Jingyi Li

Evaluation of observation-fused regional air quality model results for population air pollution exposure estimation.

In this study, Community Multiscale Air Quality (CMAQ) model was applied to predict ambient gaseous and particulate concentrations during 2001 to 2010 in 15 hospital referral regions (HRRs) using a 36-km horizontal resolution domain. An inverse distance weighting based method was applied to produce exposure estimates based on observation-fused regional pollutant concentration fields using the differences between observations and predictions at grid cells where air quality monitors were located. Although the raw CMAQ model is capable of producing satisfying results for O3 and PM2.5 based on EPA guidelines, using the observation data fusing technique to correct CMAQ predictions leads to significant improvement of model performance for all gaseous and particulate pollutants. Regional average concentrations were calculated using five different methods: 1) inverse distance weighting of observation data alone, 2) raw CMAQ results, 3) observation-fused CMAQ results, 4) population-averaged raw CMAQ results and 5) population-averaged fused CMAQ results. It shows that while O3 (as well as NOx) monitoring networks in the HRRs are dense enough to provide consistent regional average exposure estimation based on monitoring data alone, PM2.5 observation sites (as well as monitors for CO, SO2, PM10 and PM2.5 components) are usually sparse and the difference between the average concentrations estimated by the inverse distance interpolated observations, raw CMAQ and fused CMAQ results can be significantly different. Population-weighted average should be used to account for spatial variation in pollutant concentration and population density. Using raw CMAQ results or observations alone might lead to significant biases in health outcome analyses.

Significant Contributions of Isoprene to Summertime Secondary Organic Aerosol in Eastern United States

A modified SAPRC-11 (S11) photochemical mechanism with more detailed treatment of isoprene oxidation chemistry and additional secondary organic aerosol (SOA) formation through surface-controlled reactive uptake of dicarbonyls, isoprene epoxydiol and methacrylic acid epoxide was incorporated in the Community Multiscale Air Quality Model (CMAQ) to quantitatively determine contributions of isoprene to summertime ambient SOA concentrations in the eastern United States. The modified model utilizes a precursor-origin resolved approach to determine secondary glyoxal and methylglyoxal produced by oxidation of isoprene and other major volatile organic compounds (VOCs). Predicted OC concentrations show good agreement with field measurements without significant bias (MFB ∼ 0.07 and MFE ∼ 0.50), and predicted SOA reproduces observed day-to-day and diurnal variation of Oxygenated Organic Aerosol (OOA) determined by an aerosol mass spectrometer (AMS) at two locations in Houston, Texas. On average, isoprene SOA accounts for 55.5% of total predicted near-surface SOA in the eastern U.S., followed by aromatic compounds (13.2%), sesquiterpenes (13.0%) and monoterpenes (10.9%). Aerosol surface uptake of isoprene-generated glyoxal, methylglyoxal and epoxydiol accounts for approximately 83% of total isoprene SOA or more than 45% of total SOA. A domain wide reduction of NOx emissions by 40% leads to a slight decrease of domain average SOA by 3.6% and isoprene SOA by approximately 2.6%. Although most of the isoprene SOA component concentrations are decreased, SOA from isoprene epoxydiol is increased by ∼16%.

Modeling dry and wet deposition of sulfate, nitrate, and ammonium ions in Jiuzhaigou National Nature Reserve, China using a source-oriented CMAQ model: Part I. Base case model results.

A source-oriented Community Multiscale Air Quality (CMAQ) model driven by the meteorological fields generated by the Weather Research and Forecasting (WRF) model was used to study the dry and wet deposition of nitrate (NO3(-)), sulfate (SO4(2-)), and ammonium (NH4(+)) ions in the Jiuzhaigou National Nature Reserve (JNNR), China from June to August 2010 and to identify the contributions of different emission sectors and source regions that were responsible for the deposition fluxes. The model performance is evaluated in this paper and the source contribution analyses are presented in a companion paper. The results show that WRF is capable of reproducing the observed precipitation rates with a Mean Normalized Gross Error (MNGE) of 8.1%. Predicted wet deposition fluxes of SO4(2-) and NO3(-) at the Long Lake (LL) site (3100 m a.s.l.) during the three-month episode are 2.75 and 0.34 kg S(N) ha(-1), which agree well with the observed wet deposition fluxes of 2.42 and 0.39 kg S(N) ha(-1), respectively. Temporal variations in the weekly deposition fluxes at LL are also well predicted. Wet deposition flux of NH4(+) at LL is over-predicted by approximately a factor of 3 (1.60 kg N ha(-1)vs. 0.56 kg N ha(-1)), likely due to missing alkaline earth cations such as Ca(2+) in the current CMAQ simulations. Predicted wet deposition fluxes are also in general agreement with observations at four Acid Deposition Monitoring Network in East Asia (EANET) sites in western China. Predicted dry deposition fluxes of SO4(2-) (including gas deposition of SO2) and NO3(-) (including gas deposition of HNO3) are 0.12 and 0.12 kg S(N) h a(-1) at LL and 0.07 and 0.08 kg S(N) ha(-1) at Jiuzhaigou Bureau (JB) in JNNR, respectively, which are much lower than the corresponding wet deposition fluxes. Dry deposition flux of NH4(+) (including gas deposition of NH3) is 0.21 kg N ha(-1) at LL, and is also much lower than the predicted wet deposition flux. For both dry and wet deposition fluxes, predictions from the 12-km resolution nested domain are similar to those from the 36-km resolution parent domain.

Estimating population exposure to ambient polycyclic aromatic hydrocarbon in the United States - Part II: Source apportionment and cancer risk assessment.

A revised Community Multiscale Air Quality (CMAQ) model was developed to simulate the emission, reactions, transport, deposition and gas-to-particle partitioning processes of 16 priority polycyclic aromatic hydrocarbons (PAHs), as described in Part I of the two-part series. The updated CMAQ model was applied in this study to quantify the contributions of different emission sources to the predicted PAH concentrations and excess cancer risk in the United States (US) in 2011. The cancer risk in the continental US due to inhalation exposure of outdoor naphthalene (NAPH) and seven larger carcinogenic PAHs (cPAHs) was predicted to be significant. The incremental lifetime cancer risk (ILCR) exceeds 1×10-5 in many urban and industrial areas. Exposure to PAHs was estimated to result in 5704 (608-10,800) excess lifetime cancer cases. Point sources not related with energy generation and the oil and gas processes account for approximately 31% of the excess cancer cases, followed by non-road engines with 18.6% contributions. Contributions of residential wood combustion (16.2%) are similar to that of transportation-related sources (mostly motor vehicles with small contributions from railway and marine vessels; 13.4%). The oil and gas industry emissions, although large contributors to high concentrations of cPAHs regionally, are only responsible of 4.3% of the excess cancer cases, which is similar to the contributions of non-US sources (6.8%) and non-point sources (7.2%). The power generation units pose the most minimal impact on excess cancer risk, with contributions of approximately 2.3%.

Estimating population exposure to ambient polycyclic aromatic hydrocarbon in the United States – Part I: Model development and evaluation

PAHs (polycyclic aromatic hydrocarbons) in the environment are of significant concern due to their negative impact on human health. PAH measurements at the air toxics monitoring network stations alone are not sufficient to provide a complete picture of ambient PAH levels or to allow accurate assessment of public exposure in the United States. In this study, speciation profiles for PAHs were prepared using data assembled from existing emission profile data bases, and the Sparse Matrix Operator Kernel Emissions (SMOKE) model was used to generate the gridded national emissions of 16 priority PAHs in the US. The estimated emissions were applied to simulate ambient concentration of PAHs for January, April, July and October 2011, using a modified Community Multiscale Air Quality (CMAQ) model (v5.0.1) that treats the gas and particle phase partitioning of PAHs and their reactions in the gas phase and on particle surface. Predicted daily PAH concentrations at 61 air toxics monitoring sites generally agreed with observations, and averaging the predictions over a month reduced the overall error. The best model performance was obtained at rural sites, with an average mean fractional bias (MFB) of -0.03 and mean fractional error (MFE) of 0.70. Concentrations at suburban and urban sites were underestimated with overall MFB=-0.57 and MFE=0.89. Predicted PAH concentrations were highest in January with better model performance (MFB=0.12, MFE=0.69; including all sites), and lowest in July with worse model performance (MFB=-0.90, MFE=1.08). Including heterogeneous reactions of several PAHs with O3 on particle surface reduced the over-prediction bias in winter, although significant uncertainties were expected due to relative simple treatment of the heterogeneous reactions in the current model.

Modeling dry and wet deposition of sulfate, nitrate, and ammonium ions in Jiuzhaigou National Nature Reserve, China using a source-oriented CMAQ model: Part II. Emission sector and source region contributions

A source-oriented Community Multiscale Air Quality (CMAQ) model driven by the meteorological fields generated by the Weather Research and Forecasting (WRF) model was used to study the dry and wet deposition of nitrate (NO3(-)), sulfate (SO4(2-)), and ammonium (NH4(+)) ions in the Jiuzhaigou National Nature Reserve (JNNR), China from June to August 2010 and to identify the contributions of different emission sectors and source regions that were responsible for the deposition fluxes. Contributions from power plants, industry, transportation, domestic, biogenic, windblown dust, open burning, fertilizer, and manure management sources to deposition fluxes in JNNR watershed and four EANET sites are determined. In JNNR, 96%, 82%, and 87% of the SO4(2-), NO3(-) and NH4(+) deposition fluxes are in the form of wet deposition of the corresponding aerosol species. Industry and power plants are the two major sources of SO4(2-) deposition flux, accounting for 86% of the total wet deposition of SO4(2-), and industry has a higher contribution (56%) than that of power plants (30%). Power plants and industry are also the top sources that are responsible for NO3(-) wet deposition, and contributions from power plants (30%) are generally higher than those from industries (21%). The major sources of NH4(+) wet deposition flux in JNNR are fertilizer (48%) and manure management (39%). Source-region apportionment confirms that SO2 and NOx emissions from local and two nearest counties do not have a significant impact on predicted wet deposition fluxes in JNNR, with contributions less than 10%. While local NH3 emissions account for a higher fraction of the NH4(+) deposition, approximately 70% of NH4(+) wet deposition in JNNR originated from other source regions. This study demonstrates that S and N deposition in JNNR is mostly from long-range transport rather than from local emissions, and to protect JNNR, regional emission reduction controls are needed.

Sources of particulate matter in China: Insights from source apportionment studies published in 1987–2017

Particulate matter (PM) in the atmosphere has adverse effects on human health, ecosystems, and visibility. It also plays an important role in meteorology and climate change. A good understanding of its sources is essential for effective emission controls to reduce PM and to protect public health. In this study, a total of 239 PM source apportionment studies in China published during 1987-2017 were reviewed. The documents studied include peer-reviewed papers in international and Chinese journals, as well as degree dissertations. The methods applied in these studies were summarized and the main sources in various regions of China were identified. The trends of source contributions at two major cities with abundant studies over long-time periods were analyzed. The most frequently used methods for PM source apportionment in China are receptor models, including chemical mass balance (CMB), positive matrix factorization (PMF), and principle component analysis (PCA). Dust, fossil fuel combustion, transportation, biomass burning, industrial emission, secondary inorganic aerosol (SIA) and secondary organic aerosol (SOA) are the main source categories of fine PM identified in China. Even though the sources of PM vary among seven different geographical areas of China, SIA, industrial, and dust emissions are generally found to be the top three source categories in 2007-2016. A number of studies investigated the sources of SIA and SOA in China using air quality models and indicated that fossil fuel combustion and industrial emissions were the most important sources of SIA (total contributing 63.5%-88.1% of SO42-, and 47.3%-70% NO3-), and agriculture emissions were the dominant source of NH4+ (contributing 53.9%-90%). Biogenic emissions were the most important source of SOA in China in summer, while residential and industrial emissions were important in winter. Long-term changes of PM sources at two megacities of Beijing and Nanjing indicated that the contributions of fossil fuel and industrial sources have been declining after stricter emission controls in recent years. In general, dust and industrial contributions decreased and transportation contributions increased after 2000. PM2.5 emissions are predicted to decline in most regions during 2005-2030, even though the energy consumptions except biomass burning are predicted to continue to increase. Industrial, residential, and biomass burning sources will become more important in the future in the businuess-as-usual senarios. This review provides valuable information about main sources of PM and their trends in China. A few recommendations are suggested to further improve our understanding the sources and to develop effective PM control strategies in various regions of China.