Health impact assessment of a reduction in ambient PM2.5 levels in Spain

Boldo, Elena; Linares, Cristina; Lumbreras, Julio; Borge, Rafael; Narros, Adolfo; García‐Pérez, Javier; Fernández-Navarro, Pablo; Pérez‐Gómez, Beatriz; Aragonés, Núria; Ramis, Rebeca; Pollán, Marina; Moreno, Teresa; Karanasiou, Angeliki; López‐Abente, Gonzalo

doi:10.1016/j.envint.2010.10.004

Cited by 131 publications

(90 citation statements)

References 25 publications

(24 reference statements)

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“…In China, studies on PM 2.5 have been carried out since 2000, and have mainly focused on the urban areas of the Pearl River Delta (Ho et al, 2014), the Yangtze River Delta (Li et al, 2011), and the Beijing-Tianjin-Hebei region (Wang et al, 2016;Zhao et al, 2013); these studies have been similar to research in other countries (Austin et al, 2013;Boldo et al, 2011;DeGaetano and Doherty, 2004). Most previous research has focused on measuring the chemical composition and spatial-temporal variability of PM 2.5 in China (Hu et al, 2014;Liu et al, 2009).…”

Section: Introductionmentioning

confidence: 90%

Spatial-temporal characteristics of PM2.5 in China: A city-level perspective analysis

Fang

Wang

2016

J. Geogr. Sci.

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Abstract:Haze pollution has become a severe environmental problem in the daily life of the people in China. PM 2.5 makes a significant contribution to poor air quality. The spatio-temporal features of China's PM 2.5 concentrations should be investigated. This paper, based on observed data from 945 newly located monitoring sites in 2014 and industrial working population data obtained from International Standard Industrial Classification (ISIC), reveals the spatio-temporal variations of PM 2.5 concentrations in China and the correlations among different industries. We tested the spatial autocorrelation of PM 2.5 concentrations in the cities of China with the spatial autocorrelation model. A correlation coefficient to examine the correlativity of PM 2.5 concentrations and 23 characteristic variables for 190 cities in China in 2014, from which the most important ones were chosen, and then a regression model was built to further reveal the social and economic factors affecting PM 2.5 concentrations. Results: (1) The Hu Huanyong Line and the Yangtze River were the E-W divide and S-N divide between high and low values of China. (2) The PM 2.5 concentrations shows great seasonal variation, which is high in autumn and winter but low in spring and summer. The monthly average shows a U-shaped pattern, and daily average presents a periodic and impulse-shaped change. (3) PM 2.5 concentrations had a distinct characteristic of spatial agglomeration. The North China Plain was the predominant region of agglomeration, and the southeastern coastal area had stable good air quality.

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Section: Introductionmentioning

confidence: 90%

Spatial-temporal characteristics of PM2.5 in China: A city-level perspective analysis

Fang

Wang

2016

J. Geogr. Sci.

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“…Some results have also shown an association between PM and cardiovascular and respiratory disease (Oberdorster et al, 1995;Burnett et al, 1997;Ostro et al, 2000;Brunekreef and Forsberg, 2005;Kan et al, 2007). In America and Europe, epidemiologic studies of PM 2.5 have shown that long-term exposure is associated with an increase in mortality due to lung cancer and other cardiopulmonary diseases (Naess et al, 2007;Brunekreef et al, 2009;Boldo et al, 2011). Therefore, PM characteristics such as size distribution and composition are important issues impacting the health effects of these atmospheric aerosols.…”

Section: Introductionmentioning

confidence: 99%

Size Distribution and Water Soluble Ions of Ambient Particulate Matter on Episode and Non-episode Days in Southern Taiwan

Tsai

Lin

Yao

et al. 2012

Aerosol Air Qual. Res.

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Distribution of inorganic ions at an urban/industrial/agricultural complex was investigated in southern Taiwan during particulate matter (PM) episode and non-episode periods, and the Micro Orifice Uniform Deposit Impactor (MOUDI) and nano-MOUDI were employed to take PM samples. In PM episode periods, the PM significantly increased in mass concentration and in the size ranges of 0.1-1.0 and 1.8-18 μm at the site. Sulfate, nitrate and ammonium were the dominant ionic species and contributed a large fraction of PM mass in different sizes. The nitrate concentration increase on episode days could be attributed to the increase of precursor gas-NO 2 concentration during these periods. In addition, SO 2 was significantly correlated to SO 4 2-in the nuclei mode (PM 0.1 ), which indicated that the gas had transferred into particulate matter. The molar equivalent ratio of {[NO

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“…This aroused great concern. The increase in the PM 2.5 concentration not only reduces atmospheric visibility (Kan et al 2012) and impacts negatively on human health (Boldo et al 2011;Pope and Dockery, 2006), but also impacts climate change by affecting the radiation balance (Wang et al 2015b). However, the PM 2.5 concentration, affected by the intensity of emission sources, topography and climatic factors, is subject to spatial and temporal variability (Cao et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Spatial-temporal characteristics and determinants of PM2.5 in the Bohai Rim Urban Agglomeration

2016

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Health impact assessment of a reduction in ambient PM2.5 levels in Spain

Cited by 131 publications

References 25 publications

Spatial-temporal characteristics of PM2.5 in China: A city-level perspective analysis

Spatial-temporal characteristics of PM2.5 in China: A city-level perspective analysis

Size Distribution and Water Soluble Ions of Ambient Particulate Matter on Episode and Non-episode Days in Southern Taiwan

Spatial-temporal characteristics and determinants of PM2.5 in the Bohai Rim Urban Agglomeration

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