Forecasting PM10 and PM2.5 in the Aburrá Valley (Medellín, Colombia) via EnKF based data assimilation

Lopez-Restrepo, Santiago; Botero, Andrés Yarce; Pinel, Nicolás; Quintero, O. Lucía; Segers, Arjo; Heemink, A.W.

doi:10.1016/j.atmosenv.2020.117507

Cited by 31 publications

(48 citation statements)

References 35 publications

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“…For the city of Medellín, a similar under estimation of PM 2.5 and PM 10 concentrations has been observed for simulations with the LOTOS-EUROS CTM (Lopez-Restrepo, Yarce, Pinel, Quintero, Segers and Heemink, 2020), which also used the global EDGAR inventory as input. Data assimilation was used to adjust the emissions, and due to the persistent low bias the best performance was obtained by strongly increasing the emissions over the entire domain.…”

Section: Introductionsupporting

confidence: 61%

“…The input to the LOTOS-EUROS model mainly consists of meteorological data, emission inventories, and surface data such as land-use and vegetation type. LOTOS-EUROS has demonstrated its capacity through a wide use in different projects around the world (Manders, Schaap and Hoogerbrugge, 2009;Curier, Timmermans, Calabretta-Jongen, Eskes, Segers, Swart and Schaap, 2012;Mues, Kuenen, Hendriks, Manders, Segers, Scholz, Hueglin, Builtjes and Schaap, 2014;Fu, Heemink, Lu, Segers, Weber and Lin, 2016;Jin, Lin, Heemink and Segers, 2018;Lopez-Restrepo et al, 2020). For a full description of the physical processes and input data could be found in Manders et al (2017).…”

Section: Spatial Disaggregationmentioning

confidence: 99%

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Spatial Disaggregation of Particulate Matter Emission Inventory in the Metropolitan Area of Aburr\'a Valley for Air Quality Modelling

Restrepo¹,

Botero²,

Pinel³

et al. 2020

Preprint

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In this paper a local emission inventory for PM 10 and PM 2.5 is presented that has been developed using a top-down spatial disaggregation of the official emission inventory for the Metropolitan Area of the Aburrá Valley in Colombia. The local emission inventory was evaluated using the LOTOS-EUROS Chemical Transport Model in a high-resolution simulation, and compared with the global emission inventory EDGAR. A detailed analysis of the model using the local emission inventory was performed. The results showed a considerable improvement in model performance when the local emission inventory was used in comparison to the global emission inventory.

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Section: Introductionsupporting

confidence: 61%

Section: Spatial Disaggregationmentioning

confidence: 99%

Spatial Disaggregation of Particulate Matter Emission Inventory in the Metropolitan Area of Aburr\'a Valley for Air Quality Modelling

Restrepo¹,

Botero²,

Pinel³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Additionally, the country experiences a significant increase in vehicular flow in urban and rural areas. As a result, large cities face environmental problems related to air quality, mainly due to particulate matter ( Casallas et al, 2020 ; Lopez-Restrepo et al, 2020 ). Logically, the reduction in the number of vehicles and industrial operations has an impact on air quality due to the isolation and cessation of productive activities.…”

Section: Introductionmentioning

confidence: 99%

Impact of lockdown on particulate matter concentrations in Colombia during the COVID-19 pandemic

Arregocés

Rojano

Restrepo

2021

Science of The Total Environment

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The first confirmed case of COVID-19 in Colombia was reported on March 6, 2020. For this reason, on March 25, preventive isolation was declared mandatory. These measures involved the suspension of economic activities and drastically reduced the number of vehicles on the road. The objective of this study is to evaluate the impact of the lockdown due to the COVID-19 pandemic on PM 2.5 concentrations at 5 monitoring stations and aerosol optical depth values of the Terra/MODIS satellite. We analyzed and compared the weekly and monthly concentrations of PM 2.5 before and during the lockdown between the week of January 6 to June 22, 2020, and compared the daily values obtained from the Terra/MODIS satellite for the months of January–June during the years 2018–2020 to elucidate the effects of the lockdown. Similar to other monitored sites in the world, we observed substantial reductions in weekly PM 2.5 concentrations, from 41 to 84% (Bogotá), from 13 to 66% (Funza), from 17 to 57% (Boyacá), from 35 to 86% (Valledupar) and 31 at 60% (Risaralda). Unlike other studies, the aerosol optical depth values increased up to 59% during the months of lockdown compared to previous years and up to 70% of the weekly mean when compared to before the lockdown. These spatiotemporal behaviors of PM 2.5 and the aerosol optical depth in Colombia are influenced by reductions in vehicular flow during quarantine, regional rainfall, and height of the planetary boundary layer. Emissions from economic activities affect pollutant levels in the area. The analysis of the levels of pollutants during the lockdown provides a baseline for regulatory agencies to establish mitigation plans.

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“…Furthermore, most of these studies rely on large-scale analysis units (provincial units or municipal units) [ 25 , 26 ], which may lead to the potential for heterogeneity within the unit to be ignored. In addition, the time span of the selected environmental pollution data is usually short (for instance, 5 to 10 years) [ 27 , 28 ], which may reduce the reliability and accuracy of the correlation between urban expansion and pollutant emissions estimated by the model. To address the above problems, there is an urgent need for high-quality data sets of various sources and suitable models that can handle the complex spatio-temporal effects existing in the data sets.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Dynamic Spatio-Temporal Correlations between PM2.5 Emissions from Different Sources and Urban Expansion in Beijing-Tianjin-Hebei Region

Zhao

2021

IJERPH

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Due to rapid urbanization globally more people live in urban areas and, simultaneously, more people are exposed to the threat of environmental pollution. Taking PM2.5 emission data as the intermediate link to explore the correlation between corresponding sectors behind various PM2.5 emission sources and urban expansion in the process of urbanization, and formulating effective policies, have become major issues. In this paper, based on long temporal coverage and high-quality nighttime light data seen from the top of the atmosphere and recently compiled PM2.5 emissions data from different sources (transportation, residential and commercial, industry, energy production, deforestation and wildfire, and agriculture), we built an advanced Bayesian spatio-temporal autoregressive model and a local regression model to quantitatively analyze the correlation between PM2.5 emissions from different sources and urban expansion in the Beijing-Tianjin-Hebei region. Our results suggest that the overall urban expansion in the study area maintained gradual growth from 1995 to 2014, with the fastest growth rate during 2005 to 2010; the urban expansion maintained a significant positive correlation with PM2.5 emissions from transportation, energy production, and industry; different anti-haze policies should be designated according to respective local conditions in Beijing, Tianjin, and Hebei provinces; and during the period of rapid urban expansion (2005–2010), the spatial correlations between PM2.5 emissions from different sources and urban expansion also changed, with the biggest change coming from the PM2.5 emissions from the transport sector.

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Forecasting PM10 and PM2.5 in the Aburrá Valley (Medellín, Colombia) via EnKF based data assimilation

Cited by 31 publications

References 35 publications

Spatial Disaggregation of Particulate Matter Emission Inventory in the Metropolitan Area of Aburr\'a Valley for Air Quality Modelling

Spatial Disaggregation of Particulate Matter Emission Inventory in the Metropolitan Area of Aburr\'a Valley for Air Quality Modelling

Impact of lockdown on particulate matter concentrations in Colombia during the COVID-19 pandemic

Exploring the Dynamic Spatio-Temporal Correlations between PM2.5 Emissions from Different Sources and Urban Expansion in Beijing-Tianjin-Hebei Region

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