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The Community Multiscale Air Quality Model (CMAQ) is a local- to hemispheric-scale numerical air quality modeling system developed by the U.S. Environmental Protection Agency (USEPA) and supported by the Community Modeling and Analysis System (CMAS) center. CMAQ is used for regulatory purposes by the USEPA program offices and state and local air agencies and is also widely used by the broader global research community to simulate and understand complex air quality processes and for computational environmental fate and transport and climate and health impact studies. Leveraging state-of-the-science cloud computing resources for high-performance computing (HPC) applications, CMAQ is now available as a fully tested, publicly available technology stack (HPC cluster and software stack) for two major cloud service providers (CSPs). Specifically, CMAQ configurations and supporting materials have been developed for use on their HPC clusters, including extensive online documentation, tutorials and guidelines to scale and optimize air quality simulations using their services. These resources allow modelers to rapidly bring together CMAQ, cloud-hosted datasets, and visualization and evaluation tools on ephemeral clusters that can be deployed quickly and reliably worldwide. Described here are considerations in CMAQ version 5.3.3 cloud use and the supported resources for each CSP, presented through a benchmark application suite that was developed as an example of a typical simulation for testing and verifying components of the modeling system. The outcomes of this effort are to provide findings from performing CMAQ simulations on the cloud using popular vendor-provided resources, to enable the user community to adapt this for their own needs, and to identify specific areas of potential optimization with respect to storage and compute architectures. [ABSTRACT FROM AUTHOR]

The Community Multiscale Air Quality (CMAQ) model has been used for regulatory purposes at the US EPA and in the research community for decades. In 2012, we released the WRF-CMAQ coupled model that enables aerosol information from CMAQ to affect meteorological processes through direct effects on shortwave radiation. Both CMAQ and WRF-CMAQ are considered limited area models. Recently, we have extended domain coverage to global scale linking the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A, hereafter referred simply to as MPAS) with CMAQ to form the MPASCMAQ global coupled model. To configure these three different models, i.e. CMAQ (offline), WRF-CMAQ, and MPASCMAQ, we have developed the Advanced Air Quality Modelling System (AAQMS) for constructing each of them effortlessly. We evaluate this newly-built MPAS-CMAQ coupled model using two global configurations: a 120 km uniform mesh and a 92–25 km variable mesh with the finer area over North America. Preliminary computational tests show good scalability and model evaluation, a three years simulation (2014–2016) for the uniform mesh case and a monthly simulation of January and July 2016 for the variable mesh case, on ozone and PM2.5, show reasonable performance with respect to observations. The 92–25 km configuration has a high bias in wintertime surface ozone across the United States and this bias is consistent with the 120 km result. Summertime surface ozone in the 92–25 km configuration is less biased than the 120 km case. The MPAS-CMAQ system reasonably reproduces the daily variability of daily average PM from the Air Quality System (AQS) network. [ABSTRACT FROM AUTHOR]

The present trial examined the effectiveness of brief interventions for smokers who joined the Hong Kong Quit to Win Contest to quit smoking. A block randomized controlled trial allocated 1003 adult daily smokers to three groups: (i) The TEL group (n = 338) received a 5-min nurse-led telephone counselling; (ii) The SMS group (n = 335) received eight text messages through mobile phone and (iii) The CONTROL group (n = 330) did not receive the above interventions. Participants with biochemically verified abstinence at 6-month follow-up could receive cash incentive. The primary outcome was the self-reported 7-day point prevalence (PP) of tobacco abstinence at 6-month follow-up. The abstinence rate in the TEL, SMS and CONTROL group was 22.2, 20.6 and 20.3%, respectively (P for TEL versus CONTROL = 0.32; P for SMS versus CONTROL = 0.40). When abstinence at 2-, 6- and 12-month follow-up was modelled simultaneously, the TEL group had a higher abstinence than the CONTROL group (Adjusted OR = 1.38, 95% CI = 1.01--1.88, P = 0.04). In the Quit to Win Contest, the brief telephone counselling might have increased abstinence, but the text messages had no significant effect. Further studies on intensive intervention and interactive messaging services are warranted.

The Community Multiscale Air Quality (CMAQ) model has been a vital tool for air quality research and management at the United States Environmental Protection Agency (US EPA) and at government environmental agencies and academic institutions worldwide. The CMAQ model requires a significant amount of disk space to store and archive input and output files. For example, an annual simulation over the contiguous United States (CONUS) with horizontal grid-cell spacing of 12 km requires 2–3 TB of input data and can produce anywhere from 7–45 TB of output data, depending on modeling configuration and desired post-processing of the output (e.g., for evaluations or graphics). After a simulation is complete, model data are archived for several years, or even decades, to ensure the replicability of conducted research. As a result, careful disk space management is essential to optimize resources and ensure the uninterrupted progress of ongoing research and applications requiring large-scale, air quality modeling. Proper disk-space management may include applying optimal data-compression techniques that are executed on input and output files for all CMAQ simulations. There are several (not limited to) such utilities that compress files using lossless compression, such as GNU Gzip (gzip) and Basic Leucine Zipper Domain (bzip2). A new approach is proposed in this study that reduces the precision of the emission input for air quality modeling to reduce storage requirements (after a lossless compression utility is applied) and accelerate runtime. The new approach is tested using CMAQ simulations and post-processed CMAQ output to examine the impact on the performance of the air quality model. In total, four simulations were conducted, and nine cases were post-processed from direct simulation output to determine disk-space efficiency, runtime efficiency, and model (predictive) accuracy. Three simulations were run with emission input containing only five, four, or three significant digits. To enhance the analysis of disk-space efficiency, the output from the altered precision emission CMAQ simulations were additionally post-processed to contain five, four, or three significant digits. The fourth, and final, simulation was run using the full precision emission files with no alteration. Thus, in total, 13 gridded products (4 simulations and 9 altered precision output cases) were analyzed in this study. Results demonstrate that the altered precision emission files reduced the disk-space footprint by 6 %, 25 %, and 48 % compared to the unaltered emission files when using the bzip2 compression utility for files containing five, four, or three significant digits, respectively. Similarly, the altered output files reduced the required disk space by 19 %, 47 %, and 69 % compared to the unaltered CMAQ output files when using the bzip2 compression utility for files containing five, four, or three significant digits, respectively. For both compressed datasets, bzip2 performed better than gzip, in terms of compression size, by 5 %–27 % for emission data and 15 %–28 % for CMAQ output for files containing five, four, or three significant digits. Additionally, CMAQ runtime was reduced by 2 %–7 % for simulations using emission files with reduced precision data in a non-dedicated environment. Finally, the model-estimated pollutant concentrations from the four simulations were compared to observed data from the US EPA Air Quality System (AQS) and the Ammonia Monitoring Network (AMoN). Model performance statistics were impacted negligibly. In summary, by reducing the precision of CMAQ emission data to five, four, or three significant digits, the simulation runtime in a non-dedicated environment was slightly reduced, disk-space usage was substantially reduced, and model accuracy remained relatively unchanged compared to the base CMAQ simulation, which suggests that the precision of the emission data could be reduced to more efficiently use computing resources while minimizing the impact on CMAQ simulations. [ABSTRACT FROM AUTHOR]

The Community Multiscale Air Quality Model (CMAQ) has been a vital tool for air quality research and management at the United States Environmental Protection Agency (U.S. EPA), and at government environmental agencies and academic institutions worldwide. CMAQ requires a significant amount of disk space to store and archive input and output. For example, an annual simulation over the contiguous United States with horizontal grid cell spacing of 12 km requires 2-3 TB of input data and can produce anywhere from 7-45 TB of output data, depending upon modelling configuration, and desired post-processing output (e.g., for evaluations or graphics). After a simulation is complete, model data are archived for several years, or even decades, to ensure the replicability of conducted research. As a result, careful disk space management is essential to optimize resources and ensure the uninterrupted progress of ongoing research and applications requiring large scale, air quality modelling. Proper disk space management may include applying optimal data compression techniques that are executed on input and output files for all CMAQ simulations. There are several (not limited to) such utilities that compress files using losslessness compression, such as GNU Gzip and Basic Leucine Zipper Domain (bzip2). A new approach is proposed in this study that reduces the precision of the air quality model emissions input to reduce storage requirements (after a losslessness compression utility is applied) and accelerate runtime. The new approach is tested using CMAQ simulations and post-processed CMAQ output to examine the impact on the air quality model performance. In total, four simulations were conducted, and nine cases were post-processed from direct simulation output to determine disk space efficiency, runtime efficiency, and model (predictive) accuracy. Three simulations were run with emissions input containing only five, four, or three significant digits. To enhance the analysis of disk space efficiency, altered emissions CMAQ simulations were additionally post-processed to contain five, four, or three significant digits. The fourth, and final, simulation was run using the full precision emissions files with no alteration. Thus, in total, 13 gridded products (four simulations and nine cases) were analysed in this study. [ABSTRACT FROM AUTHOR]

BACKGROUND: Air quality across the northern hemisphere over the past two decades has witnessed dramatic changes, with continuous improvement in developed countries in North America and Europe, but a contrasting sharp deterioration in developing regions of Asia. OBJECTIVE: This study investigates the historical trend in the long-term exposure to [PM.sub.2.5] and [PM.sub.2.5]-related premature mortality ([PM.sub.2.5]-mortality) and its response to changes in emission that occurred during 1990-2010 across the northern hemisphere. Implications for future trends in human exposure to air pollution in both developed and developing regions of the world are discussed. METHODS: We employed the integrated exposure-response model developed by Health Effects Institute to estimate the [PM.sub.2.5]-mortality. The 1990-2010 annual average [PM.sub.2.5] concentrations were obtained from the simulations using the WRF-CMAQ model. Emission mitigation efficiencies of sulfur dioxide (S[O.sub.2]), nitrogen oxides (N[O.sub.x]), ammonia (N[H.sub.3]), and primary PM are estimated from the [PM.sub.2.5]-mortality responses to the emission variations. RESULTS: Estimated [PM.sub.2.5]-mortalities in East Asia and South Asia increased by 21% and 85% respectively, from 866,000 and 578,000 in 1990, to 1,048,000 and 1,068,000 in 2010. [PM.sub.2.5]-mortalities in developed regions (i.e., Europe and high-income North America) decreased substantially by 67% and 58% respectively. CONCLUSIONS: Over the past two decades, correlations between population and [PM.sub.2.5] have become weaker in Europe and North America due to air pollution controls but stronger in East Asia due to deteriorating air quality. Mitigation of primary PM appears to be the most efficient way for increasing health benefits (i.e., providing the largest mortality reduction per unit emissions). However, reductions in emissions of N[H.sub.3] are needed to maximize the effectiveness of N[O.sub.x] emission controls. http://dx.doi.org/10.1289/EHP298, Introduction Fine particles--those defined as aerosols with aerodynamic diameter ≤ 2.5 µm ([PM.sub.2.5])--in the atmosphere have been well known for their potential negative impacts on human health that can affect [...]

Airborne aerosols reduce surface solar radiation through light scattering and absorption (aerosol direct effects, ADE), influence regional meteorology, and further affect atmospheric chemical reactions and aerosol concentrations. Several studies have revealed that the inhibition of turbulence and the increase in atmospheric stability induced by ADE increases surface primary aerosol concentration, but the pathway of ADE impacts on secondary aerosol is still unclear. In this study, the two-way coupled WRF-CMAQ with integrated process analysis was applied to explore how ADE impacts secondary aerosol formation through changes in atmospheric dynamics and photolysis processes. Meteorological and air quality fields in Jing-Jin-Ji area (denoted JJJ, including Beijing, Tianjin and Hebei Province in China) in January and July 2013 were simulated to represent winter and summer conditions, respectively. Two pathways of ADE impacts on aerosol concentration, i.e., photolysis modification and atmospheric dynamics modification were estimated separately through scenario analysis. The results show that solar radiation is the restricting factor in winter, and the formation of sulfate is sensitive to the perturbation of solar radiation. While in summer, availability of gaseous precursors primarily dictates the levels of secondary aerosol concentrations. ADE through the attenuation of photolysis inhibits secondary aerosol formation during winter and promotes secondary aerosol formation during summer. The seasonal differences are attributed to change of effective actinic flux in winter and summer determined by aerosol optical depth, solar zenith angles, and single scattering albedo. ADE through dynamic processes is the dominant process influencing surface secondary aerosol formation due to the accumulation of gaseous precursors. Different from sulfate, the surface layer is a net-source of nitrate during winter but a sink during summer. Therefore, ADE promotes nitrate accumulation in winter and reduces nitrate accumulation in summer. [ABSTRACT FROM AUTHOR]

A mathematical method for enhancing cache performance by adjusting the mapping of data onto a direct-mapped cache in applications with sequential data access patterns was developed. It employs data offsets to accomplish a favorable mapping between primary memory and the cache. The offsetting strategy substantially improves single processor performance for each processor in a parallel processing system.

The rate of oil recovery is independent of the knowledge of the environmental costs and the rate goes to zero over time when either the environmental costs are known or not. In comparison to the risk neutral planners, the planners averse to risk start the oil recovery at a higher rate, vigorously lower the recovery rate and end with a higher speed. Estimation of the environmental costs are made on the flow related to the oil recovery rate with a stochastic component.