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atmosphere
Communication
Can We Vacuum Our Air Pollution Problem Using
Smog Towers?
Sarath Guttikunda * and Puja Jawahar
Urban Emissions, New Delhi 110019, India; puja@urbanemissions.info
*Correspondence: sguttikunda@urbanemissions.info
Received: 29 July 2020; Accepted: 28 August 2020; Published: 29 August 2020
Abstract:
In November 2019, the Supreme Court of India issued a notification to all the states in the
National Capital Region of Delhi to install smog towers for clean air and allocated INR 36 crores
(~USD 5.2 million) for a pilot. Can we vacuum our air pollution problem using smog towers?
The short answer is “no”. Atmospheric science defines the air pollution problem as (a) a dynamic
situation where the air is moving at various speeds with no boundaries and (b) a complex mixture
of chemical compounds constantly forming and transforming into other compounds. With no
boundaries, it is unscientific to assume that one can trap air, clean it, and release into the same
atmosphere simultaneously. In this paper, we outline the basics of atmospheric science to describe
why the idea of vacuuming outdoor air pollution is unrealistic, and the long view on air quality
management in Indian cities.
Keywords: India; Delhi; air quality; controls; smog towers; filtration systems
1. Introduction
Air pollution is a major health risk worldwide—outdoor PM
2.5
(particulate matter) and
Ozone pollution accounted for an estimated 3 million and 0.5 million premature deaths,
respectively, and household (indoor) air pollution for an additional 1.6 million premature deaths [
1
].
Corresponding numbers for India are 680,000 for outdoor PM
2.5
, 145,000 for outdoor ozone, and 480,000
for household pollution. Similar estimates were presented by researchers and scientists from the Indian
institutes [2–6]. In all the studies, the very young and the old are particularly vulnerable.
The year 2020 is an aberration in the pollution trends, with the COVID-19 lockdowns and a range
of restrictions for all the sectors [
7
]. Across India, ambient air pollution levels improved as much as 50%
compared to the annual trends for the same period in the previous year [
8
]. A summary of the data
from all the cities with at least one continuous air monitoring station is included in the Supplementary
Materials. Following the pandemic, epidemiological work on COVID-19 patients suggests that the risk
of mortality is higher among the population exposed to chronic PM2.5 and NO2pollution [9,10].
One key lesson from the COVID-19 lockdowns worldwide, is that air pollution can be reduced
locally and globally by reducing the emissions at the sources. This was witnessed in the data from
the ground-based monitors worldwide and satellite retrievals over India, China, Italy, and the United
States [
11
–
13
]. The measures enacted during the lockdowns are unprecedented, but the results are
evidence that we eventually need to control the emissions at the sources for “clean air”.
While the messages are clear that high air pollution is the leading cause of health impacts and
“clean air” is only possible by addressing the emissions at the sources, in November 2019, the Supreme
Court of India issued a notification to all the states in the National Capital Region of Delhi (NCR) to
install smog towers. These giant filtering systems are being pursued as a control mechanism only in the
absence of real action to control the emissions at the sources and the continuing incidence of high air
pollution levels in Delhi and other major cities. Examples discussed in the notification for replication
Atmosphere 2020,11, 922; doi:10.3390/atmos11090922 www.mdpi.com/journal/atmosphere
Atmosphere 2020,11, 922 2 of 11
are (a) a 100 m high purification tower in Xi’an, China [
14
] and (b) experimental large vacuum cleaners
called Wind Augmentation and Air Purifying Units (WAYU) were deployed in the cities of Delhi,
Mumbai, and Bengaluru, with no operational details, and (c) a smaller version of the Xi’an smog tower
in Delhi (Figure 1). The latter designs also include “mist makers” to initiate coagulation and induce wet
scavenging of the particles. The units installed in Delhi and Mumbai were designed by the National
Environmental Engineering Research Institute (NEERI) and Indian Institute of Technology (Mumbai)
and inaugurated by the then Minister of Environment [15].
Atmosphere 2020, 11, x FOR PEER REVIEW 2 of 13
While the messages are clear that high air pollution is the leading cause of health impacts and
“clean air” is only possible by addressing the emissions at the sources, in November 2019, the
Supreme Court of India issued a notification to all the states in the National Capital Region of Delhi
(NCR) to install smog towers. These giant filtering systems are being pursued as a control mechanism
only in the absence of real action to control the emissions at the sources and the continuing incidence
of high air pollution levels in Delhi and other major cities. Examples discussed in the notification for
replication are (a) a 100 m high purification tower in Xi’an, China [14] and (b) experimental large
vacuum cleaners called Wind Augmentation and Air Purifying Units (WAYU) were deployed in the
cities of Delhi, Mumbai, and Bengaluru, with no operational details, and (c) a smaller version of the
Xi’an smog tower in Delhi (Figure 1). The latter designs also include “mist makers” to initiate
coagulation and induce wet scavenging of the particles. The units installed in Delhi and Mumbai
were designed by the National Environmental Engineering Research Institute (NEERI) and Indian
Institute of Technology (Mumbai) and inaugurated by the then Minister of Environment [15].
(a) (b) (c)
Figure 1. Examples of ambient filtering systems: (a) a smog tower from Xi’an, China, (Image edited
from South China Morning Post), (b) a Wind Augmentation and Air Purifying Unit (WAYU) in Delhi,
and (c) a smaller version of Xi’an’s filtering system in Delhi.
A fundamental question remains, “can we vacuum our air pollution problem using smog towers
and mist makers”? The short answer is “no”. The idea of removing what is already in the air is
unrealistic, given the dynamic nature of air pollution, which moves and transforms simultaneously.
In this paper, we outline the basics of atmospheric science to describe why the idea of vacuuming
outdoor air pollution is unscientific, and the long view on air quality management in Indian cities. In
India, PM
2.5
is considered the main criteria pollutant for environmental compliance and public health,
and all of the discussion in this paper is about PM.
2. The Sciences
The definition of atmospheric science can be explained via the three basic sciences—
Mathematics, Physics, and Chemistry.
2.1. Mathematics
Mathematics relates to the “quantification” of the problem. In a box model version of a city
(Figure 2), the size of the city and the height of the inversion layer will determine the amount of air
present at any given instance. The inversion layer is an invisible layer of air, which determines the
total volume of air available for horizontal and vertical mixing. This height is determined by
prevalent surface temperature, air temperature at the ground and upper layers, humidity levels, and
land cover, all varying in time and space. There is seasonality associated with the inversion layer—
Figure 1.
Examples of ambient filtering systems: (
a
) a smog tower from Xi’an, China, (Image edited
from South China Morning Post), (
b
) a Wind Augmentation and Air Purifying Unit (WAYU) in Delhi,
and (c) a smaller version of Xi’an’s filtering system in Delhi.
A fundamental question remains, “can we vacuum our air pollution problem using smog towers
and mist makers”? The short answer is “no”. The idea of removing what is already in the air is
unrealistic, given the dynamic nature of air pollution, which moves and transforms simultaneously.
In this paper, we outline the basics of atmospheric science to describe why the idea of vacuuming
outdoor air pollution is unscientific, and the long view on air quality management in Indian cities.
In India, PM
2.5
is considered the main criteria pollutant for environmental compliance and public
health, and all of the discussion in this paper is about PM.
2. The Sciences
The definition of atmospheric science can be explained via the three basic sciences—Mathematics,
Physics, and Chemistry.
2.1. Mathematics
Mathematics relates to the “quantification” of the problem. In a box model version of a city
(Figure 2), the size of the city and the height of the inversion layer will determine the amount of air
present at any given instance. The inversion layer is an invisible layer of air, which determines the
total volume of air available for horizontal and vertical mixing. This height is determined by prevalent
surface temperature, air temperature at the ground and upper layers, humidity levels, and land cover,
all varying in time and space. There is seasonality associated with the inversion layer—highest during
the summer months and lowest during the winter months. This is a typical trend for most of the inland
cities in India [
16
]. The coastal cities like Chennai and Mumbai experience lesser variation across the
seasons due to the constant presence of land–sea breeze.
Atmosphere 2020,11, 922 3 of 11
Atmosphere 2020, 11, x FOR PEER REVIEW 3 of 13
highest during the summer months and lowest during the winter months. This is a typical trend for
most of the inland cities in India [16]. The coastal cities like Chennai and Mumbai experience lesser
variation across the seasons due to the constant presence of land–sea breeze.
(a) (b)
Figure 2. Depiction of a box model pollution calculation with varying inversion heights (a) for
summer months and (b) for winter months.
Pollution (in the units of μg/m
3
) is defined as mass over volume, where mass is the emission
load and volume is the amount of air present. In the summer months, a higher volume of air means
more room for lateral and vertical mixing, and vice versa for the winter months. For the same amount
of emissions in all the months, concentrations are bound to be higher in the winter months and lower
in the summer months. For “clean air” and lower concentrations, the requirement is either higher
inversion layer height or lower emissions. It is next to impossible to alter meteorology; however,
reducing emissions should be relatively easy.
In the box model, we assumed that emissions remain constant over months. This is not true.
Emissions are also seasonal, which in the case of India are higher in the winter months from space
heating needs [17] along with a lowering in mixing height, further compounding the air pollution
problem. A hypothetical case is illustrated in Table 1 for what could be the changes in the overall
pollution when the city size expands, emissions halve or double, or for changes in the meteorological
conditions. All the calculations assume a steady state condition. The worst-case scenario is when the
emissions double and the mixing height drops to a quarter of the norm, resulting in a 700% increase
in the overall pollution. During the winter haze episodes, areas between Punjab, Haryana, and Delhi
experience these conditions [18,19]—emissions nearly double compared to summer months with the
addition of agricultural residue burning and the onset of winter season requiring more biomass and
coal combustion to support space heating, with a simultaneous drop in the surface and air
temperatures. Typical day-time mixing layer heights are 1000–2000 m in the summer months and
100–200 m in the winter months. Typical night-time heights are half of this.
Table 1. A hypothetical pollution calculation for a city using a steady state box model method. W =
width of the city; L = length of the city; H = mixing height; E = emissions.
Study and Institution W L H E Pollution %Change
Base case, all as usual 1.0 1.0 1.0 1.0 1.0 0%
City size doubles in width and length and no
change in the emissions 2.0 2.0 1.0 1.0 0.25 −75%
Emission doubles, everything else is the same 1.0 1.0 1.0 2.0 2.0 +100%
Mixing height doubles, everything else is the same 1.0 1.0 2.0 1.0 0.5 −50%
Mixing height halves, everything else is the same 1.0 1.0 0.5 1.0 2.0 +100%
Figure 2.
Depiction of a box model pollution calculation with varying inversion heights (
a
) for summer
months and (b) for winter months.
Pollution (in the units of
µ
g/m
3
) is defined as mass over volume, where mass is the emission load
and volume is the amount of air present. In the summer months, a higher volume of air means more
room for lateral and vertical mixing, and vice versa for the winter months. For the same amount of
emissions in all the months, concentrations are bound to be higher in the winter months and lower
in the summer months. For “clean air” and lower concentrations, the requirement is either higher
inversion layer height or lower emissions. It is next to impossible to alter meteorology; however,
reducing emissions should be relatively easy.
In the box model, we assumed that emissions remain constant over months. This is not true.
Emissions are also seasonal, which in the case of India are higher in the winter months from space
heating needs [
17
] along with a lowering in mixing height, further compounding the air pollution
problem. A hypothetical case is illustrated in Table 1for what could be the changes in the overall
pollution when the city size expands, emissions halve or double, or for changes in the meteorological
conditions. All the calculations assume a steady state condition. The worst-case scenario is when the
emissions double and the mixing height drops to a quarter of the norm, resulting in a 700% increase in
the overall pollution. During the winter haze episodes, areas between Punjab, Haryana, and Delhi
experience these conditions [
18
,
19
]—emissions nearly double compared to summer months with the
addition of agricultural residue burning and the onset of winter season requiring more biomass and
coal combustion to support space heating, with a simultaneous drop in the surface and air temperatures.
Typical day-time mixing layer heights are 1000–2000 m in the summer months and 100–200 m in the
winter months. Typical night-time heights are half of this.
Table 1.
A hypothetical pollution calculation for a city using a steady state box model method.
W=width of the city; L =length of the city; H =mixing height; E =emissions.
Study and Institution W L H E Pollution %Change
Base case, all as usual 1.0 1.0 1.0 1.0 1.0 0%
City size doubles in width and length and no
change in the emissions 2.0 2.0 1.0 1.0 0.25 −75%
Emission doubles, everything else is the same
1.0 1.0 1.0 2.0 2.0 +100%
Mixing height doubles, everything else is
the same 1.0 1.0 2.0 1.0 0.5 −50%
Mixing height halves, everything else is
the same 1.0 1.0 0.5 1.0 2.0 +100%
Emission doubles and mixing height halves 1.0 1.0 0.5 2.0 4.0 +300%
Emission doubles and mixing height is
one quarter 1.0 1.0 0.25 2.0 8.0 +700%
Emission halves and everything else is
the same 1.0 1.0 1.0 0.5 0.5 −50%
Atmosphere 2020,11, 922 4 of 11
Mathematically, for a given set of seasonal patterns in meteorology, especially over the
Indo-Gangetic plain, the best option is to cut the emissions at the sources and disperse the emissions to
farther distances via better urban planning.
2.2. Physics
Physics relates to the “movement” of the problem. A popular saying is that “pollution knows
no boundaries”. The box model assuming closed walls in Figure 2and Table 1is good to illustrate
the point that emissions are key for any increase and decrease in pollution levels. Simultaneously,
meteorology plays an important role in determining how much of those emissions stay in the box,
determined by the horizontal wind components (U and V), or how much of those emissions will stay
close to the surface, determined by the vertical wind component (W) (Figure 3).
Atmosphere 2020, 11, x FOR PEER REVIEW 5 of 13
2.2. Physics
Physics relates to the “movement” of the problem. A popular saying is that “pollution knows no
boundaries”. The box model assuming closed walls in Figure 2 and Table 1 is good to illustrate the
point that emissions are key for any increase and decrease in pollution levels. Simultaneously,
meteorology plays an important role in determining how much of those emissions stay in the box,
determined by the horizontal wind components (U and V), or how much of those emissions will stay
close to the surface, determined by the vertical wind component (W) (Figure 3).
Figure 3. Three-dimensional motion of air through a city.
This adds two new dimensions to the air pollution problem: (a) the air is not static over the city—
between wind speeds of 1 m/s and 2 m/s, the latter is pushing twice the amount of air through the
city boundaries; (b) the air from outside the boundary carries outside emissions, which add to the
total emissions inside the city. Similarly, emissions from inside the city will be carried to a city
downwind. This is called “long-range transport” of pollution—sometimes this is an exchange of
pollution between the cities and sometimes between the states. For example, a city like Delhi is
surrounded by satellite cities Gurugram (from the state of Haryana) in the West and Noida (from the
state of Uttar Pradesh) in the East. There is constant movement of vehicles between these cities and
in a map of urban built-up area, it is difficult to draw a closed box [20]. In this case, depending on the
wind direction, emissions from each of these cities are affecting the others downwind.
The effect of long-range transport is also prominent during the seasonal dust storms (May–June)
originating from the Middle East or the Thar desert in the state of Rajasthan [21], and agricultural
residue burning (April–May and October–November) originating mostly from the states of Punjab
and Haryana [22]. In both cases, seasonal wind speeds are high enough to pick up and push the
emissions into the higher altitudes, support inter-state transport, and affect the pollution levels
downwind. The overall known horizontal advection and vertical mixing schemes are more complex
than described in this paper.
Guttikunda et al. (2019) [16] presents an analysis for 20 Indian cities, documenting contributions
of emissions inside and outside the city airsheds. On average, 30% of the pollution observed in these
cities originates outside the city limits. For cities in North India like Ludhiana, Amritsar, and
Chandigarh, the long-range transport contribution is more than 50% on an annual basis.
The movement of the pollution also includes scavenging—dry deposition when the pollutants
are in contact with a surface and wet deposition during the rains. The dry deposition rates for various
pollutants are determined by the surface roughness, soil moisture content, and wind speeds. Under
windy conditions and over dry surfaces, we have lesser deposition of the particulates, and vice versa
on the trees with enough moisture on the leaves.
Figure 3. Three-dimensional motion of air through a city.
This adds two new dimensions to the air pollution problem: (a) the air is not static over the
city—between wind speeds of 1 m/s and 2 m/s, the latter is pushing twice the amount of air through the
city boundaries; (b) the air from outside the boundary carries outside emissions, which add to the total
emissions inside the city. Similarly, emissions from inside the city will be carried to a city downwind.
This is called “long-range transport” of pollution—sometimes this is an exchange of pollution between
the cities and sometimes between the states. For example, a city like Delhi is surrounded by satellite
cities Gurugram (from the state of Haryana) in the West and Noida (from the state of Uttar Pradesh)
in the East. There is constant movement of vehicles between these cities and in a map of urban
built-up area, it is difficult to draw a closed box [
20
]. In this case, depending on the wind direction,
emissions from each of these cities are affecting the others downwind.
The effect of long-range transport is also prominent during the seasonal dust storms (May–June)
originating from the Middle East or the Thar desert in the state of Rajasthan [
21
], and agricultural
residue burning (April–May and October–November) originating mostly from the states of Punjab and
Haryana [
22
]. In both cases, seasonal wind speeds are high enough to pick up and push the emissions
into the higher altitudes, support inter-state transport, and affect the pollution levels downwind.
The overall known horizontal advection and vertical mixing schemes are more complex than described
in this paper.
Guttikunda et al. (2019) [
16
] presents an analysis for 20 Indian cities, documenting contributions of
emissions inside and outside the city airsheds. On average, 30% of the pollution observed in these cities
originates outside the city limits. For cities in North India like Ludhiana, Amritsar, and Chandigarh,
the long-range transport contribution is more than 50% on an annual basis.
The movement of the pollution also includes scavenging—dry deposition when the pollutants
are in contact with a surface and wet deposition during the rains. The dry deposition rates for
various pollutants are determined by the surface roughness, soil moisture content, and wind speeds.
Atmosphere 2020,11, 922 5 of 11
Under windy conditions and over dry surfaces, we have lesser deposition of the particulates, and vice
versa on the trees with enough moisture on the leaves.
2.3. Chemistry
Chemistry relates to the “composition” of the problem—the critical one of the three sciences,
as it links PM
2.5
, PM
10
, SO
2
, NO
2
, CO and ozone directly to all known health impacts. Of the six
pollutants, the most critical is PM
2.5
, and its chemical composition is different in space and time [
23
,
24
].
While the first five pollutants are part of direct emissions, ozone is a secondary compound formed in
the atmosphere in the presence of NOxand hydrocarbons.
A sample of PM
2.5
can provide information not only on how much pollution there is, but also
on the fuel origins of the mass on the filter. Figure 4presents a summary of the key marker metals,
elements, and compounds associated with major sources. There are overlaps between the sources
and the ratio of the markers also vary significantly, which allows for statistically apportioning source
contributions. These markers range from metals from direct combustion of fuels, like coal and diesel,
to contributions from other gases, like SO
2
forming sulphate aerosols (in a series of reactions involving
ozone and some intermediate radicals), NO
x
forming nitrate aerosols and hydrocarbons forming
secondary organic aerosols (via 500+known reactions with ozone and intermediate radicals) [
25
,
26
].
Ozone is a by-product of these 500+reactions. Most of the chemical transformation between gases
and aerosols takes place during the long-range transport—in other words, a significant portion of
the PM
2.5
samples collected in the city are there because of the emissions originating outside the
city [
16
]. The secondary nature of the PM
2.5
originating from sources not likely within a city boundary,
complicates the overall pollution control strategy.
Atmosphere 2020, 11, x FOR PEER REVIEW 6 of 13
2.3. Chemistry
Chemistry relates to the “composition” of the problem—the critical one of the three sciences, as
it links PM
2.5
, PM
10
, SO
2
, NO
2
, CO and ozone directly to all known health impacts. Of the six
pollutants, the most critical is PM
2.5
,
and its chemical composition is different in space and time
[23,24]. While the first five pollutants are part of direct emissions, ozone is a secondary compound
formed in the atmosphere in the presence of NO
x
and hydrocarbons.
A sample of PM
2.5
can provide information not only on how much pollution there is, but also on
the fuel origins of the mass on the filter. Figure 4 presents a summary of the key marker metals,
elements, and compounds associated with major sources. There are overlaps between the sources
and the ratio of the markers also vary significantly, which allows for statistically apportioning source
contributions. These markers range from metals from direct combustion of fuels, like coal and diesel,
to contributions from other gases, like SO
2
forming sulphate aerosols (in a series of reactions
involving ozone and some intermediate radicals), NO
x
forming nitrate aerosols and hydrocarbons
forming secondary organic aerosols (via 500+ known reactions with ozone and intermediate radicals)
[25,26]. Ozone is a by-product of these 500+ reactions. Most of the chemical transformation between
gases and aerosols takes place during the long-range transport—in other words, a significant portion
of the PM
2.5
samples collected in the city are there because of the emissions originating outside the
city [16]. The secondary nature of the PM
2.5
originating from sources not likely within a city boundary,
complicates the overall pollution control strategy.
Figure 4. Key metal and ion markers of various sources contributing to PM
2.5.
3. Do Smog Towers Work?
For managing outdoor air pollution, the answer is still “no”. Atmospheric science defines the air
pollution problem as (a) a dynamic situation where the air is moving at various speeds with no
boundaries, and (b) a complex mixture of chemical compounds constantly forming and transforming
into other compounds. With no boundaries, it is unscientific to assume that one can trap air, clean it,
and release into the same atmosphere simultaneously. Expecting filtering units to provide any
noticeable results at the community level is unrealistic. This is illustrated in a back-of-the-envelope
calculation for Delhi (Table 2) using two pilots under consideration, (a) T1: a smog tower in Xi’an
(China) designed to filter 10 million m
3
of air every day; (b) T2: a smaller version of T1 piloted in
Delhi’s Lajpat number market in January 2020, with a capacity of 600,000 m
3
/day.
For these calculations, we considered Delhi’s airshed, including its satellite cities Gurugram,
Noida, Greater Noida, Ghaziabad, Faridabad, and Rohtak, covering an area of 7000 sq.km (~84 km ×
Figure 4. Key metal and ion markers of various sources contributing to PM2.5.
3. Do Smog Towers Work?
For managing outdoor air pollution, the answer is still “no”. Atmospheric science defines the
air pollution problem as (a) a dynamic situation where the air is moving at various speeds with no
boundaries, and (b) a complex mixture of chemical compounds constantly forming and transforming
into other compounds. With no boundaries, it is unscientific to assume that one can trap air, clean it,
and release into the same atmosphere simultaneously. Expecting filtering units to provide any
noticeable results at the community level is unrealistic. This is illustrated in a back-of-the-envelope
calculation for Delhi (Table 2) using two pilots under consideration, (a) T1: a smog tower in Xi’an
Atmosphere 2020,11, 922 6 of 11
(China) designed to filter 10 million m
3
of air every day; (b) T2: a smaller version of T1 piloted in
Delhi’s Lajpat number market in January 2020, with a capacity of 600,000 m3/day.
Table 2. Outdoor air pollution filtering efficiency of the smog towers in Delhi’s airshed.
Variable Delhi’s Airshed T1: Xi’an Smog Tower T2: Delhi’s 2020 Pilot
Filtering capacity under full
implementation (m3/h) 400,000 25,000
Average airshed volume (m3/h),
calculated using inputs from
Table 3
1,209,600 million in the
summer 120,960 million
in the winter
Filtering efficiency as the amount
of air filtered in one hour
0.000033% in the summer
and 0.00033% in the
winter
0.000002% in the summer
and 0.00002% in the
winter
Number of towers required at
full capacity
3,024,000 units in the
summer and 302,400
units in the winter
50,000,000 units in the
summer and 5,000,000
units in the winter
Unit cost
The Supreme Court of
India allocated INR 36
crores (~USD 5.2 million)
for replication of T1
Unknown; reported pilot
cost is USD 10 million
INR 700,000 (~USD
10,000) +operations and
maintenance
Required capital cost for full
implementation in Delhi USD 15,725 billion USD 500 billion
Required operations and
maintenance costs for full
implementation in Delhi
HIGH HIGH
Table 3.
Summary of all day (AD), daytime (DT), and nighttime (NT) averages (
±
standard deviations)
of mixing heights (MH in m), near surface temperature (T in
◦
C), and near surface wind speeds (WS in
m/s) by month. Data is extracted from Weather Research Forecasting (WRF) model simulations using
the National Centers for Environmental Prediction (NCEP) reanalysis fields for the year 2018.
Variable January February March April May June July August September October November December
MH–AD 298 ±58 516 ±94 926 ±198
1075
±
254 1243
±
307 1054
±
244
573
±
240 505
±
152
462 ±123 501 ±91 350 ±73 286 ±71
MH–DT
557
±
118 974
±
187
1801
±
393 2066
±
501 2377
±
640 1855
±
485
994
±
450 906
±
269
827 ±239
959
±
184
651 ±129
534
±
140
MH–NT 39 ±8 57 ±56 51 ±18 84 ±45 109 ±60 254 ±124 153 ±85 104 ±58 97 ±105 43 ±13 50 ±33 38 ±8
T–DT
18.9
±
1.8 24.2
±
2.8
30.5 ±2.6 35.5 ±2.4 39.4 ±2.7 39.0 ±3.2
33.9
±
2.9 33.0
±
2.1
31.4 ±2.2
30.0
±
1.6
25.3 ±1.5
18.8
±
2.2
T–NT 9.9 ±1.5
15.3
±
2.5
19.6 ±1.8 26.3 ±2.2 31.1 ±1.8 34.0 ±2.3
30.6
±
2.1 29.3
±
1.3
26.5 ±1.1
21.8
±
1.8
17.5 ±1.9
11.1
±
2.8
WS–AD 2.7 ±0.7 2.8 ±0.9 3.1 ±0.6 3.8 ±0.8 3.7 ±0.9 4.5 ±1.2 3.1 ±0.7 2.7 ±0.7 2.8 ±0.9 2.5 ±0.5 2.7 ±0.7 2.4 ±0.6
For these calculations, we considered Delhi’s airshed, including its satellite cities Gurugram, Noida,
Greater Noida, Ghaziabad, Faridabad, and Rohtak, covering an area of 7000 sq.km (
~84 km ×84 km
).
Table 3presents a summary of mixing heights, near surface temperature, and wind speeds for the
year 2018. The average wind speed in the domain is 4 m/s (=14.4 km/h) in the summer months and
2 m/s (=7.2 km/h) in the winter months. Similarly, the average mixing heights are 1000 m and 200 m,
respectively. This translates to an average exchange of 1,209,600 million m
3
/h and 120,960 million
m
3
/h of air in the summer and winter months, respectively (city side * speed * mixing height)—this
calculation assumes a steady state with constant flow of air and no vertical mixing.
The concept of vacuum cleaning has worked in closed environments. For example, (a) in a closed
room, if the doors and windows remain shut, then an air purifier is an efficient way to clean the
air [
27
]. This emulates a box model containing a constant amount of air with limited movement.
When purifying the closed room, all the dust is collected on a filter, which requires either cleaning or
replacement after some time, and a clean disposal of the dust collected. During high pollution days,
the frequency of cleaning and replacement is more (b) at the end of a combustion unit, with flue gas
moving at a constant flow rate in one direction, like a power plant boiler with a chimney. The system
will include an inlet for polluted air and an outlet for cleaned air. This system is designed to trap
Atmosphere 2020,11, 922 7 of 11
emissions at the source, before entering the atmosphere at the top of the chimney. In this case, all the
dust (fly ash) from the cyclone bags or electrostatic precipitators or filters also need clean collection and
disposal [
28
]. (c) In a subway tunnel, where the air flow is limited and prone to increased exposure
levels, a purifier will induce an artificial air flow, diluting the incoming air, and thus reducing the
overall exposure levels. None of these examples present the use of filtering systems to clean the
air permanently.
In an outdoor environment, at best these systems are a demonstration of a filtering system with
negligible efficiencies (Table 2), whose performance at a power plant, or at any of the end of the pipe
applications where the emissions originate, is the most efficient.
4. Taking a Long View on Air Quality Management
The air pollution problem in India is year-round [
29
,
30
]. The winter months (November,
December, January, and February) are the worst, with stagnant meteorology stifling the lateral and
vertical movement of pollution, low temperatures pushing the need for space heating, which is
mostly met using biomass [
17
], and some seasonal emissions from agricultural residue burning [
22
].
These are in addition to the all-year combustion of petrol, diesel, gas, coal, and waste in the transport,
industrial, and domestic sectors, and resuspended from the construction activities and traffic on the
roads. The monsoon months (June, July, and August) are the best, with enhanced wet scavenging
across the country.
The air pollution problem in India is not limited to the cities. An analysis of annual average
PM
2.5
concentrations, using a combination of satellite retrievals and global emission inventories for the
period of 1998–2018, suggests that 60% of the districts do not meet the national ambient standard of
40
µ
g/m
3
and 98% do not meet the WHO guideline of 10
µ
g/m
3
[
31
]. Typically, North Indian districts
are more adversely affected from chronic air pollution.
The judicial system played a central role in several air pollution decisions in India:
•
In 1998, the Supreme Court ruled to convert public transport buses and para-transit vehicles to
run on compressed natural gas (CNG). This was a public interest litigation, which also led to
other emission control measures in Delhi [
32
,
33
]. CNG conversion was the most successful for
the transport sector and, in the early 2000s, the city of Delhi witnessed a reduction in emissions
and pollution. However, the scale of replacement has not been replicated in any other Indian city
since, and the overall bus fleet composition in Delhi has remained the same irrespective of the
growing demand [34].
•
In 2015, three toddlers filed a public interest ligation in the Supreme Court of India, to request
a full ban on the sale of fireworks. In an apparent victory for cleaner air, in November 2016,
the Court ordered a complete ban on the sale of firecrackers in the NCR. What seemed to be a
progressive measure was, however, annulled by a ‘temporary’ ruling, when the ban was lifted
with the caveat that the ban will be reinstituted if there is evidence that fireworks are a major
pollutant during the festive season.
•
In 2018, the Supreme Court ruled in favour of the introduction of BS-VI standard vehicles
nationwide, starting 1 April 2020, instead of the original plan for 2025 under the auto fuel policy.
•
In 2019, the Supreme Court ruled in favour of an immediate ban on the use of pet coke (with high
sulphur content) in all industries in the NCR by June 2019.
Time and again, judicial interventions have resulted in putting pressure on the respective agencies
to implement long-term measures for long-term benefits.
Non-judicial interventions proposed and implemented for improving air quality and health are:
•
In 2015, the Government of India launched the smart cities program for 100 cities. While air
quality was not explicitly mentioned as the environment indicator, the proposed activities were
designed to benefit overall air quality. These included a ranking system to evaluate the waste
management programs, road cleaning, and street greening in the cities.
Atmosphere 2020,11, 922 8 of 11
•
In December 2016, Delhi proposed the Graded Responsibility Action Plan (GRAP), a series
of measures to enforce under poor, very poor, severe and emergency levels of pollution [
35
].
These decisions are made based on a 48-h running average of the air quality index, calculated using
hourly PM2.5 and PM10 levels. This plan is now an example for other cities in the Indo-Gangetic
Plain to replicate. A missing link in the program is an independent body with teeth to clamp
down on offending polluters across states.
•
The Ministry of Petroleum and Natural Gas took an important first step with the Pradhan Mantri
Ujjwala Yojana (PMUY) in 2016, providing liquified petroleum gas (LPG) connections to the
poorest households. As of September 2019, the PMUY has connected 80 million beneficiaries by
directly transferring subsidies to the bank accounts of women in these households and improving
indoor and outdoor health [
36
]. While the number of connections is on the rise, there are barriers
to LPG uptake, which need to be addressed [37].
•
In April 2015, a parliamentary standing committee proposed new emission standards for all the
coal-fired thermal power plants. These standards were ratified in December 2015, tightening the
standards for PM and introducing standards for SO
2
, NO
x
, and mercury for the first time.
If implemented in full, these standards are expected to yield a 50% drop in the PM
2.5
(primary
and secondary) pollution from these plants [
38
,
39
]. All the power plants are expected to comply
in 2022.
•
Financial support from the Government of India for the Faster Adoption and Manufacturing
of Electric Vehicles (FAME) program, made electric vehicles (EVs) a new policy and economic
choice for small- and large-scale applications. The program now includes subsides for two-,
three-, and four-wheelers and the introduction of EV buses into the public transportation system.
The Delhi transport corporation is expected to receive its first 1000 buses in 2021–2022 and the
Delhi government is promoting EVs to account for 25% of new registrations by 2024.
In 2019, the Ministry of Environment, Forest and Climate Change (MoEFCC) announced the
National Clean Air Programme (NCAP) for 122 non-attainment cities from 20 states and three union
territories [
40
]. Under the NCAP, every city is required to prepare a list of actions necessary to
reduce their PM
2.5
levels by 20–30%, compared to 2017, by 2024. The authors of [
41
] present a review
of these action plans, summarizing the key action points that all the cities want to implement as:
(a) augmenting public transport, (b) eradicating road and construction dust, (c) abolishing open waste
burning, (d) promoting clean cooking, (e) implementing industrial emission standards, (f) increasing
ambient monitoring capacity, and (g) raising public awareness. While improving ambient monitoring
capacity and raising public awareness are short-term activities (with long-term maintenance), all others
are part of long-term planning, designed to reduce emissions at the sources.
Following the approval of the 102 NCAP city action plans by MoEFCC, the prevalence of
pollution episodes in October–November 2019, and limited action in the cities to counter air pollution,
the Supreme Court bench again intervened to demand the installation of smog towers and allocated
INR 36 crores (~USD 5.2 million) for the replication of the Xi’an’s smog tower design in Delhi. In
August 2020, a memorandum of understanding was signed by the Indian Institute of Technology
(Bombay) to design and construct the system. Wasting the judicial power by implementing band aid
measures is not only unscientific, but also a waste of limited financial and technical resources. We
cannot vacuum our way to “clean air”.
5. Conclusions
The city clean air action plans provide proof that there is enough technical know-how on how
much air pollution there is, the key sectors that need attention, the institutional requirements to
implement long-term strategies, and the ways in which they can be addressed [
40
,
41
]. These action
plans need institutional and financial support. At the institutional level, there are three tasks that
need immediate attention, where the judiciary can help to move the strategies forward: (1) Personnel
and Capacity— CPCB and the state pollution control boards are too understaffed to perform auditory
Atmosphere 2020,11, 922 9 of 11
and scientific operations. (2) Monitoring infrastructure—as of June 2020, there are 230 continuous
monitoring stations operated and maintained by CPCB in 124 (of 715) districts. More than half of these
districts have only one station and 70 monitors are in the vicinity of the NCR, which demonstrates
the bias in measuring and managing air pollution outside the big cities like Delhi. To spatially and
temporally represent the air pollution problem, India requires at least 4000 continuous air quality
monitoring systems (2800 in the urban areas and 1200 in the rural areas). (3) Information support—air
quality management requires information on emission loads, source contributions, costs and benefits of
interventions, and a way to prioritize actions. The funds allocated by the Supreme Court for temporary
interventions like testing smog towers are most useful for implementing these permanent solutions.
Supplementary Materials:
The following are available online at http://www.mdpi.com/2073-4433/11/9/922/s1,
Table S1: Summary of air quality in 124 cities in India for the periods before and during the 4 COVID-19 lockdowns.
Author Contributions:
Conceptualization, writing, and editing—S.G. and P.J.; Methodology, resources,
and visualization—S.G. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
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2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
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