PM-Attributable Mortality Burden Variability in the Continental U.S.

Epidemiologic studies have consistently observed associations between fine particulate matter (PM) 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.

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.

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.

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.

Mimicking atmospheric photochemical modelling with a deep neural network.

Fast and accurate prediction of ambient ozone (O) formed from atmospheric photochemical processes is crucial for designing effective O pollution control strategies in the context of climate change. The chemical transport model (CTM) is the fundamental tool for O 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.

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

Reducing PM 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.

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.

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

Monthly, high-resolution (∼2 km) ammonia (NH) 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 km. Five seasonality clusters were identified with k-means++ clustering.

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.

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 PM and O pollution. Traditional data assimilation or fusion methods are mainly focused on individual pollutants, and thus cannot support simultaneous assimilation for both PM and O. To fill the gap, this study proposed a novel multipollutant assimilation method by using an emission-concentration response model (noted as RSM-assimilation).

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 HNO, NH, 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.

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.

Examining PM concentrations and exposure using multiple models.

Epidemiologic studies have found associations between fine particulate matter (PM) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM concentration models (i.e.

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 O 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 (coefficient of determination) against observations of 0.

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).

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.

Assessing PM 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.

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

Fine particulate matter (PM) 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 PM exposures. This review article surveys publicly available exposure datasets for surface PM mass concentrations over the contiguous U.

BST2/Tetherin Overexpression Modulates Morbillivirus Glycoprotein Production to Inhibit Cell-Cell Fusion.

The measles virus (MeV), a member of the genus , is an established pathogen of humans. A key feature of morbilliviruses is their ability to spread by virus-cell and cell-cell fusion. The latter process, which leads to syncytia formation in vitro and in vivo, is driven by the viral fusion (F) and haemagglutinin (H) glycoproteins.

Development and application of observable response indicators for design of an effective ozone and fine particle pollution control strategy in China.

Designing effective control policies requires efficient quantification of the nonlinear response of air pollution to emissions. However, neither the current observable indicators nor the current indicators based on response-surface modeling (RSM) can fulfill this requirement. Therefore, this study developed new observable RSM-based indicators and applied them to ambient fine particle (PM) and ozone (O) pollution control in China.

Health benefit assessment of PM reduction in Pearl River Delta region of China using a model-monitor data fusion approach.

The Pearl River Delta (PRD), one of the most polluted and populous regions of China, experienced a 28% reduction in fine particulate matter (PM) concentration between 2013 (47 μg/m) and 2015 (34 μg/m) under a stringent national policy known as the Air Pollution Prevention and Control Action Plan (hereafter Action Plan). In this study, the health and economic benefits associated with PM reductions in PRD during 2013-2015 were estimated using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) software. To create reliable gridded PM surfaces for BenMAP-CE calculations, a data fusion tool which incorporates the accuracy of monitoring data and the spatial coverage of predictions from the Community Multiscale Air Quality (CMAQ) model has been developed.

Unexpected mode of engagement between enterovirus 71 and its receptor SCARB2.

Enterovirus 71 (EV71) is a common cause of hand, foot and mouth disease-a disease endemic especially in the Asia-Pacific region. Scavenger receptor class B member 2 (SCARB2) is the major receptor of EV71, as well as several other enteroviruses responsible for hand, foot and mouth disease, and plays a key role in cell entry. The isolated structures of EV71 and SCARB2 are known, but how they interact to initiate infection is not.

Modeling NHNO Over the San Joaquin Valley During the 2013 DISCOVER-AQ Campaign.

The San Joaquin Valley (SJV) of California experiences high concentrations of particulate matter NHNO during episodes of meteorological stagnation in winter. A rich data set of observations related to NHNO formation was acquired during multiple periods of elevated NHNO during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) field campaign in SJV in January and February 2013. Here NHNO is simulated during the SJV DISCOVER-AQ study period with the Community Multiscale Air Quality (CMAQ) model, diagnostic model evaluation is performed using the DISCOVER-AQ data set, and integrated reaction rate analysis is used to quantify HNO production rates.

Structure-Guided Identification of a Nonhuman Morbillivirus with Zoonotic Potential.

Morbilliviruses infect a broad range of mammalian hosts, including ruminants, carnivores, and humans. The recent eradication of rinderpest virus (RPV) and the active campaigns for eradication of the human-specific measles virus (MeV) have raised significant concerns that the remaining morbilliviruses may emerge in so-called vacated ecological niches. Seeking to assess the zoonotic potential of nonhuman morbilliviruses within human populations, we found that peste des petits ruminants virus (PPRV)-the small-ruminant morbillivirus-is restricted at the point of entry into human cells due to deficient interactions with human SLAMF1-the immune cell receptor for morbilliviruses.