Forecasting PM2.5 induced male lung cancer morbidity in China using satellite retrieved PM2.5 and spatial analysis
Copyright © 2017. Published by Elsevier B.V..
The present study predicts a spatial distribution of lung cancer morbidity in Chinese males due to exposure to PM2.5 concentration from 2010 to 2015. A spatial autocorrelation method was used to evaluate the spatial relationship between the lung cancer morbidities from 2006 to 2009 and satellite-derived PM2.5 atmospheric levels. A comprehensive grey correlation degree analysis was carried out to assess the simultaneous and lag associations between the lung cancer morbidity and PM2.5 concentration. These relationships were subsequently applied to predict male lung cancer morbidity in a specific year. Annual mean PM2.5 levels in this specific year and previous 8years were used as 9 independent variables to establish four statistical models. These models include ridge regression (RR), partial least squares regression (PLSR), support vector regression (SVR), and the combined forecasting model (CFM) to predict the male lung cancer morbidity in China from 2010 to 2015. The model error evaluations suggested that the partial least squares regression model performed the best in the male lung cancer morbidity forecast. We calculated the male lung cancer morbidity by the optimal method among the established statistical forecasting models at 1948 sites in China. The gridded morbidity distribution from 2010 to 2015 across the country was obtained by Kriging interpolation method. Results showed that the male lung cancer morbidity increased significantly from western to eastern China, except for the far north region. This spatial pattern is in line with the spatial distribution of PM2.5 concentration, manifesting a significant relationship between PM2.5 concentration level and lung cancer morbidity in Chinese males.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2017 |
|---|---|
| Erschienen: |
2017 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:607-608 |
|---|---|
| Enthalten in: |
The Science of the total environment - 607-608(2017) vom: 31. Dez., Seite 1009-1017 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Han, Xiao [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Air Pollutants |
|---|
| Anmerkungen: |
Date Completed 13.08.2018 Date Revised 13.08.2018 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.scitotenv.2017.07.061 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM273993771 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM273993771 | ||
| 003 | DE-627 | ||
| 005 | 20231225002505.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2017 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.scitotenv.2017.07.061 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n0913.xml |
| 035 | |a (DE-627)NLM273993771 | ||
| 035 | |a (NLM)28724219 | ||
| 035 | |a (PII)S0048-9697(17)31764-3 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Han, Xiao |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Forecasting PM2.5 induced male lung cancer morbidity in China using satellite retrieved PM2.5 and spatial analysis |
| 264 | 1 | |c 2017 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 13.08.2018 | ||
| 500 | |a Date Revised 13.08.2018 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2017. Published by Elsevier B.V. | ||
| 520 | |a The present study predicts a spatial distribution of lung cancer morbidity in Chinese males due to exposure to PM2.5 concentration from 2010 to 2015. A spatial autocorrelation method was used to evaluate the spatial relationship between the lung cancer morbidities from 2006 to 2009 and satellite-derived PM2.5 atmospheric levels. A comprehensive grey correlation degree analysis was carried out to assess the simultaneous and lag associations between the lung cancer morbidity and PM2.5 concentration. These relationships were subsequently applied to predict male lung cancer morbidity in a specific year. Annual mean PM2.5 levels in this specific year and previous 8years were used as 9 independent variables to establish four statistical models. These models include ridge regression (RR), partial least squares regression (PLSR), support vector regression (SVR), and the combined forecasting model (CFM) to predict the male lung cancer morbidity in China from 2010 to 2015. The model error evaluations suggested that the partial least squares regression model performed the best in the male lung cancer morbidity forecast. We calculated the male lung cancer morbidity by the optimal method among the established statistical forecasting models at 1948 sites in China. The gridded morbidity distribution from 2010 to 2015 across the country was obtained by Kriging interpolation method. Results showed that the male lung cancer morbidity increased significantly from western to eastern China, except for the far north region. This spatial pattern is in line with the spatial distribution of PM2.5 concentration, manifesting a significant relationship between PM2.5 concentration level and lung cancer morbidity in Chinese males | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a China | |
| 650 | 4 | |a Lung cancer | |
| 650 | 4 | |a Male | |
| 650 | 4 | |a Morbidity | |
| 650 | 4 | |a PM(2.5) | |
| 650 | 4 | |a Spatial analysis | |
| 650 | 7 | |a Air Pollutants |2 NLM | |
| 650 | 7 | |a Particulate Matter |2 NLM | |
| 700 | 1 | |a Liu, Yuqin |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Jianmin |e verfasserin |4 aut | |
| 700 | 1 | |a Mao, Xiaoxuan |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yuting |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Xudong |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t The Science of the total environment |d 1972 |g 607-608(2017) vom: 31. Dez., Seite 1009-1017 |w (DE-627)NLM000215562 |x 1879-1026 |7 nnns |
| 773 | 1 | 8 | |g volume:607-608 |g year:2017 |g day:31 |g month:12 |g pages:1009-1017 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.scitotenv.2017.07.061 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 607-608 |j 2017 |b 31 |c 12 |h 1009-1017 | ||