Urban morphology and PM10 concentration in European cities: an empirical assessment

Transcription

1 Urban morphology and PM10 concentration in European cities: an empirical assessment Federica Cappelli a, Gianni Guastella b, & Stefano Pareglio b a Roma Tre University b Fondazione Eni Enrico Mattei, Milano, IT IAERE Torino, Feb 2018

2 Facts Pollutant concentration is associated to cardiovascular and respiratory diseases Concentration above the safeguard threshold -in the EU 50mg/m3- is especially risky Pollutants are geographically concentrated near some hotspots Cities are home to more than half the world s population This share is projected to increase between 65% and 75% by 2050 Car, transport, and residential combustion contribute to air pollution to the largest extent 1

3 Objective of the research Understanding whether urban sprawl can impact the number of days in which the concentration of pollutants is larger than the safeguard threshold Larger and less compact cities may induce car-dependency and more-frequent commuting Focus on PM10 (diameter < 10 μmand can, thus, be inhaled) The empirical literature is underdeveloped and still inconclusive (and outdated) McCarty and Kaza(2015) find negative effect of total artificial area and positive effect of discontinuity on PM2.5 concentration in US counties Cárdenas Rodríguez et al. (2016) find that both the share of artificial area and the discontinuity impact positively PM10 concentration in EU cities Cho and Choi (2014) find no statistically significant relation after controlling for local specific characteristics 2

4 Objective of the research- WHAT s NEW Multidimensional conceptualization of urban sprawl and combined effects Spatial expansion of the built-up area Decreasing population density Increasing residential discontinuty Focus on exceedances, not mean concentration Control for local geophysical and climatic conditions and also for location-specific unobservable factors 3

5 The environmental impact of cities Generalised Additive Models (Wood, 2006) NDAY Pois( µ ) log( µ ) = β Z + s( sprawl) + s( Long, Lat) Linear predictor control variables Smooth non-linear predictor estimated using penalised splines main effect Smooth non-linear function of the geographical coordinates(spatial trend) unobservable effects affecting the spatial distribution of PM10 exceedances Hypothesis I: The different characters of sprawl independently contribute to PM10 exceedances Hypothesis II: the different characters of sprawl jointly contribute to PM10 exceedances ( ) = ( ) + ( ) + ( ) s sprawl s Artif s Dens s Disc ( ) = s( Artif, Dens, Disc ) s sprawl 4

6 Data Variable Description of the variables Mean SD Artif Total artificial area (CLC nomenclature level 1) - EEA SPRAWL Disc Share of discontinuous urban fabric on total artificial area - EEA Dens Population density - Eurostat Urban Audit Long Longitude AirQuality - EEA Lat Latitude AirQuality - EEA ArtifSh Share of artificial area on total area - EEA Altitude Altitude AirQuality - EEA Control Variables AgriSh Share of employment in the agricultural sector Eurostat ManuSh Share of employment in the manufacturing sector Eurostat NCars(ln) Number of private cars registered Urban Audit Temp Annual mean temperatures Prec Annual mean precipitation Wind Annual mean wind speed

7 Urban Morphology and PM10 exceedances Model I Model 2 Linear predictor 6 Intercept 0.81 *** *** (0.119) (0.118) ArtifSh (0.249) (0.246) Altitude *** *** (0.0001) (0.0001) AgriSh 1.99 *** *** (0.209) (0.212) ManuSh *** *** (0.186) (0.181) log(ncars) *** *** (0.03) (0.259) Smooth terms s(artif) *** (3.945) s(disc) *** *** s(dens) *** (3.958) (3.663) (3.487) s(artif,disc) 3.96 *** (3.999) s(long,lat) *** 13.35*** (13.985) (13.989)

8 Spatial trend in PM10 exceedances (residuals) (low) Predicted# of days of PM10 exceedances (high) 7

9 Some evidence from EU FUAs We present robust evidence relating urban sprawl to air pollution Integrated planning and transport policies can effectively mitigate the health impact due to the deteriorating quality of air Prescription: raise awareness about the negative environmental consequences of housing and transportation choices Further research: consider the time dimension, improve the quality of indicators, direct link to transport 8