NAQFC Developmental Forecast Guidance for Fine Particulate Matter (PM2.5)

2017 ◽  
Vol 32 (1) ◽  
pp. 343-360 ◽  
Author(s):  
Pius Lee ◽  
Jeffery McQueen ◽  
Ivanka Stajner ◽  
Jianping Huang ◽  
Li Pan ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Monitoring Network ◽  
Chemical Mechanism ◽  
Organic Nitrate ◽  
Fugitive Dust ◽  
Regional Variability ◽  
Dust Emissions ◽  
Numerical Predictions ◽  
Forecast Guidance

Abstract The National Air Quality Forecasting Capability (NAQFC) upgraded its modeling system that provides developmental numerical predictions of particulate matter smaller than 2.5 μm in diameter (PM2.5) in January 2015. The issuance of PM2.5 forecast guidance has become more punctual and reliable because developmental PM2.5 predictions are provided from the same system that produces operational ozone predictions on the National Centers for Environmental Prediction (NCEP) supercomputers. There were three major upgrades in January 2015: 1) incorporation of real-time intermittent sources for particles emitted from wildfires and windblown dust originating within the NAQFC domain, 2) suppression of fugitive dust emissions from snow- and/or ice-covered terrain, and 3) a shorter life cycle for organic nitrate in the gaseous-phase chemical mechanism. In May 2015 a further upgrade for emission sources was included using the U.S. Environmental Protection Agency’s (EPA) 2011 National Emission Inventory (NEI). Emissions for ocean-going ships and on-road mobile sources will continue to rely on NEI 2005. Incremental tests and evaluations of these upgrades were performed over multiple seasons. They were verified against the EPA’s AIRNow surface monitoring network for air pollutants. Impacts of the three upgrades on the prediction of surface PM2.5 concentrations show large regional variability: the inclusion of windblown dust emissions in May 2014 improved PM2.5 predictions over the western states and the suppression of fugitive dust in January 2015 reduced PM2.5 bias by 52%, from 6.5 to 3.1 μg m−3 against a monthly average of 9.4 μg m−3 for the north-central United States.

scholarly journals Exposure to Air Pollution and Particle Radioactivity With the Risk of Ventricular Arrhythmias

Circulation ◽  
2020 ◽  
Vol 142 (9) ◽  
pp. 858-867 ◽  
Author(s):  
Adjani A. Peralta ◽  
Mark S. Link ◽  
Joel Schwartz ◽  
Heike Luttmann-Gibson ◽  
Douglas W. Dockery ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Ventricular Arrhythmias ◽  
Moving Average ◽  
Fine Particulate Matter ◽  
Monitoring Network ◽  
Interquartile Range ◽  
Dew Point ◽  
High Risk Population ◽  
Air Temperatures

Background: Individuals are exposed to air pollution and ionizing radiation from natural sources through inhalation of particles. This study investigates the association between cardiac arrhythmias and short-term exposures to fine particulate matter (particulate matter ≤2.5 µm aerodynamic diameter; PM 2.5 ) and particle radioactivity. Methods: Ventricular arrhythmic events were identified among 176 patients with dual-chamber implanted cardioverter-defibrillators in Boston, Massachusetts between September 2006 and June 2010. Patients were assigned exposures based on residential addresses. Daily PM 2.5 levels were estimated at 1-km×1-km grid cells from a previously validated prediction model. Particle gross β activity was used as a surrogate for particle radioactivity and was measured from several monitoring sites by the US Environmental Protection Agency’s monitoring network. The association of the onset of ventricular arrhythmias (VA) with 0- to 21-day moving averages of PM 2.5 and particle radioactivity (2 single-pollutant models and a 2-pollutant model) before the event was examined using time-stratified case-crossover analyses, adjusted for dew point and air temperatures. Results: A total of 1,050 VA were recorded among 91 patients, including 123 sustained VA among 25 of these patients. In the single-pollutant model of PM 2.5 , each interquartile range increase in daily PM 2.5 levels for a 21-day moving average was associated with 39% higher odds of a VA event (95% CI, 12%–72%). In the single-pollutant model of particle radioactivity, each interquartile range increase in particle radioactivity for a 2-day moving average was associated with 13% higher odds of a VA event (95% CI, 1%–26%). In the 2-pollutant model, for the same averaging window of 21 days, each interquartile range increase in daily PM 2.5 was associated with an 48% higher odds of a VA event (95% CI, 15%–90%), and each interquartile range increase of particle radioactivity with a 10% lower odds of a VA event (95% CI, –29% to 14%). We found that with higher levels of particle radioactivity, the effect of PM 2.5 on VAs is reduced. Conclusions: In this high-risk population, intermediate (21-day) PM 2.5 exposure was associated with higher odds of a VA event onset among patients with known cardiac disease and indication for implanted cardioverter-defibrillator implantation independently of particle radioactivity.

scholarly journals Measurement of carbonaceous aerosol with different sampling configurations and frequencies

2015 ◽  
Vol 8 (7) ◽  
pp. 2639-2648 ◽  
Author(s):  
Y. Cheng ◽  
K.-B. He
Keyword(s):  
Particulate Matter ◽  
Chemical Speciation ◽  
Fine Particulate Matter ◽  
Monitoring Network ◽  
Airborne Particulate Matter ◽  
Carbonaceous Aerosol ◽  
Monitoring Networks ◽  
Quartz Filter ◽  
Sampling Duration ◽  
The Impact

Abstract. A common approach for measuring the mass of organic carbon (OC) and elemental carbon (EC) in airborne particulate matter involves collection on a quartz fiber filter and subsequent thermal–optical analysis. Although having been widely used in aerosol studies and in PM2.5 (fine particulate matter) chemical speciation monitoring networks in particular, this measurement approach is prone to several types of artifacts, such as the positive sampling artifact caused by the adsorption of gaseous organic compounds onto the quartz filter, the negative sampling artifact due to the evaporation of OC from the collected particles and the analytical artifact in the thermal–optical determination of OC and EC (which is strongly associated with the transformation of OC into char OC and typically results in an underestimation of EC). The presence of these artifacts introduces substantial uncertainties to observational data on OC and EC and consequently limits our ability to evaluate OC and EC estimations in air quality models. In this study, the influence of sampling frequency on the measurement of OC and EC was investigated based on PM2.5 samples collected in Beijing, China. Our results suggest that the negative sampling artifact of a bare quartz filter could be remarkably enhanced due to the uptake of water vapor by the filter medium. We also demonstrate that increasing sampling duration does not necessarily reduce the impact of positive sampling artifact, although it will enhance the analytical artifact. Due to the effect of the analytical artifact, EC concentrations of 48 h averaged samples were about 15 % lower than results from 24 h averaged ones. In addition, it was found that with the increase of sampling duration, EC results exhibited a stronger dependence on the charring correction method and, meanwhile, optical attenuation (ATN) of EC (retrieved from the carbon analyzer) was more significantly biased by the shadowing effect. Results from this study will be useful for the design of China's PM2.5 chemical speciation monitoring network, which can be expected to be inaugurated in the near future.

scholarly journals Characterization of particulate matter in iron ore mining region of Itabira, Minas Gerais, Brazil

2021 ◽  
Author(s):  
Ana Carolina Vasques Freitas ◽  
Rose-Marie Belardi ◽  
Henrique de Melo Jorge Barbosa
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Fine Particulate Matter ◽  
Monitoring Network ◽  
Principal Component ◽  
Opencast Mining ◽  
Residential Areas ◽  
Emission Data ◽  
Particulate Matter Emission ◽  
The City

Itabira has in its territory the largest complex of opencast mining in the world, which is located close to residential areas of the city. The air quality-monitoring network installed in the city is the main source of particulate matter emission data. However, these air quality stations only cover the areas near the mines and does not measure fine particulate matter. Thus, a first field campaign was carried out to characterize the particulate matter in the city and to compare the Hi-Vol data from air quality stations with the dichotomous air sampler data. Results of trajectories cluster analysis showed a long-range transport of aerosols during the sampling days from northeast (84% of the trajectories), east-southeast (12%) and south-southwest (3%) directions. Regarding to the meteorological conditions during the sampling days, negative correlations were seen between coarse particulate matter from mostly air quality stations and all meteorological parameters (but temperature). Results of the X-ray fluorescence and principal component analyses showed that the main trace elements in the coarse and fine modes are Iron and Sulfur, associated with emissions from mining activities, air mass transport from regional iron and steelmaking industry activities, vehicle emissions, local and regional biomass burning and natural biogenic emissions. This work represents the first assessment of source apportionment done in the city. Comparisons with other studies for some Brazilian larger cities showed that Itabira has comparable contributions of sulfur, iron and elements, such as copper, selenium, chromium, nickel, vanadium and lead.

Chinese emissions reductions deliver reduced PM2.5-caused mortality across China during 2015-2017

2020 ◽  
Author(s):  
Ben Silver ◽  
Luke Conibear ◽  
Carly Reddington ◽  
Christophe Knote ◽  
Steve Arnold ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Fine Particulate Matter ◽  
Monitoring Network ◽  
Minor Role ◽  
Chinese Government ◽  
Ozone Concentrations ◽  
National Monitoring ◽  
A Minor

<p>Air pollution is a serious environmental issue and leading contributor to the disease burden in China. Following severe air pollution episodes during the 2012-2013 winter, the Chinese government has prioritised efforts to reduce PM<sub>2.5</sub> emissions, and established a national monitoring network to record air quality trends. Rapid reductions in fine particulate matter (PM<sub>2.5</sub>) concentrations and increased ozone concentrations have occurred across China, during 2015 to 2017. We used measurements of particulate matter with a diameter < 2.5 µm (PM<sub>2.5</sub>) and Ozone (O<sub>3</sub>) from >1000 stations across China combined with similar datasets from Hong Kong and Taiwan to calculate trends in PM<sub>2.5</sub>, Nitrogen Dioxide, Sulphur Dioxide and O<sub>3</sub> across the greater China region during 2015-2019. We then use the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) regional air quality simulations, to explore the drivers and impacts of observed trends. Using annually varying emissions from the Multi-resolution Emission Inventory for China, we simulate air quality across China during 2015-2017, and calculate a median PM<sub>2.5</sub> trends of -3.9 µg m<sup>-3</sup> year<sup>-1</sup>. The measured nationwide median PM<sub>2.5</sub> trend of -3.4 µg m<sup>-3</sup> year<sup>-</sup>. With anthropogenic emissions fixed at 2015-levels, the simulated trend was much weaker (-0.6 µg m<sup>-3</sup> year<sup>-1</sup>), demonstrating interannual variability in meteorology played a minor role in the observed PM<sub>2.5</sub> trend. The model simulated increased ozone concentrations in line with the measurements, but underestimated the magnitude of the observed absolute trend by a factor of 2. We combined simulated trends in PM<sub>2.5</sub> concentrations with an exposure-response function to estimate that reductions in PM<sub>2.5</sub> concentrations over this period have reduced PM<sub>2.5</sub>-attribrutable premature morality across China by 150 000 deaths year<sup>-1</sup>.</p>

scholarly journals Evaluating the sensitivity of fine particulate matter (PM<sub>2.5</sub>) simulations to chemical mechanism in Delhi

2020 ◽  
Author(s):  
Chinmay Jena ◽  
Sachin D. Ghude ◽  
Rachana Kulkarni ◽  
Sreyashi Debnath ◽  
Rajesh Kumar ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Gas Phase ◽  
Optical Depth ◽  
Fine Particulate Matter ◽  
Chemical Mechanism ◽  
Aerosol Composition ◽  
Gangetic Plain ◽  
Fine Particulate ◽  
Phase Chemical

Abstract. Elevated levels of fine particulate matter (PM2.5) during winter-time have become one of the most important environmental concerns over the Indo-Gangetic Plain (IGP) region of India, and particularly for Delhi. Accurate reconstruction of PM2.5, its optical properties, and dominant chemical components over this region is essential to evaluate the performance of the air quality models. In this study, we investigated the effect of three different aerosol mechanisms coupled with gas-phase chemical schemes on simulated PM2.5 mass concentration in Delhi using the Weather Research and Forecasting model with the Chemistry module (WRF-Chem). The model was employed to cover the entire northern region of India at 10 km horizontal spacing. Results were compared with comprehensive filed data set on dominant PM2.5 chemical compounds from the Winter Fog Experiment (WiFEX) at Delhi, and surface PM2.5 observations in Delhi (17 sites), Punjab (3 sites), Haryana (4 sites), Uttar Pradesh (7 sites) and Rajasthan (17 sites). The Model for Ozone and Related Chemical Tracers (MOZART-4) gas-phase chemical mechanism coupled with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) aerosol scheme (MOZART-GOCART) were selected in the first experiment as it is currently employed in the operational air quality forecasting system of Ministry of Earth Sciences (MoES), Government of India. Other two simulations were performed with the MOZART-4 gas-phase chemical mechanism coupled with the Model for Simulating Aerosol Interactions and Chemistry (MOZART-MOSAIC), and Carbon Bond 5 (CB-05) gas-phase mechanism couple with the Modal Aerosol Dynamics Model for Europe/Secondary Organic Aerosol Model (CB05-MADE/SORGAM) aerosol mechanisms. The evaluation demonstrated that chemical mechanisms affect the evolution of gas-phase precursors and aerosol processes, which in turn affect the optical depth and overall performance of the model for PM2.5. All the three coupled schemes, MOZART-GOCART, MOZART-MOSAIC, and CB05-MADE/SORGAM, underestimate the observed concentrations of major aerosol composition (NO3−, SO42−, Cl−, BC, OC, and NH4+) and precursor gases (HNO3, NH3, SO2, NO2, and O3) over Delhi. Comparison with observations suggests that the simulations using MOZART-4 gas-phase chemical mechanism with MOSAIC aerosol performed better in simulating aerosols over Delhi and its optical depth over the IGP. The lowest NMB (−18.8 %, MB = −27.4 μg/m3) appeared for the simulations using MOZART-MOSAIC scheme, whereas the NMB was observed 32.5 % (MB = −47.5 μg/m3) for CB05-MADE/SORGAM and −53.3 % (MB = −78 μg/m3) for MOZART-GOCART scheme.

Vicious Cycle of Economic Growth, Pm2.5 and COVID-19 Case of G7 Countries

2020 ◽  
Author(s):  
Rıdvan Karacan
Keyword(s):  
Economic Growth ◽  
Air Pollution ◽  
Particulate Matter ◽  
Fossil Fuels ◽  
Fine Particulate Matter ◽  
Panel Cointegration ◽  
Air Pollutant ◽  
Population Exposure ◽  
Causality Analysis ◽  
G7 Countries

<p>Today, production is carried out depending on fossil fuels. Fossil fuels pollute the air as they contain high levels of carbon. Many studies have been carried out on the economic costs of air pollution. However, in the present study, unlike the former ones, economic growth's relationship with the COVID-19 virus in addition to air pollution was examined. The COVID-19 virus, which was initially reported in Wuhan, China in December 2019 and affected the whole world, has caused many cases and deaths. Researchers have been going on studying how the virus is transmitted. Some of these studies suggest that the number of virus-related cases increases in regions with a high level of air pollution. Based on this fact, it is thought that air pollution will increase the number of COVID-19 cases in G7 Countries where industrial production is widespread. Therefore, the negative aspects of economic growth, which currently depends on fossil fuels, is tried to be revealed. The research was carried out for the period between 2000-2019. Panel cointegration test and panel causality analysis were used for the empirical analysis. Particulate matter known as PM2.5[1] was used as an indicator of air pollution. Consequently, a positive long-term relationship has been identified between PM2.5 and economic growth. This relationship also affects the number of COVID-19 cases.</p><p><br></p><p><br></p><p>[1] "Fine particulate matter (PM2.5) is an air pollutant that poses the greatest risk to health globally, affecting more people than any other pollutant (WHO, 2018). Chronic exposure to PM2.5 considerably increases the risk of respiratory and cardiovascular diseases in particular (WHO, 2018). For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator" (OECD.Stat).</p>

The Mortality Impact of Fine Particulate Matter in China

2020 ◽  
Author(s):  
Yazhen Gong ◽  
Shanjun Li ◽  
Nicholas Sanders ◽  
Guang Shi
Keyword(s):  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Fine Particulate
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