1. NIEHS G. P. Patil

2. This report is very disappointing. What kind of software are you using?

3. Stone-Age Space-Age Syndrome Stone-age data Space-age data Stone-age analysis Space-age analysis

4. NIEHS EC Presentation-1 Environmental Justice Environmental Justice –Spacetime Persistent Poverty –Pollution Proximity and Vulnerability Biosurveillance and Biosecurity Biosurveillance and Biosecurity –Elevated Cluster Detection and Delineation –Early Warning for Outbreaks

5. NIEHS EC Presentation-2 Disease Mapping and Evaluation Disease Mapping and Evaluation –Hotspot Detection and Delineation –Hotspot Rating –Hotspots, Coldspots: Prioritization Agent Orange Dioxin Vietnam Agent Orange Dioxin Vietnam –Hotspot Coldspot Midspot Detection, Prioritization, and Evaluation –Multispectral and Multiband Change Detection and Evaluation

6. NIEHS EC Presentation-3 Sound solid sophisticated statistical methods Sound solid sophisticated statistical methods Circle-based and ellipse-based space and time scan statistics, and beyond Circle-based and ellipse-based space and time scan statistics, and beyond Upper-level-set-connected-component-based space and time scan statistics Upper-level-set-connected-component-based space and time scan statistics Partial-order-set-based Prioritization tool Partial-order-set-based Prioritization tool User friendly downloadable software system User friendly downloadable software system

7. NIEHS EC Presentation-4 National Center, Nationwide Projects, and Innovative Synergistics National Center, Nationwide Projects, and Innovative Synergistics –Geoinformatic Inferential Statistics and Visualization –Hotspots Coldspots Midspots Detection and Delineation –Prioritization, Evaluation, Early Warning –Monitoring, Assessment, Management

8. Environmental Justice Is environmental degradation worse in poor and minority communities? Is environmental degradation worse in poor and minority communities? Identification of persistent poverty and its trajectories Identification of persistent poverty and its trajectories Identification of pollution proximity and vulnerability Identification of pollution proximity and vulnerability Co-location research hampered by the lack of methods and tools of investigation Co-location research hampered by the lack of methods and tools of investigation

10. Dimensions of Tract Poverty in Four Metropolitan Areas 1970-1990 Concentrated Persistent Camden, NJ Detroit, MI Oakland, CA Memphis, TN Growing Shifting

11. Growing poverty

12. Persistent poverty

13. Oakland 1970 Poverty dataOakland 1980 Poverty data Oakland 1990 Poverty data Shifting poverty

14. Camden 1990 Poverty data Concentrated poverty Camden 1970 Poverty dataCamden 1980 Poverty data

15. Hotspot Persistence Space (census tract) 1970 1980 1990 2000 Time (census year) Persistent Hotspot Long Duration Space (census tract) 1970 1980 1990 2000 Time (census year) One-shot Hotspot Short Duration Persistence is a property of space-time hotspots Persistence can be assessed by the projection of the space-time hotspot onto the time axis

16. Typology of Persistent Space-Time Hotspots- 1 Space (census tract) 1970 1980 1990 2000 Time (census year) Stationary Hotspot Space (census tract) 1970 1980 1990 2000 Time (census year) Shifting Hotspot Space (census tract) 1970 1980 1990 2000 Time (census year) Expanding Hotspot Space (census tract) 1970 1980 1990 2000 Time (census year) Contracting Hotspot

17. Typology of Persistent Space-Time Hotspots- 2 Space (census tract) 1970 1980 1990 2000 Time (census year) Bifurcating Hotspot These hotspots are connected in space-time However, certain time slices of the hotspot are disconnected in space Space (census tract) 1970 1980 1990 2000 Time (census year) Merging Hotspot Spatially disconnected time slice

18. Trajectory of a Persistent Space-Time Hotspot A space-time hotspot is a three-dimensional object Visualization can be done by displaying the sequence of time slices---called the trajectory of the hotspot Time slices of space-time hotspot Space (census tract) 1970 1980 1990 2000 Time (census year) Merging Hotspot

19. Trajectory of a Merging Hotspot 19701980 1990 2000

20. Trajectory of a Shifting Hotspot 19701980 1990 2000

21. Syndromic Surveillance Symptoms of disease such as diarrhea, respiratory problems, headache, etc Symptoms of disease such as diarrhea, respiratory problems, headache, etc Earlier reporting than diagnosed disease Earlier reporting than diagnosed disease Less specific, more noise Less specific, more noise

22. Early Detection of Disease Outbreaks with Applications in New York City Prospective Disease Surveillance Prospective Disease Surveillance The Spatial Scan Statistic The Spatial Scan Statistic Dead Birds and West-Nile Virus Surveillance in New York City Dead Birds and West-Nile Virus Surveillance in New York City Other Current Applications Other Current Applications

23. The Spatial Scan Statistic Move a circular window across the map. Move a circular window across the map. Use a variable circle radius, from zero up Use a variable circle radius, from zero up to a maximum where 50 percent of the population is included.

24. A small sample of the circles used

25. For each circle: – Obtain actual and expected number of cases inside and outside the circle. – Calculate Likelihood Function Compare Circles: – Pick circle with maximum likelihood. This is the most likely cluster, i.e., the cluster least likely to have occurred by chance Inference: – Generate random replicas of the data set under the null- hypothesis of no clusters (Monte Carlo sampling) – Compare most likely clusters in real and random data sets (Likelihood ratio test)

26. Spatial Scan Statistic: Properties – Adjusts for inhomogeneous population density. – Simultaneously tests for clusters of any size and any location, by using circular windows with continuously variable radius. – Accounts for multiple testing. – Possibility to include confounding variables, such as age, sex or socio-economic variables. – Aggregated or non-aggregated data (states, counties, census tracts, block groups, households, individuals).

27. Detecting Emerging Clusters Instead of a circular window in two dimensions, we use a cylindrical window in three dimensions. Instead of a circular window in two dimensions, we use a cylindrical window in three dimensions. The base of the cylinder represents space, while the height represents time. The base of the cylinder represents space, while the height represents time. The cylinder is flexible in its circular base and starting date, but we only consider those cylinders that reach all the way to the end of the study period. Hence, we are only considering ‘alive’ clusters. The cylinder is flexible in its circular base and starting date, but we only consider those cylinders that reach all the way to the end of the study period. Hence, we are only considering ‘alive’ clusters.

28. West Nile Virus Surveillance in New York City 2000 Data: Simulation/Testing of Prospective Surveillance System 2000 Data: Simulation/Testing of Prospective Surveillance System 2001 Data: Real Time Implementation of Daily Prospective Surveillance 2001 Data: Real Time Implementation of Daily Prospective Surveillance

29. 2000 Data - Dead birds reported by the public - Simulation of a daily prospective surveillance system - Start date: June 1, 2000.

32. Major epicenter on Staten Island Dead bird surveillance system: June 14 Dead bird surveillance system: June 14 Positive bird report: July 16 (coll. July 5) Positive bird report: July 16 (coll. July 5) Positive mosquito trap: July 24 (coll. July 7) Positive mosquito trap: July 24 (coll. July 7) Human case report: July 28 (onset July 20) Human case report: July 28 (onset July 20)

33. Hospital Emergency Admissions in New York City Hospital emergency admissions data from a majority of New York City hospitals. Hospital emergency admissions data from a majority of New York City hospitals. At midnight, hospitals report last 24 hour of At midnight, hospitals report last 24 hour of data to New York City Department of Health A spatial scan statistic analysis is performed every morning A spatial scan statistic analysis is performed every morning If an alarm, a local investigation is conducted If an alarm, a local investigation is conducted

34. Other Syndromic Surveillance Data Sources 911 Ambulance Dispatches 911 Ambulance Dispatches Pharmacy Sales Pharmacy Sales Employee Sick Leave Employee Sick Leave Physician Visits Physician Visits Veterinary Clinic Visits Veterinary Clinic Visits

35. Aggregation-Resolution Issues

37. Ridge and Valley Hotspots Ridge and Valley hotspots within hotspots. Hotspots within Hotspots

38. Candidate Zones for Hotspots Goal: Identify geographic zone(s) in which a response is significantly elevated relative to the rest of a region Goal: Identify geographic zone(s) in which a response is significantly elevated relative to the rest of a region A list of candidate zones Z is specified a priori A list of candidate zones Z is specified a priori –This list becomes part of the parameter space and the zone must be estimated from within this list –Each candidate zone should generally be spatially connected, e.g., a union of contiguous spatial units or cells –Longer lists of candidate zones are usually preferable –Expanding circles or ellipses about specified centers are a common method of generating the list

39. Poor Hotspot Delineation by Circular Zones Hotspot Circular zone approximations Circular zones may represent single hotspot as multiple hotspots

40. Circular spatial scan statistic zonation Cholera outbreak along a river flood plain Small circles miss much of the outbreak Large circles include many unwanted cells

41. Crime Rate Hotspots

42. SIDS HOTSPOTS

43. Outbreak expanding in time Small cylinders miss much of the outbreak Large cylinders include many unwanted cells Space Time Cylindrical space-time scan statistic zonation

44. Some Space-Time Hotspots and Their Cylindrical Approximations Hotspot Cylindrical approximation Cylindrical approximation sees single hotspot as multiple hotspots Space Time

45. ULS Candidate Zones Question: Are there data-driven (rather than a priori) ways of selecting the list of candidate zones? Question: Are there data-driven (rather than a priori) ways of selecting the list of candidate zones? Motivation for the question: A human being can look at a map and quickly determine a reasonable set of candidate zones and eliminate many other zones as obviously uninteresting. Can the computer do the same thing? Motivation for the question: A human being can look at a map and quickly determine a reasonable set of candidate zones and eliminate many other zones as obviously uninteresting. Can the computer do the same thing? A data-driven proposal: Candidate zones are the connected A data-driven proposal: Candidate zones are the connected components of the upper level sets of the response surface. The candidate zones have a tree structure (echelon tree is a subtree), which may assist in automated detection of multiple, but geographically separate, elevated zones. Null distribution: If the list is data-driven (i.e., random), its variability must be accounted for in the null distribution. A new list must be developed for each simulated data set. Null distribution: If the list is data-driven (i.e., random), its variability must be accounted for in the null distribution. A new list must be developed for each simulated data set.

46. Data-adaptive approach to reduced parameter space  0 Data-adaptive approach to reduced parameter space  0 Zones in  0 are connected components of upper level sets of the empirical intensity function G a = Y a / A a Zones in  0 are connected components of upper level sets of the empirical intensity function G a = Y a / A a Upper level set (ULS) at level g consists of all cells a where G a  g Upper level set (ULS) at level g consists of all cells a where G a  g Upper level sets may be disconnected. Connected components are Upper level sets may be disconnected. Connected components are the candidate zones in  0 These connected components form a rooted tree under set inclusion. These connected components form a rooted tree under set inclusion. –Root node = entire region R –Leaf nodes = local maxima of empirical intensity surface –Junction nodes occur when connectivity of ULS changes with falling intensity level Tree-Structured SATScan

47. Upper Level Set (ULS) of Intensity Surface Hotspot zones at level g (Connected Components of upper level set)

48. Changing Connectivity of ULS as Level Drops

49. ULS Connectivity Tree Schematic intensity “surface” N.B. Intensity surface is cellular (piece-wise constant), with only finitely many levels A, B, C are junction nodes where multiple zones coalesce into a single zone A B C

50. Ingredients: Ingredients: – Tessellation of a geographic region: –Intensity value G on each cell. Determines a cellular (piece- wise constant) surface with G as elevation. Imagine surface initially inundated with water Imagine surface initially inundated with water Water evaporates gradually exposing the surface which appears as islands in the sea Water evaporates gradually exposing the surface which appears as islands in the sea How does connectivity (number of connected components) of the exposed surface change with falling water level? How does connectivity (number of connected components) of the exposed surface change with falling water level? ULS Connectivity Tree -1 a c b k d e f g h i j a, b, c, … are cell labels

51. Think of the tessellated surface as a landform Think of the tessellated surface as a landform Initially the entire surface is under water Initially the entire surface is under water As the water level recedes, more and more of the landform is As the water level recedes, more and more of the landform isexposed At each water level, cells are colored as follows: At each water level, cells are colored as follows: –Green for previously exposed cells (green = vegetated) –Yellow for newly exposed cells (yellow = sandy beach) –Blue for unexposed cells (blue = under water) For each newly exposed cell, one of three things happens: For each newly exposed cell, one of three things happens: –New island emerges. Cell is a local maximum. Morse index=2. Connectivity increases. –Existing island increases in size. Cell is not a critical point. Connectivity unchanged. –Two (or more) islands are joined. Cell is a saddle point Morse index=1. Connectivity decreases. ULS Connectivity Tree -2

52. a c b k d e f g h i j ULS Connectivity Tree -3 ULS Tree a a a b,c b k d e f g h i j c Newly exposed island Island grows

53. ULS Connectivity Tree -4 ULS Tree a a b,c b k d e f g h i j c Second island appears d a a b,c b k d e f g h i j c Both islands grow d e f,g New leaf node (local maximum)

54. ULS Connectivity Tree -5 ULS Tree a a b,c b k d e f g h i j c Islands join – saddle point d e f,g h Junction node a a b,c b d e f g h i j c Exposed land grows d e f,g h k i,j,k Root node

55. Agreement/Disagreement regarding hotspot locus Agreement/Disagreement regarding hotspot locus Comparative plausibility and accuracy of hotspot delineation Comparative plausibility and accuracy of hotspot delineation Execution time and computer efficiency Execution time and computer efficiency Comparison of Tree-Structured and Circle-Based SATScan

56. A hotspot confidence set with two connected components is shown on the ULS tree. The connected components correspond to different hotspot loci while the nodes within a connected component correspond to different delineations of that hotspot – all at the appropriate confidence level. Confidence Region on ULS Tree

57. Estimation Uncertainty in Hotspot Delineation Outer envelope Inner envelope MLE

58. Determine a confidence set for the hotspot Determine a confidence set for the hotspot Each member of the confidence set is a zone which is a statistically plausible delineation of the hotspot at specified confidence Each member of the confidence set is a zone which is a statistically plausible delineation of the hotspot at specified confidence Confidence set lets us rate individual cells a for hotspot membership Confidence set lets us rate individual cells a for hotspot membership Rating for cell a is percentage of zones in confidence set that contain a. (More generally, use weighted proportion.) Rating for cell a is percentage of zones in confidence set that contain a. (More generally, use weighted proportion.) Map of cell ratings: Map of cell ratings: –Inner envelope = cells with 100% rating –Outer envelope = cells with positive rating Hotspot Delineation and Hotspot Rating

59. Benefits Of ULS Scan Statistic Approach Identifies arbitrary shaped clusters Identifies arbitrary shaped clusters Computationally efficient Computationally efficient Confidence set, hotspot rating Confidence set, hotspot rating Applicable to continuous response Applicable to continuous response Generalizes to space-time scan Generalizes to space-time scan

61. Ranking Possible Disease Clusters in the State of New York Data Matrix

62. Hotspot Prioritization and Poset Ranking Multiple hotspots with intensities significantly elevated relative to the rest of the region Multiple hotspots with intensities significantly elevated relative to the rest of the region Ranking based on likelihood values, and additional attributes: raw intensity values, socio-economic and demographic factors, feasibility scores, excess cases, seasonal residence, atypical demographics, etc. Ranking based on likelihood values, and additional attributes: raw intensity values, socio-economic and demographic factors, feasibility scores, excess cases, seasonal residence, atypical demographics, etc. Multiple attributes, multiple indicators Multiple attributes, multiple indicators Ranking without having to integrate the multiple indicators into a composite index Ranking without having to integrate the multiple indicators into a composite index

63. First stage screening First stage screening –Significant clusters by SaTScan and/or upper level sets upper level sets Second stage screening Second stage screening –Multicriteria noteworthy clusters by partially ordered sets and Hass diagrams Final stage screening Final stage screening –Follow up clusters for etiology, intervention based on multiple criteria using Hass diagrams based on multiple criteria using Hass diagrams Multiple Criteria Analysis, Multiple Indicators and Choices, Health Statistics, Disease Etiology, Health Policy, Resource Allocation

64. HUMAN ENVIRONMENT INTERFACE LAND, AIR, WATER INDICATORS RANK COUNTRYLANDAIRWATER 1Sweden 2Finland 3Norway 5 Iceland 13 Austria 22 Switzerland 39 Spain 45 France 47 Germany 51 Portugal 52 Italy 59 Greece 61 Belgium 64 Netherlands 77 Denmark 78 United Kingdom 81 Ireland 69.01 76.46 27.38 1.79 40.57 30.17 32.63 28.34 32.56 34.62 23.35 21.59 21.84 19.43 9.83 12.64 9.25 35.24 19.05 63.98 80.25 29.85 28.10 7.74 6.50 2.10 14.29 6.89 3.20 0.00 1.07 5.04 1.13 1.99 100 98 100 82 100 98 100 for land - % of undomesticated land, i.e., total land area-domesticated (permanent crops and pastures, built up areas, roads, etc.) for air - % of renewable energy resources, i.e., hydro, solar, wind, geothermal for water - % of population with access to safe drinking water

65. Hasse Diagram (Western Europe )

66. Ranking Partially Ordered Sets – 5 Linear extension decision tree a b dc e f a c e b b d ff d ed f e e f c f d ed f e e f d f e e f c f e e f c c f d ed f e e f d f e e f c b a b a d Jump Size: 1 3 3 2 3 5 4 3 3 2 4 3 4 4 2 2 Poset (Hasse Diagram)

67. Cumulative Rank Frequency Operator – 5 An Example of the Procedure In the example from the preceding slide, there are a total of 16 linear extensions, giving the following cumulative frequency table. Rank Element123456 a91416161616 b71215161616 c0410161616 d026121616 e00141016 f0000616 Each entry gives the number of linear extensions in which the element (row label) receives a rank equal to or better that the column heading

68. Cumulative Rank Frequency Operator – 6 An Example of the Procedure 16 The curves are stacked one above the other and the result is a linear ordering of the elements: a > b > c > d > e > f

69. Cumulative Rank Frequency Operator – 7 An example where F must be iterated Original Poset (Hasse Diagram) a f eb c g d h a f e b ad c h g a f e b ad c h g F F 2

70. Cumulative Rank Frequency Operator – 8 An example where F results in ties Original Poset (Hasse Diagram) a cb d a b, c (tied) d F Ties reflect symmetries among incomparable elements in the original Hasse diagram Elements that are comparable in the original Hasse diagram will not become tied after applying F operator

72. Network-based Surveillance Subway system surveillance Subway system surveillance Drinking water distribution system surveillance Drinking water distribution system surveillance Stream and river system surveillance Stream and river system surveillance Postal System Surveillance Postal System Surveillance Road transport surveillance Road transport surveillance

73. Figure 1. Upper Juniata river network with superimposed network of Pennsylvania Department of Environmental Protection (DEP) sampling stations. In Phase 1, scan statistics methods will be used to identify hotspots of biological impairment. Phase 2 incorporates potential explanatory factors and identifies residual hotspots that are subjected to detailed network modeling in Phase 3.

74. Disease Mapping and Evaluation Elevated rate cluster detection and assessment Elevated rate cluster detection and assessment Geographic surveillance and evaluation Geographic surveillance and evaluation Comparative studies involving statistical power Comparative studies involving statistical power Geometric accuracy in cluster estimation Geometric accuracy in cluster estimation Real data projects and validations Real data projects and validations Computing and software Computing and software

75. Agent Orange Dioxin Hotspots, coldspots, and midspots Hotspots, coldspots, and midspots Identification and prioritization Identification and prioritization Persistent hotspot trajectories Persistent hotspot trajectories Spatial disease monitoring, modeling, and tracking Spatial disease monitoring, modeling, and tracking Health effect surveillance system Health effect surveillance system

76. Agent Orange Dioxin Ecosystem impact assessment Ecosystem impact assessment Deforestation, defoliation, remote sensing Deforestation, defoliation, remote sensing Hyperspectral change detection and accuracy assessment Hyperspectral change detection and accuracy assessment Emergent advanced raster map analysis system Emergent advanced raster map analysis system

77. Geospatial Patterns and Pattern Metrics Geospatial Patterns and Pattern Metrics –Landscape patterns, disease patterns, mortality patterns Surface Topology and Spatial Structure Surface Topology and Spatial Structure –Hotspots, outbreaks, critical areas –Intrinsic hierarchical decomposition, study areas, reference areas –Change detection, change analysis, spatial structure of change Multiscale Advanced Raster Map Analysis

78. Needed Downloadable geoinformatic analysis system Downloadable geoinformatic analysis system Identification of arbitrary shape hotspots, coldspots, midspots Identification of arbitrary shape hotspots, coldspots, midspots Multi-criteria prioritization and ranking Multi-criteria prioritization and ranking Synoptic and network-based surveillance Synoptic and network-based surveillance Multiple scales and aggregation levels Multiple scales and aggregation levels Space, time, and space-time Space, time, and space-time Zonal tree scan system Zonal tree scan system

79. Terra Seer Long Island Study-1 August 13, 2002 GEOGRAPHIC TRACE OF CANCER The Geographic Distribution of BLC Cancers in Long Island NY The Geographic Distribution of BLC Cancers in Long Island NY Geographic Trace of Cancer (the pattern of cases over time and space) may even become as important as genetic research in the search for causes Geographic Trace of Cancer (the pattern of cases over time and space) may even become as important as genetic research in the search for causes Need of an Exploratory, Integrative, Need of an Exploratory, Integrative, Multi-scalar Approach

80. Terra Seer Long Island Study-2 August 13, 2002 STRENGTHENING SATSCAN The techniques we employ are not ‘better’ or ‘more accurate’ than the scan statistic The techniques we employ are not ‘better’ or ‘more accurate’ than the scan statistic Using a battery of approaches allows us to quantify different aspects of univariate and multivariate clusters, to explore different scales of clustering, and to evaluate how sensitive the results are to different definitions of clustering Using a battery of approaches allows us to quantify different aspects of univariate and multivariate clusters, to explore different scales of clustering, and to evaluate how sensitive the results are to different definitions of clustering The methods we have used are not designed to replace the scan approach, rather they should be used with the scan statistic to develop a more comprehensive understanding of geographic variation of cancer morbidity The methods we have used are not designed to replace the scan approach, rather they should be used with the scan statistic to develop a more comprehensive understanding of geographic variation of cancer morbidity

81. Terra Seer Long Island Study-3 August 13, 2002 HOTSPOTS, PUBLIC AND AGENCIES Public: Being singled out as an elevated cluster community is such a source of dread for many people Public: Being singled out as an elevated cluster community is such a source of dread for many people Agency: Hotspot status creates intense pressure to study individual clusters instead of broader cancer patterns Agency: Hotspot status creates intense pressure to study individual clusters instead of broader cancer patterns Need: Hotspot rating along with hotspot detection, prioritization, and ranking interval with multicriteria Need: Hotspot rating along with hotspot detection, prioritization, and ranking interval with multicriteria

82. Terra Seer Long Island Study-4 August 13, 2002 Issues and Approaches Issues and Approaches –Local Moran test, Boundary Detection, Boundary Overlap, Multi-Scalar, and Spatially Constrained Clustering Adjacency: Centroid, common border, upper level set tree neighbor Adjacency: Centroid, common border, upper level set tree neighbor Multiscalar: Multivariate response, Multivariate Binomial, Poisson, Gamma, Lognormal Multiscalar: Multivariate response, Multivariate Binomial, Poisson, Gamma, Lognormal Scale of Study: Spatial extent, Hotspot in Hotspots Scale of Study: Spatial extent, Hotspot in Hotspots Scale of Process: Resolution, Aggregation Level Scale of Process: Resolution, Aggregation Level

83. Recommendations Research, education, and outreach initiative Research, education, and outreach initiative National Center National Center Multi-focal multi-disciplinary thrust Multi-focal multi-disciplinary thrust Concepts, methods, tools, validations Concepts, methods, tools, validations Solid, sound, sophisticated and yet user- friendly Priority activities and regional projects Priority activities and regional projects Downloadable user-friendly software system Downloadable user-friendly software system Innovative Synergistics Innovative Synergistics

84. Penn State Involvements Center for Statistical Ecology and Environmental Statistics/NSF,EPA Center for Statistical Ecology and Environmental Statistics/NSF,EPA Poverty Research Center/Ford Foundation Poverty Research Center/Ford Foundation GeoVista Center/NCI, NSF GeoVista Center/NCI, NSF Geovisualization and Spatial Analysis of Cancer Data NCI Program For: Geographic-based Research in Cancer Control and Epidemiology Appalachia Cancer Network/NCI Appalachia Cancer Network/NCI Environmental Consortium on Biosurveillance Environmental Consortium on Biosurveillance Cooperative Wetlands Center/EPA Cooperative Wetlands Center/EPA Center for Remote Sensing of Earth Resources/NASA, EPA Center for Remote Sensing of Earth Resources/NASA, EPA

85. Logo for Statistics, Environment, Health, Ecology, and Society