CA-TIMES (McCollum et al., 2012; Yang et al., 2014, 2015) is a bottom-up energy–economic model originally based on the TIMES (The Integrated MARKAL-EFOM System) model (Loulou et al., 2016). CA-TIMES is a cost-minimization optimization model that balances energy supply and demand system-wide from all energy sectors of the economy. Demand includes the transportation, industrial, residential, commercial, and agricultural sectors. Fuel and electricity supply includes electric, biofuel, hydrogen production plants, and biofuel and petroleum refineries. Demand was assumed fixed for the scenarios considered (Yang et al., 2014, 2015). CA-TIMES allows imports from out of state, such as oil, natural gas, and electricity. Renewables and biomass are handled separately and modeled explicitly as located in or out of state and imports are determined on a cost basis. CA-TIMES contains capital and operation costs for each technology and diverse fuel and energy carriers, and it calculates GHG emissions for CO2, CH4, and N2O.

The case studies considered in the present study focus on two CA-TIMES scenarios in 2050: (i) a business-as-usual (BAU) scenario that achieves the goals outlined in California Assembly Bill 32 (AB32), the Global Warming Solutions Act of 2006 and (ii) a climate-friendly GHG-Step scenario that achieves an 80 % reduction (relative to 1990 level) in GHG emissions by 2050. Statewide GHG emissions under each scenario are summarized in Fig. 1. In the GHG-Step scenario a “step” GHG emissions constraint in which a constant 2020 cap is held until 2050 is applied, and then an 80 % reduction is applied from 2050 onward. This allows the model freedom to adopt strategies that lower GHG emissions prior to 2049 if those strategies minimize costs. This 2050 GHG constraint causes aggressive change over the period 2040–2049 but does not shock to the energy system in 2050 because the CA-TIMES model has perfect foresight and optimally minimizes the energy system cost (with a 4 % discount factor) over the entire period from 2010 to 2050, making investment decisions to meet targets. Also, CA-TIMES investments in low-GHG technologies start slowly and grow to reach the required market share to meet the targets since technologies have finite lifetimes and cannot take over respective markets instantaneously. The criteria pollutant emissions between 2010 and 2049 were not analyzed in the current study but a summary of CA-TIMES results for intermediate years is provided by Yang et al. (2015). Both BAU and GHG-Step scenarios include current and sunset GHG regulations in California: Corporate Average Fuel Economy (CAFE) standards (California Air Resources Board, 2005, 2009b, 2010b), the Zero Emission Vehicle (ZEV) mandate (California Air Resources Board, 2012b, c, d, e, f), Low Carbon Fuel Standard (LCFS) (California Air Resources Board, 2009c, 2011c), Cap-and-Trade Program (California Air Resources Board, 2011d, 2017), and federal and state incentives (tax credits and subsidies). CA-TIMES predicts total annual energy consumption in California for the year 2050 to be 8763 PJ in the BAU scenario and 7679 PJ in the GHG-Step scenario (reference value for 2010 is approximately 7500 PJ) (Yang et al., 2015).

The methods to estimate criteria emissions for different sources developed in the current paper take advantage of the best available information describing future energy and emissions as a function of location. The quality of this information varied considerably for each major source category and so the details of the methodology also varied. Figure 2 illustrates an overview of the general procedure. The changes in energy consumption and GHG emissions produced by CA-TIMES for each energy sector in the year 2050 were translated into changes in criteria pollutant emissions by accounting for changing energy activity levels or fuel switching. Literature searches were conducted to identify any previous studies describing spatial locations of future emissions within California. Altered emissions for the year 2050 were then projected from a 2010 emissions inventory with 4 km spatial resolution provided by the California Air Resources Board (CARB). Additional details for each major source type are discussed below.

On-road mobile sources include passenger cars, light-duty trucks (LDTs), medium-duty trucks (MDTs), heavy-duty trucks (HDTs), buses, motorcycles, and motor homes. On-road emissions were generated in a multistep process summarized in Fig. 3. In the first step, 2010–2035 emissions projection trends from the Emission Factor (EMFAC) 2011 model (California Air Resources Board, 2011a) were used to extrapolate further to 2050. In the second step, an intermediate 4 km vehicular emissions inventory was generated by combining EMFAC 2050 projections with a 2010 4 km emissions inventory as a spatial surrogate. In the third step, the 2050 fossil fuel vehicular emissions rates that were projected from EMFAC as well as new emissions rates gathered from alternative fuel emissions literature were used to scale the 4 km intermediate mobile emissions inventory based on the vehicle miles traveled (VMTs), trips, and vehicle class and (conventional and alternative) fuel consumption output produced for each CA-TIMES scenario.

Aviation sources include commercial, civil, agricultural, or military use and primarily run on jet fuel or aviation gasoline. The rail emissions sources include passenger, commuter, switching, and hauling trains, which currently run primarily on diesel-fueled generators powering an electric drivetrain. Off-road equipment includes industrial, agricultural, and construction equipment; port and rail operations; and lawn and garden equipment. The list of aviation, rail, and off-road emissions source categorizations are based on the EICs listed in Table S17 in the Supplement (including new EICs created to represent sources operating on alternative fuels previously not in the CARB inventory).

The marine emissions source category includes all ocean-going vessels (OGVs), commercial harbor craft, and recreational boats (see Table S19 in the Supplement). An intermediate OGV emissions inventory was predicted for the year 2050 based on the extrapolation of Port of Los Angeles and Port of Long Beach 2020 trends (Starcrest Consulting Group, 2009; The Port of Los Angeles and The Port of Long Beach, 2010) (see Table S20 in the Supplement). All other OGV emissions (not listed in Table S20 in the Supplement) in California were held constant at 2010 levels in the intermediate 2050 inventory prior to modifications from CA-TIMES.

Major emissions sources within the residential and commercial sectors include natural gas combustion (space heating and water heating), biomass combustion (fireplaces and stoves), and food cooking (especially charbroiling and frying). The residential and commercial emissions associated with natural gas and food cooking were assumed to scale according to population growth projected for each county (Table S24 in the Supplement) (State of California, 2013) to produce an intermediate emissions inventory. These intermediate residential and commercial gridded emissions were then scaled to reflect 2010 vs. 2050 results from CA-TIMES (Fig. 9).

Natural gas consumption in the commercial sector was reduced by half (325 to 162 PJ) in the GHG-Step scenario relative to the BAU scenario in 2050. Most of commercial energy reduction is due to efficiency gains and switch from natural gas to electrification of end uses. Natural gas consumption in the residential sector also decreases (615 to 507 PJ) under the GHG-Step scenario relative to the BAU scenario. Much of the energy that would have been supplied by natural gas is replaced by renewable sources such as solar (155 PJ), which was assumed to have no criteria pollutant emissions in California. Improved energy efficiency and conservation also plays a role, with residential electricity consumption decreasing (402 to 313 PJ) in the GHG-Step scenario. Other combustion sources, including wood burning and distillate oil fuel consumption, were allowed to compete in CA-TIMES subject to the constraint that they could not increase above the 2010 levels in order to maintain compliance with current air quality regulations.

The electricity generation emissions category includes all fuel-burning and renewable power plants for industrial, residential, commercial, or transportational use. Annual generation totals for different types of California power plants were extracted from national power plant data (US Energy Information Administration Independent Statistics and Analysis, 2012; US Environmental Protection Agency, 2014). Emissions rates per unit of fuel burned were estimated for each power plant described in the base case 2010 emissions inventory.

CA-TIMES finds that non-hydro renewable (geothermal, tidal, solar, wind, and biomass) increases from 10 % (22 938 GWh) of the electricity generation mix in 2010 (144 825 GWh) to 35 and 76 % (489 493 GWh) in the 2050 BAU and 2050 GHG-Step scenarios, respectively (see Fig. 10). However, total in-state and out-of-state electricity generation in the GHG-Step scenario is one-third larger than the BAU scenario (416 219 GWh vs. 643 373 GWh) to meet the increased demand from sectors such as the on-road vehicles with growing hybridization and electrification needed to meet the 2050 carbon constraint. Statewide scaling factors for electricity generation in the 2050 BAU scenario vs. 2010 and the 2050 GHG-Step scenario vs. 2010 are listed in Table S25 in the Supplement.

CA-TIMES calculates aggregated statewide energy totals but energy resources (especially for renewables) are not uniformly distributed across the state. In the current study, renewable electricity production in 2050 was spatially allocated in a manner that was consistent with the energy resource potential in 12 regions (Fig. S5 in the Supplement) as projected in 15 scenarios by the grid load distribution model SWITCH (Fripp, 2012; Johnston et al., 2013; Nelson et al., 2013). Table S26 in the Supplement lists the electrical generation by energy source for each SWITCH region averaged across these 15 scenarios. This profile of resource potential was then applied to the CA-TIMES predictions summarized in Table S25 in the Supplement yielding the 2050 and 2010 scaling factors for the BAU scenario (Table S27 in the Supplement) and the GHG-Step scenario (Table S28 in the Supplement).

The scaling factors summarized in Tables S27 and S28 assume that the out-of-state portion of electricity generation for a given fuel or energy resource in the year 2050 remained constant at 2010 levels. CA-TIMES does not provide additional information describing out-of-state generation except for a few renewables. This out-of-state portion of the electricity generation was subtracted from the CA-TIMES totals prior to scaling emissions from each power plant in California. Table S29 in the Supplement summarizes the out-of-state portion of electricity generation for each fuel in 2010 and assumed portions in each of the 2050 scenarios.

Additional emissions adjustments were made for new renewable fuels such as those produced by the biomass integrated gasification combined cycle (IGCC), a process that gasifies biomass for electricity production. Much of the biomass electricity generation projected by CA-TIMES for 2050 in the BAU scenario uses biomass IGCC (see Tables S30 through S32). There are currently several coal IGCC plants in the US (US Department of Energy National Energy Technology Laboratory, 2010, 2015) but no biomass IGCC plants (Lundqvist, 1993; Ståhl and Neergaard, 1998; US Department of Energy National Energy Technology Laboratory, 2010). Future biomass IGCC emissions in California were estimated using several models that incorporate biomass IGCC, such as GREET, CA-GREET (Argonne National Laboratory Transportation Technology R&D Center, 2014; California Air Resources Board, 2009a, 2015), and a National Renewable Energy Laboratory analysis (Mann and Spath, 1997). Ultimately, biomass IGCC power plant emissions were estimated from conversion of conventional steam turbines in the 2010 CARB inventory based on emissions rates inferred from CA-GREET1.8 for 2050 (Table S33 in the Supplement). An inter-comparison study among GREET1.8, GREET 2014, and CA-GREET2.0 showed that the CA-GREET1.8b model had the best agreement with emissions rates from approximately 30 biomass plants operating on wood residue in California (California Air Resources Board, 2011b; US Environmental Protection Agency, 2014).

The industrial and agricultural emissions category covers many manufacturing industries such as metal, wood, glass, textiles, mining, and chemicals. Food and agricultural sectors include farming livestock, crops, food production, bakeries, and breweries. Most of these industries were unchanged in the CA-TIMES energy scenarios, with the notable exception that biofuel and hydrogen fuel production replaced some traditional petroleum production, causing changes in refinery and storage emissions (shown in Figs. S6–S8).

Figure 11 illustrates PM emissions of tire and brake wear from on-road vehicles under the BAU and GHG-Step scenarios. The fine spatial distribution of the emissions reflects the spatial distribution of tire- and brake-wear emissions in the base 2010 inventory that is updated using EMFAC predictions to produce the intermediate 2050 emissions inventory. The technology changes inherent in the CA-TIMES BAU and GHG-Step scenarios are then applied uniformly across the state, yielding virtually identical spatial distributions for the final 2050 BAU and GHG-Step scenario emissions. Tire- and brake-wear emissions patterns illustrated in Fig. 11 essentially follow predicted vehicle activity patterns in the state. Predicted emissions are highest in major urban centers and along major transportation corridors. Although increase in vehicular activity was part of this study, expansion of roadways between 2010 and 2050 was not considered in this study and may be updated in newer versions of the model.

California's environmental regulations apply uniformly across the state, which supports the assumption of uniform GHG emissions reductions for on-road vehicles. Despite the uniform regulatory landscape, some of the measures described in the CA-TIMES GHG-Step scenario rely on modified behavioral patterns and willingness or ability to adopt new technologies, which may change by region. Education levels, personal wealth, and environmental attitudes vary sharply across California. Capturing these trends in subregions of the state will require surveys of consumer choice and predictions of future behavior that are beyond the scope of the current paper.

Figure 12 illustrates the PM emissions from tailpipe exhaust under the 2050 BAU scenario and the 2050 GHG-Step scenario. Similar to the tire- and brake-wear emissions, the spatial pattern for mobile sources is identical under both scenarios because the technology changes specified by the CA-TIMES model are applied uniformly over the entire state. Tailpipe PM emissions once again follow patterns of vehicle activity as predicted by EMFAC. Of greater interest is the prediction that tire- and brake-wear emissions (Fig. 11) will exceed tailpipe emissions (Fig. 12) in both the 2050 BAU and GHG-Step scenarios due to the adoption of increasingly clean vehicle technology. Tailpipe emissions in the GHG-Step scenario are a factor of ∼1.8 lower than tailpipe emissions in the BAU scenario. In contrast, tire- and brake-wear emissions are predicted to decrease by a factor of +3 under the GHG-Step scenario. This reflects the fact that BAU gasoline and diesel tailpipe emissions already incorporate significant emissions control technology yielding fewer opportunities for further improvement. Tire- and brake-wear emissions have almost no control technology in the BAU scenario, which makes the widespread adoption of electric or hybrid drivetrains using regenerative braking particularly effective at reducing emissions.

The current analysis assumes that no new major highways will be built in California and population growth is accommodated partially through increased urban density such that traffic volumes increase uniformly across the transportation network. These assumptions are simplistic but a previous study of smart growth in the San Joaquin Valley indicated that more detailed accounting of population growth had minimal impact on air quality (Hixson et al., 2010).

PM emissions from off-road and rail sources are plotted in Fig. 13 for the BAU and GHG-Step scenarios examined in the current study. Maximum statewide PM emissions for this source category are centered at the location of major construction projects with lower emissions rates for “routine” off-road emissions distributed more broadly according to typical activity patterns for smaller construction projects, rail, etc. The 2010 emissions inventory that acts as the basis for the 2050 projections in the current project correctly identified replacement of the east span of the Bay Bridge in the San Francisco Bay Area as the leading construction project with the highest overall emissions in the state. This ∼USD 6.5 billion project spanned more than 10 years, with the new bridge completed in 2013 and final decommissioning and demolition of the old eastern span scheduled for 2018.

It is difficult to predict the location of major construction projects in 2050 but it is reasonable to expect that several large projects will be active in that timeframe. Candidate projects currently under discussion include additional replacement of California's numerous highways and bridges, upgrading California's water conveyance systems to better withstand earthquakes, development of high-speed rail lines, reinforcement or expansion of seawalls to protect property, etc. Each of these projects will potentially emit criteria pollutants that would affect air quality over major urban centers. In the present study, the peak emissions associated with the major construction project around the Bay Bridge were retained in the future scenario as an example of a major construction project near an urban area. Future model analysis that uses these emissions should conduct sensitivity tests to ensure that the assumed placement of this example major construction project does not influence the overall conclusions of the study.

Maximum PM emissions shown in Fig. 13 decrease by a factor of approximately 1.64 in the GHG-Step scenario relative to the BAU scenario. Adoption of biomass-based fuels was also found to reduce emissions of SOx, HC, PM, and occasionally CO from off-road and rail sources, but NOx emissions increased for some fuel choices.

PM emissions from marine and aviation sources are shown in Fig. 14 for the BAU and GHG-Step scenarios considered in the present study. The highest PM emissions rates occur in offshore shipping lanes that converge on the Port of Los Angeles, the Port of Long Beach, and the Port of Oakland. Emissions rates change with proximity to California shores due to regulations governing sulfur content of marine fuel or ship speed. Emissions patterns at inland locations reflect shipping activity on inland waterways or activity surrounding small regional airports.

Maximum PM emissions rates from marine sources increase under the GHG-Step scenario as illustrated most clearly in the right panels of Fig. 14. CA-TIMES determined that the available biofuel capacity could be more efficiently used to offset traditional fossil fuels for on-road transportation sources and so the GHG-Step scenario is predicted to incorporate additional fossil fuels for marine sources under the GHG-Step scenario vs. the BAU scenario. The net result of the disbenefits associated with increased marine emissions vs. the benefits of the decreased on-road emissions will be considered in future studies that include analysis with regional air quality models.

Figure 15 illustrates PM emissions from residential and commercial sources under the 2050 BAU and GHG-Step scenarios. The spatial patterns of emissions largely follow the estimated population projections in California in the year 2050 as summarized in Table S24 in the Supplement. Population growth was assumed to be identical under the BAU and GHG-Step scenarios yielding virtually identical spatial distributions for both scenarios. The adoption of new technologies and altered behavioral patterns predicted by the CA-TIMES model under the GHG-Step scenario were applied uniformly over the state without modification by income, education level, or regional differences in environmental attitudes. Predicted changes to PM emissions from residential and commercial sources are modest with slight reductions of ∼10 % mostly attributed to energy efficiency measures. Widespread adoption of biomethane to replace natural gas is predicted in the GHG-Step scenario but this fuel change has little impact on criteria pollutant emissions.

Figure 16 illustrates predicted emissions of PM from combustion processes used to generate electricity. These emissions are represented as point sources and so only the grid cells containing an electrical generation unit are colored. The highest emissions rates for individual grid cells are associated with a small number of major electrical generation stations typically powered by natural gas in the BAU scenario. The majority of the colored grid cells in Fig. 16 are associated with smaller backup generators that operate intermittently and therefore have very low emissions. These backup units are typically powered by a fossil fuel such as diesel fuel in the BAU scenario, with a shift to biofuels in the GHG-Step scenario. This fuel switch has a modest impact on total emissions given the low utilization of these units.

Peak emissions rates of PM decrease by a factor of ∼1.7 in the GHG-Step scenario primarily due to a reduction in fossil fuel electricity generation in favor of a shift to solar and wind sources (see Fig. 10). All generating stations are assumed to continue operation at a reduced rate in the GHG-Step scenario rather than selectively decommissioning some stations. The age and efficiency of existing natural-gas-generating stations will likely be key factors determining how they are operated in the future scenarios. Solar and wind electricity generation does not emit criteria pollutants and so the location of these facilities is not shown in Fig. 16.

Figure 17 shows the locations of refineries producing biofuels (biorefineries) in California under the BAU and GHG-Step scenarios considered in the present study. The location of future biorefineries was chosen to minimize transportation costs for the raw materials feeding into the refinery and the delivery of fuel to the final point of end use. Additional zoning constraints were considered to prevent the placement of biorefineries near schools, hospitals, or other locations with sensitive populations. More generally, a constraint was considered to restrict the placement of new biorefineries in regions that currently violate the NAAQS. The top panels of Fig. 11 therefore do not allow the placement of biorefineries in either the San Joaquin Valley or the South Coast Air Basin (SoCAB), while the less constrained scenarios illustrated in the lower panels of Fig. 17 do not impose this restriction. In practice, biorefineries were generally sited near landfills or industrial or agricultural areas within each city selected as economically optimal within the specified constraints. The enforcement of NAAQS constraints on biorefineries led to a smaller number of larger refineries under both the BAU and GHG-Step scenarios. Note that overall biorefining output is higher in the BAU scenario than in the GHG-Step scenario. Biofuels have lower associated GHG emissions than traditional fossil fuels but their carbon intensity is still too high to meet the GHG emissions target represented in the GHG-Step scenario. The CA-TIMES model therefore predicts that a portion of the energy supplied by biofuels in the BAU scenario will instead be supplied by wind and solar in the GHG-Step scenario.

Figure 18a illustrates the net change in emissions related to criteria pollutants in California in the GHG-Step scenario vs. the BAU scenario analyzed in the current study. Emissions of each pollutant are broken down by the major emissions categories analyzed in Sect. 2. The miscellaneous category is equivalent in the BAU and GHG-Step scenarios and hence is not plotted. Contributions below 0 % indicate emissions reductions, while contributions above 0 % indicate emissions increases. Each of these changes represents the statewide average for the sources within the indicated sector. Note that the changes within each sector may not be uniform across the entire state. The net change in total emissions is indicated by the black horizontal line for each species. It is immediately apparent that the emissions reductions illustrated in Fig. 18a are not uniform for all pollutants. Maximum reductions of ∼60 % are observed for CO2 and particulate copper (Cu) emissions. In contrast, emissions of particulate SO42-, gaseous CO, and gaseous SOx actually increase under the GHG-Step scenario due to trade-offs in the technologies adopted in the off-road mobile categories (rail, marine, aviation, etc.) needed to optimize the overall GHG emissions across the state. Emissions of pollutants that experience increasing trends in Fig. 18a are minor in the present-day inventory and so they do not currently trigger NAAQS violations. Changes in key highly emitted pollutants fall in between the extreme cases described above (see results for particulate elemental carbon (EC), particulate OC, and gaseous NOx). Each of these pollutants experiences a net decrease in total emissions averaged across California, but emissions changes are not uniform across all categories. Some technology and fuel changes cause higher emissions, which are offset by savings in other categories. This complex mixture of trade-offs reflects the optimal economic approach to GHG reductions determined by the CA-TIMES model.

The changing activity patterns, fuels, and technologies included in the GHG-Step scenario lead to changes in the emitted particle size and composition distribution. This leads to differences in the response of primary PM with aerodynamic diameter less than 2.5 µm (PM2.5) and less than 0.1 µm (PM0.1; ultrafine particles). Ultrafine particles are an emerging pollutant of concern expected to influence public health (Delfino et al., 2005; Hoek et al., 2010; Knol et al., 2009). The results shown in Fig. 18a illustrate that the GHG-Step scenario leads to only a 4 % decrease in primary PM2.5 emissions but a much larger 36 % reduction in PM0.1 emissions. Recent epidemiology results indicate that PM0.1 is associated with mortality in the California Teachers Study (Ostro et al., 2015). Likewise, toxicology studies indicate that ultrafine particles are more toxic than larger particles per unit mass (Donaldson et al., 2001, 2002; Elder et al., 2006; Kreyling et al., 2004; Oberdorster et al., 2002). Enhanced PM0.1 emissions reductions could amplify the potential health benefits of the future GHG-Step scenario beyond the level expected from PM2.5 emissions reductions.

Figure 18b shows the net change in criteria pollutant emissions predicted using the expert analysis approach described by Shindell et al. (2012). These results are presented as a comparison point to the results illustrated in Fig. 18a and listed in Tables S36 through S38 in the Supplement. The expert analysis scenario focused on a small number of measures targeted for countries which are in the early stages of adopting policies to reduce GHG emissions or mitigate regional air quality problems. As a result, the measures described by Shindell et al. have a large impact on global public health but they will have a very minor impact on California (or any other major state or country that has already implemented significant emissions controls).

Comparison of Fig. 18a and b illustrates that only reductions in particulate EC are comparable in the Shindell et al. and CA-TIMES scenarios due to the mitigation of emissions from off-road diesel engines. CA-TIMES accomplishes this reduction through a combined switch in fuels and adoption of diesel particle filters on remaining diesel and biodiesel sources to achieve a combined reduction in GHG emissions and criteria pollutant emissions. Shindell et al. assume uniform adoption of diesel particle filters on all off-road diesel engines with no fuel switching. Shindell et al. also specify the adoption of digesters for dairy waste and increased use of landfill gas as renewable methane sources. CA-TIMES predicts similar adoption resulting in a ∼35–40 % reduction in ammonia (NH3) emissions from these sources. The CA-TIMES approach considered in the present study additionally considers how the emissions of biomethane differ from the emissions of traditional natural gas. The only other significant measure specified by Shindell et al. that could reduce criteria pollutant emissions in California is a complete ban on burning of agricultural waste. California already limits agriculture burns to avoid stagnation periods. Thus, even the apparent savings associated with reduced agricultural burns shown in Fig. 18b are likely to have limited practical impact on air quality in the state. Shindell et al. do not consider the adoption of low-carbon fuels or electrification of on-road vehicles, which are necessary to achieve deep GHG reductions in CA.

Overall, the analysis presented by Shindell et al. (2012) is appropriately targeted at global health but the measures considered in this analysis do not achieve California's GHG objectives and the criteria pollutant emissions changes associated with them will not support calculations for future air quality in California. Energy economic models such as CA-TIMES represent a more realistic tool for development of scenarios in regions like California that have already considered all simple measures. Careful analysis is required to understand the resulting complex pattern of trade-offs between emissions in different categories that result from these scenarios.

Figure 19 illustrates examples of spatial patterns of emissions changes under the GHG-Step scenario predicted by CA-TIMES in the current study. The offsetting increasing and decreasing emissions changes illustrated in Fig. 18 do not occur uniformly over the state but instead appear as regions of localized increasing and decreasing emissions. As an even greater complication, the spatial pattern of increasing and decreasing emissions changes for each pollutant. The top panels of Fig. 19 illustrate changes in the commercial and residential sector for NOx emissions (Fig. 19a) and OC emissions (Fig. 19b) in central California. Patterns of emissions increases or decreases are similar in major urban centers (San Francisco and Sacramento) but different patterns are predicted for emissions of NOx and OC in the heavily polluted San Joaquin Valley (Fresno and Bakersfield). The lower panels of Fig. 19 illustrate even stronger variation in the spatial pattern of emissions changes in the off-road and rail categories in southern California. The spatial pattern of the change in particulate EC emissions (Fig. 19c) differs strongly from the spatial pattern of the change in particulate OC emissions (Fig. 19d).

All of the emissions illustrated in Fig. 19 will produce regions of increased or decreased pollutant concentrations. Given that each region is highly populated, these emissions patterns will have a direct effect on population exposure. Detailed analysis with regional air quality models at a resolution of 4 km or finer will be required to understand the health implications of these changing emissions. California requires this level of fine-scale emissions analysis to accurately predict the air quality impacts of future GHG mitigation strategies in the state. Similar efforts will be required to analyze the effects of GHG mitigation strategies on criteria pollutants in other highly populated regions that have already moved beyond simple emissions regulations banning obvious sources of air pollution.

The CA-REMARQUE projections for criteria pollutant emissions associated with optimal climate policies in California should not be directly extrapolated to other regions or countries. Instead, the methods used by CA-REMARQUE should be applied to each new region to fully consider the appropriate energy resources available, consumption patterns, equipment vintages, aftertreatment regulations, and population and economic growth rates. Each region may have a different optimal set of GHG mitigation technologies and policies that will lead to different rates and spatial patterns of emissions compared to the changes predicted in California. Many developing regions will be able to select less expensive GHG mitigation strategies that also reduce GHG and criteria pollutant emissions relative to their BAU scenario. Within developed regions such as other US states, the elements of the mobile emissions inventory maintained by the US EPA (MOVES and mobile portion of the National Emissions Inventory) can be adapted to replace the corresponding California information (EMFAC, mobile portion of the CARB inventory). Changes to off-road emissions would need to be estimated following procedures similar to those employed in the CARB off-road VISION model. Effort would be needed to estimate how changes to marine fuel sources would influence emissions at major ports. Studies would need to be conducted describing potential locations for new facilities producing low-carbon fuels and the resulting emissions from those facilities. This information would support a fully resolved analysis of the criteria pollutant emissions associated with climate policies outside of California.