Rahul Goel

Particulate and gaseous emissions in two coastal cities—Chennai and Vishakhapatnam, India

The presence of land sea breezes is advantageous to Chennai and Vishakhapatnam. With most industrial and power plant emissions dispersed to the sea, their overall impact on the urban air quality is lessened. However, the same is not true for the diffused emissions, such as the vehicle exhaust, domestic cooking, open waste burning, and road dust, which are steadily increasing. The annual averages for 2012 in Chennai are 121.5 ± 45.5, 12.1 ± 3.5, and 20.8 ± 7.0 and in Vishakhapatnam are 70.4 ± 29.7, 18.9 ± 14.4, and 15.6 ± 6.3, for PM10, SO2, and NO2 respectively. All the concentrations are reported in micrograms per cubic millimeter. In this paper, we present sector-specific emissions inventory for particulate and gaseous pollutants, which is spatially disaggregated at 0.01° resolution, suitable for atmospheric dispersion modeling. For the urban airshed, the ambient particulate concentrations were modeled using the ATMoS dispersion model, which when overlaid on gridded population, resulted in estimated 4,850 and 1,250 premature deaths and 390,000 and 110,000 asthma attacks in year 2012, for the Greater Chennai and the Greater Vishakhapatnam regions, respectively. The total emissions are also projected to 2030. Under the current growth rates and policy assumptions, the pollution levels are likely to further increase, if the expected changes in the industrial energy efficiency, environmental regulations in the power plants, and fuel standards for the vehicles are not introduced as planned.

Estimating city-level travel patterns using street imagery: A case study of using Google Street View in Britain.

Background Street imagery is a promising and growing big data source providing current and historical images in more than 100 countries. Studies have reported using this data to audit road infrastructure and other built environment features. Here we explore a novel application, using Google Street View (GSV) to predict travel patterns at the city level. Methods We sampled 34 cities in Great Britain. In each city, we accessed 2000 GSV images from 1000 random locations. We selected archived images from time periods overlapping with the 2011 Census and the 2011–2013 Active People Survey (APS). We manually annotated the images into seven categories of road users. We developed regression models with the counts of images of road users as predictors. The outcomes included Census-reported commute shares of four modes (combined walking plus public transport, cycling, motorcycle, and car), as well as APS-reported past-month participation in walking and cycling. Results We found high correlations between GSV counts of cyclists (‘GSV-cyclists’) and cycle commute mode share (r = 0.92)/past-month cycling (r = 0.90). Likewise, GSV-pedestrians was moderately correlated with past-month walking for transport (r = 0.46), GSV-motorcycles was moderately correlated with commute share of motorcycles (r = 0.44), and GSV-buses was highly correlated with commute share of walking plus public transport (r = 0.81). GSV-car was not correlated with car commute mode share (r = –0.12). However, in multivariable regression models, all outcomes were predicted well, except past-month walking. The prediction performance was measured using cross-validation analyses. GSV-buses and GSV-cyclists are the strongest predictors for most outcomes. Conclusions GSV images are a promising new big data source to predict urban mobility patterns. Predictive power was the greatest for those modes that varied the most (cycle and bus). With its ability to identify mode of travel and capture street activity often excluded in routinely carried out surveys, GSV has the potential to be complementary to new and traditional data. With half the world’s population covered by street imagery, and with up to 10 years historical data available in GSV, further testing across multiple settings is warranted both for cross-sectional and longitudinal assessments.

Contextualising Safety in Numbers: a longitudinal investigation into change in cycling safety in Britain, 1991–2001 and 2001–2011

Introduction The ’Safety in Numbers’ (SiN) phenomenon refers to a decline of injury risk per time or distance exposed as use of a mode increases. It has been demonstrated for cycling using cross-sectional data, but little evidence exists as to whether the effect applies longitudinally —that is, whether changes in cycling levels correlate with changes in per-cyclist injury risks. Methods This paper examines cross-sectional and longitudinal SiN effects in 202 local authorities in Britain, using commuting data from 1991, 2001 and 2011 censuses plus police -recorded data on ’killed and seriously injured’ (KSI) road traffic injuries. We modelled a log-linear relationship between number of injuries and number of cycle commuters. Second, we conducted longitudinal analysis to examine whether local authorities where commuter cycling increased became safer (and vice versa). Results The paper finds a cross-sectional SiN effect exists in the 1991, 2001 and 2011 censuses. The longitudinal analysis also found a SiN effect, that is, places where cycling increased were more likely to become safer than places where it had declined. Finally, these longitudinal results are placed in the context of changes in pedestrian, cyclist and motorist safety. While between 1991 and 2001 all modes saw declines in KSI risk (37% for pedestrians, 36% for cyclists and 27% for motor vehicle users), between 2001 and 2011 pedestrians and motorists saw even more substantial declines (41% and 49%), while risk for cyclists increased by 4%. Conclusion The SiN mechanism does seem to operate longitudinally as well as cross-sectionally. However, at a national level between 2001–11 it co-existed with an increase in cyclist injury risk both in absolute terms and in relation to other modes.

Modelling of road traffic fatalities in India

Highlights • First study in India to account for exposure of six different modes of transport for injury modelling.• Auto rickshaws or tuk-tuks are found to be the only motorised mode associated with higher safety.• Increase in walking and cycling is associated with higher safety.• Inverse U-shaped pattern of road deaths results from a mode shift from 2W to cars.