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

Romero, Yovitza; Díaz, César; Meldrum, Ian; Velasquez, Ricardo Arias; Noel, Julien

doi:10.1016/j.heliyon.2020.e04029

Cited by 26 publications

(17 citation statements)

References 21 publications

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“…They found that PM 2.5 concentrations at six of the ten regulatory monitors in Lima exceeded the World Health Organization (WHO) daily guideline on 77% of the days between 2014 and 2015. PM 2.5 concentrations in Lima show seasonal trends, with the highest levels observed in the summer (Romero et al, 2020b).…”

Section: Introductionmentioning

confidence: 98%

“…Vehicular emissions are the dominant source of pollution in the city (Arias Veĺasquez et al, 2019;Silva et al, 2017). The diurnal peaks of PM 2.5 during rush hours (Romero et al, 2020b;Sánchez Ccoyllo et al, 2011), as well as the association between proximity to roads and the concentrations of PM 2.5 and Black Carbon (BC) is evidence of this (Underhill et al, 2015). The government has attempted to mitigate vehicular pollution by phasing out lead from gasoline, reducing the sulfur content in diesel and reducing the permissible age of vehicles.…”

Section: Introductionmentioning

confidence: 99%

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The Impact of The Pico Y Placa Policy On Fine Particulate Matter In Lima, Peru

Romero

deSouza

Duarte

et al. 2021

Preprint

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Lima has been ranked among the top most polluted cities in the Americas. Vehicular emissions are the dominant source of pollution in the city. In order to reduce congestion and pollution levels during the XVIII Pan- and Parapan-American Games, Lima government officials enacted the pico y placa policy to restrict the number of vehicles on certain heavily trafficked roads in the city at rush hours between Monday to Thursday based on the last digit of their license plates. This policy was retained after the Games. In this paper we evaluate the impact of this policy on fine particulate matter concentration levels (PM2.5) at a background site in the city using a difference-in-difference approach. We find that the policy resulted in increases on PM2.5 levels on Monday-Thursday compared to Friday-Sunday levels after the policy was enacted, compared to previous years. However, such an increase was not significant. These results suggest the need for additional policies to reduce pollution due to traffic in Lima. It also suggests the need to track the response to this policy over time to evaluate its efficacy.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

The Impact of The Pico Y Placa Policy On Fine Particulate Matter In Lima, Peru

Romero

deSouza

Duarte

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…In 2015 air pollution, in general, are responsible for over 6 million deaths in the world [10,11]. There is more than 400,000 premature death in Europe [12], and around 7 million worldwide [13].…”

Section: Introductionmentioning

confidence: 99%

“…For example, 80.49% of emissions in Beijing, China, were produced by motor vehicles [14]. It was also found that 80% of air pollution in the Lima Metropolitan Area was produced from automobiles [13]. in China, 85% of emissions were from transport [15], while in the United Kingdom was 92% [16], and 75% in Malaysia [17], but in the United States, automobiles are responsible for emitting 57% of air pollution [7].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Artificial Neural Network Model using Genetic Algorithm for Prediction of Traffic Emission Concentrations

Abdullah¹,

Usmani²,

Pillai³

et al. 2021

IJACSA

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Global warming and climate change have become universal issues recently. One of the leading sources of climate change is automobiles. Automobiles are the prime source of air pollution in urban areas globally. This has resulted in a problematic and chaotic state in the development of an automatic traffic management system for capturing and monitoring vehicles' hourly and daily passage. With the significant advancement of sensor technology, atmospheric information such as air pollution, meteorological, and motor vehicle data can be harvested and stored in databases. However, due to the complexity and non-linear associations between air quality, meteorological, and traffic variables, it is difficult for the traditional statistical and mathematical models to analyze them. Recently, machine learning algorithms in the field of traffic emissions prediction have become a popular tool. Meteorological and traffic variables influence the variation and the trend of the traffic pollutants. In this paper, an optimized artificial neural network (OANN) was developed to enhance the existing artificial neural network (ANN) model by updating the initial weights in the network using a Genetic Algorithm (GA). The OANN model was implemented to predict the concentration of CO, N O, N O2, and N Ox pollutants produced by motor vehicles in Kuala Lumpur, Malaysia. OANN was compared with Artificial Neural Network (ANN), Random Forest (RF), and Decision Tree (DT) models. The results show that the developed OANN model performed better than the ANN, RF, and DT models with the lowest MSE values of 0.0247 for CO, 0.0365 for N O, 0.0542 N O2, and 0.1128 for N Ox. It can be concluded that the developed OANN model is a better choice in predicting traffic emission concentrations. The developed OANN model can help environmental agencies monitor traffic-related air pollution levels efficiently and take necessary measures to ensure the effectiveness of traffic management policy. The OANN model can also help decision-makers mitigate traffic emissions to protect citizens living in the neighborhood of highways.

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“…There are relatively few studies on PM matter in many South American countries, including Peru. Those few studies investigating air quality in Peru have found extreme cases of air pollution due to limited atmospheric circulation in high altitude cities, lack of government oversight on large emission sources such as factories, and the prevalence of older vehicles still in use in many regions [6,20]. The city of Arequipa is an important industrial and commercial center of Peru with over 1 million residents.…”

Section: Introductionmentioning

confidence: 99%

Geochemical Characterization and Heavy Metal Sources in PM10 in Arequipa, Peru

Michalski

Olson

et al. 2021

Atmosphere

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Particulate matter smaller than 10 μm (PM10) is an important air pollutant that adversely affects human health by increasing the risk of respiratory and cardiovascular diseases. Recent studies reported multiple extreme PM10 levels at high altitude Peruvian cities, which resulted from a combination of high emissions and limited atmospheric circulation at high altitude. However, the emission sources of the PM10 still remain unclear. In this study, we collected PM10 samples from four sites (one industrial site, one urban site, and two rural sites) at the city of Arequipa, Peru, during the period of February 2018 to December 2018. To identify the origins of PM10 at each site and the spatial distribution of PM10 emission sources, we analyzed major and trace element concentrations of the PM10. Of the observed daily PM10 concentrations at Arequipa during our sampling period, 91% exceeded the World Health Organization (WHO) 24-h mean PM10 guideline value, suggesting the elevated PM10 strongly affected the air quality at Arequipa. The concentrations of major elements, Na, K, Mg, Ca, Fe, and Al, were high and showed little variation, suggesting that mineral dust was a major component of the PM10 at all the sites. Some trace elements, such as Mn and Mo, originated from the mineral dust, while other trace elements, including Pb, Sr, Cu, Ba, Ni, As and V, were from additional anthropogenic sources. The industrial activities at Rio Seco, the industrial site, contributed to significant Pb, Cu, and possibly Sr emissions. At two rural sites, Tingo Grande and Yarabamba, strong Cu emissions were observed, which were likely associated with mining activities. Ni, V, and As were attributed to fossil fuel combustion emissions, which were strongest at the Avenida Independencia urban site. Elevated Ba and Cu concentrations were also observed at the urban site, which were likely caused by heavy traffic in the city and vehicle brake wear emissions.

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

Cited by 26 publications

References 21 publications

The Impact of The Pico Y Placa Policy On Fine Particulate Matter In Lima, Peru

The Impact of The Pico Y Placa Policy On Fine Particulate Matter In Lima, Peru

An Optimized Artificial Neural Network Model using Genetic Algorithm for Prediction of Traffic Emission Concentrations

Geochemical Characterization and Heavy Metal Sources in PM10 in Arequipa, Peru

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