Influence of Urbanization on the Spatial Distribution of Associations Between Air Pollution and Mortality in Beijing, China.

This study investigated the influence of urbanization on the intra-city spatial distribution of associations between air pollution and mortality in Beijing, China. First, we utilized the generalized additive model to establish the exposure-response associations of PM2.5, O3, with nonaccidental and cardiorespiratory mortality between urban and suburban areas. Second, we assessed district-specific air pollution-related mortality and analyzed how these associations were affected by the degree of urbanization. Finally, we analyzed the changes in air pollution-related mortality before and after the enforcement of the Air Pollution Prevention and Control Action Plan (referred to as the Action Plan). The effect estimates of PM2.5 for nonaccidental mortality were 0.20% (95% CI: 0.12-0.28) in urban areas and 0.46% (95% CI: 0.35-0.58) in suburban areas per 10 µg/m3 increase in PM2.5 concentrations. The corresponding estimates of O3 were 0.13% (95% CI: -0.04-0.29) in urban areas and 0.34% (95% CI: 0.12-0.56) in suburban areas per 10 µg/m3 increase in O3 concentrations; however, the difference between the estimates of O3 in urban and suburban areas was not statistically significant. The district-specific results suggested that the estimated risks increased along with urban vulnerability levels for the effects of PM2.5. Implementing the Action Plan reduced the mortality risks of PM2.5, but the risks of O3 increased in some districts. However, the difference in the estimates between the pre- and post-emission reductions was not statistically significant. Our study indicated that populations living in less urbanized areas are more vulnerable to the adverse effects of air pollution in Beijing, particularly for PM2.5.

Assessment of black carbon exposure level and health economic loss in China.

Based on the geographic information system (GIS) software and the application of the black carbon (BC) and fine particulate matter ([Formula: see text]) ratio method, this paper analyzed and calculated the national BC distribution from 2015 to 2017 and evaluated the national human exposure to BC. The results showed that from 2015 to 2017, 2/3 of the national land area and nearly half of the population were exposed to 1-3 [Formula: see text], and the area and population exposed to a concentration less than 2 [Formula: see text] increased yearly, while the area and population exposed to a concentration higher than 9 [Formula: see text] decreased yearly. The estimated economic loss showed that 77.3% of the targeted districts or counties claimed a loss per square kilometer of 50 million Chinese Yuan (CNY) or less from the perspective of annual changes, and districts and counties in Beijing-Tianjin-Hebei and Hunan with annual losses between 50 and 500 million CNY showed an increasing trend. The BC ratio (the proportion of BC economic loss to GDP) of Beijing-Tianjin-Hebei and Hunan also showed an increasing trend yearly.

Assessment of resident's exposure level and health economic costs of PM10 in Beijing from 2008 to 2012.

Epidemiological studies have asserted a negative association between atmospheric particulates and human health, especially particulate matter (PM10), which can cause a noticeable damage to human health. In recent years, PM10 has become the primary pollutant in major cities in China. It is crucial to evaluate the health impacts of PM10 to make pollution control policies and protect public health. For health-based assessments, human exposure evaluation is a key step, which is related to offering an exact exposure date for assessment. Using high-density PM10 and population data based on the Geographic Information System (GIS), this study estimated the impact of PM10 on human exposure levels and combined the exposure-response function with the health-economic loss relationship to assess the effect of PM10 on human health in Beijing from 2008 to 2012 quantitatively. The results showed that the population distribution was highly centralized in urban areas, especially inside the fifth ring road. A high proportion, 63.4% of the population, was exposed to the range of 120 to 130µg/m(3). Approximately 44.1% of that population was located inside the fifth ring road, and approximately 55.9% of it was located outside of the fifth ring road. The spatial distribution of the economic cost associated with PM10 from 2008 to 2012 was uneven, being highly centralized in urban areas, especially inside the fifth ring road, similar to the population densities. The economic cost increased from 2008 to 2012, similar to GDP. The proportion of economic cost to Beijing's GDP decreased from 2008 to 2012. The average economic cost of 5years inside the fifth ring road was 4.55billion US$; that of the outside was 4.95billion US$. The proportions of average economic losses compared with GDP inside and outside of the fifth ring road changed slightly in the period from 2008 to 2012.

Assessment of population exposure to PM10 for respiratory disease in Lanzhou (China) and its health-related economic costs based on GIS.

BACKGROUND: Evaluation of the adverse health effects of PM10 pollution (particulate matter less than 10 microns in diameter) is very important for protecting human health and establishing pollution control policy. Population exposure estimation is the first step in formulating exposure data for quantitative assessment of harmful PM10 pollution. METHODS: In this paper, we estimate PM10 concentration using a spatial interpolation method on a grid with a spatial resolution 0.01° × 0.01°. PM10 concentration data from monitoring stations are spatially interpolated, based on accurate population data in 2000 using a geographic information system. Then, an interpolated population layer is overlaid with an interpolated PM10 concentration layer, and population exposure levels are calculated. Combined with the exposure-response function between PM10 and health endpoints, economic costs of the adverse health effects of PM10 pollution are analyzed. RESULTS: The results indicate that the population in Lanzhou urban areas is distributed in a narrow and long belt, and there are relatively large population spatial gradients in the XiGu, ChengGuan and QiLiHe districts. We select threshold concentration C0 at: 0 µg m(-3) (no harmful health effects), 20 µg m(-3) (recommended by the World Health Organization), and 50 µg m(-3) (national first class standard in China) to calculate excess morbidity cases. For these three scenarios, proportions of the economic cost of PM10 pollution-related adverse health effects relative to GDP are 0.206%, 0.194% and 0.175%, respectively. The impact of meteorological factors on PM10 concentrations in 2000 is also analyzed. Sandstorm weather in spring, inversion layers in winter, and precipitation in summer are important factors associated with change in PM10 concentration. CONCLUSIONS: The population distribution by exposure level shows that the majority of people live in polluted areas. With the improvement of evaluation criteria, economic damage of respiratory disease caused by PM10 is much bigger. The health effects of Lanzhou urban residents should not be ignored. The government needs to find a better way to balance the health of residents and economy development. And balance the pros and cons before making a final policy.

An assessment of China's PM10-related health economic losses in 2009.

Using GIS software and based on exposure-response functions, this paper estimated the health-related economic losses that China suffered in 2009 due to the presence of particulate matter (PM(10)). The results show that China suffered a health-related economic loss due to PM(10) of US$ 106.5 billion, or 2.1% of China's GDP, for the year 2009. Some urban areas, including Beijing, Tianjin, Shanghai, Guangzhou, Chongqing, and Changsha, reported large health-related economic losses due to PM(10), with a value of US$ 1.5 million per square kilometre or greater. Some parts of Beijing, Ji'nan, and Chongqing reported health-related economic losses due to PM(10) as being greater than 4% of the 2009 GDP.