Simulation of Daily Variation of Suspended Particulate Matter over Delhi

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Simulation of Daily Variation of Suspended Particulate Matter over Delhi: Relative Roles of Vehicular Emission, Dust, and Domestic Appliances P. GOSWAMI

AND

J. BARUAH

CSIR Centre for Mathematical Modeling and Computer Simulation, Bangalore, India (Manuscript received 20 September 2007, in final form 29 January 2008) ABSTRACT The massive growth in the size and the population of cities over the past few decades has led to serious deterioration in the quality of air. One of the important constituents of airborne pollutants, which is a major health hazard, is suspended particulate matter (SPM). SPM is also an important source of cloud condensation nuclei; accurate simulations of SPM with sufficiently long lead thus have many applications, from issuing health advisories to forecasting fog. One of the biggest challenges in modeling air pollution in general and SPM in particular is to identify and mathematically represent the (location specific) sources and sinks. In this study the authors present a dynamical model for daily values of SPM over Delhi, India. The meteorological parameters are taken from the daily values from NCEP reanalysis. The validation is carried out against observations generated by the Central Pollution Control Board (CPCB) in India for the period 2000–05. Error statistics show that the model can capture a significant part of the observed variability of SPM. An evaluation of the relative contributions of various sources show that while vehicular pollution accounts for a large fraction of the SPM throughout the year, steep increases in the winter and the premonsoon periods are accounted for by fossil fuel burning and wind blown dust, respectively. Simulation with a doubling scenario for traffic congestion shows the effect to have strong seasonality. Such a model can be also interfaced with a seasonal forecast model or a climate model for enhanced scope of seasonal forecasts or for an investigation of the impact of SPM on regional climate change.

1. Introduction The solid particles suspended in open air, such as soot generated by combustion, dust, and exhaust from automobiles, with diameters of 30 ␮m (1 ␮m ⫽ 0.00l mm) or less are called suspended particulate matter (SPM). Sources of SPM can be categorized according to the type of emissions: natural and manmade. Natural sources include plant pollen, wind blown dust, volcanic eruptions, and lightning-generated forest fires. Manmade sources include transportation vehicles, industrial processes, power plants, and others. In general, the smaller and lighter a particle is, the longer it will stay in the air. Larger particles (⬎30 ␮m in diameter) tend to settle to the ground because of gravity in a matter of hours whereas the smallest particles (⬍1 ␮m) can stay

Corresponding author address: P. Goswami, CSIR Centre for Mathematical Modeling and Computer Simulation, C-MMACS, NAL Belur Campus, Wind Tunnel Road, Bangalore-560 037, India. E-mail: [email protected] DOI: 10.1175/2008MWR2386.1 © 2008 American Meteorological Society

in the atmosphere for weeks and are mostly removed by precipitation and advection. The main chemical component of SPM that is of major concern is lead, others being nickel, arsenic, and those present in diesel exhaust. These particles, when breathed in, lodge in the lung tissues causing effects including the slowing down of the exchange of oxygen and carbon dioxide in the blood, resulting in shortness of breath and straining of the heart. SPM is thus a serious threat to people with heart problems, or respiratory diseases like emphysema, bronchitis, and asthma. The World Health Organization (WHO/UNEP 1992) carries net estimates of worldwide premature death due to lung cancer and cardiovascular and respiratory diseases caused by outdoor pollution. According to these estimates, between 100 and 150 million people around the globe have asthma and the figure is increasing. Over 180 000 people die worldwide each year because of asthma. India has an estimated population of 15–20 million asthmatics (Anonymous 2004). Several studies have shown a close relationship between air pollution and mortality (Pope et al. 2002, 1995) as well as

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other ailments (Dockery et al. 1993; Samet et al. 2000). SPM also affects our environment in a variety of ways like the corroding of metals and masonry, inhibition of plant growth through covering of leaves, and increasing usage of cleaning agents and paints. SPM also strongly modulates the local weather through the supply of condensation nuclei. Accurate and advanced estimates of SPM can therefore significantly aid forecasting of fog and health advisories. While a number of techniques have been applied to model air pollution in general (Cope and Hess 2005; Gery et al.1988; Grivas and Chaloulakou 2006), the use of dynamical, in particular, mesoscale models for modeling pollution is relatively recent (Otte et al. 2005; McHenry et al. 2004; Ziomass et al.1995). This is, however, a potential tool as SPM exhibits strong daily and seasonal fluctuations due to variation in the vehicular, industrial, and atmospheric activities over a location. Dynamical modeling of air pollution in general and SPM in particular has not reached the desired level in India. The present work is an attempt to fill this gap. We present a dynamical model for SPM and evaluate its potential as a simulation as well as forecast model by comparing simulations with observed SPM compiled by the Central Pollution Control Board (CPCB) in India. In section 2 we describe the basic model along with representations of various sources and sinks. Results on the comparison of our simulations with observations are presented in section 3. Section 4 contains our conclusions and perspectives.

2. The SPM model and simulation methodology As mentioned earlier, the biggest challenge in modeling SPM is to identify the sources and sinks (Main et al. 1994; Moitra and Shukla 1994; Wangkiat et al. 2004) for a location and represent them mathematically so that a consistent and comprehensive dynamical model can be built.

a. The dynamical model The basic dynamics of SPM is governed by the continuity equation for a scalar variable (Byun and Schere 2005; Cope and Hess 2005): ⭸sⲐ⭸t ⫹ u⭸sⲐ⭸x ⫹ ␷⭸sⲐ⭸y ⫹ w⭸sⲐ⭸z ⫽ S⫹ ⫺ S⫺,

共1兲

where s represents area-averaged (Delhi, India) SPM concentration in an atmospheric column (␮g m⫺3) and u, ␷, and w are the wind velocity along the x, y, and z directions, respectively. The terms S⫹ and S⫺ represent, respectively, sources and sinks of SPM:

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S⫹ ⫽ SV ⫹ SW ⫹ SD and S⫺ ⫽ SP ⫹ SA, where SV, SW, and SD, respectively, denote contributions to SPM due to vehicular exhaust, wind blown dust, and domestic appliances. The terms comprising the sink of SPM are precipitation (SP) and removal due to advection (SA). As the downward settling time due to gravity is generally longer than a day, we do not include a sink due to settling of SPM to the ground. Equation (1) is then integrated with the observed SPM on day 1 as the initial condition for 365 days forced by observed National Centers for Environmental Prediction (NCEP) values of meteorological variables.

b. Sources and sinks The largest contribution to SPM, especially over metropolitan areas, appears to be from vehicular exhaust (SV). The contribution of vehicular exhaust to SPM, however, is a complex function of several parameters. While some of these parameters, like the number of vehicles, are relatively easy (at least in principle) to estimate, others like the idling time of vehicles are much harder to determine accurately. We define the contribution of vehicular exhaust (SV) to SPM as NT

SV ⫽

N␷

兺 兺

nt ⫽ 1 n␷ ⫽ 1

r共t兲n␷共nt兲ER共nt兲TE共nt兲,

共2兲

where NT is the total number of types of vehicles, N␷ is the total number of vehicles of type nt, ER(nt) is the average emission rate from a vehicle of type nt, and TE(nt) is the average effective duration of emission in a day. The effective duration of emission in a day is modeled as TE 共nt兲 ⫽ 关d共nt兲 Ⲑ␷0兴 ⫹ TI 共nt兲,

共3兲

where TI (nt) is the average idling time, d(nt) is the average distance traveled by vehicles of type nt (km), and ␷0 is the average speed of the vehicle. Both TI and ␷0 are assumed to depend on the total volume of traffic (total number of vehicles): TI 共nt兲 ⫽ TI␾关1 ⫹ a共nt兲兴, TI ⱖ 0.

␷0 ⫽ ␷u共1 ⫺ bNT N␷兲, ␷0 ⱖ 0.

共4兲 共5兲

Equation (4) assumes that although in a metropolitan area like Delhi, all vehicles would move with the same average speed (regulated by traffic control); the idling time changes based on the type of vehicle, with the smallest (largest) idling time for the lightest (heaviest) vehicle, and the increase in the number of vehicles re-

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TABLE 1. Parameters for vehicular source in the SPM model (reference: Centre for Science and Environment). The numbers 1, 2, 3, and 4 represent the labels for the types of vehicles: petrol-driven car, diesel-driven car, two wheeler, and heavy vehicle, respectively. Typical value for vehicle type Serial No.

Description

Symbol

1

2

3

4

1 2 3

Avg distance traveled by different types of vehicles (km) Avg speed of different types of vehicles (km h⫺1) Avg idling time (h)

dnv snv TI

21.37 20.68 1

19.37 22.28 1.0

22.71 12.04 0.5

11.84 13.84 2

duces the average speed below a free traffic speed ␷u. In Eq. (2), r(t) is a positive random number between 0.8 and 1 that represents (random) fluctuations in the traffic volume with time (day). The parameters like the number of vehicles, average distance traveled, and idling time for Delhi (Table 1) were taken from data compiled by the Centre for Science and Environment (available online at www. gobartimes.org/july1999/gtimes_cov3.htm). The data for emission rates of different pollutants for different vehicular type, given in Table 2, were adopted from Goyal and Rama Krishna (1998) and Das and Parikh (2003); other parameters were estimated through a process of calibration. The calibration was carried out by searching for a value of the parameter, varied within its range of uncertainty, to produce optimum performance (minimum error) on a few (15–20) days spread across a year (2000). These values were then kept constant for the other days and the other years. In addition to the vehicular exhaust, wind blown dust (SW), emissions from various domestic appliances, and the burning of fossil fuel are also potential sources of SPM. These two sources are, however, highly seasonal in nature and depend significantly on meteorological variables. The seasonality in SPM sources with respect to domestic appliances may come from a number of sources. While the hot premonsoon months may see the extensive use of air conditioners, coolers, and refrigerators, the cold winter months may witness more fossil fuel and biomass burning. We represent the contribution from wind blown dust as Sw ⫽ wd ⫻ w.

共6兲

Here wd is a constant that determines the efficiency of the wind in raising the dust; wd is essentially a function of soil type with larger (smaller) value for loose (compact) soil. The value of near-surface wind speed (w) has to be obtained from a separate model or observed data. The modeling of the domestic source of SPM is considered essentially a function of temperature, although it may also depend on wind and humidity: SD ⫽ d1 Ⲑ共T ⫺ Tl兲 ⫹ d2共Th ⫺ T兲,

共7兲

where d1 and d2 are constants and Tl and Th represent the low and high temperature threshold, respectively. Once again the ambient temperature has to be supplied by observation or a (mesoscale) model. The primary sinks of SPM are downward movement under gravity, washing down by rain, and removal by advection. The function of precipitation as an efficient sink of SPM can be seen from significant negative correlations between daily values of SPM and rainfall (NCEP) for each year (Table 3). Ideally, the atmospheric variables like wind, temperature, and precipitation should be taken from a mesoscale forecast. In the present work the large-scale atmospheric fields are taken from daily reanalyses of NCEP data available over a 2.5° ⫻ 2.5° global grid (Kalnay et al. 1997). The daily values of SPM over Delhi are adopted from data made available by the CPCB. Some details of the measurements are given in the appendix (Anonymous 2004). The samples for SPM are collected for every 8 h throughout the day. CPCB compiles the data from its network and provides daily values of SPM and other pollutants over Delhi.

TABLE 2. Data on vehicular emission (Goyal and Rama Krishna 1998; Das and Parikh 2003). Emission rate (ER) of pollutants in g s⫺1 by different vehicles and number of vehicles (nt) Vehicle type

Petrol-driven car

Diesel-driven car

Two wheeler

Heavy vehicles

Pollutants

ER

nt

ER

nt

ER

nt

ER

nt

NOx HC CO SPM

0.000 075 0.001 711 0.011 42 0.000 33

1 103 407 1 103 407 1 103 407 1 103 407

0.000 075 0.001 711 0.011 42 0.002

45 000 45 000 45 000 45 000

0.000 522 0.001 807 0.016 17 0.0002

2 148 893 2 148 893 2 148 893 2 148 893

0.028 34 0.004 56 0.003 77 0.003

19 455 19 455 19 455 19 455

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TABLE 3. Correlation coefficients between daily values of rain and SPM. Correlation coef between rain and obs SPM Serial No.

Year

Jun–Sep

Annual

1 2 3 4 5 6

2000 2001 2002 2003 2004 2005

⫺0.25 ⫺0.35 ⫺0.37 ⫺0.40 ⫺0.40 ⫺0.48

⫺0.3 ⫺0.4 ⫺0.4 ⫺0.35 ⫺0.38 ⫺0.4

3. Results and discussion Our methodology here is to evaluate the model for hindcasts of daily values of SPM over Delhi by taking the area-averaged fields of NCEP as representative forecasts from a dynamical model. In an actual forecast situation these fields will be generated by running an appropriate model configuration in a forecast model or using fields available from other forecasts.

a. Climatology of observed and simulated SPM We first compare the climatology (2000–05) of daily values of SPM over Delhi from observations (CPCB) and model simulations (solid and dash lines, respectively, Fig. 1). The four panels in Fig. 1 represent simulations with different combinations of sources and sinks, as indicated in each panel. It can be seen that the model simulations follow the observed features quite closely, as reflected by a correlation coefficient of above 0.6, highly significant (99% confidence level) for the degrees of freedom involved. In particular, the simulated SPM follows the distinct annual cycle in observed SPM fairly well. We note that the observed annual cycle of SPM has three maxima: January– February, April–May, and October–November. The winter maxima (especially during January–February) appear to be significantly controlled by domestic sources, as a comparison of climatology of simulated SPM (Fig. 1) with and without this source shows. The premonsoon maximum, on the other hand, has a significant contribution from natural dust; the simulated climatology without natural dust is significantly below the observed climatology (Fig. 1, third panel from top) during this period for the case without this source. This is consistent with the fact that during May–June, the winds over Delhi turn into westerlies, bringing the dust from the Thar Desert and arid regions to the west of Delhi (Goyal and Sidhartha 2002). The relatively high values of SPM in the premonsoon periods especially after March are attributed to the strengthening of winds associated with the monsoon currents and the resulting

FIG. 1. Average values of SPM over Delhi for different cases as indicated. The meteorological variables have been adopted from NCEP daily reanalysis over a 2.5° ⫻ 2.5° box over Delhi. The observed SPM data are from CPCB.

dust storms. Consistent with our finding of significant negative correlation between precipitation and SPM, there is a steep fall in SPM in the monsoon months. The sharp fall in SPM in the monsoon season shows the effect of rain on clearing the atmosphere. A close look at the four panels in Fig. 1 shows that vehicular pollution accounts for the largest fraction of SPM throughout the year. After the short rainy spell, the concentration of SPM rises again both in observations and in model simulations.

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FIG. 2. Monthly average values of SPM over Delhi from observation (hollow bars), simulation with only vehicular sources (dark bars), and simulation with all sources (shaded bars) for the 6 yr.

b. Multiscale variability In addition to the climatological features revealed in Fig. 1, SPM exhibits variability at different time scales. A comparison of monthly values of observed and simulated SPM over Delhi during the period 2000–05 (Fig. 2) highlights the interannual variability in monthly SPM and the model’s ability to capture it. It can be seen that the agreement between observed and simulated monthly SPM is realized for each of the years and that the climatology depicted in Fig. 1 does not have any significant bias from any given year. A scrutiny of Fig. 2 also reveals that while the effect of nonvehicular sources is in general small, it can be significant in some cases. A statistical evaluation of the model’s performance in simulating the interannual variability is pre-

sented in Table 4, which shows the correlation between observed and predicted monthly SPM for the six years. Table 4 also indicates the relative importance of vehicular and other sources of SPM. In particular, the inclusion of nonvehicular sources seems to have only a marginal effect in improving correlation. However, nonvehicular sources do have significant contribution, as shown by relative percentage error

冉

| predicted-observed | ⫻ 100 observed

冊

in simulated values of monthly SPM. In particular, inclusion of nonvehicular sources often reduces the error (hollow bars, Fig. 3); however, these effects also vary from year to year.

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TABLE 4. Correlation coefficients between observed and simulated monthly SPM over Delhi (2000–05). Correlation coef Year

Only vehicle

All sources

2000 2001 2002 2003 2004 2005 Avg

0.45 0.76 0.74 0.65 0.37 0.62 0.75

0.45 0.74 0.76 0.88 0.58 0.56 0.89

The daily values of simulated SPM also show close agreement with the observations. The two correlation coefficients (TC and AC) in Table 5 represent, respectively, correlation coefficients between observed and simulated values of total and anomalies (with respect to

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corresponding 6-yr mean) of daily SPM. These correlation coefficients are significant, except for the years 2000 and 2004, at the 99% confidence level for the degrees of freedom involved; in the years 2000 and 2004, the significance for TC is above the 95% confidence level. Further, correlation among SPM anomalies is generally better (except for the year 2003) than that between total anomalies, implying significant skill in the forecast of daily variability of SPM. It may be seen that inclusion of other processes like natural dust or domestic appliances do not necessarily or significantly improve the correlation. However, the important role of the nonvehicular sources can be appreciated from a comparison of the annual SPM load from different scenarios with the observed load (Table 6). In 5 out of 6 yr, as well as in the climatology, the inclusion of nonvehicular sources significantly improves the degree of agreement between the observed and the simulated an-

FIG. 3. Relative percentage error in simulated monthly values of SPM for the 6 yr for only vehicular sources (filled bars) and all sources (hollow bars).

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TABLE 5. Correlation coefficients between observed and simulated daily SPM over Delhi (2000–05). Correlation coef Only vehicle

Vehicle ⫹ natural

Vehicle ⫹ domestic

Vehicle ⫹ natural ⫹ domestic

Serial No.

Year

TC

AC

TC

AC

TC

AC

TC

AC

1 2 3 4 5 6 7

2000 2001 2002 2003 2004 2005 Avg

0.20 0.41 0.41 0.34 0.21 0.40 0.59

0.37 0.25 0.27 0.05 0.33 0.42 0.71

0.21 0.44 0.41 0.47 0.21 0.37 0.66

0.39 0.3 0.23 0.08 0.33 0.4 0.57

0.20 0.39 0.40 0.44 0.21 0.38 0.57

0.38 0.26 0.31 0.03 0.32 0.39 0.73

0.21 0.41 0.41 0.51 0.21 0.36 0.64

0.34 0.26 0.31 0.1 0.38 0.39 0.60

nual SPM load. Another assessment of the contribution of nonvehicular sources to SPM can be obtained by comparing the mean and the variability (standard deviation) for different scenarios. The normalized mean (with respect to standard deviation for observed SPM) is about 2.2 (Table 7) on average, while that in simulation with only the vehicular source is only about 1.1. Inclusion of nonvehicular sources generally brings this normalized mean closer, only if marginally, to the observed value.

c. Skill evaluation In terms of statistical properties like the mean and the standard deviation, the simulated values are somewhat lower than the observed ones (Table 7). Although there is some improvement with inclusion of various sources, this improvement is only about 10%; on average, the simulated mean is about 90% of the observed mean (last row, Table 7). This is consistent with the results in Table 5, which also show only marginal changes with respect to various processes. Although the present model is driven by rather coarse-resolution daily meteorological parameters, we have carried out an assessment of the potential of such a model for forecasting daily values of SPM, and thus TABLE 6. Annual load (g m⫺3) of observed and predicted SPM (2000–05). Predicted SPM

Year

Obs SPM

Only vehicle

Vehicle ⫹ natural

Vehicle ⫹ domestic

Vehicle ⫹ natural ⫹ domestic

2000 2001 2002 2003 2004 2005 Avg SPM

0.17 0.17 0.19 0.20 0.18 0.18 0.18

0.13 0.11 0.16 0.15 0.12 0.15 0.14

0.13 0.12 0.19 0.19 0.13 0.16 0.15

0.13 0.14 0.18 0.18 0.13 0.16 0.15

0.14 0.15 0.19 0.20 0.14 0.17 0.16

other pollutants, by evaluating certain error statistics. Table 8 presents the average relative percentage error for 6 yr for different scenarios. Once again, inclusion of nonvehicular sources does not provide significant and consistent improvement in forecasts of daily values. The average relative error, around 60%, appears high; however, for many practical applications such as issuing advisories, high precision is not necessary since such (but not all) forecasts depend on thresholds (threat levels). A measure of the usefulness of the forecasts for such application is provided by the maximum error and the distribution of error. The number of days in different error bins (Fig. 4) for the 6 yr shows on average the distribution to be nearly Gaussian for most of these years. It may be seen that the number of days with error between ⫺500 and ⫹500 ␮g m⫺3 is above 300 in general and the number of days with error between ⫺250 and ⫹250 ␮g m⫺3 (approximately one standard deviation in observed data) is about 200. Taking 500 ␮g m⫺3 as the tolerance level of uncertainty for practical applications such as issuing health advisories or traffic regulations (based on threat levels), more than 300 days on average are within this error limit for each year.

4. Conclusions SPM forecasts generated at high spatiotemporal resolution and interfaced with GIS data can be used to issue advisories as well as to initiate regulatory measures (like traffic planning). Further, such forecasts can be an important input for forecasting events like fog, as SPM is an important source of cloud condensation nuclei. One of the objectives of the present work has been to explore and evaluate the potential of a dynamical forecast model as a tool for generating daily SPM forecasts. Based on our simulations of daily SPM for 6 yr, it is reasonable to conclude that 24-h forecasting of SPM with a dynamical model has significant potential. While the present study provides a potential platform for advance forecasting of SPM over a city like Delhi,

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TABLE 7. Mean (␮g m⫺3) and normalized mean (with respect to associated standard deviations) in daily values of observed and simulated SPM over Delhi: (2000–05). Note that x is the mean of SPM values (365 days), ␴ is the standard deviation, and x/␴ is the normalized mean.

Obs

Only vehicle

Vehicle ⫹ natural

Vehicle ⫹ domestic

Vehicle ⫹ natural ⫹ domestic

Simulated

Simulated

Simulated

Simulated

Year

x

x/␴

x

x/␴

x

x/␴

x

x/␴

x

x/␴

2000 2001 2002 2003 2004 2005 Avg

483 474 529 547 505 506 507

2.1 2.3 2.4 1.8 2.3 2.5 2.2

356 323 456 432 373 422 393

1.1 1.1 1.2 1.4 1.2 1.3 1.2

366 346 529 528 387 461 436

1.1 1.2 1.3 1.4 1.3 1.2 1.3

386 390 469 560 394 443 440

1.2 1.2 1.3 1.7 1.2 1.3 1.3

387 414 542 561 408 478 465

1.2 1.2 1.4 1.4 1.3 1.3 1.3

there are several improvements possible that need to be implemented in a phased manner. It is clear that both accurate estimates of the meteorological parameters and the sources and sinks are important for estimating the variations of SPM. In particular, the meteorological parameters from NCEP daily reanalysis used in the present study are at a resolution of more than 250 km and are unlikely to capture the short-term, local variations in these parameters. Mesoscale dust storms in the premonsoon season may significantly add to SPM during this period; such events are unlikely to be captured in coarse-resolution NCEP data. It would be desirable to carry out the calibration and validation with data on a finer grid. However, dynamical parameters on a finer grid for a long period over India are still not available. While the India Meteorological Department (IMD) has now prepared long-period (1951–2003) daily rainfall on 1° ⫻ 1° grid (Rajeevan et al. 2006), it does not cover the complete period of 2000–05. Using data from different sources like NCEP and IMD, on the other hand, involves issues of consistency and compatibility. Use of mutually consistent meteorological parameters on a finer grid is a potential source of improvement in the simulations.

TABLE 8. Average relative percentage error (2000–05). Percentage error

Year

Only vehicle

Vehicle ⫹ natural

Vehicle ⫹ domestic

Vehicle ⫹ natural ⫹ domestic

2000 2001 2002 2003 2004 2005 Avg

62.1 55.5 55.2 55.3 58.9 60.9 58

60.0 54.6 60.3 58.0 55.7 66.2 59.1

62.0 61.4 53.7 53.7 59.5 64.3 59.1

59.4 62.7 57.8 58.7 56.3 67.9 60.4

Another shortcoming of the present study is that the forecast values are 24-hourly; it would be more useful to consider hourly forecasts for more effective use. While it is now possible to generate forecasts at fairly high resolution using state-of-the-art mesoscale models like the fifth-generation Pennsylvania State University– National Center for Atmospheric Research Mesoscale Model (MM5) and techniques like dynamical downscaling, a sufficiently dense network of observations with more frequent sampling is also a critical requirement for model validation and calibration. In terms of health hazards, a distinction needs to be made between respiratory SPM (RSPM) and general SPM. Once again, both careful mapping of sources and sinks as well as observations of RSPM are necessary to develop and validate a model for RSPM. One of the most challenging aspects of SPM modeling and pollution modeling in general is to adequately define the source and the sink functions. Both type and distribution of sources are location specific. A source function for Delhi will not necessarily work for another location. It is also necessary to include other minor and seasonal sources of SPM like pollen. Special events like Diwali and Holi can be days on which SPM source is augmented. Similarly, the vehicular load can show significant day to day modulation depending on a number of factors. Further traffic congestion and thus parameters like idling time and average flow speed of traffic are functions of road width and other parameters. Consideration of these aspects becomes important for location-specific forecasts of SPM. Also discussed earlier, in an actual forecast situation, the meteorological parameters should come from forecasts from a (mesoscale) model. The error statistics of the model is likely to change when such forecasts are used. The long-period validation presented here is, however, a necessary step before attempting (effort intensive) simulation of a large number (6 ⫻ 365) of events.

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FIG. 4. Histogram of errors showing the number of days in different error bins (predicted ⫺ observed) for the 6 yr. The simulations are with combined sources of vehicular emission, natural dust, and domestic appliances.

An accurate SPM simulation model also provides an effective tool for impact assessment for various uses. Figure 5 shows a doubling of vehicle (and hence also idling time) scenarios for SPM concentration over Delhi. These simulations are generated by averaging over six simulations for 6 yr (2000–05) for control (as in Fig. 1) and with doubling. It can be seen that such a doubling will have adverse effects during March–June. The highly seasonal nature of the effect of doubling is a reflection of the saturation capacity of (meteorological) sink. The doubling has very little effect in the monsoon months implying that the washing-down capacity of

rain over Delhi can carry this extra load on average. For winter months, the advection sink is just above saturation showing marginal increase with doubling. Among other potential applications, such a dynamical model of SPM (and other pollutants) can be interfaced to a climate model for high-resolution analysis of the impact of SPM on regional climate. While an increase in SPM is generally expected to favor cloud formation and precipitation, changes in wind pattern (advection) and rainfall rates (due to other factors) can significantly affect SPM concentration, setting up a feedback cycle. Another potential application is to incorporate the

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of the Models3 Community Multiscale Air Quality (CMAQ) modeling system. Appl. Mech. Rev., 59, 51–77. Cope, M., and D. Hess, 2005: Air quality forecasting: A review and comparison of the approaches used internationally and in Australia. Clean Air Environ. Quality, 39, 52–60. CPCB, 2001: National ambient air quality statistics of India. Central Pollution Control Board, Parivesh Bhavan, Delhi, India. Das, A., and J. Parikh, 2003: Transport scenarios in two metropolitan cities in India: Delhi and Mumbai. Energy Convers. Manage., 45, 2603–2625. Dockery, D. W., III, C. A. Pope, X. Xiping, J. D. Spengler, J. H. Ware, M. A. Fay Jr., B. G. Ferries, and F. E. Speizer, 1993: An association between air pollution and mortality in six US cities. New Engl. J. Med., 329, 1753–1759. Gery, M. W., G. Z. Whitten, and J. P. Killus, 1988: Development and testing of the CBMIV for urban and regional modeling. U.S. Environmental Protection Agency, EPA600/388012.

FIG. 5. Effect of traffic congestion (doubling of idling time and doubling of total number of vehicles). The dashed line represents projected average (2000–05) daily SPM if the total number of vehicles and the idling time were doubled. The corresponding observed values (solid line) and the standard simulations (dotted line) are presented for comparison.

SPM model into the seasonal forecast cycle to enhance scope and quality of the forecasts. Acknowledgments. This work was supported by a research grant under a Network project, CSIR, Government of India.

APPENDIX Protocols Generally Adopted for SPM Monitoring in India TABLE A1. Sampling and Analytical Protocols generally adopted for SPM monitoring in India. Particulars

SPM

Sampling instruments Sampling principle Flow rate Sampling period

High volume sampler (HVS) Filtration of aerodynamic sizes 0.8–1.2 m3 min⫺1 8-hourly change of filter paper, 24-hourly reporting Twice a week, 104 days observation per year Electronic balance Gravimetric

Sampling Analytical instrument Analytical method

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