Learn the Tricks to Get the Best from YourCity Ambient Air Quality Monitoring NetworkCase of Mumbai, IndiaDr Prasad ModakE...
First let us get to the basics
Why Ambient Air Quality Monitoring?• Know the background ?(locations of least “sourceinfluence” or local variability)• Exp...
What needs to be decided?• Which parameters? (e.g. Gaseous, Particulatesand particulate based; Multimedia?)• Deciding on T...
Number, Locations and Siting Guidelines• For point sources : Three location philosophy;Background, Influence• Urban areas ...
Timing, Duration, Frequency, Sample size• Winter as critical month – Periods of low mixing heights,frequent inversion cond...
What to measure? And How?• Criteria pollutants (Routine and recently added )• Source specific parameters• Multimedia measu...
What do we do with the collected data?• Statistical analyses• Data acceptability• Long term data (Correlations and Trends,...
Case study of Mumbai, India1997-1999 data
Diurnal variationsAn analysis of the 8 hourly averages for Mumbaifor the years 1997, 98 and 99 indicates that theconcentra...
020406080100120140ColabaBabulaTankWorliDadarParelSewreeSionKharS.TankAndheriSakinakaJogeshwariGhatkoparBhandupMulundBoriva...
020406080100120140160180ColabaBabulaTankWorliDadarParelSewreeSionKharS.TankAndheriSakinakaJogeshwariGhatkoparBhandupMulund...
Similarities were observed between the pattern of contours drawn for90thpercentile concentrations and the annual means.Ann...
Higher value of CVindicates morefluctuations in themonitored data.Values of CV arerather high forammoniaCV for NO2Check on...
Interpret 90th Percentile ValuesInterpret 90th Percentile ValuesGenerally, SO2concentrationsare well withinstandards, exce...
90th Percentile Values90th Percentile ValuesThecontourmap forNO2indicates acorridoreffect dueto trafficemissionsalong thew...
Following observations can be made from resultsof trend analyses and exceedence overstandards;Mulund, Bhandup, Ghatkopar a...
Stations such as Khar (next toSupari Tank), Sion and Maravali(close to Mankhurd) show some ofthe higher level of exceedenc...
Figure 4.2 a Percent Deviation from Regional Means for 1997-100-50050100150ColabaBabulaTankWorliDadarParelSewreeSionKharS....
Let us understand NetworkMorphology
Network MorphologyNetwork MorphologyNumber of Monitoring StationsNetwork morphology involves the decision on the number of...
Network MorphologyNetwork MorphologyNumber of Monitoring StationsIS 5182 (Part 14 – 1985), Indian Standards (IS) suggests ...
Method/Thumb rule Result CommentsUS EPA 1971 basedon population15 high frequency or40 low frequencyambient air qualitymoni...
Configuring Monitoring StationsConfiguring Monitoring StationsConfiguration of monitoring stations is influenced by the go...
Configuring Monitoring StationsConfiguring Monitoring Stations• Locate kerbside air quality monitoring stations at streets...
Application to Mumbai
Suggested zones for sitingColaba BackgroundBorivali BackgroundParel* AmbientAndheri*Khar*SionMaravali / source orientedBha...
What should be avoided?What should be avoided?The obstruction of tree cover behind is visible in the photograph ofthe moni...
The obstruction of the staircase headroom and the building behindcould lead to unreliable and incorrect data as can be see...
What happens when two agenciesmonitor at same location?
COMPARISON OF SPMR2= 0.6741$0$50$100$150$200$250$300$350$400$4500 100 200 300 400 500NEER ICOMPARISON OF NO2R2=0.014101020...
What should we do?• Urban AQ Monitoring Guidelines - covering all aspects (manyneed some defogging, adaptations etc)• Emph...
Want to analyze your CityAmbient AQ Network?Write toDr Prasad ModakPrasad.modak@emcentre.com
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Learn the Tricks to Get the Best from Your City Ambient Air Quality Monitoring Network Case of Mumbai, India

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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

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Learn the Tricks to Get the Best from Your City Ambient Air Quality Monitoring Network Case of Mumbai, India

  1. 1. Learn the Tricks to Get the Best from YourCity Ambient Air Quality Monitoring NetworkCase of Mumbai, IndiaDr Prasad ModakEnvironmental Management Centrewww.emcentre.com
  2. 2. First let us get to the basics
  3. 3. Why Ambient Air Quality Monitoring?• Know the background ?(locations of least “sourceinfluence” 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 models05/03/13 3Dr Prasad Modak
  4. 4. What needs to be decided?• Which parameters? (e.g. Gaseous, Particulatesand particulate based; Multimedia?)• Deciding on Timing and frequency (Samplinginternal, sample size)• Where? (i.e. location)• How? (Method)05/03/13 4Dr Prasad Modak
  5. 5. Number, Locations and Siting Guidelines• For point sources : Three location philosophy;Background, Influence• Urban areas (Area sources): Land use and populationdriven “network”; Staggered frequencies, fixed andmoving stations philosophy• Traffic junctions (Kerbside air quality)• Special cases - indoor air quality; exposure monitoring;receptor modeling05/03/13 5Dr Prasad Modak
  6. 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 (CVtypically over 20%), Low confidence around means,Problem of trend detection05/03/13 6Dr Prasad Modak
  7. 7. What to measure? And How?• Criteria pollutants (Routine and recently added )• Source specific parameters• Multimedia measurements : Rainwater and Particulateconstituents – Chemical Mass Balances• High frequency automatic stations• Issues on methods, practicing of standard protocols,QA/QC systems05/03/13 7Dr Prasad Modak
  8. 8. What do we do with the collected data?• Statistical analyses• Data acceptability• Long term data (Correlations and Trends, Multivariateanalyses (Factor analyses and Clustering), Interventionanalyses• Short term intensive data (Distribution analyses, PercentExeedence, Extreme value functions)05/03/13 8Dr Prasad Modak
  9. 9. Case study of Mumbai, India1997-1999 data
  10. 10. Diurnal variationsAn analysis of the 8 hourly averages for Mumbaifor the years 1997, 98 and 99 indicates that theconcentrations for all the pollutants in the night(i.e. sampling period of 20-04 hrs) are relativelyhigher than those in the day.05/03/13 10Dr Prasad ModakLook at Data VariationsPlot them intelligently
  11. 11. 020406080100120140ColabaBabulaTankWorliDadarParelSewreeSionKharS.TankAndheriSakinakaJogeshwariGhatkoparBhandupMulundBorivaliTilakNagarChemburMaravaliAniknagarMahulMankhurdMonitoring StationsPercentExceedenceforthreeyears(97,98,99)NO2SO2SPMExceedenceAverage percentage ofexceedence forNO2 is 19%SO2 is 11%SPM is 78%Number of outliers (4 sigma test) in the data are negligible05/03/13 11Dr Prasad ModakCheck on Outliers
  12. 12. 020406080100120140160180ColabaBabulaTankWorliDadarParelSewreeSionKharS.TankAndheriSakinakaJogeshwariGhatkoparBhandupMulundBorivaliTilakNagarChemburMaravaliAniknagarMahulMankhurdMonitoring Stations%CoefficientofVariationNO2SO2SPMNH3CV values are generally high(>40) for all three years(particularly for Ammonia)Coefficient of Variation05/03/13 12Dr Prasad ModakCheck on Variability
  13. 13. Similarities were observed between the pattern of contours drawn for90thpercentile concentrations and the annual means.AnnualAverage forNO290thPercentilefor NO2InterpretInterpretContoursContoursContours are based on 1999 data05/03/13 13Dr Prasad Modak
  14. 14. Higher value of CVindicates morefluctuations in themonitored data.Values of CV arerather high forammoniaCV for NO2Check on variabilityCheck on variabilityof “linked”of “linked”parametersparametersContours are based on 1999 dataCV for NH3Max for NH3 160%Max for NO2 100%05/03/13 14Dr Prasad Modak
  15. 15. Interpret 90th Percentile ValuesInterpret 90th Percentile ValuesGenerally, SO2concentrationsare well withinstandards, exceptin industrialareas.There is clearlyan island effect atChembur(characterized bythe localinfluence ofFertilizer industry- RCF) for NH3emissions.90th Percentile values: SO290th Percentilevalues: NH305/03/13 15Dr Prasad Modak
  16. 16. 90th Percentile Values90th Percentile ValuesThecontourmap forNO2indicates acorridoreffect dueto trafficemissionsalong thewesternandeasternsuburbroads.90th Percentilevalues: NO290th Percentilevalues: SPM05/03/13 16Dr Prasad Modak
  17. 17. Following observations can be made from resultsof trend analyses and exceedence overstandards;Mulund, Bhandup, Ghatkopar and Mankhurd,Aniknagar , Sion and Worli show a statisticallysignificant downward trend over the period of1997-1999 for SPM.Despite such a downward trend in the easternsuburbs, results show that almost all the stationsin Mumbai have a considerable exceedence overstandards. Average percentage of exceedence is70% that is indeed very significant.In the case of NO2, no station reports astatistically downward trend. Two stations viz.Supari Tank and Mankhurd show statisticallyupward trend in the period of 1997-1999.05/03/13 17Dr Prasad ModakTrends on exceedence
  18. 18. Stations such as Khar (next toSupari Tank), Sion and Maravali(close to Mankhurd) show some ofthe higher level of exceedence.These observations corroborate thatemissions of NO2 in Wards H, Gand M are on the rise mainly due toemissions of traffic.A group of stations consisting ofMaravali, Supari Tank, Andheri andJogeshwari show a statisticallyupward trend for SO2. Despite sucha trend, the exceedence overstandards is only marginal of theorder of between 5 to 10% in thisarea.05/03/13 18Dr Prasad ModakDo Source Interpretation
  19. 19. Figure 4.2 a Percent Deviation from Regional Means for 1997-100-50050100150ColabaBabulaTankWorliDadarParelSewreeSionKharS.TankAndheriSakinakaJogeshwariGhatkoparBhandupMulundBorivaliTilakNagarChemburMaravaliAniknagarMahulMankhurdMonitoring StationsPercentDeviationfromRegionalMeanNO2SO2SPMAt Colaba ,Supari Tank,Andheri,Sakinaka, andBorivali, forinstance, forall the threeparametersviz. SO2, NO2and SPM, andfor all thethree years,station annualaverages aregenerallybelow theregionalmeans.Compare with Regional MeansCompare with Regional MeansMost of theambientstations showaveragevalues belowthe regionalmean for allthe pollutantsConsistentbehavior is seenat Khar andMaravali withrespect to theregional mean.05/03/13 19Dr Prasad Modak
  20. 20. Let us understand NetworkMorphology
  21. 21. Network MorphologyNetwork MorphologyNumber of Monitoring StationsNetwork morphology involves the decision on the number of monitoring stationsand their configuration.Number of Monitoring Stations could be decided based on several approachessuch as:Using distance criterion (proximity analysis) – this is based only on optimizingnetwork density so as to have a spatially well distributed network. Does notconsider air quality influence and hence can be used only as a supportiveapproach.US EPA has developed design curves relating the populations and the numberof monitoring stations considering the type of monitoring stations (such asmanual or automatic) based on a detailed qualitative evaluation of several citiesin USA. These curves could be used to determine the gross number of stationswhich could then be refined with other approaches.05/03/13 21Dr Prasad Modak
  22. 22. Network MorphologyNetwork MorphologyNumber of Monitoring StationsIS 5182 (Part 14 – 1985), Indian Standards (IS) suggests two empiricalmethods for the estimation of number of monitoring stations. Onemethod is based on population exposed and the other is based on thecomparison with standard and 90thpercentile concentrations ofpollutants.Amongst the analytical techniques, methods based on the estimation ofregional mean have also been proposed to arrive at the number ofmonitoring stations. These methods could be used for estimation ofnumber of monitoring stations for a pollutant if its coefficient of variation(CV) is known.05/03/13 22Dr Prasad Modak
  23. 23. Method/Thumb rule Result CommentsUS EPA 1971 basedon population15 high frequency or40 low frequencyambient air qualitymonitoring stationsData base outdated,High and lowfrequency are notprecisely defined.IS 5182 (Part I4 –1985) – populationexposure criteria10 ambient and 4kerbside air qualitymonitoring stationsDoes not comment onthe requiredfrequencyIS 5182 (Part I4 –1985) – based oncomparison between90thpercentile andstandard7 ambient air qualitymonitoring stationsResults can bespurious dependingon the limitations ofthe dataKeagy’s nomograph 30 low frequencymonitoring stationsResults can bespurious dependingon the limitations ofthe dataIt is prudentthat therequirednumber ofmonitoringstations isarrived at byexamining theneededmonitoringconfiguration.This approachbrings in therequired urbanspecificity.Summary of Various Recommendations on the Number of Air Quality Monitoring StationsSummary of Various Recommendations on the Number of Air Quality Monitoring StationsThe guidelines provided by IS 5182 (Part 14) 1985seem to be appropriate.05/03/13 23Dr Prasad Modak
  24. 24. Configuring Monitoring StationsConfiguring Monitoring StationsConfiguration of monitoring stations is influenced by the governing or sitespecific objective. Criteria for configuration of monitoring stations shouldnot be equated to that of the siting protocol.Typical guidelines for choosing a configuration for an urbanAQMN are,• Locate an ambient air quality monitoring station to capturevarious 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 oneambient air quality monitoring station that is distanced fromurban emission sources and is therefore broadly representativeof city-wide background conditions.• 05/03/13 24Dr Prasad Modak
  25. 25. Configuring Monitoring StationsConfiguring Monitoring Stations• Locate kerbside air quality monitoring stations at streets thatexhibit heavy traffic and pedestrian congestion.• Few (at least two or three) ambient air quality monitoringstations may be located to capture influence of any majorsources (point or area) present in the urban area.05/03/13 25Dr Prasad Modak
  26. 26. Application to Mumbai
  27. 27. Suggested zones for sitingColaba BackgroundBorivali BackgroundParel* AmbientAndheri*Khar*SionMaravali / source orientedBhandup4 kerbside monitoring stations at congested trafficjunctions.In addition, two more zones for ambient monitoringwill be recommended.All of the above zones will be reviewed in task 2.Task 2 will also include identification of specificlocations for the sites* candidates for automatic monitoringRecommended monitoring stations05/03/1327Dr Prasad Modak
  28. 28. What should be avoided?What should be avoided?The obstruction of tree cover behind is visible in the photograph ofthe monitoring station at Maravali05/03/13 28Dr Prasad Modak
  29. 29. The obstruction of the staircase headroom and the building behindcould lead to unreliable and incorrect data as can be seen fromthis photograph at Parel where MCGM as well as NEERImonitored ambient air quality.What should be avoided?What should be avoided?05/03/13 29Dr Prasad Modak
  30. 30. What happens when two agenciesmonitor at same location?
  31. 31. COMPARISON OF SPMR2= 0.6741$0$50$100$150$200$250$300$350$400$4500 100 200 300 400 500NEER ICOMPARISON OF NO2R2=0.014101020304050607080900 10 20 30 40 50 60 70NEERIComparison between NEERI and BMC monitoring at ParelComparison between NEERI and BMC monitoring at ParelThe monitoring station at Parelwhere both BMC and NEERI conductambient air quality monitoringshowed little correlation for all thepollutants.The scatter diagrams on the leftshow the low R squared values ofdata of NEERI and BMC for SPMand NO2.Although the sampling frequencies ofNEERI and BMC differ, monthlyaverages are expected to showreasonably similar patterns. It seemsthat even at the same location ofsampling, the monthly averages cangreatly differ when the station isoperated by different agencies atdifferent sampling times.05/03/13 31Dr Prasad Modak
  32. 32. What should we do?• Urban AQ Monitoring Guidelines - covering all aspects (manyneed 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 examplesin Training• Provide support software for better AQ data interpretation• Campaign against poor ambient Air Quality data05/03/13 32Dr Prasad Modak
  33. 33. Want to analyze your CityAmbient AQ Network?Write toDr Prasad ModakPrasad.modak@emcentre.com

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