Dataset Information

Forecasting PM_2.5-induced lung cancer mortality and morbidity at county level in China using satellite-derived PM_2.5 data from 1998 to 2016: a modeling study.

ABSTRACT: The serious ambient fine particulate matter (PM_2.5) is one of the key risk factors for lung cancer. However, existing studies on the health effects of PM_2.5 in China were less considered the regional transport of PM_2.5 concentration. In this study, we aim to explore the association between lung cancer and PM_2.5 and then forecast the PM2.5-induced lung cancer morbidity and mortality in China. Ridge regression (RR), partial least squares regression (PLSR), model tree-based (MT) regression, regression tree (RT) approach, and the combined forecasting model (CFM) were alternative forecasting models. The result of the Pearson correlation analysis showed that both local and regional scale PM_2.5 concentration had a significant association with lung cancer mortality and morbidity and compared with the local lag and regional lag exposure to ambient PM_2.5; the regional lag effect (0.172~0.235 for mortality; 0.146~0.249 for morbidity) was not stronger than the local lag PM_2.5 exposure (0.249~0.294 for mortality; 0.215~0.301 for morbidity). The overall forecasting lung cancer morbidity and mortality were 47.63, 47.86, 39.38, and 39.76 per 100,000 population. The spatial distributions of lung cancer morbidity and mortality share a similar spatial pattern in 2015 and 2016, with high lung cancer morbidity and mortality areas mainly located in the central to east coast areas in China. The stakeholders would like to implement a cross-regional PM_2.5 control strategy for the areas characterized as a high risk of lung cancer.

SUBMITTER: Liao WB

PROVIDER: S-EPMC7293676 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Forecasting PM2.5-induced lung cancer mortality and morbidity at county level in China using satellite-derived PM2.5 data from 1998 to 2016: a modeling study.

Liao Wei-Bin WB Ju Ke K Zhou Qian Q Gao Ya-Min YM Pan Jay J

Environmental science and pollution research international 20200423 18

The serious ambient fine particulate matter (PM2.5) is one of the key risk factors for lung cancer. However, existing studies on the health effects of PM2.5 in China were less considered the regional transport of PM2.5 concentration. In this study, we aim to explore the association between lung cancer and PM2.5 and then forecast the PM2.5-induced lung cancer morbidity and mortality in China. Ridge regression (RR), partial least squares regression (PLSR ...[more]

PMID: 32328997

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Project description:Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System's Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources. Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275 m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R 2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2.5, 0.88 and 0.65 for NO3 -, 0.78 and 0.23 for SO4 2-, and 1.01 for NH+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30 % and 13 %, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO4 2- cross-validation values showed the largest spatial and spatiotemporal R2 improvement, with a 43 % increase. Assessing this physical technique in a well- instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent.

Project description:BackgroundAsthma affects millions of people worldwide. Lima, Peru is one of the most polluted cities in the Americas but has insufficient ground PM2.5 (particulate matter that are 2.5 μm or less in diameter) measurements to conduct epidemiologic studies regarding air pollution. PM2.5 estimates from a satellite-driven model have recently been made, enabling a study between asthma and PM2.5.ObjectiveWe conducted a daily time-series analysis to determine the association between asthma emergency department (ED) visits and estimated ambient PM2.5 levels in Lima, Peru from 2010 to 2016.MethodsWe used Poisson generalized linear models to regress aggregated counts of asthma on district-level population weighted PM2.5. Indicator variables for hospitals, districts, and day of week were included to account for spatial and temporal autocorrelation while assessing same day, previous day, day before previous and average across all 3-day exposures. We also included temperature and humidity to account for meteorology and used dichotomous percent poverty and gender variables to assess effect modification.ResultsThere were 103,974 cases of asthma ED visits during the study period across 39 districts in Lima. We found a 3.7% (95% CI: 1.7%-5.8%) increase in ED visits for every interquartile range (IQR, 6.02 μg/m3) increase in PM2.5 same day exposure with no age stratification. For the 0-18 years age group, we found a 4.5% (95% CI: 2.2%-6.8%) increase in ED visits for every IQR increase in PM2.5 same day exposure. For the 19-64 years age group, we found a 6.0% (95% CI: 1.0%-11.0%) increase in ED visits for every IQR in average 3-day exposure. For the 65 years and up age group, we found a 16.0% (95% CI: 7.0%-24.0%) decrease in ED visits for every IQR increase in PM2.5 average 3-day exposure, although the number of visits in this age group was low (4,488). We found no effect modification by SES or gender.DiscussionResults from this study provide additional literature on use of satellite-driven exposure estimates in time-series analyses and evidence for the association between PM2.5 and asthma in a low- and middle-income (LMIC) country.

Dataset Information

Forecasting PM_2.5-induced lung cancer mortality and morbidity at county level in China using satellite-derived PM_2.5 data from 1998 to 2016: a modeling study.

Publications

Forecasting PM<sub>2.5</sub>-induced lung cancer mortality and morbidity at county level in China using satellite-derived PM<sub>2.5</sub> data from 1998 to 2016: a modeling study.

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