Estimating the spatial distribution of environmental suitability for female lung cancer mortality in China based on a novel statistical method View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-02-12

AUTHORS

Xiao Han, Yanlong Guo, Hong Gao, Jianmin Ma, Manjie Sang, Sheng Zhou, Tao Huang, Xiaoxuan Mao

ABSTRACT

Lung cancer as one of the major causes of cancer mortality has been demonstrated to be closely related to the ambient atmospheric environment, but little has been done in the synthetic evaluation of the linkage between cancer mortality and combined impact of ambient air pollution and meteorological conditions. The present study determined the environmental suitability for female lung cancer mortality associated with air contaminants and meteorological variables. A novel fuzzy matter-element method was applied to identify the spatial distribution and regions for the environmental suitability for the female lung cancer mortality across China in 2013. The membership functions between the cancer mortality and 6 environmental factors, including PM2.5, NO2, SO2, PM10, the annual mean wind speed, and mean temperature, were generated and the weights of each of the environmental factors were established by the maximum entropy (MaxEnt) model. We categorized the environmental suitability combined with GIS spatial analysis into three zones, including low-suitable, medium-suitable, and high-suitable region where the cancer mortality ranging from low to high rate was identified. These three zones were quantified by the MaxEnt model taking different air pollutants and meteorological variables into consideration. We identified that NO2 was a most significant factor among the 6 environmental factors with the weight of 24.88%, followed by the annual mean wind speed, SO2, and PM2.5. The high-suitable area, mainly in the North China Plain which is a most heavily contaminated region by air pollution in China, covers 1.6195 million square kilometers, accounting for 17.85% of the total area investigated in this study. Identification of the impact of various environmental factors on cancer mortality in the different suitable area provides a scientific basis for the environmental management, risk assessment, and lung cancer control. More... »

PAGES

1-14

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11356-019-04444-3

DOI

http://dx.doi.org/10.1007/s11356-019-04444-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112093569

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30756355


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11356-019-04444-3'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11356-019-04444-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11356-019-04444-3'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11356-019-04444-3'


 

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