The assessment of health damage caused by air pollution and its implication for policy making in Taiyuan, Shanxi, China

Zhang, Daisheng; Aunan, Kristin; Seip, H. M.; Larssen, Steinar; Liu, Jianhui; Zhang, Dingsheng

doi:10.1016/j.enpol.2009.09.039

Cited by 66 publications

(31 citation statements)

References 43 publications

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“…The city is surrounded by mountains on three sides, which lead to poor atmospheric diffusion and severe air pollution. PM 10 is the major pollutant of concern (Zhang et al, 2010). The main PM 10 compositions in Taiyuan City are generally organic carbon (OC), water-soluble ions, and levoglucosan.…”

Section: Area Descriptionmentioning

confidence: 99%

Evaluation of PM₁₀ forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City, China

Zhang

Liu

Shi

et al. 2013

Journal of the Air & Waste Management Association

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(BPANN) model with various air quality parameters. The predicted results of the models were consistent with the observations with a correlation coefficient of 0.72. The PM 10 yearly average concentrations combined with the population data from 2002 to 2008 were given into the Intake Fraction (IF) model to calculate the IFs, which are defined as the integrated incremental intake of a pollutant released from a source category or a region over all exposed individuals. The results in this study are only for main stationary sources of the research area, and the traffic sources have not been included. The computed IFs results are therefore under-estimations. The IFs of PM 10 from Taiyuan with a mean of 8.5 per million were relatively high compared with other IFs of the United States, Northern Europe and other cities in China. The results of this study indicate that the artificial neural network is an effective method for PM 10 pollution modeling, and the Intake Fraction model provides a rapid population risk estimate for pollutant emission reduction strategies and policies.Implications: The PM 10 (particulate matter with an aerodynamic diameter 10 µm) yearly average concentration of Taiyuan, with a mean of 0.176 mg/m 3 , was higher than the 65 µg/m 3 recommended by the U.S. Environmental Protection Agency (EPA). The spatial distribution of PM 10 yearly average concentrations showed that wind direction and wind speed played an important role, whereas temperature and humidity had a lower effect than expected. Intake fraction estimates of Taiyuan were relatively high compared with those observed in other cities. Population density was the major factor influencing PM 10 spatial distribution. The results indicated that the artificial neural network was an effective method for PM 10 pollution modeling.

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Section: Area Descriptionmentioning

confidence: 99%

Evaluation of PM₁₀ forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City, China

Zhang

Liu

Shi

et al. 2013

Journal of the Air & Waste Management Association

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“…In recent years researchers have cast doubts on some aspects of these methods: the use in a developing country of dose-response functions from the US has been questioned and the validity of benefit transfer has been disputed (Hammitt and Zhou, 2006;Liu et al, 1997;Ostro, 2004;Wang and Mullahy, 2006); some researchers have considered using quality adjusted life years (QALYs) or years of life lost (YOLL) rather than mortality (Kenkel, 2006); WTP is preferred in the developed world, but the alternative adjusted human capital (AHC) approach has been more widely used in China (Zhang et al, 2010).…”

Section: Methods Of Calculation: Sustainability Benefitmentioning

confidence: 99%

The role of offsets in a post-Kyoto climate agreement: The power sector in China

Partridge

Gamkhar

2010

Energy Policy

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“…Therefore, some outcomes that could not be quantified, such as reduced lung function, were not included in this analysis even though there is evidence that they are associated with exposure to air pollution (Zhang et al 2010, Chen et al 2007). …”

Section: Health Benefit Estimation Limitations and Uncertaintiesmentioning

confidence: 99%

“…The C-R coefficients also assume a linear dose-response, and thus a given exposure concentration reductions amount to the same risk of mortality or morbidity regardless of the initial concentration levels. This is likely one of the largest sources of uncertainty because different countries have different levels and components of air pollution as well as differences in population health status, sensitivity, and age distribution (Zhang et al 2010, Chen et al 2007). The sensitivity analysis of health benefits in Section 6 addresses this issue by looking at varying C-R coefficients.…”

Section: Health Benefit Estimation Limitations and Uncertaintiesmentioning

confidence: 99%

Quantifying the Co-benefits of Energy-Efficiency Programs: A Case Study of the Cement Industry in Shandong Province, China

Hasanbeigi

Lobscheid

Dai

et al. 2012

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Executive SummaryChina's cement industry accounted for more than half of the world's total cement production in 2010. The cement industry is one of the most energy-intensive and highest carbon dioxide (CO 2 )-emitting industries and one of the key industrial contributors to air pollution in China. For example, it is the largest source of particulate matter (PM) emissions in China, accounting for 40 percent of industrial PM emissions and 27 percent of total national PM emissions. Although specific regulations and policies are needed to reduce the pollutant emissions from the cement industry, air pollution can also be reduced as a co-benefit of energy efficiency and climate-change mitigation policies and programs. Quantifying and accounting for these cobenefits when evaluating energy efficiency and climate-change mitigation programs reveals benefits beyond the programs' energy and global warming impacts and adds to their cost effectiveness.In this study, we quantify the co-benefits of PM 10 and sulfur dioxide (SO 2 ) emissions reductions that result from energy-saving measures in China's cement industry. We use a modified form of the cost of conserved energy (CCE) equation to incorporate the value of these co-benefits: CCE co-ben = (annualized capital cost + annual change in operations&maintenance costs -annual co-benefits) annual energy savings (Equation ES-1)The annualized capital cost can be calculated as follows:where: d = discount rate (assumed 30 percent in this study) n = lifetime of the energy-efficiency measureWe used the following methodology to calculate CCE with co-benefits (CCE co-ben ):1. We established the year 2008 as the base year for energy, materials use, and production in 16 representative cement plants in Shandong Province. We also used 2008 data when modeling air quality and health impacts, as described below.2. We compiled a list of 34 commercially available technologies Out of the 34 measures, 29 are applicable to the cement plants in our study, 23 are electricity-saving measures, and 6 are fuel-saving measures. To quantify the air pollution emissions (PM and SO 2 ) reductions associated with the electricity-saving measures, we used relevant average emission factors for the electricity grid. We did not conduct the air quality modeling or analyze health impacts of the electricity-saving measures because the air pollution from electricity generation is emitted by power plants that are dispersed around the region which is beyond the scope of this study, and our goal in this study is in the air pollution effects of the cement plants themselves. Therefore, in quantifying co-benefits to be included in the CCE calculation, we focused only on the six fuel-saving measures because those measures reduce air pollution at the cement plant site.3. We assessed the potential application of the energy-efficiency technologies and measures in the 16 Shandong cement plants based on information collected from the plants.4. We calculated energy savings and CO 2 and air pollutant (PM 10 and SO 2 ) emissions reduc...

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The assessment of health damage caused by air pollution and its implication for policy making in Taiyuan, Shanxi, China

Cited by 66 publications

References 43 publications

Evaluation of PM₁₀ forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City, China

Evaluation of PM₁₀ forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City, China

The role of offsets in a post-Kyoto climate agreement: The power sector in China

Quantifying the Co-benefits of Energy-Efficiency Programs: A Case Study of the Cement Industry in Shandong Province, China

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The assessment of health damage caused by air pollution and its implication for policy making in Taiyuan, Shanxi, China

Cited by 66 publications

References 43 publications

Evaluation of PM10 forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City, China

Evaluation of PM10 forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City, China

The role of offsets in a post-Kyoto climate agreement: The power sector in China

Quantifying the Co-benefits of Energy-Efficiency Programs: A Case Study of the Cement Industry in Shandong Province, China

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Evaluation of PM₁₀ forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City, China

Evaluation of PM₁₀ forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City, China