Matriz sobre medición de la calidad y uso de datos abiertos

In this section concentrations from development sites were used to develop two different versions of hourly adjustments for LUR predictions of annual mean concentrations for 2010. Applied adjustments include A) direct adjustment by monitoring data, and B) adjustments calculated from monitoring data aggregated by temporal pattern. As mentioned above both of these types of adjustments have been used in the literature. Little comparison between the methods or evaluation with ambient monitoring data had however been undertaken. Adjustment version A has been calculated to evaluate to what extent hourly variability at background stations reflects variability at other locations, and whether it therefore can be used for predictions. Adjustment version B uses temporal trends that reflect certain temporal changes in PM concentrations. This is considered a more transferable method as the repeated pattern can be used to predict concentrations for different time periods or areas for which monitoring data might not be available. In the following paragraphs describe how the two adjustments are calculated.

Station Station

156

Development of temporal adjustments:

For adjustment A, the average of all background stations (as described above) was divided directly by the annual mean of 14.08µg/m3 _{for PM}

2.5 and 20.82µg/m3 for PM10. The resulting ratios are presented in Figure 31. The two graphs for hourly ratios at the top of Figure 31 show that calculating ratios directly from the hourly background concentrations results in an adjustment ratio with high variability from 0.15 up to 10.33 for PM2.5 and from 0.11 to 8.56 for PM10.

PM2.5 PM10

Figure 31: Temporal adjustments A for hourly, daily and weekly LUR predictions of PM2.5 and PM10, based on 2010 data.

The highest ratios both for PM2.5 and PM10 have been calculated for a short time period in November. These values reflect a pollution episode referred to by the LAQN as the ‘Guy Fawkes and Diwali event celebrations’ (www.londonair.org.uk). Ratios for all other times remain below 6 for both PM2.5 and PM10 with the majority of hourly ratios between 0 and 2. For daily ratios (graphs in the middle of Figure 31), peaks are less pronounced compared to hourly ratios. All ratios are

157 between 0 and 4 for PM2.5 and between 0 and 3 for PM10. For weekly ratios (Figure 31, bottom) a further reduction in variability can be observed with all ratios between 0 and 2 both for PM2.5 and for PM10.

Next, adjustment B was calculated. As a first step, temporal patterns were identified, which describe repeated patterns of pollution variability. Hour of day and day of the week form recognised patterns in particle pollution following differences in traffic source activities (Beevers et al. 2009). The third recognised temporal pattern for particle variability is season. Seasonal differences have been described as part of monthly differences, as well as differences between winter, spring, summer and autumn (Arx et al. 2004; Beevers et al. 2009). Seasonal differences in particle concentrations reflect climatological changes throughout the year.

For this application hour of day, day of week and seasonal patterns were combined to calculate adjustment B. For the seasonal pattern four seasons were used instead of the monthly difference, as weather, and as a consequence related particle concentration changes, can vary substantially between the same months of different years. Differences between the four seasons are more stable between years. As a first step the hourly monitoring concentrations for the average of London background stations were used to calculate the mean concentrations by hour of day, day of week and season. These three averages were then each divided by the 2010 annual mean. The resulting ratios were combined to adjustments B, as presented in Figure 32. To calculate hourly ratios, first the seasonal ratio was multiplied by the day of the week, then the hour of day was combined with the results. All resulting ratios of adjustment B are presented in Appendix H. In comparison to the direct hourly adjustment A (Figure 31) the graphs show a more repetitive pattern, as well as much less variability with the highest ratios well below 2 for both PM2.5 and PM10. For the daily adjustment B, only the seasonal and day of the week ratios were combined. Weeks were adjusted only by seasonal ratios. Ratios for adjustment versions A and B were subsequently multiplied with ESCAPE- LUR estimates, calculated for each station of the prediction monitoring sites.

158 P M 2 .5 : A d ju st m e n t B P M 1 0 : A d ju st m e n t B

159

Adjustment by ratio and adjustment by total difference in comparison

Applying ratios (and in particular combining several ratios) to a dataset can potentially provide a scaling problem as the same ratio adds more to a high value compared to a lower value. A different approach is to calculate and apply the direct difference to a long term mean. Adjusting by total difference to a long term mean would add the same value to a low and a high concentration. Adjustment by difference can however result in negative values, ratio adjustment is therefore preferred here, as well as by many other studies (Aguilera et al. 2009; Aguilera et al. 2010; Brauer et al. 2008; Dons, Van Poppel, Int Panis, et al. 2014) as a method. In order to test if the adjustment produces problems with scaling, an adjustment by difference has been calculated both for adjustment A and B and is compared in this section.

In order to calculate adjustment A using difference, the average (hourly, daily, and weekly) for all background monitoring stations is taken minus the annual average. Similarly, adjustment B is developed using total difference to the annual average for hour of day, day of week, and season. These three differences are then added together (as shown in Figure 32). To calculate adjusted LUR estimates the adjustments by difference are added to LUR estimates of the prediction sites. Predictions of monitoring stations were compared between both adjustment variations to assess if adjustment by ratio performed less well, which would indicate problems with its application.

For comparison between ratio and difference adjustments COD (Coefficient of Divergence) was calculated between monitored and modelled. COD has been introduced in chapter 3. A COD of 0 means values are the same, while a Pearson correlation of 1 refers to a perfect correlation (Gaines Wilson & Zawar-reza 2006). The COD is more suited than Pearson’s correlation to identify a potential problem of scaling as it compares not only the variability, but also the total values.

COD results for PM2.5 are presented in Table 37. Result show that most COD differ little between adjustment by ratio and by total difference for both adjustments A and B, as well as for different levels of temporal aggregation. Only for weekly concentrations at one station (station 5) for adjustment B the difference in COD between the two approaches was higher than 0.05. Furthermore, there is no clear indication if adjustment by ratio or by difference generally show better COD results. For both adjustment A and B, as well as for all periods of temporal aggregation, COD are lower at a relatively equal number of stations for either adjustment. In summary, the results for PM2.5 show no clear evidence that one or the other version of adjustment has better agreement with concentrations at the different stations.

160

Hourly Daily Weekly

Station ID Adjustment A Adjustment B Adjustment A Adjustment B Adjustment A Adjustment B Ratio Diff. Ratio Diff. Ratio Diff. Ratio Diff. Ratio Diff. Ratio Diff. 1 0.216 0.205 0.265 0.255 0.160 0.147 0.199 0.188 0.126 0.111 0.119 0.104 2 0.181 0.190 0.280 0.254 0.134 0.142 0.229 0.202 0.105 0.112 0.174 0.135 3 0.173 0.187 0.292 0.279 0.111 0.123 0.238 0.226 0.101 0.106 0.184 0.169 4 0.167 0.166 0.293 0.282 0.112 0.112 0.241 0.229 0.104 0.104 0.176 0.161 5 0.287 0.320 0.240 0.271 0.251 0.279 0.182 0.226 0.215 0.236 0.134 0.188 6 0.302 0.337 0.254 0.299 0.279 0.303 0.237 0.285 0.269 0.282 0.229 0.277 7 0.223 0.209 0.251 0.245 0.180 0.167 0.199 0.197 0.155 0.143 0.138 0.136 8 0.219 0.259 0.257 0.266 0.200 0.227 0.222 0.239 0.193 0.207 0.197 0.215 9 0.173 0.186 0.295 0.281 0.108 0.123 0.240 0.223 0.080 0.079 0.165 0.137

Table 37: COD (Coefficient of Divergence) for adjustments by ratio and difference of the temporal LUR adjustment versions A (direct adjustment by monitoring data) and B (adjustment based on temporal patterns) for hourly, daily, and weekly PM2.5 concentrations. Lower COD results between ratio and difference by more than 0.01 are coloured in blue and bold.

Hourly Daily Weekly

Adjustment A Adjustment B Adjustment A Adjustment B Adjustment A Adjustment B ID Ratio Diff. Ratio Diff. Ratio Diff. Ratio Diff. Ratio Diff. Ratio Diff.

1 0.267 0.299 0.258 0.281 0.250 0.268 0.234 0.261 0.248 0.255 0.227 0.254 2 0.169 0.164 0.235 0.226 0.117 0.113 0.178 0.175 0.098 0.095 0.123 0.122 3 0.121 0.128 0.231 0.220 0.082 0.085 0.178 0.168 0.065 0.064 0.117 0.104 4 0.193 0.211 0.290 0.274 0.128 0.144 0.220 0.205 0.097 0.108 0.158 0.140 5 0.200 0.192 0.209 0.212 0.175 0.169 0.165 0.181 0.160 0.158 0.139 0.160 6 0.223 0.207 0.241 0.237 0.200 0.185 0.192 0.198 0.174 0.164 0.151 0.164 7 0.105 0.108 0.228 0.212 0.062 0.068 0.179 0.164 0.046 0.053 0.127 0.108 8 0.221 0.221 0.266 0.267 0.181 0.181 0.202 0.214 0.170 0.170 0.161 0.181 9 0.107 0.108 0.235 0.219 0.068 0.070 0.187 0.173 0.051 0.055 0.132 0.115 10 0.153 0.147 0.226 0.221 0.120 0.116 0.177 0.176 0.112 0.110 0.132 0.135 11 0.196 0.183 0.228 0.224 0.167 0.153 0.171 0.175 0.138 0.129 0.117 0.130 12 0.168 0.179 0.296 0.282 0.119 0.130 0.241 0.226 0.115 0.120 0.180 0.161 13 0.144 0.144 0.254 0.241 0.098 0.098 0.195 0.183 0.067 0.067 0.132 0.117 14 0.148 0.135 0.204 0.200 0.115 0.104 0.152 0.156 0.101 0.095 0.107 0.117 15 0.120 0.125 0.222 0.208 0.081 0.081 0.164 0.153 0.061 0.057 0.104 0.089 16 0.164 0.140 0.198 0.193 0.119 0.096 0.144 0.142 0.085 0.068 0.086 0.085 17 0.219 0.218 0.287 0.269 0.129 0.129 0.205 0.183 0.092 0.092 0.146 0.114 18 0.140 0.150 0.244 0.230 0.100 0.108 0.192 0.179 0.078 0.084 0.137 0.120 19 0.184 0.185 0.269 0.256 0.151 0.148 0.195 0.190 0.134 0.131 0.140 0.137 19b 0.181 0.182 0.267 0.253 0.132 0.128 0.182 0.175 0.105 0.102 0.120 0.114 20 0.228 0.342 0.244 0.258 0.200 0.230 0.202 0.223 0.192 0.204 0.177 0.202 21 0.154 0.136 0.203 0.203 0.110 0.099 0.157 0.158 0.088 0.079 0.098 0.100 22 0.135 0.145 0.265 0.248 0.098 0.106 0.213 0.197 0.086 0.089 0.155 0.135 23 0.207 0.242 0.242 0.250 0.191 0.209 0.204 0.219 0.186 0.194 0.171 0.194 24 0.173 0.156 0.208 0.199 0.119 0.097 0.138 0.135 0.095 0.085 0.107 0.104 25 0.296 0.331 0.281 0.294 0.287 0.306 0.261 0.284 0.280 0.287 0.257 0.280 26 0.198 0.190 0.240 0.234 0.164 0.152 0.182 0.182 0.120 0.103 0.102 0.108

Table 38: COD (Coefficient of Divergence) for adjustments by ratio and difference of the temporal LUR adjustment A (direct adjustment by monitoring data) and B (adjustment based on temporal patterns) for hourly, daily, and weekly PM10 concentrations. Lower COD results between ratio and difference by more than 0.01 are coloured in blue and bold.

161 Results for PM10 are presented in Table 38. Results show that at a large number of stations COD differs by less than 0.01 between adjustments by ratio and by total difference. For adjustment A both for hourly, as well as for daily concentrations, COD differ by less than 0.01 at 14 out of 26 stations. Of all comparisons between ratio and difference adjustments presented in the table only 19 COD results differ by more than 0.02, and only for one comparison COD results differ by more than 0.05 (hourly, adjustment A, station 20). In addition, results do not show consistently better agreement (lower COD) with concentrations at monitoring stations for one or the other adjustment version. In summary, for PM10 (as above for PM2.5) the comparison between predictions by ratio and difference both for adjustments A and B, as well as for all levels of temporal aggregation show very similar results.

For both PM size fractions, the adjustment by ratio did not, as such show any lower performance in comparison with application of the total difference. As mentioned before, adjustment by difference can produce negative concentrations. Ratio adjustments were therefore chosen over adjustment by total difference and have been used in steps three and four of this analysis.