Estimating the global abundance of ground level presence of particulate matter (PM2.5)

Abstract

Abstract. With the increasing awareness of the health impacts of particulate matter, there is a growing need to comprehend the spatial and temporal variations of the global abundance of ground level airborne particulate matter with a diameter of 2.5 microns or less (PM 2.5). Here we use a suite of remote sensing and meteorological data products together with groundbased observations of particulate matter from 8,329 measurement sites in 55 countries taken 1997-2014 to train a machinelearning algorithm to estimate the daily distributions of PM 2.5 from 1997 to the present. In this first paper of a series, we present the methodology and global average results from this period and demonstrate that the new PM 2.5 data product can reliably represent global observations of PM 2.5 for epidemiological studies.

How much incident lung cancer was missed globally in 2012? An ecological country-level study

“…This particular kind of air pollution could be influenced by a large range of pollutants that includes radon, asbestos, chromium, cadmium and arsenic, organic chemicals, radiation and coal smoke. A recent study has also demonstrated the potential of remote sensed and meteorological data products as being reliable representation of global observations of PM2.5 for epidemiological studies (Lary et al, 2014). Our bivariate results also indicated a positive association between GDP and lung cancer incidence illustrating the fact that rising affluence in developing countries may possibly be associated with an increase in smoking (Ezzati et al, 2005).…”

“…The Globocan 2012 report shows marked inter-country incidence differences that are compounded by intra-country differences across regions, ethnic groups (WHO-IARC, 2014;Lortet-Tieulent et al, 2014), gender (Islami et al, 2015), a long latency period (30 years) and the degree of inhalation (Farnsworth et al, 2004). Although smoking is widely accepted as the primary agent that triggers lung cancer (Bruce et al, 2000;Cohen et al, 2005), evidence exists that shows that the disease is also linked to other social determinants like passive smoking, indoor cooking, occupational hazards, motor vehicle emissions and industrial pollution (Lary et al, 2014;Youlden et al, 2008). A combination of factors therefore suggests diverse inter-and intra-country differences in the incidence of lung cancer that are further magnified by the inequality gap between socioeconomic groups (Deaton, 2013).…”

The Neighborhood Scale Variability of Airborne Particulates

“…This area represented 1 pixel of data in the satellite-based ground level PM 2.5 concentration estimate of Lary et al (2014) [12]. This project used a mobile sensor package to gather data throughout the city mounted 1.5 m above the ground.…”

“…We see higher mean pressure, humidity, and PM 2.5 concentration on May 23. Figure 2 shows the approximate 10 km × 10 km area covered during daily measurement-gathering drives (outlined in red), representing 1 pixel of the global estimate from Lary et al (2014) [12]. This drive area was chosen to include several different environments including residential areas, retail/commercial areas, and major highways: State Highway 75 is roughly down the center of the region of interest, President George Bush Tollway is across the northern border, and Interstate 635 is across the southern border.…”

“…Typically there is a lack of neighborhood-scale observations of airborne particulates, and many towns have no observations at all. To help address this issue, a machine learning approach was previously developed, by Lary et al (2014) [12], to estimate daily global abundance of airborne particulates from multiple big, environmental, data sets. Lary et al (2014) [12] used ground-based observations along with satellite-based remote sensing (from NASA's seawifs and the two modis), meteorological data products (from the NASA merra), and machine learning to estimate global daily ground level PM 2.5 concentrations from 1997-2014.…”