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California has large and diverse biomass resources and provides a pertinent example of how biomass use is changing and needs to change, in the face of climate mitigation policies. As in other areas of the world, California needs to optimize its use of biomass and waste to meet environmental and socioeconomic objectives. We used a systematic review to assess biomass use pathways in California and the associated impacts on climate and air quality. Biomass uses included the production of renewable fuels, electricity, biochar, compost, and other marketable products. For those biomass use pathways recently developed, information is available on the effects—usually beneficial—on greenhouse gas (GHG) emissions, and there is some, but less, published information on the effects on criteria pollutants. Our review identifies 34 biomass use pathways with beneficial impacts on either GHG or pollutant emissions, or both—the "good." These included combustion of forest biomass for power and conversion of livestock‐associated biomass to biogas by anaerobic digestion. The review identified 13 biomass use pathways with adverse impacts on GHG emissions, criteria pollutant emissions, or both—the "bad." Wildfires are an example of one out of eight pathways which were found to be bad for both climate and air quality, while only two biomass use pathways reduced GHG emissions relative to an identified counterfactual but had adverse air quality impacts. Issues of high interest for the "future" included land management to reduce fire risk, future policies for the dairy industries, and full life‐cycle analysis of biomass production and use. [ABSTRACT FROM AUTHOR]

A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in P M 2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality ( O 3 and CO ) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O 3 and CO , respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly P M 2.5 and P M 10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for P M 2.5 and P M 10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily P M 2.5 and P M 10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978. [ABSTRACT FROM AUTHOR]

Due to the increasingly serious air pollution problem, air quality prediction has been an important approach for air pollution control and prevention. Many prediction methods have been proposed in recent years to improve the prediction accuracy. However, most of the existing methods either did not consider the spatial relationships between monitoring stations or overlooked the strength of the correlation. Excluding the spatial correlation or including too much weak spatial inputs could influence the modeling and reduce the prediction accuracy. To overcome the limitation, this paper proposes a correlation filtered spatial-temporal long short-term memory (CFST-LSTM) model for air quality prediction. The model is designed based on the original LSTM model and is equipped with a spatial-temporal filter (STF) layer. This layer not only takes into account the spatial influence between stations, but also can extract highly correlated sequential data and drop weaker ones. To evaluate the proposed CFST-LSTM model, hourly PM2.5 concentration data of California are collected and preprocessed. Several experiments are conducted. The experimental results show that the CFST-LSTM model can effectively improve the prediction accuracy and has great generalization. [ABSTRACT FROM AUTHOR]

Climate change has, and will continue to have, a disproportionate impact on communities of color. Already, it is clear that systemic racism has led to increased temperatures in predominantly Black neighborhoods as compared to white neighborhoods in the same cities. A legacy of discriminatory housing policies in California is correlated with worse air quality and health disparities, both of which could be further exacerbated as temperatures rise. As cities and states begin developing climate change adaptation plans, it is imperative that they develop equity-based solutions that take into account how discriminatory practices are leading to disproportionate climate impacts. If such impacts are not accounted for, they will be exacerbated in the future. This paper analyzes equity-based climate adaptation strategies for heat, which is already the deadliest weather-related disaster in the U.S., and how they could be applied to Los Angeles. [ABSTRACT FROM AUTHOR]

Air pollution is a serious concern to people who want to engage in physical activities to improve and maintain their health. However, air quality data collected at the nearest monitoring site may not be the best source of information because the data may be incomplete (e.g., some pollutants are not monitored) or not representative. This paper puts forward a method that uses air quality data from a large area to derive a diurnal profile of air quality variation for that area and identify the time window in which the air quality is typically the best during a day. If people exercise in that time window then they can minimize their exposure to air pollution. Three years' worth of air quality data in five California counties were analyzed to identify the general pattern of diurnal variation of air quality. The analysis shows the pattern of air quality variation is very similar among those five counties which represent diverse geographical and meteorological conditions. The analysis further reveals that in California air quality is generally the best in early mornings; as such, people should exercise in the early morning if their daily schedule allows it. A similar analysis can be performed for other areas to help people choose the best time window to exercise. [ABSTRACT FROM AUTHOR]

Predicting air quality is a complex task due to the dynamic nature, volatility, and high variability in time and space of pollutants and particulates. At the same time, being able to model, predict, and monitor air quality is becoming more and more relevant, especially in urban areas, due to the observed critical impact of air pollution on citizens' health and the environment. In this paper, we employ a popular machine learning method, support vector regression (SVR), to forecast pollutant and particulate levels and to predict the air quality index (AQI). Among the various tested alternatives, radial basis function (RBF) was the type of kernel that allowed SVR to obtain the most accurate predictions. Using the whole set of available variables revealed a more successful strategy than selecting features using principal component analysis. The presented results demonstrate that SVR with RBF kernel allows us to accurately predict hourly pollutant concentrations, like carbon monoxide, sulfur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, as well as the hourly AQI for the state of California. Classification into six AQI categories defined by the US Environmental Protection Agency was performed with an accuracy of 94.1% on unseen validation data. [ABSTRACT FROM AUTHOR]

Many previous studies have shown that open-source technologies help democratize information and foster collaborations to enable addressing global physical and societal challenges. The outbreak of the novel coronavirus has imposed unprecedented challenges to human society. It affects every aspect of livelihood, including health, environment, transportation, and economy. Open-source technologies provide a new ray of hope to collaboratively tackle the pandemic. The role of open source is not limited to sharing a source code. Rather open-source projects can be adopted as a software development approach to encourage collaboration among researchers. Open collaboration creates a positive impact in society and helps combat the pandemic effectively. Open-source technology integrated with geospatial information allows decision-makers to make strategic and informed decisions. It also assists them in determining the type of intervention needed based on geospatial information. The novelty of this paper is to standardize the open-source workflow for spatiotemporal research. The highlights of the open-source workflow include sharing data, analytical tools, spatiotemporal applications, and results and formalizing open-source software development. The workflow includes (i) developing open-source spatiotemporal applications, (ii) opening and sharing the spatiotemporal resources, and (iii) replicating the research in a plug and play fashion. Open data, open analytical tools and source code, and publicly accessible results form the foundation for this workflow. This paper also presents a case study with the open-source spatiotemporal application development for air quality analysis in California, USA. In addition to the application development, we shared the spatiotemporal data, source code, and research findings through the GitHub repository. [ABSTRACT FROM AUTHOR]

Air pollution monitoring using mobile ground-based measurement platforms can provide high-quality spatiotemporal air pollution information. As mobile air quality monitoring campaigns extend to entire fleets of vehicles and integrate smaller-scale air quality sensors, it is important to address the need to assess these measurements in a scalable manner. We explore the collocation-based evaluation of air quality measurements in a mobile platform using fixed regulatory sites as a reference. We compare two approaches: a standard collocation assessment technique, in which the mobile platform is parked near the fixed regulatory site for a period of time, and an expanded approach, which uses measurements while the mobile platform is in motion in the general vicinity of the fixed regulatory site. Based on the availability of fixed-reference-site data, we focus on three pollutants (ozone, nitrogen dioxide, and nitric oxide) with distinct atmospheric lifetimes and behaviors. We compare measurements from a mobile laboratory with regulatory site measurements in Denver, CO, USA, and in the San Francisco Bay Area, CA, USA. Our 1-month Denver dataset includes both parked collocation periods near the fixed regulatory sites and general driving patterns around the sites, allowing a direct comparison of the parked and mobile collocation techniques on the same dataset. We show that the mobile collocation approach produces similar performance statistics, including coefficients of determination and mean bias errors, to the standard parked collocation technique. This is particularly true when the comparisons are restricted to specific road types, with residential streets showing the closest agreement and highways showing the largest differences. We extend our analysis to a larger (yearlong) dataset in California, where we explore the relationships between the mobile measurements and the fixed reference sites on a larger scale. We show that using a 40 h running median converges to within ± 4 ppbv of the fixed reference site for nitrogen dioxide and ozone and up to about 8 ppbv for nitric oxide. We propose that this agreement can be leveraged to assess instrument performance over time during large-scale mobile monitoring campaigns. We demonstrate an example of how such relationships can be employed during large-scale monitoring campaigns using small sensors to identify potential measurement biases. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of climate change on meteorology and air quality conditions in California by dynamically downscaling Parallel Climate Model (PCM) data to high resolution (4 km) using the Weather Research and Forecast (WRF) model. This paper evaluates the present years'' (2000--06) downscaling results driven by either PCM or National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) reanalysis data. The analyses focused on the air quality--related meteorological variables, such as planetary boundary layer height (PBLH), surface temperature, and wind. The differences of the climatology from the two sets of downscaling simulations and the driving global datasets were compared, which illustrated that most of the biases of the downscaling results were inherited from the driving global climate model (GCM). The downscaling process added mesoscale features but also introduced extra biases into the driving global data. The main source of bias in the PCM data is an imprecise prediction of the location and strength of the Pacific subtropical high (PSH). The analysis implied that using simulation results driven by PCM data as the input for air quality models will likely underestimate air pollution problems in California. Regional-averaged statistics of the downscaling results were estimated for two highly polluted areas, the South Coast Air Basin (SoCAB) and the San Joaquin Valley (SJV), by comparing to observations. The simulations driven by GFS data overestimated surface temperature and wind speed for most of the year, indicating that WRF has systematic errors in these two regions. The simulation matched the observations better during summer than winter in terms of bias. WRF has difficulty reproducing weak surface wind, which normally happens during stagnation events in these two regions. The shallow summer PBLH in the Central Valley is caused by the dominance of high pressure systems over the valley and the strong valley wind during summer. The change of meteorology and air quality in California due to climate change will be explored in Part II of this study, which compares the future (2047--53) and present (2000--06) simulation results driven by PCM data and is presented in a separate paper. [ABSTRACT FROM AUTHOR]

In response to increasing wildfire risks, California plans to expand the use of prescribed fire. We characterized the anticipated change in health impacts from exposure to smoke under a future fire‐management scenario relative to a historical period (2008–2016). Using dispersion models, we estimated daily fine particulate matter (PM2.5) emissions from hypothetical future prescribed fires on 500,000‐acres classified as high priority. To evaluate health impacts, we calculated excess daily cardiorespiratory emergency department visit rates attributed to all‐source PM2.5, distinguishing the portion of the burden attributed to prescribed fire. The total burden was differentiated by fire type and by smoke strata‐specific days to calculate strata‐specific burden rates, which were then applied to estimate the burden in the future scenario. This analysis suggests that the exposure to prescribed fire smoke, measured as the number of persons exposed per year, would be 15 times greater in the future. However, these exposures were associated with lower concentrations compared to the historical period. The increased number of exposure days led to an overall increase in the future health burden. Specifically, the northern, central, and southern regions experienced the largest burden increase. This study introduces an approach that integrates spatiotemporal exposure differences, baseline morbidity, and population size to assess the impacts of prescribed fire under a future scenario. The findings highlight the need to consider both the level and frequency of exposure to guide strategies to safeguard public health as well as aid forest management agencies in making informed decisions to protect communities while mitigating wildfire risks. Plain Language Summary: Prescribed fire is a forest management strategy for reducing the risks of wildfires. While some fires are ecologically beneficial, smoke from fires is a major source of airborne particle pollution, which is harmful to human health. This study examined the change in health impacts resulting from an expected increase in the use of prescribed fire within California's high‐priority wildfire risk areas. We used daily counts of cardiorespiratory emergency department visits attributed to air quality combined with model‐generated measures of smoke pollution to estimate health impacts. We compared exposures and the associated health burden on days impacted by wildfire or prescribed fire smoke in the past to the impacts in the hypothetical future scenario with increased prescribed fire. Projections of future prescribed burning in high priority areas suggest that more people would experience smoke more often, although exposures would occur at lower concentrations. With more frequent lower‐level exposure days near populated areas, the health burden would increase relative to past prescribed fire. Understanding the potential impact of prescribed fire may simultaneously help protect public health and increase safety from wildfires. Key Points: A California‐based model of future prescribed burning in high‐priority wildfire risk areas suggested more people will experience smokeAn increased number of exposure days in the future scenario led to an overall increase in the future health burdenThe excess future health burden was due to the cumulative impact of lower exposure days and high population density in high‐priority areas [ABSTRACT FROM AUTHOR]

Ammonia is a significant precursor of PM 2.5 particles and thus contributes to poor air quality in many regions. Furthermore, ammonia concentrations are rising due to the increase of large-scale, intensive agricultural activities, which are often accompanied by greater use of fertilizers and concentrated animal feedlots. Ammonia is highly reactive and thus highly variable and difficult to measure. Satellite-based instruments, such as the Atmospheric Infrared Sounder (AIRS) and the Cross-Track Infrared Sounder (CrIS), have been shown to provide much greater temporal and spatial coverage of ammonia distribution and variability than is possible with in situ networks or aircraft campaigns, but the validation of these data is limited. Here we evaluate MUSES (multi-spectra, multi-species, multi-sensors) ammonia retrievals from AIRS and CrIS against ammonia measurements from aircraft in the California Central Valley and in the Colorado Front Range. These are small datasets taken over high-source regions under very different conditions: winter in California and summer in Colorado. Direct comparisons of the surface values of the retrieved profiles are biased very low in California (∼ 40 ppbv) and slightly high in Colorado (∼ 4 ppbv). This bias appears to be primarily due to smoothing error, since applying the instrument operator effectively reduces the bias to zero; even after the smoothing error is accounted for, the average uncertainty at the surface is in the 20 %–30 % range. We also compare 3 years of CrIS ammonia against an in situ network in the Magic Valley in Idaho We show that CrIS ammonia captures both the seasonal signal and the spatial variability in the Magic Valley, although it is biased low here also. In summary, this analysis substantially adds to the validation record but also points to the need for more validation under many different conditions and at higher altitudes. [ABSTRACT FROM AUTHOR]

apidly changing wildfire regimes across the Western United States have driven more frequent and severe wildfires, resulting in wide-ranging societal threats from wildfires and wildfire-generated smoke. However, common measures of fire severity focus on what is burned, disregarding the societal impacts of smoke generated from each fire. We combine satellite-derived fire scars, air parcel trajectories from individual fires, and predicted smoke PM2.5 to link source fires to resulting smoke PM2.5 and health impacts experienced by populations in the contiguous United States from April 2006 to 2020. We quantify fire-specific accumulated smoke exposure based on the cumulative population exposed to smoke PM2.5 over the duration of a fire and estimate excess asthma-related emergency department (ED) visits as a result of this exposure. We find that excess asthma visits attributable to each fire are only moderately correlated with common measures of wildfire severity, including burned area, structures destroyed, and suppression cost. Additionally, while recent California fires contributed nearly half of the country’s smoke-related excess asthma ED visits during our study period, the most severe individual fire was the 2007 Bugaboo fire in the Southeast. We estimate that a majority of smoke PM2.5 comes from sources outside the local jurisdictions where the smoke is experienced, with 87% coming from fires in other counties and 60% from fires in other states. Our approach could enable broad-scale assessment of whether specific fire characteristics affect smoke toxicity or impact, inform cost-effectiveness assessments for allocation of suppression resources, and help clarify the growing transboundary nature of local air quality. [ABSTRACT FROM AUTHOR]

Zero-emission vehicle (ZEV) adoption is a key climate mitigation tool, but its environmental justice implications remain unclear. Here, we quantify ZEV adoption at the census tract level in California from 2015 to 2020 and project it to 2035 when all new passenger vehicles sold are expected to be ZEVs. We then apply an integrated traffic model together with a dispersion model to simulate air quality changes near roads in the Greater Los Angeles. We found that per capita ZEV ownership in non-disadvantaged communities (non-DACs) as defined by the state of California is 3.8 times of that in DACs. Racial and ethnic minorities owned fewer ZEVs regardless of DAC designation. While DAC residents receive 40% more pollutant reduction than non-DACs due to intercommunity ZEV trips in 2020, they remain disproportionately exposed to higher levels of traffic-related air pollution. With more ZEVs in 2035, the exposure disparity narrows. However, to further reduce disparities, the focus must include trucks, emphasizing the need for targeted ZEV policies that address persistent pollution burdens among DAC and racial and ethnic minority residents. Zero-emission vehicle adoption brings near-roadway air quality benefits to all communities in California, yet equity gaps persist in disadvantaged communities, calling for targeted policies. [ABSTRACT FROM AUTHOR]

As low-cost air quality sensors become more widely utilized, more tools and methods are needed to help users access/process sensor data, identify poorly performing sensors, and analyze/visualize sensor data. Free and open-source software (FOSS) packages developed for use on FOSS data science platforms are well-suited to support this need by offering replicable and shareable tools that can be adapted to meet a user or project's specific needs. This paper describes enhancements to the FOSS AirSensor R package (version 1.0) and the DataViewer web application (version 1.0.1) that have been developed to support data access, processing, analysis, and visualization for the PurpleAir PA-II sensor. This paper also demonstrates how these enhancements may be used to track and assess the health of air sensors in real-time or for large historical datasets. The dataset used for this analysis was collected during a multi-year project (with sensors deployed from October 2017 to October 2020) involving the distribution of approximately 400 PA-II sensors across 14 communities in southern, central, and northern California. Applying the tools in the AirSensor package revealed a dramatic variability in sensor performance, mainly driven by seasonal trends or particulate matter source type. These results also indicate that this sensor can provide useful data for at least three years with little evidence of substantial or consistent drift. Further, high agreement was observed between co-located sensors deployed at different times, indicating that it may be reasonable to compare data from old and new PA-II sensors. In addition to assessing the long-term performance and reliability of the PA-II sensor, this analysis serves as a model for how data from large sensor networks may be effectively processed, evaluated, interpreted, and communicated. • An open-source R package has been enhanced to better support sensor data analysis. • Package is used to assess multiple years of data from multiple sensor networks. • Analysis reveals seasonal variability in the performance of air quality sensors. • Though there is little indication of substantial or consistent drift over time. • Example illustrates how the package can be used with real-time or historical data. [ABSTRACT FROM AUTHOR]

Chronic high surface ozone (O3) levels and the increasing sulfur oxides (SOx = SO2+SO4) ambient concentrations over South Coast (SC) and other areas of California (CA) are affected by both local emissions and long-range transport. In this paper, multi-scale tracer, full-chemistry and adjoint simulations using the STEM atmospheric chemistry model are conducted to assess the contribution of local emission sources to SC O3 and to evaluate the impacts of transported sulfur and local emissions on the SC sulfur budget during the ARCTAS-CARB experiment period in 2008. Senstivity simulations quantify contributions of biogenic and fire emissions to SC O3 levels. California biogenic and fire emissions contribute 3-4ppb to near-surface O3 over SC, with larger contributions to other regions in CA. During a long-range transport event from Asia starting from 22 June, high SOx levels (up to ~0.7 ppb of SO2 and ~ 1.3 ppb of SO4) is observed above ~6 km, but they did not affect CA surface air quality. The elevated SOx observed at 1-4 km is estimated to enhance surface SOx over SC by ~0.25 ppb (upper limit) on ~24 June. The near-surface SOx levels over SC during the flight week are attributed mostly to local emissions. Two anthropogenic SOx emission inventories (Els) from the California Air Resources Board (CARB) and the US Environmental Protection Agency (EPA) are compared and applied in 60 km and 12 km chemical transport simulations, and the results are compared with observations. The CARB El shows improvements over the National Emission Inventory (NEI) by EPA, but generally underestimates surface SC SOx, by about a factor of two. Adjoint sensitivity analysis indicated that SO2 levels at 00:00 UTC (17:00 local time) at six SC surface sites were influenced by previous day maritime emissions over the ocean, the terrestrial emissions over nearby urban areas, and by transported SO2 from the north through both terrestrial and maritime areas. Overall maritime emissions contribute 10-70% of SO2 and 20-60% fine SO4 on-shore and over the most terrestrial areas, with contributions decreasing with inland distance from the coast. Maritime emissions also modify the photochemical environment, shifting O3 production over coastal SC to more VOC-limited conditions. These suggest an important role for shipping emission controls in reducing fine particle and 03 concentrations in SC. [ABSTRACT FROM AUTHOR]

The escalating frequency and severity of global wildfires necessitate an in-depth understanding and monitoring of wildfire smoke impacts, specifically its contribution to fine particulate matter (PM 2.5 ). We propose a data-fusion method to study wildfire contribution to PM 2.5 using satellite-derived smoke plume indicators and PM 2.5 monitoring data. Our study incorporates two types of monitoring data, the high-quality but sparse Air Quality System (AQS) stations and the abundant but less accurate PurpleAir (PA) sensors that are gaining popularity among citizen scientists. We propose a multi-resolution spatiotemporal model specified in the spectral domain to calibrate the PA sensors against accurate AQS measurements, and leverage the two networks to estimate wildfire contribution to PM 2.5 in California in 2020 and 2021. A Bayesian approach is taken to incorporate all uncertainties and our prior intuition that the dependence between networks, as well as the accuracy of PA network, vary by frequency. We find that 1% to 3% increase in PM 2.5 concentration due to wildfire smoke, and that leveraging PA sensors improves accuracy. [ABSTRACT FROM AUTHOR]

There have been consistent efforts to improve the spatiotemporal representations of biogenic/anthropogenic emission sources for photochemical transport modeling for better accuracy of local/regional air quality forecasts. While biogenic emissions, bi-directional NH 3 from fertilizer applications, and point source plume rise are dynamically coupled in the Community Multiscale Air Quality (CMAQ) "inline", there are still known meteorology-induced emissions sectors (e.g., on-road mobile sources, residential heating, and livestock waste), with little or no accounting for the meteorological impacts in the currently operational chemical and aerosol forecasts, but they are represented with static, not weather-aware annual or monthly county total emissions and standard monthly, weekly, or daily temporal allocation profiles to disaggregate them on finer timescales for the hourly air quality forecasts. It often results in poor forecasting performance due to the poor spatiotemporal representations of precursor pollutants during high ozone and PM 2.5 episodes. The main focus of this study is to develop a dynamic inline coupler within the CMAQ system for the on-road mobile emission sector that requires significant computational resources in the current modeling application. To improve their accuracy and spatiotemporal representations, we developed the inline coupler module called CMAQ-MetEmis (for meteorology-induced emission sources within CMAQ version 5.3.2 modeling system). It can dynamically estimate meteorology-induced hourly gridded on-road mobile emissions within the CMAQ, using simulated meteorology without any computational burden to the CMAQ modeling system. To understand the impacts of meteorology-driven on-road mobile emissions on local air quality, the CMAQ is applied over the continental U.S. for 2 months (January and July 2019) for two emissions scenarios, namely (a) "static" on-road vehicle emissions based on static temporal profiles and (b) inline CMAQ-MetEmis on-road vehicle emissions. Overall, the CMAQ-MetEmis coupler allows us to dynamically simulate on-road vehicle emissions from the MOtor Vehicle Emission Simulator (MOVES) on-road emission model for CMAQ, with a better spatiotemporal representation based on the simulated meteorology inputs when compared to the static scenario. The domain total of daily volatile organic compound (VOC) emissions from the inline scenario shows that the largest impacts are from the local meteorology, which is approximately 10 % lower than the ones from the static scenario. In particular, the major difference in the VOC estimates was shown over the California region. These local meteorology impacts on the on-road vehicle emissions via CMAQ-MetEmis revealed an improvement in the hourly NO 2 , daily maximum ozone, and daily average PM 2.5 patterns, with a higher agreement and correlation with daily ground observations. [ABSTRACT FROM AUTHOR]

Over the past few decades, criteria emissions such as carbon monoxide (CO), hydrocarbons (HCs), nitrogen oxides (NOx) and particulate matter (PM) from transportation have decreased significantly, thanks to stricter emission standards and the widespread adoption of cleaner technologies. While air quality is a complex problem that is not solely dependent on transportation emissions, it does play a significant role in both regional and global air quality levels. Emission standards such as Euro 1–6 in Europe, Corporate Average Fuel Economy (CAFE) regulations, Tier I—III standards in the US and the low emission vehicle (LEV) program in California have all played a huge role in bringing down transportation emissions and hence improving air quality overall. This article reviews the effect of emissions from transportation, primarily focusing on criteria emissions from road transport emissions and highlights the impact of some of the novel technological advances that have historically helped meet these strict emission norms. The review also notes how modern road engine vehicles emissions compare with national and international aviation and shipping and discusses some of the suggested Euro 7 emissions standards and their potential to improve air quality. [ABSTRACT FROM AUTHOR]

On-road mobile sources play a significant role in air quality modeling and these models require gridded, hourly emission inputs. Due to its geographical and meteorological diversity and stringent air quality regulations, California state always poses big challenge for air quality modelers and policy makers. At the same time, the impact of ambient temperature on vehicle emissions has been well researched in the past few decades and it is vital to prepare a reliable on-road gridded emission inventory for air quality modeling. This technical paper introduces a gridding method that takes temperature impacts into account, calculates emissions from grid-level to county, and attempts to quantify the likely effects of such a bottom-up, temperature sensitive approach on a gridded on-road emission inventory. To provide confidence in the proposed SMOKE-EMFAC method, a detailed analysis was carried out to compare the results with the default EMFAC output, and the results were within ±1%. Applying detailed grid level temperatures, we also found that criteria pollutant distributions are sensitive to them, and they are in accordance with previous US-EPA study. The proposed method could be very useful while testing different complex emission regulations and policies due to its inherent flexibility. [ABSTRACT FROM AUTHOR]

Bus systems in small California cities are widely believed to produce air quality, energy conservation, and traffic congestion benefits by inducing car-to-transit mode shifts. This paper tests the validity of these claims for a conventional bus system in Davis, California. Results from the Davis case fail to support claims for transit's benefits. The paper concludes that questions raised by the unexpected effects of transit in Davis and the lack of adequate data to evaluate similar systems in other cities should be addressed by collecting and analyzing additional data. Otherwise, transportation planners and public officials may be misled by inadequate or invalid information when formulating transportation policy or committing public resources to conventional bus systems. [ABSTRACT FROM AUTHOR]

The Camp Fire was one of California's deadliest and most destructive wildfires, and its widespread smoke threatened human health over a large area in Northern California in November 2018. To analyze the Camp Fire influence on air quality on a 200 km distant site in Berkeley, highly time-resolved total carbon (TC), black carbon (BC), and organic carbon (OC) were measured using the Carbonaceous Aerosol Speciation System (CASS, Aerosol Magee Scientific), comprising two instruments, a Total Carbon Analyzer TCA08 in tandem with an Aethalometer AE33. During the period when the air quality was affected by wildfire smoke, the BC concentrations increased four times above the typical air pollution level presented in Berkeley before and after the event, and the OC increased approximately ten times. High-time-resolution measurements allow us to study the aging of OC and investigate how the characteristics of carbonaceous aerosols evolve over the course of the fire event. A higher fraction of secondary carbonaceous aerosols was observed in the later phase of the fire. At the same time, the amount of light-absorbing organic aerosol (brown carbon) declined with time. [ABSTRACT FROM AUTHOR]

This 2-year, 28-day study examined whether adolescents felt greater fatigue and emotional distress the same day and the day after air quality was worse. We linked objective daily air quality measurements to daily self-reports from 422 Mexican–American adolescents in Los Angeles County, California from 2009 to 2011 (50% girls, MAge = 15 years). A robust, within-subject analysis of 9696 observations revealed that adolescents with ongoing physical complaints reported greater fatigue and emotional distress on days that the air contained higher levels of nitrogen dioxide (NO2) and carbon monoxide (CO). Regardless of physical complaints, adolescents on average also reported greater fatigue the day after NO2 levels were higher. The same-day and next-day associations between air pollution and distress were mediated via daily increases in fatigue. Results were robust when controlling for day of the week, and daily temperature and humidity. Sulfur dioxide (SO2), ozone (O3), PM2.5 and PM10 were not related to daily fatigue or distress. [ABSTRACT FROM AUTHOR]

In 2006, California introduced the Global Warming Solutions Act (Assembly Bill 32), which requires the state to reduce greenhouse gas emissions to 1990 levels by 2020. “Cool community” strategies, including cool roofs, cool pavements, cool walls and urban vegetation, have been identified as voluntary measures with potential to reduce statewide emissions. In addition, cool community strategies provide co-benefits for residents of California, such as reduced utility bills, improved air quality and enhanced urban livability. To achieve these savings, Lawrence Berkeley National Laboratory (LBNL) has worked with state and local officials, non-profit organizations, school districts, utilities, and manufacturers for 4 years to advance the science and implementation of cool community strategies. This paper summarizes the accomplishments of this program, as well as recent developments in cool community policy in California and other national and international efforts. We also outline lessons learned from these efforts to characterize successful programs and policies to be replicated in the future. [ABSTRACT FROM AUTHOR]

Recent regulatory and legislative developments in the state of California (e.g., Assembly Bill (AB) 1647 and AB 617) will increase the extent of air quality monitoring in communities near petroleum refineries and at facility fencelines. This work reports results over a three-year period for currently-installed community and fenceline air quality monitoring for the Chevron refinery in Richmond, California, USA. This paper presents the most comprehensive air quality dataset that has been published to date for a community near a petroleum refinery, including concentration of different air toxics (e.g., benzene, toluene, xylenes, etc.) and criteria air pollutants (e.g., ozone (O3), sulfur dioxide (SO2), and particulate matter (PM)). Instrumental techniques such as ultra-violet differential absorption spectroscopy (UV-DOAS), laser-based spectroscopy and real-time gas chromatography (GC) were used in the community and fenceline monitors. From 2015 to 2017, measured concentrations at community monitors near the Richmond refinery were generally below California thresholds for acute and chronic health (only two exceedances were observed for 8-h average benzene concentrations during the three-year monitoring period). Although more detailed speciation for volatile organic compounds (VOCs) and fine particulate matter (PM2.5) would be needed to confirm certain source profile identities, preliminary application of source apportionment methods indicates the prevalence of typical urban emission profiles, such as from traffic, in the measured community data. [ABSTRACT FROM AUTHOR]

Air quality associated public health co-benefit may emerge from climate and energy policies aimed at reducing greenhouse gas (GHG) emissions. However, the distribution of these co-benefits has not been carefully studied, despite the opportunity to tailor mitigation efforts so they achieve maximum benefits within socially and economically disadvantaged communities (DACs). Here, we quantify such health co-benefits from different long-term, low-carbon scenarios in California and their distribution in the context of social vulnerability. The magnitude and distribution of health benefits, including within impacted communities, is found to varies among scenarios which reduce economy wide GHG emissions by 80% in 2050 depending on the technology- and fuel-switching decisions in individual end-use sectors. The building electrification focused decarbonization strategy achieves ~15% greater total health benefits than the truck electrification focused strategy which uses renewable fuels to meet building demands. Conversely, the enhanced electrification of the truck sector is shown to benefit DACs more effectively. Such tradeoffs highlight the importance of considering environmental justice implications in the development of climate mitigation planning. Air quality is found to be more equitable through two salient decarbonization pathways for California in 2050 with the relative justice of decarbonization scenarios quantified at the neighborhood level and the tradeoffs between pathways evaluated. [ABSTRACT FROM AUTHOR]

Smoke from the 2018 Camp Fire in Northern California blanketed a large part of the region for 2 weeks, creating poor air quality in the "unhealthy" range for millions of people. The NOAA Global System Laboratory's HRRR-Smoke model was operating experimentally in real time during the Camp Fire. Here, output from the HRRR-Smoke model is compared to surface observations of PM2.5 from AQS and PurpleAir sensors as well as satellite observation data. The HRRR-Smoke model at 3-km resolution successfully simulated the evolution of the plume during the initial phase of the fire (8-10 November 2018). Stereoscopic satellite plume height retrievals were used to compare with model output (for the first time, to the authors' knowledge), showing that HRRR-Smoke is able to represent the complex 3D distribution of the smoke plume over complex terrain. On 15-16 November, HRRR-Smoke was able to capture the intensification of PM2.5 pollution due to a high pressure system and subsidence that trapped smoke close to the surface; however, HRRR-Smoke later underpredicted PM2.5 levels due to likely underestimates of the fire radiative power (FRP) derived from satellite observations. The intensity of the Camp Fire smoke event and the resulting pollution during the stagnation episodes make it an excellent test case for HRRR-Smoke in predicting PM2.5 levels, which were so high from this single fire event that the usual anthropogenic pollution sources became insignificant. The HRRR-Smoke model was implemented operationally at NOAA/NCEP in December 2020, now providing essential support for smoke forecasting as the impact of U.S. wildfires continues to increase in scope and magnitude. [ABSTRACT FROM AUTHOR]

As air quality sensors increasingly become commercially available, a deeper consideration of their usability and usefulness is needed to ensure effective application by the public. Much of the research related to sensors has focused on data quality and potential applications. While this information is important, a greater understanding of users' experience with sensors would provide complementary information. Under a U.S. EPA-funded Science to Achieve Results grant awarded to the South Coast Air Quality Management District in California, titled "Engage, Educate, and Empower California Communities on the Use and Applications of Low-Cost Air Monitoring Sensors", approximately 400 air quality sensors were deployed with 14 California communities. These communities received sensors and training, and they participated in workshops. Widely varying levels of sensor installation and engagement were observed across the 14 communities. However, despite differences between communities (in terms of participation, demographics, and socioeconomic factors), many participants offered similar feedback on the barriers to sensor use and strategies leading to successful sensor use. Here, we assess sensor use and participant feedback, as well as discuss the development of an educational toolkit titled "Community in Action: A Comprehensive Toolkit on Air Quality Sensors". This toolkit can be leveraged by future community and citizen science projects to develop networks designed to collect air quality information that can help reduce exposure to and the emissions of pollutants, leading to improved environmental and public health. [ABSTRACT FROM AUTHOR]

Annual burned areas in the United States have increased 2-fold during the past decades. With more large fires resulting in more emissions of fine particulate matter, an accurate prediction of fire emissions is critical for quantifying the impacts of fires on air quality, human health, and climate. This study aims to construct a machine learning (ML) model with game-theory interpretation to predict monthly fire emissions over the contiguous US (CONUS) and to understand the controlling factors of fire emissions. The optimized ML model is used to diagnose the process-based models in the Fire Modeling Intercomparison Project (FireMIP) to inform future development. Results show promising performance for the ML model, Community Land Model (CLM), and Joint UK Land Environment Simulator-Interactive Fire And Emission Algorithm For Natural Environments (JULES-INFERNO) in reproducing the spatial distributions, seasonality, and interannual variability of fire emissions over the CONUS. Regional analysis shows that only the ML model and CLM simulate the realistic interannual variability of fire emissions for most of the subregions (r>0.95 for ML and r=0.14∼0.70 for CLM), except for Mediterranean California, where all the models perform poorly (r=0.74 for ML and r<0.30 for the FireMIP models). Regarding seasonality, most models capture the peak emission in July over the western US. However, all models except for the ML model fail to reproduce the bimodal peaks in July and October over Mediterranean California, which may be explained by the smaller wind speeds of the atmospheric forcing data during Santa Ana wind events and limitations in model parameterizations for capturing the effects of Santa Ana winds on fire activity. Furthermore, most models struggle to capture the spring peak in emissions in the southeastern US, probably due to underrepresentation of human effects and the influences of winter dryness on fires in the models. As for extreme events, both the ML model and CLM successfully reproduce the frequency map of extreme emission occurrence but overestimate the number of months with extremely large fire emissions. Comparing the fire PM 2.5 emissions from the ML model with process-based fire models highlights their strengths and uncertainties for regional analysis and prediction and provides useful insights into future directions for model improvements. [ABSTRACT FROM AUTHOR]

The University of Colorado Airborne Solar Occultation Flux (CU AirSOF) instrument conducted the first suborbital carbon monoxide (CO) mass flux measurements on the scale of large wildfires, showing that the destructive fires in northern California in October 2017 emitted 2,040 ± 316 tonnes CO hr−1. Pyrogenic estimates from seven satellite‐based emission inventories bracket the observed flux, but their range spans a factor of 83. The simulated air quality impacts in the form of ozone and fine particulate matter scale primarily with these uncertain emission amounts, and range from insignificant to very severe. This uncertainty in predicting emissions is reduced to a factor of ∼2 by the CU AirSOF flux measurements, with potential for future improvements. The uncertainty is primarily the result of uncertain vegetation types and sources of radiative power measurements, and to a lesser extent uncertain emission factors and fire diurnal cycles. Plain Language Summary: Wildfire smoke is a major source of air pollution that affects public health and natural areas, but the amounts of vegetation that go up in smoke and the emitted amounts of smoke are not well known, due to a lack of direct measurements. The accuracy of models used to predict smoke impacts on public health in affected communities is significantly impacted by their reliance on uncertain emissions estimates. In this study, a new instrument, the University of Colorado Airborne Solar Occultation Flux (CU AirSOF), measured the amount of carbon monoxide (CO) produced by the destructive fires in northern California during October 2017. These are the first airborne emission measurements on the scale of a large wildfire. The measured CO emissions from the fires fall within the large range among satellite‐based emission estimates, reducing the uncertainty in fire emissions. Air quality impacts in the form of ozone (O3) and fine particulate matter (PM2.5) range from insignificant to very severe, in direct relationship to the uncertain satellite‐based emission estimates. Key Points: First suborbital carbon monoxide flux measurements on the scale of large wildfiresCarbon monoxide fire emission inventories span a factor of 83 for a case study day of the northern California fires in October 2017Predicted ozone and fine particulate matter impacts vary from insignificant to very severe depending primarily on uncertain emission amounts [ABSTRACT FROM AUTHOR]

Abstract: The growth of commercial aviation has fueled concerns over air quality around airports and the surrounding communities. Airports must expand their operations to meet the increase in air traffic, but expansion plans have been delayed or canceled due to concerns over local air quality. This paper presents the methodology for real-time measurements of aircraft engine specific Particulate Matter (PM) emissions and analysis of the associated high resolution data acquired during normal Landing and Take-Off (LTO) operations 100–300 m downwind of an active taxi-/runway at the Oakland International Airport. The airframe–engine combinations studied included B737-300 with CFM56-3B engines, B737-700/800 with CFM56-7B engines, A320 with V2500-A5 engines, MD-80 with JT-8D engines, A300 with CF6-80 engines, DC-10 with CF6-50 engines, and CRJ-100/200 with CF34-3B engines. For all engine types studied, the size distributions were typically bimodal in nature with a nucleation mode comprised of freshly nucleated PM and an accumulation mode comprised mostly of PM soot with some condensed volatile material. The PM number-based emission index observed ranged between 7 × 1015–3 × 1017 particles kg−1 fuel burned at idle/taxi and between 4 × 1015–2 × 1017 particles kg−1 fuel burned at take-off, and the associated PM mass-based emission index (EIm) ranged between 0.1 and 0.7 g kg−1 fuel burned at both the idle/taxi and take-off conditions. Older technology engines such as the CFM56-3B and JT8D engines were observed to have as much as 3× higher PM EIm values at take-off compared to newer engine technology such as the CFM56-7B engine. The results from this study provide information for better characterizing evolving PM emissions from in-service commercial aircraft under normal LTO operations and assessing their impact on local and regional air quality and health related impacts. [Copyright &y& Elsevier]

Abstract: We can control asthma through proper clinical and environmental management and education. The U.S. population is growing, urbanizing and aging; seniors of low-to-middle income families are working and living longer. We conducted community-based participatory research in Visalia, Tulare County, California with a prospective, cross-sectional repeated measures design and quantitative and qualitative process; home environment and health-related outcomes data were collected. In this paper, we presented results of the air quality sampling—pollen, carbon dioxide (CO2) and particulate matter (PM) outdoors away from most major sources (agricultural fields, large pollinating trees, etc)—at a community central site close to a mobile line source and participant homes in the cooling season, July, 2009. Weather was hot and dry with light winds; diurnal variation ranged between 65–107°F (18–42°C) and 12–76% relative humidity at the study’s central site. Co-located active (reference) and passive (PAAS) samplers were used for pollen; passive monitoring for CO2 (Telaire 7001) and active sampling for PM were conducted. Overall, we observed spatial variability in CO2, fine PM (PM2.5), and pollen counts. Weekday and study week average CO2 and PM2.5 concentrations were higher near study homes compared to central site sampling points, but peak measures and overnight/pre-dawn time period averages were elevated at the central site. Pollen counts were typically lower at homes—even if grass, trees, flowers or potted plants were present—compared to the central site closer to and generally downwind from commercial agricultural tree production. Data are new; the nine-county San Joaquin Valley has one pollen count station in the national network, and two of four government outdoor air monitoring stations in the county are in national parks. We suggest—given poor air quality in large part due to PM—adding routine pollen counts to regional/state agency air monitoring sites and more CO2 and PM monitoring. [Copyright &y& Elsevier]

Abstract: This paper presents an analysis of the aerosol chemical composition, optical properties and size distributions for a range of conditions encountered during a field measurement campaign conducted between July 7–29, 2005 at Point Reyes National Seashore, north of San Francisco, CA. The fractional mass loading derived from hourly measurements of an Aerodyne Mass Spectrometer (AMS) during this period are compared with filter-pack measurements from the Pt. Reyes IMPROVE station with good agreement found between the two if it assumed that chloride is primarily from large sea-salt particles (not measured by the AMS). During the first half of the campaign (July 7–17), conditions at the site were largely maritime while flow during the second half of the campaigns (July 18–29) was influenced by a thermal trough that added a cyclonic twist to the incoming marine air, bringing it from the south with a more extensive over-land trajectory. Neither flow regime was associated with air coming from the San Francisco Bay area to the south. The AMS measurements are partitioned into clear and foggy conditions which are then used to calculate the equivalent molar ratio of ammonium to the sum of sulfate, nitrate and chloride. Ratios calculated from measurements made before the onset of the thermal trough on July 18th were associated with acidic or near-neutral particles. Measurements made after July 18th yield ratios that appear to have excess ammonium. Model calculations of the equilibrium gas-phase mixing ratio of NH3 suggest very high values which we attribute to agricultural practices within the park. Reported as an incidental finding is evidence for the cloud droplet activation of large particles (DP > 0.2 μm) with a corresponding reduction in the single scattering albedo of the non-activated particles, followed by a return in the particle size spectrum to the pre-fog conditions immediately afterwards. [Copyright &y& Elsevier]

This paper reviews and compares two air quality benefit assessments completed for California's South Coast Air Basin in 1989 and 2008. Specifically, we separate the influence of changes in population and air quality from that of newer health concentration–response relationships and changing economic values. The dynamic interaction of key variables, including health and economic, as well as changes in population and air quality, lead to significant changes in results over time. Results show dramatic reductions in exposures to ozone and particulate concentrations between the two time periods, a continually evolving health literature, and in contrast, fairly constant real economic unit values assigned to adverse health outcomes. Such research is important because highly technical analyses of the expected benefits of proposed air quality regulatory programs have become an increasingly important component of many decision-making processes. [Copyright &y& Elsevier]

A comprehensive air quality modeling project was carried out to simulate size and composition resolved airborne particulate matter concentrations in northern and central California using the pollutant concentration and meteorological data collected during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS) from December 15, 2000 to January 7, 2001. Measured 24-h average PM2.5 concentrations during this time period exceeded 180μgm−3 at Bakersfield, making it the most severe PM2.5 air quality episode ever recorded in the United States with a rigorous measurement database to support modeling. In this paper, the UCD/CIT source-oriented air quality model is used to predict the concentrations of O3, NO, NO2, CO, elemental carbon (EC), organic compounds (OC), nitrate and PM2.5 mass concentration over a 24-day period using a horizontal resolution of 4km×4km to cover all of central California. This is the first extensive evaluation of an air quality model in central California using the fine spatial resolution appropriate for the mountain–valley topography of the region combined with the relatively long multi-week time scales associated with winter stagnation events. Fractional bias (FB) values were calculated at all sites on each day of the study to quantify model performance. The CO (FB=−0.5 to +0.3), O3 (FB=−0.5 to +0.25), NO (FB=−0.9 to −0.1) and NO2 (FB=0 to +0.4) concentrations predicted by the UCD/CIT model are in general agreement with observations at most monitoring stations throughout the Valley. The predicted PM2.5 concentrations (FB=−0.5 to +0.75) generally agree with observations at Bethel Island, Sacramento, Fresno and Bakersfield spanning the entire length of the model domain. PM2.5 concentrations are over-predicted at the remote monitoring site Angiola in the central portion of the domain. Part of the over-prediction is due to excess fugitive dust emissions. CO, NO, EC and OC were all under-predicted at Angiola, indicating possible missing combustion sources in the emission inventory. The regional nitrate (FB=−1.5 to +1.25) formation dynamics were correctly reproduced by the model simulation but imperfect wind fields cause differences between the predicted vs. measured spatial distribution of nitrate during the last several days of simulation leading to the broader range of fraction bias. Overall, the results of the current study confirm the ability of the air quality model to capture the major features of a severe particulate air pollution event in northern and central California providing a foundation for future studies on source apportionment and emissions control. [Copyright &y& Elsevier]

A summer air quality monitoring campaign focusing on the evolution of ultrafine (<180nm in diameter) particle concentrations was conducted at an urban site in Los Angeles during June–July 2006. Previous observations suggest that ultrafine aerosol at this site are generally representative of the Los Angeles urban environment. Continuous and intermittent gas and aerosol measurements were made over 4 weeks with consistent daily meteorological conditions. Monthly averages of the data suggest the strong influence of commute traffic emissions on morning observations of ultrafine particle concentrations. By contrast, in the afternoon our measurements provide evidence of secondary photochemical reactions becoming the predominant formation mechanism of ultrafine aerosols. The ultrafine number concentration peak occurs in the early afternoon, before the maximum ozone concentration is observed. The source of this offset is unknown and requires further investigation. It is possible that the chemical mechanisms responsible for secondary organic aerosol formation evolve as atmospheric conditions change and/or secondary semi-volatile components of the aerosol re-volatilize due to the elevated peak temperatures observed (ca. 30–35°C) combined with the increased atmospheric dilution during that time. Measurements of the volatility of the ultrafine aerosol are consistent with this interpretation as overall volatility increases in the afternoon and there is less evidence of external mixing. Composition data presented in the companion paper support these conclusions [Ning et al., 2007. Daily variation in chemical characteristics of urban ultrafine aerosols and inference of their sources. Environmental Science and Technology, in press]. [Copyright &y& Elsevier]

This paper describes a novel school-based, visual environmental public health educational intervention intended to help reduce the exposure of children — and adults — to outdoor air pollution, including known environmental asthma triggers like ozone and particles. The overarching goal was to enhance the learning, recreational, and work environments of students and staff. The specific purpose of the Asthma-Friendly Outdoor (Ambient) Air Quality Flag Program was to establish an education and communication tool for Central California communities that would accomplish two things: 1) Establish permanent local policy change to existing operating procedures in school districts and schools to help reduce the exposure of students, teachers, staff, and nearby communities to outdoor environmental asthma triggers and 2) provide education on air quality and potential health effects of exposure to air pollutants. Data on the program from its initial years are presented. To date, the following important ' lessons have been learned: 1) Science-based, simple, visual, low-cost school-based educational interventions to help reduce human exposure to outdoor environmental asthma triggers (i.e., ozone, particles, and pollens) can work in socioeconomically and ethnically diverse urban and rural or agricultural communities, and 2) local health and environmental justice groups such as asthma coalitions can successfully lead school-based environmental interventions to help improve children's quality of life. [ABSTRACT FROM AUTHOR]

Current modeling methods are inadequate for producing gridded mobile source inventories suitable for simulating fine-scale changes in emissions for transportation planning or regulatory control purposes. In this paper, we present a new model that more precisely simulates small-scale emission changes that can arise from, for example, implementation of traffic control measures in transportation networks. The new model, UCDrive, directly interfaces with most travel demand models and with California''s regulatory emission factor model EMFAC2001/2002. The model improves the sensitivity of emission estimates to variations in vehicle speeds and uses a theoretically consistent approach to spatially disaggregating mobile emissions. To demonstrate application of the model we use the Sacramento area network to perform a comparison study. We show that regional emissions with the new model replicate regulatory inventories, which is critical for using the model for regulatory purposes. We also show that the new model''s results better replicate regulatory totals than those derived with a frequently used gridded emissions inventory model. The new model should improve the ability to better simulate changes in mobile emissions from transportation projects and help to bridge the gap between gridded mobile emission inventories and regional mobile emission inventories. [Copyright &y& Elsevier]

In response to the coronavirus disease of 2019 (COVID-19), California issued statewide stay-at-home orders, bringing about abrupt and dramatic reductions in air pollutant emissions. This crisis offers us an unprecedented opportunity to evaluate the effectiveness of emission reductions in terms of air quality. Here we use the Weather Research and Forecasting model with Chemistry (WRF-Chem) in combination with surface observations to study the impact of the COVID-19 lockdown measures on air quality in southern California. Based on activity level statistics and satellite observations, we estimate the sectoral emission changes during the lockdown. Due to the reduced emissions, the population-weighted concentrations of fine particulate matter (PM 2.5) decrease by 15 % in southern California. The emission reductions contribute 68 % of the PM 2.5 concentration decrease before and after the lockdown, while meteorology variations contribute the remaining 32 %. Among all chemical compositions, the PM 2.5 concentration decrease due to emission reductions is dominated by nitrate and primary components. For O 3 concentrations, the emission reductions cause a decrease in rural areas but an increase in urban areas; the increase can be offset by a 70 % emission reduction in anthropogenic volatile organic compounds (VOCs). These findings suggest that a strengthened control on primary PM 2.5 emissions and a well-balanced control on nitrogen oxides and VOC emissions are needed to effectively and sustainably alleviate PM 2.5 and O 3 pollution in southern California. [ABSTRACT FROM AUTHOR]

Background: This study seeks to answer two questions about the impacts of the 2020 Environmental Protection Agency's enforcement regulation rollbacks: is this suspension bolstering the economic viability of industries as oil and manufacturing executives claim they will and are these regulations upholding the agency's mission of protecting the environment? Results: To answer the former question, we utilized 6 months of state employment level data from California, United States, as a method of gauging the economic health of agency-regulated industries. We implemented a machine learning model to predict weekly employment data and a t-test to indicate any significant changes in employment. We found that, following California's state-issued stay-at-home order and the agency's regulation suspension, oil and certain manufacturing industries had statistically significant lower employment values. To answer the latter question, we used 10 years of PM2.5 levels in California, United States, as a metric for local air quality and treatment–control county pairs to isolate the impact of regulation rollbacks from the impacts of the state lockdown. Using the agency's data, we performed a t-test to determine whether treatment–control county pairs experienced a significant change in PM2.5 levels. Even with the statewide lockdown—a measure we hypothesized would correlate with decreased mobility and pollution levels—in place, counties with oil refineries experienced the same air pollution levels when compared to historical data averaged from the years 2009 to 2019. Conclusions: In contrast to the expectation that the suspension would improve the financial health of the oil and manufacturing industry, we can conclude that these industries are not witnessing economic growth with the suspension and state shutdown in place. Additionally, counties with oil refineries could be taking advantage of these rollbacks to continue emitting the same amount of PM2.5, in spite of state lockdowns. For these reasons, we ask international policymakers to reconsider the suspension of enforcement regulations as these actions do not fulfill their initial expectations. We recommend the creation and maintenance of pollution control and prevention programs that develop emission baselines, mandate the construction of pollution databases, and update records of pollution emissions. [ABSTRACT FROM AUTHOR]

Extensive, severe wildfires, and wildfire‐induced smoke occurred across the western and central United States since August 2020. Wildfires resulting in the loss of habitats and emission of particulate matter and volatile organic compounds pose serious threatens to wildlife and human populations, especially for avian species, the respiratory system of which are sensitive to air pollutions. At the same time, the extreme weather (e.g., snowstorms) in late summer may also impact bird migration by cutting off their food supply and promoting their migration before they were physiologically ready. In this study, we investigated the environmental drivers of massive bird die‐offs by combining socioecological earth observations data sets with citizen science observations. We employed the geographically weighted regression models to quantitatively evaluate the effects of different environmental and climatic drivers, including wildfire, air quality, extreme weather, drought, and land cover types, on the spatial pattern of migratory bird mortality across the western and central US during August‐September 2020. We found that these drivers affected the death of migratory birds in different ways, among which air quality and distance to wildfire were two major drivers. Additionally, there were more bird mortality events found in urban areas and close to wildfire in early August. However, fewer bird deaths were detected closer to wildfires in California in late August and September. Our findings highlight the important impact of extreme weather and natural disasters on bird biology, survival, and migration, which can provide significant insights into bird biodiversity, conservation, and ecosystem sustainability. Key Points: We investigated the environmental drivers of massive bird die‐offs by combining earth observations with citizen science observationsWe found distance to wildfire and air quality were the major factors that affect the birth mortality eventsOur findings highlight the important impact of extreme weather and natural disasters on bird biology, survival, and migration [ABSTRACT FROM AUTHOR]

Using county data from California between 2004 and 2017, this article exploits changes in households' exposure to hazardous waste recycling to identify the effects on potential environmental and economic outcomes. While a 10% rise in recycling per household is associated with a 0.28% and 0.94% improvement in air quality and a decline in the number of unhealthy days in a year, recycling is associated with a rise in net energy generation and fuel consumption and not associated with economic activity. There is some evidence that households in zip codes with higher housing values and population density value reductions in hazardous waste. [ABSTRACT FROM AUTHOR]

• Integrate multiple types of measures of an air pollutant into one single modeling framework. • Deal with repeated measures for annual air pollution model development. • Difference-in-difference analysis to identify differential improvements in air pollution exposure. Over the past decade, researchers and policy-makers have become increasingly interested in regulatory and policy interventions to reduce air pollution concentrations and improve human health. Studies have typically relied on relatively sparse environmental monitoring data that lack the spatial resolution to assess small-area improvements in air quality and health. Few studies have integrated multiple types of measures of an air pollutant into one single modeling framework that combines spatially- and temporally-rich monitoring data. In this paper, we investigated the differential effects of California emissions reduction plan on reducing air pollution between those living in the goods movement corridors (GMC) that are within 500 m of major highways that serve as truck routes to those farther away or adjacent to routes that prohibit trucks. A mixed effects Deletion/Substitution/Addition (D/S/A) machine learning algorithm was developed to model annual pollutant concentrations of nitrogen dioxide (NO 2) by taking repeated measures into consideration and by integrating multiple types of NO 2 measurements, including those through government regulatory and research-oriented saturation monitoring into a single modeling framework. Difference-in-difference analysis was conducted to identify whether those living in GMC demonstrated statistically larger reductions in air pollution exposure. The mixed effects D/S/A machine learning modeling result indicated that GMC had 2 ppb greater reductions in NO 2 concentrations from pre- to post-policy period than far away areas. The difference-in-difference analysis demonstrated that the subjects living in GMC experienced statistically significant greater reductions in NO 2 exposure than those living in the far away areas. This study contributes to scientific knowledge by providing empirical evidence that improvements in air quality via the emissions reductions plan policies impacted traffic-related air pollutant concentrations and associated exposures most among low-income Californians with chronic conditions living in GMC. The identified differences in pollutant reductions across different location domains may be applicable to other states or other countries if similar policies are enacted. [ABSTRACT FROM AUTHOR]

The Northern California Camp Fire that took place in November 2018 was one of the most damaging environmental events in California history. Here, we analyze ground-based station observations of airborne particulate matter that has a diameter <2.5 µm (PM 2.5) across Northern California and conduct numerical simulations of the Camp Fire using the Weather Research and Forecasting model online coupled with chemistry (WRF-Chem). Simulations are evaluated against ground-based observations of PM 2.5 , black carbon, and meteorology, as well as satellite measurements, such as Tropospheric Monitoring Instrument (TROPOMI) aerosol layer height and aerosol index. The Camp Fire led to an increase in Bay Area PM 2.5 to over 50 µgm-3 for nearly 2 weeks, with localized peaks exceeding 300 µgm-3. Using the Visible Infrared Imaging Radiometer Suite (VIIRS) high-resolution fire detection products, the simulations reproduce the magnitude and evolution of surface PM 2.5 concentrations, especially downwind of the wildfire. The overall spatial patterns of simulated aerosol plumes and their heights are comparable with the latest satellite products from TROPOMI. WRF-Chem sensitivity simulations are carried out to analyze uncertainties that arise from fire emissions, meteorological conditions, feedback of aerosol radiative effects on meteorology, and various physical parameterizations, including the planetary boundary layer model and the plume rise model. Downwind PM 2.5 concentrations are sensitive to both flaming and smoldering emissions over the fire, so the uncertainty in the satellite-derived fire emission products can directly affect the air pollution simulations downwind. Our analysis also shows the importance of land surface and boundary layer parameterization in the fire simulation, which can result in large variations in magnitude and trend of surface PM 2.5. Inclusion of aerosol radiative feedback moderately improves PM 2.5 simulations, especially over the most polluted days. Results of this study can assist in the development of data assimilation systems as well as air quality forecasting of health exposures and economic impact studies. [ABSTRACT FROM AUTHOR]

Background: Nationally, ischemic heart disease mortality has declined significantly due to advancements in managing traditional risk factors of hypertension, diabetes, hyperlipidemia, smoking, and obesity and acute intervention. However geographic disparities persist that may, in part, be attributed to environmental effects. Methods: Ischemic heart disease age-adjusted mortality were obtained from the CDC database for years 1999 through 2014 by county, gender, race, and Hispanic origin for the Central Valley of California. Results: There was an increase in mortality from north to south of 14.9 (95% CI: 8.0-21.9, p value <0.0001) in time period 1, 7.9 (95% CI: 0.8-15, p value <0.05) in time period 2, and 9.2 (95% CI: 4.0-14.3, p value <0.001) in time period 3. In time period 1, the ambient particulate matter ≤2.5 micrometers (PM2.5) level increased from north to south by 0.84 mg/m³ (95% CI: 0.71-0.96), in time period 2 there was a 0.87 mg/m³ increase (95% CI: 0.74-1.0), and a 1.0 mg/m³ increase in time period 3 (95% CI: 0.87-1.1). PM2.5 level was correlated to IHD mortality in all time periods (Period 1 r²=0.46, p=0.0001; Period 2, r²=0.34, p=0.008; Period 3 r²=0.51, p value <0.0001). Conclusion: Continued declines in ischemic heart disease mortality will depend on the concerted efforts of clinicians in continuing management of the traditional risk factors with appropriate medication use, acute interventions for coronary syndromes, the necessity of patient self-management of high risk behaviors associated with smoking and obesity, and the development of coordinated actions with policy makers to reduce environmental exposure in their respective communities. [ABSTRACT FROM AUTHOR]

The San Joaquin Valley of California is known for excessive ozone air pollution owing to local production combined with terrain-induced flow patterns that channel air in from the highly populated San Francisco Bay area and stagnate it against the surrounding mountains. During the summer, ozone violations of the National Ambient Air Quality Standards (NAAQS) are notoriously common, with the San Joaquin Valley having an average of 115 violations of the current 70 ppb standard each year between 2012 and 2016. Because regional photochemical production peaks with actinic radiation, most studies focus on the daytime, and consequently the nocturnal chemistry and dynamics that contribute to these summertime high-ozone events are not as well elucidated. Here we investigate the hypothesis that on nights with a strong low-level jet (LLJ), ozone in the residual layer (RL) is more effectively mixed down into the nocturnal boundary layer (NBL) where it is subject to dry deposition to the surface, the rate of which is itself enhanced by the strength of the LLJ, resulting in lower ozone levels the following day. Conversely, nights with a weaker LLJ will sustain RLs that are more decoupled from the surface, retaining more ozone overnight, and thus lead to more fumigation of ozone the following mornings, giving rise to higher ozone concentrations the following afternoon. The relative importance of this effect, however, is strongly dependent on the net chemical overnight loss of Ox (here [ Ox ] ≡ [ O3 ] + [ NO2 ]), which we show is highly uncertain, without knowing the ultimate chemical fate of the nitrate radical (NO3). We analyze aircraft data from a study sponsored by the California Air Resources Board (CARB) aimed at quantifying the role of RL ozone in the high-ozone events in this area. By formulating nocturnal scalar budgets based on pairs of consecutive flights (the first around midnight and the second just after sunrise the following day), we estimate the rate of vertical mixing between the RL and the NBL and thereby infer eddy diffusion coefficients in the top half of the NBL. The average depth of the NBL observed on the 12 pairs of flights for this study was 210(±50) m. Of the average -1.3 ppb h -1 loss of Ox in the NBL during the overnight hours from midnight to 06:00 PST, -0.2 ppb h -1 was found to be due to horizontal advection, -1.2 ppb h -1 due to dry deposition, -2.7 ppb h -1 to chemical loss via nitrate production, and +2.8 ppb h -1 from mixing into the NBL from the RL. Based on the observed gradients of Ox in the top half of the NBL, these mixing rates yield eddy diffusivity estimates ranging from 1.1 to 3.5 m 2 s -1 , which are found to inversely correlate with the following afternoon's ozone levels, providing support for our hypothesis. The diffusivity values are approximately an order of magnitude larger than the few others reported in the extant literature for the NBL, which further suggests that the vigorous nature of nocturnal mixing in this region, due to the LLJ, may have an important control on daytime ozone levels. Additionally, we propose that the LLJ is a branch of what is colloquially referred to as the Fresno eddy, which has been previously proposed to recirculate pollutants. However, vertical mixing from the LLJ may counteract this effect, which highlights the importance of studying the LLJ and Fresno eddy as a single interactive system. The synoptic conditions that are associated with strong LLJs are found to contain deeper troughs along the California coastline. The LLJs observed during this study had an average centerline height of 340 m, average speed of 9.9 m s -1 (σ=3.1 m s -1) , and a typical peak timing around 23:00 PST. A total of 7 years of 915 MHz radioacoustic sounding system and surface air quality network data show an inverse correlation between the jet strength and ozone the following day, further suggesting that air quality models need to forecast the strength of the LLJ in order to more accurately predict ozone violations. [ABSTRACT FROM AUTHOR]

Semi-volatile and intermediate-volatility organic compounds (SVOCs and IVOCs) from anthropogenic sources are likely to be important precursors of secondary organic aerosol (SOA) in urban airsheds, yet their treatment in most models is based on limited and obsolete data or completely missing. Additionally, gas-phase oxidation of organic precursors to form SOA is influenced by the presence of nitric oxide (NO), but this influence is poorly constrained in chemical transport models. In this work, we updated the organic aerosol model in the UCD/CIT (University of California at Davis/California Institute of Technology) chemical transport model to include (i) a semi-volatile and reactive treatment of primary organic aerosol (POA), (ii) emissions and SOA formation from IVOCs, (iii) the NOx influence on SOA formation, and (iv) SOA parameterizations for SVOCs and IVOCs that are corrected for vapor wall loss artifacts during chamber experiments. All updates were implemented in the statistical oxidation model (SOM) that simulates the oxidation chemistry, thermodynamics, and gas–particle partitioning of organic aerosol (OA). Model treatment of POA, SVOCs, and IVOCs was based on an interpretation of a comprehensive set of source measurements available up to the year 2016 and resolved broadly by source type. The NOx influence on SOA formation was calculated offline based on measured and modeled VOC:NOx ratios. Finally, the SOA formation from all organic precursors (including SVOCs and IVOCs) was modeled based on recently derived parameterizations that accounted for vapor wall loss artifacts in chamber experiments. The updated model was used to simulate a 2-week summer episode over southern California at a model resolution of 8 km. When combustion-related POA was treated as semi-volatile, modeled POA mass concentrations were reduced by 15 %–40 % in the urban areas in southern California but were still too high when compared against "hydrocarbon-like organic aerosol" factor measurements made at Riverside, CA, during the Study of Organic Aerosols at Riverside (SOAR-1) campaign of 2005. Treating all POA (except that from marine sources) to be semi-volatile, similar to diesel exhaust POA, resulted in a larger reduction in POA mass concentrations and allowed for a better model–measurement comparison at Riverside, but this scenario is unlikely to be realistic since this assumes that POA from sources such as road and construction dust are semi-volatile too. Model predictions suggested that both SVOCs (evaporated POA vapors) and IVOCs did not contribute as much as other anthropogenic precursors (e.g., alkanes, aromatics) to SOA mass concentrations in the urban areas (< 5 % and < 15 % of the total SOA respectively) as the timescales for SOA production appeared to be shorter than the timescales for transport out of the urban airshed. Comparisons of modeled IVOC concentrations with measurements of anthropogenic SOA precursors in southern California seemed to imply that IVOC emissions were underpredicted in our updated model by a factor of 2. Correcting for the vapor wall loss artifact in chamber experiments enhanced SOA mass concentrations although the enhancement was precursor-dependent as well as NOx -dependent. Accounting for the influence of NOx using the VOC:NOx ratios resulted in better predictions of OA mass concentrations in rural/remote environments but still underpredicted OA mass concentrations in urban environments. The updated model's performance against measurements combined with the results from the sensitivity simulations suggests that the OA mass concentrations in southern California are constrained within a factor of 2. Finally, simulations performed for the year 2035 showed that, despite reductions in VOC and NOx emissions in the future, SOA mass concentrations may be higher than in the year 2005, primarily from increased hydroxyl radical (OH) concentrations due to lower ambient NO2 concentrations. [ABSTRACT FROM AUTHOR]

This article examines the effect of air quality information on immigration and emigration of households in California counties by measuring the change in the number of air quality alert days. Based on panel data for 2000–2014, I find evidence suggesting that more frequent air quality alerts reduce the rate of population growth in a county by decreasing immigration of households. This is driven by "Unhealthy" air alerts, which signals weaker air quality than "Unhealthy for Sensitive Groups." The negative impacts on immigration are larger for high household income counties and are strongest when there is a sharp increase in the number of air quality alerts. [ABSTRACT FROM AUTHOR]

Background: Policies to mitigate climate change by reducing greenhouse gas (GHG) emissions can yield public health benefits by also reducing emissions of hazardous co-pollutants, such as air toxics and particulate matter. Socioeconomically disadvantaged communities are typically disproportionately exposed to air pollutants, and therefore climate policy could also potentially reduce these environmental inequities. We sought to explore potential social disparities in GHG and co-pollutant emissions under an existing carbon trading program-the dominant approach to GHG regulation in the US and globally.Methods and Findings: We examined the relationship between multiple measures of neighborhood disadvantage and the location of GHG and co-pollutant emissions from facilities regulated under California's cap-and-trade program-the world's fourth largest operational carbon trading program. We examined temporal patterns in annual average emissions of GHGs, particulate matter (PM2.5), nitrogen oxides, sulfur oxides, volatile organic compounds, and air toxics before (January 1, 2011-December 31, 2012) and after (January 1, 2013-December 31, 2015) the initiation of carbon trading. We found that facilities regulated under California's cap-and-trade program are disproportionately located in economically disadvantaged neighborhoods with higher proportions of residents of color, and that the quantities of co-pollutant emissions from these facilities were correlated with GHG emissions through time. Moreover, the majority (52%) of regulated facilities reported higher annual average local (in-state) GHG emissions since the initiation of trading. Neighborhoods that experienced increases in annual average GHG and co-pollutant emissions from regulated facilities nearby after trading began had higher proportions of people of color and poor, less educated, and linguistically isolated residents, compared to neighborhoods that experienced decreases in GHGs. These study results reflect preliminary emissions and social equity patterns of the first 3 years of California's cap-and-trade program for which data are available. Due to data limitations, this analysis did not assess the emissions and equity implications of GHG reductions from transportation-related emission sources. Future emission patterns may shift, due to changes in industrial production decisions and policy initiatives that further incentivize local GHG and co-pollutant reductions in disadvantaged communities.Conclusions: To our knowledge, this is the first study to examine social disparities in GHG and co-pollutant emissions under an existing carbon trading program. Our results indicate that, thus far, California's cap-and-trade program has not yielded improvements in environmental equity with respect to health-damaging co-pollutant emissions. This could change, however, as the cap on GHG emissions is gradually lowered in the future. The incorporation of additional policy and regulatory elements that incentivize more local emission reductions in disadvantaged communities could enhance the local air quality and environmental equity benefits of California's climate change mitigation efforts. [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compounds (BVOC) participate in reactions that can lead to secondarily formed ozone and particulate matter (PM) impacting air quality and climate. BVOC emissions are important inputs to chemical transport models applied on local to global scales but considerable uncertainty remains in the representation of canopy parameterizations and emission algorithms from different vegetation species. The Biogenic Emission Inventory System (BEIS) has been used to support both scientific and regulatory model assessments for ozone and PM. Here we describe a new version of BEIS which includes updated input vegetation data and canopy model formulation for estimating leaf temperature and vegetation data on estimated BVOC. The Biogenic Emission Landuse Database (BELD) was revised to incorporate land use data from the Moderate Resolution Imaging Spectroradiometer (MODIS) land product and 2006 National Land Cover Database (NLCD) land coverage. Vegetation species data are based on the US Forest Service (USFS) Forest Inventory and Analysis (FIA) version 5.1 for 2002-2013 and US Department of Agriculture (USDA) 2007 census of agriculture data. This update results in generally higher BVOC emissions throughout California compared with the previous version of BEIS. Baseline and updated BVOC emission estimates are used in Community Multiscale Air Quality (CMAQ) Model simulations with 4 km grid resolution and evaluated with measurements of isoprene and monoterpenes taken during multiple field campaigns in northern California. The updated canopy model coupled with improved land use and vegetation representation resulted in better agreement between CMAQ isoprene and monoterpene estimates compared with these observations. [ABSTRACT FROM AUTHOR]