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Abstract To demonstrate how a mega city can lead in decarbonizing beyond legal mandates, the city of Los Angeles (LA) developed science-based, feasible pathways towards utilizing 100% renewable energy for its municipally-owned electric utility. Aside from decarbonization, renewable energy adoption can lead to co-benefits such as improving urban air quality from reductions in combustion-related emissions of oxides of nitrogen (NO_x), primary fine particulate matter (PM_2.5) and others. Herein, we quantify changes to air pollutant concentrations and public health from scenarios of 100% renewable electricity adoption in LA in 2045, alongside aggressive electrification of end-use sectors. Our analysis suggests that while ensuring reliable electricity supply, reductions in emissions of air pollutants associated with the 100% renewable electricity scenarios can lead to 8% citywide reductions of PM_2.5concentration while increasing ozone concentration by 5% relative to a 2012 baseline year, given identical meteorology conditions. The combination of these concentration changes could result in net monetized public health benefits (driven by avoided deaths) of up to $1.4 billion in year 2045 in LA, results potentially replicable for other city-scale decarbonization scenarios. 

Abstract Global cooling capacity is expected to triple by 2050, as rising temperatures and humidity levels intensify the heat stress that populations experience. Although air conditioning (AC) is a key adaptation tool for reducing exposure to extreme heat, we currently have a limited understanding of patterns of AC ownership. Developing high resolution estimates of AC ownership is critical for identifying communities vulnerable to extreme heat and for informing future electricity system investments as increases in cooling demand will exacerbate strain placed on aging power systems. In this study, we utilize a segmented linear regression model to identify AC ownership across Southern California by investigating the relationship between daily household electricity usage and a variety of humid heat metrics (HHMs) for ~160000 homes. We hypothesize that AC penetration rate estimates, i.e. the percentage of homes in a defined area that have AC, can be improved by considering indices that incorporate humidity as well as temperature. We run the model for each household with each unique heat metric for the years 2015 and 2016 and compare differences in AC ownership estimates at the census tract level. In total, 81% of the households were identified as having AC by at least one heat metric while 69% of the homes were determined to have AC with a consensus across all five of the heat metrics. Regression results also showed that ther²values for the dry bulb temperature (DBT) (0.39) regression were either comparable to or higher than ther²values for HHMs (0.15–0.40). Our results suggest that using a combination of heat metrics can increase confidence in AC penetration rate estimates, but using DBT alone produces similar estimates to other HHMs, which are often more difficult to access, individually. Future work should investigate these results in regions with high humidity. 

Abstract Extreme heat events are increasing in frequency and intensity, challenging electricity infrastructure due to growing cooling demand and posing public health risks to urbanites. In order to minimize risks from increasing extreme heat, it is critical to (a) project increases in electricity use with urban warming, and (b) identify neighborhoods that are most vulnerable due in part to a lack of air conditioning (AC) and inability to afford increased energy. Here, we utilize smart meter data from 180 476 households in Southern California to quantify increases in residential electricity use per degree warming for each census tract. We also compute AC penetration rates, finding that air conditioners are less prevalent in poorer census tracts. Utilizing climate change projections for end of century, we show that 55% and 30% of the census tracts identified as most vulnerable are expected to experience more than 16 and 32 extreme heat days per year, respectively. 

Abstract Climate change, urbanization, and economic growth are expected to drive increases in the installation of new air conditioners, as well as increases in utilization of existing air conditioning (AC) units, in the coming decades. This growth will provide challenges for a diversity of stakeholders, from grid operators charged with maintaining a reliable and cost-effective power system, to low-income communities that may struggle to afford increased electricity costs. Despite the importance of building a quantitative understanding of trends in existing and future AC usage, methods to estimate AC penetration with high spatial and temporal resolution are lacking. In this study we develop a new classification method to characterize AC penetration patterns with unprecedented spatiotemporal resolution (i.e. at the census tract level), using the Greater Los Angeles Area as a case study. The method utilizes smart meter data records from 180 476 households over two years, along with local ambient temperature records. When spatially aggregated, the overall AC penetration rate of the Greater Los Angeles Area is 69%, which is similar to values reported by previous studies. We believe this method can be applied to other regions of the world where household smart meter data are available. 

Abstract Climate change is expected to exacerbate the urban heat island (UHI) effect in cities worldwide, increasing the risk of heat-related morbidity and mortality. Solar reflective ‘cool pavement’ is one of several mitigation strategies that may counteract the negative effects of the UHI effect. An increase in pavement albedo results in less heat absorption, which results in reduced surface temperatures ( T surface ). Near surface air temperatures ( T air ) could also be reduced if cool pavements are deployed at sufficiently large spatial scales, though this has never been confirmed by field measurements. This field study is the first to conduct controlled measurements of the impacts of neighborhood-scale cool pavement installations. We measured the impacts of cool pavement on albedo, T surface , and T air . In addition, pavement albedo was monitored after installation to assess its degradation over time. The field site (∼0.64 km 2 ) was located in Covina, California; ∼30 km east of Downtown Los Angeles. We found that an average pavement albedo increase of 0.18 (from 0.08 to 0.26) corresponded to maximum neighborhood averaged T surface and T air reductions of 5 °C and 0.2 °C, respectively. Maximum T surface reductions were observed in the afternoon, while minimum reductions of 0.9 °C were observed in the morning. T air reductions were detected at 12:00 local standard time (LST), and from 20:00 LST to 22:59 LST, suggesting that cool pavement decreases T air during the daytime as well as in the evening. An average albedo reduction of 30% corresponded to a ∼1 °C reduction in the T surface cooling efficacy. Although we present here the first measured T air reductions due to cool pavement, we emphasize that the tradeoffs between T air reductions and reflected shortwave radiation increases are still unclear and warrant further investigation in order to holistically assess the efficacy of cool pavements, especially with regards to pedestrian thermal comfort. 

Abstract. The effects of atmospheric black carbon (BC) on climate and public health have been well established, but large uncertainties remain regarding the extent of the impacts of BC at different temporal and spatial scales. These uncertainties are largely due to the heterogeneous nature of BC in terms of its spatiotemporal distribution, mixing state, and coating composition. Here, we seek to further understand the size and mixing state of BC emitted from various sources and aged over different timescales using field measurements in the Los Angeles region. We measured refractory black carbon (rBC) with a single-particle soot photometer (SP2) on Catalina Island, California (∼70 km southwest of downtown Los Angeles) during three different time periods. During the first campaign (September 2017), westerly winds were dominant and measured air masses were representative of well-aged background over the Pacific Ocean. In the second and third campaigns (December 2017 and November 2018, respectively), atypical Santa Ana wind conditions allowed us to measure biomass burning rBC (BCbb) from air masses dominated by large biomass burning events in California and fossil fuel rBC (BCff) from the Los Angeles Basin. We observed that the emissions source type heavily influenced both the size distribution of the rBC cores and the rBC mixing state. BCbb had thicker coatings and larger core diameters than BBff. We observed a mean coating thickness (CTBC) of ∼40–70 nm and a count mean diameter (CMD) of ∼120 nm for BCbb. For BCff, we observed a CTBC of ∼5–15 nm and a CMD of ∼100 nm. Our observations also provided evidence that aging led to an increased CTBC for both BCbb and BCff. Aging timescales < ∼1 d were insufficient to thickly coat freshly emitted BCff. However, CTBC for aged BCff within aged background plumes was ∼35 nm thicker than CTBC for fresh BCff. Likewise, we found that CTBC for aged BCbb was ∼18 nm thicker than CTBC for fresh BCbb. The results presented in this study highlight the wide variability in the BC mixing state and provide additional evidence that the emissions source type and aging influence rBC microphysical properties.