Take-Home Message. Opportunities to mine routine monitoring data for insights into ambient particulate matter climatology

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Madlyn Barber
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Weight of Evidence Tools and Examples for Fine Particulate Matter

Engineering Washington University in St.

Opportunities to mine routine monitoring data for insights into

1 Weight of Evidence Tools and Examples for Fine Particulate Matter State Implementation Plans Energy, Environmental and Chemical Engineering Washington University in St. Louis Baltimore, MD September 27-28, 21 1 Take-Home Message Opportunities to mine routine monitoring data for insights into ambient particulate matter climatology Today s examples focus on inter-site variability 2 September 27-28, 21 1

2 PM Attainment Demonstration SIPs Demonstration Framework Chemical transport modeling Relative reduction factors for PM components Speciation Data SANDWICH analysis to define PM components Species Modeled Attainment Test (SMAT) Weight-of-Evidence (WOE) analyses to support the Attainment Demonstration (see Mike Koerber s slide for list of analyses) Today s presentation: example tools/analyses for WOE, and PM conceptual models in general 3 Data-Driven WOE Tools (not exhaustive) Basic trends analyses Receptor Modeling, e.g. Chemical Mass Balance (CMB) UNMIX Positive Matrix Factorization (PMF) Other data analyses, especially those that exploit routine monitoring data PM mass and speciation Surface winds and synoptic weather e.g. Chu et al. (29) Environ. Sci. Technol., 43, ; (21) Aerosol Sci. Technol., 44, September 27-28, 21 2

3 An Example Detroit Metropolitan Area Southeast Michigan (greater Detroit area) with violating monitors (at least until recently) for both daily- and annual-average PM 2. NAAQS Weight-of-evidence approach has been used for PM air quality planning and management Objectives Overall: Develop a conceptual model for PM 2. over Southeast Michigan Characteristics of, and drivers for, high PM on exceedance and near-exceedance days Today s presentation: Methodology to isolate and characterize local emission source contributions using PM 2 2. mass (FRM) data and surface winds Represents only one component of the conceptual model 6 September 27-28, 21 3

4 Conceptual Model for High PM 2. Concentration Days over Southeast Michigan Conceptual Model Version 1., October 28 (available from SEMCOG) Trends Analysis (Section 3) and Climatology of High PM Days (Section 4) included data through 26 Relationships between Site-Specific Excess Mass and Surfaces Winds (Section ) included data through 27 Analyses updated in Summer 21 to include data through 29 Additional refinements in progress 7 Fine PM Excess Mass Analysis Methodology 1. Define an urban/regional scale baseline for each monitoring day using the PM 2 2. compliance ce monitoring network data 2. Determine the day-specific excess PM 2. mass concentration at each site compared to this networkwide baseline 3. Methodology ogy identifies es the location o and magnitude of impacts from local sources and/or the urban plume 8 September 27-28, 21 4

the split depends on the monitoring network characteristics 9

5 Fine PM Excess Mass Analysis Not a rigorous urban/regional split but results are informative Precise methodology to obtain the split depends on the monitoring network characteristics 9 Southeast Michigan Network Included in this Study 1 September 27-28, 21

6 Site-specific Concentration Values versus Base Solid diagonal line is the 1:1 line Dashed horizontal line is the 24-hour PM 2. NAAQS 8 7 Livonia 8 7 Oak Park site e-specific concentration, ug/m site e-specific concentration, ug/m base concentration, ug/m base concentration, ug/m 3 Exceedances generally observed only on high-baseline days 11 Site-specific Concentration Values versus Base 8 7 Allen Park 8 7 SW High Sch. site-specific concentration, ug/ /m site-specific concentration, ug/ /m base concentration, ug/m base concentration, ug/m 3 Exceedances generally observed only on high baseline days Urban- and/or finer-scale impacts present, modest at Allen Park, more prevalent at SW High School 12 September 27-28, 21 6

7 Site-specific Concentration Values versus Base 8 7 Wyandotte 8 7 Dearborn site-specific concentration, ug g/m site-specific concentration, ug g/m base concentration, ug/m base concentration, ug/m 3 Significant urban- and/or finer-scale impacts, in some cases leading to exceedances on relatively low baseline days 13 Network-wide Base Concentration by Year network-wide baseline conce entration, ug/m ( 84) (14) (11) (113) (117) (121) ( 73) (119) (116) (118) What differences be attributed to emissions reductions and what differences are due to synoptic weather patterns? Donna Kenski s CART and Cox and Chu analyses should help to sort this out (see Mike Koerber s slide for meteorology-adjusted ozone trends) 14 September 27-28, 21 7

8 1-D Nonparametric Wind Regression on Excess Mass (relative to network base) 1. Results expressed as the expected (average) concentration when the winds come from a given direction Good for identifying sources and their emission strengths, and tracking accountability -vs- 2. Contribution to average excess mass for winds from a given direction (wind sector apportionment), obtained by weighting of (1) by the wind direction frequency distribution Good for apportioning average source impacts 1 Local PM Excess at Dearborn 1-D NWR, rings are 2 μg/m 3 excess above netwo ork-wide baseline, ug/m 3 Dearborn 2 ( 76) ( 98) ( 96) (1) (1) (114) ( 7) (114) D NWR, units μg/m 3 12 Dearborn, Dearborn, D NWR excess mass expected concentra ation, μg/m 3 [24-26] minus [2-23] wind direction, o N 16 September 27-28, 21 8

9 Local PM Excess by Wind Direction (24-26) 1-D NWR (normalized) YPSILANTI LIVONIA OAK PARK EAST 7 MILE NEW HAVEN ALLEN PARK Update to the 28 Analyses Recent patterns in the annual base concentration Presumably captures regional contributions and a portion of the urban-scale contributions Recent patterns in the annual site-specific excess mass Presumably the excess mass captures urban- and finer-scale emission source impacts To what extent do surface wind variations explain recent changes in site-specific excess mass? Seek to separate variations in wind direction frequency from changes in emissions 18 September 27-28, 21 9

Network-wide Base Concentration by Year ration, ug/m 3 netw

(117) (121) ( 73) (119) (116) (118) 2 21 22 23 24 2 26 27 28 29

(11) ( 66) (11) (114) (19) 1 2 21 22 23 24 2 26 27 28 29 1-D NWR,

10 Network-wide Base Concentration by Year ration, ug/m 3 netw work-wide baseline concentr ( 84) (14) (11) (113) (117) (121) ( 73) (119) (116) (118) Should examine this pattern using synoptic typing tools, air mass back trajectories, etc. 19 Local PM Excess at Linwood Linwood ( 8) ( 98) ( 91) (16) (1) (11) ( 66) (11) (114) (19) D NWR, rings are 2 μg/m 3 1-D NWR, units μg/m 3 excess above networ rk-wide baseline, ug/m September 27-28, 21 1

Local PM Excess at Allen Park Allen Park (

(113) (113) 1 2 21 22 23 24 2 26 27 28 29

units μg/m 3 excess above networ rk-wide

Southwest High School 1 Southwest High Sch.

11 Local PM Excess at Allen Park Allen Park ( 79) ( 9) ( 91) (18) (18) (118) ( 67) (116) (113) (113) D NWR, rings are 2 μg/m 3 1-D NWR, units μg/m 3 excess above networ rk-wide baseline, ug/m Local PM Excess at Southwest High School 1 Southwest High Sch. ( 82) (12) ( 94) (1) (11) (114) ( 7) (112) (11) (114) D NWR, rings are 2 μg/m 3 1-D NWR, units μg/m 3 excess above networ rk-wide baseline, ug/m September 27-28, 21 11

Local PM Excess at Wyandotte Wyandotte (

98) ( 96) (1) (1) (114) ( 7) (114) (114)

NWR, 4-6 (pink) & 7-9 (red), rings are 2

12 Local PM Excess at Wyandotte Wyandotte ( 82) ( 99) ( 9) (1) (17) (112) ( 69) (11) (11) (17) 1 1-D NWR, rings are 2 μg/m 3 rk-wide baseline, ug/m 3 excess above networ D NWR, units μg/m 3 23 Local PM Excess at Dearborn excess above networ rk-wide baseline, ug/m 3 Dearborn ( 76) ( 98) ( 96) (1) (1) (114) ( 7) (114) (114) (114) D NWR, 4-6 (pink) & 7-9 (red), rings are 2 μg/m 3 1-D NWR, units μg/m 3 24 September 27-28, 21 12

13 Dearborn Sector Apportionment Expected concentrations from NWR weighted by surface wind direction frequency distribution 6 6 cumulative excess concentration, ug g/m cumulative excess concentration, ug g/m wind direction, o N wind direction, o N Southwest (SW) sector o N Period-average contribution, μg/m 3 Period SW sector Other Sectors Dearborn PMF Modeling & Sector Apportionment Wade et al. (28) PMF modeling for Dearborn May 22 December 26 8-factor solution Two factors with conditional probability function (CPF) bearings to the southwest Fe/Mn/K factor: 1.3 μg/m 3 Zn factor: 1. μg/m μg/m 3 Excess mass southwest sector apportionment for the same time period: 2.7 μg/m 3 26 September 27-28, 21 13

14 Summary Substantial information can be extracted from routine network data Nature of high concentration days Local emission source contributions to specific monitors Urban plume impacts at downwind sites Such analyses can feed into the weight-of-evidence approach taken for SIP Attainment Demonstrations Also used to probe robustness of chemical transport modeling and receptor modeling, and to develop conceptual models 27 Acknowledgements Southeastern Michigan Council of Governments (SEMCOG) Joan Weidner, Chuck Hersey Michigan Department of Natural Resources and Environment Mary Ann Heindorf, Cynthia Hodges, Amy Robinson Southeastern Michigan Ozone Study (SEMOS) group Sonoma Technology, Inc. Steve Brown, Hilary Hafner, Katie Wade Lake Michigan Air Directors Consortium Donna Kenski, Mike Koerber Washington University in St. Louis Jen Garlock 28 September 27-28, 21 14

Air Pollution in Detroit: Status and Trends

Air Pollution in Detroit: Status and Trends Craig Fitzner Air Quality Evaluation Section MDEQ-Air Quality Division May 11, 2016 (517) 284-6743 Fitznerc@Michigan.gov Overview Core functions of the Air Quality