibration The mass concentrations of PM 1, PM 2.5 and PM 1 measured by the DustTrak sensors were corrected for effects by RH. A calibration curve for each specific sensor was derived by comparing DustTrak data with samples collected by a gravimetric sampler. A.1 Calibration Procedure During the calibration sampling, DustTraks were fitted with a 2.5 µm inlet impactor to ensure that only PM 2.5 was sampled. Both DustTraks were set to log at 1 minute intervals using a flow rate of.3 m 3 min -1. The gravimetric sampler used was a MiniVol Portable Air Sampler (Airmetrics) also fitted with a PM 2.5 impactor. Aerosols were collected on 47 mm diameter, 2. µm thick Teflon filters (Pall Corporation P/N R2PJ47). The flow rate for the MiniVol was set to 5 m 3 min -1. Filters were conditioned in a temperature and relative humidity controlled box (T: 22 C and RH: 32%) before each weighing. Each filter was weighed on a.1 mg precision Sartorius MC5 Microbalance. Each filter was weighed three times and the mean weight was taken. Samples with erroneous results due to weighing problems or other issues were rejected. 2% of the 65 samples collected were affected and subsequently excluded from analysis. The DustTraks and MiniVol samplers were set up in a rooftop laboratory located at Block E2 within the National University of Singapore (NUS). Sampling tubes connected the instruments to an air sampling box which was connected to ambient air via a main sampling line (Figure A-1). 141

86 Figure A-1: Instrument set-up for the gravimetric calibration. Two separate sampling periods were carried out. The first sampling period took place from 23 June to 5 July 212. Ambient aerosols were sampled for three different averaging periods: 3, 12 and 24 hours. The second sampling period was carried out from 1 September to 1 November 212. For this sampling, all samples were used and averaged over 24 hours intervals. A few days of the second sampling period were impacted by transboundary haze from wildfires in neighboring Sumatra, Indonesia, thus higher concentrations of PM2.5 were observed, providing a wider range of concentrations than during the first sampling period. A.2 Temperature and Relative Humidity data As part of the DustTrak calibration, T and RH data were obtained from the Geography Weather Station (GWS) located above the elevator shaft of Block E2 in NUS. An issue with the power supply meant there was no meteorological data logged during the second half of the first sampling period. Data from the SERIS weather station located 13 m away at Block E3A of NUS was used to supplement the missing data. For the second sampling period, GWS data was used throughout. The instruments used at both weather stations are listed in Table A-2. Data from both 142

87 stations from 23 to 27 June 212 were compared to find if there were differences within the range of accuracy for the instruments. Table A-1: Details of meteorological instruments at each weather station. Geography Weather Station SERIS weather station Sensor Vaisala CS5 Thies CLIMA T accuracy ±.5 C ±.1 C RH accuracy ± 2.5% RH ±.2% RH A.3 Humidity correction Effects of humidity were corrected using the following equation from Ramachandran et al. (23): PM2.5 =., CF = ( ) (2) RH refers to the relative humidity measured over the same period as the DustTrak, expressed as a fraction of 1%. CF is the correction factor. Eq. (2) becomes unstable for very high RH, although the exact threshold is unknown. The calculated CF may overcorrect for the DustTrak PM 2.5 values in environments with very high RH. This is a concern in Singapore which experiences a tropical climate where relative humidity ranges from 65 to 9%. Figure A-2 shows that the most frequent values of RH recorded throughout both sampling periods are between 75 and 82%, which is quite high and may result in exceptionally high CF. However this is the best available method for dealing with the effect of humidity on the DustTrak readings. 143

88 Figure A-2: Distribution of RH values during the two sampling periods. A.4 Gravimetric correction A calibration curve was developed for the relationship between the gravimetric PM 2.5 and RH-corrected DustTrak measurements. Data from the DustTraks were first corrected for humidity effects using Eq. (2) then averaged over the respective time periods. Data from both sampling periods were combined with the aim to use a larger set of sampling points (N = 52). Initial regression equations performed poorly, which was attributed to the different sampling intervals and the effect of high humidity (Figure A-3). To improve the calibration curve, data from the 12 and 3 hour sampling periods were removed. Samples experiencing > 85% RH for more than 1% of the time were also removed to avoid an overcorrection effect from excessively large values of CF. Following this, DustTrak PM 2.5 values which overestimated the gravimetric measurements were also removed, based on the assumption that the optical technique overestimates the mass concentrations of fine particles (Ramachandran et al., 23). Following these corrections the regression coefficients improved significantly (Table A-2). 144

89 6. 5. (a) y =.2529x R² = (b) y = 1.2x.3385 R² =.268 Gravimetric PM2.5 (µg/m 3 ) y =.2271x R² =.2151 Gravimetric PM2.5 (µg/m 3 ) y = x.2962 R² = DT21 DT211 DT21 DT211 Linear (DT21) Linear (DT211) Power (DT21) Power (DT211) DustTrak PM2.5(µg/m 3 ) DustTrak PM2.5(µg/m 3 ) Figure A-3: Scatterplots of all data from both measurement periods, showing (a) linear regression and (b) power regression. 145

90 Table A-2: Regression coefficients derived from data when RH-corrected DustTrak PM 2.5 values are greater than gravimetric filter values. D = measured data and G = calibrated data. (N = 22). Sensor Linear regression r 2 Power regression r 2 DT21 G =.5539D (3).78 G = D.795 (5).83 DT211 G =.5358D (4).83 G = D.7567 (6).87 The data was also analysed according to different time periods. This was done in order to remove the additional source of uncertainty from the different human influence during handling of the filters. Sample points taken during the first half of second sampling period resulted in the best performing calibration equations (Table A-3). Table A-3: Regression curves derived from data of the first-half of the second sampling period only. D = measured data and G = calibrated data. (N = 24). Linear regression r 2 Power regression r 2 DT21 G =.514D (7).77 G = D.6614 (9).84 DT211 G =.5236D (8).84 G = D.6842 (1).89 Eqs. (7) (1) (Table A-4) with the best r 2 were compared to find if there is a major difference between the linear and power regression methods. The equations were applied to all data points sampled. The mean difference between the equations (Linear Power) was relatively small at.57 µg m -3. Equations (9) and (1) were selected as the calibration equation as they had the highest r 2 and accounted for zeropoint calibration. An important caveat is that these equations are strictly only valid over the range of values they were derived from. Thus, care should be taken when interpreting values that have been corrected outside these ranges (Figure A-3). 146

91 Appendix B Description of a trip on each transport mode B.1 Bus For the Bus mode, a trip included walking to the nearest bus-stop from the start point, waiting time for the bus, travel on the bus, and walking to the end point. Getting to the bus-stop from the start point included walking to the basement of the starting shopping mall, through the connecting underpass, and up to the street level. The busstop was located a short distance away from the underpass exit. Measurements were usually taken in the middle (lengthwise) of the bus, near the exit door. All buses sampled in this study were fully air-conditioned and had closed windows. It was not possible to confirm the recirculation settings. B.2 Mass Rapid Transit (MRT) Similar to the Bus mode transects, a single trip for the MRT mode included walking to and from the station, waiting times, and actual travel on the trains. For most samples, measurements were taken in the middle of the train carriage depending on how many people were in the train. In some cases, the train carriage was too full, thus volunteers had to stand by the door. B.3 Taxi A trip on the Taxi mode consisted of walking to the nearest taxi-stand from the starting point, waiting at the taxi-stand, travel in the vehicle, and walking to the end point. The shortest routes between the start and end points and the taxi-stand were taken, which happened to pass through shopping malls. Within the vehicle, the instrument backpack was kept in the middle of the back seat of the car. The DustTrak 147

92 and CPC were placed on the researcher s lap. No instructions were given to the driver regarding driving style. Windows were kept closed and air-conditioning was on recirculation, the usual settings the drivers use. Where possible, the driver was interviewed to find out the age of the vehicle and last time the engine was serviced or the air-conditioner filter replaced. B.4 Walk For the Walk mode transects, measurements were taken along the middle of the sidewalk, approximately 1.5 m away from the road. To enable sampling on both sides of the road to avoid a bias in concentration due to the vortex effect described in Section 2.2.2, volunteers crossed to the other side of the road at approximately 6 m from the start point. Traffic signals were obeyed at all times. 148

93 Appendix C Anderson-Darling Test for Normality Table C-1: Summary of statistics from Anderson-Darling test for Normality. AD = test statistic. Parameter N Mean (sd) AD p-value PM (7.48) 1.42 <.5 PM (7.474) 1.41 <.5 PM (8.213) <.5 PN (1384) <.5 ASA (27) ppahs (39) <.5 BC (3.389) 4.92 <.5 CO (.817) 8.44 <.5 149

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