Tracey Holloway

Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet's resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.

Understanding the surface O 3 response over a “receptor” region to emission changes over a foreign “source” region is key to evaluating the potential gains from an international approach to abate ozone (O 3 ) pollution. We apply an ensemble of 21 global and hemispheric chemical transport models to estimate the spatial average surface O 3 response over east Asia (EA), Europe (EU), North America (NA), and south Asia (SA) to 20% decreases in anthropogenic emissions of the O 3 precursors, NO x , NMVOC, and CO (individually and combined), from each of these regions. We find that the ensemble mean surface O 3 concentrations in the base case (year 2001) simulation matches available observations throughout the year over EU but overestimates them by >10 ppb during summer and early fall over the eastern United States and Japan. The sum of the O 3 responses to NO x , CO, and NMVOC decreases separately is approximately equal to that from a simultaneous reduction of all precursors. We define a continental‐scale “import sensitivity” as the ratio of the O 3 response to the 20% reductions in foreign versus “domestic” (i.e., over the source region itself) emissions. For example, the combined reduction of emissions from the three foreign regions produces an ensemble spatial mean decrease of 0.6 ppb over EU (0.4 ppb from NA), less than the 0.8 ppb from the reduction of EU emissions, leading to an import sensitivity ratio of 0.7. The ensemble mean surface O 3 response to foreign emissions is largest in spring and late fall (0.7–0.9 ppb decrease in all regions from the combined precursor reductions in the three foreign regions), with import sensitivities ranging from 0.5 to 1.1 (responses to domestic emission reductions are 0.8–1.6 ppb). High O 3 values are much more sensitive to domestic emissions than to foreign emissions, as indicated by lower import sensitivities of 0.2 to 0.3 during July in EA, EU, and NA when O 3 levels are typically highest and by the weaker relative response of annual incidences of daily maximum 8‐h average O 3 above 60 ppb to emission reductions in a foreign region (<10–20% of that to domestic) as compared to the annual mean response (up to 50% of that to domestic). Applying the ensemble annual mean results to changes in anthropogenic emissions from 1996 to 2002, we estimate a Northern Hemispheric increase in background surface O 3 of about 0.1 ppb a −1 , at the low end of the 0.1–0.5 ppb a −1 derived from observations. From an additional simulation in which global atmospheric methane was reduced, we infer that 20% reductions in anthropogenic methane emissions from a foreign source region would yield an O 3 response in a receptor region that roughly equals that produced by combined 20% reductions of anthropogenic NO x , NMVOC, and CO emissions from the foreign source region.

Biofuels from land-rich tropical countries may help displace foreign petroleum imports for many industrialized nations, providing a possible solution to the twin challenges of energy security and climate change. But concern is mounting that crop-based biofuels will increase net greenhouse gas emissions if feedstocks are produced by expanding agricultural lands. Here we quantify the 'carbon payback time' for a range of biofuel crop expansion pathways in the tropics. We use a new, geographically detailed database of crop locations and yields, along with updated vegetation and soil biomass estimates, to provide carbon payback estimates that are more regionally specific than those in previous studies. Using this cropland database, we also estimate carbon payback times under different scenarios of future crop yields, biofuel technologies, and petroleum sources. Under current conditions, the expansion of biofuels into productive tropical ecosystems will always lead to net carbon emissions for decades to centuries, while expanding into degraded or already cultivated land will provide almost immediate carbon savings. Future crop yield improvements and technology advances, coupled with unconventional petroleum supplies, will increase biofuel carbon offsets, but clearing carbon-rich land still requires several decades or more for carbon payback. No foreseeable changes in agricultural or energy technology will be able to achieve meaningful carbon benefits if crop-based biofuels are produced at the expense of tropical forests.

Health is inextricably linked to climate change. It is important for clinicians to understand this relationship in order to discuss associated health risks with their patients and to inform public policy.To provide new US-based temperature projections from downscaled climate modeling and to review recent studies on health risks related to climate change and the cobenefits of efforts to mitigate greenhouse gas emissions.We searched PubMed and Google Scholar from 2009 to 2014 for articles related to climate change and health, focused on governmental reports, predictive models, and empirical epidemiological studies. Of the more than 250 abstracts reviewed, 56 articles were selected. In addition, we analyzed climate data averaged over 13 climate models and based future projections on downscaled probability distributions of the daily maximum temperature for 2046-2065. We also compared maximum daily 8-hour average ozone with air temperature data taken from the National Oceanic and Atmospheric Administration, National Climate Data Center.By 2050, many US cities may experience more frequent extreme heat days. For example, New York and Milwaukee may have 3 times their current average number of days hotter than 32°C (90°F). High temperatures are also strongly associated with ozone exceedance days, for example, in Chicago, Illinois. The adverse health aspects related to climate change may include heat-related disorders, such as heat stress and economic consequences of reduced work capacity; respiratory disorders, including those exacerbated by air pollution and aeroallergens, such as asthma; infectious diseases, including vectorborne diseases and waterborne diseases, such as childhood gastrointestinal diseases; food insecurity, including reduced crop yields and an increase in plant diseases; and mental health disorders, such as posttraumatic stress disorder and depression, that are associated with natural disasters. Substantial health and economic cobenefits could be associated with reductions in fossil fuel combustion. For example, greenhouse gas emission policies may yield net economic benefit, with health benefits from air quality improvements potentially offsetting the cost of US and international carbon policies.Evidence over the past 20 years indicates that climate change can be associated with adverse health outcomes. Health care professionals have an important role in understanding and communicating the related potential health concerns and the cobenefits from policies to reduce greenhouse gas emissions.

We present an analysis of the barriers and opportunities for incorporating air quality co-benefits into climate policy assessments. It is well known that many strategies for reducing greenhouse gas emissions also decrease emissions of health-damaging air pollutants and precursor species, including particulate matter, nitrogen oxides, and sulfur dioxide. In a survey of previous studies we found a range of estimates for the air quality co-benefits of climate change mitigation of $2- 196/tCO2 with a mean of $49/tCO2, and the highest co-benefits found in developing countries. These values, although of a similar order of magnitude to abatement cost estimates, are only rarely included in integrated assessments of climate policy. Full inclusion of these co-benefits would have pervasive implications for climate policy in areas including: optimal policy stringency, overall costs, distributional effects, robustness to discount rates, incentives for international cooperation, and the value of adaptation, forests, and climate engineering relative to mitigation. Under-valuation results in part from uncertainty in climatic damages, valuation inconsistency, and institutional barriers. Because policy debates are framed in terms of cost minimization, policy makers are unlikely to fully value air quality co-benefits unless they can be compared on an equivalent basis with the benefits of avoided climatic damages. While air quality co-benefits have been prominently portrayed as a hedge against uncertainty in the benefits of climate change abatement, this assessment finds that full inclusion of co-benefits depends on—rather than substitutes for—better valuation of climate damages.

Abstract Surface ozone (O 3 ) air pollution in populated regions has been attributed to emissions of nitrogen oxides (NO + NO 2 = NO x ) and reactive volatile organic compounds (VOCs). These constituents react with hydrogen oxide radicals (OH + HO 2 = HO x ) in the presence of sunlight and heat to produce O 3 . The question of whether to reduce NO x emissions, VOC emissions, or both is complicated by spatially and temporally heterogeneous ozone‐NO x ‐VOC sensitivity. This study characterizes spatial and temporal variations in O 3 sensitivity by analyzing the ratio of formaldehyde (HCHO, a marker of VOCs) to nitrogen dioxide (NO 2 ), a metric known as the formaldehyde nitrogen ratio (FNR). Level 3 gridded retrievals from the Ozone Monitoring Instrument (OMI) aboard the NASA Aura satellite were used to calculate FNR, with our analysis focusing on China. Based on previous studies, we take FNR < 1.0 as indicating VOC‐limited regimes, FNR > 2.0 as indicating NO x ‐limited regime, and FNR between 1.0 and 2.0 as indicating transitional regime (where either NO x reductions or VOC reductions would be expected to reduce O 3 ). We find that the transitional regime is widespread over the North China Plain (NCP), the Yangtze River Delta, and the Pearl River Delta during the ozone season (defined as having near‐surface air temperatures >20°C at the early afternoon OMI overpass time). Outside of these regions, the NO x ‐limited regime is dominant. Because HCHO and NO 2 have distinct seasonal patterns, FNR also has a pronounced seasonality, consistent with the seasonal cycle of surface O 3 . Examining trends from 2005 to 2013 indicates rapid growth in NO 2 , especially over less‐developed areas where O 3 photochemistry is NO x limited. Over this time period, HCHO decreased in southern China, where VOC emissions are dominated by biogenic sources, but increased slightly over the NCP, where VOC emissions are dominated by anthropogenic sources. A linear regression approach suggests that most of China (70% of grid cells) will be characterized by a transitional regime during the O 3 season by 2030. However, in megacities such as Guangzhou, Shanghai, and Beijing, NO 2 has decreased such that the chemical regime has shifted from VOC limited in 2005 to transitional in 2013.

Automobile exhaust contains precursors to ozone and fine particulate matter (PM ≤ 2.5 µm in aerodynamic diameter; PM2.5), posing health risks. Dependency on car commuting also reduces physical fitness opportunities.In this study we sought to quantify benefits from reducing automobile usage for short urban and suburban trips.We simulated census-tract level changes in hourly pollutant concentrations from the elimination of automobile round trips ≤ 8 km in 11 metropolitan areas in the upper midwestern United States using the Community Multiscale Air Quality (CMAQ) model. Next, we estimated annual changes in health outcomes and monetary costs expected from pollution changes using the U.S. Environmental Protection Agency Benefits Mapping Analysis Program (BenMAP). In addition, we used the World Health Organization Health Economic Assessment Tool (HEAT) to calculate benefits of increased physical activity if 50% of short trips were made by bicycle.We estimate that, by eliminating these short automobile trips, annual average urban PM2.5 would decline by 0.1 µg/m3 and that summer ozone (O3) would increase slightly in cities but decline regionally, resulting in net health benefits of $4.94 billion/year [95% confidence interval (CI): $0.2 billion, $13.5 billion), with 25% of PM2.5 and most O3 benefits to populations outside metropolitan areas. Across the study region of approximately 31.3 million people and 37,000 total square miles, mortality would decline by approximately 1,295 deaths/year (95% CI: 912, 1,636) because of improved air quality and increased exercise. Making 50% of short trips by bicycle would yield savings of approximately $3.8 billion/year from avoided mortality and reduced health care costs (95% CI: $2.7 billion, $5.0 billion]. We estimate that the combined benefits of improved air quality and physical fitness would exceed $8 billion/year.Our findings suggest that significant health and economic benefits are possible if bicycling replaces short car trips. Less dependence on automobiles in urban areas would also improve health in downwind rural settings.

This study explores the evolution and distribution of carbon monoxide (CO) using the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory three‐dimensional global chemical transport model (GFDL GCTM). The work aims to gain an improved understanding of the global carbon monoxide budget, specifically focusing on the contribution of each of the four source terms to the seasonal variability of CO. The sum of all CO sources in the model is 2.5 Pg CO/yr (1 Pg = 10 3 Tg), including fossil fuel use (300 Tg CO/yr), biomass burning (748 Tg CO/yr), oxidation of biogenic hydrocarbons (683 Tg CO/yr), and methane oxidation (760 Tg CO/yr). The main sink for CO is destruction by the hydroxyl radical, and we assume a hydroxyl distribution based on three‐dimensional monthly varying fields given by Spivakovsky et al . [1990], but we increase this field by 15% uniformly to agree with a methyl chloroform lifetime of 4.8 years [ Prinn et al , 1995]. Our simulation produces a carbon monoxide field that agrees well with available measurements from the NOAA/Climate Monitoring and Diagnostics Laboratory global cooperative flask sampling network and from the Jungfraujoch observing station of the Swiss Federal Laboratories for Materials Testing and Research (EMPA) (93% of seasonal‐average data points agree within ±25%) and flight data from measurement campaigns of the NASA Global Tropospheric Experiment (79% of regional‐average data points agree within ±25%). For all 34 ground‐based measurement sites we have calculated the percentage contribution of each CO source term to the total model‐simulated distribution and examined how these contributions vary seasonally due to transport, changes in OH concentration, and seasonality of emission sources. CO from all four sources contributes to the total magnitude of CO in all regions. Seasonality, however, is usually governed by the transport and destruction by OH of CO emitted by fossil fuel and/or biomass burning. The sensitivity to the hydroxyl field varies spatially, with a 30% increase in OH yielding decreases in CO ranging from 4–23%, with lower sensitivities near emission regions where advection acts as a strong local sink. The lifetime of CO varies from 10 days over summer continental regions to well over a year at the winter poles, where we define lifetime as the turnover time in the troposphere due to reaction with OH.

Abstract Abstract Problem: Explicitly prohibited from regulating the land use planning activities of municipal and county governments by the Clean Air Act (42 U.S.C. 131), the U.S. Environmental Protection Agency (EPA) has been forced to pursue an end-of-the-pipe approach to air quality management that has not proved successful in fully reducing ozone and fine particulate matter below health-based standards in many large U.S. cities. The persistence of these pollutants, in combination with a rapid rise in vehicle travel in recent decades, has raised concerns within the planning and public health communities about the long-term success of an air quality management program that is effectively divorced from the land use planning process. Purpose: This work, which is part of an EPA-sponsored study titled Projecting the Impact of Land Use and Transportation on Future Air Quality (PLUTO), was intended to assess the effectiveness of compact growth in improving air quality at a geographic scale compatible with secondary pollution formation and transport and over a planning horizon sufficient to capture the longer-term benefits of regional land use change. Methods: Future air quality is associated with alternative land development scenarios in this study through the integration of three separate and previously unrelated modeling components. These components consist of a set of standard population projection techniques, a household vehicle travel activity framework, and a mobile source emissions model (MOBILE 6) developed by the EPA. Results and conclusions: The results of our analysis find the median elasticity of vehicle travel with respect to density change over time to be −0.35, suggesting metropolitan areas can expect a 10% increase in population density to be associated with a 3.5% reduction in household vehicle travel and emissions. In addition, vehicle elasticities derived for urban and suburban census tracts across the 11 metro regions suggest density increments within urban zones (−0.43) to be more than twice as effective in reducing vehicle travel and emissions as density increments within suburban zones (−0.19). Takeaway for practice: We found compactness to be associated with greater reductions in vehicle travel than in previous studies, which suggests land use change can play a measurable role in improving regional air quality over time. Importantly, we found where compact growth occurs to be critically important to determining the extent to which higher density development reduces vehicle travel and emissions. We found the densifi-cation of urban zones to be more than twice as effective in reducing vehicle miles of travel and emissions as the densification of suburban zones, suggesting compact growth to be better for air quality than historical patterns of growth when densifying urban zones is given priority over non-urban zones. Key Words: air pollutionclimate changecompact growthsmart growthgrowth managementtravel behavior

We employ the Geophysical Fluid Dynamics Laboratory (GFDL) global chemistry transport model (GCTM) to address the episodic nature of trans‐Pacific pollution. The strongest Asian CO episodes over North America (NA), occurring most frequently between February and May, are often associated with disturbances that entrain pollution over eastern Asia and amplify over the western Pacific Ocean. Using 55 ppb of Asian CO as a criterion for major events, we find that during a typical year three to five Asian pollution events analogous to those observed by Jaffe et al. [1999] are expected in the boundary layer all along the U.S. West Coast between February and May. In contrast to CO, Asia currently has a small impact on the magnitude and variability of background ozone arriving over NA from the west. Direct and indirect Asian contributions to episodic O 3 events over the western United States are generally in the 3–10 ppbv range. The two largest total O 3 events (>60 ppbv), while having trajectories which pass over Asia, show negligible impact from Asian emissions. However, this may change. A future emission scenario in which Asian NOx emissions increase by a factor of 4 from those in 1990 produces late spring ozone episodes at the surface of California with Asian contributions reaching 40 ppb. Such episodic contributions are certain to exacerbate local NA pollution events, especially in elevated areas more frequently exposed to free tropospheric and more heavily Asian‐influenced air.

This study presents a consistent, national-level evaluation of potential biodiesel volumes and prices, replicated across 226 countries, territories, and protectorates. Utilizing all commercially exported lipid feedstocks from existing agricultural lands, we compare the upper-limit potential for expanded biodiesel production in terms of absolute biodiesel volumes, profitable potential from biodiesel exports, and potential from expanded vegetable oil production through agricultural yield increases. Country findings are compared across a variety of economic, energy, and environmental metrics. Our results show an upper-limit worldwide volume potential of 51 billion liters from 119 countries; 47 billion of which could be produced profitably at today's import prices. Also significant production gains are possible through increasing agricultural yields: a 12-fold increase over existing potential, primarily hinging on better management of tropical oilseed varietals.

Despite the well-established links between air pollution and human health, vegetation, and aquatic ecosystems, less attention has been paid to the potential impact of reactive atmospheric gases and aerosols on avian species. In this literature review, we summarize findings published since 1950 regarding avian responses to air pollution and discuss knowledge gaps that could be addressed in future studies. We find consistent evidence for adverse health impacts on birds attributable to exposure to gas-phase and particulate air pollutants, including carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), smoke, and heavy metals, as well as mixtures of urban and industrial emissions. Avian responses to air pollution include respiratory distress and illness, increased detoxification effort, elevated stress levels, immunosuppression, behavioral changes, and impaired reproductive success. Exposure to air pollution may furthermore reduce population density, species diversity, and species richness in bird communities.

Fine particulate matter (PM2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM2.5 exposures. This review article surveys publicly available exposure datasets for surface PM2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM2.5 exposure data are ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources.Implications: Fine particulate matter (PM2.5) has large impacts on human morbidity and mortality. Even though the methods for generating the PM2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors.

We examine the emergence of InterContinental Transport (ICT) of air pollution on the agendas of the air quality and climate communities and consider the potential for a new treaty on hemispheric air pollution. ICT is the flow of air pollutants from a source continent (e.g., North America) to a receptor continent (e.g., Europe). ICT of air pollutants occurs through two mechanisms: (i) episodic advection and (ii) increasing the global background, which enhances surface concentrations. We outline the current scientific evidence for ICT of aerosols and ozone, both of which contribute to air pollution and radiative forcing. The growing body of scientific evidence for ICT suggests that a hemispheric-scale treaty to reduce air pollutant concentrations may be appropriate to address climate and air quality concerns simultaneously. Such a treaty could pave the way for future climate agreements.

Results from the Model Intercomparison Study Asia Phase II (MICS-Asia II) are presented. Nine different regional modeling groups simulated chemistry and transport of ozone (O3), secondary aerosol, acid deposition, and associated precursors, using common emissions and boundary conditions derived from a global model. Four-month-long periods, representing 2 years and three seasons (i.e., March, July, and December in 2001, and March in 2002), are analyzed. New observational data, obtained under the EANET (the Acid Deposition Monitoring Network in East Asia) monitoring program, were made available for this study, and these data provide a regional database to compare with model simulations. The analysis focused around seven subject areas: O3 and related precursors, aerosols, acid deposition, global inflow of pollutants and precursor to Asia, model sensitivities to aerosol parameterization, analysis of emission fields, and detailed analyses of individual models, each of which is presented in a companion paper in this issue of Atmospheric Environment. This overview discusses the major findings of the study, as well as information on common emissions, meteorological conditions, and observations.

Abstract. Understanding the exchange processes between the atmospheric boundary layer and the free troposphere is crucial for estimating hemispheric transport of air pollution. Most studies of hemispheric air pollution transport have taken a large-scale perspective using global chemical transport models with fairly coarse spatial and temporal resolutions. In support of United Nations Task Force on Hemispheric Transport of Air Pollution (TF HTAP; www.htap.org), this study employs two high-resolution atmospheric chemistry models (WRF-Chem and CMAQ; 36×36 km) driven with chemical boundary conditions from a global model (MOZART; 1.9×1.9°) to examine the role of fine-scale transport and chemistry processes in controlling pollution export and import over the Asian continent in spring (March 2001). Our analysis indicates the importance of rapid venting through deep convection that develops along the leading edge of frontal system convergence bands, which are not adequately resolved in either of two global models compared with TRACE-P aircraft observations during a frontal event. Both regional model simulations and observations show that frontal outflows of CO, O3 and PAN can extend to the upper troposphere (6–9 km). Pollution plumes in the global MOZART model are typically diluted and insufficiently lofted to higher altitudes where they can undergo more efficient transport in stronger winds. We use sensitivity simulations that perturb chemical boundary conditions in the CMAQ regional model to estimate that the O3 production over East Asia (EA) driven by PAN decomposition contributes 20% of the spatial averaged total O3 response to European (EU) emission perturbations in March, and occasionally contributes approximately 50% of the total O3 response in subsiding plumes at mountain observatories (at approximately 2 km altitude). The response to decomposing PAN of EU origin is strongly affected by the O3 formation chemical regimes, which vary with the model chemical mechanism and NOx/VOC emissions. Our high-resolution models demonstrate a large spatial variability (by up to a factor of 6) in the response of local O3 to 20% reductions in EU anthropogenic O3 precursor emissions. The response in the highly populated Asian megacities is 40–50% lower in our high-resolution models than the global model, suggesting that the source-receptor relationships inferred from the global coarse-resolution models likely overestimate health impacts associated with intercontinental O3 transport. Our results highlight the important roles of rapid convective transport, orographic forcing, urban photochemistry and heterogeneous boundary layer processes in controlling intercontinental transport; these processes may not be well resolved in the large-scale models.

Ambient air pollution in India contributes to negative health impacts and early death. Ground-based monitors often used to quantify health impacts are located in urban regions, yet approximately 70% of India's population lives in rural communities. We simulate high-resolution concentrations of fine particulate matter (PM) and ozone from the regional Community Multi-scale Air Quality model over northern India, including updated estimates of anthropogenic emissions for transportation, residential combustion and location-based industrial and electrical generating emissions in a new anthropogenic emissions inventory. These simulations inform seasonal air quality and health impacts due to anthropogenic emissions, contrasting urban versus rural regions. For our northern India domain, we estimate 463 200 (95% confidence interval: 444 600–482 600) adults die prematurely each year from PM2.5 and that 37 800 (28 500–48 100) adults die prematurely each year from O3. This translates to 5.8 deaths per 10 000 attributable to air pollution out of an annual rate of 72 deaths per 10 000 (8.1% of deaths) using 2010 estimates. We estimate that the majority of premature deaths resulting from PM2.5 and O3 are in rural (383 600) as opposed to urban (117 200) regions, where we define urban as cities and towns with populations of at least 100 000 people. These findings indicate the need for rural monitoring and appropriate health studies to understand and mitigate the effects of ambient air pollution on this population in addition to supporting model evaluation.

Climate change negatively impacts human health through heat stress and exposure to worsened air pollution, amongst other pathways. Indoor use of air conditioning can be an effective strategy to reduce heat exposure. However, increased air conditioning use increases emissions of air pollutants from power plants, in turn worsening air quality and human health impacts. We used an interdisciplinary linked model system to quantify the impacts of heat-driven adaptation through building cooling demand on air-quality-related health outcomes in a representative mid-century climate scenario.We used a modeling system that included downscaling historical and future climate data with the Weather Research and Forecasting (WRF) model, simulating building electricity demand using the Regional Building Energy Simulation System (RBESS), simulating power sector production and emissions using MyPower, simulating ambient air quality using the Community Multiscale Air Quality (CMAQ) model, and calculating the incidence of adverse health outcomes using the Environmental Benefits Mapping and Analysis Program (BenMAP). We performed simulations for a representative present-day climate scenario and 2 representative mid-century climate scenarios, with and without exacerbated power sector emissions from adaptation in building energy use. We find that by mid-century, climate change alone can increase fine particulate matter (PM2.5) concentrations by 58.6% (2.50 μg/m3) and ozone (O3) by 14.9% (8.06 parts per billion by volume [ppbv]) for the month of July. A larger change is found when comparing the present day to the combined impact of climate change and increased building energy use, where PM2.5 increases 61.1% (2.60 μg/m3) and O3 increases 15.9% (8.64 ppbv). Therefore, 3.8% of the total increase in PM2.5 and 6.7% of the total increase in O3 is attributable to adaptive behavior (extra air conditioning use). Health impacts assessment finds that for a mid-century climate change scenario (with adaptation), annual PM2.5-related adult mortality increases by 13,547 deaths (14 concentration-response functions with mean incidence range of 1,320 to 26,481, approximately US$126 billion cost) and annual O3-related adult mortality increases by 3,514 deaths (3 functions with mean incidence range of 2,175 to 4,920, approximately US$32.5 billion cost), calculated as a 3-month summer estimate based on July modeling. Air conditioning adaptation accounts for 654 (range of 87 to 1,245) of the PM2.5-related deaths (approximately US$6 billion cost, a 4.8% increase above climate change impacts alone) and 315 (range of 198 to 438) of the O3-related deaths (approximately US$3 billion cost, an 8.7% increase above climate change impacts alone). Limitations of this study include modeling only a single month, based on 1 model-year of future climate simulations. As a result, we do not project the future, but rather describe the potential damages from interactions arising between climate, energy use, and air quality.This study examines the contribution of future air-pollution-related health damages that are caused by the power sector through heat-driven air conditioning adaptation in buildings. Results show that without intervention, approximately 5%-9% of exacerbated air-pollution-related mortality will be due to increases in power sector emissions from heat-driven building electricity demand. This analysis highlights the need for cleaner energy sources, energy efficiency, and energy conservation to meet our growing dependence on building cooling systems and simultaneously mitigate climate change.

Abstract. The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite is a valuable source of information to monitor the NOx emissions that adversely affect air quality. We conduct a series of experiments using a 4×4 km2 Comprehensive Air Quality Model with Extensions (CAMx) simulation during April–September 2019 in eastern Texas to evaluate the multiple challenges that arise from reconciling the NOx emissions in model simulations with TROPOMI. We find an increase in NO2 (+17 % in urban areas) when transitioning from the TROPOMI NO2 version 1.3 algorithm to the version 2.3.1 algorithm in eastern Texas, with the greatest difference (+25 %) in the city centers and smaller differences (+5 %) in less polluted areas. We find that lightning NOx emissions in the model simulation contribute up to 24 % of the column NO2 in the areas over the Gulf of Mexico and 8% in Texas urban areas. NOx emissions inventories, when using locally resolved inputs, agree with NOx emissions derived from TROPOMI NO2 version 2.3.1 to within 20 % in most circumstances, with a small NOx underestimate in Dallas–Fort Worth (−13 %) and Houston (−20 %). In the vicinity of large power plant plumes (e.g., Martin Lake and Limestone) we find larger disagreements, i.e., the satellite NO2 is consistently smaller by 40 %–60 % than the modeled NO2, which incorporates measured stack emissions. We find that TROPOMI is having difficulty distinguishing NO2 attributed to power plants from the background NO2 concentrations in Texas – an area with atmospheric conditions that cause short NO2 lifetimes. Second, the NOx/NO2 ratio in the model may be underestimated due to the 4 km grid cell size. To understand ozone formation regimes in the area, we combine NO2 column information with formaldehyde (HCHO) column information. We find modest low biases in the model relative to TROPOMI HCHO, with −9 % underestimate in eastern Texas and −21 % in areas of central Texas with lower biogenic volatile organic compound (VOC) emissions. Ozone formation regimes at the time of the early afternoon overpass are NOx limited almost everywhere in the domain, except along the Houston Ship Channel, near the Dallas/Fort Worth International airport, and in the presence of undiluted power plant plumes. There are likely NOx-saturated ozone formation conditions in the early morning hours that TROPOMI cannot observe and would be well-suited for analysis with NO2 and HCHO from the upcoming TEMPO (Tropospheric Emissions: Monitoring Pollution) mission. This study highlights that TROPOMI measurements offer a valuable means to validate emissions inventories and ozone formation regimes, with important limitations.

Clean energy policy can provide substantial health benefits through improved air quality. As ambitious clean energy proposals are increasingly considered and adopted across the United States (US), quantifying the benefits of removal of such large air pollution emissions sources is crucial to understanding potential societal impacts of such policy. In this study, we estimate health benefits resulting from the elimination of emissions of fine particulate matter (PM2.5), sulfur dioxide, and nitrogen oxides from the electric power, transportation, building, and industrial sectors in the contiguous US. We use EPA's CO-Benefits Risk Assessment screening tool to estimate health benefits resulting from the removal of PM2.5-related emissions from these energy-related sectors. We find that nationwide efforts to eliminate energy-related emissions could prevent 53,200 (95% CI: 46,900-59,400) premature deaths each year and provide $608 billion ($537-$678 billion) in benefits from avoided PM2.5-related illness and death. We also find that an average of 69% (range: 32%-95%) of the health benefits from emissions removal remain in the emitting region. Our study provides an indication of the potential scale and distribution of public health benefits that could result from ambitious regional and nationwide clean energy and climate mitigation policy.

The combination of wind energy generation and energy storage can produce a source of electricity that is functionally equivalent to a baseload coal or nuclear power plant. A model was developed to assess the technical and environmental performance of baseload wind energy systems using compressed air energy storage. The analysis examined several systems that could be operated in the midwestern United States under a variety of operating conditions. The systems can produce substantially more energy than is required from fossil or other primary sources to construct and operate them. By operation at a capacity factor of 80%, each evaluated system achieves an effective primary energy efficiency of at least five times greater than the most efficient fossil combustion technology, with greenhouse gas emission rates less than 20% of the least emitting fossil technology currently available. Life-cycle emission rates of NOx and SO2 are also significantly lower than fossil-based systems.

Projections from general circulation model (GCM) simulations must be downscaled to the high spatial resolution needed for assessing local and regional impacts of climate change, but uncertainties in the downscaling process are difficult to quantify. We employed a multiple linear regression model and the MM5 dynamical model to downscale June, July, and August monthly mean surface temperature over eastern North America under greenhouse gas‐driven climate change simulation by the NASA GISS GCM. Here we examine potential sources of apparent agreement between the two classes of models and show that arbitrary parameters in a statistical model contribute significantly to the level of agreement with dynamical downscaling. We found that the two methods and all permutations of regression parameters generally exhibited comparable skill at simulating observations, although spatial patterns in temperature across the region differed. While the two methods projected similar regional mean warming over the period 2000–2087, they developed different spatial patterns of temperature across the region, which diverged further from historical differences. We found that predictor domain size was a negligible factor for current conditions, but had a much greater influence on future surface temperature change than any other factor, including the data sources. The relative importance of SD model inputs to downscaled skill and domain‐wide agreement with MM5 for summertime surface temperature over North America in descending order is Predictor Domain; Training Data/Predictor Model; Predictor Variables; and Predictor Grid Resolution. Our results illustrate how statistical downscaling may be used as a proxy for dynamical models in sensitivity analysis.

Eight regional Eulerian chemical transport models (CTMs) are compared with each other and with an extensive set of observations including ground-level concentrations from EANET, ozone soundings from JMA and vertical profiles from the TRACE-P experiment to evaluate the models’ abilities in simulating O3 and relevant species (SO2, NO, NO2, HNO3 and PAN) in the troposphere of East Asia and to look for similarities and differences among model performances. Statistical analysis is conducted to help estimate the consistency and discrepancy between model simulation and observation in terms of various species, seasons, locations, as well as altitude ranges. In general, all models show a good skill of simulating SO2 for both ground level and the lower troposphere, although two of the eight models systematically overpredict SO2 concentration. The model skills for O3 vary largely with region and season. For ground-level O3, model results are best correlated with observations in July 2001. Comparing with O3 soundings measured in the afternoon reveals the best consistency among models in March 2001 and the largest disparity in O3 magnitude in July 2001, although most models produce the best correlation in July as well. In terms of the statistics for the four flights of TRACE-P experiment, most models appear to be able to accurately capture the variability in the lower troposphere. The model performances for NOx are relatively poor, with lower correlation and with almost all models tending to underpredict NOx levels, due to larger uncertainties in either emission estimates or complex chemical mechanism represented. All models exhibit larger RMSE at altitudes <2 km than 2–5.5 km, mainly due to a consistent tendency of these models towards underprediction of the magnitude of intense plumes that often originate from near surface. Relatively lower correlation at altitudes 2–5.5 km may be attributed to the models’ limitation in representing convection or potential chemical processes. Most of the key features in species distribution have been consistently reproduced by the participating models, such as the O3 enhancement in the western Pacific Ocean in March and in northeast Asia in July, respectively, although the absolute model values may differ considerably from each other. Large differences are found among models in the southern parts of the domain for all the four periods, including southern China and northern parts of some Southeast Asia countries where the behaviors of chemical components and the ability of these models are still not clearly known because of a lack of observational databases.

Region-to-grid source–receptor (S/R) relationships are established for sulfur and reactive nitrogen deposition in East Asia, using the Eulerian-type Community Multiscale Air Quality (CMAQ) model with emission and meteorology data for 2001. We proposed a source region attribution methodology by analyzing the non-linear responses of the CMAQ model to emission changes. Sensitivity simulations were conducted where emissions of SO2, NOx, and primary particles from a source region were reduced by 25%. The difference between the base and sensitivity simulations was multiplied by a factor of four, and then defined as the contribution from that source region. The transboundary influence exhibits strong seasonal variation and generally peaks during the dry seasons. Long-range transport from eastern China contributes a significant percentage (>20%) of anthropogenic reactive nitrogen as well as sulfur deposition in East Asia. At the same time, northwestern China receives approximately 35% of its sulfur load and 45% of its nitrogen load from foreign emissions. Sulfur emissions from Miyakejima and other volcanoes contribute approximately 50% of the sulfur load in Japan in 2001. Sulfur inflows from regions outside the study domain, which is attributed by using boundary conditions derived from the MOZART global atmospheric chemistry model, are pronounced (10–40%) over most parts of Asia. Compared with previous studies using simple Lagrangian models, our results indicate higher influence from long-range transport. The estimated S/R relationships are believed to be more realistic since they include global influence as well as internal interactions among different parts of China.

Abstract. This study employs the regional Community Multiscale Air Quality (CMAQ) model to examine seasonal and diurnal variations of boundary layer ozone (O3) over East Asia. We evaluate the response of model simulations of boundary layer O3 to the choice of chemical mechanisms, meteorological fields, boundary conditions, and model resolutions. Data obtained from surface stations, aircraft measurements, and satellites are used to advance understanding of O3 chemistry and mechanisms over East Asia and evaluate how well the model represents the observed features. Satellite measurements and model simulations of summertime rainfall are used to assess the impact of the Asian monsoon on O3 production. Our results suggest that summertime O3 over Central Eastern China is highly sensitive to cloud cover and monsoonal rainfall over this region. Thus, accurate simulation of the East Asia summer monsoon is critical to model analysis of atmospheric chemistry over China. Examination of hourly summertime O3 mixing ratios from sites in Japan confirms the important role of diurnal boundary layer fluctuations in controlling ground-level O3. By comparing five different model configurations with observations at six sites, the specific mechanisms responsible for model behavior are identified and discussed. In particular, vertical mixing, urban chemistry, and dry deposition depending on boundary layer height strongly affect model ability to capture observed behavior. Central Eastern China appears to be the most sensitive region in our study to the choice of chemical mechanisms. Evaluation with TRACE-P aircraft measurements reveals that neither the CB4 nor the SAPRC99 mechanisms consistently capture observed behavior of key photochemical oxidants in springtime. However, our analysis finds that SAPRC99 performs somewhat better in simulating mixing ratios of H2O2 (hydrogen peroxide) and PAN (peroxyacetyl nitrate) at flight altitudes below 1 km. The high level of uncertainty associated with O3 production in Central Eastern China poses a major problem for regional air quality management. This highly polluted, densely populated region would greatly benefit from comprehensive air quality monitoring and the development of model chemical mechanisms appropriate to this unique atmospheric environment.

Aggressive renewable energy policies have helped the biofuels industry grow at a rate few could have predicted. However, while discourse on the energy balance and environmental impacts of agricultural biofuel feedstocks are common, the potential they hold for additional production has received considerably less attention. Here we present a new biofuel yield analysis based on the best available global agricultural census data. These new data give us the first opportunity to consider geographically-specific patterns of biofuel feedstock production in different regions, across global, continental, national and sub-national scales. Compared to earlier biofuel yield tables, our global results show overestimates of biofuel yields by ∼100% or more for many crops. To encourage the use of regionally-specific data for future biofuel studies, we calculated complete results for 20 feedstock crops for 238 countries, states, territories and protectorates.

Past studies have established strong connections between meteorology and air quality, via chemistry, transport, and natural emissions. A less understood linkage between weather and air quality is the temperature-dependence of emissions from electricity generating units (EGUs), associated with high electricity demand to support building cooling on hot days. This study quantifies the relationship between ambient surface temperatures and EGU air emissions (CO2, SO2, and NOX) using historical data. We find that EGUs in the Eastern U.S. region from 2007 to 2012 exhibited a 3.87% ± 0.41% increase in electricity generation per °C increase during summer months. This is associated with a 3.35%/°C ± 0.50%/°C increase in SO2 emissions, a 3.60%/°C ± 0.49%/°C increase in NOX emissions, and a 3.32%/°C ± 0.36%/°C increase in CO2 emissions. Sensitivities vary by year and by pollutant, with SO2 both the highest sensitivity (5.04% in 2012) and lowest sensitivity (2.19% in 2007) in terms of a regional average. Texas displays 2007–2012 sensitivities of 2.34%/°C ± 0.28%/°C for generation, 0.91%/°C ± 0.25%/°C for SO2 emissions, 2.15%/°C ± 0.29%/°C for NOX emissions, and 1.78%/°C ± 0.22%/°C for CO2 emissions. These results suggest demand-side and supply side technological improvements and fuel choice could play an important role in cost-effective reduction of carbon emissions and air pollution.

Studies quantifying the air quality and associated human health "co-benefits" from climate mitigation strategies represent a growing area of research and policy analysis. Still, these studies are relatively sparse, reflecting the historical disconnect between literature quantifying the air quality and health, as compared to other aspects of climate and energy policy evaluation. While linkages between energy and transportation sector decarbonization and air pollution are qualitatively well established, quantifying the air quality co-benefits of climate, clean energy, and transportation electrification policies requires models and analysis methods that span social, physical, chemical, and biological systems. Studies in the peer-reviewed literature (n=32) have evaluated carbon pricing, renewable portfolio standards, energy efficiency, renewable energy deployment, and clean transportation. A number of major findings have emerged from these studies: 1) decarbonization strategies can reduce air pollution disproportionally on the most polluted days; 2) renewable energy deployment and climate policies offer the highest health and economic benefits in regions with greater reliance on coal generation; 3) monetized air quality health co-benefits can offset costs of climate policy implementation; 4) monetized co-benefits typically exceed the levelized cost of electricity (LCOE) of renewable energies; 5) climate mitigation strategies can have adverse effects on air quality; 6) Electric vehicle (EV) adoption generally improves air quality on peak pollution days, but can result in ozone disbenefits in urban centers due to the titration of ozone with nitrogen oxides. Drawing from these published studies, we review the state of knowledge on climate co-benefits to air quality and health, identifying opportunities for policy action and further research.

Data from satellite instruments provide estimates of gas and particle levels relevant to human health, even pollutants invisible to the human eye. However, the successful interpretation of satellite data requires an understanding of how satellites relate to other data sources, as well as factors affecting their application to health challenges. Drawing from the expertise and experience of the 2016-2020 NASA HAQAST (Health and Air Quality Applied Sciences Team), we present a review of satellite data for air quality and health applications. We include a discussion of satellite data for epidemiological studies and health impact assessments, as well as the use of satellite data to evaluate air quality trends, support air quality regulation, characterize smoke from wildfires, and quantify emission sources. The primary advantage of satellite data compared to in situ measurements, e.g., from air quality monitoring stations, is their spatial coverage. Satellite data can reveal where pollution levels are highest around the world, how levels have changed over daily to decadal periods, and where pollutants are transported from urban to global scales. To date, air quality and health applications have primarily utilized satellite observations and satellite-derived products relevant to near-surface particulate matter <2.5 μm in diameter (PM2.5) and nitrogen dioxide (NO2). Health and air quality communities have grown increasingly engaged in the use of satellite data, and this trend is expected to continue. From health researchers to air quality managers, and from global applications to community impacts, satellite data are transforming the way air pollution exposure is evaluated.

▪ Abstract Energy and energy technologies have a central role in social and economic development at all scales, from household and community to regional and national. Among its welfare effects, energy is closely linked with public health both positively and negatively, the latter through environmental pollution and degradation. We review the current research on how energy use and energy technologies influence public health, emphasizing the risks associated with indoor and ambient air pollution from energy use, and the links between the local and global environmental health impacts of energy use. This review illustrates that, despite their large public health implications, most energy policies and programs in the developing world are fundamentally treated as components of overall economic development, without explicit assessment of their health benefits or hazards. Closer integration of health in energy management can facilitate the development of policies and programs that increase welfare and minimize negative health outcomes. Renewable energy technologies are used as an example of how an integrated energy-health approach can be used in policy analysis and formulation.

This paper focuses on the comparison of chemical deposition of eight regional chemical models used in Model Inter-Comparison Study for Asia (MICS-Asia) II. Monthly-mean depositions of chemical species simulated by these models, including dry deposition of SO2, HNO3, NH3, sulfate, nitrate and ammonium and wet deposition of SO42−, NO3− and NH4+, have been provided for four periods (March, July, December 2001 and March 2002) in this work. Observations at 37 sites of the Acid Deposition Monitoring Network in East Asia (EANET) are compared with SO42−, NO3− and NH4+ wet deposition model results. Significant correlations appeared between the observation and computed ensemble mean of participant models. Also, differences among modeled sulfur and nitrogen dry depositions have been studied at the EANET sites. Based on the analysis of acid deposition for various species from different models, total depositions of sulfur (SO2 and sulfate) and nitrogen (nitrate and ammonium) have been evaluated as the ensemble mean of the eight models. In general, all models capture the observed spatial distribution of sulfur and nitrogen deposition, although the absolute values may differ from measurements. High deposition often occurs in eastern China, Japan, the Republic of Korea, Thailand, Vietnam, Philippines and other parts of Southeast Asia. The magnitude of model bias is quite large for many of the models. In examining the reasons for model–measurement disagreement, we find that differences in chemical processes, deposition parameterization, and modeled precipitation are the main reasons for large model disparities.

Future climate change is expected to alter the spatial and temporal distribution of surface wind speeds (SWS), with associated impacts on electricity generation from wind energy. However, the predictions for the direction and magnitude of these changes hinge critically on the assessment methods used. Many climate change impact analyses, including those focused on wind energy, use individual climate models and/or statistical downscaling methods rooted in historical observations. Such studies may individually suggest an unrealistically high level of scientific certainty due to the absence of competing projections (over the same region, time period, etc). A new public data archive, the North American Regional Climate Change Assessment Program (NARCCAP), allows for a more comprehensive perspective on regional climate change impacts, here applied to three wind farm sites in California.

Since the end of World War II, global agriculture has undergone a period of rapid intensification achieved through a combination of increased applications of chemical fertilizers, pesticides, and herbicides, the implementation of best management practice techniques, mechanization, irrigation, and more recently, through the use of optimized seed varieties and genetic engineering. However, not all crops and not all regions of the world have realized the same improvements in agricultural intensity. In this study we examine both the magnitude and spatial variation of new agricultural production potential from closing of 'yield gaps' for 20 ethanol and biodiesel feedstock crops. With biofuels coming under increasing pressure to slow or eliminate indirect land-use conversion, the use of targeted intensification via established agricultural practices might offer an alternative for continued growth. We find that by closing the 50th percentile production gap—essentially improving global yields to median levels—the 20 crops in this study could provide approximately 112.5 billion liters of new ethanol and 8.5 billion liters of new biodiesel production. This study is intended to be an important new resource for scientists and policymakers alike—helping to more accurately understand spatial variation of yield and agricultural intensification potential, as well as employing these data to better utilize existing infrastructure and optimize the distribution of development and aid capital.

Climate change will increase extreme heat-related health risks. To quantify the health impacts of mid-century climate change, we assess heat-related excess mortality across the eastern USA. Health risks are estimated using the US Environmental Protection Agency’s Environmental Benefits Mapping and Analysis Program (BenMAP). Mid-century temperature estimates, downscaled using the Weather Research and Forecasting model, are compared to 2007 temperatures at 36 km and 12 km resolutions. Models indicate the average apparent and actual summer temperatures rise by 4.5° and 3.3° C, respectively. Warmer average apparent temperatures could cause 11,562 additional annual deaths (95% confidence interval, CI: 2641–20,095) due to cardiovascular stress in the population aged 65 years and above, while higher minimum temperatures could cause 8767 (95% CI: 5030–12,475) additional deaths each year. Modeled future climate data available at both coarse (36 km) and fine (12 km) resolutions predict significant human health impacts from warmer climates. The findings suggest that currently available information on future climates is sufficient to guide regional planning for the protection of public health. Higher resolution climate and demographic data are still needed to inform more targeted interventions.

This study uses statistical downscaling to estimate the impact of future climate change on air quality. We employ historical observations of surface ozone (O 3 ) over the Chicago area, large‐scale climate variables from the National Center for Environmental Protection (NCEP) reanalysis data, and climate projections from three GCMs (GFDL, PCM, and HadCM3), driven by two SRES emission scenarios (A1FI and B1 for GFDL and PCM; A2 and B1 for HadCM3). This approach calculates historic relationships between meteorology and O 3 , and considers how future meteorology would affect ground‐level O 3 if these relationships remain constant. Ozone mixing ratios over Chicago are found to be most sensitive to surface temperature, horizontal surface winds, surface relative humidity, incoming solar radiation, and cloud cover. Considering the change in O 3 due to global climate change alone, summertime (June, July, and August) mean mixing ratios over Chicago are projected to increase by 6–17 ppb by the end of the century, depending on assumptions about global economic growth and choice of GCM. Changes are greater under higher climate emissions scenarios and more sensitive climate models (e.g. 24 ppb for GFDL A1FI as compared to 2 ppb for PCM B1). However, this approach does not take into account changes in O 3 ‐precursor emissions nor changes in local and lake‐effect meteorology, which could combine with climate change to either enhance or diminish the projected change in local mixing ratios. Statistical downscaling is performed with the Statistical DownScaling Model (SDSM v. 4.1, a publicly available scientific analysis and decision‐support tool.

The Community Multiscale Air Quality model (CMAQ) is used to simulate aerosol mass and composition in the Great Lakes region of North America in an annual study for 2002. Model predictions are evaluated against daily and weekly average speciated fine particle (PM 2.5 ) and bulk (PM 2.5 and PM 10 ) mass concentration measurements taken throughout the region by the Interagency Monitoring of Protected Visual Environments (IMPROVE), Speciation Trends Network (STN), and Clean Air Status and Trends Network (CASTNet) monitoring networks, and number concentration is evaluated using hourly observations at a rural site. Through detailed evaluation of model‐measurement agreement over urban and remote areas, major features of aerosol seasonality are examined. Whereas nitrate (winter maximum) and sulfate (summer maximum) seasonal patterns are driven by climatic influence on aerosol thermodynamics, seasonality of ammonium and organic mass (OM) is driven by emissions. Production of anthropogenic secondary organic aerosol (SOA) and summertime ozone formation both reach regional maxima over the southern Great Lakes, where they are also most strongly temporally correlated. Although primary OM is more prevalent, insufficient SOA formation leads to summertime OM underprediction of more than 50%. By comparing temporal patterns in aerosol species between model and observations, we find that elemental carbon, OM, and PM 2.5 are overly correlated in CMAQ, suggesting that the model misses chemical, transport, or emissions processes differentiating these constituents. In contrast, sulfate and PM 2.5 are not sufficiently correlated in CMAQ, although CMAQ simulates sulfate with a high level of skill. Performance relative to ad hoc regional modeling goals and previous studies is average to excellent for most species throughout the year, and seasonal patterns are captured.

We present an examination of the potential emissions and air quality benefits of shifting freight from truck to rail in the upper Midwestern United States. Using a novel, freight-specific emissions inventory (the Wisconsin Inventory of Freight Emissions, WIFE) and a three-dimensional Eulerian photochemical transport model (the Community Multiscale Air Quality Model, CMAQ), we quantify how specific freight mode choices impact ambient air pollution concentrations. Using WIFE, we developed two modal shift scenarios: one focusing on intraregional freight movements within the Midwest and a second on through-freight movements through the region. Freight truck and rail emissions inventories for each scenario were gridded to a 12 km × 12 km horizontal resolution as input to CMAQ, along with emissions from all other major sectors, and three-dimensional time-varying meteorology from the Weather Research and Forecasting model (WRF). The through-freight scenario reduced monthly mean (January and July) localized concentrations of nitrogen dioxide (NO2) by 28% (-2.33 ppbV) in highway grid cells, and reduced elemental carbon (EC) by 16% (-0.05 μg/m(3)) in highway grid cells. There were corresponding localized increases in railway grid cells of 25% (+0.83 ppbV) for NO2, and 22% (+0.05 μg/m(3)) for EC. The through-freight scenario reduced CO2 emissions 31% compared to baseline trucking. The through-freight scenario yields a July mean change in ground-level ambient PM2.5 and O3 over the central and eastern part of the domain (up to -3%).

We evaluate how fine particulate matter (PM2.5) and precursor emissions could be reduced if 17% of electricity generation was replaced with solar photovoltaics (PV) in the Eastern United States. Electricity generation is simulated using GridView, then used to scale electricity-sector emissions of sulfur dioxide (SO2) and nitrogen oxides (NOX) from an existing gridded inventory of air emissions. This approach offers a novel method to leverage advanced electricity simulations with state-of-the-art emissions inventories, without necessitating recalculation of emissions for each facility. The baseline and perturbed emissions are input to the Community Multiscale Air Quality Model (CMAQ version 4.7.1) for a full accounting of time- and space-varying air quality changes associated with the 17% PV scenario. These results offer a high-value opportunity to evaluate the reduced-form AVoided Emissions and geneRation Tool (AVERT), while using AVERT to test the sensitivity of results to changing base-years and levels of solar integration. We find that average NOX and SO2 emissions across the region decrease 20% and 15%, respectively. PM2.5 concentrations decreased on average 4.7% across the Eastern U.S., with nitrate (NO3−) PM2.5 decreasing 3.7% and sulfate (SO42−) PM2.5 decreasing 9.1%. In the five largest cities in the region, we find that the most polluted days show the most significant PM2.5 decrease under the 17% PV generation scenario, and that the greatest benefits are accrued to cities in or near the Ohio River Valley. We find summer health benefits from reduced PM2.5 exposure estimated as 1424 avoided premature deaths (95% Confidence Interval (CI): 284 deaths, 2 732 deaths) or a health savings of $13.1 billion (95% CI: $0.6 billion, $43.9 billion) These results highlight the potential for renewable energy as a tool for air quality managers to support current and future health-based air quality regulations.

While it is known that energy efficiency (EE) lowers power sector demand and emissions, study of the air quality and public health impacts of EE has been limited. Here, we quantify the air quality and mortality impacts of a 12% summertime (June, July, and August) reduction in baseload electricity demand. We use the AVoided Emissions and geneRation Tool (AVERT) to simulate plant-level generation and emissions, the Community Multiscale Air Quality (CMAQ) model to simulate air quality, and the Environmental Benefits Mapping and Analysis Program (BenMAP) to quantify mortality impacts. We find EE reduces emissions of NOx by 13.2%, SO2 by 12.6%, and CO2 by 11.6%. On a nationwide, summer average basis, ambient PM2.5 is reduced 0.55% and O3 is reduced 0.45%. Reduced exposure to PM2.5 avoids 300 premature deaths annually (95% CI: 60 to 580) valued at $2.8 billion ($0.13 billion to $9.3 billion), and reduced exposure to O3 averts 175 deaths (101 to 244) valued at $1.6 billion ($0.15 billion to $4.5 billion). This translates into a health savings rate of $0.049/kWh ($0.031/kWh for PM2.5 and $0.018/kWh for O3). These results illustrate the importance of capturing the health benefits of EE and its potential as a strategy to achieve air standards.

Abstract The 2018 NASA Health and Air Quality Applied Science Team (HAQAST) “Indicators” Tiger Team collaboration between NASA‐supported scientists and civil society stakeholders aimed to develop satellite‐derived global air pollution and climate indicators. This Commentary shares our experience and lessons learned. Together, the team developed methods to track wildfires, dust storms, pollen counts, urban green space, nitrogen dioxide concentrations and asthma burdens, tropospheric ozone concentrations, and urban particulate matter mortality. Participatory knowledge production can lead to more actionable information but requires time, flexibility, and continuous engagement. Ground measurements are still needed for ground truthing, and sustained collaboration over time remains a challenge.

This study quantifies the seasonality and geographic variability of global pollutant inflow to Asia. Asia is often looked to as a major source of intercontinental air pollution transport with rising emissions and efficient pollutant export processes. However, the degree to which foreign emissions have been imported to Asia has not been thoroughly examined. The Model Inter-Comparison Study for Asia (MICS-Asia) is an international collaboration to study air pollution transport and chemistry in Asia. Using the global atmospheric chemistry Model of Ozone and Related Tracers (MOZART v. 2.4), and comparing results with a suite of regional models participating in MICS-Asia, we find that imported O3 contributes significantly throughout Asia. The choice of upper boundary condition is found to be particularly important for O3, even for surface concentrations. Both North America and Europe contribute to ground-level O3 concentrations throughout the region, though the seasonality of these two sources varies. North American contributions peak at over 10% of monthly mean O3 during winter months in East Asia, compared to Europe's spring- and autumn-maxima (5–8%). In comparison to observed data from the Acid Deposition Monitoring Network in East Asia (EANET), MOZART concentrations for O3 generally fall within the range of the MICS models, but MOZART is unable to capture the fine spatial variability of shorter-lived species as well as the regional models.

Abstract. Quantitative analysis of three atmospheric mercury species – gaseous elemental mercury (Hg0), reactive gaseous mercury (RGHg) and particulate mercury (PHg) – has been limited to date by lack of ambient measurement data as well as by uncertainties in numerical models and emission inventories. This study employs the Community Multiscale Air Quality Model version 4.6 with mercury chemistry (CMAQ-Hg), to examine how local emissions, meteorology, atmospheric chemistry, and deposition affect mercury concentration and deposition the Great Lakes Region (GLR), and two sites in Wisconsin in particular: the rural Devil's Lake site and the urban Milwaukee site. Ambient mercury exhibits significant biases at both sites. Hg0 is too low in CMAQ-Hg, with the model showing a 6% low bias at the rural site and 36% low bias at the urban site. Reactive mercury (RHg = RGHg + PHg) is over-predicted by the model, with annual average biases &gt;250%. Performance metrics for RHg are much worse than for mercury wet deposition, ozone (O3), nitrogen dioxide (NO2), or sulfur dioxide (SO2). Sensitivity simulations to isolate background inflow from regional emissions suggests that oxidation of imported Hg0 dominates model estimates of RHg at the rural study site (91% of base case value), and contributes 55% to the RHg at the urban site (local emissions contribute 45%).

Abstract We evaluate nitrogen dioxide (NO 2 ) simulations from a widely used air quality model, the Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model, using ground‐ and satellite‐based observations. In addition to direct comparison of modeled and measured variables, we compare the response of NO 2 to meteorological conditions and the ability of the model to capture these sensitivities over the continental U.S. during winter and summer periods of 2007. This is the first study to evaluate relationships between NO 2 and meteorological variables using satellite data, the first to apply these relationships for model validation, and the first to characterize variability in sensitivities over a wide geographic and temporal scope. We find boundary layer height, wind speed, temperature, and relative humidity to be the most important variables in determining near‐surface NO 2 variability. Consistent with earlier studies on NO 2 ‐meteorology relationships, we find that, in general, NO 2 responds negatively to planetary boundary height, negatively to wind speed, and negatively to insolation. Unlike previous studies, we find a slight positive association between precipitation and NO 2 , and we find a consistently positive average association between temperature and NO 2 . CMAQ agreed with relationships observed in ground‐based data from the EPA Air Quality System and the Ozone Monitoring Instrument over most regions. However, we find that the southwest U.S. is a problem area for CMAQ, where modeled NO 2 responses to insolation, boundary layer height, and other variables are at odds with the observations.

The impact of air emissions from electricity generation depends on the spatial distribution of power plants and electricity dispatch decisions. Thus, any realistic evaluation of the air quality impacts of lower-carbon electricity must account for the spatially heterogeneous changes in associated emissions. Here, we present an analysis of the changes in fine particulate matter (PM2.5) associated with current, expected, and proposed energy efficiency and renewable energy policies in Wisconsin. We simulate the state's electricity system and its potential response to policies using the MyPower electricity-sector model, which calculates plant-by-plant reductions in NOx and SO2 emissions. We find that increased efficiency and renewable generation in a 2024 policy scenario substantially reduce statewide emissions of NOx and SO2 (55% and 59% compared to 2008, 32% and 33% compared to 2024 business-as-usual, BAU). PM2.5 is quantified across the Great Lakes region using the EPA Community Multiscale Air Quality (CMAQ) model for some emissions scenarios. We find that summer mean surface concentrations of sulfate and PM2.5 are less sensitive to policy changes than emissions. In the 2024 policy scenario, sulfate aerosol decreases less than 3% over most of the region relative to BAU and 3–13% relative to 2008 over most of Wisconsin. The lower response of these secondary aerosols arises from chemical and meteorological processing of electricity emissions, and mixing with other emission sources. An analysis of model performance and response to emission reduction at five sites in Wisconsin shows good model agreement with observations and a high level of spatial and temporal variability in sulfate and PM2.5 reductions. In this case study, the marginal improvements in emissions and air quality associated with carbon policies were less than the technology, renewable, and conservation assumptions under a business-as-usual scenario. However, this analysis for Wisconsin shows how integrated modeling can quantify the emission and air quality co-benefits associated with carbon reduction measures, and this approach can be applied to other regions and larger geographical scales.

To evaluate uncertainty in the spatial distribution of air emissions over India, we compare satellite and surface observations with simulations from the U.S. Environmental Protection Agency (EPA) Community Multi-Scale Air Quality (CMAQ) model. Seasonally representative simulations were completed for January, April, July, and October 2010 at 36 km × 36 km using anthropogenic emissions from the Greenhouse Gas-Air Pollution Interaction and Synergies (GAINS) model following version 5a of the Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants project (ECLIPSE v5a). We use both tropospheric columns from the Ozone Monitoring Instrument (OMI) and surface observations from the Central Pollution Control Board (CPCB) to closely examine modeled nitrogen dioxide (NO2) biases in urban and rural regions across India. Spatial average evaluation with satellite retrievals indicate a low bias in the modeled tropospheric column (−63.3%), which reflects broad low-biases in majority non-urban regions (−70.1% in rural areas) across the sub-continent to slightly lesser low biases reflected in semi-urban areas (−44.7%), with the threshold between semi-urban and rural defined as 400 people per km2. In contrast, modeled surface NO2 concentrations exhibit a slight high bias of +15.6% when compared to surface CPCB observations predominantly located in urban areas. Conversely, in examining extremely population dense urban regions with more than 5000 people per km2 (dense-urban), we find model overestimates in both the column (+57.8) and at the surface (+131.2%) compared to observations. Based on these results, we find that existing emission fields for India may overestimate urban emissions in densely populated regions and underestimate rural emissions. However, if we rely on model evaluation with predominantly urban surface observations from the CPCB, comparisons reflect model high biases, contradictory to the knowledge gained using satellite observations. Satellites thus serve as an important emissions and model evaluation metric where surface observations are lacking, such as rural India, and support improved emissions inventory development.

Abstract This study characterizes the degree to which current polar-orbiting satellites can evaluate the daytime change in NO 2 vertical column density (VCD) in urban, suburban, and rural areas. We examine these issues by considering the diurnal cycle of NO 2 over the United States, using the large NO 2 monitoring network supported by states, tribes, and the US Environmental Protection Agency (EPA). Through this analysis, we identify the potential opportunities and limitations of current space-based NO 2 data in capturing diurnal change. Ground-based monitoring data from the US EPA are compared with satellite retrievals of NO 2 from the KNMI Tropospheric Emissions Monitoring Internet Service (TEMIS) for two instruments: GOME-2 with a mid-morning overpass, and OMI with an early afternoon overpass. Satellite data show evidence of higher morning NO 2 in the vicinity of large urban areas. Both satellites and ground monitors show ∼1.5–2x greater NO 2 abundance between morning and afternoon in urban areas. Despite differences in horizontal resolution and overpass time, the two satellite retrievals show similar agreement with ground-based NO 2 measurements. When analyzed on a pixel-by-pixel basis, we find evidence for spatial structure in the diurnal change in NO 2 between city center and surrounding areas in Southern California. Wider analysis of urban-suburban structure in diurnal NO 2 change is hindered by resolution differences in the two satellite instruments, which have the potential to create data artefacts. This study highlights the value of future geostationary instruments to provide comparable satellite retrievals for NO 2 over the course of a day, and research needs related to the effective utilization of NO 2 satellite data for air quality applications.

This study evaluates formaldehyde (HCHO) over the U.S. from 2006 to 2015 by comparing ground monitor data from the Air Quality System (AQS) and a satellite retrieval from the Ozone Monitoring Instrument (OMI). Our comparison focuses on the utility of satellite data to inform patterns, trends, and processes of ground-based HCHO across the U.S. We find that cities with higher levels of biogenic volatile organic compound (BVOC) emissions, including primary HCHO, exhibit larger HCHO diurnal amplitudes in surface observations. These differences in hour-to-hour variability in surface HCHO suggests that satellite agreement with ground-based data may depend on the distribution of emission sources. On a seasonal basis, OMI exhibits the highest correlation with AQS in summer and the lowest correlation in winter. The ratios of HCHO in summer versus other seasons show pronounced seasonal variability in OMI, likely due to seasonal changes in the vertical HCHO distribution. The seasonal variability in HCHO from satellite is more pronounced than at the surface, with seasonal variability 20–100% larger in satellite than surface observations. The seasonal variability also has a latitude dependency, with more variability in higher latitude regions. OMI agrees with AQS on the interannual variability in certain periods, whereas AQS and OMI do not show a consistent decadal trend. This is possibly due to a rather large interannual variability in HCHO, which makes the small decadal drift less significant. Temperature also explains part of the interannual variabilities. Small temperature variations in the western U.S. are reflected with more quiescent HCHO interannual variability in that region. The decrease in summertime HCHO in the southeast U.S. could also be partially explained by a small and negative trend in local temperatures.

Transitioning to a sustainable energy system poses a massive challenge to communities, nations, and the global economy in the next decade and beyond. A growing portfolio of satellite data products is available to support this transition. Satellite data complement other information sources to provide a more complete picture of the global energy system, often with continuous spatial coverage over targeted areas or even the entire Earth. We find that satellite data are already being applied to a wide range of energy issues with varying information needs, from planning and operation of renewable energy projects, to tracking changing patterns in energy access and use, to monitoring environmental impacts and verifying the effectiveness of emissions reduction efforts. While satellite data could play a larger role throughout the policy and planning lifecycle, there are technical, social, and structural barriers to their increased use. We conclude with a discussion of opportunities for satellite data applications to energy and recommendations for research to maximize the value of satellite data for sustainable energy transitions.

Abstract This study analyzes future changes in population-weighted degree-days in 48 states over the contiguous U.S. Using temperature data from the NASA Earth Exchange Global Daily Downscaled Projects and population data from NASA Socioeconomic Data and Applications Center, we computed population-weighted degree-days (PHDD and PCDD) and EDD (energy degree-days, PHDD + PCDD) over the 21st century, under a business-as-usual scenario. Results show that although the rising temperature is the primary driver, population distribution and projection play undeniable roles in estimating state-level heating and cooling demand. Throughout the 21st century, the U.S. is projected to experience a heating-to-cooling shift in energy demand, with the number of heating-dominant states dropping from 37 to 17 and the length of cooling seasons extending by two months (indicating a corresponding reduction in heating seasons) in all states by late-century. Meanwhile, a more homogenous EDD pattern is expected due to the increasing PCDD and decreasing PHDD, and the peak EDD month will switch from winter to summer in 15 out of 48 states. Our study provides a more nuanced understanding of future heating and cooling demand by examining both annual and monthly variations in the demands and how their relative dominance in a single framework may evolve over time. The study's state-level perspective can provide valuable insights for policymakers, energy providers, and other stakeholders regarding the forthcoming shift in demand patterns and related building operations and energy consumption at both state and regional levels.

A simple model of chemistry and transport, ATMOS-N, has been developed to calculate source–receptor relationships for reactive nitrogen species within Asia. The model is intended to support discussion of energy and environmental issues in Asia, to compare sulfate and nitrate contributions to regional acidification, and to estimate how each nation's acid deposition and air quality relates to domestic versus foreign emissions. ATMOS-N is a Lagrangian "puff" model in which non-interacting puffs of emissions are advected horizontally and mixed between three vertical layers. Results are compared with wet nitrate deposition observations in Asia. On an annual average, the model estimates that long-range transport contributes a significant percentage of total nitrate deposition throughout east Asia. China, the largest emitter of the region, contributes 18% to nitrate deposition in Taiwan, 18% in Japan, 46% in North Korea, and 26% in South Korea. South Korea contributes 12% to nitrate deposition in Japan, due to its close upwind proximity. Compared with total acid deposition (nitrate+sulfate), nitrate contributes 30–50% over northern Japan, 30–60% in India, and 50–90% in southeast Asia where biomass burning emits high levels of NOx. The percentage contribution of nitrate is very low in China, where emissions and deposition of sulfur are extraordinarily high.

Several proposed utility-scale energy storage systems in the U.S. will use the spare output capacity of existing electric power systems to create the equivalent of new load-following plants that can rapidly respond to fluctuations in electricity demand and increase the flexibility of baseload generators. New energy storage systems using additional generation from existing plants can directly compete with new traditional sources of load-following and peaking electricity, yet this application of energy storage is not required to meet many of the Clean Air Act standards required of new electricity generators (e.g., coal- or gas-fired power plants). This study evaluates the total emissions that will likely result from the operation of a new energy storage facility when coupled with an average existing U.S. coal-fired power plant and estimates that the emission rates of SO2 and NOx will be considerably higher than the rate of a new plant meeting Clean Air Act standards, even accounting for the efficiency benefits of energy storage. This study suggests that improved emissions "accounting" might be necessary to provide accurate environmental comparisons between energy storage and more traditional sources of electricity generation.

This study has conducted a comprehensive model evaluation to help identify major uncertainties of regional air quality model in predicting long-range transport and deposition of acidifying substances in East Asia. Annual predictions of the Community Multiscale Air Quality (CMAQ) model are carried out at two horizontal scales: an 81 km domain over East Asia and a 27 km domain over Northeast Asia. The model successfully reproduces the magnitudes and diurnal variations of SO2 mixing ratios at most sites of the Acid Deposition Monitoring Network in East Asia (EANET). Through the comparison with tropospheric NO2 columns from the Global Ozone Monitoring Experiment (GOME), the model is shown to be able to capture major spatial and seasonal variations of NO2 observed from space over East Asia. Regarding the magnitudes, however, CMAQ underpredicts the GOME retrieval over industrial area of eastern China in March and December, and over the remote western China in July. Primary reasons for the discrepancy over eastern China are the uncertainties both in emission inventory and in the GOME retrieval in wintertime. For the wet season the soil-biogenic NO emission estimates need to be reviewed regarding the intensity and timing of fertilizer applications, and the magnitude of rain-induced pulsing. The sensitivities of predicted NO2 columns, NOx mixing ratios, and wet nitrate deposition to 50% increase of NOx emissions are studied. Due to the underpredictions of NOx and also to the uncertainty in modeled precipitation and nitrate formation, CMAQ has a tendency to underpredict annual wet deposition loads of nitrate observed by the EANET network.

Eight chemical transport models participate in a model intercomparison study for East Asia, MICS-Asia II. This paper analyzes calculated results for particulate matter of sulfate, nitrate and ammonium through comparisons with each other and with monthly measurements at EANET (the acid deposition monitoring network in East Asia) and daily measurements at Fukue, Japan. To the EANET measurements, model ensemble means better agree with model individual results for sulfate and total ammonium, although total nitrate is consistently and considerably underestimated. To measurements at Fukue, the models show better agreement than for the EANET measurements. This is likely because Fukue is centered in many of the model domains, whereas the EANET stations are mostly in Southeast Asia and Russia. Moreover, it would be important that Fukue is in Northeast Asia, where the emission inventory is more reliable than Southeast Asia. The model–model comparisons are made in view of the total amount in the atmosphere, vertical profile, coefficient of variation in surface concentrations, and transformation changes with distance. All the models show reasonable tendencies in vertical profiles and composition ratios. However, total amounts in the atmosphere are discrepant among the models. The consistency of the total amount in the atmosphere would influence source–receptor analysis. It seems that model results would be consistent, if the models take into account the primitive processes like emission, advection/diffusion, chemical transformation and dry/wet deposition, no matter the processes are modeled simply or comprehensively. Through the comparison study, we learned that it would be difficult to find any problems from one comparison (model-observation comparison with one data or many but at one station or in a short period). Modelers tend to examine model performances only from model-observation comparisons. However, taking budget in a certain or whole model domain would be important, before the models are applied to source–receptor analysis.

This paper presents the results of a study on the effectiveness of smart growth development patterns and vehicle fleet hybridization in reducing mobile source emissions of carbon dioxide (CO2) across 11 major metropolitan regions of the Midwestern U.S. over a 50-year period. Through the integration of a vehicle travel activity modeling framework developed by researchers atthe Oak Ridge National Laboratory with small area population projections, we model mobile source emissions of CO2 associated with alternative land development and technology change scenarios between 2000 and 2050. Our findings suggest that under an aggressive smart growth scenario, growth in emissions expected to occur under a business as usual scenario is reduced by 34%, while the full dissemination of hybrid-electric vehicles throughout the light vehicle fleet is found to offset the expected growth in emissions by 97%. Our results further suggest that high levels of urban densification could achieve reductions in 2050 CO2 emissions equivalent to those attainable through the full dissemination of hybrid-electric vehicle technologies.

Nitrogen dioxide (NO2) is emitted during high temperature combustion from anthropogenic and natural sources. Human exposure to high NO2 concentrations causes cardiovascular and respiratory illnesses. The EPA operates ground monitors across the U.S. which take hourly measurements of NO2 concentrations, providing precise measurements for assessing human pollution exposure but with sparse spatial distribution. Satellite-based instruments capture NO2 amounts through the atmospheric column with global coverage at regular spatial resolution, but do not directly measure surface NO2. This study compares regression methods using satellite NO2 data from the TROPospheric Ozone Monitoring Instrument (TROPOMI) to estimate annual surface NO2 concentrations in varying geographic and land use settings across the continental U.S. We then apply the best-performing regression models to estimate surface NO2 at 0.01o by 0.01o resolution, and we term this estimate as quasi-NO2 (qNO2). qNO2 agrees best with measurements at suburban sites (cross-validation (CV) R2 = 0.72) and away from major roads (CV R2 = 0.75). Among U.S. regions, qNO2 agrees best with measurements in the Midwest (CV R2 = 0.89) and agrees least in the Southwest (CV R2 = 0.65). To account for the non-Gaussian distribution of TROPOMI NO2, we apply data transforms, with the Anscombe transform yielding highest agreement across the continental U.S. (CV R2 = 0.78). The interpretability, minimal computational cost, and health relevance of qNO2 facilitates use of satellite data in a wide range of air quality applications.

Abstract As policy organizations consider strategies to mitigate climate change, decarbonization initiatives can also reduce health-impacting air pollutants and may affect the associated racial disparities of adverse effects. With the U.S. Environmental Protection Agency CO-Benefits Risk Assessment Health Impacts Screening Tool (COBRA), we compare three decarbonization scenarios and their impacts at the regional and county scales. COBRA calculates changes in county-level ambient fine particulate matter (PM 2.5 ), and associated mortality impacts, for each decarbonization scenario. We compare these patterns with demographic data to evaluate the relative exposure reduction benefit across race and ethnicity. Carbon-free electricity would reduce national average ambient PM 2.5 concentrations by 0.21 μ g m −3 , compared with a 0.19 μ g m −3 reduction associated with carbon-free industrial activity, and a 0.08 μ g m −3 reduction associated with carbon-free light duty vehicle (LDV) transportation. Decarbonization strategies also vary in terms of the racial groups most benefitting from each scenario, due to regional and urban/rural patterns in emission sources and population demographics. Black populations are the only group to experience relative exposure reduction benefits compared to the total population in every scenario, with industrial decarbonization yielding 23% greater reductions in ambient PM 2.5 concentrations for Black populations than for the total U.S. population. The largest relative reduction in PM 2.5 exposure was found for Asian populations in the carbon-free LDV transportation scenario (53%). The magnitudes of total air quality improvements by scenario vary across regions of the U.S., and generally do not align with the decarbonization policy that achieves the largest equity goal. Only the transportation decarbonization scenario meets the criteria of the Justice40 Initiative nationwide, fulfilling the 2021 commitment by U.S. President Biden that federal investments in clean energy are designed to allocate at least 40% of benefits to disadvantaged communities.

Future changes in climate and precursor emissions will likely have important consequences on ground-level ozone concentrations for the City of Chicago and its surrounding suburban/rural areas. Here we use a regional climate–air quality modeling system to evaluate the combined and individual effects of climate warming (and resulting biogenic emissions increases) and anthropogenic emissions perturbations from 1996–2000 to 2048–2052 and 2095–2099 in this region. Two scenarios are considered, including A1FI (higher warming with increasing anthropogenic emissions) and B1 (less warming with reduced anthropogenic emissions). Relative to 1996–2000, projected changes in climate and anthropogenic emissions together lead to little ozone change for the City of Chicago under A1FI but 5.0–7.8 ppb increases under B1 by 2048–2052 and 2095–2099. For A1FI, the decreasing ratio of volatile organic compounds (VOCs) to nitrogen oxides (NOx) reduces ozone concentrations over Chicago, despite the increasing emissions for both NOx and VOCs. Averaged over the Chicago urban and surrounding suburban area, however, surface ozone increase 2.3–7.1 ppb under A1FI by 2095–2099. Additionally, the seasonal ozone variation is projected to increase 84–127% under A1FI but decrease 23–30% under B1 over the Chicago area. By comparison, projected climate warming alone increases the surface ozone by 2.1–8.7 ppb and its seasonal variation by 22–89% over the Chicago area from 1996–2000 to 2095–2099 under both scenarios. Therefore, effective emission regulation and climate considerations are both important to pollution mitigation in the Chicago area.

Most US energy consumption occurs in buildings, with cooling demands anticipated to increase net building electricity use under warmer conditions. The electricity generation units that respond to this demand are major contributors to sulfur dioxide (SO2) and nitrogen oxides (NOx), both of which have direct impacts on public health, and contribute to the formation of secondary pollutants including ozone and fine particulate matter.

Abstract To assess climate change impacts on hydrology, conservation biology, and air quality, impact studies typically require future climate data with spatial resolution high enough to resolve urban‐rural gradients, complex topography, and sub‐synoptic atmospheric phenomena. We present here an approach to dynamical downscaling using analysis nudging, where the entire domain is constrained to coarser‐resolution parent data. Here meteorology from the North American Regional Reanalysis and the North American Regional Climate Change Assessment Program data archive are used as parent data and downscaled with the Advanced Research version of the Weather Research and Forecasting model to a 12 km × 12 km horizontal resolution over the Eastern U.S. Our results show when analysis nudging is applied to all variables at all levels, mean fractional errors relative to parent data are less than 2% for maximum 2 m temperatures, less than 15% for minimum 2 m temperatures, and less than 18% for10 m wind speeds. However, the skill of representing fields that are not nudged, such as boundary layer height and precipitation, is less clear. Our results indicate that though nudging can be a useful tool for consistent, comparable studies of downscaling climate for regional and local impacts, which variables are nudged and at what levels should be carefully considered based on the climate impact(s) of study.

Tropospheric NO2 concentrations obtained from the measurements of the Ozone Monitoring Instrument on board the NASA Aura satellite from 2005 to 2016 were studied to identify major NO2 emission hot spots, trends, and seasonal variations over Kazakhstan. Emission hot spots are observed over the locations of thermal power plants (Ekibastuz) and major urban and industrial regions (Almaty and Shymkent), as well as the capital city (Astana). Some decreasing trends have been observed for NO2 over Ekibastuz, whereas the regions of Almaty and Shymkent showed increasing trends due to industrial growth. The seasonal pattern of the NO2 concentration shows a difference between three industrial cities of Almaty, Shymkent, and Ekibastuz versus the rest of Kazakhstan. In these three cities, a NO2 maximum is found during wintertime, which we attribute to seasonality of emissions associated with electricity production and the longer chemical lifetime of NO2 in winter. In contrast, in Astana and the rest of Kazakhstan, the NO2 concentration reaches a maximum in the summer.

Starting in 2011, a team-based approach has been developed to connect NASA science with air quality and health communities. These teams, funded by the NASA Applied Sciences Program, promote collaboration within the team, communication with end-user communities, and the rapid advancement of applied research. The team structure provides increased flexibility to address high-priority research areas, better aligning research questions with user needs. The first NASA team built on this structure was the Air Quality and Applied Sciences Team (AQAST, 2011 to 2016), and continued with the Health and Applied Sciences Team (HAQAST, 2016 to 2019). Over the years of AQAST and HAQAST, we have experimented with different approaches to manage an Applied Sciences Team. We have adjusted our approach based on lessons learned and feedback gathered from stakeholders, team members, program mangers, and meeting attendees. We have found that this type of team succeeds by building a culture of collaboration, advancing communication with stakeholder communities, and identifying issues where the team structure can provide a rapid response. AQAST and HAQAST represent a model of funding and research with positive outcomes for air quality and public health engagement with NASA data and tools. This team-based approach is well suited to mission-driven, applied science activities.

Abstract Air pollution is highly variable, such that source contributions to air pollution can vary even within a single city. However, few tools exist to support city-scale air quality analyses, including impacts of energy system changes. We present a methodology that utilizes regional ground-based monitor measurements to scale speciation data from the Intervention Model for Air Pollution (InMAP), a national-scale reduced-complexity model. InMAP, like all air quality models, has biases in its concentration estimates; these biases may be pronounced when examining a single city. We apply the bias correction methodology to Madison, Wisconsin and estimate the relative contributions of sources to annual-average fine particulate matter (PM 2.5 ), as well as the impacts of coal power plant retirements and electric vehicle (EV) adoption. We find that the largest contributors to ambient PM 2.5 concentrations in Madison are on-road transportation, contributing 21% of total PM 2.5 ; non-point sources, 16%; and electricity generating units, 14%. State-wide coal power plant closures from 2014 to 2020 and planned closures through 2025 were modeled to assess air quality benefits. The largest relative reductions are seen in areas north of Milwaukee (up to 7%), though population-weighted PM 2.5 was reduced by only 3.8% across the state. EV adoption scenarios lead to a relative reduction in PM 2.5 over Madison of 0.5% to 13.7% or a 9.3% reduction in total PM 2.5 from a total replacement of light-duty vehicles (LDVs) with EVs. Similar percent reductions are calculated for population-weighted concentrations over Madison. Replacing 100% of LDVs with EVs reduced CO 2 emissions by over 50%, highlighting the potential benefits of EVs to both climate and air quality. This work illustrates the potential of combining data from models and monitors to inform city-scale air quality analyses, supporting local decision-makers working to reduce air pollution and improve public health.

Air quality models can support pollution mitigation design by simulating policy scenarios and conducting source contribution analyses. The Intervention Model for Air Pollution (InMAP) is a powerful tool for equitable policy design as its variable resolution grid enables intra-urban analysis, the scale of which most environmental justice inquiries are levied. However, InMAP underestimates particulate sulfate and overestimates particulate ammonium formation, errors that limit the model's relevance to city-scale decision-making. To reduce InMAP's biases and increase its relevancy for urban-scale analysis, we calculate and apply scaling factors (SFs) based on observational data and advanced models. We consider both satellite-derived speciated PM2.5 from Washington University and ground-level monitor measurements from the U.S. Environmental Protection Agency, applied with different scaling methodologies. Relative to ground-monitor data, the unscaled InMAP model fails to meet a normalized mean bias performance goal of <±10% for most of the PM2.5 components it simulates (pSO4: -48%, pNO3: 8%, pNH4: 69%), but with city-specific SFs it achieves the goal benchmarks for every particulate species. Similarly, the normalized mean error performance goal of <35% is not met with the unscaled InMAP model (pSO4: 53%, pNO3: 52%, pNH4: 80%) but is met with the city-scaling approach (15%-27%). The city-specific scaling method also improves the R2 value from 0.11 to 0.59 (ranging across particulate species) to the range of 0.36-0.76. Scaling increases the percent pollution contribution of electric generating units (EGUs) (nationwide 4%) and non-EGU point sources (nationwide 6%) and decreases the agriculture sector's contribution (nationwide -6%).

Abstract Formaldehyde (HCHO), a known carcinogen classified as a hazardous pollutant by the United States Environmental Protection Agency (U.S. EPA), is measured through monitoring networks across the U.S. Since these data are limited in spatial and temporal extent, model simulations from the U.S. EPA Community Multiscale Air Quality (CMAQ) model are used to estimate ambient HCHO exposure for the EPA National Air Toxics Assessment (NATA). Here, we employ satellite HCHO retrievals from the Ozone Monitoring Instrument (OMI)—the NASA retrieval developed by the Smithsonian Astrophysical Observatory (SAO), and the European Union Quality Assurance for Essential Climate Variables (QA4ECV) retrieval—to evaluate three CMAQ configurations, spanning the summers of 2011 and 2016, with differing biogenic emissions inputs and chemical mechanisms. These CMAQ configurations capture the general spatial and temporal behavior of both satellite retrievals, but underestimate column HCHO, particularly in the western U.S. In the southeastern U.S., the comparison with OMI HCHO highlights differences in modeled meteorology and biogenic emissions even with differences in satellite retrievals. All CMAQ configurations show low daily correlations with OMI HCHO ( r = 0.26–0.38), however, we find higher monthly correlations ( r = 0.52–0.73), and the models correlate best with the OMI‐QA4ECV product. Compared to surface observations, we find improved agreement over a 24‐h period compared to afternoon‐only, suggesting daily HCHO amounts are captured with more accuracy than afternoon amounts. This work highlights the potential for synergistic improvements in modeling and satellite retrievals to support near‐surface HCHO estimates for the NATA and other applications.

Satellites offer an unprecedented opportunity to evaluate patterns and trends in air pollution, especially in regions with few or no ground-based monitors. We evaluate the 100 most populous cities worldwide, comparing nitrogen dioxide (NO2) vertical column densities from the Aura satellite to population, gross urban product (GUP), and emissions estimates. We find a positive relationship between GUP and NO2 for 38 of the 56 low-income cities, where NO2 increases with GUP, and a negative relationship for the 7 high-income cities, where NO2 decreases with GUP. This trend is consistent with the environmental Kuznets curve model. However, we found the GUP of 36 cities in the middle range of incomes did not display any consistent relationship with NO2. This partial Kuznets curve relationship arises across the 2005 to 2011 years of our study period, where specific cities show a positive or negative trend in NO2 with GUP growth over time. We analyze a global emissions inventory to compare the relationship between GUP per capita and pollution, which shows a similar relationship. The difference between observed NO2 and the emissions inventory could be due to atmospheric processes or could be due to city-specific changes in energy that are not well captured in the inventory.

Although measurements of individual exposure remain the “gold standard” for evaluating health risks associated with air pollution, atmospheric models have become important tools for understanding the links between energy use, air pollution, and public health. The spatial and temporal coverage of model data typically far exceeds that of measurement data; the cost of simulating air pollution concentrations with a model is low compared with the equipment and personnel costs to operate a network of measurement stations; and models permit analysis of future projections and implications of air quality management policy. Still, important limitations remain. No model is perfect, and data quality is limited by model accuracy, meteorological input, and quantification of emissions. Exploiting the strengths of models in public health analysis, while appropriately dealing with model uncertainty, poses challenges that span public health and atmospheric science disciplines. Here we review past studies in which models have been employed in air pollution health analyses, and we discuss new directions in public health that capitalize on atmospheric model strengths.

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Even in well-studied, data-rich regions of the United States and Europe, understanding ambient particulate matter (PM, aka aerosols) remains a challenge. Atmospheric aerosols exhibit chemical heterogeneity, spatial and seasonal variability, and result in a wide range of health impacts (mortality, respiratory disease, cardiovascular disease, eye irritation, and others). In addition, aerosols play an important role in climate, exerting warming effects (black carbon), cooling effects (sulfate and organic carbon), and affecting precipitation and cloud cover. Characterizing the emission sources, concentrations, transport patterns, and impacts is particularly difficult in developing countries, where data are scarce, emissions are high, and health impacts are often severe.

Concentrations of atmospheric mercury observed during July and August of 2005 in Riverside, CA and during August of 2006 at sites throughout the Los Angeles Basin indicate that a diurnal pattern of elemental mercury frequently exists within the basin during summer months. During these diurnal cycles, elemental mercury is observed to abruptly spike well above global background levels during the morning hours. These peak events were observed to be coincident across several monitoring sites throughout the Basin suggesting that mercury spikes were not a result of source plumes unique to each site but rather the impact of a basin‐wide phenomenon. Atmospheric temperature profiles measured by a Radio Acoustic Sounding System (RASS) located in Moreno Valley, CA indicate that peak events coincided with the shift in surface temperature profile from stable to neutral indicating the presence of fumigation episodes, the occurrence of which is supported by a generic model of basin dispersion. The presence of mercury in the stable layer aloft is a function of point sources within the basin, and in particular a single, elevated point source located in the Port of Long Beach. The unique dynamics of atmospheric mercury observed throughout the Los Angeles Basin combined with the location of this major point source upwind of the Basin provide a novel method of directly observing atmospheric mixing associated with the break‐up of the nocturnal inversion layer.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).CORRESPONDING AUTHOR: Ben Kaldunski, ben.kaldunski@gmail.com; bkaldunski@wisc.edu

With a growing focus on energy and environment, there is need to include emission constraints in electricity markets. Current methods to reduce emissions include the Acid Rain Program, NOx Trading Programs and Cross-State Air Pollution Rule (CSAPR). Even with these cap-and-trade programs in effect, there are substantial Nonattainment Areas in the USA with respect to the National Ambient Air Quality Standards. In this paper, we present a novel approach to reduce emissions by coupling the US Environmental Protection Agency (EPA) atmospheric model CMAQ, and an electricity market model. We include weather, chemistry, time and locational aspects of NOx and SOx emissions to reduce specific pollutants which cap-and-trade programs do not implement. We introduce a linear emission constraint within our electricity model which implicitly involves the atmospheric effects. We present an example to successfully reduce a sulfate particulate matter (ASO4) concentration at a specific location and at a specific time period.

Abstract Climatic design conditions are widely used by the building community as environmental parameters informing the size and energy requirements for heating, ventilation and air conditioning systems, along with other building design characteristics. Climatic design conditions are calculated by the American Society of Heating, Refrigerating and Air-conditioning Engineers using historical climate data. Our work advances methods for projecting future climate design conditions based on data from global climate models. These models do not typically archive the hourly data required for climate design condition calculations, and they often exhibit large biases in extreme conditions, daily minimum temperatures and daily maximum temperatures needed for climatic design conditions. We present a method for rescaling historical hourly data under future climatic states to estimate the impact of climate change on future building climatic design conditions. This rescaling method is then used to calculate future climatic design conditions in Madison, Wisconsin, throughout the 21st century for two future greenhouse gas emissions scenarios. The results are consistent with a warming climate and show increases in heating, cooling, humidification and dehumidification design conditions, suggesting less extreme cold conditions and more extreme hot and humid conditions in Madison. The design conditions used for estimating energy demand, degree days, show that under a business-as-usual scenario, by the mid-century, building heating and cooling in Madison (climate zone 5A) will be similar to the current heating demand in Chicago, IL (climate zone 5A) and cooling demand in Baltimore, MD (climate zone 4A); by the late-century, building heating and cooling in Madison will resemble the current heating demand in St Louis, MO (climate zone 4A) and cooling demand in Augusta, GA (climate zone 3A). Given the rapid pace of climate change in the 21st century, our work suggests that historical design conditions may become obsolete during even the initial stages of a building’s expected life span. Changes in climatic design conditions in Madison highlight the importance of considering future climatic changes in building design to ensure that buildings built today meet the performance needs of the future.

In considering the environmental health impacts of air pollution, it is important to consider the degree to which local, regional, and global emission sources each play a role. Ozone, fine particulate matter, and mercury are three main health-relevant air pollutants with significant global transport. Still, local and regional sources typically dominate health impacts. Ground-level ozone is a powerful oxidant, with high concentrations associated with respiratory disease, heart disease, and premature mortality. Fine particular matter is small enough to penetrate deep into the lungs, which subsequently poses a greater health risk than coarser particles, and is light enough to stay in the atmosphere for about a week and transport far from emission sources. Human exposure to toxic methylmercury occurs primarily through consumption of contaminated fish and aquatic species, with mercury entering aquatic systems via the deposition from the atmosphere. Of particular health concern is prenatal exposure, which can lead to developmental delays in speech, motor skills, and mental capacity. Effective control of these environmental health risks requires understanding how different emission source contribute to population exposure.

ADVERTISEMENT RETURN TO ISSUEPREVCorrespondence/Rebut...Correspondence/RebuttalNEXTResponse to Comment on “Intercontinental Transport of Air Pollution: Will Emerging Science Lead to a New Hemispheric Treaty?”Tracey Holloway, Arlene Fiore, and Meredith Galanter HastingsView Author Information Gaylord Nelson Institute for Environmental Studies University of WisconsinMadison 1710 University Avenue, Room 201A Madison, Wisconsin 53726 NOAA Geophysical Fluid Dynamics Laboratory Forrestal Campus, U.S. Route 1 P.O. Box 308 Princeton University Princeton, New Jersey 08542 Department of Geosciences Princeton University B-78 Guyot Hall Princeton, New Jersey 08544Cite this: Environ. Sci. Technol. 2004, 38, 6, 1914Publication Date (Web):February 14, 2004Publication History Published online14 February 2004Published inissue 1 March 2004https://doi.org/10.1021/es0498944Copyright © 2004 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views187Altmetric-Citations2LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (19 KB) Get e-AlertsSUBJECTS:Air pollution,Atmospheric chemistry,Climate,Environmental transport,Quality management Get e-Alerts

Abstract. Mechanisms controlling surface ozone (O3) over East Asia are examined using the regional Community Multiscale Air Quality (CMAQ) model at two horizontal scales: 81 km and 27 km. Through sensitivity studies and comparison with recently available satellite data and surface measurements in China and Japan, we find that the O3 budget over East Asia shows complex interactions among photochemical production, regional transport, meteorological conditions, burning of agricultural residues, and global inflows. For example, wintertime surface O3 over northern domain is sensitive to boundary conditions derived from the MOZART (Model for Ozone and Related Tracers) global model, whereas summertime O3 budget is controlled by the competitive processes between photochemical production and monsoonal intrusion of low-O3 marine air masses from tropical Pacific. We find that simulated surface O3 for 2001 does not exhibit the same sharp drop in July and August concentrations that is observed at two mountaintop sites (Tai and Hua) for 2004 and Beijing for 1995–2005. CMAQ sensitivity tests with two widely used photochemical schemes demonstrate that over the industrial areas in East Asia north of 30° N, SAPRC99 produces higher values of mean summertime O3 than CBIV, amounting to a difference of 10 ppb. In addition, analysis of NCEP winds and geopotential heights suggests that southwesterly monsoonal intrusion in central east China is weakened in August 2001 as compared with the climatologically mean for 1980–2005. Further examination of the O3 diurnal cycle at nine Japanese sites shows that boundary layer evolution has an important effect on the vertical mixing of ground-level O3, and error in near surface meteorology might contribute to overprediction of nighttime O3 in urban and rural areas. In conclusion, the uncertainties in simulating cloud activities and convection mixing, Asian monsoon circulation, photochemical production, and nighttime cooling explain why CMAQ with 81 km horizontal scale overpredicts the observed surface O3 in July and August over central east China and central Japan by 5–15 ppb (CBIV) and 15–25 ppb (SAPRC99). The results suggest clear benefits in evaluating atmospheric chemistry over Asia with high resolution regional model.

In considering the environmental health impacts of air pollution, it is important to consider the degree to which local, regional, and global emission sources each play a role. Ozone, fine particulate matter, and mercury are three health-damaging air pollutants subject to intercontinental transport, though local and regional sources typically play a larger role in determining health impacts. Inhalation exposure to ground-level ozone, a powerful oxidant, and fine particulate matter is associated with respiratory disease, cardiovascular disease, and premature mortality. Human exposure to toxic methylmercury occurs primarily through consumption of contaminated fish and aquatic species, with mercury entering aquatic ecosystems via atmospheric deposition. Prenatal exposure to methylmercury is associated with neurodevelopmental delays in newborns. Effective control of these environmental health risks requires understanding how different emission sources and subsequent transport contribute to population exposure.

Power plants contribute substantially to fine particulate pollution which results in hazardous environmental effects. Air quality can be improved by relocation of real power output between power plants. However, this relocation should include spatial, temporal and chemical effects that contribute towards formation of particulate matter. This paper presents a novel air quality constrained optimal power flow which implicitly includes locational, temporal and chemical aspects. A quadratic response surface of an air quality model is included as an environmental constraint in a DC optimal power flow (OPF). Simulation results on an equivalent model of North Eastern Power Coordinating Council (NPCC) demonstrates the successful implementation of the model to reduce particulate matter at an arbitrary location and time period (NY state) on 14th July 2005.

Abstract We use a combination of petroleum–diesel models, datasets and tools along with biodiesel-specific corrections to create a roadway-level emissions inventory capable of evaluating spatial, temporal and scale aspects of fuel distribution options for the Midwestern US. Specifically, we compare the emissions of a year-round “low-blend” biodiesel implementation scenario, already under consideration in a variety of states, with a more strategic summer-only, interstate-only “high-blend” scenario. Our results indicate that spatial and seasonal distribution decisions do affect the overall emissions impacts of any biodiesel deployment, even those at low-blend levels. However, we also finds that changes in emissions due to biodiesel are considerably smaller than those anticipated from improvements to engine and control technologies.

Abstract. Quantitative analysis of three atmospheric mercury species – gaseous elemental mercury (Hg0), reactive gaseous mercury (RGHg) and particulate mercury (PHg) – has been limited to date by lack of ambient measurement data as well as by uncertainties in numerical models and emission inventories. This study employs the Community Multiscale Air Quality Model version 4.6 with mercury chemistry (CMAQ-Hg), to examine how local emissions, meteorology, atmospheric chemistry, and deposition affect mercury concentration and deposition the Great Lakes Region (GLR), and two sites in Wisconsin in particular: the rural Devil's Lake site and the urban Milwaukee site. Ambient mercury exhibits significant biases at both sites. Hg0 is too low in CMAQ-Hg, with the model showing a 6% low bias at the rural site and 36% low bias at the urban site. Reactive mercury (RHg = RGHg + PHg) is over-predicted by the model, with annual average biases &gt;250%. Performance metrics for RHg are much worse than for mercury wet deposition, ozone (O3), nitrogen dioxide (NO2), or sulfur dioxide (SO2). Sensitivity simulations to isolate background inflow from regional emissions suggests that oxidation of imported Hg0 dominates model estimates of RHg at the rural study site (91% of base case value), and contributes 55% to the RHg at the urban site (local emissions contribute 45%). Limited evidence on the lifetime of RHg transported to the rural site suggests that modeled dry deposition rates are too high, possibly compensating for the erroneously high RHg values.

Abstract. Understanding the exchange processes between the atmospheric boundary layer and the free troposphere is crucial for estimating hemispheric transport of air pollution. Most studies of hemispheric air pollution transport have taken a large-scale perspective: using global chemical transport models and focusing on synoptic-scale export events. These global models have fairly coarse spatial and temporal resolutions, and thus have a limited ability to represent boundary layer processes and urban photochemistry. In support of United Nations Task Force on Hemispheric Transport of Air Pollution (TF HTAP; http://www.htap.org), this study employs two high-resolution atmospheric chemistry models (WRF-Chem and CMAQ; 36×36 km) coupled with a global model (MOZART; 1.9×1.9°) to examine the importance of fine-scale transport and chemistry processes in controlling pollution export and import over the Asian continent. We find that the vertical lifting and outflow of Asian pollution is enhanced in the regional models throughout the study period (March 2001) as contrast to the global model. Episodic outflow of CO, PAN, and O3 to the upper troposphere during cold frontal passages is twice as great in the WRF-Chem model as compared with the MOZART model. The TRACE-P aircraft measurements indicate that the pollution plumes in MOZART are too weak and too low in the altitude, which we attribute to the global model's inability to capture rapid deep convection that develops along the leading edge of the convergence band during frontal events. In contrast to pollution export from Asia, we find little difference in the regional vs. global model transport of European (EU) pollution into surface air over East Asia (EA). Instead, the local surface characteristics – sensitivity – strongly influence surface O3 responses. For instance, the O3 response to 20% decreases in EU emissions imported into our regional model domain is strongest (0.4–0.6 ppbv) over mountainous regions and weakest (0.1–0.3 ppbv) in megacities. The spatial averaged O3 response over EA estimated by our regional models is ~0.1 ppbv lower than global model estimates. Our results suggest that global models tend to underestimate the total budget of Asian pollutants exported to the free troposphere given their limited ability to properly capture vertical convection and lifting. Due to the compensating effects on surface O3 responses over downwind continents, future high-resolution hemispheric model analysis should provide additional insights into how the export and import processes interact, and will help to narrow the uncertainty of intercontinental source-receptor relationships.

Early air pollution control efforts were prompted by urban episodes due to local emissions, such as the 1952 London smog associated with sulphur from burning coal (see Chapter 1). Although local areas typically experience the highest levels of health- and ecosystemdamaging air pollution, many species remain in the atmosphere for days, months, or even years. The longer a pollutant stays in the atmosphere, the farther from its original source it travels. For example, it takes about five days for a pollutant to cross the Pacific Ocean, but over a year for pollution to cross from the Northern Hemisphere mid-latitudes to the Southern Hemisphere. An example of such a pollutant plume as seen by satellite is shown in Figure 3.1.