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City Ambient Air Quality Monitoring

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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 …

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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  • 1. Slide 1Learn the Tricks to Get the Best from YourCity Ambient Air Quality Monitoring Network:The Case of Mumbai, IndiaByDr. Prasad Modak,Director, Ekonnect Knowledge Foundation©2013 Ekonnect Knowledge Foundation
  • 2. First let us get to the basics…©2013 Ekonnect Knowledge FoundationSlide 2
  • 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 models©2013 Ekonnect Knowledge FoundationSlide 3
  • 4. What needs to be decided?• Which parameters? (e.g. Gaseous, Particulates andparticulate based; Multimedia?)• Deciding on Timing and frequency (Sampling internal,sample size)• Where? (i.e. location)• How? (Method)©2013 Ekonnect Knowledge FoundationSlide 4
  • 5. Number, Locations and SitingGuidelines• 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 modeling©2013 Ekonnect Knowledge FoundationSlide 5
  • 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 detection©2013 Ekonnect Knowledge FoundationSlide 6
  • 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 systems©2013 Ekonnect Knowledge FoundationSlide 7
  • 8. What do we do with the collected data?Statistical analysesData acceptabilityLong term data (Correlations and Trends, Multivariateanalyses (Factor analyses and Clustering), InterventionanalysesShort term intensive data (Distribution analyses, PercentExeedence, Extreme value functions)©2013 Ekonnect Knowledge FoundationSlide 8
  • 9. Case study of Mumbai, India1997-1999 data©2013 Ekonnect Knowledge FoundationSlide 9
  • 10. Illustration of Diurnal Variation in MumbaisAir Quality (1997 monthly data for NO2 for all monitoring stations)0.0010.0020.0030.0040.0050.0060.0070.0080.0090.00JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DECMonthNO2inug/m312 pm to 4 pm4 pm to 4 am4 am to 12 pmDiurnal variationsAn analysis of the 8 hourly averages forMumbai for the years 1997, 98 and 99 indicatesthat the concentrations for all the pollutants inthe night (i.e. sampling period of 20-04 hrs) arerelatively higher than those in the day.Look at Data VariationsPlot them intelligently©2013 Ekonnect Knowledge FoundationSlide 10
  • 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 negligibleCheck on Outliers©2013 Ekonnect Knowledge FoundationSlide 11
  • 12. 020406080100120140160180ColabaBabulaTankWorliDadarParelSewreeSionKharS.TankAndheriSakinakaJogeshwariGhatkoparBhandupMulundBorivaliTilakNagarChemburMaravaliAniknagarMahulMankhurdMonitoring Stations%CoefficientofVariationNO2SO2SPMNH3CV values are generally high (>40)for all three years (particularly forAmmonia)Coefficient of VariationCheck on Variability©2013 Ekonnect Knowledge FoundationSlide 12
  • 13. NNSimilarities were observed between the pattern of contours drawn for90th percentile concentrations and the annual means.AnnualAverage forNO290thPercentilefor NO2InterpretContoursContours are based on 1999 data©2013 Ekonnect Knowledge FoundationSlide 13
  • 14. Higher value of CVindicates morefluctuations in themonitored data.Values of CV arerather high forammonia NCV for NO2Check on variabilityof “linked”parametersContours are based on 1999 dataNCV for NH3Max for NH3 160%Max for NO2 100%©2013 Ekonnect Knowledge FoundationSlide 14
  • 15. Interpret 90th Percentile ValuesGenerally, SO2concentrationsare well withinstandards,except inindustrial areas.There is clearlyan island effect atChembur(characterized bythe localinfluence ofFertilizer industry- RCF) for NH3emissions.90th Percentile values: SO290th Percentilevalues: NH3©2013 Ekonnect Knowledge FoundationSlide 15
  • 16. 90th Percentile ValuesThe contourmap forNO2indicates acorridoreffect due totrafficemissionsalong thewestern andeasternsuburbroads.90th Percentilevalues: NO290th Percentilevalues: SPM©2013 Ekonnect Knowledge FoundationSlide 16
  • 17. Following observations can be made fromresults of trend analyses and exceedenceover standards;Mulund, Bhandup, Ghatkopar and Mankhurd,Aniknagar , Sion and Worli show astatistically significant downward trend overthe period of 1997-1999 for SPM.Despite such a downward trend in the easternsuburbs, results show that almost all thestations in Mumbai have a considerableexceedence over standards. Averagepercentage of exceedence is 70% that isindeed 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.AN.DWGN NTrends on exceedence©2013 Ekonnect Knowledge FoundationSlide 17
  • 18. EMC 2D/MMRDAFINAL/DATA/ACADFILES/BASEPLAN.DWGN N NStations such as Khar (next to SupariTank), Sion and Maravali (close toMankhurd) show some of the higherlevel of exceedence. Theseobservations corroborate thatemissions of NO2 in Wards H, G and Mare on the rise mainly due to emissionsof traffic.A group of stations consisting ofMaravali, Supari Tank, Andheri andJogeshwari show a statistically upwardtrend for SO2. Despite such a trend, theexceedence over standards is onlymarginal of the order of between 5 to10% in this area.Do Source Interpretation©2013 Ekonnect Knowledge FoundationSlide 18
  • 19. Figure 4.2 a Percent Deviation from Regional Means for 1997-100-50050100150ColabaBabulaTankWorliDadarParelSewreeSionKharS.TankAndheriSakinakaJogeshwariGhatkoparBhandupMulundBorivaliTilakNagarChemburMaravaliAniknagarMahulMankhurdMonitoring StationsPercentDeviationfromRegionalMeanNO2SO2SPMAt Colaba ,Supari Tank,Andheri,Sakinaka,and Borivali,for instance,for all thethreeparametersviz. SO2,NO2 andSPM, and forall the threeyears,stationannualaverages aregenerallybelow theregionalmeans.Compare with Regional Means05010015020025030035040045019781979198019811982198319841985198619871988198919901991199719981999YearRegionalMeaninug/m3SO2NO2SPMMost of theambientstations showaverage valuesbelow theregional meanfor all thepollutantsConsistent behavioris seen at Khar andMaravali withrespect to theregional mean.©2013 Ekonnect Knowledge FoundationSlide 19
  • 20. ©2013 Ekonnect Knowledge FoundationLet us understand Network MorphologySlide 20
  • 21. Network Morphology• Network morphology involves the decision on the number of monitoring stations andtheir configuration.• Number of Monitoring Stations could be decided based on several approaches suchas:• Using distance criterion (proximity analysis) – this is based only on optimizing networkdensity so as to have a spatially well distributed network. Does not consider air qualityinfluence and hence can be used only as a supportive approach.• US EPA has developed design curves relating the populations and the number ofmonitoring stations considering the type of monitoring stations (such as manual orautomatic) based on a detailed qualitative evaluation of several cities in USA. Thesecurves could be used to determine the gross number of stations which could then berefined with other approaches.Number of Monitoring Stations©2013 Ekonnect Knowledge FoundationSlide 21
  • 22. Network Morphology• IS 5182 (Part 14 – 1985), Indian Standards (IS) suggests two empirical methods forthe estimation of number of monitoring stations. One method is based on populationexposed and the other is based on the comparison with standard and 90th percentileconcentrations of pollutants.• Amongst the analytical techniques, methods based on the estimation of regional meanhave also been proposed to arrive at the number of monitoring stations. Thesemethods could be used for estimation of number of monitoring stations for a pollutant ifits coefficient of variation (CV) is known.Number of Monitoring Stations©2013 Ekonnect Knowledge FoundationSlide 22
  • 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 between90th percentile 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 prudent thatthe requirednumber ofmonitoring stationsis arrived at byexamining theneeded monitoringconfiguration. Thisapproach brings inthe required urbanspecificity.The guidelines provided by IS 5182 (Part 14) 1985 seem to be appropriate.SUMMARY OF VARIOUS RECOMMENDATIONS ON THENUMBER OF AIR QUALITY MONITORING STATIONS©2013 Ekonnect Knowledge FoundationSlide 23
  • 24. Configuration 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 urban AQMN are,• Locate an ambient air quality monitoring station to capture variousdevelopment zones i.e. city center and suburban areas. Prioritizelocation based on population and sensitivity• To obtain a background air quality, locate at least one ambient airquality monitoring station that is distanced from urban emissionsources and is therefore broadly representative of city-widebackground conditions.CONFIGURING MONITORING STATIONS©2013 Ekonnect Knowledge FoundationSlide 24
  • 25. • Locate kerbside air quality monitoring stations at streets that exhibit heavytraffic and pedestrian congestion.• Few (at least two or three) ambient air quality monitoring stations may belocated to capture influence of any major sources (point or area) presentin the urban area.CONFIGURING MONITORING STATIONS©2013 Ekonnect Knowledge FoundationSlide 25
  • 26. ©2013 Ekonnect Knowledge FoundationApplication to MumbaiSlide 26
  • 27. Stationsbeing monitored sinceJan 2000MPCBSionMulundBMC1. 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 atTraffic Junctions (BMC)Wadala, Andheri and MahimNEERI (under GEMS)ParelKalbadeviBandraI - IndustrialC - CommercialR - ResidentialLegendZones Suggested 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 stations©2013 Ekonnect Knowledge FoundationSlide 27
  • 28. What should be avoided?The obstruction of tree cover behind is visible in the photograph of the monitoringstation at Maravali©2013 Ekonnect Knowledge FoundationSlide 28
  • 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.©2013 Ekonnect Knowledge FoundationWhat should be avoided?Slide 29
  • 30. ©2013 Ekonnect Knowledge FoundationWhat happens when two agenciesmonitor at same location?Slide 30
  • 31. Comparison between NEERI and BMC monitoring at ParelThe monitoring station at Parel whereboth BMC and NEERI conduct ambientair quality monitoring showed littlecorrelation for all the pollutants.The scatter diagrams on the left show thelow R squared values of data of NEERIand BMC for SPM and NO2.Although the sampling frequencies ofNEERI and BMC differ, monthly averagesare expected to show reasonably similarpatterns. It seems that even at the samelocation of sampling, the monthlyaverages can greatly differ when thestation is operated by different agenciesat different sampling times.©2013 Ekonnect Knowledge FoundationSlide 31
  • 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 variousobjectives• Hands on Training on data generation and analyses• Build case studies like Mumbai AQ Data and use theexamples in Training• Provide support software for better AQ datainterpretation• Campaign against poor ambient Air Quality data©2013 Ekonnect Knowledge FoundationSlide 32
  • 33. ©2013 Ekonnect Knowledge FoundationWant to analyze your City AmbientAQ Network?Write to:Dr Prasad Modakprasad.modak@emcentre.comorprasad.modak@ekonnect.netSlide 33

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