Cities operate ambient air quality monitoring networks but often do not analyze and interpret the data. Data gets simply "stacked". Networks are not configured correctly capturing the data trends and monitoring objectives. This presentation provides guidance and uses Mumbai's ambient air quality data to illustrate application

1. Learn the Tricks to Get the Best from Your
City Ambient Air Quality Monitoring Network
Case of Mumbai, India
Dr Prasad Modak
Environmental Management Centre
www.emcentre.com

2. First let us get to the basics

3. 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
05/03/13 3Dr Prasad Modak

4. 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)
05/03/13 4Dr Prasad Modak

5. 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
05/03/13 5Dr Prasad Modak

6. 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
05/03/13 6Dr Prasad Modak

7. 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
05/03/13 7Dr Prasad Modak

8. 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)
05/03/13 8Dr Prasad Modak

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

10. 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.
05/03/13 10Dr Prasad Modak
Look at Data Variations
Plot them intelligently

11. 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
05/03/13 11Dr Prasad Modak
Check on Outliers

12. 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
05/03/13 12Dr Prasad Modak
Check on Variability

13. 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
InterpretInterpret
ContoursContours
Contours are based on 1999 data05/03/13 13Dr Prasad Modak

14. Higher value of CV
indicates more
fluctuations in the
monitored data.
Values of CV are
rather high for
ammonia
CV for NO2
Check on variabilityCheck on variability
of “linked”of “linked”
parametersparameters
Contours are based on 1999 data
CV for NH3
Max for NH3 160%
Max for NO2 100%
05/03/13 14Dr Prasad Modak

15. Interpret 90th Percentile ValuesInterpret 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
05/03/13 15Dr Prasad Modak

16. 90th Percentile Values90th 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
05/03/13 16Dr Prasad Modak

17. 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.
05/03/13 17Dr Prasad Modak
Trends on exceedence

18. 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.
05/03/13 18Dr Prasad Modak
Do Source Interpretation

19. 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 MeansCompare with Regional Means
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.
05/03/13 19Dr Prasad Modak

20. Let us understand Network
Morphology

21. Network MorphologyNetwork Morphology
Number of Monitoring Stations
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.
05/03/13 21Dr Prasad Modak

22. Network MorphologyNetwork Morphology
Number of Monitoring Stations
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.
05/03/13 22Dr Prasad Modak

23. 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.
Summary of Various Recommendations on the Number of Air Quality Monitoring StationsSummary of Various Recommendations on the Number of Air Quality Monitoring Stations
The guidelines provided by IS 5182 (Part 14) 1985seem to be appropriate.
05/03/13 23Dr Prasad Modak

24. Configuring Monitoring StationsConfiguring Monitoring Stations
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.
• 05/03/13 24Dr Prasad Modak

25. Configuring Monitoring StationsConfiguring Monitoring Stations
• 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.
05/03/13 25Dr Prasad Modak

26. Application to Mumbai

27. Suggested zones 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
05/03/13
27Dr Prasad Modak

28. What should be avoided?What should be avoided?
The obstruction of tree cover behind is visible in the photograph of
the monitoring station at Maravali
05/03/13 28Dr Prasad Modak

29. 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?What should be avoided?
05/03/13 29Dr Prasad Modak

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

31. COMPARISON OF SPM
R2
= 0.6741
$0
$50
$100
$150
$200
$250
$300
$350
$400
$450
0 100 200 300 400 500
NEER I
COMPARISON OF NO2
R2
=0.0141
0
10
20
30
40
50
60
70
80
90
0 10 20 30 40 50 60 70
NEERI
Comparison between NEERI and BMC monitoring at ParelComparison 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.
05/03/13 31
Dr Prasad Modak

32. 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
05/03/13 32Dr Prasad Modak

33. Want to analyze your City
Ambient AQ Network?
Write to
Dr Prasad Modak
Prasad.modak@emcentre.com