Viral PIC-pocketing: RSV sequestration of translational preinitiation complexes into bi-phasic biomolecular condensates.

Orthopneumoviruses characteristically form membrane-less cytoplasmic inclusion bodies (IBs) wherein RNA replication and transcription occur. Here, we report a strategy whereby the orthopneumoviruses sequester various components of the translational preinitiation complex machinery into viral inclusion bodies to facilitate translation of their own mRNAs-PIC-pocketing. Electron microscopy of respiratory syncytial virus (RSV)-infected cells revealed bi-phasic organization of IBs, specifically, spherical "droplets" nested within the larger inclusion. Using correlative light and electron microscopy, combined with fluorescence in situ hybridization, we showed that the observed bi-phasic morphology represents functional compartmentalization of the inclusion body and that these domains are synonymous with the previously reported inclusion body-associated granules (IBAGs). Detailed analysis demonstrated that IBAGs concentrate nascent viral mRNA, the viral M2-1 protein as well as components of eukaryotic translation initiation factors (eIF), eIF4F and eIF3, and 40S complexes involved in translation initiation. Interestingly, although ribopuromycylation-based imaging indicates that the majority of viral mRNA translation occurs in the cytoplasm, there was some evidence for intra-IBAG translation, consistent with the likely presence of ribosomes in a subset of IBAGs imaged by electron microscopy. Mass spectrometry analysis of sub-cellular fractions from RSV-infected cells identified significant modification of the cellular translation machinery; however, interestingly, ribopuromycylation assays showed no changes to global levels of translation. The mechanistic basis for this pathway was subsequently determined to involve the viral M2-1 protein interacting with eIF4G, likely to facilitate its transport between the cytoplasm and the separate phases of the viral inclusion body. In summary, our data show that these viral organelles function to spatially regulate early steps in viral translation within a highly selective bi-phasic biomolecular condensate. IMPORTANCE: Respiratory syncytial viruses (RSVs) of cows and humans are a significant cause of morbidity and mortality in their respective populations. These RNA viruses replicate in the infected cells by compartmentalizing the cell's cytoplasm into distinct viral microdomains called inclusion bodies (IBs). In this paper, we show that these IBs are further compartmentalized into smaller structures that have significantly different density, as observed by electron microscopy. Within smaller intra-IB structures, we observed ribosomal components and evidence for active translation. These findings highlight that RSV may additionally compartmentalize translation to favor its own replication in the cell. These data contribute to our understanding of how RNA viruses hijack the cell to favor replication of their own genomes and may provide new targets for antiviral therapeutics in vivo.

PM2.5-Attributable Mortality Burden Variability in the Continental U.S.

Epidemiologic studies have consistently observed associations between fine particulate matter (PM2.5) exposure and premature mortality. These studies use air quality concentration information from a combination of sources to estimate pollutant exposures and then assess how mortality varies as a result of differing exposures. Health impact assessments then typically use a single log-linear hazard ratio (HR) per health outcome to estimate counts of avoided human health effects resulting from air quality improvements. This paper estimates the total PM2.5-attributable premature mortality burden using a variety of methods for estimating exposures and quantifying PM2.5-attributable deaths in 2011 and 2028. We use: 1) several exposure models that apply a wide range of methods, and 2) a variety of HRs from the epidemiologic literature that relate long-term PM2.5 exposures to mortality among the U.S. population. We then further evaluate the variability of aggregated national premature mortality estimates to stratification by race and/or ethnicity or exposure level (e.g., below the current annual PM2.5 National Ambient Air Quality Standards). We find that unstratified annual adult mortality burden incidence estimates vary more (e.g., ~3-fold) by HR than by exposure model (e.g., <10%). In addition, future mortality burden estimates stratified by race/ethnicity are larger than the unstratified estimates of the entire population, and studies that stratify PM2.5-attributable mortality HRs by an exposure concentration threshold led to substantially higher estimates. These results are intended to provide transparency regarding the sensitivity of mortality estimates to upstream input choices.

Hydrogen chloride (HCl) at ground sites during CalNex 2010 and insight into its thermodynamic properties.

Gas phase hydrogen chloride (HCl) was measured at Pasadena and San Joaquin Valley (SJV) ground sites in California during May and June 2010 as part of the CalNex study. Observed mixing ratios were on average 0.83 ppbv at Pasadena, ranging from below detection limit (0.055 ppbv) to 5.95 ppbv, and were on average 0.084 ppbv at SJV with a maximum value of 0.776 ppbv. At both sites, HCl levels were highest during midday and shared similar diurnal variations with HNO3. Coupled phase partitioning behavior was found between HCl/Cl- and HNO3/NO3 - using thermodynamic modelling and observations. Regional modeling of Cl- and HCl using CMAQ captures some of the observed relationships but underestimates measurements by a factor of 5 or more. Chloride in the 2.5-10 µm size range in Pasadena was sometimes higher than sea salt abundances, based on co-measured Na+, implying that sources other than sea salt are important. The acid-displacement of HCl/Cl- by HNO3/NO3 - (phase partitioning of semi-volatile acids) observed at the SJV site can only be explained by aqueous phase reaction despite low RH conditions and suggests the temperature dependence of HCl phase partitioning behavior was strongly impacted by the activity coefficient changes under relevant aerosol conditions (e.g., high ionic strength). Despite the influence from activity coefficients, the gas-particle system was found to be well constrained by other stronger buffers and charge balance so that HCl and Cl- concentrations were reproduced well by thermodynamic models.

Membrane Interactions and Uncoating of Aichi Virus, a Picornavirus That Lacks a VP4.

Kobuviruses are an unusual and poorly characterized genus within the picornavirus family and can cause gastrointestinal enteric disease in humans, livestock, and pets. The human kobuvirus Aichi virus (AiV) can cause severe gastroenteritis and deaths in children below the age of 5 years; however, this is a very rare occurrence. During the assembly of most picornaviruses (e.g., poliovirus, rhinovirus, and foot-and-mouth disease virus), the capsid precursor protein VP0 is cleaved into VP4 and VP2. However, kobuviruses retain an uncleaved VP0. From studies with other picornaviruses, it is known that VP4 performs the essential function of pore formation in membranes, which facilitates transfer of the viral genome across the endosomal membrane and into the cytoplasm for replication. Here, we employ genome exposure and membrane interaction assays to demonstrate that pH plays a critical role in AiV uncoating and membrane interactions. We demonstrate that incubation at low pH alters the exposure of hydrophobic residues within the capsid, enhances genome exposure, and enhances permeabilization of model membranes. Furthermore, using peptides we demonstrate that the N terminus of VP0 mediates membrane pore formation in model membranes, indicating that this plays an analogous function to VP4. IMPORTANCE To initiate infection, viruses must enter a host cell and deliver their genome into the appropriate location. The picornavirus family of small nonenveloped RNA viruses includes significant human and animal pathogens and is also a model to understand the process of cell entry. Most picornavirus capsids contain the internal protein VP4, generated from cleavage of a VP0 precursor. During entry, VP4 is released from the capsid. In enteroviruses this forms a membrane pore, which facilitates genome release into the cytoplasm. Due to high levels of sequence similarity, it is expected to play the same role for other picornaviruses. Some picornaviruses, such as Aichi virus, retain an intact VP0, and it is unknown how these viruses rearrange their capsids and induce membrane permeability in the absence of VP4. Here, we have used Aichi virus as a model VP0 virus to test for conservation of function between VP0 and VP4. This could enhance understanding of pore function and lead to development of novel therapeutic agents that block entry.

Mimicking atmospheric photochemical modelling with a deep neural network.

Fast and accurate prediction of ambient ozone (O3) formed from atmospheric photochemical processes is crucial for designing effective O3 pollution control strategies in the context of climate change. The chemical transport model (CTM) is the fundamental tool for O3 prediction and policy design, however, existing CTM-based approaches are computationally expensive, and resource burdens limit their usage and effectiveness in air quality management. Here we proposed a novel method (noted as DeepCTM) that using deep learning to mimic CTM simulations to improve the computational efficiency of photochemical modeling. The well-trained DeepCTM successfully reproduces CTM-simulated O3 concentration using input features of precursor emissions, meteorological factors, and initial conditions. The advantage of the DeepCTM is its high efficiency in identifying the dominant contributors to O3 formation and quantifying the O3 response to variations in emissions and meteorology. The emission-meteorology-concentration linkages implied by the DeepCTM are consistent with known mechanisms of atmospheric chemistry, indicating that the DeepCTM is also scientifically reasonable. The DeepCTM application in China suggests that O3 concentrations are strongly influenced by the initialized O3 concentration, as well as emission and meteorological factors during daytime when O3 is formed photochemically. The variation of meteorological factors such as short-wave radiation can also significantly modulate the O3 chemistry. The DeepCTM developed in this study exhibits great potential for efficiently representing the complex atmospheric system and can provide policymakers with urgently needed information for designing effective control strategies to mitigate O3 pollution.

Predicting the Nonlinear Response of PM2.5 and Ozone to Precursor Emission Changes with a Response Surface Model.

Reducing PM2.5 and ozone concentrations is important to protect human health and the environment. Chemical transport models, such as the Community Multiscale Air Quality (CMAQ) model, are valuable tools for exploring policy options for improving air quality but are computationally expensive. Here, we statistically fit an efficient polynomial function in a response surface model (pf-RSM) to CMAQ simulations over the eastern U.S. for January and July 2016. The pf-RSM predictions were evaluated using out-of-sample CMAQ simulations and used to examine the nonlinear response of air quality to emission changes. Predictions of the pf-RSM are in good agreement with the out-of-sample CMAQ simulations, with some exceptions for cases with anthropogenic emission reductions approaching 100%. NOX emission reductions were more effective for reducing PM2.5 and ozone concentrations than SO2, NH3, or traditional VOC emission reductions. NH3 emission reductions effectively reduced nitrate concentrations in January but increased secondary organic aerosol (SOA) concentrations in July. More work is needed on SOA formation under conditions of low NH3 emissions to verify the responses of SOA to NH3 emission changes predicted here. Overall, the pf-RSM performs well in the eastern U.S., but next-generation RSMs based on deep learning may be needed to meet the computational requirements of typical regulatory applications.

Coupled Air Quality and Boundary-Layer Meteorology in Western U.S. Basins during Winter: Design and Rationale for a Comprehensive Study.

Wintertime episodes of high aerosol concentrations occur frequently in urban and agricultural basins and valleys worldwide. These episodes often arise following development of persistent cold-air pools (PCAPs) that limit mixing and modify chemistry. While field campaigns targeting either basin meteorology or wintertime pollution chemistry have been conducted, coupling between interconnected chemical and meteorological processes remains an insufficiently studied research area. Gaps in understanding the coupled chemical-meteorological interactions that drive high pollution events make identification of the most effective air-basin specific emission control strategies challenging. To address this, a September 2019 workshop occurred with the goal of planning a future research campaign to investigate air quality in Western U.S. basins. Approximately 120 people participated, representing 50 institutions and 5 countries. Workshop participants outlined the rationale and design for a comprehensive wintertime study that would couple atmospheric chemistry and boundary-layer and complex-terrain meteorology within western U.S. basins. Participants concluded the study should focus on two regions with contrasting aerosol chemistry: three populated valleys within Utah (Salt Lake, Utah, and Cache Valleys) and the San Joaquin Valley in California. This paper describes the scientific rationale for a campaign that will acquire chemical and meteorological datasets using airborne platforms with extensive range, coupled to surface-based measurements focusing on sampling within the near-surface boundary layer, and transport and mixing processes within this layer, with high vertical resolution at a number of representative sites. No prior wintertime basin-focused campaign has provided the breadth of observations necessary to characterize the meteorological-chemical linkages outlined here, nor to validate complex processes within coupled atmosphere-chemistry models.

Monthly Patterns of Ammonia Over the Contiguous United States at 2-km Resolution.

Monthly, high-resolution (∼2 km) ammonia (NH3) column maps from the Infrared Atmospheric Sounding Interferometer (IASI) were developed across the contiguous United States and adjacent areas. Ammonia hotspots (95th percentile of the column distribution) were highly localized with a characteristic length scale of 12 km and median area of 152 km2. Five seasonality clusters were identified with k-means++ clustering. The Midwest and eastern United States had a broad, spring maximum of NH3 (67% of hotspots in this cluster). The western United States, in contrast, showed a narrower midsummer peak (32% of hotspots). IASI spatiotemporal clustering was consistent with those from the Ammonia Monitoring Network. CMAQ and GFDL-AM3 modeled NH3 columns have some success replicating the seasonal patterns but did not capture the regional differences. The high spatial-resolution monthly NH3 maps serve as a constraint for model simulations and as a guide for the placement of future, ground-based network sites.

Furthering a partnership: Air quality modeling and improving public health.

Air pollution is one of the top five risk factors for population health globally. In recent years, advances in air pollution data and modeling have occurred simultaneously with advances in data and methods available for health studies. To realize the potential of such advances, the air quality modeling and public health communities should continue to strengthen their engagements and build effective interdisciplinary teams. These partnerships recognize the tight coupling between air quality and health data and methods and the value of expertise from multiple fields to ensure that this information is applied appropriately with a deep understanding of its capabilities and limitations. Building effective multidisciplinary teams takes a sustained commitment to engage with partners with different expertise to establish working partnerships and collaborations to better address public exposures to air pollution. Effective partnerships enable better targeting of research resources to answer important questions and provide essential information to protect public health.Implications: Air quality models are an effective tool that can be used to estimate air pollution exposure in epidemiologic studies and risk assessments. Working together in collaborative multidisciplinary teams will lead to greater advancements in understanding of air pollution impacts and in useful information informing actions to improve public health.

A comparative study of two-way and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US: performance evaluation and impacts of chemistry-meteorology feedbacks on air quality.

The two-way coupled Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model has been developed to more realistically represent the atmosphere by accounting for complex chemistry-meteorology feedbacks. In this study, we present a comparative analysis of two-way (with consideration of both aerosol direct and indirect effects) and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US. Long-term (5 years from 2008 to 2012) simulations using WRF-CMAQ with both offline and two-way coupling modes are carried out with anthropogenic emissions based on multiple years of the U.S. National Emission Inventory and chemical initial and boundary conditions derived from an advanced Earth system model (i.e., a modified version of the Community Earth System Model/Community Atmospheric Model). The comprehensive model evaluations show that both two-way WRF-CMAQ and WRF-only simulations perform well for major meteorological variables such as temperature at 2 m, relative humidity at 2 m, wind speed at 10 m, precipitation (except for against the National Climatic Data Center data), and shortwave and longwave radiation. Both two-way and offline CMAQ also show good performance for ozone (O3) and fine particulate matter (PM2.5). Due to the consideration of aerosol direct and indirect effects, two-way WRF-CMAQ shows improved performance over offline coupled WRF and CMAQ in terms of spatiotemporal distributions and statistics, especially for radiation, cloud forcing, O3, sulfate, nitrate, ammonium, elemental carbon, tropospheric O3 residual, and column nitrogen dioxide (NO2). For example, the mean biases have been reduced by more than 10 W m-2 for shortwave radiation and cloud radiative forcing and by more than 2 ppb for max 8 h O3. However, relatively large biases still exist for cloud predictions, some PM2.5 species, and PM10 that warrant follow-up studies to better understand those issues. The impacts of chemistry-meteorological feedbacks are found to play important roles in affecting regional air quality in the US by reducing domain-average concentrations of carbon monoxide (CO), O3, nitrogen oxide (NO x ), volatile organic compounds (VOCs), and PM2.5 by 3.1% (up to 27.8%), 4.2% (up to 16.2%), 6.6% (up to 50.9%), 5.8% (up to 46.6%), and 8.6% (up to 49.1%), respectively, mainly due to reduced radiation, temperature, and wind speed. The overall performance of the two-way coupled WRF-CMAQ model achieved in this work is generally good or satisfactory and the improved performance for two-way coupled WRF-CMAQ should be considered along with other factors in developing future model applications to inform policy making.

Examining PM2.5 concentrations and exposure using multiple models.

Epidemiologic studies have found associations between fine particulate matter (PM2.5) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM2.5 concentrations in 2011, ii) potential changes in PM2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 µg m-3) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM2.5 concentrations were estimated to decrease by about 1 µg m-3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 µg m-3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM2.5 concentrations less than 10 µg m-3 in 2011 and PM2.5 improvements of about 2 µg m-3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average.

The SARS-CoV-2 Spike protein has a broad tropism for mammalian ACE2 proteins.

SARS Coronavirus 2 (SARS-CoV-2) emerged in late 2019, leading to the Coronavirus Disease 2019 (COVID-19) pandemic that continues to cause significant global mortality in human populations. Given its sequence similarity to SARS-CoV, as well as related coronaviruses circulating in bats, SARS-CoV-2 is thought to have originated in Chiroptera species in China. However, whether the virus spread directly to humans or through an intermediate host is currently unclear, as is the potential for this virus to infect companion animals, livestock, and wildlife that could act as viral reservoirs. Using a combination of surrogate entry assays and live virus, we demonstrate that, in addition to human angiotensin-converting enzyme 2 (ACE2), the Spike glycoprotein of SARS-CoV-2 has a broad host tropism for mammalian ACE2 receptors, despite divergence in the amino acids at the Spike receptor binding site on these proteins. Of the 22 different hosts we investigated, ACE2 proteins from dog, cat, and cattle were the most permissive to SARS-CoV-2, while bat and bird ACE2 proteins were the least efficiently used receptors. The absence of a significant tropism for any of the 3 genetically distinct bat ACE2 proteins we examined indicates that SARS-CoV-2 receptor usage likely shifted during zoonotic transmission from bats into people, possibly in an intermediate reservoir. Comparison of SARS-CoV-2 receptor usage to the related coronaviruses SARS-CoV and RaTG13 identified distinct tropisms, with the 2 human viruses being more closely aligned. Finally, using bioinformatics, structural data, and targeted mutagenesis, we identified amino acid residues within the Spike-ACE2 interface, which may have played a pivotal role in the emergence of SARS-CoV-2 in humans. The apparently broad tropism of SARS-CoV-2 at the point of viral entry confirms the potential risk of infection to a wide range of companion animals, livestock, and wildlife.

An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States.

In this paper, we integrated multiple types of predictor variables and three types of machine learners (neural network, random forest, and gradient boosting) into a geographically weighted ensemble model to estimate the daily maximum 8 h O3 with high resolution over both space (at 1 km × 1 km grid cells covering the contiguous United States) and time (daily estimates between 2000 and 2016). We further quantify monthly model uncertainty for our 1 km × 1 km gridded domain. The results demonstrate high overall model performance with an average cross-validated R2 (coefficient of determination) against observations of 0.90 and 0.86 for annual averages. Overall, the model performance of the three machine learning algorithms was quite similar. The overall model performance from the ensemble model outperformed those from any single algorithm. The East North Central region of the United States had the highest R2, 0.93, and performance was weakest for the western mountainous regions (R2 of 0.86) and New England (R2 of 0.87). For the cross validation by season, our model had the best performance during summer with an R2 of 0.88. This study can be useful for the environmental health community to more accurately estimate the health impacts of O3 over space and time, especially in health studies at an intra-urban scale.

Deep Learning for Prediction of the Air Quality Response to Emission Changes.

Efficient prediction of the air quality response to emission changes is a prerequisite for an integrated assessment system in developing effective control policies. Yet, representing the nonlinear response of air quality to emission controls with accuracy remains a major barrier in air quality-related decision making. Here, we demonstrate a novel method that combines deep learning approaches with chemical indicators of pollutant formation to quickly estimate the coefficients of air quality response functions using ambient concentrations of 18 chemical indicators simulated with a comprehensive atmospheric chemical transport model (CTM). By requiring only two CTM simulations for model application, the new method significantly enhances the computational efficiency compared to existing methods that achieve lower accuracy despite requiring 20+ CTM simulations (the benchmark statistical model). Our results demonstrate the utility of deep learning approaches for capturing the nonlinearity of atmospheric chemistry and physics and the prospects of the new method to support effective policymaking in other environment systems.

Large-scale optimization of multi-pollutant control strategies in the Pearl River Delta region of China using a genetic algorithm in machine learning.

A scientifically sound integrated assessment modeling (IAM) system capable of providing optimized cost-benefit analysis is essential in effective air quality management and control strategy development. Yet scenario optimization for large-scale applications is limited by the computational expense of optimization over many control factors. In this study, a multi-pollutant cost-benefit optimization system based on a genetic algorithm (GA) in machine learning has been developed to provide cost-effective air quality control strategies for large-scale applications (e.g., solution spaces of ~1035). The method was demonstrated by providing optimal cost-benefit control pathways to attain air quality goals for fine particulate matter (PM2.5) and ozone (O3) over the Pearl River Delta (PRD) region of China. The GA was found to be >99% more efficient than the commonly used grid searching method while providing the same combination of optimized multi-pollutant control strategies. The GA method can therefore address air quality management problems that are intractable using the grid searching method. The annual attainment goals for PM2.5 (< 35 µg m-3) and O3 (< 80 ppb) can be achieved simultaneously over the PRD region and surrounding areas by reducing NOx (22%), volatile organic compounds (VOCs, 12%), and primary PM (30%) emissions. However, to attain stricter PM2.5 goals, SO2 reductions (> 9%) are needed as well. The estimated benefit-to-cost ratio of the optimal control strategy reached 17.7 in our application, demonstrating the value of multi-pollutant control for cost-effective air quality management in the PRD region.

The Acidity of Atmospheric Particles and Clouds.

Acidity, defined as pH, is a central component of aqueous chemistry. In the atmosphere, the acidity of condensed phases (aerosol particles, cloud water, and fog droplets) governs the phase partitioning of semi-volatile gases such as HNO3, NH3, HCl, and organic acids and bases as well as chemical reaction rates. It has implications for the atmospheric lifetime of pollutants, deposition, and human health. Despite its fundamental role in atmospheric processes, only recently has this field seen a growth in the number of studies on particle acidity. Even with this growth, many fine particle pH estimates must be based on thermodynamic model calculations since no operational techniques exist for direct measurements. Current information indicates acidic fine particles are ubiquitous, but observationally-constrained pH estimates are limited in spatial and temporal coverage. Clouds and fogs are also generally acidic, but to a lesser degree than particles, and have a range of pH that is quite sensitive to anthropogenic emissions of sulfur and nitrogen oxides, as well as ambient ammonia. Historical measurements indicate that cloud and fog droplet pH has changed in recent decades in response to controls on anthropogenic emissions, while the limited trend data for aerosol particles indicates acidity may be relatively constant due to the semi-volatile nature of the key acids and bases and buffering in particles. This paper reviews and synthesizes the current state of knowledge on the acidity of atmospheric condensed phases, specifically particles and cloud droplets. It includes recommendations for estimating acidity and pH, standard nomenclature, a synthesis of current pH estimates based on observations, and new model calculations on the local and global scale.

Data assimilation of ambient concentrations of multiple air pollutants using an emission-concentration response modeling framework.

Data assimilation for multiple air pollutant concentrations has become an important need for modeling air quality attainment, human exposure and related health impacts, especially in China that experiences both PM2.5 and O3 pollution. Traditional data assimilation or fusion methods are mainly focused on individual pollutants, and thus cannot support simultaneous assimilation for both PM2.5 and O3. To fill the gap, this study proposed a novel multipollutant assimilation method by using an emission-concentration response model (noted as RSM-assimilation). The new method was successfully applied to assimilate precursors for PM2.5 and O3 in the 28 cities of the North China Plain (NCP). By adjusting emissions of five pollutants (i.e., NOx, SO2, NH3, VOC and primary PM2.5) in the 28 cities through RSM-assimilation, the RMSEs (root mean square errors) of O3 and PM2.5 were reduced by about 35% and 58% from the original simulations. The RSM-assimilation results small sensitivity to the number of observation sites due to the use of prior knowledge of the spatial distribution of emissions; however, the ability to assimilate concentrations at the edge of the control region is limited. The emission ratios of five pollutants were simultaneously adjusted during the RSM-assimilation, indicating that the emission inventory may underestimate NO2 in January, April and October, and SO2 in April, but overestimate NH3 in April and VOC in January and October. Primary PM2.5 emissions are also significantly underestimated, particularly in April (dust season in NCP). Future work should focus on expanding the control area and including NH3 observations to improve the RSM-assimilation performance and emission inventories.

Assessing PM2.5 Model Performance for the Conterminous U.S. with Comparison to Model Performance Statistics from 2007-2015.

Previous studies have proposed that model performance statistics from earlier photochemical grid model (PGM) applications can be used to benchmark performance in new PGM applications. A challenge in implementing this approach is that limited information is available on consistently calculated model performance statistics that vary spatially and temporally over the U.S. Here, a consistent set of model performance statistics are calculated by year, season, region, and monitoring network for PM2.5 and its major components using simulations from versions 4.7.1-5.2.1 of the Community Multiscale Air Quality (CMAQ) model for years 2007-2015. The multi-year set of statistics is then used to provide quantitative context for model performance results from the 2015 simulation. Model performance for PM2.5 organic carbon in the 2015 simulation ranked high (i.e., favorable performance) in the multi-year dataset, due to factors including recent improvements in biogenic secondary organic aerosol and atmospheric mixing parameterizations in CMAQ. Model performance statistics for the Northwest region in 2015 ranked low (i.e., unfavorable performance) for many species in comparison to the 2007-2015 dataset. This finding motivated additional investigation that suggests a need for improved speciation of wildfire PM2.5emissions and modeling of boundary layer dynamics near water bodies. Several limitations were identified in the approach of benchmarking new model performance results with previous results. Since performance statistics vary widely by region and season, a simple set of national performance benchmarks (e.g., one or two targets per species and statistic) as proposed previously are inadequate to assess model performance throughout the U.S. Also, trends in model performance statistics for sulfate over the 2007 to 2015 period suggest that model performance for earlier years may not be a useful reference for assessing model performance for recent years in some cases. Comparisons of results from the 2015 base case with results from five sensitivity simulations demonstrated the importance of parameterizations of NH3 surface exchange, organic aerosol volatility and production, and emissions of crustal cations for predicting PM2.5 species concentrations.

Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models.

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.

A System for Developing and Projecting PM2.5 Spatial Fields to Correspond to Just Meeting National Ambient Air Quality Standards.

PM2.5 concentration fields that correspond to just meeting national ambient air quality standards (NAAQS) are useful for characterizing exposure in regulatory assessments. Computationally efficient methods that incorporate predictions from photochemical grid models (PGM) are needed to realistically project baseline concentration fields for these assessments. Thorough cross validation (CV) of hybrid spatial prediction models is also needed to better assess their predictive capability in sparsely monitored areas. In this study, a system for generating, evaluating, and projecting PM2.5 spatial fields to correspond with just meeting the PM2.5 NAAQS is developed and demonstrated. Results of ten-fold CV based on standard and spatial cluster withholding approaches indicate that performance of three spatial prediction models improves with decreasing distance to the nearest neighboring monitor, improved PGM performance, and increasing distance from sources of PM2.5 heterogeneity (e.g., complex terrain and fire). An air quality projection tool developed here is demonstrated to be effective for quickly projecting PM2.5 spatial fields to just meet NAAQS using realistic spatial response patterns based on air quality modeling. PM2.5 tends to be most responsive to primary PM2.5 emissions in urban areas, whereas response patterns are relatively smooth for NOx and SO2 emission changes. On average, PM2.5 is more responsive to changes in anthropogenic primary PM2.5 emissions than NOx and SO2 emissions in the contiguous U.S.