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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium Public Health

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium Ambient Air Pollution in Shanghai: A Health-Based Assessment HAIDONG KAN and BINGHENG CHEN Department of Environmental Health School of Public Health Fudan University CHANGHONG CHEN Shanghai Academy of Environmental Science To investigate the potential impact of ambient air pollution on public health and the economy in Shanghai, we estimated exposure levels of the general population under various planned energy scenarios and then assessed potential health impacts using concentration-response functions derived from available epidemiologic studies. We then estimated the corresponding economic values of the health effects based on the unit values of the health outcomes. The results showed that ambient air pollution could have a significant impact on the health of Shanghai residents, both in physical and monetary terms. Compared with the base-case (BC) scenario, implementation of various planned energy scenarios could prevent many premature deaths (from 608 to 5,144 in 2010 and from 1,189 to10,462 in 2020) and could substantially reduce the number of pollution-related diseases. The expected economic benefits vary widely depending on the underlying assumptions. Thus, energy and environmental policy could not only reduce air pollution and improve air quality, but could also protect public health and benefit the economy. Energy and related health issues are of growing concern worldwide. Fossil fuels, the primary sources of energy, are also the greatest sources of ambient air pollution—nitrogen oxides (NOx), sulfur dioxide (SO2), dust, soot, smoke, and other suspended fine particulate matter (PM). These pollutants can lead to serious public health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. The burning of fossil fuels is also the major source of carbon dioxide (CO2) emissions, a primary contributor to global warming. In China, a developing country, coal has been the primary energy source.

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium Rapid economic growth has been accompanied by a rapid increase in the demand for energy, and the relatively inefficient energy technology currently used in China has caused high pollution emissions. However, China has been making great efforts to save energy, optimize its energy structure, and increase energy efficiency to balance energy consumption and environmental needs. As a result of these efforts, the increase in energy consumption has been lower than the increase in gross domestic product (GDP) in China during the past decade. Clearly, energy-policy decisions today will have a significant impact on future air pollution levels and public health. In the present study, we estimate the public health impact of ambient air pollution under various energy scenarios in Shanghai, one of the fastest growing urban areas in China, and place monetary values on the estimated health effects. METHODS Development of Energy Scenarios The MARKAL (MARKet ALlocation) optimization model was used to estimate pollutant emissions under various energy scenarios in Shanghai. MARKAL is a dynamic, linear programming model that optimizes a technology-rich network representation of an energy system. MARKAL models the economy of a region as a system, represented by processes and physical and monetary flows among those processes. Details about the application of MARKAL for energy and environmental policies in Shanghai have been discussed elsewhere (Gielen and Chen, 2001). The energy scenarios developed for Shanghai can be classified into four TABLE 1 Elements of the Scenarios   Economic Growth Improvement in Energy Efficiency Maximum Coal Use Electricity Imports BC √       EFF √ √     NG √ √ √ √ SO2 √ √ √ √ NOx √ √ √ √ CO2 √ √ √ √

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium categories: (1) base-case (BC); (2) energy options (i.e., use of efficiency measures [eff] or natural gas [NG]); (3) pollution targets (i.e., constraints on SO2 or NOx); and (4) control of CO2 emissions. The details of the scenarios are shown in Table 1. Concentrations of Ambient Air Pollutants Based on the principle of transfer matrix, an air-quality model (the Exposure Level Model) was developed to link MARKAL emission scenarios and concentrations of ambient air pollutants. Input to the fundamental matrix was a long-range transport and deposition model (ATMOS model) for SO2 and primary PM10 (particulates with diameters of 10 mm or less). The ATMOS model is a Lagrangian parcel model with three vertical layers (Calori and Carmichael, 1999). For the Shanghai project, the ATMOS model provided a 4 × 4 km resolution of the concentration of SO2 and primary PM10. The total area of Greater Shanghai, 6341 km2, was divided into 487 grids. Based on matrix output of the ATMOS model, the Shanghai Exposure Level model was developed in Excel to link MARKAL predictions and provide exposure levels for the analysis of health impacts. Exposure Levels Because strong epidemiologic evidence supports the association of PM10 with adverse health effects, PM10 was selected as a useful indicator of several sources of outdoor air pollution, such as fossil-fuel combustion (Wilson and Spengler, 1996). Availability of Natural Gas Control of SO2 Emissions Control of NOx Emissions Control of PM10 Emissions Tax on CO2 √         √ √       √ √ √     √ √ √ √ √

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium Everyone living in Greater Shanghai was considered to be in the exposed population in this analysis. The number of Shanghai residents in each 4 × 4 km grid cell was estimated based on population data from the Shanghai Bureau of Statistics. Combining the PM10 level and population in each grid cell, we estimated exposure levels to outdoor air pollution under various scenarios in 2010 and 2020. Estimations of Health Effects To develop estimates of public health impacts of air pollution, we used concentration-response (C-R) coefficients derived from published, peer-reviewed studies in China and worldwide. Because most epidemiologic studies linking air pollution and health effects are based on a relative-risk model in the form of a Poisson regression, health effects at a given concentration, C, could be given by the following equation: (1) C and C0 are the PM10 concentrations, and E and E0 are the corresponding health effects under a specific scenario and the BC scenario, respectively. β is the exposure-response function. Exposure-response functions (β) link air quality changes and heath outcomes. Our preference was to use C-R functions from Chinese studies whenever they were available. Results in the international peer-reviewed literature were used only when the selected end points could not be found in the Chinese literature. If there were several studies describing a C-R function for the same health end point, we used the pooled estimate to get the mean and 95 percent confidence interval (CI) of the coefficient. The results of the analysis were given as a comparison of the health effects under a specific scenario with the health effects under the BC scenario in 2010 and 2020, respectively. Economic Value of Health Effects The analysis was based mainly on the concept of willingness to pay (WTP); the cost of illness (COI) was used as an alternative for morbidity end points that could not be valued based on the existing literature. The effect of air pollution on mortality was assessed by using the value of a statistical life (VOSL). The literature on VOSL and WTP to avoid a statistical premature death is mainly from the United States. However, because of our limited time and budget, we used a contingent valuation study (CVM) conducted in Chongqing, China, (Wang, 2001) for the estimate of Shanghai VOSL. In the Chongqing study, Wang reported an average WTP of US$34,750. The marginal effect of increased income on WTP was also reported: with an increase in annual

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium income of US$145.80, the increase in WTP was US$14,550. Taking the annual income difference in 2001 between Chongqing (US$495.7) and Shanghai (US$1,234.5) into account, we did a conversion based on Chongqing’s coefficient between marginal WTP and income to get an estimate for the VOSL in Shanghai. Because data for China were not available for some end points of morbidity, we used a value based on the conversion used by the U.S. Environmental Protection Agency. The ratio for conversion was based on the per capita income of U.S. and Shanghai residents, and the income elasticity was assumed to be 1. The COI for the end point of hospital admissions and outpatient visits was calculated from data from Shanghai because no studies based on WTP were available for those end points. The economic value of a change in the incidence of an adverse health outcome was calculated as the change in incidence (the number of avoidable deaths) multiplied by the unit monetary value (the value of a single case avoided). Because of inherent uncertainties in the health and economic impact assessment, the results were given as a range (mean and 95 percent CI). Because both health outcomes and unit values were distributions rather than constants, we conducted a Monte Carlo simulation to calculate the economic values. RESULTS Exposures to Particulates in Shanghai Table 2 summarizes the percentage of the population exposed to different levels of PM10 under various scenarios in 2010 and 2020. It should be emphasized that the PM10 levels in Table 2 are much lower than the actual concentrations in Shanghai because only PM10 from energy consumption was included. PM10 from other sources, such as natural sources, construction sites, and so forth, was not included. Estimates of Health Effects Table 3 summarizes the PM10 exposure-response coefficients (mean and 95 percent CI) and baseline rates of selected health outcomes. The excess cases in each scenario (compared to the BC scenario) were computed based on the change in exposure levels to PM10 under each scenario, exposure-response functions, and baseline rates for the health outcomes. The potential health benefits in 2010 and 2020 shown in Table 4 clearly indicate that the choice of energy scenario could have a significant impact on the health of Shanghai residents. Compared with the BC scenario, the implementation of other energy scenarios could prevent 608 to 5,144 PM10-related deaths in 2010 and 1,189 to 10,462 PM10-related deaths in 2020.

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium TABLE 2 Exposure Levels of PM10 under Different Scenarios for 2010 and 2020   2010 PM10 level (µg/m3) BC EFF NG SO2 NOx CO2 <5 – – 0.1 0.1 0.4 0.8 10–15 2.0 2.3 4.5 4.7 7.5 9.6 15–20 6.4 6.8 9.2 9.8 12.5 13.3 20–25 8.2 8.5 11.1 10.8 8.3 7.9 25–30 8.3 8.0 5.1 5.5 6.1 8.2 30–35 4.4 4.2 5.3 5.8 13.0 44.6 35–40 3.3 4.5 9.1 12.6 51.6 15.6 40–45 4.8 5.2 35.7 38.2 0.6 – 45–50 7.0 11.1 19.9 12.5 – – 50–55 18.1 31.5 – – – – 55–60 – – – – – – 60–65 – – – – – – 65–70 – – – – – – 70–75 – – – – – – >75 – – – – – – Economic Value of Estimated Health Effects Table 5 summarizes the unit values (mean and 95 percent CI) for various end points in 2000 in Shanghai and the specific approach used to derive them. By combining the health benefits and unit values, we computed the economic benefits compared to the BC scenario. Table 6 shows the results for 2010 and 2020. DISCUSSION The link between energy and health must be taken into account in decisions addressing rapid economic growth and sustainable development in Shanghai. This study, which is based on the same approaches used internationally for assessments of environmental impacts, shows that an effective energy and environmental policy will be an important factor in reducing air pollution, improving air quality, and promoting public health. The quantification of the impact of air pollution on public health is a critical component in environmental policy decisions. Given the gaps in scientific knowledge about the health effects of air pollution and the wide range of uncertainties characterizing many aspects of the process, analyzing the total burden of ambient air pollution on public health is a challenging task.

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium 2020 BC EFF NG SO2 NOx CO2 – – – – 0.3 0.9 0.3 0.5 2.9 2.9 6.9 9.6 2.6 2.9 7.6 7.6 12.8 13.8 5.1 5.6 9.9 10.0 7.9 7.6 5.7 6.2 6.5 6.5 5.7 10.6 6.8 6.4 4.6 4.7 12.1 50.1 5.2 5.4 4.2 5.0 44.4 7.4 3.0 2.7 8.4 7.5 9.9 – 2.9 2.9 18.8 19.8 – – 2.4 3.3 37.1 36.0 – – 4.0 3.9 – – – – 2.4 8.9 – – – – 12.4 16.0 – – – – 13.3 35.3 – – – – 33.9 – – – – – Our estimates were conservative for three reasons. First, in the present analysis we selected only PM10 as an indicator of outdoor air pollution and probably overlooked adverse health effects from exposure to other air pollutants; thus, we probably underestimated the health effects attributable to total air pollution. Although PM10 may be a good indicator, there is clear evidence that other pollutants, such as ozone, NOx, and SO2, have independent health effects. In addition, we did not include synergistic effects among air pollutants or with cofactors, such as pollen and other allergens. Second, the ATMOS model we used could only deal with primary PM10 and SO2. Thus, we underestimated the health effects attributable to secondary PM10, such as sulfate and nitrate. Previous studies have shown that ammonium sulfate and nitrate account for substantial proportions of fine particles in Shanghai (Ye et al., 2003). Third, we focused only on health outcomes that could be quantitatively estimated and then translated into monetary values for further assessment. Some end points, such as subclinical symptoms and decreased pulmonary function, were not included in this analysis, although there is evidence of an association between them and exposure to air pollution. We did not estimate cancer-related

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium TABLE 3 Exposure-Response Coefficients and Baseline Rate for Exposure to PM10 (per person) Health Outcome (by age group) Mean (95 percent CI) Reference Frequency Reference Long-term mortality (adult ≥ 30) 0.00430 (0.00260–0.00610) Dockery et al., 1993; Pope et al., 1995 0.01077 Shanghai Municipal Bureau of Public Health, 2000 Chronic bronchitis (all ages) 0.00450 (0.00127–0.00773) Ma and Hong, 1992; Jin et al., 2000 0.01390 China Ministry of Health, 1998 Respiratory hospital admission (all ages) 0.00130 (0.00010–0.00250) Zmirou et al., 1998; Wordley et al., 1997; Prescott et al., 1998 0.01240 Shanghai Municipal Bureau of Public Health, 2000 Cardiovascular hospital admission (all ages) 0.00130 (0.00070–0.00190) Wordley et al., 1997; Prescott et al., 1998 0.00850 Shanghai Municipal Bureau of Public Health, 2000 Outpatient visits—internal medicine (all ages) 0.00034 (0.00019–0.00049) Xu et al., 1995 3.26000 Shanghai Municipal Bureau of Public Health, 2000 Outpatient visits—pediatrics (all ages) 0.00039 (0.00014–0.00064) Xu et al., 1995 0.30000 Shanghai Municipal Bureau of Public Health, 2000 Acute bronchitis (all ages) 0.00550 (0.00189–0.00911) Jin et al., 2000 0.39000 Wang et al., 1994 Asthma attack (children < 15 years) 0.00440 (0.00270–0.00620) Roemer et al., 1993; Segala et al., 1998; Gielen et al., 1997 0.06930 Ling et al., 1996 Asthma attack (adults ≥ 15 years) 0.00390 (0.00190–0.00590) Dusseldorp et al., 1995; Hiltermann et al., 1998; Neukirch et al., 1998 0.05610 Ling et al., 1996

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium effects linked to exposures to ambient air pollution, although recent studies in the United States have suggested an association (e.g., Pope et al., 2002). Because some of the exposure-response functions we used in this analysis were not available in Chinese studies, we relied on international studies, conducted mostly in the United States and Western Europe. This raises the question of whether results from a developed country can be transferred to a developing country. For example, Chinese studies generally report lower coefficients for exposure-response relationships between air pollution and adverse health effects than studies in the United States and Europe. This is probably because of differences in levels of air pollution, local population sensitivity, age distribution, and especially air pollutant components. For instance, the composition of the motor vehicle fleet in Western Europe and the United States, where most of the epidemiological studies were performed, and the motor vehicle fleet in China differs substantially. Another major difference is the widespread use of coal in China, which suggests that the air pollution mix also differs substantially. Ideally, when exposure-response functions from developed countries are applied to other regions, for example, Shanghai, they should be revised to account for local conditions, such as the physical (diameter, etc.) and chemical (components) properties of particulates, the socioeconomic status of local populations, etc. However, no reference data are available for such revisions. Until locally derived exposure-response functions become available, this will probably be the weakest part of an analysis. Because no valuation study of the health end points associated with air pollution in Shanghai had been done before, we had to estimate values from previous studies of similar changes, a procedure called “benefit transfer” or “value transfer” in economics. Characteristics of the concerned population (e.g., age distribution, income, health status, culture) may have contextual effects on the valuation results. For example, different social and health-insurance systems greatly influence people’s risk perception, which affects the WTP to avoid the risk. If we had transferred the U.S. VOSL directly to the Shanghai study, after accounting for the income difference between the two sites, the VOSL would have been US$780,000, which is much higher than the VOSL estimated in the Chongqing study. The number would be even higher if we had used purchasing power parity (PPP) as the income definition. Obviously, the estimate used in the Chongqing study is better fitted to the Shanghai study in terms of the economic and social situation. Therefore, we tried to use Chinese studies wherever they were available and attempted to stay on the conservative side with a range of reasonable estimates. There are also inherent uncertainties in transferring values from other study sites, whether in China or elsewhere. Therefore, we strongly suggest that a WTP study for the avoidance of air-pollution-related health risks in Shanghai be undertaken, especially on the WTP to reduce the risk of premature death from air

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium TABLE 4 Health Benefits of Different Energy Scenarios Compared with the BC Scenario for 2010 and 2020 (mean value)   2010   EFF NG SO2 NOx CO2 Premature death 608 2,761 3,079 4,266 5,144 Chronic bronchitis 1,315 5,964 6,649 9,210 11,100 Respiratory hospital admissions 377 1,740 1,943 2,712 3,286 Cardiovascular hospital admissions 260 1,197 1,336 1,865 2,260 Outpatient visits (internal medicine) 27,080 125,400 14,0200 196,000 237,900 Outpatient visits (pediatrics) 2,807 13,000 14,530 20,320 24,660 Acute bronchitis 49,490 223,500 249,000 344,200 414,200 Asthma attacks 1,508 6,858 7,649 10,610 12,790 TABLE 5 Unit Values for End Points in 2000 (in 2000 US$) End Point Mean (95 percent CI) Approach Premature death 108,500 (101,900–115,100) WTP Chronic bronchitis 6,050 (807–20,130) WTP Respiratory hospital admissions 710a COI Cardiovascular hospital admissions 1,043a COI Outpatient visits (internal medicine) 14a COI Outpatient visits (pediatrics) 14a COI Acute bronchitis 7.2 (2.6–11.9) WTP Asthma attacks 5.3 (2.3–8.3) WTP aThe available data did not provide a distribution of values. pollution. Additional research will be necessary to improve our evaluations of the health outcomes associated with air pollution, which should include: factors that influence WTP (e.g., age, income, education level, pollution level); the relationship between WTP and quality of life; the relationship between private costs and lost output; people’s preferences in trade-offs among risks. In summary, despite the limitations of our analysis, the results highlight the importance of considering air-pollution-related health effects in decisions about energy options in Shanghai. As we move toward sustainable development and

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium 2020 EFF NG SO2 NOx CO2 1,189 6,424 6,541 9,219 10,462 2,580 13,910 14,160 19,940 22,620 704 3,918 3,991 5,707 6,522 485 2,694 2,744 3,924 4,485 49,850 279,600 284,900 409,200 468,600 5,173 29,000 29,540 42,430 48,590 98,520 526,400 535,900 751,300 850,800 2,937 15,910 16,200 22,860 25,960 health, close collaboration between public health policy and energy policy will increase the chances of preventing avoidable health hazards. The approaches recommended in this analysis can be used to evaluate other regions in China for local and nationwide air-pollution-related health-risk assessments and economic evaluations. But health-impact assessment methods must be improved and refined, especially in dealing with uncertainties, transferring exposure-response functions, and using more common health indicators, such as disability-adjusted life years (DALYs). These improvements will require close cooperation among air-pollution modelers, epidemiologists, economists, and policy makers. ACKNOWLEDGEMENT This project was cofunded by the U.S. Environmental Protection Agency (EPA) / U.S. National Renewable Energy Laboratory (NREL) Integrated Environmental Strategies Program (IES), China Council for International Cooperation on Environment and Development, China State Environmental Protection Bureau, China Ministry of Health, and Shanghai Environmental Protection Bureau.

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium TABLE 6 Economic Benefits of the BC Scenario for 2010 and 2020 (in millions of 2000 US$) (mean value)   2010   EFF NG SO2 NOx CO2 Premature death 104.0 469.0 524.0 729.2 873.2 Chronic bronchitis 7.5 34.0 36.9 52.4 60.7 Respiratory hospital admissions 0.4 1.8 2.0 2.9 3.5 Cardiovascular hospital admissions 0.4 1.8 2.1 2.9 3.5 Outpatient visits (internal medicine) 0.6 2.6 2.9 4.1 4.9 Outpatient visits (pediatrics) 0.1 0.3 0.3 0.4 0.5 Acute bronchitis 0.5 2.2 2.5 3.5 4.1 Asthma attacks 0.0 0.1 0.1 0.1 0.1 Total 113.5 511.8 570.8 795.5 950.5 BIBLIOGRAPHY AND REFERENCES Calori, G., and G.R. Carmichael. 1999. An urban trajectory model for sulfur in Asian megacities: model concepts and preliminary application. Atmospheric Environment 33(19): 3109–3117. China Ministry of Health. 1998. Second National Health Service Survey. Beijing: China Ministry of Health. Dockery, D.W., C.A. Pope, X. Xu, J.D. Spengler, J.H. Ware, M.E. Fay, B.G. Ferris, Jr., and F.E. Speizer. 1993. An association between air pollution and mortality in six U.S. cities. New England Journal of Medicine 329(24): 1753–1759. Dusseldorp, A., H. Kruize, B. Brunekreef, P. Hofschreuder, G. de Meer, and A.B. van Oudvorst. 1995. Association of PM10 and airborne iron with respiratory health of adults living near a steel factory. American Journal of Respiratory and Critical Care Medicine 152(6Pt1): 1932–1939. Gielen, D., and C. Chen. 2001. The CO2 emission reduction benefits of Chinese energy policies and environmental policies: a case study for Shanghai, from 1995–2020. Ecological Economics 39(2): 257–270. Gielen, M.H., S.C. van der Zee, J.H. van Wijnen, C.J. van Steen, and B. Brunekreef. 1997. Acute effects of summer air pollution on respiratory health of asthmatic children. American Journal of Respiratory and Critical Care Medicine 155(6): 2105–2108. Hiltermann, T.J., J. Stolk, S.C. van der Zee, B. Brunekreef, C.R. de Bruijne, P.H. Fischer, C.B. Ameling, P.J. Sterk, P.S. Hiemstra, and L. van Bree. 1998. Asthma severity and susceptibility to air pollution. European Respiratory Journal 11(3): 686–693. Jin, L.B., Y. Qin, and Z. Xu. 2000. Relationship between air pollution and acute and chronic respiratory disease in Benxi. Journal of Environment and Health 17: 268–270. In Chinese. Ling, S., J. Peng, and Z. Wang. 1996. A sample survey and multiple factor analysis on asthma in urban districts of Shanghai. Chinese Journal of Tubercular and Respiratory Disease 19: 11–13.

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium 2020 EFF NG SO2 NOx CO2 300.4 1618.4 1647.6 2340.0 2646.0 21.6 117.3 118.5 161.8 188.8 1.1 6.1 6.2 8.9 10.2 1.1 6.2 6.3 9.0 10.3 1.5 8.6 8.7 12.6 14.4 0.2 0.9 0.9 1.3 1.5 1.5 7.8 7.8 11.1 12.8 0.0 0.2 0.2 0.3 0.3 327.4 1765.5 1796.2 2544.5 2884.3 Ma, H.B., and C.J. Hong. 1992. Effects of particulate air pollution on respiratory disease. Chinese Journal of Public Health 11: 229–232. Neukirch, F., C. Ségala, Y. Le Moullec, M. Korobaeff, and M. Aubier. 1998. Short-term effects of low-level winter pollution on respiratory health of asthmatic adults. Archives of Environmental Health 53(5): 320–328. Pope, C.A., R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. Journal of American Medical Association 287(9): 1132–1141. Pope, C.A., M.J. Thun, M.M. Namboodiri, D.W. Dockery, J.S. Evans, F.E. Speizer, and C.W. Heath, Jr. 1995. Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults. American Journal of Respiratory and Critical Care Medicine 151(3Pt1): 669–674. Prescott, G.J., G.R. Cohen, R.A. Elton, F.G. Fowkes, and R.M. Agius. 1998. Urban air pollution and cardiopulmonary ill health: a 14.5 year time series study. Occupational and Environmental Medicine 55(10): 697–704. Roemer, W., G. Hoek, and B. Brunekreef. 1993. Effects of ambient winter air pollution on respiratory health of children with chronic respiratory symptoms. American Review of Respiratory Disease 147(1): 118–124. Segala, C., B. Fauroux, J. Just, L. Pascual, A. Grimfeld, and F. Neukirch. 1998. Short-term effect of winter air pollution on respiratory health of asthmatic children in Paris. European Respiratory Journal 11(3): 677–685. Shanghai Municipal Bureau of Public Health. 2000. Shanghai Health Yearbook. Pp. 54–58. Shanghai: Shanghai Science and Technology Literature Publishing House. Wang, H. 2001. Willingness to Pay for Reducing the Risk of Death by Improving Air Quality: A Contingent Valuation Study in Chongqing, China. Presented at 3rd International Health Economic Association Conference, July 22–25, 2001, York, England.

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium Wang, J., Q. Wang, and Z. Bi. 1994. A survey on acute respiratory disease. Chinese Journal of Epidemiology 15(3): 141–144. Wilson, R., and J. Spengler. 1996. Particles in Our Air: Concentrations and Health Effects. Cambridge, Mass.: Harvard University Press. Wordley, J., S. Walters, and J.G. Ayres. 1997. Short term variations in hospital admissions and mortality and particulate air pollution. Occupational and Environmental Health 54(2): 108–116. Xu, X., D.W. Dockery, D.C. Christiani, B. Li, and H. Huang. 1995. Association of air pollution with hospital outpatient visits in Beijing. Archives of Environmental Health 50(3): 214–220. Ye, B., X. Ji, H. Yang, X. Yao, C.K. Chan, S.H. Cadle, T. Chan, and P.A. Mulawa. 2003. Concentration and chemical composition of PM2.5 in Shanghai for a 1-year period. Atmospheric Environment 37(4): 499–510. Zmirou, D., J. Schawartz, M. Saez, A. Zanobetti, B. Wojtyniak, G. Touloumi, C. Spix, A. Ponce de Leon, Y. Le Moullec, L. Bacharova, J. Schouten, A. Ponka, and K. Katsouyanni. 1998. Time-series analysis of air pollution and cause-specific mortality. Epidemiology 9(5): 495–503.