Quantifying and spatial disaggregation of air pollution emissions from ground transportation in a developing country context: Case study for the Lima Metropolitan Area in Peru

Romero, Yovitza; Chicchon, Norvic; Noel, Julien; Ratti, Carlo; Nyhan, Marguerite

doi:10.1016/j.scitotenv.2019.134313

Cited by 52 publications

(23 citation statements)

References 42 publications

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“…In general, in the coastal area of the city, there are lower concentrations due to the coastal wind that blows from the ocean to the mountains (Silva et al 2017). The main sources of outdoor air pollution are automotive fleet for these the reason the area of greatest impact turns out to be the downtown of the city where most vehicle traffic is focused and where important economic activity takes place (Romero et al 2020;Silva et al 2017). Likewise, the wind disperses the atmospheric pollution emitted in the downtown area towards the mountain slopes.…”

Section: Resultsmentioning

confidence: 99%

Effects of COVID-19 pandemic control measures on air pollution in Lima metropolitan area, Peru in South America

Rojas

Urdanivia

Garay

et al. 2021

Air Qual Atmos Health

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Graphical abstract The sanitary measures implemented to control and prevent an increase in infections due to the COVID-19 pandemic have produced an improvement in the air quality of many urban areas around the world. We assessed air quality during the COVID-19 pandemic for particulate matter (PM 2.5 and PM 10 ), NO 2 and O 3 in in metropolitan area of Lima, Peru between pre-lockdown period (February 1 and March 15 of 2020), historical period (March 16 to April 30 2017–2019) and lockdown period (March 16 to April 30, 2020). The complete national lockdown that was implemented in Peru produced statistically significant reductions in the in-air pollutant (PM 10 (-40% and -58%), PM 2.5 (-31% and -43%) and NO 2 (-46% and -48%)), as recorded by the by the ground-based air quality monitoring network throughout the metropolitan area, compared with the corresponding concentrations for the previous weeks and over the same period for 2017–2019. Analysis of the spatial Distribution of satellite data also show decreases in the concentrations of PM 10, PM 2.5 and NO 2 as a result of the containment measures and suspension of activities implemented by the Peruvian government. The concentrations of O 3 significantly increased (11% and 170%) as a result of the decrease in the concentration of NO 2 , confirming that the study area is a hydrocarbon-limited system, as previously reported. The results obtained contribute to the assessment by the regulatory agencies of the possible strategies of control and monitoring of air pollution in the study area. Supplementary Information The online version contains supplementary material available at 10.1007/s11869-021-00990-3.

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Section: Resultsmentioning

confidence: 99%

Effects of COVID-19 pandemic control measures on air pollution in Lima metropolitan area, Peru in South America

Rojas

Urdanivia

Garay

et al. 2021

Air Qual Atmos Health

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“…In Figure 1, the difference between summer (Figure 1a) and winter (Figure 1b) is clear in this zone, latitude and longitude of Lima, Peru are -12.046374, -77.042793 respectively., with strong influence with Pacific Ocean for wind velocity and direction, as well as, particulate matter. On the other hands, LMA's one of the main air quality problem is associated with ground transportation (Dawidowski et al, 2014;MINAM (Perús Ministry of Environment), 2010;Romero et al, 2019). In addition, the districts of "Ate, Independencia, La Victoria, Santa Anita and Rımac", in that order, are those that reported the most complaints regarding particulate matter (dust) (INEI (National Institute of Statistics and Informatics), 2016).…”

Section: Study Areamentioning

confidence: 99%

“…Peru has a population of 31,2 million in 2017, besides, it was found that about 2,300 premature deaths annually result from particulates matter (PMs) pollutant at outdoor level, and about 3,000 by using biomass burning stoves at indoor level. On the other hand, Lima has become one of the most critical city associated to air pollution and vehicle traffic in South America regional level, with a population of 8,785 million, and 1,752 million motor vehicles in 2018 (the old units are working across Lima), where the projection of total vehicle growth is 7 % per year, in the last ten year (Posada, 2018), then, vehicle emission are the main sources due to the continuous growth of vehicle units and their old age (more than 14 years old mainly) according to (Romero et al, 2019), which results in about 80% contribution to the total pollutant emission in LMA (San Miguel, 2006). Particulate matter is also affecting the ground or water by changing the nutrient balance in coastal waters and large rivers basins affecting the diversity of ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Temporal and spatial analysis of traffic – Related pollutant under the influence of the seasonality and meteorological variables over an urban city in Peru

Romero

Díaz

Meldrum

et al. 2020

Heliyon

Self Cite

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A four yearlong air monitoring data (since 2015 to 2018) was collected to assess the impact of meteorological parameters such as temperature, relative humidity, wind speed and wind direction on the spatial and temporal variability of CO (carbon monoxide), NO 2 (nitrogen oxide), O 3 (ozone), SO 2 (sulfur dioxide), PM 10 and PM 2.5 (particulate matter with diameter aerodynamic less than 2.5 μm and 10 μm respectively) in Lima Metropolitan Area (LMA). Data from eight air quality monitoring stations, currently under the supervision of the National Meteorological and Hydrological Service (SENAMHI), was used to capture trafficrelated pollutants concentrations under various local traffic conditions. Effects on meteorological events on seasonal traffic-related pollutants concentration variability were identified. Using average data from the eight stations, it was found that the monthly mean traffic-related pollutants since 2015 to 2018 were higher in spring and winter and lower in autumn and summer. Low-wind speed was associated with events that contribute to elevated seasonal PM 2.5 levels. Correlation coefficients between PM 2.5 concentrations and meteorological variables fluctuated significaly across different seasons. High PM 2.5 concentrations were associated with low relative humidity and high wind conditions in spring. Results suggest that there exist a seasonal variation of PM 2.5 concentration, and set the need of future work on the understanding of the air pollution effects on human health.

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“…It is important to highlight that different studies worldwide (e.g., Sun et al, 2016;Romero et al, 2020) reported the use of only road segment length density as the method for vehicular emissions disaggregation. Hence, this type of approach included in DROVE is a common and reliable methodology for vehicular emissions distribution.…”

Section: Spatial Disaggregation Of On-road Vehicular Pm10 Emission In Manizales Using the Three Disaggregation Optionsmentioning

confidence: 99%

“…The lack of detailed and accurate information about ground transport is a common issue in majority of Latin American countries, hence, there are difficulties not only for estimating precise macro-scale total emission fluxes at a national or regional level, but also for representing detailed spatial and temporal distribution of vehicular emissions (Romero et al, 2020). This fact establishes the need to count on reliable and flexible emission inventories (EI), that integrally describe the contribution of urban sources to atmospheric pollutants, following together a macro-scale approach and an accurate representation of disaggregated emissions at a local level.…”

Section: Introductionmentioning

confidence: 99%

DROVE: An Algorithm for Spatial and Temporal Disaggregation of On-road Vehicle Emission Inventories

González

Gómez

Aristizábal

2020

Aerosol Air Qual. Res.

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A key component of air quality management in urban areas is the analysis of pollutant emission distribution and hotspots, which are valuable information for developing emission reduction strategies and as input in air quality models. For vehicular emissions, this task is difficult because the sources are in constant movement and the availability of accurate and realistic vehicular activity information is scarce; a common issue in cities of emerging countries. Hence, exercises for estimating atmospheric pollutant emissions are developed only in a macro-scale way, without allocating in space and time the emissions. In this study, we present a new computational algorithm named DROVE, for the disaggregation of on-road vehicle emissions in space and time. DROVE is a free code developed in R and provides emission fluxes distribution from the estimation of disaggregation factors. It was designed with three possible approximations for spatial emissions disaggregation, considering the input information that would be available in the city: length of road segments (LRS), LRS + type of roads; and LRS + traffic flows. Two temporal distribution options are available for obtaining gridded hourly emissions. We evaluated the capabilities of DROVE for performing the PM10 emission distribution fluxes in the medium-sized cities of Manizales, Colombia, Antofagasta, Chile, and the megacity of Bogotá, Colombia. Results suggest that DROVE was able to allocate emission hotspots in zones of high traffic and main avenues (when information of the type of roads or traffic flow is available). Emissions distribution did not reflect this behavior when only LRS was used as input data, obtaining 50% of grid cells with percentage emission differences higher than 100% against the use of LRS + traffic flows. DROVE can be implemented in any region worldwide, could contribute with air quality management and provide disaggregated emission fluxes for air quality modeling.

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Quantifying and spatial disaggregation of air pollution emissions from ground transportation in a developing country context: Case study for the Lima Metropolitan Area in Peru

Cited by 52 publications

References 42 publications

Effects of COVID-19 pandemic control measures on air pollution in Lima metropolitan area, Peru in South America

Effects of COVID-19 pandemic control measures on air pollution in Lima metropolitan area, Peru in South America

Temporal and spatial analysis of traffic – Related pollutant under the influence of the seasonality and meteorological variables over an urban city in Peru

DROVE: An Algorithm for Spatial and Temporal Disaggregation of On-road Vehicle Emission Inventories

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