High-resolution satellite remote sensing of provincial PM2.5 trends in China from 2001 to 2015

Lin, Changqing; Liu, Gaohuan; Lau, Alexis K.H.; Li, Ying; Li, Chengcai C.; Fung, Jimmy Chi Hung; Lao, Xiang Qian

doi:10.1016/j.atmosenv.2018.02.045

Cited by 141 publications

(85 citation statements)

References 44 publications

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“…This is comparable to Zheng et al (2017), who find that the annual mean PM 2.5 across 74 Chinese cities decreased by 23.6% between 2013-2015 (−7.9% year −1 ). Lin et al (2018) used satellite data to suggest the Chinese PM 2.5 trend steepened from −0.65 μg m −3 year −1 between 2006-2010 to −2.3 μg m −3 year −1 between 2011-2015. Our work suggests that the rate of PM 2.5 decline has been sustained, or possibly even become faster, between 2015-2017.…”

Section: Methodsmentioning

confidence: 99%

Substantial changes in air pollution across China during 2015–2017

Silver

Reddington

Arnold

et al. 2018

Environ. Res. Lett.

190

123

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China's rapid industrialisation and urbanisation has led to poor air quality. The Chinese government have responded by introducing policies to reduce emissions and setting ambitious targets for ambient PM 2.5 , SO 2 , NO 2 and O 3 concentrations. Previous satellite and modelling studies indicate that concentrations of these pollutants have begun to decline within the last decade. However, prior to 2012, air quality data from ground-based monitoring stations were difficult to obtain, limited to a few locations in major cities, and often unreliable. Since then, a comprehensive monitoring network, with over 1000 stations across China has been established by the Ministry of Ecology and Environment (MEE). We use a three-year (2015-2017) dataset consisting of hourly PM 2.5 , O 3 , NO 2 and SO 2 concentrations obtained from the MEE, combined with similar data from Taiwan and Hong Kong. We find that at 53% and 59% of stations, PM 2.5 and SO 2 concentrations have decreased significantly, with median rates across all stations of −3.4 and −1.9 μg m −3 year −1 respectively. At 50% of stations, O 3 maximum daily 8 h mean (MDA8) concentrations have increased significantly, with median rates across all stations of 4.6 μg m −3 year −1 . It will be important to understand the relative contribution of changing anthropogenic emissions and meteorology to the changes in air pollution reported here.

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Section: Methodsmentioning

confidence: 99%

Substantial changes in air pollution across China during 2015–2017

Silver

Reddington

Arnold

et al. 2018

Environ. Res. Lett.

190

123

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“…We obtained the available ground observations of PM 2.5 concentration from multiple sources (e.g., national and regional ground monitoring networks, observations by the U.S. consulate, and a series of published papers) in the study region from 2001 to 2015. Evaluation of the satellite-derived PM 2.5 concentration against the ground observations obtained a correlation coefficient of >0.9 and a mean absolute percentage error within ±20% [14]. In addition, the satellite-derived PM 2.5 concentrations experienced consistent long-term variations with the ground observations in multiple metropolises (e.g., Beijing, Shanghai, Guangzhou, Hong Kong, and Taipei).…”

Section: Study Regionmentioning

confidence: 78%

“…First, spectral data from the two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites were used to build aerosol optical depth (AOD) data at a resolution of 0.01 • × 0.01 • in eastern China [26]. Second, an observational data-driven algorithm, which took the ground-observed visibility and relative humidity data as inputs, was developed to derive the ground-level PM 2.5 concentration from the AOD [14,27]. To validate the long-term satellite-derived PM 2.5 concentration, we applied a method used in similar studies [28,29].…”

Section: Study Regionmentioning

confidence: 99%

“…We considered all 24 administrative regions as "provinces in eastern China" in this study. Lin et al [14] built a long-term PM 2.5 dataset at a spatial resolution of 0.01 • × 0.01 • in the study region from 2001 to 2015 (http://envf.ust.hk/dataview/aod2pm/current). We took advantage of this satellite dataset to characterize the long-term variation of PM 2.5 concentration in eastern China.…”

Section: Study Regionmentioning

confidence: 99%

“…During recent Five-Year Plan (FYP) periods, the Chinese government promulgated a series of control measures to reduce the PM 2.5 concentration level in China [11][12][13]. Lin et al [14] assessed the long-term variation in PM 2.5 concentration over China during the past three FYP periods…”

Section: Introductionmentioning

confidence: 99%

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Difference in PM2.5 Variations between Urban and Rural Areas over Eastern China from 2001 to 2015

Lin

Lau

et al. 2018

Atmosphere

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To more effectively reduce population exposure to PM 2.5 , control efforts should target densely populated urban areas. In this study, we took advantage of satellite-derived PM 2.5 data to assess the difference in PM 2.5 variations between urban and rural areas over eastern China during the past three Five-Year Plan (FYP) periods (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). The results show that urban areas experienced less of a decline in PM 2.5 concentration than rural areas did in more than half of the provinces during the 11th FYP period (2006)(2007)(2008)(2009)(2010). In contrast, most provinces experienced a greater reduction of PM 2.5 concentration in urban areas than in rural areas during the 10th and 12th FYP periods (2001-2005 and 2011-2015, respectively). During the recent 12th FYP period, the rates of decline in PM 2.5 concentration in urban areas were more substantial than in rural areas by as much as 1.5 µg·m −3 ·year −1 in Beijing and 2.0 µg·m −3 ·year −1 in Tianjin. These results suggest that the spatial difference in PM 2.5 change was conducive to a reduction in the population exposure to PM 2.5 in most provinces during recent years.

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Habitual exercise is associated with reduced risk of diabetes regardless of air pollution: a longitudinal cohort study

et al. 2021

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Aims/hypothesis Physical activity may increase a person's inhalation of air pollutants and exacerbate the adverse health effects. This study aimed to investigate the combined associations of chronic exposure to particulate matter with an aerodynamic diameter less than 2.5 μm (PM 2.5 ) and habitual physical activity with the incidence of type 2 diabetes in Taiwan. Methods We selected 156,314 non-diabetic adults (≥18 years old) who joined an ongoing longitudinal cohort between 2001 and 2016. Incident type 2 diabetes was identified at the follow-up medical examinations. Two-year mean PM 2.5 exposure was estimated at each participant's address using a satellite-based spatiotemporal model. Information on physical activity and a wide range of covariates was collected using a standard self-administered questionnaire. We analysed the data using a Cox regression model with time-varying covariates. An interaction term between PM 2.5 and physical activity was included to examine the overall interaction effects. Results Compared with high physical activity, moderate and inactive/low physical activity were associated with a higher risk of diabetes (HR [95% CI] 1.31 [1.22, 1.41] and 1.56 [1.46, 1.68], respectively). Participants with moderate/high PM 2.5 had a higher risk of type 2 diabetes than the participants exposed to low PM 2.5 (HR 1.31 [1.22, 1.40] and 1 .94 [1.76, 2.14], respectively). The participants with high physical activity and low PM 2.5 had a 64% lower risk of type 2 diabetes than those with inactive/low physical activity and high PM 2.5 . Conclusions/interpretation Higher physical activity and lower PM 2.5 exposure are associated with lower risk of type 2 diabetes. Habitual physical activity can reduce the risk of diabetes regardless of the levels of PM 2.5 exposure. Our results indicate that habitual physical activity is a safe diabetes prevention strategy for people residing in relatively polluted regions. Keywords Chinese adults . Long-term exposure . Physical activity . PM 2.5 air pollution . Type 2 diabetes Abbreviations AOD Aerosol optical depth FPG Fasting plasma glucose MET Metabolic equivalents PM 2.5 Particulate matter with an aerodynamic diameter less than 2.5 μm Cui Guo and Hsiao Ting Yang are joint first authors * Xiang Qian Lao

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High-resolution satellite remote sensing of provincial PM2.5 trends in China from 2001 to 2015

Cited by 141 publications

References 44 publications

Substantial changes in air pollution across China during 2015–2017

Substantial changes in air pollution across China during 2015–2017

Difference in PM2.5 Variations between Urban and Rural Areas over Eastern China from 2001 to 2015

Habitual exercise is associated with reduced risk of diabetes regardless of air pollution: a longitudinal cohort study

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