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Environmental Effects of Urban Traffic - A case study of Jaipur City
Sandeep Maithani, B S Sokhi and A P Subudhi
Human Settlement Analysis Group,
Indian Institute of Remote Sensing, Dehra Dun, India
K B Herath
Survey Department of Sri Lanka,
Growing Urban centres necessitate the sprawling of transportation network, increasing distance between places of residence and work which needs to be covered in minimum time. The increased socio-economic status of urban population coupled with inadequacy of public transport has encouraged personalized means of transport. This craze for owning vehicles in urban centres, has led to considerable noise and air pollution, especially in big cities. (Table 1)
Table 1. Noise levels in major Indian cities
Jaipur is the second largest city in Rajasthan. It is located between 26°4815" to 27°0015"N latitude and 75°4115" to 75°5345"E longitude, covering an area of 350 km² with 70 municipal wards. The population of the city was 1,518,235 in 1991 as per census and 2,536,669 according to a study carried out by UNICEF in 1997. The city falls within semi arid climatic zone with an annual rainfall of 60 cm. The urban area and its hinterland are mainly covered with thick mantle of soil, wind blown sand and alluvium. The eastern and northen parts are formed of Aravali hill ranges.
Considering the development status, the physical development can be categorized into two parts (i) Walled city area (ii) Outside walled city area. The walled city area is conventionally developed area having densely populated residential and commercial landuses with no scope for physical expansion. This has pressurized development in the southern and western side (as physical constraints are imposed by Aravali ranges in the north and east side of city). To serve these areas the road network has been extended.
The main arterial roads considered in this study are:
Table 2. Example of Air pollution dataset
Table 3 : Buffer Zones of Air Pollution
Data Used and Methodology The Survey of India topographical sheets (45N/13 and 45N/9 ) surveyed in 1971 on 1:50,000 scale and guide map of Jaipur city on 1:25,000 scale surveyed in 1971 were used for preparing the base maps. IRS-1C geocoded LISS-III FCC of 14 April 1998 on 1:50,000 scale and IRS-1C geocoded PAN data of 12 March 1998 on 1:25,000 scale were visually interpreted for making the existing landuse and arterial road network map (Figure 1 & 2) Field verification survey was carried out to check the interpretation accuracy and to collect secondary data on traffic, pollution and population density (Figure 3). The maps were digitized and incorporated within GIS domain (ArcInfo and ILWIS ), for creating thematic layers like landuse, road network, ward-wise population density and calculating length of roads and areas of various landuses.
Figure 1: Landuse map of jaipur 1998
Figure 2: Transportation network jaipur city
For the purpose of analysis ward-wise population density maps were crossed with catchment areas (based on models) of environmental parameters (air and noise pollution), to find population affected by each environmental parameter.
Figure 3: Ward wise population density jaipur city
Air Pollution Dispersion model
The main pollutants from automobile exhaust are oxides of nitrogen, sulphur, carbon ( NOX, SOX, COX resp.), ammonia and suspended particulate matter. At the roadside the concentration is highest while it decreases away from the road. For each pollutant a buffer zone was calculated within which concentration of pollutant exceeds air standard limits.
Table 4. Example of Noise Pollution Dataset
Table 5 : Buffer Zones of Air Pollution
Buffer zones were calculated based on Passquill and Smith (1983) dispersion model and using data sets (an example of dataset used is shown in Table 2)
C (x,z) = [(2Q/L)/ ((2p)½ usz)] exp (-z2/2s2z) ............. (Eq.1)
Q/L = Emission per unit length of road (mg/second metre)
sz = Gaussian coefficient for vertical dispersion (metre)
u = Mean wind speed (metre / second)
C = concentration of pollutant (mg/metre3)
The two buffer zones calculated were:
Noise Level Prediction Level
Noise can be defined as unwanted sound in the wrong place at the wrong time. The highway noise prediction model used in this study (Lyons, 1973) is based on the principal: Noise is produced by traffic and is then attenuated by distance before it reaches the listener. The noise level can be well predicted using Lyons empirical model:
L = 10logV - 15logD + 30 logS + 10log[tanh (1.19*10-3 )* VD/S] + 29 ..(Eq.2)
V= Volume of Traffic per hour (vehicles/hour)
S= Average vehicle speed (miles/hour)
D = Distance from centreline of road to sound receptor (feet)
L= Predicted noise level (dB)
Keeping in view the acceptable noise level of 50 dB (as prescribed by the Ministry of Environment and Forests 1989) two buffer zones were calculated (Table 5) using Eq.2 and data sets (an example of dataset used is shown in Table 3).
Table 6 : Population affected by air pollution in Jaipur city (Figure 4)
Table 7 : Population affected by noise pollution in Jaipur city (Figure 5)
These buffers zones layers were overlaid on the population density layer to find out the population affected by these two pollutants. The affected population is shown in table 7.
Figure 4: Air pollution bufer zones
Figure 5: Noise pollution bufer zones
Results and Discussion
It was found that significant numbers of population were affected by air and noise pollution (94.3% and 34.8% of total population respectively). 52.7% of total population lying in 0-425 m buffer zone was affected by all air pollutants and 41.6% of total population lying in 425-1500 m buffer zone was affected only by suspended particulate matter only.
2.3% of total population lying in 0-30 m buffer zone was affected by noise pollution, the minimum noise level being 60 dB and 32.6% of total population lying in 30-250 m buffer zone was subjected to noise level ranging from 50-60 dB.
Thus, 2.3% of total population (57,587) was subjected to maximum air and noise pollution. With increasing vehicular traffic the impact of noise and air pollution would increase within this buffer zone (0-30 m) on the existing population.
The maximum intensity of noise and air pollution was recorded at Hawa Sarak, M.I.Road and Jhotwara Road . The high intensities of pollution in above-mentioned roads was mainly due to connection of these roads directly to commercial areas, industrial areas and offices. J.L.N. Marg where the Rajasthan University is located, is also having high intensity of pollution during office hours because this is the only road linking the residential areas with the state and central offices and business centres. Agra Road is also having high intensity of pollution since it carries heavy inter-state traffic.
The KHAT coefficient calculated after field survey for measuring classification accuracy was 85.8% therefore proving that remote sensing data can be used for providing spatial information for Environment Impact Studies.
The authors are grateful to Dr P S Roy , Dean, Indian Institute of Remote Sensing for providing all necessary help and guidance during the work.
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