ISSN (Print) - 0012-9976 | ISSN (Online) - 2349-8846

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Have CNG Regulations in Delhi Done Their Job?

The impact of compressed natural gas regulations on the spatial dynamics of air pollution in Delhi and its surroundings is evaluated and the levels of air pollution in Delhi is compared vis-a-vis major cities in India. Two significant findings emerge. First, the post-regulation state of air quality in Delhi and its surroundings is significantly worse than the domestic and international standards of air quality. Second, the data reveals that no significant improvement in air quality has been made in the post-regulation period, alluding to the fact that the reduction in air pollution because of cng regulations on buses, taxis and autorickshaws has been offset by a phenomenal increase in the number of private vehicles, particularly cars and non-cng heavy vehicles.

ir quality interventions are a new phenomenon in the rapidly growing megacities of developing countries. The enforcement of

Kilometres Panipat Sonipat Rohtak Delhi Meerut Ghaziabad Bulandshahr Rewari Gurgaon Faridabad Alwar Delhi National Capital Region Air Quality Regulation and Population Density

, were downloaded from the web site [ 2007].

There is only one monitoring station in each city where data on these three pollutants are gathered continuously. Therefore, readers should be cautioned that these data may not truly represent the state of air quality for the entire city.

Met One Instruments, Inc [Met One Inc 2003], .

The instrument uses laser technology, which is different from gravimetric measurements. T

uses the information from the scattered particles to calculate a mass per unit volume. A mean particle diameter is calculated for each of the five different sizes. This mean particle diameter is used to calculate a volume (cubic metres), which is then multiplied by the number of particles and a generic density (μgm-3) that is a conglomeration of typical aerosols. The resulting mass is divided by the volume of air sampled for a mass per unit volume measurement (μgm-3).

This instrument also recorded relative humidity ( ) and temperature with every sample. The main concern about the instrument is that the mass values can be easily inflated with an increase in , especially when exceeds 40 per cent [Thomas and Gebhart 1994]. A standard relationship between photometric and gravimetric measurements, as discussed by Ramachandran et al (2003), was used to calibrate the data for :

SPECIAL ARTICLEdecember 22, 2007 Economic & Political Weekly52the major roads, industrial clusters, buses and heavy vehicles per minute, using an ordinary least squares regression (OLS) model: τi = α+ Xi’ β+ εi ...(2)where βis a vector of regression coefficients, and εi = unobservable.One of the main assumptions of the OLS model is that εi ~ N(0,σ2). Since the error term observed spatial autocorrelation, we used a spatial autoregressive model. Three different models, namely conditional autoregressive (CAR), simultaneous autoregressive (SAR) and spatial autoregressive models (SAM), are suggested to account for spatial structure in the data. In the first model, spatial dependence in the residuals is expected to have a condi-tional distribution. In the SAR model, however, a joint distribu-tion is expected. In the third model (i e,SAM), we could represent the residuals that are not explained in the OLS with a variable (e g,Ri). We could treat this new variable as a predictor variable in the appropriate model. This way, we would no longer need to assume dependency for the outcome variable. One of the advan-tages of this approach is that it is easily understandable. Another advantage is that we could avoid the problem of counterintuitive results inSARandCAR as demonstrated by Wall (2004).More formally, assuming that the response variable is normal-ly distributed, we could define the model as follows:τ= α+ X’β+ ρRi + εi ...(3)where Ri = information not explained in the OLS ρ= parameter coefficientsεi ~ N(0,σ2) iid There are various ways to estimate Ri. Institutively, Ri can be estimated as an inverse distance weighted average of residuals at the neighbouring sites, as 1 k -ωRi = ———Σrjdij ...(4)k -ω j=1 Σdijj=1 where rj = residual at jth neighbouring site k = number of neighbouring sites dij=distance between ith site and jth neighbouring site and dij≤ h h = distance range determined using anempirical semi-variogram ω = distanceexponent The distance range (h) and distance exponent (ω) can be esti-mated with the aid of an empirical semivariogram. The distance range refers to the distance threshold where the semivariogram levels off to a nearly constant value, called the sill. The shape of the semivariogram helps us determine the distance exponent. ForPM2.5 andPM10, distance ranges were 4.5 and 3.0km, respec-tively, and the distance exponent for both was -2. 3 Results3.1 DescriptiveAnalysisThe levels ofPM2.5 andPM10inDelhi and its surroundings were sig-nificantly higher than the standards recommendedbytheCPCB, the Environmental ProtectionAgency(EPA) of the United States andWHO. According to the EPA, the three-year average of PM2.5 must be less than 15μgm-3, but the average PM2.5 in Delhi over a five-month period was recorded as 28.2±1.8μgm-3 (95 per cent CI), andPM10 andTSP averages were 157±18.1μgm-3 and 189±21.8μgm-3, respectively (Table 3a). The summarystatistics of different sources of air pollutants are presentedinTable3b. Among automobiles, cars and non-CNGheavy vehicles recorded the highest (18.9±3.5) and lowest frequency(1.8±0.2),respec-tively. The average distance to the industrial cluster was 2.4±0.37km.As mentioned above, 16 of the 113 sites were located outside Delhi but within 3km of its border. It is interesting to note that the concentration of ambient particles in the area outside Delhi is significantly higher than that inside the Delhi border; the aver-age PM2.5, PM10 andTSP outside Delhi were recorded as 33.4±6.9μgm-3, 213.1±83.6μgm-3 and 250.3±98.9μgm-3, respec-tively, compared with 27.3±1.7μgm-3, 147±14.9μgm-3 and 178.5±18.3μgm-3 inside Delhi. The section 3.3.2 extends this analysis by evaluating the air pollution distribution against dif-ferent distance intervals from the Delhi border in both directions (inside and outside Delhi). 3.2 Spatial Pattern of Air PollutionFigures 4, 5 and 6 show the spatial distribution of PM2.5, PM10 and TSP. The trend of spatial distribution is similar in all three maps, and two striking observations emerge from the visual analysis of these maps. First, the levels of air pollution in central parts of Delhi were relatively low as compared to those observed in the peripheral areas. Despite this, the levels of PM2.5 and PM10 inside and outside Delhi were much higher than the standards recom-mended by WHO [WHO 2006]. The absence of air quality regula-tions and migration of pollution industries and vehicles that were subject to regulations might have been responsible for the elevat-ed concentration of air pollution in the areas outside the Delhi border. Second, among industrial areas, Ashok Vihar, Sahibabad, Table 3a: Ambient Air Pollutants in Delhi and Its Surroundings, July 23 to December 3, 2003 – Summary StatisticsVariables Inside Delhi Outside Delhi Total Inter-MeanInter-Mean Inter-Mean Quartile (± 95% CI) Quartile (± 95% CI) Quartile (± 95% CI) RangeRangeRange PM2.5 (μgm-3) Aerosol 11.9 39.1(±2.3) 21.2 46.7(±7.1) 14.0 40.2(±2.3)PM2.5 (μgm-3) Gravimetric 8.5 27.3(±1.7) 14.9 33.4(±6.9) 8.8 28.2(±1.8)PM7 (μgm-3) Aerosol 91.7 172.9(±18.0) 210.7 241.1(±66.7) 94.7 183.1(±18.7)PM7 (μgm-3) Gravimetric 67.7 120.4(±11.8) 82.8 173.1(±63.3) 67.9 128.3(±14.1)PM10 (μgm-3) Aerosol 113.8 207.1(±22.2) 254.7 291.9(±86.3) 125.8 219.9(±23.4)PM10 (μgm-3) Gravimetric 91.3 147.1(±14.9) 108.7 213.1(±83.6) 90.2 157.0(±18.1)Total suspended aerosol (μgm-3) 144.6 250.6(±27.1) 287.9 343.3(±103.9) 149.7 264.6(±28.3)Total suspended aerosol (μgm-3)Gravimetric 107.4178.5(±18.3)129.0250.3(±98.9)113.0189.3(±21.8)Relative Humidity (%) 5.5 46.8(±0.7) 3.2 45.7(±1.4) 5.5 46.6(±0.6)Temperature (ºC) 0.8 32.4(±0.1) 0.6 31.9(±0.3) 0.9 32.3(±0.1)Table 3b: Sources of Air Pollution in Delhi and Surroundings – Summary StatisticsVariable Inside Delhi Outside Delhi Total Inter-Mean Inter-Mean Inter-Mean Quartile (± 95% CI) Quartile (± 95% CI) Quartile (± 95% CI) Range Range Range Two-wheeler/minute 15.016.8(±2.6)17.613.7(±5.2)14.916.3(±2.3)Cars/minute 21.818.9(±3.5)18.615.3(±6.3)20.818.4(±3.1)Buses/minute 4.4 2.8(±0.6) 1.8 1.4(±0.7) 4.1 2.6(±0.5)Trucks/minute 1.81.2(±0.2)2.01.5(±0.8)1.81.3(±0.2)Distance to the closest road (m) 0.98 0.87(±.25) 1.4 1.37(±0.84) 1.11 0.95(±0.25)Distance to the closest industrial clusters (m) 2.5 2.47(±0.38) 1.14 0.7(±0.39) 2.6 2.2.0(±0.35)Distance to Delhi border (km) 5.5 6.8(±0.7) 1.4 1.6(±0.6) 6.5 6.1(±0.7)Distance to the city centre (m) 7.5 10.7(±1.1) 6.1 16.3(±2.0) 7.6 11.6(±1.0)
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