Contributions of mobile, stationary and biogenic sources to air pollution in the Amazon rainforest: a numerical study with the WRF-Chem model

Rafee, Sameh Adib Abou; Martins, Leila Droprinchinski; Kawashima, Ana Beatriz; Almeida, Daniela S. de; Morais, Marcos Vinicius Bueno de; Souza, Renan; Oliveira, Maria Betânia Leal de; Souza, Rodrigo Augusto Ferreira de; Medeiros, Adam S. S; Urbina, Viviana; Freitas, Edmílson Dias de; Martin, Scot T.; Martins, Jorge Alberto

doi:10.5194/acp-17-7977-2017

Cited by 53 publications

(50 citation statements)

References 78 publications

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“…Overall, the comparison shows that the simulations satisfactorily represent average regional afternoon concentrations of NO x and O 3 . A companion study by Abou Rafee et al (2017) focuses on the comparison of simulated and measured pollutant concentrations for GoAmazon2014/5. Figure 3 shows examples of the spatial distribution of ozone concentration for each of cases A, B, and C for the single afternoon of 1 February 2014.…”

Section: Resultsmentioning

confidence: 99%

Power plant fuel switching and air quality in a tropical, forested environment

et al. 2017

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Abstract. How a changing energy matrix for electricity production affects air quality is considered for an urban region in a tropical, forested environment. Manaus, the largest city in the central Amazon Basin of Brazil, is in the process of changing its energy matrix for electricity production from fuel oil and diesel to natural gas over an approximately 10-year period, with a minor contribution by hydropower. Three scenarios of urban air quality, specifically afternoon ozone concentrations, were simulated using the Weather Research and Forecasting (WRF-Chem) model. The first scenario used fuel oil and diesel for electricity production, which was the reality in 2008. The second scenario was based on the fuel mix from 2014, the most current year for which data were available. The third scenario considered nearly complete use of natural gas for electricity production, which is the anticipated future, possibly for 2018. For each case, inventories of anthropogenic emissions were based on electricity generation, refinery operations, and transportation. Transportation and refinery operations were held constant across the three scenarios to focus on effects of power plant fuel switching in a tropical context. The simulated NO x and CO emissions for the urban region decrease by 89 and 55 %, respectively, after the complete change in the energy matrix. The results of the simulations indicate that a change to natural gas significantly decreases maximum afternoon ozone concentrations over the population center, reducing ozone by > 70 % for the most polluted days. The sensitivity of ozone concentrations to the fuel switchover is consistent with a NO xlimited regime, as expected for a tropical forest having high emissions of biogenic volatile organic compounds, high water vapor concentrations, and abundant solar radiation. There are key differences in a shifting energy matrix in a tropical, forested environment compared to other world environments. Policies favoring the burning of natural gas in place of fuel oil and diesel have great potential for ozone reduction and improved air quality for growing urban regions located in tropical, forested environments around the world.

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Section: Resultsmentioning

confidence: 99%

Power plant fuel switching and air quality in a tropical, forested environment

et al. 2017

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“…VEIN will add several new speciations to these functions, such as those in the EMEP/EEA guidelines (Ntziachristos and Samaras, 2016). In the case of Brazil, there are several studies of tropospheric ozone, which use the speciation of VOC emissions as input (Vara-Vela et al, 2016;Abou Rafee et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…In this case, the NMHC speciations from Ibarra (2017) cover emissions from vehicles consuming gasohol (gasoline with 25 % of ethanol), ethanol and diesel from exhaust, evaporative and liquid fuel released to the atmosphere. This speciation is based on measurements made in Brazil during the studies of Rafee (2015), Ynoue (2004), Albuquerque (2005), Oliveira (2007), Miranda (2001), Vara-Vela et al 2016and Andrade et al (2015). Hence, it is advisable that users from other nations/locations use suitable data for their local conditions.…”

Section: Speciation Of Emissions In Chemical Subcomponentsmentioning

confidence: 99%

VEIN v0.2.2: an R package for bottom–up vehicular emissions inventories

Ibarra¹,

Ynoue²,

O’Sullivan

et al. 2018

Geosci. Model Dev.

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Abstract. Emission inventories are the quantification of pollutants from different sources. They provide important information not only for climate and weather studies but also for urban planning and environmental health protection. We developed an open-source model (called Vehicular Emissions Inventory – VEIN v0.2.2) that provides high-resolution vehicular emissions inventories for different fields of studies. We focused on vehicular sources at street and hourly levels due to the current lack of information about these sources, mainly in developing countries.The type of emissions covered by VEIN are exhaust (hot and cold) and evaporative considering the deterioration of the factors. VEIN also performs speciation and incorporates functions to generate and spatially allocate emissions databases. It allows users to load their own emission factors, but it also provides emission factors from the road transport model (Copert), the United States Environmental Protection Agency (EPA) and Brazilian databases. The VEIN model reads, distributes by age of use and extrapolates hourly traffic data, and it estimates emissions hourly and spatially. Based on our knowledge, VEIN is the first bottom–up vehicle emissions software that allows input to the WRF-Chem model. Therefore, the VEIN model provides an important, easy and fast way of elaborating or analyzing vehicular emissions inventories under different scenarios. The VEIN results can be used as an input for atmospheric models, health studies, air quality standardizations and decision making.

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“…In recent years, several articles were published in which the model mentioned above, was used to different cities [13][14][15][16][17][18]. In the particular case of Peru, the authors of ref.…”

Section: Introductionmentioning

confidence: 99%

SO₂ and NO₂ simulation and validation in Metropolitan Lima using WRF-Chem model

2020

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In recent times, air pollution in Peru is attracting the attention of the population and the government as well, who finally makes the policies that help us to preserve good air quality. In this research, we used the chemical-meteorological model Weather research and Forecasting coupled with Chemical (WrF-Chem v3.8) to predict pollution scenarios. We studied and analyzed three 2017 months of summer (January, February and March) and three months of winter (July, august and September) to evaluate and forecast two pollutants concentration, sulfur dioxide (SO 2) and nitrogen dioxide (NO 2) over the city of lima. We also considered the meteorological variables such as the wind speed and its direction, average temperature, relative humidity and atmospheric pressure. besides, we used fixed industrial sources inventory as emission data and the global Forecast System (gFS) as border data for the meteorological components. Within the WrF-Chem model, we implemented the grell-Freitas parameterization of convection to represent the clouds; we used rrTMg for the shortwave/longwave radiation scheme, and the Monin-Obukhov for the processes in the surface layer, among others. On the other hand, for the gas phase chemistry, we used the raDM2 scheme, for the aerosol module we utilized the MaDE-SOrgaM, and finally, we employed the Fast-j photolysis scheme. We finally compared the results with the data provided by the ten monitoring stations that belong to the National Service of Meteorology and hydrology (SENaMhI) which are located in strategic zones in lima. lastly, we showed that the variables studied are within the environmental quality standard authorized by the Ministry of the Environment, and we also demonstrated that the simulations given by the model are, in general, overlapping the values measured experimentally in all of the monitoring stations evaluated.

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Contributions of mobile, stationary and biogenic sources to air pollution in the Amazon rainforest: a numerical study with the WRF-Chem model

Cited by 53 publications

References 78 publications

Power plant fuel switching and air quality in a tropical, forested environment

Power plant fuel switching and air quality in a tropical, forested environment

VEIN v0.2.2: an R package for bottom–up vehicular emissions inventories

SO₂ and NO₂ simulation and validation in Metropolitan Lima using WRF-Chem model

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Contributions of mobile, stationary and biogenic sources to air pollution in the Amazon rainforest: a numerical study with the WRF-Chem model

Cited by 53 publications

References 78 publications

Power plant fuel switching and air quality in a tropical, forested environment

Power plant fuel switching and air quality in a tropical, forested environment

VEIN v0.2.2: an R package for bottom–up vehicular emissions inventories

SO2 and NO2 simulation and validation in Metropolitan Lima using WRF-Chem model

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Product

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SO₂ and NO₂ simulation and validation in Metropolitan Lima using WRF-Chem model