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The Emission Scenario Projection (ESP) method produces future-year air pollutant emissions for mesoscale air quality modeling applications. We present ESP v2.0, which expands upon ESP v1.0 by spatially allocating future-year emissions to account for projected population and land use changes. In ESP v2.0, US Census Division-level emission growth factors are developed using an energy system model. Regional factors for population-related emissions are spatially disaggregated to the county level using population growth and migration projections. The county-level growth factors are then applied to grow a base-year emission inventory to the future. Spatial surrogates are updated to account for future population and land use changes, and these surrogates are used to map projected county-level emissions to a modeling grid for use within an air quality model. We evaluate ESP v2.0 by comparing US 12 km emissions for 2005 with projections for 2050. We also evaluate the individual and combined effects of county-level disaggregation and of updating spatial surrogates. Results suggest that the common practice of modeling future emissions without considering spatial redistribution over-predicts emissions in the urban core and under-predicts emissions in suburban and exurban areas. In addition to improving multi-decadal emission projections, a strength of ESP v2.0 is that it can be applied to assess the emissions and air quality implications of alternative energy, population and land use scenarios. [ABSTRACT FROM AUTHOR]

Presents abstract of papers presented at the 64th annual meeting of the Association of American Geographers in Washington D. C. Automobile Commuting in Large Suburbs: A Comparative Analysis of Private Car Use in the Daily Journey to Work; Drought and Municipal Water Resources Management; Local Air Circulations-A Case Study in Air Pollution Climatology.

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transformation, transport and fate of the many different air pollutant species that comprise particulate matter (PM), including dust (or soil). The CMAQ model version 5.0 (CMAQv5.0) has several enhancements over the previous version of the model for estimating the emission and transport of dust, including the ability to track the specific elemental constituents of dust and have the modelderived concentrations of those elements participate in chemistry. The latest version of the model also includes a parameterization to estimate emissions of dust due to wind action. The CMAQv5.0 modeling system was used to simulate the entire year 2006 for the continental United States, and the model estimates were evaluated against daily surface based measurements from several air quality networks. The CMAQ modeling system generally did well replicating the observed soil concentrations in the western United States; however the model consistently overestimated the observed soil con centrations in the eastern United States, regardless of season. The performance of the individual trace metals was generally good at the rural network sites, with relatively small biases for Fe, Al, Si and Ti throughout the year, while Ca, K and Mn were overestimated and Mg underestimated. For the urban sites, Fe, Mg and Mn, while overestimated, had comparatively better performance throughout the year than the other trace metals, which were consistently overestimated, including very large overestimations of Al, Ti and Si in the fall. An underestimation of nighttime mixing in the urban areas appears to contribute to the overestimation of trace metals. Removing the anthropogenic fugitive dust (AFD) emissions and the effects of wind-blown dust (WBD) lowered the model soil concentrations. However, even with both AFD emissions and WBD effects removed, soil concentrations were still often overestimated, suggesting that there are other sources of errors in the modeling system that contribute to the overestimation of soil components. Efforts are underway to improve both the nighttime mixing in urban areas and the spatial and temporal distribution of dust related emissions sources in the emissions inventory. [ABSTRACT FROM AUTHOR]

The article offers information on the modification of the U.S. Environmental Protection Agency (EPA) regional emission model known as SMOKE for Europe (SMOKE-EU). It mentions that SMOKE-EU is capable of producing Community Multiscale Air Quality (CMAQ) ready emissions not only in Europe but also in Western Russia, Turkey and North Africa. The purpose of the modification is to develop a flexible model capable of producing high resolution emission data for Chemical Transport Models (CTM).

Dust is an important and complex constituent of the atmospheric system, having significant impacts on the environment, climate, air quality, and human health. Although dust events are common across many regions of the United States, their impacts are not often prioritized in air quality mitigation strategies. We argue that there are at least three factors that result in underestimation of the social and environmental impact of dust events, making them receive less attention. These include (a) sparse monitoring stations with irregular spatial distribution in dust‐influenced regions, (b) inconsistency with dust sampling methods, and (c) sampling frequency and schedules, which can lead to missed dust events or underestimation of dust particle concentrations. Without addressing these three factors, it is challenging to characterize and understand the full air quality impacts of dust events in the United States. This paper highlights the need for additional monitoring to measure these events so that we can more fully evaluate and understand their impacts, as they are predicted to increase with climate change. Plain Language Summary: Dust is an important and complex component of the atmospheric system, having significant impacts on the environment, climate, air quality, and human health. Yet, dust events are underestimated and therefore do not receive the level of attention necessary to fully understand their impacts. This is due in part to the fact that monitoring stations are sparse in dust‐influenced regions; there is a lack of consistency in dust definitions and sampling methods, and there is a lack of continuous monitoring, which results in missed dust events or underestimations of their particle concentration. This commentary highlights the need for additional monitoring designed to measure dust so that we can more fully understand its impacts, which are particularly important as dust is expected to increase with climate change. Key Points: Different factors result in an underestimation of the social and environmental impact of dust events, making them receive less attentionThere is a lack of consistency in dust definitions and sampling methodsSampling frequency and schedules of dust events can lead to missed dust events or underestimation of dust particle concentrations [ABSTRACT FROM AUTHOR]

From the ensemble of stations that monitor surface air quality over the United States and Europe, we identify extreme ozone pollution events and find that they occur predominantly in clustered, multi-day episodes with spatial extents of more than 1000 km. Such scales are amenable to forecasting with current global atmospheric chemistry models. We develop an objective mapping algorithm that uses the heterogeneous observations of the individual surface sites to calculate surface ozone averaged over 1° by 1° grid cells, matching the resolution of a global model. Air quality extreme (AQX) events are identified locally as statistical extremes of the ozone climatology and not as air quality exceedances. With the University of California, Irvine chemistry-transport model (CTM) we find there is skill in hindcasting these extreme episodes, and thus identify a new diagnostic using global chemistry-climate models (CCM) to identify changes in the characteristics of extreme pollution episodes in a warming climate. [ABSTRACT FROM AUTHOR]

The article presents a study to examine aerosols measured from the ground and space change over a regional scale in the U.S. Analysis from aerosol data from 2000-2009 reveals 50% large concentration in the summer in Atlanta, Georgia. The study uses datasets representing measurements use by Environmental Protection Agency (EPA) for the dissemination of air quality information.

Regional-scale air pollution models are routinely being used worldwide for research, forecasting air quality, and regulatory purposes. It is well recognized that there are both reducible (systematic) and irreducible (unsystematic) errors in the meteorology–atmospheric-chemistry modeling systems. The inherent (random) uncertainty stems from our inability to properly characterize stochastic variations in atmospheric dynamics and chemistry and from the incommensurability associated with comparisons of the volume-averaged model estimates with point measurements. Because stochastic variations are not being explicitly simulated in the current generation of regional-scale meteorology–air quality models, one should expect to find differences between the model estimates and corresponding observations. This paper presents an observation-based methodology to determine the expected errors from current-generation regional air quality models even when the model design, physics, chemistry, and numerical analysis, as well as its input data, were "perfect". To this end, the short-term synoptic-scale fluctuations embedded in the daily maximum 8 h ozone time series are separated from the longer-term forcing using a simple recursive moving average filter. The inherent uncertainty attributable to the stochastic nature of the atmosphere is determined based on 30+ years of historical ozone time series data measured at various monitoring sites in the contiguous United States (CONUS). The results reveal that the expected root mean square error (RMSE) at the median and 95th percentile is about 2 and 5 ppb , respectively, even for perfect air quality models driven with perfect input data. Quantitative estimation of the limit to the model's accuracy will help in objectively assessing the current state of the science in regional air pollution models, measuring progress in their evolution, and providing meaningful and firm targets for improvements in their accuracy relative to ambient measurements. [ABSTRACT FROM AUTHOR]

Wildfires have long been regarded as one chief culprit in regional air pollution, and pose great impacts on climate change. Although climate forcing of wildfire smoke has been widely investigated, its influence on synoptic systems remains unclear. Based on measurement and modeling analysis, the impact of wildfire smoke on the development of a mid‐latitude cyclone was revealed for Canadian wildfires in early June of 2023. The radiative forcing induced by smoke at surface and in the atmosphere reached up to −150 and 100 W m−2, posing opposite tendencies of atmospheric stratification over the land and ocean. Such perturbations contributed to the enhancement and stagnation of the cyclone, which favored the transport of smoke from the fire‐intensive region, indicated by nearly 40% increment of PM2.5 mass flux. With escalating wildfire risk in the future, the inclusion of smoke aerosols' impacts on meteorology in weather forecast models is of great importance. Plain Language Summary: Wildfires are uncontrolled fires that burn in the wildland vegetation, posing great challenges to regional air quality and global climate. Wildfire smoke has been known to exert great climate forcing via aerosol‐radiation interaction yet its impact on synoptic scales needs further investigation. Here, based on comprehensive observations and modeling analysis for the extreme Canadian wildfires in early June of 2023, smoke aerosol is revealed to induce significant radiative forcing and yield opposite modifications of temperature stratification over the land and ocean, resulting in intensified and stagnant mid‐latitude cyclone. Such perturbations favored the transport of smoke from fire‐intensive region to downwind cities in Canada and United States, and the subsequent long‐range transport dominated by the cyclone. Key Points: Smoke from intensive Canadian wildfires in early June of 2023 degraded the air quality of cities in eastern Canada and United StatesThe wildfire smoke enhanced the development of the mid‐latitude cyclone via smoke aerosol‐radiation interactionThe intensification and stagnation of the cyclone facilitated the transport of smoke to downwind cities in northeastern United States [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) air pollution has been recognized as a major source of mortality in the United States for at least 25 years, yet much remains unknown about which sources are the most harmful, let alone how best to target policies to mitigate them. Such efforts can be improved by employing high-resolution geographically explicit methods for quantifying human health impacts of emissions of PM2.5 and its precursors. Here, we provide a detailed examination of the health and economic impacts of PM2.5 pollution in the United States by linking emission sources with resulting pollution concentrations. We estimate that anthropogenic PM2.5 was responsible for 107,000 premature deaths in 2011, at a cost to society of $886 billion. Of these deaths, 57% were associated with pollution caused by energy consumption [e.g., transportation (28%) and electricity generation (14%)]; another 15% with pollution caused by agricultural activities. A small fraction of emissions, concentrated in or near densely populated areas, plays an outsized role in damaging human health with the most damaging 10% of total emissions accounting for 40% of total damages. We find that 33% of damages occur within 8 km of emission sources, but 25% occur more than 256 km away, emphasizing the importance of tracking both local and long-range impacts. Our paper highlights the importance of a fine-scale approach as marginal damages can vary by over an order of magnitude within a single county. Information presented here can assist mitigation efforts by identifying those sources with the greatest health effects. [ABSTRACT FROM AUTHOR]

Concentrations of atmospheric trace species in the United States have changed dramatically over the past several decades in response to pollution control strategies, shifts in domestic energy policy and economics, and economic development (and resulting emission changes) elsewhere in the world. Reliable projections of the future atmosphere require models to not only accurately describe current atmospheric concentrations, but to do so by representing chemical, physical and biological processes with conceptual and quantitative fidelity. Only through incorporation of the processes controlling emissions and chemical mechanisms that represent the key transformations among reactive molecules can models reliably project the impacts of future policy, energy and climate scenarios. Efforts to properly identify and implement the fundamental and controlling mechanisms in atmospheric models benefit from intensive observation periods, during which collocated measurements of diverse, speciated chemicals in both the gas and condensed phases are obtained. The Southeast Atmosphere Studies (SAS, including SENEX, SOAS, NOMADSS and SEAC4RS) conducted during the summer of 2013 provided an unprecedented opportunity for the atmospheric modeling community to come together to evaluate, diagnose and improve the representation of fundamental climate and air quality processes in models of varying temporal and spatial scales. This paper is aimed at discussing progress in evaluating, diagnosing and improving air quality and climate modeling using comparisons to SAS observations as a guide to thinking about improvements to mechanisms and parameterizations in models. The effort focused primarily on model representation of fundamental atmospheric processes that are essential to the formation of ozone, secondary organic aerosol (SOA) and other trace species in the troposphere, with the ultimate goal of understanding the radiative impacts of these species in the southeast and elsewhere. Here we address questions surrounding four key themes: gas-phase chemistry, aerosol chemistry, regional climate and chemistry interactions, and natural and anthropogenic emissions. We expect this review to serve as a guidance for future modeling efforts. [ABSTRACT FROM AUTHOR]

The demand for air quality depends on health impacts and defensive investments, but little research assesses the empirical importance of defenses. A rich quasi-experiment suggests that the Nitrogen Oxides (NOx) Budget Program (NBP), a cap-and-trade market, decreased NOx emissions, ambient ozone concentrations, pharmaceutical expenditures, and mortality rates. The annual reductions in pharmaceutical purchases, a key defensive investment, and mortality are valued at about $800 million and $1.3 billion, respectively, suggesting that defenses are over one-third of willingness-to-pay for reductions in NOx emissions. Further, estimates indicate that the NBP's benefits easily exceed its costs and that NOx reductions have substantial benefits. (JEL I12, Q51, Q53, Q58) [ABSTRACT FROM AUTHOR]

Atmospheric aerosols have important impacts on air quality and the Earth–atmospheric energy balance. However, as computing power is limited, Earth system models generally use coarse spatial grids and parameterize finer-scale atmospheric processes. These parameterizations and the simulation of atmospheric aerosols are often sensitive to model horizontal resolutions. Understanding the sensitivities is necessary for the development of Earth system models at higher resolutions with the deployment of more powerful supercomputers. Using the Energy Exascale Earth System Model (E3SM) version 1, this study investigates the impact of horizontal grid spacing on the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols (ERF aer) over the contiguous United States. We examine the resolution sensitivity by comparing the nudged simulation results for 2016 from the low-resolution model (LR) and the regional refinement model (RRM). As expected, the simulated emissions of natural dust, sea salt, and marine organic matter are substantially higher in the RRM than in the LR. In addition, RRM simulates stronger aqueous-phase production of sulfate through the enhanced oxidation of sulfur dioxide by hydrogen peroxide due to increased cloud liquid water content. In contrast, the gas-phase chemical production of sulfate is slightly suppressed. The RRM resolves more large-scale precipitation and produces less convective precipitation than the LR, leading to increased (decreased) aerosol wet scavenging by large-scale (convective) precipitation. Regarding aerosol effects on clouds, RRM produces larger temporal variabilities in the large-scale liquid cloud fractions than LR, resulting in increased microphysical cloud processing of aerosols (more interstitial aerosols are converted to cloud-borne aerosols via aerosol activation) in RRM. Water vapor condensation is also enhanced in RRM compared to LR. Consequently, the RRM simulation produces more cloud droplets, a larger cloud droplet radius, a higher liquid water path, and a larger cloud optical depth than the LR simulation. A comparison of the present-day and pre-industrial simulations indicates that, for this contiguous United States domain, the higher-resolution increases ERF aer at the top of the model by about 12 %, which is mainly attributed to the strengthened indirect effect associated with aerosol–cloud interactions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic resulted in stay-at-home policies and other social distancing behaviors in the United States in spring of 2020. This paper examines the impact that these actions had on emissions and expected health effects through reduced personal vehicle travel and electricity consumption. Using daily cell phone mobility data for each U.S. county, we find that vehicle travel dropped about 40% by mid-April across the nation. States that imposed stay-at-home policies before March 28 decreased travel slightly more than other states, but travel in all states decreased significantly. Using data on hourly electricity consumption by electricity region (e.g., balancing authority), we find that electricity consumption fell about 6% on average by mid-April with substantial heterogeneity. Given these decreases in travel and electricity use, we estimate the county-level expected improvements in air quality, and, therefore, expected declines in mortality. Overall, we estimate that, for a month of social distancing, the expected premature deaths due to air pollution from personal vehicle travel and electricity consumption declined by approximately 360 deaths, or about 25% of the baseline 1500 deaths. In addition, we estimate that CO2 emissions from these sources fell by 46 million metric tons (a reduction of approximately 19%) over the same time frame. [ABSTRACT FROM AUTHOR]

Modeling of inhalation exposure and risks resulting from exposure to mobile source air toxics can be used to evaluate impacts of reductions from control programs on overall risk, as well as changes in relative contributions of different source sectors to risk, changes in contributions of different pollutants to overall risk, and changes in geographic distributions of risk. Such analysis is useful in setting regulatory priorities, and informing the decision-making process. In this paper, we have conducted national-scale air quality, exposure, and risk modeling for the US in the years 2015, 2020, and 2030, using similar tools and methods as the 1999 National-Scale Air Toxics Assessment. Our results suggest that US Environmental Protection Agency emission control programs will substantially reduce average inhalation cancer risks and potential noncancer health risks from exposure to mobile source air toxics. However, cancer risk and noncancer hazard due to inhalation of air toxics will continue to be a public health concern.Journal of Exposure Science and Environmental Epidemiology (2007) 17, 95–105. doi:10.1038/sj.jes.7500529; published online 27 September 2006 [ABSTRACT FROM AUTHOR]

Voluntary environmental programs are codes of progressive environmental conduct that firms pledge to adopt. This paper investigates whether ISO 14001, a voluntary program with a weak sword—a weak monitoring and sanctioning mechanism—can mitigate shirking and improve participants' environmental performance. Sponsored by the International Organization for Standardization (ISO), ISO 14001 is the most widely adopted voluntary environmental program in the world. Our analysis of over 3,000 facilities regulated as major sources under the U.S. Clean Air Act suggests that ISO 14001-certified facilities reduce their pollution emissions more than non-certified facilities. This result persists even after controlling for facilities' emission and regulatory compliance histories as well as addressing potential endogeneity issues between facilities' environmental performance and their decisions to join ISO 14001. © 2005 by the Association for Public Policy Analysis and Management [ABSTRACT FROM AUTHOR]

What determines the environmental regulatory regime of a country or region? This paper addresses the question in detail, using the US and its widely varying environmental policies as the case study. What factors lead some US states to pass strict environmental regulations, while others are content with the baseline standards required at the national level? This work outlines the state environmental choice as a trade-off between the desires of consumers (who want better environmental quality) and of producers (who want less restrictive environmental standards). A rational state legislator maximises her chances of being re-elected by balancing these two competing forces when setting environmental policy. I test this model by directly analysing the state decision to adopt more restrictive sulfur dioxide regulations than those required by the federal government under the Environmental Protection Agency's "National Ambient Air Quality Standards" program. The statistical results suggest that legislators weigh the relative influence of consumer and producer groups when setting sulfur dioxide standards, in addition to accounting for meteorological influences that affect the cost of compliance with stricter environmental regulations. Limited evidence is also provided to support an inverted-U shaped relationship between income levels and environmental regulations. [ABSTRACT FROM AUTHOR]

Emissions trading takes place from two alternative baselines: 1) emission reduction credits (ERCs) in which the baseline is existing regulations which are often activity based; or 2) capand trade which specifies the toted allowable emissions. This paper examines the effects of these two tradable permit systems on marginal and average costs for the firm, using electricity, generation as an example. The ERC system subsidizes the activity level to which it is tied and fails to incorporate the full cost of external harm into the product price. If the permit limit is chosen efficiently, the capand trade system is more efficient. [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]

Evidence of the physical and economic impacts of climate change is a critical input to policy development and decision-making. In addition to the magnitude of potential impacts, detailed estimates of where, when, and to whom those damages may occur; the types of impacts that will be most damaging; uncertainties in these damages; and the ability of adaptation to reduce potential risks are all interconnected and important considerations. This study utilizes the reduced-complexity model, the Framework for Evaluating Damages and Impacts (FrEDI), to rapidly project economic and physical impacts of climate change across 10 000 future scenarios for multiple impact sectors, regions, and populations within the contiguous United States (US). Results from FrEDI show that net national damages increase overtime, with mean climate-driven damages estimated to reach USD 2.9 trillion (95 % confidence interval (CI): USD 510 billion to USD 12 trillion) annually by 2090. Detailed FrEDI results show that for the analyzed sectors the majority of annual long-term (e.g., 2090) damages are associated with climate change impacts to human health, including mortality attributable to climate-driven changes in temperature and air pollution (O 3 and PM 2.5) exposure. Regional results also show that annual long-term climate-driven damages vary geographically. The Southeast (all regions are as defined in Fig. 5) is projected to experience the largest annual damages per capita (mean: USD 9300 per person annually; 95 % CI: USD 1800–USD 37 000 per person annually), whereas the smallest damages per capita are expected in the Southwest (mean: USD 6300 per person annually; 95 % CI: USD 840–USD 27 000 per person annually). Climate change impacts may also broaden existing societal inequalities, with, for example, Black or African Americans being disproportionately affected by additional premature mortality from changes in air quality. Lastly, FrEDI projections are extended through 2300 to estimate the net present climate-driven damages within US borders from marginal changes in greenhouse gas emissions. Combined, this analysis provides the most detailed illustration to date of the distribution of climate change impacts within US borders. [ABSTRACT FROM AUTHOR]

Air quality in the United States has improved dramatically over the past 50 years in large part due to the introduction of the Clean Air Act and the creation of the Environmental Protection Agency to enforce it. This article is a reflection on the 50-year anniversary of the formation of the Environmental Protection Agency, describing what economic research says about the ways in which the Clean Air Act has shaped our society—in terms of costs, benefits, and important distributional concerns. We conclude with a discussion of how recent changes to both policy and technology present new opportunities for researchers in this area. [ABSTRACT FROM AUTHOR]

Wildland fires produce smoke plumes that impact air quality and human health. To understand the effects of wildland fire smoke on humans, the amount and composition of the smoke plume must be quantified. Using a fire emissions inventory is one way to determine the emissions rate and composition of smoke plumes from individual fires. There are multiple fire emissions inventories, and each uses a different method to estimate emissions. This paper presents a comparison of four emissions inventories and their products: Fire INventory from NCAR (FINN version 1.5), Global Fire Emissions Database (GFED version 4s), Missoula Fire Labs Emissions Inventory (MFLEI (250 m) and MFLEI (10 km) products), and Wildland Fire Emissions Inventory System (WFEIS (MODIS) and WFEIS (MTBS) products). The outputs from these inventories are compared directly. Because there are no validation datasets for fire emissions, the outlying points from the Bayesian models developed for each inventory were compared with visible images and fire radiative power (FRP) data from satellite remote sensing. This comparison provides a framework to check fire emissions inventory data against additional data by providing a set of days to investigate closely. Results indicate that FINN and GFED likely underestimate emissions, while the MFLEI products likely overestimate emissions. No fire emissions inventory matched the temporal distribution of emissions from an external FRP dataset. A discussion of the differences impacting the emissions estimates from the four fire emissions inventories is provided, including a qualitative comparison of the methods and inputs used by each inventory and the associated strengths and limitations. [ABSTRACT FROM AUTHOR]

Mobile sources are responsible for a substantial controllable portion of the reactive organic carbon (ROC) emitted to the atmosphere, especially in urban environments of the United States. We update existing methods for calculating mobile source organic particle and vapor emissions in the United States with over a decade of laboratory data that parameterize the volatility and organic aerosol (OA) potential of emissions from on-road vehicles, nonroad engines, aircraft, marine vessels, and locomotives. We find that existing emission factor information from Teflon filters combined with quartz filters collapses into simple relationships and can be used to reconstruct the complete volatility distribution of ROC emissions. This new approach consists of source-specific filter artifact corrections and state-of-the-science speciation including explicit intermediate-volatility organic compounds (IVOCs), yielding the first bottom-up volatility-resolved inventory of US mobile source emissions. Using the Community Multiscale Air Quality model, we estimate mobile sources account for 20 %–25 % of the IVOC concentrations and 4.4 %–21.4 % of ambient OA. The updated emissions and air quality model reduce biases in predicting fine-particle organic carbon in winter, spring, and autumn throughout the United States (4.3 %–11.3 % reduction in normalized bias). We identify key uncertain parameters that align with current state-of-the-art research measurement challenges. [ABSTRACT FROM AUTHOR]

The capability of chemical transport models to represent fine particulate matter (PM 2.5) over the course of a day is of vital importance for air quality simulation and assessment. In this work, we used the nested GEOS-Chem model at 0.25∘×0.3125∘ resolution to simulate the diel (24 h) variation in PM 2.5 mass concentrations over the contiguous United States (US) in 2016. We evaluate the simulations with in situ measurements from a national monitoring network. Our base case simulation broadly reproduces the observed morning peak, afternoon dip, and evening peak of PM 2.5 , matching the timings of these features within 1–3 h. However, the simulated PM 2.5 diel amplitude in our base case was 106 % biased high, relative to observations. We find that temporal resolution of emissions, subgrid vertical gradient between surface model-level center and observations, and biases in boundary layer mixing and aerosol nitrate are the major causes for this inconsistency. We applied an hourly anthropogenic emission inventory, converted the PM 2.5 mass concentrations from the model-level center to the height of surface measurements by correcting for aerodynamic resistance, adjusted the boundary layer heights in the driving meteorological fields using aircraft observations, and constrained nitrate concentrations using in situ measurements. The bias in the PM 2.5 diel amplitude was reduced to - 12 % in the improved simulation. Gridded hourly emissions rather than diel scaling factors applied to monthly emissions reduced biases in simulated PM 2.5 overnight. Resolving the subgrid vertical gradient in the surface model level aided the capturing of the timings of the PM 2.5 morning peak and afternoon minimum. Based on the improved model, we find that the mean observed diel variation in PM 2.5 for the contiguous US is driven by (1) building up of PM 2.5 by 10 % in early morning (04:00–08:00 local time, LT), due to increasing anthropogenic emissions into a shallow mixed layer; (2) decreasing PM 2.5 by 22 % from mid-morning (08:00 LT) through afternoon (15:00 LT), associated with mixed-layer growth; (3) increasing PM 2.5 by 30 % from mid-afternoon (15:00 LT) though evening (22:00 LT) as emissions persist into a collapsing mixed layer; and (4) decreasing PM 2.5 by 10 % overnight (22:00–04:00 LT) as emissions diminish. [ABSTRACT FROM AUTHOR]

The Community Multiscale Air Quality (CMAQ) model version 4.7.1 was used to simulate mercury wet and dry deposition for a domain covering the continental United States (US). The simulations used MM5-derived meteorological input fields and the US Environmental Protection Agency (EPA) Clear Air Mercury Rule (CAMR) emissions inventory. Using sensitivity simulations with different boundary conditions and tracer simulations, this investigation focuses on the contributions of boundary concentrations to deposited mercury in the Southwest (SW) US. Concentrations of oxidized mercury species along the boundaries of the domain, in particular the upper layers of the domain, can make significant contributions to the simulated wet and dry deposition of mercury in the SWUS. In order to better understand the contributions of boundary conditions to deposition, inert tracer simulations were conducted to quantify the relative amount of an atmospheric constituent transported across the boundaries of the domain at various altitudes and to quantify the amount that reaches and potentially deposits to the land surface in the SW US. Simulations using alternate sets of boundary concentrations, including estimates from global models (Goddard Earth Observing System-Chem (GEOS-Chem) and the Global/Regional Atmospheric Heavy Metals (GRAHM) model), and alternate meteorological input fields (for different years) are analyzed in this paper. CMAQ dry deposition in the SW US is sensitive to differences in the atmospheric dynamics and atmospheric mercury chemistry parameterizations between the global models used for boundary conditions. [ABSTRACT FROM AUTHOR]

This paper describes an evaluation of the US Bureau of Labor Statistics' American Time Use Survey (ATUS) for potential use in modeling human exposures to environmental pollutants. The ATUS is a large, on-going, cross-sectional survey of where Americans spend time and what activities they undertake in those locations. The data are reported as a series of sequential activities over a 24-h time period - a 'diary day' - starting at 0400 hours. Between 12,000 and 13,000 surveys are obtained each year and the Bureau has plans to continue ATUS for the foreseeable future. The ATUS already has about 73,000 diary days of data, more than twice as many as that which currently exists in the US Environmental Protection Agency's (EPA) 'Consolidated Human Activity Database' (CHAD) that the Agency uses for exposure modeling purposes. There are limitations for using ATUS in modeling human exposures to environmental pollutants. The ATUS does not report the location for a number of activities regarded as 'personal.' For 2006, personal activities with missing location information totaled 572 min/day, on average, for survey participants: about 40% of their day. Another limitation is that ATUS does not distinguish between indoor and outdoor activities at home, two of the traditional locational demarcations used in human exposure modeling. This lack of information affects exposure estimates to both indoor and outdoor air pollutants and potentially affects non-dietary ingestion estimates for children, which can vary widely depending on whether or not a child is indoors. Finally, a detailed analysis of the work travel activity in a subsample from ATUS 2006 indicates that the coding scheme is not fully consistent with a CHAD-based exposure modeling approach. For ATUS respondents in this subsample who reported work as an activity, roughly 48% of their days were missing work travel at one or both ends of the work shift or reported within work-shift travel inconsistently. An extensive effort would be needed to recode work travel data from ATUS for EPA's exposure modeling purposes. [ABSTRACT FROM AUTHOR]

The Clean Air Act mandates that the US Environmental Protection Agency (EPA) develop National Ambient Air Quality Standards for criteria air pollutants and conduct periodic reviews of the standards based on new scientific evidence. In recent reviews, evidence from epidemiologic studies has played a key role. Epidemiologic studies often provide evidence for effects of several air pollutants. Determining whether there are independent effects of the separate pollutants is a challenge. Among the many issues confronting the interpretation of epidemiologic studies of multi-pollutant exposures and health effects are those specifically related to statistical modeling. The EPA convened a workshop on 13 and 14 December 2006 in Chapel Hill, North Carolina, USA, to discuss these and other issues; Session Three of the workshop was devoted specifically to statistical modeling. Prominent statistical modeling issues in epidemiologic studies of air pollution include (1) measurement error across the co-pollutants; (2) correlation and multi-collinearity among the co-pollutants; (3) the timing of the concentration–response function; (4) confounding; and (5) spatial analyses.The views expressed in this paper are those of the authors and do not necessarily reflect the views of policies of the US Environmental Protection Agency.Journal of Exposure Science and Environmental Epidemiology (2007) 17, S90–S96; doi:10.1038/sj.jes.7500631 [ABSTRACT FROM AUTHOR]

The observed scatter of observations about air quality model predictions stems from a combination of naturally occurring stochastic variations that are impossible for any model to simulate explicitly and variations arising from limitations in knowledge and from imperfect input data. In this paper, historical tracer experiments of atmospheric dispersion were analyzed to develop algorithms to characterize the observed stochastic variability in the ground-level crosswind concentration profile. The algorithms were incorporated into a Lagrangian puff model (“INPUFF”) so that the consequences of variability in the dispersion could be simulated using Monte Carlo methods. The variability in the plume trajectory was investigated in a preliminary sense by tracking the divergence in trajectories from releases adjacent to the actual release location. The variability in the near-centerline concentration values not described by the Gaussian crosswind profile was determined to be on the order of a factor of 2. The variability in the trajectory was determined as likely to be larger than the plume width, even with local wind observations for use in characterizing the transport. Two examples are provided to illustrate how estimates of variability 1) can provide useful information to inform decisions for emergency response and 2) can provide a basis for sound statistical designs for model performance assessments. [ABSTRACT FROM AUTHOR]

Ozone is produced by chemical interactions involving nitrogen oxides (NOx) and volatile organic compounds in the presence of sunlight. At high concentrations, ground-level ozone has been shown to be harmful to human health and to the environment. It has been recognized that ozone is a regional-scale problem and that regionwide control strategies would be needed to improve ozone air quality in the eastern United States. To mitigate interstate transport of ozone and its precursors, the U.S. Environmental Protection Agency issued a regional rule in 1998 known as the “NOx State Implementation Plan (SIP) Call,” requiring 21 states in the eastern United States to reduce their summertime NOx emissions by 30 May 2004. In this paper, the effectiveness of the new emission control measures mandated by the NOx SIP Call is assessed by quantifying the changes that occurred in the daily maximum 8-h ozone concentrations measured at nearly 50 locations, most of which are rural (33 sites of the Clean Air Status and Trend Network and 16 sites of the Air Quality System), over the eastern United States. Given the strong dependence of ozone formation and accumulation on meteorological conditions, the incidence of the latter is first mitigated, and meteorologically adjusted ozone concentrations are extracted using a multiple regression technique. By examining the differences between the cumulative distribution functions of the meteorologically adjusted ozone concentrations, it is shown that ozone concentrations in the eastern United States are now on average 13% less than those prior to the NOx SIP Call. Using back-trajectory analyses, it is also shown that emission controls on the electricity-generating units located in the Ohio River Valley have contributed toward the improvement of ozone air quality in downwind regions, especially east and northeast of the Ohio River Valley. [ABSTRACT FROM AUTHOR]

This paper presents model estimates of the effect of chlorine emissions on atmospheric ozone concentrations in the eastern United States. The model included anthropogenic molecular chlorine emissions, anthropogenic hypochlorous acid emissions from cooling towers and swimming pools, and chlorine released from sea-salt aerosols. The release of chlorine emissions from sea-salt aerosols was modeled using heterogeneous reactions involving chloride ions in aerosols and three gas-phase species. The gas-phase chlorine chemistry was combined with the Carbon Bond Mechanism and incorporated into the Community Multiscale Air Quality modeling system. Air quality model simulations were performed for July 2001 and the results obtained with and without chlorine emissions were analyzed. When chlorine emissions were included in the model, ozone concentrations increased in the Houston, Texas, and New York–New Jersey areas. The daily maximum 1-h ozone concentrations increased by up to 12 parts per billion by volume (ppbv) in the Houston area and 6 ppbv in the New York–New Jersey area. The daily maximum 8-h ozone concentrations increased by up to 8 ppbv in the Houston area and 4 ppbv in the New York–New Jersey area. The monthly average daily maximum 1-h ozone concentration increased by up to 3 ppbv in the Houston area, but the increases in the monthly average daily maximum 1-h ozone concentration in the New York–New Jersey area were small. Chlorine emissions and chemistry enhanced the volatile organic compound oxidation rates and, thereby, increased the ozone production rate. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) flies on NASA's Earth Observing System AURA satellite, launched in July 2004. OMI is an ultraviolet/visible (UV/VIS) nadir solar backscatter spectrometer, which provides nearly global coverage in one day, with a spatial resolution of 13 km × 24 km. Trace gases measured include O3, NO2, SO2, HCHO, BrO, and OCIO. In addition OMI measures aerosol characteristics, cloud top heights and cloud coverage, and UV irradiance at the surface. OMI's unique capabilities for measuring important trace gases with daily global coverage and a small footprint will make a major contribution to our understanding of stratospheric and tropospheric chemistry and climate change along with Aura's other three instruments. OMI's high spatial resolution enables detection of air pollution at urban scales. Total Ozone Mapping Spectrometer and differential optical absorption spectroscopy heritage algorithms, as well as new ones developed by the international (Dutch, Finnish, and U.S.) OMI science team, are used to derive OMI's advanced backscatter data products. In addition to providing data for Aura's prime objectives, OMI will provide near-real-time data for operational agencies in Europe and the U.S. Examples of OMI's unique capabilities are presented in this paper. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) flies on the National Aeronautics and Space Adminsitration's Earth Observing System Aura satellite launched in July 2004. OMI is a ultraviolet/visible (UV/VIS) nadir solar backscatter spectrometer, which provides nearly global coverage in one day with a spatial resolution of 13 km × 24 km. Trace gases measured include O3, NO2, SO2, HCHO, BrO, and OClO. In addition, OMI will measure aerosol characteristics, cloud top heights, and UV irradiance at the surface. OMI's unique capabilities for measuring important trace gases with a small footprint and daily global coverage will be a major contribution to our understanding of stratospheric and tropospheric chemistry and climate change. OMI's high spatial resolution is unprecedented and will enable detection of air pollution on urban scale resolution. In this paper, the instrument and its performance will be discussed. [ABSTRACT FROM AUTHOR]

Dispersed development, also referred to as urban growth or sprawl, is a pattern of low-density development spread over previously rural landscapes. Such growth can result in adverse impacts to air quality, water quality, human health, aquatic and terrestrial ecosystems, agricultural land, military training areas, water supply and wastewater treatment, recreational resources, viewscapes, and cultural resources. The U.S. Environmental Protection Agency (U.S. EPA) is charged with protecting public health and the environment, which includes consideration of impacts from dispersed development. Specifically, because federal infrastructure projects can affect the progress of dispersed development, the secondary impacts resulting from it must be assessed in documents prepared under the National Environmental Policy Act (NEPA). The Council on Environmental Quality (CEQ) has oversight for NEPA and Section 309 of the Clean Air Act requires that U.S. EPA review and comment on federal agency NEPA documents. The adverse effects of dispersed development can be induced by federal infrastructure projects including transportation, built infrastructure, modifications in natural infrastructure, public land conversion and redevelopment of properties, construction of federal facilities, and large traffic or major growth generation developments requiring federal permits. This paper presents an approach that U.S. EPA reviewers and NEPA practitioners can use to provide accurate, realistic, and consistent analysis of secondary impacts of dispersed development resulting from federal infrastructure projects. It also presents 24 measures that can be used to mitigate adverse impacts from dispersed development by modifying project location and design, participating in preservation or restoration activities, or informing and supporting local communities in planning. [ABSTRACT FROM AUTHOR]

A real-time photochemical air quality forecast system has been implemented for the Puget Sound region to support public awareness of air quality issues. The Air Indicator Report for Public Access and Community Tracking (AIRPACT) forecast system uses daily numerical weather forecasts from the fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) to drive the California Meteorological Model (CALMET)/California photochemistry grid model (CALGRID) Eulerian photochemical modeling suite. Hourly forecasts of ozone and other pollutant concentrations, including primary particulate emissions from diesel sources, are produced for urban Seattle and environs within a gridded domain consisting of 62 × 67 grid cells (4 km × 4 km) with 13 vertical layers. Detailed gridded emission inventories are adjusted dynamically for time of day, day of the week, month, and gridded ambient temperatures to generate requisite emissions. The forecast system also uses hourly pollutant observation data, reported daily, to perform automated evaluations of forecast accuracy. Forecasts and verification results are provided on a daily basis via the Web (see ). This paper describes the forecast system and presents preliminary forecast evaluation results from the Pacific Northwest 2001 (PNW2001) field program and from selected months using the routine monitoring network. AIRPACT is unique nationally as the sole numerical modeling system producing daily, year-round, high-resolution regional air quality forecasts with daily verification. [ABSTRACT FROM AUTHOR]

In February 2000, a detailed measurement campaign was conducted over a 9-day period within a mercury (Hg) cell chlor-alkali plant in the southeastern United States (US). The principal focus of this study was to determine the fugitive (non-ducted) airborne Hg emissions from the main production equipment located in the “cell building” during extended period operations, including maintenance events. Other Hg monitoring conducted in and around the plant also included surveys of process areas surrounding the cell building, measurement of Hg fluxes from soil and other exposed surfaces, and speciated Hg measurements of ambient air both “upwind” and “downwind” of the production area as well as the determination of Hg contained in output products and wastes. The study was a collaborative effort among scientists from the US Environmental Protection Agency, the US Department of Energy''s Oak Ridge National Laboratory, and the University of Michigan. This paper provides a description of the facility, the rationale and design of the study, and an introduction to companion papers containing the observational data. [Copyright &y& Elsevier]

The western United States has experienced severe drought in recent decades, and climate models project increased drought risk in the future. This increased drying could have important implications for the region’s interconnected, hydropower-dependent electricity systems. Using power-plant level generation and emissions data from 2001 to 2021, we quantify the impacts of drought on the operation of fossil fuel plants and the associated impacts on greenhouse gas (GHG) emissions, air quality, and human health. We find that under extreme drought, electricity generation from individual fossil fuel plants can increase up to 65% relative to average conditions, mainly due to the need to substitute for reduced hydropower. Over 54% of this drought-induced generation is transboundary, with drought in one electricity region leading to net imports of electricity and thus increased pollutant emissions from power plants in other regions. These drought-induced emission increases have detectable impacts on local air quality, as measured by proximate pollution monitors. We estimate that the monetized costs of excess mortality and GHG emissions from drought-induced fossil generation are 1.2 to 2.5x the reported direct economic costs from lost hydro production and increased demand. Combining climate model estimates of future drying with stylized energy- transition scenarios suggests that these drought-induced impacts are likely to remain large even under aggressive renewables expansion, suggesting that more ambitious and targeted measures are needed to mitigate the emissions and health burden from the electricity sector during drought. [ABSTRACT FROM AUTHOR]

Exposure to traffic-related air pollutants (TRAPs) has been associated with numerous adverse health effects. TRAP concentrations are highest meters away from major roads, and disproportionately affect minority (i.e., non-white) populations often considered the most vulnerable to TRAP exposure. To demonstrate an improved assessment of on-road emissions and to quantify exposure inequity in this population, we develop and apply a hybrid data fusion approach that utilizes the combined strength of air quality observations and regional/local scale models to estimate air pollution exposures at census block resolution for the entire U.S. We use the regional photochemical grid model CMAQ (Community Multiscale Air Quality) to predict the spatiotemporal impacts at local/regional scales, and the local scale dispersion model, R-LINE (Research LINE source) to estimate concentrations that capture the sharp TRAP gradients from roads. We further apply the Regionalized Air quality Model Performance (RAMP) Hybrid data fusion technique to consider the model's nonhomogeneous, nonlinear performance to not only improve exposure estimates, but also achieve significant model performance improvement. With a R2 of 0.51 for PM2.5 and 0.81 for NO2, the RAMP hybrid method improved R2 by ~0.2 for both pollutants (an increase of up to ~70% for PM2.5 and ~31% NO2). Using the RAMP Hybrid method, we estimate 264,516 [95% confidence interval [CI], 223,506–307,577] premature deaths attributable to PM2.5 from all sources, a ~1% overall decrease in CMAQ-estimated premature mortality compared to RAMP Hybrid, despite increases and decreases in some locations. For NO2, RAMP Hybrid estimates 138,550 [69,275–207,826] premature deaths, a ~19% increase (22,576 [11,288 – 33,864]) compared to CMAQ. Finally, using our RAMP hybrid method to estimate exposure inequity across the U.S., we estimate that Minorities within 100 m from major roads are exposed to up to 15% more PM2.5 and up to 35% more NO2 than their White counterparts. [ABSTRACT FROM AUTHOR]

To improve air quality near large swine production sites and manure application fields, Kentucky has legislated setback requirements. Unlike other states, however, Kentucky requires swine producers to control all acreage in the setback. Furthermore, the setback length for surface application, an odorous option, is longer than that for incorporation. This paper evaluates how much longer the setback length for surface application must be to encourage soil incorporation of swine manure. Results indicate that Kentucky's setback lengths do not encourage odor control via incorporation; the setback length for surface application must be substantially longer than that for incorporation. [ABSTRACT FROM AUTHOR]

Switchgrass is a promising bioenergy feedstock, but industrial-scale production may lead to negative environmental effects. This study considers one such potential consequence: the life cycle monetized damages to human health from air pollution. We estimate increases in mortality from long-term exposure to fine particulate matter (PM 2.5 ), which is emitted directly (“primary PM 2.5 ”) and forms in the atmosphere (“secondary PM 2.5 ”) from precursors of nitrogen oxides (NO x ), sulfur oxides (SO x ), ammonia (NH 3 ), and volatile organic compounds (VOCs). Changes in atmospheric concentrations of PM 2.5 (primary + secondary) from on-site production and supporting supply chain activities are considered at 2694 locations (counties in the Central and Eastern US), for two biomass yields (9 and 20 Mg ha −1 ), three nitrogen fertilizer rates (50, 100, and 150 kg ha −1 ), and two nitrogen fertilizer types (urea and urea ammonium nitrate). Results indicate that on-site processes dominate life-cycle emissions of NH 3 , NO x , primary PM 2.5 , and VOCs, whereas SO x is primarily emitted in upstream supply chain processes. Total air quality impacts of switchgrass production, which are dominated by NH 3 emissions from fertilizer application, range widely depending on location, from 2 to 553 $ Mg −1 (mean: 45) of dry switchgrass at a biomass yield of 20 Mg ha −1 and fertilizer application of 100 kg ha −1 N applied as urea. Switching to urea ammonium nitrate solution lowers damages to 2 to 329 $ Mg −1 (mean: 28). This work points to human health damage from air pollution as a potentially large social cost from switchgrass production and suggests means of mitigating that impact via strategic geographical deployment and management. Furthermore, by distinguishing the origin of atmospheric emissions, this paper advances the current emerging literature on ecosystem services and disservices from agricultural and bioenergy systems. [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]

This paper addresses the direct and indirect relationships between the National Environmental Policy Act (NEPA) and the new conformity requirements. This paper concludes that NEPA and transportation/air quality conformity processes should be concurrent. The need for concurrent determinations is supported by five arguments: 1) the legislative history of conformity indicates that project conformity determinations be made during the NEPA process; 2) general NEPA requirements specify coordination between environmental processes; 3) the level of technical detail required for conformity analyses meets or exceeds the level already required for NEPA; 4) unless conformity is taken into account, alternatives and mitigation measures generated during the NEPA analytical process may later result in a negative conformity determination; and 5) public comment periods, unless coordinated, would run consecutively rather than concurrently, potentially delaying project implementation. [ABSTRACT FROM AUTHOR]

The problems associated with air pollution in urban areas have received much attention in the past decade. Very recently, many facets of the general problem have been examined including the distribution of air quality and different methods of controls of air pollution. In extending this line of research, the primary contributions of the present study are two-fold. First, a theoretical model is outlined which suggests that the relationship between externalities and property values may be interpreted in a preference context. Second, researchers deal explicitly with the problem of multicollinearity among the independent variables by employing both principal component analysis and ordinary least squares regression analysis in the empirical portion of the study. This paper examines the relationship between four environmental externalities and household preferences over alternative housing units. The estimates of the impacts are expressed as housing price differentials due to the externality. The goal in this paper has been to improve upon earlier studies that examine the relationship between environmental externalities and housing prices. The theoretical model presented earlier examines explicitly the relationship between household preferences and environmental externalities as measured by housing price differentials.

The attainment and enforcement of air quality standards is a complicated process. A crucial element in this process is the measurement of ambient air quality. The selection of locations for a limited number of monitors and their sampling frequencies is an important problem in the design of a regional air quality measurement system. This paper presents a mathematical model and procedure for solving the problem and demonstrates the conflicting nature of the principal guidelines specified by EPA. The method uses atmospheric simulation to assess the likelihood of standards violations, and linear programming to select the monitor locations and sampling frequencies. Since atmospheric simulation is required by EPA, the method appears to be accessible to virtually all local agencies. The use of linear programming permits extensive sensitivity analysis, which is illustrated in a case study. In particular, the trade-offs between conflicting EPA guidelines can be explicitly analyzed. The computational burden is primarily in the atmospheric simulation and is roughly proportional to the area of the region being studied. The added computational burden represented by the model solution is quite small. [ABSTRACT FROM AUTHOR]

This study aims to generate a spatially complete planetary boundary layer height (PBLH) product over the contiguous United States (CONUS). An eXtreme Gradient Boosting (XGB) regression model was developed using selected meteorological and geographical data fields as explanatory variables to fit the PBLH values derived from Aircraft Meteorological DAta Relay (AMDAR) reports hourly profiles at 13:00–14:00 LST (local solar time) during 2005–2019. A preprocessing step was implemented to exclude AMDAR data points that were unexplainable by the predictors, mostly under stable conditions. The PBLH prediction by this work as well as PBLHs from three reanalysis datasets (the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate – ERA5; the Modern-Era Retrospective analysis for Research and Applications, Version 2 – MERRA-2; and the North American Regional Reanalysis – NARR) were compared to reference PBLH observations from spaceborne lidar (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, CALIPSO), airborne lidar (High Spectral Resolution Lidar, HSRL), and in situ research aircraft profiles from the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaigns. Compared with PBLHs from reanalysis products, the PBLH prediction from this work shows closer agreement with the reference observations, with the caveat that different PBLH products and estimates have different ways of identifying the PBLH; thus, their comparisons should be interpreted with caution. The reanalysis products show significant high biases in the western CONUS relative to the reference observations. One direct application of the dataset generated by this work is that it enables sampling of the PBLH at the sounding locations and times of sensors aboard satellites with an overpass time in the early afternoon, e.g., the Afternoon Train (A-train), the Suomi National Polar-orbiting Partnership (Suomi NPP), the Joint Polar Satellite System (JPSS), and the Sentinel-5 Precursor (Sentinel-5P) satellite sensors. As both AMDAR and ERA5 are continuous at hourly resolution, the observational-data-driven PBLHs may be extended to other daytime hours. [ABSTRACT FROM AUTHOR]

The Copernicus Atmosphere Monitoring Service (CAMS) provides routine analyses and forecasts of trace gases and aerosols on a global scale. The core is the European Centre for Medium Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), where modules for atmospheric chemistry and aerosols have been introduced and which allows for data assimilation of satellite retrievals of composition. We have updated both the homogeneous and heterogeneous NOx chemistry applied in the three independent tropospheric–stratospheric chemistry modules maintained within CAMS, referred to as IFS(CB05BASCOE), IFS(MOCAGE) and IFS(MOZART). Here we focus on the evaluation of main trace gas products from these modules that are of interest as markers of air quality, namely lower-tropospheric O3 , NO2 and CO , with a regional focus over the contiguous United States. Evaluation against lower-tropospheric composition reveals overall good performance, with chemically induced biases within 10 ppb across species for regions within the US with respect to a range of observations. The versions show overall equal or better performance than the CAMS reanalysis, which includes data assimilation. Evaluation of surface air quality aspects shows that annual cycles are captured well, albeit with variable seasonal biases. During wintertime conditions there is a large model spread between chemistry schemes in lower-tropospheric O3 (∼ 10 %–35 %) and, in turn, oxidative capacity related to NOx lifetime differences. Analysis of differences in the HNO3 and PAN formation, which act as reservoirs for reactive nitrogen, revealed a general underestimate in PAN formation over polluted regions, likely due to too low organic precursors. Particularly during wintertime, the fraction of NO2 sequestered into PAN has a variability of 100 % across chemistry modules, indicating the need for further constraints. Notably, a considerable uncertainty in HNO3 formation associated with wintertime N2O5 conversion on wet particle surfaces remains. In summary, this study has indicated that the chemically induced differences in the quality of CAMS forecast products over the United States depends on season, trace gas, altitude and region. While analysis of the three chemistry modules in CAMS provide a strong handle on uncertainties associated with chemistry modeling, the further improvement of operational products additionally requires coordinated development involving emissions handling, chemistry and aerosol modeling, complemented with data-assimilation efforts. [ABSTRACT FROM AUTHOR]

Adopting electric end-use technologies instead of fossil-fueled alternatives, known as electrification, is an important economy-wide decarbonization strategy that also reduces criteria pollutant emissions and improves air quality. In this study, we evaluate CO2 and air quality co-benefits of electrification scenarios by linking a detailed energy systems model and a full-form photochemical air quality model in the United States. We find that electrification can substantially lower CO2 and improve air quality and that decarbonization policy can amplify these trends, which yield immediate and localized benefits. In particular, transport electrification can improve ozone and fine particulate matter (PM2.5), though the magnitude of changes varies regionally. However, growing activity from non-energy-related PM2.5 sources—such as fugitive dust and agricultural emissions—can offset electrification benefits, suggesting that additional measures beyond CO2 policy and electrification are needed to meet air quality goals. We illustrate how commonly used marginal emissions approaches systematically underestimate reductions from electrification. Electrification is a decarbonization strategy that has the potential to reduce pollutant emissions and improve air quality. Here the authors evaluate CO2 and air quality co-benefits of electrification scenarios in the United States and find that electrification can substantially lower CO2 and improve ozone and fine particulate matter regionally. [ABSTRACT FROM AUTHOR]

Air pollution levels in the United States have decreased dramatically over the past decades, yet national racial-ethnic exposure disparities persist. For ambient fine particulate matter (PM2.5), we investigate three emission-reduction approaches and compare their optimal ability to address two goals: 1) reduce the overall population average exposure ("overall average") and 2) reduce the difference in the average exposure for the most exposed racial-ethnic group versus for the overall population ("national inequalities"). We show that national inequalities in exposure can be eliminated with minor emission reductions (optimal: ~1% of total emissions) if they target specific locations. In contrast, achieving that outcome using existing regulatory strategies would require eliminating essentially all emissions (if targeting specific economic sectors) or is not possible (if requiring urban regions to meet concentration standards). Lastly, we do not find a trade-off between the two goals (i.e., reducing overall average and reducing national inequalities); rather, the approach that does the best for reducing national inequalities (i.e., location-specific strategies) also does as well as or better than the other two approaches (i.e., sector-specific and meeting concentration standards) for reducing overall averages. Overall, our findings suggest that incorporating location-specific emissions reductions into the US air quality regulatory framework 1) is crucial for eliminating long-standing national average exposure disparities by race-ethnicity and 2) can benefit overall average exposures as much as or more than the sector-specific and concentration-standards approaches. [ABSTRACT FROM AUTHOR]

Understanding how individual pollution sources contribute to ambient sulfate pollution is critical for assessing past and future air quality regulations. Since attribution to specific sources is typically not encoded in spatial air pollution data, we develop a mechanistic model which we use to estimate, with uncertainty, the contribution of ambient sulfate concentrations attributable specifically to sulfur dioxide (SO2) emissions from individual coal-fired power plants in the central United States. We propose a multivariate Ornstein–Uhlenbeck (OU) process approximation to the dynamics of the underlying space-time chemical transport process, and its distributional properties are leveraged to specify novel probability models for spatial data that are viewed as either a snapshot or time-averaged observation of the OU process. Using US EPA SO2 emissions data from 193 power plants and state-of-the-art estimates of ground-level annual mean sulfate concentrations, we estimate that in 2011—a time of active power plant regulatory action—existing flue-gas desulfurization (FGD) technologies at 66 power plants reduced population-weighted exposure to ambient sulfate by 1.97 μg/m3 (95% CI: 1.80–2.15). Furthermore, we anticipate future regulatory benefits by estimating that installing FGD technologies at the five largest SO2-emitting facilities would reduce human exposure to ambient sulfate by an additional 0.45 μg/m3 (95% CI: 0.33–0.54). for this article are available online. [ABSTRACT FROM AUTHOR]

Recent literature has suggested a link between poor air quality and worse COVID-19 outcomes. In the United States, this link is particularly noteworthy because of residential sorting along ethnic lines within the US population; minorities are disproportionately exposed to health hazards, including air pollution. The impacts of the COVID-19 pandemic have also been disproportionately concentrated amongst minorities. We explore the association between air quality and COVID-19 outcomes, using county level data for the United States from the first wave of the pandemic in 2020, and test whether exposure to more polluted air can account for some of the observed disparities in COVID-19 outcomes among minorities. [ABSTRACT FROM AUTHOR]

Comparison of lightning flash data from the National Lightning Detection Network (NLDN) and from the World Wide Lightning Location Network (WWLLN) over the contiguous United States (CONUS) for the 2016–2018 period reveals temporally and spatially varying flash rates that would influence lightning NOx (LNOx) production due to known detection efficiency differences especially during summer months over land (versus over ocean). However, the lightning flash density differences between the two networks show persistent seasonal patterns over geographical regions (e.g., land versus ocean). Since the NLDN data are considered to have higher accuracy (lightning detection with >95% efficiency), we developed scaling factors for the WWLLN flash data based on the ratios of WWLLN to NLDN flash data over time (months of year) and space. In this study, sensitivity simulations using the Community Multiscale Air Quality (CMAQ) model are performed utilizing the original data sets (both NLDN and WWLLN) and the scaled WWLLN flash data for LNOx production over the CONUS. The model performance of using the different lightning flash datasets for ambient O3 and NOx mixing ratios that are directly impacted by LNOx emissions and the wet and dry deposition of oxidized nitrogen species that are indirectly impacted by LNOx emissions is assessed based on comparisons with ground-based observations, vertical profile measurements, and satellite products. During summer months, the original WWLLN data produced less LNOx emissions (due to its lower lightning detection efficiency) compared to the NLDN data, which resulted in less improvement in model performance than the simulation using NLDN data as compared to the simulation without any LNOx emissions. However, the scaled WWLLN data produced LNOx estimates and model performance comparable with the NLDN data, suggesting that scaled WWLLN may be used as a substitute for the NLDN data to provide LNOx estimates in air quality models when the NLDN data are not available (e.g., due to prohibitive cost or lack of spatial coverage). [ABSTRACT FROM AUTHOR]