Assessment of health burden caused by particulate matter in southern China using high-resolution satellite observation

Lu, Xingcheng; Lin, Changqing; Li, Ying; Yao, Tingting; Fung, Jimmy Chi Hung; Lau, Alexis K.H.

doi:10.1016/j.envint.2016.11.002

Cited by 72 publications

(32 citation statements)

References 52 publications

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“…Air pollution, caused by multiple-pollutant emissions and vehicle exhaust, has been aggravating yearly with the rapid urbanization and development of transportation infrastructure in China [22]. Accumulating evidence indicates that air pollution has a signi cant impact on cardiovascular-and chronic cardiopulmonary-related morbidity and mortality in China [23][24][25]. Some studies have also shown that air pollution is associated with eye diseases, such as dry eye [26][27], conjunctivitis and pterygium [28].…”

Section: Discussionmentioning

confidence: 99%

Machine learning prediction on number of patient due to conjunctivitis based on air pollutants: A preliminary study

Cheng

Feng

Zhou

et al. 2020

Preprint

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Background Accurate prediction of the number of patients with conjunctivitis plays an important role in providing adequate treatment at the hospital, but such accurate predictive model currently does not exist. The current study sought to use machine learning (ML) prediction based on past patient for conjunctivitis and several air pollutants. The optimal machine learning prediction model was selected to predict conjunctivitis-related number patients. Methods The average daily air pollutants concentrations (CO, O3, NO2, SO2, PM10, PM2.5) and weather data (highest and lowest temperature) were collected. Data were randomly divided into training dataset and test dataset, and normalized mean square error (NMSE) was calculated by 10 fold cross validation, comparing between the ability of seven ML methods to predict the number of patient due to conjunctivitis (Lasso penalized liner model, Decision tree, Boosting regression, Bagging regression, Random forest, Support vector, and Neural network). According to the accuracy of impact prediction, the important air and weather factors that affect conjunctivitis were identified. Results A total of 84977 cases to treat conjunctivitis were obtained from the ophthalmology center of the Affiliated Hospital of Hangzhou Normal University. For all patients together, the NMSE of the different methods were as follows: Lasso penalized liner regression: 0.755, Decision tree: 0.710, Boosting regression: 0.616, Bagging regression: 0.615, Random forest: 0.392, Support vectors: 0.688, and Neural network: 0.476. Further analyses, stratified by gender and age at diagnosis, supported Random forest as being superior to others ML methods. The main factors affecting conjunctivitis were: O3, NO2, SO2 and air temperature. Conclusion Machine learning algorithm can predict number of patients due to conjunctivitis, among which, the Random forest algorithm had the highest accuracy. Machine learning algorithm could provide accurate information for hospitals dealing with conjunctivitis caused by air factors.

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Section: Discussionmentioning

confidence: 99%

Machine learning prediction on number of patient due to conjunctivitis based on air pollutants: A preliminary study

Cheng

Feng

Zhou

et al. 2020

Preprint

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“…Studies showed that air pollution leads to more than one million premature deaths and 76 million disability-adjusted life years each year in China [1]. These concomitant human capital losses to the entire society have inspired research to measure the monetary value of health impact caused by air pollution in different study areas of China [2][3][4]. Assessing health economic losses spatially can not only provide the spatial distribution of health economic losses related to air pollution within China, but also provide more detailed information for the government to implement specifically targeted pollution control policies for various regions.…”

Section: Introductionmentioning

confidence: 99%

“…Quantifying spatial distribution of health impact in terms of health economic losses, has a large room for improvement in the conventional assessment approaches because of the low spatial resolution of data source [2]. Health impact is defined as the adverse effect of air pollutants on the human body and mainly be quantified as the number of deaths and illnesses caused by air pollution.…”

Section: Introductionmentioning

confidence: 99%

“…The economic losses related to health impact caused by ambient pollutants is quantified by using monetary values. Many researchers used the amended human capital (AHC) approach in China [13], willingness to pay (WTP) method in urban areas of Beijing [14] and the value of statistical life (VSL) method in southern China [2] to measure the monetary value of health impact. However, most of these researches focused on statistically quantifying and neglected the spatial distribution character of pollutants concentration and population density.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Assessment of Health Economic Losses from Exposure to Ambient Pollutants in China

Wang

et al. 2020

Remote Sensing

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Increasing emissions of ambient pollutants have caused considerable air pollution and negative health impact for human in various regions of China over the past decade. The resulting premature mortality and excessive morbidity caused huge human economic losses to the entire society. To identify the differences of health economic losses in various regions of China and help decision-making on targeting pollutants control, spatial assessment of health economic losses due to ambient pollutants in China is indispensable. In this study, to better represent the spatial variability, the satellite-based retrievals of the concentrations of various pollutants (PM 10 , PM 2.5 , O 3 , NO 2 , SO 2 and CO) for the time period from 2007 to 2017 in China are used instead of using in-situ data. Population raster data were applied together with exposure-response function to calculate the spatial distribution of health impact and then the health impact is further quantified by using amended human capital (AHC) approach. The results which presented in a spatial resolution of 0.25 • × 0.25 • , show the signification contribution from the spatial assessment to revealing the spatial distribution and variance of health economic losses in various regions of China. Spatial assessment of overall health economic losses is different from conventional result due to more detail spatial information. This spatial assessment approach also provides an alternative method for losses measurement in other fields.

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“…Aerosols are small, suspended liquid and solid particles in the atmosphere that affect weather [1], climate [2], and human health [3]. Acting as cloud condensation nuclei, they can affect the formation and life of the cloud and increase precipitation [4].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Comparison of Long-Term MODIS C5.1 and C6 Products against AERONET Observations over China

et al. 2017

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MODIS (MODerate Resolution Imaging Spectroradiometer) aerosol products are the most widely used satellite retrieved aerosol optic depth (AOD) products, which compensate for the spatial lack of ground-based sun photometer observations. The newly released Collection 6 (C6) aerosol products have some improvements compared to the Collection 5.1 (C5.1) products with optimized algorithms and newly revised upstream products. Additionally, a three-kilometer resolution AOD product was added in the C6 product. In this study, the accuracies and regional applicability of long-term (2001-2015) different MODIS C5.1 and C6 aerosol products in China were evaluated against the 16 AERONET (Aerosol Robotic Network) observations with observations over more than three years. The overall analysis indicates that the C6 DT (Dark Target) 10 km products slightly improved the retrieval accuracies, with about 3% more data falling within the Expected Error (EE) envelope. However, for Deep Blue (DB) products, the C6 algorithm significantly improved the accuracy over all of China, and increased the successful retrieval number by extending retrieval coverages. Regional analysis demonstrated that the C6 DT 10 km product did not perform well in East China, with only 33.5% of retrievals falling within the EE envelope. For the DB product, the C6 algorithm significantly increased the number successfully retrieved, and was more accurate in all four regions in China. The validation of the DT 3 km product suggests large differences existed between the Terra and Aqua results. The accuracy of the Aqua DT 3 km product is obviously higher than that of the Terra DT 3 km product. The results of the study suggest that proper AOD products need to be considered when evaluating aerosol loading situations in different regions in China.

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Assessment of health burden caused by particulate matter in southern China using high-resolution satellite observation

Cited by 72 publications

References 52 publications

Machine learning prediction on number of patient due to conjunctivitis based on air pollutants: A preliminary study

Machine learning prediction on number of patient due to conjunctivitis based on air pollutants: A preliminary study

Spatial Assessment of Health Economic Losses from Exposure to Ambient Pollutants in China

Evaluation and Comparison of Long-Term MODIS C5.1 and C6 Products against AERONET Observations over China

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