Evaluation of emissions and air quality in megacities

ARTICLE IN PRESS

Atmospheric Environment 42 (2008) 1593–1606 www.elsevier.com/locate/atmosenv

Evaluation of emissions and air quality in megacities B.R. Gurjara,, T.M. Butlerb, M.G. Lawrenceb, J. Lelieveldb a

Civil Engineering Department, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India Atmospheric Chemistry Division, Max Planck Institute for Chemistry, P.O. Box 3060, D-55020 Mainz, Germany

b

Received 22 March 2007; received in revised form 22 October 2007; accepted 26 October 2007

Abstract Several concepts and indicators exist to measure and rank urban areas in terms of their socio-economic, infrastructural, and environment-related parameters. The World Bank regularly publishes the World Development Indicators (WDI), and the United Nations reports the City Development Index (CDI) and also ranks megacities on the basis of their population size. Here, we evaluate and rank megacities in terms of their trace gas and particle emissions and ambient air quality. Besides ranking the megacities according to their surface area and population density, we evaluate them based on carbon monoxide (CO) emissions per capita, per year, and per unit surface area. Further, we rank the megacities according to ambient atmospheric concentrations of criteria pollutants, notably total suspended particles (TSP), sulfur dioxide (SO2), and nitrogen dioxide (NO2). We propose a multi-pollutant index (MPI) considering the combined level of the three criteria pollutants (i.e., TSP, SO2, and NO2) in view of the World Health Organization (WHO) Guidelines for Air Quality. Of 18 megacities considered here 5 classify as having ‘‘fair’’ air quality, and 13 as ‘‘poor’’. The megacities with the highest MPI, Dhaka, Beijing, Cairo, and Karachi, most urgently need reduction of air pollution. r 2007 Elsevier Ltd. All rights reserved. Keywords: Megacity; Emissions; Urban air pollution; Air quality index; Environmental Kuznets curve

1. Introduction Urban air pollution poses a significant threat to human health, property and the environment throughout both the developed and developing parts of the world. The issue of urban air quality is receiving increasing attention as a growing share of the world’s population is now living in urban centers and demanding a cleaner urban environment. The United Nations (UN) estimates that 4.9 billion inhabitants out of 8.1 billion will be living in Corresponding author. Tel.: +91 1332 285881; fax: +91 1332 275568/273560. E-mail address: [email protected] (B.R. Gurjar).

1352-2310/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2007.10.048

cities by 2030 (UNCSD, 2001). Rapid urbanization has resulted in increasing air pollution emissions due to transportation, energy production and industrial activity, all concentrated in densely populated areas. The environmental impacts are particularly severe in cities of about 10 million or more inhabitants—also known as megacities (e.g., Gurjar and Lelieveld, 2005), especially in Asia where some countries (e.g., China and India) combine strong industrial expansion, high population density and number, and intense motor vehicle use. Several previous studies have examined emissions from megacities and their effects. For instance, Guttikunda et al. (2003) carried out a study covering

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a period of 25 years (1975–2000) examining the contribution of megacities to sulfur emissions and pollution in Asia. It was found that although Asian megacities cover o2% of the land area they emit 16% of the total anthropogenic sulfur in Asia. It is also observed that urban sulfur emissions contribute more than 30% to regional pollution levels in large parts of Asia. Studies of megacities on different spatial scales using various modeling tools are required to understand their local-to-regional-toglobal impacts and implications (Gurjar and Lelieveld, 2005). For instance, looking beyond the local impacts of megacity pollution, Lawrence et al. (2007) have employed a global model to examine the outflow characteristics of pollutants from megacities, demonstrating the tradeoffs between pollutant buildup in the region surrounding each megacity versus export to downwind regions or to the upper troposphere. Unfortunately, the coarse grid resolution of global atmospheric models and source inventories still have difficulties capturing the details of the development of megacity emissions temporally and spatially (Gurjar et al., 2004). For example, global models typically run at a resolution of about 2.81 (T42) and about 11 (T106) at present, regional models down to about 25 km grid cells. Megacityspecific emissions data are generally available for the city as a whole, or occasionally at 2 km  2 km resolution (Mohan et al., 2007). Global inventories tend to be at 11  11, or some have been 0.51  0.51 (e.g., the RETRO inventory) (Butler et al., 2007). EDGAR is expected to release its version 4 inventory soon with a much higher resolution of 0.11  0.11. In particular, reliable emission inventories are a prerequisite for modeling studies; however, there are substantial (often 450%) differences between the representations of megacities in the various global emissions data sets that are currently available (Butler et al., 2007). Moreover, focusing back on the urban scale, evaluation of emission estimates and local air quality along with risk–benefit analysis is required in defining control measures to tackle the air pollution problem. In this paper, we develop a new approach to evaluate and compare the emissions and pollution concentrations in different megacities to assess their air quality.

2. Environmental quality indices The need for carefully constructed indices and metrics for pollution control and natural resource

management is urgent according to UN millennium development goals (MDGs), which call upon nations to make progress on a range of critical development issues. However, the environmental dimension of the MDGs is often criticized as being insufficiently defined and inadequately measured (Esty et al., 2006). There is, in fact, an inherent complexity in creating accessible, understandable, and uniformly acceptable indices of environmental quality (Longhurst, 2005). Nevertheless, the use of appropriate indices (for air pollution) has been in practice to assess and evaluate environmental quality and associated health effects. Multiple linear regression models have been used to derive indices for investigating most important and interactive effects of air pollutants (Katsouyanni et al., 1993). Also, there have been efforts to express air or water pollution by an aggregation of pollutant subindices (e.g., Ott, 1978; Swami and Tyagi, 1999). Most aggregation methods, however, suffer from three shortcomings: ambiguity, eclipsing, and rigidity (Swami and Tyagi, 2007). Ambiguity problems exist when all the subindices indicate acceptable air or water quality for a given use, but the aggregated index does not. Eclipsing problems exist when the aggregated index fails to reflect the poor environmental quality of one or more variables. Rigidity problems exist when additional variables are included in the index to address specific environmental quality concerns, but the incorrect aggregation function might artificially reduce the value of the total index such that it does not accurately reflect the true environmental quality. Thus, when ambiguous, the aggregations can raise an unnecessary alarm, and conversely when eclipsed, a false sense of security may be provided. In view of the above, air pollution indices and other environmental quality indicators have often been the subject of debate (Longhurst, 2005), and the role of subjective judgment is not ruled out in the interpretation and application of prevailing pollution indices. 2.1. Air quality index (AQI): an illustration The AQI is a rating scale for reporting the ambient air pollution recorded at monitoring sites on a particular time scale (e.g., daily). The two main objectives of AQI are (a) to inform and caution the public about the risk of exposure to daily pollution levels and (b) to enforce required regulatory measures for immediate local impact

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(Stieb et al., 2005). The higher the AQI value, the greater the level of air pollution and health risk. Although the AQI itself is simply a number that reflects some aspect(s) of air quality, in practice it is associated with color schemes, graphics, air quality category labels (e.g., ‘‘good,’’ ‘‘moderate,’’ or ‘‘poor’’), and various messages so that its meaning is easily understood by the public (Stieb et al., 2005). The conventional AQI is calculated by comparing individual pollutants to standards (such as the one elaborated in following example), whereas Stieb et al. (2005) proposed an alternative no-threshold, multipollutant AQI based on the concentration–response (mortality) relationship of CO, NO2, O3, SO2, and PM2.5 in Canadian cities in a daily time-series study. Since no-threshold concentration–response relationships do not provide obvious cut points between AQI categories, the Stieb et al. approach is criticized for its inherent dependence on subjective judgment to define the required AQI categories. In contrast, the conventional AQI approach incorporates the current understanding of the health effects associated with exposure to the air pollutants and accordingly revise the AQI categories and descriptors (EPA, 1999) from time to time. A New Delhi-based public interest organization ‘‘Centre for Science and Environment (CSE)’’ has used the following method to develop an AQI (Source: http://www.cseindia.org/html/lab/lab_air_ pollution_aqi_measuring_faq.htm) for a particular location (i.e., Tughlakabad Institutional Area) within the category of ‘‘sensitive’’ area (e.g., hospitals, schools). In this case, an AQI of 100 is considered to be equivalent to the National Ambient Air Quality Standards (NAAQS) value for 24 h for sensitive areas. For each pollutant, an intermediate value of 50 is considered equivalent to one-half the value of NAAQS, which is the upper bound of the ‘‘good’’ category (see Table 1; adapted from EPA, 1999). TSP measurements in Table 1

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refer to particles in the size range of 0.1–100 mm diameter (CPCB (Central Pollution Control Board), 2001) and hence shown as PM100. An AQI greater than 100 is considered to be above NAAQS and is given index values of 200, 300, and 300+ to represent poor, very poor, and critical air quality, respectively. The following formula is used by CSE for calculating AQI for a day: IP ¼ ½ðIHI  ILOÞ=ðBPHI  BPLOÞ  ðCP  BPLOÞ þ ILO;

ð1Þ

where IP is the AQI for pollutant ‘‘P’’ (rounded to the nearest integer), CP the actual ambient concentration of pollutant ‘‘P’’, BPHI the upper end breakpoint concentration in Table 1 that is greater than or equal to CP (e.g., 150 mg m3 if CP of PM10 ¼ 141 mg m3), and BPLO the lower end breakpoint concentration in Table 1 that is less than or equal to CP (e.g., 76 mg m3 if CP of PM10 ¼ 141 mg m3), ILO the subindex or AQI value in Table 1 corresponding to BPLO (e.g., 101 when BPLO ¼ 76 mg m3 and CP of PM10 ¼ 141 mg m3), and IHI the subindex or AQI value in Table 1 corresponding to BPHI (e.g., 200 when BPLO ¼ 76 mg m3 and CP of PM10 ¼ 141 mg m3). Ultimately, the concentration of each pollutant is converted into AQI for the pollutant using the above-mentioned formula. The pollutant with the highest AQI on a given day becomes the AQI reading for that day. It can be used to describe the impact of the pollutants on human health and the environment. The pollutant with the highest AQI number becomes the ‘‘overall’’ AQI for a particular location. The higher the AQI value, the greater the level of air pollution and the larger the danger to human health. If the AQI for suspended particulate matter (SPM) is 305 and is the highest out of the five pollutants, it is reported as the overall AQI or quality of air for a particular location. It would be reported as an ‘‘AQI of 305; reason: SPM.’’ Citing a

Table 1 Air quality index (AQI) values for a local monitoring station (i.e. Tughlakabad Institutional Area) in New Delhi Index value

Description

PM100a (mg m3)

PM10 (mg m3)

SO2 (mg m3)

NO2 (mg m3)

0–50 51–100 101–200 201–300 301+

Good Marginal (moderate) Unhealthy (poor) Very unhealthy (very poor) Critical

0–50 51–100 101–200 201–400 401+

0–37.5 38.5–75 76–150 151–300 301+

0–15 16–30 31–60 61–120 121+

0–15 16–30 31–60 61–120 121+

a

Particles in the size range of 0.1–100 mm diameter.

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case study of the city of Pittsburgh, Longhurst (2005) has argued that the indeterminate nature of the atmosphere and the many contestable scientific and technical decisions involved in the creation of a numerical representation subjects the resulting index to political debate rather than providing a uniformly understood and accepted measurement. Variation in individuals’ health and perceptions, health care system, and medical facilities from place to place makes the issue further complicated. Not surprisingly it has been difficult to establish uniform air quality indices in the United States and elsewhere. It remains to be seen to what degree the above-mentioned AQI based on observations at a particular locality in New Delhi will be successful in educating people and enforcing requisite regulations on public demand, e.g., unceasing efforts made by public interest groups such as CSE have helped New Delhi witness the successful implementation of compressed natural gas (CNG) as a fuel in the public transport fleet. 2.2. Multi-pollutant index (MPI) The simple concept and formulation of the MPI is proposed to reduce the possible role of subjective judgments on the degree of pollution in a megacity, which may arise due to individual perceptions based on pollution levels (or emissions) of one or more pollutants in a given megacity. The MPI index is expressed mathematically as below: hX MPI ¼ ð1=2nÞ fðACi  GCi Þ=GCi g i X þ fðAEi  GEi Þ=GEi g , ð2Þ where i is the pollutant in question (e.g., TSP, SO2, NO2, etc.), n the total number of pollutants taken into consideration, ACi the atmospheric concentration of a pollutant in ambient air of a megacity, GCi the guideline concentration of the pollutant as recommended by a national or an international agency like the World Health Organisation (WHO) (e.g., WHO, 1987, 2000, 2006), AEi the atmospheric emission (per year or per capita, etc.) of a pollutant in a megacity, and GEi is the guideline emission (per year or per capita, etc.) cap of a pollutant, e.g., as recommended by an international agency. Since no information was found on guideline emission caps (per year or per capita or per unit energy consumption, etc.) of criteria pollutants recommended by a national or international reg-

ulatory agency, we have removed the second part of Eq. (2) pending further information, and used Eq. (3) to calculate a provisional MPI index. The role of emissions in MPI is discussed in Section 3.4: hX i (3) MPI ¼ ð1=nÞ fðACi  GCi Þ=GCi g . If the values of all ACi were ever to be zero in the above equation (perhaps in case of a utopian superclean city), the MPI would be 1. If the values of all ACi are equal to their respective GCi the index value will be estimated as 0. Consequently, the MPI will always be higher than 1, and its value will reflect whether a megacity is more polluted or cleaner relative to other megacities in question. In the present case, we have taken the MPI as a quantitative measure of pollution in a megacity expressing the combined pollution levels of three criteria pollutants (i.e., TSP, SO2, and NO2) relative to the WHO Guidelines for Air Quality (e.g., WHO, 1987, 2000, 2006). As an illustration, Table 2 gives the ranking of megacities based on ambient atmospheric concentrations of TSP, SO2, and NO2 and also in terms of their total concentration as well as MPI indices; the results in the table are discussed further in Section 3. If we solve Eq. (2) for the condition when the MPI is equal to zero, i.e., a megacity with borderline air quality, Eq. (2) can be rewritten as Eq. (4) to estimate the ratio of actual emission to guideline emission of a pollutant or group of pollutants in ambient air of a megacity with the given level of annually averaged ambient air concentrations of criteria pollutants and their emissions. This relationship can be used to assess if emissions in a megacity are acceptable in view of the actual and guideline ambient air concentrations and emissions: X X ðAEi =GEi Þ ¼ ½fð2  GCi Þ  ACi g=GCi . (4) From Sections 2.1 and 2.2, it is apparent that the MPI enables the comparison of air quality of megacities (or any urban area) in terms of aggregated pollution levels, whereas the individual contribution of pollutants is clearer in the AQI. The latter allows the definition of air quality with respect to particulate matter (PM) or gases, but the criteria cannot be objectively continued if we intend to evaluate urban air quality as a collective function of several air pollutants. Moreover, the concept of the MPI can be extended to estimate allowable or guideline emissions of a particular or group of air pollutants in a given megacity or urban area. With

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Table 2 Ranking of megacities based on ambient air quality measurements and MPI Megacities in 2000

TSP (mg m3) [Rank]

SO2 (mg m3) [Rank]

NO2 (mg m3) [Rank]

MPI [Rank]

Tokyo Mexico City New York-Newark Sa˜o Paulo Mumbai (Bombay) Kolkata (Calcutta) Shanghai Buenos Aires Delhi Los Angeles-Long Beach-Santa Ana Osaka-Kobe Jakarta Beijing Rio de Janeiro Cairo Dhaka Moscow Karachi

40 201 27 53 243 312 246 185 405 39 34 271 377 139 593 516 150 668

19 47 22 18 19 19 53 20 18 9 19 35 90 15 37 120 15 13

55 56 63 47 43 37 73 20 36 66 45 120 122 60 59 83 170 30

0.27 0.52 0.23 0.29 0.39 0.59 0.87 0.01 0.92 0.25 0.37 1.24 2.01 0.11 1.93 2.40 1.07 1.81

[15] [10] [18] [14] [9] [6] [8] [11] [4] [16] [17] [7] [5] [13] [2] [3] [12] [1]

[9] [4] [7] [10] [9] [9] [3] [8] [10] [13] [9] [6] [2] [11] [5] [1] [11] [12]

[11] [10] [7] [12] [14] [15] [5] [18] [16] [6] [13] [3] [2] [8] [9] [4] [1] [17]

[16] [10] [14] [17] [11] [9] [8] [13] [7] [15] [18] [5] [2] [12] [3] [1] [6] [4]

Note: 1. Out of 54 (i.e. 18  3) observations, sample sizes for different years were as follows: 1990 (n ¼ 1), 1992–1994 (n ¼ 3), 1995 (n ¼ 7), 1998–1999 (n ¼ 13), 2000–2001 (n ¼ 30). 2. GCi values for different pollutants used in computing the MPI in this table are as follows: TSP ¼ 90 mg m3 (WHO, 1987), SO2 ¼ 50 mg m3 (WHO, 1987, 2000), and NO2 ¼ mg m3 (WHO, 1997, 2000).

the MPI, it would also be possible to weight pollutants unevenly for different purposes (e.g., including both PM10 and PM2.5, but weighting PM2.5 higher for health purposes, while O3 could be weighted higher for agriculture, etc.). 3. Results and discussions Given the constraints on availability of data and to study megacities in a uniform time frame, we have used data for the year 2000 or late 1990s. We could not find the desired data for most of the megacities beyond 2000. Fig. 1 shows population, surface area, and population density of megacities for the year 2000. Tokyo has the highest population (34.5 million) and Karachi the lowest (10 million) of the cities considered (UN, 2004). Los Angeles has the greatest surface area (27,800 km2) resulting in the least dense population (Molina and Molina, 2002). Cairo’s population (10.4 million) is reported to be confined in 214 km2 (Decker et al., 2000), making it the most densely populated megacity. 3.1. CO emissions in megacities Annual CO emission (Gg year1) estimates for 2000 in each megacity are shown in Fig. 2. It is

interesting to observe how different units and scales influence the relative position of the megacities in terms of CO emissions. For example, Tokyo, Beijing, Shanghai, and Los Angeles have the maximum CO emissions on annual basis and Kolkata, Dhaka, Mumbai, Cairo, and Rio de Janeiro have the minimum emissions. Cairo, Tokyo, and Moscow rank among the highest emitters of CO per unit of surface area, and Rio de Janeiro and Los Angeles rank among the smallest emitters. On the other hand, Beijing, Shanghai, and Los Angeles are among the highest per capita CO emitters, and Mumbai, Kolkata, Dhaka, and Cairo emit the least per capita CO. It is worth noting that megacity CO emission estimates are highly uncertain due to several assumptions. For instance, (i) in many cases due to unavailability of megacity-specific emission inventories we have extrapolated city traffic emissions to total emissions based on the population growth trend or ratio of traffic emission to the total city emission (e.g., for Buenos Aires, Cairo, Dhaka, Jakarta, Karachi, Moscow, Rio de Janeiro, and Sa˜o Paulo) or (ii) in two cases (Tokyo and Osaka-Kobe) we have estimated megacity emissions based on the country-level emissions (given in EDGAR emission inventory), scaled by the ratio of the megacity population to the national population.

ARTICLE IN PRESS B.R. Gurjar et al. / Atmospheric Environment 42 (2008) 1593–1606 Population (Million)

Surface area (sq. km)

Population density (per sq. km)

50000

35 30 Population (million)

40000 25 30000

20 15

20000

10 10000

Karachi

Dhaka

Moscow

Cairo

Rio de Janeiro

Jakarta

Beijing

Osaka-Kobe

Los Angeles

Delhi

Buenos Aires

Kolkata

Shanghai

Mumbai

São Paulo

New York

Mexico City

0

Tokyo

5 0

Surface area (sq. km), Population density (km-2)

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Megacities Fig. 1. Megacity population (million), surface area (km2), and population density (number of people per km2) in 2000.

CO emission (kg/capita/yr)

2500

250

2000

200

1500

150

1000

100

500

50 Karachi

Moscow

Dhaka

Cairo

Rio de Janeiro

Beijing

Jakarta

Osaka-Kobe

Delhi

Los Angeles

Buenos Aires

Kolkata

Shanghai

Mumbai

São Paulo

New York

0 Mexico City

0

CO emission (kg/capita/yr)

CO emission (Mg/sq. km/yr)

300

Tokyo

CO emission (Gg/yr), CO emission (Mg/km-2/yr)

CO emission (Gg/year)

3000

Megacities

Fig. 2. Annual CO emission estimates for 2000 (Gg year1).

For remaining megacities (e.g., Beijing, Delhi, Kolkata, Los Angeles-Long Beach-Santa Ana, Mexico City, Mumbai, New York-Newark, and Shanghai) we have used city-level emission data from secondary sources with required temporal adjustments (through extrapolation) in some cases (e.g., Kolkata and Mexico City) where emission data were available for other than the year 2000. The sources of baseline emission data are given in Table 3. The degree of uncertainty is reflected in our comparison of estimated emissions based on

city-specific and country-level emissions (see Table 3). Table 3 shows that in Shanghai and Beijing, where CO emission estimates are based on country-level data, sources are underestimated by a factor of 2–3.5 compared with estimates based on city-specific baseline data. In the case of Karachi and Sa˜o Paulo the city-specific CO emissions estimated based on the traffic data surrogate turns out to be almost equal to the emissions scaled down from national emission data from the EDGAR inventory. For Tokyo and Osaka, we could not find city level or any surrogate

ARTICLE IN PRESS B.R. Gurjar et al. / Atmospheric Environment 42 (2008) 1593–1606 Table 3 Comparison of megacity emissions (Gg year1) derived from city specific and country level baseline data Megacities (in descending order of estimated CO emissions)

Megacity CO emission in 2000

Based on city level (traffic or total) baseline data but interpolated/ extrapolated to 2000 (total)

Tokyoe Beijingc Shanghaic Los Angeles-Long Beach-Santa Anac Mexico Cityc New YorkNewarkc Moscowd

Osaka-Kobee Sa˜o Paulod Buenos Airesd Jakartad Karachid Delhic Rio de Janeirod Cairod Mumbai (Bombay)c Dhakad

Kolkata (Calcutta)c a

Based on EDGAR 1995 country level baseline data but interpolated/ extrapolated to 2000a

2955a 2728 (Streets et al., 2002) 2158 (Streets et al., 2002) 2027 (ARB (Air Resources Board), 2004) 1820 (Molina and Molina, 2002) 1420b

2955 782 930 3689

1324 (Myagkov, M.S., personal communication) 958a 956 (Jacobi et al., 1999) 733 ( Mazzeo and Venegas, 2003) 694 ( Aboeprajitno, 2002) 613 (Faiz et al., 1990) 503 (Gurjar et al., 2004) 396 (Faiz et al., 1990) 319 (Faiz et al., 1990) 255 (TERI, 2002)

1089

238 (Karim, 1999, and personal communication) 212 (ESS, 2002)

2060 5572

958 951 964 951 622 722 601 527 934 617

758

EDGAR 3.2—precursor gases of tropospheric ozone. URL: http://arch.rivm.nl/env/int/coredata/edgar/ b Projected from 1996 baseline data obtained from http:// www.epa.gov/region02/air/ c Borrowed or extrapolated from megacity-specific inventory data. d Extrapolated from surrogate traffic emission data in absence of megacity-specific emission data. e Neither city-specific nor surrogate traffic emission data were available for Tokyo and Osaka-Kobe, thus CO emissions for these megacities were derived from country level EDGAR emission inventory data.

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data, so we took their emissions as equal to those estimated from EDGAR data. Nevertheless, in the majority of the megacities listed in Table 3, the CO emission estimates based on city-specific data are significantly lower than the estimates based on country-level data. Thus, the megacity emissions derived from national-level inventories tend to be overestimated in most cases. 3.2. Air quality in megacities The air quality of different megacities in terms of ambient air concentrations of SO2, NO2, and TSP is shown in Figs. 3–5. Fig. 3 illustrates averaged annual ambient air concentrations of SO2 in different megacities in the late 1990s, showing that SO2 levels violate the WHO guideline (50 mg m3) (WHO, 1987, 2000) in Dhaka (Karim, personal communication), Beijing, and Shanghai (1995 data from Baldasano et al., 2003). Los Angeles (1999 data from Baldasano et al., 2003), Karachi (1998–1999 data from Guttikunda et al., 2003), and Rio de Janeiro (2000–2001 data from Castro, 2004) have relatively low SO2 concentrations. Further, as shown in Fig. 4, NO2 concentrations in several megacities exceed the WHO guideline (40 mg m3) (WHO, 1997, 2000). Moscow (Mygkov, personal communication), Beijing (1995 data from Baldasano et al., 2003), and Jakarta (1992–1993 data from Shah and Nagpal, 1997) are highly polluted by NO2, whereas Buenos Aires (1998 data from Baldasano et al., 2003) and Karachi (1998–1999 data from Suparco, 1999) have lower ambient concentrations of NO2. Nevertheless, it is to be noted that the ambient pollutant concentrations discussed here are taken from different secondary sources, such as those discussed above, and others e.g., OECD (2002), TERI (2002), AFPR (Auto Fuel Policy Report) (2002), CPCB (Central Pollution Control Board) (2001), Makoto Koike (personal communication), EIMP (2002), and Parekh et al. (2001). Thus the collected air quality data are subject to unknown uncertainties and errors related to methodology, instruments, time period, etc. The evidence on airborne PM and its public health impact consistently shows adverse health effects at exposures that are currently experienced by urban populations in both developed and developing countries. The epidemiological evidence shows adverse effects of PM following both shortand long-term exposures. The range of health effects

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1600

Sulphur dioxide

WHO guideline (WHO, 2000)

125

SO2 (µg/m3)

100 75 50 25

Moscow

Karachi

Moscow

Karachi

Dhaka

Cairo

Rio de Janeiro

Beijing

Jakarta

Osaka-Kobe

Delhi

Los Angeles

Buenos Aires

Shanghai

Kolkata

Mumbai

São Paulo

New York

Mexico City

Tokyo

0

Megacities Fig. 3. Annual average ambient air concentrations of SO2 in the late 1990s.

180 NO2 (µg/m3)

Nitrogen dioxide

WHO guideline (WHO, 2000)

120

60

Dhaka

Cairo

Rio de Janeiro

Beijing

Jakarta

Osaka-Kobe

Los Angeles

Delhi

Buenos Aires

Shanghai

Kolkata

Mumbai

São Paulo

New York

Mexico City

Tokyo

0

Megacities Fig. 4. Annual average ambient air concentrations of NO2 in the late 1990s.

is broad but predominantly relates to the respiratory and cardiovascular systems (WHO, 2006). The entire population is affected, but susceptibility to pollution may vary with health or age. Several studies have reported a relationship between particulate air pollution and daily mortality rates (e.g., Dockery et al., 1993; Evans et al., 1984; Lave and Seskin, 1977; Ozkaynak and Thurston, 1987). The risk for various outcomes has been shown to increase with exposure and there is little evidence to suggest a threshold below which no adverse health effects would be anticipated. Ultrafine (UF) particles, i.e., particles smaller than 0.1 mm in diameter, have recently attracted significant scientific and medical attention. These are usually

measured as a number concentration (WHO, 2006). Since we could not find emission or air quality data pertaining to PM10 and lower size particles, we did not include them in our study but relied upon TSP observations. TSP levels in several megacities violate the old WHO guideline (WHO, 1987) for TSP (90 mg m3) as demonstrated in Fig. 5. Karachi, Cairo, Dhaka (Salam et al., 2003), Delhi, and Beijing have maximum TSP levels, whereas New York, Osaka-Kobe, Los Angeles, Sa˜o Paulo, and Tokyo have the lowest levels. Note that the new guidelines of the WHO (e.g., WHO, 2000, 2006) do not recommend a safe limit (specific guideline concentration) for TSP as it has been established that a threshold value could not be identified in case of PM

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1601

700 Total suspended particles (TSP)

WHO guideline (WHO, 1987)

600 TSP (µg/m3)

500 400 300 200

Karachi

Moscow

Dhaka

Cairo

Rio de Janeiro

Beijing

Jakarta

Osaka-Kobe

Los Angeles

Delhi

Buenos Aires

Shanghai

Kolkata

Mumbai

São Paulo

New York

Mexico City

0

Tokyo

100

Megacities Fig. 5. Annual average ambient air concentrations of TSP in the late 1990s.

below which no adverse effects on health occurred. Studies with animals have shown that the coarse, fine, and UF fractions of ambient PM induce health effects (WHO, 2003). On a mass basis, small particles generally induce more inflammation than larger particles, due to a relatively larger surface area. The coarse fraction of ambient PM may, on the other hand, be more potent to induce inflammation than smaller particles due to differences in chemical composition (Soukup and Becker, 2001). Thus, it is unlikely that any standard or guideline value will lead to complete protection for every individual against all possible adverse health effects of PM (WHO, 2006). Furthermore, while there is considerable toxicological evidence of potential detrimental effects of UF particles on human health, the existing body of epidemiological evidence is insufficient to reach a conclusion on the exposure–response relationship of UF particles. Therefore, no recommendations can at present be provided for guideline concentrations of UF particles. Given this situation, it would be interesting to see, in follow-up studies to the present work, in what way the nothreshold value for PM could influence the formulation and application of the proposed index. The particle size-related study is important because the PM size is a predictor of the particle’s ability to enter and/or lodge in the respiratory system, as opposed to TSP (Morawska et al., 2005). Even when compared with all combustion and non-combustion sources in urban areas, road transport can account for close to 60% of UF, i.e., particles with diameters o0.1 mm (Morawska, 2003). Clearly, there is a need for construction of megacity-level activity-based emissions inventories and

comparisons that take account of the size distribution of particles. 3.3. MPI-based ranking MPI-based total air pollution levels, as described in Section 2.2, are shown in Fig. 6. Table 2 presents the ranking of megacities based on ambient air quality measurements of TSP, SO2, and NO2, and MPI values. It is interesting that Karachi ranks as the most polluted megacity in terms of TSP, whereas according to the MPI ranking it is categorized as the fourth most polluted megacity in the world, due to the lower levels of NO2 and SO2. On the other hand, Dhaka, which is the fourth most polluted megacity in terms of ambient air concentration of NO2, and third in terms of TSP, ranks as the most polluted megacity in the world when judged according to the MPI. Similarly, Osaka, Sa˜o Paulo, and Tokyo are ranked towards the middle in terms of the SO2 concentration, but they emerge as the least polluted megacities when evaluated using the MPI. Based on present MPI values, it is evident from Fig. 6 and Table 2 that Dhaka, Beijing, Cairo, and Karachi appear to be most polluted, whereas Osaka-Kobe, Tokyo, Sa˜o Paulo, Los Angeles, New York, and Buenos Aires are the least polluted megacities. 3.4. Role of emissions in MPI It needs to be emphasized that in Eq. (2) equal weightings have been given to air pollution emissions

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Megacity pollution index (MPI) Fair air quality -1.0

Poor air quality

-0.5

0.0

0.5

1.5

1.0

2.0

2.5

Tokyo (-0.3) Mexico City (0.5) New York (-0.2) São Paulo (-0.3) Mumbai (0.4) Kolkata (0.6) Shanghai (0.9) Buenos Aires (0.0) Delhi (0.9) Los Angeles (-0.2) Osaka-Kobe (-0.4) Jakarta (1.2) Beijing (2.0) Rio de Janeiro (0.1) Cairo (1.9) Dhaka (2.4) Moscow (1.1) Karachi (1.8)

Fig. 6. MPI-based total pollution level in megacities.

and their ambient air concentrations. These do not need to be evenly weighted, but it is worth noting that they are closely related because the atmospheric assimilative capacity or assimilation potential of an urban air shed, which is governed by the ventilation coefficient (VC) of the concerned region, directly influences the emission cap and ambient air quality. The assimilative capacity of the atmosphere is defined as the maximum amount of air pollution load (i.e., emission cap) that can be discharged into the atmosphere without violating the best-designated usage of the air environment in the concerned region. It is possible to estimate the assimilative capacity of an air shed on the basis of ventilation-coefficient approach (e.g., Goyal et al., 2003; Goyal and Chalapati Rao, 2007) and hence to prescribe the guideline emission caps per unit of time or activity. The VC is the product of two meteorological parameters, namely, the mixing height and average wind speed, through the mixing layers. The VC can be estimated by VC ¼ Zi  U, where Zi ¼ atmospheric boundary layer height or mixing P height (m) and U ¼ average wind velocity (U ¼ Ui for i ¼ 1 to Zi) in the mixed layer (m s–1). Thus, lower values of the VC indicate less dispersion potential of pollutants in the atmosphere or greater chances of poor air quality. The higher the coefficient, the greater the ability of the atmosphere to disperse the pollutants and hence the higher the possibility of having fair air quality. It is also to be noted that the

removal of the emission part from Eq. (2) may reduce the absolute value of the resulting MPI values but it will not alter the meaning of the final result, i.e., the resulting MPI values will correctly indicate which megacities are relatively worse or better in terms of air pollution and ambient air quality. The removal of the emission part from Eq. (2) would, however, underestimate the absolute value of the city-specific MPI and thus need to be cautiously applied in case of individual megacities. 3.5. MPI versus world knowledge competitiveness index (WKCI) It is discernible from Fig. 6 that most of the Asian megacities suffer from poor air quality. South Asia alone contains one-third of the 15 largest megacities, with a combined population of about 70 million. The economy in the Indian subcontinent is growing rapidly, and megacities are playing crucial roles as their relatively better infrastructure and skilled workers attract international businesses and investments, which enhance the overall employment capacity of these cities (Gurjar, 2005). Similar arguments apply to the Chinese megacities. The improving employment opportunities attract the rural population to migrate into the cities. Because of the growing industrial activity and energy use, the burgeoning megacities are growing into huge conglomerates of air pollution sources with local,

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regional, and global consequences for air quality and climate (Gurjar and Lelieveld, 2005). Huggins et al. (2004) have published the WKCI as an integrated and overall benchmark of the knowledge capacity, capability, and sustainability of 125 regions (including several megacities) across the globe. The WKCI also represents the extent to which this knowledge is translated into economic value, and how it is transferred into the wealth of the citizens of these regions, utilizing 19 knowledge economy benchmarks, including employment levels in the knowledge economy, patent registrations, R&D investment by the private and public sectors, education expenditure, information and communication technology infrastructure, and access to private equity. The WKCI report also includes the regionspecific knowledge intensity ratio (KIR), which is calculated on the basis of each region’s WKCI index score relative to its index of GDP per capita. Such a measure appears to be the best available derivative of the relative importance of knowledge and knowledge-based activities to the overall economic performance and structure of each region (Huggins et al., 2004). Assuming that the investment in knowledge-based economic sectors indirectly represents and influences the environmental quality of the region, we have attempted to assess the covariation trends in MPI and KIR values (see Fig. 7a and b). It appears that megacities with negative MPI values (i.e., cleaner megacities such as Tokyo, New York, Los Angeles, and Osaka) have higher KIR values (40.8), whereas megacities with poor air quality (e.g., Mumbai, Shanghai, and Beijing) have relatively lower KIR values (o0.8). It can be seen that all cleaner megacities correspond to developed economies (e.g., Japan and USA), whereas megacities in developing or emerging economies (e.g., India and China) are characterized by poor air quality. It seems air quality in megacities is following the concept of the environmental Kuznets curve, which proposes that there is an inverted U-shape relation between environmental degradation and income per capita, so that, eventually, growth reduces the environmental impact of economic activity (Stern et al., 1996). Here, it is to be noted that the MPI values discussed above correspond to the year 2000, whereas the KIR values pertain to the year 2004. Since the environmental and infrastructure quality of a city and its economic activities interact and influence each other with a time lag of years (e.g., investment in implementation

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of CNG in public transport system of megacity Delhi reduced TSP levels but increased NOx concentrations in subsequent years), the above comparison seems to be reasonable. 4. Conclusions and recommendations We have proposed a simple concept and formulation of a MPI to evaluate atmospheric emissions and urban air quality, particularly in megacities. Our index can help monitor air quality changes over time, and relate these to other indices that provide information about the—often rapidly— changing state of megacities. We have chosen averaged annual air concentrations of SO2, NO2, and TSP, and WHO guidelines (e.g., WHO, 1987, 2000, 2006) for the present calculations and comparisons. The conventional indices based on the concentration of a single pollutant are limited in their scope, and fail to identify where the overall air quality is poorest and potentially presents the greatest health risk. The proposed MPI, referenced to WHO guidelines, currently provides a viable option for doing this. The results could be useful to attract funding from organizations such as the World Bank and the UN to fund the introduction of specific mitigation strategies, which could have the benefit of reducing transboundary transport of air pollution from megacities to other regions. While a lack of air quality data for several megacities and other data gaps (e.g., the lack of availability of megacity scale comprehensive emission inventories) constrains the current effort, we expect that over time this methodology will facilitate the evaluation and ranking of megacity emissions and air quality. The proposed index can be refined and improved in the future when more comprehensive air quality measurements, also for other pollutants (e.g., CO, benzene, and ozone), become available. Owing to the lack of information on city-specific emission data in several cases, we have used traffic emission data as surrogate to estimate the megacity-level data, which makes our estimates highly uncertain. Therefore, we feel a greater need for high-quality emissions data from all megacities, which can provide adequate information about megacity-level activity-based emissions and comparisons that also take account of the size distribution of particles (e.g., TSP, PM10, PM2.5, PM1.0 or PM0.1). The possibility of considering air quality standards for different size categories of PM

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2.50 MPI-2000

KIR-2004

MPI-2000 & KIR-2004

2.00

1.50

1.00

0.50

0.00

-0.50

Tokyo

NewYork

Mumbai

Shanghai

Los Angeles

Osaka

Beijing

Megacities 1.6 1.4 1.2

KIR-2004

1 0.8 0.6 0.4 0.2

-0.50

0 0.00

0.50

1.00 MPI-2000

1.50

2.00

2.50

Fig. 7. (a) Comparison of MPI-2000 and KIR-2004 values in selected megacities. (b) Co-variation of MPI-2000 and KIR-2004.

into our MPI calculations (or taking into account ‘‘no-air-quality-standards’’, i.e., ‘‘no-threshold’’ for PM as per new WHO guidelines) and their effect on the MPI requires further study. Another key issue related to monitoring in megacities is the specific location of air quality measurements, which can produce biased results and influence the resulting index values. This calls for standardization of siting criteria and number of sites needed per megacity in working towards better global tracking of air quality in megacities.

Acknowledgments We thank several researchers (especially Mikhail S. Myagkov, Makoto Koike, Samudra Vijay, Tom Beer, Toshiaki Ichinose, Laura Venegas, Sabino Palmieri, Sanghun Kim, Masud Karim, and Arnaldo Cardoso) who helped in collecting air quality and emissions data. The Max Planck Society, Munich, and the Max Planck Institute for Chemistry, Mainz, Germany, have supported this study through the Max Planck Partner Group for Megacities and

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Global Change established at Indian Institute of Technology Roorkee, India. We also thank the anonymous reviewers whose constructive suggestions have greatly helped improve the original manuscript.

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