Identifying PM<sub>2.5</sub> and PM<sub>0.1</sub> Sources for Epidemiological Studies in California

Hu, Jianlin; Zhang, Hongliang; Chen, Shu Hua; Ying, Qi; Wiedinmyer, Christine; Vandenberghe, François; Kleeman, Michael J.

doi:10.1021/es404810z

Cited by 79 publications

(70 citation statements)

References 71 publications

(83 reference statements)

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“…The University of California Davis/CIT_Primary (UCD_P) chemical transport model (Hu et al, 2014a) estimated primary ground-level PM element concentrations across densely populated areas of California including Los Angeles County at a 4 km Â 4 km grid resolution for particles ranging from 0.01 to 20 mm from approximately 900 sources. In the present study, the simulated PM concentrations were calculated for two particle size fractions (PM 2.5 and PM 0.1 ) for the period of 2000-2006.…”

Section: Chemical Transport Modelingmentioning

confidence: 99%

“…The UCD_P model was developed to track primary PM (emitted directly from sources) through a simulation of emission, advection, diffusion and deposition. The detailed descriptions of the model and its validation are the purpose of other publications (Hu et al, 2014a(Hu et al, , 2014b, but its main components are summarized below. Size and composition resolved particle emissions were derived from a library of primary particle source profiles measured during actual source tests.…”

Section: Chemical Transport Modelingmentioning

confidence: 99%

“…Size and composition resolved particle emissions were derived from a library of primary particle source profiles measured during actual source tests. Gridded emissions were prepared using the raw emissions inventory provided by the California Air Resources Board (Hu et al, 2014a). Meteorological inputs were prepared using the Weather Research and Forecast (WRF) model version 3.1 (Skamarock et al, 2008).…”

Section: Chemical Transport Modelingmentioning

confidence: 99%

“…Chemical transport models (CTMs) have been used to address issues of the lack of source information and measurement scarcity for certain air pollutants (Hu et al, 2014a). For instance CTMs can predict the detailed size and chemical composition of primary PM with reasonable temporal and spatial resolution, while keeping track of source information.…”

Section: Introductionmentioning

confidence: 99%

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Sources and contents of air pollution affecting term low birth weight in Los Angeles County, California, 2001–2008

Olivier

et al. 2014

Environmental Research

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111

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a b s t r a c tBackground: Low birth weight (LBW, o 2500 g) has been associated with exposure to air pollution, but it is still unclear which sources or components of air pollution might be in play. The association between ultrafine particles and LBW has never been studied. Objectives: To study the relationships between LBW in term born infants and exposure to particles by size fraction, source and chemical composition, and complementary components of air pollution in Los Angeles County (California, USA) over the period [2001][2002][2003][2004][2005][2006][2007][2008]. Methods: Birth certificates (n ¼ 960,945) were geocoded to maternal residence. Primary particulate matter (PM) concentrations by source and composition were modeled. Measured fine PM, nitrogen dioxide and ozone concentrations were interpolated using empirical Bayesian kriging. Traffic indices were estimated. Associations between LBW and air pollution metrics were examined using generalized additive models, adjusting for maternal age, parity, race/ethnicity, education, neighborhood income, gestational age and infant sex. Results: Increased LBW risks were associated with the mass of primary fine and ultrafine PM, with several major sources (especially gasoline, wood burning and commercial meat cooking) of primary PM, and chemical species in primary PM (elemental and organic carbon, potassium, iron, chromium, nickel, and titanium but not lead or arsenic). Increased LBW risks were also associated with total fine PM mass, nitrogen dioxide and local traffic indices (especially within 50 m from home), but not with ozone. Stronger associations were observed in infants born to women with low socioeconomic status, chronic hypertension, diabetes and a high body mass index. Conclusions: This study supports previously reported associations between traffic-related pollutants and LBW and suggests other pollution sources and components, including ultrafine particles, as possible risk factors.

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Section: Chemical Transport Modelingmentioning

confidence: 99%

Section: Chemical Transport Modelingmentioning

confidence: 99%

Section: Chemical Transport Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sources and contents of air pollution affecting term low birth weight in Los Angeles County, California, 2001–2008

Olivier

et al. 2014

Environmental Research

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111

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“…The uncertainties in emission inventories are carried into CTMs simulations, leading to uncertainties in air quality predictions, which need to be carefully evaluated to identify the useful information for health effect studies (Hu et al, 2017b(Hu et al, , 2014c(Hu et al, , b, 2015bTao et al, 2014). An evaluation of 1-year air pollutants predictions using the Weather Research and Forecasting (WRF) / Community Multi-scale Air Quality (CMAQ) modeling system with the Multi-resolution Emission Inventory for China (MEIC) has been reported .…”

Section: Introductionmentioning

confidence: 99%

Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

Huang

et al. 2017

Atmos. Chem. Phys.

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Abstract. Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O 3 and PM 2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM 2.5 in the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25 to −0.16) and MFE (0.26-0.31) of individual simulations. The ensemble annual daily maximum 1 h O 3 (O 3 -1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06-0.19 and MNE of 0.16-0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM 2.5 and O 3 -1h. The study demonstrates that ensemble predictions from combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories, and the results are publicly available for future health effect studies.

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A review of AirQ Models and their applications for forecasting the air pollution health outcomes

Conti

Heibati

Kloog

et al. 2017

Environ Sci Pollut Res

117

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Even though clean air is considered as a basic requirement for the maintenance of human health, air pollution continues to pose a significant health threat in developed and developing countries alike. Monitoring and modeling of classic and emerging pollutants is vital to our knowledge of health outcomes in exposed subjects and to our ability to predict them. The ability to anticipate and manage changes in atmospheric pollutant concentrations relies on an accurate representation of the chemical state of the atmosphere. The task of providing the best possible analysis of air pollution thus requires efficient computational tools enabling efficient integration of observational data into models. A number of air quality models have been developed and play an important role in air quality management. Even though a large number of air quality models have been discussed or applied, their heterogeneity makes it difficult to select one approach above the others. This paper provides a brief review on air quality models with respect to several aspects such as prediction of health effects.

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Identifying PM_2.5 and PM_0.1 Sources for Epidemiological Studies in California

Cited by 79 publications

References 71 publications

Sources and contents of air pollution affecting term low birth weight in Los Angeles County, California, 2001–2008

Sources and contents of air pollution affecting term low birth weight in Los Angeles County, California, 2001–2008

Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

A review of AirQ Models and their applications for forecasting the air pollution health outcomes

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Identifying PM2.5 and PM0.1 Sources for Epidemiological Studies in California

Cited by 79 publications

References 71 publications

Sources and contents of air pollution affecting term low birth weight in Los Angeles County, California, 2001–2008

Sources and contents of air pollution affecting term low birth weight in Los Angeles County, California, 2001–2008

Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

A review of AirQ Models and their applications for forecasting the air pollution health outcomes

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Identifying PM_2.5 and PM_0.1 Sources for Epidemiological Studies in California