Best Practices in Air Quality Monitoring

Transcription

1 Best Practices in Air Quality Monitoring Prof. Shun cheng LEE Dr. Wing tat HUNG Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University KEY REFERENCES 1. URBAN AIR QUALITY MANAGEMENT STRATEGY IN ASIA GUIDEBOOK By Steinar Larssen, Knut Erik Grønskei, Norwegian Institute for Air Research 2. Kjeller, Norway and M. C. Hanegraaf, Huib Jansen, O. J. Kuik, F. H. Oosterhuis, Xander A. Olsthoorn, Institute for Environmental Studies, The Free Unversity 3. Amsterdam, the Netherlands. 4. Air Quality Monitoring Programme Design By Bjarne Sivertsen, Norwegian Institute for Air Research, Kjeller, Norway. NILU: F 2/22 REFERENCE: 5. O DATE: FEBRUARY Guidelines for Ambient Air Quality Monitoring By CENTRAL POLLUTION CONTROL BOARD (Ministry of Environment & Forests, Govt. of India), NATIONAL AMBIENT AIR QUALITY MONITORING SERIES : NAAQMS/... / GUIDELINES FOR DEVELOPING NATIONAL STRATEGIES TO USE AIR QUALITY 8. MONITORING AS AN ENVIRONMENTAL POLICY TOOL, United Nations Economic and Social Council, ECE/CEP/29/1, 14 October National Air Pollution Surveillance Network Quality Assurance and Quality Control Guidelines, Environment Canada, Environmental Protection Service, Environmental Technology Advancement Directorate, Analysis and Air Quality Division, Environmental Technology Centre, Report No. AAQD GUIDANCE FOR NETWORK DESIGN AND OPTIMUM SITE EXPOSURE FOR PM2,5 AND PM1, PREPARED BY John G. Watson, Judith C. Chow, David DuBois, Mark Green, Neil Frank, Marc Pitchford, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency 1

2 Essential Elements of Good Air Quality Monitoring System Summary of Essential Attributes of Good Practices in implementing AQM system ATTRIBUTE/CATEGORY A B Ability to properly plan and implement a AQM network to a compatible international standard Ability to plan and implement a QA/QC process INDICATORS Evidence of proper planning documents (for example guidelines) and planning process for AQM system (in particular, number and locations of AQM stations Evidence of a proper equipment sourcing and procurement procedure (for example, tendering documents) Evidence of qualification requirements for personnel (including responsible officers and technicians) Evidence of a QA/QC process (for example, a published requirements and guides for data quality, i.e., acceptable accuracy and lid d t t i l l ) Evidence of remedial measures in case of un-reliable / uncertain data records (for example, a model to rectify the faulty data) Evidence of qualification requirements for independent QA/QC agent Evidence of published data and reports (hardcopy of electronic forms) Evidence of formal established channels to disseminate data and results to stakeholders Evidence of channels receiving feedbacks from stakeholders Evidence of changes in policy resulting from AQM results (for example, changes in legislation and implementation of clean air programme) Evidence of stakeholders using the AQM data records and results AQM t valid data capturing levels) C D E Ability to disseminate AQM data and analytical results to stakeholders Ability to utilize the AQM results to improve AQ control policy Ability to provide manpower and financial resources to sustain the AQM system Evidence of staff development and training programmes for personnel in AQM system Evidence of funding commitment for sustaining the expenses of the AQM system Evidence of continuous review exercise to improve AQM system SUPPORTING DOCUMENTS Guidelines for siting and planning of AQM station Lists of AQ monitoring equipment, equipment specifications QA/QC guidelines for AQM system Samples of air quality data record sheets Local air quality standards Manpower structure, personnel qualification requirements Costs : i) initial site capital cost; ii) costs of spare parts and consumables; iii) Operating costs,electricity, calibration gases, filter papers and iv) maintenance cost Monthly/ Annual running costs for AQM system 2

3 World Health Organization Guidelines The Role of Monitoring in Air Quality Management 3

4 AQM NETWORK SIZE DESIGN Good practice to check the necessity of station, the types of pollutant monitoring, the appropriate equipment to use 4

5 US ENVIRONMENTAL PROTECTION AGENCY Summarized below is the minimum number of stations per metropolitan statistical area (MSA) for PM 1, PM 2.5 and O 3 under the US EPA monitoring guidelines. PM 1 Minimum Monitoring Requirements MSA Population HIGH CONCENTRATION (exceeds NAAQS by 2% or more) MEDIUM CONCENTRATION (exceeds NAAQS by 8%) LOW CONCENTRATION (less than 8% of NAAQS) >1,, , 1,, , 5, , 25, Source: Federal Register / Vol. 71, No. 2 / October 17, 26 / Rules and Regulations PM2.5 Minimum Monitoring Requirements MSA Population Most recent 3 year design value 85% of any PM2.5 NAAQS Most recent 3 year design value <85% of any PM2.5 NAAQS >1,, 3 2 5, 1,, 2 1 5, 5, 1 Source: Federal Register / Vol. 71, No. 2 / October 17, 26 / Rules and Regulations O3 Minimum Monitoring Requirements MSA Population Most recent 3 Most recent 3 year design value year design value 85% of any PM2.5 NAAQS <85% of any PM2.5 NAAQS >1 million million , 4 million 2 1 5, 35, 1 Source: Federal Register / Vol. 71, No. 2 / October 17, 26 / Rules and Regulations Australian Specification The Peer Review Committee (PRC), Australian National Environment Protection Council (NEPC) National Environment Protection (Ambient Air Quality) Measure Technical Paper No. 4 on Screening Procedures, May 21 Fewer performance monitoring stations may be needed where it can be demonstrated that pollutant levels are reasonably expected to be consistently lower than the standards mentioned in this Measure. 5

6 Equipment Considerations AQM Management and manpower training The case of California Air Resources Board (CARB) TRAINING The ARB has recruitment and screening procedures to ensure that station operators are experienced and qualified instrument technicians. On the job training is completed by all new station operators before they are allowed to independently operate field stations. 6

7 The primary purpose of the QAPP is to provide an overview of the project, describe the need for the measurements, and define QA/QC activities to be applied to the project, all within a single document. Common Issues and Problems in Asian Cities 1. Inadequate political drive deficiency of public awareness and no champion in government senior management 2. Insufficient funds to sustain the recurrent and replacement costs of air monitoring system 3. Deficiency in data analysis and especially in QA/QC requirement and processing insufficient fund and technical ability 4. Discrepancies i in data dissemination i i stakeholders have no access to raw data 5. Insufficient use of data to make policy changes to improve air quality 7

8 THE CASE OFHONG KONG AND PEARL RIVER DELTA AQM NETWORK Problems/ Issues in early 198s 1. Serious air pollution from factories 2. Inadequate public awareness on air pollution 3. Inadequate funds to set up AQM system 4. No political leader(s) to advocate abatement of air pollution. How the problems were tackled? 1. Air pollution control work started in late 198s when the British Hong Kong Government implemented the international obligation on Hong Kong; the political drive came from the UK government and expatriates working in HK 2. The AQM network started with a few stations and gradually expanded 3. The planning, operation, QA/QC systems were borrowed directly from UK 8

9 Air quality monitoring network in Hong Kong Hong Kong has 14 air quality monitoring stations for measuring concentrations of major air pollutants. Land Use Type Land Use Characteristics Air Monitoring Stations Urban Densely populated 1. Central/ western residential areas mixed 2. Eastern with some commercial 3. Kwai Chung and/ or industrial area 4. Kwun Tong 5. Sham Shui Po It consists of Tsuen Wan general stations for New Town Mainly residential area 7. Sha Tin monitoring ambient 8. Tai Po air quality and 3 9. Tung Chung roadside stations for 1. Yuen Long measuring street level Rural Rural area 11. Tap Mum air quality since 1999 (background station) Roadside Urban roadside in mixed residential/ commercial 12. Causeway Bay area with heavy traffic 13. Central and surrounded by many tall buildings 14. Mong Kok Air quality monitoring network in Hong Kong 9

10 General stations and Roadside stations Contents General station Roadside station Purposes Stations cost Height above ground level Distance to nearest major roadway Parameter Maintenance Schedule Represent an area of mixed residential and commercial/industrial activities or only residential activities US$ 385 for capital and US$ 22 for Annual operation/ development From 11 meters (Tap Mun) to 27.5 meter (Tung Chung) From 2 meter (Sham Shui Po) to 2 meters (Sha Tin) SO2, NOx, NO, NO2, CO, O3, RSP, TSP Less frequent Monitor street-level emissions from nearby vehicle exhaust and road dust US$ 51 for capital and US$ 22 for Annual operation/ development Quite low, at 2 to 3 meters All less than 5 meters SO2, NOx, NO, NO2, CO, RSP More frequent API Comes from measurements at 11 general AQM stations Comes from measurements at 3 roadside AQM stations Hok Tsui Station Hok Tsui was established by the Civil & Structural Engineering Department of Hong Kong Polytechnic University in It is the first long term background site Parameter: ozone, solar ultraviolet radiation, aerosol, CO and NO x,pm 2.5, Rn Equipment CO analyze; O 3 analyzer; Multi gas calibrator; Standard gas; UV photometric t O 3 calibrator; Brewer Spectrophotometer; external UVB lamp; Data logger; temperature and Humidity data logger; Three fine particle collecting instruments (belong to EPD); Radon analyzer; Radon calibration unit; Telephone lines; Pump; dehumidifier control equipment; air condition. 1

11 With the rapid economic development in the Mainland, air quality in HK is severely affected, there is a great problem to identify responsibilities, Pearl River Delta AQM network was set up ten years ago to identify our source apportioning Mobile station 11

12 How to use the data? 1.Determine air pollution levels and trend 2.Determine source region for measured plume using wind and back trajectories 3.Improving emission inventories 4.Determine what precursors control photochemical production of ozone 5.Modelling Emission Based models and Observation Based models 1 day back trajectories arriving HK for days with PM 2.5 samples (November 2 October 21) (Wang, Final Report to EPD, 23) 12

13 Local vs. regional contribution to PM 2.5 in coastal air mass 4 HKEPD) (PM2.5 data courtesy of ) Chemical Composition (μg/m3) Rural (Hok Tsui) Urban (Tsuen Wan) Sulfate Nitrate Ammonium EC OC PM 2.5 Source apportionments of NMHCs Urban Hong Kong Rural Lin an 5.2%.1% 3% 8% 2% 19.4% 36.4% Use of solvent Vehicle emission LPG or natural gas leakage Industrial sources Biogenic emissions 17% 9% Gasoline evaporation Biomass burning Natural gas leakage Industrial sources Biogenic emissions 61% LPG leakage 38.9% Vehicle and solvent Biofuel/biomass Guo et al., EP, 23, AE 24 13

14 Contribution of different pollutants to light extinction (Bext) Bext (Mm 1 ) = 3f(rh) [Sulfate]+ 3f(rh)[Nitrate]+ 4 [Organic] + 1[Soil]+.6[Coarse Mass]+ 1[EC] +.175[NO 2 ] + 1 f(rh) Hygroscopic species growth function Relative Humidity (%) [Sulfate] = (NH 4 ) 2 SO 4 [Nitrate] = NH 4 NO 3 [Organic] = 1.4[OC] [Soil] = 2.2[Al] [Si] [Ca] [Fe] [Ti] [Coarse Mass] = [RSP]-[FSP] f(rh)= hygroscopic species growth function Inverse model result (Y. X. Wang et al., JGR, 24) 14

15 Roadside emissions problems: unclear characteristics and factors influences the roadside pollution concerntrations Solution: carried out a detailed study and analaysis with a Roadside Supersite at PolyU Supersite Objectives Evaluate measurement methods (Intercomparisons, practicality, operating procedures) Understand atmospheric processes and source contributions (Chemical/size characteristics, Influencing factors, effects of meteorology) Establish PM/health relationships (epidemiology, human exposure, toxicological responses, practical PM indicators of health heffects) 15

16 Traffic Patterns at Supersite c counts (# hour -1 ) Traffi Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Diesel fueled vehicle Gasoline fueled vehicle Taxis Total Research Grade Supersite Measurements Continuous, high time resolution measurements of particle mass, number, and chemical components Ultrafine, fine, and coarse particle size distributions Standard and advanced chemical and morphological characterization Precursor gases, co pollutants, and meteorology Collocated instrument testing 16

17 Observation and Method Avg Time Frequency Filter Filter Mass and Chemistry PM2.5 mass, ions, carbon (RP sequential /Quartz 24-hr Daily Quartz (47mm) filter) PM1 mass, ions, carbon (RP sequential /Quartz 24-hr Daily Quzrts (47mm) filter) PM1, PM2.5, PM1 Mass, ions, Elements, carbon including water soluble organic carbon (URG 24-hr Every six days Teflon (47mm) Quartz (47mm) sampler with Quartz/Teflon filters) Positive OC artifact for PM1 and PM2.5 (URG sampler with Quartz/Teflon filters) 24-hr Intensive Teflon (47mm) Quartz (47mm) Diurnal variation of PM2.5 and major chemical composition (RP sequential /Quartz filter) 2-hr Intensive Quartz (47mm) Particle Sizes Mass, ion and elemental size distribution (MOUDI m in 11 fraction with Teflon & IC, AC, XRF; and Aluminum & OC/EC analyser) Mass, ion and elemental size distribution (MOUDI m in 11 fraction with Aluminum foil & IC, OC/EC analyzer) Carbon size distribution (Anderson Impactor.43-1 m in 8 fraction with Quartz & IC, OC/EC analyzer) Continuous Particle Mass and Chemistry 48-hr to 12- hr intensive Teflon (47mm and 37 mm) Quartz (47mm and 37 mm) 48-hr to 72-hr Intensive Aluminium foil (47mm) Quartz (37mm) Teflon (47 mm) 24-hr Weekly Quartz (81mm) PM2.5 mass (TEOM) 3-min Every day Connected to a computer EC (Aethalometer Model AE-3) 5-min Every day Kimoto PMcoarse/fine/OBC 1-hr Every day Total Particulate PAH 1-min Every day Particle number Concentration μm size distribution (TSI min Every day Connected to a computer SMPS).1-2 μm size distribution (PMS 13) 3-min Every day Morphology Particles on Nucleapore filter (SEM) 3-min - Nuclepore (47 mm) Gases NO-NO2-NOx 1-min Every day Connected to a computer NH3 15-min Every day PM Measurements filter BGI frmomni TM Ambient Air Sampler Airmetrics Mini Vol portable samplers URG 3ABC multichannel samplers Partisol Plus Sequential Air Sampler (225) MSP MOUDI model 11 and model 115 (Nano MOUDI) Roadside Medium Vol Air Sampler (PM 2.5 ) High Vol Air Sampler (PM 2.5 ) Eight Stage Non Viable Impacor (.4 1 m) High Vol PUF sampler (PAHs) 17

18 Aerosol Physical Properties Near real time automated measurements SMPS 38 Electrostatic Classifiers and 322A Condensation Particle Counters (CPCs), measuring highresolution size distributions of ultrafine particles. display data using 162 size channels from 1 to 1 nm Sizing sensitivities from.1 2. microns (.1,.2,.3,.4,.5,.7, 1. and 2. µm) OPC = optical particle counter, SMPS = scanning mobility particle spectrometer Continuous Measurements Li Cor CO 2 and H 2 O NOx and NH 3 BC Single and sevenwavelength aethalometer BC Kimoto SPM 613D Dichotomous Monitor PAH EcoChem R&P TEOM 18

19 Consistency Tests for PM Measurements Chemical Analyses consistency tests include: (1) comparisons between mass concentrations and the weighted sum of chemical species; (2) comparisons between concentrations of the same species measured by different analysis methods (e.g., sulfate by IC versus total sulfur by XRF; soluble potassium by IC versus total potassium by XRF; and chloride by IC versus chlorine by XRF); (3) charge balances between anions and cations; and (4) comparisons between mass and chemical concentrations in different size fractions (e.g., PM concentrations must always be less than or equal to PM 1 concentrations). 19

20 Mass Concentrations versus the Sum of Chemical Species 1:1 line 12 Sum of specie es of PM 1. (ug m -3 ) y=.8(.3)+1.5(1.67) R 2 =.96 n=4 y/x=1.9(.13) Teflon particulate mass (ug m -3 ) Different Analytical Methods g m -3 ) Sulfate (ug Chloride (ug m -3 ) y=2.67(.6)x-.21(.32) R 2 =.95 n= Sulfur (ug m -3 ) y=.8(.3)x+.25(.3) R 2 =.9 n= Chlorine (ug m -3 ) um (ug m -3 ) Soluble potassiu g m -3 ) Calculated ammonium (ug y=.89(.2)x-.6(.3) R 2 =.93 n= Total potassium (ug m -3 ) Ammonium sulfate y=1.87(.6)x+.44(.18) 6)x+ 18) R 2 =.9, n=12 Ammonium bisullfate y=1.7(.4)x+.32(.16) R 2 =.87, n= Measured ammonium (ug m -3 ) 2

21 Different Size Fractions 12 s concentration (ug m -3 ) PM 2.5 mass y=1.3(.5)-1.53(1.77) R 2 =.96 PM 1. /PM 2.5 =.81(.18) PM 1. mass concentration (ug m -3 ) Inter comparisons of Different Instruments Measurement methods developed by different organizations may give different results when sampling the same atmosphere even though the techniques appear to be similar (e.g., Hitzenberger, et al., 24). 21

22 Comparison of different PM 2.5 samplers Instrument BGI High Volume Mini Volume URG Y-Shape Kimoto Rel. Ref.* Mass Concentration, μgm -3 Diff. to Rel. Ref.* *Rel. Ref. represents the Relative Reference Value, Relative Reference Value is the average mass concentrations obtained by different filter-based samplers. Time Series Analysis 22

23 Inter comparisons of hourly PM 2.5 between Kimoto (beta gauge) and TEOM (weighing) 12 o hourly PM2.5 (μg m -3 ) Kimot y =.96(.1)x (.51) R 2 =.72 P<.1, n= TEOM hourly PM2.5 (μg m -3 ) Results: The Characteristics of Fine (PM 2.5 ) and Coarse (PM ) Particles 23

24 Seasonal Variations concentration (ug m -3 ) PM PM2.5 at PU roadside Jan Feb Mar Apr May Jun Jul Month Aug Sep Oct Nov Dec concentration (ug m -3 ) PM 5 PMcoarse at PU roadside Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month The PM data was determined by Kimoto SPM 613D Dichotomous Monitor Weekly cycle PM concentration (ug m -3 ) PM2.5 PM1 3 Sun Mon Tue Wed Thu Fri Sat R PM 2.5 PM coarse BC Diesel-fueled vehicle Concentration (ug m -3 ) PMcoarse BC Gasoline-fueled vehicle Taxis The PM data were determined by Kimoto SPM 613D Dichotomous Monitor Traffic counts (# hour -1 ) Sun Mon Tue Wed Thu Fri Sat Diesel fueled vehicle Gasoline fueled vehicle Taxis Sun Mon Tue Wed Thu Fri Sat 24

25 2% 7%3%% 19% 42% 27% 12/17/212 Median concentrations of PM coarse for each.4 m s 1 wind speed bin PM coarse PM coarse coarse R=.98, P< Wind speed (m s -1 ) The PM data was determined by Kimoto SPM 613D Dichotomous Monitor Chemical Composition PM 2.5 (55.5±25.6 μg m 3 ) PM coarse (25.9±15.5 μg m 3 ) 6% 5% 2% 6% 27% 17% 13% 8% 8% 25% 32% 11% 29% 11% URG 3ABC multichannel samplers OM(OC*1. 4) EC Ammoni um sulfate Ammoni um nitrate Sea- sal t Mi ner al mat er i al and t r ace el ement Uni dent i f i ed 25

26 The diurnal patterns of OC/EC ratios 3. OC/EC Ratio :-4: 8:-1: 12:2: 6: 14:22: 8: 16: : 1:18: 4: 12:2: 6: 14:22: Time (h) Average OC/EC ratio: ~.6 during 8: 1:; ~1.6 during 1: 4: The samples were collected by RP225 Results: Pollution Episodes the air pollution in Hong Kong has close relations to synoptic systems, especially continental high pressure in cold season (Pathak et al., 23; Wang et al., 1997; 23; Louie et al., 25b) and tropical storms in the warm season (Cheng et al., 26; Wang et al., 26) 26

27 Aerosols during Pollution Episodes Jan 21 May 31, 24 Episode days: Jan 3 Feb Apr PM1. at PU roadside station PM2.5 at PU roadside station 5 On episode days, the average OC concentration in PM 1. and PM 2.5 increased 7% and 1%, respectively, compared to average values. EC showed only a 2 3% increase. The samples were collected by RP225 Aerosols during Pollution Episodes Jul 2 Jul 28, 25 ffic counts (# h -1 ) Traf entration (ug m -3 ) The samples were collected by RP225 Mass conce Case I Case II Case III LPG Taxi Diesel vehicle Gasoline vehicle PM 2.5 EC OC Sulfate Ammnonium Nitrate 14: : 12: 22: 1: 2: 8: 18: 6: 16: 4: 14: : Time (hour) SOC (a) (b) (c) (d) 27

28 Results: Size distributions SMPS OPC MSP MOUDI model 11 and model 115 (Nano MOUDI) Size Distributions of EC and Ions dm/d dlogdp dm/dlogdp 25 Sample 6 EC 2 Sample 7 15 Sample Diameter (µm) 1 8 Sample 6 NO3 - Sample 7 6 Sample Diameter (µm) dm/dlogdp dm/d dlogdp 6 5 Sample 6 SO4 2-4 Sample 7 Sample Diameter (µm) 1 8 Sample 6 NH4 + Sample 7 6 Sample Diameter (µm) 28

29 Size Distributions of Metals 3 dm/dlogdp dm M/dlogDp dm/dlogdp.9 Na V Cu Al Fe Zn Si Cl Sn K Ni Sb.2.1 dm/dlogdp Br Rb Ba Pb Diameter (µm) Diameter (µm) Diameter (µm) Diameter (µm) Elements with similar size distributions: 1. Al, Si Crustal elements 2. V and Ni ship emissions (Yu et al., 23) 3. Cu, Zn, and Ba brake dust 4. Br, Rb, and Pb incinerator (Louie et al., 25a) Typical size distribution of particle numbers (7 2 nm) in roadside environment in winter 7 (a) () winter 6 SMPS Lasair OPC 5 /logdp (# cm -3 ) dn/ Midpoint diameter (nm) 29

30 The relationship between ultrafine/accumulation mode particle numbers and PM mass (# cm -3 ) 8 R 2 = R 2 = R 2 =.26 R 2 = ) Ultrafine particle (# cm -3 Accumulation mode particle PM 2.5 (µg m -3 ) PM 1 (µg m -3 ) Evolution of ultrafine particle size distribution in a typical day 217 Size distribution dn/dlogdp 7 Time of day (hour) 3

31 Diurnal patterns of ultrafine particle, BC, total traffic number and meteorological parameters # cm -3 ) Ultrafine particle (# Wind speed (m s -1 ) BC )Ultrafine particle Jan 6 Jan 11 Jan 2 Jan 21 Wind speed MixingHeight R 2 = BC (µg cm -3 ) Mixing height (m) Solar radiation (mw cm -2 ) Total vehicle number Solar radiation 4: 9: 14:19: : 5: 1:15:2: 1: 6: 11:16:21: 2: 7: 12:17:22: Totoal vehicle number Summary Continuous mass and chemical monitors are available and practical for long term measurement programs practical for long term measurement programs Fine particles were mainly from vehicle exhausts, while coarse particles were mainly from the resuspension of road dust by wind and vehicle generated turbulence Continuous particle number measurements indicate ultrafine concentrations are fresh emissions from vehicles The seasonal variations of PM is influenced by Asian monsoon For day to day variations, regional or long range transport have significant impact on the loading of some chemical species during pollution episode days Besides traffic, wind speed and mixing height influence the loading of hourly PM 31