Qi Ying

Formation of Urban Fine Particulate Matter

Urban air pollution represents one of the greatest environmental challenges facing mankind in the 21st century. Noticeably, many developing countries, such as China and India, have experienced severe air pollution because of their fast-developing economy and urbanization. Globally, the urbanization trend is projected to continue: 70% of the world population will reside in urban centers by 2050, and there will exist 41 megacities (with more than 10 million inhabitants) by 2030. Air pollutants consist of a complex combination of gases and particulate matter (PM). In particular, fine PM (particles with the aerodynamic diameter smaller than 2.5 μm or PM_(2.5)) profoundly impacts human health, visibility, the ecosystem, the weather, and the climate, and these PM effects are largely dependent on the aerosol properties, including the number concentration, size, and chemical composition. PM is emitted directly into the atmosphere (primary) or formed in the atmosphere through gas-to-particle conversion (secondary) (Figure 1). Also, primary and secondary PM undergoes chemical and physical transformations and is subjected to transport, cloud processing, and removal from the atmosphere.

Spatial and temporal variations of six criteria air pollutants in 31 provincial capital cities in China during 2013-2014.

Long-term air pollution data with high temporal and spatial resolutions are needed to support the research of physical and chemical processes that affect the air quality, and the corresponding health risks. However, such datasets were not available in China until recently. For the first time, this study examines the spatial and temporal variations of PM2.5, PM10, CO, SO2, NO2, and 8 h O3 in 31 capital cities in China between March 2013 and February 2014 using hourly data released by the Ministry of Environmental Protection (MEP) of China. The annual mean concentrations of PM2.5 and PM10 exceeded the Chinese Ambient Air Quality Standards (CAAQS), Grade I standards (15 and 40 μg/m(3) for PM2.5 and PM10, respectively) for all cities, and only Haikou, Fuzhou and Lasa met the CAAQS Grade II standards (35 and 70 μg/m(3) for PM2.5 and PM10, respectively). Observed PM2.5, PM10, CO and SO2 concentrations were higher in cities located in the North region than those in the West and the South-East regions. The number of non-attainment days was highest in the winter, but high pollution days were also frequently observed in the South-East region during the fall and in the West region during the spring. PM2.5 was the largest contributor to the air pollution in China based on the number of non-attainment days, followed by PM10, and O3. Strong correlation was found between different pollutants except for O3. These results suggest great impacts of coal combustion and biomass burning in the winter, long range transport of windblown dust in the spring, and secondary aerosol formation throughout the year. Current air pollution in China is caused by multiple pollutants, with great variations among different regions and different seasons. Future studies should focus on improving the understanding of the associations between air quality and meteorological conditions, variations of emissions in different regions, and transport and transformation of pollutants in both intra- and inter-regional contexts.

Relationships between meteorological parameters and criteria air pollutants in three megacities in China

Meteorological conditions play a crucial role in ambient air pollution by affecting both directly and indirectly the emissions, transport, formation, and deposition of air pollutants. In this study, the relationships between meteorological parameters and ambient air pollutants concentrations in three megacities in China, Beijing, Shanghai, and Guangzhou were investigated. A systematic analysis of air pollutants including PM2.5, PM10, CO, SO2, NO2, and O3 and meteorological parameters including temperature, wind speed (WS), wind direction (WD) and relative humanity (RH) was conducted for a continuous period of 12 months from March 2013 to February 2014. The results show that all three cities experienced severe air quality problems. Clear seasonal trends were observed for PM2.5, PM10, CO, SO2 and NO2 with the maximum concentrations in the winter and the minimum in the summer, while O3 exhibited an opposite trend. Substantially different correlations between air pollutants and meteorological parameters were observed among these three cities. WS reversely correlated with air pollutants, and temperature positively correlated with O3. Easterly wind led to the highest PM2.5 concentrations in Beijing, westerly wind led to high PM2.5 concentrations in Shanghai, while northern wind blew air parcels with the highest PM2.5 concentrations to Guangzhou. In Beijing, days of top 10% PM2.5, PM10, CO, and NO2 concentrations were with higher RH compared to days of bottom 10% concentrations, and SO2 and O3 showed no distinct RH dependencies. In Guangzhou, days of top 10% PM2.5, PM10, CO, SO2, NO2 and O3 concentrations were with lower RH compared to days of bottom 10% concentrations. Shanghai showed less fluctuation in RH between top and bottom 10%. These results confirm the important role of meteorological parameters in air pollution formation with large variations in different seasons and geological areas. These findings can be utilized to improve the understanding of the mechanisms that produce air pollution, enhance the forecast accuracy of the air pollution under different meteorological conditions, and provide effective measures for mitigating the pollution.

Evaluation of a seven-year air quality simulation using the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) models in the eastern United States

The performance of the Weather Research and Forecasting (WRF)/Community Multi-scale Air Quality (CMAQ) system in the eastern United States is analyzed based on results from a seven-year modeling study with a 4-km spatial resolution. For 2-m temperature, the monthly averaged mean bias (MB) and gross error (GE) values are generally within the recommended performance criteria, although temperature is over-predicted with MB values up to 2K. Water vapor at 2-m is well-predicted but significant biases (>2 g kg(-1)) were observed in wintertime. Predictions for wind speed are satisfactory but biased towards over-prediction with 0<MB<0.5 m s(-1) and root mean square error (RMSE) around 1.5 to 2 m s(-1). Wind direction, predicted without observation nudging, is not well-reproduced with GE values as large as 50° in summertime. Performance in other months is better with RMSE around 20-30° and MB within ± 10°. O3 performance meets the EPA criteria of mean normalized bias (MNB) within ± 0.15 and accuracy of unpaired peak (AUP) within 0.2. Normalized gross error (NGE) is mostly below 0.25, lower than the criteria of 0.35. Performance of PM10 is satisfactory with mean fractional bias (MFB) within ± 0.6, but a large under-prediction in springtime was frequently observed. Performance of PM2.5 and its components is mostly within performance goals except for organic carbon (OC), which is universally under-predicted with MFB values as large as -0.8. The predicted frequency distribution of PM2.5 generally agrees with observations although the predictions are slightly biased towards more frequent high concentrations in most areas. Elemental carbon (EC), nitrate and sulfate concentrations are also well reproduced. The other unresolved PM2.5 components (OTHER) are significantly overestimated by more than a factor of two. No conclusive explanations can be made regarding the possible cause of this universal overestimation, which warrants a follow-up study to better understand this problem.

Preconception and early pregnancy air pollution exposures and risk of gestational diabetes mellitus

BACKGROUND Air pollution has been linked to gestational diabetes mellitus (GDM) but no studies have evaluated impact of preconception and early pregnancy air pollution exposures on GDM risk. METHODS Electronic medical records provided data on 219,952 singleton deliveries to mothers with (n=11,334) and without GDM (n=208,618). Average maternal exposures to particulate matter (PM) ≤ 2.5μm (PM2.5) and PM2.5 constituents, PM ≤ 10μm (PM10), nitrogen oxides (NOx), carbon monoxide, sulfur dioxide (SO2) and ozone (O3) were estimated for the 3-month preconception window, first trimester, and gestational weeks 1-24 based on modified Community Multiscale Air Quality models for delivery hospital referral regions. Binary regression models with robust standard errors estimated relative risks (RR) for GDM per interquartile range (IQR) increase in pollutant concentrations adjusted for study site, maternal age and race/ethnicity. RESULTS Preconception maternal exposure to NOX (RR=1.09, 95% CI: 1.04, 1.13) and SO2 (RR=1.05, 1.01, 1.09) were associated with increased risk of subsequent GDM and risk estimates remained elevated for first trimester exposure. Preconception O3 was associated with lower risk of subsequent GDM (RR=0.93, 0.90, 0.96) but risks increased later in pregnancy. CONCLUSION Maternal exposures to NOx and SO2 preconception and during the first few weeks of pregnancy were associated with increased GDM risk. O3 appeared to increase GDM risk in association with mid-pregnancy exposure but not in earlier time windows. These common exposures merit further investigation.

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.

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

The University of California-Davis_Primary (UCD_P) model was applied to simultaneously track ∼ 900 source contributions to primary particulate matter (PM) in California for seven continuous years (January 1st, 2000 to December 31st, 2006). Predicted source contributions to primary PM2.5 mass, PM1.8 elemental carbon (EC), PM1.8 organic carbon (OC), PM0.1 EC, and PM0.1 OC were in general agreement with the results from previous source apportionment studies using receptor-based techniques. All sources were further subjected to a constraint check based on model performance for PM trace elemental composition. A total of 151 PM2.5 sources and 71 PM0.1 sources contained PM elements that were predicted at concentrations in general agreement with measured values at nearby monitoring sites. Significant spatial heterogeneity was predicted among the 151 PM2.5 and 71 PM0.1 source concentrations, and significantly different seasonal profiles were predicted for PM2.5 and PM0.1 in central California vs southern California. Population-weighted concentrations of PM emitted from various sources calculated using the UCD_P model spatial information differed from the central monitor estimates by up to 77% for primary PM2.5 mass and 148% for PM2.5 EC because the central monitor concentration is not representative of exposure for nearby population. The results from the UCD_P model provide enhanced source apportionment information for epidemiological studies to examine the relationship between health effects and concentrations of primary PM from individual sources.

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%.

Source apportionment of sulfate and nitrate particulate matter in the Eastern United States and effectiveness of emission control programs

Reducing population exposure to PM2.5 in the eastern US will require control of secondary sulfate and nitrate. A source-oriented Community Multi-scale Air Quality (CMAQ) model is used to determine contributions of major emission sources to nitrate and sulfate concentrations in the seven eastern US cities (New York City, Pittsburgh, Baltimore, Chicago, Detroit, St. Paul, and Winston-Salem) in January and August of 2000 and 2006. Identified major nitrate sources include on-road gasoline-powered vehicles, diesel engines, natural gas and coal combustion. From 2000 to 2006, January nitrate concentrations decreased by 25-68% for all the seven cities. On average, ~53% of this change was caused by emissions controls while 47% was caused by meteorology variations. August nitrate concentrations decreased by a maximum of 68% in New York City but Detroit experienced increasing August nitrate concentrations by up to 33%. On average, ~33% of the reduction in nitrate is offset by increases associated with meteorological conditions that favor nitrate formation. Coal combustion and natural gas are the dominant sources for sulfate in both seasons. January sulfate decrease from 2000 to 2006 in all cities by 4-58% except New York City, which increases by 13%. On average, ~93% of the reduction in sulfate was attributed to emission controls with 7% associated with changes in meteorology. August sulfate concentrations decrease by 11-44% in all cities. On average, emission controls alone between 2000 and 2006 would have caused 6% more reduction but the effectiveness of the controls was mitigated by meteorology conditions more favorable to sulfate production in 2006 vs. 2000. The results of this study suggest that regional emissions controls between 2000 and 2006 have been effective at reducing population exposure to PM2.5 in the eastern US, but yearly variations in meteorology must be carefully considered when assessing the exact magnitude of the control benefits.

Atmospheric wet deposition of sulfur and nitrogen in Jiuzhaigou National Nature Reserve, Sichuan Province, China

In the last two decades, remarkable ecological changes have been observed in Jiuzhaigou National Nature Reserve (JNNR). Some of these changes might be related to excessive deposition of sulfur (S) and nitrogen (N), but the relationship has not been quantified due to lack of monitoring data, particularly S and N deposition data. In this study, we investigated the concentrations, fluxes, and sources of S and N wet deposition in JNNR from April 2010 to May 2011. The results show that SO4(2-), NO3-, and NH4+ concentrations in the wet deposition were 39.4-170.5, 6.2-34.8, and 0.2-61.2 μeq L(-1), with annual Volume-Weighted Mean (VWM) concentrations of 70.5, 12.7, and 13.4 μeq L(-1), respectively. Annual wet deposition fluxes of SO4(2-), NO3-, and NH4+ were 8.06, 1.29, and 1.39 kg S(N)ha(-1), respectively, accounting for about 90% of annual atmospheric inputs of these species at the monitoring site. The results of Positive Matrix Factorization (PMF) analysis show that fossil fuel combustion, agriculture, and aged sea salt contributed to 99% and 83% of annual wet deposition fluxes of SO4(2-) and NO3-, respectively. Agriculture alone contributed to 89% of annual wet deposition flux of NH4+. Although wet deposition in JNNR was polluted by anthropogenic acids, the acidity was largely neutralized by the Ca2+ from crust and 81% of wet deposition samples had a pH higher than 6.00. However, acid rain mainly caused by SO4(2-) continued to occur in the wet season, when ambient alkaline dust concentration was lower. Since anthropogenic emissions have elevated S and N deposition and caused acid rain in JNNR, further studies are needed to better quantify the regional sources and ecological effects of S and N deposition for JNNR.