Download - City Ambient Air Quality Monitoring
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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
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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)
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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
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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
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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
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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)
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Case study of Mumbai, India1997-1999 data
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Illustration of Diurnal Variation in Mumbai's Air Quality (1997 monthly data for NO2 for all monitoring stations)
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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 VariationsPlot them intelligently
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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
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NO2
SO2
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NH3
CV values are generally high (>40) for all three years (particularly for Ammonia)
Coefficient of Variation
Check on Variability
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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
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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%
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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
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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
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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
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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
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Figure 4.2 a Percent Deviation from Regional Means for 1997
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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
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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.
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©2013 Ekonnect Knowledge Foundation
Let us understand Network Morphology
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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
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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
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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
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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
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• 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
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©2013 Ekonnect Knowledge Foundation
Application to Mumbai
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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
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What should be avoided?
The obstruction of tree cover behind is visible in the photograph of the monitoring station at Maravali
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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.
©2013 Ekonnect Knowledge Foundation
What should be avoided?
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©2013 Ekonnect Knowledge Foundation
What happens when two agencies monitor at same location?
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COMPARISON OF SPM
R2 = 0.6741
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COMPARISON OF NO2
R2 = 0.0141
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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.
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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
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©2013 Ekonnect Knowledge Foundation
Want to analyze your City Ambient AQ Network?
Write to:Dr Prasad Modak
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