Research Project

Public Health Impacts in Urban Environments of Greenhouse Gas Emissions Reduction Strategies (PURGE)

Dinesh Mohan and Geetam Tiwari

Project Details

OBJECTIVE

  1. With the input of panels of experts, to define the critical policy choices for mitigation in selected settings in Europe, China and India, and the parameters within which those choices are constrained. 
  2. To delineate sets of specific realistic interventions, tailored to local needs, which meet the demanding abatement trajectories at several future time points (2020, 2030, 2050) as set out by the reports of the Intergovernmental Panel on Climate Change and in national or regional assessments.

Publishable Summary

Short descriptions of the two case study cities- Delhi and Vishakhapatnam considered for health impact modelling in India:

 

Delhi (India), capital city of India, covers an area of 1,500 square kilometres. Delhi along with its satellite cities- Gurgaon, Noida, Greater Noida, Faridabad, and Ghaziabad collectively called as Delhi Metropolitan Area (DMA) has an area of 2500 square kilometre. The region has grown rapidly over the past 20 years -- in 1990, its total population stood at 9.8 million and in 2011 at 21.5 million.

 

Vishakhapatnam (India):Vishakhapatnam is a port city situated on the south-east coast of India and has a population of 1.7 million (2011). It is one of India’s largest sea ports and has several heavy industries as well as a steel plant. Owing to its large industrial base, the city has been growing at an unprecedented rate doubling its population in the last decade. Table 1 shows major characteristics of the two cities.

 

Table 1: Major characteristics of the case study cities from India

Attributes

Delhi

Visakhapatnam

Population (million)

17

1.7

Total Area (sq. km.)

1480

530

Percent Built-up Area

~50

~30

Population Density (persons per hectare)

230

130

Number of in-use Cars per 100 persons

7.6

2.4

Number of in-use MTWs1 per 100 persons

13.5

17.6

Number of Road Fatalities per 100,000 persons

~11

~24

  1. MTW= motorised two-wheelers

Description of the work performed since the beginning of the project and the main results achieved so far

  • Household travel surveys have been conducted at Delhi and Visakhapatnam. With primary data collection, there is much better understanding of travel patterns of the population in the two cities. Also, it is possible to carry out exposure analysis disaggregated by age and gender as well as socio-economic status. This data is very crucial for designing future scenarios as well as to measure corresponding health effects.
  • In the literature on Delhi and Visakhapatnam as well as other Indian settings, we found lack of data from secondary sources on vehicle activity required for estimation of emission inventory. The major vehicle activity variables required are annual mileage, fuel efficiency, number of in-use vehicles, age distribution of vehicles and share of different fuel types (petrol, diesel and CNG). We conducted surveys at fuel stations and used database from pollution check centres in order to estimate vehicle activity data for the two settings. One of the major findings is that vehicle registration data overestimates the actual number of in-use vehicles by up to 100%. The findings from the study have been crucial as inputs to emission inventory for the two cities. The annual mileage values for different modes will also be used for injury model.
  • A survey was conducted to understand the travel characteristics of Delhi metro users. One of the major findings of the study is that mode shift from private vehicles to metro took place for trips longer than 10 km. Also, in the absence of metro, car owners are much more likely to use their cars than buses. Motorised two-wheeler (MTW) owners, on the other hand, are more likely to use bus than MTWs. Among all the trips in Delhi, the proportion of trips longer than 10 km is less than 20 percent. Majority of the access and egress trips to metro are by walking and  cycle rickshaw followed by three wheeler taxis. Trips longer than 20 kms are more dependent on motorised feeder systems.
  • Emission inventories and dispersion models have been completed for both Delhi and Visakhapatnam. The models for the two cities are built using bottom-up emission inventories from multiple sectors contributing to emissions in Indian cities. Also, preliminary health effects calculations have been carried out in both the settings for baseline as well as future years with BAU scenario. Due to inclusion of multiple sectors in the model, health effects of interventions in sectors other than transport can also be estimated.
  1. Some details for the emission inventory and dispersion modelling for Delhi follows. Emission inventory developed for the city of Delhi covers a modeling domain of 80kmx80km and including all known sectors like road and non-road transport, industries, power plants, domestic cooking and heating, generator sets and waste burning. For the base year 2010, we estimate emissions of 62,700 tons of fine particulates with diameter < 2.5 mm, 113,900 tons of fine particulates with diameter < 10.0 mm, 36,950 tons of sulfur dioxide, 375,900 tons of nitrogen oxides, 1.42 million tons of carbon monoxide, and 260,450 tons of volatile organic compounds. The inventory is further spatially disaggregated into 80 x 80 grids at 0.01 degree resolution.
  2. The GIS based spatial inventory coupled with temporal resolution of 1 hour, was utilized for chemical transport modeling using the ATMoS dispersion model. The modeled average PM2.5 concentrations ranged 122 ± 10 mg/m3 for South Delhi; 90 ± 20 mg/m3 for Gurgaon and Dwarka; 93 ± 26 mg/m3 for North-West Delhi; 93 ± 23 mg/m3 for North-East Delhi; 42 ± 10 mg/m3 for Greater Noida; 77 ± 11 mg/m3 for Faridabad industrial area. The results are compared to measured ambient particulate pollution to validate the seasonality of the emissions inventory.
  3. For six residential and industrial regions with more than 10,000 people per square km, the sector contributions to ambient PM2.5 concentrations ranged 16-34% for vehicle exhaust, 20-27% for diffused sources, 14-21% for industries, 3-16% diesel generator sets, and 4-17% brick kilns.
  4. For baseline scenarios in 2010 and BAU in 2020, we estimate 5,700 and 7,600 premature deaths due to exposure to ambient PM2.5 concentrations.
  5. The 2020 scenarios resulted in a total savings of 2,060 cases of premature deaths (27%) and 2.2 million cases of asthma attacks upon implementation of eight interventions in 2020 for transport, power generation, waste management, brick kilns and other industry. These interventions collectively are expected to reduce 20,000 tons of PM2.5 (30%), 5,200 tons of sulfur dioxide (14%), 132,600 tons of nitrogen oxide (31%), and 390,800 tons of carbon monoxide (24%) emissions per year in 2020.
    • An ongoing paper is looking at the time series trend of transport emissions in Delhi both retrospectively (since 1990) and prospectively (till 2030). From the retrospective analysis, the paper will highlight the extent to which different transport emission policies as well as changing fuel shares have been able to improve air quality and reduce GHG emissions.
    • Road injury analysis for the two settings in India will only be carried out for fatal accidents as representative data and overall estimates for injury accidents are not available for Indian cities. Therefore, fatal accident data analysis has been carried out for the two settings. Visakhapatnam has a fatality rate (24 per 100,000 persons) which is double that of Delhi (~11). Matrices for striking vehicle and victims have been obtained for the two cities. Ongoing efforts are classifying the accidents by different road types which would then have an impact on risk factors for different roads.
    • Under WP-14, a database of cities has been prepared. The database has been completed for 32 Indian cities and is ongoing for 30 non-Chinese Asian cities. For Indian cities, an exhaustive search for publicly available database has been carried out. The databases include Census, National Sample Survey (NSS), city-specific reports such as city development and mobility plans and other government based domains for meteorological and pollution data. While most data attributes has been obtained for Indian cities, most cities do not have nutrition related data as well as transport patterns such as modal share, number of cycling trips and injury related data such as share of victim types for fatal accidents.
    • Within the framework of cities database, a paper reviewed the current understanding of air quality, air quality monitoring, urban air pollution sources, and key messages from the national and international programs to control and manage air pollution in the Indian cities. The paper also presents a database of 40 Indian cities with parameters such as area, population density, vehicle ownership, fuel used for cooking and average ambient concentration values of PM10, SO2 and NO2 obtained from air quality monitoring stations in the cities.
    • List of publications in press or under review- attached as appendix
  1. Emission Inventory of Delhi
  2. Health impact of particulate pollution in a megacity- Delhi, India- Sarath Guttikunda and Rahul Goel- published in Environmental Development
  3. Access-egress and other travel characteristics of metro users in Delhi- Rahul Goel and Geetam Tiwari- submitted to Transport Policy
  4. Moving around in Indian cities- Dinesh Mohan- published in Economic and Political weekly
  5. Nature of air pollution, emission sources, and management in the Indian cities- Sarath Guttikunda, Rahul Goel and Pallavi Pant- submitted to Atmospheric Environment
  6. Benchmarking Vehicle and Passenger Travel Characteristics in Delhi for On-Road Emissions Analysis- Rahul Goel, Sarath Guttikunda, Dinesh Mohan and Geetam Tiwari submitted to Transportation Research- Part D

Ongoing work for Delhi and Visakhapatnam

This section describes details of the work which have not been published as papers.

  1. Household Travel Survey of Delhi residents- travel characteristics, understanding trip length distributions relevant for designing scenarios

In the absence of detailed data on transport characteristics from a secondary source, we carried out a household travel survey in Delhi. In the survey questionnaire, we asked about the demographics and socio-economic characteristics of all the household members and other household level characteristics. For travel related information, we asked about all the trips made by all the household members during the previous day. The survey questionnaire also used International Physical Activity Questionnaire (ipaq)[1] in order to quantify overall physical activity levels of the respondents. With this, we have sufficient information required for ITHIM modeling framework. This section describes results from the survey data which are relevant to understanding current travel patterns as well as to design future scenarios for GHG reduction.

               Trip-based modal share for main modes

Up to 55% of the trips use non-motorised modes (walk, cycle and cycle rickshaw), 25% use public transportation (auto rickshaw, shared auto rickshaw, bus, metro, rail and taxi) while 20% use private motorized vehicles (cars and two-wheelers).

In case of females, due to a small number of cases, disaggregation by age is not presented. Both males and females, have similar distribution of walking across different age categories with the major difference among older than 60 in which males have twice the walking share than females. Among males, cycling is prevalent over a smaller number of age categories- 91% within 15 to 59 years.

Up to 55% of all the trips have a trip length within 3 km. Among private motorized modes, motorized two-wheelers (MTWs) have 30% trips within 3 km and 45% trips within 5 km while these proportions are 17% and 30%, respectively in case of cars. In case of buses, share of trips within 5 km is 25% which is similar to that of cars while metro has only a marginal share of 3% within this distance.

 

 


[1] http://www.ipaq.ki.se/ipaq.htm

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