City Ambient Air Quality Monitoring

Learn the Tricks to Get the Best from Your
City Ambient Air Quality Monitoring Network:
The Case of Mumbai, India
By
Dr. Prasad Modak,
Director, Ekonnect Knowledge Foundation
©2013 Ekonnect Knowledge Foundation

First let us get to the basics…
Slide 2

Why Ambient Air Quality Monitoring?
• Know the background ?(locations of least “source
influence” or local variability)
• Exposure Levels – Health, material, vegetation damage
• Impact zones - Compliance with ambient standards
• Assessing a specific source of influence
• Validation of air quality models
Slide 3

What needs to be decided?
• Which parameters? (e.g. Gaseous, Particulates and
particulate based; Multimedia?)
• Deciding on Timing and frequency (Sampling internal,
sample size)
• Where? (i.e. location)
• How? (Method)
Slide 4

Number, Locations and Siting
Guidelines
• For point sources : Three location philosophy;
Background, Influence
• Urban areas (Area sources): Land use and population
driven “network”; Staggered frequencies, fixed and
moving stations philosophy
• Traffic junctions (Kerbside air quality)
• Special cases - indoor air quality; exposure monitoring;
receptor modeling
Slide 5

Timing, Duration, Frequency, Sample Size
• Winter as critical month – Periods of low mixing heights,
frequent inversion conditions
• 24 hours, 8 hourly, 1 hour, continuous
• Once in a season, once a month, weekly, bi-weekly
• Staggered and simultaneous monitoring campaigns
• Sample size critical, considering data variability (CV
typically over 20%), Low confidence around means,
Problem of trend detection
Slide 6

What to measure? And How?
• Criteria pollutants (Routine and recently added )
• Source specific parameters
• Multimedia measurements : Rainwater and Particulate
constituents – Chemical Mass Balances
• High frequency automatic stations
• Issues on methods, practicing of standard protocols,
QA/QC systems
Slide 7

What do we do with the collected data?
Statistical analyses
Data acceptability
Long term data (Correlations and Trends, Multivariate
analyses (Factor analyses and Clustering), Intervention
analyses
Short term intensive data (Distribution analyses, Percent
Exeedence, Extreme value functions)
Slide 8

Case study of Mumbai, India
1997-1999 data
Slide 9

Illustration of Diurnal Variation in Mumbai'sAir Quality (1997 monthly data for NO2 for all monitoring stations)
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
NO2inug/m3
12 pm to 4 pm
4 pm to 4 am
4 am to 12 pm
Diurnal variations
An analysis of the 8 hourly averages for
Mumbai for the years 1997, 98 and 99 indicates
that the concentrations for all the pollutants in
the night (i.e. sampling period of 20-04 hrs) are
relatively higher than those in the day.
Look at Data Variations
Plot them intelligently
Slide 10

0
20
40
60
80
100
120
140
Colaba
BabulaTank
Worli
Dadar
Parel
Sewree
Sion
Khar
S.Tank
Andheri
Sakinaka
Jogeshwari
Ghatkopar
Bhandup
Mulund
Borivali
TilakNagar
Chembur
Maravali
Aniknagar
Mahul
Mankhurd
Monitoring Stations
PercentExceedenceforthreeyears(97,98,99)
NO2
SO2
SPM
Exceedence
Average percentage of
exceedence for
NO2 is 19%
SO2 is 11%
SPM is 78%
Number of outliers (4 sigma test) in the data are negligible
Check on Outliers
Slide 11

0
20
40
60
80
100
120
140
160
180
Colaba
BabulaTank
Worli
Dadar
Parel
Sewree
Sion
Khar
S.Tank
Andheri
Sakinaka
Jogeshwari
Ghatkopar
Bhandup
Mulund
Borivali
TilakNagar
Chembur
Maravali
Aniknagar
Mahul
Mankhurd
Monitoring Stations
%CoefficientofVariation
NO2
SO2
SPM
NH3
CV values are generally high (>40)
for all three years (particularly for
Ammonia)
Coefficient of Variation
Check on Variability
Slide 12

NN
Similarities were observed between the pattern of contours drawn for
90th percentile concentrations and the annual means.
Annual
Average for
NO2
90th
Percentile
for NO2
Interpret
Contours
Contours are based on 1999 data©2013 Ekonnect Knowledge Foundation
Slide 13

Higher value of CV
indicates more
fluctuations in the
monitored data.
Values of CV are
rather high for
ammonia N
CV for NO2
Check on variability
of “linked”
parameters
Contours are based on 1999 data
N
CV for NH3
Max for NH3 160%
Max for NO2 100%
Slide 14

Interpret 90th Percentile Values
Generally, SO2
concentrations
are well within
standards,
except in
industrial areas.
There is clearly
an island effect at
Chembur
(characterized by
the local
influence of
Fertilizer industry
- RCF) for NH3
emissions.
90th Percentile values: SO2
90th Percentile
values: NH3
Slide 15

90th Percentile Values
The contour
map for
NO2
indicates a
corridor
effect due to
traffic
emissions
along the
western and
eastern
suburb
roads.
90th Percentile
values: NO2
90th Percentile
values: SPM
Slide 16

Following observations can be made from
results of trend analyses and exceedence
over standards;
Mulund, Bhandup, Ghatkopar and Mankhurd,
Aniknagar , Sion and Worli show a
statistically significant downward trend over
the period of 1997-1999 for SPM.
Despite such a downward trend in the eastern
suburbs, results show that almost all the
stations in Mumbai have a considerable
exceedence over standards. Average
percentage of exceedence is 70% that is
indeed very significant.
In the case of NO2, no station reports a
statistically downward trend. Two stations viz.
Supari Tank and Mankhurd show statistically
upward trend in the period of 1997-1999.
AN.DWG
N N
Trends on exceedence
Slide 17

EMC 2D/MMRDAFINAL/DATA/ACADFILES/BASEPLAN.DWG
N N N
Stations such as Khar (next to Supari
Tank), Sion and Maravali (close to
Mankhurd) show some of the higher
level of exceedence. These
observations corroborate that
emissions of NO2 in Wards H, G and M
are on the rise mainly due to emissions
of traffic.
A group of stations consisting of
Maravali, Supari Tank, Andheri and
Jogeshwari show a statistically upward
trend for SO2. Despite such a trend, the
exceedence over standards is only
marginal of the order of between 5 to
10% in this area.
Do Source Interpretation
Slide 18

Figure 4.2 a Percent Deviation from Regional Means for 1997
-100
-50
0
50
100
150
Colaba
BabulaTank
Worli
Dadar
Parel
Sewree
Sion
Khar
S.Tank
Andheri
Sakinaka
Jogeshwari
Ghatkopar
Bhandup
Mulund
Borivali
TilakNagar
Chembur
Maravali
Aniknagar
Mahul
Mankhurd
Monitoring Stations
PercentDeviationfromRegionalMean
NO2
SO2
SPM
At Colaba ,
Supari Tank,
Andheri,
Sakinaka,
and Borivali,
for instance,
for all the
three
parameters
viz. SO2,
NO2 and
SPM, and for
all the three
years,
station
annual
averages are
generally
below the
regional
means.
Compare with Regional Means
0
50
100
150
200
250
300
350
400
450
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1997
1998
1999
YearRegionalMeaninug/m3
SO2
NO2
SPM
Most of the
ambient
stations show
average values
below the
regional mean
for all the
pollutants
Consistent behavior
is seen at Khar and
Maravali with
respect to the
regional mean.
Slide 19

Let us understand Network Morphology
Slide 20

Network Morphology
• Network morphology involves the decision on the number of monitoring stations and
their configuration.
• Number of Monitoring Stations could be decided based on several approaches such
as:
• Using distance criterion (proximity analysis) – this is based only on optimizing network
density so as to have a spatially well distributed network. Does not consider air quality
influence and hence can be used only as a supportive approach.
• US EPA has developed design curves relating the populations and the number of
monitoring stations considering the type of monitoring stations (such as manual or
automatic) based on a detailed qualitative evaluation of several cities in USA. These
curves could be used to determine the gross number of stations which could then be
refined with other approaches.
Number of Monitoring Stations
Slide 21

Network Morphology
• IS 5182 (Part 14 – 1985), Indian Standards (IS) suggests two empirical methods for
the estimation of number of monitoring stations. One method is based on population
exposed and the other is based on the comparison with standard and 90th percentile
concentrations of pollutants.
• Amongst the analytical techniques, methods based on the estimation of regional mean
have also been proposed to arrive at the number of monitoring stations. These
methods could be used for estimation of number of monitoring stations for a pollutant if
its coefficient of variation (CV) is known.
Number of Monitoring Stations
Slide 22

Method/Thumb rule Result Comments
US EPA 1971 based
on population
15 high frequency or
40 low frequency
ambient air quality
monitoring stations
Data base outdated,
High and low
frequency are not
precisely defined.
IS 5182 (Part I4 –
1985) – population
exposure criteria
10 ambient and 4
kerbside air quality
monitoring stations
Does not comment on
the required
frequency
IS 5182 (Part I4 – 1985)
– based on
comparison between
90th percentile and
standard
7 ambient air quality
monitoring stations
Results can be
spurious depending
on the limitations of
the data
Keagy’s nomograph 30 low frequency
monitoring stations
Results can be
spurious depending
on the limitations of
the data
It is prudent that
the required
number of
monitoring stations
is arrived at by
examining the
needed monitoring
configuration. This
approach brings in
the required urban
specificity.
The guidelines provided by IS 5182 (Part 14) 1985 seem to be appropriate.
SUMMARY OF VARIOUS RECOMMENDATIONS ON THE
NUMBER OF AIR QUALITY MONITORING STATIONS
Slide 23

Configuration of monitoring stations is influenced by the governing or site
specific objective. Criteria for configuration of monitoring stations should
not be equated to that of the siting protocol.
Typical guidelines for choosing a configuration for an urban AQMN are,
• Locate an ambient air quality monitoring station to capture various
development zones i.e. city center and suburban areas. Prioritize
location based on population and sensitivity
• To obtain a background air quality, locate at least one ambient air
quality monitoring station that is distanced from urban emission
sources and is therefore broadly representative of city-wide
background conditions.
CONFIGURING MONITORING STATIONS
Slide 24

• Locate kerbside air quality monitoring stations at streets that exhibit heavy
traffic and pedestrian congestion.
• Few (at least two or three) ambient air quality monitoring stations may be
located to capture influence of any major sources (point or area) present
in the urban area.
CONFIGURING MONITORING STATIONS
Slide 25

Application to Mumbai
Slide 26

Stationsbeing monitored since
Jan 2000
MPCB
Sion
Mulund
BMC
1. Colaba (C/R)
2. Babula Tank (I/R)
3. Worli Naka (C)
4. Dadar (C)
5. Parel (I/C/R)
6. Sewree (I)
7. Sion (C)
8. Khar (C/R)
9. Supari Tank (R)
10. Andheri (I/C)
11. Saki Naka (I)
12. Jogeshwari (I)
13. Ghatkopar (I/C/R)
14. Bhandup (I)
15. Mulund (I)
16. Borivali (R)
17. Tilaknagar (C)
18. Chembur Naka (C/R)
19. Maravali (I)
20. Aniknagar (I)
21. Mahul (I)
22. Mankhurd (R)
Mobile Monitoring at
Traffic Junctions (BMC)
Wadala, Andheri and Mahim
NEERI (under GEMS)
Parel
Kalbadevi
BandraI - Industrial
C - Commercial
R - Residential
Legend
Zones Suggested for siting
Colaba Background
Borivali Background
Parel* Ambient
Andheri*
Khar*
Sion
Maravali / source oriented
Bhandup
4 kerbside monitoring stations at congested traffic
junctions.
In addition, two more zones for ambient monitoring
will be recommended.
All of the above zones will be reviewed in task 2.
Task 2 will also include identification of specific
locations for the sites
* candidates for automatic monitoring
Recommended monitoring stations
Slide 27

What should be avoided?
The obstruction of tree cover behind is visible in the photograph of the monitoring
station at Maravali
Slide 28

The obstruction of the staircase headroom and the building behind
could lead to unreliable and incorrect data as can be seen from
this photograph at Parel where MCGM as well as NEERI
monitored ambient air quality.
What should be avoided?
Slide 29

What happens when two agencies
monitor at same location?
Slide 30

Comparison between NEERI and BMC monitoring at Parel
The monitoring station at Parel where
both BMC and NEERI conduct ambient
air quality monitoring showed little
correlation for all the pollutants.
The scatter diagrams on the left show the
low R squared values of data of NEERI
and BMC for SPM and NO2.
Although the sampling frequencies of
NEERI and BMC differ, monthly averages
are expected to show reasonably similar
patterns. It seems that even at the same
location of sampling, the monthly
averages can greatly differ when the
station is operated by different agencies
at different sampling times.
Slide 31

What should we do?
• Urban AQ Monitoring Guidelines - covering all aspects
(many need some defogging, adaptations etc)
• Emphasis on end objectives and cost-effectiveness -
Demonstrating how data should be used for various
objectives
• Hands on Training on data generation and analyses
• Build case studies like Mumbai AQ Data and use the
examples in Training
• Provide support software for better AQ data
interpretation
• Campaign against poor ambient Air Quality data
Slide 32

Want to analyze your City Ambient
AQ Network?
Write to:
Dr Prasad Modak
prasad.modak@emcentre.com
or
prasad.modak@ekonnect.net
Slide 33

City Ambient Air Quality Monitoring

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