## Appendix B

Analysis to Identify Freezing Regions and Evaluate Impacts

of Expanding Type “A” AreasIntentionally Blank PageAppendix B B-1



## Analysis to Identify Freezing Regions and Evaluate Impacts

of Expanding Type “A” Areas

This appendix presents the data sources and methodo logy staff used to evaluate areas experience freezing temperatures in the winter mont hs to determine whether, and to what extent, additional areas of California should be added to Type “A” areas.



### A. Data Sources

To identify areas experiencing freezing temperature s, staff used average monthly minimum temperature data from the Parameter-elevati on Regressions on Independent Slopes Model (PRISM) developed by Dr. Christopher D aly of the PRISM Climate Group at Oregon State University. PRISM is a hybrid stat istical-geographic approach to mapping climate, using discrete point measurements of climatic elements such as precipitation and temperature, with a digital eleva tion model and knowledge of climatic extremes to generate continuous, gridded estimates of monthly, annual, and eventbased climatic parameters (PRISM, 2012).

PRISM data sets are recognized world-wide as the hi ghest-quality spatial climate data sets currently available and are widely accepted by the meteorological community. PRISM climatologies are used in a variety of weathe r and climate related projects such as the 2012 United States Department of Agriculture ’s (U.S.D.A.) Plant Hardiness Zone Map; the official U.S.D.A. precipitation and temper ature maps for all 50 states and Caribbean and Pacific Islands; the official climate atlas for the U.S.D.A.; a 112-year series of monthly temperature, precipitation, and d ew point maps for the conterminous 48 states; National Weather Service Forecasts; and several climate change research efforts; to name a few. PRISM data sets are the of ficial spatial climate data (Daly et al., 2008).

PRISM generated gridded average monthly minimum tem perature data for the climatological period 1971-2000 were obtained and u sed in this analysis. These gridded data sets are 30-year climatologies produce d by the PRISM modeling system, by distributing the normal mean monthly minimum tem perature at each discrete station to a spatial grid. A climate normal is defined as the arithmetic mean of a climatological element computed over three consecutive decades (WM O, 1989). Thus, the normal mean monthly minimum temperature for each station i s the 30-year mean of the monthly means, where the monthly means are computed from the daily values for the same 30-year period. Detailed discussions on creat ing climate data sets using PRISM are well documented in the literature (Daly and Joh nson 1998, Daly 2006, Daly et al. 2002, 2008).

PRISM generated spatially gridded average monthly m inimum temperature data were specifically chosen for this analysis, because thes e data are representative of the average minimum temperatures in a region. Further, the PRISM data are distributed on a uniform ~800m grid, thus providing a dense covera ge of the climate variable and areAppendix B B-2

in a digital form that allows the data to be easily incorporated in calculations and analysis using a Geographic Information System (GIS ) approach.

These data allowed staff to identify areas of Calif ornia that experience typical freezing weather ( i.e. , temperatures less than 32 degrees Fahrenheit) dur ing the winter. To encompass all of winter, staff selected the average monthly minimum temperature of

the months of December to March. Areas that fell i nto these typically freezing areas were referred to as “freezing zones.” These areas are shown in blue in Figure B-1. Existing Type “A” areas are shown in gray.

To translate the identified “freezing zones” to a l ist of recognizable spatial entities, staff used existing United States (U.S.) Postal Service Z IP code boundaries to identify specific ZIP codes having typical freezing weather in winter. The U.S. ZIP code boundary along with 2010 Census population dataset developed by the Environmental Systems Research Institute (ESRI) were also used (A rcGIS, 2012).

Staff used a GIS approach for the analysis, from pr ocessing and averaging of the datasets, to overlay and statistical analyses, to t he identification and mapping of “freezing zones” and ZIP codes. ESRI designs and d evelops the world’s leading GIS technology. GIS is an important tool used by natio nal mapping agencies, aeronautical and nautical organizations, and commercial map and chart publishers around the world.



### A. Methodology

Determining Freezing Zones

PRISM 30-arcsec (~800m) 1971-2000 average monthly m inimum temperature data for December through March were obtained from the PRISM Climate Group for the entire United States (PRISM, 2012). Using GIS, the data w ere clipped to the California extent, reprojected, and converted from a floating point gr id to an integer grid. The individual monthly grids were then averaged to generate a seas onal average monthly minimum temperature grid (Dec.-Mar. average) within GIS. R egions experiencing temperatures below 32 degrees Fahrenheit (“freezing zones”) were then identified and a shapefile was generated for mapping and further analysis.

Existing Type A

The existing Type “A” areas that cover 18 counties were also identified and mapped. Portions of the “freezing zones” that are outside o f the existing Type “A” were separated and used in the identification of ZIP codes experie ncing freezing temperatures through a GIS overlay approach. Staff is proposing to expa nd Type “A” areas with these

identified ZIP codes.Appendix B B-3

Existing Type “A” areas are identified by either co unty or air basin boundaries. The additional areas identified as having freezing zone s in the current analysis encompass parts of several counties. Staff evaluated various approaches to describe the areas being considered, while minimizing the potential em ission impact of allowing the higher VOC products to be sold. ZIP codes appear to be th e best descriptor of these areas; they closely match the freezing zones. Figure B-2 shows the ZIP codes to be added to describe the additional Type “A” areas as a pink li ne. 

### Figure B-1. Areas Outside of Existing Type “A” wit h Freezing Temperatures

Air Basins in Type “A”: North East Plateau (NEP) Mountain Counties (MC) Lake Tahoe (LT) Great Basin Valley (GBV)

Counties in Type “A”:

Del Norte

Shasta TrinityAppendix B B-4



### Figure B-2. Zip Codes that Would Describe Addition al Type “A” AreasAppendix B B-5



### B. Results

Through a GIS based approach, staff identified the ZIP codes, along with their respective populations, that would be added to Type “A” areas. Forty-three ZIP codes were identified from this analysis as having freezi ng conditions outside of the existing Type “A” areas (Figure B-2). Staff is proposing to add these ZIP codes for Type “A”

areas into the definition for Automotive Windshield Washer Fluid (AWWF) in section 94508(a).



### C. Emissions Impact

The estimated total population (2010 census based) in the new proposed Type “A” ( i.e

. identified ZIP codes), was used to estimate the pot ential VOC emissions that may result from allowing the higher VOC products to be sold in these areas. Staff calculated the estimated emission increase by taking the per capita use of AWWF products and applying it to the population that would be added t o Type “A” area. The per capita use was calculated by taking sales information, for AWW F, from the Initial Statement of Reasons for Proposed Amendments to the California C onsumer Products Regulation (ISOR), released on September 29, 2006 (ARB, 2006).

In that staff report, the sales and emissions of Au tomotive Windshield Washer Fluid, for Type “A” areas, are based on the results of the 200 3 Consumer and Commercial Products Survey. These data showed that, in 2003, 10,982 pounds of AWWF was sold in Type “A” areas.

In order to calculate a per capita use, staff grew the sales data by the population o f the State in 2010, as obtained by ESRI. Dividing this sales number by our modeled population number for Type “A” area in 2010 (848, 2 44) yields the per capita use of 0.01 pounds (lbs) of product per day.

Multiplying our per capita use rate by the estimate d population in the new Type “A” areas, we estimate the total volume of 25 percent b y weight VOC product that would be sold in these new areas. Multiplying sales data by 24 percent yields the estimated increase in VOC emissions resulting from the propos al.

Lastly, the data were grown to representative 2012 numbers using population data from the California Department of Finance webpage (CADOF , 2012). Using the ZIP code data, the population added to Type “A” areas would be approximately 101,000 people. These calculations yield an increase in VOC emissio ns of 0.12 tons per day (tpd). In the context of having reduced VOC emissions by over 25 tpd from this category, staff believes the increase to be quite small. Table B-1 presents the estimated emission increase by air basin.Appendix B B-6 Table B-1 Estimated Increase in VOC emissions by Air Basin

Air Basin Emission (TPD)*

Mojave Desert 0.02
San Joaquin Valley 0.03

South Coast 0.04

Northern Air Basins** 0.02

San Diego < 0.01

Total 0.12

* Values are rounded
** Northern Air Basins include Sacramento Valley, N orth Coast, and Lake County Air

Basin

The estimated statewide total emissions increase of 0.12 tpd is conservative. To the

extent that dilutable AWWF is used at a concentrati on greater than 1 percent by weight VOC in the areas proposed for addition to Type “A,” as allowed by regulation, the

emissions increase would be lower.Appendix B B-7



## References for Appendix B

1. Air Resources Board. Initial Statement of Reason s for Proposed Amendments to the California Consumer Products Regulation and the Aer osol Coatings Regulation. September 29, 2006. (ARB, 2006)

2. ArcGIS Online, ESRI. Esri Maps and Data. http://www.arcgis.com/home/index.html

. Accessed July 2012. (ArcGIS, 2012)

3. California Department of Finance. Interim Popul ation Projections for California and

its Counties 2010-2050. http://www.dof.ca.gov/research/demographic/reports/ projections/interim/view.php Accessed August 2012. (CADOF, 2012)

4. Daly, C., Johnson, G. Climate Mapping with PRISM : PRISM Spatial Climate Layers.

1998. http://prism.oregonstate.edu/pub/prism/docs/prisgui d.pdf . Accessed July 2012 (Daly and Johnson, 1998)

5. Daly, C., Gibson, W.P., Taylor, G.H., Jonhson, G .L., Pasteris, P. A knowledge-based approach to the statistical mapping of climate. 200 2. Climate research. Volume 22: 99-113. (Daly et al., 2002)

6. Daly, C. Guidelines for assessing the suitabilit y of spatial climate data sets. 2006. International Journal of Climatology. Volume 26: 70 7–721. (Daly, 2006)

7. Daly, C., Halbleib, M., Smith, J.I., Gibson,W.P. , Doggett, M.K., Taylor, G.H., Curtis, J. and Pasteris, P.P. Physiographically sensitive m apping of climatological temperature and precipitation across the contermino us United States. 2008. International Journal of Climatology. Volume 28: 20 31-2064. (Daly et al., 2008)

8. PRISM Climate Group, Oregon State University. http://prism.oregonstate.edu/

. Accessed July 2012. (PRISM, 2012)

9. World Meteorological Organization. Calculation o f Monthly and Annual 30-Year Standard Normals. 1989. WCDP-No. 10, WMO-TD/No. 341 . (WMO, 1989)