Combined use of land use regression and BenMAP for estimating public health benefits of reducing PM2.5 in Tianjin, China

Chen, Li; Shi, Mengshuang; Li, Suhuan; Zhang, Bai; Wang, Zhongliang

doi:10.1016/j.atmosenv.2016.12.023

Cited by 31 publications

(10 citation statements)

References 46 publications

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“…NO2 models had better R 2 than PM2.5 in most cities. Our city scale performance is comparable to some city scale models for Beijing (R 2 0.78 vs. 0.58 for PM2.5) [17] and Shanghai (R 2 0.70 vs. 0.61 for NO2) [19], but worse in some other locations (0.25 vs. 0.73 for PM2.5 in Tianjin) [56]. Regional or city-scale models may better capture within-city variability than national models such as ours, especially for PM2.5 for which pollution sources can be complex and vary by regions in China.…”

Section: Within-city Variationsupporting

confidence: 67%

National PM2.5 and NO2 Exposure Models for China Based on Land Use Regression, Satellite Measurements, and Universal Kriging

Xu¹,

Bechle²,

Wang³

et al. 2018

ISEE Conference Abstracts

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Outdoor air pollution is a major killer worldwide and the fourth largest contributor to the burden of disease in China. China is the most populous country in the world and also has the largest number of air pollution deaths per year, yet the spatial resolution of existing national air pollution estimates for China is generally relatively low. We address this knowledge gap by developing and evaluating national empirical models for China incorporating land-use regression (LUR), satellite 2 / 32 measurements, and universal kriging (UK). We test the resulting models in several ways, including (1) comparing models developed using forward stepwise linear regression vs. partial least squares (PLS) regression modeling, (2) comparing models developed with and without satellite measurements, and with and without UK, and (3) 10-fold cross-validation (CV), leave-one-province-out CV (LOPO-CV), and leave-one-city-out CV (LOCO-CV). Satellite data and kriging are complementary in making predictions more accurate: kriging improved the models in well-sampled areas; satellite data substantially improved performance at locations far away from monitors. Stepwise forward selection performs similarly to PLS in 10-fold CV, but better than PLS in LOPO-CV. Our best models employ forward selection and UK, with 10-fold CV R 2 of 0.89 (for both 2014 and 2015) for PM2.5 and of 0.73 (year-2014) and 0.78 (year-2015) for NO2. Population-weighted concentrations during 2014-2015 decreased for PM2.5 (58.7 μg/m 3 to 52.3 μg/m 3 ) and NO2 (29.6 μg/m 3 to 26.8 μg/m 3 ).We produced the first high resolution national LUR models for annual-average concentrations in China. Models were applied on 1 km grid to support future research.In 2015, more than 80% of the Chinese population lived in areas that exceed the Chinese national PM2.5 standard, 35 g/m 3 . Results here will be publicly available and may be useful for epidemiology, risk assessment, and environmental justice research.

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Section: Within-city Variationsupporting

confidence: 67%

National PM2.5 and NO2 Exposure Models for China Based on Land Use Regression, Satellite Measurements, and Universal Kriging

Xu¹,

Bechle²,

Wang³

et al. 2018

ISEE Conference Abstracts

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“…Under these circumstances, some studies accessing the public health benefits of reducing PM 2.5 in China emphasized the significant of air quality improvement. For instance, Chen et al ( 2017 ) created an annual air quality surface with a land use regression (LUR) model to evaluate avoided cases of mortality and morbidity in Tianjin, assuming the achievement of China’s current air quality daily and annual standards (no. GB3095-2012).…”

Section: Discussionmentioning

confidence: 99%

“…GB3095-2012). It was estimated in the study (Chen et al 2017 ) that if the daily average PM 2.5 was reduced to the daily Class II standard (75 μg/m 3 ), the avoided deaths for cardiovascular disease would be 2000 (95% CI 920–3100) per year and the monetary values were 180–4800 million yuan per year in 2015 in Tianjin; if the annual average PM 2.5 was reduced to the annual Class II standard (35 μg/m 3 ), the avoided deaths for cardiovascular disease were 1400 (95% CI 640–2100) and the monetary values were 130 to 3400 million yuan.…”

Section: Discussionmentioning

confidence: 99%

The contribution of PM2.5 to cardiovascular disease in China

Zhang

Routledge

2020

Environ Sci Pollut Res

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China is experiencing rapid urbanization and industrialization with correspondingly high levels of air pollution. Although the harm of PM2.5 has been long reported, it is only quite recently that there is increasing concern in China for its possible adverse health effects on cardiovascular disease. We reviewed the epidemiologic evidence of potential health effects of PM2.5 on cardiovascular disease reported from recent studies in China (2013 onwards). There is clear evidence for the contribution of PM2.5 to cardiovascular outcomes, including mortality, ischemic heart disease, and stroke from studies based in various regions in China. This evidence adds to the global evidence that PM2.5 contributes to adverse cardiovascular health risk and highlights the need for improved air quality in China.

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“…Studies have shown that decreases in PM 2.5 concentrations represent significant decreases in the number of hospitalizations and deaths due to respiratory, cardiovascular, and other diseases, as well as reductions in Public Health System costs (29). The concentrations of NO 2 allow a better evaluation of the effects of air pollutants on respiratory health than the PM 10 particles (30).…”

Section: Discussionmentioning

confidence: 99%

Effects of exposure to fine particulate matter in elderly hospitalizations due to respiratory diseases in the South of the Brazilian Amazon

Machín

Nascimento

Mantovani

et al. 2019

Braz J Med Biol Res

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Exposure to air pollution is an important cause of hospital admissions due to respiratory diseases. Nevertheless, few studies use pollutant concentration data estimated by mathematical models. A time-series ecological study was developed, using data from hospitalizations due to respiratory diseases in people over 60 years of age, residents of Cuiabá, Brazil, during 2012, obtained from the Brazilian Ministry of Health. The independent variables were the concentrations of fine particulate matter (PM2.5) and carbon monoxide (CO) estimated by mathematical modeling, minimum temperature, and relative humidity (obtained from the Brazilian Meteorological Agency), and the number of forest fires. The generalized linear regression model of Poisson was used, with lags of 0 to 7 days. The coefficients obtained were transformed into relative risk of hospitalization, with respective 95% confidence intervals; alpha=5% was adopted. In that year, 591 hospitalizations were evaluated, with a daily average of 1.61 (SD=1.49), the PM2.5 average concentration was 15.7 µg/m3, and the CO average concentration was 144.2 ppb. Significant associations between exposure to these contaminants and hospitalizations in lags 3 and 4 in 2012 were observed. There was a hospitalization risk increase of 31.8%, with an increase of 3.5 µg/m3 of PM2.5 concentrations and an increase of 188 in the total number of hospitalizations, with an expense of more than ≈US$ 96,000 for the Brazilian Public Health System. This study provided information on the cost of air pollution to the health system and the feasibility of using a mathematical model to estimate environmental concentration of air pollutants.

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Combined use of land use regression and BenMAP for estimating public health benefits of reducing PM2.5 in Tianjin, China

Cited by 31 publications

References 46 publications

National PM2.5 and NO2 Exposure Models for China Based on Land Use Regression, Satellite Measurements, and Universal Kriging

National PM2.5 and NO2 Exposure Models for China Based on Land Use Regression, Satellite Measurements, and Universal Kriging

The contribution of PM2.5 to cardiovascular disease in China

Effects of exposure to fine particulate matter in elderly hospitalizations due to respiratory diseases in the South of the Brazilian Amazon

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