2010

Perspectives on the challenge posed by potential future climate change are presented including a discussion of prospects for carbon capture followed either by sequestration or reuse including opportunities for alternatives to the use of oil in the transportation sector. The potential for wind energy as an alternative to fossil fuel energy as a source of electricity is outlined including the related opportunities for cost effective curtailment of future growth in emissions of CO₂.

Although China has surpassed the United States as the world’s largest carbon dioxide emitter, in situ measurements of atmospheric CO2 have been sparse in China. This paper analyzes hourly CO2 and its correlation with CO at Miyun, a rural site near Beijing, over a period of 51 months (Dec 2004 through Feb 2009). The CO2-CO correlation analysis evaluated separately for each hour of the day provides useful information with statistical significance even in the growing season. We found that the intercept, representing the initial condition imposed by global distribution of CO2 with influence of photosynthesis and respiration, exhibits diurnal cycles differing by season. The background CO2 (CO2,b) derived from Miyun observations is comparable to CO2 observed at a Mongolian background station to the northwest. Annual growth of overall mean CO2 at Miyun is estimated at 2.7 ppm yr−1 while that of CO2,b is only 1.7 ppm yr−1 similar to the mean growth rate at northern mid-latitude background stations. This suggests a relatively faster increase in the regional CO2 sources in China than the global average, consistent with bottom-up studies of CO2 emissions. For air masses with trajectories through the northern China boundary layer, mean winter CO2/CO correlation slopes (dCO2/dCO) increased by 2.8±0.9 ppmv/ppmv or 11% from 2005–2006 to 2007–2008, with CO2 increasing by 1.8 ppmv. The increase in dCO2/dCO indicates improvement in overall combustion efficiency over northern China after winter 2007, attributed to pollution reduction measures associated with the 2008 Beijing Olympics. The observed CO2/CO ratio at Miyun is 25% higher than the bottom-up CO2/CO emission ratio, suggesting a contribution of respired CO2 from urban residents as well as agricultural soils and livestock in the observations and uncertainty in the emission estimates.

A new methodology is developed to constrain
Chinese anthropogenic emissions of nitrogen oxides (NOx)
from four major sectors (industry, power plants, mobile and
residential) in July 2008. It combines tropospheric NO2 column
retrievals from GOME-2 and OMI, taking advantage
of their different passing time over China (10:00 a.m. LT
(local time) versus 02:00 p.m.) and consistent retrieval algorithms.
The approach is based on the difference of NOx
columns at the overpass times of the two instruments; it thus
is less susceptible to the likely systematic errors embedded
in individual retrievals that are consistent with each other.
Also, it explicitly accounts for diurnal variations and uncertainties
of NOx emissions for individual sources. Our best
top-down estimate suggests a national budget of 6.8 TgN/yr
(5.5 TgN/yr for East China), close to the a priori bottom-up
emission estimate from the INTEX-B mission for the year of
2006. The top-down emissions are lower than the a priori
near Beijing, in the northeastern provinces and along the east
coast; yet they exceed the a priori over many inland regions.
Systematic errors in satellite retrievals are estimated to lead
to underestimation of top-down emissions by at most 17%
(most likely 10%). Effects of other factors on the top-down
estimate are typically less than 15% each, including lightning,
soil emissions, mixing in planetary boundary layer, anthropogenic
emissions of carbon monoxide and volatile organic
compounds, magnitude of a priori emissions, assumptions
on emission diurnal variations, and uncertainties in the
four sectors. The a posteriori emission budget is 5.7 TgN/yr
for East China.

The book offers a comprehensive account of how the world evolved to its present state in which humans now exercise a powerful, in many cases dominant, influence for global environmental change. It outlines the history that led to this position of dominance, in particular the role played by our increasing reliance on fossil sources of energy, on coal, oil and natural gas, and the problems that we are now forced to confront as a result of this history. The concentration of carbon dioxide in the atmosphere is greater now than at any time over at least the past 650,000 years with prospects to increase over the next few decades to levels not seen since dinosaurs roamed the Earth 65 million years ago. Comparable changes are evident also for methane and nitrous oxide and for a variety of other constituents of the atmosphere including species such as the ozone depleting chlorofluorocarbons for which there are no natural analogues.

Increases in the concentrations of so-called greenhouse gases in the atmosphere are responsible for important changes in global and regional climate with consequences for the future of global society which, though difficult to predict in detail, are potentially catastrophic for a world poorly equipped to cope. Changes of climate in the past were repetitively responsible for the demise of important civilizations. These changes, however, were generally natural in origin in contrast to the changes now underway for which humans are directly responsible. The challenge is to transition to a new energy economy in which fossil fuels will play a much smaller role. We need as a matter of urgency to cut back on emissions of climate altering gases such as carbon dioxide while at the same time reducing our dependence on unreliable, potentially disruptive, though currently indispensable, sources of energy such as oil, the lifeblood of the global transportation system. The book concludes with a discussion of options for a more sustainable energy future, highlighting the potential for contributions from wind, sun, biomass, geothermal and nuclear, supplanting currently unsustainable reliance on coal, oil and natural gas.

Field measurements and data investigations were conducted for developing an emission factor database for inventories of atmospheric pollutants from Chinese coal-fired power plants. Gaseous pollutants and particulate matter (PM) of different size fractions were measured using a gas analyzer and an electric low-pressure impactor (ELPI), respectively, for ten units in eight coal-fired power plants across the country. Combining results of field tests and literature surveys, emission factors with 95% confidence intervals (CIs) were calculated by boiler type, fuel quality, and emission control devices using bootstrap and Monte Carlo simulations. The emission factor of uncontrolled SO₂ from pulverized combustion (PC) boilers burning bituminous or anthracite coal was estimated to be 18.0S kg t⁻¹ (i.e., 18.0 × the percentage sulfur content of coal, S) with a 95% CI of 17.2S–18.5S. NO_X emission factors for pulverized-coal boilers ranged from 4.0 to 11.2 kg t⁻¹, with uncertainties of 14–45% for different unit types. The emission factors of uncontrolled PM_2.5, PM₁₀, and total PM emitted by PC boilers were estimated to be 0.4A (where A is the percentage ash content of coal), 1.5A and 6.9A kg t⁻¹, respectively, with 95% CIs of 0.3A–0.5A, 1.1A–1.9A and 5.8A–7.9A. The analogous PM values for emissions with electrostatic precipitator (ESP) controls were 0.032A (95% CI: 0.021A–0.046A), 0.065A (0.039A–0.092A) and 0.094A (0.0656A–0.132A) kg t⁻¹, and 0.0147A (0.0092–0.0225A), 0.0210A (0.0129A–0.0317A), and 0.0231A (0.0142A–0.0348A) for those with both ESP and wet flue-gas desulfurization (wet-FGD). SO₂ and NO_X emission factors for Chinese power plants were smaller than those of U.S. EPA AP-42 database, due mainly to lower heating values of coals in China. PM emission factors for units with ESP, however, were generally larger than AP-42 values, because of poorer removal efficiencies of Chinese dust collectors. For units with advanced emission control technologies, more field measurements are needed to reduce emission factor uncertainties.

Mixing in the planetary boundary layer (PBL) affects vertical distributions of air tracers in the lower troposphere. An accurate representation of PBL mixing is critical for chemical-transport models (CTMs) for applications sensitive to simulations of the vertical profiles of tracers. The full mixing assumption in the widely used global CTM GEOS-Chem has recently been supplemented with a non-local PBL scheme. This study analyzes the impact of the non-local scheme on model representation of PBL mixing, consequences for simulations of vertical profiles of air tracers and surface air pollution, and implications for model applications to the interpretation of data retrieved from satellite remote sensing. The non-local scheme significantly improves simulations of the vertical distributions for NO₂ and O₃, as evaluated using aircraft measurements in summer 2004. It also reduces model biases over the U.S. by more than 10 ppb for surface ozone concentrations at night and by 2–5 ppb for peak ozone in the afternoon, as evaluated using ground observations. The application to inverse modeling of anthropogenic NO_x emissions for East China using satellite retrievals of NO₂ from OMI and GOME-2 suggests that the full mixing assumption results in 3–14% differences in top–down emission budgets as compared to the non-local scheme. The top–down estimate combining the non-local scheme and the Lin et al. inverse modeling approach suggests a magnitude of 6.6 TgN yr⁻¹ for emissions of NO_x over East China in July 2008 and 8.0 TgN yr⁻¹ for January 2009, with the magnitude and seasonality in good agreement with bottom–up estimates.

This study uses geographically weighted regressions and multilevel models to understand the implications of location and attitudinal characteristics for travel behavior in Chengdu, China. In particular, the estimated distance traveled and the mode choice of nonmotorized versus motorized vehicles for work- and school-related trips were examined by using a recent household trip diary data set. The results suggest that location characteristics may be influential in the prediction of travel behavior but cannot be fully captured by simple categorization such as inner ring location versus peripheral location. Variations in travel behavior can be related to socioeconomic and location variables in ways that vary by location in a complex manner. Policy makers should therefore reconsider the role that location and attitudinal implications may play in meeting travel demand in rapidly developing cities like Chengdu.

The rising prevalence of private cars in the developing world is causing serious congestion and pollution. In China, private cars started to emerge as an important travel mode in the past decade. Prospective research on the relationship between urban form and car ownership is relatively uncommon in the developing world, and China offers a unique study opportunity, given the tremendous increases in private cars and fast-paced urbanization over the past decade. This study investigates the influence of urban form on car ownership as well as the impact of other socioeconomic and demographic factors on private car ownership across megacities in China. Analysis was conducted through the use of data from 36 megacities and two household survey data sets collected in Beijing and the city of Chengdu, China. Ordinary least squares regression and discrete choice models were employed to execute the aggregate and disaggregate analysis of the urban form impact on private car ownership across cities. The statistical model results demonstrate that urban affluence, urban scale, and road infrastructure supply factors have significant positive effects on the city level of private car ownership across cities. Population density calculated at the subdistrict level, however, had a significant negative effect on private car ownership across cities. Households with private cars were found to prefer to live close to urban centers where amenities were readily available. The results provide evidence for urban planners and policy makers.

The Chinese government has moved aggressively since 2005 to reduce emissions of a number of pollutants including primary particulate matter (PM) and sulfur dioxide (SO₂), efforts inadvertently aided since late 2008 by economic recession. Satellite observations of aerosol optical depth (AOD) and column nitrogen dioxide (NO₂) provide independent indicators of emission trends, clearly reflecting the sharp onset of the recession in the fall of 2008 and rebound of the economy in the latter half of 2009. Comparison of AOD with ground-based observations of PM over a longer period indicate that emission-control policies have not been successful in reducing concentrations of aerosol pollutants at smaller size range over industrialized regions of China. The lack of success is attributed to the increasing importance of anthropogenic secondary aerosols formed from precursor species including nitrogen oxides (NO_x), non-methane volatile organic compounds (NMVOC), and ammonia (NH₃).

While transport modelers in developed countries are accustomed to working with relatively rich datasets including transport networks and land use data, such databases are rarely available in developing countries. However, developing countries such as China with its immense rate of economic growth are, arguably, most in need of demand models. The research addressed in this paper is how to develop mode choice models for planning and policy analysis when high quality level of service data are not available. The research makes use of a 1,001 household travel and activity survey from Chengdu collected by the China Project at Harvard University in 2005. Chengdu has an urban population of over 3 million and a GDP growth rate of over 20% per year. The survey contains a rich array of self-assessed information on available modes and accessibility and also includes a number of attitudinal questions. The approach taken here is to treat level of service as a latent (i.e., unobservable) variable. Measurement equations (from the structural equation model paradigm) are used to infer latent level of service, and these equations are integrated with the mode choice model. Our initial results indicate that models that do not correct for measurement error may significantly underestimate travelers' values of time. The methodological approach employed has potential for improving models estimated with higher quality network data, because it can correct for measurement error that exists, for example, in network-derived level of service variables.

We examine seasonal variations of carbon monoxide (CO), ozone (O₃), and their relationships observed over the course of 3 yr (2005–2007) at Miyun, a rural site 100 km north of Beijing. Monthly mean afternoon mixing ratios of CO have broad maxima in winter and a secondary peak in June. Monthly mean afternoon O₃ shows a clear seasonal pattern with a major peak in June (85 ppb), a secondary peak in September (65 ppb) and minimum in winter (50–55 ppb). The seasonal cycles of O₃ and CO are associated with seasonal changes in dominant synoptic pattern. Substantial interannual variability is found for CO which is attributed to the interannual variability of meteorology and emissions from biomass burning. The seasonality and magnitude of background CO and O₃ derived at Miyun are consistent with observations at upwind remote continental sites. The O₃–CO correlation slope is about 0.07 ppb ppb⁻¹ on average in summer, significantly lower than the typical slope of 0.3 ppb ppb⁻¹ reported for developed countries. The O₃–CO correlation slope shows large gradients for different types of air masses (0.133 ± 0.017 ppb ppb⁻¹ in aged urban pollution plumes and 0.047 ± 0.008 ppb ppb⁻¹ in biomass burning plumes), suggesting that the conventional method of direct scaling the mean O₃–CO slope by CO emissions to deduce O₃ production rate is subject to large uncertainties if applied for China.