each window it takes the value 1 for all time periods after the extension date, and 0 for periods before it.<sup>15</sup><sup>**x**</sup> _t_ ,<sup>is the vector of covariates and includes controls for</sup> weather;<sup>16</sup> for hour of the day,<sup>17</sup> day of the week, and interactions between these two; and for public holidays and festivals such as Diwali.<sup>18</sup> **P** ( _t_ ) is a third-order _l_ , _m_ polynomial in time and captures all smooth variations in pollutant levels.<sup>_u_</sup> _t_ is the _l_ , _m_ error term. The coefficient  1 measures the proportionate change in pollutant level at location _l_ as a result of extension _m_ . It is to be interpreted as the immediate localized (at location _l_ ) effect on pollution as a result of that particular extension. Since we expect the traffic creation effect to be negligible in the short run, we do _l_ , _m l_ , _m_ not expect  1 to be positive. If there is a strong traffic diversion effect,  1 will be negative, otherwise it will be insignificant.

Our identification strategy is similar to that used in Chen and Whalley (2012) (henceforth CW).<sup>19</sup> CW look at the effect of the introduction of the Taipei Metro (TM) on air quality in Taipei City. While they use the discontinuity arising from the _opening_ of the metro system, we exploit future discontinuities arising from various _extensions_ of the network. Unlike CW, we do not use the first opening of the metro for two reasons. First, we do not have pollution data that

> 15. We exclude the 24-hour data pertaining to the day of the extension because we do not know the exact hour when the new line became operational.

> 16. Controls for weather include current and up to 4-hour lags of temperature, relative humidity, wind speed, and rainfall and quartics of both current and 1-hour lags of these weather variables.

> 17. Figures S1.1a through S1.1c in the supplementary appendix S1 show how the pollutants at _ITO_ vary by hour of the day and by season. Once again we see that pollution is higher in winter compared to summer. Also, there is substantial intraday variation with peak levels reached between 8 pm and 2 am. Similar patterns are observed at _Siri Fort_ . One plausible reason for why pollution peaks during these night hours could be a citywide ban on the entry and movement of heavy goods vehicles (mostly diesel powered trucks) between 6:00 am and 9:00 pm. The substantial intraday variation in pollution calls for inclusion of hour of the day fixed effects in order to improve the precision of our estimates. In most specifications season-fixed effects are not included because of short window lengths, typically nine weeks. Choice of window lengths is discussed later in this section.

> 18. Diwali is a Hindu festival that falls in winter, typically in October or November. It spreads over several days and is celebrated with an ostentatious bursting of firecrackers. It has been documented that air pollution in Delhi shoots up during and immediately following Diwali (CPCB 2012). It is therefore important to control for this source of pollution.

19. Before Chen and Whalley, Davis (2008) used similar identification to estimate the effect of driving restrictions on air pollution in Mexico City.

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