Appendix B 

Analysis to Identify Freezing Regions and Evaluate Impacts 

of Expanding Type “A” Areas 
Intentionally Blank Page 
Appendix B B-1

Analysis to Identify Freezing Regions and Evaluate Impacts 

of Expanding Type “A” Areas 

This appendix presents the data sources and methodology staff used to evaluate areas 

experience freezing temperatures in the winter months 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 temperatures, staff used average monthly 

minimum temperature data from the Parameter-elevation Regressions on Independent 

Slopes Model (PRISM) developed by Dr. Christopher Daly of the PRISM Climate Group 

at Oregon State University. PRISM is a hybrid statistical-geographic approach to 

mapping climate, using discrete point measurements of climatic elements such as 

precipitation and temperature, with a digital elevation 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 highest-quality spatial climate data 

sets currently available and are widely accepted by the meteorological community. 

PRISM climatologies are used in a variety of weather 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 temperature 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 dew 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 official spatial climate data (Daly et al., 

2008). 

PRISM generated gridded average monthly minimum temperature data for the 

climatological period 1971-2000 were obtained and used in this analysis. These 

gridded data sets are 30-year climatologies produced by the PRISM modeling system, 

by distributing the normal mean monthly minimum temperature 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 (WMO, 1989). Thus, the normal 

mean monthly minimum temperature for each station is 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 creating climate data sets using PRISM 

are well documented in the literature (Daly and Johnson 1998, Daly 2006, Daly et al. 

2002, 2008). 

PRISM generated spatially gridded average monthly minimum temperature data were 

specifically chosen for this analysis, because these 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 coverage of the climate variable and are 
Appendix 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 California that experience typical freezing 

weather (i.e., temperatures less than 32 degrees Fahrenheit) during the winter. To 

encompass all of winter, staff selected the average monthly minimum temperature of 

the months of December to March. Areas that fell into 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 list of recognizable spatial entities, staff 

used existing United States (U.S.) Postal Service ZIP 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 (ArcGIS, 2012). 

Staff used a GIS approach for the analysis, from processing and averaging of the 

datasets, to overlay and statistical analyses, to the identification and mapping of 

“freezing zones” and ZIP codes. ESRI designs and develops the world’s leading GIS 

technology. GIS is an important tool used by national 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 minimum temperature data for 

December through March were obtained from the PRISM Climate Group for the entire 

United States (PRISM, 2012). Using GIS, the data were clipped to the California extent, 

reprojected, and converted from a floating point grid to an integer grid. The individual 

monthly grids were then averaged to generate a seasonal average monthly minimum 

temperature grid (Dec.-Mar. average) within GIS. Regions 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 of the existing Type “A” were separated 

and used in the identification of ZIP codes experiencing freezing temperatures through 

a GIS overlay approach. Staff is proposing to expand Type “A” areas with these 

identified ZIP codes. 
Appendix B B-3

Existing Type “A” areas are identified by either county or air basin boundaries. The 

additional areas identified as having freezing zones in the current analysis encompass 

parts of several counties. Staff evaluated various approaches to describe the areas 

being considered, while minimizing the potential emission impact of allowing the higher 

VOC products to be sold. ZIP codes appear to be the 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 line. 

Figure B-1. Areas Outside of Existing Type “A” with 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 

 Trinity 
Appendix B B-4

Figure B-2. Zip Codes that Would Describe Additional Type “A” Areas 
Appendix 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 freezing 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 potential 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 to Type “A” area. The per capita use 

was calculated by taking sales information, for AWWF, from the Initial Statement of 

Reasons for Proposed Amendments to the California Consumer Products Regulation 

(ISOR), released on September 29, 2006 (ARB, 2006).

In that staff report, the sales and emissions of Automotive Windshield Washer Fluid, for 

Type “A” areas, are based on the results of the 2003 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 of the 

State in 2010, as obtained by ESRI. Dividing this sales number by our modeled 

population number for Type “A” area in 2010 (848, 244) yields the per capita use of 

0.01 pounds (lbs) of product per day. 

Multiplying our per capita use rate by the estimated population in the new Type “A” 

areas, we estimate the total volume of 25 percent by 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 proposal. 

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 emissions 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, North 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 concentration 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 Reasons for Proposed Amendments to the 

California Consumer Products Regulation and the Aerosol 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 Population 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/prisguid.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. 2002. Climate research. Volume 22: 

99-113. (Daly et al., 2002) 

6. Daly, C. Guidelines for assessing the suitability of spatial climate data sets. 2006. 

International Journal of Climatology. Volume 26: 707–721. (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 mapping of climatological 

temperature and precipitation across the conterminous United States. 2008. 

International Journal of Climatology. Volume 28: 2031-2064. (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 of Monthly and Annual 30-Year 

Standard Normals. 1989. WCDP-No. 10, WMO-TD/No. 341. (WMO, 1989)