USFS Region 1 SAS Analysis of Lake Chemistry, NADP, and IMPROVE data
This presentation is the property of its rightful owner.
Sponsored Links
1 / 35

USFS Region 1 SAS Analysis of Lake Chemistry, NADP, and IMPROVE data PowerPoint PPT Presentation


  • 90 Views
  • Uploaded on
  • Presentation posted in: General

USFS Region 1 SAS Analysis of Lake Chemistry, NADP, and IMPROVE data. Jill Grenon and Mark Story, Gallatin NF. R1 Air Quality Monitoring Program Overview Non Parametric Statistical Methods Statistical Test Results Tentative Conclusions. USFS R1 Wilderness Air Quality Monitoring Plan

Download Presentation

USFS Region 1 SAS Analysis of Lake Chemistry, NADP, and IMPROVE data

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Usfs region 1 sas analysis of lake chemistry nadp and improve data

USFS Region 1 SAS Analysis of Lake Chemistry, NADP, and IMPROVE data

Jill Grenon and Mark Story, Gallatin NF

  • R1 Air Quality Monitoring Program Overview

  • Non Parametric Statistical Methods

  • Statistical Test Results

  • Tentative Conclusions


Usfs region 1 sas analysis of lake chemistry nadp and improve data

USFS R1 Wilderness Air Quality Monitoring Plan

Mark Story - Gallatin NF, Thomas Dzomba - USFS R1/R4 AFA, Jill Grenon - Gallatin NF/MSU

2/1/2008

INTRODUCTION

Protection of air quality values is a key component of both the Clean Air Act and Wilderness Act. The USFS Region 1 has 13 designated Wilderness areas. In terms of air quality, seven are designated as Class I Wilderness areas and six are designated as Class II Wilderness Areas. Class l areas in USFS R1 were designated by the Clean Air Act amendments of 1977. The 1977 Clean Air Act amendments assigned the Forest Service an “affirmative responsibility” to protect the Air Quality Related Values (AQRV’s) of Class l areas. Class II areas include all other areas of the country that are not Class I. Class II Wilderness areas are Class II for the Clean Air Act Prevention of Significant Deterioration (PSD) regulations. Air quality protection authority (beyond ambient air quality standards and PSD increments) for Class II Wilderness areas therefore relies primarily upon the Wilderness Act with the air quality values titled Wilderness Air Quality Values (WAQV’s).

Region 1 has been actively monitoring AQRVs and WAQVs since 1989. Formal AQRV monitoring plans for regional Class I Wilderness areas were developed between 1989 and 1996. For Class II Wilderness areas, formal WAQV plans were developed in 2007 and 2008 in accordance with the 10-Year Wilderness Challenge. The following table summarizes the plan development for each Wilderness area; each plan is referenced at the end of this plan and tabulated below.

http://www.fs.fed.us/r1/gallatin/resources/air/


Usfs region 1 sas analysis of lake chemistry nadp and improve data

USFS R1Class I Wilderness Areas

AQRV’s


Usfs region 1 sas analysis of lake chemistry nadp and improve data

USFS R1Class II Wilderness Areas

WAQV’s


Usfs region 1 sas analysis of lake chemistry nadp and improve data

Example of an IMPROVE baseline graph


R1 air quality beyond excel

R1 AIR QUALITYBEYOND EXCEL

  • Excel limited in statistical power

  • Used SAS to run non parametric tests to test for statistically significant trends in USFS R1 AQ Data

  • Analyzed R1 Lakes, NADP, and IMPROVE data.


Statistics

Statistics

  • SAS Institute statistical software was used to run analyses following draft USFS Data Analysis Protocol (DAP) recommendations in coordination with Lori Porth, RMRS Statistician

  • Non-parametric test that can work with non-normal distributions and are not affected by errors, gross outliers, or missing data in the data set.

  • A trend is detectable and considered significant if it meets our designated alpha level of α = 0.1 also shown as 90% confidence level. Additional confidence levels used were 95 (α = 0.05), 99 (α = 0.01), and 99.9 (α = 0.001). (Salmi et.al 2002).


Statistical tests used

Statistical Tests Used

  • Mann-Kendall- run to see if there were significant trends for each parameter

  • Kruskal-Wallace- run to see if seasons in the data set were statistically different

  • Seasonal Mann-Kendall-run to look for trends while taking seasonality into account

  • Sens slope estimator- magnitude of slope


Usfs dap protocols

USFS DAP Protocols


Our hypothesis

Our Hypothesis

  • Ho = Lake chemistry, air chemistry, and visibility show no trend through time

  • H1 = Lake chemistry, air chemistry, and visibility are not stable and have either an increasing or decreasing trend over time


Usfs region 1 sas analysis of lake chemistry nadp and improve data

R1 AQ Lake data limitations

Mann-Kendall test was used for analysis of lakes.

No seasonal data available

More than 10 years of data available but from an array of months


Usfs region 1 sas analysis of lake chemistry nadp and improve data

Cabinet Mountain Wilderness

Lower Libby

Upper Libby

Selway-Bitterroot Wilderness

North Kootenai

Shasta

Absaroka-Beartooth Wilderness

Stepping Stone

Twin Island


Trends in annual lake ph

Trends in Annual Lake pH


Trends in annual lake conductivity

Trends in Annual Lake Conductivity


Nadp sites in and around mt

NADP sites in and around MT

Glacier

Clancy

Lost Trail Pass

Little Bighorn

Tower Junction

Cratersof the Moon


Annual nh4 at nadp sites

Annual NH4+ at NADP sites


Usfs region 1 sas analysis of lake chemistry nadp and improve data

Annual Sulfate Concentration at NADP sites


Glacier nadp site percent confidence level and trend direction

Glacier NADP site percent confidence level and trend direction


Glacier nadp spring trends

Glacier NADP Spring Trends


Glacier nadp seasonal sulfate trends

Glacier NADP Seasonal Sulfate Trends


Glacier nadp seasonal sulfate trends1

Glacier NADP Seasonal Sulfate Trends


Improve sites

IMPROVE SITES

GLAC1

CABI1

MONT1

GAMO1

SULA1

YELL2


Yell2 improve site on a clear day and on a hazy day

Yell2 IMPROVE site on a clear day and on a hazy day

Spectrum Seriesdv=0 Bext=10 SVR=390

Spectrum Seriesdv=17 Bext=52 SVR=75


Improve parameter dictionary

IMPROVE Parameter Dictionary


Usfs region 1 sas analysis of lake chemistry nadp and improve data

Key IMPROVE Components

  • PM2.5 components measured:

    • Sulfate (SO4)

    • Nitrate (NO3)

    • Organic Carbon (OMC)

    • Elemental Carbon (EC) also (LAC)

    • Coarse Particulate Matter (ECM)

    • Sea Salt

    • Fine Soils


Annual trends in svr at improve sites

Annual Trends in SVR at IMPROVE sites


Yell2 svr best and worst days

YELL2 SVR best and worst days


Usfs region 1 sas analysis of lake chemistry nadp and improve data

YELL2 dv 20% best and worst days


Conclusions

Conclusions

  • Trend interpretation, particularly cause/effect is difficult and complex

  • Lake ANC decrease not statistically validated except at Stepping Stone Lake. The pH increasing trend and decrease in lake cation trends are not readily explainable

  • Consistent NH4 increase trend at all of the NADP sites. This may be partially due to increased agriculture emissions such as feedlots in E. Oregon and E. Washington

  • NO3 trend increases in lakes and NADP not as consistent as NH4 increase

  • Consistent decrease SO4 at NADP sites is consistent with US trends the last 2 decades with reduced industrial sulfate emissions

  • Consistent improvement in visibility at most of the IMPROVE sites as expressed in increased SVR, decreased deciviews, and reduced extinction

  • 20% best and worst visibility day trends visually correlates well with wildfire emissions.

  • More work in interpretation needs to be done before report finalization


Usfs region 1 sas analysis of lake chemistry nadp and improve data

Danke, Gracias, THANKS

  • Laurie Porth – RMRS

  • Scott Copeland – USFS/CSU Lander

  • Greg Bevenger – Shoshone NF

  • Thomas Dzomba – USFS R1

Danke, Gracias, THANKS


  • Login