Geographical Analysis of Lung Cancer Mortality Rate and PM2.5 Using Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth

Abstract

Exposure to particulate matter with an aerodynamic diameter of less than 2.5 μm (PM 2.5 ) may increase risk of lung cancer. The repetitive and broad-area coverage of satellites may allow atmospheric remote sensing to offer a unique opportunity to monitor air quality and help fill air pollution data gaps that hinder efforts to study air pollution and protect public health. This geographical study explores if there is an association between PM 2.5 and lung cancer mortality rate in the conterminous USA. Lung cancer (ICD-10 codes C34-C34) death count and population at risk by county were extracted for the period from 2001 to 2010 from the U.S. CDC WONDER online database. The 2001-2010 Global Annual Average PM 2.5 Grids from MODIS and MISR Aerosol Optical Depth dataset was used to calculate a 10 year average PM 2.5 pollution. Exploratory spatial data analyses, spatial regression (a spatial lag and a spatial error model), and spatially extended Bayesian Monte Carlo Markov Chain simulation found that there is a significant positive association between lung cancer mortality rate and PM 2.5 . The association would justify the need of further toxicological investigation of the biological mechanism of the adverse effect of the PM 2.5 pollution on lung cancer. The Global Annual Average PM 2.5 Grids from MODIS and MISR Aerosol Optical Depth dataset provides a continuous surface of concentrations of PM 2.5 and is a useful data source for environmental health research.

A geographically weighted regression approach to investigate air pollution effect on lung cancer: A case study in Portugal

“…A geographic approach with remote sensing can help to fill in data gaps that hamper current efforts to study air pollution. A study by Hu and Baker (2017) shows that there is a significant positive association between mortality from this type of cancer and PM 2.5 . This result was achieved using data from the MODIS satellite sensor and MISR Annual Global Grid PM 2.5 data (Hu and Baker, 2017).…”

“…A study by Hu and Baker (2017) shows that there is a significant positive association between mortality from this type of cancer and PM 2.5 . This result was achieved using data from the MODIS satellite sensor and MISR Annual Global Grid PM 2.5 data (Hu and Baker, 2017). Nonetheless, the statistically significant association between lung cancer mortality and presence of PM 2.5 may be indicative of a potential effect of air pollution; the authors suggest that the same association would require a toxicological approach in order to observe the adverse biological mechanism of PM 2.5 pollution (Hu and Baker, 2017).…”

“…This result was achieved using data from the MODIS satellite sensor and MISR Annual Global Grid PM 2.5 data (Hu and Baker, 2017). Nonetheless, the statistically significant association between lung cancer mortality and presence of PM 2.5 may be indicative of a potential effect of air pollution; the authors suggest that the same association would require a toxicological approach in order to observe the adverse biological mechanism of PM 2.5 pollution (Hu and Baker, 2017). The model developed in this study yielded satisfactory results and is in line with other similar studies using GWR models (Fu et al, 2015;Ren et al, 2016), or other spatial analyst tools (Bilancia et al, 2009;López-Cima et al, 2011).…”

Conceptual Model of Ecosystem Service Flows from Carbon Dioxide to Blue Carbon in Coastal Wetlands: An Empirical Study Based on Yancheng, China

“…where NPP s and NPP u are the simulated values of the surface and underground dry mass of plants, respectively (g•m −2 •y −1 ); W s is the measured value of the surface litter weight; W a is the measured value of the aboveground dry weight of plants; W u is the measured value of the underground dry weight of plants (g•m −2 , measured underground/aboveground dry weight, i.e., root-shoot ratio); BC_flow represents the amount of blue carbon in (BC_flow < 0) or out (BC_flow > 0) of the carbon pool (g•m −2 •y −1 ); SOC is the annual accumulation of soil organic carbon (g•y −1 ); soc is the carbon sequestration capacity (g•kg −1 ); ρ, a, and h are the soil bulk density (kg•m −3 ), area (m 2 ), and depth (m, assuming a uniform distribution of organic carbon in the soil surface), respectively. This paper also tested the visualizing multivariate spatial correlation between NPP and NDVI by Arc GIS 10.0; the Bivariate Moran's I statistic is represented as the values of NDVI across all neighboring units and plotted against NPP in each unit [29]; if the slope on the scatter plot is significantly different to zero then there is an association between NPP and NDVI.…”