Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations

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

doi:10.5194/acp-15-3445-2015

Cited by 60 publications

(72 citation statements)

References 107 publications

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“…A previous study has revealed that air pollution levels are associated with these parameters in highly polluted regions in China (Y. . It is also demonstrated that bias in predicted meteorological parameters by WRF contributes to bias in PM 2.5 prediction (Hu et al, 2015c;Zhang et al, 2014a, b). A companion study is undergoing to evaluate the sensitivity of predictions to meteorological fields.…”

Section: Discussionmentioning

confidence: 99%

One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system

et al. 2016

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Abstract. China has been experiencing severe air pollution in recent decades. Although an ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research and Forecasting (WRF) model and the Community Multi-scale Air Quality (CMAQ) model was conducted to provide detailed temporal and spatial information of ozone (O 3 ), total PM 2.5 , and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O 3 and PM 2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, overprediction of O 3 generally occurs at low concentration range while underprediction of PM 2.5 happens at low concentration range in summer. Spatially, the model has better performance in southern China than in northern China, central China, and Sichuan Basin. Strong seasonal variations of PM 2.5 exist and wind speed and direction play important roles in high PM 2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO 2− 4 ), nitrate (NO − 3 ), ammonium (NH + 4 ), and primary organic aerosol (POA) are the most important PM 2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of the CMAQ model to reproduce severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.

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Section: Discussionmentioning

confidence: 99%

One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system

et al. 2016

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“…Air quality was simulated using the UCD-CIT (University of California, Davis -California Institute of Technology) 3-D regional chemical transport model (Kleeman and Cass, 2001;Ying et al, 2007;Hu et al, 2015). The SAPRC11 chemical mechanism was used to represent gas-phase chemical reactions.…”

Section: Regional Chemical Transport Model Configuration and Simulationmentioning

confidence: 99%

Low-carbon energy generates public health savings in California

et al. 2018

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Abstract. California's goal to reduce greenhouse gas (GHG) emissions to a level that is 80 % below 1990 levels by the year 2050 will require adoption of low-carbon energy sources across all economic sectors. In addition to reducing GHG emissions, shifting to fuels with lower carbon intensity will change concentrations of short-lived conventional air pollutants, including airborne particles with a diameter of less than 2.5 µm (PM 2.5 ) and ozone (O 3 ). Here we evaluate how business-as-usual (BAU) air pollution and public health in California will be transformed in the year 2050 through the adoption of low-carbon technologies, expanded electrification, and modified activity patterns within a low-carbon energy scenario (GHG-Step). Both the BAU and GHG-Step statewide emission scenarios were constructed using the energy-economic optimization model, CA-TIMES, that calculates the multi-sector energy portfolio that meets projected energy supply and demand at the lowest cost, while also satisfying scenario-specific GHG emissions constraints. Corresponding criteria pollutant emissions for each scenario were then spatially allocated at 4 km resolution to support air quality analysis in different regions of the state. Meteorological inputs for the year 2054 were generated under a Representative Concentration Pathway (RCP) 8.5 future climate. Annual-average PM 2.5 and O 3 concentrations were predicted using the modified emissions and meteorology inputs with a regional chemical transport model. In the final phase of the analysis, mortality (total deaths) and mortality rate (deaths per 100 000) were calculated using established exposure-response relationships from air pollution epidemiology combined with simulated annual-average PM 2.5 and O 3 exposure. Net emissions reductions across all sectors are −36 % for PM 0.1 mass, −3.6 % for PM 2.5 mass, −10.6 % for PM 2.5 elemental carbon, −13.3 % for PM 2.5 organic carbon, −13.7 % for NO x , and −27.5 % for NH 3 . Predicted deaths associated with air pollution in 2050 dropped by 24-26 % in California (1537-2758 avoided deaths yr −1 ) in the "climatefriendly" 2050 GHG-Step scenario, which is equivalent to a 54-56 % reduction in the air pollution mortality rate (deaths per 100 000) relative to 2010 levels. These avoided deaths have an estimated value of USD 11.4-20.4 billion yr −1 based on the present-day value of a statistical life (VSL) equal to USD 7.6 million. The costs for reducing California GHG emissions 80 % below 1990 levels by the year 2050 depend strongly on numerous external factors such as the global price of oil. Best estimates suggest that meeting an intermediate target (40 % reduction in GHG emissions by the year 2030) using a non-optimized scenario would reduce personal income by USD 4.95 billion yr −1 (−0.15 %) and lower overall state gross domestic product by USD 16.1 billion yr −1 (−0.45 %). The public health benefits described here are comparable to these cost estimates, making a compelling argument for the adoption of low-carbon energy in California, with implic...

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“…Performance of the ensemble predictions at the city was calculated using the withheld observations to evaluate the performance. The evaluation process was repeated for each of the 60 cities and the performance was compared to that with individual inventories (shown in Table S3 Hu et al, 2014bHu et al, , 2015b. Ensemble predictions were also calculated with daily and monthly averages for PM 2.5 , in addition to the calculation with annual averages discussed above.…”

Section: Ensemble Predictionsmentioning

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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Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations

Cited by 60 publications

References 107 publications

One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system

One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system

Low-carbon energy generates public health savings in California

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

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