1 Air Quality Interventions and Spatial Dynamics of Air Pollution in

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Air Quality Interventions and Spatial Dynamics of Air Pollution in Delhi and its
Neighboring Areas
Naresh Kumar⊗ and Andrew D. Fosterα
Abstract: In recent years, soaring air pollution levels and their adverse health effects
have begun to trigger air pollution regulations in many rapidly expanding cities in
developing countries. Enforcements of compressed natural gas (CNG) in Delhi (India)
and Lahore (Pakistan) are two examples of such regulations. Using the air pollution data
monitored at 113 sites between July and December 2003 this article examines the spatial
patterns of air pollution in Delhi and its surroundings with references to the
recommended air quality standards, and evaluates the impact of air quality regulations on
the spatial patterns of air pollution in Delhi vis-à-vis its surroundings. From the analysis
of data three important findings emerge. First, even with the regulations in place, air
pollution levels in Delhi and in its surroundings were significantly higher than the
standards set by the World Health Organization (WHO), Central Pollution Control Board
(CPCB) and Environmental Protection Agency (EPA); the peripheral areas of the city
(inside and outside its border) have witnessed two to three times higher levels of air
pollution than the EPA standards. Second, air quality regulations in Delhi have resulted
in air pollution redistribution and adversely affected the air quality of areas surrounding
Delhi, because these areas were not subject to these regulations. Third, industries and
trucks continue to the most important sources of fine and coarse particles in the study
area.
Keywords: Air pollution, environmental interventions, air quality, Delhi.
⊗
Assistant Professor, Department of Geography, University of Iowa, Iowa City, IA 52242, USA &
Adjunct Assistant Professor, PSTC, Brown University, Providence, RI 20912. Email: [email protected] or [email protected]
α
Professor and Head, Department of Economics, Brown University, Providence, RI 20912. Email:
[email protected]
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1. INTRODUCTION: The soaring levels of air pollution that high-income countries
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witnessed in the 1950s and 1960s have begun to threaten public health in the rapidly
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growing middle-income countries, including India and China, in recent years. As result,
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these countries have begun to enforce environmental regulations, such as enforcement of
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the compressed natural gas (CNG) in Delhi (India) and Lahore (Pakistan) by their
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respective Supreme Courts are two examples of these regulations, which have drawn the
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attention of media and researchers worldwide, and there is an increasing interest in the
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understanding of the impact of these regulations on the time-space dynamics of air
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pollution and its effect, in turn, on human health. Ideally, time-series data are required to
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assess the effects of these interventions, but the limited spatial-temporal coverage of air
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pollution restricts our ability to study the effect of these regulations. Nevertheless, a
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cross-sectional approach can be adopted in which air quality of an area with the
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regulations is contrasted with an adjacent area (as a control) without such regulations.
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Building on this approach, the spatial distribution of air pollution in Delhi (after the
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regulations) and its surroundings can help us answer three important questions – (a) is the
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post-regulation state of air quality adequate to protect human health, (b) does air pollution
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vary geographically within Delhi and its surroundings, and (c) has the air quality
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regulations in Delhi adversely affected the air quality of areas in its surroundings in the
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absence of such regulations. Although there are various measures of air pollution, we rely
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on coarse ≤10μm (PM10) and fine ≤2.5μm (PM2.5) particles, because these are accepted as
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standard measures of air quality worldwide (WHO, 2000). The use of the term air
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quality/air pollution in the remaining parts of this paper will be referring to the mass of
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PM2.5 and PM10 in the ambient environment.
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The remainder of this article is organized into four sections. The first section presents a
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background of the proposed work, followed by a description on data and methodology in
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the second section. The fourth section examines the spatial distribution of air pollution in
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Delhi and its surrounding, and examine the effect of air quality regulation using three
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different approaches – (a) comparison of air pollution in Delhi with the other mega cities
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from 2001-2005, (b) air pollution levels in- and out-side Delhi border during the year
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2003 and (c) air pollution with reference to different sources (of air pollution). The final
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section presents a discussion, concludes the main findings and draws our attention to
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future research direction in this field.
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2. Background: Rising smoke from chimneys, once used to be a symbol of prosperity,
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began to pose serious health concerns towards the middle of 20th century. Alarming
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health effects of air pollution episodes in 1950s, including that of smog in Donora,
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Pennsylvania in 1948, in Poza Rica, Mexico in 1950 and famous London smog in 1952,
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not only drew public attention worldwide but also triggered the enforcement of air quality
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regulations in western countries, such as the UK Clean Air Act in 1956 and the Air
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Pollution Control Act in US in 1955, which was replaced by the Clean Air Act in 1963.
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Throughout the second half of the 20th century, air pollution and its health effects have
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been subject to intensive research investigation in western countries. A substantial body
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of literature has documented the adverse health effects of different types of air pollutants.
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Over a decade ago, the major focus of research in this field was on the association
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between aggregate estimates of respirable ambient particulate ≤10µm (PM10) and
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mortality (Pope and Dockery, 2006, Pope et al., 1995, Schwartz et al., 1995). After
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controlling for seasonality and other confounders, recent literature reiterates this
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association between mortality/morbidity and air pollution with the special emphasis on
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the relevance of fine particulates (PM2.5) in the research on health effects of exposure to
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ambient air pollution (Dominici et al., 2003, Samet et al., 2000,
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National Research Council, 2001). A recent review of literature on this topic is available
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in Davidson et al. (2005). While the health effects of air pollution are examined at length
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in high-income countries, limited air pollution and health data constrain researchers’
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ability to pursue research in the field in low-income countries.
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The main focus of this article is study the spatial patterns of air pollution and the effect of
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air quality regulations in Delhi on air pollution redistribution in Delhi and its
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surroundings. The sequential details on these regulations and associated environmental
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laws are discussed in detail in (Bell et al., 2004). Review of literature suggests that there
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is limited research available on air pollution distribution in Delhi. Research reported by
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Kathuria (2002) and Khillare et al. (2004) are particularly relevant to this study. Using
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the data collected by high volume samplers between July 1997 and June 1998 at four
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locations, (2004) examined spatial and temporal variations in the concentration of
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suspended particulates matter (SPM) and heavy metals, namely Pb, Cd, Cr, Ni, and Fe, in
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the atmospheric aerosols in Delhi. Their main findings revealed that the SPM
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concentration in Delhi was three times higher than the national standards (140µg/m3).
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Their analysis further showed that (a) the concentration of heavy metals and SPM were
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land-use specific, for example they found elevated levels of SMP and heavy metals in
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industrial area, and (b) the main sources of SPM and heavy metals (in ambient aerosol)
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were emission from automobiles and industries. While the research reported by Khillare
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et al. (2004) provides valuable insight into air pollution estimates in pre air quality
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regulation periods, it is difficult to generalize spatial patterns of air pollution for the
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entire city using the data at monitored at four locations only.
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Kathuria (2002) examined the effects of recent regulations related to vehicular pollution
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on air quality in Delhi. Based on air pollution data from the central pollution control
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board (CPCB) before and after the interventions, he concluded that recent interventions
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have led to little improvement in air quality in the City. This research conveys important
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messages regarding soaring levels of air pollution in Delhi as compared to WHO
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standards (WHO, 2000), and required measures to check air pollution from vehicle
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emission at three different stages – (a) pre-combustion stage, i.e. improvement in the
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quality of fuel, (b) combustion stage, which refers to engine efficiency, and (c) post-
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combustion stage, i.e. exhaust treatment, e.g. use of catalytic converters. However, the
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findings regarding the effect of CNG interventions on air quality can be questioned for
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two important reasons. First, the conversion of commercial vehicles to CNG was
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completed by the end of year 2002, but the data used in the analysis was until 2001,
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which fails to capture the full effect of CNG regulations. Second, the analysis is based on
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air pollution data collected by the CPCB. These data, however, do not capture air
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pollution scenario for the entire city, because there are only 9 monitoring sites, of which
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only one near income tax department (ITO) is operational continuously.
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A review of literature suggests that few studies are available on the spatial analysis of air
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pollution in Delhi. The proposed research will augment a number of literatures. First, it is
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the first systematic attempt to analyze spatially detailed patterns of air quality, measured
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by fine and coarser particulate matters (PM2.5 and PM10, respectively) in Delhi and its
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surroundings. Second, this research examines the impact of emission from different
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sources on air pollution at a given location. Third, this article sheds light on how air
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quality regulations can guide the course of air pollution redistribution in a city (with the
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air quality regulations in place) and in its surroundings, largely unaffected by such
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regulations. Finally, this research identifies the significance of Environmental Kuznets
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Curve (EKC) in the context of a developing country. The proposed research will answer
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the following research questions:
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a. What is the spatial distribution of air quality in Delhi and its surroundings, and is
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the current state of air quality with reference to the recommended air quality
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standards adequate to protect human health?
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b. What is the impact of air quality regulations on the spatial distribution of air
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pollution in the study area, and have the CNG regulations done their job?
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c. What are the main sources of fine and coarse particles in the study area?
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3. STUDY AREA, DATA AND METHODS:
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and its surroundings. Delhi and its 10 neighboring districts of three other states form the
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National Capital Region (NCR), which was enacted to divert the burden of Delhi’s
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growth in the surrounding areas (Table 1, Figure 1). Delhi is the second largest
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metropolitan in India, and its population has increased from 9.4 million in 1991 to 13.2
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million in 2001 (at the rate of 3.34% annual exponential growth rate). The number of
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industrial units in Delhi increased from 8,000 in 1951 to 125,000 in 1991, and number of
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automobiles increased from 235,000 in 1975 to 4,236,675 in 2003-04
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(Government of India, 2006). Consequently, Delhi has been experiencing a phenomenal
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increase in air-pollution levels. According to WHO estimates, Delhi was reported to be
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the tenth most polluted city in the world in terms of suspended particulate matter (NCT
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Fact Sheet Delhi 1999) that posed a serious threat to human health. In part as a result of
3.1 Delhi and its surroundings: This study focuses on air pollution distribution Delhi
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this concern over air quality in Delhi, there is a need to collect good air pollution
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longitudinal data for the management and surveillance of air quality in the city.
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Delhi is an instructive location for examining the relationship between air pollution and
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health in context to recently implemented air quality regulations as a consequence of a
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series of rulings by the Indian Supreme Court. Of particular importance was the
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September 2000 ruling directed at so-called “non-conforming areas”. These are areas in
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which most industrial activity is supposed to be excluded, but in which industrial growth
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has continued unabated. The potential impact of these rulings is very large. According to
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a survey of the Delhi government 98,000 of the total of 125,000 industries were in non-
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conforming areas. Subsequent to the decision 38,000 establishments in sectors with
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particularly high levels of pollution were subject to this ruling. A large number of
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industrial plots on the periphery of the city were developed to house roughly 24,000
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establishments. While this ruling has met with substantial resistance and change has
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proceeded slowly, the ruling remains in force and concerted attempts are being made to
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relocate industries (TERI, 2001b). It is also anticipated that new investments will have
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been influenced by these rulings regardless of the eventual extent of their enforcement.
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An important feature of this ruling is that it means that similarly polluting industries in
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different parts of the city are differentially affected by the law.
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The second Supreme Court ruling mandated conversion of buses and three-wheel
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scooters from gasoline/diesel to compressed natural gas (CNG) in 2000 and 2001. It took
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the state about two years to implement this ruling, and the final bus registered in Delhi
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was converted to CNG in December 2002. The conversion of a gasoline engine to CNG
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costs roughly five times the cost of a new gasoline operated vehicle; thus after CNG
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regulation, most gasoline operated commercial vehicles were migrated to the neighboring
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districts (in NCR) that were unaffected by CNG regulation. Moreover, it is anticipated
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that different parts of the region will have been differentially affected by these initiatives
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given differences in level of traffic through these regions. There is some question about
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the extent to which these changes have improved air quality as yet, given the phase in
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time for some components of the new regulations, but a carefully constructed model
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suggested that total declines in vehicular emissions are likely to become evident starting
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in 2003 (TERI, 2001a). Though the CNG and industrial zoning regulations are expected
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to reduce the pollution level in Delhi, we expect that the air pollution has been
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importantly redistributed since most polluting vehicles and industries that were subject to
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these regulations moved to neighboring states. Spatially detailed air pollution data and a
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cross-sectional comparison of air pollution in Delhi vis-à-vis outside Delhi border can
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provide insight into the effect of these regulations on air pollution redistribution.
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3.2 Data: The air pollution data for this research come from two different sources – (a)
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Central Pollution Control Board (CPCB) and (b) field campaign from July-December
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2003.
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Air pollution data from CPCB: Air pollution data monitored at a central location for
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seven mega cities were acquired from CPCB to contrast the trend of air pollution in Delhi
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with other megacities from 2000 to 2005. Data on three pollutants – SO2, NO2 and
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particles ≤ 10μm in aerodynamic diameter (PM10) – were collected. PM10 data are
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collected in three different shifts 6:00 to 14:00, 14:00 to 22:00 and 22:00 to 6:00 (of the
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next day) using high volume samplers. CPCB facilities, however, are not equipped to
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monitor PM2.5. There is only one monitoring station in each city where data on these
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three pollutants are monitored continuously. Therefore, readers should be cautioned about
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these data, because these may not truly represent the state of air quality of the entire city,
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though these data may provide insight into the change in air pollution with respect to
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policy interventions.
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Air sampling field campaign: Given the limited spatial coverage and non-availability of
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data on fine particulate matter (PM2.5), the existing air pollution data collected by CPCB
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were inadequate for evaluating the spatial patterns of air pollution. Therefore, a field
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campaign was conducted from July 23 to December 3, 2003 to monitor ambient particles
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at 113 sites in Delhi and its surroundings. Since one of the major goals of the campaign
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was to evaluate spatial variability in the mass of airborne particulate matter (PM), a
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spatially dispersed sampling design was adopted, in which sample sites were identified
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using a two-step process. In the first, a rectangular grid was overlaid onto the entire study
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area, to ensure full coverage of the area. In the second step, a random location was
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simulated within each cell (of size 1x1.5km), and then the simulated locations were
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transferred to a Garmin Global Positioning System (GPS) in order to navigate them and
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examine their suitability. Some sites, which were inaccessible, were discarded and re-
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simulated, resulting in a final sample of 113 suitable sites (Figure 2a and 2b). At each site
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air was sampled at two different times every third day. Each sample involved four
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readings – two each in mass and count modes. In the mass mode, each reading sampled
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air for two minutes and in the count mode for one minute. Therefore, each sample
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involved six minutes of sampling. Although air was sampled at different times between
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7:30AM and 10:00PM, the PM data for the present analysis was restricted ±150 minutes
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of the time interval of AOD data, generally 10:30AM, in order to minimize the temporal
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noise in PM Data.
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The Aerocet 531, a photometric sampler, from Met One Instruments, Inc., was used to
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collect air pollution data (Met One Inc, 2003). It is an automatic instrument that can
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monitor particulate mass (PM) in a range of ≤1, ≤2, ≤5, ≤7 and ≤10µm in aerodynamic
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diameters in mass mode, and PM ≤ 0.5 and PM ≤ 10µm in count mode. The instrument
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uses laser technology and uses a right angle scattering method at 0.78μm, which is
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different from gravimetric measurements. The source light travels at a right angle to the
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collection system and detector, and the instrument uses the information from the scattered
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particles to calculate a mass per unit volume. A mean particle diameter is calculated for
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each of the 5 different sizes. This mean particle diameter is used to calculate a volume
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(cubic meters), which is then multiplied by the number of particles and then a generic
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density (µgm-3) that is a conglomeration of typical aerosols. The resulting mass is divided
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by the volume of air sampled for a mass per unit volume measurement (µg/m3).
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This instrument also recorded relative humidity and temperature with every sample. The
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main flaw of the instrument is that the mass values can be easily inflated with the
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increase in relative humidity (Thomas and Gebhart, 1994). A standard relationship
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between photometric and gravimetric measurements as discussed by Ramachandran et al.
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(2003) was used to calibrate the data for relative humidity:
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D/D0 = 1 + (0.25 (RH2/(1-RH))
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(1)
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Where D and D0 are wet (photometric estimates) and dry particles (gravimetric
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measurement), respectively; RH is relative humidity (proportion). Sioutas and others
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(2000) suggest correction for relative humidity using different particle characteristics
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including molecular weight of dry particles. These data were not available for the study
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area. Thus, we relied on the above notation to estimate dry mass of PM2.5, PM10 and TSP,
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though the analysis was performed on both original and calibrated dataset.
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Data on the source of air pollution: This article focuses on two main sources of air
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pollutants, namely traffic and industries. Data for traffic were collected as a part of air
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pollution data. Number of vehicles were counted during each sample and then classified
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by type – buses, trucks, cars, two wheeler and others. Data on industrial clusters were
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generated from the Eicher Map of Delhi (EICHER, 2001), which is largely based on
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Survey of India’s large scale topographic maps.
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3.3 Methods: Out analysis is based on three different methods – (a) proximity analysis
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for data integration, (b) spatial interpolation of air pollution surfaces and (c) regression
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modeling. These methods are discussed below:
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3.3.1 Data Processing: As mentioned earlier data were collected at each site at two
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different times of every third day. On an average we have more than 65 samples at each
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site, which represent sampling at different times of a day and different days of a week.
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Each sample included two readings (four minutes of sampling). The data used in the
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analysis are the averages over July 23 through December 3, 2003. The frequency of
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vehicles is the average number of vehicles (by their types) every one minute for the same
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period. Proximity to road and industrial cluster were computed using spatial join in
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ArcGIS Ver 9.x (ESRI, 2005), which computed straight line distance of all sample sites
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to the closest sources (roads and industrial clusters in our case).
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3.3.2 Spatial Interpolation: Various methods of interpolation are available to interpolate
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continuous surface. We employed Kriging, which estimates air pollution at given pixel as
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an inverse function of distance weighted by spatial autocorrelation among the sample
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sites (Cressie, 1990, Isaaks and Srivastava, 1989), to interpolate surfaces of PM2.5, PM10
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and TSP. Kriging uses a semivariogram, developed from the spatial structure in the data,
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to determine the weight. Other advantage of Kriging is that it yields a set of spatial
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predictions at sampled locations and also provides an associated variance that measures
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the uncertainty in the predictions. The optimal parameters, such as distance range,
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distance exponent, were computed by minimizing variance between actual and estimated
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values at the sample sites (Table 2):
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min | σ 2 |=
2
1 n
(
)
−
∑
ˆ
z
z
i
i
n i =1
(1)
where n = number of sample sites
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ẑ i = estimated value at ith site
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zi = observed value at ith site
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3.3.3 Air pollution and its sources – regression model: Two different sets of regression
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models were employed to examine the contribution of different sources (of air pollution)
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on the levels of air pollution observed at the sample sites. To begin with air pollution at
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sample site τi was modeled as a function of the selected covariates Xi, which included
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proximity to the major roads, industrial clusters, frequency of buses and trucks per
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minute, using an ordinary least square regression (OLS) model:
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τi = α + Xi' β + εi
(2)
Where β is a vector of regression coefficients, and
εi = unobservable
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One of the main assumptions of OLS model is that εi ~ N(0,σ2). Since the error term
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observed spatial autocorrelation, we used spatial autoregressive model. Three different
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models, namely conditional autoregressive (CAR), simultaneous autoregressive (SAR)
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and spatial autoregressive models (SAM), are suggested to account for spatial structure in
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the data. In the first, the spatial dependence in the residuals is expected to have a
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conditional distribution and joint distribution in the second. In the third one (SAM) we
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could represent the residuals that are not explained in the OLS with a variable (e.g., Ri ),
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we could treat this new variable as a predictor variable in the appropriate model. This
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way, we would no longer need to assume dependency for the outcome variable. One of
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the advantages of this approach is that it is easily understandable. Another advantage is
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that we could avoid the problem of counterintuitive results in SAR and CAR as
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demonstrated by (Wall, 2004).
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More formally, assuming that the response variable is normally distributed, we could
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define the model as proposed above as following:
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τ(si) = α + X(si)' β + θRi + εi
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Where Ri = information not explained in the OLS
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θ = parameter coefficients
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ε'i ~ N(0,σ2) iid
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There are various ways to estimate Ri. Institutively, Ri can be estimated as an inverse
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distance weighted average of residuals at the neighboring sites, as
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Ri =
k
1
k
∑d
−
ij
∑r d
ω
j =1
j
−ω
ij
j =1
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Where rj = residual of jth neighboring site
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k = number of neighboring sites
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dij = distance between ith site and jth neighboring site, and dij < h
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h = distance range
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ω = distance exponent
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The distance range (h) and distance exponent (ω) can be estimated with the aid of
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empirical semivariogram. The distance range refers to the distance threshold where the
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semivariogram levels off to nearly a constant value, called as the sill, and then shape of
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semivariogram can help us determine the distance exponent. For PM2.5 and PM10,
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distance ranges were 4.5 and 3.0km, respectively, and distance exponent for both was -2.
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4. RESULTS
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4.1 Data validation – photometric and gravimetric measurements: Spatially detailed
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PM data were not available for the study area. Therefore, we conducted a field campaign
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to collect these data using photometric samplers. Conventionally, however, high volume
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samplers are employed to monitor ambient PM, which requires a minimum of eight hours
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of sampling. Filters are weighed before and after the sampling, and based on the mass
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gained during the sampling period and the amount of air sampled, the mass of PM is
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computed. Given the cost of gravimetric samplers, it was not plausible to deploy these
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samplers at a large number of locations. Therefore, we monitored air pollution with the
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aid of photometric samplers at relatively large number of locations. To assess the
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accuracy of photometric samplers, first, we compare data from these samplers with the
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CPCB data.
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During August through November 2003, the daily averages of PM10 from CPCB site and
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our instrument were 203±26.8μgm-3 and 153±33.5μgm-3, respectively. The photometric
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estimates were significantly lower than the gravimetric estimates. Given the differences
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in the method of operation and duration of sampling by gravimetric (24 hour average)
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and real time photometric measurements (six minutes for each sample), a difference of
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49.5±31μgm-3 (95% CI) seem reasonable. In addition, the regression analysis suggests a
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statistically significant positive association in the temporal variability in PM10 measured
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by both methods (Figure 3). It was not possible to validate PM2.5 because of the non-
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availability of PM2.5 data. Based on other research, the difference between photometric
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and gravimetric estimates is likely to be smaller for PM2.5, and photometric estimates can
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be calibrated to gravimetric standards by adjusted for relative humidity as suggested by
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Ramachandran and others (2003).
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4.2 Descriptive analysis: The concentration of both fine and coarse particles in the study
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area is much higher than the standards set by the Central Pollution Control Board
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(CPCB), the Environmental Protection Agency (EPA) of the United States and the World
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Health Organization (WHO). According to EPA, three year average of PM2.5 must be less
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than 15µg/m3, while the average mass of PM2.5, PM10 and TSP (five months averages)
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was recorded as 28.2±1.8µgm-3 (95% CI), 157±18.1µgm-3 and 189±21.8µgm-3,
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respectively (Table 3a). These values are significantly higher than the air quality
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standards recommended by the World Health Organization, the Environmental Protection
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Agency and the Central Pollution Control Board. The summary statistics of different
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sources of air pollutants is presented in table 3b. Among automobiles, cars and trucks
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recorded the highest (18.9±3.5) and lowest frequency (1.8±0.2), respectively; the average
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distance to the industrial cluster was 2.4±0.37km.
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As mentioned above, 16 of the 113 sites were located outside Delhi but adjacent to its
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border. It is interesting to note that the concentration of ambient particles in the area
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outside Delhi (but closer to Delhi border) is significantly higher than that in Delhi; the
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average mass of PM2.5, PM10 and TSP outside Delhi were 33.4±6.9µgm-3,
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213.1±83.6µgm-3 and 250.3±98.9µgm-3 as against 27.3±1.7µgm-3, 147±14.9µgm-3 and
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178.5±18.3µgm-3, respectively, in Delhi. This clearly indicates that the state of air quality
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in the areas outside Delhi border is relative poor as compared to that in Delhi. This is
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further discussed in the section 4.4.2.
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4.3 Spatial pattern of air pollution: Figure 4, 5 and 6 show the spatial distribution of
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PM2.5, PM10 and TSP in the study area. The trend of spatial distribution is similar in all
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three maps. In addition, two striking observations emerge from these maps. First, the
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central part of Delhi experience relatively lower concentration of air pollution as
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compared to that observed in the peripheral areas, albeit the minimum concentration of
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ambient particles in- and out-side Delhi is much higher than WHO, CPCB and EPA
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standards. The absence of air quality regulations and migration of pollution industries and
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vehicles that were banned in Delhi can be responsible for the elevated concentration of
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air pollution in areas outside Delhi border. Second, among industrial areas, Ashok Vihar,
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Sahibabad, Okhla and Industrial areas surrounding Delhi-Gurgaon border (around the
27
intersection of National Highway 8 and Delhi border) witness high concentration of air
28
pollution, which indicates that industries continue to be an important source of air
29
pollution in the study area.
30
31
4.4 Air Quality Regulations and Air pollution: In the absence of spatially detailed time
32
series air pollution data, we rely on three alternate approaches to assess the effect of air
13
1
quality regulation on time-space dynamics of air pollution in Delhi and its neighboring
2
areas. First, we compare air pollution levels in Delhi from 2001-2002 to 2004-2005 with
3
reference to other mega cities. Second, compare air pollution inside and outside Delhi
4
border, especially in the eastern and southern parts where urban growth has continued
5
irrespective of the city boundary. Third, examine air pollution with reference to pollution
6
sources, which have been subject to these regulations.
7
8
4.4.1 Air pollution in Delhi and other mega cities: Air pollution data on three air
9
pollutants, namely SO2, NO2 and PM10, from 2001 to 2005 for seven megacities were
10
acquired from the Central Pollution Control Board. These data come from one
11
monitoring station (located centrally) in each city. The average estimates of air pollutants
12
were computed for the pre-regulation (year 2001-02) and post regulation (2004-05),
13
periods; the summary of these calculations is available in the table 4. Among all cities
14
Delhi recorded the highest levels SO2, NO2 and PM10 during the pre-regulation periods,
15
which undoubtedly indicated very poor state of air quality in Delhi prior to the regulation
16
periods. Although the levels of air pollution during the post regulation period continue to
17
be high, there was not a significant decline in the concentration of PM10 in Delhi. Air
18
pollution data for Mumbai were not available for the year 2001-02, but during the year
19
2004-05 Mumbai, which is the most populous city in India, witnessed the worst type of
20
air quality in terms of the selected three air pollutants – PM10, SO2, and NO2.
21
22
A comparison of the trend of air pollution in Delhi with that in Kanpur can shed some
23
light on the effect of air quality regulation in Delhi, because Kanpur and Delhi are
24
situated in identical topographic and climatic conditions, and Kanpur is the largest city
25
closer to Delhi. The average PM10 in Delhi declined slightly from 240.2±22.7μgm-3 in
26
2001-02 to 239.8±10.9μgm-3 2004-05. In Kanpur, however, the PM10 increased from
27
178.5±12.8μgm-3 in 2001-02 to 198.3±15.3μgm-3 in 2004-05, a net increase of about
28
20.42μgm-3 over a span of just four years. This clearly indicates that while the
29
enforcement of environmental regulations in Delhi did not results in a significant
30
improvement in air quality, these regulations have been protecting air quality from
31
further deterioration. In Kanpur, however, the air quality has deteriorated significantly
14
1
over a span of four years and this deterioration can be attributed to the absence of air
2
quality regulations.
3
4
Interpretation of these results should be used with caution, because PM10 is not a robust
5
indicator of air quality, especially in semi-dry climate where dust is a major contributor
6
of PM10 mass. Data on PM2.5 mass, which results from combustion, are not available for
7
any of the cities. Therefore, using a cross-sectional approach a comparison of PM2.5 mass
8
(and other pollutant) inside and outside Delhi border can shed light on the effect of these
9
regulations on air pollution redistribution.
10
11
4.4.2 Air Pollution inside and outside Delhi border: As evident from the section 4.2,
12
areas in- and out-side Delhi recorded relatively high concentration of ambient particles.
13
In this section, air pollutant is examined in- and out-side Delhi border at different
14
distance intervals. This can allow us to evaluate the effect of recent environmental
15
regulations on air pollution redistribution. The main assumption behind this analysis,
16
however, is that the air quality of areas bordering Delhi (both inside and outside) was
17
same prior to the regulations, because urban growth has spread across Delhi’s border in
18
eastern, south eastern and southern parts (Figure 7), and functional characteristics of
19
these areas were expected to the same. Therefore, prior to the regulations the sources and
20
levels of air pollution should have been the same in these areas. Based on this
21
assumption, we hypothesize that the differences in air pollution levels inside and outside
22
short distances (<2km) of Delhi border are the result of differential impact of air quality
23
regulations.
24
25
The analysis of our data suggests that the levels of air pollution within 2km outside
26
Delhi’s border were significantly higher than the areas inside 2km of Delhi’s border, and
27
the ratio of outside to inside air pollution declines with distance from the border (Table
28
5). This proves our hypothesis and indicates the differential impact of air quality
29
regulations. While the regulations, namely conversion of buses to CNG and closer of
30
polluting industries, are likely to improve the air quality in Delhi, these seem to have
31
adversely affected the air quality of areas outside Delhi border, because these areas seem
32
to have attracted a large number of polluting industries and vehicles that were banned in
15
1
Delhi. As a result the air quality of these areas is likely to deteriorate further in the
2
absence of air quality regulations in place.
3
4
4.4.3 Sources of air pollution: The main assumption behind this analysis is that if the
5
sources of air pollution have been subject to environmental regulations, proximity to
6
these sources should not show any significant association with the levels of air pollution
7
(monitored at spatially dispersed sites). In the study area, there are four main sources of
8
air pollution, namely industries (including three thermal plants), automobiles, cooking
9
and air conditioning buildings. Among these, only diesel buses and a limited number of
10
polluting industries were banned. In order to examine the impact of ban on these
11
activities, we examine air pollution with respect to (indirect) sources of air pollution,
12
namely frequency of different types of vehicles (particularly buses and trucks) and
13
proximity to industrial clusters. Two different sets models were examined – (a) in the
14
first set, the impact of air pollution sources was examined on PM2.5 and PM10 in- and out-
15
side Delhi (Table 6a and 6b) and (b) in the second set, PM2.5 and P10 were regressed on
16
the important sources of air pollution using ordinary least square regression model and
17
spatial autoregressive model (Table 7).
18
19
As hypothesized, the frequency of buses, which were converted to the natural compressed
20
gas (during 2001 to 2002) observed an insignificant association with PM2.5 and PM10,
21
particularly inside Delhi. In contrast, the frequency of trucks, which were not subject to
22
CNG regulations, emerged the most important predictor of both PM2.5 and PM10 (Figure
23
8a and 8b); it explains 21 and 12 % of the total variability in PM2.5 and PM10,
24
respectively (Table 7). Among other sources, proximity to industrial clusters emerge the
25
second most important predictor of PM2.5 and PM10 (Figure 8c and 8d) and proximity to
26
road shows a significant positive association with PM10, but its association with PM2.5
27
was insignificant, which indicates that dust is one of the major contributors of PM10.
28
Combustion from trucks and industries are two major contributors of fine particles
29
(PM2.5). These two sources together explain about one third of the total variability in
30
PM2.5 (Table 7).
31
16
1
It is interesting to note that the regression coefficient of the frequency of trucks is
2
stronger outside Delhi as compared to inside Delhi (Table 6a and 6b). The impact of
3
proximity to industrial cluster on PM2.5 and PM10, however, was stronger inside Delhi.
4
The frequency of buses did not register a significant association with fine particles, but
5
showed a negative association with PM10 inside Delhi and a positive association with
6
PM10 outside Delhi, because buses not registered in Delhi were not subject to these
7
regulations, and hence these buses in the areas outside Delhi continue to use diesel and
8
hence likely to be an important source of fine particles (PM2.5).
9
10
Our analysis clearly supports the hypothesis that the sources that were subject to
11
regulation have done their job, for example conversion of buses to compressed natural
12
gas has helped improve air quality in Delhi, but trucks and industries continue to be a
13
major source of fine particles in the study area. PM10 shows a statistically significant
14
spatial autocorrelation, because coarse particles settle by gravity as distance increases and
15
fine particles stay aloft longer distances and for longer duration.
16
17
18
5. DISCUSSION AND CONCLUSION
19
According to the Articles 39 (e), 47 and 48A of the Indian Constitution, it is the
20
responsibility of the State to secure and improve people’s health and protect the
21
environment. The air quality in Delhi continued to deteriorate throughout the 1980s and
22
1990s notwithstanding a number environmental laws were enacted during this period.
23
Because of government’s failure to discharge its constitutional responsibility and growing
24
public discontent over the unabated increase in air pollution, the Indian Supreme Court
25
stepped in and in its series of rulings directed Delhi Administration to improve air quality
26
by adopting such measures as the closure of polluting industries in non-conforming areas
27
and switching commercial vehicles, particularly buses and auto-rickshaws, from
28
conventional fuel (diesel/gasoline) to compressed natural gas (CNG). There is a decade
29
and half long history behind these regulations. A detailed discussion on the background
30
of these regulations and associated environmental laws is available in Bell et al.
31
17
1
From the air quality interventions in Delhi we can learn a number of lessons. First, these
2
regulations clearly indicate how an independent judicial branch in a democratic society
3
can enact environmental laws, generally reserved to legislators and specialized regulator
4
bodies of executive branch, and direct the executive branch to enforce these laws when
5
political will fails to do so (Bell et al., 2004). Second, until recently it was believed that
6
Environmental Kuznets Curve (EKC), which maps the course of air pollution as an
7
inverted U-shape function of economic growth, was applicable to western countries
8
(Krupitsky et al., 2005, Stern, 2004). Developing countries, however, have begun to
9
address environment pollution in recent years (Dasgupta et al., 2002). Delhi, which has
10
enforced two major environmental regulations in recent years, is an example of these
11
attempts. The similar CNG regulations are being enforced in Lahore, Pakistan. These
12
interventions are expected to reverse trend the increasing trend of air pollution in the
13
City, and its entry in the second phase of EKC, in which air pollution declines, to a
14
certain level, with the positive economic growth. Although the validity of EKC in terms
15
of econometric precision in air pollution-economic growth association is questioned
16
(Stern, 2004), the general idea behind it is still relevant for both developed and
17
developing countries. Now the question before us, particularly in the context of
18
developing countries, is to investigate whether the EKC will follow the same course of
19
air pollution (in relation to economic growth) as it did in developed countries. Although
20
this topic is beyond the scope of this research, it will useful to pursue the application of
21
EKC in Delhi for future research.
22
23
Although recent air pollution regulations in Delhi have drawn public attention worldwide,
24
most literature and media coverage has been restricted to such topics as the role of
25
politics and the Indian Supreme Court behind these interventions and the role of
26
government agencies in implementing these regulations, particularly CNG regulation and
27
closure of pollution industries in non-conforming area. Little attention was paid to
28
quantify the state of air quality before and after the regulations and spatial variability in
29
air pollution in- and outside the City, because air pollution data are not available at high
30
spatial-temporal resolutions. Therefore, we attempted to collect spatially detailed air
31
pollution data at 113 sites from July-December 2003, and examined these data to assess
18
1
the spatial patterns of air pollution with reference of the recommended air pollution
2
standards and the effect of these regulations on air pollution redistribution in- and out-
3
side Delhi.
4
5
Although these regulations were expected to improve air quality in Delhi, the analysis of
6
our data does not support a significant improvement in air quality within the City.
7
Moreover, substantial variations were observed in the spatial distribution of air pollution
8
in- and out-side Delhi border. These regulations have two major impacts on air pollution
9
redistribution in- and out-side Delhi. First, closure of H-Class - hazardous, noxious,
10
heavy and large polluting – industries in non-conforming areas resulted in either their
11
relocation to newly developed industrial estates, namely Bawana Industrial Estate in
12
North and Kanjawala in North-West, regularization of industries in five non-conforming
13
areas (namely Lawrence Road, Wazirpur, Naraina Phase, Naraina Phase-2 and G.T.
14
Karnal Road) or their migration to the neighboring states, which were not subject to these
15
regulations. As result, air quality at the destination of these polluting industries (whether
16
within the regularized industrial estates in Delhi or outside Delhi) has been affected
17
adversely. Second, most non-CNG buses that were banned in Delhi migrated to
18
neighboring states of Delhi. Therefore, in the absence of stringent air quality regulations
19
the areas surrounding Delhi have served as magnet to attract polluting industries and
20
vehicles, and could explain significantly high concentration of PM2.5, PM10 and TSP.
21
Polluting industries, particularly, prefer proximity to the city center and less stringent
22
environmental regulation. A cross-sectional analysis of data clearly indicates that the
23
elevated concentration of air pollution in the areas outside Delhi could be the result of
24
differential impact of these regulations. Although the air pollution levels in most parts of
25
Delhi and its surroundings were significantly higher than the WHO, EPA and CPCB
26
standards, areas nearing Delhi border (±2km) witnessed three to four times higher than
27
the recommended air pollution levels.
28
29
From the analysis of air pollution and its association with sources of air pollution two
30
important findings emerge. First, the conversion of buses to CNG seems to have been
31
working, because the frequency of buses does not show a significant association with the
32
mass of PM2.5 or PM10, particularly in Delhi. Second, trucks and industrials are the most
19
1
important sources of air pollution, particularly that of fine particles (PM2.5), which are by
2
product of combustion. The proximity to industrial locations and frequency of trucks
3
account for one third of the variability in PM2.5. It seems that land-zoning regulations
4
were not enforced as vigorously as the CNG regulations, and trucks, plying in the city,
5
might not be registered in the Delhi and hence has not been subject to CNG regulations.
6
In addition, a large number of personal vehicles (including diesel cars) are added every
7
year (Waldman, 2005). Therefore, emission reduced by the CNG regulations could have
8
been neutralized by the addition of new diesel based cars and unchecked emission from
9
industries and trucks.
10
11
While more stringent regulations are required to check air pollution from industries, truck
12
and personal vehicles within the city, areas surrounding Delhi also need to implement
13
similar air quality regulations otherwise increasing concentration of air pollution in these
14
areas is like to have severe health effects. In addition, there is also a need for spatially
15
detailed longitudinal data for effective air quality monitoring and management, because
16
the limited sites in Delhi cannot be used to estimate spatially detailed air pollution
17
surfaces. The use of real-time photometric samplers and satellite remote sensing are two
18
substitutes for constructing spatially detailed time-series data (Kumar et al., 2007). These
19
data are critical for computing exposure in micro-environments to study the health effects
20
of air pollution.
20
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24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
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How Delhi Broke the Logjam on Air Quality Reforms. Environment Magazine,
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Environmental Kuznets Curve. Journal of Economic Perspectives, 16, 147-168.
DAVIDSON, C. I., PHALEN, R. & SOLOMON, P. (2005) Airborne Particulate Matter
and Human Health: A Review. Aerosol Science and Technology, 39, 737–749.
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particulate matter and mortality: Timescale effects in four US cities. American
Journal of Epidemiology, 157, 1055-1065.
EICHER (2001) Delhi: City Map, New Delhi, Eicher Goodearth Ltd.
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ISAAKS, E. H. & SRIVASTAVA, R. M. (1989) An Introduction to Applied
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KATHURIA, V. (2002) Vehicular Pollution Control in Delhi - Need for Integrated
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KHILLARE, P. S., BALACHANDRAN, S. & R., M. B. (2004) Spatial and Temporal
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KUZNETSOVA, M., ZVARTAU, E. & SAMET, J. H. (2005) Alcohol use and
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KUMAR, N., CHU, A. & FOSTER, A. (2007) An Empirical Relationship between PM2.5
and Aerosol Optical Depth in Delhi Metropolitan. Atmospheric Environment
(forthcoming).
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Characterizing Indoor and Outdoor 15 Minute Average PM2.5 Concentrations in
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22
Table-1: Population Growth in National Capital Region, 1981-1991
District/State
Name
1991
1981
Total
Rural
Urban
Total
Rural
Panipat
677157
467561
209596
492279
340841
Sonipat
754866
576841
178025
593681
467821
Rohtak
1867193
1423133
444060
1516142
1223830
Faridabad
1477240
759727
717513
986076
577446
Gurgaon
1146090
913386
232704
863865
694634
623301
528101
95200
496813
437494
Haryana
6545847
4668749
1877098
4948856
3742065
Alwar
1395513
1115704
279809
1048886
861095
Rajasthan
1395513
1115704
279809
1048886
861095
Meerut
3447912
2171355
1276557
2767185
1903270
Ghaziabad
2703933
1455673
1248260
1843297
1214180
Bulandshahr
2849859
2257064
592795
2358569
1902422
Rewari
Annual Exponential
Growth Rate (%)
Tota Rur
Urban
Urban
l
al
15143
3.24 3.21
3.30
3
12586
2.43 2.12
3.53
0
29231
2.10 1.52
4.27
2
40863
4.12 2.78
5.79
0
16923
2.87 2.78
3.24
1
59319 2.29 1.90
4.84
12067
2.84 2.24
4.52
86
18779
2.90 2.62
4.07
1
18779
2.90 2.62
4.07
1
86391
2.22 1.33
3.98
5
62911
3.91 1.83
7.09
7
45614
1.91 1.72
2.65
7
23
Uttar
Pradesh
9001704
5884092
3117612
6969051
5019871
Delhi
9420644
949019
8471625
6220300
452216
Delhi UT
9420644
949019
8471625
6220300
452216
2636370
8
12617564
NCR
1374614 1918709 1007524
4
3
7
Source: Census of India 1981 and 1991
19491
80
57680
84
57680
84
91118
41
2.59
1.60
4.81
4.24
7.69
3.92
4.24
7.69
3.92
3.23
2.28
4.20
24
Table 2 Parameters used for Kriging to create air pollution surface.
Parameter
Value
Semivariogram model
Exponential
Anisotropic direction
306°
Major range (DD)
0.39
Minor range (DD)
0.18
Lag size (degree decimals)
No. of points included
Neighbors to include
0.033
12
15 or at least 10 for each
angular sector
4
Angular sector
Note: the sill and nugget were computed using the automatic function within
ArcMap to obtain the best fit for the semivariogram.
25
Inside Delhi
Variables
PM2.5 (μgm-3)
Aerosol
PM2.5 (μgm-3)
Gravimetric
PM7 (μgm-3)
Aerosol
PM7 (μgm-3)
Gravimetric
PM10 (μgm-3)
Aerosol
PM10 (μgm-3)
Gravimetric
Total suspended aerosol
(μgm-3)
Total suspended aerosol
(μgm-3)
Gravimetric
Outside Delhi
Total
InterQuartile
Range
Mean
(± 95% CI)
InterQuartile
Range
Mean
(± 95% CI)
InterQuartile
Range
Mean
(± 95% CI)
11.9
39.1(±2.3)
21.2
46.7(±7.1)
14.0
40.2(±2.3)
8.5
27.3(±1.7)
14.9
33.4(±6.9)
8.8
28.2(±1.8)
91.7 172.9(±18.0)
210.7
241.1(±66.7)
94.7 183.1(±18.7)
67.7 120.4(±11.8)
82.8
173.1(±63.3)
67.9 128.3(±14.1)
113.8 207.1(±22.2)
254.7
291.9(±86.3)
125.8 219.9(±23.4)
91.3 147.1(±14.9)
108.7
213.1(±83.6)
90.2 157.0(±18.1)
144.6 250.6(±27.1)
287.9
343.3(±103.9)
149.7 264.6(±28.3)
107.4 178.5(±18.3)
129.0
250.3(±98.9)
113.0 189.3(±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 3a: Ambient air pollutants in Delhi and its surroundings July 23 to December 3, 2003 - summary Statistics
26
Inside Delhi
variable
Outside Delhi
Total
InterQuartile
Range
Mean
(± 95% CI)
InterQuartile
Range
Two
wheeler/minute
15.0
16.8(±2.6)
17.6
13.7(±5.2)
14.9
16.3(±2.3)
Cars/minute
21.8
18.9(±3.5)
18.6
15.3(±6.3)
20.8
18.4(±3.1)
Buses/minute
4.4
2.8(±0.6)
1.8
1.4(±0.7)
4.1
2.6(±0.5)
Trucks/minute
1.8
1.2(±0.2)
2.0
1.5(±0.8)
1.8
1.3(±0.2)
Mean Inter-Quartile
(± 95% CI)
Range
Distance to the
0.98
0.87(±.25)
1.4
1.37(±0.84)
1.11
closest road (m)
Distance to the
closest industrial
2.5
2.47(±0.38)
1.14
0.7(±0.39)
2.6
clusters (m)
Distance to Delhi
5.5
6.8(±0.7)
1.4
1.6(±0.6)
6.5
border (km)
Distance to the
7.5
10.7(±1.1)
6.1
16.3(±2.0)
7.6
city center (m)
Table 3b: Sources of air pollution in Delhi and Surroundings – summary statistics.
Mean
(± 95% CI)
0.95(±0.25)
2.2.0(±0.35)
6.1(±0.7)
11.6(±1.0)
27
2001
2002
2001-02
2004
2005
2004-05
2001-02 2004-05
SO2
13.6(±0.70)
10.5(±0.52)
11.8(±0.45)
8.8(±0.51)
9.0(±0.40)
8.9(±0.32)
-2.90(±0.56)
NO2
70.6(±2.38)
76.2(±2.30)
73.8(±1.68)
86.8(±3.70)
84.6(±2.36)
85.4(±2.02)
11.55(±2.62)
178.7(±21.9) 284.8(±34.7) 240.2(±22.7) 210.1(±13.7) 257.3(±15.0) 239.8(±10.9)
-0.83(±25.9)
City Name
Delhi (ITO B.S.Z.
Marg)
Kanpur (Vikas
Nagar)
Vadodara
Mumbai
SO2
4.4(±0.23)
4.0(±0.06)
4.2(±0.11)
4.3(±0.19)
4.1(±0.04)
4.1(±0.07)
-0.07(±0.15)
NO2
30.4(±2.59)
19.4(±1.24)
24.4(±1.46)
18.6(±1.68)
20.2(±0.97)
19.6(±0.87)
-4.80(±1.93)
200.8(±22.4) 160.3(±14.1) 178.5(±12.8) 185.8(±27.6) 205.2(±18.2) 198.3(±15.3)
20.42(±20.1)
PM10
SO2
4.0(±0.07)
4.0(±0.04)
4.0(±0.03)
5.5(±0.57)
5.5(±0.27)
5.5(±0.29)
1.44(±0.36)
NO2
15.6(±2.71)
10.0(±0.67)
11.2(±0.85)
12.8(±1.00)
21.3(±1.27)
17.6(±1.02)
6.44(±1.43)
PM10
60.1(±8.7)
37.3(±3.8)
41.7(±3.8)
58.1(±6.0)
56.5(±4.1)
57.2(±3.5)
15.45(±5.3)
SO2
5.2(±0.34)
5.1(±0.34)
5.2(±0.24)
6.7(±1.09)
7.4(±0.63)
7.1(±0.59)
1.89(±0.57)
NO2
42.9(±3.13)
42.8(±2.65)
42.8(±2.03)
38.1(±2.96)
48.1(±2.78)
43.9(±2.12)
1.01(±3.00)
PM10
74.9(±8.8)
95.9(±8.3)
86.1(±6.1) 157.0(±17.2) 153.1(±16.1) 154.8(±11.8)
68.37(±12.3)
SO2
6.6(±0.77)
5.2(±0.47)
5.8(±0.44)
9.2(±2.06)
6.0(±0.85)
7.2(±0.96)
1.43(±0.93)
NO2
24.1(±2.98)
25.9(±2.22)
25.1(±1.81)
22.2(±3.15)
20.0(±2.20)
20.8(±1.82)
-4.35(±2.73)
PM10
72.1(±8.9)
76.9(±6.6)
74.8(±5.4)
66.4(±10.8)
81.8(±7.8)
76.1(±6.4)
1.28(±8.5)
SO2
NA
NA
NA
16.6(±2.03)
24.9(±1.11)
22.0(±1.11)
NA
NO2
NA
NA
NA
92.3(±8.81)
91.8(±7.47)
91.9(±5.75)
NA
NA
NA
NA 526.1(±71.0) 213.3(±6.1) 318.8(±30.1)
PM10
Table 4: Ambient air pollutants in Delhi and other mega cities 2001-02 and 2004-05 (μgm-3 (95% CI))
(Source: Central Pollution Control Board)
NA
Chennai (Adyar)
Kolkata
PM10
28
Distance to Delhi
Border (km)
Delhi
Outside Delhi
Difference
<=1
36.9(±10.6; 6)
59.2(±17.4; 5)
-22.3 (0.053)
<=2
41.6(±8.8; 11)
50.6(±9.4; 11)
-9.0 (0.092)
<=3
40.7(±5.4; 18)
47.5(±7.4; 16)
-6.8 (0.075)
All Sites
39.0(±2.4; 95)
47.5(±7.4; 16)
-8.4 (0.012)
<=1
183.8(±67.7; 6)
423.7(±226.0; 5)
-239.8 (0.029)
<=2
244.7(±82.7; 11)
330.0(±119.6; 11)
-85.3 (0.131)
<=3
236.8(±54.2; 18)
299.5(±90.5; 16)
-62.7 (0.120)
All Sites
207.2(±22.4; 95)
299.5(±90.5; 16)
-92.3 (0.007)
<=1
226.7(±82.7; 6)
505.8(±271.0; 5)
-279.1 (0.032)
<=2
290.8(±93.9; 11)
387.4(±145.2; 11)
-96.6 (0.143)
<=3
282.3(±61.5; 18)
352.2(±109.0; 16)
-69.9 (0.134)
All Sites
250.6(±27.4; 95)
352.2(±109.0; 16)
-101.6 (0.014)
PM2.5(μgm-3)
PM10(μgm-3)
TSP(μgm-3)
Table 5: Distribution of ambient particles with reference to distance from Delhi border, July-December 2003.
29
Covariate
s
Pollution
Source
(Trucks/Minute)^0.5
(Buses/Minute)^0.5
(Cars/Minute)^0.5
Distance to the
closest industrial
cluster (km)
Model1
Model2
Model1
Model2
Model1
Model2
Model1
Model2
0.166
0.162
0.036
0.048
0.014
0.016
-0.004
-0.003
(4.47)**
(4.48)**
-1.400
-1.900
-1.100
-1.310
(3.65)**
(2.80)**
Outside
Delhi = 1
-1.620
-1.420
0.166
0.197
0.183
0.1
0.168
(2.73)**
(3.00)**
(2.79)**
-1.44
(2.57)*
3.761
3.579
(94.0)*
*
112
3.49
3.469
3.599
3.553
3.593
3.557
3.802
(84.3)**
(84.7)**
(86.8)**
(83.0)**
(66.9)**
(66.2)**
(78.2)**
Obs
112
112
112
112
112
112
112
R-squared
0.15
0.21
0.02
0.09
0.01
0.08
0.11
Constant
Distance to the
closest road
(km)
Model Model
1
2
0.002
0.002
3.598
(93.9)*
(66.7)**
*
112
112
0.12
0.02
0.08
Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1%
Table 6a: Log(PM2.5) and pollution sources
30
Covariate
s
Pollution
Source
(Trucks/Minute)^0.5
Obs
R-squared
(Cars/Minute)^0.5
Model1
Model2
Model1
Model2
Model1
Model2
Model1
Model2
0.201
0.194
-0.015
0.004
-0.017
-0.013
-0.007
-0.006
(2.90)**
(2.86)**
(0.31)
(0.08)
(0.75)
(0.60)
(3.86)**
Outside
Delhi = 1
Constant
(Buses/Minute)^0.5
Distance to the
closest industrial
cluster (km)
Distance to the
closest road
(km)
Model Model
1
2
0.005
0.004
(3.07)** (2.17)*
(1.98)
0.29
0.30
0.30
0.15
0.28
(2.52)*
(2.53)*
(2.51)*
(1.22)
(2.40)*
5.49
5.11
(75.5)*
*
112.00
5.07
5.03
5.28
5.21
5.32
5.26
5.55
(65.4)**
(65.3)**
(70.6)**
(66.8)**
(55.3)**
(54.3)**
(64.3)**
112.00
112.00
112.00
112.00
112.00
112.00
112.00
0.07
0.12
0.00
0.06
0.01
0.06
0.12
5.14
(75.8)*
(54.6)**
*
112.00 112.00
0.13
0.04
0.09
Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1%
Table 6b: Log(PM10) and pollution sources
31
(Trucks/minute)^0.5
SQRT(Distance to Industrial Cluster (m))
SQRT(Distance to the major road)
SQRT(Buses/minute)
Outside Delhi Border = 1
Log(PM2.5)
OLS
0.258
(3.66)**
-0.002
(1.53)
0.002
(1.52)
-0.067
(1.46)
0.079
(1.14)
Spatial Autocorrelation
Constant
Observations
R-squared
3.52
(40.25)**
111
0.3
SAM
0.261
(3.71)**
-0.002
(1.62)
0.002
(1.46)
-0.068
(1.48
0.086
(1.25)
0.24
(1.31)
3.523
(40.41)**
111
0.31
Log(PM10)
OLS
SAM
0.477
0.46
(3.82)**
(3.76)**
-0.003
-0.004
(1.46)
(1.77)
0.003
0.003
(1.51)
(1.37)
-0.215
-0.203
(2.63)**
(2.55)*
0.09
0.102
(0.73)
(0.85)
0.385
(2.46)*
5.14
5.172
(33.13)**
(34.01)**
111
111
0.28
0.32
Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1%
Table 7: Ambient particles and sources of air pollution – OLS and Spatial Autoregressive Models.
32
Figure 1: National Capital Regions: population density and CNG regulations.
33
Figure 2a: Delhi and other megacities in Northern India.
34
Figure 2b: Air pollution monitoring sites and sources of air pollution in Delhi and its
surroundings.
35
400
0
PM10 (Photometric Sampler)
100
200
300
100
200
300
PM10 (High Volume Sampler)
Figure 3: PM10 from photometric and gravimetric samplers at ITO, Delhi, July 23-December
2003.
36
Figure 4: PM2.5 (μgm-3) in Delhi and its surroundings, July-December 2003.
37
Figure 5: PM10 (μgm-3) in Delhi and its surroundings, July-December 2003.
38
Figure 6: TSP (μgm-3) in Delhi and its surroundings, July-December 2003.
39
Figure 7: Urban sprawl in the southern west part of Delhi’s border.
40
6.5
4.5
4.5
log(PM10 (microgram/cubic m))
5
5.5
6
log(PM2.5 (microgram/cubic m))
3.5
4
3
0
.5
1
1.5
SQRT(Trucks/minute)
2
2.5
0
1
1.5
SQRT(Trucks/minute)
2
2.5
Figure 8c: PM2.5 and proximity to industries.
4.5
4.5
log(PM10 (microgram/cubic m))
5
5.5
6
log(PM10 (microgram/cubic m))
5
5.5
6
6.5
6.5
Figure 8a: PM2.5 and frequency of trucks.
.5
0
.5
1
1.5
SQRT(Trucks/minute)
2
2.5
0
Figure 8b: PM10 and frequency of trucks.
20
40
60
SQRT(Distance to Industrial Cluster (m))
Figure 8d: PM10 and proximity to industries.
41
80