City Ambient Air Quality Monitoring

Slide 1

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…

©2013 Ekonnect Knowledge Foundation 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


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)


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


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


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


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)


Case study of Mumbai, India1997-1999 data


Illustration of Diurnal Variation in Mumbai's Air Quality (1997 monthly data for NO2 for all monitoring stations)

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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 VariationsPlot them intelligently


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NO2 is 19% SO2 is 11%

SPM is 78%

Number of outliers (4 sigma test) in the data are negligible

Check on Outliers


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CV values are generally high (>40) for all three years (particularly for Ammonia)

Coefficient of Variation

Check on Variability


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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

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CV for NO2

Check on variability of “linked” parameters

Contours are based on 1999 data

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CV for NH3

Max for NH3 160%

Max for NO2 100%


Interpret 90th Percentile ValuesGenerally, 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


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


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.

EMC 2D/ MMRDAFINAL/ DATA/ ACADFILES/ BASEPLAN.DWG

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Trends on exceedence


EMC 2D/MMRDAFINAL/DATA/ACADFILES/ BASEPLAN.DWG

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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


Figure 4.2 a Percent Deviation from Regional Means for 1997

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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

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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.


©2013 Ekonnect Knowledge Foundation

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


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 90 th 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

©2013 Ekonnect Knowledge FoundationSlide 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


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


• 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



Application to Mumbai

Slide 26

Stations being monitored sinceJan 2000

MPCBSionMulund

BMC1. Colaba (C/R)2. Babula Tank (I/R)3. Worl i 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)ParelKalbadeviBandraI - Industrial

C - CommercialR - Residential

Legend

Zones Suggested for sitingColaba 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 monitoringRecommended monitoring stations


What should be avoided?

The obstruction of tree cover behind is visible in the photograph of the monitoring station at Maravali


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 OF SPM

R2 = 0.6741

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COMPARISON OF NO2

R2 = 0.0141

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NEERI

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.


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



Want to analyze your City Ambient AQ Network?

Write to:Dr Prasad Modak

[email protected]

[email protected]

Slide 33

mailto:[email protected]

mailto:[email protected]

Education

City Ambient Air Quality Monitoring