scholarly journals Daily Concentrations of PM2.5 in the Valencian Community Using Random Forest for the Period 2008–2018

Proceedings ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 13
Author(s):  
Represa ◽  
Palomar-Vázquez ◽  
Porta ◽  
Fernández-Sarría
Keyword(s):  
Climate Change ◽  
Random Forest ◽  
Cross Validation ◽  
Meteorological Parameters ◽  
Fine Particulate Matter ◽  
Ground Level ◽  
Test Set ◽  
Quality Models ◽  
Final Model ◽  
Fine Particulate

Fine particulate matter (PM2.5) is a global problem that affects the population health and contributes to climate change. Remote sensing provides useful information for the development of air quality models. This work aims to obtain a daily model of PM2.5 levels in the Valencian Community with a resolution of 1 km for the period 2008–2018. MODIS-MAIAC images, meteorological parameters of the MERRA-2 project, land cover information and ground level measurements of PM2.5 levels were analysed with Random Forest. The verification of the model was carried out using cross-validation repeated ten times, and an evaluation of a test set with 20% of the collected information. The final model was used to generate maps of the daily concentrations of PM2.5 for the area of the Valencian Community throughout the study period.

scholarly journals Impacts of historical climate and land cover changes on fine particulate matter (PM<sub>2.5</sub>) air quality in East Asia over 1980–2010

2016 ◽  
Author(s):  
Yu Fu ◽  
Amos P. K. Tai ◽  
Hong Liao
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Particulate Matter ◽  
Air Quality ◽  
Land Cover ◽  
East Asia ◽  
Fine Particulate Matter ◽  
Anthropogenic Emissions ◽  
Voc Emissions ◽  
Fine Particulate

Abstract. To examine the effects of changes in climate, land cover and land use (LCLU), and anthropogenic emissions on fine particulate matter (PM2.5) between the 5-year periods 1981–1985 and 2007–2011 in East Asia, we perform a series of simulations using a global chemical transport model (GEOS-Chem) driven by assimilated meteorological data and a suite of land cover and land use data. Our results indicate that climate change alone could lead to a decrease in wintertime PM2.5 concentration by 4.0–12.0 μg m−3 in northern China, but an increase in summertime PM2.5 by 6.0–8.0 μg m−3 in those regions. These changes are attributable to the changing chemistry and transport of all PM2.5 components driven by long-term trends in temperature, wind speed and mixing depth. The concentration of secondary organic aerosol (SOA) is simulated to increase by 0.2–0.8 μg m−3 in both summer and winter in most regions of East Asia due to climate change alone, mostly reflecting higher biogenic volatile organic compound (VOC) emissions under warming. The impacts of LCLU change alone on PM2.5 (−2.1 to +1.3 μg m−3) are smaller than that of climate change, but among the various components the sensitivity of SOA and thus organic carbon to LCLU change (−0.4 to +1.2 μg m−3) is quite significant especially in summer, which is driven mostly by changes in biogenic VOC emissions following cropland expansion and changing vegetation density. The combined impacts show that while the effect of climate change on PM2.5 air quality is more pronounced, LCLU change could offset part of the climate effect in some regions but exacerbate it in others. As a result of both climate and LCLU changes combined, PM2.5 levels are estimated to change by −12.0 to +12.0 μg m−3 across East Asia between the two periods. Changes in anthropogenic emissions remain the largest contributor to deteriorating PM2.5 air quality in East Asia during the study period, but climate and LCLU changes could lead to a substantial modification of PM2.5 levels.

scholarly journals Optimal estimation for global ground-level fine particulate matter concentrations

2013 ◽  
Vol 118 (11) ◽  
pp. 5621-5636 ◽  
Author(s):  
Aaron van Donkelaar ◽  
Randall V. Martin ◽  
Robert J. D. Spurr ◽  
Easan Drury ◽  
Lorraine A. Remer ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Optimal Estimation ◽  
Ground Level ◽  
Fine Particulate

Satellite-based estimates of ground-level fine particulate matter during extreme events: A case study of the Moscow fires in 2010

2011 ◽  
Vol 45 (34) ◽  
pp. 6225-6232 ◽  
Author(s):  
Aaron van Donkelaar ◽  
Randall V. Martin ◽  
Robert C. Levy ◽  
Arlindo M. da Silva ◽  
Michal Krzyzanowski ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Extreme Events ◽  
Fine Particulate Matter ◽  
Ground Level ◽  
Fine Particulate

scholarly journals Continuous mapping of fine particulate matter (PM<sub>2.5</sub>) air quality in East Asia at daily 6×6 km<sup>2</sup> resolution by application of a random forest algorithm to 2011–2019 GOCI geostationary satellite data

2021 ◽  
Author(s):  
Drew C. Pendergrass ◽  
Daniel J. Jacob ◽  
Shixian Zhai ◽  
Jhoon Kim ◽  
Ja-Ho Koo ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Random Forest ◽  
South Korea ◽  
East Asia ◽  
Fine Particulate Matter ◽  
Eastern China ◽  
Ambient Air ◽  
Population Exposure ◽  
Fine Particulate

Abstract. We use 2011–2019 aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) instrument over East Asia to infer 24-h daily surface fine particulate matter (PM2.5) concentrations at continuous 6x6 km2 resolution over eastern China, South Korea, and Japan. This is done with a random forest (RF) algorithm applied to the gap-filled GOCI AODs and other data and trained with PM2.5 observations from the three national networks. The predicted 24-h PM2.5 concentrations for sites entirely withheld from training in a ten-fold crossvalidation procedure correlate highly with network observations (R2 = 0.89) with single-value precision of 26–32 % depending on country. Prediction of annual mean values has R2 = 0.96 and single-value precision of 12 %. The RF algorithm is only moderately successful for diagnosing local exceedances of the National Ambient Air Quality Standard (NAAQS) because these exceedances are typically within the single-value precisions of the RF, and also because of RF smoothing of extreme PM2.5 concentrations. The area-weighted and population-weighted trends of RF PM2.5 concentrations for eastern China, South Korea, and Japan show steady 2015–2019 declines consistent with surface networks, but the surface networks in eastern China and South Korea underestimate population exposure. Further examination of RF PM2.5 fields for South Korea identifies hotspots where surface network sites were initially lacking and shows 2015–2019 PM2.5 decreases across the country except for flat concentrations in the Seoul metropolitan area. Inspection of monthly PM2.5 time series in Beijing, Seoul, and Tokyo shows that the RF algorithm successfully captures observed seasonal variations of PM2.5 even though AOD and PM2.5 often have opposite seasonalities. Application of the RF algorithm to urban pollution episodes in Seoul and Beijing demonstrates high skill in reproducing the observed day-to-day variations in air quality as well as spatial patterns on the 6 km scale. Comparison to a CMAQ simulation for the Korean peninsula demonstrates the value of the continuous RF PM2.5 fields for testing air quality models, including over North Korea where they offer a unique resource.

scholarly journals COVID-19 lockdowns cause global air pollution declines

2020 ◽  
Vol 117 (32) ◽  
pp. 18984-18990 ◽  
Author(s):  
Zander S. Venter ◽  
Kristin Aunan ◽  
Sourangsu Chowdhury ◽  
Jos Lelieveld
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Ground Level ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Economic Activities ◽  
Long Distance ◽  
Mobility Data ◽  
Fine Particulate

The lockdown response to coronavirus disease 2019 (COVID-19) has caused an unprecedented reduction in global economic and transport activity. We test the hypothesis that this has reduced tropospheric and ground-level air pollution concentrations, using satellite data and a network of >10,000 air quality stations. After accounting for the effects of meteorological variability, we find declines in the population-weighted concentration of ground-level nitrogen dioxide (NO2: 60% with 95% CI 48 to 72%), and fine particulate matter (PM2.5: 31%; 95% CI: 17 to 45%), with marginal increases in ozone (O3: 4%; 95% CI: −2 to 10%) in 34 countries during lockdown dates up until 15 May. Except for ozone, satellite measurements of the troposphere indicate much smaller reductions, highlighting the spatial variability of pollutant anomalies attributable to complex NOxchemistry and long-distance transport of fine particulate matter with a diameter less than 2.5 µm (PM2.5). By leveraging Google and Apple mobility data, we find empirical evidence for a link between global vehicle transportation declines and the reduction of ambient NO2exposure. While the state of global lockdown is not sustainable, these findings allude to the potential for mitigating public health risk by reducing “business as usual” air pollutant emissions from economic activities. Explore trends here:https://nina.earthengine.app/view/lockdown-pollution.

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