TY - JOUR
T1 - Air quality in Enclosed Railway Stations: quantifying the impact of diesel trains through deployment of multi-site measurement and random forest modelling
AU - Font, Anna
AU - Tremper, A.H.
AU - Lin, Chun
AU - Priestman, M.
AU - Marsh, D.
AU - Woods, M.
AU - Heal, Mathew R.
AU - Green, D.C.
PY - 2020/4/30
Y1 - 2020/4/30
N2 - Concentrations of the air pollutants (NO2 and particulate matter) were measured for several months and at multiple locations inside and outside two enclosed railway stations in the United Kingdom – Edinburgh Waverly (EDB) and London King’s Cross (KGX) – which, respectively, had at the time 59% and 18% of their train services powered by diesel engines. Average concentrations of NO2 were above the 40 µg m-3 annual limit value outside the stations and were further elevated inside, especially at EDB. Concentrations of PM2.5 inside the stations were 30-40% higher at EDB than outside and up to 20% higher at KGX. Concentrations of both NO2 and PM2.5 were highest closer to the platforms, especially those with a higher frequency of diesel services. A random-forest regression model was used to quantify the impact of numbers of different types of diesel trains on measured concentrations allowing prediction of the impact of individual diesel-powered rolling stock.
AB - Concentrations of the air pollutants (NO2 and particulate matter) were measured for several months and at multiple locations inside and outside two enclosed railway stations in the United Kingdom – Edinburgh Waverly (EDB) and London King’s Cross (KGX) – which, respectively, had at the time 59% and 18% of their train services powered by diesel engines. Average concentrations of NO2 were above the 40 µg m-3 annual limit value outside the stations and were further elevated inside, especially at EDB. Concentrations of PM2.5 inside the stations were 30-40% higher at EDB than outside and up to 20% higher at KGX. Concentrations of both NO2 and PM2.5 were highest closer to the platforms, especially those with a higher frequency of diesel services. A random-forest regression model was used to quantify the impact of numbers of different types of diesel trains on measured concentrations allowing prediction of the impact of individual diesel-powered rolling stock.
U2 - 10.1016/j.envpol.2020.114284
DO - 10.1016/j.envpol.2020.114284
M3 - Article
SN - 0269-7491
VL - 262
JO - Environmental Pollution
JF - Environmental Pollution
M1 - 114284
ER -