Modeling Study of the Particulate Matter in Lima with the WRF-Chem Model: Case Study of April 2016

Sánchez-Ccoyllo, Odón R.; Ordoñez-Aquino, Carol G; Muñoz, Ángel G.; Llacza, Alan; Andrade, Mauro; Liu, Yang; Reátegui-Romero, Warren; Brasseur, Guy

doi:10.37622/ijaer/13.11.2018.10129-10141

Cited by 19 publications

(11 citation statements)

References 17 publications

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“…[57]. When looking at the results of this report, we can say that there is poor air quality in the south (VMT Station), as well as in the east (ATE and STA Stations), areas with high vehicular traffic as well as having industries there [58]. The air quality at the CDM station and the SBJ station is as good as the air quality in residential areas.…”

Section: Introductionmentioning

confidence: 94%

Behavior of the Average Concentrations As Well As Their PM10 and PM2.5 Variability in the Metropolitan Area of Lima, Peru: Case Study February and July 2016

Reátegui-Romero¹,

Alvarez²,

Pacsi-Valdivia³

et al. 2021

IJESD

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This research focused on analyzing the behavior of the hourly average concentrations of PM10 and PM2.5 in relation to vehicular traffic, as well as the effect of relative humidity on these concentrations. Measurements of hourly particulate matter concentrations were recorded by the National Meteorology and Hydrology Service of Peru (SENAMHI) at five surface air quality stations. The profiles of PM10 concentrations are related to traffic behavior, showing high levels of concentrations at peak hours, while the PM2.5 profiles are flatter and better related to traffic in February (summer). The decrease in relative humidity between 80 to 65% in the mornings has a greater effect on the increase in PM10 and PM2.5 concentrations in February than in July (winter), and the increase in relative humidity between 65 to 80 % in the afternoon, it has a greater effect on the decrease in the concentration of PM2.5 in February than in July. The air quality in the north (PPD and CRB stations) and east (SJL station) of the Metropolitan Area of Lima (MAL) are the most polluted. The factors that relate PM10 concentrations with the Peruvian standard in February at these stations were 2.79, 1.78 and 1.26, and in July 2.74, 1.28 and 1.36 respectively. The highest and lowest variability of PM10 and PM2.5 in February and July occurred in the northern area (PPD and SMP stations).

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Section: Introductionmentioning

confidence: 94%

Behavior of the Average Concentrations As Well As Their PM10 and PM2.5 Variability in the Metropolitan Area of Lima, Peru: Case Study February and July 2016

Reátegui-Romero¹,

Alvarez²,

Pacsi-Valdivia³

et al. 2021

IJESD

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“…The geo-physical parameters such as orography was taken from ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer, Global Digital Elevation Model), and land use was taken from USGS (U.S. Geological Survey) datasets. The anthropogenic emissions of air contamination in a 5 km horizontal resolution grid domain [4] were based on the emission vehicular model [7]. This model considers that air atmospheric emission only comes from on-road vehicles [7].…”

Section: Wrf-chem Modeling Setupmentioning

confidence: 99%

“…Detailed descriptions of the WRF-Chem model can be found on the website (https://ruc.noaa.gov/wrf/wrf-chem/). The WRF-Chem model performance was evaluated by many researchers in the world, for example [4,5,6]. However, there are only few studies about numerical estimation of air quality in the MALC for example [4].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Predicting Aerosol PM10 Concentrations with the WRF-Chem Model, in Lima, in February 2018

Sánchez-Ccoyllo¹,

Llacza²,

Castesana³

2021

World Congress on Civil, Structural, and Environmental Engineering

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Particulate matter with aerodynamic diameters equal to or less than 10 µm (PM10) concentration in the Metropolitan Area of Lima -Callao (MALC) frequently exceeds the daily Peruvian National Ambient Air Quality Standard for PM10 (100 µg/m 3 ) and the daily World Health Organization Guideline (50 µg/m 3 ). The aim of this study was to simulate the hourly PM10 concentration and evaluate the model performance. The prediction of PM10 concentration was done using the "Weather Research and Forecasting Model Coupled with Chemistry" (WRF-Chem) air pollution modeling system. Therefore, in this study, WRF-Chem (version 4.0) was applied to simulate PM10 concentrations from January 30, 2018 to February 28, 2018. The first 2 days were used as spin-up in order to minimize the influence of the initial conditions with a 5-km-by-5-km grid over MALC. This PM10 concentrations prediction was validated against ground-based observations. For validating the aerosol PM10 simulations, hourly PM10 in-situ measurements from two traffic air quality monitoring stations named Ate and Puente Piedra in the MALC were used. The results showed that the prediction of PM10 mass concentration at the (Ate and Puente Piedra) traffic air quality monitoring stations reasonably captures the observed PM10 temporal. However, PM10 concentrations estimated were under-estimated by the WRF-Chem model.

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“…The concentration of PM 10 in South Lima is worrisome because South Lima has industry and poor greens areas. The MEAL's population is breathing high concentrations of PM 2.5 [30]. High PM concentrations compromise the health at risk of people in Lima [31].…”

mentioning

confidence: 99%

Describing Aerosol and Assessing Health Effects in Lima, Peru

Sánchez-Ccoyllo¹,

Ordoñez-Aquino²,

Arratea-Morán³

et al. 2021

IJESD

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The current study aims to discover the chemical-morphological characteristics of PM10 through the analysis of Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) and Scanning Electron Microscopy (SEM), respectively, in the Metropolitan Area of Lima (MEAL) in the period from January 04 to 18, 2019. The study also aims to analyze the aerosol optical depth (AOD) in the period from January 1, 2014 to October 15, 2020. The effect of the PM2.5 concentrations on health in 2016 has also been quantified. The results indicate that the daily PM10 value of 50 μg/m3 recommended by the World Health Organization was exceeded on 75% of the monitored days. The morphological analysis of PM10 demonstrated the presence of particles of anthropogenic and geogenic origin. Particles from building activities and secondary aerosol formation were found, as well as particles associated with the resuspension of soil and marine aerosols. In 2016, 718, 1426, and 4295 cases of all-cause mortality, on average, could have been avoided in the MEAL if the annual average of PM2.5 would have decreased from 26.53 μg/m3 to 23.88 μg/m3, 21.22 μg/m3, or 10 μg/m3, respectively. In 2016, 1.58%, 3.14% and 9.47% of all causes are respectively attributed to fine aerosol (PM2.5) over 23.88 μg/m3, 21.22 μg/m3, and 10 μg/m3. Policy makers to legally reduce the PM2.5 concentrations in the MEAL could use this result.

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Modeling Study of the Particulate Matter in Lima with the WRF-Chem Model: Case Study of April 2016

Cited by 19 publications

References 17 publications

Behavior of the Average Concentrations As Well As Their PM10 and PM2.5 Variability in the Metropolitan Area of Lima, Peru: Case Study February and July 2016

Behavior of the Average Concentrations As Well As Their PM10 and PM2.5 Variability in the Metropolitan Area of Lima, Peru: Case Study February and July 2016

Evaluating Predicting Aerosol PM10 Concentrations with the WRF-Chem Model, in Lima, in February 2018

Describing Aerosol and Assessing Health Effects in Lima, Peru

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